Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatio-Temporal Characterization of Synchrophasor Data Against Spoofing Attacks in Smart Grids

“Source ID Mix” has emerged as a new type of highly deceiving attack which can alter the source information of synchrophasor data measured by multiple phasor measurement units, thereby paralyzing many wide-area measurement systems applications. To address such sophisticated cyber attacks, we have exploited the spatio-temporal characteristics of synchrophasor data for authenticating measurements’ source information. Specifically, the source authentication is performed when the measurements are subjected to three types of spoofing attacks. Some practical difficulties in applying the proposed method on real-time authentication caused by insufficient measurement data have also been solved. Experimental results with real synchrophasor measurements have validated the effectiveness of the proposed method in detecting such complicated data spoofing and improving power systems’ cyber security.
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation

This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased representation from decentralized and heterogeneous local data. It employs a two-sided knowledge distillation with contrastive learning as a core component, allowing the federated system to function without requiring clients to share any data features. Furthermore, its adaptable architecture can be used as an add-on module for existing unsupervised algorithms in federated settings. Experiments show that our model improves performance significantly (1.58--5.52pp) on five unsupervised algorithms.
A Community-Aware Framework for Social Influence Maximization

We consider the problem of Influence Maximization (IM), the task of selecting $k$ seed nodes in a social network such that the expected number of nodes influenced is maximized. We propose a community-aware divide-and-conquer framework that involves (i) learning the inherent community structure of the social network, (ii) generating candidate solutions by solving the influence maximization problem for each community, and (iii) selecting the final set of seed nodes using a novel progressive budgeting scheme. Our experiments on real-world social networks show that the proposed framework outperforms the standard methods in terms of run-time and the heuristic methods in terms of influence. We also study the effect of the community structure on the performance of the proposed framework. Our experiments show that the community structures with higher modularity lead the proposed framework to perform better in terms of run-time and influence.
The fine structure of a chromospheric rosette

We have studied the spatial behaviour of the physical properties of dark mottles forming a well-defined rosette. The observations were obtained with the Multichannel Subtractive Double Pass (MSDP) spectrograph, operating in Hα at the Pic du Midi Observatory. From these observations, intensity fluctuations and Doppler shift velocities at different wavelengths were derived over a two dimensional field of view. The observed contrast profiles were matched with theoretical contrast profiles using Beckers'cloud model and 4 parameters were derived for the dark mottles: the source function, the line-of-sight velocity, the Doppler width and the optical depth
ReBoot: Distributed statistical learning via refitting Bootstrap samples

In this paper, we study a one-shot distributed learning algorithm via refitting Bootstrap samples, which we refer to as ReBoot. Given the local models that are fit on multiple independent subsamples, ReBoot refits a new model on the union of the Bootstrap samples drawn from these local models. The whole procedure requires only one round of communication of model parameters. Theoretically, we analyze the statistical rate of ReBoot for generalized linear models (GLM) and noisy phase retrieval, which represent convex and non-convex problems respectively. In both cases, ReBoot provably achieves the full-sample statistical rate whenever the subsample size is not too small. In particular, we show that the systematic bias of ReBoot, the error that is independent of the number of subsamples, is $O(n ^ {-2})$ in GLM, where n is the subsample size. This rate is sharper than that of model parameter averaging and its variants, implying the higher tolerance of ReBoot with respect to data splits to maintain the full-sample rate. Simulation study exhibits the statistical advantage of ReBoot over competing methods including averaging and CSL (Communication-efficient Surrogate Likelihood) with up to two rounds of gradient communication. Finally, we propose FedReBoot, an iterative version of ReBoot, to aggregate convolutional neural networks for image classification, which exhibits substantial superiority over FedAve within early rounds of communication.
The Detectability of Rocky Planet Surface and Atmosphere Composition with the JWST: The Case of LHS 3844b

The spectroscopic characterization of terrestrial exoplanets over a wide spectral range from the near- to the mid-infrared will be made possible for the first time with the JWST. One challenge is that it is not known a priori whether such planets possess optically thick atmospheres or even any atmospheres altogether. However, this challenge also presents an opportunity, the potential to detect the surface of an extrasolar world. This study explores the feasibility of characterizing with the JWST the atmosphere and surface of LHS 3844b, the highest signal-to-noise rocky thermal emission target among planets that are cool enough to have nonmolten surfaces. We model the planetary emission, including the spectral signal of both the atmosphere and surface, and we explore all scenarios that are consistent with the existing Spitzer 4.5 μm measurement of LHS 3844b from Kreidberg et al. In summary, we find a range of plausible surfaces and atmospheres that are within 3σ of the observationless reflective metal-rich, iron-oxidized, and basaltic compositions are allowed, and atmospheres are restricted to a maximum thickness of 1 bar, if near-infrared absorbers at ≳100 ppm are included. We further make predictions on the observability of surfaces and atmospheres and find that a small number (∼3) of eclipse observations should suffice to differentiate between surface and atmospheric features. We also perform a Bayesian retrieval analysis on simulated JWST data and find that the surface signal may make it harder to precisely constrain the abundance of atmospheric species and may falsely induce a weak H2O detection.
Identity Testing for High-Dimensional Distributions via Entropy Tensorization

We present improved algorithms and matching statistical and computational lower bounds for the problem of identity testing n-dimensional distributions. In the identity testing problem, we are given as input an explicit distribution μ, an ε > 0, and access to a sampling oracle for a hidden distribution π. The goal is to distinguish whether the two distributions μ and π are identical or are at least ε-far apart. When there is only access to full samples from the hidden distribution π, it is known that exponentially many samples may be needed for identity testing, and hence previous works have studied identity testing with additional access to various conditional sampling oracles. We consider here a significantly weaker conditional sampling oracle, which we call the Coordinate Oracle, and provide a fairly complete computational and statistical characterization of the identity testing problem in this new model. We prove that if an analytic property known as approximate tensorization of entropy holds for the visible distribution μ, then there is an efficient identity testing algorithm for any hidden π that uses Õ(n/ε) queries to the Coordinate Oracle. Approximate tensorization of entropy is a classical tool for proving optimal mixing time bounds of Markov chains and concentration of Lipschitz functions for high-dimensional distributions. Recent work shows that spectral independence implies approximate tensorization and consequently establishes it for many families of n-dimensional distributions. We complement our algorithmic result for identity testing with a matching Ω(n/ε) statistical lower bound for the number of queries under the Coordinate Oracle. We also prove a computational phase transition: for a well-studied class of n-dimensional distributions, specifically sparse antiferromagnetic Ising models over {+1,−1}n, we show that in the regime where approximate tensorization of entropy fails, there is no efficient identity testing algorithm unless RP=NP. Department of Computer Science and Engineering, Pennsylvania State University. Email: ablanca@cse.psu. edu. Research supported in part by NSF grant CCF-1850443. Department of Mathematics, Massachusetts Institute of Technology. Email: zongchen@mit.edu. Department of Computer Science, University of Rochester. Email: stefanko@cs.rochester. edu. Research supported in part by NSF grant CCF-1563757. Department of Computer Science, University of California, Santa Barbara. Email: vigoda@ucsb.edu. Research supported in part by NSF grant CCF-2147094.
Robustar: Interactive Toolbox Supporting Precise Data Annotation for Robust Vision Learning

We introduce the initial release of our software Robustar, which aims to improve the robustness of vision classiﬁcation machine learning models through a data-driven perspective. Building upon the recent understanding that the lack of machine learning model’s robustness is the tendency of the model’s learning of spurious features, we aim to solve this problem from its root at the data perspective by removing the spurious features from the data before training. In particular, we introduce a software that helps the users to better prepare the data for training image classiﬁcation models by allowing the users to annotate the spurious features at the pixel level of images. To facilitate this process, our software also leverages recent advances to help identify potential images and pixels worthy of attention and to continue the training with newly annotated data. Our software is hosted at the GitHub Repository https://github.com/HaohanWang/Robustar.
Radiative transfer in the interplanetary medium at Lyman alpha

We present here a method used to solve the transfer equation in the interplanetary medium at Lyman α for realistic distributions of atomic hydrogen. To compute the source function in the heliosphere, we have combined an iterative numerical solution for small values of optical depth from the sun with a Monte Carlo simulation of radiative transfer at large optical depth. The intensity can then be computed by integration of the source function along the line of sight, taking into account extinction. It is shown that radiative transfer effects on the source function are quite large, with a maximum increase in the downwind region, depending on the interstellar and solar parameters
ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the Wild

Estimating the pose of a moving camera from monocular video is a challenging problem, especially due to the presence of moving objects in dynamic environments, where the performance of existing camera pose estimation methods are susceptible to pixels that are not geometrically consistent. To tackle this challenge, we present a robust dense indirect structure-from-motion method for videos that is based on dense correspondence initialized from pairwise optical flow. Our key idea is to optimize long-range video correspondence as dense point trajectories and use it to learn robust estimation of motion segmentation. A novel neural network architecture is proposed for processing irregular point trajectory data. Camera poses are then estimated and optimized with global bundle adjustment over the portion of long-range point trajectories that are classified as static. Experiments on MPI Sintel dataset show that our system produces significantly more accurate camera trajectories compared to existing state-of-the-art methods. In addition, our method is able to retain reasonable accuracy of camera poses on fully static scenes, which consistently outperforms strong state-of-the-art dense correspondence based methods with end-to-end deep learning, demonstrating the potential of dense indirect methods based on optical flow and point trajectories. As the point trajectory representation is general, we further present results and comparisons on in-the-wild monocular videos with complex motion of dynamic objects. Code is available at https://github.com/bytedance/particle-sfm.
P4TE: PISA Switch Based Traffic Engineering in Fat-Tree Data Center Networks

This work presents P4TE, an in-band traffic monitoring, load-aware packet forwarding, and flow rate controlling mechanism for traffic engineering in fat-tree topology-based data center networks using PISA switches. It achieves sub-RTT reaction time to change in network conditions, improved flow completion time, and balanced link utilization. Unlike the classical probe-based monitoring approach, P4TE uses an in-band monitoring approach to identify traffic events in the data plane. Based on these events, it re-adjusts the priorities of the paths. It uses a heuristic-based load-aware forwarding path selection mechanism to respond to changing network conditions and control the flow rate by sending feedback to the end hosts. It is implementable on emerging v1model.p4 architecture-based programmable switches and capable of maintaining the line-rate performance. Our evaluation shows that P4TE uses a small amount of resources in the PISA pipeline and achieves an improved flow completion time than ECMP and HULA.
A Massively-Parallel 3D Simulator for Soft and Hybrid Robots

Simulation is an important step in robotics for creating control policies and testing various physical parameters. Soft robotics is a field that presents unique physical challenges for simulating its subjects due to the nonlinearity of deformable material components along with other innovative, and often complex, physical properties. Because of the computational cost of simulating soft and heterogeneous objects with traditional techniques, rigid robotics simulators are not well suited to simulating soft robots. Thus, many engineers must build their own one-off simulators tailored to their system, or use existing simulators with reduced performance. In order to facilitate the development of this exciting technology, this work presents an interactive-speed, accurate, and versatile simulator for a variety of types of soft robots. Cronos, our open-source 3D simulation engine, parallelizes a mass-spring model for ultra-fast performance on both deformable and rigid objects. Our approach is applicable to a wide array of nonlinear material configurations, including high deformability, volumetric actuation, or heterogenous stiffness. This versatility provides the ability to mix materials and geometric components freely within a single robot simulation. By exploiting the flexibility and scalability of nonlinear Hookean mass-spring systems, this framework simulates soft and rigid objects via a highly parallel model for near real-time speed. We describe an efficient GPU CUDA implementation, which we demonstrate to achieve computation of over 1 billion elements per second on consumer-grade GPU cards. Dynamic physical accuracy of the system is validated by comparing results to Euler-Bernoulli beam theory, natural frequency predictions, and empirical data of a soft structure under large deformation.
Content-Aware Scalable Deep Compressed Sensing

To more efficiently address image compressed sensing (CS) problems, we present a novel content-aware scalable network dubbed CASNet which collectively achieves adaptive sampling rate allocation, fine granular scalability and high-quality reconstruction. We first adopt a data-driven saliency detector to evaluate the importance of different image regions and propose a saliency-based block ratio aggregation (BRA) strategy for sampling rate allocation. A unified learnable generating matrix is then developed to produce sampling matrix of any CS ratio with an ordered structure. Being equipped with the optimization-inspired recovery subnet guided by saliency information and a multi-block training scheme preventing blocking artifacts, CASNet jointly reconstructs the image blocks sampled at various sampling rates with one single model. To accelerate training convergence and improve network robustness, we propose an SVD-based initialization scheme and a random transformation enhancement (RTE) strategy, which are extensible without introducing extra parameters. All the CASNet components can be combined and learned end-to-end. We further provide a four-stage implementation for evaluation and practical deployments. Experiments demonstrate that CASNet outperforms other CS networks by a large margin, validating the collaboration and mutual supports among its components and strategies. Codes are available at https://github.com/Guaishou74851/CASNet.
The Dynamics of Human Central Arterial System

The paper presents mathematical and numerical models of the blood flow in human arteries. We describe selected modelling techniques for the mechanical phenomena occurring in the arteries: blood flow, displacement of the wall and the fluid-structure interaction between the blood and the wall. The paper concentrates on the theoretical results showing the conditions of applicability of presented models. We describe variational models for the Casson flow of blood as well as stochastic Fluid Particle Model (FPM) modified for the nonlinear flows. For the artery wall we describe the model which is the physically nonlinear Koiter shell and the Finite Element Method (FEM). We also present the simulations of the fluid-structure interaction that uses the weakly coupled approach of FPM for blood with FEM for the wall.
Incentive Design for Large Congestion Games: Publicness-Specific Bayes Correlated Wardrop Equilibrium (Extended Abstract)

The travel costs of the players (travelers) in anonymous congestion games depend on their choices of routes and also on the states of the transportation network such as incidents, weather, and road work. In this work, we consider an incomplete-information environment in which the realizations of the states are unobserved by the travelers. We study how a planner can incentivize the travelers to behave in her favor by strategically designing what and how the travelers get informed about the realizations of the states.
Automatic Repair and Deadlock Detection for Parameterized Systems

We present an algorithm for the repair of parameterized systems. The repair problem is, for a given process implementation, to find a refinement such that a given safety property is satisfied by the resulting parameterized system, and deadlocks are avoided. Our algorithm uses a parameterized model checker to determine the correctness of candidate solutions and employs a constraint system to rule out candidates. We apply this algorithm on systems that can be represented as well-structured transition systems (WSTS), including disjunctive systems, pairwise rendezvous systems, and broadcast protocols. Moreover, we show that parameterized deadlock detection can be decided in EXPTIME for disjunctive systems, and that deadlock detection is in general undecidable for broadcast protocols.
Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers

Atom-thick two-dimensional materials usually possess unique properties compared to their bulk counterparts. Their properties are significantly affected by defects, which could be uncontrollably introduced by irradiation. The effects of electromagnetic irradiation and particle irradiation on 2H MoS 2 two-dimensional nanolayers are reviewed in this paper, covering heavy ions, protons, electrons, gamma rays, X-rays, ultraviolet light, terahertz, and infrared irradiation. Various defects in MoS 2 layers were created by the defect engineering. Here we focus on their influence on the structural, electronic, catalytic, and magnetic performance of the 2D materials. Additionally, irradiation-induced doping is discussed and involved.
Parcours en largeur lexicographique : un algorithme de partitionnement. Application aux graphes et généralisations

Les parcours en largueur lexicographiques ont ete introduits en 1976 pour reconnaitre les graphes triangules. Depuis des proprietes interessantes de ce parcours ont ete mises en evidence (notamment pour la reconnaissance des graphes d'intervalles). Pour mieux comprendre ces parcours, nous avons, dans un premier temps, etudie leur application d'origine : la reconnaissance des graphes triangules. Nous obtenons une generalisation des algorithmes de reconnaissance des graphes triangules. Nous en deduisons une unique preuve de ces algorithmes. Nous mettons ensuite en evidence des proprietes diametrales des parcours en largeur lexicographiques pour deux classes de graphes : les graphes triangules et les graphes sans triplet asteroide. Nous montrons que, deux parcours permettent de trouver un sommet dont l'excentricite differe du diametre d'au plus 1. Les parcours en largeur lexicographiques peuvent etre compris comme un processus d'affinage de partition. Cette technique est utilisee dans de nombreuses applications : tri de chaines de caracteres, minimisation d'automates finis deterministes, orientation transitive d'un graphe, reconnaissance des graphes d'intervalles nous proposons une structure de donnees et un algorithme generique pour toutes ces applications. Ainsi nous mettons en evidence des liens entre ces problemes et pouvons esperer de nouveaux resultats algorithmiques.
Radial viscous fingering induced by an infinitely fast chemical reaction

Abstract A numerical insight into the radial displacement of two viscously stable reactants undergoing an infinitely fast chemical reaction is obtained. This work broadens the numerical knowledge about the interaction between chemical reaction and hydrodynamic instability. A suitable transformation is utilised to deal with an infinite dimensionless parameter and reduce computational cost. Viscous fingering instability originates when the product has a different viscosity than the reactants. We calculate the onset time $t_{on}$ when the instability begins to appear for different reactants by varying the log mobility ratio $R_c$ and the Péclet number $Pe$. Based on $t_{on}$, we divide the time domain into stable and unstable zones with respect to instability. This helps to characterise reactants so as to have a flow with/without instability. For a fixed $Pe$ and $\vert R_c \vert$, it is found that $t_{on}$ is early for $R_c > 0$ in contrast to the result for rectilinear displacement. This results in a wider stable zone for $R_c < 0$. Interestingly, it is found that the stable zone can be made independent of $Pe$ by using a careful rescaling and we obtain the dependence of the onset time of instability on $R_c$ and $Pe$ as $t_{on} \propto (\vert R_c\vert Pe^{\beta _1}-\beta _2)^{\beta _3}$ where the constant of proportionality and $\beta _i$, $i=1,2,3$, depend upon the sign of $R_c$. In the unstable zone, we find that the length of the fingers depends upon the sign of $R_c$, which is not observed for any radial displacement of reactants undergoing a slow or moderately fast chemical reaction.