October 2023 arXiv papers — page 21

Qi Zhang, Yifei Wang, Yisen Wang

Existing contrastive learning methods rely on pairwise sample contrast $z_x^\top z_{x'}$ to learn data representations, but the learned features often lack clear interpretability from a human perspective. Theoretically, it lacks feature identifiability and different initialization may lead to totally different features. In this paper, we study a new method n

Yan-Hong Yang, Ying-Lin Liu, Ying-Hui Shao

Drawing inspiration from the significant impact of the ongoing Russia-Ukraine conflict and the recent COVID-19 pandemic on global financial markets, this study conducts a thorough analysis of three key crude oil futures markets: WTI, Brent, and Shanghai (SC). Employing the visibility graph (VG) methodology, we examine both static and dynamic characteristics

James Scott-Brown, Alexis Pister, Benjamin Bach

This paper introduces NetPanorama, a domain-specific language and declarative grammar for interactive network visualization design that supports multivariate, temporal, and geographic networks. NetPanorama allows users to specify network visualizations as combinations of primitives and building blocks. These support network creation and transformation, inclu

Zhe Li, H. Hoe Tan, Chennupati Jagadish, Lan Fu

Single-photon detector (SPD), an essential building block of the quantum communication system, plays a fundamental role in developing next-generation quantum technologies. In this work, we propose an efficient modeling workflow of nanowire SPDs utilizing avalanche breakdown at reverse-biased conditions. The proposed workflow is explored to maximize computati

Dong An, Konstantina Trivisa

High-dimensional fractional reaction-diffusion equations have numerous applications in the fields of biology, chemistry, and physics, and exhibit a range of rich phenomena. While classical algorithms have an exponential complexity in the spatial dimension, a quantum computer can produce a quantum state that encodes the solution with only polynomial complexit

Zhen Qian, Min Chen, Zhuo Sun, Fan Zhang

Understanding urban dynamics and promoting sustainable development requires comprehensive insights about buildings. While geospatial artificial intelligence has advanced the extraction of such details from Earth observational data, existing methods often suffer from computational inefficiencies and inconsistencies when compiling unified building-related data

Zikun Qin, Malay Ghosh

The paper addresses asymptotic estimation of normal means under sparsity. The primary focus is estimation of multivariate normal means where we obtain exact asymptotic minimax error under global-local shrinkage prior. This extends the corresponding univariate work of Ghosh and Chakrabarti (2017). In addition, we obtain similar results for the Dirichlet-Lapla

Shinhoo Kang, Emil M. Constantinescu

Computational advances have fundamentally transformed the landscape of numerical simulations, enabling unprecedented levels of complexity and precision in modeling physical phenomena. While these high-fidelity simulations offer invaluable insights for scientific discovery and problem solving, they impose substantial computational requirements. Consequently,

Y. L. Wang, H. J. Zhao, Y. K. Hsiao

We explore the two-body non-leptonic weak decays of $\Omega_c^0$ into final states ${\bf B}^{(*)}M$ and ${\bf B}^{(*)}V$, where ${\bf B}^{(*)}$ denotes an octet (a decuplet) baryon and $M(V)$ represents a pseudoscalar (vector) meson. We employ the topological $SU(3)_f$ approach to depict and parameterize the $W$-emission and $W$-exchange processes. We find t

Jingzhou Sun, Lehan Wang, Zhaojun Nan, Yuxuan Sun

Intelligent real-time applications, such as video surveillance, demand intensive computation to extract status information from raw sensing data. This poses a substantial challenge in orchestrating computation and communication resources to provide fresh status information. In this paper, we consider a scenario where multiple energy-constrained devices serve

Tianqin Li, Ziqi Wen, Yangfan Li, Tai Sing Lee

Current deep-learning models for object recognition are known to be heavily biased toward texture. In contrast, human visual systems are known to be biased toward shape and structure. What could be the design principles in human visual systems that led to this difference? How could we introduce more shape bias into the deep learning models? In this paper, we

Li Ding, Masrour Zoghi, Guy Tennenholtz, Maryam Karimzadehgan

We introduce EV3, a novel meta-optimization framework designed to efficiently train scalable machine learning models through an intuitive explore-assess-adapt protocol. In each iteration of EV3, we explore various model parameter updates, assess them using pertinent evaluation methods, and then adapt the model based on the optimal updates and previous progre

Viesturs Spūlis, Daniels Gorovojs, Jānis Pudāns, Rolands Lopatko

In this work we show that macroscopic experiments can be used to investigate microscopic systems. Such macroscopic experiments enable testing the assumptions and results obtained by theoretical considerations or simulations that can not be obtained under microscope (e.g., the orientation of a spherical particle). To emulate the dynamics of a single hematite

Luca Crosato, Kai Tian, Hubert P. H Shum, Edmond S. L. Ho

Interaction-aware Autonomous Driving (IAAD) is a rapidly growing field of research that focuses on the development of autonomous vehicles (AVs) that are capable of interacting safely and efficiently with human road users. This is a challenging task, as it requires the autonomous vehicle to be able to understand and predict the behaviour of human road users.

Jiatai Wang, Zhiwei Xu, Xin Wang, Tao Li

Existing multi-stage clustering methods independently learn the salient features from multiple views and then perform the clustering task. Particularly, multi-view clustering (MVC) has attracted a lot of attention in multi-view or multi-modal scenarios. MVC aims at exploring common semantics and pseudo-labels from multiple views and clustering in a self-supe

Zhongyang Gu

The $bmo$ space, also known as the local $BMO$ space, is the $BMO$ space which is uniformly locally $L^1$ in addition. In this article, we establish an extension theorem for the $bmo$ space defined in an arbitrary uniformly $C^2$ domain. This extension theorem results in a product estimate for $bmo$ functions defined in an arbitrary uniformly $C^2$ domain.

Zecheng Zhang, Christian Moya, Lu Lu, Guang Lin

Neural operators have been applied in various scientific fields, such as solving parametric partial differential equations, dynamical systems with control, and inverse problems. However, challenges arise when dealing with input functions that exhibit heterogeneous properties, requiring multiple sensors to handle functions with minimal regularity. To address

Yihong Sun, Bharath Hariharan

Unsupervised monocular depth estimation techniques have demonstrated encouraging results but typically assume that the scene is static. These techniques suffer when trained on dynamical scenes, where apparent object motion can equally be explained by hypothesizing the object's independent motion, or by altering its depth. This ambiguity causes depth estimato

Nikolai V. Ivanov

The first goal of the present paper it to present a simple and elementary proof of the standard Seifert-van Kampen theorem based on ideas of P. Olum. The key tool is the singular cohomology theory with non-abelian coefficients in dimensions 0 and 1. After this we apply non-abelian cohomology to prove Crowell-Fox version of Seifert-van Kampent theorem and its

Tapas Tripura, Souvik Chakraborty

Machine learning has witnessed substantial growth, leading to the development of advanced artificial intelligence models crafted to address a wide range of real-world challenges spanning various domains, such as computer vision, natural language processing, and scientific computing. Nevertheless, the creation of custom models for each new task remains a reso

Teng Xiao, Huaisheng Zhu, Zhengyu Chen, Suhang Wang

Graph Contrastive Learning (GCL) has shown superior performance in representation learning in graph-structured data. Despite their success, most existing GCL methods rely on prefabricated graph augmentation and homophily assumptions. Thus, they fail to generalize well to heterophilic graphs where connected nodes may have different class labels and dissimilar

Hongxu Chen, Jinchi Chen, Ke Wei

In this paper, we consider a distributed stochastic non-convex optimization problem, which is about minimizing a sum of $n$ local cost functions over a network with only zeroth-order information. A novel single-loop Decentralized Zeroth-Order Variance Reduction algorithm, called DZOVR, is proposed, which combines two-point gradient estimation, momentum-based

Changwoo Lee, Hun-Seok Kim

This paper investigates efficient deep neural networks (DNNs) to replace dense unstructured weight matrices with structured ones that possess desired properties. The challenge arises because the optimal weight matrix structure in popular neural network models is obscure in most cases and may vary from layer to layer even in the same network. Prior structured

Hongye Yu, Tzu-Chieh Wei

Current noisy quantum computers have multiple types of errors, which can occur in the state preparation, measurement/readout, and gate operation, as well as intrinsic decoherence and relaxation. Partly motivated by the booming of intermediate-scale quantum processors, measurement and gate errors have been recently extensively studied, and several methods of

Naoto Shiraishi, Hal Tasaki

We show, without relying on any unproven assumptions, that a low-density free fermion chain exhibits thermalization in the following (restricted) sense. We choose the initial state as a pure state drawn randomly from the Hilbert space in which all particles are in half of the chain. This represents a nonequilibrium state such that the half chain containing a

Benjamin D. Woods, Hudaiba Soomro, E. S. Joseph, Collin C. D. Frink

Engineering conduction-band valley couplings is a key challenge for Si-based spin qubits. Recent work has shown that the most reliable method for enhancing valley couplings entails adding Ge concentration oscillations to the quantum well. However, ultrashort oscillation periods are difficult to grow, while long oscillation periods do not provide useful impro

Mohamed Ali Hamza, Yuta Wakasugi, Shuji Yoshikawa

In this article we investigate the asymptotic profile of solutions for the Cauchy problem of the nonlinear damped beam equation with two variable coefficients: \[ \partial_t^2 u + b(t) \partial_t u - a(t) \partial_x^2 u + \partial_x^4 u = \partial_x \left( N(\partial_x u) \right). \] In the authors' previous article [17], the asymptotic profile of solutions

Isaac Slaughter, Craig Greenberg, Reva Schwartz, Aylin Caliskan

Previous work has established that a person's demographics and speech style affect how well speech processing models perform for them. But where does this bias come from? In this work, we present the Speech Embedding Association Test (SpEAT), a method for detecting bias in one type of model used for many speech tasks: pre-trained models. The SpEAT is inspire

Rong Qiu, Qiyu Zeng, Ke Chen, Xiaoxiang Yu

Thermal conductivity $\kappa$ of MgO plays a fundamental role in understanding the thermal evolution and mantle convection in the interior of terrestrial planets. However, previous theoretical calculations deviate from each other and the $\kappa$ of high-pressure B2 phase remains undetermined. Here, by combining molecular dynamics and deep potential trained

Wenzhe Xu, Daniel B. Williamson, Frederic Hourdin, Romain Roehrig

Computer model calibration involves using partial and imperfect observations of the real world to learn which values of a model's input parameters lead to outputs that are consistent with real-world observations. When calibrating models with high-dimensional output (e.g. a spatial field), it is common to represent the output as a linear combination of a smal

Weiyi Xue, Fan Lu, Guang Chen

Outdoor LiDAR point clouds are typically large-scale and complexly distributed. To achieve efficient and accurate registration, emphasizing the similarity among local regions and prioritizing global local-to-local matching is of utmost importance, subsequent to which accuracy can be enhanced through cost-effective fine registration. In this paper, a novel hi

Pintu Debnath, Sayan Goswami

$IP$ sets play fundamental role in arithmetic Ramsey theory. A set is called an additive $IP$ set if it is of the form $FS\left(\langle x_{n}\rangle_{n\in \mathbb{N}}\right)=\left\{ \sum_{t\in H}x_{t}:H\right.$ is a nonempty finite subset of $\left.\mathbb{N}\right\}$, whereas it is called a multiplicative $IP$ set if it is of the form $FP\left(\langle x_{n}

Juan J. Villarreal

We describe the intertwiners between modules of a vertex algebra using the language of lambda bracket. We apply this formalism to obtain some classical results on conformal field theory.

Lei Xu, Xinlei Yi, Guanghui Wen, Yang Shi

In this paper, we study the distributed nonconvex optimization problem, which aims to minimize the average value of the local nonconvex cost functions using local information exchange. To reduce the communication overhead, we introduce three general classes of compressors, i.e., compressors with bounded relative compression error, compressors with globally b

Ely Sandine

Our result is a construction of infinitely many radial self-similar implosion profiles for the gravitational Euler-Poisson system. The problem can be expressed as solving a system of non-autonomous non-linear ODEs. The first rigorous existence result for a non-trivial solution to these ODEs is due to Guo, Had\v{z}i\'c and Jang [Comm. Math. Phys. 386.3 (2021)

Anthony Kling

Let $N\geq3$ and $r\geq1$ be integers and $p\geq2$ be a prime such that $p\nmid N$. One can consider two different integral structures on the space of modular forms over $\mathbb{Q}$, one coming from arithmetic via $q$-expansions, the other coming from geometry via integral models of modular curves. Both structures are stable under the Hecke operators; furth

Shuli Jiang, Pranay Sharma, Gauri Joshi

We study the problem of communication-efficient distributed vector mean estimation, a commonly used subroutine in distributed optimization and Federated Learning (FL). Rand-$k$ sparsification is a commonly used technique to reduce communication cost, where each client sends $k < d$ of its coordinates to the server. However, Rand-$k$ is agnostic to any correl

Rubaba Amyeen

Question generation has numerous applications in the educational context. Question generation can prove helpful for students when reviewing content and testing themselves. Furthermore, a question generation model can aid teachers by lessening the burden of creating assessments and other practice material. This paper aims to find the best method to generate q

M. Assafin

PRAIA - Package for the Reduction of Astronomical Images Automatically - is a suite of astrometric and photometric tasks designed to cope with huge amounts of heterogeneous observations with fast processing, no human intervention, minimum parametrization and yet maximum possible accuracy and precision. It is the main tool used to analyse astronomical observa

Muhammad Umar Farooq, Rehan Ahmad, Thomas Hain

Student-teacher learning or knowledge distillation (KD) has been previously used to address data scarcity issue for training of speech recognition (ASR) systems. However, a limitation of KD training is that the student model classes must be a proper or improper subset of the teacher model classes. It prevents distillation from even acoustically similar langu

Zhanduo Tang, Swagato Mukherjee, Peter Petreczky, Ralf Rapp

We utilize a previously constructed thermodynamic $T$-matrix approach to the quark-gluon plasma (QGP) to calculate Wilson line correlators (WLCs) of a static quark-antiquark pair and apply them to the results from 2+1-flavor lattice-QCD (lQCD) computations with realistic pion mass. The self-consistent $T$-matrix results, which include constraints from the lQ

Manuel Fernandez

In this paper we prove that the $\ell_0$ isoperimetric coefficient for any axis-aligned cubes, $\psi_{\mathcal{C}}$, is $\Theta(n^{-1/2})$ and that the isoperimetric coefficient for any measurable body $K$, $\psi_K$, is of order $O(n^{-1/2})$. As a corollary we deduce that axis-aligned cubes essentially "maximize" the $\ell_0$ isoperimetric coefficient: Ther

Homa Hosseinmardi, Samuel Wolken, David M. Rothschild, Duncan J. Watts

The potential for a large, diverse population to coexist peacefully is thought to depend on the existence of a ``shared reality:'' a public sphere in which participants are exposed to similar facts about similar topics. A generation ago, broadcast television news was widely considered to serve this function; however, since the rise of cable news in the 1990s

Anirudh Srinivasan, Venkata S Govindarajan, Kyle Mahowald

Techniques in causal analysis of language models illuminate how linguistic information is organized in LLMs. We use one such technique, AlterRep, a method of counterfactual probing, to explore the internal structure of multilingual models (mBERT and XLM-R). We train a linear classifier on a binary language identity task, to classify tokens between Language X

Srinivasa Pranav, José M. F. Moura

We present P2PL, a practical multi-device peer-to-peer deep learning algorithm that, unlike the federated learning paradigm, does not require coordination from edge servers or the cloud. This makes P2PL well-suited for the sheer scale of beyond-5G computing environments like smart cities that otherwise create range, latency, bandwidth, and single point of fa

Shu Yu Tew, Mario Boley, Daniel F. Schmidt

We present a novel method for tuning the regularization hyper-parameter, $\lambda$, of a ridge regression that is faster to compute than leave-one-out cross-validation (LOOCV) while yielding estimates of the regression parameters of equal, or particularly in the setting of sparse covariates, superior quality to those obtained by minimising the LOOCV risk. Th

Zhixu Du, Shiyu Li, Yuhao Wu, Xiangyu Jiang

Mixture-of-Experts (MoE) has emerged as a favorable architecture in the era of large models due to its inherent advantage, i.e., enlarging model capacity without incurring notable computational overhead. Yet, the realization of such benefits often results in ineffective GPU memory utilization, as large portions of the model parameters remain dormant during i

Jun Hu, Ibtihal Alanazi, Zhe Wang

Given a gamma population with known shape parameter $\alpha$, we develop a general theory for estimating a function $g(\cdot)$ of the scale parameter $\beta$ with bounded variance. We begin by defining a sequential sampling procedure with $g(\cdot)$ satisfying some desired condition in proposing the stopping rule, and show the procedure enjoys appealing asym

Mordecai Waegell

It has recently been shown that relativistic quantum theory leads to a local interpretation of quantum mechanics wherein the universal wavefunction in configuration space is entirely replaced with an ensemble of local fluid equations in spacetime. For want of a fully relativistic quantum fluid treatment, we develop a model using the nonrelativistic Madelung

Kangdi Yu, Murat C. Sarihan, Jin Ho Kang, Madeline Taylor

The field of superconducting quantum computing, based on Josephson junctions, has recently seen remarkable strides in scaling the number of logical qubits. In particular, the fidelities of one- and two-qubit gates have reached the breakeven point with the novel error mitigation and correction methods. Parallel to these advances is the effort to expand the Hi

Tamara Kucherenko, Martin Schmoll, Christian Wolf

We study ergodic-theoretic properties of coded shift spaces. A coded shift space is defined as a closure of all bi-infinite concatenations of words from a fixed countable generating set. We derive sufficient conditions for the uniqueness of measures of maximal entropy and equilibrium states of Hoelder continuous potentials based on the partition of the coded

Israel Quiros

Understanding of Weyl gauge symmetry is rarely associated with underlying postulates. Here we show that such an omission leads to discrepancies in regard to the reach and consequences of gauge symmetry within gravitational theories. Replacement of the postulates which underlie the conventional approach, by just the opposite assumptions, leads to a radically

Jason Cantarella, Henrik Schumacher

We present the first algorithm for sampling random configurations of closed $n$-gons with any fixed edgelengths $r_1, \dots, r_n$ in any dimension $d$ which is proved to sample correctly from standard probability measures on these spaces. We generate open $n$-gons as weighted sets of edge vectors on the unit sphere and close them by taking a Möbius transform

Rodrigo Braz Teixeira, Giorgio Carugno, Izaak Neri, Pablo Sartori

Biological mixtures, such as the cellular cytoplasm, are composed of a large number of different components. From this heterogeneity, ordered mesoscopic structures emerge, such as liquid phases with controlled composition. These structures compete with each other for the same components. This raises several questions, such as what types of interactions allow

Chase Yakaboski, Gregory Hyde, Clement Nyanhongo, Eugene Santos

AI for Science (AI4Science), particularly in the form of self-driving labs, has the potential to sideline human involvement and hinder scientific discovery within the broader community. While prior research has focused on ensuring the responsible deployment of AI applications, enhancing security, and ensuring interpretability, we also propose that promoting

Rafid Buksh, Samim Ul Islam

While the Standard Model remains the prevailing description of natural phenomena, several observed phenomena continue to elude its explanation. To address these challenges, we investigate the Minimal Left-Right Symmetric Model, an immediate extension of the Standard Model. This model adeptly resolves issues related to parity violation and neutrino mass small

Shentong Mo, Zhun Sun, Chao Li

Data augmentation has become a standard component of vision pre-trained models to capture the invariance between augmented views. In practice, augmentation techniques that mask regions of a sample with zero/mean values or patches from other samples are commonly employed in pre-trained models with self-/semi-/fully-supervised contrastive losses. However, the

Abdelrahman Seleem, André F. R. Guarda, Nuno M. M. Rodrigues, Fernando Pereira

In the current golden age of multimedia, human visualization is no longer the single main target, with the final consumer often being a machine which performs some processing or computer vision tasks. In both cases, deep learning plays a undamental role in extracting features from the multimedia representation data, usually producing a compressed representat

K. G. Zoysa, S. R. Munasinghe

Traffic management is a serious problem in many cities around the world. Even the suburban areas are now experiencing regular traffic congestion. Inappropriate traffic control wastes fuel, time, and the productivity of nations. Though traffic signals are used to improve traffic flow, they often cause problems due to inappropriate or obsolete timing that does

Lu Chen, Rou Jiang, Maochun Zhu

Let $F$ be convex and homogeneous of degree $1$, its polar $F^{o}$ represent a finsler metric on $\mathbb{R}^{n}$, and $\Omega$ be any bounded open set in $\mathbb{R}^{n}$. In this paper, we first construct the theoretical structure of anisotropic harmonic transplantation. Using the anisotropic harmonic transplantation, co-area formula, limiting Sobolev appr

Kiran Lekkala, Chen Liu, Laurent Itti

Sim2Real transfer has gained popularity because it helps transfer from inexpensive simulators to real world. This paper presents a novel system that fuses components in a traditional World Model into a robust system, trained entirely within a simulator, that Zero-Shot transfers to the real world. To facilitate transfer, we use an intermediary representation

Amirhossein Kazerouni, Reza Azad, Alireza Hosseini, Dorit Merhof

Implicit Neural Representations (INRs) have revolutionized signal representation by leveraging neural networks to provide continuous and smooth representations of complex data. However, existing INRs face limitations in capturing fine-grained details, handling noise, and adapting to diverse signal types. To address these challenges, we introduce INCODE, a no

Rita Garcia, Christoph Treude, Andrew Valentine

Collaboration is used in Software Engineering (SE) to develop software. Industry seeks SE graduates with collaboration skills to contribute to productive software development. SE educators can use Collaborative Learning (CL) to help students develop collaboration skills. This paper uses a Systematic Mapping Study (SMS) to examine the application of the CL ed

Mo Tiwari, Ryan Kang, Donghyun Lee, Sebastian Thrun

Clustering is a fundamental task in data science with wide-ranging applications. In $k$-medoids clustering, cluster centers must be actual datapoints and arbitrary distance metrics may be used; these features allow for greater interpretability of the cluster centers and the clustering of exotic objects in $k$-medoids clustering, respectively. $k$-medoids clu

M. Hollenbach, C. Kasper, D. Erb, L. Bischoff

There is an urgent need to increase the global data storage capacity, as current approaches lag behind the exponential growth of data generation driven by the Internet, social media and cloud technologies. In addition to increasing storage density, new solutions should provide long-term data archiving that goes far beyond traditional magnetic memory, optical

Patrick Kim, Stefan Buller, Rory Conlin, William Dorland

We present new stellarator equilibria that have been optimized for reduced turbulent transport using nonlinear gyrokinetic simulations within the optimization loop. The optimization routine involves coupling the pseudo-spectral GPU-native gyrokinetic code GX with the stellarator equilibrium and optimization code DESC. Since using GX allows for fast nonlinear

Shuyao Li, Stephen J. Wright

We consider minimization of a smooth nonconvex function with inexact oracle access to gradient and Hessian (without assuming access to the function value) to achieve approximate second-order optimality. A novel feature of our method is that if an approximate direction of negative curvature is chosen as the step, we choose its sense to be positive or negative

Hai Wang, Xiaoyu Xiang, Yuchen Fan, Jing-Hao Xue

Personalized text-to-image (T2I) synthesis based on diffusion models has attracted significant attention in recent research. However, existing methods primarily concentrate on customizing subjects or styles, neglecting the exploration of global geometry. In this study, we propose an approach that focuses on the customization of 360-degree panoramas, which in

Syed Sarosh Mahdi, Zaib Ullah, Gopi Battineni, Muneer Gohar Babar

Blockchain technology provides a secure and decentralized platform for storing and transferring sensitive medical data, which can be utilized to enable remote medical consultations. This paper proposes a theoretical framework for creating a blockchain-based digital entity to facilitate telemedicine services. The proposed framework utilizes blockchain technol

Graham D. Joe, Cleaven Chia, Benjamin Pingault, Michael Haas

Phonons are envisioned as coherent intermediaries between different types of quantum systems. Engineered nanoscale devices such as optomechanical crystals (OMCs) provide a platform to utilize phonons as quantum information carriers. Here we demonstrate OMCs in diamond designed for strong interactions between phonons and a silicon vacancy (SiV) spin. Using op

Khaled I Alsharif, Alexander H Pesch, Vamsi Borra, Pedro Cortes

This study examines the discharge behaviour of a cylindrical LiFeS2 cell to evaluate the parameters that can be used to predict and estimate the nonlinear dynamic response of a battery. A linear model is developed to simulate the discharge behaviour and examine the thermal behaviour. In particular, a commercial-grade battery is discharged with the industry-s

Michael C. Sitarz, Mikhail V. Medvedev, Alex L. Ford

An electron-positron cascade in the magnetospheres of Kerr Black Holes (BH) is a fundamental ingredient to fueling the relativistic $\gamma$-ray jets seen at the polar regions of galactic supermassive BHs (SMBH). This leptonic cascade occurs in the "spark gap" region of a BH magnetosphere where the unscreen electric field parallel to the magnetic field is pr

Michael P. Leung

The literature on cluster-randomized trials typically allows for interference within but not across clusters. This may be implausible when units are irregularly distributed across space without well-separated communities, as clusters in such cases may not align with significant geographic, social, or economic divisions. This paper develops methods for reduci

Fulin Guo

This paper studies the action dynamics of network coordination games with bounded-rational agents. I apply the experience-weighted attraction (EWA) model to the analysis as the EWA model has several free parameters that can capture different aspects of agents' behavioural features. I show that the set of possible long-term action patterns can be largely diff

Domenico Cotroneo, Alessio Foggia, Cristina Improta, Pietro Liguori

Evaluating the correctness of code generated by AI is a challenging open problem. In this paper, we propose a fully automated method, named ACCA, to evaluate the correctness of AI-generated code for security purposes. The method uses symbolic execution to assess whether the AI-generated code behaves as a reference implementation. We use ACCA to assess four s

Hamed Alemansour, S. O. Reza Moheimani

The invention of scanning tunneling microscope (STM) dates back to the work of Binnig and Rohrer in the early 1980s, whose seminal contribution was rewarded by the 1986 Nobel Prize in Physics for the design of the scanning tunneling microscope. Forty years later, the STM remains the best existing tool for studying electronic, chemical, and physical propertie

Yash Gupta, Runtian Zhai, Arun Suggala, Pradeep Ravikumar

Several recent works have studied the societal effects of AI; these include issues such as fairness, robustness, and safety. In many of these objectives, a learner seeks to minimize its worst-case loss over a set of predefined distributions (known as uncertainty sets), with usual examples being perturbed versions of the empirical distribution. In other words

Tomáš Dvořák, Mei-Mei Gu

The burnt pancake graph $BP_n$ is the Cayley graph of the hyperoctahedral group using prefix reversals as generators. Let $\{u,v\}$ and $\{x,y\}$ be any two pairs of distinct vertices of $BP_n$ for $n\geq 4$. We show that there are $u-v$ and $x-y$ paths whose vertices partition the vertex set of $BP_n$ even if $BP_n$ has up to $n-4$ faulty elements. On the o

Rricha Jalota, Koel Dutta Chowdhury, Cristina España-Bonet, Josef van Genabith

Translated texts exhibit systematic linguistic differences compared to original texts in the same language, and these differences are referred to as translationese. Translationese has effects on various cross-lingual natural language processing tasks, potentially leading to biased results. In this paper, we explore a novel approach to reduce translationese i

Boris Kopeliovich, Irina Potashnikova, Ivan Schmidt

The observed enhancement of low-kT photons in comparison with incorrect calculations, should not be treated as a puzzle. The paper by Low considered a large rapidity gap process of diffractive excitation of a hadron, h -> h+\gamma, rather than multiple hadron production spanning all over the rapidity interval between colliding hadrons. The optical theorem co

Sotatsu Otabe, Wataru Usukura, Kaido Suzuki, Kentaro Komori

We propose and experimentally demonstrate the generation of enhanced optical springs using the optical Kerr effect. A nonlinear optical crystal is inserted into a Fabry-Perot cavity with a movable mirror, and a chain of second-order nonlinear optical effects in the phase-mismatched condition induces the Kerr effect. The optical spring constant is enhanced by

Yujian Liu, Xinliang Frederick Zhang, Kaijian Zou, Ruihong Huang

Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets. But while much attention has been devoted to media bias via overt ideological language or topic selection, a more unobtrusive way in which the media shape opinion is via the strategic inclusion or omissio

Nerijus Sidorovas, Dmitri Tseluiko, Wooyoung Choi, Karima Khusnutdinova

We consider the outward-propagating nonlinear concentric water waves within the scope of the 2D Boussinesq system. The problem is axisymmetric, and we derive the slow radius versions of the cylindrical Korteweg - de Vries (cKdV) and extended cKdV (ecKdV) models. Numerical runs are initially performed using the full axisymmetric Boussinesq system. At some dis

Daniel Ortiz-Arroyo

During the last decades, a myriad of fuzzy time series models have been proposed in scientific literature. Among the most accurate models found in fuzzy time series, the high-order ones are the most accurate. The research described in this paper tackles three potential limitations associated with the application of high-order fuzzy time series models. To beg

Daniel Robert-Nicoud, Andreas Krause, Viacheslav Borovitskiy

Various applications ranging from robotics to climate science require modeling signals on non-Euclidean domains, such as the sphere. Gaussian process models on manifolds have recently been proposed for such tasks, in particular when uncertainty quantification is needed. In the manifold setting, vector-valued signals can behave very differently from scalar-va

Yogesh Sapkota, Suman Devkota, Vamsi Borra, Pedro Cortes

As the demands for electric vehicles, electric aircrafts, unmanned aircraft systems, and other motor-driven systems increase, high-performance motor drives employing variable frequency control with higher efficiency and reliability are becoming increasingly important parts of the ever-changing technological landscape. This study proposes a Field Programmable

Chao Jiang, Bo Hui, Bohan Liu, Da Yan

Despite the success of diffusion models, the training and inference of diffusion models are notoriously expensive due to the long chain of the reverse process. In parallel, the Lottery Ticket Hypothesis (LTH) claims that there exists winning tickets (i.e., aproperly pruned sub-network together with original weight initialization) that can achieve performance

D. G. Pasternak, A. M. Romshin, R. H. Bagramov, A. I. Galimov

In fluorescence spectra of nanodiamonds (NDs) synthesized at high pressure from adamantane and other organic compounds, very narrow (~1 nm) lines of unknown origin are observed in a wide spectroscopic range from ~500 to 800 nm. Here, we propose and experimentally substantiate the hypothesis that these mysterious lines arise from radiative recombination of do

Éva Rácz, László Ruppert, Radim Filip

Current advances in nonlinear optics have made it possible to perform a homodyne-like tomography of an unknown state without highly efficient detectors or a strong local oscillator. Thereby, a new experimental direction has been opened into multimode and large-bandwidth quantum optics. An optical parametric amplifier (OPA) allows us to reconstruct the quadra

Subhash Lakshminarayana, Carsten Maple, Andrew Larkins, Daryl Flack

This article focuses on cyber security threats from IoT-enabled energy smart appliances (ESAs) such as smart heat pumps, electric vehicle chargers, etc., to power grid operations. It presents an in-depth analysis of the demand side threats, including (i) an overview of the vulnerabilities in ESAs and the wider risk from the demand-side response (DSR) ecosyst

Siu A. Chin

By use of a special wave function derived from similarly transformed propagators, this work shows that the energy of a thousand spin-balanced fermions in a three-dimensional harmonic potential can be accurately computed using the Monte Carlo method.

Omkar Bait, Sanchayeeta Borthakur, Daniel Schaerer, Emmanuel Momjian

Sources that leak Lyman-continuum (LyC) photons and lead to the reionisation of the universe are intensely studied using multiple observing facilities. Recently, the Low-redshift LyC Survey (LzLCS) has found the first large sample of LyC emitting galaxies at low redshift ($z\sim 0.3$) with the Hubble Space Telescope/Cosmic Origins Spectrograph. The LzLCS sam

Wenxuan Bao, Tianxin Wei, Haohan Wang, Jingrui He

Personalized federated learning algorithms have shown promising results in adapting models to various distribution shifts. However, most of these methods require labeled data on testing clients for personalization, which is usually unavailable in real-world scenarios. In this paper, we introduce a novel setting called test-time personalized federated learnin

Pramit Saha, Divyanshu Mishra, J. Alison Noble

The most challenging, yet practical, setting of semi-supervised federated learning (SSFL) is where a few clients have fully labeled data whereas the other clients have fully unlabeled data. This is particularly common in healthcare settings where collaborating partners (typically hospitals) may have images but not annotations. The bottleneck in this setting

Yan Wang, Huaiqing Wu, Dan Nettleton

We establish stability of random forests under the mild condition that the squared response ($Y^2$) does not have a heavy tail. In particular, our analysis holds for the practical version of random forests that is implemented in popular packages like \texttt{randomForest} in \texttt{R}. Empirical results show that stability may persist even beyond our assump

Qidong Su, Christina Giannoula, Gennady Pekhimenko

Large Language Models (LLMs) like GPT are state-of-the-art text generation models that provide significant assistance in daily routines. However, LLM execution is inherently sequential, since they only produce one token at a time, thus incurring low hardware utilization on modern GPUs. Batching and speculative decoding are two techniques to improve GPU hardw

Jennifer Crawford, Haoli Yin, Luke McDermott, Daniel Cummings

Multimodal Re-Identification (ReID) is a popular retrieval task that aims to re-identify objects across diverse data streams, prompting many researchers to integrate multiple modalities into a unified representation. While such fusion promises a holistic view, our investigations shed light on potential pitfalls. We uncover that prevailing late-fusion techniq

Ammar N. Abbas, Georgios C. Chasparis, John D. Kelleher

The difficulty of identifying the physical model of complex systems has led to exploring methods that do not rely on such complex modeling of the systems. Deep reinforcement learning has been the pioneer for solving this problem without the need for relying on the physical model of complex systems by just interacting with it. However, it uses a black-box lea

M. Abu-Shady, R. R. Luz, G. X. A. Petronilo, A. E. Santana

Using the formalism of generalized fractional derivatives, a two-dimensional non-relativistic meson system is studied. The mesons are interacting by a Cornell potential. The system is formulated in the domain of the symplectic quantum mechanics by means of the generalized fractional Nikiforov-Uvarov method. The corresponding Wigner function and the energy ei

Siu A. Chin

The direct integration of the harmonic oscillator path integral obscures the fundamental structure of its discrete, imaginary time propagator (density matrix). This work, by first proving an operator identity for contracting two free propagators into one in the presence of interaction, derives the discrete propagator by simple algebra without doing any integ