October 2023 arXiv papers — page 176

Jeongbhin Seo, Dongsu Ryu, Hyesung Kang

Nearby radio galaxies (RGs) of Fanaroff-Riley Class I (FR-I) are considered possible sites for the production of observed ultra-high-energy cosmic rays (UHECRs). Among those, some exhibit blazar-like inner jets, while others display plume-like structures. We reproduce the flow dynamics of FR-I jets using relativistic hydrodynamic simulations. Subsequently, w

Leigh Foster, Benjamin Young

A plane partition, whose 3D Young diagram is made of unit cubes, can be approximated by a ``coarser" plane partition, made of cubes of side length 2. Indeed, there are two such approximations obtained by ``rounding up" or ``rounding down" to the nearest cube. We relate this coarsening (or downsampling) operation to the squish map introduced by the second aut

Juliane Dannberg, Rene Gassmoeller, Daniele Thallner, Frederick LaCombe

The Earth's magnetic field is generated by a dynamo in the outer core and is crucial for shielding our planet from harmful radiation. Despite the established importance of the core-mantle boundary heat flux as driver for the dynamo, open questions remain about how heat flux heterogeneities affect the magnetic field. Here, we explore the distribution of core-

Su Jiang, Louis J. Durlofsky

History matching based on monitoring data will enable uncertainty reduction, and thus improved aquifer management, in industrial-scale carbon storage operations. In traditional model-based data assimilation, geomodel parameters are modified to force agreement between flow simulation results and observations. In data-space inversion (DSI), history-matched qua

Otte Heinävaara

Given two Hermitian matrices, $A$ and $B$, we introduce a new type of spectral measure, a $\textit{tracial joint spectral measure}$ $\mu_{A, B}$ on the plane. Existence of this measure implies the following two results: 1) any two-dimensional subspace of the Schatten-$p$ class is isometric to a subspace of $L_{p}$, and 2) if $f : \mathbb{R} \to \mathbb{R}$ h

Xinyu Si, Lei Fang

It has been proposed that biologically generated turbulence plays an important role in material transport and ocean mixing. Both experimental and numerical studies have reported evidence of the non-negligible mixing by moderate Reynolds number swimmers in quiescent water, such as zooplankton, especially at aggregation scales. However, the interaction between

Akifumi Wachi, Wataru Hashimoto, Xun Shen, Kazumune Hashimoto

Safe exploration is essential for the practical use of reinforcement learning (RL) in many real-world scenarios. In this paper, we present a generalized safe exploration (GSE) problem as a unified formulation of common safe exploration problems. We then propose a solution of the GSE problem in the form of a meta-algorithm for safe exploration, MASE, which co

Arian Eamaz, Farhang Yeganegi, Mojtaba Soltanalian

We explore the impact of coarse quantization on matrix completion in the extreme scenario of dithered one-bit sensing, where the matrix entries are compared with time-varying threshold levels. In particular, instead of observing a subset of high-resolution entries of a low-rank matrix, we have access to a small number of one-bit samples, generated as a resul

Tony Shen, Seonghwan Seo, Grayson Lee, Mohit Pandey

Searching the vast chemical space for drug-like molecules that bind with a protein pocket is a challenging task in drug discovery. Recently, structure-based generative models have been introduced which promise to be more efficient by learning to generate molecules for any given protein structure. However, since they learn the distribution of a limited protei

Alan Frieze, Wesley Pegden

The greedy and nearest-neighbor TSP heuristics can both have $\log n$ approximation factors from optimal in worst case, even just for $n$ points in Euclidean space. In this note, we show that this approximation factor is only realized when the optimal tour is unusually short. In particular, for points from any fixed $d$-Ahlfor's regular metric space (which i

Yijia Xiao, Dylan Steinecke, Alexander Russell Pelletier, Yushi Bai

Knowledge graphs (KGs) have emerged as a powerful framework for representing and integrating complex biomedical information. However, assembling KGs from diverse sources remains a significant challenge in several aspects, including entity alignment, scalability, and the need for continuous updates to keep pace with scientific advancements. Moreover, the repr

Mitchell L. Krock, Julie Bessac, Michael L. Stein

Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., environmental or financial applications). Neural networks learn complicated nonlinear relationships from large datasets under limited parametric assumptions. By definition, the number o

Yunhyung Cho, Eunjeong Lee, Mikiya Masuda, Seonjeong Park

The $c_1$-cohomological rigidity conjecture states that two smooth toric Fano varieties are isomorphic as varieties if there is a $c_1$-preserving isomorphism between their integral cohomology rings. In this paper, we confirm the conjecture for smooth toric Fano varieties of Picard number two.

Peiyu Yu, Yaxuan Zhu, Sirui Xie, Xiaojian Ma

Latent space Energy-Based Models (EBMs), also known as energy-based priors, have drawn growing interests in the field of generative modeling due to its flexibility in the formulation and strong modeling power of the latent space. However, the common practice of learning latent space EBMs with non-convergent short-run MCMC for prior and posterior sampling is

Jean-Guillaume Durand, Arthur Dubois, Robert J. Moss

Over the past decade, machine learning has demonstrated impressive results, often surpassing human capabilities in sensing tasks relevant to autonomous flight. Unlike traditional aerospace software, the parameters of machine learning models are not hand-coded nor derived from physics but learned from data. They are automatically adjusted during a training ph

Daniel Duarte, Arturo E. Giles Flores

We begin the study of Lipschitz saturation for germs of toric singularities. By looking at their associated analytic algebras, we prove that if (X,0) is a germ of toric singularity with smooth normalization then its Lipschitz saturation is again toric. Finally we show how to calculate the Lipschitz saturation for some families of toric singularities starting

Harold Blas

We study a non-Hermitian (NH) $sl(2)$ affine Toda model coupled to fermions through soliton theory techniques and the realizations of the pseudo-chiral and pseudo-Hermitian symmetries. The interplay of non-Hermiticity, integrability, nonlinearity, and topology significantly influence the formation and behavior of a continuum of bound state modes (CBM) and ex

Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant

Most large language models (LLMs) are trained once and never updated; thus, they lack the ability to dynamically adapt to our ever-changing world. In this work, we perform a detailed study of the factuality of LLM-generated text in the context of answering questions that test current world knowledge. Specifically, we introduce FreshQA, a novel dynamic QA ben

M. H. Hooshmand, M. M. Yousefian Arani

Recently, sub-indices and sub-factors of groups with connections to number theory, additive combinatorics, and factorization of groups have been introduced and studied. Since all group subsets are considered in the theory and there are many basic open problems, conjectures, and questions, their computational aspects are particularly important. In this paper,

N. Aucar Boidi, K. Hallberg, A. Aharony, O. Entin-Wohlman

We study fermions on a finite chain, interacting repulsively when residing on the same and on nearest-neighbor sites, and subjected to a Wannier-Stark linearly-varying potential. Using the density matrix renormalization-group numerical technique to solve this generalized extended Hubbard model, the ground state exhibits a staircase of (quasi) plateaus in the

Martin Ulaga, Jure Kokalj, Alexander Wietek, Andrej Zorko

Motivated by recent experiments on a compound {displaying Ising-like short-range correlations on the triangular lattice, we study the anisotropic easy-axis spin-$1/2$ Heisenberg model on the triangular and kagome lattice} by performing numerical calculations of finite-temperature properties, in particular of static spin structure factor and of thermodynamic

Teeratorn Kadeethum, Daniel O'Malley, Youngsoo Choi, Hari S. Viswanathan

Data-driven modeling can suffer from a constant demand for data, leading to reduced accuracy and impractical for engineering applications due to the high cost and scarcity of information. To address this challenge, we propose a progressive reduced order modeling framework that minimizes data cravings and enhances data-driven modeling's practicality. Our appr

Davis M. Welakuh, Vasil Rokaj, Michael Ruggenthaler, Angel Rubio

In this work we investigate the effects that multi-mode photonic environments, e.g., optical cavities, have on the properties of quantum matter. We highlight the importance of the non-perturbative mass renormalization procedure for ab initio quantum electrodynamics simulations and how it connects to common approximations used in polaritonic chemistry and cav

Samaneh Javadinia, Amirali Baniasadi

Convolutional Neural Networks (CNNs) have produced state-of-the-art results for image classification tasks. However, they are limited in their ability to handle rotational and viewpoint variations due to information loss in max-pooling layers. Capsule Networks (CapsNets) employ a computationally-expensive iterative process referred to as dynamic routing to a

Utsav Garg, Erhan Bas

Instruction-tuned large language models (LLMs) have demonstrated promising zero-shot generalization capabilities across various downstream tasks. Recent research has introduced multimodal capabilities to LLMs by integrating independently pretrained vision encoders through model grafting. These multimodal variants undergo instruction tuning, similar to LLMs,

Erfan Al-Hossami, Razvan Bunescu, Justin Smith, Ryan Teehan

When employing the Socratic method of teaching, instructors guide students toward solving a problem on their own rather than providing the solution directly. While this strategy can substantially improve learning outcomes, it is usually time-consuming and cognitively demanding. Automated Socratic conversational agents can augment human instruction and provid

L. Medel Onofre, A. Martín-Ruiz

The planar Hall effect (PHE), the appearance of an in-plane transverse voltage in the presence of coplanar electric and magnetic fields, has been ascribed to the chiral anomaly and Berry curvature effects in Weyl semimetals. In the presence of position- and time-dependent perturbations, such as strain, Weyl semimetals react as if they would be subjected to e

JohnA. Hodge, Kumar Vijay Mishra, Brian M. Sadler, Amir I. Zaghloul

Higher spectral and energy efficiencies are the envisioned defining characteristics of high data-rate sixth-generation (6G) wireless networks. One of the enabling technologies to meet these requirements is index modulation (IM), which transmits information through permutations of indices of spatial, frequency, or temporal media. In this paper, we propose nov

Ioannis Eleftheriadis

We characterise the slices of the category of graphs that are algebraically universal in terms of the structure of the slicing graph. In particular, we show that algebraic universality is obtained if, and only if, the slicing graph contains one of four fixed graphs as a subgraph.

Ting-Jui Chang, Shahin Shahrampour

This paper addresses the distributed online control problem over a network of linear time-invariant (LTI) systems (with possibly unknown dynamics) in the presence of adversarial perturbations. There exists a global network cost that is characterized by a time-varying convex function, which evolves in an adversarial manner and is sequentially and partially ob

Kim Youwang, Lee Hyun, Kim Sung-Bin, Suekyeong Nam

We propose NeuFace, a 3D face mesh pseudo annotation method on videos via neural re-parameterized optimization. Despite the huge progress in 3D face reconstruction methods, generating reliable 3D face labels for in-the-wild dynamic videos remains challenging. Using NeuFace optimization, we annotate the per-view/-frame accurate and consistent face meshes on l

David R. Miller, Ilaria Caiazzo, Jeremy Heyl, Harvey B. Richer

We searched the Gaia DR3 database for ultramassive white dwarfs with kinematics consistent with having escaped the nearby Hyades open cluster, identifying three such candidates. Two of these candidates have masses estimated from Gaia photometry of approximately 1.1 solar masses; their status as products of single stellar evolution that have escaped the clust

Gaia Colombani, Giulia Bertagnolli, Oriol Artime

The self-avoiding random walk (SARW) is a stochastic process whose state variable avoids returning to previously visited states. This non-Markovian feature has turned SARWs a powerful tool for modelling a plethora of relevant aspects in network science, such as network navigability, robustness and resilience. We analytically characterize self-avoiding random

Qifan Zhang, Xuesong Bai, Xiang Li, Haixin Duan

Domain Name System (DNS) is a critical component of the Internet. DNS resolvers, which act as the cache between DNS clients and DNS nameservers, are the central piece of the DNS infrastructure, essential to the scalability of DNS. However, finding the resolver vulnerabilities is non-trivial, and this problem is not well addressed by the existing tools. To li

Amit Acharya

A methodology for defining variational principles for a class of PDE models from continuum mechanics is demonstrated, and some of its features explored. The scheme is applied to quasi-static and dynamic models of rate-independent and rate-dependent, single crystal plasticity at finite deformation.

Hsiu-Ping Lin, Suman Chauhan, Yougender Chauhan, Nagender Chauhan

This paper uses the dataset of Amazon to predict the books ratings listed on Amazon website. As part of this project, we predicted the ratings of the books, and also built a recommendation cluster. This recommendation cluster provides the recommended books based on the column's values from dataset, for instance, category, description, author, price, reviews

Dmitry Teytelman

This volume contains contributions presented at the 11th Low-Level RF Workshop which was held in Gyeongju, South Korea on October 22-27, 2023. This workshop continued the series of successful international workshops held in Newport News, USA (2001), Geneva, Switzerland (2005), Knoxville, USA (2007), Tsukuba, Japan (2009), Hamburg, Germany (2011), Tahoe City,

Boyang Zhao, Huandong Chen, Ragib Ahsan, Fei Hou

Chalcogenide perovskites, such as BaZrS$_3$, are emerging semiconductors with potential for high photovoltaic power conversion efficiency. The role of defects in the efficiency of the generation and collection of photo-excited carriers has not been experimentally investigated extensively. We study the effect of processing-induced defects on the photoconducti

Koichi Yamawaki

The anomalous dimension $\gamma_m =1$ in the infrared region near conformal edge in the broken phase of the large $N_f$ QCD has been shown by the ladder Schwinger-Dyson equation and also by the lattice simulation for $N_f=8$ for $ N_c=3$. Recently Zwicky claimed another independent argument (without referring to explicit dynamics) for the same result, $\gamm

Thomas C. Paul

The Joint Experiment Missions for Extreme Universe Observatory comprises a collection of complementary missions dedicated to pioneering technologies and techniques for a future space-based multi-messenger observatory which will have sufficient sensitivity and exposure to measure properties of extremely rare ultra-high energy (E>50 EeV) cosmic rays and very h

Maziyar Khadivi, Todd Charter, Marjan Yaghoubi, Masoud Jalayer

Machine scheduling aims to optimize job assignments to machines while adhering to manufacturing rules and job specifications. This optimization leads to reduced operational costs, improved customer demand fulfillment, and enhanced production efficiency. However, machine scheduling remains a challenging combinatorial problem due to its NP-hard nature. Deep Re

Qiang Huo, Rong Yuan

In this paper, we introduce mean dimension and rate distortion dimension for $\mathbb{Z}^{k}$-actions dynamical system $(\mathcal{X},\mathbb{Z}^k,T)$. Suppose $(\mathcal{X},\mathbb{Z}^k,T)$ has the marker property. Taking these two variables, the metric $d$ on $\mathcal{X}$ and $\mathbb{Z}^{k}$-invariant measure $\mu$, into consideration, a minimax-type vari

Hancheng Cao, Jesse Dodge, Kyle Lo, Daniel A. McFarland

In recent years, funding agencies and journals increasingly advocate for open science practices (e.g. data and method sharing) to improve the transparency, access, and reproducibility of science. However, quantifying these practices at scale has proven difficult. In this work, we leverage a large-scale dataset of 1.1M papers from arXiv that are representativ

Rania Abdelghani, Hélène Sauzéon, Pierre-Yves Oudeyer

Generative Artificial Intelligence (GAI) can be seen as a double-edged weapon in education. Indeed, it may provide personalized, interactive and empowering pedagogical sequences that could favor students' intrinsic motivation, active engagement and help them have more control over their learning. But at the same time, other GAI properties such as the lack of

Jeremy Dao, Helei Duan, Alan Fern

In this work we propose a learning-based approach to box loco-manipulation for a humanoid robot. This is a particularly challenging problem due to the need for whole-body coordination in order to lift boxes of varying weight, position, and orientation while maintaining balance. To address this challenge, we present a sim-to-real reinforcement learning approa

Gilles Germain, Yvik Swan

We introduce a version of Stein's method of comparison of operators specifically tailored to the problem of bounding the Wasserstein-1 distance between continuous and discrete distributions on the real line. Our approach rests on a new family of weighted discrete derivative operators, which we call bespoke derivatives. We also propose new bounds on the deriv

Xinyu Zhang, Sujit Ghosh

In the field of global optimization, many existing algorithms face challenges posed by non-convex target functions and high computational complexity or unavailability of gradient information. These limitations, exacerbated by sensitivity to initial conditions, often lead to suboptimal solutions or failed convergence. This is true even for Metaheuristic algor

Jordi Casacuberta, Stefan Hickel, Marios Kotsonis

A novel mechanism is identified, through which a spanwise-invariant surface feature (a two-dimensional forward-facing step) significantly stabilizes the stationary crossflow instability of a three-dimensional boundary layer. The mechanism is termed here as reverse lift-up effect, inasmuch as it acts reversely to the classic lift-up effect; that is, kinetic e

Zhe Zhao, Qingyun Liu, Huan Gui, Bang An

Many recent breakthroughs in machine learning have been enabled by the pre-trained foundation models. By scaling up model parameters, training data, and computation resources, foundation models have significantly advanced the state-of-the-art in many applications. However, it is still an open question of how to use these models to perform downstream tasks ef

Wentao Tang

This paper focuses on the model-free synthesis of state observers for nonlinear autonomous systems without knowing the governing equations. Specifically, the Kazantzis-Kravaris/Luenberger (KKL) observer structure is leveraged, where the outputs are fed into a linear time-invariant (LTI) system to obtain the observer states, which can be viewed as the states

Rajkumar Vasudeva Raju, Zhe Li, Scott Linderman, Xaq Pitkow

Patterns of microcircuitry suggest that the brain has an array of repeated canonical computational units. Yet neural representations are distributed, so the relevant computations may only be related indirectly to single-neuron transformations. It thus remains an open challenge how to define canonical distributed computations. We integrate normative and algor

Long Pham, Barry O'Sullivan, Tai Tan Mai

This study examines the key factors that affect European reactions to artificial intelligence (AI) in the context of both full and flawed democracies in Europe. Analysing a dataset of 4,006 respondents, categorised into full democracies and flawed democracies based on the Democracy Index developed by the Economist Intelligence Unit (EIU), this research ident

Xiaohan Fu, Zihan Wang, Shuheng Li, Rajesh K. Gupta

Large Language Models (LLMs) are being enhanced with the ability to use tools and to process multiple modalities. These new capabilities bring new benefits and also new security risks. In this work, we show that an attacker can use visual adversarial examples to cause attacker-desired tool usage. For example, the attacker could cause a victim LLM to delete c

Zachary Levonian, Chenglu Li, Wangda Zhu, Anoushka Gade

For middle-school math students, interactive question-answering (QA) with tutors is an effective way to learn. The flexibility and emergent capabilities of generative large language models (LLMs) has led to a surge of interest in automating portions of the tutoring process - including interactive QA to support conceptual discussion of mathematical concepts.

Hui Xu, Mircea D. Grigoriu

Finite dimensional (FD) models, i.e., deterministic functions of time/space and finite sets of random variables, are constructed for target vector-valued random processes/fields. They are required to have two properties. First, standard Monte Carlo algorithms can be used to generate their samples, referred to as FD samples. Second, under some conditions spec

An Yan, Yu Wang, Yiwu Zhong, Zexue He

Medical image classification is a critical problem for healthcare, with the potential to alleviate the workload of doctors and facilitate diagnoses of patients. However, two challenges arise when deploying deep learning models to real-world healthcare applications. First, neural models tend to learn spurious correlations instead of desired features, which co

Filippo de Feo, Andrzej Święch, Lukas Wessels

We study optimal control problems governed by abstract infinite dimensional stochastic differential equations using the dynamic programming approach. In the first part, we prove Lipschitz continuity, semiconcavity and semiconvexity of the value function under several sets of assumptions, and thus derive its $C^{1,1}$ regularity in the space variable. Based o

Mikhail Mozikov, Ilya Makarov, Alexandr Bulkin, Daria Taniushkina

In this paper we discuss and address the challenges of predicting extreme atmospheric events like intense rainfall, hail, and strong winds. These events can cause significant damage and have become more frequent due to climate change. Integrating climate projections with machine learning techniques helps improve forecasting accuracy and identify regions wher

Min Dai, Jaemin Lee, Aaron D. Ames

Drawing inspiration from human multi-domain walking, this work presents a novel reduced-order model based framework for realizing multi-domain robotic walking. At the core of our approach is the viewpoint that human walking can be represented by a hybrid dynamical system, with continuous phases that are fully-actuated, under-actuated, and over-actuated and d

Liangqi Yuan, Ziran Wang, Christopher G. Brinton

The Internet of Things (IoT) consistently generates vast amounts of data, sparking increasing concern over the protection of data privacy and the limitation of data misuse. Federated learning (FL) facilitates collaborative capabilities among multiple parties by sharing machine learning (ML) model parameters instead of raw user data, and it has recently gaine

Xingsheng Sun

We employ two different atomistic methods to investigate solute-defect interactions in nanosized palladium-hydrogen (Pd-H) systems across multiple time scales. The first method, referred to as Diffusive Molecular Dynamics (DMD), focuses on capturing hydride phase transformation and the evolution of solute-induced lattice defects over a diffusive time scale.

Iván Díaz, Hana Lee, Emre Kıcıman, Mouna Akacha

Recognizing the importance of real-world data (RWD) for regulatory purposes, the United States (US) Congress passed the 21st Century Cures Act1 mandating the development of Food and Drug Administration (FDA) guidance on regulatory use of real-world evidence. The Forum on the Integration of Observational and Randomized Data (FIORD) conducted a meeting bringin

Md Sadik Awal, Md Tauhidur Rahman

Discovering new vulnerabilities and implementing security and privacy measures are important to protect systems and data against physical attacks. One such vulnerability is impedance, an inherent property of a device that can be exploited to leak information through an unintended side channel, thereby posing significant security and privacy risks. Unlike tra

Mosab Rezaei, Hamed Alhoori, Mona Rahimi

Frequent modifications of unit test cases are inevitable due to software's continuous underlying changes in source code, design, and requirements. Since manually maintaining software test suites is tedious, timely, and costly, automating the process of generation and maintenance of test units will significantly impact the effectiveness and efficiency of soft

Zishun Yu, Yunzhe Tao, Liyu Chen, Tao Sun

Program synthesis aims to create accurate, executable programs from problem specifications, specifically from natural language descriptions in our context. Recent studies have leveraged the power of reinforcement learning (RL) in conjunction with large language models (LLMs), significantly enhancing code generation capabilities. The application of RL focuses

Darren Chiu, Radhika Nagpal, Bahar Haghighat

Robot swarms can be tasked with a variety of automated sensing and inspection applications in aerial, aquatic, and surface environments. In this paper, we study a simplified two-outcome surface inspection task. We task a group of robots to inspect and collectively classify a 2D surface section based on a binary pattern projected on the surface. We use a dece

Alyvia Walters, Tawfiq Ammari, Kiran Garimella, Shagun Jhaver

Visual culture has long been deployed by actors across the political spectrum as tools of political mobilization, and have recently incorporated new communication tools, such as memes, GIFs, and emojis. In this study, we analyze the top-circulated Facebook memes relating to critical race theory (CRT) from May 2021 - May 2022 to investigate their visual and t

Ioannis Charalampidis, Joseph Barker

The stability of magnetic skyrmions has been investigated in the past, but mostly in the absence of thermal fluctuations. However, thermal spin fluctuations modify the magnetic properties (exchange stiffness, Dzyaloshinskii-Moriya interaction (DMI) and anisotropy) that define skyrmion stability. Thermal magnons also excite internal skrymion dynamics, deformi

Denis Khimin, Johannes Lankeit, Marc C. Steinbach, Thomas Wick

The purpose of this work is the formulation of optimality conditions for phase-field optimal control problems. The forward problem is first stated as an abstract nonlinear optimization problem, and then the necessary optimality conditions are derived. The sufficient optimality conditions are also examined. The choice of suitable function spaces to ensure the

Arjun Bagchi, Kedar S. Kolekar, Taniya Mandal, Ashish Shukla

Gubser flow provides an analytic model for describing the spacetime dynamics of the quark-gluon plasma produced in heavy-ion collisions. Along with boost and rotation invariance along the beam axis, the model assumes invariance under a combination of translations and special conformal transformations in the transverse plane, leading to a flow profile which e

Biagio Montaruli, Luca Demetrio, Maura Pintor, Luca Compagna

Machine-learning phishing webpage detectors (ML-PWD) have been shown to suffer from adversarial manipulations of the HTML code of the input webpage. Nevertheless, the attacks recently proposed have demonstrated limited effectiveness due to their lack of optimizing the usage of the adopted manipulations, and they focus solely on specific elements of the HTML

Leonid Berlyand, Etienne Sandier, Yitzchak Shmalo, Lei Zhang

We explore the applications of random matrix theory (RMT) in the training of deep neural networks (DNNs), focusing on layer pruning that is reducing the number of DNN parameters (weights). Our numerical results show that this pruning leads to a drastic reduction of parameters while not reducing the accuracy of DNNs and CNNs. Moreover, pruning the fully conne

Xingche Guo, Bin Yang, Ji Meng Loh, Qinxia Wang

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a potential biomarker for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions.

Junbo Li, Ang Li, Chong Tian, Qirong Ho

Weight decay is a standard technique to improve generalization performance in modern deep neural network optimization, and is also widely adopted in federated learning (FL) to prevent overfitting in local clients. In this paper, we first explore the choices of weight decay and identify that weight decay value appreciably influences the convergence of existin

Hui Zhong, Chenpei Huang, Xinyue Zhang, Miao Pan

The Metaverse is a virtual world, an immersive experience, a new human-computer interaction, built upon various advanced technologies. How to protect Metaverse personal information and virtual properties is also facing new challenges, such as new attacks and new expectations of user experiences. While traditional methods (e.g., those employed in smartphone a

Victor Vadakechirayath George

Inspired by recent developments in attention models for image classification and natural language processing, we present various Attention based architectures in reinforcement learning (RL) domain, capable of performing well on OpenAI Gym Atari-2600 game suite. In spite of the recent success of Deep Reinforcement learning techniques in various fields like ro

Maxim Mazanov, Diego Román-Cortés, Gabriel Cáceres-Aravena, Christofer Cid

The concepts of topology provide a powerful tool to tailor the propagation and localization of light. While electromagnetic waves have only two polarization states, engineered degeneracies of photonic modes provide novel opportunities resembling orbital or spin degrees of freedom in condensed matter. Here, we tailor such degeneracies for the array of femtose

Dimitri Bertsekas

We consider the classical linear assignment problem, and we introduce new auction algorithms for its optimal and suboptimal solution. The algorithms are founded on duality theory, and are related to ideas of competitive bidding by persons for objects and the attendant market equilibrium, which underlie real-life auction processes. We distinguish between two

Jiri Navratil, Benjamin Elder, Matthew Arnold, Soumya Ghosh

Accurate quantification of model uncertainty has long been recognized as a fundamental requirement for trusted AI. In regression tasks, uncertainty is typically quantified using prediction intervals calibrated to an ad-hoc operating point, making evaluation and comparison across different studies relatively difficult. Our work leverages: (1) the concept of o

Christoph F. Strnadl

Ecosystems enjoy increasing attention due to their flexibility and innovative power. It is well known, however, that this type of network-based economic governance structures occupies a potentially unstable position between the two stable (governance) endpoints, namely the firm (i.e., hierarchical governance) and the (open) market (i.e., coordination through

Ziyao Wang, Jianyu Wang, Ang Li

The theoretical landscape of federated learning (FL) undergoes rapid evolution, but its practical application encounters a series of intricate challenges, and hyperparameter optimization is one of these critical challenges. Amongst the diverse adjustments in hyperparameters, the adaptation of the learning rate emerges as a crucial component, holding the prom

Yi Zhu, Evgueni T. Filipov

Existing Civil Engineering structures have limited capability to adapt their configurations for new functions, non-stationary environments, or future reuse. Although origami principles provide capabilities of dense packaging and reconfiguration, existing origami systems have not achieved deployable metre-scale structures that can support large loads. Here, w

CMS Collaboration

The results of a search for stealth supersymmetry in final states with two photons and jets, targeting a phase space region with low missing transverse momentum ($p_\text{T}^\text{miss}$), are reported. The study is based on a sample of proton-proton collisions at $\sqrt{s}$ = 13 TeV collected by the CMS experiment, corresponding to an integrated luminosity

Ivan Losev

The goal of this paper is to relate the quantum category $\mathcal{O}$ (known also as the category of modules over the mixed quantum group) at an odd root of unity to the affine Hecke category. Namely, we prove equivalences of highest weight categories between integral blocks of the affine category $\mathcal{O}$ and the heart of the so called ``new'' t-struc

Zihao Wang, Yongqiang Chen, Yang Duan, Weijiang Li

Machine Learning (ML) techniques have found applications in estimating chemical kinetic properties. With the accumulated drug molecules identified through "AI4drug discovery", the next imperative lies in AI-driven design for high-throughput chemical synthesis processes, with the estimation of properties of unseen reactions with unexplored molecules. To this

Sumit Pandey, Kuan-Fu Chen, Erik B. Dam

This paper introduces a comprehensive approach for segmenting regions of interest (ROI) in diverse medical imaging datasets, encompassing ultrasound, CT scans, and X-ray images. The proposed method harnesses the capabilities of the YOLOv8 model for approximate boundary box detection across modalities, alongside the Segment Anything Model (SAM) and High Quali

Atefeh Khoshkhahtinat, Hoda Mohammadzade

Epilepsy is a neurological disorder identified by sudden and recurrent seizures, which are believed to be accompanied by distinct changes in brain dynamics. Exploring the dynamic changes of brain network states during seizures can pave the way for improving the diagnosis and treatment of patients with epilepsy. In this paper, the connectivity brain network i

Herbert Woisetschläger, Alexander Isenko, Shiqiang Wang, Ruben Mayer

Large Language Models (LLM) and foundation models are popular as they offer new opportunities for individuals and businesses to improve natural language processing, interact with data, and retrieve information faster. However, training or fine-tuning LLMs requires a vast amount of data, which can be challenging to access due to legal or technical restriction

Nicholas Konz, Charles Godfrey, Madelyn Shapiro, Jonathan Tu

By now there is substantial evidence that deep learning models learn certain human-interpretable features as part of their internal representations of data. As having the right (or wrong) concepts is critical to trustworthy machine learning systems, it is natural to ask which inputs from the model's original training set were most important for learning a co

Son Nguyen, Adam Wang, Albert Montillo

Traditional deep learning (DL) models have two ubiquitous limitations. First, they assume training samples are independent and identically distributed (i.i.d), an assumption often violated in real-world datasets where samples have additional correlation due to repeat measurements (e.g., on the same participants in a longitudinal study or cells from the same

M. Yu. Melnikov, A. A. Shashkin, S. -H. Huang, C. W. Liu

We report the observation of two-threshold voltage-current characteristics accompanied by a peak of broadband current noise between the two threshold voltages in the insulating state at low densities in the 2D electron system in ultra-high mobility SiGe/Si/SiGe heterostructures. The observed results can be described by a phenomenological theory of the collec

Shisheng Li, Yung-Chang Lin, Yiling Chiew, Yunyun Dai

Layer number-dependent band structures and symmetry are vital for the electrical and optical characteristics of two-dimensional (2D) transition metal dichalcogenides (TMDCs). Harvesting 2D TMDCs with tunable thickness and properties can be achieved through top-down etching and bottom-up growth strategies. In this study, we report a pioneering technique that

Yong Deng, Min Dong

This paper studies MapReduce-based heterogeneous coded distributed computing (CDC) where, besides different computing capabilities at workers, input files to be accessed by computing jobs have nonuniform popularity. We propose a file placement strategy that can handle an arbitrary number of input files. Furthermore, we design a nested coded shuffling strateg

Ivo Saviane, Irina Yegorova, Dominique Proust

We investigate the mass-metallicity relation for galaxies in the Abell cluster AC114 from 7 hours of VIMOS/MR data collected at the ESO-VLT telescope in 2009. The dynamical analysis completed in our previous paper allowed us to select cluster members, whose spectra are here analyzed with stellar population synthesis models. Active and passive galaxies are id

Bryan Bo Cao, Abrar Alali, Hansi Liu, Nicholas Meegan

Tracking subjects in videos is one of the most widely used functions in camera-based IoT applications such as security surveillance, smart city traffic safety enhancement, vehicle to pedestrian communication and so on. In the computer vision domain, tracking is usually achieved by first detecting subjects with bounding boxes, then associating detected boundi

Moun Meenakshi, Dipanjan Mukherjee, Gianluigi Bodo, Paola Rossi

We investigate the effect of the jet's immediate surroundings on the non-thermal synchrotron emission and its polarization properties. The ambient medium is equipped with a turbulent magnetic field, which is compressed and amplified by the jets as they progress. This leads to high polarization at the forward shock surface. The randomness in the magnetic pola

Erik Schnaubelt, Sina Atalay, Mariusz Wozniak, Julien Dular

For finite element (FE) analysis of no-insulation (NI) high-temperature superconducting (HTS) pancake coils, the high aspect ratio of the turn-to-turn contact layer (T2TCL) leads to meshing difficulties which result in either poor quality mesh elements resulting in a decrease of the solution accuracy or a high number of degrees of freedom. We proposed to mit

Eddie Guo, Christopher Perlette, Mojtaba Sharifi, Lukas Grasse

This study presents a speech-based motion planning strategy (SBMP) developed for lower limb exoskeletons to facilitate safe and compliant human-robot interaction. A speech processing system, finite state machine, and central pattern generator are the building blocks of the proposed strategy for online planning of the exoskeleton's trajectory. According to ex

Salvatore Capozziello, Rocco D'Agostino

We consider non-local modifications of General Relativity given by a distortion function in terms of the inverse of the d'Alembert operator. The inclusion of these terms is motivated by the possibility of reproducing the current accelerated expansion of the Universe starting from non-local gravity. In particular, we propose a model-independent method, based

Keitarou Matsumoto, Satoshi Masuda, Masumi Shimojo, Hisashi Hayakawa

Large solar flares occasionally trigger significant space-weather disturbances that affect the technological infrastructures of modern civilization, and therefore require further investigation. Although these solar flares have been monitored by satellite observations since the 1970s, large solar flares occur only infrequently and restrict systematic statisti