November 2022 arXiv papers — page 55

Mario Bonk, Luca Capogna, Xiaodan Zhou

We study existence and uniqueness of Green functions for the Cheeger $Q$-Laplacian in metric measure spaces that are Ahlfors $Q$-regular and support a $Q$-Poincar\'e inequality with $Q>1$. We prove uniqueness of Green functions both in the case of relatively compact domains, and in the global (unbounded) case. We also prove existence of global Green function

Zhiyan Ding, Lin Lin

We develop a phase estimation method with a distinct feature: its maximal runtime (which determines the circuit depth) is $\delta/\epsilon$, where $\epsilon$ is the target precision, and the preconstant $\delta$ can be arbitrarily close to $0$ as the initial state approaches the target eigenstate. The total cost of the algorithm satisfies the Heisenberg-limi

Adam Gleave, Mohammad Taufeeque, Juan Rocamonde, Erik Jenner

imitation provides open-source implementations of imitation and reward learning algorithms in PyTorch. We include three inverse reinforcement learning (IRL) algorithms, three imitation learning algorithms and a preference comparison algorithm. The implementations have been benchmarked against previous results, and automated tests cover 98% of the code. Moreo

Chunyu Li, Taisuke Hashimoto, Eiichi Matsumoto, Hiroharu Kato

Three-dimensional (3D) object reconstruction based on differentiable rendering (DR) is an active research topic in computer vision. DR-based methods minimize the difference between the rendered and target images by optimizing both the shape and appearance and realizing a high visual reproductivity. However, most approaches perform poorly for textureless obje

J. Nathan Kutz, Aminur Rahman, Megan R. Ebers, James Koch

Damped-driven systems are ubiquitous in engineering and science. Despite the diversity of physical processes observed in a broad range of applications, the underlying instabilities observed in practice have a universal characterization which is determined by the overall gain and loss curves of a given system. The universal behavior of damped-driven systems c

Rhythm Shimakawa, Nobuhiro Okabe, Masato Shirasaki, Masayuki Tanaka

This paper reports our discovery of the most massive supercluster, termed the King Ghidorah Supercluster (KGSc), at $z=0.50-0.64$ in the Third Public Data Release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP PDR3) over 690 deg$^2$, as well as an initial result for a galaxy and dark matter mapping. The primary structure of the KGSc comprises tri

T. Anderson, E. Anderssen, M. Askins, A. J. Bacon

EOS is a technology demonstrator, designed to explore the capabilities of hybrid event detection technology, leveraging both Cherenkov and scintillation light simultaneously. With a fiducial mass of four tons, EOS is designed to operate in a high-precision regime, with sufficient size to utilize time-of-flight information for full event reconstruction, flexi

Zhu Xiaoxu, Fan kecai, He hai, Zhang Ziyu

More children in Shandong Province are stunted than any other province in China. Data on more than 122,000 children show a dramatic increase in height advantage with birth order in Shandong relative to the average of other provinces. We suggest that the steep birth order gradient in Shandong is due to a preference for the eldest child, which influences paren

Diptarka Chakraborty, Gunjan Kumar, Kuldeep S. Meel

We consider the problem of estimating the support size of a distribution $D$. Our investigations are pursued through the lens of distribution testing and seek to understand the power of conditional sampling (denoted as COND), wherein one is allowed to query the given distribution conditioned on an arbitrary subset $S$. The primary contribution of this work i

Pu Du, Shuo Zhai, Jian-Min Wang

Reverberation mapping (RM) is a widely-used method for probing the physics of broad-line regions (BLRs) in active galactic nuclei (AGNs). There are increasing preliminary evidences that the RM behaviors of broad emission lines are influenced by BLR densities, however, the influences have not been investigated systematically from theoretical perspective. In t

Juan C. Quiroz, David Brieger, Louisa Jorm, Raymond W Sy

Objective: To develop prognostic survival models for predicting adverse outcomes after catheter ablation treatment for non-valvular atrial fibrillation (AF). Methods: We used a linked dataset including hospital administrative data, prescription medicine claims, emergency department presentations, and death registrations of patients in New South Wales, Austra

Chenglin Li, Yuanzhen Xie, Chenyun Yu, Bo Hu

Cross-domain recommendation is an important method to improve recommender system performance, especially when observations in target domains are sparse. However, most existing techniques focus on single-target or dual-target cross-domain recommendation (CDR) and are hard to be generalized to CDR with multiple target domains. In addition, the negative transfe

Rodolfo Valiente, Behrad Toghi, Mahdi Razzaghpour, Ramtin Pedarsani

It is expected that autonomous vehicles(AVs) and heterogeneous human-driven vehicles(HVs) will coexist on the same road. The safety and reliability of AVs will depend on their social awareness and their ability to engage in complex social interactions in a socially accepted manner. However, AVs are still inefficient in terms of cooperating with HVs and strug

Bing Yi, Gil Ho Yoon, Ran Zheng, Long Liu

Topology optimization is one of the engineering tools for finding efficient design. For the material interpolation scheme, it is usual to employ the SIMP (Solid Isotropic Material with Penalization) or the homogenization based interpolation function for the parameterization of the material properties with respect to the design variables assigned to each fini

Hai Wu, Chenglu Wen, Wei Li, Xin Li

3D object detection received increasing attention in autonomous driving recently. Objects in 3D scenes are distributed with diverse orientations. Ordinary detectors do not explicitly model the variations of rotation and reflection transformations. Consequently, large networks and extensive data augmentation are required for robust detection. Recent equivaria

Arghya Chakraborty, Rahul Vaze

The classic online facility location problem deals with finding the optimal set of facilities in an online fashion when demand requests arrive one at a time and facilities need to be opened to service these requests. In this work, we study two variants of the online facility location problem; (1) weighted requests and (2) congestion. Both of these variants a

Xue Jiang, Xiulian Peng, Yuan Zhang, Yan Lu

Recently end-to-end neural audio/speech coding has shown its great potential to outperform traditional signal analysis based audio codecs. This is mostly achieved by following the VQ-VAE paradigm where blind features are learned, vector-quantized and coded. In this paper, instead of blind end-to-end learning, we propose to learn disentangled features for rea

Yinbo Yu, Jiajia Liu

Deep reinforcement learning (DRL) is one of the most popular algorithms to realize an autonomous driving (AD) system. The key success factor of DRL is that it embraces the perception capability of deep neural networks which, however, have been proven vulnerable to Trojan attacks. Trojan attacks have been widely explored in supervised learning (SL) tasks (e.g

Jianqing Fan, Zhipeng Lou, Mengxin Yu

A stylized feature of high-dimensional data is that many variables have heavy tails, and robust statistical inference is critical for valid large-scale statistical inference. Yet, the existing developments such as Winsorization, Huberization and median of means require the bounded second moments and involve variable-dependent tuning parameters, which hamper

Xuan Sheng, Zhaoyang Han, Piji Li, Xiangmao Chang

Deep learning is becoming increasingly popular in real-life applications, especially in natural language processing (NLP). Users often choose training outsourcing or adopt third-party data and models due to data and computation resources being limited. In such a situation, training data and models are exposed to the public. As a result, attackers can manipul

Jianqing Fan, Zhipeng Lou, Weichen Wang, Mengxin Yu

This paper considers ranking inference of $n$ items based on the observed data on the top choice among $M$ randomly selected items at each trial. This is a useful modification of the Plackett-Luce model for $M$-way ranking with only the top choice observed and is an extension of the celebrated Bradley-Terry-Luce model that corresponds to $M=2$. Under a unifo

Yuanpeng Li

This survey includes systematic generalization and a history of how machine learning addresses it. We aim to summarize and organize the related information of both conventional and recent improvements. We first look at the definition of systematic generalization, then introduce Classicist and Connectionist. We then discuss different types of Connectionists a

Fabian Beck, Noboru Sakamoto

In this contribution, the optimal stabilization problem of periodic orbits is studied via invariant manifold theory and symplectic geometry. The stable manifold theory for the optimal point stabilization case is generalized to the case of periodic orbit stabilization, where a normally hyperbolic invariant manifold plays the role of a hyperbolic equilibrium p

Gabriel Mancino-Ball, Shengnan Miao, Yangyang Xu, Jie Chen

Consider a network of $N$ decentralized computing agents collaboratively solving a nonconvex stochastic composite problem. In this work, we propose a single-loop algorithm, called DEEPSTORM, that achieves optimal sample complexity for this setting. Unlike double-loop algorithms that require a large batch size to compute the (stochastic) gradient once in a wh

Shaoming Duan, Chuanyi Liu, Peiyi Han, Tianyu He

Non-independent and identically distributed (non-IID) data is a key challenge in federated learning (FL), which usually hampers the optimization convergence and the performance of FL. Existing data augmentation methods based on federated generative models or raw data sharing strategies for solving the non-IID problem still suffer from low performance, privac

Linjiang Huang, Kaixin Lu, Guanglu Song, Liang Wang

In this paper, we present a novel training scheme, namely Teach-DETR, to learn better DETR-based detectors from versatile teacher detectors. We show that the predicted boxes from teacher detectors are effective medium to transfer knowledge of teacher detectors, which could be either RCNN-based or DETR-based detectors, to train a more accurate and robust DETR

Chao Yin, Zhenhuan Liu

We calculate the amount of entanglement shared by two intervals in the ground state of a (1+1)-dimensional conformal field theory (CFT), quantified by an entanglement measure $\mathcal{E}$ based on the computable cross norm (CCNR) criterion. Unlike negativity or mutual information, we show that $\mathcal{E}$ has a universal expression even for two disjoint i

Shuo Zheng, Bojie Lv, Tong Zhang, Yinfei Xu

An active reconfigurable intelligent surface (RIS) has been shown to be able to enhance the sum-of-degrees-of-freedom (DoF) of a two-user multiple-input multiple-output (MIMO) interference channel (IC) with equal number of antennas at each transmitter and receiver. However, for any number of receive and transmit antennas, when and how an active RIS can help

Sunwook Hwang, Youngseok Kim, Seongwon Kim, Saewoong Bahk

Semi-supervised Learning (SSL) has received increasing attention in autonomous driving to reduce the enormous burden of 3D annotation. In this paper, we propose UpCycling, a novel SSL framework for 3D object detection with zero additional raw-level point cloud: learning from unlabeled de-identified intermediate features (i.e., smashed data) to preserve priva

Song Li, Jiandong Zhou, Chong MO, Jin LI

Graph convolutional networks (GCNs) is a class of artificial neural networks for processing data that can be represented as graphs. Since financial transactions can naturally be constructed as graphs, GCNs are widely applied in the financial industry, especially for financial fraud detection. In this paper, we focus on fraud detection on cryptocurrency truct

Hasibul Jamil, Elvis Rodrigues, Jacob Goldverg, Tevfik Kosar

Efficient data transfers over high-speed, long-distance shared networks require proper utilization of available network bandwidth. Using parallel TCP streams enables an application to utilize network parallelism and can improve transfer throughput; however, finding the optimum number of parallel TCP streams is challenging due to nondeterministic background t

Zhe Chang, Yu-Ting Kuang, Xukun Zhang, Jing-Zhi Zhou

We study the second order scalar and density perturbations generated by the Gaussian curvature perturbations and primordial gravitational waves in the radiation-dominated era. After presenting all the possible second-order source terms, we obtain the explicit expressions of the kernel functions and the power spectra of the second order scalar perturbations.

Joshua Introne

There is growing concern about misinformation and the role online media plays in social polarization. Analyzing belief dynamics is one way to enhance our understanding of these problems. Existing analytical tools, such as survey research or stance detection, lack the power to correlate contextual factors with population-level changes in belief dynamics. In t

Shenwang Jiang, Jianan Li, Jizhou Zhang, Ying Wang

Label noise and class imbalance commonly coexist in real-world data. Previous works for robust learning, however, usually address either one type of the data biases and underperform when facing them both. To mitigate this gap, this work presents a novel meta-learning based dynamic loss that automatically adjusts the objective functions with the training proc

Ralph M. Kaufmann, Michael Monaco

Motivated by viewing categories as bimodule monoids over their isomorphism groupoids, we construct monoidal structures called plethysm products on three levels: that is for bimodules, relative bimodules and factorizable bimodules. For the bimodules, we work in the general setting of actions by categories. We give a comprehensive theory linking these levels t

J. Tian, G. E. Anderson, P. J. Hancock, J. C. A. Miller-Jones

We present a targeted search for low-frequency (144--215\,MHz) FRB emission from five repeating FRBs using 23.3\,hr of archival data taken with the Murchison Widefield Array (MWA) Voltage Capture System (VCS) between 2014 September and 2020 May. This is the first time that the MWA VCS has been used to search for FRB signals from known repeaters, which enable

Zhen Guo, Zhiguang Liu, Qihang Zhang, George Barbastathis

We propose a noise-resilient deep reconstruction algorithm for X-ray tomography. Our approach shows strong noise resilience without obtaining noisy training examples. The advantages of our framework may further enable low-photon tomographic imaging.

Yuhei Otsubo, Akira Otsuka, Mamoru Mimura

Bit-stream recognition (BSR) has many applications, such as forensic investigations, detection of copyright infringement, and malware analysis. We propose the first BSR that takes a bare input bit-stream and outputs a class label without any preprocessing. To achieve our goal, we propose a centrifuge mechanism, where the upstream layers (sub-net) capture glo

Pengyuan Shi, Yuetong Wang, Saad Abbasi, Alexander Wong

As the COVID-19 pandemic continues to put a significant burden on healthcare systems worldwide, there has been growing interest in finding inexpensive symptom pre-screening and recommendation methods to assist in efficiently using available medical resources such as PCR tests. In this study, we introduce the design of COVID-Net Assistant, an efficient virtua

Qibin Hou, Cheng-Ze Lu, Ming-Ming Cheng, Jiashi Feng

This paper does not attempt to design a state-of-the-art method for visual recognition but investigates a more efficient way to make use of convolutions to encode spatial features. By comparing the design principles of the recent convolutional neural networks ConvNets) and Vision Transformers, we propose to simplify the self-attention by leveraging a convolu

Berk Cirisci, Constantin Enea, Azadeh Farzan, Suha Orhun Mutluergil

Partial order reduction (POR) is a classic technique for dealing with the state explosion problem in model checking of concurrent programs. Theoretical optimality, i.e., avoiding enumerating equivalent interleavings, does not necessarily guarantee optimal overall performance of the model checking algorithm. The computational overhead required to guarantee op

Arpit Das, Naveen Balaji Umasankar

In this work we extend the study of arXiv:2210.07186 by investigating two- and three-character MLDEs for Fricke groups at prime levels. We have constructed these higher-character MLDEs by using a $\mathit{novel}$ Serre-Ramanujan type derivative operator which maps $k$-forms to $(k+2)$-forms in $\Gamma^{+}_0(p)$. We found that this $\mathit{novel}$ derivative

William S. Armstrong, Spencer Drakontaidis, Nicholas Lui

Images of spacecraft photographed from other spacecraft operating in outer space are difficult to come by, especially at a scale typically required for deep learning tasks. Semantic image segmentation, object detection and localization, and pose estimation are well researched areas with powerful results for many applications, and would be very useful in auto

Jing Sun, Shuo Chen, Cong Zhang, Yining Ma

Opponent modelling has proven effective in enhancing the decision-making of the controlled agent by constructing models of opponent agents. However, existing methods often rely on access to the observations and actions of opponents, a requirement that is infeasible when such information is either unobservable or challenging to obtain. To address this issue,

Yi Guo, Xiaomei Zhang, Dirui Xu, Xinju Guo

Laser-plasma instability (LPI) is one of the main obstacles in laser-driven inertial confinement fusion (ICF) for achieving predictable and reproducible fusion at high gain. For the first time we have proved analytically and confirmed with three-dimensional particle-in-cell simulations that angular incoherence has additional and much stronger suppression of

Mahsa Shahbandeh, Mahsa Amin Salehi, Maryam Soltanyzadeh, Mehrnaz Mirzaei

Burn wounds can severely trouble the health system and life quality of patients. The present study aimed to analyze the synergistic healing properties of curcumin, honey, and potassium alum substances merged in a newly-devised burn ointment on second-degree burn wounds in rats. The MIC and MBC tests on 200 clinical isolates of Pseudomonas aeruginous are comp

Minki Jeong, Changick Kim

Real-world data often have a long-tailed distribution, where the number of samples per class is not equal over training classes. The imbalanced data form a biased feature space, which deteriorates the performance of the recognition model. In this paper, we propose a novel long-tailed recognition method to balance the latent feature space. First, we introduce

Yutaka Nagashima

A deductive program synthesis tool takes a specification as input and derives a program that satisfies the specification. The drawback of this approach is that search spaces for such correct programs tend to be enormous, making it difficult to derive correct programs within a realistic timeout. To speed up such program derivation, we improve the search strat

Rishi Athavale, Lakshmi Sritan Motati, Rohan Kalahasty

Gaze estimation has grown rapidly in accuracy in recent years. However, these models often fail to take advantage of different computer vision (CV) algorithms and techniques (such as small ResNet and Inception networks and ensemble models) that have been shown to improve results for other CV problems. Additionally, most current gaze estimation models require

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

The decay $\eta_c(2S)\to\pipieta$ is searched for through the radiative transition $\psi(3686) \to\gamma\eta_c(2S)$ using 448 million $\psi$(3686) events accumulated at the BESIII detector. The first evidence of $\eta_c(2S)\to\pi^+\pi^-\eta$ is found with a statistical significance of 3.5$\sigma$. The product of the branching fractions of $\psi(3686)\to\gamm

Sebastian Scheid

In this contribution the latest measurements of real and virtual photons in Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV from the ALICE Collaboration are presented. The extracted photon spectra are compared to predictions from state-of-the-art models that explicitly include the whole evolution of the heavy-ion collision as well as measurements at lower

Kevin Coulembier, Pavel Etingof, Alexander Kleshchev, Victor Ostrik

We study invariant theory of the general linear supergroup in positive characteristic. In particular, we determine when the symmetric group algebra acts faithfully on tensor superspace and demonstrate that the symmetric group does not always generate all invariants.

Shoya Motonaga, Mao Shinoda

One of the fundamental results of ergodic optimization asserts that for any dynamical system on a compact metric space with the specification property and for a generic continuous function $f$ every invariant probability measure that maximizes the space average of $f$ must have zero entropy. We establish the analogical result in the context of constraint erg

Alakh Aggarwal, Jikai Wang, Steven Hogue, Saifeng Ni

Recent research works have focused on generating human models and garments from their 2D images. However, state-of-the-art researches focus either on only a single layer of the garment on a human model or on generating multiple garment layers without any guarantee of the intersection-free geometric relationship between them. In reality, people wear multiple

O. Imanuvilov, M. Yamamoto

We consider a parabolic equation in a bounded domain $\OOO$ over a time interval $(0,T)$ with the homogeneous Neumann boundary condition. We arbitrarily choose a subboundary $\Gamma \subset \ppp\OOO$. Then, we discuss an inverse problem of determining a zeroth-order spatially varying coefficient by extra data of solution $u$: $u\vert_{\Gamma \times (0,T)}$ a

Zhiqiang Wei, Yingyi Wu, Bin Xu

A conformal metric ${\rm d}s^{2}$ with finitely many conical singularities of constant Gaussian curvature $K=1$ on a compact Riemann surface is referred to as a spherical conical metric. When the associated monodromy group of ${\rm d}s^{2}$ is diagonalizable, we refer to ${\rm d}s^{2}$ as a reducible spherical conical metric. The simplest case of a reducible

Marcella Medeiros Siqueira Coutinho de Almeida, Thiago Emmanuel Pereira, Fabio Morais

Preliminary data obtained from a partnership between the Federal University of Campina Grande and an ecommerce company indicates that some applications have issues when dealing with variable demand. This happens because a delay in scaling resources leads to performance degradation and, in literature, is a matter usually treated by improving the auto-scaling.

Carlos Espinosa-Valdéz, Luis Núñez-Betancourt, Yuriko Pitones

We show that the generalized minimum distance function is non-increasing as the degree varies for reduced standard graded algebras over a field. This allows us to define its regularity index and its stabilization value. The stabilization value is computed for every cases. We study how the regularity index varies as the number of polynomial increases, and use

Lin Yang, Qilong Zhai, Ran Zhang

In this paper, we develop a new weak Galerkin finite element scheme for the Stokes interface problem with curved interfaces. We take a unique vector-valued function at the interface and reflect the interface condition in the variational problem. Theoretical analysis and numerical experiments show that the errors can reach the optimal convergence order under

Arthur Josi, Mahdi Alehdaghi, Rafael M. O. Cruz, Eric Granger

The re-identification (ReID) of individuals over a complex network of cameras is a challenging task, especially under real-world surveillance conditions. Several deep learning models have been proposed for visible-infrared (V-I) person ReID to recognize individuals from images captured using RGB and IR cameras. However, performance may decline considerably i

Jiacheng Xu, Caiming Xiong, Silvio Savarese, Yingbo Zhou

Modern natural language generation paradigms require a good decoding strategy to obtain quality sequences out of the model. Beam search yields high-quality but low diversity outputs; stochastic approaches suffer from high variance and sometimes low quality, but the outputs tend to be more natural and creative. In this work, we propose a deterministic search

Lingxiao Huang, Jian Li, Xuan Wu

Constructing small-sized coresets for various clustering problems in different metric spaces has attracted significant attention for the past decade. A central problem in the coreset literature is to understand what is the best possible coreset size for $(k,z)$-clustering in Euclidean space. While there has been significant progress in the problem, there is

Jing Cheng, Yasser G. Alqaham, Amit K. Sanyal, Zhenyu Gan

Precise trajectory tracking for legged robots can be challenging due to their high degrees of freedom, unmodeled nonlinear dynamics, or random disturbances from the environment. A commonly adopted solution to overcome these challenges is to use optimization-based algorithms and approximate the system with a simplified, reduced-order model. Additionally, deep

Yunqi Miao, Jiankang Deng, Guiguang Ding, Jungong Han

Unsupervised person re-identification (ReID) aims to train a feature extractor for identity retrieval without exploiting identity labels. Due to the blind trust in imperfect clustering results, the learning is inevitably misled by unreliable pseudo labels. Albeit the pseudo label refinement has been investigated by previous works, they generally leverage aux

Ivan Damnjanović, Marko Milošević, Dragan Stevanović

We note here that the problem of determining extremal values of Sombor index for trees with a given degree sequence fits within the framework of results by Hua Wang from [Cent. Eur. J. Math. 12 (2014) 1656-1663], implying that the greedy tree has the minimum Sombor index, while an alternating greedy tree has the maximum Sombor index.

Travis Barton, Elizabeth Gross, Colby Long, Joseph Rusinko

Phylogenetic networks provide a means of describing the evolutionary history of sets of species believed to have undergone hybridization or gene flow during their evolution. The mutation process for a set of such species can be modeled as a Markov process on a phylogenetic network. Previous work has shown that a site-pattern probability distributions from a

Jai Prakash, Michele Vignati, Daniele Vignarca, Edoardo Sabbioni

Teleoperation provides human operator sophisticated perceptual and cognitive skills into an over the network control loop. It gives hope of addressing some challenges related to vehicular autonomy which is based on artificial intelligence by providing a backup plan. Variable network time delays in data transmission is the major problem in teleoperating a veh

Dimitrios Bralios, Efthymios Tzinis, Gordon Wichern, Paris Smaragdis

Traditional source separation approaches train deep neural network models end-to-end with all the data available at once by minimizing the empirical risk on the whole training set. On the inference side, after training the model, the user fetches a static computation graph and runs the full model on some specified observed mixture signal to get the estimated

Riccardo Massidda, Atticus Geiger, Thomas Icard, Davide Bacciu

Causal abstraction provides a theory describing how several causal models can represent the same system at different levels of detail. Existing theoretical proposals limit the analysis of abstract models to "hard" interventions fixing causal variables to be constant values. In this work, we extend causal abstraction to "soft" interventions, w

Keya Prakash, K. S. Thejaswini

We consider the model of history-deterministic one-counter nets (OCNs). History-determinism is a property of transition systems that allows for a limited kind of non-determinism which can be resolved 'on-the-fly'. Token games, which have been used to characterise history-determinism over various models, also characterise history-determinism over OCNs

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

We present a search for the lepton flavor violating decay $J/ψ\to e^{\pm}μ^{\mp}$ using $8.998\times 10^{9} J/ψ$ events collected with the BESIII detector at the BEPCII $e^+e^-$ storage ring. No excess of signal above background is observed; we therefore set an upper limit on the branching fraction of $\mathcal{B}(J/ψ\to e^{\pm}μ^{\mp})<4.5\times10^{-9}$ at

Debobroto Das Robin, Javed I. Khan

Very few of the innovations in deep networking have seen data center scale implementation. Because the Data Center network's extreme scale performance requires hardware implementation, which is only accessible to a few. However, the emergence of reconfigurable match-action table (RMT) paradigm-based switches have finally opened up the development life cycle

Yu. A. Demidov, M. G. Kozlov, A. E. Barzakh, V. A. Yerokhin

The magnetic hyperfine structure constants have been calculated for low-lying levels in neutral potassium atom taking into account the Bohr--Weisskopf (BW) and Breit--Rosenthal (BR) effects. According to our results the $4p_{1/2}$ state of K~I is free from both BR and BW corrections on the level of the current theoretical uncertainties. Using this finding an

Iberê Kuntz, Roldao da Rocha

The presence of a functional measure is scrutinized on both sides of the dual gauge/gravity correspondence. Corrections to the transport coefficients in relativistic hydrodynamics are obtained using the linear response procedure. In particular, using first-order hydrodynamics, the shear viscosity, entropy density, diffusion constant, and speed of sound are s

Moe Kayali, Dan Suciu

We propose quasi-stable coloring, an approximate version of stable coloring. Stable coloring, also called color refinement, is a well-studied technique in graph theory for classifying vertices, which can be used to build compact, lossless representations of graphs. However, its usefulness is limited due to its reliance on strict symmetries. Real data compres

Navdeep Gill, Abhishek Mathur, Marcos V. Conde

Organizations of all sizes, across all industries and domains are leveraging artificial intelligence (AI) technologies to solve some of their biggest challenges around operations, customer experience, and much more. However, due to the probabilistic nature of AI, the risks associated with it are far greater than traditional technologies. Research has shown t

C. Reichhardt, C. J. O. Reichhardt

We consider simulations of Wigner crystals interacting with random quenched disorder in the presence of thermal fluctuations. When quenched disorder is absent, there is a well defined melting temperature determined by the proliferation of topological defects, while for zero temperature, there is a critical quenched disorder strength above which topological d

Matthew Feickert, Mihir Katare, Mark Neubauer, Avik Roy

Extracting scientific results from high-energy collider data involves the comparison of data collected from the experiments with synthetic data produced from computationally-intensive simulations. Comparisons of experimental data and predictions from simulations increasingly utilize machine learning (ML) methods to try to overcome these computational challen

David W. Kribs, Jeremy Levick, Rajesh Pereira, Mizanur Rahaman

Quantum entanglement can be studied through the theory of completely positive maps in a number of ways, including by making use of the Choi-Jamilkowski isomorphism, which identifies separable states with entanglement breaking quantum channels, and optimal ensemble length with entanglement breaking rank. The multiplicative domain is an important operator stru

Piergiuseppe Mallozzi, Inigo Incer, Pierluigi Nuzzo, Alberto Sangiovanni-Vincentelli

We address the problem of modeling, refining, and repairing formal specifications for robotic missions using assume-guarantee contracts. We show how to model mission specifications at various levels of abstraction and implement them using a library of pre-implemented specifications. Suppose the specification cannot be met using components from the library. I

Xiyue Han, Alexander Schied

We study the problem of reconstructing the Faber--Schauder coefficients of a continuous function $f$ from discrete observations of its antiderivative $F$. For instance, this question arises in financial mathematics when estimating the roughness of volatility from the integrated volatility of an asset price trajectory. Our approach starts with mathematically

Gunnar Pruessner, Rosalba Garcia-Millan

Active particles that translate chemical energy into self-propulsion can maintain a far-from-equilibrium steady state and perform work. The entropy production measures how far from equilibrium such a particle system operates and serves as a proxy for the work performed. Field theory offers a promising route to calculating entropy production, as it allows for

V. De Romeri, O. G. Miranda, D. K. Papoulias, G. Sanchez Garcia

The observation of coherent elastic neutrino nucleus scattering has opened the window to many physics opportunities. This process has been measured by the COHERENT Collaboration using two different targets, first CsI and then argon. Recently, the COHERENT Collaboration has updated the CsI data analysis with a higher statistics and an improved understanding o

Yuval Dagan, Constantinos Daskalakis, Anthimos Vardis Kandiros

We study the optimization landscape of the log-likelihood function and the convergence of the Expectation-Maximization (EM) algorithm in latent Gaussian tree models, i.e. tree-structured Gaussian graphical models whose leaf nodes are observable and non-leaf nodes are unobservable. We show that the unique non-trivial stationary point of the population log-lik

Purva Tendulkar, Dídac Surís, Carl Vondrick

Synthesizing 3D human avatars interacting realistically with a scene is an important problem with applications in AR/VR, video games and robotics. Towards this goal, we address the task of generating a virtual human -- hands and full body -- grasping everyday objects. Existing methods approach this problem by collecting a 3D dataset of humans interacting wit

Hyeongdon Moon, Yoonseok Yang, Jamin Shin, Hangyeol Yu

The automatic generation of Multiple Choice Questions (MCQ) has the potential to reduce the time educators spend on student assessment significantly. However, existing evaluation metrics for MCQ generation, such as BLEU, ROUGE, and METEOR, focus on the n-gram based similarity of the generated MCQ to the gold sample in the dataset and disregard their educatio

Tomasz Tarkowski, Nevill Gonzalez Szwacki

This work describes the implementation of a genetic algorithm-based strategy combined with first-principles computations for identifying the structure of the most stable boron 1D structures. We focus our attention on the structure of ultrathin 1D boron structures given the lack of previous experimental and theoretical work on this topic. Our methodology yiel

G. Azuelos, B. Broerman, D. Bryman, W. C. Chen

At the Montreal Tandem accelerator, an experiment is being set up to measure internal pair creation from the decay of nuclear excited states using a multiwire proportional chamber and scintillator bars surrounding it from the DAPHNE experiment. The acceptance covers a solid angle of nearly 4$\pi$. Preamplifiers and the data acquisition hardware have been des

Ramtin Amintaheri

TeV-scale dark matter is well motivated by notions of naturalness as the new physics threshold is expected to emerge in the TeV regime. We extend the Standard Model by adding an arbitrary SU(2) dark matter multiplet in non-chiral representation. The pseudo-real representations can be viable DM candidates providing that one includes a higher dimensional mass-

Lin Zhang, Harry Joe, Natalia Nolde

Conditions are obtained for a Gaussian vector autoregressive time series of order $k$, VAR($k$), to have univariate margins that are autoregressive of order $k$ or lower-dimensional margins that are also VAR($k$). This can lead to $d$-dimensional VAR($k$) models that are closed with respect to a given partition $\{S_1,\ldots,S_n\}$ of $\{1,\ldots,d\}$ by spe

Tomoya Yoshida

In 2009, Ghani, Hancock and Pattinson gave a tree-like representation of stream processors $A^{\mathbb{N}} \rightarrow B^{\mathbb{N}}$. In 2021, Garner showed that this representation can be established in terms of algebraic theory and comodels: the set of infinite streams $A^{\mathbb{N}}$ is the final comodel of the algebraic theory of $A$-valued input $\ma

Carson Denison, Badih Ghazi, Pritish Kamath, Ravi Kumar

A well-known algorithm in privacy-preserving ML is differentially private stochastic gradient descent (DP-SGD). While this algorithm has been evaluated on text and image data, it has not been previously applied to ads data, which are notorious for their high class imbalance and sparse gradient updates. In this work we apply DP-SGD to several ad modeling task

Oriol Rubies-Bigorda, Stefan Ostermann, Susanne F. Yelin

Ordered atomic arrays with subwavelength lattice spacing emit light collectively. For fully inverted atomic arrays, this results in an initial burst of radiation and a fast build up of coherences between the atoms at initial times. Based on a cumulant expansion of the equations of motion, we derive exact analytical expressions for the emission properties and

Wei Wang, Junjie Li, Lijun Wu, Jennifer Sears

The Verwey transition in magnetite (Fe3O4 ) is the prototypical metal-insulator transition and has eluded a comprehensive explanation for decades. A major element of the challenge is the complex interplay between charge order and lattice distortions. Here we use ultrafast electron diffraction (UED) to disentangle the roles of charge order and lattice distort

Yijun Chen, Guodong Shi

One of the most widely used models for studying the geographical economics of climate change is the Regional Integrated model of Climate and the Economy (RICE). In this paper, we investigate how cooperation and competition arise in regional climate policies under the RICE framework from the standpoints of game theory and optimal control. First, we show that

Francesca E. D. Raimondi, Andrew R. Lawrence, Hana Chockler

This paper proposes a method for measuring fairness through equality of effort by applying algorithmic recourse through minimal interventions. Equality of effort is a property that can be quantified at both the individual and the group level. It answers the counterfactual question: what is the minimal cost for a protected individual or the average minimal co

Dong Min Roh, Zhaojun Bai, Ren-Cang Li

Much like the classical Fisher linear discriminant analysis (LDA), the recently proposed Wasserstein discriminant analysis (WDA) is a linear dimensionality reduction method that seeks a projection matrix to maximize the dispersion of different data classes and minimize the dispersion of same data classes via a bi-level optimization. In contrast to LDA, WDA c

Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans

Careful prompt design is critical to the use of large language models in zero-shot or few-shot learning. As a consequence, there is a growing interest in automated methods to design optimal prompts. In this work, we propose Test-time Prompt Editing using Reinforcement learning (TEMPERA). In contrast to prior prompt generation methods, TEMPERA can efficiently

Paul Goyes, Edwin Vargas, Claudia Correa, Yu Sun

Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging. Pre-processing approaches, such as missing trace or shot interpolation, are typically employed to enhance seismic data in such cases. Recently, deep learning has been used to address the trace interpolation problem at the expense of large amount

Tianyu Pan, Weining Shen, Clintin P. Davis-Stober, Guanyu Hu

We propose a novel nonparametric Bayesian IRT model in this paper by introducing the clustering effect at question level and further assume heterogeneity at examinee level under each question cluster, characterized by the mixture of Binomial distributions. The main contribution of this work is threefold: (1) We demonstrate that the model is identifiable. (2)