February 2023 - cypherpunks - lists.cpunks.org

From Big Dog to Big House - the oxmoronic nature of national-anarchy
by professor rat 26 Feb '23

26 Feb '23

The confronting pictures that capture El Salvador's crackdown on gangsters 11 hours ago The Guardian More El Salvador moves suspected gang members to 40,000-capacity ‘megaprison’ Yesterday Reuters More The 'mega prison' in El Salvador's gang crackdown

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“We are looking to kill Trump."
by professor rat 26 Feb '23

26 Feb '23

Take a number and get in line Amirali Hajizadeh, the head of the Revolutionary Guards Aerospace Force, during a TV interview, said that Iran has plans to kill former United States President Donald Trump https://www.news18.com/news/world/irans-cruise-missile-may-raise-us-concern… APster style deadpool on the perp https://stiffs.com/celebrity/donaldtrump Cypherpunk revolution into KILLING US PRESIDENTS SINCE 1999

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[ot] painful research joke: mind control of rat over wifi
by Undescribed Horrific Abuse, One Victim & Survivor of Many 26 Feb '23

26 Feb '23

The painful joke is because members of this list have likely experienced a more conventional kind of mind control (that involving abuse and messaging and possibly drugs and other things including sometimes implants) for “ratting” of powerful criminals. The messaging can at times include stories from the abuser that an implant has been placed in their brain, and/or that the abuser controls them with their mind alone and the victim is helpless, and/or that the victim is a “worthless rat”. Computer viruses and spy devices are called implants now, and some people have found biological implants as well I understand. In this normal recent research paper, wireless devices are attached to the heads of a human and a biological rat such that the rat is forced to do what the human directs. I imagine you could also wire it the other way around, so that a human has to do what a rat wants. Studies may be in progress. This paper came up in my machine learning feed. The twitter account usually tweets popular mainstream data science papers. Not sure why they tweeted this one. I expect that others would agree that, given experience of being mind controlled, experiments like this are horrifying and highly unethical, even though the subject is a tiny rat. Having your brain forced to ignore everything that it is there to do for you, in order to attend to moment-to-moment commands coming from invisible stimulation is world-ending hellish torture that makes painful and confused lifelong activism, uncommunicable complex trauma, and tragic loss of beautiful independent spirits and life potential. Victims need studies like these to somehow represent respect and inclusion of the control subject. Still, I strongly celebrate that research like this is producing mainstream discourse and knowledge, and find it quite necessary. It is one of the paths along which therapies for detection and recovery can develop. I apologize that this is not actual mkultra recovery research, which likely also exists. This paper, the sharing of which unfortunately supports harmful messaging, is still much easier for me to think of and look at than more relevant ones. The paste below is poor quality partly because I am not sure of actual utility of the paper. https://twitter.com/hardmaru/status/1628963052560482305 https://www.nature.com/articles/s41598-018-36885-0 Human Mind Control of Rat Cyborg’s Continuous Locomotion with Wireless Brain-to-Brain Interface shaomin Zhang1,3,4, Sheng Yuan1,3,4, Lipeng Huang2, Xiaoxiang Zheng1,3,4, Zhaohui Wu2, Kedi Xu1,3,4 & Gang pan 2 Brain-machine interfaces (BMIs) provide a promising information channel between the biological brain and external devices and are applied in building brain-to-device control. Prior studies have explored the feasibility of establishing a brain-brain interface (BBI) across various brains via the combination of BMIs. However, using BBI to realize the efficient multidegree control of a living creature, such as a rat, to complete a navigation task in a complex environment has yet to be shown. In this study, we developed a BBI from the human brain to a rat implanted with microelectrodes (i.e., rat cyborg), which integrated electroencephalogram-based motor imagery and brain stimulation to realize human mind control of the rat’s continuous locomotion. Control instructions were transferred from continuous motor imagery decoding results with the proposed control models and were wirelessly sent to the rat cyborg through brain micro-electrical stimulation. The results showed that rat cyborgs could be smoothly and successfully navigated by the human mind to complete a navigation task in a complex maze. Our experiments indicated that the cooperation through transmitting multidimensional information between two brains by computer-assisted BBI is promising. Direct communication between brains has long been a dream for people, especially for those with difficulty in verbal or physical language. Brain-machine interfaces (BMIs) provide a promising information channel between the brain and external devices. As a potential human mind reading technology, many previous BMI studies have successfully decoded brain activity to control either virtual objects1–3 or real devices4,5. On the other hand, BMIs can also be established in an inverse direction of information flow, where computer-generated information can be used to modulate the function of a specific brain region6–8 or import tactile information back to the brain9–11. The combination of different types of BMI systems can thus help to realize direct information exchange between two brains to form a new brain-brain interface (BBI). However, very few previous studies have explored BBIs across different brains12. Miguel Pais-Vieira et al. established a BBI to realize the real-time transfer of behaviorally meaningful sensorimotor information between the brains of two rats13. While an encoder rat performed a senso- rimotor task, samples of its cortical activity were transmitted to matching cortical areas of a “decoder” rat using intracortical micro-electrical stimulation (ICMS) on its somatosensory cortex. Guided solely by the information provided by the encoder rat’s brain, the decoder rat learned to make similar behavioral selections. BBIs between humans have also been preliminary explored. One example of a BBI between humans detected motor intention with EEG signals recorded from one volunteer and transmitted this information over the internet to the motor cortex region of another volunteer by transcranial magnetic stimulation, which resulted in the direct information transmission from one human brain to another using noninvasive means14. In addition to information transfer between two brains of the same type of organism, the BBI enables information to be transferred from a human brain to another organism’s brain. Yoo et al. used steady-state visual evoked potential (SSVEP)-based BMI to Figure 1. Experiment setup. (a) Overview of the BBI system. In the brain control sessions, EEG signal was acquired and sent to the host computer where the motor intent was decoded. The decoding results were then transferred into control instructions and sent to the stimulator on the back of the rat cyborg with preset parameters. The rat cyborg would then respond to the instructions and finish the task. For the eight-arm maze, the width of each arm was 12 cm and the height of the edge was 5 cm. The rat cyborg was located in the end of either arm at the beginning of each run. And preset turning directions were informed vocally by another participant when a new trial started. (b) Flowchart of the proposed brain-to-brain interface. extract human intention and sent it to an anesthetized rat using transcranial focused ultrasound stimulation on its brain, thereby controlling the tail movement of the anesthetized rat by the human brain15. In a very recent work, a BBI was developed to implement motion control of a cyborg cockroach by combining a human’s SSVEP BMI and electrical nerve stimulation on the cockroach’s antennas16. The cyborg cockroach could then be navigated by the human brain to complete walking along an S-shaped track. Although the feasibility of BBIs has been preliminarily proven, it is still a big challenge to build an efficient BBI for the multidegree control for the continuous locomotion of a mammal in a complex environment. In the current study, we present a wireless brain-to-brain interface, through which a human can mind control a live rat’s contin- uous locomotion. Different from the control of lifeless devices, it is critical to have highly demanding instantane- ity in the control of a living creature in real time due to its agility and self-consciousness. For this purpose, the BBI system requires timely reactions and a high level of accuracy in terms of information decoding and importing, as well as real-time visual feedback of the rat’s movement. The SSVEP-based BMI, as used for brain intention decod- ing in previous BBI works that have depended on visual stimulation, may distract the manipulator from reacting promptly to real-time visual feedback. As an alternative solution, motor imagery-based BMI has the advantages of rapid response and a low level of distraction from the visual feedback. Therefore, the BBI system established in the current study integrates control instructions decoded by noninvasive motor imagery with neural feedback, and the instructions are sent back to the rat’s brain by ICMS in real time. We also proposed and compared two different control models for our BBI system, the thresholding model (TREM) and the gradient model (GRAM), to provide a more natural and easier process for the manipulator during steering control. With this interface, our manipulators were able to mind control a rat cyborg to smoothly complete maze navigation tasks. Results Set up of BBI system and task design. The BBI system in the current study consisted of two parts: a noninvasive EEG-based BMI and a rat cyborg system17 (Fig. 1(a)). The EEG-based BMI decoded the motor intent of left and right arm movement, which corresponded to the generation of instruction Left and Right turning, respectively. In the current study, the average EEG signal control accuracy of all 6 manipulators was 77.86 ± 12.4% over all the experiments conducted. The eye blink signals in the EEG were used to elicit the instruction Forward/ Reward, which was detected by the amplitude of EEG signal in the frontopolar channel. The rat cyborgs were prepared based on previous works17–20 and were well-trained before experiments were conducted in this study (see Methods for more details). Two parts of the system were connected through an integration platform, sending decoded instructions from motor intent to the rat cyborgs, and providing visual information feedback in real time. An overview of the BBI system is presented in Fig. 1. The control effect of the rat cyborgs was evaluated by a turning task on an eight-arm maze. A complete run of the turning task contained a total of 16 turning trials, with eight left turnings and eight right turnings. To avoid the influence of the memory and training experience of the rats, the turning direction sequence was randomly Figure 2. (a) Performance of manual control stage. The mean CPT of each rat cyborg for manual control across all sessions. (Note: For display, only positive standard deviations are presented as error bars). (b) Different areas assigned in the investigation for the optimal area. The simplified plus-maze was modified from the original eight-arm maze by blocking four crossing arms. (c) The averaged success rate (mean ± SD) of each area for the rat cyborgs to receive instructions with manual control. generated by computer before each task run. The targeted turning direction of each trial was informed vocally by other experimenters at the start of each trial during the turning control experiments. For each run, the rats were placed at the end of one of the eight arms as a starting point. The rat was then driven towards the center of the maze and guided to turn into one of the adjacent arms. A trial was regarded as successful when the rat performed a correct turning and reached the end of the target arm. A new trial would then start when the rat reached the end of one arm and turned its head back towards the center of the maze. If the rat failed to complete one turning trial, the same turning direction trail was repeated until the rat succeeded. The total time from the start to the end of completing 16 correct trials was recorded as the completion time (CPT) of each run. The turning accuracy (TA) was then calculated as the ratio of the number of correct turns to the total number of turns performed. The entire experiment contained three stages, one manual control stage and two brain control stages, with each stage containing 5 sessions and being performed on five consecutive days. Each session consisted of 3 inde- pendent runs, with an interval break time between each run of at least eight minutes. The entire procedure was video recorded, and the mouse clicking sequences during manual control stage were recorded for further analysis. In the second and third stages, two different control models (GRAM and TREM, see details in the Methods) were applied. To further test the applicability of brain control, the rat cyborgs were controlled to complete a navigation task in a more complicated maze. Manual control of rat cyborg. During the manual control stage, the rat cyborgs were controlled by experi- enced operators. We found that the turning accuracy of a well-trained rat cyborg could achieve an exceptionally high rate of nearly 100%. As displayed in Fig. 2(a), the average CPT of all rat cyborgs at the first session of manual control was 190.03 ± 75.41 s and decreased to 132.56 ± 12.39 s at the fifth session. Most of the rats showed an obvi- ous learning curve through the manual control stage. The CPT of each rat cyborg became very close at the end of the manual control stage, indicating that they were becoming familiar with the task environment and the control Figure 3. (a) Average CPT across all rat cyborgs for the three consecutive stages. (b) Average turning accuracy across all rat cyborgs for the three consecutive stages. Error bars indicate the standard deviation. *Indicates p < 0.05. instructions delivered into their brains. There was no significant difference (paired T-test, p > 0.05) between the average CPT of the last two sessions of the manual control stage for each rat cyborg, which indicated that the rat cyborgs were in a steady state. During the manual control sessions, we noticed that the successful turning behavior of a rat cyborg was highly dependent on the timing of the turning instructions (Fig. 2(b)). To optimize the instruction timing, an additional experiment was conducted. In this experiment, the rats were placed at the end of the plus-maze, which was modified from the original eight-arm maze, to wait for instructions to turn left or right. By delivering turning instructions while the rats’ bodies were located in different sections along the straight arm, the instruction timing could be evaluated by the turning success of the rats. Figure 2(c) shows the overall performance of the turning success rate at five equally divided sections of the maze. According to the success rate of this plus-maze test, the best location for the rat cyborg to receive turning instructions was the area near the intersection (areas C and D in Fig. 2(b)). When considering brain control conditions, motor imagery should be initiated slightly before the optimal point for manual control because the decoding process and instruction generation take a short period of time. Thus, in our study, the manipulators were asked to start motor imagery when the rats arrived at areas D and E. BBI evaluation. After stage 1 of manual control, two further brain control stages were performed by several brain control manipulators. In the two brain control stages, the manipulators controlled the rat cyborgs with a BBI (Fig. 1(a)) based on one of the two proposed control models. During the first brain control stage (stage 2), the gradient model (GRAM) was applied, and in the second brain control stage, the thresholding model (TREM) was applied. The two control models were based on different threshold calculating strategies. The thresholds were used to differentiate the decoding results attributed to real intention or noise (see Methods for a detailed expla- nation of thresholds). The results of the two control models are shown in Fig. 3. The overall CPT value remained stable in both brain control stages, with no significant difference between the two sessions inside each stage (Fig. 3(a), paired T-test for the average CPT, p > 0.05). However, a comparison between the two brain control stages showed that a longer time was taken to complete the same maze tasks with the TREM-based BBI system. The average CPT of all rat cyborgs across the GRAM-based stage 2 was shorter than the TREM-based stage 3 Figure 4. (a) Average number of turning instructions for all the rat cyborgs across all the sessions and a comparison of the group-level number of turning instructions between different stages. (b) Average number of Forward instructions for all the rat cyborgs across all sessions and a comparison of the group-level number of Forward instruction between different stages. ***indicates p < 0.01, *indicates p < 0.05, paired T-test. (243.41 ± 12.73 s vs. 275.05 ± 14.47 s, paired T-test, p < 0.05), demonstrating that the GRAM model was better than the TREM model for the proposed BBI system. As shown in Fig. 3(b), the average turning accuracy of all rat cyborgs dropped approximately 15% at the first session of brain control stage 2 compared to that in the manual control stage. The turning accuracy then grad- ually increased back to 98.08 ± 2.31% at the last session in stage 2, indicating that the rat cyborgs could quickly be accustomed to the transition of different control styles. The drop of the fourth session was most likely due to the poor performance (81.67 ± 5.44%) of one rat cyborg. When the brain control model changed from GRAM at stage 2 to TREM at stage 3, the turning accuracy slightly dropped to 90.35 ± 5.03% in the first session of stage 3 and then generally increased across the remainder of the last stage. The group level of turning accuracy on average for stage 2 and 3 was 91.75 ± 3.85% and 93.32 ± 1.73%, respectively (stage 2 vs. stage 3, paired T-test, p > 0.05). Overall, the turning accuracy of stage 2 and stage 3 demonstrated stable behavior results of brain control on rat cyborgs at the group level. We further analyzed the sending number of different instructions among the three stages. Figure 4(a) shows the average number of Left and Right turning instructions to complete an experimental run across sessions of all the rat cyborgs tested. Theoretically, the minimum number of turning instructions given in a 100% accuracy run is 16, which can hardly be reached even by experienced manual control. Compared with the GRAM-based and the TREM-based brain control stages, the group-level number of turning instructions were 60.15 ± 7.33 and 87.98 ± 56.30 (stage 2 vs. stage 3, paired T-test, p < 0.01), respectively. Thus, more turning instructions were needed to steer the rat cyborg with TREM-based brain control. Since the number of turning instructions was largely affected by the accuracy of the instructions, the extra instructions in TREM were most likely used to compensate the effect of wrong turning behavior. As we mentioned above, instructions given with a proper timing contributed to fewer mistakes; therefore, the lower number of turning instructions in the GRAM-based brain control stage demonstrated that there was less error turning correction in GRAM-based stage 2 than in TREM-based stage 3. Figure 5. The delay between the start of decoding result output and the instruction generation refers to the thresholds for GRAM and TREM. ***indicates p < 0.01, T-test. As shown in Fig. 4(b), the group level average of Forward instructions across the sessions of GRAM-based and TREM-based brain control was 228.14 ± 44.44 and 286.70 ± 13.57, respectively. The statistical analysis indi- cated that the sending number of Forward instructions had no significant difference (stage 2 vs. stage 3, paired T-test, p = 0.09) between the two brain control stages. This may be due to the large fluctuation in the first two sessions of stage 2, which might have been caused by the transition from manual control to brain control. On one hand, the brain-control manipulators needed to gain experiences in controlling rats. On the other hand, the rat cyborgs also needed time to get adapted to new controlling strategy, especially the different stimulation timing and frequency from manual control. When only the later three sessions of stage 2 and stage 3 were compared, the sending Forward instruction did show a significant difference (later three sessions, stage 2 vs. stage 3, paired T-test, p = 0.03). This result demonstrated that the TREM-based brain control model requires more Forward instructions for the rat cyborgs to complete the same turning tasks. The reason for more Forward instructions with the TREM-based brain control model was the rat cyborgs had a worse performance with the TREM model and required more turning and forward instructions to correct the wrong behavior. To explain the different performances of GRAM- and TREM- based brain control strategies, we also calcu- lated the short delays occurred between decoding result output from EEG device and instructions generated by two different control models. Our results showed a nearly 70% reduction of instruction generation delay with GRAM (155.01 ± 3.10 ms) compared to TREM (494.70 ± 47.22 ms) (Shown in Fig. 5). Turning instructions were thus generated and sent much quicker after the motor imagery with the GRAM model, which ensured less wrong turning behavior of the rat cyborgs and better turning performance. The BBI system was further tested in a maze of higher complexity to test its applicability and stability. The rats were asked to complete a series of preset navigation tasks such as climbing and descending steps, turning left or right, and going through a tunnel in a three-dimensional maze under control of the BBI system. When the rat went into a wrong direction or turned into an unexpected route, the manipulator needed to guide the rat back to the correct route (Fig. 6, see more details in Supplementary Video 1). 5 minutes completion time for each run was limited as the criterion for evaluating success rate. A successful run was defined as the rat cyborgs finish all of preset navigation tasks following the route within the limited time. All rats participated in turning tasks were tested with the optimized GRAM-based brain control model in the maze task. The rats all performed well with high success rate in 10 consecutive tests (Table 1). Discussion Our study demonstrated the feasibility of cultivating an information pathway between a human brain and a rat brain. With our BBI system, a rat cyborg could accurately complete turning and forward behavior under the control of a human mind, and could perform navigation tasks in a complicated maze. Our work extended and explored the further possibility of functional information transmission from brain to brain. Unlike mechanical robots, the rat cyborgs have self-consciousness and flexible motor ability, which means the rat cyborgs will have unexpected movements depending on their own will during the control period. The BBI system should thus be designed with high instantaneity and real-time feedback for a better control effect. Previous brain-to-brain sys- tems have mainly been based on the SSVEP paradigm15,16. In the SSVEP paradigm, the manipulators must switch their attention between the feedback screen and the flickers. However, rat cyborgs move quickly and require a minimum frequency of Forward instructions above 3 Hz. It is thus difficult for the human manipulator to send a high frequency of Forward instructions and simultaneously watch the locomotion of the rat cyborgs in the feed- back screen. Compared with previous works15,16, we used motor imagery and eye blink as manipulative protocols and provided real-time visual feedback of the rat cyborg, which is comparably more viable and avoids the visual fatigue of the manipulators. In addition, during the rat cyborgs brain control experiments, the overall perfor- mance was influenced by several major factors: Figure 6. The rat cyborg was navigated by human brain control in a more complex maze (see more details in Supplementary Video 1). The three-dimensional maze was more complicated, consisting of a start point and an end point, slops and stairs for climbing and descending, a raised platform with a height of half a meter, pillars to be avoided and a tunnel to be passed through. The rat cyborgs were asked to complete the navigation task along the preset route (red arrowed) within 5 minutes. Rat cyborg Success Total Success rate A01 8 A02 9 A03 8 A04 9 A05 10 A06 10 Average 9 10 80% 10 90% 10 80% 10 90% 10 100% 10 100% 10 90% Table 1. Success rate of brain control in the complex maze. (1) The accuracy of instructions. The decoding correctness of motor imagery and the appropriate timing of control instructions influence the control performance the most. Furthermore, the instruction should be sent with high instantaneity, especially when an unexpected mistake occurs. In our brain control sessions, the correctness mainly depended on the threshold value and the timeliness of triggering instruction determined by the control models. The better performance (less CPT and number of turning and forward instructions) for GRAM-based BBI is most likely due to less delay between the start of the decoding results and the release of instructions. Comparatively, the longer delay occurred in TREM may probably contrib- ute to a longer CPT, which in turn resulted in greater amount of instructions needed to complete the task. Besides, the longer delay in the TREM model also leads to obstruction of motor imagery. The manipulators reported that the delay of instruction release during TREM brain control could not readily be perceived. Although the manipulators tried to begin imagery in advance, it was difficult to decide the concrete timing and difficult to operate when instructions were needed to be released over a short period. In contrast, with the short response duration in GRAM, the manipulators were able to start motor imagery at the optimal instruction-receiving time, and switching between Left and Right instructions was much easier. (2) Adaption of the manipulators to brain control task. The mental status of a manipulator can be influenced by disturbance, such as environmental noise, and fatigue caused by long-duration imagery. The ability to overcome these could be improved after several practice sessions. The noninvasive EEG-based BMI used in this study translates the sensorimotor rhythms detected in the bilateral motor areas to the control signal for the rat cyborg. This is not intuitive to the manipulators at the beginning of the experiment, but becomes more intuitive as the experiment goes on. The manipulators gradually learn what instruction should be sent and when their imagery should begin according to the movements and locations of the rat cyborg, thereby cultivating a tacit understanding between the human and the rat cyborg. The stable level of perfor- mance seen in the latter stage 2 and stage 3 indicates this mutual adaption. (3) The inherent adaptive ability of rat cyborgs. Rat cyborgs possess an inherent adaptive ability to their environment and the control method. The overall decrease of average CPT in the manual control stage indicates the adaption of rat cyborgs to the control instructions. The variation trend of each line indicates the various adaption abilities among rat cyborgs. Intriguingly, the final CPT of each rat cyborg reached a similar level. It is likely that all of the rat cyborgs adapted to the same control pattern of the operator. In addition, the rat cyborgs can also adapt to the changes of instruction release due to their excellent learning ability. The results showed that the performance was adversely affected by changes in the control mode (stage 1, session 5 vs. stage 2, session 1 and stage 2, session 5 vs. stage 3, session 1 in Fig. 3(a,b)) but subse- quently stabilized. The decrease in the turning accuracy from stage 1 to stage 2 was much sharper than the change from stage 2 to stage 3. This may be because the control pattern is more distinct between manual control and brain control. While between different brain control stages, the manipulator’s control pattern was not likely to dramatically alter. (4) In conclusion, our findings suggest that computer-assisted BBI that transmits information between two entities is intriguingly possible. The control model proposed here could transfer the decoding results of motor imagery-based EEG-BMI to other external devices with remarkable instantaneity. In the future, er- ror-related potentials (ErrPs)21 could be used to detect false generated instructions, thereby eliminating the wrong instructions before sending them to the rat cyborgs. Furthermore, information flow will be made bidirectional and communicative between two human individuals. Methods Participants and ethics statement. Six rats were engaged in this study. All methods were carried out in accordance with the National Research Council’s Guide for the Care and Use of Laboratory Animals. All exper- imental protocols were approved by the Ethics Committee of Zhejiang University, China. Informed consent was obtained from all manipulators. Rat cyborg preparation. The rat cyborg system had long been developed in our previous research work. Briefly, bipolar stimulating electrodes were made from pairs of insulated nichrome wires (65 μm in diameter), with a 0.5 mm vertical tip separation. Microelectrodes were implanted into the rat’s brain for the control of their locomotion. Two pairs of electrodes were implanted in the bilateral medial forebrain bundle (MFB)22 for virtual reward stimulation and instruction of forward moving. The other two pairs of electrodes were implanted sym- metrically in both sides of the whisker barrel fields of somatosensory cortices (SIBF)23 for turning cue stimula- tion. The rats were allowed to recover from the surgery for one week before the experiments. Once recovered, the rat cyborgs were first trained to correlate the stimulations with the corresponding locomotion behaviors17. The parameters of the electrical stimulation that were sent into the rat’s brain were based on our previous works24, which can activate appropriate behavior but avoid seizures even after a long duration of stimulation. During the training and control sessions, electrical stimulations were delivered through a wireless microstimulator mounted on the rat’s back. Control instructions were given by operators with a computer program wirelessly connected to the microstimulator through Bluetooth. Decoding in the BBI. A commercial EEG device, Emotiv EPOC (Emotiv Inc., USA)25 was used in this study for EEG data recording. EEG data were acquired with a 14-channel neuroheadset, with all electrode impedances kept below 10 kΩ. During the brain control experiments, the EEG signals were sampled at the rate of 256 Hz. The recorded data were then wirelessly transmitted to a host computer through Bluetooth and further processed with the help of Emotiv SDK. Through trained imagination, the manipulators learned to modulate their sensorimotor rhythm amplitude in the upper mu (10–14 Hz) frequency band26,27. The power spectrum of left and right compo- sition was then obtained as the intensity of motor imagery by common spatial pattern (CSP)28, i.e., xL(t) and xR(t), respectively. Details of the common spatial pattern filter are described as follows: Let XR and XL denote the preprocessed EEG during right- or left-hand movements with dimensions N × T, where N is the number of channels and T is the number of samples per channel. The common spatial pattern filter is acquired as follows: (1) Calculate the normalized channel covariance of XR and XL as: cov(XL) trace(XLXL) (1) cov(XR) T CL = CR = trace(XRXR) (2) (2) Average the CL and CR on all of the left- and right-hand movement EEG trials; the composite spatial covari- T Scientific RepoRts | 00 (4) Perform whitening transform on CL and CR, and the transformed spatial covariance matrixes are: SL = PCLPT (5) SR = P CRPT (6) where, (5) Perform eigenvalue decomposition on the transformed spatial covariance matrix, where: S =UΣUT (8) L LLL S = U Σ UT (9) R RRR (Note that ΣL + ΣR must be an identity matrix); (6) The eigenvectors corresponding to the largest eigenvalue in ΣL and ΣR are chosen to calculate the common spatial pattern filters for right- and left-hand movements, which can be written as: SF = U (i|argmax Σ (i))P LL iL (10) SF = U (j|argmax Σ (j))P RR jR (11) (7) Let x(t) be the preprocessed EEG signal recorded in movement imaginary application, the intensity of left- and right-hand movement imagery can be given as: xL(t) = SFLx(t) (12) xR(t) = SFRx(t) (13) (8) Finally, calculate the power spectral density of xL(t) and xR(t), and aggregate the band power within the overlapping window length of k. (14) (15) where P(x(t)) indicates the power spectral density of x(t). The intensity of motor intent was then mapped to a value ranged from 0 to 1, and the normalized B(t) was used as the input of the control model. Set up of BBI system. As the BBI system consisted of a noninvasive EEG-based BMI and a rat cyborg system, a controlling program written in Visual C++ was applied to acquire EEG raw data from Emotiv SDK, generate instructions with the control models and trigger the release of instructions to the rat cyborg. The loco- motion and location of the rat cyborg in the entire experimental scene was video captured by a top-viewed cam- era and visually delivered back to the manipulators on an LCD screen in real time. The decoding results of motor imaginary were relayed using a flashing instruction feedback panel that was integrated in the bottom of the LCD by an OpenCV (Open Source Computer Vision Library, http://opencv.org)-based self-written program. The EEG decoding results and motor control instructions were recorded with a J2EE (Java 2 Platform Enterprise Edition)-based program for further analysis. Control models for BBI. The inputs of the control model included the decoding results of Left or Right motor imagery and eye blink detection. The collected EEG signals were projected by a common spatial pattern (CSP) spatial filter. Next, the power spectrum of left and right composition was obtained as the intensity of motor imagery, i.e., xL(t) and xR(t), respectively. Eye blink, xF(t), was detected when the EEG signal (E(t)) amplitude of channels near the eyes exceeds a threshold →θEOG.  The output of the control model was a control signal for the microelectrical stimulations. xL(t), YR(t) and YF(t) represent the Left, Right and Forward instructions, respectively. For the safety of the rat cyborgs, instructions should be sent under the following rule: If two instructions were presented continuously, the latter instruction would only be sent when the time interval was larger than a prede- fined threshold ΔT. Adjacent instructions were defined as tuples <C1, C2>, C1, C2 ∈{Left, Right, Forward}. Five P = Σ−1/2UT (7) 0 t B (t) = P(x (t)) L∑L t−k t B (t) = P(x (t)) R∑R t−k →→ 1, E(t) ≥ θ xF(t) =  EOG 0, Otherwise (16) → Figure 7. Samples of decoding results and their corresponding gradients of motor imagery in a preliminary experiment. The blue curve is the result of right imagery (Right) and the orange curve is the result of left imagery (Left). During the right turning period shown in the figure, only right imagery occurred, while in the left turning period, both left and right results appeared. The right decoding results were deemed to be caused by noise. In addition, the left decoding results appearing in the blank period (no imagination) are regarded as noise as well. The gradL (yellow) and gradR (light blue) curves represent the left and right gradient of corresponding decoding results, respectively. θL and θR are the optimal thresholds for left and right motor imagery in TREM. For GRAM, the optimal thresholds are θL and θR. out of nine types of tuples were restricted, namely, ΔT<F,F>, ΔT<L,L>, ΔT<R,R>, ΔT<L,F> and ΔT<R,F>. These five were determined by the number distribution of the interval for each tuple based on the manual control sequence record. To guarantee the proper reaction, the level of excitement and the safety of the rat cyborgs, the intervals of ΔT<F,F>, ΔT<L,L>, ΔT<R,R>, ΔT<L,F> and ΔT<R,F> for brain control were set to be 200 ms, 500 ms, 500 ms, 350 ms and 350 ms, respectively, according to our previous work17. The minimum time interval was not restricted for F-L, F-R, R-L and L-R because the manipulator needed to send the first turning command as quickly as possible. We defined n = 0, 1, ... as the n-th generation of instruction, and tL(n), tR(n) and tF(n) were the times that an instruction occurred. Initially, tL(n), tR(n) and tF(n) were equal to 0 (n = 0). The generation of Forward was the same for the two models, as described below: (17)    t (n)−t (n−1)≥ΔT ,   F F <F,F>   t (n) − t (n − 1) ≥ ΔT , FL <L,F> 1, t∈tF Y(t)=   t (n)−t (n−1)≥ΔT  F         F a n d R x F ( t F ) = 1 <R,F>   0, otherwise  Two models (Fig. 1(b)) for generating Left and Right instructions were proposed. One was called the thresh- olding model (TREM), in which the instructions were generated when the decoding results exceeded a threshold (θ). The other model was the gradient model (GRAM), in which the instructions were generated when the gradi- ent value between two decoding results transcended a threshold (θ′). The thresholds were used to differentiate the decoding results attributed to real intention or noise. Figure 7 demonstrates typical decoding results of a left and right imagery and their corresponding gradients. Thresholding Control Model. For TREM, controlling impulses were generated when the intensity of left or right exceeded a threshold θ. A turning instruction was generated if xL(t) > θL or xR(t) > θR. Therefore, the function of TREM is described as follows:  1, t∈{t|t(n)−t(n−1)≥ΔT and x(t)≥θ} LLL <L,L>LLL YL(t) =  0, otherwise  (18)  1, t∈{t|t(n)−t(n−1)≥ΔT and x(t)≥θ} RRR <R,R>RRR YR(t) =  0, otherwise  (19) Gradient Control Model. Although the threshold in TREM could differentiate the decoding results attributed to real intention or floating background noise, the delay between the start of the decoding results and the generation of instruction was too long. We proposed an improved model, GRAM, that outperformed in both differentiation and instantaneity. For GRAM, instructions were generated when the gradient value between two decoding win- dows transcended a threshold θ′. The gradient value was calculated as follows: Gradx(t) = x(t) − x(t − 1) (20) A turning instruction was generated if Grad x(t) > θ′. Accordingly, the function of GRAM is described as follows:  1, t∈{t|t(n)−t(n−1)≥ΔT  LL L <L,L> and Gradx(t)≥θ′} LL L and Gradx(t)≥θ′} RR R YL(t) =  0, otherwise (21) (22)   1, t∈{t|t(n)−t(n−1)≥ΔT  RR R <R,R> YR(t) =  0, otherwise  The thresholds θ and θ′ were decided prior to the implementation of brain control. To ascertain the optimal threshold, a preliminary experiment was conducted. The manipulators were asked to complete three rounds of eight motor imagery tasks. Intents were decoded in real time, and the decoding results were recorded. The best threshold was determined with a receiver operating characteristic (ROC) curve. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References 1. Royer, A. S., Doud, A. J., Rose, M. L. & He, B. EEG control of a virtual helicopter in 3-dimensional space using intelligent control strategies. Ieee T Neur Sys Reh 18, 581 (2010). 2. Xia, B. et al. A combination strategy based brain–computer interface for two-dimensional movement control. J Neural Eng 12, 46021 (2015). 3. Wolpaw, J. R. & McFarland, D. J. Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. P Natl Acad Sci Usa 101, 17849 (2004). 4. Carlson, T. & Millan, J. D. R. Brain-controlled wheelchairs: a robotic architecture. Ieee Robot Autom Mag 20, 65 (2013). 5. Meng, J. et al. Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks. Sci Rep-Uk 6, 38565 (2016). 6. Hoy, K. E. & Fitzgerald, P. B. Brain stimulation in psychiatry and its effects on cognition. Nat Rev Neurol 6, 267 (2010). 7. Chen, R. et al. Depression of motor cortex excitability by low‐frequency transcranial magnetic stimulation. Neurology 48, 1398 (1997). 8. Nguyen, J. et al. Repetitive transcranial magnetic stimulation combined with cognitive training for the treatment of Alzheimer’s disease. Neurophysiologie Clinique/Clinical Neurophysiology 47, 47 (2017). 9. O Doherty, J. E. et al. Active tactile exploration using a brain–machine–brain interface. Nature 479, 228 (2011). 10. Flesher, S. N. et al. Intracortical microstimulation of human somatosensory cortex. Sci Transl Med 8, 141r (2016). 11. Romo, R., Hernández, A., Zainos, A. & Salinas, E. Somatosensory discrimination based on cortical microstimulation. Nature 392, 387 (1998). 12. Min, B., Marzelli, M. J. & Yoo, S. Neuroimaging-based approaches in the brain-computer interface. Trends Biotechnol 28, 552 (2010). 13. Pais-Vieira,M.,Lebedev,M.,Kunicki,C.,Wang,J.&Nicolelis,M.A.Abrain-to-braininterfaceforreal-timesharingofsensorimotor information. Sci Rep-Uk 3, 1319 (2013). 14. Rao, R. P. et al. A direct brain-to-brain interface in humans. Plos One 9, e111332 (2014). 15. Yoo, S., Kim, H., Filandrianos, E., Taghados, S. J. & Park, S. Non-invasive brain-to-brain interface (BBI): establishing functional links between two brains. Plos One 8, e60410 (2013). 16. Li, G. & Zhang, D. Brain-computer interface controlled cyborg: establishing a functional information transfer pathway from human brain to cockroach brain. Plos One 11, e150667 (2016). 17. Feng, Z. et al. A remote control training system for rat navigation in complicated environment. J Zhejiang Univ-Sc A 8, 323 (2007). 18. Talwar, S. K. et al. Behavioural neuroscience: Rat navigation guided by remote control. Nature 417, 37 (2002). 19. Wang, Y. et al. Visual cue-guided rat cyborg for automatic navigation [research frontier]. IEEE Comput Intell M 10, 42 (2015). 20. Yu, Y. et al. Intelligence-augmented rat cyborgs in maze solving. Plos One 11, e147754 (2016). 21. Chavarriaga, R., Sobolewski, A. & Millán, J. D. R. Errare machinale est: the use of error-related potentials in brain-machine interfaces. Front Neurosci-Switz 8, 208 (2014). 22. Hermer-Vazquez, L. et al. Rapid learning and flexible memory in “habit” tasks in rats trained with brain stimulation reward. Physiol Behav 84, 753 (2005). 23. Paxinos, G. & Watson, C. The rat brain in stereotaxic coordinates. (Elsevier, Academic Press, Amsterdam, 2014). 24. Xu, K., Zhang, J., Zhou, H., Lee, J. C. T. & Zheng, X. A novel turning behavior control method for rat-robot through the stimulation of ventral posteromedial thalamic nucleus. Behav Brain Res 298, 150 (2016). 25. Martinez-Leon, J., Cano-Izquierdo, J. & Ibarrola, J. Are low cost Brain Computer Interface headsets ready for motor imagery applications? Expert Syst Appl 49, 136 (2016). 26. Pfurtscheller, G., Neuper, C., Flotzinger, D. & Pregenzer, M. EEG-based discrimination between imagination of right and left hand movement. Electroencephalography and clinical Neurophysiology 103, 642 (1997). 27. Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G. & Vaughan, T. M. Brain–computer interfaces for communication and control. Clin Neurophysiol 113, 767 (2002). 28. Kumar, S., Mamun, K. & Sharma, A. CSP-TSM: Optimizing the performance of Riemannian tangent space mapping using common spatial pattern for MI-BCI. Comput Biol Med 91, 231 (2017).

1 1

Cryptocurrency: Markets 101
by grarpamp 26 Feb '23

26 Feb '23

How To Crypto 51: What is an order book, what are maker/taker fees, what are order types and what is slippage? How To Crypto 51: What is an order book, what are maker/taker fees, what are order types and what is slippage? A guide by MalletSwinging TLDR There is no TLDR for this post. I try to include them when possible but there is no way for me to summarize this information. This is a long, thorough and informative post so be prepared to spend some time reading it if you decide to continue. Intro Welcome to another post in my series, How To Crypto by MalletSwinging. I decided to write these posts because I see a lot of new Redditors on this sub every single day. I know how confusing and overwhelming this space can be and I want to make it easier for our greener members to navigate crypto without feeling too embarrassed to ask questions. In this post I am getting back to crypto basics. On my last post I received a comment from a reader who asked ‘what is a stop loss?’ It got me thinking that maybe instead of continuing with more complicated trading posts I should make a few that can act as a foundation for more advanced concepts I’ll introduce in later articles. Today we are going to talk about order types available on a centralized exchange, or CEX. A CEX is an exchange like Coinbase, Binance, Crypto.com, Kucoin or one of the many other smaller players in this space. We’ve seen centralized exchanges like FTX and Voyager go belly up recently and users of those exchanges have lost some or all of their crypto. This is why picking a reputable exchange to use as a fiat onramp is an important choice. Trading is also possible on a decentralized exchange (DEX) like Sushiswap, Uniswap, Bancor and others. Many of the concepts in this document pertain to DEX trades as well but for the purposes of this article we will be looking at CEX orders and order books only. I’m not here to debate CEX vs DEX or the (de)merits of each CEX; there are plenty of posts in this sub that discuss those. This is a purely informative post and contains no advice beyond what to expect when placing buy or sell orders. To learn any of these concepts we must first understand what an order book is. What is an order book? An order book is a list maintained by each centralized exchange that contains every current and active buy or sell order on a specific trading pair. What’s a trading pair you ask? Great question, Mallet! A trading pair contains exactly two assets. At least one of these on a CEX will always be a cryptocurrency. The other can be another cryptocurrency but it can also be a unit of fiat currency like USD or EUR. Many times the second asset will be a stablecoin like USDT, USDC or (gasp) BUSD. A trading pair allows you to trade one of the two assets for the other. If a trading pair is BTC/USD it means that I can trade BTC for USD or I can trade USD for BTC. These are the only two assets available to trade in this particular trading pair. If I have USDT and I want to trade it for BTC I need to look for BTC/USDT which is a completely separate pair from BTC/USD. Each trading pair has its own order book. An order book on a CEX for BTC/USD has every single current and active order that has been placed on that exchange to trade BTC for USD or USD for BTC. They do not cross over from one exchange to another, meaning the order book on Coinbase will have different order amounts and prices compared to the order book for the same pair on Binance. Let’s take a look at how this is represented visually on Binance.us: https://preview.redd.it/180sjlmnbdka1.png?width=577&format=png&auto=webp&v=… There is a lot of information to unpack here! First, the green numbers on the left represent buy orders. This means these are orders from users who have USD and want to buy BTC. The red numbers on the left represent sell orders. These are orders from users who have BTC and want to sell it for USD. These are real orders placed by accounts on Binance.us. Some people may have placed these orders manually but the majority of them are placed by bots or automated trading strategies. For practical purposes we don’t really care who placed these orders; we only care that they exist. Breakdown of what we are seeing in this order book: The green and red bars on the right show relative order book sizes of the crypto asset at the dollar price listed on the left. Since we are coming at this scientifically we will use the actual numbers instead of the bars. Bids The top green number ($24,880.40) represents the highest USD price someone is willing to pay for BTC. This line is known as a bid. Each green number represents a bid price. Each line represents real USD that someone has locked and is willing to pay to buy BTC. The white number directly to the right of the highest bid price (0.130726) represents how much BTC they are willing to buy at that price. If you look to the second white number right of the first white number you will see the total USD price ($3,252.52) that you would receive if you sold them that 0.130726 BTC at their bid price. These two numbers don’t necessarily mean that one person wants to buy all that BTC at that bid price; this can be one or more parties that cumulatively want to buy that amount of BTC at that bid price. As we go down the list of bids we see subsequently lower green numbers on each line. These represent the next highest bids currently in the order book. Note that they will always be in descending numeric order by price with the highest bid price at the top of the list. There are more bids (aka buy orders) below the lowest one on this list but the list is heavily truncated due to space constraints. Different exchanges will show different amounts of both buy and sell orders depending on how their site is designed. Most have the option to view only buy orders or only sell orders. Last sale price The large red number right above that ($24,880.40) represents the last sale price of any amount of BTC on Binance.us. Note that in this case it is the same as the highest bid price. This is not always the case but it is here. This likely means that a buy order at $24,880.40 was only partially filled and what we are seeing is the remainder of that buy order still on the order book. The red arrow down arrow to the right of the last sale price represents the direction the last sale price moved from the sale price before that. All this tells us is that over the last two trades the sale price of BTC went down by at least $0.01. If the arrow were green and pointed up it would tell us that over the last two trades the sale price of BTC went up by at least $0.01. If you were to look on TradingView or any other charting platform this sale price is what constitutes the current price of an asset. Asks Next, let’s look at the smallest red number directly above the last sale price. This line is known as an ask. The red number ($24,882.06) represents the lowest ask price which is the lowest price someone is willing to sell BTC, priced in USD. Each red number represents an ask price. Each line represents real BTC that someone has locked and is willing to sell for USD. The white number directly to the right of that (0.156284) represents the amount of BTC someone has listed for sale at the lowest ask price. This is real BTC that someone has locked for sale at the price listed on the ask line. *** Note to more advanced readers: There are enough arguments about whether CEXs actually have the crypto assets in question in this sub; please don’t bombard the comments section with this as it will just confuse new users. Let’s assume for the sake of this lesson that they do. The second white number directly to the right of the first white number is the total dollar amount it would cost to buy the entire 0.156284 of BTC at the lowest ask price of $24,882.06. As we go up the list of asks we see subsequently higher red numbers on each line. These represent the next lowest asks currently in the order book. Note that they will always be in ascending numeric order (from the bottom up) by price with the lowest bid price at the bottom of the red list. There are more asks (aka sell orders) above the highest one on this list but the list is heavily truncated due to space constraints. Different exchanges will show different amounts of both buy and sell orders depending on how their site is designed. Maker and taker fees If you look at any exchange’s fee structure you will see two different commission pricings; a maker fee and a taker fee. Since we are trading BTC/USD on Binance.us (or Binance.com) there are technically no fees for orders however most other trading pairs are charged a percentage fee on the total value of each trade that is successfully executed. As far as I know all non-Binance exchanges charge fees on all trades regardless of trading pair. Some do charge tiered fees and some give discounts for trading volume or for paying in their exchange token but that’s not really important to understand here. Here is the base Binance.us fee structure for pairs other than BTC/USD(T) or ETH/USD(T): https://preview.redd.it/noqhsgnubdka1.png?width=350&format=png&auto=webp&v=… Maker orders If you place an order on an order book that is not immediately filled but does remain on the exchange’s order book it is called a maker order. In other words, maker orders occur when you create liquidity on an order book by putting one of the two assets in a trading pair up to exchange for the other at a trade price that will not immediately be filled. Limit orders can be maker or taker orders. If you place a maker order and it is filled you are charged the maker percentage (in this case 0.3%) on the entire trade value by the exchange. Taker orders An order that removes liquidity from an order book immediately is considered a taker order. Market orders are always taker orders because they will always be filled immediately and will therefore take liquidity off an order book. Limit orders that are priced in such a way that they are filled immediately and remove liquidity from an order book will be considered taker orders. If you place a taker order you are charged the taker order percentage-based fee (in this case a whopping 0.45%) on the entire trade value by the exchange. It is possible for part of a limit order to be filled as a taker and the rest to be filled as a maker. It is also possible for part to be placed as a taker and for the rest to not get filled at all depending on how you place your order and what happens in order book by other parties after your order is placed. Order types Now that we understand the basics of how an order book works and how maker/taker fees are determined let’s take a look at order types. In crypto there are two main order types, limit and market. I will go over Stop-limit orders (aka conditional orders) in my next post about stop losses in the interest of keeping things simple here. All orders require an asset pair so for the sake of simplicity we will again use BTC/USD for our examples and assume we are looking at the same order book I used above. Market orders A market order is an order that allows you to fill in a dollar amount (in the case of a fiat or stablecoin-based pair like BTC/USD) and will purchase that much crypto at the best price available from the exchange’s order book. This option gives you the least amount of control over how much you pay for an asset. Market orders, especially larger ones and/or on low liquidity pairs, are subject to something called slippage which I will go over in a moment. Market orders are always taker orders. Let’s say I have $5,000 and assume the order book I used in the example above is currently in place. I want to buy $5,000 worth of BTC and I don’t care what price I pay for it as long as it’s close to the last sale price. I fill out my market order form like this: https://preview.redd.it/ldvcmlvxbdka1.png?width=974&format=png&auto=webp&v=… I can choose to place this as a total USD amount or a total BTC amount in the field market Total with the down arrow next to it. Here I’ve chosen total USD amount for the sake of being consistent. Here is the same order book for the sake of not have to scroll up or down: https://preview.redd.it/de4plawybdka1.png?width=577&format=png&auto=webp&v=… The moment press Buy I will purchase the exchange will buy $5,000 of BTC at the best available price. Assuming the order book was exactly as the image above at the moment I pressed Buy here I will buy: 0.156284 BTC at $24,882.06 for $3,888.670.007498 BTC at $24,882.73 for $186.570.037163 BTC at $24,883.30 for $924.76 This is a good segue into the next section, slippage. Slippage As you can see in the example above, the last sale price of BTC was $24,880.40 but our average buy price was around $2 higher than that. This is known as slippage. Slippage occurs when there is not enough liquidity on an order book to cover an entire buy or sell at the last sale price. In the example above there was a relatively low amount of slippage (less than 0.01%.) When trading high liquidity, high volume pairs in small amounts there is usually relatively low slippage like this. When trading relatively low liquidity, low volume pairs like JASMY/USDT there will usually be considerably more slippage because the order book is often less competitive and therefore more spread out. Note that if I had placed a sell market order for 0.1 BTC I would have experienced no slippage in this case as there is enough liquidity on the order book to cover my entire sell order at the last sale price. Slippage is something I personally usually try to avoid as I like to know exactly how much I’ll pay for something before I buy it. If you don’t like the sound of slippage either, welcome to the world of limit orders. For funsies, here is what a chart looks like when someone places a BIG market sell order on an order book and incurs a ton of slippage. You can see the long red wick indicating that a market sell order cleared out an order book. This drove the last sale price down before other others swept in and filled the order book back up, bringing the last sale price back into congruence with the rest of the chart. https://preview.redd.it/w03oyyh6fdka1.png?width=1333&format=png&auto=webp&v… Limit orders A limit order is a buy or sell order in which a user has a specific amount of one asset that they wish to trade for a specific amount of another asset. As I mentioned earlier these can be either maker or taker orders depending on how you place them Let’s say that we have $1,000 and we want to buy 0.05 BTC with that exact amount of dollars. A little basic math tells us that in order to buy 0.05 BTC with $1,000 the price for one BTC will need to be $20,000. Here’s the math behind that: [our dollar amount] / [our desired amount of BTC] = [price of BTC required for trade] / [one BTC] Since we are trying to figure out the price of BTC required for trade we can effectively make that variable X and then solve for said X. I hope you remember your high school algebra! $1,000 / 0.05 = X / 1 In this case X is equal to $20,000. If you get stuck on this it might be a good idea to do a quick refresher: https://www.khanacademy.org/math/algebra-basics This means that if I want to buy 0.05 BTC for $1,000 I need the price to come down from $24,880.40 to $20,000 and there needs to be at least 0.05 BTC available at $20,000. Since I don’t want to pay more than $20,000 but I also don’t want to sit at my computer or phone and wait for the price to drop I decide to use a limit order and leave it on the order book permanently as a GTC (good ‘til canceled) order. Here is an image of how I would place this order. The black boxes are me obfuscating some info I don’t want to share on here: https://preview.redd.it/r7c843t3cdka1.png?width=975&format=png&auto=webp&v=… If I fill in the price and BTC amount I want to purchase the total USD amount will be filled in automatically. You must have the USD amount in the Total box available and unlocked in your trading account in order to place this trade. When I press the green Buy BTC button the trade will be placed. The TIF (time in force) option specifies how long your order will remain on the order book. Binance.us gives these common options: GTC, or Good ‘til Canceled – this means that the order will stay on the order book in perpetuity, or until it is filled or canceled. If I had used the $1,000 example buy at $20,000 per BTC above as a GTC order my $1,000 will remain locked meaning I cannot use it for other trades or withdraw it from the exchange. If I had placed a GTC order for 1 BTC at $24,882.06 (assuming we are still using the order book above) I would have purchased 0.156284 BTC for $3,888.67 and the remainder or my order (in this case $20,993.39) would remain on the order book as a buy order for the remaining 0.843716 BTC at a price of $24,882.06. The $20,993.39 would be locked, unavailable for other trades or withdrawals. The trade that was executed for $3,888.67 would be considered a taker order and the other $20,993.39 would be considered a maker order. IOC, or Immediate or Cancel – this means that my order will be executed immediately. If it cannot be filled in full it will fill as much of the order as possible and then cancel the rest of the order. If I had used the $1,000 example buy at $20,000 per BTC above as an IOC order, since the current price of BTC is $24,880 none of my $1,000 order would be filled and my order would be canceled. If I had placed an IOC order for 1 BTC at $24,882.06 (assuming we are still using the order book above I would have purchased 0.156284 BTC for $3,888.67 and the rest of the order would be canceled. the remainder of my $24,882.06 (in this case $20,993.39) would remain in my account balance and it would be unlocked, available for other trades or withdrawal. The $3,888.67 I spent would be considered a taker order. FOK, or Fill or Kill – this means that if my entire order cannot be placed for the exact amount I specified, none of the order will be filled and it will be immediately canceled. If I had used the $1,000 example buy at $20,000 per BTC above, since the best available price for BTC is $24,882.06 none of my $1,000 order above would be filled and my order would be canceled. If I had placed a FOK order for 1 BTC at $24,882.06 (assuming we are still using the order book above) I would have purchased no BTC at all since there is not enough on the order book for me to buy 1 BTC at that price. The entirety of my $24,882.06 would remain in my account balance and it would be unlocked, available for other trades or withdrawal. Other order options There are some other options we can check here, namely Iceberg and Post Only. These options probably won’t be relevant to you as a newer trader but I’ll explain them anyway. Post only – this option means that your order will only be processed as a maker order and not a taker order. If you place an order as Post Only and it is impossible for it to be placed onto the order book without it being filled it will be immediately canceled. Iceberg – this option allows you to break a very large order up into smaller chunks to avoid disrupting a market with one giant buy or sell. As a newer trader it is unlikely that you will need this option, at least not during the beginning of your journey. Once you click this option it will allow you to specify a chunk size that will break your order up into multiple smaller, more obfuscated orders. Sell orders are identical to buy orders except you are interacting with the other side of the order book. I considered redoing all of these examples as sell orders but I think anyone still reading will understand the concept. What happens if I place a limit order that will clear multiple levels of an order book? Another great question! Thanks for asking, Mallet; you really ask the best questions. Let’s again assume this is our order book: https://preview.redd.it/6hgamiy6cdka1.png?width=577&format=png&auto=webp&v=… Let’s assume I want to buy 1 BTC and I’m willing to pay up to $24,883.66 for it but not a penny more. I fill out my form and press Buy BTC. https://preview.redd.it/z6zm4tt7cdka1.png?width=434&format=png&auto=webp&v=… As long as the order book has not changed I will immediately buy: 0.156284 BTC at $24,882.06 for $3,888.670.007498 BTC at $24,882.73 for $186.570.221031 BTC at $24,883.30 for $5,499.980.413122 BTC at $24,883.66 for $10,279.99 This means I will have purchased a cumulative total of 0.797935 BTC for a cumulative dollar amount of $19,855.21. The amount I have purchased here will be considered a taker order. I will then see the remainder my buy order appear on the order book for the remainder of my requested BTC (0.20207BTC) at a price of $24,883.66. If the remainder of my buy order is filled by someone else’s subsequent sell order this part of it will be considered a maker order. Conclusion That’s about it! At this point you hopefully have a clear understanding of how an order book works, how to determine the fees you will pay on trades and the difference between market and limit orders. Armed with this knowledge you might be ready to jump into one of my more advance articles: How to crypto 101: How to grid trade How to crypto 201: What is leverage? Stay tuned for my next post which will be How To Crypto 53: What is a conditional order and how do I use one to set up a stop loss? What is a stop loss, anyway? ***Edit: thank you for the awards, u/tommo_graham, u/rootpl and u/Bailszy! You absolutely didn't have to do that but they are much appreciated.

1 0

Cryptocurrency: Attn: GovBankCorpPol - FYI, You Can't Stop Crypto
by grarpamp 26 Feb '23

26 Feb '23

https://www.youtube.com/watch?v=DjYbsq3FXfM https://v.redd.it/joqa2xfdl7ka1 https://gource.io/

1 0

Cryptocurrency: Action - Join Digi Currency Trade Alliance - Bleat Args At US Congress
by grarpamp 26 Feb '23

26 Feb '23

Change the script, argue for Freedom, Privacy, Transparent Exchanges, Privacy Coins, DEX, Alternate Currencies... https://joindcta.org/ Stop the SEC attack on crypto - email congress today using our free tool! (self.CryptoCurrency) submitted 5 hours ago by DCTAorg☑️ DCTA Official and call them on Monday... Hi r/cryptocurrency! Many of you know about the Digital Currency Traders Alliance (DCTA) - we are a nonprofit coalition of retail investors, consumers, traders, businesses, and thought leaders in the Digital Currency space focused on ensuring the future of digital currency is equitable and open to all. Our latest project is DCTA’s #StoptheSEC Action Days 2023, aimed at getting cryptocurrency consumers engaged in the national legislative process in order to reign in the SEC. Our goal is to connect consumers with their federal representatives so they can communicate how much the SEC has hurt their investments. As you all know, the SEC continues to refuse to proactively issue regulatory guidance for the sector and instead prefers to set policy through enforcement actions. This doesn’t help consumers - the SEC’s inaction harms us by not tackling bad actors when it is needed and their overreaction after the fact. Take 30 seconds out of your day and contact Congress using our free and easy tool at https://joindcta.org/advocacy/stop-the-sec/. It will directly connect you with your representatives and even includes a sample script for you to use. we (the Digital Currency Traders Alliance) are working with some of the members of the California Legislature on legislation to help reduce scam / fraudulent projects, with a specific focus on pump and dumps, account hacks, and phishing attempts. The legislators we are working with asked us to gather some examples of how you, everyday users, have been impacted or fallen victim to these kinds of scams. We are reaching out to hear your stories and experiences -.thank you in advance for your help. In response to the recent regulatory actions at the state and federal levels aimed at the digital currency sector, the Digital Currency Traders Alliance (DCTA) has launched its Legislative Advocacy Portal, which will allow consumers to track and get updates on crypto legislation at the federal and state level. The Advocacy Portal Automatically tracks who their state and federal representatives are and allows them to automatically send their representatives a letter and a link to the DCTA Legislative Crypto Consumer Handbook, which outlines best practices for protecting consumers when legislators are considering crypto currency regulations. Right now, we have a once-in-a-lifetime opportunity for retail cryptocurrency consumers to organize and speak up as our government creates the foundation for how the crypto and blockchain sector will be regulated for decades to come. It is imperative that your voice is heard and recognized in the legislative process! Go to https://joindcta.org/ today to let your lawmakers hear from you! The Digital Currency Traders Alliance is looking to expand its team! (self.CryptoCurrency) submitted 5 months ago by DCTAorg☑️ DCTA Official Hey everyone! This post is for those of you that are looking for an opportunity to get involved in the politics of this emerging sector! The Digital Currency Traders Alliance is looking for volunteers/interns (the entire team is all volunteers right now) to help with social media and communications. Some of the duties for the position are as follows: Assist staff with implementing DCTA’s 2022 Strategic Plan Assist with updating and maintaining DCTA’s social media presence, including Reddit Discord Twitter Instagram TikTok. Assist with additional administrative tasks as necessary We are looking to bring on several individuals to assist us. If you feel you can only accomplish part of the listed duties we would still ask that you apply. Skills Needed: Deep knowledge of social media platforms and insight into content that performs best on each platform. Familiarity with crypto/blockchain blogs and resources, including Twitter personalities, crypto subreddits, and Discord channels. Basic knowledge of the cryptocurrency and blockchain sectors. Firm grasp of available tools and platforms in the social media space. An effective communicator, both written and oral. Ability to communicate in a professional manner with press and community contacts. Self-motivated, good organizational skills, detail-oriented, ability to prioritize, multi-task and meet deadlines. Enthusiasm for the mission of DCTA and the consumers we serve. We are looking for applicants who are interested in working hard, but most importantly working hard for a cause that matters and in a place they can really make a difference in the crypto policies shaping our country moving forward. Applicants should support the goals of the Digital Currency Traders Alliance. This position will require 8-12 hours per week and College/University credit is available. Who We Are The Digital Currency Traders Alliance is a nonprofit coalition of retail investors, consumers, traders, businesses, and thought leaders in the Digital Currency space founded by a team of California-based lobbyists and public policy experts. Our mission at DCTA is to ensure that the future of digital currency trading is equitable and open to all by educating policymakers about the digital currency sector, promoting consumer protections, and giving a voice to everyday consumers. We want to ensure that decision-makers have the knowledge that they need to craft and adopt regulations and best practices that appropriately balance growth and innovation with robust consumer protections. For more information or to submit a resume for consideration please email: [nate@joinDCTA.org](mailto:Nate@joinDCTA.org). I know a lot of folks here are pretty anti-regulation/government. But I can’t help but think how some pretty basic regulations could have helped reduce the severity of the recent meltdown and the contagion from Terra > Celsius > other big funds/service providers. Things like mandatory disclosures to users, rules for use of held customer funds, protections for bankruptcy, or deposit insurance a la FDIC. So I did a quick writeup on some of the pros and cons of regulating crypto and look at what that future might look like and what the legislators we’ve been talking to are actually trying to regulate. To be clear: right now U.S. regulators are mainly focused on regulating centralized service providers like Celsius. Most regulators know enough to know that they have little control over what actually happens on-chain, so the current focus is on businesses that provide services off-chain that can be regulated (if they want to operate in a given jurisdiction) Downsides of regulation Increased compliance burden - service providers would have to be licensed and provide more thorough insight into operations and data. This will result in costs passed down to users, and may also prevent smaller players from being able to operate. Restricted services - similar to how Celsius restricted Earn to accredited investors (prior to their meltdown) or how Coinbase/COMP’s fixed APR products were killed last year, we would undoubtedly see restrictions on what services providers can offer in the first place. Stifling innovation - the above two points add up to big burdens for small players that would likely stop many small teams from being able to operate. Upsides of regulation Preventing Bad Actors - team filings and mandatory disclosures (risks, marketing, influencers, token issuance, etc) would make it harder for anonymous teams to manipulate markets and run rug pulls/PnDs. Tax Clarity - it’s hard to imagine worse tax law for crypto than we have now. Letting users make small transactions and taxing mining/staking/airdrops when disposed would eliminate a lot of tax headache. User protections - codifying things like bankruptcy protections, customer service/uptime requirements, user privacy/security standards, etc. The Lummis-Gillibrand RFIA had provisions for this included. Green-lighting innovation - regulatory clarity would give many businesses the confidence needed to expand and invest in U.S. operations. This could help offset the loss from other businesses that are driven away due to regulation. Widespread adoption - not everyone is technical enough to interact with DeFi and manage their wallets safely. Entities like Celsius were a great way to onboard nontechnical users, assuming they were safe and had proper user protections. Check out the full post here: https://joindcta.org/crypto-regulations-pros-cons/ Sure, there are going to be some growing pains, and there’s always the chance that regulators get this wrong and we end up stuck with some bad laws. This is why we want to make it easy for crypto users to track crypto legislation and contact their reps. Legislators absolutely will listen to us if we make enough noise together, and now is the time to make sure our rights as users are codified as the first real crypto laws are written. It's also a great time to let reps know how important this issue is going into midterms. Check out https://joindcta.org/advocacy/ if you want to learn more and make your voice heard. We want to make this a useful resource - are there any pros/cons of regulation we forgot to mention above? What are your biggest concerns as crypto starts to get regulated? Stop the SEC attack on crypto - email congress today using our free tool! by DCTAorg in CryptoCurrency [–]DCTAorg[S] 2 points 5 hours ago we've got a lot of requests over the last few days to do an international Call to Action. We're working on the plan right now! permalink save context full comments (123) report Stop the SEC attack on crypto - email congress today using our free tool! by DCTAorg in CryptoCurrency [–]DCTAorg[S] 4 points 5 hours ago It really does!! permalink save context full comments (123) report Stop the SEC attack on crypto - email congress today using our free tool! by DCTAorg in CryptoCurrency [–]DCTAorg[S] 2 points 5 hours ago Thanks!! permalink save context full comments (123) report Stop the SEC attack on crypto - email congress today using our free tool! by DCTAorg in CryptoCurrency [–]DCTAorg[S] 6 points 5 hours ago Right now it's just the states. But we've got a lot of requests over the last few days to do an international Call to Action. We're working on the plan right now and should be announcing an international version in the next couple weeks. permalink save context full comments (123) report 134 Stop the SEC attack on crypto - email congress today using our free tool! (self.CryptoCurrency) submitted 5 hours ago by DCTAorg to r/CryptoCurrency 123 comments share save hide report AOC criticizes Christian Super Bowl ads, says Jesus would not fund commercials to 'make fascism look benign' by GDPisnotsustainable in politics [–]DCTAorg 1 point 11 days ago Amen!!! As a fully grown pastor’s kid I have been screaming this for years!!! permalink save context full comments (4603) report 12 California lawmakers are looking for stories about common crypto scams (self.CryptoCurrency) submitted 25 days ago by DCTAorg to r/CryptoCurrency 23 comments share save hide report 0 The Digital Currency Traders Alliance is looking to expand its team! (self.CryptoCurrency) submitted 5 months ago by DCTAorg to r/CryptoCurrency 7 comments share save hide report The Government is regulating crypto, make sure your voice is heard! by DCTAorg in CryptoCurrency [–]DCTAorg[S] 2 points 5 months ago Thanks! permalink save context full comments (21) report The Government is regulating crypto, make sure your voice is heard! by DCTAorg in CryptoCurrency [–]DCTAorg[S] 2 points 5 months ago Our executive director did cannabis policy for over 10 years and heard the “our voice doesn't matter" line countless times. You would be shocked at how legislators react when they start regularly hearing from educated constituents on any specific issue. permalink save context full comments (21) report The Government is regulating crypto, make sure your voice is heard! by DCTAorg in CryptoCurrency [–]DCTAorg[S] 3 points 5 months ago One of the biggest frauds of our lifetime is that big money has been able to convince average voters that their voice doesn't matter. Our team have been advocates for over a decade and have successfully taken on corporate interests time and time again. permalink save context full comments (21) report The Government is regulating crypto, make sure your voice is heard! by DCTAorg in CryptoCurrency [–]DCTAorg[S] 3 points 5 months ago The portal is actually free to use! People who want to support our work are also free to do it! permalink save context full comments (21) report 7 The Government is regulating crypto, make sure your voice is heard! (self.CryptoCurrency) submitted 5 months ago by DCTAorg to r/CryptoCurrency 21 comments share save hide report New Bipartisan US Bill Puts CFTC In Charge Of Regulating Bitcoin And Ethereum by jakkkmotivator in CryptoCurrencies [–]DCTAorg 1 point 6 months ago Good, rather the CFTC than the SEC. Same as was proposed in June in the RFIA bill. It looks like the legislative consensus is that most coins that serve the purpose of directly helping a network run through PoS staking or PoW rewards will be commodities under the CFTC's jurisdiction. The problem that this bill still does not address is how to make the distinction between commodity and security for the other 99% of tokens. The RFIA proposed "commodity by default" and then any tokens that granted the following would be considered a security: Voting rights with respect to that entity. Rights to interest, dividend payments, or profits with respect to that entity. A debt or equity interest in that entity. Liquidation rights with respect to that entity. Meaning whatever bill ends up passing, the SEC will probably get jurisdiction over anything DeFi and many of the interesting things being done in the ecosystem, and future legislation will probably not rock that boat. So yeah, great to see continued support for BTC and ETH to be commodities under the CFTC, but still a lot of things for regulators to figure out about the rest of the market and use cases. permalink save context full comments (32) report US Senators propose bill to exclude crypto transactions under $50 from taxes. Another step in the right direction. by partymsl in CryptoCurrency [–]DCTAorg 1 point 6 months ago Yup. Which is one of the many reasons why we desperately need to be talking to legislators and get this changed to "taxed when disposed of, not earned." The Lummis-Gillibrand RFIA proposed back in June would do exactly this, although that bill seems unlikely to pass at this point. permalink save context full comments (589) report Sure centralized platforms and projects might be easier to navigate but this comes at the huge cost of custody. Never forget that. by [deleted] in defi [–]DCTAorg 1 point 7 months ago Generally agreed, but you have to admit that these centralized platforms do play a positive role in crypto adoption. Not everybody is capable of being their own bank, managing their own keys, or using a web3 wallet. If you want to bring these users into the ecosystem, a centralized crypto platform is a great solution. Remember all the posts last year about people who onboarded friends/family to the benefits of crypto via celsius? The problem is that there are no rules on what these platforms can do, and so they generally end up doing shady shit when they think no one is looking. What we need is sensible regulation and protections on these custodial service providers to keep them from doing shady stuff behind the scenes. Things like: requirements on how customer funds can be used bankruptcy protections so users can recover their assets (or deposit insurance) required disclosures for product risks The recently proposed RFIA bill in the senate had clauses for user protections on all the above points, although that bill is currently on hold til 2023 from the looks of it. The future of crypto isn't 100% pure decentralization all the time, it's going to be about integrating blockchain with existing tech and frameworks for a better, more transparent and equitable system. DeFi will always be there for the users that want it, and centralized service providers will be there for those who aren't ready to make that leap yet. permalink save context full comments (28) report 1 Regulation could have prevented some of this… (self.CryptoCurrency) submitted 7 months ago by DCTAorg to r/CryptoCurrency 21 comments share save hide report Celsius lawyers claim users gave up legal rights to their crypto. by Socialinfluencing in CryptoCurrency [–]DCTAorg 5 points 7 months ago Everyone saying "not your keys not your crypto" is ignoring the fact that apps like Celsius help bring crypto adoption to non-technical users by abstracting away complexity. A year ago everyone was onboarding their friends & family through Celsius, and it was generally seen as a big victory for the space. Do you all trust your parents or grandparents to manage their own private keys? Should we just gatekeep out all the non-technical folks? Instead of saying "I told you so" after a centralized platform loses user funds, why don't we instead try to proactively regulate these platforms before something bad happens? For example the RFIA bill that was proposed last month had clauses specifically for: restricting use of deposited customer funds (no yield farming w/ deposits) protecting user deposits in the event of a bankruptcy Celsius can put whatever they want in their ToS, but at the end of the day it all still has to comply with existing laws and be upheld in court. That said, laws specific to crypto are minimal and vague right now which just drives home the point that we need smart regulation and consumer protections in this space to prevent big players from abusing their end-users like this. I know many folks here are probably resistant to regulation in crypto, but now is literally the best time to get involved and make your voice heard to ensure we get proper consumer protections as crypto becomes more mainstream. permalink save context full comments (318) report US Senator Says Too Many Crypto Firms Are Able to Scam Customers — Urges SEC to Regulate by Beyonderr in CryptoCurrency [–]DCTAorg 1 point 7 months ago Regulation was coming long before Luna & Celsius melted down, they just became focal points for the conversation since a lot of people lost a lot of money in a very short timeframe. Many here are generally against regulation, but what's so bad about regulating centralized service providers from making sure they can't do shady things with user funds? Regulation is also how we get things like deposit insurance, customer service standards, and other basic user protections. The recent crypto regulatory framework that was proposed in June literally has clauses that would have prevented exactly what Celsius was doing and might have prevented this whole market-wide implosion. I guess my point is: regulations are coming to crypto, like it or not. If you're concerned regulators will get it wrong, then get involved in the political process, contact your reps about crypto, and make your voice heard. permalink save context full comments (335) report 2 Verification Request: DCTA (self.CryptoCurrencyMeta) submitted 7 months ago by DCTAorg to r/CryptoCurrencyMeta 2 comments share save hide report Yesterday European Commission agreed on a deal for stringent and invasive AML for all crypto services, including linking your onchain wallets to user's KYC details. This drastically destroys user's privacy by Set1Less in CryptoCurrency [–]DCTAorg 1 point 7 months ago I understand the gut reaction to think "this is invasive and an overreach on the EU's part, the government is going to ruin crypto," but this is a pretty positive step. Everyone here wants crypto to go mainstream and help build a better, more equitable financial system - but you don't get there without the blessing of the world's major governments. This ruling gives crypto more formal legitimacy in the EU. P2P transactions and anything purely onchain would be unaffected, so you can always go that route if you're concerned. Much of this info is already easy enough to figure out with chain analysis if you've ever done any KYC with any centralized exchange, this just formalizes the process by making you confirm what they already know. If this sort of news worries you take a step back, look at the bigger picture, and realize that this is actually a big step forward for crypto adoption. permalink save context full comments (851) report [deleted by user] by [deleted] in CryptoCurrency [–]DCTAorg 1 point 8 months ago Do you trust your grandparents to manage their funds on a hardware wallet? What about the friend that regularly has their social media accounts compromised by clicking on phishing links? Not your keys, not your coins is simply not a viable long-term solution for the average user. Hardware wallets are great for the OGs in this space, but if you want wider adoption and crypto to actually be used globally, it needs to have a better user experience for casual users and late adopters. permalink save context full comments (228) report [deleted by user] by [deleted] in CryptoCurrency [–]DCTAorg 1 point 8 months ago You aren't wrong, but how do you realistically expect a newbie to this space to vet every single claim that is thrown at them? The sheer volume of misinformation is astounding, and it takes many users in this space months or years to even have a basic and partial understanding. The whole point of regulation is to pass part of this burden from the end-user up to the regulators and service providers directly, making it easier for users to safely adopt. Of course, no government can really regulate DeFi and DAPPs, so those products will always be available to the technically savvy like you & me. But how many people were using Celsius or other CeFi platforms to casually onboard friends and family to crypto? These centralized platforms can and should be regulated, especially if we ever want crypto to see more widespread use than it currently does. Regardless, regulation is coming, so best be a part of that conversation when it does. permalink save context full comments (228) report [deleted by user] by [deleted] in CryptoCurrency [–]DCTAorg 1 point 8 months ago Many in this space don't want to admit it, but cases like this are why regulation exists, and it isn't the boogeyman many think it is. If we all want crypto to become mainstream and act as the backbone of a new financial system, there must be regulations in place to prevent bad actors and protect the less technical users out there. For example, the recent draft of the Responsible Financial Innovation Act had multiple clauses that would have expressly prohibited what Celsius was doing with user funds, and protections in place for users if/when they go bankrupt. That bill is far from perfect, but the consumer protections are a step in the right direction and the sort of language that we need to reiterate with lawmakers as they begin to draft crypto legislation over the next 1-2 years. Make no mistake, regulation is coming to this space, and it's coming fast. All you can do as an end-user is make your voice heard to ensure that politicians don't mess it up when it does happen. permalink save context full comments (228) report I’m having a hard time making sense of DeFi by imnessal in defi [–]DCTAorg 1 point 8 months ago Speaking to point #2 on regulation: as a grassroots crypto advocacy group, what we've seen in a lot of early discussions with legislators and draft bills is that it's hard for regulators not to accidentally target DeFi as they try to write laws that target CeFi. Almost none of them are trying to target DeFi right now; to /u/Terrorbear's point, most regulators don't care as long as nothing blatantly illegal is going on. But in writing laws that affect money transmission and KYC/AML, it's hard not accidentally target DeFi operators. Right now most politicians are playing catch up from 5 years ago as they struggle to understand the basics of cryptocurrencies and digital assets, and figure out how the hell to classify all this stuff legally. They generally aren't looking at the nuances of DeFi yet, but that will likely come over the next 2-3 years once they've figured out the basics of the industry. Shameless plug: if you're concerned that regulators will get this wrong, check out our website joindcta.org. We're organizing a coalition of crypto users to speak up and make sure our voices are heard as crypto legislation is being drafted around the U.S. permalink save context full comments (39) report view more: next › Another crypto day for the government by ResponsibleResort195 in CryptoMarkets [–]DCTAorg 1 point 8 months ago Literally none of the legislators we've spoken to want to ban crypto (here in the US), they all want to understand and figure out how to stop their constituents from getting scammed and then bring revenue into their jurisdiction. They also largely understand that trying to stop everything that happens on blockchain is impossible, and will just be an endless game of regulatory whack-a-mole that accomplishes nothing. Most legislators are concerned with how private crypto businesses operate within their jurisdiction - the Coinbases and Crypto.coms of the world - and making sure they're compliant and not screwing their customers. At the end of the day regulation is coming to this space, and it really isn't that scary. No one is going to ban anything in the US (again, they really can't and they know it) and most legislators want to stop the obvious BS scams and pump n dumps more than anything else, same as we all want. If you're really concerned about big govt messing this up, then consider getting involved in crypto grassroots advocacy and make sure your voice is heard as the inevitable regs come our way. permalink save context full comments (78) report Ethereum's cofounder Vitalik Buterin says we'll soon use 'soulbound tokens' to verify things like school and employment — all stored in a 'souls' wallet by AptitudeSky in CryptoCurrency [–]DCTAorg 1 point 9 months ago This proposal is both incredibly exciting and incredibly scary at the same time. The futurologist in me is excited by the possibilities here, but the realist in me is worried about the eventual hacks and information leaks from bringing personal information directly onto the blockchain. Things like the Ledger hack a few years back have shown how tying your crypto ID to your real-world ID is usually never a good idea. Imagine if a government was issuing NFT IDs like this and a leak of that magnitude occurred? That said, any new technology will face these problems. SBTs will have lots of growing pains, but the hope is that in 10+ years it will be mature enough (and secure enough) for widespread adoption. Things like ZK proofs will help tremendously in the implementation, but that tech is also in its infancy and years away from practical use in this area. I also have a hard time imagining this could ever be mandatory; moving real-world docs or ID to blockchain would likely have to be opt-in for users that want it and can actually handle it. At the end of the day, this is an early draft of an interesting idea and the sort of thing that does continually push the space forward. It's also the sort of thing that will need to be watched carefully to make sure it cannot be abused and that user privacy is never compromised. permalink save context full comments (1118) report At 4PM EST the Digital Currency Traders Alliance will be hosting an AMA to talk about what we are doing to prevent government overreach and ensure consumer privacy as the government begins to regulate crypto. by jwinterm in CryptoCurrency [–]DCTAorg 1 point 10 months ago Thank you to everyone who attended and for the great discussion on how we can impact the future of crypto regulation. As mentioned, we're just getting started and will have a lot of educational resources and advocacy campaigns coming in the next couple of weeks. To track crypto legislation in your state or contact your representatives go to https://joindcta.org/advocacy/. You can also support us with crypto donations at https://joindcta.org/support/ or sign up for a membership at https://joindcta.org/membership/. If you just want updates on what we're doing you can follow us on twitter or sign up for email updates here. We have an unprecedented opportunity to help write the laws that will govern our future financial system - now is the time to get involved and make your voice heard. permalink save context full comments (21) report 1 Verification Request: [Entity Name] (self.CryptoCurrencyMeta) submitted 11 months ago by DCTAorg to r/CryptoCurrencyMeta 1 comment share save hide report

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Offshore Wind Halt Urged By Native Americans Seeking Sway
by Gunnar Larson 24 Feb '23

24 Feb '23

Offshore Wind Halt Urged By Native Americans Seeking Sway https://www.bloomberg.com/news/articles/2023-02-23/offshore-wind-halt-urged… The National Congress of American Indians on Thursday called for a moratorium on offshore wind development along US coasts, insisting the Biden administration do a better job protecting tribal interests.

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Larson and Pritzker Will Do What It Takes to Keep Both DeSantis and Trump Out of the White House
by Gunnar Larson 24 Feb '23

24 Feb '23

Larson and Pritzker Will Do What It Takes to Keep Both DeSantis and Trump Out of the White House: https://www.bloomberg.com/news/articles/2023-02-23/pritzker-vows-to-block-g…

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RED FLAGS! Corporate Officers - Directors and Officers - United States
by Gunnar Larson 24 Feb '23

24 Feb '23

As many Cypherpunks can agree, xNY.io has been screaming about red flags out of New York, concerning digital asset innovation at the cross border level. Here is a terrific example of how board directors who have ignored red flags at the United Nations (aka JP Morgan) probably have a solvency issue. Elon Musk said the same thing, before the Twitter LBO about the failing United Nations. Also, Mr. Musk said that the CEO that banks the United Nations hates the SpaceX CEO ... Meanwhile, the Southern District of New York seemingly has never heard of Bank.org and/or the United Nations. https://www.mondaq.com/unitedstates/directors-and-officers/1284994/delaware… Until the Delaware Court of Chancery issued its recent decision in In re McDonald's Corp. Stockholder Derivative Litigation1 ("McDonalds"), it was unclear if claims for breach of the fiduciary duty of loyalty premised on a lack of oversight first established by In re Caremark International Inc. Derivative Litigation2 ("Caremark") in 1996, with respect to directors, also applied to corporate officers of Delaware corporations. In McDonalds, the Delaware Court of Chancery pronounced, unequivocally, that "[t]his decision clarifies that corporate officers owe a duty of oversight. The same policies that motivated [the Delaware Court of Chancery in Caremark] to recognize the duty of oversight for directors apply equally, if not to a greater degree, to officers."3 Legal Background and Analysis Under the Caremark test, as later adopted by the Delaware Supreme Court in Stone v. Ritter4, liability to directors for failing to properly discharge their duty of oversight arises under two different "prongs" of the test, where either: "(1) directors utterly failed to implement any reporting or information system or controls; or (2) having implemented such a system or controls, consciously failed to monitor or oversee its operations thus disabling themselves from being informed of risks or problems requiring their attention."5 First Prong: Information System Claims. The court in McDonalds referred to the first prong as an "Information Systems Claim", whereby "the board lacked the requisite information systems and controls".6 To make an Information Systems Claim, the board must have consciously failed to make a good faith effort to establish a board-level information and oversight system designed to provide timely and accurate information and, at a minimum, address "essential and mission-critical" legal compliance.7 The validity of an Information Systems Claim does not depend on whether the oversight system was actually effective, but only whether the system existed and was monitored by the board. Second Prong: Red-Flags Claims. The McDonalds court referred to the second prong as a "Red-Flags Claim", whereby "the board's information systems generated red flags indicating wrongdoing and that the directors failed to respond."8 The basis for liability requires a demonstration that (i) directors consciously disregarded evidence of red flag wrongdoing or misconduct in bad faith, and (ii) that the corporate trauma in question must be sufficiently similar to the red flag misconduct such that the board's bad faith and conscious inaction proximately caused the trauma.9 Must Act in Bad Faith; Exculpation Unavailable. The McDonalds court found that the two prongs of the Caremark test would apply equally to officers and directors, but that the specifics of an officer's duty is context-dependent, as discussed in more detail below. Regardless, under either prong of the Caremark test, directors and officers will only be liable for violations of the duty of oversight if a plaintiff can provide evidence that they acted in bad faith and, therefore, disloyally to the corporation. 10 This requires a showing of scienter, i.e., that the officer consciously failed to make a good faith effort to establish information systems or the officer consciously ignored red flags.11 As a practical matter, this means that Section 102(b)(7) of the Delaware General Corporation Law, which permits a Delaware corporation to include an exculpatory provision in its certificate of incorporation that eliminates the personal liability of a director or officer for breaches of certain fiduciary duties, will not apply to Caremark claims because Section 102(b)(7) specifically excludes bad faith misconduct and breaches of the duty of loyalty. See "Elimination of the Duty of Care in Delaware? Statutory Exculpation of Officers: Recent Amendment to Section 102(b)(7) of the Delaware General Corporation Law". Applicability to Corporate Officers. The Court of Chancery in McDonalds then went on to explain how the situational aspects of a corporate officer's duty of oversight may differ from that of the board of directors in a couple of important ways. For example, although the board has oversight duties regarding the whole corporation because it has "plenary authority" concerning the management of the corporation, a particular officer only has a duty to establish information systems and follow-up on red flags issues that comes within the scope of his or her authority and responsibility (e.g., a chief financial officer would be responsible for creating financial, but not human resources or legal, reporting systems and controls).12 The court then went on to note, however, that if a red flag is sufficiently material, then an officer may have a duty to report upward on such red flag even if it is outside of his her or her area of responsibility.13 From the McDonalds decision, it is unclear which officers of the corporation will be charged with a duty of oversight, i.e., if it will include all corporate officers or just senior officers. Based on language from the McDonalds opinion, a reasonable inference can be drawn that it applies to all corporate officers: "the officers are optimally positioned to identify red flags and either address them or report upward to senior officers [emphasis added] or to the board."14 Section 142(a) of the Delaware General Corporation Law defines the term "officer" as "such officers with such titles and duties as shall be stated in the bylaws or in a resolution of the board of directors." Further, in the matter of In re Walt Disney Co. Derivative Litigation,15 the Delaware Court of Chancery established a bright-line rule whereby officers and directors become fiduciaries only when they are officially installed, and receive "the formal investiture of authority that accompanies such office of directorship." Taken together, the fiduciary duty of oversight would seem to apply only to those corporate officers specified in the bylaws or appointed by board resolution. In the derivative shareholder lawsuit16 at issue in the McDonalds case, the plaintiffs did not allege that the Chief People Officer of McDonalds failed to make a good faith effort to establish information systems (i.e., an Information Systems Claim), and, instead, made a Red-Flags Claim by asserting that the Chief People Officer breached his duty of oversight by consciously ignoring red flags.17 In particular, the complaint cited statements from employees that the human resources function, under the supervision of the Chief People Officer, "turned a blind eye" to complaints about sexual harassment, including coordinated complaints filed by restaurant workers and a ten-city strike. Further, because the Chief People Officer also allegedly engaged in acts of sexual harassment, the court concluded that "it is reasonable to infer that the officer consciously ignored red flags about similar behavior of others."18 Practice Points Regarding Oversight Duties of Corporate Officers In order to minimize exposure to liability for a breach of the duty of oversight by corporate officers, we recommend that management take the following actions: Each corporate officer should identify, at a minimum, the essential and mission-critical compliance with laws or regulatory mandates facing the company that are within the scope of the officer's authority and establish a monitoring systems that timely and accurately brings this information to the officer's attention. There may be heightened risk for a Caremark claim where risk to life or health or the company's obligation to comply with positive laws or regulations are involved. Once the oversight system is in place, the officer should pay attention to any "red flag" issues that may evidence non-compliance, report that information to the officer's superior(s), and take corrective actions, as needed, to address the red flag of non-compliance. Document all of the above actions, as litigation actions involving alleged breach of the duty of oversight are preceded by Section 220 books and records requests under the Delaware General Corporation Law. If this happens, one should be able to produce ample evidence that such officer made good faith efforts to properly execute his or her duty of oversight, including documenting: (i) that an oversight system was established, (ii) that the officer reviewed and discussed compliance issues, and (iii) that the officer followed-up on all red-flag issues, and addressed them, as needed.

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Judge Carter Enjoins New York's New Online Hate Speech Law On First Amendment Grounds - Trials & Appeals & Compensation - United States
by Gunnar Larson 23 Feb '23

23 Feb '23

. . . The potential chilling effect to social media users is exacerbated by the indefiniteness of some of the Hateful Conduct Law's key terms. It is not clear what the terms like "vilify" and "humiliate" mean for the purposes of the law. While it is true that there are readily accessible dictionary definitions of those words, the law does not define what type of "conduct" or "speech" could be encapsulated by them. For example, could a post using the hashtag "BlackLivesMatter" or "BlueLivesMatter" be considered "hateful conduct" under the law? Likewise, could social media posts expressing anti-American views be considered conduct that humiliates or vilifies a group based on national origin? It is not clear from the face of the text, and thus the law does not put social media users on notice of what kinds of speech or content is now the target of government regulation. https://www.mondaq.com/unitedstates/trials-amp-appeals-amp-compensation/128…

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