October 2023 arXiv papers — page 162

Zhihao Du, Jiaming Wang, Qian Chen, Yunfei Chu

Generative Pre-trained Transformer (GPT) models have achieved remarkable performance on various natural language processing tasks, and have shown great potential as backbones for audio-and-text large language models (LLMs). Previous mainstream audio-and-text LLMs use discrete audio tokens to represent both input and output audio; however, they suffer from pe

Ziheng Wu, Jiaqi Xu, Xinyi Zou, Kunzhe Huang

Stable Diffusion web UI (SD-WebUI) is a comprehensive project that provides a browser interface based on Gradio library for Stable Diffusion models. In this paper, We propose a novel WebUI plugin called EasyPhoto, which enables the generation of AI portraits. By training a digital doppelganger of a specific user ID using 5 to 20 relevant images, the finetune

Korawat Charoenpitaks, Van-Quang Nguyen, Masanori Suganuma, Masahiro Takahashi

This paper addresses the problem of predicting hazards that drivers may encounter while driving a car. We formulate it as a task of anticipating impending accidents using a single input image captured by car dashcams. Unlike existing approaches to driving hazard prediction that rely on computational simulations or anomaly detection from videos, this study fo

Ho-Yeon Won, Hyun-Chul Kim, June-Young Kim

We investigate the gravitational form factors (GFFs) of the nucleon and the baryon octet, decomposed into their flavor components, utilizing a pion mean-field approach grounded in the large $N_c$ limit of Quantum Chromodynamics (QCD). Our focus is on the contributions from the twist-2 operators to the flavor-triplet and octet GFFs, and we decompose the mass,

Yuanyuan Liu, Zhuo-Xu Cui, Shucong Qin, Congcong Liu

Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging. This study aims to accelerate 3D-MC-CMR acquisition by a novel method based on score-based diffusion models with self-supervised learning. Specifically, we first establish a mapping between the undersampled k-sp

Zhikai Chen, Haitao Mao, Hongzhi Wen, Haoyu Han

In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to ensure promising performance. In contrast, Large Language Models (LLMs) exhibit impressive zero-shot proficiency on text-attributed graphs. Yet, they face challenges in efficiently

Yuuki Ishiwatari, Takahiro Yokoyama, Tomoya Kojima, Taisuke Banno

Amphiphilic molecules spontaneously form self-assembly structures based on physical conditions such as molecular structure, concentration, and temperature. These structures exhibit various useful functions according to their morphology. The concept of the critical packing parameter serves to correlate self-organized structures with chemical composition. Howe

Zhi Li, Min Du, Victor P. Debattista, Juntai Shen

Gas flows in the presence of two independently-rotating nested bars remain not fully understood, which is likely to play an important role in fueling the central black hole. We use high-resolution hydrodynamical simulations with detailed models of subgrid physics to study this problem. Our results show that the inner bar in double-barred galaxies can help dr

Chen-Te Ma

We review the various aspects of the 3D Einstein gravity theory with a negative cosmological constant and its boundary description. We also explore its connections to CFTs, modular symmetry, and holography. It is worth noting that this particular theory is topological in nature, which means that all the physical degrees of freedom are located on the boundary

Jie Zhu, Yuan Zong, Jingang Shi, Cheng Lu

This paper focuses on the research of micro-expression recognition (MER) and proposes a flexible and reliable deep learning method called learning to rank onset-occurring-offset representations (LTR3O). The LTR3O method introduces a dynamic and reduced-size sequence structure known as 3O, which consists of onset, occurring, and offset frames, for representin

Gaojie Zhang, Qingyuan Luo, Xiaokun Wen, Hao Wu

The discovery and manipulation of topological Hall effect (THE), an abnormal magnetoelectric response mostly related to the Dzyaloshinskii-Moriya interaction (DMI), are promising for next-generation spintronic devices based on topological spin textures such as magnetic skyrmions. However, most skyrmions and THE are stabilized in a narrow temperature window e

Heitor Rapela Medeiros, Fidel A. Guerrero Pena, Masih Aminbeidokhti, Thomas Dubail

A powerful way to adapt a visual recognition model to a new domain is through image translation. However, common image translation approaches only focus on generating data from the same distribution as the target domain. Given a cross-modal application, such as pedestrian detection from aerial images, with a considerable shift in data distribution between in

Sylvain Carpentier, Gahng Sahn Lee, Uhi Rinn Suh

In this paper, we introduce a class of super Adler-type operators associated with the Lie superalgebra $\mathfrak{gl}(m|n)$. We show that these operators generate Poisson vertex superalgebras which are isomorphic to the classical $\mathcal{W}$-superalgebras associated with $\mathfrak{gl}(m|n)$ and some rectangular nilpotent elements. We use this isomorphism

Ruoqi Yu, Peng Ding

Many scientific questions in biomedical, environmental, and psychological research involve understanding the effects of multiple factors on outcomes. While factorial experiments are ideal for this purpose, randomized controlled treatment assignment is generally infeasible in many empirical studies. Therefore, investigators must rely on observational data, wh

Tianlong Ma, Xian'an Jin, Weiling Yang

The multivariate arithmetic Tutte polynomial of arithmetic matroids is a generalization of the multivariate Tutte polynomial of matroids. In this note, we give the convolution formulas for the multivariate arithmetic Tutte polynomial of the product of two arithmetic matroids. In particular, the convolution formulas for the multivariate arithmetic Tutte polyn

Zhiyuan Wang, Haoran Li, Tianting Zhong, Qi Zhao

Orbital angular momentum (OAM) recognition of vortex beams is critical for applications ranging from optical communications to quantum technologies. However, conventional approaches designed for free-space propagation struggle when light passes through scattering media, such as multimode fibers (MMF), and often rely on high-resolution sensors with tens of th

Kaixun Huang, Ao Zhang, Binbin Zhang, Tianyi Xu

The attention-based deep contextual biasing method has been demonstrated to effectively improve the recognition performance of end-to-end automatic speech recognition (ASR) systems on given contextual phrases. However, unlike shallow fusion methods that directly bias the posterior of the ASR model, deep biasing methods implicitly integrate contextual informa

Yue Luo, Xiangcheng Zheng, Lei Zhang

The high-index saddle dynamics (HiSD) method [J. Yin, L. Zhang, and P. Zhang, {\it SIAM J. Sci. Comput., }41 (2019), pp.A3576-A3595] serves as an efficient tool for computing index-$k$ saddle points and constructing solution landscapes. Nevertheless, the conventional HiSD method often encounters slow convergence rates on ill-conditioned problems. To address

Ziyi Yin, Muchao Ye, Tianrong Zhang, Tianyu Du

Vision-Language (VL) pre-trained models have shown their superiority on many multimodal tasks. However, the adversarial robustness of such models has not been fully explored. Existing approaches mainly focus on exploring the adversarial robustness under the white-box setting, which is unrealistic. In this paper, we aim to investigate a new yet practical task

Huai-Ke Guo, Song Li, Yang Xiao, Jin Min Yang

In order to study the validity of analytical formulas used in the calculation of characteristic physical quantities related to vacuum bubbles, we conduct several numerical simulations of bubble kinematics in the context of cosmological first-order phase transitions to determine potentially existing systematic uncertainties. By comparing with the analytical r

Hamed Nosrati, Stephanie Smith, Douglas B. Hayman

In order to operationalize the Australia Telescope Compact Array (ATCA) for space situational awareness (SSA) applications, we develop a system model for range and direction of arrival (DOA) estimation based on the interferometric data. We employ the observational data collected from global positioning system (GPS) satellites to evaluate the developed model

Krishna Acharya, Eshwar Ram Arunachaleswaran, Sampath Kannan, Aaron Roth

We study the problem of online prediction, in which at each time step $t$, an individual $x_t$ arrives, whose label we must predict. Each individual is associated with various groups, defined based on their features such as age, sex, race etc., which may intersect. Our goal is to make predictions that have regret guarantees not just overall but also simultan

Ali Nikkhah, Scott Jordan

Internet users have suffered collateral damage in tussles over paid peering between large ISPs and large content providers. In order to qualify for settlement-free peering, large Internet Service Providers (ISPs) require that peers meet certain requirements. However, the academic literature has not yet shown the relationship between these settlement-free pee

Chi Zhang, Fu-Wen Shu

In our previous work, we have explored quantum gravity induced entanglement of mass (QGEM) in curved spacetime, observing entanglement formation between particles moving along geodesics in a Schwarzschild spacetime background. We find that long interaction time induces entanglement, even for particles with microscopic mass, addressing decoherence concerns. I

Bowen He, Xiao Zheng, Yuan Chen, Weinan Li

The rapid adoption of AI and convenience offered by cloud services have resulted in the growing demands for GPUs in the cloud. Generally, GPUs are physically attached to host servers as PCIe devices. However, the fixed assembly combination of host servers and GPUs is extremely inefficient in resource utilization, upgrade, and maintenance. Due to these issues

Zhang A-Man, Li Shi-Min, Cui Pu, Li Shuai

This paper aims to use the unified bubble dynamics equation to investigate bubble behavior in complex scenarios involving hybrid free surface/wall boundaries and interactions between multiple bubbles. The effect of singularity movement on the unified equation's form is analyzed after deriving the bubble pulsation equation using a moving point source and a di

Tim Mitchell, Michael L. Overton

We make an experimental comparison of methods for computing the numerical radius of an $n\times n$ complex matrix, based on two well-known characterizations, the first a nonconvex optimization problem in one real variable and the second a convex optimization problem in $n^{2}+1$ real variables. We make comparisons with respect to both accuracy and computatio

Peili Chen, Linyang He, Li Fu, Lu Fan

Speech and language models trained through self-supervised learning (SSL) demonstrate strong alignment with brain activity during speech and language perception. However, given their distinct training modalities, it remains unclear whether they correlate with the same neural aspects. We directly address this question by evaluating the brain prediction perfor

Jiawei Li, Chunxu Guo, Li Fu, Lu Fan

Reconstructing natural speech from neural activity is vital for enabling direct communication via brain-computer interfaces. Previous efforts have explored the conversion of neural recordings into speech using complex deep neural network (DNN) models trained on extensive neural recording data, which is resource-intensive under regular clinical constraints. H

Yue Luo, Lei Zhang, Pingwen Zhang, Zhiyi Zhang

We analyze the semi-implicit scheme of high-index saddle dynamics, which provides a powerful numerical method for finding the any-index saddle points and constructing the solution landscape. Compared with the explicit schemes of saddle dynamics, the semi-implicit discretization relaxes the step size and accelerates the convergence, but the corresponding nume

Chuanan Wei, Ce Xu

Riemann zeta function is important in a lot of branches of number theory. With the help of the operator method and several transformation formulas for hypergeometric series, we prove four series involving Riemann zeta function. Two of them are series expansions for $\zeta(7)$ and $\zeta(3)^2$ recently conjectured by Z.-W. Sun.

Ali Nikkhah, Scott Jordan

Disagreements over peering fees have risen to the level of potential government regulation. ISPs assert that content providers should pay them based on the volume of downstream traffic. Transit providers and content providers assert that consumers have already paid ISPs to transmit the content they request and that peering agreements should be settlement-fre

Raymond Chu, Inwon Kim, Young-Heon Kim, Kyeongsik Nam

We study the nonlocal Stefan problem, where the phase transition is described by a nonlocal diffusion as well as the change of enthalpy functions. By using a stochastic optimization approach introduced for the local case, we construct global-time weak solutions and give a probabilistic interpretation for the solutions. An important ingredient in our analysis

Lei Zhang, Hao Chen, Shu Hu, Bin Zhu

Generative adversarial networks (GANs) have remarkably advanced in diverse domains, especially image generation and editing. However, the misuse of GANs for generating deceptive images, such as face replacement, raises significant security concerns, which have gained widespread attention. Therefore, it is urgent to develop effective detection methods to dist

Meiqing Xu

In this paper, we focus on the partial differential equation \begin{equation*} (-\Delta)^\frac{\alpha}{2} u(x)=f(x,u(x))\;\;\;\;\text{ in }\mathbb{R}^n, \end{equation*} where $0<\alpha\leq 2$. By the direct method of scaling spheres investigated by Dai and Qin (\cite{dai2023liouville}, \textit{International Mathematics Research Notices, 2023}), we derive a L

Shuai Li, Siwei Lyu, Jeff Trinkle

Due to the limitations of the robotic sensors, during a robotic manipulation task, the acquisition of the object's state can be unreliable and noisy. Combining an accurate model of multi-body dynamic system with Bayesian filtering methods has been shown to be able to filter out noise from the object's observed states. However, efficiency of these filtering m

Takamori Park, Leon Balents

Inspired by the recent work by Delacretaz et. al., we rigorously derive an exact and simple method to bosonize a non-interacting fermionic system with a Fermi surface starting from a microscopic Hamiltonian. In the long-wavelength limit, we show that the derived bosonized action is exactly equivalent to the action obtained by Delacretaz et. al. In addition,

Yukari Yamauchi, Landon Buskirk, Pablo Giuliani, Kyle Godbey

We present a computational framework for efficient learning, sampling, and distribution of general Bayesian posterior distributions. The framework leverages a machine learning approach for the construction of normalizing flows for the general probability distributions typically encountered in Bayesian uncertainty quantification studies. This normalizing flow

Paul Bastide, Carla Groenland, Maria-Romina Ivan, Tom Johnston

Given a finite poset $\mathcal P$, we say that a family $\mathcal F$ of subsets of $[n]$ is $\mathcal P$-saturated if $\mathcal F$ does not contain an induced copy of $\mathcal P$, but adding any other set to $\mathcal F$ creates an induced copy of $\mathcal P$. The induced saturation number of $\mathcal P$, denoted by $\text{sat}^*(n,\mathcal P)$, is the si

Xinhua Wang, Houping Yue, Zizheng Wang, Liancheng Xu

Cross-domain sequential recommenders (CSRs) are gaining considerable research attention as they can capture user sequential preference by leveraging side information from multiple domains. However, these works typically follow an ideal setup, i.e., different domains obey similar data distribution, which ignores the bias brought by asymmetric interaction dens

Joel Niklaus, Robin Mamié, Matthias Stürmer, Daniel Brunner

Releasing court decisions to the public relies on proper anonymization to protect all involved parties, where necessary. The Swiss Federal Supreme Court relies on an existing system that combines different traditional computational methods with human experts. In this work, we enhance the existing anonymization software using a large dataset annotated with en

Matin Amoozadeh, David Daniels, Daye Nam, Aayush Kumar

Generative artificial systems (GenAI) have experienced exponential growth in the past couple of years. These systems offer exciting capabilities, such as generating programs, that students can well utilize for their learning. Among many dimensions that might affect the effective adoption of GenAI, in this paper, we investigate students' \textit{trust}. Trust

Wei Peng, Tomas Bosschieter, Jiahong Ouyang, Robert Paul

Generative AI models hold great potential in creating synthetic brain MRIs that advance neuroimaging studies by, for example, enriching data diversity. However, the mainstay of AI research only focuses on optimizing the visual quality (such as signal-to-noise ratio) of the synthetic MRIs while lacking insights into their relevance to neuroscience. To gain th

Ziqi Zhang, Xiao Xiong, Zifeng Zhuang, Jinxin Liu

Studying how to fine-tune offline reinforcement learning (RL) pre-trained policy is profoundly significant for enhancing the sample efficiency of RL algorithms. However, directly fine-tuning pre-trained policies often results in sub-optimal performance. This is primarily due to the distribution shift between offline pre-training and online fine-tuning stages

Victor Kalvin

We study the zeta-regularized spectral determinant of the Friedrichs Laplacians on the singular spheres obtained by cutting and glueing copies of constant curvature (hyperbolic, spherical, or flat) double triangle. The determinant is explicitly expressed in terms of the corresponding Belyi functions and the determinant of the Friedrichs Laplacian on the doub

Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan

How does scaling the number of parameters in large language models (LLMs) affect their core capabilities? We study two natural scaling techniques -- weight pruning and simply training a smaller or larger model, which we refer to as dense scaling -- and their effects on two core capabilities of LLMs: (a) recalling facts presented during pre-training and (b) p

Yuanyuan Su, Francoise Combes, Valeria Olivares, Gianluca Castignani

We present a multiwavelength observation of a cool core that does not appear to be associated with any galaxy, in a nearby cluster, Abell~1142. Its X-ray surface brightness peak of $\lesssim2$ keV is cooler than the ambient intracluster gas of $\gtrsim3$ keV, and is offset from its brightest cluster galaxy (BCG) by 80 kpc in projection, representing the larg

Vanicka Arora, Liam Magee, Luke Munn

Built heritage has been both subject and product of a gaze that has been sustained through moments of colonial fixation on ruins and monuments, technocratic examination and representation, and fetishisation by aglobal tourist industry. We argue that the recent proliferation of machine learning and vision technologies create new scopic regimes for heritage: s

Liam Collins, Shanshan Wu, Sewoong Oh, Khe Chai Sim

In many applications of federated learning (FL), clients desire models that are personalized using their local data, yet are also robust in the sense that they retain general global knowledge. However, the presence of data heterogeneity across clients induces a fundamental trade-off between personalization (i.e., adaptation to a local distribution) and robus

Leticia López-Flores, Monica Olvera de la Cruz

Acid-base equilibria directly influence the functionality and behavior of particles in a system. Due to the ionizing effects of acid-base functional groups, particles will undergo charge exchange. The degree of ionization and their intermolecular and electrostatic interactions are controlled by varying the pH and salt concentration of the solution in a syste

Callum McDougall, Arthur Conmy, Cody Rushing, Thomas McGrath

We present a single attention head in GPT-2 Small that has one main role across the entire training distribution. If components in earlier layers predict a certain token, and this token appears earlier in the context, the head suppresses it: we call this copy suppression. Attention Head 10.7 (L10H7) suppresses naive copying behavior which improves overall mo

Andrew Y. Joe, Kateryna Pistunova, Kristen Kaasbjerg, Ke Wang

Employing flux-grown single crystal WSe$_2$, we report charge carrier scattering behaviors measured in $h$-BN encapsulated monolayer field effect transistors. We perform quantum transport measurements across various hole densities and temperatures and observe a non-monotonic change of transport mobility $\mu$ as a function of hole density in the degenerately

Atsushi Ueshima, Shayegan Omidshafiei, Hirokazu Shirado

Cooperation is challenging in biological systems, human societies, and multi-agent systems in general. While a group can benefit when everyone cooperates, it is tempting for each agent to act selfishly instead. Prior human studies show that people can overcome such social dilemmas while choosing interaction partners, i.e., strategic network rewiring. However

Artem K. Pimachev, Manoj Settipalli, Sanghamitra Neogi

We propose a physics-aware generative adversarial network model, FluxGAN, capable of simultaneously generating high-quality images of large microstructures and description of their thermal properties. During the training phase, the model learns about the relationship between the local structural features and the physical processes, such as the heat flux in t

Fred Hohman, Mary Beth Kery, Donghao Ren, Dominik Moritz

On-device machine learning (ML) promises to improve the privacy, responsiveness, and proliferation of new, intelligent user experiences by moving ML computation onto everyday personal devices. However, today's large ML models must be drastically compressed to run efficiently on-device, a hurtle that requires deep, yet currently niche expertise. To engage the

Evan Sidrow, Nancy Heckman, Alexandre Bouchard-Côté, Sarah M. E. Fortune

Hidden Markov models (HMMs) are popular models to identify a finite number of latent states from sequential data. However, fitting them to large data sets can be computationally demanding because most likelihood maximization techniques require iterating through the entire underlying data set for every parameter update. We propose a novel optimization algorit

Amit Kundu, Debasis Sarkar

Defining nonlocality in a no-input closed quantum network scenario is a new area of interest nowadays. Gisin, in[Entropy 21, 325 (2019)], proposed a possible condition for non-tri-locality of the trivial no-input closed network scenario, triangle network, by introducing a new kind of joint measurement bases and a probability bound. In[npj Quantum Information

Weihang Tan, Yingjie Lao, Keshab K. Parhi

CRYSTAL-Kyber (Kyber) is one of the post-quantum cryptography (PQC) key-encapsulation mechanism (KEM) schemes selected during the standardization process. This paper addresses optimization for Kyber architecture with respect to latency and throughput constraints. Specifically, matrix-vector multiplication and number theoretic transform (NTT)-based polynomial

Alireza Rezazadeh, Athreyi Badithela, Karthik Desingh, Changhyun Choi

Learning multi-object dynamics from visual data using unsupervised techniques is challenging due to the need for robust, object representations that can be learned through robot interactions. This paper presents a novel framework with two new architectures: SlotTransport for discovering object representations from RGB images and SlotGNN for predicting their

Othman Abad, Hassane Zguitti

This paper is a continuation of our paper [Med. J. Math 19, Article number: 31 (2022)] in which we extended the notion of generalized Drazin-Riesz invertible operators to closed operators. We establish here, results relating the notion of closed generalized Drazin-Riesz invertibility with the theory of $C_{0}$-semigroups. Firstly, we generalize results obtai

Boyang Zhao, Guodong Ren, Hongyan Mei, Vincent C. Wu

Structural disorder has been shown to enhance and modulate magnetic, electrical, dipolar, electrochemical, and mechanical properties of materials. However, the possibility of obtaining novel optical and optoelectronic properties from structural disorder remains an open question. Here, we show unambiguous evidence of disorder in the form of anisotropic, picos

Hanmin Li, Avetik Karagulyan, Peter Richtárik

Matrix-stepsized gradient descent algorithms have been shown to have superior performance in non-convex optimization problems compared to their scalar counterparts. The det-CGD algorithm, as introduced by Li et al. (2023), leverages matrix stepsizes to perform compressed gradient descent for non-convex objectives and matrix-smooth problems in a federated man

Byungkyun Kang, Patrick Semon, Corey Melnick, Mancheon Han

ComDMFT is a parallel computational package designed to study the electronic structure of correlated quantum materials from first principles. Our approach is based on the combination of first-principles methods and dynamical mean field theories. In version 2.0, we implemented fully-diagrammatic GW+EDMFT from first-principles. In this approach, correlated ele

Yu Wang, Tong Zhao, Yuying Zhao, Yunchao Liu

Graph Neural Networks (GNNs) have shown great promise in learning node embeddings for link prediction (LP). While numerous studies aim to improve the overall LP performance of GNNs, none have explored its varying performance across different nodes and its underlying reasons. To this end, we aim to demystify which nodes will perform better from the perspectiv

Ryosuke T. Tominaga, Hidekazu Tanaka

Streaming instability is considered to be one of the dominant processes to promote planetesimal formation by gravitational collapse of dust clumps. The development of streaming instability is expected to form dust clumps in which the local dust density is strongly enhanced and even greater than the Roche density. The resulting clumps can collapse to form pla

Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li

In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors from drug development to renewable energy. To answer this call, we present DeepSpeed4Science initiative (deepspee

Roland Bauerschmidt, Thierry Bodineau, Benoit Dagallier

Glauber dynamics of the Ising model on a random regular graph is known to mix fast below the tree uniqueness threshold and exponentially slowly above it. We show that Kawasaki dynamics of the canonical ferromagnetic Ising model on a random $d$-regular graph mixes fast beyond the tree uniqueness threshold when $d$ is large enough (and conjecture that it mixes

Ricardo Coronado-Leija, Ali Abdollahzadeh, Hong-Hsi Lee, Santiago Coelho

Biophysical modeling of diffusion MRI (dMRI) offers the exciting potential of bridging the gap between the macroscopic MRI resolution and microscopic cellular features, effectively turning the MRI scanner into a noninvasive in vivo microscope. In brain white matter, the Standard Model (SM) interprets the dMRI signal in terms of axon dispersion, intra- and ex

Murali Emani, Sam Foreman, Varuni Sastry, Zhen Xie

Artificial intelligence (AI) methods have become critical in scientific applications to help accelerate scientific discovery. Large language models (LLMs) are being considered as a promising approach to address some of the challenging problems because of their superior generalization capabilities across domains. The effectiveness of the models and the accura

Jianqing Fan, Cheng Gao, Jason M. Klusowski

This paper addresses challenges in robust transfer learning stemming from ambiguity in Bayes classifiers and weak transferable signals between the target and source distribution. We introduce a novel quantity called the ''ambiguity level'' that measures the discrepancy between the target and source regression functions, propose a simple transfer learning pro

Andrew Rosemberg, Mathieu Tanneau, Bruno Fanzeres, Joaquim Garcia

The Optimal Power Flow (OPF) problem is integral to the functioning of power systems, aiming to optimize generation dispatch while adhering to technical and operational constraints. These constraints are far from straightforward; they involve intricate, non-convex considerations related to Alternating Current (AC) power flow, which are essential for the safe

Naren Dhyani, Jianqiao Mo, Minsu Cho, Ameya Joshi

The Vision Transformer (ViT) architecture has emerged as the backbone of choice for state-of-the-art deep models for computer vision applications. However, ViTs are ill-suited for private inference using secure multi-party computation (MPC) protocols, due to the large number of non-polynomial operations (self-attention, feed-forward rectifiers, layer normali

Rodolfo Brandão, David Saintillan, Ehud Yariv

We consider phoretic self-propulsion of a chemically active colloid where solute is consumed at both the colloid boundary and within the bulk solution. Assuming first-order kinetics, the dimensionless transport problem is governed by the surface Damk\"ohler number ${\mathcal{S}}$ and the bulk Damk\"ohler number ${\mathcal B}$. The dimensionless colloid veloc

Giselle Sosa Jones, Catalin Trenchea

We propose and analyze a second-order partitioned time-stepping method for a two-phase flow problem in porous media. The algorithm is based on a refactorization of Cauchy's one-leg $\theta$-method. The main part consists of the implicit backward Euler method on $[t^n, t^{n+\theta}]$, while part two uses a linear extrapolation on $[t^{n+\theta},t^{n+1}]$ to o

Philip A. Bernstein

This article is a summary of eight of Jim Gray's transaction papers. It was written at the invitation of Pat Helland to be a chapter of a forthcoming book in the ACM Turing Award winners' series, "Curiosity, Clarity, and Caring: How Jim Gray's Passion for Learning, Teaching, and People Changed Computing."

Pasquale Bosso, Octavio Obregón, Saeed Rastgoo, Wilfredo Yupanqui

In a previous work we studied the interior of the Schwarzschild black hole implementing an effective minimal length, by applying a modification to the Poisson brackets of the theory. In this work we perform a proper quantization of such a system. Specifically, we quantize the interior of the Schwarzschild black hole in two ways: once by using the standard qu

Nina H. Amini, Maël Bompais, Clément Pellegrini

We show that quantum trajectories become exponentially fast supported by one of their minimal invariant subspaces. Exponential convergence is shown in expectation using Lyapunov techniques. The proof is based on an in-depth study of the identifiability of the probability distributions generated in the different subspaces. We furthermore introduce a feedback

Tamara Cucumides, Daniel Daza, Pablo Barceló, Michael Cochez

The challenge of answering graph queries over incomplete knowledge graphs is gaining significant attention in the machine learning community. Neuro-symbolic models have emerged as a promising approach, combining good performance with high interpretability. These models utilize trained architectures to execute atomic queries and integrate modules that mimic s

Nikola Paunkovic, Marko Vojinovic

We discuss the issues with tentative generalisations of the process matrix formalism from finite-dimensional mechanical systems all the way to quantum field theory. We present a detailed overview of possible open problems that arise when one attempts to move from particle ontology into the realm of field ontology, i.e., when one transitions from mechanics to

Martin Burger, Thomas Schuster, Anne Wald

We consider time-dependent inverse problems in a mathematical setting using Lebesgue-Bochner spaces. Such problems arise when one aims to recover parameters from given observations where the parameters or the data depend on time. There are various important applications being subject of current research that belong to this class of problems. Typically invers

Liang Chen, Sue Ann Campbell

We consider a large network of Izhikevich neurons. Each neuron has a quadratic integrate-and-fire type model with a recovery variable modelling spike frequency adaptation (SFA). We introduce a biologically motivated synaptic current expression and a delay in the synaptic transmission. Following the Ott-Antonsen theory, we reduce the network model to a mean-f

Surjya Ray, Pratik Mehta, Hongen Zhang, Ada Chaman

The precipitous rise and adoption of Large Language Models (LLMs) have shattered expectations with the fastest adoption rate of any consumer-facing technology in history. Healthcare, a field that traditionally uses NLP techniques, was bound to be affected by this meteoric rise. In this paper, we gauge the extent of the impact by evaluating the performance of

Bruno L. Dias, Felipe Avila, Armando Bernui

We investigate the transition scale to homogeneity, $R_H$, using as cosmic tracer the spectroscopic sample of blue galaxies from the Sloan Digital Sky Survey (SDSS). Considering the spatial distribution of the galaxy sample we compute the two point correlation function $\xi(r)$, the scaled counts in spheres $\mathcal{N}(<r)$, and the fractal dimension $\math

Alexis Akira Toda

This paper shows the usefulness of the Perov contraction theorem, which is a generalization of the classical Banach contraction theorem, for solving Markov dynamic programming problems. When the reward function is unbounded, combining an appropriate weighted supremum norm with the Perov contraction theorem yields a unique fixed point of the Bellman operator

Jeremiah Milbauer, Ziqi Ding, Zhijin Wu, Tongshuang Wu

Reading and understanding the stories in the news is increasingly difficult. Reporting on stories evolves rapidly, politicized news venues offer different perspectives (and sometimes different facts), and misinformation is rampant. However, existing solutions merely aggregate an overwhelming amount of information from heterogenous sources, such as different

Elena Dzifcakova, Jaroslav Dudik, Martina Pavelkova, Bozena Solarova

The KAPPA package is designed for calculations of optically thin spectra for the non-Maxwellian \k{appa}-distributions. This paper presents extension of the database to allow calculations of the spectra for extreme values of \k{appa} < 2, which are important for accurate diagnostics of the \k{appa}-distributions in the outer solar atmosphere. In addition, tw

Jacob Sacks, Rwik Rana, Kevin Huang, Alex Spitzer

A major challenge in robotics is to design robust policies which enable complex and agile behaviors in the real world. On one end of the spectrum, we have model-free reinforcement learning (MFRL), which is incredibly flexible and general but often results in brittle policies. In contrast, model predictive control (MPC) continually re-plans at each time step

Elia Anzuoni, Tommaso Gagliardoni

We present Shufflecake, a new plausible deniability design to hide the existence of encrypted data on a storage medium making it very difficult for an adversary to prove the existence of such data. Shufflecake can be considered a ``spiritual successor'' of tools such as TrueCrypt and VeraCrypt, but vastly improved: it works natively on Linux, it supports any

Samantha C. Wu, Janosz W. Dewberry, Jim Fuller

The orbital architectures of short-period exoplanet systems are shaped by tidal dissipation in their host stars. For low-mass M-dwarfs whose dynamical tidal response comprises a dense spectrum of inertial modes at low frequencies, resolving the frequency dependence of tidal dissipation is crucial to capturing the effect of tides on planetary orbits throughou

Jason Parker

It is a classical result of categorical algebra, due to Lawvere and Linton, that finitary varieties of algebras (in the sense of Birkhoff) are dually equivalent to finitary monads on $Set$. Recent work of Ad\'amek, Dost\'al, and Velebil has established that analogous results also hold in certain enriched contexts. Specifically, taking $V$ to be one of the ca

Zuotian Li, Xiang Liu, Zelei Cheng, Yingjie Chen

Randomized controlled trials (RCT) are the gold standards for evaluating the efficacy and safety of therapeutic interventions in human subjects. In addition to the pre-specified endpoints, trial participants' experience reveals the time course of the intervention. Few analytical tools exist to summarize and visualize the individual experience of trial partic

Diego Marcondes, Mariana Feldman, Junior Barrera

There have been attempts to insert mathematical morphology (MM) operators into convolutional neural networks (CNN), and the most successful endeavor to date has been the morphological neural networks (MNN). Although MNN have performed better than CNN in solving some problems, they inherit their black-box nature. Furthermore, in the case of binary images, the

Avinash Deshmukh, Riley A. Stewart, Pinrui Shen, James L. Booth

We present a comprehensive mathematical model and experimental measurements for the evolution of a trapped particle ensemble driven by collisions with a room-temperature background vapor. The model accommodates any trap geometry, confining potential, initial trapped distribution, and other experimental details; it only depends on the the probability distribu

Zhengyi Luo, Jinkun Cao, Josh Merel, Alexander Winkler

We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. Due to the high dimensionality of humanoids and the inherent difficulties in reinforcement learning, prior methods have focused on learning skill embeddings for a narrow range of movement styles (e.g. locomotion, game charac

William Q. Erickson, Markus Hunziker

The first half of this paper is largely expository, wherein we present a systematic combinatorial approach to the theory of polynomial (semi)invariants and multilinear invariants of several vectors and covectors, for the classical groups. This culminates in a graphical description of graded linear bases. By applying well-known results of lattice path combina

David Fellner, Thomas I. Strasser, Wolfgang Kastner, Feizifar Behnam

The changes in the electric energy system toward a sustainable future are inevitable and already on the way today. This often entails a change of paradigm for the electric energy grid, for example, the switch from central to decentralized power generation which also has to provide grid-supporting functionalities. However, due to the scarcity of distributed s

Tao Li, Juan Guevara, Xinhong Xie, Quanyan Zhu

Offline reinforcement learning (RL) suffers from the distribution shift between the offline dataset and the online environment. In multi-agent RL (MARL), this distribution shift may arise from the nonstationary opponents in the online testing who display distinct behaviors from those recorded in the offline dataset. Hence, the key to the broader deployment o

Cong Jiang, Denis Talbot, Sara Carazo, Mireille E Schnitzer

The test-negative design (TND), which is routinely used for monitoring seasonal flu vaccine effectiveness (VE), has recently become integral to COVID-19 vaccine surveillance, notably in Qu\'ebec, Canada. Some studies have addressed the identifiability and estimation of causal parameters under the TND, but efficiency bounds for nonparametric estimators of the

Xinyu Si, Lei Fang

In recent years, research topics concerning active matter have attracted interest from diverse communities. It has been suggested that active matter-as represented by zooplankton-has potential in ocean mixing due to its intrinsic mobility and the sheer amount of biomass. However, prior investigations have predominantly overlooked the influence of external ba

Yuri Matsumura, Suguru Otani

We theoretically prove why statistically rejecting the null hypothesis of perfect competition is challenging, known as a common problem in the literature. We also assess the finite sample performance of the conduct parameter test in homogeneous goods markets, showing that statistical power increases with the number of markets, a larger conduct parameter, and