Miracle-dz/UniFAR-FTUs huggingface dataset:
Relativistic Effects and GRB Polarization in Power-Law Evolution

Despite decades of polarization observations and high-significance polarized $\gamma$-ray, X-ray, optical, and radio emissions in gamma-ray bursts (GRBs) have been accumulating in dozens of cases, people have yet to find a consistent scenario for understanding the globally observed timing properties of GRB polarization to date. Here, we report that the observed properties of GRB polarization exhibit a four-segment timing evolution at the cosmological distance: (I) an initial hump early on (within the first few seconds); (II) a later on power-law decay (from $\sim$10$^{1}$ to $\sim$10$^{4}$ s), which takes the form of $\pi_{\rm obs} \propto t^{-0.50 \pm 0.02}$; (III) afterwards a late-time rebrightening hump (from $\sim$10$^{4}$ to $\sim$10$^{5}$ s); and (IV) finally a flatting power-law decay (from $\sim$ 10$^{5}$ to $\sim$ 10$^{7}$ s), with the the form of $\pi_{\rm obs} \propto t^{-0.21 \pm 0.08}$. These findings may present a challenge to the mainstream of polarization models that assume the polarization time evolution change in different emission regions. We show that these results can be explained by relativistic and geometric effects of a highly relativistic and magnetized jet generated by the central engine, and"magnetic patches"distributed as a globally random but locally coherent form. Our analysis suggests that there is a single dominant mechanism that might account for the global observational properties of GRB polarization, and other emission mechanisms and effects might play a role in spatially local and temporally short effects on GRB polarization.
Federated Adversarial Learning: A Framework with Convergence Analysis

Federated learning (FL) is a trending training paradigm to utilize decentralized training data. FL allows clients to update model parameters locally for several epochs, then share them to a global model for aggregation. This training paradigm with multi-local step updating before aggregation exposes unique vulnerabilities to adversarial attacks. Adversarial training is a popular and effective method to improve the robustness of networks against adversaries. In this work, we formulate a general form of federated adversarial learning (FAL) that is adapted from adversarial learning in the centralized setting. On the client side of FL training, FAL has an inner loop to generate adversarial samples for adversarial training and an outer loop to update local model parameters. On the server side, FAL aggregates local model updates and broadcast the aggregated model. We design a global robust training loss and formulate FAL training as a min-max optimization problem. Unlike the convergence analysis in classical centralized training that relies on the gradient direction, it is significantly harder to analyze the convergence in FAL for three reasons: 1) the complexity of min-max optimization, 2) model not updating in the gradient direction due to the multi-local updates on the client-side before aggregation and 3) inter-client heterogeneity. We address these challenges by using appropriate gradient approximation and coupling techniques and present the convergence analysis in the over-parameterized regime. Our main result theoretically shows that the minimum loss under our algorithm can converge to $\epsilon$ small with chosen learning rate and communication rounds. It is noteworthy that our analysis is feasible for non-IID clients.
Regional Synchronization during Economic Contraction: The Case of the U.S. and Japan

ABSTRACT Two decades of studies have found significant regional differences in the timing of transitions in national business cycles and their durations. Earlier studies partly detect regional synchronization during business cycle expansions and contractions in Europe, the United States, and Japan. We examine this possibility by applying a sophisticated method for identifying the time-varying degree of synchronization to regional business cycle data in the U.S. and Japan. The method is prominent in nonlinear sciences but has been infrequently applied in business cycle studies. We find that the degree of synchronization in regional business cycles increased during contractions and decreased during expansions throughout the period under study. Such asymmetry between the contraction and expansion phases of a business cycle will contribute to our better understanding of the phenomenon of business cycles.
Parabolic Relaxation for Quadratically-constrained Quadratic Programming - Part II: Theoretical & Computational Results

In the first part of this work [32], we introduce a convex parabolic relaxation for quadratically-constrained quadratic programs, along with a sequential penalized parabolic relaxation algorithm to recover near-optimal feasible solutions. In this second part, we show that starting from a feasible solution or a near-feasible solution satisfying certain regularity conditions, the sequential penalized parabolic relaxation algorithm convergences to a point which satisfies Karush-Kuhn-Tucker optimality conditions. Next, we present numerical experiments on benchmark non-convex QCQP problems as well as large-scale instances of system identification problem demonstrating the efficiency of the proposed approach.
Estimating Topic Exposure for Under-Represented Users on Social Media

Online Social Networks (OSNs) facilitate access to a variety of data allowing researchers to analyze users' behavior and develop user behavioral analysis models. These models rely heavily on the observed data which is usually biased due to the participation inequality. This inequality consists of three groups of online users: the lurkers - users that solely consume the content, the engagers - users that contribute little to the content creation, and the contributors - users that are responsible for creating the majority of the online content. Failing to consider the contribution of all the groups while interpreting population-level interests or sentiments may yield biased results. To reduce the bias induced by the contributors, in this work, we focus on highlighting the engagers' contributions in the observed data as they are more likely to contribute when compared to lurkers, and they comprise a bigger population as compared to the contributors. The first step in behavioral analysis of these users is to find the topics they are exposed to but did not engage with. To do so, we propose a novel framework that aids in identifying these users and estimates their topic exposure. The exposure estimation mechanism is modeled by incorporating behavioral patterns from similar contributors as well as users' demographic and profile information.
Interpretable Disentangled Parametrization of Measured BRDF with β-VAE

Finding a low dimensional parametric representation of measured BRDF remains challenging. Currently available solutions are either not interpretable, or rely on limited analytical solutions, or require expensive test subject based investigations. In this work, we strive to establish a parametrization space that affords the data-driven representation variance of measured BRDF models while still offering the artistic control of parametric analytical BRDFs. We present a machine learning approach that generates an interpretable disentangled parameter space. A disentangled representation is one in which each parameter is responsible for a unique generative factor and is insensitive to the ones encoded by the other parameters. To that end, we resort to a $\beta$-Variational AutoEncoder ($\beta$-VAE), a specific architecture of Deep Neural Network (DNN). After training our network, we analyze the parametrization space and interpret the learned generative factors utilizing our visual perception. It should be noted that perceptual analysis is called upon downstream of the system for interpretation purposes compared to most other existing methods where it is used upfront to elaborate the parametrization. In addition to that, we do not need a test subject investigation. A novel feature of our interpretable disentangled parametrization is the post-processing capability to incorporate new parameters along with the learned ones, thus expanding the richness of producible appearances. Furthermore, our solution allows more flexible and controllable material editing possibilities than manifold exploration. Finally, we provide a rendering interface, for real-time material editing and interpolation based on the presented new parametrization system.
A Survey of ADMM Variants for Distributed Optimization: Problems, Algorithms and Features

By coordinating terminal smart devices or microprocessors to engage in cooperative computation to achieve systemlevel targets, distributed optimization is incrementally favored by both engineering and computer science. The well-known alternating direction method of multipliers (ADMM) has turned out to be one of the most popular tools for distributed optimization due to many advantages, such as modular structure, superior convergence, easy implementation and high flexibility. In the past decade, ADMM has experienced widespread developments. The developments manifest in both handling more general problems and enabling more effective implementation. Specifically, the method has been generalized to broad classes of problems (i.e.,multi-block, coupled objective, nonconvex, etc.). Besides, it has been extensively reinforced for more effective implementation, such as improved convergence rate, easier subproblems, higher computation efficiency, flexible communication, compatible with inaccurate information, robust to communication delays, etc. These developments lead to a plentiful of ADMM variants to be celebrated by broad areas ranging from smart grids, smart buildings, wireless communications, machine learning and beyond. However, there lacks a survey to document those developments and discern the results. To achieve such a goal, this paper provides a comprehensive survey on ADMM variants. Particularly, we discern the five major classes of problems that have been mostly concerned and discuss the related ADMM variants in terms of main ideas, main assumptions, convergence behaviors and main features. In addition, we figure out several important future research directions to be addressed. This survey is expected to work as a tutorial for both developing distributed optimization in broad areas and identifying existing theoretical research gaps.
Constructing Large-Scale Real-World Benchmark Datasets for AIOps

Recently, AIOps (Artificial Intelligence for IT Operations) has been well studied in academia and industry to enable automated and effective software service management. Plenty of efforts have been dedicated to AIOps, including anomaly detection, root cause localization, incident management, etc. However, most existing works are evaluated on private datasets, so their generality and real performance cannot be guaranteed. The lack of public large-scale real-world datasets has prevented researchers and engineers from enhancing the development of AIOps. To tackle this dilemma, in this work, we introduce three public real-world, large-scale datasets about AIOps, mainly aiming at KPI anomaly detection, root cause localization on multi-dimensional data, and failure discovery and diagnosis. More importantly, we held three competitions in 2018/2019/2020 based on these datasets, attracting thousands of teams to participate. In the future, we will continue to publish more datasets and hold competitions to promote the development of AIOps further.
Spin Hall conductivity of interacting two-dimensional electron systems

We consider a two-dimensional electron system subjected to a short-ranged nonmagnetic disorder potential, Coulomb interactions, and Rashba spin-orbit coupling. The path-integral approach incorporated within the Keldysh formalism is used to derive the kinetic equation for the semiclassical Green's function and applied to compute the spin current within the linear response theory. We discuss the frequency dependence of the spin Hall conductivity and further elucidate the role of electron interactions at finite temperatures for both the ballistic and diffusive regimes of transport. We argue that interaction corrections to the spin Hall effect stem from the quantum interference processes whose magnitude is estimated in terms of parameters of the considered model.
Carbon Dynamics in Canada's Managed Forests from 1991 to 2006: A Comparison of InTEC and CBM

This study examined the annual C balance and its shifting trend in Canada’s managed forests from 1991 to 2006 using the Integrated Terrestrial Ecosystem C-budget (InTEC) model. During this period, Canada’s managed forests remained a moderate C sink of 58 Mt C yr on average, but displayed an apparent trend of shifting towards a C source. The combined risk of climate change and increased disturbances are weakening the C sink in Canada’s managed forests. This study also compared the results from InTEC with those from CBM-CFS (Carbon Budget Model of the Canadian Forest Sector) at both national and regional levels. InTEC shows larger inter-annual variability and regional difference than CBM-CFS due to its incorporation of both disturbance and non-disturbance factors. In comparison, CBM-CFS3 has likely underestimated both the true C loss and the C sink potential of Canada’s managed forests, given that it does not account for the non-disturbance factors.
Protein Folding: From Classical Issues to a New Perspective

The Levinthal paradox exposes many critical questions on the protein folding problem, among which we could point out why proteins can reach their native state in a biologically reasonable time. A proper answer to this question is of foremost importance for evolutive biology since it enables us to understand life as we know it. Preliminary results, based on the upper bound protein marginal-stability limit, together with transition state theory arguments, lead us to show that two-state proteins must reach their native state in, at most, seconds rather than ($\sim 10^{27}$) years -- as indicated by a naive solution of the Levinthal paradox. This outcome -- added to the amide hydrogen-exchange protection factors analysis -- makes it possible for us to suggest how a protein point mutations and/or post-translational modifications impact its folding time scales but not its upper bound limit that obeys the physics ruling the process. Noteworthy for almost 50 years, the protein folding problem --mas the Levinthal paradox -- has been a topic of passionate debate because Anfinsen's challenge -- how a sequence encodes its folding -- remains unsolved despite the smashing success of accurately predicting the protein tridimensional structures by state-of-the-art numerical-methods. Aimed to unlock this long-standing challenge, we propose a new perspective of protein folding, specifically, as a problem that should be devised as an 'analytic whole' -- a Leibniz&Kant's notion. This viewpoint might help us decode Anfinsen's challenge and, thus, open new avenues for future research in the protein folding field.
Expend, a Gauss Programme for Non-Linear GMM Estimation of Exponential Models with Endogenous Regressors for Cross Section and Panel Data

ExpEnd is a Gauss programme for non-linear generalised method of moments (GMM) estimation of exponential models with endogenous regressors for cross section and panel data. The estimators included in this package are simple Poisson pseudo ML; GMM for cross section data using moment conditions based on multiplicative or additive errors; within groups fixed effects Poisson for panel data; GMM estimation using quasi-differenced moment conditions eliminating unobserved heterogeneity and allowing for predetermined or endogenous regressors; and quasi-differenced GMM for a dynamic linear feedback model. This manual describes in detail the various estimators, the data and software requirements, and the programme commands. The programme can be downloaded here [zip file, 435KB].
Neural optimization machine: a neural network approach for optimization and its application in additive manufacturing with physics-guided learning

Neural networks (NNs) are increasingly used in design to construct the objective functions and constraints, which leads to the needs of optimization of NN models with respect to design variables. A Neural Optimization Machine (NOM) is proposed for constrained single/multi-objective optimization by appropriately designing the NN architecture, activation function and loss function. The NN's built-in backpropagation algorithm conducts the optimization and is seamlessly integrated with the additive manufacturing (AM) process-property model. The NOM is tested using several numerical optimization problems. It is shown that the increase in the dimension of design variables does not increase the computational cost significantly. Next, a brief review of the physics-guided machine learning model for fatigue performance prediction of AM components is given. Finally, the NOM is applied to design processing parameters in AM to optimize the mechanical fatigue properties through the physics-guided NN under uncertainties. One novel contribution of the proposed methodology is that the constrained process optimization is integrated with physics/knowledge and the data-driven AM process-property model. Thus, a physics-compatible process design can be achieved. Another significant benefit is that the training and optimization are achieved in a unified NN model, and no separate process optimization is needed. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.
Detecting Algorithmically Generated Domains Using a GCNN-LSTM Hybrid Neural Network

Domain generation algorithm (DGA) is used by botnets to build a stealthy command and control (C&C) communication channel between the C&C server and the bots. A DGA can periodically produce a large number of pseudo-random algorithmically generated domains (AGDs). AGD detection algorithms provide a lightweight, promising solution in response to the existing DGA techniques. In this paper, a GCNN (gated convolutional neural network)-LSTM (long short-term memory) Hybrid Neural Network (GLHNN) for AGD detection is proposed. In GLHNN, GCNN is applied to extract the informative features from domain names on top of LSTM which further processes the feature sequence. GLHNN is experimentally validated using representative AGDs covering six classes of DGAs. GLHNN is compared with the state-of-the-art detection models and demonstrates the best overall detection performance among these tested models.
Connection-oriented and Connectionless Quantum Internet Considering Quantum Repeaters

With the rapid development of quantum information and technology in recent years, the construction of quantum internet for interconnecting all kinds of quantum devices, such as quantum processors and sensors, will be the next trend for practical quantum applications. In this paper, we propose the protocols for construction of connection-oriented and connectionless quantum networks by considering the concrete quantum repeater (QR) nodes. Four classes of QRs networks are considered first and designed with two types of protocols in link layer, i.e. simultaneous and one-by-one link. Based on those two link models, the connection-oriented protocol is presented for all classes of QRs networks and the connectionless protocol is proposed for the first, second and third classes QRs networks by only one-by-one link. Furthermore, we introduce a new hybrid connection model of quantum networks combined with connection-oriented and connectionless for practical uses. Our work is a new attempt to study the model of the network layer for different kinds of QR networks and paves the way for developing the protocol stack of universal large-scale quantum internet.
Synchronization analysis between exchange rates on the basis of purchasing power parity using the Hilbert transform

Synchronization is a phenomenon in which a pair of ﬂuctuations adjust their rhythms when interacting with each other. We measure the degree of synchronization between the U.S. dollar (USD) and euro exchange rates and between the USD and Japanese yen exchange rates on the basis of purchasing power parity (PPP) over time. We employ a method of synchronization analysis using the Hilbert transform, which is common in the ﬁeld of nonlinear science. We ﬁnd that the degree of synchronization is high most of the time, suggesting the establishment of PPP. The degree of synchronization does not remain high across periods with economic events with asymmetric eﬀects, such as the U.S. real estate bubble.
Comparison Conundrum and the Chamber of Visualizations: An Exploration of How Language Influences Visual Design

The language for expressing comparisons is often complex and nuanced, making supporting natural language-based visual comparison a non-trivial task. To better understand how people reason about comparisons in natural language, we explore a design space of utterances for comparing data entities. We identified different parameters of comparison utterances that indicate what is being compared (i.e., data variables and attributes) as well as how these parameters are specified (i.e., explicitly or implicitly). We conducted a user study with sixteen data visualization experts and non-experts to investigate how they designed visualizations for comparisons in our design space. Based on the rich set of visualization techniques observed, we extracted key design features from the visualizations and synthesized them into a subset of sixteen representative visualization designs. We then conducted a follow-up study to validate user preferences for the sixteen representative visualizations corresponding to utterances in our design space. Findings from these studies suggest guidelines and future directions for designing natural language interfaces and recommendation tools to better support natural language comparisons in visual analytics.
Eight Years of Face Recognition Research: Reproducibility, Achievements and Open Issues

Automatic face recognition is a research area with high popularity. Many different face recognition algorithms have been proposed in the last thirty years of intensive research in the field. With the popularity of deep learning and its capability to solve a huge variety of different problems, face recognition researchers have concentrated effort on creating better models under this paradigm. From the year 2015, state-of-the-art face recognition has been rooted in deep learning models. Despite the availability of large-scale and diverse datasets for evaluating the performance of face recognition algorithms, many of the modern datasets just combine different factors that influence face recognition, such as face pose, occlusion, illumination, facial expression and image quality. When algorithms produce errors on these datasets, it is not clear which of the factors has caused this error and, hence, there is no guidance in which direction more research is required. This work is a followup from our previous works developed in 2014 and eventually published in 2016, showing the impact of various facial aspects on face recognition algorithms. By comparing the current state-of-the-art with the best systems from the past, we demonstrate that faces under strong occlusions, some types of illumination, and strong expressions are problems mastered by deep learning algorithms, whereas recognition with low-resolution images, extreme pose variations, and open-set recognition is still an open problem. To show this, we run a sequence of experiments using six different datasets and five different face recognition algorithms in an open-source and reproducible manner. We provide the source code to run all of our experiments, which is easily extensible so that utilizing your own deep network in our evaluation is just a few minutes away.