Miracle-dz/UniFAR-FTUs huggingface dataset:
Searching for Gravitationally Lensed Gamma-Ray Bursts with Their Afterglows

Gamma-ray bursts (GRBs) at high redshifts are expected to be gravitationally lensed by objects of different mass scales. Other than a single recent claim, no lensed GRB has been detected so far by using gamma-ray data only. In this paper, we suggest that multiband afterglow data might be an efficient way to search for lensed GRB events. Using the standard afterglow model, we calculate the characteristics of the lensed afterglow lightcurves under the assumption of two popular analytic lens models: the point-mass and singular isothermal sphere models. In particular, when different lensed images cannot be resolved, their signals would be superimposed together with a given time delay. In this case, the X-ray afterglows are likely to contain several X-ray flares of similar width in linear scale and similar spectrum, and the optical afterglow lightcurve will show re-brightening signatures. Since the lightcurves from the image arriving later would be compressed and deformed in the logarithmic timescale, the larger time delay (i.e., the larger mass of the lens), the easier it is to identify the lensing effect. We analyzed the archival data of optical afterglows and found one potential candidate of the lensed GRB (130831A) with time delay ∼500 s; however, observations of this event in gamma-ray and X-ray bands seem not to support the lensing hypothesis. In the future, with the cooperation of the all-sky monitoring gamma-ray detectors and multiband sky survey projects, the method proposed in this paper would be more efficient in searching for strongly lensed GRBs.
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees

Models recently used in the literature proving residual networks (ResNets) are better than linear predictors are actually different from standard ResNets that have been widely used in computer vision. In addition to the assumptions such as scalar-valued output or single residual block, the models fundamentally considered in the literature have no nonlinearities at the final residual representation that feeds into the final affine layer. To codify such a difference in nonlinearities and reveal a linear estimation property, we define ResNEsts, i.e., Residual Nonlinear Estimators, by simply dropping nonlinearities at the last residual representation from standard ResNets. We show that wide ResNEsts with bottleneck blocks can always guarantee a very desirable training property that standard ResNets aim to achieve, i.e., adding more blocks does not decrease performance given the same set of basis elements. To prove that, we first recognize ResNEsts are basis function models that are limited by a coupling problem in basis learning and linear prediction. Then, to decouple prediction weights from basis learning, we construct a special architecture termed augmented ResNEst (A-ResNEst) that always guarantees no worse performance with the addition of a block. As a result, such an A-ResNEst establishes empirical risk lower bounds for a ResNEst using corresponding bases. Our results demonstrate ResNEsts indeed have a problem of diminishing feature reuse; however, it can be avoided by sufficiently expanding or widening the input space, leading to the above-mentioned desirable property. Inspired by the densely connected networks (DenseNets) that have been shown to outperform ResNets, we also propose a corresponding new model called Densely connected Nonlinear Estimator (DenseNEst). We show that any DenseNEst can be represented as a wide ResNEst with bottleneck blocks. Unlike ResNEsts, DenseNEsts exhibit the desirable property without any special architectural re-design.
Social Fraud Detection Review: Methods, Challenges and Analysis

Social reviews have dominated the web and become a plausible source of product information. People and businesses use such information for decision-making. Businesses also make use of social information to spread fake information using a single user, groups of users, or a bot trained to generate fraudulent content. Many studies proposed approaches based on user behaviors and review text to address the challenges of fraud detection. To provide an exhaustive literature review, social fraud detection is reviewed using a framework that considers three key components: the review itself, the user who carries out the review, and the item being reviewed. As features are extracted for the component representation, a feature-wise review is provided based on behavioral, text-based features and their combination. With this framework, a comprehensive overview of approaches is presented including supervised, semi-supervised, and unsupervised learning. The supervised approaches for fraud detection are introduced and categorized into two sub-categories; classical, and deep learning. The lack of labeled datasets is explained and potential solutions are suggested. To help new researchers in the area develop a better understanding, a topic analysis and an overview of future directions is provided in each step of the proposed systematic framework.
Are Transformers More Robust Than CNNs?

Transformer emerges as a powerful tool for visual recognition. In addition to demonstrating competitive performance on a broad range of visual benchmarks, recent works also argue that Transformers are much more robust than Convolutions Neural Networks (CNNs). Nonetheless, surprisingly, we find these conclusions are drawn from unfair experimental settings, where Transformers and CNNs are compared at different scales and are applied with distinct training frameworks. In this paper, we aim to provide the first fair&in-depth comparisons between Transformers and CNNs, focusing on robustness evaluations. With our unified training setup, we first challenge the previous belief that Transformers outshine CNNs when measuring adversarial robustness. More surprisingly, we find CNNs can easily be as robust as Transformers on defending against adversarial attacks, if they properly adopt Transformers' training recipes. While regarding generalization on out-of-distribution samples, we show pre-training on (external) large-scale datasets is not a fundamental request for enabling Transformers to achieve better performance than CNNs. Moreover, our ablations suggest such stronger generalization is largely benefited by the Transformer's self-attention-like architectures per se, rather than by other training setups. We hope this work can help the community better understand and benchmark the robustness of Transformers and CNNs. The code and models are publicly available at https://github.com/ytongbai/ViTs-vs-CNNs.
Evolution of the Spin, Spectrum and Superorbital Period of the Ultraluminous X-Ray Pulsar M51 ULX7

M51 ULX7 is among a small group of known ultraluminous X-ray pulsars (ULXPs). The neutron star powering the source has a spin period of 2.8 s, orbits its companion star with a period of 2 days, and a superorbital period of 38 days is evident in its X-ray lightcurve. Here we present NuSTAR and XMM-Newton data on the source from 2019 obtained when the source was near its peak brightness. We detect the pulsations, having spun up at a rate of 3 ± 0.5 × 10−10 s s−1 since they were previously detected in 2018. The data also provide the first high-quality broadband spectrum of the source. We find it to be very similar to that of other ULXPs, with two disk-like components, and a high-energy tail. When combined with XMM-Newton data obtained in 2018, we explore the evolution of the spectral components with superorbital phase, finding that the luminosity of the hotter component drives the superorbital flux modulation. The inclination the disk components appear to change with phase, which may support the idea that these superorbital periods are caused by disk precession. We also reexamine the superorbital period with 3 yr of Swift/XRT monitoring, finding that the period is variable, increasing from 38.2 ± 0.5 days in 2018–2019 to 44.2 ± 0.9 days in 2020–2021, which rules out alternative explanations for the superorbital period.
Important Sentence Identiﬁcation in Legal Cases Using Multi-Class Classiﬁcation

—The advancement of Natural Language Processing (NLP) is spreading through various domains in forms of practical applications and academic interests. Inherently, the legal domain contains a vast amount of data in text format. Therefore it requires the application of NLP to cater to the analytically demanding needs of the domain. Identifying important sentences, facts and arguments in a legal case is such a tedious task for legal professionals. In this research we explore the usage of sentence embeddings for multi-class classiﬁcation to identify important sentences in a legal case, in the perspective of the main parties present in the case. In addition, a task-speciﬁc loss function is deﬁned in order to improve the accuracy restricted by the straightforward use of categorical cross entropy loss.
A mechanism for FIMPy baryogenesis

We present a simple mechanism which allows the simultaneous generation of the baryon asymmetry of the Universe along with its dark matter content. To this goal, we employ the out-of-equilibrium decays of heavy bath states into a feebly coupled dark matter particle and Standard Model charged fermions. These decays lead to dark matter production via the freeze-in mechanism and, assuming that they further violate CP can generate a viable matter-antimatter asymmetry in the resonant regime. We illustrate this mechanism by studying a particular realisation of this general scenario, where the role of the heavy bath particles is played by SU(3)c × SU(2)L-singlet vector-like fermions with a non-zero hypercharge and dark matter is identified with a gauge-singlet real scalar field. We show that in the context of this simple model the cosmological constraints for the dark matter abundance and the baryon asymmetry are satisfied for masses of heavy vector-like fermion states of a few TeV, potentially within reach of the High-Luminosity Run of the Large Hadron Collider. Dark matter, in turn, is predicted to be rather light, with a mass of a few keV. ar X iv :2 11 1. 05 74 0v 1 [ he pph ] 1 0 N ov 2 02 1
Space-time homogenization problems for porous medium equations with nonnegative initial data

This paper concerns a space-time homogenization limit of nonnegative weak solutions to porous medium equations. In particular, the so-called homogenized matrix will be characterized in terms of solutions to cell problems, which drastically vary in a scaling parameter r > 0 . A similar problem has already been studied in [1], where the growth of the power nonlinearity is strictly restricted due to some substantial obstacles. In the present paper, such obstacles will be overcome by developing local uniform estimates for the gradients of nonnegative weak solutions.
LUMINOUS: Indoor Scene Generation for Embodied AI Challenges

Learning-based methods for training embodied agents typically require a large number of high-quality scenes that contain realistic layouts and support meaningful interactions. However, current simulators for Embodied AI (EAI) challenges only provide simulated indoor scenes with a limited number of layouts. This paper presents Luminous, the first research framework that employs state-of-the-art indoor scene synthesis algorithms to generate large-scale simulated scenes for Embodied AI challenges. Further, we automatically and quantitatively evaluate the quality of generated indoor scenes via their ability to support complex household tasks. Luminous incorporates a novel scene generation algorithm (Constrained Stochastic Scene Generation (CSSG)), which achieves competitive performance with human-designed scenes. Within Luminous, the EAI task executor, task instruction generation module, and video rendering toolkit can collectively generate a massive multimodal dataset of new scenes for the training and evaluation of Embodied AI agents. Extensive experimental results demonstrate the effectiveness of the data generated by Luminous, enabling the comprehensive assessment of embodied agents on generalization and robustness.
Stationary solutions and local equations for interacting diffusions on regular trees

We study the invariant measures of infinite systems of stochastic differential equations (SDEs) indexed by the vertices of a regular tree. These invariant measures correspond to Gibbs measures associated with certain continuous specifications, and we focus specifically on those measures which are homogeneous Markov random fields. We characterize the joint law at any two adjacent vertices in terms of a new two-dimensional SDE system, called the"local equation", which exhibits an unusual dependence on a conditional law. Exploiting an alternative characterization in terms of an eigenfunction-type fixed point problem, we derive existence and uniqueness results for invariant measures of the local equation and infinite SDE system. This machinery is put to use in two examples. First, we give a detailed analysis of the surprisingly subtle case of linear coefficients, which yields a new way to derive the famous Kesten-McKay law for the spectral measure of the regular tree. Second, we construct solutions of tree-indexed SDE systems with nearest-neighbor repulsion effects, similar to Dyson's Brownian motion.
Well-defined forward operators in dynamic diffractive tensor tomography using viscosity solutions of transport equations

We consider a general setting for dynamic tensor field tomography in an inhomogeneous refracting and absorbing medium as inverse source problem for the associated transport equation. Following Fermat's principle the Riemannian metric in the considered domain is generated by the refractive index of the medium. There is wealth of results for the inverse problem of recovering a tensor field from its longitudinal ray transform in a static euclidean setting, whereas there are only few inversion formulas and algorithms existing for general Riemannian metrics and time-dependent tensor fields. It is a well-known fact that tensor field tomography is equivalent to an inverse source problem for a transport equation where the ray transform serves as given boundary data. We prove that this result extends to the dynamic case. Interpreting dynamic tensor tomography as inverse source problem represents a holistic approach in this field. To guarantee that the forward mappings are well-defined, it is necessary to prove existence and uniqueness for the underlying transport equations. Unfortunately, the bilinear forms of the associated weak formulations do not satisfy the coercivity condition. To this end we transfer to viscosity solutions and prove their unique existence in appropriate Sobolev (static case) and Sobolev-Bochner (dynamic case) spaces under a certain assumption that allows only small variations of the refractive index. Numerical evidence is given that the viscosity solution solves the original transport equation if the viscosity term turns to zero.
Constrained Instance and Class Reweighting for Robust Learning under Label Noise

Deep neural networks have shown impressive performance in supervised learning, enabled by their ability to fit well to the provided training data. However, their performance is largely dependent on the quality of the training data and often degrades in the presence of noise. We propose a principled approach for tackling label noise with the aim of assigning importance weights to individual instances and class labels. Our method works by formulating a class of constrained optimization problems that yield simple closed form updates for these importance weights. The proposed optimization problems are solved per mini-batch which obviates the need of storing and updating the weights over the full dataset. Our optimization framework also provides a theoretical perspective on existing label smoothing heuristics for addressing label noise (such as label bootstrapping). We evaluate our method on several benchmark datasets and observe considerable performance gains in the presence of label noise.
New Insights of BTI Degradation in MOSFETs with SiON Gate Dielectrics

In this paper, we shall summarize our recent research achievements on the topic of BTI degradation of MOSFETs with SiON gate dielectrics. It is widely recognized that recovery during measurement delay seriously misleads the interpretation of BTI phenomena. We have developed a modified Charge Pumping method to measure the interface trap generation under stress with negligible recovery during measurement delay. The method simply extends the inversion level Vinv of the CP pulse to the stress voltage and keep the duty cycle of Vinv as large as possible, therefore the stress is still “on” during measurement[1,2]. How to exclude the contribution of oxide charge trapping/de-trapping to the CP current in this modified CP method is an important issue, and is carefully considered and discussed. Using this modified CP method and the fast pulsed Id-Vg method (FPM) we developed in the past, we systematically investigate and compare the BTI degradation of p-MOSFETs with thermal (TNO) and plasma (PNO) nitrided SiON gate dielectrics with the same nitrogen surface concentration, and compare with previous data reported by other groups using different measurement approaches. We also systematically measure four stress configurations of NBTI and PBTI degradations for both n-MOSFETs and p-MOSFETs. The following results are obtained: (1) The oxide charge degradation in TNO SiON devices is several times larger than those in PNO devices under the same stress condition, due to the much larger charge moment in TNO devices. Particularly, when the stress voltage is reduced to the operation voltage, the fast transient BTI component due to the oxide traps is reduced to negligibly small in the PNO devices , however is still large in the TNO devices, and therefore will affect the device performance in real circuit application. This information provides an important base for the circuit design. (2) Although TNO and PNO devices have very different oxide trap BTI degradation behaviors, however both types of devices have the same interface trap degradation behavior, same temperature dependent power law index n of the interface trap generation, n is linear dependent of temperature when T is lower than 100C and is saturated at around 0.18 when T is higher than 100C, also have the same interface trap activation energy EA=0.17 eV. This information provides an important base for the interface trap modeling. (3) There are interface trap generations under stresses for all four different configurations. The generation rates under comparable stress conditions ( namely same oxide field) are the highest for p-MOSFETs under positive stress and lowest for n-MOSFETs under positive stress. The experimental data indicates that hot holes are more effective in interface trap generation. These results provide additional valuable information for BTI modeling, as well as re-assessing the impact of BTI in logic and analog circuits and SRAM applications. [1] W.J.Liu et al, IEDM 2007, p.813 [2] D.M. Huang et al, TED to be published. 215th ECS Meeting, Abstract #794, © The Electrochemical Society
Continuous metrics and a conjecture of Schoen

A classical theorem in conformal geometry states that on a manifold with non-positive Yamabe invariant, a smooth metric achieving the invariant must be Einstein. In this work, we extend it to the singular case and show that in all dimension, if a continuous metric is smooth outside a compact set of high co-dimension and achieves the Yamabe invariant, then the metric is Einstein away from the singularity and can be extended to be smooth on the manifold in a suitable sense. As an application of the method, we prove a Positive Mass Theorem for asymptotically flat manifolds with analogous singularities.
Power-law fluctuations near critical point in semiconductor lasers with delayed feedback

Since the analogy between laser oscillation and second-order phase transition was indicated in the 1970s, dynamical fluctuations on lasing threshold inherent in critical phenomena have gained significant interest. Here, we numerically and experimentally demonstrate that a semiconductor laser subject to delayed optical feedback can exhibit unusual large intensity fluctuations characterized by power-law distributions. Such an intensity fluctuation consists of distinct intermittent bursts of light intensity, whose peak values attain tens of times the intensity of the maximum gain mode. This burst behavior emerges when a laser with a long time delay (over 100 ns) and an optimal feedback strength operates around the lasing threshold. The intensity and waiting time statistics follow power-law-like distributions. This implies the emergence of nonequilibrium critical phenomena, namely self-organized criticality. In addition to numerical results, we report experimental results that suggest the power-law intensity dynamics in a semiconductor laser with delayed feedback.
Toward Designing Optimal Sensing Matrices for Generalized Linear Inverse Problems

We consider an inverse problem <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {y}= f(\boldsymbol {Ax})$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {x}\in \mathbb {R}^{n}$ </tex-math></inline-formula> is the signal of interest, <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {A}$ </tex-math></inline-formula> is the sensing matrix, <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> is a nonlinear function and <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {y} \in \mathbb {R}^{m}$ </tex-math></inline-formula> is the measurement vector. In many applications, we have some level of freedom to design the sensing matrix <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {A}$ </tex-math></inline-formula>, and in such circumstances we could optimize <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {A}$ </tex-math></inline-formula> to achieve better reconstruction performance. As a first step towards optimal design, it is important to understand the impact of the sensing matrix on the difficulty of recovering <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {x}$ </tex-math></inline-formula> from <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {y}$ </tex-math></inline-formula>. In this paper, we study the performance of one of the most successful recovery methods, i.e., the expectation propagation (EP) algorithm. We define a notion of spikiness for the spectrum of <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {A}$ </tex-math></inline-formula> and show the importance of this measure for the performance of EP. We show that whether a spikier spectrum can hurt or help the recovery performance depends on <inline-formula> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula>. Based on our framework, we are able to show that, in phase-retrieval problems, matrices with spikier spectrums are better for EP, while in 1-bit compressed sensing problems, less spiky spectrums lead to better performance. Our results unify and substantially generalize existing results that compare Gaussian and orthogonal matrices, and provide a platform towards designing optimal sensing systems.