1,258 research outputs found

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ļ¬fth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ļ¬elds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiļ¬ed Proportional Conļ¬‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiļ¬ers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiļ¬cation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiļ¬cation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiļ¬cation, and hybrid techniques mixing deep learning with belief functions as well

    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 steerā€”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

    Modern meat: the next generation of meat from cells

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    Modern Meat is the first textbook on cultivated meat, with contributions from over 100 experts within the cultivated meat community. The Sections of Modern Meat comprise 5 broad categories of cultivated meat: Context, Impact, Science, Society, and World. The 19 chapters of Modern Meat, spread across these 5 sections, provide detailed entries on cultivated meat. They extensively tour a range of topics including the impact of cultivated meat on humans and animals, the bioprocess of cultivated meat production, how cultivated meat may become a food option in Space and on Mars, and how cultivated meat may impact the economy, culture, and tradition of Asia

    The impact of head orientation with respect to B0 on diffusion tensor MRI measures

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    Diffusion tensor MRI (DT-MRI) remains the most commonly used approach to characterise white matter (WM) anisotropy. However, DT estimates may be affected by tissue orientation w.r.t. Bā†’0 due to local gradients and intrinsic T2 orientation dependence induced by the microstructure. This work aimed to investigate whether and how diffusion tensor MRI-derived measures depend on the orientation of the head with respect to the static magnetic field, Bā†’0 ā . By simulating WM as two compartments, we demonstrated that compartmental T2 anisotropy can induce the dependence of diffusion tensor measures on the angle between WM fibres and the magnetic field. In in vivo experiments, reduced radial diffusivity and increased axial diffusivity were observed in white matter fibres perpendicular to Bā†’0 compared to those parallel to Bā†’0 ā . Fractional anisotropy varied by up to 20% as a function of the angle between WM fibres and the orientation of the main magnetic field. To conclude, fibre orientation w.r.t. Bā†’0 is responsible for up to 7% variance in diffusion tensor measures across the whole brain white matter from all subjects and head orientations. Fibre orientation w.r.t. Bā†’0 may introduce additional variance in clinical research studies using diffusion tensor imaging, particularly when it is difficult to control for (e.g. fetal or neonatal imaging, or when the trajectories of fibres change due to e.g. space occupying lesions)

    Individualisation of transcranial electric stimulation to improve motor function after stroke:Current challenges and future perspective

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    Transcranial electric stimulation (tES) is a non-invasive brain stimulation technique that could potentially improve motor rehabilitation after stroke. However, the effects of tES are in general stronger in healthy individuals compared to people with stroke. Interindividual variability in brain structure and function due to stroke potentially explain this difference in effects. This thesis describes the development of methods to facilitate the individualisation of tES in people with stroke and identifies objective neurophysiological correlates of motor learning that could potentially help to monitor the response to tES.In chapter 2, EEG correlates of explicit motor task learning were derived in healthy, young participants. Chapter 3 investigated the effects of 3 different tDCS configurations (sham, targeting contralateral M1 and targeting the full resting motor network) on corticospinal excitability. Both conventional and motor network tDCS did not increase corticospinal excitability relative to sham stimulation. Chapter 4 describes methods to create head models of people with stroke and assesses the effects of stroke lesions on the electric fields within stimulation targets. Chapter 5 describes a method to experimentally determine the electric conductivity of the stroke lesion. Finally, Chapter 6 analyses the electric fields generated by conventional tDCS in people with stroke and age-matched controls. It is shown that the one-size-fits-all approach results in more variable electric fields in people with stroke compared to controls. Optimisation of the electrode positions to maximise the electric field in stimulation targets increases the electric fields in people with stroke to the same level as found in healthy controls.This thesis shows anatomical and motor function variability exists between people with stroke due to differences in lesion characteristics. While there are several opportunities to individualise tES, more research is needed to investigate if this improves the effects of tES. As such, clinical implementation of tES seems unrealistic in the foreseeable future.<br/

    System-wide stress testing & systemic risk

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    The financial crisis of 2007-2009, which brought the entire system at the brink of collapse, renewed efforts to guard against financial instability. A key pillar of the post-crisis regulatory toolkit is "stress testing". Stress tests provide a forward-looking examination of firmsā€™ potential losses during severely ad- verse conditions. And enable timely action to recapitalise those firms who experience capital shortfalls in such crisis scenarios. Todayā€™s regulatory stress tests do not heed the key lesson of the financial crisis: amplifications in the networked financial system must be taken into account to be able to assess systemic risk. Because of this, these tests are unable to assess systemic risk and ergo to address it ā€“ defeating their raison dā€™Ė†etre. The overarching research question in this thesis is whether new building blocks ā€“ expressing the heterogeneity of institutions, contracts, markets, constraints and behaviour in the interconnected financial system ā€“ can be supplied for system-wide stress tests to better capture the endogenous amplification of shocks in order to improve the assessment of systemic risk and the evaluation of prudential policies to address financial fragility. The cornerstone of my thesis is the development of a generic network-based method, comprised of these five building blocks (i.e. institutions, contracts, markets, constraints and behaviour), for system-wide stress testing ā€“ which has gained traction from leading central banks, including the Bank of England and the European Central Bank. Using this method, I implement two data-driven models to address some of the most salient financial stability questions of today. First, we ask how the regulatory buffer size and its usability under Basel III affect systemic risk? We find that financial resilience decreases if regulatory buffers are seen to be less usable by banks. If regulatory buffers are not treated as usable, then regulatory buffers de facto act as capital requirements. In such case, if an adverse shock threatens an institution to breach its capital buffers constraints, it is forced to delever, which tends to have a destabilising effect on the financial markets. We show that the size of usable regulatory buffers that is required to maintain stability is underestimated if the interaction between exposure loss contagion, funding contagion, overlapping portfolio contagion and margin call contagion is not taken into account. Second, we inquire what the systemic implications are of the bail-in design to resolve systemically important banks? First of all, we find that the bail-in design tremendously matters for whether bail-ins can be credibly executed in system-wide financial crises and cases of large systemically impor- tant bank failures, without significantly exacerbating financial distress. Our results demonstrate that an early bail-in, strong recapitalisation and fair distribution of equity compensation by means of debt-to-equity conversion rates makes bail-in a feasible option on the table for idiosyncratic cases of bank failure and limits ā€“ but not eliminates ā€“ contagion in cases of system-wide distress. We further show that excluding run-prone, short-term debt from the application of the bail-in tool, increasing the requirements on loss absorbing debt and providing investors with certainty about the bail-in design lowers contagion in system-wide crises to manageable levels. Our findings highlight that while well-designed bail-ins could be credibly administered in system-wide crises, it is not clear that the current bail-in design is in the regime of stability. Altogether, the methods and findings of this thesis emphasise the promise that system-wide stress tests hold for regulators to efficaciously assess systemic risk and calibrate prudential policies constituting the financial architecture

    Development and application of NMR methods to study biomolecular dynamics

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    Structural biology has generated profound insights into biomolecular machines. The molecular basis of processes like binding, folding, catalysis and regulation, which underlie the inner working of living organisms would have largely remained unexplored without the thousands of structures that have been solved over the years. But these machines, formed by proteins and nucleic acids, are inherently dynamic, and information about this fourth dimension, the modulation of their structure with time, is often lacking. Nuclear magnetic resonance (NMR) is exquisitely suited to characterize dynamics over a wide timescale, from picoseconds, where amplitudes and correlation times can be extracted, to microsecond, milliseconds and seconds, where in favourable cases information about the kinetics, the thermodynamics and the structure of an excited state can be retrieved. With increasing size of the molecular system under consideration, however, this characterization is progressively challenging for NMR, and the analysis often focuses on 13CH3 spin systems in a perdeuterated background. As an alternative approach, fluorine NMR has grown in popularity. The 19F isotope can be introduced site-specifically, it gives rise to background-free one-dimensional spectra and the technique bypasses the need for perdeuteration. In my disseration, I expanded the existing toolkit of 19F NMR, applied 19F experiments that report on dynamics to high-molecular weight systems and combined their advantages with established methyl group NMR techniques. Development of 19F relaxation dispersion experiments To develop 19F relaxation dispersion (RD) experiments, I used a 7.5 kDa cold shock protein from the thermophilic organism Thermotoga maritima as a protein folding/unfolding model system. The global analysis of three RD experiments showed consistent results for the two-state exchange process. Our new rotating frame relaxation pulse sequences allowed to extract the absolute chemical shift of the unfolded state and significantly extended the range of timescales that can be assessed experimentally. Employing a 360 kDa double heptameric complex, I validated the applicability of the experiments on a highly challenging assembly. Conformational changes in the exoribonuclease Xrn2 The 5'-3' exoribonuclease Xrn2 operates in the nucleus in RNA processing and RNA turn-over pathways. Static structures of its cytoplasmic homologue Xrn1 in the presence of substrates implicate that the enzymes undergo conformational changes to progress through the catalytic cycle. Here, I solved the X-ray structure of Xrn2 from the thermophilic organism Chaetomium thermophilum to 3 ƅ resolution and combined methyl group and fluorine relaxation dispersion to characterize the exchange in a 100 kDa apo protein core construct in solution. Upon binding of a substrate, the conformational equilibrium is substantially shifted towards the active state. Importantly, the 19F experiments allowed to characterize dynamics in these unstable samples and I could show that the exchange of the enzyme:substrate complex are largely suppressed. Multi-site exchange in a neomycin-sensing riboswitch The existence of multiple sparsely populated states complicates the characterization of an exchanging system. Using a synthetic neomycin-binding riboswitch bound to different aminoglycoside ligands, I demonstrated that fluorine NMR can be employed to study exchange topologies with up to four states. To this end, I take advantage of an additional off-resonance technique, 19F chemical exchange saturation transfer. Combined with 19F RD and longitudinal exchange experiments, the results support the notion of a modular impact of aminoglycoside functional groups on the riboswitch dynamics. Taken together, these results expand and complement the NMR toolbox to study exchanging systems, with an emphasis on high-molecular weight systems and intricate exchange topologies involving more than two states. Furthermore, they elucidate the molecular dynamics in the 5'-3' exoribonuclease Xrn2 and provide a conceptional framework to study dynamics in related systems such as Xrn1

    2015 GREAT Day Program

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    SUNY Geneseoā€™s Ninth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1009/thumbnail.jp

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum
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