Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Entropy-regularized Markov Decision Processes

Stochastic and soft optimal policies resulting from entropy-regularized Markov decision processes (ER-MDP) are desirable for exploration and imitation learning applications. Motivated by the fact that such policies are sensitive with respect to the state transition probabilities, and the estimation of these probabilities may be inaccurate, we study a robust version of the ER-MDP model, where the stochastic optimal policies are required to be robust with respect to the ambiguity in the underlying transition probabilities. Our work is at the crossroads of two important schemes in reinforcement learning (RL), namely, robust MDP and entropy regularized MDP. We show that essential properties that hold for the non-robust ER-MDP and robust unregularized MDP models also hold in our settings, making the robust ER-MDP problem tractable. We show how our framework and results can be integrated into different algorithmic schemes including value or (modified) policy iteration, which would lead to new robust RL and inverse RL algorithms to handle uncertainties. Analyses on computational complexity and error propagation under conventional uncertainty settings are also provided.
Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size

Establishing a fast rate of convergence for optimization methods is crucial to their applicability in practice. With the increasing popularity of deep learning over the past decade, stochastic gradient descent and its adaptive variants (e.g. Adagrad, Adam, etc.) have become prominent methods of choice for machine learning practitioners. While a large number of works have demonstrated that these first order optimization methods can achieve sub-linear or linear convergence, we establish local quadratic convergence for stochastic gradient descent with adaptive step size for problems such as matrix inversion.
Multiscale Concrete Modeling of Aging Degradation

In this work a numerical finite element framework is implemented to enable the integration of coupled multiscale and multiphysics transport processes. A User Element subroutine (UEL) in Abaqus is used to simultaneously solve stress equilibrium, heat conduction, and multiple diffusion equations for 2D and 3D linear and quadratic elements. Transport processes in concrete structures and their degradation mechanisms are presented along with the discretization of the governing equations. The multiphysics modeling framework is theoretically extended to the linear elastic fracture mechanics (LEFM) by introducing the eXtended Finite Element Method (XFEM) and based on the XFEM user element implementation of Giner et al. [2009]. A damage model that takes into account the damage contribution from the different degradation mechanisms is theoretically developed. The total contribution of damage is forwarded to a Multi-Stage Fatigue (MSF) model to enable the assessment of the fatigue life and the deterioration of reinforced concrete structures in a nuclear power plant. Finally, two examples are presented to illustrate the developed multiphysics user element implementation and the XFEM implementation of Giner et al. [2009].
Learning to Predict 3D Lane Shape and Camera Pose from a Single Image via Geometry Constraints

Detecting 3D lanes from the camera is a rising problem for autonomous vehicles. In this task, the correct camera pose is the key to generating accurate lanes, which can transform an image from perspective-view to the top-view. With this transformation, we can get rid of the perspective effects so that 3D lanes would look similar and can accurately be fitted by low-order polynomials. However, mainstream 3D lane detectors rely on perfect camera poses provided by other sensors, which is expensive and encounters multi-sensor calibration issues. To overcome this problem, we propose to predict 3D lanes by estimating camera pose from a single image with a two-stage framework. The first stage aims at the camera pose task from perspective-view images. To improve pose estimation, we introduce an auxiliary 3D lane task and geometry constraints to benefit from multi-task learning, which enhances consistencies between 3D and 2D, as well as compatibility in the above two tasks. The second stage targets the 3D lane task. It uses previously estimated pose to generate top-view images containing distance-invariant lane appearances for predicting accurate 3D lanes. Experiments demonstrate that, without ground truth camera pose, our method outperforms the state-of-the-art perfect-camera-pose-based methods and has the fewest parameters and computations. Codes are available at https://github.com/liuruijin17/CLGo.
AutoFITS: Automatic Feature Engineering for Irregular Time Series

A time series represents a set of observations collected over time. Typically, these observations are captured with a uniform sampling frequency (e.g. daily). When data points are observed in uneven time intervals the time series is referred to as irregular or intermittent. In such scenarios, the most common solution is to reconstruct the time series to make it regular, thus removing its intermittency. We hypothesise that, in irregular time series, the time at which each observation is collected may be helpful to summarise the dynamics of the data and improve forecasting performance. We study this idea by developing a novel automatic feature engineering framework, which focuses on extracting information from this point of view, i.e., when each instance is collected. We study how valuable this information is by integrating it in a time series forecasting workflow and investigate how it compares to or complements state-of-the-art methods for regular time series forecasting. In the end, we contribute by providing a novel framework that tackles feature engineering for time series from an angle previously vastly ignored. We show that our approach has the potential to further extract more information about time series that significantly improves forecasting performance.
Structural Model and Mathematical Foundation of Information Space

"#$%&'$(((( In today's era, there is no doubt about the importance of information. However, people still have different opinions on information, and the research on information measurement is also inconclusive. Based on the researches of predecessors, this paper summarizes the important progress and main shortcomings in understanding and measurement of information, discusses the basic problems such as the essential connotation of objectivity of information, puts forward the mathematical model of information and the basic properties of information, and deduces and establishes the universality, delicacy, persistence, richness, inclusiveness, delay, etc. The structural model and mathematical basic theoretical framework of information space are formed. The motion law of information flow in information space is systematically and comprehensively analyzed, and the information dynamics for complex information system is preliminarily discussed. ( )*+(,-%. #((( Information Space, Structural Model, Information Measurement, Information Dynamics
IRS-aided MIMO Systems over Double-scattering Channels: Impact of Channel Rank Deficiency

Intelligent reflecting surfaces (IRSs) are promising enablers for next-generation wireless communications due to their reconfigurability and high energy efficiency in improving poor propagation condition of channels, e.g., limited scattering environment. However, most existing works assumed full-rank channels requiring rich scatters, which may not be available in practice. To analyze the impact of rank-deficient channels and mitigate the ensued performance loss, we consider a large-scale IRS-aided MIMO system with statistical channel state information (CSI), where the double-scattering channel is adopted to model rank deficiency. By leveraging random matrix theory (RMT), we first derive a deterministic approximation (DA) of the ergodic rate with low computational complexity and prove the existence and uniqueness of the DA parameters. Then, we propose an alternating optimization algorithm for maximizing the DA with respect to phase shifts and signal covariance matrices. Numerical results will show that the DA is tight and our proposed method can effectively mitigate the performance loss induced by channel rank deficiency.
A Universal LiDAR SLAM Accelerator System on Low-cost FPGA

LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) serves as a basis for indoor cleaning, navigation, and many other useful applications in both industry and household. From a series of LiDAR scans, it constructs an accurate, globally consistent model of the environment and estimates a robot position inside it. SLAM is inherently computationally intensive; it is a challenging problem to realize a fast and reliable SLAM system on mobile robots with a limited processing capability. To overcome such hurdles, in this paper, we propose a universal, low-power, and resource-efficient accelerator design for 2D LiDAR SLAM targeting resource-limited FPGAs. As scan matching is at the heart of SLAM, the proposed accelerator consists of dedicated scan matching cores on the programmable logic part, and provides software interfaces to facilitate the use. Our accelerator can be integrated to various SLAM methods including the ROS (Robot Operating System)-based ones, and users can switch to a different method without modifying and re-synthesizing the logic part. We integrate the accelerator into three widely-used methods, i.e., scan matching, particle filter, and graph-based SLAM. We evaluate the design in terms of resource utilization, speed, and quality of output results using real-world datasets. Experiment results on a Pynq-Z2 board demonstrate that our design accelerates scan matching and loop-closure detection tasks by up to 14.84x and 18.92x, yielding 4.67x, 4.00x, and 4.06x overall performance improvement in the above methods, respectively. Our design enables the real-time performance while consuming only 2.4W and maintaining accuracy, which is comparable to the software counterparts and even the state-of-the-art methods.
Simultaneous Execution of Quantum Circuits on Current and Near-Future NISQ Systems

In the noisy intermediate-scale quantum (NISQ) era, the idea of quantum multiprogramming, running multiple quantum circuits (QCs) simultaneously on the same hardware, helps to improve the throughput of quantum computation. However, the crosstalk, unwanted interference between qubits on NISQ processors, may cause performance degradation when using multiprogramming. To address this challenge, we introduce palloq (parallel allocation of QCs), a novel compilation protocol. Palloq improves the performance of quantum multiprogramming on NISQ processors, while paying attention to 1) the combination of QCs chosen for parallel execution and 2) the assignment of program qubit variables to physical qubits, to reduce unwanted interference among the active set of QCs. We also propose a software-based crosstalk detection protocol using a new combination of randomized benchmarking methods. Our method successfully characterizes the suitability of hardware for multiprogramming with relatively low detection costs. We found a tradeoff between the success rate and execution time of the multiprogramming. Our results will be of value when device throughput becomes a significant bottleneck. Until service providers have enough quantum processors available to more than meet demand, this approach will be attractive to the service providers and users who want to optimize job management and throughput of the processor.
A New Approach to Rotational Weingarten Surfaces

Weingarten surfaces are those whose principal curvatures satisfy a functional relation, whose set of solutions is called the curvature diagram or the W-diagram of the surface. Making use of the notion of geometric linear momentum of a plane curve, we propose a new approach to the study of rotational Weingarten surfaces in Euclidean 3-space. Our contribution consists of reducing any type of Weingarten condition on a rotational surface to a first-order differential equation on the momentum of the generatrix curve. In this line, we provide two new classification results involving a cubic and an hyperbola in the W-diagram of the surface characterizing, respectively, the non-degenerated quadric surfaces of revolution and the elasticoids, defined as the rotational surfaces generated by the rotation of the Euler elastic curves around their directrix line. As another application of our approach, we deal with the problem of prescribing mean or Gauss curvature on rotational surfaces in terms of arbitrary continuous functions depending on distance from the surface to the axis of revolution. As a consequence, we provide simple new proofs of some classical results concerning rotational surfaces, such as Euler’s theorem about minimal ones, Delaunay’s theorem on constant mean curvature ones, and Darboux’s theorem about constant Gauss curvature ones.
Overdetermined ODEs and rigid periodic states in network dynamics

We consider four long-standing Rigidity Conjectures about synchrony and phase patterns for hyperbolic periodic orbits of admissible ODEs for networks. Proofs of stronger local versions of these conjectures, published in 2010-12, are now known to have a gap, but remain valid for a broad class of networks. Using different methods we prove local versions of the conjectures under a stronger condition, `strong hyperbolicity', which is related to a network analogue of the Kupka-Smale Theorem. Under this condition we also deduce global versions of the conjectures and an analogue of the $H/K$ Theorem in equivariant dynamics. We prove the Rigidity Conjectures for all 1- and 2-colourings and all 2- and 3-node networks by proving that strong hyperbolicity is generic in these cases.
P2P-Loc: Point to Point Tiny Person Localization

Bounding-box annotation form has been the most frequently used method for visual object localization tasks. However, bounding-box annotation relies on a large amount of precisely annotating bounding boxes, and it is expensive and laborious. It is impossible to be employed in practical scenarios and even redundant for some applications (such as tiny person localization) that the size would not matter. Therefore, we propose a novel point-based framework for the person localization task by annotating each person as a coarse point (CoarsePoint) instead of an accurate bounding box that can be any point within the object extent. Then, the network predicts the person's location as a 2D coordinate in the image. Although this greatly simplifies the data annotation pipeline, the CoarsePoint annotation inevitably decreases label reliability (label uncertainty) and causes network confusion during training. As a result, we propose a point self-refinement approach that iteratively updates point annotations in a self-paced way. The proposed refinement system alleviates the label uncertainty and progressively improves localization performance. Experimental results show that our approach has achieved comparable object localization performance while saving up to 80$\%$ of annotation cost.
Fast Learning of MNL Model from General Partial Rankings with Application to Network Formation Modeling

Multinomial Logit (MNL) is one of the most popular discrete choice models and has been widely used to model ranking data. However, there is a long-standing technical challenge of learning MNL from many real-world ranking data: exact calculation of the MNL likelihood of partial rankings is generally intractable. In this work, we develop a scalable method for approximating the MNL likelihood of general partial rankings in polynomial time complexity. We also extend the proposed method to learn mixture of MNL. We demonstrate that the proposed methods are particularly helpful for applications to choice-based network formation modeling, where the formation of new edges in a network is viewed as individuals making choices of their friends over a candidate set. The problem of learning mixture of MNL models from partial rankings naturally arises in such applications. And the proposed methods can be used to learn MNL models from network data without the strong assumption that temporal orders of all the edge formation are available. We conduct experiments on both synthetic and real-world network data to demonstrate that the proposed methods achieve more accurate parameter estimation and better fitness of data compared to conventional methods.
N-body Simulations of the Solar System with CPU-based Parallel Methods

The gravitational N-body simulation in the Solar system was performed using different parallel approaches with the comparisons in the computational times and speed-up values being carried out under different model sizes and the number of processors. The numerical integration used is a second-order velocity Verlet approach which gives the acceptable accuracy in the orbits of major bodies and asteroids with a time step size of 0.1 days.
Entanglement and seniority

J. Kovács, A. T. Kruppa, 2 P. Salamon, Ö. Legeza, 4, 5, ∗ and G. Zaránd 6 Institute for Nuclear Research, Debrecen, Hungary Department of Theoretical Physics, Budapest University of Technology and Economics, H-1111 Budapest, Hungary Wigner Research Centre for Physics, H-1525 Budapest, Hungary Fachbereich Physik, Philipps-Universität Marburg, 35032 Marburg, Germany Institute for Advanced Study, Technical University of Munich, Lichtenbergstrasse 2a, 85748 Garching, Germany MTA-BME Quantum Dynamics and Correlations Research Group, Eötvös Loránd Research Network (ELKH), Budapest University of Technology and Economics
H2-Optimal Reduction of Positive Networks Using Riemannian Augmented Lagrangian Method

In this letter, we formulate the model reduction problem of a stable and positive network system as a constrained Riemannian optimization problem with the <inline-formula> <tex-math notation="LaTeX">$H^{2}$ </tex-math></inline-formula>-error objective function of the original and reduced network systems. We improve the reduction performance of the clustering-based method, which is one of the most known methods for model reduction of positive network systems, by using the output of the clustering-based method as the initial point for the proposed method. The proposed method reduces the dimension of the network system while preserving the properties of stability, positivity, and interconnection structure by applying the Riemannian augmented Lagrangian method (RALM) and deriving the Riemannian gradient of the Lagrangian. To check the efficiency of our method, we conduct a numerical experiment and compare it with the clustering-based method in the sense of <inline-formula> <tex-math notation="LaTeX">$H^{2}$ </tex-math></inline-formula>-error and <inline-formula> <tex-math notation="LaTeX">$H^{\infty }$ </tex-math></inline-formula>-error.
Deconfounded Training for Graph Neural Networks

Learning powerful representations is one central theme of graph neural networks (GNNs). It requires refining the critical information from the input graph, instead of the trivial patterns, to enrich the representations. Towards this end, graph attention and pooling methods prevail. They mostly follow the paradigm of “learning to attend”. It maximizes the mutual information between the attended subgraph and the ground-truth label. However, this training paradigm is prone to capture the spurious correlations between the trivial subgraph and the label. Such spurious correlations are beneficial to in-distribution (ID) test evaluations, but cause poor generalization in the out-of-distribution (OOD) test data. In this work, we revisit the GNN modeling from the causal perspective. On the top of our causal assumption, the trivial information serves as a confounder between the critical information and the label, which opens a backdoor path between them and makes them spuriously correlated. Hence, we present a new paradigm of deconfounded training (DTP) that better mitigates the confounding effect and latches on the critical information, to enhance the representation and generalization ability. Specifically, we adopt the attention modules to disentangle the critical subgraph and trivial subgraph. Then we make each critical subgraph fairly interact with diverse trivial subgraphs to achieve a stable prediction. It allows GNNs to capture a more reliable subgraph whose relation with the label is robust across different distributions. We conduct extensive experiments on synthetic and real-world datasets to demonstrate the effectiveness.
An Empirical Study of Security Practices for Microservices Systems

Despite the numerous benefits of microservices systems, security has been a critical issue in such systems. Several factors explain this difficulty, including a knowledge gap among microservices practitioners on properly securing a microservices system. To (partially) bridge this gap, we conducted an empirical study. We first manually analyzed 861 microservices security points, including 567 issues, 9 documents, and 3 wiki pages from 10 GitHub open-source microservices systems and 306 Stack Overflow posts concerning security in microservices systems. In this study, a microservices security point is referred to as"a GitHub issue, a Stack Overflow post, a document, or a wiki page that entails 5 or more microservices security paragraphs". Our analysis led to a catalog of 28 microservices security practices. We then ran a survey with 74 microservices practitioners to evaluate the usefulness of these 28 practices. Our findings demonstrate that the survey respondents affirmed the usefulness of the 28 practices. We believe that the catalog of microservices security practices can serve as a valuable resource for microservices practitioners to more effectively address security issues in microservices systems. It can also inform the research community of the required or less explored areas to develop microservices-specific security practices and tools.
AutoCast: scalable infrastructure-less cooperative perception for distributed collaborative driving

Autonomous vehicles use 3D sensors for perception. Cooperative perception enables vehicles to share sensor readings with each other to improve safety. Prior work in cooperative perception scales poorly even with infrastructure support. AUTOCAST1 enables scalable infrastructure-less cooperative perception using direct vehicle-to-vehicle communication. It carefully determines which objects to share based on positional relationships between traffic participants, and the time evolution of their trajectories. It coordinates vehicles and optimally schedules transmissions in a distributed fashion. Extensive evaluation results under different scenarios show that, unlike competing approaches, AUTOCAST can avoid crashes and near-misses which occur frequently without cooperative perception, its performance scales gracefully in dense traffic scenarios providing 2-4x visibility into safety critical objects compared to existing cooperative perception schemes, its transmission schedules can be completed on the real radio testbed, and its scheduling algorithm is near-optimal with negligible computation overhead.
A Benchmark Dataset for Micro-video Thumbnail Selection

The thumbnail, as the first sight of a micro-video, plays a pivotal role in attracting users to click and watch. Although several pioneer efforts have been dedicated to jointly considering the quality and representativeness for selecting the thumbnail, they are limited in exploring the influence of users` interests. While in the real scenario, the more the thumbnails satisfy the users, the more likely the micro-videos will be clicked. In this paper, we aim to select the thumbnail of a given micro-video that meets most users` interests. Towards this end, we construct a large-scale dataset for the micro-video thumbnails. Ultimately, we conduct several baselines on the dataset and demonstrate the effectiveness of our dataset.