A PROMISE WITHOUT A REMEDY: THE SUPPOSED INCOMPATIBILITY OF THE GPLV2 AND APACHE V2 LICENSES

License “incompatibility” in free and open source software licensing means that, when two differently licensed pieces of software are combined, one cannot comply with both licenses at the same time. It is commonly accepted that the GNU General Public License version 2 is incompatible with the Apache License, version 2 because certain provisions of the Apache License would be considered “further restrictions” not permitted by the GPLv2. However, this article will explain why there is no legally cognizable claim for combining the two, either under a copyright infringement theory or a breach of contract theory

Of Reptiles and Velcro: The Brain\u27s Negativity Bias and Persuasion

Negative political advertising has become commonplace for one simple reason it works Cognitive pyschologists attribute this to a phenomenon they call the brain\u27s ÔÇ£negativity biasÔÇØ That is our brains are more apt to process and retain negative information as opposed to positive information As one neuropsychologist has put it ÔÇ£your brain is like Velcro for negative experiences and Teflon for positive onesÔÇØCognitive psychologists have concluded that bad stimuli have significantly more power across a broad range of psychological phenomena What are the implications of this finding for legal writing For example how do judges respond to negative themes in briefs Should lawyers phrase their legal arguments in terms of avoiding bad outcomes instead of promoting good outcomes Should rule statements in briefs highlight the possible negative consequences of a particular ruling as opposed to a positive outcome Should advocates adopt a negative or aggressive tone in their writing Does this finding change the way lawyers should do or at least think about counteranalysis Does a judge\u27s negative opinion of an advocate have more power than a potential positive view of the clientAnswering these questions in the affirmative might be controversial Many a judge as well as many legal writing professors counsel lawyers and law students to avoid the negative and emphasize the positive Given the near ubiquitousness of this advice it seems that the cognitive psychology on negativity bias is worth studying Have we all been giving bad advice all this time This article discusses the cognitive psychology findings then suggests some hypotheses for how they might inform choices that advocates might make It is intended to open a conversation about how the negativity bias might affect the process of persuasio

Wireless multi-channel sensor for neurodynamic studies

Journal ArticleThis paper presents the design of a bio-compatible, implantable neural recording device for Aplysia californica, a common sea slug. Low-voltage extracellular neural signals (<100 μV) are recorded using a high-performance, low-power, low-noise preamplifier that is integrated with programmable data acquisition and control, and FSK telemetry that provides 5-kbps wireless neural data through 18 cm of saltwater. The telemetry utilizes an 8-cm electric dipole antenna matched to 50 Ω by exposing the ends of the antenna to the saltwater. A 3-V lithium ion battery (160 mAh) allows 16 hours of recording. Neural data obtained using extracellular nerve electrodes and a wired interface to this device have 2.5-µVrms noise, comparable to commercial neural recording equipment

MacCrate (in)Action: The Case for Enhancing the Upper-Level Writing Requirement in Law Schools

In 2001, the American Bar Association amended the Standards for Accreditation of Law Schools to require, for the first time, a “rigorous writing experience after the first year.” During the summer of 2004 the author conducted a nationwide survey to determine how law schools responded to this change. The author found that most schools did little more than to require students to take at least one course which was evaluated by means of an academic paper rather than an examination. The author concludes that this is probably not the response the ABA had hoped for, but suggests that a 2005 amendment to the Standards, which now require “writing in a legal context”, holds more promise for encouraging law schools to focus more on practical legal writing skills

A simulation study on the effects of neuronal ensemble properties on decoding algorithms for intracortical brain-machine interfaces

Background: Intracortical brain-machine interfaces (BMIs) harness movement information by sensing neuronal activities using chronic microelectrode implants to restore lost functions to patients with paralysis. However, neuronal signals often vary over time, even within a day, forcing one to rebuild a BMI every time they operate it. The term &quot;rebuild&quot; means overall procedures for operating a BMI, such as decoder selection, decoder training, and decoder testing. It gives rise to a practical issue of what decoder should be built for a given neuronal ensemble. This study aims to address it by exploring how decoders&apos; performance varies with the neuronal properties. To extensively explore a range of neuronal properties, we conduct a simulation study. Methods: Focusing on movement direction, we examine several basic neuronal properties, including the signal-to-noise ratio of neurons, the proportion of well-tuned neurons, the uniformity of their preferred directions (PDs), and the non-stationarity of PDs. We investigate the performance of three popular BMI decoders: Kalman filter, optimal linear estimator, and population vector algorithm. Results: Our simulation results showed that decoding performance of all the decoders was affected more by the proportion of well-tuned neurons that their uniformity. Conclusions: Our study suggests a simulated scenario of how to choose a decoder for intracortical BMIs in various neuronal conditions

Reach and grasp by people with tetraplegia using a neurally controlled robotic arm

Paralysis following spinal cord injury (SCI), brainstem stroke, amyotrophic lateral sclerosis (ALS) and other disorders can disconnect the brain from the body, eliminating the ability to carry out volitional movements. A neural interface system (NIS)1–5 could restore mobility and independence for people with paralysis by translating neuronal activity directly into control signals for assistive devices. We have previously shown that people with longstanding tetraplegia can use an NIS to move and click a computer cursor and to control physical devices6–8. Able-bodied monkeys have used an NIS to control a robotic arm9, but it is unknown whether people with profound upper extremity paralysis or limb loss could use cortical neuronal ensemble signals to direct useful arm actions. Here, we demonstrate the ability of two people with long-standing tetraplegia to use NIS-based control of a robotic arm to perform three-dimensional reach and grasp movements. Participants controlled the arm over a broad space without explicit training, using signals decoded from a small, local population of motor cortex (MI) neurons recorded from a 96-channel microelectrode array. One of the study participants, implanted with the sensor five years earlier, also used a robotic arm to drink coffee from a bottle. While robotic reach and grasp actions were not as fast or accurate as those of an able-bodied person, our results demonstrate the feasibility for people with tetraplegia, years after CNS injury, to recreate useful multidimensional control of complex devices directly from a small sample of neural signals

Estimating Risk for Future Intracranial, Fully Implanted, Modular Neuroprosthetic Systems: A Systematic Review of Hardware Complications in Clinical Deep Brain Stimulation and Experimental Human Intracortical Arrays

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155940/1/ner13069.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155940/2/ner13069_am.pd

A Spiking Neural Network Decoder for Implantable Brain Machine Interfaces and its Sparsity-aware Deployment on RISC-V Microcontrollers

Implantable Brain-machine interfaces (BMIs) are promising for motor rehabilitation and mobility augmentation, and they demand accurate and energy-efficient algorithms. In this paper, we propose a novel spiking neural network (SNN) decoder for regression tasks for implantable BMIs. The SNN is trained with enhanced spatio-temporal backpropagation to fully leverage its capability to handle temporal problems. The proposed SNN decoder outperforms the state-of-the-art Kalman filter and artificial neural network (ANN) decoders in offline finger velocity decoding tasks. The decoder is deployed on a RISC-V-based hardware platform and optimized to exploit sparsity. The proposed implementation has an average power consumption of 0.50 mW in a duty-cycled mode. When conducting continuous inference without duty-cycling, it achieves an energy efficiency of 1.88 uJ per inference, which is 5.5X less than the baseline ANN. Additionally, the average decoding latency is 0.12 ms for each inference, which is 5.7X faster than the ANN implementation

Fascicle localisation within peripheral nerves through evoked activity recordings: A comparison between electrical impedance tomography and multi-electrode arrays

BACKGROUND: The lack of understanding of fascicular organisation in peripheral nerves limits the potential of vagus nerve stimulation therapy. Two promising methods may be employed to identify the functional anatomy of fascicles within the nerve: fast neural electrical impedance tomography (EIT), and penetrating multi-electrode arrays (MEA). These could provide a means to image the compound action potential within fascicles in the nerve. NEW METHOD: We compared the ability to localise fascicle activity between silicon shanks (SS) and carbon fibre (CF) multi-electrode arrays and fast neural EIT, with micro-computed tomography (MicroCT) as an independent reference. Fast neural EIT in peripheral nerves was only recently developed and MEA technology has been used only sparingly in nerves and not for source localisation. Assessment was performed in rat sciatic nerves while evoking neural activity in the tibial and peroneal fascicles. RESULTS: Recorded compound action potentials were larger with CF compared to SS (∼700μV vs ∼300μV); however, background noise was greater (6.3μV vs 1.7μV) leading to lower SNR. Maximum spatial discrimination between Centres-of-Mass of fascicular activity was achieved by fast neural EIT (402±30μm) and CF MEA (414±123μm), with no statistical difference between MicroCT (625±17μm) and CF (p>0.05) and between CF and EIT (p>0.05). Compared to CF MEAs, SS MEAs had a lower discrimination power (103±51μm, p<0.05). COMPARISON WITH EXISTING METHODS: EIT and CF MEAs showed localisation power closest to MicroCT. Silicon MEAs adopted in this study failed to discriminate fascicle location. Re-design of probe geometry may improve results. CONCLUSIONS: Nerve EIT is an accurate tool for assessment of fascicular position within nerves. Accuracy of EIT and CF MEA is similar to the reference method. We give technical recommendations for performing multi-electrode recordings in nerves