Miracle-dz/UniFAR-FTUs huggingface dataset:
A significant hardening and rising shape detected in the MeV/GeV νFν spectrum from the recently discovered very-high-energy blazar S4 0954+65 during the bright optical flare in 2015 February

We report on Fermi Large Area Telescope (LAT) and multi-wavelength results on the recently-discovered very-high-energy (VHE, $E>$ 100 GeV) blazar S4 0954+65 ($z=0.368$) during an exceptionally bright optical flare in 2015 February. During the time period (2015 February, 13/14, or MJD 57067) when the MAGIC telescope detected VHE $\gamma$-ray emission from the source, the Fermi-LAT data indicated a significant spectral hardening at GeV energies, with a power-law photon index of $1.8 \pm 0.1$---compared with the 3FGL value (averaged over four years of observation) of $2.34 \pm 0.04$. In contrast, Swift/XRT data showed a softening of the X-ray spectrum, with a photon index of $1.72 \pm 0.08$ (compared with $1.38 \pm 0.03$ averaged during the flare from MJD 57066 to 57077), possibly indicating a modest contribution of synchrotron photons by the highest-energy electrons superposed on the inverse Compton component. Fitting of the quasi-simultaneous ($ $ 100 MeV) and a hard spectral index of $\Gamma_{\rm GeV} < 2.0$ detected by Fermi-LAT on daily time scales is a promising target for TeV follow-up by ground-based Cherenkov telescopes to discover high-redshift blazars, investigate their temporal variability and spectral features in the VHE band, and also constrain the intensity of the extragalactic background light.
Computer Modelling in Tomography and Ill-Posed Problems

Mathematical basis of the method of computerized tomography basic notions of the theory of ill-posed problems problem of integral geometry the Radon transfer Radon problem as an example of an ill-posed problem the algorithm of inversion of the two-dimensional Radon trans form based on the convolution with the generalized function 1/z2 cone-beam tomography reconstruction reducing the inversion formulas of cone-beam tomography reconstruction to the form convenient for constructing numerical algorithms elements of the theory of generalized functions in application to problems of inversion of the ray transformation the relations between the Radon, Fourier and ray transformations inverse kinematic problem in the tomographic setting direct kinematic problem and numerical solution for three-dimensional regular media formulation of the inverse kinematic problem with the use of a tomography system of data gathering deduction on the basic inversion formula and the algorithm of solving the inverse kinematic problem in three-dimensional linearized formulation model experiment and numerical study of the algorithm solution of the inverse kinematic problem by the method of computerized tomography for media with opaque inclusions. Appendix: reconstruction with the use of the standard model.
Predicting the traffic flux in the city of Valencia with Deep Learning

Traffic congestion is a major urban issue due to its adverse effects on health and the environment, so much so that reducing it has become a priority for urban decision-makers. In this work, we investigate whether a high amount of data on traffic flow throughout a city and the knowledge of the road city network allows an Artificial Intelligence to predict the traffic flux far enough in advance in order to enable emission reduction measures such as those linked to the Low Emission Zone policies. To build a predictive model, we use the city of Valencia traffic sensor system, one of the densest in the world, with nearly 3500 sensors distributed throughout the city. In this work we train and characterize an LSTM (Long Short-Term Memory) Neural Network to predict temporal patterns of traffic in the city using historical data from the years 2016 and 2017. We show that the LSTM is capable of predicting future evolution of the traffic flux in real-time, by extracting patterns out of the measured data.
COPILOT: Human Collision Prediction and Localization from Multi-view Egocentric Videos

—To produce safe human motions, assistive wearable exoskeletons must be equipped with a perception system that enables anticipating potential collisions from egocentric observations. However, previous approaches to exoskeleton perception greatly simplify the problem to speciﬁc types of environments, limiting their scalability. In this paper, we propose the challenging and novel problem of predicting human-scene collisions for diverse environments from multi-view egocentric RGB videos captured from an exoskeleton. By classifying which body joints will collide with the environment and predicting a collision region heatmap that localizes potential collisions in the environment, we aim to develop an exoskeleton perception system that generalizes to complex real-world scenes and provides actionable outputs for downstream control. We propose COPILOT, a video transformer-based model that performs both collision prediction and localization simultaneously, leveraging multi-view video inputs via a proposed joint space-time-viewpoint attention operation. To train and evaluate the model, we build a synthetic data generation framework to simulate virtual humans moving in photo-realistic 3D environments. This framework is then used to establish a dataset consisting of 8.6M egocentric RGBD frames to enable future work on the problem. Extensive experiments suggest that our model achieves promising performance and generalizes to unseen scenes as well as real world. We apply COPILOT to a downstream collision avoidance task, and successfully reduce collision cases by 29% on unseen scenes using a simple closed-loop control algorithm.
Force-Aware Interface via Electromyography for Natural VR/AR Interaction

While tremendous advances in visual and auditory realism have been made for virtual and augmented reality (VR/AR), introducing a plausible sense of physicality into the virtual world remains challenging. Closing the gap between real-world physicality and immersive virtual experience requires a closed interaction loop: applying user-exerted physical forces to the virtual environment and generating haptic sensations back to the users. However, existing VR/AR solutions either completely ignore the force inputs from the users or rely on obtrusive sensing devices that compromise user experience. By identifying users' muscle activation patterns while engaging in VR/AR, we design a learning-based neural interface for natural and intuitive force inputs. Specifically, we show that lightweight electromyography sensors, resting non-invasively on users' forearm skin, inform and establish a robust understanding of their complex hand activities. Fuelled by a neural-network-based model, our interface can decode finger-wise forces in real-time with 3.3% mean error, and generalize to new users with little calibration. Through an interactive psychophysical study, we show that human perception of virtual objects' physical properties, such as stiffness, can be significantly enhanced by our interface. We further demonstrate that our interface enables ubiquitous control via finger tapping. Ultimately, we envision our findings to push forward research towards more realistic physicality in future VR/AR.
Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization

Bayesian optimization (BO) is a class of popular methods for expensive black-box optimization, and has been widely applied to many scenarios. However, BO suffers from the curse of dimensionality, and scaling it to high-dimensional problems is still a challenge. In this paper, we propose a variable selection method MCTS-VS based on Monte Carlo tree search (MCTS), to iteratively select and optimize a subset of variables. That is, MCTS-VS constructs a low-dimensional subspace via MCTS and optimizes in the subspace with any BO algorithm. We give a theoretical analysis of the general variable selection method to reveal how it can work. Experiments on high-dimensional synthetic functions and real-world problems (i.e., NAS-bench problems and MuJoCo locomotion tasks) show that MCTS-VS equipped with a proper BO optimizer can achieve state-of-the-art performance.
Random Weight Factorization Improves the Training of Continuous Neural Representations

Continuous neural representations have recently emerged as a powerful and flexible alternative to classical discretized representations of signals. However, training them to capture fine details in multi-scale signals is difficult and computationally expensive. Here we propose random weight factorization as a simple drop-in replacement for parameterizing and initializing conventional linear layers in coordinate-based multi-layer perceptrons (MLPs) that significantly accelerates and improves their training. We show how this factorization alters the underlying loss landscape and effectively enables each neuron in the network to learn using its own self-adaptive learning rate. This not only helps with mitigating spectral bias, but also allows networks to quickly recover from poor initializations and reach better local minima. We demonstrate how random weight factorization can be leveraged to improve the training of neural representations on a variety of tasks, including image regression, shape representation, computed tomography, inverse rendering, solving partial differential equations, and learning operators between function spaces.
Exit Game With Private Information

The timing of strategic exit is one of the most important but difficult business decisions, especially under competition and uncertainty. Motivated by this problem, we examine a stochastic game of exit in which players are uncertain about their competitor's exit value. We construct an equilibrium for a large class of payoff flows driven by a general one-dimensional diffusion. In the equilibrium, the players employ sophisticated exit strategies involving both the state variable and the posterior belief process. These strategies are specified explicitly in terms of the problem data and a solution to an auxiliary optimal stopping problem. The equilibrium we obtain is further shown to be unique within a wide subclass of symmetric Bayesian equilibria.
Using Entropy Measures for Monitoring the Evolution of Activity Patterns

In this work, we apply information theory inspired methods to quantify changes in daily activity patterns. We use in-home movement monitoring data and show how they can help indicate the occurrence of healthcare-related events. Three different types of entropy measures namely Shannon's entropy, entropy rates for Markov chains, and entropy production rate have been utilised. The measures are evaluated on a large-scale in-home monitoring dataset that has been collected within our dementia care clinical study. The study uses Internet of Things (IoT) enabled solutions for continuous monitoring of in-home activity, sleep, and physiology to develop care and early intervention solutions to support people living with dementia (PLWD) in their own homes. Our main goal is to show the applicability of the entropy measures to time-series activity data analysis and to use the extracted measures as new engineered features that can be fed into inference and analysis models. The results of our experiments show that in most cases the combination of these measures can indicate the occurrence of healthcare-related events. We also find that different participants with the same events may have different measures based on one entropy measure. So using a combination of these measures in an inference model will be more effective than any of the single measures.
Life cycle of cosmic-ray electrons in the intracluster medium

We simulate the evolution of relativistic electrons injected into the medium of a small galaxy cluster by a central radio galaxy, studying how the initial jet power affects the dispersal and the emission properties of radio plasma. By coupling passive tracer particles to adaptive-mesh cosmological MHD simulations, we study how cosmic-ray electrons are dispersed as a function of the input jet power. We also investigate how the latter affects the thermal and non-thermal properties of the intracluster medium, with differences discernible up to $\sim$ Gyr after the start of the jet. We evolved the energy spectra of cosmic-ray electrons, subject to energy losses that are dominated by synchrotron and inverse Compton emission as well as energy gains via re-acceleration by shock waves and turbulence. We find that in the absence of major mergers the amount of re-acceleration experienced by cosmic-ray electrons is not enough to produce long-lived detectable radio emissions. However, for all simulations the role of re-acceleration processes is crucial to maintain a significant and volume-filling reservoir of fossil electrons ($\gamma \sim 10^3$) for several Gyrs after the first injection by jets. This is important to possibly explain recent discoveries of cluster-wide emission and other radio phenomena in galaxy clusters.
Enhanced skyrmion metastability under applied strain in FeGe

Mechanical straining of skyrmion hosting materials has previously demonstrated increased phase stability through the expansion of the skyrmion equilibrium pocket. Additionally, metastable skyrmions can be generated via rapid field-cooling to form significant skyrmion populations at low temperatures. Using small-angle x-ray scattering and x-ray holographic imaging on a thermally strained 200 nm thick FeGe lamella, we observe temperature-induced strain effects on the structure and metastability of the skyrmion lattice. We find that in this sample orientation (H || [1 1 0]) with no strain, metastable skyrmions produced by field cooling through the equilibrium skyrmion pocket vanish from the sample upon dropping below the well known helical reorientation temperature. However, when strain is applied along [110] axis, and this procedure is repeated, a substantial volume fraction of metastable skyrmions persist upon cooling below this temperature down to 100 K. Additionally, we observe a large number of skyrmions retained after a complete magnetic field polarity reversal, implying that the metastable energy barrier protecting skyrmions from decay is enhanced.
Large‐scale turbulence, shocks, and charged‐particle acceleration

We discuss the physics of particle acceleration by shocks moving in turbulent astrophysical plasmas. We suggest that the observed lack of agreement of energetic particles and shock properties in the heliosphere is likely to be a result of fluctuations and turbulence in the upstream fluid. Shock acceleration takes time, so the observations of particles at any given point and time at a shock reflects some combination of the history prior to the time of observation. Further, since the particles are mobile, there is spatial averaging as well. It follows that the shock properties observed by a spacecraft should not correlate well with the accelerated particles. Additionally, the upstream turbulence, and in particular, the fluctuations in plasma density have a large effect on the downstream magnetic field. These effects may also help to understand the magnetic field observed by Voyager 1 in the heliosheath.
A note on M2-branes in opposite charge

The flux singularity that arise when branes are put in an oppositely charged background is a wildly discussed issue and in this paper the problem is investigated further. New AdS world volume (anti-)M2-brane solutions are constructed by placing the brane in a background where the charge dissolved in the flux is opposite that of the brane. A topological obstruction is derived to show how the singularity develops during localisation of this solution and any similarly constructed solution. Furthermore the singularity is shown not to be possible to hide behind a horizon, which would have given credence to a possible M-theory resolution. 1 ar X iv :1 30 9. 26 40 v1 [ he pth ] 1 0 Se p 20 13
Estimating the hardness of SAT encodings for Logical Equivalence Checking of Boolean circuits

In this paper we investigate how to estimate the hardness of Boolean satisfiability (SAT) encodings for the Logical Equivalence Checking problem (LEC). Meaningful estimates of hardness are important in cases when a conventional SAT solver cannot solve a SAT instance in a reasonable time. We show that the hardness of SAT encodings for LEC instances can be estimated \textit{w.r. t. } some SAT partitioning. We also demonstrate the dependence of the accuracy of the resulting estimates on the probabilistic characteristics of a specially defined random variable associated with the considered partitioning. The paper proposes several methods for constructing partitionings, which, when used in practice, allow one to estimate the hardness of SAT encodings for LEC with good accuracy. In the experimental part we propose a class of scalable LEC tests that give extremely complex instances with a relatively small input size $n$ of the considered circuits. For example, for $n = 40$, none of the state-of-the-art SAT solvers can cope with the considered tests in a reasonable time. However, these tests can be solved in parallel using the proposed partitioning methods.
Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients

We consider escaping saddle points of nonconvex problems where only the function evaluations can be accessed. Although a variety of works have been proposed, the majority of them require either second or first-order information, and only a few of them have exploited zeroth-order methods, particularly the technique of negative curvature finding with zeroth-order methods which has been proven to be the most efficient method for escaping saddle points. To fill this gap, in this paper, we propose two zeroth-order negative curvature finding frameworks that can replace Hessian-vector product computations without increasing the iteration complexity. We apply the proposed frameworks to ZO-GD, ZO-SGD, ZO-SCSG, ZO-SPIDER and prove that these ZO algorithms can converge to $(\epsilon,\delta)$-approximate second-order stationary points with less query complexity compared with prior zeroth-order works for finding local minima.
GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction

In this paper, we propose a Graph Inception Diffusion Networks(GIDN) model. This model generalizes graph diffusion in different feature spaces, and uses the inception module to avoid the large amount of computations caused by complex network structures. We evaluate GIDN model on Open Graph Benchmark(OGB) datasets, reached an 11% higher performance than AGDN on ogbl-collab dataset.
Dense Prediction Transformer for Scale Estimation in Monocular Visual Odometry

Monocular visual odometry consists of the estimation of the position of an agent through images of a single camera, and it is applied in autonomous vehicles, medical robots, and augmented reality. However, monocular systems suffer from the scale ambiguity problem due to the lack of depth information in 2D frames. This paper contributes by showing an application of the dense prediction transformer model for scale estimation in monocular visual odometry systems. Experimental results show that the scale drift problem of monocular systems can be reduced through the accurate estimation of the depth map by this model, achieving competitive state-of-the-art performance on a visual odometry benchmark.
LOPR: Latent Occupancy PRediction using Generative Models

Environment prediction frameworks are integral for autonomous vehicles, enabling safe navigation in dynamic environments. LiDAR generated occupancy grid maps (L-OGMs) offer a robust bird's eye-view scene representation that facilitates joint scene predictions without relying on manual labeling unlike commonly used trajectory prediction frameworks. Prior approaches have optimized deterministic L-OGM prediction architectures directly in grid cell space. While these methods have achieved some degree of success in prediction, they occasionally grapple with unrealistic and incorrect predictions. We claim that the quality and realism of the forecasted occupancy grids can be enhanced with the use of generative models. We propose a framework that decouples occupancy prediction into: representation learning and stochastic prediction within the learned latent space. Our approach allows for conditioning the model on other available sensor modalities such as RGB-cameras and high definition maps. We demonstrate that our approach achieves state-of-the-art performance and is readily transferable between different robotic platforms on the real-world NuScenes, Waymo Open, and a custom dataset we collected on an experimental vehicle platform.
Safe and Stable Control Synthesis for Uncertain System Models via Distributionally Robust Optimization

This paper considers enforcing safety and stability of dynamical systems in the presence of model uncertainty. Safety and stability constraints may be specified using a control barrier function (CBF) and a control Lyapunov function (CLF), respectively. To take model uncertainty into account, robust and chance formulations of the constraints are commonly considered. However, this requires known error bounds or a known distribution for the model uncertainty, and the resulting formulations may suffer from over-conservatism or over-confidence. In this paper, we assume that only a finite set of model parametric uncertainty samples is available and formulate a distributionally robust chance-constrained program (DRCCP) for control synthesis with CBF safety and CLF stability guarantees. To facilitate efficient computation of control inputs during online execution, we present a reformulation of the DRCCP as a second-order cone program (SOCP). Our formulation is evaluated in an adaptive cruise control example in comparison to 1) a baseline CLF-CBF quadratic programming approach, 2) a robust approach that assumes known error bounds of the system uncertainty, and 3) a chance-constrained approach that assumes a known Gaussian Process distribution of the uncertainty.
Effective and Robust Non-Prehensile Manipulation via Persistent Homology Guided Monte-Carlo Tree Search

Performing object retrieval in real-world workspaces must tackle challenges including \emph{uncertainty} and \emph{clutter}. One option is to apply prehensile operations, which can be time consuming in highly-cluttered scenarios. On the other hand, non-prehensile actions, such as pushing simultaneously multiple objects, can help to quickly clear a cluttered workspace and retrieve a target object. Such actions, however, can also lead to increased uncertainty as it is difficult to estimate the outcome of pushing operations. The proposed framework in this work integrates topological tools and Monte-Carlo Tree Search (MCTS) to achieve effective and robust pushing for object retrieval. It employs persistent homology to automatically identify manageable clusters of blocking objects without the need for manually adjusting hyper-parameters. Then, MCTS uses this information to explore feasible actions to push groups of objects, aiming to minimize the number of operations needed to clear the path to the target. Real-world experiments using a Baxter robot, which involves some noise in actuation, show that the proposed framework achieves a higher success rate in solving retrieval tasks in dense clutter than alternatives. Moreover, it produces solutions with few pushing actions improving the overall execution time. More critically, it is robust enough that it allows one to plan the sequence of actions offline and then execute them reliably on a Baxter robot.