Miracle-dz/UniFAR-FTUs huggingface dataset:
FastTrack: An open-source software for tracking varying numbers of deformable objects

Analyzing the dynamical properties of mobile objects requires to extract trajectories from recordings, which is often done by tracking movies. We compiled a database of two-dimensional movies for very different biological and physical systems spanning a wide range of length scales and developed a general-purpose, optimized, open-source, cross-platform, easy to install and use, self-updating software called FastTrack. It can handle a changing number of deformable objects in a region of interest, and is particularly suitable for animal and cell tracking in two-dimensions. Furthermore, we introduce the probability of incursions as a new measure of a movie’s trackability that doesn’t require the knowledge of ground truth trajectories, since it is resilient to small amounts of errors and can be computed on the basis of an ad hoc tracking. We also leveraged the versatility and speed of FastTrack to implement an iterative algorithm determining a set of nearly-optimized tracking parameters—yet further reducing the amount of human intervention—and demonstrate that FastTrack can be used to explore the space of tracking parameters to optimize the number of swaps for a batch of similar movies. A benchmark shows that FastTrack is orders of magnitude faster than state-of-the-art tracking algorithms, with a comparable tracking accuracy. The source code is available under the GNU GPLv3 at https://github.com/FastTrackOrg/FastTrack and pre-compiled binaries for Windows, Mac and Linux are available at http://www.fasttrack. sh.
Toward the Fully Physics-Informed Echo State Network - an ODE Approximator Based on Recurrent Artificial Neurons

Inspired by recent theoretical arguments, physics-informed echo state network (ESN) is discussed on the attempt to train a reservoir model absolutely in physics-informed manner. As the plainest work on such a purpose, an ODE (ordinary differential equation) approximator is designed to replicate the solution in sequence with respect to the recurrent evaluations. On the principal invariance of differential equations, the constraint in recurrence just takes shape to secure a proper regression method for the ESN-based ODE approximator. After then, the actual training process is established on the idea of two-pass strategy for regression. Aiming at the fully physics-informed reservoir model, a couple of nonlinear dynamical problems are demonstrated as the computations obtained from the proposed method in this study.
Elaboration et caractérisation de micro- et nano-composites alumine-zircone pour application orthopédique

Ce travail de these a pour objectif l'elaboration et la caracterisation de micro- et nano-composites alumine-zircone appliques aux tetes femorales de protheses de hanches. Les micro-composites ont ete elabores par dispersion atomisation, puis pressage. La dispersion electrosterique a permis de determiner le taux de dispersant (Darvan C) optimal, pour l'alumine et les zircones (pure et yttriee). Ce taux est de 0. 25 wt%. Cependant, ce type de stabilisation s'est montree moins efficace que la stabilisation electrostatique, comme en temoignet des agregats de zircone caracterises au sein des suspensions par des mesures de rheologie, et par MEB dans les microstructures des composites frittes. En revanche, par dispersion electrostatique, il est possible d'obtenir des composites homogenes, soit a pH 4,5 pour un melange d'alumine et de zircone pure ou yttriee, soit a pH 11, pour un melange d'alumine et de zircone pure. Les micro-composites denses sont caracterises en terme de proprietes mecaniques, resistance au vieillissement, proprietes biologiques et tribologiques. Pour eviter tout vieillissement, nous avons montre qu'il est necessaire de proscrire tout agregat de zircone de la microstructure et d'utiliser un taux de zircone a 16 vol%. Les tests mecaniques d'indentation Vickers, par la methode d'Anstis, nous ont permis de determiner les seuils de propagation des fissures de nos differentes nuances, et de selectionner une nuance contenant 10 vol% de zircone pure. Les caracteristiques en frottement sont par ailleurs comparables a celles des tetes en alumine (reference pour cette application). L'absence de cytotoxicite de notre composite en presence de fibroblastes et d'osteoblastes a ete demontree par des tests MTT. Enfin, l'elaboration de nanocomposites a necessite des techniques d'elaboration particulieres (dispersion, coulage, et frittage SPS). Le nanocomposite obtenu presente des tailles de grains d'alumine et de zircone de 200nm et 95nm, pour une densite de 98%. Les proprietes mecaniques d'un tel nanocomposite, evaluees par indentation (methode d'Anstis), se sont averees similaires a celles d'un composite microstructure
Black hole spin evolution in warped accretion discs

Massive black holes (BHs) inhabiting galactic nuclei can be described by two parameters only, i.e. mass and spin, that change through cosmic time in response to accretion and merger events. While most numerical simulations accurately track the BH mass, spin evolution is rarely taken into account. In this work, we implement and validate a self-consistent sub-grid model for the evolution of the BH mass and spin via gas accretion in the hydrodynamics code gizmo. The model assumes that accretion from resolved scales does not occur instantaneously but is mediated by a sub-grid geometrically thin α-disc. After validating our model semi-analytically, we test it in an idealized environment consisting of a circumnuclear disc, where gas accretion on to the accretion disc is consistently determined by gizmo. In the absence of any accretion-related feedback, the spin evolution closely traces that observed in the semi-analytical models, and depends on the free parameters of our implementation, such as the initial BH spin, angular momentum of the accretion disc, and radius at which the gas inflow circularizes. In gizmo, we also couple our model with the biconical-outflow model presented in a companion paper, wherein the feedback axis is always aligned with the BH spin. In this last case, the evolution of the central BH differs significantly from the previous cases, since the feedback process modifies the gas dynamics and its inflow rates from resolved scales. Such an interaction cannot be modelled by simple semi-analytical models and should be treated using full N-body hydrodynamical simulations.
Encoded Value-at-Risk: A Predictive Machine for Financial Risk Management

Measuring risk is at the center of modern financial risk management. As the world economy is becoming more complex and standard modeling assumptions are violated, the advanced artificial intelligence solutions may provide the right tools to analyze the global market. In this paper, we provide a novel approach for measuring market risk called Encoded Value-at-Risk (Encoded VaR), which is based on a type of artificial neural network, called Variational Auto-encoders (VAEs). Encoded VaR is a generative model which can be used to reproduce market scenarios from a range of historical cross-sectional stock returns, while increasing the signal-to-noise ratio present in the financial data, and learning the dependency structure of the market without any assumptions about the joint distribution of stock returns. We compare Encoded VaR out-of-sample results with eleven other methods and show that it is competitive to many other well-known VaR algorithms presented in the literature.
Coordinated Motion Control and Event-based Obstacle-crossing for Four Wheel-leg Independent Motor-driven Robotic System via MPC

This work presents the coordinated motion control and obstacle-crossing problem for the four wheel-leg independent motor-driven robotic systems via a model predictive control (MPC) approach based on an event-triggering mechanism. The modeling of a wheel-leg robotic control system with a dynamic supporting polygon is organized. The system dynamic model is 3 degrees of freedom (DOF) ignoring the pitch, roll and vertical motions. The single wheel dynamic is analyzed considering the characteristics of motor-driven and the Burckhardt nonlinear tire model. As a result, an over-actuated predictive model is proposed with the motor torques as inputs and the system states as outputs. As the supporting polygon is only adjusted at certain conditions, an event-based triggering mechanism is designed to save hardware resources and energy. The MPC controller is evaluated on a virtual prototype as well as a physical prototype. The simulation results guide the parameter tuning for the controller implementation in the physical prototype. The experimental results on these two prototypes verify the efficiency of the proposed approach.
Learning Predictive Models for Ergonomic Control of Prosthetic Devices

We present Model-Predictive Interaction Primitives -- a robot learning framework for assistive motion in human-machine collaboration tasks which explicitly accounts for biomechanical impact on the human musculoskeletal system. First, we extend Interaction Primitives to enable predictive biomechanics: the prediction of future biomechanical states of a human partner conditioned on current observations and intended robot control signals. In turn, we leverage this capability within a model-predictive control strategy to identify the future ergonomic and biomechanical ramifications of potential robot actions. Optimal control trajectories are selected so as to minimize future physical impact on the human musculoskeletal system. We empirically demonstrate that our approach minimizes knee or muscle forces via generated control actions selected according to biomechanical cost functions. Experiments are performed in synthetic and real-world experiments involving powered prosthetic devices.
Robotic self-representation improves manipulation skills and transfer learning

Cognitive science suggests that the self-representation is critical for learning and problem-solving. However, there is a lack of computational methods that relate this claim to cognitively plausible robots and reinforcement learning. In this paper, we bridge this gap by developing a model that learns bidirectional action-effect associations to encode the representations of body schema and the peripersonal space from multisensory information, which is named multimodal BidAL. Through three different robotic experiments, we demonstrate that this approach significantly stabilizes the learning-based problem-solving under noisy conditions and that it improves transfer learning of robotic manipulation skills.
A GAN-based Approach for Mitigating Inference Attacks in Smart Home Environment

The proliferation of smart, connected, always listening devices have introduced significant privacy risks to users in a smart home environment. Beyond the notable risk of eavesdropping, intruders can adopt machine learning techniques to infer sensitive information from audio recordings on these devices, resulting in a new dimension of privacy concerns and attack variables to smart home users. Techniques such as sound masking and microphone jamming have been effectively used to prevent eavesdroppers from listening in to private conversations. In this study, we explore the problem of adversaries spying on smart home users to infer sensitive information with the aid of machine learning techniques. We then analyze the role of randomness in the effectiveness of sound masking for mitigating sensitive information leakage. We propose a Generative Adversarial Network (GAN) based approach for privacy preservation in smart homes which generates random noise to distort the unwanted machine learning-based inference. Our experimental results demonstrate that GANs can be used to generate more effective sound masking noise signals which exhibit more randomness and effectively mitigate deep learning-based inference attacks while preserving the semantics of the audio samples.
NuSTAR Non-detection of a Faint Active Galactic Nucleus in an Ultraluminous Infrared Galaxy with Kpc-scale Fast Wind

Large-scale outflows are generally considered to be possible evidence that active galactic nuclei (AGNs) can severely affect their host galaxies. Recently, an ultraluminous infrared galaxy (ULIRG) at z = 0.49, AKARI J0916248+073034, was found to have a galaxy-scale [O iii] λ5007 outflow with one of the highest energy-ejection rates at z < 1.6. However, the central AGN activity estimated from its torus mid-infrared (MIR) radiation is weak relative to the luminous [O iii] emission. In this work we report the first NuSTAR hard X-ray follow-up of this ULIRG to constrain its current AGN luminosity. The intrinsic 2–10 keV luminosity shows a 90% upper limit of 3.0 × 1043 erg s−1 assuming Compton-thick obscuration (NH = 1.5 × 1024 cm−2), which is only 3.6% of the luminosity expected from the extinction-corrected [O iii] luminosity. Using the NuSTAR observation, we successfully identify that this ULIRG has a very extreme case of X-ray deficit among local ULIRGs. A possible scenario to explain the drastic decline in both the corona (X-ray) and torus (MIR) is that the primary radiation from the AGN accretion disk is currently in a fading status, as a consequence of a powerful nuclear wind suggested by powerful ionized outflow in a galaxy scale.
Continual Learning with Deep Artificial Neurons

Neurons in real brains are enormously complex computational units. Among other things, they're responsible for transforming inbound electro-chemical vectors into outbound action potentials, updating the strengths of intermediate synapses, regulating their own internal states, and modulating the behavior of other nearby neurons. One could argue that these cells are the only things exhibiting any semblance of real intelligence. It is odd, therefore, that the machine learning community has, for so long, relied upon the assumption that this complexity can be reduced to a simple sum and fire operation. We ask, might there be some benefit to substantially increasing the computational power of individual neurons in artificial systems? To answer this question, we introduce Deep Artificial Neurons (DANs), which are themselves realized as deep neural networks. Conceptually, we embed DANs inside each node of a traditional neural network, and we connect these neurons at multiple synaptic sites, thereby vectorizing the connections between pairs of cells. We demonstrate that it is possible to meta-learn a single parameter vector, which we dub a neuronal phenotype, shared by all DANs in the network, which facilitates a meta-objective during deployment. Here, we isolate continual learning as our meta-objective, and we show that a suitable neuronal phenotype can endow a single network with an innate ability to update its synapses with minimal forgetting, using standard backpropagation, without experience replay, nor separate wake/sleep phases. We demonstrate this ability on sequential non-linear regression tasks.
Temperature-Dependent Current Dispersion Study in β-Ga₂O₃ FETs Using Submicrosecond Pulsed I–V Characteristics

A comprehensive study of drain current dispersion effects in <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub> field-effect transistors (FETs) has been done using dc and pulsed measurements. Both the virtual gate effect in the gate–drain access region and mobile traps under the gate are the most plausible explanations for the experimentally observed pulsed current dispersion and high-temperature threshold voltage shift, respectively. Unpassivated devices show significant current dispersion between dc and pulsed <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> response in gate lag measurements characterized by time constants in the range of 400–690 <inline-formula> <tex-math notation="LaTeX">$\mu \text{s}$ </tex-math></inline-formula> both at room temperature and higher temperatures. The increasing time constant with temperature suggests a complex electron capture and emission process in the virtual gate. The reactive ion etching step during the device fabrication is most likely responsible for introducing the traps. The effect of traps can be minimized by using surface passivation layers, in this case, silicon nitride, which shows significant improvement in the current dispersion. This work demonstrates the detrimental effect that the traps can have on the current dispersion, which significantly limits the high-frequency operation of the device.
INTEGRAL discovery of a high-energy tail in the microquasar Cygnus X-3

Context. The X-ray spectra of X-ray binaries are dominated by emission of either soft or hard X-rays which defines their soft and hard spectral states. While the generic picture is relatively well understood, little is known about the interplay of the various media at work, or about the reasons why some sources do not follow common behavior. Cygnus X-3 is amongst the list of X-ray binaries that show quite complex behavior, with various distinct spectral states not seen in other sources. These states have been characterized in many studies. Because of its softness and intrinsic low flux above typically 50 keV, very little is known about the hard X/soft gamma-ray (100–1000 keV) emission in Cygnus X-3. Aims. Using the whole INTEGRAL data base, we aim to explore the 3–1000 keV spectra of Cygnus X-3. This allows to probe this region with the highest sensitivity ever, and search for the potential signature of a high-energy non-thermal component as sometimes seen in other sources. Methods. Our work is based on state classification carried out in previous studies with data from the Rossi X-Ray Timing Explorer. We extend this classification to the whole INTEGRAL data set in order to perform a long-term state-resolved spectral analysis. Six stacked spectra were obtained using 16 years of data from JEM-X (3–25 keV), ISGRI (25–300 keV), and SPI (20–400 keV). Results. We extract stacked images in three different energy bands, and detect the source up to 200 keV. In the hardest states, our purely phenomenological approach clearly reveals the presence of an additonnal component > 50 keV in addition to the component usually interpreted as thermal Comptonization. We apply a more physical model of hybrid thermal/nonthermal corona (EQPAIR) to characterize this nonthermal component and compare our results with those of previous studies and analyses. Our modeling indicates a more efficient acceleration of electrons in states where major ejections are observed. We also evaluate and find a dependence of the photon index of the power law as a function of the strong orbital modulation of the source in the Flaring InterMediate state. This dependence could be due to a higher absorption when Cygnus X-3 is behind its companion. However, the uncertainties on the density column prevent us from drawing any firm conclusions.
Tactile SLAM: Real-time inference of shape and pose from planar pushing

Tactile perception is central to robot manipulation in unstructured environments. However, it requires contact, and a mature implementation must infer object models while also accounting for the motion induced by the interaction. In this work, we present a method to estimate both object shape and pose in real-time from a stream of tactile measurements. This is applied towards tactile exploration of an unknown object by planar pushing. We consider this as an online SLAM problem with a nonparametric shape representation. Our formulation of tactile inference alternates between Gaussian process implicit surface regression and pose estimation on a factor graph. Through a combination of local Gaussian processes and fixed-lag smoothing, we infer object shape and pose in real-time. We evaluate our system across different objects in both simulated and real-world planar pushing tasks.
Learning Latent Representations to Influence Multi-Agent Interaction

Seamlessly interacting with humans or robots is hard because these agents are non-stationary. They update their policy in response to the ego agent's behavior, and the ego agent must anticipate these changes to co-adapt. Inspired by humans, we recognize that robots do not need to explicitly model every low-level action another agent will make; instead, we can capture the latent strategy of other agents through high-level representations. We propose a reinforcement learning-based framework for learning latent representations of an agent's policy, where the ego agent identifies the relationship between its behavior and the other agent's future strategy. The ego agent then leverages these latent dynamics to influence the other agent, purposely guiding them towards policies suitable for co-adaptation. Across several simulated domains and a real-world air hockey game, our approach outperforms the alternatives and learns to influence the other agent.
Weak convergence of the scaled jump chain and number of mutations of the Kingman coalescent.

The Kingman coalescent is an important and well studied process in population genetics modelling the ancestry of a sample of individuals backwards in time. In this paper, weak convergence is proved for a sequence of Markov chains consisting of two components related to the Kingman coalescent, as the size of the initial configuration, the sample size, grows to infinity. The first component is the normalised jump chain of the block counting processes of the Kingman coalescent with a finite number of $d$ genetic types. The second component is a $d^2$-dimensional process counting the number of mutations between types occurring in the Kingman coalescent. Time is scaled by the sample size. The limiting process consists of a deterministic $d$-dimensional component describing the limit of the jump chain and $d^2$ independent Poisson processes with state-dependent intensities, exploding at the origin, describing the limit of the number of mutations. The weak convergence result is first proved, using a generator approach, in the setting of parent independent mutations. A change of measure argument is used to extend the weak convergence result to include parent dependent mutations.
Synthetic Data - A Privacy Mirage

Synthetic datasets produced by generative models are advertised as a silver-bullet solution to privacy-preserving data sharing. Claims about the privacy benefits of synthetic data, however, have not been supported by a rigorous privacy analysis. In this paper, we introduce an evaluation framework that enables data holders to (I) quantify the privacy gain of publishing a synthetic dataset instead of the raw data, and (II) compare the privacy properties of generative model training algorithms. We illustrate the utility of the framework and quantify privacy gain with respect to two concerns, the risk of re-identification via linkage and the risk of attribute disclosure, on synthetic data produced by a range of generative models, from simple independent histograms to differentially private GANs. We find that, across the board, synthetic data provides little privacy gain even under a black-box adversary with access to a single synthetic dataset only. Moreover, we observe that some target records receive substantially less protection than others and that the more complex the generative model, the more difficult it is to predict which targets will remain vulnerable to privacy attacks. Our findings highlight the need to re-consider whether synthetic data is an appropriate strategy to privacy-preserving data publishing.