569 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    SuperCDMS HVeV Run 2 Low-Mass Dark Matter Search, Highly Multiplexed Phonon-Mediated Particle Detector with Kinetic Inductance Detector, and the Blackbody Radiation in Cryogenic Experiments

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    There is ample evidence of dark matter (DM), a phenomenon responsible for ≈ 85% of the matter content of the Universe that cannot be explained by the Standard Model (SM). One of the most compelling hypotheses is that DM consists of beyond-SM particle(s) that are nonluminous and nonbaryonic. So far, numerous efforts have been made to search for particle DM, and yet none has yielded an unambiguous observation of DM particles. We present in Chapter 2 the SuperCDMS HVeV Run 2 experiment, where we search for DM in the mass ranges of 0.5--10⁴ MeV/c² for the electron-recoil DM and 1.2--50 eV/c² for the dark photon and the Axion-like particle (ALP). SuperCDMS utilizes cryogenic crystals as detectors to search for DM interaction with the crystal atoms. The interaction is detected in the form of recoil energy mediated by phonons. In the HVeV project, we look for electron recoil, where we enhance the signal by the Neganov-Trofimov-Luke effect under high-voltage biases. The technique enabled us to detect quantized e⁻h⁺ creation at a 3% ionization energy resolution. Our work is the first DM search analysis considering charge trapping and impact ionization effects for solid-state detectors. We report our results as upper limits for the assumed particle models as functions of DM mass. Our results exclude the DM-electron scattering cross section, the dark photon kinetic mixing parameter, and the ALP axioelectric coupling above 8.4 x 10⁻³⁴ cm², 3.3 x 10⁻¹⁴, and 1.0 x 10⁻⁹, respectively. Currently every SuperCDMS detector is equipped with a few phonon sensors based on the transition-edge sensor (TES) technology. In order to improve phonon-mediated particle detectors' background rejection performance, we are developing highly multiplexed detectors utilizing kinetic inductance detectors (KIDs) as phonon sensors. This work is detailed in chapter 3 and chapter 4. We have improved our previous KID and readout line designs, which enabled us to produce our first ø3" detector with 80 phonon sensors. The detector yielded a frequency placement accuracy of 0.07%, indicating our capability of implementing hundreds of phonon sensors in a typical SuperCDMS-style detector. We detail our fabrication technique for simultaneously employing Al and Nb for the KID circuit. We explain our signal model that includes extracting the RF signal, calibrating the RF signal into pair-breaking energy, and then the pulse detection. We summarize our noise condition and develop models for different noise sources. We combine the signal and the noise models to be an energy resolution model for KID-based phonon-mediated detectors. From this model, we propose strategies to further improve future detectors' energy resolution and introduce our ongoing implementations. Blackbody (BB) radiation is one of the plausible background sources responsible for the low-energy background currently preventing low-threshold DM experiments to search for lower DM mass ranges. In Chapter 5, we present our study for such background for cryogenic experiments. We have developed physical models and, based on the models, simulation tools for BB radiation propagation as photons or waves. We have also developed a theoretical model for BB photons' interaction with semiconductor impurities, which is one of the possible channels for generating the leakage current background in SuperCDMS-style detectors. We have planned for an experiment to calibrate our simulation and leakage current generation model. For the experiment, we have developed a specialized ``mesh TES'' photon detector inspired by cosmic microwave background experiments. We present its sensitivity model, the radiation source developed for the calibration, and the general plan of the experiment.</p

    Manipulation of uncooperative rotating objects in space with a modular self-reconfigurable robot

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    The following thesis is a feasibility study for the controlled deployment of robotic scaffolding structures on randomly tumbling objects with low-magnitude gravitational field for use in space applications such as space debris removal, spacecraft maintenance and asteroids capture and mining. The proposed solution is based on the novel use of self-reconfigurable modular robots performing deployments on randomly tumbling objects as a task-driven reconfiguration or manipulation through reconfiguration. The robot design focused on its control strategy which used a decentralised modular controller with two levels. One high-level behaviour-based component and one low-level component generating commands via a constrained optimisation using either a linear or a non-linear model predictive control approach and constituting a novel control method for rotating objects via angular momentum exchanges and mass distribution changes. The controller design relied on modelling the robot modules and the object as a rotating discretised deformable continuum whose rigid part, the object, was an ellipsoid. All parameters were normalised when possible and disturbances, sensors and actuator errors were modelled respectively as biased white noises and coloured noises. The correctness of the overall control algorithm was ensured. The main objective of the MPC controllers was to control the deployment of a module from the tip of the spinning axis to the plane containing the object’s centre of mass while coiling around the spinning axis and ensuring the object’s rotational state tracked a reference state. Simulations showed that the nonlinear MPC controller should be preferred over a linear one and that, for a mass ratio of the object’s to the module’s equal to 10000, the nonlinear MPC controller is best suited to stability maintenance and meets the deployment requirement, suggesting that the proposed solution would be acceptable for medium-size objects such as asteroids
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