Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectrum and Morphology of the Very-high-energy Source HAWC J2019+368

The MGRO J2019+37 region is one of the brightest sources in the sky at TeV energies. It was detected in the second HAWC catalog as 2HWC J2019+367 and here we present a detailed study of this region using data from HAWC. This analysis resolves the region into two sources: HAWC J2019+368 and HAWC J2016+371. We associate HAWC J2016+371 with the evolved supernova remnant CTB 87, although its low significance in this analysis prevents a detailed study at this time. An investigation of the morphology (including possible energy-dependent morphology) and spectrum for HAWC J2019+368 is the focus of this work. We associate HAWC J2019+368 with PSR J2021+3651 and its X-ray pulsar wind nebula, the Dragonfly nebula. Modeling the spectrum measured by HAWC and Suzaku reveals a ∼7 kyr pulsar and nebula system producing the observed emission at X-ray and γ-ray energies.
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning

Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection has been extensively studied in and broadly applied to many real-world network problems. Classical approaches to community detection typically utilize probabilistic graphical models and adopt a variety of prior knowledge to infer community structures. As the problems that network methods try to solve and the network data to be analyzed become increasingly more sophisticated, new approaches have also been proposed and developed, particularly those that utilize deep learning and convert networked data into low dimensional representation. Despite all the recent advancement, there is still a lack of insightful understanding of the theoretical and methodological underpinning of community detection, which will be critically important for future development of the area of network analysis. In this paper, we develop and present a unified architecture of network community-finding methods to characterize the state-of-the-art of the field of community detection. Specifically, we provide a comprehensive review of the existing community detection methods and introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We then discuss in detail the main idea behind each method in the two categories. Furthermore, to promote future development of community detection, we release several benchmark datasets from several problem domains and highlight their applications to various network analysis tasks. We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
A Priori Generalization Analysis of the Deep Ritz Method for Solving High Dimensional Elliptic Equations

This paper concerns the a priori generalization analysis of the Deep Ritz Method (DRM) [W. E and B. Yu, 2017], a popular neural-network-based method for solving high dimensional partial differential equations. We derive the generalization error bounds of two-layer neural networks in the framework of the DRM for solving two prototype elliptic PDEs: Poisson equation and static Schr\"odinger equation on the $d$-dimensional unit hypercube. Specifically, we prove that the convergence rates of generalization errors are independent of the dimension $d$, under the a priori assumption that the exact solutions of the PDEs lie in a suitable low-complexity space called spectral Barron space. Moreover, we give sufficient conditions on the forcing term and the potential function which guarantee that the solutions are spectral Barron functions. We achieve this by developing a new solution theory for the PDEs on the spectral Barron space, which can be viewed as an analog of the classical Sobolev regularity theory for PDEs.
HIGH RESOLUTION GENERATION EXPANSION PLANNING CONSIDERING FLEXIBILITY NEEDS: THE CASE OF SWITZERLAND IN 2030

This paper presents a static generation expansion planning formulation in which operational and investment decisions for a wide range of technologies are co-optimized from a centralized perspective. The location, type and quantity of new generation and storage capacities are provided such that system demand and flexibility requirements are satisfied. Depending on investments in new intermittent renewables (wind, PV), the flexibility requirements are adapted in order to fully capture RES integration costs and ensure normal system operating conditions. To position candidate units, we incorporate DC constraints, nodal demand and production of existing generators as well as imports and exports from other interconnected zones. To show the capabilities of the formulation, high-temporal resolution simulations are conducted on a 162-bus system consisting of the full Swiss transmission grid and aggregated neighboring countries.
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing

Semantic parsing has long been a fundamental problem in natural language processing. Recently, cross-domain context-dependent semantic parsing has become a new focus of research. Central to the problem is the challenge of leveraging contextual information of both natural language utterance and database schemas in the interaction history. In this paper, we present a dynamic graph framework that is capable of effectively modelling contextual utterances, tokens, database schemas, and their complicated interaction as the conversation proceeds. The framework employs a dynamic memory decay mechanism that incorporates inductive bias to integrate enriched contextual relation representation, which is further enhanced with a powerful reranking model. At the time of writing, we demonstrate that the proposed framework outperforms all existing models by large margins, achieving new state-of-the-art performance on two large-scale benchmarks, the SParC and CoSQL datasets. Specifically, the model attains a 55.8% question-match and 30.8% interaction-match accuracy on SParC, and a 46.8% question-match and 17.0% interaction-match accuracy on CoSQL.
CycleSegNet: Object Co-Segmentation With Cycle Refinement and Region Correspondence

Image co-segmentation is an active computer vision task that aims to segment the common objects from a set of images. Recently, researchers design various learning-based algorithms to undertake the co-segmentation task. The main difficulty in this task is how to effectively transfer information between images to make conditional predictions. In this paper, we present CycleSegNet, a novel framework for the co-segmentation task. Our network design has two key components: a region correspondence module which is the basic operation for exchanging information between local image regions, and a cycle refinement module, which utilizes ConvLSTMs to progressively update image representations and exchange information in a cycle and iterative manner. Extensive experiments demonstrate that our proposed method significantly outperforms the state-of-the-art methods on four popular benchmark datasets — PASCAL VOC dataset, MSRC dataset, Internet dataset, and iCoseg dataset, by 2.6%, 7.7%, 2.2%, and 2.9%, respectively.
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications

In this work, we study a novel class of projection-based algorithms for linearly constrained problems (LCPs) which have a lot of applications in statistics, optimization, and machine learning. Conventional primal gradient-based methods for LCPs call a projection after each (stochastic) gradient descent, resulting in that the required number of projections equals that of gradient descents (or total iterations). Motivated by the recent progress in distributed optimization, we propose the delayed projection technique that calls a projection once for a while, lowering the projection frequency and improving the projection efficiency. Accordingly, we devise a series of stochastic methods for LCPs using the technique, including a variance reduced method and an accelerated one. We theoretically show that it is feasible to improve projection efficiency in both strongly convex and generally convex cases. Our analysis is simple and unified and can be easily extended to other methods using delayed projections. When applying our new algorithms to federated optimization, a newfangled and privacy-preserving subfield in distributed optimization, we obtain not only a variance reduced federated algorithm with convergence rates better than previous works, but also the first accelerated method able to handle data heterogeneity inherent in federated optimization.
Radio halos in a mass-selected sample of 75 galaxy clusters

Context. Radio halos are synchrotron diffuse sources at the centre of a fraction of galaxy clusters. The study of large samples of clusters with adequate radio and X-ray data is necessary to investigate the origin of radio halos and their connection with the cluster dynamics and formation history. Aims. The aim of this paper is to compile a well-selected sample of galaxy clusters with deep radio observations to perform an unbiased statistical study of the properties of radio halos. Methods. We selected 75 clusters with M ≥ 6 × 1014 M⊙ at z = 0.08 − 0.33 from the Planck Sunyaev-Zel’dovich catalogue. Clusters without suitable radio data were observed with the Giant Metrewave Radio Telescope and/or the Jansky Very Large Array to complete the information about the possible presence of diffuse emission. We used archival Chandra X-ray data to derive information on the clusters’ dynamical states. Results. This observational campaign led to the detection of several cluster-scale diffuse radio sources and candidates that deserve future follow-up observations. Here we summarise their properties and add information resulting from our new observations. For the clusters where we did not detect any hint of diffuse emission, we derived new upper limits to their diffuse flux. Conclusions. We have built the largest mass-selected (> 80% complete in mass) sample of galaxy clusters with deep radio observations available to date. The statistical analysis of the sample, which includes the connection between radio halos and cluster mergers, the radio power – mass correlation, and the occurrence of radio halos as a function of the cluster mass, will be presented in Paper II.
Explaining the difference between men’s and women’s football

Women’s football is gaining supporters and practitioners worldwide, raising questions about what the differences are with men’s football. While the two sports are often compared based on the players’ physical attributes, we analyze the spatio-temporal events during matches in the last World Cups to compare male and female teams based on their technical performance. We train an artificial intelligence model to recognize if a team is male or female based on variables that describe a match’s playing intensity, accuracy, and performance quality. Our model accurately distinguishes between men’s and women’s football, revealing crucial technical differences, which we investigate through the extraction of explanations from the classifier’s decisions. The differences between men’s and women’s football are rooted in play accuracy, the recovery time of ball possession, and the players’ performance quality. Our methodology may help journalists and fans understand what makes women’s football a distinct sport and coaches design tactics tailored to female teams.
Effect of demineralization on the macromolecular structure of coals

It appears that HCl/HF demineralization has little, if any, effect on the macromolecular structure of coals apart from those due to ion exchange and the removal of inorganics. Six coals varying in carbon content between 69% and 86% were demineralized in HCl/HF, dried, and extracted with pyridine, and the amount and molecular weight distribution of the pyridine extracts were determined. The observed alterations in the amounts and molecular weight distributions of the extracts from these three coals due to demineralization are probably due to solubility changes caused by converting acid salts to acids by ions exchange
Extraction, isolation, structure elucidation and evaluation of toxicity, anti-inflammatory and analgesic activity of Pituranthos scoparius constituents

JURY: Chair: KHENNOUF Seddik Pr. UFA Sétif 1 Supervisor: CHAREF Noureddine Pr. UFA Sétif 1 Co-Supervisor: ABU-ZARGA Musa H. Pr. The University of Jordan, Amman Examiners: ARRAR Lekhmici Pr. UFA Sétif 1 GHERRAF Noureddine Pr. University of Oum El Bouaghi ZELLAGUI Amar Pr. University of Oum El Bouaghi University Ferhat Abbas Setif 1 Faculty of Natural and Life Sciences سابع تاحرف ةعماج فيطس 1 مولع ةيلك ةايحلاو ةعيبطلا
Nonlinear Filter for Simultaneous Localization and Mapping on a Matrix Lie Group Using IMU and Feature Measurements

Simultaneous localization and mapping (SLAM) is a process of concurrent estimation of the vehicle’s pose and feature locations with respect to a frame of reference. This article proposes a computationally cheap geometric nonlinear SLAM filter algorithm structured to mimic the nonlinear motion dynamics of the true SLAM problem posed on the matrix Lie group of $\mathbb {SLAM}_{n}(3)$ . The nonlinear filter on manifold is proposed in continuous form and it utilizes available measurements obtained from group velocity vectors, feature measurements, and an inertial measurement unit (IMU). The unknown bias attached to velocity measurements is successfully handled by the proposed estimator. Simulation results illustrate the robustness of the proposed filter in discrete form, demonstrating its utility for the six-degrees-of-freedom (6 DoF) pose estimation as well as feature estimation in three-dimensional (3-D) space. In addition, the quaternion representation of the nonlinear filter for SLAM is provided.
On the global convergence of randomized coordinate gradient descent for non-convex optimization

In this work, we analyze the global convergence property of coordinate gradient descent with random choice of coordinates and stepsizes for non-convex optimization problems. Under generic assumptions, we prove that the algorithm iterate will almost surely escape strict saddle points of the objective function. As a result, the algorithm is guaranteed to converge to local minima if all saddle points are strict. Our proof is based on viewing coordinate descent algorithm as a nonlinear random dynamical system and a quantitative finite block analysis of its linearization around saddle points.
Symmetry and control of spin-scattering processes in two-dimensional transition metal dichalcogenides

Transition metal dichalcogenides (TMDs) combine interesting optical and spintronic properties in an atomically thin material, where the light polarization can be used to control the spin and valley degrees of freedom for the development of novel optospintronic devices. These promising properties emerge due to their large spin-orbit coupling in combination with their crystal symmetries. Here, we provide simple symmetry arguments in a group-theory approach to unveil the symmetry-allowed spin-scattering mechanisms, and indicate how one can use these concepts towards an external control of the spin lifetime. We perform this analysis for both monolayer (inversion asymmetric) and bilayer (inversion symmetric) crystals, indicating the different mechanisms that play a role in these systems. We show that in monolayer TMDs, electrons and holes transform fundamentally differently----leading to distinct spin-scattering processes. We find that one of the electronic states in the conduction band is partially protected by time-reversal symmetry, indicating a longer spin lifetime for that state. In bilayer and bulk TMDs, a hidden spin polarization can exist within each layer despite the presence of global inversion symmetry. We show that this feature enables control of the interlayer spin-flipping scattering processes via an out-of-plane electric field, providing a mechanism for electrical control of the spin lifetime.
Dynamics and rheology of ring-linear blend semidilute solutions in extensional flow: Single molecule experiments

Ring polymers exhibit unique flow properties due to their closed chain topology. Despite recent progress, we have not yet achieved a full understanding of the nonequilibrium flow behavior of rings in nondilute solutions where intermolecular interactions greatly influence chain dynamics. In this work, we directly observe the dynamics of DNA rings in semidilute ring-linear polymer blends using single molecule techniques. We systematically investigate ring polymer relaxation dynamics from high extension and transient and steady-state stretching dynamics in a planar extensional flow for a series of ring-linear blends with varying ring fraction. Our results show multiple molecular subpopulations for ring relaxation in ring-linear blends, as well as large conformational fluctuations for rings in a steady extensional flow, even long after the initial transient stretching process has subsided. We further quantify the magnitude and characteristic time scales of ring conformational fluctuations as a function of blend composition. Interestingly, we find that the magnitude of ring conformational fluctuations follows a nonmonotonic response with increasing ring fraction, first increasing at low ring fraction and then substantially decreasing at large ring fraction in ring-linear blends. A unique set of ring polymer conformations are observed during the transient stretching process, which highlights the prevalence of molecular individualism and supports the notion of complex intermolecular interactions in ring-linear polymer blends. In particular, our results suggest that transient intermolecular structures form in ring-linear blends due to a combination of direct forces due to linear chains threading through open rings and indirect forces due to hydrodynamic interactions; these combined effects lead to large conformational fluctuations of rings over distributed time scales. Taken together, our results provide a new molecular understanding of ring polymer dynamics in ring-linear blends in the nonequilibrium flow. © 2021 The Society of Rheology. https://doi.org/10.1122/8.0000219
Model-based Fault Injection Experiments for the Safety Analysis of Exoskeleton System

Model-based fault injection methods are widely used for the evaluation of fault tolerance in safety-critical control systems. In this paper, we introduce a new model-based fault injection method implemented as a highly-customizable Simulink block called FIBlock. It supports the injection of typical faults of essential heterogeneous components of Cyber-Physical Systems, such as sensors, computing hardware, and network. The FIBlock GUI allows the user to select a fault type and configure multiple parameters to tune error magnitude, fault activation time, and fault exposure duration. Additional trigger inputs and outputs of the block enable the modeling of conditional faults. Furthermore, two or more FIBlocks connected with these trigger signals can model chained errors. The proposed fault injection method is demonstrated with a lower-limb EXO-LEGS exoskeleton, an assistive device for the elderly in everyday life. The EXO-LEGS model-based dynamic control is realized in the Simulink environment and allows easy integration of the aforementioned FIBlocks. Exoskeletons, in general, being a complex CPS with multiple sensors and actuators, are prone to hardware and software faults. In the case study, three types of faults were investigated: 1) sensor freeze, 2) stuck-at-0, 3) bit-flip. The fault injection experiments helped to determine faults that have the most significant effects on the overall system reliability and identify the fine line for the critical fault duration after that the controller could no longer mitigate faults.
Model Reduction Captures Stochastic Gamma Oscillations on Low-Dimensional Manifolds

Gamma frequency oscillations (25–140 Hz), observed in the neural activities within many brain regions, have long been regarded as a physiological basis underlying many brain functions, such as memory and attention. Among numerous theoretical and computational modeling studies, gamma oscillations have been found in biologically realistic spiking network models of the primary visual cortex. However, due to its high dimensionality and strong non-linearity, it is generally difficult to perform detailed theoretical analysis of the emergent gamma dynamics. Here we propose a suite of Markovian model reduction methods with varying levels of complexity and apply it to spiking network models exhibiting heterogeneous dynamical regimes, ranging from nearly homogeneous firing to strong synchrony in the gamma band. The reduced models not only successfully reproduce gamma oscillations in the full model, but also exhibit the same dynamical features as we vary parameters. Most remarkably, the invariant measure of the coarse-grained Markov process reveals a two-dimensional surface in state space upon which the gamma dynamics mainly resides. Our results suggest that the statistical features of gamma oscillations strongly depend on the subthreshold neuronal distributions. Because of the generality of the Markovian assumptions, our dimensional reduction methods offer a powerful toolbox for theoretical examinations of other complex cortical spatio-temporal behaviors observed in both neurophysiological experiments and numerical simulations.
Handling Mobility in Low-Power Wide-Area Network

Despite the proliferation of mobile devices in various wide-area Internet of Things applications (e.g., smart city, smart farming), current Low-Power Wide-Area Networks (LPWANs) are not designed to effectively support mobile nodes. In this paper, we propose to handle mobility in SNOW (Sensor Network Over White spaces), an LPWAN that operates in the TV white spaces. SNOW supports massive concurrent communication between a base station (BS) and numerous low-power nodes through a distributed implementation of OFDM. In SNOW, inter-carrier interference (ICI) is more pronounced under mobility due to its OFDM based design. Geospatial variation of white spaces also raises challenges in both intra- and inter-network mobility as the low-power nodes are not equipped to determine white spaces. To handle mobility impacts on ICI, we propose a dynamic carrier frequency offset estimation and compensation technique which takes into account Doppler shifts without requiring to know the speed of the nodes. We also propose to circumvent the mobility impacts on geospatial variation of white space through a mobility-aware spectrum assignment to nodes. To enable mobility of the nodes across different SNOWs, we propose an efficient handoff management through a fast and energy-efficient BS discovery and quick association with the BS by combining time and frequency domain energy-sensing. Experiments through SNOW deployments in a large metropolitan city and indoors show that our proposed approaches enable mobility across multiple different SNOWs and provide robustness in terms of reliability, latency, and energy consumption under mobility.