October 2022 arXiv papers — page 94

Nam Hyeon-Woo, Kim Yu-Ji, Byeongho Heo, Dongyoon Han

The favorable performance of Vision Transformers (ViTs) is often attributed to the multi-head self-attention (MSA). The MSA enables global interactions at each layer of a ViT model, which is a contrasting feature against Convolutional Neural Networks (CNNs) that gradually increase the range of interaction across multiple layers. We study the role of the dens

Takahiro Inayama

In this paper, we introduce a new concept of $L^2$-extension indices. This index is a function that gives the minimum constant with respect to the $L^2$-estimate of an Ohsawa--Takegoshi-type extension at each point. By using this notion, we propose a new way to study the positivity of curvature. We prove that there is an equivalence between how sharp the $L^

Luiza Labazanova, Shuang Peng, Liuming Qiu, Hoi-Yin Lee

In this paper, we propose a novel design of a hybrid mobile robot with controllable stiffness and deformable shape. Compared to conventional mobile agents, our system can switch between rigid and compliant phases by solidifying or melting Field's metal in its structure and, thus, alter its shape through the motion of its active components. In the soft state,

Jin-Min Liang, Qiao-Qiao Lv, Shu-Qian Shen, Ming Li

Finding the ground state of a Hamiltonian system is of great significance in many-body quantum physics and quantum chemistry. We propose an improved iterative quantum algorithm to prepare the ground state of a Hamiltonian. The crucial point is to optimize a cost function on the state space via the quantum gradient descent (QGD) implemented on quantum devices

Ang Li, Song Jiang, Yizhou Sun, Judea Pearl

This paper deals with the problem of learning the probabilities of causation of subpopulations given finite population data. The tight bounds of three basic probabilities of causation, the probability of necessity and sufficiency (PNS), the probability of sufficiency (PS), and the probability of necessity (PN), were derived by Tian and Pearl. However, obtain

Ning Han, Xun Yang, Ee-Peng Lim, Hao Chen

Cross-modal video retrieval aims to retrieve the semantically relevant videos given a text as a query, and is one of the fundamental tasks in Multimedia. Most of top-performing methods primarily leverage Visual Transformer (ViT) to extract video features [1, 2, 3], suffering from high computational complexity of ViT especially for encoding long videos. A com

Runsheng Xu, Jinlong Li, Xiaoyu Dong, Hongkai Yu

Existing multi-agent perception algorithms usually select to share deep neural features extracted from raw sensing data between agents, achieving a trade-off between accuracy and communication bandwidth limit. However, these methods assume all agents have identical neural networks, which might not be practical in the real world. The transmitted features can

Hongjiang Chen, Yang Wang, Leibo Liu, Shaojun Wei

Due to user privacy and regulatory restrictions, federate learning (FL) is proposed as a distributed learning framework for training deep neural networks (DNN) on decentralized data clients. Recent advancements in FL have applied Neural Architecture Search (NAS) to replace the predefined one-size-fit-all DNN model, which is not optimal for all tasks of vario

Elena Nozdrinova, Olga Pochinka, Ekaterina Tsaplina

The classical approach to the study of dynamical systems consists in representing the dynamics of the system in the form of a "source-sink", that means identifying an attractor-repeller pair, which are attractor-repellent sets for all other trajectories of the system. If there is a way to choose this pair so that the space orbits in its complement (the chara

Jason M. Altschuler, Kunal Talwar

Sampling from a high-dimensional distribution is a fundamental task in statistics, engineering, and the sciences. A canonical approach is the Langevin Algorithm, i.e., the Markov chain for the discretized Langevin Diffusion. This is the sampling analog of Gradient Descent. Despite being studied for several decades in multiple communities, tight mixing bounds

Qi-Yang Zheng

We prove that $d_k(n)=d_k(n+B)$ infinitely often for any positive integers $k$ and $B$, where $d_k(n)$ denotes the number of divisors of $n$ coprime to $k$.

Hsu-Chieh Hu, Joseph Zhou, Gregory J. Barlow, Stephen F. Smith

We introduce a heuristic scheduling algorithm for real-time adaptive traffic signal control to reduce traffic congestion. This algorithm adopts a lane-based model that estimates the arrival time of all vehicles approaching an intersection through different lanes, and then computes a schedule (i.e., a signal timing plan) that minimizes the cumulative delay in

Yuntian Deng, Volodymyr Kuleshov, Alexander M. Rush

Language models have demonstrated the ability to generate highly fluent text; however, it remains unclear whether their output retains coherent high-level structure (e.g., story progression). Here, we propose to apply a statistical tool, model criticism in latent space, to evaluate the high-level structure of the generated text. Model criticism compares the

Jing Ma, Ruocheng Guo, Saumitra Mishra, Aidong Zhang

Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted label?". The comparison of this instance before and after perturbation can enhance human interpretation. Most existing studies on counterfactual explanations are limited in tabular da

Yejia Liu, Wang Zhu, Shaolei Ren

Continual learning faces a crucial challenge of catastrophic forgetting. To address this challenge, experience replay (ER) that maintains a tiny subset of samples from previous tasks has been commonly used. Existing ER works usually focus on refining the learning objective for each task with a static memory construction policy. In this paper, we formulate th

Jiangang Ying, Yushu Zheng

The main interest of this article is the one-sided boundedness of the local discrepancy of $\alpha\in\mathbb{R}\setminus\mathbb{Q}$ on the interval $(0,c)\subset(0,1)$ defined by \[D_n(\alpha,c)=\sum_{j=1}^n 1_{\{\{j\alpha\}<c\}}-cn.\] We focus on the special case $c\in (0,1)\cap\mathbb{Q}$. Several necessary and sufficient conditions on $\alpha$ for $(D_n(\

Sirag Erkol, Satyaki Sikdar, Filippo Radicchi, Santo Fortunato

The exponentially growing number of scientific papers stimulates a discussion on the interplay between quantity and quality in science. In particular, one may wonder which publication strategy may offer more chances of success: publishing lots of papers, producing a few hit papers, or something in between. Here we tackle this question by studying the scienti

Zhongyi Zhang, Shengshan Qin, Chen Fang, Jiangping Hu

We show that there are diversified Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions between magnetic impurities, mediated by itinerant electrons, in a centrosymmetric crystal respecting a nonsymmorphic space group. We take the $P4/nmm$ space group as an example. We demonstrate that the different type of interactions, including the Heisenberg-type, the Dzyal

Petr Navratil

I present an overview of experiments at TRIUMF ARIEL and ISAC facilities covering both the current and the future envisioned programs. I also briefly review theory program at TRIUMF that relates to the ARIEL experimental program. I highlight several recent experimental results from the nuclear astrophysics, nuclear structure, fundamental symmetries, and the

JUNO Collaboration, Jie Zhao, Baobiao Yue, Haoqi Lu

The physics potential of detecting $^8$B solar neutrinos will be exploited at the Jiangmen Underground Neutrino Observatory (JUNO), in a model independent manner by using three distinct channels of the charged-current (CC), neutral-current (NC) and elastic scattering (ES) interactions. Due to the largest-ever mass of $^{13}$C nuclei in the liquid-scintillato

Xiao-Yan Wang, Xiang-Jun Chen

Neutrino flavor conversion is assumed to be induced by right-handed neutrino flavor conversion via seesaw mechanism. Neutrino oscillation is the macroscopic phenomenon before all neutrino flavor-flip interactions reaching equilibrium. The model for the hypothesis is the extension by introducing large mass Majorana right-handed neutrino into the Standard Mode

Xin Xin, Jiyuan Yang, Hanbing Wang, Jun Ma

Modern recommender systems are trained to predict users potential future interactions from users historical behavior data. During the interaction process, despite the data coming from the user side recommender systems also generate exposure data to provide users with personalized recommendation slates. Compared with the sparse user behavior data, the system

Wei-Min Liu, Lev Yungelson, Alexandre Kuranov

Ultracompact cataclysmic variables (CVs) of the AM CVn type are deemed to be important verification sources for the future space gravitational wave detectors such as the Laser Interferometer Space Antenna (LISA). We model the present-day Galactic population of AM CVn stars with He-star donors. Such a population has long expected to exist, though only a coupl

Shixuan Zhang, Xu Andy Sun

This paper presents a novel algorithmic study with extensive numerical experiments of distributionally robust multistage convex optimization (DR-MCO). Following the previous work on dual dynamic programming (DDP) algorithmic framework for DR-MCO, we focus on data-driven DR-MCO models with Wasserstein ambiguity sets that allow probability measures with infini

WL Zhang, YF Shen, H Song, Zh Zhang

TCP/IP network stack is irreplaceable for Web services in datacenter front-end servers, and the demand for which is growing rapidly for emerging high concurrency network service applications (including Internet, Internet of Things, mobile Internet, etc.) especially. The existing network stack schemes often face the dilemma between the data center server reso

Zhaofeng Wu, Hao Peng, Nikolaos Pappas, Noah A. Smith

Document-level machine translation leverages inter-sentence dependencies to produce more coherent and consistent translations. However, these models, predominantly based on transformers, are difficult to scale to long documents as their attention layers have quadratic complexity in the sequence length. Recent efforts on efficient attention improve scalabilit

Chandni Saxena, Muskan Garg, Gunjan Ansari

With recent developments in Social Computing, Natural Language Processing and Clinical Psychology, the social NLP research community addresses the challenge of automation in mental illness on social media. A recent extension to the problem of multi-class classification of mental health issues is to identify the cause behind the user's intention. However, mul

Pengtao Yang, Ruixi Liu Zhe Yuan, Yi Liu

Magnetization relaxation in the two-dimensional itinerant ferromagnetic van der Waals material Fe$_3$GeTe$_2$, below the Curie temperature, is fundamentally important for applications to low-dimensional spintronics devices. We use first-principles scattering theory to calculate the temperature-dependent Gilbert damping for bulk and single-layer Fe$_3$GeTe$_2

Yu-Long Zhou

We consider the spatially inhomogeneous quantum Boltzmann equation for bosons with a singular collision kernel, the weak-coupling limit of a large system of Bose-Einstein particles interacting through inverse power law. Global well-posedness of the corresponding Cauchy problem is proved in a periodic box near equilibrium for initial data satisfying high temp

Guoqing Zhang, Long Wang

In continuum robotics, real-time robust shape estimation is crucial for planning and control tasks that involve physical manipulation in complex environments. In this paper, we present a novel stochastic observer-based shape estimation framework designed specifically for continuum robots. The shape state space is uniquely represented by the modal coefficient

Daniel Q. Naiman, Fred Torcaso

Let $X_i,i=0,1,\ldots$ be a sequence of iid random variables whose distribution is continuous. Associated with this sequence is the sequence $(i,X_i),i=0,1,\ldots$. Let ${\cal R}_{n}$ denote the set of Pareto optimal elements of $\{ (i,X_i):i=0,\ldots,n\}.$ We refer to the elements of ${\cal R}_{n}$ as the current records at time $n,$ and we define $R_n=\ver

Zhiyong Si

This paper develops a generalized scalar auxiliary variable (SAV) method for the time-dependent Ginzburg-Landau equations. The backward Euler is used for discretizing the temporal derivative of the time-dependent Ginzburg-Landau equations. In this method, the system is decoupled and linearized to avoid solving the non-linear equation at each step. The theore

Yu-Hau Tseng, Te-Sheng Lin, Wei-Fan Hu, Ming-Chih Lai

In this paper, we propose a cusp-capturing physics-informed neural network (PINN) to solve discontinuous-coefficient elliptic interface problems whose solution is continuous but has discontinuous first derivatives on the interface. To find such a solution using neural network representation, we introduce a cusp-enforced level set function as an additional fe

Tushar Sangam, Ishan Rajendrakumar Dave, Waqas Sultani, Mubarak Shah

Drone-to-drone detection using visual feed has crucial applications, such as detecting drone collisions, detecting drone attacks, or coordinating flight with other drones. However, existing methods are computationally costly, follow non-end-to-end optimization, and have complex multi-stage pipelines, making them less suitable for real-time deployment on edge

Kexin Chen, Hoi Ying Wong

This study investigates an optimal consumption--investment problem in which the unobserved stock trend is modulated by a hidden Markov chain that represents different economic regimes. In the classical approach, the hidden state is estimated from historical asset prices, but recent advancements in technology enable investors to consider alternative data in t

Guowen Xu, Shengmin Xu, Jianting Ning, Tianwei Zhang

Sparse inner product (SIP) has the attractive property of overhead being dominated by the intersection of inputs between parties, independent of the actual input size. It has intriguing prospects, especially for boosting machine learning on large-scale data, which is tangled with sparse data. In this paper, we investigate privacy-preserving SIP problems that

Ayaka Ishikawa, Sho Kubota, Etsuo Segawa

We consider a quantum walk model on a finite graph which has an interaction with the outside. Here a quantum walker from the outside penetrates the graph and also a quantum walker in the graph goes out to the outside at every time step. This dynamics of the quantum walk converges to a stationary state. In this paper, we estimate the speed of the convergence

Yishi Xu, Dongsheng Wang, Bo Chen, Ruiying Lu

Embedded topic models are able to learn interpretable topics even with large and heavy-tailed vocabularies. However, they generally hold the Euclidean embedding space assumption, leading to a basic limitation in capturing hierarchical relations. To this end, we present a novel framework that introduces hyperbolic embeddings to represent words and topics. Wit

Thomas A. Trainor

In previous analyses a two-component (soft+hard) model (TCM) was developed for identified-hadron (PID) spectra from 5 TeV $p$-Pb and 13 TeV $p$-$p$ collisions. Spectrum data are generally described within their statistical uncertainties. Within the model are coefficients $z_{si}(n_s)$ and $z_{hi}(n_s)$ that denote the fractions of hadron species $i$ within t

Guowen Xu, Xingshuo Han, Gelei Deng, Tianwei Zhang

In this paper, we present VerifyML, the first secure inference framework to check the fairness degree of a given Machine learning (ML) model. VerifyML is generic and is immune to any obstruction by the malicious model holder during the verification process. We rely on secure two-party computation (2PC) technology to implement VerifyML, and carefully customiz

Qiaoyuan Cheng, Engui Fan

In this paper, we prove the existence of global solutions in $H^3(\mathbb{R})\cap H^{2,1}(\mathbb{R})$ to the Fokas-Lenells (FL) equation on the line when the initial data includes solitons.A key tool in proving this result is a newly modified Darboux transformation, which adds or subtracts a soliton with given spectral and scattering parameters. In this way

Fei Wang

We construct a special D4-D2-brane configuration on the K3 fibered Calabi-Yau threefolds: canonical D4-branes on K3 fiberations and another D2-branes on its base $\mathbb{P}_1$, by using the ordinary nonlinear sigma model in two dimensions. We would derive the exact expression of $Ext$ groups of this construction. By using the Gepner model, we achieve the fe

Robert Reynolds, Allan Stauffer

In this paper we use a contour integral method to derive a generating function in the form of a double series involving the product of two Chebyshev polynomials over generalized independent indices expressed in terms of the incomplete gamma function. The generating function represents a more generalized form relative to current literature. A possible applica

Zhi-Chao Zhao, Yong Zhou, Sai Wang

It is reported that the Large High Altitude Air Shower Observatory (LHAASO) observed thousands of very-high-energy photons up to $\sim$18 TeV from GRB 221009A. We study the survival rate of these photons via considering the fact that they are absorbed by the extragalactic background light. By performing a set of $10^6$ Monte-Carlo simulations, we explore the

Yitzchak Shmalo

We study the stability of accuracy during the training of deep neural networks (DNNs). In this context, the training of a DNN is performed via the minimization of a cross-entropy loss function, and the performance metric is accuracy (the proportion of objects that are classified correctly). While training results in a decrease of loss, the accuracy does not

Alan R. Wagner, Colin Holbrook, Daniel Holman, Brett Sheeran

This paper describes our recent effort to use virtual reality to simulate threatening emergency evacuation scenarios in which a robot guides a person to an exit. Our prior work has demonstrated that people will follow a robot's guidance, even when the robot is faulty, during an emergency evacuation. Yet, because physical in-person emergency evacuation experi

Ilsang Yoon, Christopher L. Carilli, Seiji Fujimoto, Marco Castellano

We report the ALMA observation of a $z\gtrsim10$ galaxy candidate (GHZ1) discovered from the GLASS-JWST Early Release Science Program. Our ALMA program aims to detect the [OIII] emission line at the rest-frame 3393.0062 GHz ($88.36\mu$m) and far-IR continuum emission with the spectral window setup seamlessly covering a 26.125 GHz frequency range ($10.10<z<11

Bharat Prakash, Nicholas Waytowich, Tim Oates, Tinoosh Mohsenin

Learning to solve long horizon temporally extended tasks with reinforcement learning has been a challenge for several years now. We believe that it is important to leverage both the hierarchical structure of complex tasks and to use expert supervision whenever possible to solve such tasks. This work introduces an interpretable hierarchical agent framework by

Yu-An Chen, Alexey V. Gorshkov, Yijia Xu

Utilizing the framework of $\mathbb{Z}_2$ lattice gauge theories in the context of Pauli stabilizer codes, we present methodologies for simulating fermions via qubit systems on a two-dimensional square lattice. We investigate the symplectic automorphisms of the Pauli module over the Laurent polynomial ring. This enables us to systematically increase the code

Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu, Cho-Jui Hsieh

Extreme multi-label classification (XMC) is a popular framework for solving many real-world problems that require accurate prediction from a very large number of potential output choices. A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an ML model to efficiently search this ind

Gwenevere Frank, Scott Makeig, Arnaud Delorme

Independent component analysis (ICA), is a blind source separation method that is becoming increasingly used to separate brain and non-brain related activities in electroencephalographic (EEG) and other electrophysiological recordings. It can be used to extract effective brain source activities and estimate their cortical source areas, and is commonly used i

Ruipeng Zhang, Chenning Yu, Jingkai Chen, Chuchu Fan

Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments. For the more challenging problem of planning in dynamic environments, such as multi-arm assembly tasks and human-robot interaction, motion planners need to consider the trajectories of the dynamic obstacles and reason ab

Sabir Ramazanov, Rome Samanta

We discuss production of QCD axion dark matter in a novel scenario, which assumes time-varying scale of Peccei-Quinn symmetry breaking. The latter decreases as the Universe's temperature at early times and eventually stabilises at a large constant value. Such behavior is caused by the portal interaction between the complex field carrying Peccei-Quinn charge

Michael P. Adams, Evelyn Pratami-Sinaga, Andreas Michels

We calculate the magnetic SANS cross section of dilute ensembles of uniformly magnetized and randomly-oriented Stoner-Wohlfarth particles using the Landau-Lifshitz equation. The focus of our study is on the angular anisotropy of the magnetic SANS signal as it can be seen on a two-dimensional position-sensitive detector. Depending on the symmetry of the magne

Farhad Naderian

This article explores the concept of transferability within communication channels, with a particular focus on the inability to transmit certain situations through these channels. The Channel Non-Transferability Theorem establishes that no encoding-decoding mechanism can fully transmit all propositions, along with their truth values, from a transmitter to a

Todd Gamblin, Massimiliano Culpo, Gregory Becker, Sergei Shudler

Modern scientific software stacks have become extremely complex, using many programming models and libraries to exploit a growing variety of GPUs and accelerators. Package managers can mitigate this complexity using dependency solvers, but they are reaching their limits. Finding compatible dependency versions is NP-complete, and modeling the semantics of pac

Aneesh Rangnekar, Christopher Kanan, Matthew Hoffman

Obtaining human per-pixel labels for semantic segmentation is incredibly laborious, often making labeled dataset construction prohibitively expensive. Here, we endeavor to overcome this problem with a novel algorithm that combines semi-supervised and active learning, resulting in the ability to train an effective semantic segmentation algorithm with signific

Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon

Groundbreaking language-vision architectures like CLIP and DALL-E proved the utility of training on large amounts of noisy image-text data, without relying on expensive accurate labels used in standard vision unimodal supervised learning. The resulting models showed capabilities of strong text-guided image generation and transfer to downstream tasks, while p

Tonima Tasnim Ananna, C. Megan Urry, Claudio Ricci, Priyamvada Natarajan

We constrain the intrinsic Eddington ratio (\lamEdd ) distribution function for local AGN in bins of low and high obscuration (log NH <= 22 and 22 < log NH < 25), using the Swift-BAT 70-month/BASS DR2 survey. We interpret the fraction of obscured AGN in terms of circum-nuclear geometry and temporal evolution. Specifically, at low Eddington ratios (log lamEdd

Atish Dixit, Ahmed Elsheikh

Reinforcement learning (RL) is a promising method to solve control problems. However, model-free RL algorithms are sample inefficient and require thousands if not millions of samples to learn optimal control policies. A major source of computational cost in RL corresponds to the transition function, which is dictated by the model dynamics. This is especially

Saibal De, Eduardo Corona, Paramsothy Jayakumar, Shravan Veerapaneni

We propose a framework for discrete scientific data compression based on the tensor-train (TT) decomposition. Our approach is tailored to handle unstructured output data from discrete element method (DEM) simulations, demonstrating its effectiveness in compressing both raw (e.g. particle position and velocity) and derived (e.g. stress and strain) datasets. W

Ruofan Liang, Jiahao Zhang, Haoda Li, Chen Yang

Neural radiance fields (NeRFs) have recently emerged as a promising approach for 3D reconstruction and novel view synthesis. However, NeRF-based methods encode shape, reflectance, and illumination implicitly and this makes it challenging for users to manipulate these properties in the rendered images explicitly. Existing approaches only enable limited editin

Jason M. Gregory, Sarah Al-Hussaini, Ali-akbar Agha-mohammadi, Satyandra K. Gupta

Experimental design in field robotics is an adaptive human-in-the-loop decision-making process in which an experimenter learns about system performance and limitations through interactions with a robot in the form of constructed experiments. This can be challenging because of system complexity, the need to operate in unstructured environments, and the compet

Douglas M. Hall, Mark J. Stevens, Gregory M. Grason

Geometric frustration offers a pathway to soft matter self-assembly with controllable finite sizes. While the understanding of frustration in soft matter assembly derives almost exclusively from continuum elastic descriptions, a current challenge is to understand the connection between microscopic physical properties of misfitting ``building blocks" and emer

Yuanran Zhu

We introduce a combinatorial version Mori-Zwanzig theory and develop from it a family of self-consistent evolution equations for the correlation function or Green's function of interactive many-body systems. The core idea is to use an ansatz to rewrite the memory kernel (self-energy) of the regular Mori-Zwanzig equation as a function composition of the corre

Sizhe An, Yin Li, Umit Ogras

The ability to estimate 3D human body pose and movement, also known as human pose estimation (HPE), enables many applications for home-based health monitoring, such as remote rehabilitation training. Several possible solutions have emerged using sensors ranging from RGB cameras, depth sensors, millimeter-Wave (mmWave) radars, and wearable inertial sensors. D

Xi Chen, Wenbo Jing, Weidong Liu, Yichen Zhang

The development of modern technology has enabled data collection of unprecedented size, which poses new challenges to many statistical estimation and inference problems. This paper studies the maximum score estimator of a semi-parametric binary choice model under a distributed computing environment without pre-specifying the noise distribution. An intuitive

Erqian Tang, Xiaotian Guo, Todor Stefanov

Nowadays, many AI applications utilizing resource-constrained edge devices (e.g., small mobile robots, tiny IoT devices, etc.) require Convolutional Neural Network (CNN) inference on a distributed system at the edge due to limited resources of a single edge device to accommodate and execute a large CNN. There are four main partitioning strategies that can be

Shiyuan Huang, Robinson Piramuthu, Shih-Fu Chang, Gunnar A. Sigurdsson

In Video Question Answering (VideoQA), answering general questions about a video requires its visual information. Yet, video often contains redundant information irrelevant to the VideoQA task. For example, if the task is only to answer questions similar to "Is someone laughing in the video?", then all other information can be discarded. This paper investiga

Rohan Chandra, Rahul Maligi, Arya Anantula, Joydeep Biswas

We propose an extension to the MAPF formulation, called SocialMAPF, to account for private incentives of agents in constrained environments such as doorways, narrow hallways, and corridor intersections. SocialMAPF is able to, for instance, accurately reason about the urgent incentive of an agent rushing to the hospital over another agent's less urgent incent

Na Li

Motivated by the increasing need of saving search effort by obtaining relevant video clips instead of whole videos, we propose a new task, named Semantic Video Moments Retrieval at scale (SVMR), which aims at finding relevant videos coupled with re-localizing the video clips in them. Instead of a simple combination of video retrieval and video re-localizatio

Ajay Jaiswal, Kumar Ashutosh, Justin F Rousseau, Yifan Peng

AI-powered Medical Imaging has recently achieved enormous attention due to its ability to provide fast-paced healthcare diagnoses. However, it usually suffers from a lack of high-quality datasets due to high annotation cost, inter-observer variability, human annotator error, and errors in computer-generated labels. Deep learning models trained on noisy label

Antonio Bellon, Mareike Dressler, Vyacheslav Kungurtsev, Jakub Marecek

We present an online algorithm for time-varying semidefinite programs (TV-SDPs), based on the tracking of the solution trajectory of a low-rank matrix factorization, also known as the Burer-Monteiro factorization, in a path-following procedure. There, a predictor-corrector algorithm solves a sequence of linearized systems. This requires the introduction of a

Lindsay Bassman Oftelie, Roel Van Beeumen, Daan Camps, Wibe A. de Jong

The physics of dirty bosons highlights the intriguing interplay of disorder and interactions in quantum systems, playing a central role in describing, for instance, ultracold gases in a random potential, doped quantum magnets, and amorphous superconductors. Here, we demonstrate how quantum computers can be used to elucidate the physics of dirty bosons in one

Zhiwen Tan, Chang Shen, Padmaja Subbarao, Wendy Lou

In epidemiological and clinical studies, identifying patients' phenotypes based on longitudinal profiles is critical to understanding the disease's developmental patterns. The current study was motivated by data from a Canadian birth cohort study, the CHILD Cohort Study. Our goal was to use multiple longitudinal respiratory traits to cluster the participants

Yi Gu, Shunyu Yao, Chuang Gan, Joshua B. Tenenbaum

Text games present opportunities for natural language understanding (NLU) methods to tackle reinforcement learning (RL) challenges. However, recent work has questioned the necessity of NLU by showing random text hashes could perform decently. In this paper, we pursue a fine-grained investigation into the roles of text in the face of different RL challenges,

Christopher T. Kenny, Shiro Kuriwaki, Cory McCartan, Evan T. R. Rosenman

In "Differential Perspectives: Epistemic Disconnects Surrounding the US Census Bureau's Use of Differential Privacy," boyd and Sarathy argue that empirical evaluations of the Census Disclosure Avoidance System (DAS), including our published analysis, failed to recognize how the benchmark data against which the 2020 DAS was evaluated is never a ground truth o

Gonçalo Gonçalves, Márcio Ferreira, João Aveiro, Antonio Onofre

The use of the Audio Spectrogram Transformer (AST) model for gravitational-wave data analysis is investigated. The AST machine-learning model is a convolution-free classifier that captures long-range global dependencies through a purely attention-based mechanism. In this paper a model is applied to a simulated dataset of inspiral gravitational wave signals f

Satoshi Tanioka, Daniel Vander-Hyde, Garrett D. Cole, Steven D. Penn

Thermal noise in high-reflectivity mirror coatings is a limiting factor in ground-based gravitational wave detectors. Reducing this coating thermal noise improves the sensitivity of detectors and enriches the scientific outcome of observing runs. Crystalline gallium arsenide and aluminum-alloyed gallium arsenide (referred to as AlGaAs) coatings are promising

Friedrich Hübner

We study solutions to the Lippmann-Schwinger equation in systems where a slow subsystem is coupled to a fast subsystem via an impurity. Such situations appear when a high-frequency Floquet-driven impurity is introduced into a low-energy system, but the driving frequency is at resonance with a high-energy band. In contrast to the case of resonant bulk driving

Y. S. Lo, R. I. Woodward, T. Roger, V. Lovic

The development and performance of quantum technologies heavily relies on the properties of the quantum states, which often require careful optimization of the driving conditions of all underlying components. In quantum key distribution (QKD), optical injection locking (OIL) of pulsed lasers has recently been shown as a promising technique to realize high-sp

Xian Wang, Paul Kairys, Sri Hari Krishna Narayanan, Jan Hückelheim

Quantum optimal control problems are typically solved by gradient-based algorithms such as GRAPE, which suffer from exponential growth in storage with increasing number of qubits and linear growth in memory requirements with increasing number of time steps. Employing QOC for discrete lattices reveals that these memory requirements are a barrier for simulatin

Bartosz Paprocki, Agnieszka Pregowska, Janusz Szczepanski

Realistic modeling of brain involves large number of neurons. The important question is how this size affects transmission efficiency? Here, these issue is studied in terms of Shannon's Theory. Mutual Information between input and output signals for simple class of networks with an increasing number of neurons is analyzed theoretically and numerically. Levy-

Navidreza Asadi, Maziar Goudarzi

Two major techniques are commonly used to meet real-time inference limitations when distributing models across resource-constrained IoT devices: (1) model parallelism (MP) and (2) class parallelism (CP). In MP, transmitting bulky intermediate data (orders of magnitude larger than input) between devices imposes huge communication overhead. Although CP solves

Chiyu Max Jiang, Mahyar Najibi, Charles R. Qi, Yin Zhou

Continued improvements in deep learning architectures have steadily advanced the overall performance of 3D object detectors to levels on par with humans for certain tasks and datasets, where the overall performance is mostly driven by common examples. However, even the best performing models suffer from the most naive mistakes when it comes to rare examples

Soufian El Yadmani, Robin The, Olga Gadyatskaya

Exploit proof-of-concepts (PoCs) for known vulnerabilities are widely shared in the security community. They help security analysts to learn from each other and they facilitate security assessments and red teaming tasks. In the recent years, PoCs have been widely distributed, e.g., via dedicated websites and platforms, and public code repositories such as Gi

R. A. Konoplya

The well-known duality between quasinormal modes of any stationary, spherically symmetric and asymptotically flat or de Sitter black hole and parameters of the circular null geodesic was initially claimed for gravitational and test field perturbations. According to this duality the real and imaginary parts of the $\ell \gg n$ quasinormal mode (where $\ell$ a

Marwan Zeggari, Renaud Lambiotte, Aydin Abadi, Louise Axon

While online interactions and exchanges have grown exponentially over the past decade, most commercial infrastructures still operate through centralized protocols, and their success essentially depends on trust between different economic actors. Digital advances such as blockchain technology has led to a massive wave of \textit{Decentralized Ledger Technolog

Zhao Song, Yitan Wang, Zheng Yu, Lichen Zhang

Sketching is one of the most fundamental tools in large-scale machine learning. It enables runtime and memory saving via randomly compressing the original large problem into lower dimensions. In this paper, we propose a novel sketching scheme for the first order method in large-scale distributed learning setting, such that the communication costs between dis

Stacie Baumann, Joseph Briggs

An \emph{$(n,k,t)$-graph} is a graph on $n$ vertices in which every set of $k$ vertices contains a clique on $t$ vertices. Tur\'an's Theorem, rephrased in terms of graph complements, states that the unique minimum $(n,k,2)$-graph is an equitable disjoint union of cliques. We prove that minimum $(n,k,t)$-graphs are always disjoint unions of cliques for any $t

Cheng-Chia Tsai, Xiaoyan Huang, Zhicheng Wu, Zongfu Yu

Recent years have seen a considerable surge of research on developing heuristic approaches to realize analog computing using physical waves. Among these, neuromorphic computing using light waves is envisioned to feature performance metrics such as computational speed and energy efficiency exceeding those of conventional digital techniques by many orders of m

Nikhil Tilak, Guohong Li, Takashi Taniguchi, Kenji Watanabe

Artificially twisted heterostructures of semiconducting transition metal dichalcogenides (TMDs) offer unprecedented control over their electronic and optical properties via the spatial modulation of interlayer interactions and structural reconstruction. Here we study twisted MoS2 bilayers in a wide range of twist angles near 0{\deg} using Scanning Tunneling

Yinglun Zhu, Robert Nowak

Deep neural networks have great representation power, but typically require large numbers of training examples. This motivates deep active learning methods that can significantly reduce the amount of labeled training data. Empirical successes of deep active learning have been recently reported in the literature, however, rigorous label complexity guarantees

Leora E. Dresselhaus-Marais, Bernard Kozioziemski, Theodor S. Holstad, Trygve Magnus Ræder

The structures, strain fields, and defect distributions in solid materials underlie the mechanical and physical properties across numerous applications. Many modern microstructural microscopy tools characterize crystal grains, domains and defects required to map lattice distortions or deformation, but are limited to studies of the (near) surface. Generally s

Alexander Mazurenko, Vladimir A. Stukopin

We construct a minimalistic presentation of Drinfeld super Yangians in the case of special linear superalgebra associated with an arbitrary Dynkin diagram. This gives us a possibility to introduce Hopf superalgebra structure on Drinfeld super Yangians. Using complete Weyl group we classify Drinfeld super Yangians endowed with mentioned Hopf superalgebra stru

Smriti Nandan Paul, Phillip Logan Sheridan, Richard J. Licata, Piyush M. Mehta

Ambitious satellite constellation projects by commercial entities and the ease of access to space in recent times have led to a dramatic proliferation of low-Earth space traffic. It jeopardizes space safety and long-term sustainability, necessitating better space traffic management (STM). Correct modeling of uncertainties in force models and orbital states,

Tian Yu Liu, Baharan Mirzasoleiman

Data augmentation is essential to achieve state-of-the-art performance in many deep learning applications. However, the most effective augmentation techniques become computationally prohibitive for even medium-sized datasets. To address this, we propose a rigorous technique to select subsets of data points that when augmented, closely capture the training dy

Shangbin Feng, Zhaoxuan Tan, Zilong Chen, Ningnan Wang

Modeling the ideological perspectives of political actors is an essential task in computational political science with applications in many downstream tasks. Existing approaches are generally limited to textual data and voting records, while they neglect the rich social context and valuable expert knowledge for holistic ideological analysis. In this paper, w

Yichuan Deng, Zhao Song, Yitan Wang, Yuanyuan Yang

A coreset is a point set containing information about geometric properties of a larger point set. A series of previous works show that in many machine learning problems, especially in clustering problems, coreset could be very useful to build efficient algorithms. Two main measures of an coreset construction algorithm's performance are the running time of th

Parker C. Lusk, Kaveh Fathian, Jonathan P. How

We present a multiway fusion algorithm capable of directly processing uncertain pairwise affinities. In contrast to existing works that require initial pairwise associations, our MIXER algorithm improves accuracy by leveraging the additional information provided by pairwise affinities. Our main contribution is a multiway fusion formulation that is particular