October 2023 arXiv papers — page 71

Jiale Tao, Shuhang Gu, Wen Li, Lixin Duan

Unsupervised face animation aims to generate a human face video based on the appearance of a source image, mimicking the motion from a driving video. Existing methods typically adopted a prior-based motion model (e.g., the local affine motion model or the local thin-plate-spline motion model). While it is able to capture the coarse facial motion, artifacts c

Yuteng Zhang, Yongchang Hui, Junrong Song, Shurong Zheng

Large-scale matrix data has been widely discovered and continuously studied in various fields recently. Considering the multi-level factor structure and utilizing the matrix structure, we propose a multilevel matrix factor model with both global and local factors. The global factors can affect all matrix times series, whereas the local factors are only allow

Stuart A. Newman

The applicability of computational and dynamical systems models to organisms is scrutinized, using examples from developmental biology and cognition. Developmental morphogenesis is dependent on the inherent material properties of developing tissues, a non-computational modality, but cell differentiation, which utilizes chromatin-based revisable memory banks

Yi-Xiang Wang, Fuxiang Li

Recent experiments performed the nonreciprocal magneotransport in ZrTe$_5$ and obtained a giant magnetochiral anisotropy (MCA) coefficient $\gamma'$. The existing theoretical analysis was based on the semiclassical Boltzmann equation. In this paper, we develop a full quantum theory to calculate $\gamma'$ and further explore the underlying physics. We reveal

Dhwanit Agarwal, George Biros

In this paper, we present a novel numerical scheme for simulating deformable and extensible capsules suspended in a Stokesian fluid. The main feature of our scheme is a partition-of-unity (POU) based representation of the surface that enables asymptotically faster computations compared to spherical-harmonics based representations. We use a boundary integral

Eric A. Bai, Madeleine Beckner, Botao Ju, Jerome P. Reiter

Many population surveys do not provide information on respondents' residential addresses, instead offering coarse geographies like zip code or higher aggregations. However, fine resolution geography can be beneficial for characterizing neighborhoods, especially for relatively rare populations such as immigrants. One way to obtain such information is to link

Junwei You, Ying Chen, Zhuoyu Jiang, Zhangchi Liu

Effective classification of autonomous vehicle (AV) driving behavior emerges as a critical area for diagnosing AV operation faults, enhancing autonomous driving algorithms, and reducing accident rates. This paper presents the Gramian Angular Field Vision Transformer (GAF-ViT) model, designed to analyze AV driving behavior. The proposed GAF-ViT model consists

Bobo Hua, Genggeng Huang, Jiaxuan Wang

We prove the existence of topological solutions to the self-dual Chern-Simons model and the Abelian Higgs system on the lattice graphs Z^n for n>1. This extends the results in Huang, Lin and Yau [HLY20] from finite graphs to lattice graphs.

Kenta Hashizume

Let $(X,\Delta)$ be a normal pair with a projective morphism $X \to Z$ and let $A$ be a relatively ample $\mathbb{R}$-divisor on $X$. We prove the termination of some minimal model program on $(X,\Delta+A)/Z$ and the abundance conjecture for its minimal model under assumptions that the non-nef locus of $K_{X}+\Delta+A$ over $Z$ does not intersect the non-lc

Kaizhi Wang, Jun Yan, Kai Zhao

This paper concerns with the time periodic viscosity solution problem for a class of evolutionary contact Hamilton-Jacobi equations with time independent Hamiltonians on the torus $\mathbb{T}^n$. Under certain suitable assumptions we show that the equation has a non-trivial $T$-periodic viscosity solution if and only if $T\in D$, where $D$ is a dense subset

Gaurab Rimal, Ryan B. Comes

Molecular beam epitaxy (MBE), a workhorse of the semiconductor industry, has progressed rapidly in the last few decades in the development of novel materials. Recent developments in condensed matter and materials physics have seen the rise of many novel quantum materials that require ultra-clean and high-quality samples for fundamental studies and applicatio

Carmel Fiscko, Aayushya Agarwal, Yihan Ruan, Soummya Kar

We present a stochastic first-order optimization method specialized for deep neural networks (DNNs), ECCO-DNN. This method models the optimization variable trajectory as a dynamical system and develops a discretization algorithm that adaptively selects step sizes based on the trajectory's shape. This provides two key insights: designing the dynamical system

Hamid Bateni, Keyvan Kambakhsh

The Private Proof of Solvency is a groundbreaking solution in the realm of Proof of Solvency, offering a secure, efficient, and privacy-preserving method for crypto custody providers such as centralized cryptocurrency exchanges or enterprise custody providers. By leveraging the inherent state concept of every blockchain and pioneering cryptographic technique

Kun Song, Wenhang Liu, Gaoming Chen, Xiang Xu

Topological maps are favorable for their small storage compared to geometric map. However, they are limited in relocalization and path planning capabilities. To solve this problem, a feature-based hierarchical topological map (FHT-Map) is proposed along with a real-time map construction algorithm for robot exploration. Specifically, the FHT-Map utilizes both

Andy Lin, Cameron Torres, Errett C. Hobbs, Jaydeep Bardhan

Enumerated threat agent lists have long driven biodefense priorities. The global SARS-CoV-2 pandemic demonstrated the limitations of searching for known threat agents as compared to a more agnostic approach. Recent technological advances are enabling agent-agnostic biodefense, especially through the integration of multi-modal observations of host-pathogen in

Andy Yang, David Chiang, Dana Angluin

The expressive power of transformers over inputs of unbounded size can be studied through their ability to recognize classes of formal languages. In this paper, we establish exact characterizations of transformers with hard attention (in which all attention is focused on exactly one position) and attention masking (in which each position only attends to posi

Eason Chen, Ray Huang, Justa Liang, Damien Chen

This paper presents the latest progress of GPTutor: a ChatGPT-powered programming tool extension in Visual Studio Code. The emergence of Large Language Models (LLMs) has improved software development efficiency, but their performance can be hindered by training data limitations and prompt design issues. Existing LLM development tools often operate as black b

Seonglae Cho, Yonggi Cho, HoonJae Lee, Myungha Jang

In this paper, we present RTSUM, an unsupervised summarization framework that utilizes relation triples as the basic unit for summarization. Given an input document, RTSUM first selects salient relation triples via multi-level salience scoring and then generates a concise summary from the selected relation triples by using a text-to-text language model. On t

Mashari Alatawi, Nitesh Saxena

The authentication ceremony plays a crucial role in verifying the identities of users before exchanging messages in end-to-end encryption (E2EE) applications, thus preventing impersonation and man-in-the-middle (MitM) attacks. Once authenticated, the subsequent communications in E2EE apps benefit from the protection provided by the authentication ceremony. H

Erfan Darzi, Florian Dubost, Nanna. M. Sijtsema, P. M. A van Ooijen

In this paper, we delve into the susceptibility of federated medical image analysis systems to adversarial attacks. Our analysis uncovers a novel exploitation avenue: using gradient information from prior global model updates, adversaries can enhance the efficiency and transferability of their attacks. Specifically, we demonstrate that single-step attacks (e

Mengyue Yang, Xinyu Cai, Furui Liu, Weinan Zhang

Learning representations purely from observations concerns the problem of learning a low-dimensional, compact representation which is beneficial to prediction models. Under the hypothesis that the intrinsic latent factors follow some casual generative models, we argue that by learning a causal representation, which is the minimal sufficient causes of the who

Stefan Knirck, Gabe Hoshino, Mohamed H. Awida, Gustavo I. Cancelo

We present first results from a dark photon dark matter search in the mass range from 44 to 52 $\mu{\rm eV}$ ($10.7 - 12.5\,{\rm GHz}$) using a room-temperature dish antenna setup called GigaBREAD. Dark photon dark matter converts to ordinary photons on a cylindrical metallic emission surface with area $0.5\,{\rm m}^2$ and is focused by a novel parabolic ref

Dylan Warman, Muhammad Ashad Kabir

Fake news has emerged as a critical global issue, magnified by the COVID-19 pandemic, underscoring the need for effective preventive tools. Leveraging machine learning, including deep learning techniques, offers promise in combatting fake news. This paper goes beyond by establishing BERT as the superior model for fake news detection and demonstrates its util

Maximilian Stölzle, Daniela Rus, Cosimo Della Santina

Parallel robots based on Handed Shearing Auxetics (HSAs) can implement complex motions using standard electric motors while maintaining the complete softness of the structure, thanks to specifically designed architected metamaterials. However, their control is especially challenging due to varying and coupled stiffness, shearing, non-affine terms in the actu

Liyuan Wang, Jingyi Xie, Xingxing Zhang, Hang Su

In this work, we present a general framework for continual learning of sequentially arrived tasks with the use of pre-training, which has emerged as a promising direction for artificial intelligence systems to accommodate real-world dynamics. From a theoretical perspective, we decompose its objective into three hierarchical components, including within-task

Raul E. Curto, Hamza El Azhar, Youssef Omari, El Hassan Zerouali

For recursively generated shifts, we provide definitive answers to two outstanding problems in the theory of unilateral weighted shifts: the Subnormality Problem ({\bf SP}) (related to the Aluthge transform) and the Square Root Problem ({\bf SRP}) (which deals with Berger measures of subnormal shifts). We use the Mellin Transform and the theory of exponentia

Mohammad Al-Jarrah, Niyizhen Jin, Bamdad Hosseini, Amirhossein Taghvaei

This paper is concerned with the problem of nonlinear filtering, i.e., computing the conditional distribution of the state of a stochastic dynamical system given a history of noisy partial observations. Conventional sequential importance resampling (SIR) particle filters suffer from fundamental limitations, in scenarios involving degenerate likelihoods or hi

W. Arendt, A. F. M. ter Elst, M. Sauter

Nittka gave an efficient criterion on a form defined on $L_2(\Omega)$ which implies that the associated semigroup is $L_p$-invariant for some given $p \in (1,\infty)$. We extend this criterion to the Hilbert space valued~$L_2(\Omega,H)$. As an application we consider elliptic systems of pure second order. Our main result shows that the induced semigroup is $

Aida Abiad, Bryan A. Curtis, Mary Flagg, H. Tracy Hall

The inverse eigenvalue problem studies the possible spectra among matrices whose off-diagonal entries have their zero-nonzero patterns described by the adjacency of a graph $G$. In this paper, we refer to the $i$-nullity pair of a matrix $A$ as $(\operatorname{null}(A), \operatorname{null}(A(i))$, where $A(i)$ is the matrix obtained from $A$ by removing the

Qiuhao Hu, Mohammad Reza Amini, Ashley Wiese, Ilya Kolmanovsky

This paper explores the synergies between integrated power and thermal management (iPTM) and battery charging in an electric vehicle (EV). A multi-objective model predictive control (MPC) framework is developed to optimize the fast charging performance while enforcing the constraints in the power and thermal loops. The approach takes into account the couplin

Di Guo, Runmin Xu, Jinyu Wu, Meijin Lin

Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering. However, NMR signals are easily contaminated by noise during the data acquisition, which can affect subsequent quantitative analysis. Therefore, denoising NMR signals has been a long-time concern. In this work, we propose an optimiza

Masahito Hayashi, Yanling Chen

This paper studies the secrecy results for the two-way wiretap channel (TW-WC) with an external eavesdropper under a strong secrecy metric. Employing non-adaptive coding, we analyze the information leakage and the decoding error probability, and derive inner bounds on the secrecy capacity regions for the TW-WC under strong joint and individual secrecy constr

M Krithika, P Vanchinathan

For a field $K$, and a root $\alpha$ of an irreducible polynomial over $K$ (in some algebraic closure) the number of roots of $f(x)$ lying in $K(\alpha)$ is studied here. Given such an $f(x)$ of degree $n$ for which $r$ of the roots are i n $K(\alpha)$, we describe a construction that yields, for $d\ge2$, irreducible polynomials of degree $nd$ and with exact

Lavinia Corina Ciungu

Starting from involutive BE algebras, we redefine the orthomodular algebras, by introducing the notion of implicative-orthomodular algebras. We investigate properties of implicative-orthomodular algebras, and give characterizations of these algebras. Then we define and study the notions of filters and deductive systems, and characterize certain classes of fi

Ashwith Prabhu, Jennifer Parra-Contreras, Elizabeth A. Goldschmidt, Kanu Sinha

We present a framework for quantization of electromagnetic field in the presence of dielectric media with time-varying optical properties. Considering a microscopic model for the dielectric as a collection of matter fields interacting with the electromagnetic environment, we allow for the possibility of dynamically varying light-matter coupling. We obtain th

Wilmar Cardona, Jose L. Palacios-Córdoba, César A. Valenzuela-Toledo

Since current challenges faced by $\Lambda$CDM might be hinting at new unravelled physics, here we investigate a plausible cosmological model where a vector field acts as source of dark energy. In particular, we examine whether an energy-momentum exchange between dark energy and dark matter could provide an explanation for current discrepancies in cosmologic

Bissmella Bahaduri, Zuheng Ming, Fangchen Feng, Anissa Mokraou

Object detection in Remote Sensing Images (RSI) is a critical task for numerous applications in Earth Observation (EO). Differing from object detection in natural images, object detection in remote sensing images faces challenges of scarcity of annotated data and the presence of small objects represented by only a few pixels. Multi-modal fusion has been dete

Kunhao Ji, Ian Davidson, Jayantha Sahu, David. J. Richardson

Novel fundamental notions helping in the interpretation of the complex dynamics of nonlinear systems are essential to our understanding and ability to exploit them. In this work we predict and demonstrate experimentally a fundamental property of Kerr-nonlinear media, which we name mode rejection and takes place when two intense counter-propagating beams inte

Linh H. Nghiem, Cornelis J. Potgieter

In the classic measurement error framework, covariates are contaminated by independent additive noise. This paper considers parameter estimation in such a linear errors-in-variables model where the unknown measurement error distribution is heteroscedastic across observations. We propose a new generalized method of moment (GMM) estimator that combines a momen

William R. Dunn, Dimitra Koutroumpa, Jennifer A. Carter, Kip D. Kuntz

The Line Emission Mapper's (LEM's) exquisite spectral resolution and effective area will open new research domains in Astrophysics, Planetary Science and Heliophysics. LEM will provide step-change capabilities for the fluorescence, solar wind charge exchange (SWCX) and auroral precipitation processes that dominate X-ray emissions in our Solar System. The obs

Daniel J. Díaz-González, Roberto Galván-Madrid, Adam Ginsburg, Frédérique Motte

We present the combination of ALMA-IMF and single-dish continuum images from the Mustang-2 Galactic Plane Survey (MGPS90) at 3 millimeters and the Bolocam Galactic Plane Survey (BGPS) at 1 millimeter. Six and ten out of the fiffteen ALMA-IMF fields are combined with MGPS90 and BGPS, respectively. The combination is made via the feathering technique. We used

Lukas Karapin-Springorum, Asia Sarycheva, Andrew Dopilka, Hyungyeon Cha

Further technological advancement of both lithium-ion and emerging battery technologies can be catalyzed by an improved understanding of the chemistry and working mechanisms of the solid electrolyte interphases (SEIs) that form at electrochemically active battery interfaces. However, collecting and interpreting spectroscopy results of SEIs is difficult for s

Peter Macgregor, He Sun

Constructing a similarity graph from a set $X$ of data points in $\mathbb{R}^d$ is the first step of many modern clustering algorithms. However, typical constructions of a similarity graph have high time complexity, and a quadratic space dependency with respect to $|X|$. We address this limitation and present a new algorithmic framework that constructs a spa

Maximiliano Isi, Will M. Farr

Cotesta et al. (2022) reanalyze the GW150914 ringdown, arguing against the presence of an overtone and suggesting claims of its detection in Isi et al. (2019) were driven by noise. Here we point out a number of technical errors in that analysis, including a software bug, and show that features highlighted as problematic are in fact expected and encountered i

Loïc Moczko, Sven Reichardt, Aditya Singh, Xin Zhang

Quantised lattice vibrations (i.e., phonons) in solids are robust and unambiguous fingerprints of crystal structures and of their symmetry properties. In metals and semimetals, strong electron-phonon coupling may lead to so-called Kohn anomalies in the phonon dispersion, providing an image of the Fermi surface in a non-electronic observable. Kohn anomalies b

Ruizhe Wu, Yunkun Zhao, Leilei Tang, Jigang Wang

As the brightness of synchrotron radiation (SR) light sources improves, the operation stability of light sources is weakened. To explore various beam instability related issues in light sources, one transverse beam diagnostics system for bunch-by-bunch (BbB) profile measurement has been established at Hefei Light Source-II (HLS-II). In this paper, one in-sit

Grzegorz A. Rempala

This brief note highlights a largely overlooked similarity between the SIR ordinary differential equations used for epidemics on the configuration model of a Poisson network and the classical mass-action SIR equations introduced nearly a century ago by Kermack and McKendrick. We demonstrate that the decline pattern in susceptibles is identical for both model

Yi-ze Cai, Zi-qiang Zhang

We perform the potential analysis for the holographic Schwinger effect in spinning Myers-Perry black holes. We compute the potential between the produced pair by evaluating the classical action of a string attaching on a probe D3-brane sitting at an intermediate position in the AdS bulk. It turns out that increasing the angular momentum reduces the potential

Wenjun Hou, Yi Cheng, Kaishuai Xu, Wenjie Li

Automating radiology report generation can significantly alleviate radiologists' workloads. Previous research has primarily focused on realizing highly concise observations while neglecting the precise attributes that determine the severity of diseases (e.g., small pleural effusion). Since incorrect attributes will lead to imprecise radiology reports, streng

Ronak Mehta, Vincent Roulet, Krishna Pillutla, Zaid Harchaoui

We consider the distributionally robust optimization (DRO) problem with spectral risk-based uncertainty set and $f$-divergence penalty. This formulation includes common risk-sensitive learning objectives such as regularized condition value-at-risk (CVaR) and average top-$k$ loss. We present Prospect, a stochastic gradient-based algorithm that only requires t

Ludovick Bouthat, Javad Mashreghi, Frédéric Morneau-Guérin

In the first of this series of two articles, we studied some geometrical aspects of the Birkhoff polytope, the compact convex set of all $n \times n$ doubly stochastic matrices, namely the Chebyshev center, and the Chebyshev radius of the Birkhoff polytope associated with metrics induced by the operator norms from $\ell_n^p$ to $\ell_n^p$ for $1 \leq p \leq

Ludovick Bouthat, Javad Mashreghi, Frédéric Morneau-Guérin

The geometry of the Birkhoff polytope, i.e., the compact convex set of all $n \times n$ doubly stochastic matrices, has been an active subject of research. While its faces, edges and facets as well as its volume have been intensely studied, other geometric characteristics such as the center and radius were left off, despite their natural uses in some areas o

Yuqi Jia, Minghong Fang, Neil Zhenqiang Gong

Decentralized federated learning (DFL) enables clients (e.g., hospitals and banks) to jointly train machine learning models without a central orchestration server. In each global training round, each client trains a local model on its own training data and then they exchange local models for aggregation. In this work, we propose SelfishAttack, a new family o

Jihoon Chung, Zhenyu Kong

Sensor technology developments provide a basis for effective fault diagnosis in manufacturing systems. However, the limited number of sensors due to physical constraints or undue costs hinders the accurate diagnosis in the actual process. In addition, time-varying operational conditions that generate nonstationary process faults and the correlation informati

Fatemeh Daneshmandi, Hassan Hessari, Tahmineh Nategh, Ali Bai

Background: Employee innovation is a crucial aspect of organizations in the current era. Therefore, studying the factors influencing individual innovation is vital and unavoidable. Undoubtedly, job satisfaction is a significant variable in management sciences. Nowadays, all organizations are interconnected with technology. Objective: This research explores t

Clàudia Climent, Joseph E. Subotnik, Abraham Nitzan

We apply the Kubo-Anderson stochastic theory of molecular spectral lineshape to the case of polaritons formed in the collective strong coupling regime. We investigate both the fast and slow limits of the random frequency modulation of the emitter as well as the intermediate regime and show how the interplay between the characteristic timescales of the cavity

Yoo Yeon Sung, Jordan Boyd-Graber, Naeemul Hassan

Polarization and the marketplace for impressions have conspired to make navigating information online difficult for users, and while there has been a significant effort to detect false or misleading text, multimodal datasets have received considerably less attention. To complement existing resources, we present multimodal Video Misleading Headline (VMH), a d

Amit Kumar Jaiswal, Haiming Liu, Prayag Tiwari

This paper focuses on affective emotion recognition, aiming to perform in the subject-agnostic paradigm based on EEG signals. However, EEG signals manifest subject instability in subject-agnostic affective Brain-computer interfaces (aBCIs), which led to the problem of distributional shift. Furthermore, this problem is alleviated by approaches such as domain

Linh H. Nghiem, Francis K. C. Hui, Samuel Mueller, A. H. Welsh

We consider the problem of surrogate sufficient dimension reduction, that is, estimating the central subspace of a regression model, when the covariates are contaminated by measurement error. When no measurement error is present, a likelihood-based dimension reduction method that relies on maximizing the likelihood of a Gaussian inverse regression model on t

Victor Hoffmann, Ilias Nahmed, Parisa Rastin, Guénaël Cabanes

The increased adoption of reinforced polymer (RP) composite materials, driven by eco-design standards, calls for a fine balance between lightness, stiffness, and effective vibration control. These materials are integral to enhancing comfort, safety, and energy efficiency. Dynamic Mechanical Analysis (DMA) characterizes viscoelastic behavior, yet there's a gr

Haolin Cong, Sasan Razmkhah, Mustafa Altay Karamuftuoglu, Massoud Pedram

Single flux quantum (SFQ) technology has garnered significant attention due to its low switching power and high operational speed. Researchers have been actively pursuing more advanced devices and technologies to further reduce the reliance on inductors, bias, and dynamic power. Recently, innovative magnetic Josephson junction devices have emerged, enhancing

Sagnik Ray Choudhury, Jushaan Kalra

Edge probing tests are classification tasks that test for grammatical knowledge encoded in token representations coming from contextual encoders such as large language models (LLMs). Many LLM encoders have shown high performance in EP tests, leading to conjectures about their ability to encode linguistic knowledge. However, a large body of research claims th

Xinyu Hu, Pengfei Tang, Simiao Zuo, Zihan Wang

Large language models (LLMs) have made impressive progress in natural language processing. These models rely on proper human instructions (or prompts) to generate suitable responses. However, the potential of LLMs are not fully harnessed by commonly-used prompting methods: many human-in-the-loop algorithms employ ad-hoc procedures for prompt selection; while

Jonathan Patsenker, Henry Li, Yuval Kluger

The exponential moving average (EMA) is a commonly used statistic for providing stable estimates of stochastic quantities in deep learning optimization. Recently, EMA has seen considerable use in generative models, where it is computed with respect to the model weights, and significantly improves the stability of the inference model during and after training

Sebin Gracy, Ji Liu, Tamer Basar, Cesar A. Uribe

The paper deals with the analysis of a discrete-time networked competitive bivirus susceptible-infected-susceptible (SIS) model. More specifically, we suppose that virus 1 and virus 2 are circulating in the population and are in competition with each other. We show that the model is strongly monotone, and that, under certain assumptions, it does not admit an

Yifei He, Haoxiang Wang, Bo Li, Han Zhao

Unsupervised domain adaptation (UDA) adapts a model from a labeled source domain to an unlabeled target domain in a one-off way. Though widely applied, UDA faces a great challenge whenever the distribution shift between the source and the target is large. Gradual domain adaptation (GDA) mitigates this limitation by using intermediate domains to gradually ada

Yu Wu, Ying Chen, Qiaoqiao Li, Kui Xue

Chalcogenide perovskites provide a promising avenue for non-toxic, stable thermoelectric materials. Here, thermal transport and thermoelectric properties of BaZrS$_3$ as a typical orthorhombic perovskite are investigated. An extremely low lattice thermal conductivity $\kappa_L$ of 1.84 W/mK at 300 K is revealed for BaZrS$_3$, due to the softening effect of B

Nan-Jiang Jiang, Chenhao Tan, Marie-Catherine de Marneffe

Human label variation, or annotation disagreement, exists in many natural language processing (NLP) tasks, including natural language inference (NLI). To gain direct evidence of how NLI label variation arises, we build LiveNLI, an English dataset of 1,415 ecologically valid explanations (annotators explain the NLI labels they chose) for 122 MNLI items (at le

Minkyu Choi, Kuan Han, Xiaokai Wang, Yizhen Zhang

The human visual system uses two parallel pathways for spatial processing and object recognition. In contrast, computer vision systems tend to use a single feedforward pathway, rendering them less robust, adaptive, or efficient than human vision. To bridge this gap, we developed a dual-stream vision model inspired by the human eyes and brain. At the input le

Priyanka Ranade, Anupam Joshi

Narrative construction is the process of representing disparate event information into a logical plot structure that models an end to end story. Intelligence analysis is an example of a domain that can benefit tremendously from narrative construction techniques, particularly in aiding analysts during the largely manual and costly process of synthesizing even

Orhan Donmez

The study of a disturbed accretion disk holds great significance in the realm of astrophysics, as such events play a crucial role in revealing the nature of disk structure, the release of energy, and the generation of shock waves. Consequently, they can help explain the causes of X-ray emissions observed in black hole accretion disk systems. In this paper, w

Paul Carter

We consider planar traveling fronts between stable steady states in two-component singularly perturbed reaction-diffusion-advection equations, where a small quantity $\delta^2$ represents the ratio of diffusion coefficients. The fronts under consideration are large amplitude and contain a sharp interface, induced by traversing a fast heteroclinic orbit in a

Kaiqi Yang, Haoyu Han, Wei Jin, Hui Liu

Graph Contrastive Learning (GCL) has demonstrated remarkable effectiveness in learning representations on graphs in recent years. To generate ideal augmentation views, the augmentation generation methods should preserve essential information while discarding less relevant details for downstream tasks. However, current augmentation methods usually involve ran

Jaeseoung Park, Ashwani Kumar, Yucheng Zhou, Sangheon Oh

Resistive memory-based reconfigurable systems constructed by CMOS-RRAM integration hold great promise for low energy and high throughput neuromorphic computing. However, most RRAM technologies relying on filamentary switching suffer from variations and noise leading to computational accuracy loss, increased energy consumption, and overhead by expensive progr

Eric Haag, Noah Rhodes, Line Roald

The Optimal Power Shutoff (OPS) problem is an optimization problem that makes power line de-energization decisions in order to reduce the risk of igniting a wildfire, while minimizing the load shed of customers. This problem, with DC linear power flow equations, has been used in many studies in recent years. However, using linear approximations for power flo

Zhexian Zhang, Daniel Morrall, Kiyohiro Yabuuchi

Tube processing and aging effects in FeCrAl ODS steels are investigated in four mechanical alloyed ferritic ODS steels, Fe15Cr (SP2), Fe15Cr5Al (SP4), Fe15Cr7Al (SP7) and Fe18Cr7Al (SP11). These steels were made into 0.3mm thick plates by simulated tube processing (STP). Strengthening after partial recrystallization was achieved after the last cold rolling a

Philippe Chlenski, Ethan Turok, Antonio Moretti, Itsik Pe'er

Hyperbolic geometry is gaining traction in machine learning for its effectiveness at capturing hierarchical structures in real-world data. Hyperbolic spaces, where neighborhoods grow exponentially, offer substantial advantages and consistently deliver state-of-the-art results across diverse applications. However, hyperbolic classifiers often grapple with com

B. Barbosa, J. P. Palastro, D. Ramsey, K. Weichman

Electron-positron pair creation occurs throughout the universe in the environments of extreme astrophysical objects, such as pulsar magnetospheres and black hole accretion disks. The difficulty of emulating these environments in the laboratory has motivated the use of ultrahigh-intensity laser pulses for pair creation. Here we show that the phase offset betw

Natanael G. de Isídio, K. Menéndez-Delmestre, T. S. Gonçalves, M. Grossi

Our current understanding of how dark matter (DM) is distributed within the Milky Way halo, particularly in the solar neighborhood, is based on either careful studies of the local stellar orbits, model assumptions on the global shape of the MW halo, or from direct acceleration measurements. In this work, we undertake a study of external galaxies, with the in

Yiqun Liu, Marc Riviere, Thomas Guionnet, Aline Roumy

The Versatile Video Coding (VVC) standard has been recently finalized by the Joint Video Exploration Team (JVET). Compared to the High Efficiency Video Coding (HEVC) standard, VVC offers about 50% compression efficiency gain, in terms of Bjontegaard Delta-Rate (BD-rate), at the cost of a 10-fold increase in encoding complexity. In this paper, we propose a me

Marija Čuić, Goran Duplančić, Krešimir Kumerički, Kornelija Passek-K.

We revisit the next-to-leading order (NLO) perturbative QCD corrections for the deeply virtual meson production (DVMP) process, exploring its phenomenology both in isolation and in a multichannel fit combined with deeply virtual Compton scattering (DVCS). Our approach involves the conformal partial wave (CPaW) formalism, which allows for the straightforward

Stefano Menasce, Rafael Libanori, Fergal Coulter, André R. Studart

Self-healing silicones that are able to restore the functionalities and extend the lifetime of soft devices hold great potential in many applications. However, currently available silicones need to be triggered to self-heal or suffer from creep-induced irreversible deformation during use. Here, we design and print silicone objects that are programmed at the

Max Vargas, Adam Tsou, Andrew Engel, Tony Chiang

Sampling biases can cause distribution shifts between train and test datasets for supervised learning tasks, obscuring our ability to understand the generalization capacity of a model. This is especially important considering the wide adoption of pre-trained foundational neural networks -- whose behavior remains poorly understood -- for transfer learning (TL

Jeremie Laydevant, Logan G. Wright, Tianyu Wang, Peter L. McMahon

Human brains and bodies are not hardware running software: the hardware is the software. We reason that because the microscopic physics of artificial-intelligence hardware and of human biological "hardware" is distinct, neuromorphic engineers need to be cautious (and yet also creative) in how we take inspiration from biological intelligence. We should focus

Linus Bao, Christy Hazel, Tia Karkos, Alice Kessler

We prove that Hill's characteristic function $\chi$ for transfer systems on a lattice $P$ surjects onto interior operators for $P$. Moreover, the fibers of $\chi$ have unique maxima which are exactly the saturated transfer systems. In order to apply this theorem in examples relevant to equivariant homotopy theory, we develop the theory of saturated transfer

F. Corrales-Machín, G. Viera-López, R. Bartali, Y. Nahmad-Molinari

Laboratory made granular-granular impact craters have been used as model analogues of planetary impact craters. These kind of craters have been observed and studied using profilometry techniques that allow to retrieve important morphologic features from the impacted surface. In this work, we propose to use a Time-of-Flight camera (Microsoft Kinect One) for t

Mufei Li, Eleonora Kreačić, Vamsi K. Potluru, Pan Li

Large-scale graphs with node attributes are increasingly common in various real-world applications. Creating synthetic, attribute-rich graphs that mirror real-world examples is crucial, especially for sharing graph data for analysis and developing learning models when original data is restricted to be shared. Traditional graph generation methods are limited

Jianyu Ma

Given a complete Riemannian manifold $M$ with a lower Ricci curvature bound, we consider barycenters in the Wasserstein space $\mathcal{W}_2(M)$ of probability measures on $M$. We refer to them as Wasserstein barycenters, which by definition are probability measures on $M$. The goal of this article is to present a novel approach to proving their absolute con

Tommaso Guffanti, Daniele Gammelli, Simone D'Amico, Marco Pavone

Reliable and efficient trajectory optimization methods are a fundamental need for autonomous dynamical systems, effectively enabling applications including rocket landing, hypersonic reentry, spacecraft rendezvous, and docking. Within such safety-critical application areas, the complexity of the emerging trajectory optimization problems has motivated the app

Qing An, Mehdi Zafari, Chris Dick, Santiago Segarra

As wireless communication systems strive to improve spectral efficiency, there has been a growing interest in employing machine learning (ML)-based approaches for adaptive modulation and coding scheme (MCS) selection. In this paper, we introduce a new adaptive MCS selection framework for massive MIMO systems that operates without any feedback from users by s

Puoya Tabaghi, Yusu Wang

A main object of our study is multiset functions -- that is, permutation-invariant functions over inputs of varying sizes. Deep Sets, proposed by \cite{zaheer2017deep}, provides a \emph{universal representation} for continuous multiset functions on scalars via a sum-decomposable model. Restricting the domain of the functions to finite multisets of $D$-dimens

Shawn Shan, Wenxin Ding, Josephine Passananti, Stanley Wu

Data poisoning attacks manipulate training data to introduce unexpected behaviors into machine learning models at training time. For text-to-image generative models with massive training datasets, current understanding of poisoning attacks suggests that a successful attack would require injecting millions of poison samples into their training pipeline. In th

J. A. Posligua, T. Heibges, H. Wistrand, C. Guépin

Very-high-energy neutrinos can be observed by detecting air shower signals. Detection of transient target of opportunity (ToO) neutrino sources is part of a broader multimessenger program. The Extreme Universe Space Observatory on a Super Pressure Balloon 2 (EUSO-SPB2) Mission, launched on May 12, 2023, was equipped with an optical Cherenkov Telescope (CT) d

Matias Lopez, Jake Bowers

We introduce a method for calculating \(p\)-values to test causal hypotheses in qualitative research \emph{a la} process tracing. As in an experiment, our \(p\)-value tells us how often one would make the same or more compelling observations favoring one theory while entertaining a rival theory. We adapt Fisher's (1935) randomization-based urn model to the r

Alvin Y. Sukmadji, Frank R. Kschischang, Mohannad Shehadeh

Zipper codes with irregular variable degree are studied. Two new interleaver maps -- chevron and half-chevron -- are described. Simulation results with shortened double-error-correcting Bose--Chaudhuri--Hocquenghem constituent codes show that zipper codes with chevron and half-chevron interleaver maps outperform staircase codes when the rate is below 0.86 an

Soo Hyun Ryu

The goal of this paper is to explore how Transformer language models process semantic knowledge, especially regarding the plausibility of noun-verb relations. First, I demonstrate GPT2 exhibits a higher degree of similarity with humans in plausibility processing compared to other Transformer language models. Next, I delve into how knowledge of plausibility i

Howard Georgi

I bosonize the position-space correlators of flavor-diagonal scalar fermion bilinears in arbitrary generalizations of the Schwinger model with $n_F$ massless fermions coupled to $n_A$ gauge bosons for $n_F\geq n_A$. For $n_A=n_F$, the fermion bilinears can be bosonized in terms of $n_F$ scalars with masses proportional to the gauge couplings. As in the Schwi

T. Broeren, K. G. Klein, J. M. TenBarge

Space plasma studies frequently use in situ magnetic field measurements taken from many spacecraft simultaneously. A useful data product of these measurements is the reconstructed magnetic field in a volume near the spacecraft observatory. We compare a standard method of computing the magnetic field at arbitrary spatial points, the Curlometer, to two novel a

Binchi Zhang, Yushun Dong, Chen Chen, Yada Zhu

Fairness-aware graph neural networks (GNNs) have gained a surge of attention as they can reduce the bias of predictions on any demographic group (e.g., female) in graph-based applications. Although these methods greatly improve the algorithmic fairness of GNNs, the fairness can be easily corrupted by carefully designed adversarial attacks. In this paper, we

Nathael Da Costa, Cyrus Mostajeran, Juan-Pablo Ortega, Salem Said

Kernel methods are powerful tools in machine learning. Classical kernel methods are based on positive-definite kernels, which map data spaces into reproducing kernel Hilbert spaces (RKHS). For non-Euclidean data spaces, positive-definite kernels are difficult to come by. In this case, we propose the use of reproducing kernel Krein space (RKKS) based methods,