Miracle-dz/UniFAR-FTUs huggingface dataset:
GRB X-Ray Flare Properties among Different GRB Subclasses

Gamma-ray bursts (GRBs) can be divided into three subclasses: X-ray flash (XRF), X-ray rich (XRR), and classical GRB (C-GRB). An X-ray flare is the rebrightening emission shown in the early X-ray afterglow of some GRBs. In this paper, we comprehensively examine the X-ray flare properties among XRF, XRR, and C-GRB subclasses. We utilize the XRF, XRR, and C-GRB subclass samples obtained from the Swift-BAT3 catalog, and the X-ray flare observational properties are collected from Falcone et al., Chincarini et al., and Yi et al. We find that XRFs and XRRs have more bright X-ray flares than C-GRBs. The ratio of the X-ray flare fluence to the prompt emission fluence has different distributions between XRF and C-GRB subclasses. The linear correlation between the duration and the peak time of the X-ray flares is also different between XRF and C-GRB subclasses. We are inclined to identify the GRBs with the bright X-ray flares as XRFs or XRRs. We discuss some issues that are related to the XRF/XRR/C-GRB classification. We also caution the selection effects and the instrument bias in our investigation. Large samples are required in the future to further confirm our results.
Pictures tell a story: antecedents of rich-media curation in social network sites

ABSTRACT This study explores a new aspect of online social networking, in which textual information has been largely replaced by the curation of rich-media content. We examine these ‘curation-based’ networks (CBN) and develop a theoretical model of the antecedents of intentions to curate rich-media content. Results from the empirical analysis of 449 users of Pinterest, a popular CBN, show that users undertake curation tasks to achieve objectives around the management, discovery, and presentation of rich-media content. The findings have implications for researchers and practitioners interested in the changing nature of online social networks and the role of rich-media in online communication.
Ballistic correction to the density of states in an interacting three-dimensional metal

We study the tunneling density of states (DOS) in an interacting disordered three-dimensional metal and calculate its energy dependence in the quasiballistic regime, for the deviation from the Fermi energy, $E-E_F$, exceeding the elastic scattering rate. In this region, the DOS correction originates from the interplay of the interaction and single-impurity scattering. Depending on the distance between the interaction point and the impurity, one should distinguish (i) the smallest scales of the order of the Fermi wavelength and (ii) larger spatial scales of the order of $\hbar v_F/|E-E_F|$. In two dimensions, the large-scale contribution prevails, resulting in a nearly universal DOS correction. The peculiarity of Friedel oscillations in three dimensions is that the contributions from small and large scales are typically comparable, making the DOS correction sensitive to the details of the interaction and demonstrating a significant particle-hole asymmetry. On the other hand, we show that the non-analytic part of the DOS is determined by large scales and can be expressed in terms of the Fermi-surface characteristics only.
Electron-Induced State Conversion in Diamond NV Centers Measured with Pump–Probe Cathodoluminescence Spectroscopy

Nitrogen-vacancy (NV) centers in diamond are reliable single-photon emitters, with applications in quantum technologies and metrology. Two charge states are known for NV centers, NV0 and NV–, with the latter being mostly studied due to its long electron spin coherence time. Therefore, control over the charge state of the NV centers is essential. However, an understanding of the dynamics between the different states still remains challenging. Here, conversion from NV– to NV0 due to electron-induced carrier generation is shown. Ultrafast pump–probe cathodoluminescence spectroscopy is presented for the first time, with electron pulses as pump and laser pulses as probe, to prepare and read out the NV states. The experimental data are explained with a model considering carrier dynamics (0.8 ns), NV0 spontaneous emission (20 ns), and NV0 → NV– back transfer (500 ms). The results provide new insights into the NV– → NV0 conversion dynamics and into the use of pump–probe cathodoluminescence as a nanoscale NV characterization tool.
On the Joint Distribution of Descents and Signs of Permutations

We study the joint distribution of descents and sign for elements of the symmetric group and the hyperoctahedral group (Coxeter groups of types $A$ and $B$). For both groups, this has an application to riffle shuffling: for large decks of cards the sign is close to random after a single shuffle. In both groups, we derive generating functions for the Eulerian distribution refined according to sign, and use them to give two proofs of central limit theorems for positive and negative Eulerian numbers.
Parity-time symmetry in wavelength space with spatial singularity.

Implementation of a parity-time (PT) symmetric microwave photonic system in the optical wavelength space with spatial singularity is proposed. In the proposed PT-symmetric microwave photonic system, the gain and loss modes are confined in a single spatial resonator, which is different from a conventional PT-symmetric system in which the two modes are localized in two physically separated resonators to form one-dimensional spatial potential symmetry as required by the simplest one-dimensional parity transformation. We show that PT-symmetry can be implemented between subspaces in non-spatial parameter spaces, in which the gain and loss modes can perfectly overlay spatially but are distinguishable in the designated parameter space. The resultant spatial singularity enables the possibility in implementing PT-symmetric systems with increased structural simplicity, integration density and long-term stability. To prove the concept, a PT-symmetric optoelectronic oscillator (OEO) in the optical wavelength space is implemented. The OEO has a single-loop architecture, with the gain and loss microwave modes carried by two optical wavelengths to form two mutually coupled wavelength-space resonators (WSRs). PT-symmetry is achieved by controlling the wavelength spacing and the power contrast. The operation of PT symmetry in the OEO is verified by the generation of a 10-GHz microwave signal with a low phase noise of 129.3 dBc/Hz at 10-kHz offset frequency and a high sidemode suppression ratio (SMSR) of 66.22 dB. Compared with a conventional spatial PT-symmetric system, one in the wavelength space features a much simpler configuration, better stability and greater resilience to environmental interferences.
On existence of equilibrium under social coalition structures

Abstract In a strategic-form game, a strategy profile is an equilibrium if no viable coalition of agents (or players) benefits (in the Pareto sense) from jointly changing their strategies. Weaker or stronger equilibrium notions can be defined by considering various restrictions on coalition formation. For instance, in a Nash equilibrium, it is assumed that viable coalitions are singletons, and in a super strong equilibrium, it is assumed that every coalition is viable. Restrictions on coalition formation can be justified by communication limitations, coordination problems, or institutional constraints. In this paper, inspired by social structures in various real-life scenarios, we introduce certain restrictions on coalition formation, and on their basis, we introduce a number of equilibrium notions. As an application, we study our equilibrium notions in resource selection games (RSGs), and we present a complete set of existence and nonexistence results for general RSGs and their important special cases.
Transition between dissipatively stabilized helical states

We analyze a $XXZ$ spin-1/2 chain which is driven dissipatively at its boundaries. The dissipative driving is modelled by Lindblad jump operators which only act on both boundary spins. In the limit of large dissipation, we find that the boundary spins are pinned to a certain value and at special values of the interaction anisotropy, the steady states are formed by a rank-2 mixture of helical states with opposite winding numbers. Contrarily to previous stabilization of topological states, these helical states are not protected by a gap in the spectrum of the Lindbladian. By changing the anisotropy, the transition between these steady states takes place via mixed states of higher rank. In particular, crossing the value of zero anisotropy a totally mixed state is found as the steady state. The transition between the different winding numbers via mixed states can be seen in the light of the transitions between different topological states in dissipatively driven systems. The results are obtained developing a perturbation theory in the inverse dissipative coupling strength and using the numerical exact diagonalization and matrix product state methods.
Geometric and Martin boundaries
of a Cartan-Hadamard surface

We give a general criterion for the Dirichlet problem at infinity (DPI) on a Cartan-Hadamard surface to be solvable, which we primarily use to give the best possible upper radial radial curvature bound for solvability of the DPI, but which is also flexible enough to accommodate flats. In particular, any (upper) radial curvature bound which implies transience also implies solvability of the DPI, which is perhaps surprising. Taking advantage of the structure provided by uniformization, we show that solvability of the DPI implies there is a natural continuous surjection of the Martin boundary onto the geometric boundary at infinity. Finally, we give matched upper and lower radial curvature bounds that imply the natural identification of the geometric and Martin boundaries (for Cartan-Hadamard surfaces) that are more generous than the bounds that are known in arbitrary dimension.
Representing All Stable Matchings by Walking a Maximal Chain

The seminal book of Gusfield and Irving [GI89] provides a compact and algorithmically useful way to represent the collection of stable matches corresponding to a given set of preferences. In this paper, we reinterpret the main results of [GI89], giving a new proof of the characterization which is able to bypass a lot of the "theory building" of the original works. We also provide a streamlined and efficient way to compute this representation. Our proofs and algorithms emphasize the connection to well-known properties of the deferred acceptance algorithm.
Joint Design of Measurement Matrix and Sparse Support Recovery Method via Deep Auto-Encoder

Sparse support recovery arises in many applications in communications and signal processing. Existing methods tackle sparse support recovery problems for a given measurement matrix, and cannot flexibly exploit the properties of sparsity patterns for improving performance. In this letter, we propose a data-driven approach to jointly design the measurement matrix and support recovery method for complex sparse signals, using auto-encoder in deep learning. The proposed architecture includes two components, an auto-encoder and a hard thresholding module. The proposed auto-encoder successfully handles complex signals using standard auto-encoder for real numbers. The proposed approach can effectively exploit properties of sparsity patterns, and is especially useful when these underlying properties do not have analytic models. In addition, the proposed approach can achieve sparse support recovery with low computational complexity. Experiments are conducted on an application example, device activity detection in grant-free massive access for massive machine type communications (mMTC). Numerical results show that the proposed approach achieves significantly better performance with much less computation time than classic methods, in the presence of extra structures in sparsity patterns.
Khovanov homology also detects split links

Extending ideas of Hedden-Ni, we show that the module structure on Khovanov homology detects split links. We also prove an analogue for untwisted Heegaard Floer homology of the branched double cover. Technical results proved along the way include two interpretations of the module structure on untwisted Heegaard Floer homology in terms of twisted Heegaard Floer homology and the fact that the module structure on the reduced Khovanov complex of a link is well-defined up to quasi-isomorphism.
Common Throughput Maximization for UAV-Enabled Interference Channel With Wireless Powered Communications

This paper studies an unmanned aerial vehicle (UAV)-enabled two-user interference channel for wireless powered communication networks (WPCNs). In this system, two UAVs wirelessly charge two low-power Internet-of-things (IoT)-devices on the ground and collect information from them. We consider two scenarios when both UAVs cooperate in energy transmission and/or information reception via interference coordination and coordinated multi-point (CoMP), respectively. For both scenarios, the UAVs’ trajectories are designed to not only enhance the wireless power transfer (WPT) efficiency in the downlink, but also mitigate the co-channel interference for wireless information transfer (WIT) in the uplink. In particular, the objective is to maximize the uplink common (minimum) throughput of the two IoT-devices over a finite UAV mission period, by jointly optimizing the trajectories of both UAVs and the downlink/uplink wireless resource allocation, subject to the maximum flying speed and collision avoidance constraints for UAVs, as well as the individual energy neutrality constraints at IoT-devices. Under both scenarios with interference coordination and CoMP, we first obtain the optimal solutions to the two common-rate maximization problems for the special case with sufficiently long UAV mission duration. Next, we obtain high-quality solutions for the practical case with finite UAV mission duration by using the alternating optimization and successive convex approximation (SCA). Numerical results show that the proposed designs significantly outperform benchmark schemes, and the utilization of CoMP achieves much higher uplink throughput than interference coordination.
Discovery of a Photoionized Bipolar Outflow toward the Massive Protostar G45.47+0.05

Massive protostars generate strong radiation feedback, which may help set the mass that they achieve by the end of the accretion process. Studying such feedback is therefore crucial for understanding the formation of massive stars. We report the discovery of a photoionized bipolar outflow toward the massive protostar G45.47+0.05 using high-resolution observations at 1.3 mm with the Atacama Large Millimeter/Submillimeter Array (ALMA) and at 7 mm with the Karl G. Jansky Very Large Array (VLA). By modeling the free–free continuum, the ionized outflow is found to be a photoevaporation flow with an electron temperature of 10,000 K and an electron number density of ∼1.5 × 107 cm−3 at the center, launched from a disk of radius of 110 au. H30α hydrogen recombination line emission shows strong maser amplification, with G45 being one of very few sources to show such millimeter recombination line masers. The mass of the driving source is estimated to be 30–50 M⊙ based on the derived ionizing photon rate, or 30–40 M⊙ based on the H30α kinematics. The kinematics of the photoevaporated material is dominated by rotation close to the disk plane, while accelerated to outflowing motion above the disk plane. The mass loss rate of the photoevaporation outflow is estimated to be ∼(2–3.5) × 10−5 M⊙ yr−1. We also found hints of a possible jet embedded inside the wide-angle ionized outflow with nonthermal emissions. The possible coexistence of a jet and a massive photoevaporation outflow suggests that, in spite of the strong photoionization feedback, accretion is still ongoing.
On Recognizing Texts of Arbitrary Shapes with 2D Self-Attention

Scene text recognition (STR) is the task of recognizing character sequences in natural scenes. While there have been great advances in STR methods, current methods which convert two-dimensional (2D) image to one-dimensional (1D) feature map still fail to recognize texts in arbitrary shapes, such as heavily curved, rotated or vertically aligned texts, which are abundant in daily life (e.g. restaurant signs, product labels, company logos, etc). This paper introduces an architecture to recognize texts of arbitrary shapes, named Self-Attention Text Recognition Network (SATRN). SATRN utilizes the self-attention mechanism, which is originally proposed to capture the dependency between word tokens in a sentence, to describe 2D spatial dependencies of characters in a scene text image. Exploiting the full-graph propagation of self-attention, SATRN can recognize texts with arbitrary arrangements and large inter-character spacing. As a result, our model outperforms all existing STR models by a large margin of 4.5pp on average in "irregular text" benchmarks and also achieved state-of-the-art performance in two "regular text" benchmarks. We provide empirical analyses that illustrate the inner mechanisms and the extent to which the model is applicable (e.g. rotated and multi-line text). We will open- source the code.1
Self-emergence of chaos in the identification of irregular periodic behavior.

Various forms of chaotic synchronization have been proposed as ways of realizing associative memories and/or pattern recognizers. To exploit this kind of synchronization phenomena in temporal pattern recognition, a chaotic dynamical system representing the class of signals that are to be recognized must be established. This system can be determined by means of identification techniques. The fulfillment of the chaotic condition could be imposed as a constraint. However, it is shown here that, even for a very simple identification algorithm, chaos emerges by itself, allowing us to model the diversity of nearly periodic signals.
Review of permanent-magnet brushless DC motor basic drives based on analysis and simulation study

Brushless DC (BLDC) motors and their drives have been increasingly considered in a broad range of applications due to their significant features. The implementation of these motors is possible thanks to firstly, the progress of permanent-magnet (PM) technologies which provide high efficiency, power density, and torque for these motors. Secondly, the structure and special features of these motors have prepared a basis for simpler control and smaller size compared to those with the same power. In this paper, the basic drives of BLDC motors have been reviewed in order to provide a useful reference for primary research in conventional methods of these types of motors. To present a proper insight to various drive techniques in these motors a systematic classification to control strategies with principles of these techniques has been made. In addition, computer simulations have been utilized in order to achieve a more accurate evaluation, provide the possibility of comparative analysis between various strategies, and place emphasis on the constraints and features of each method. Apart from the comparison of different methods of each strategy, a general comparison among the different methods of various strategies has been made based on the torque ripple, analysis of frequency, and losses of the BLDC motor drive as well as various applications of the different controlling methods. Moreover, considering the importance of electric vehicles (EVs) in industry, selection of the best controlling method for this type of applications together with energy regeneration has been discussed.
Model-free prediction of spatiotemporal dynamical systems with recurrent neural networks: Role of network spectral radius

A common difficulty in applications of machine learning is the lack of any general principle for guiding the choices of key parameters of the underlying neural network. Focusing on a class of recurrent neural networks - reservoir computing systems that have recently been exploited for model-free prediction of nonlinear dynamical systems, we uncover a surprising phenomenon: the emergence of an interval in the spectral radius of the neural network in which the prediction error is minimized. In a three-dimensional representation of the error versus time and spectral radius, the interval corresponds to the bottom region of a "valley. " Such a valley arises for a variety of spatiotemporal dynamical systems described by nonlinear partial differential equations, regardless of the structure and the edge-weight distribution of the underlying reservoir network. We also find that, while the particular location and size of the valley would depend on the details of the target system to be predicted, the interval tends to be larger for undirected than for directed networks. The valley phenomenon can be beneficial to the design of optimal reservoir computing, representing a small step forward in understanding these machine-learning systems.
Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS). It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state-of-the-art traffic monitoring systems focusing on the major functionality–vehicle classification. We organize various vehicle classification systems, examine research issues and technical challenges, and discuss hardware/software design, deployment experience, and system performance of vehicle classification systems. Finally, we discuss a number of critical open problems and future research directions in an aim to provide valuable resources to academia, industry, and government agencies for selecting appropriate technologies for their traffic monitoring applications.
Causality and deceit: Do androids watch action movies?

We seek causes through science, religion, and in everyday life. We get excited when a big rock causes a big splash, and we get scared when it tumbles without a cause. But our causal cognition is usually biased. The 'why' is influenced by the 'who'. It is influenced by the 'self', and by 'others'. We share rituals, we watch action movies, and we influence each other to believe in the same causes. Human mind is packed with subjectivity because shared cognitive biases bring us together. But they also make us vulnerable. An artificial mind is deemed to be more objective than the human mind. After many years of science-fiction fantasies about even-minded androids, they are now sold as personal or expert assistants, as brand advocates, as policy or candidate supporters, as network influencers. Artificial agents have been stunningly successful in disseminating artificial causal beliefs among humans. As malicious artificial agents continue to manipulate human cognitive biases, and deceive human communities into ostensive but expansive causal illusions, the hope for defending us has been vested into developing benevolent artificial agents, tasked with preventing and mitigating cognitive distortions inflicted upon us by their malicious cousins. Can the distortions of human causal cognition be corrected on a more solid foundation of artificial causal cognition? In the present paper, we study a simple model of causal cognition, viewed as a quest for causal models. We show that, under very mild and hard to avoid assumptions, there are always self-confirming causal models, which perpetrate self-deception, and seem to preclude a royal road to objectivity.