December 2024 arXiv papers — page 159

Pu Zhao, Xuan Shen, Zhenglun Kong, Yixin Shen

Recently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities. Leading this evolution are proprietary LLMs like GPT-4 and GPT-o1, which have captured widespread attention in the AI community due to their remarkable performance and versatility. Simultaneously, open-source L

Ruchira Manke, Mohammad Wardat, Foutse Khomh, Hridesh Rajan

Deep Learning (DL) is a class of machine learning algorithms that are used in a wide variety of applications. Like any software system, DL programs can have bugs. To support bug localization in DL programs, several tools have been proposed in the past. As most of the bugs that occur due to improper model structure known as structural bugs lead to inadequate

Mingtao Xu, Wei Yi, De-Huan Cai

In quantum dynamics, the Loschmidt amplitude is analogous to the partition function in the canonical ensemble. Zeros in the partition function indicate a phase transition, while the presence of zeros in the Loschmidt amplitude indicates a dynamical quantum phase transition. Based on the classical-quantum correspondence, we demonstrate that the partition func

Daijiro Suematsu

Absolute values of the neutrino mass are not known still now although their upper bounds are constrained through several experiments and observations. Recent analyses of cosmological observations present severe constraint on the sum of neutrino masses. It might suggest an interesting possibility for the absolute values of neutrino mass and their ordering. In

In Won Yeu, Annika Stuke, Jon L. pez-Zorrilla, James M. Stevenson

Artificial neural network (ANN) potentials enable highly accurate atomistic simulations of complex materials at unprecedented scales. Despite their promise, training ANN potentials to represent intricate potential energy surfaces (PES) with transferability to diverse chemical environments remains computationally intensive, especially when atomic force data a

Olga Maryeva

Today, we have a sufficiently complete picture of what the Wolf--Rayet (WR) stars are. Predictions of stellar evolution theory are in a good agreement with their parameters, estimated from observational data using stellar atmospheres codes; predictions of population synthesis also agree well with number of known WR stars. This article provides an overview of

Alex Rich, Noah Stier, Pradeep Sen, Tobias Höllerer

The promise of unsupervised multi-view-stereo (MVS) is to leverage large unlabeled datasets, yet current methods underperform when training on difficult data, such as handheld smartphone videos of indoor scenes. Meanwhile, high-quality synthetic datasets are available but MVS networks trained on these datasets fail to generalize to real-world examples. To br

Azwad Tamir, Jiann-Shiun Yuan

It is a common practice in modern medicine to prescribe multiple medications simultaneously to treat diseases. However, these medications could have adverse reactions between them, known as Drug-Drug Interactions (DDI), which have the potential to cause significant bodily injury and could even be fatal. Hence, it is essential to identify all the DDI events b

Chu Sun

Grid-forming (GFM) converter is deemed as one enabler for high penetration of renewable energy resources in power system. However, as will be pointed out in this letter, the conventional power-to-frequency (P-f) GFM control will face a dilemma in keeping power limit and grid synchronization when the energy resource of the converter reaches the limit. To addr

Greyson Brothers, Willa Mannering, Amber Tien, John Winder

We explore the Iterative Inference Hypothesis (IIH) within the context of transformer-based language models, aiming to understand how a model's latent representations are progressively refined and whether observable differences are present between correct and incorrect generations. Our findings provide empirical support for the IIH, showing that the nth toke

Xiaoyu Luo, Qiongxiu Li

Adversarial robustness, the ability of a model to withstand manipulated inputs that cause errors, is essential for ensuring the trustworthiness of machine learning models in real-world applications. However, previous studies have shown that enhancing adversarial robustness through adversarial training increases vulnerability to privacy attacks. While differe

Miles Hutson, Isaac Kauvar, Nick Haber

Model-based reinforcement learning (MBRL) is a promising route to sample-efficient policy optimization. However, a known vulnerability of reconstruction-based MBRL consists of scenarios in which detailed aspects of the world are highly predictable, but irrelevant to learning a good policy. Such scenarios can lead the model to exhaust its capacity on meaningl

Fermin Moscoso del Prado Martin

In many fields, such as language acquisition, neuropsychology of language, the study of aging, and historical linguistics, corpora are used for estimating the diversity of grammatical structures that are produced during a period by an individual, community, or type of speakers. In these cases, treebanks are taken as representative samples of the syntactic st

Gaetan Raynaud, Karen Mulleners

High-speed imaging is central to the experimental investigation of fast phenomena, like flapping flags. Event-based cameras use new types of sensors that address typical challenges such as low illumination conditions, large data transfer, and the trade-off between increasing repetition rate and measurement duration more efficiently and at reduced costs compa

Taban Baghfalaki, Reza Hashemi, Catherine Helmer, Helene Jacqmin-Gadda

Collecting multiple longitudinal measurements and time-to-event outcomes is a common practice in clinical and epidemiological studies, often focusing on exploring associations between them. Joint modeling is the standard analytical tool for such data, with several R packages available. However, as the number of longitudinal markers increases, the computation

Greg Markowsky, Clayton McDonald

Given a planar domain $D$, the harmonic measure distribution function $h_D(r)$, with base point $z$, is the harmonic measure with pole at $z$ of the parts of the boundary which are within a distance $r$ of $z$. Equivalently it is the probability Brownian motion started from $z$ first strikes the boundary within a distance $r$ from $z$. We call $h_D$ the $h$-

Chris U. Carmona, Ross A. Haines, Max Anderson Loake, Michael Benskin

Traditional methods for spatial inference estimate smooth interpolating fields based on features measured at well-located points. When the spatial locations of some observations are missing, joint inference of the fields and locations is possible as the fields inform the locations and vice versa. If the number of missing locations is large, conventional Baye

Enrico Pajer, Dong-Gang Wang, Bowei Zhang

We revisit axion monodromy inflation in the context of UV-inspired models and point out that its cosmological observables are sensitive to heavy fields with masses far above the Hubble scale, such as the moduli of flux compactifications. By studying a string-inspired two-field extension of axion monodromy with a small turning rate, we reveal that the oscilla

Sarosh Nagar, David Eaves

Artificial intelligence (AI) and digital public infrastructure (DPI) are two technological developments that have taken center stage in global policy discourse. Yet, to date, there has been relatively little discussion about how AI and DPI can mutually enhance the public value provided by each other. Therefore, in this paper, we describe both the opportuniti

Yoonsoo Kim, Elias R. Most, Andrei M. Beloborodov, Bart Ripperda

The merger of a black hole (BH) and a neutron star (NS) in most cases is expected to leave no material around the remnant BH; therefore, such events are often considered as sources of gravitational waves without electromagnetic counterparts. However, a bright counterpart can emerge if the NS is strongly magnetized, as its external magnetosphere can experienc

Jing Zhou, Chunlin Li

Understanding how changes in explanatory features affect the unconditional distribution of the outcome is important in many applications. However, existing black-box predictive models are not readily suited for analyzing such questions. In this work, we develop an approximation method to compute the feature importance curves relevant to the unconditional dis

Reed Chen, Courtney Trutna Paley, Wren Wightman, Lisa Hobson-Webb

Research ultrasound scanners such as the Verasonics Vantage often lack the advanced image processing algorithms used by clinical systems. Image quality is even lower in plane wave imaging - often used for shear wave elasticity imaging (SWEI) - which sacrifices spatial resolution for temporal resolution. As a result, delay-and-summed images acquired from SWEI

Shruti Singh, Shantanu Kumar

This study investigates the impact of gradient compression on distributed training performance, focusing on sparsification and quantization techniques, including top-k, DGC, and QSGD. In baseline experiments, random-k compression results in severe performance degradation, highlighting its inefficacy. In contrast, using top-k and DGC at 50 times compression y

Azeddine Zaidni, Saad Benjelloun, Radouan Boukharfane

We study the anisotropic, incompressible Cahn-Hilliard-Navier-Stokes system with variable density in a bounded smooth domain $\Omega \subset \mathbb{R}^d$. This work extends previous results on the isotropic case by incorporating anisotropic surface energy, represented by $\mathfrak{F}= \int_{\Omega} \frac{\epsilon}{2}\, \Gamma^2(\nabla \phi) $. The thermody

Wenliang Zhong, Weizhi An, Feng Jiang, Hehuan Ma

Composed Image Retrieval (CIR) involves retrieving a target image based on a composed query of an image paired with text that specifies modifications or changes to the visual reference. CIR is inherently an instruction-following task, as the model needs to interpret and apply modifications to the image. In practice, due to the scarcity of annotated data in d

Daren Chen, Ian Zemke, Hugo Zhou

We consider satellite operators where the corresponding 2-component link is an L-space link. This family includes many commonly studied satellite operators, including cabling operators, the Whitehead operator, and a family of Mazur operators. We give a formula which computes the knot Floer complex of a satellite of $K$ in terms of the knot Floer complex of $

Yi Wang, Bingxian Mu

This paper introduces Bidirectional Guidance Informed Trees (BIGIT*),~a new asymptotically optimal sampling-based motion planning algorithm. Capitalizing on the strengths of \emph{meet-in-the-middle} property in bidirectional heuristic search with a new lazy strategy, and uniform-cost search, BIGIT* constructs an implicitly bidirectional preliminary motion t

Ehsan Latif, Yifan Zhou, Shuchen Guo, Lehong Shi

This study evaluates the performance of OpenAI's o1-preview model in higher-order cognitive domains, including critical thinking, systematic thinking, computational thinking, data literacy, creative thinking, logical reasoning, and scientific reasoning. Using established benchmarks, we compared the o1-preview models's performance to human participants from d

Luiz L. Lopes, Jose C. Jimenez, Luis B. Castro, Cesar V. Flores

We investigated the radial and non-radial fundamental ($f$) mode oscillations of self-bound (quark) stars obtained after employing the Vector MIT (vMIT) bag model. Within this model, we computed the equation of state for strange quark matter satisfying thermodynamic consistency. This allowed us to obtain the corresponding behavior of the speed of sound, mass

Saeed Noori Gashti, Behnam Pourhassan, Izzet Sakalli

This paper studies the thermodynamic topology through the bulk-boundary and restricted phase space (RPS) frameworks. In bulk-boundary framework, we observe two topological charges $(\omega = +1, -1)$ concerning the non-extensive Barrow parameter and with ($\delta=0$) in Bekenstein-Hawking entropy. For Renyi entropy, different topological charges are observed

Andrea Giorgini, Jingning He, Hao Wu

We analyze a diffuse interface model that describes the dynamics of incompressible two-phase flows influenced by interactions with a soluble chemical substance, encompassing the chemotaxis effect, mass transport, and reactions. In the resulting coupled evolutionary system, the macroscopic fluid velocity field $\boldsymbol{v}$ satisfies a Navier--Stokes syste

Onur Agirseven, M. A. Ollis

We propose a "Coprime Buratti-Horak-Rosa (BHR) Conjecture": If $L$ is a multiset of size $v-1$ with support contained in $\{1, 2, \ldots, \lfloor v/2 \rfloor\}$ such that $\gcd(v,x) = 1$ for all $x \in L$, then $L$ is realizable. This is a specialization of the well-known BHR Conjecture and it includes Buratti's original conjecture. We argue that the most ef

Namrata Das, Rakshya Panta, Neelam Karki, Ruchi Manandhar

In an era of widespread influence of Natural Language Processing (NLP), there have been multiple research efforts to supplant traditional manual coding techniques with automated systems capable of generating solutions autonomously. With rapid research for code generation and a sole focus on large language models, there emerges a need to compare and evaluate

Taehyeun Kim, Robin Inho Kee, Ilya Kolmanovsky, Anouck Girard

This paper develops a Time Shift Governor (TSG)-based control scheme to enforce constraints during rendezvous and docking (RD) missions in the setting of the Two-Body problem. As an add-on scheme to the nominal closed-loop system, the TSG generates a time-shifted Chief spacecraft trajectory as a target reference for the Deputy spacecraft. This modification o

Constantinos Daskalakis, Ian Gemp, Yanchen Jiang, Renato Paes Leme

Stories are records of our experiences and their analysis reveals insights into the nature of being human. Successful analyses are often interdisciplinary, leveraging mathematical tools to extract structure from stories and insights from structure. Historically, these tools have been restricted to one dimensional charts and dynamic social networks; however,

Anna C. Gilbert, Joon-Hyeok Yim

A metric space $(X,d)$ is said to be $\delta$-hyperbolic if $d(x,y)+d(z,w)$ is at most $\max(d(x,z)+d(y,w), d(x,w)+d(y,z))$ by $2 \delta$. A geodesic space is $\delta$-slim if every geodesic triangle $\Delta(x,y,z)$ is $\delta$-slim. It is well-established that the notions of $\delta$-slimness, $\delta$-hyperbolicity, $\delta$-thinness and similar concepts a

D. Stoppacher, A. D. Montero-Dorta, M. C. Artale, A. Knebe

Investigating the assembly history of the most massive and passive galaxies will enhance our understanding of why galaxies exhibit such a remarkable diversity in structure and morphology. In this paper, we simultaneously investigate the assembly history and redshift evolution of semi-analytically modelled galaxy properties of central galaxies between 0.56 <

Suvrojit Ghosh, Koulik Khamaru, Tirthankar Dasgupta

D-Optimal designs for estimating parameters of response models are derived by maximizing the determinant of the Fisher information matrix. For non-linear models, the Fisher information matrix depends on the unknown parameter vector of interest, leading to a weird situation that in order to obtain the D-optimal design, one needs to have knowledge of the param

Gerald C. Nwalozie, Andre L. F. de Almeida, Martin Haardt

In this paper, we consider a double-RIS (D-RIS)-aided flat-fading MIMO system and propose an interference-free channel training and estimation protocol, where the two single-reflection links and the one double-reflection link are estimated separately. Specifically, by using the proposed training protocol, the signal measurements of a particular reflection li

Kaustav Mukherjee, Johannes Schachenmayer, Shannon Whitlock, Sebastian Wüster

Configurable arrays of optically trapped Rydberg atoms are a versatile platform for quantum computation and quantum simulation, also allowing controllable decoherence. We demonstrate theoretically, that they also enable proof-of-principle demonstrations for a technique to build models for open quantum dynamics by machine learning with artificial neural netwo

Gabriela Juncosa, Taha Yasseri, Julia Koltai, Gerardo Iniguez

Quantifying how individuals react to social influence is crucial for tackling collective political behavior online. While many studies of opinion in public forums focus on social feedback, they often overlook the potential for human interactions to result in self-censorship. Here, we investigate political deliberation in online spaces by exploring the hypoth

Lajos Diósi

We consider $N$ clones of the quantized world, interacting with each other via quantum gravity, coupled by the downscaled Newton constant $G/N$. In the limit $N\rightarrow\infty$, we obtain the semiclassical Einstein equation for every single cloneworld. In the non-relativistic limit, De Filippo had already obtained the semiclassical Schr\"odinger-Newton equ

James Dylan Douthitt, James Oxley

In earlier work, we characterized the class of matroids with no $M(C_4)$ as an induced minor and the class of matroids with no member of $\{M(C_4),M(K_4)\}$ as an induced minor. In this paper, for every two matroids in $\{M(C_4),M(K_4\backslash e),M(K_4),F_7\}$, we determine the class of matroids that have neither of the chosen pair as an induced minor. Addi

Hyunchul Lim, Minghan Gao, Franklin Mingzhe Li, Nam Anh Dang

Emotional voice communication plays a crucial role in effective daily interactions. Deaf and hard-of-hearing (DHH) individuals often rely on facial expressions to supplement sign language to convey emotions, as the use of voice is limited. However, in American Sign Language (ASL), these facial expressions serve not only emotional purposes but also as linguis

Alessandro Marcelletti, Edoardo Marangone, Michele Kryston, Claudio Di Ciccio

Blockchain enables novel, trustworthy Process-Aware Information Systems (PAISs) by enforcing the security, robustness, and traceability of operations. In particular, transparency ensures that all information exchanges are openly accessible, fostering trust within the system. Although this is a desirable property to enable notarization and auditing activities

Eduardo Schirmer Finn, Eduardo Horta

For highly skewed or fat-tailed distributions, mean or median-based methods often fail to capture the central tendencies in the data. Despite being a viable alternative, estimating the conditional mode given certain covariates (or mode regression) presents significant challenges. Nonparametric approaches suffer from the "curse of dimensionality", while semip

Zohair Shafi, Germans Savcisens, Tina Eliassi-Rad

Machine learning models for graphs in real-world applications are prone to two primary types of uncertainty: (1) those that arise from incomplete and noisy data and (2) those that arise from uncertainty of the model in its output. These sources of uncertainty are not mutually exclusive. Additionally, models are susceptible to targeted adversarial attacks, wh

Yuzhou Nie, Zhun Wang, Ye Yu, Xian Wu

Recent studies have discovered that large language models (LLM) may be ``fooled'' to output private information, including training data, system prompts, and personally identifiable information, under carefully crafted adversarial prompts. Existing red-teaming approaches for privacy leakage either rely on manual efforts or focus solely on system prompt extra

Nora Weickgenannt, Jean-Paul Blaizot

We study the longitudinal spin polarization of a relativistic fluid of massive spin-1/2 particles undergoing a boost-invariant expansion in the longitudinal direction and rotating in the transverse plane. We express the polarization vector in terms of spin moments and derive closed equations of motion for the latter using spin kinetic theory with a nonlocal

Kyle Binder

We introduce the singular cohomology ring of a matroid which extends the Chow ring of a matroid. This is defined as the singular cohomology ring of a certain quasi-projective toric variety associated to the matroid. Using the matroidal flips of Adiprasito, Huh, and Katz, we prove sharp vanishing results for the cohomology ring and compute the dimension of th

Mengming Michael Dong, Theophanis C. Stratopoulos, Victor Xiaoqi Wang

This paper provides a review of recent publications and working papers on ChatGPT and related Large Language Models (LLMs) in accounting and finance. The aim is to understand the current state of research in these two areas and identify potential research opportunities for future inquiry. We identify three common themes from these earlier studies. The first

A. Acus, A. Dargys

Explicit formulas to calculate MV functions in a basis-free representation are presented for an arbitrary Clifford geometric algebra Cl(p,q). The formulas are based on analysis of the roots of minimal MV polynomial and covers defective MVs, i.e. the MVs that have non-diagonalizable matrix representations. The method may be generalized straightforwardly to ma

Richa Mishra, S. O. Reza Moheimani

We introduce a novel control mode for Scanning Tunneling Microscopy (STM) that leverages di/dz feedback. By superimposing a high-frequency sinusoidal modulation on the control signal, we extract the amplitude of the resulting tunneling current to obtain a di/dz measurement as the tip is scanned over the surface. A feedback control loop is then closed to main

Ranjan Sapkota, Manoj Karkee

In this study, we developed a customized instance segmentation model by integrating the Convolutional Block Attention Module (CBAM) with the YOLO11 architecture. This model, trained on a mixed dataset of dormant and canopy season apple orchard images, aimed to enhance the segmentation of tree trunks and branches under varying seasonal conditions throughout t

Illych Alvarez

This work redefines the framework of chaos in dynamical systems by extending Devaney's definition to multiple mappings, emphasizing the pivotal role of nonlinearity. We propose a novel theorem demonstrating how nonlinear dynamics within a single mapping can induce chaos across a collective system, even when other components lack sensitivity. To validate thes

Animesh Basak, Rajat K. Dey

Based on a Monte Carlo simulation study of vertical extensive air showers (EAS) at the KASCADE location we introduce a new simple observable $\eta_{\rho{(45;310)}}$ (in short $\eta_{\rho}$) - the ratio between two lateral electron densities of an EAS measured at two well-defined radial distances indicated by the characteristic radial feature of the local age

Yuriy Shlepnev

In the realm of PCB and packaging interconnect design, electromagnetic analysis tools have transitioned from optional to essential over the last two decades, as data rates soared beyond 6 Gbps. Today, with standard data rates eclipsing 6 Gbps and reaching thresholds of 224 Gbps, these tools are indispensable for designing reliable interconnects. The goals of

Nathan Wycoff, Lisa O. Singh, Ali Arab, Katharine M. Donato

Augmenting a smooth cost function with an $\ell_1$ penalty allows analysts to efficiently conduct estimation and variable selection simultaneously in sophisticated models and can be efficiently implemented using proximal gradient methods. However, one drawback of the $\ell_1$ penalty is bias: nonzero parameters are underestimated in magnitude, motivating tec

Aditya Chinchure, Sahithya Ravi, Raymond Ng, Vered Shwartz

The commonsense reasoning capabilities of vision-language models (VLMs), especially in abductive reasoning and defeasible reasoning, remain poorly understood. Most benchmarks focus on typical visual scenarios, making it difficult to discern whether model performance stems from keen perception and reasoning skills, or reliance on pure statistical recall. We a

FNU Neha, Deepshikha Bhati, Deepak Kumar Shukla, Md Amiruzzaman

Generative Adversarial Networks (GANs) have proven to be a powerful tool in generating artistic images, capable of mimicking the styles of renowned painters, such as Claude Monet. This paper introduces a tiered GAN model to progressively refine image quality through a multi-stage process, enhancing the generated images at each step. The model transforms rand

Haizhou Shi, Yibin Wang, Ligong Han, Huan Zhang

Estimating the uncertainty of responses from Large Language Models (LLMs) remains a critical challenge. While recent Bayesian methods have demonstrated effectiveness in quantifying uncertainty through low-rank weight updates, they typically require complex fine-tuning or post-training procedures. In this paper, we propose Training-Free Bayesianization (TFB),

Ziyuan Qin, Dongjie Cheng, Haoyu Wang, Huahui Yi

Contemporary Text-to-Image (T2I) models frequently depend on qualitative human evaluations to assess the consistency between synthesized images and the text prompts. There is a demand for quantitative and automatic evaluation tools, given that human evaluation lacks reproducibility. We believe that an effective T2I evaluation metric should accomplish the fol

Sicong Tian, Haiyu Wu, Michael C. King, Kevin W. Bowyer

One-to-many facial identification is documented to achieve high accuracy in the case where both the probe and the gallery are "mugshot quality" images. However, an increasing number of documented instances of wrongful arrest following one-to-many facial identification have raised questions about its accuracy. Probe images used in one-to-many facial identific

Yael Kapon, Lilach Brann, Shira Yochelis, Jonas Fransson

The chiral-induced spin selectivity (CISS) effect demonstrates a strong coupling between electron spin and molecular chirality, enabling spin-controlled interactions between chiral molecules and magnetic surfaces. While CISS experiments have revealed robust changes in the spin-polarization properties of magnetic materials upon chiral molecular adsorption, th

Kateřina Škardová, Alexandre Daby-Seesaram, Martin Genet

We present the Finite Element Neural Network Interpolation (FENNI) framework, a sparse neural network architecture extending previous work on Embedded Finite Element Neural Networks (EFENN) introduced with the Hierarchical Deep-learning Neural Networks (HiDeNN). Due to their mesh-based structure, EFENN requires significantly fewer trainable parameters than f

Harshit Sikchi, Siddhant Agarwal, Pranaya Jajoo, Samyak Parajuli

The reward hypothesis states that all goals and purposes can be understood as the maximization of a received scalar reward signal. However, in practice, defining such a reward signal is notoriously difficult, as humans are often unable to predict the optimal behavior corresponding to a reward function. Natural language offers an intuitive alternative for ins

Niloufar Saeidi Mobarakeh, Behzad Khamidehi, Chunlin Li, Hamidreza Mirkhani

The primary goal of motion planning is to generate safe and efficient trajectories for vehicles. Traditionally, motion planning models are trained using imitation learning to mimic the behavior of human experts. However, these models often lack interpretability and fail to provide clear justifications for their decisions. We propose a method that integrates

A. Faulconnier, S. Job, J. Brocail, N. Peyret

This article explores the hysteretic behavior and the damping features of sheared granular media using discrete element method (DEM) simulations. We consider polydisperse non-cohesive frictional spherical particles, enclosed in a container with rigid but moving walls, submitted to a cyclic simple shear superimposed to a confining pressure. The mechanical res

R. McOwen, P. Topalov

We prove that the Navier-Stokes equation is well-posed in function spaces on $\mathbb{R}^d$, $d\ge 2$, that contain vector fields of order $O(|x|^\kappa)$ as $|x|\to\infty$ with $\kappa<1/2$. The corresponding solutions depend continuously on the viscosity parameter $\nu\ge 0$ and converge to the solutions of the Euler equation as $\nu\to 0+$. Our proof is b

Aletta Jooste, Kerstin Jordaan

In this paper we consider interlacing of the zeros of polynomials from different sequences $\{p_n\}$ and $\{g_n\}$. In our main result we consider a mixed recurrence equation necessary for existence of a linear term $(x-A)$ so that the $(n+1)$ zeros of $(x-A)g_n(x)$ interlace with the $n$ zeros of $p_n$. We apply our result to Meixner-Pollaczek, Pseudo-Jacob

Alexandre Daby-Seesaram, Kateřina Škardová, Martin Genet

This work introduces a hybrid approach that combines the Proper Generalised Decomposition (PGD) with deep learning techniques to provide real-time solutions for parametrised mechanics problems. By relying on a tensor decomposition, the proposed method addresses the curse of dimensionality in parametric computations, enabling efficient handling of high-dimens

Anthony Pietz, Karin John, Uwe Thiele

Bacteria invade surfaces by forming dense colonies encased in a polymer matrix. Successful settlement of founder bacteria, early microcolony development and later macroscopic spreading of these biofilms on surfaces rely on complex physical mechanisms. Recent data show that on soft hydrogels, substrate rigidity is an important determinant for biofilm initiati

Nnamdi C. Chikere, Sofia Lozano Voticky, Quang D. Tran, Yasemin Ozkan-Aydin

Traditional locomotion strategies become ineffective at low Reynolds numbers, where viscous forces predominate over inertial forces. To adapt, microorganisms have evolved specialized structures like cilia and flagella for efficient maneuvering in viscous environments. Among these organisms, Phytophthora zoospores demonstrate unique locomotion mechanisms that

Matthias Beckmann, Carla Dittert

This paper proves a novel analytical inversion formula for the so-called modulo Radon transform (MRT), which models a recently proposed approach to one-shot high dynamic range tomography. It is based on the solution of a Poisson problem linking the Laplacian of the Radon transform (RT) of a function to its MRT in combination with the classical filtered back

Soumya Suvra Ghosal, Soumyabrata Pal, Koyel Mukherjee, Dinesh Manocha

Large Language Models (LLMs) have recently demonstrated impressive few-shot learning capabilities through in-context learning (ICL). However, ICL performance is highly dependent on the choice of few-shot demonstrations, making the selection of the most optimal examples a persistent research challenge. This issue is further amplified in low-resource Indic lan

Zhenhao Cai, Jian Ding

In this paper, we establish the existence and equivalence of four types of incipient infinite clusters (IICs) for the critical Gaussian free field (GFF) level-set and the critical loop soup on the metric graph $\widetilde{\mathbb{Z}}^d$ for all $d\ge 3$ except the critical dimension $d=6$. These IICs are defined as four limiting conditional probabilities, in

Paramita Das, Amartya Roy, Ritabrata Chakraborty, Animesh Mukherjee

Although Wikipedia is the largest multilingual encyclopedia, it remains inherently incomplete. There is a significant disparity in the quality of content between high-resource languages (HRLs, e.g., English) and low-resource languages (LRLs, e.g., Hindi), with many LRL articles lacking adequate information. To bridge these content gaps, we propose a lightwei

Youssef Shoeb, Nazir Nayal, Azarm Nowzad, Fatma Güney

Detecting road obstacles is essential for autonomous vehicles to navigate dynamic and complex traffic environments safely. Current road obstacle detection methods typically assign a score to each pixel and apply a threshold to generate final predictions. However, selecting an appropriate threshold is challenging, and the per-pixel classification approach oft

Zhenhao Cai, Jian Ding

We prove quasi-multiplicativity for critical level-sets of Gaussian free fields (GFF) on the metric graphs $\widetilde{\mathbb{Z}}^d$ ($d\ge 3$). Specifically, we study the probability of connecting two general sets located on opposite sides of an annulus with inner and outer radii both of order $N$, where additional constraints are imposed on the distance o

Guilherme B. Kanegae, Alexandre F. Fonseca

Graphyne is a porous two-dimensional carbon allotrope of graphene that possesses interesting physical properties, including non-null bandgap. It is composed of carbon hexagonal rings or carbon-carbon bonds connected by acetylenic chains. The diverse forms of these connections yield a variety of graphyne structures. In a previous study, we have obtained the e

A. A. Melnikov, Yu. G. Selivanov, D. G. Poydashev, S. V. Chekalin

We have studied the ultrafast optical response of a PbSe crystal to an intense picosecond terahertz pulse with a peak electric field strength of up to $\sim$ 500 kV/cm. The reflectivity anisotropy signal contains oscillations at the fundamental frequency of the resonant infrared-active phonon mode as well as its second, third, and fourth harmonics. The effec

Nguyen N. Hung, A. A. Schaeffer Fry

We propose and present evidence for a conjectural global-local phenomenon concerning the $p$-rationality of $p$-height-zero characters. Specifically, if $\chi$ is a height-zero character of a finite group $G$ and $D$ is a defect group of the $p$-block of $G$ containing $\chi$, then the $p$-rationality of $\chi$ can be captured inside the normalizer $N_G(D)$.

Duarte Fontes, Robert Szafron

Muon conversion is one of the best probes of charged lepton flavor violation. The experimental limit is soon expected to improve by four orders of magnitude, thus calling for precise predictions of the shape of the signal spectrum. Equally important are precise predictions for muon decay-in-orbit, the main background for muon conversion. While the calculatio

Chad Berner, Noah Giddings, John Herr, Palle Jorgensen

In this paper we study 2D Fourier expansions for a general class of planar measures $\mu$, generally singular, but assumed compactly supported in $\mathbb{R}^2$. We focus on the following question: When does $L^2(\mu)$ admit a 2D system of Fourier expansions? We offer concrete conditions allowing an affirmative answer to the question for a large class of Bor

Saqib Javed, Ahmad Jarrar Khan, Corentin Dumery, Chen Zhao

Recent advancements in high-fidelity dynamic scene reconstruction have leveraged dynamic 3D Gaussians and 4D Gaussian Splatting for realistic scene representation. However, to make these methods viable for real-time applications such as AR/VR, gaming, and rendering on low-power devices, substantial reductions in memory usage and improvements in rendering eff

Peter K. Friz, Khoa Lê, Huilin Zhang

We study stochastic optimal control of rough stochastic differential equations (RSDEs). This is in the spirit of the pathwise control problem (Lions--Souganidis 1998, Buckdahn--Ma 2007; also Davis--Burstein 1992), with renewed interest and recent works drawing motivation from filtering, SPDEs, and reinforcement learning. Results include regularity of rough v

Orizon P. Ferreira, Boris S. Mordukhovich, Wilkreffy M. S. Santos, João Carlos O. Souza

In this paper, we introduce an inexact approach to the Boosted Difference of Convex Functions Algorithm (BDCA) for solving nonconvex and nondifferentiable problems involving the difference of two convex functions (DC functions). Specifically, when the first DC component is differentiable and the second may be nondifferentiable, BDCA utilizes the solution fro

Yan Zhang, Pengcheng Zheng, Chengxiao Zeng, Bin Xiao

Remote Sensing (RS) image deblurring and Super-Resolution (SR) are common tasks in computer vision that aim at restoring RS image detail and spatial scale, respectively. However, real-world RS images often suffer from a complex combination of global low-resolution (LR) degeneration and local blurring degeneration. Although carefully designed deblurring and S

Yuqi Tan, Xiang Liu, Shuzhao Xie, Bin Chen

3D Gaussian Splatting (3DGS) has emerged as a pivotal technique for 3D scene representation, providing rapid rendering speeds and high fidelity. As 3DGS gains prominence, safeguarding its intellectual property becomes increasingly crucial since 3DGS could be used to imitate unauthorized scene creations and raise copyright issues. Existing watermarking method

Leonardo Pina, Yongmin Li

This study presents a novel method for generating music visualisers using diffusion models, combining audio input with user-selected artwork. The process involves two main stages: image generation and video creation. First, music captioning and genre classification are performed, followed by the retrieval of artistic style descriptions. A diffusion model the

Michael R. Metel, Boxing Chen, Mehdi Rezagholizadeh

Several works have developed eviction policies to remove key-value (KV) pairs from the KV cache for more efficient inference. The focus has been on compressing the KV cache after the input prompt has been processed for faster token generation. In settings with limited GPU memory, and when the input context is longer than the generation length, we show that b

Shiming Wen, Wei Zhang, Lin Ma, Yunning Zhao

Using data from the LAMOST Medium-Resolution Spectroscopic Survey of Nebulae, we create a sample of 17,821 diffuse ionized gas (DIG) spectra in the anti-center region of the Milky Way, by excluding fibers in the directions of H II regions and supernova remnants. We then analyze the radial and vertical distributions of three line ratios ([N II]/H$\alpha$, [S

Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham

Large Language Models (LLMs) generating unsafe responses to toxic prompts is a significant issue in their applications. While various efforts aim to address this safety concern, previous approaches often demand substantial human data collection or rely on the less dependable option of using another LLM to generate corrective data. In this paper, we aim to ta

Daniel Kornbluth, Alexey Kushnir

Prevailing methods of course allocation at undergraduate institutions involve reserving seats to give priority to designated groups of students. We introduce a competitive equilibrium-based mechanism that assigns course seats using student preferences and course priorities. This mechanism satisfies approximate notions of stability, efficiency, envy-freeness,

Arturo Rodriguez, Ashesh Chattopadhyay, Piyush Kumar, Luis F. Rodriguez

Physics-informed neural networks (PINNs) commonly address ill-posed inverse problems by uncovering unknown physics. This study presents a novel unsupervised learning framework that identifies spatial subdomains with specific governing physics. It uses the partition of unity networks (POUs) to divide the space into subdomains, assigning unique nonlinear model

Masoud Shabaninezhad, Hamid Mehrvar, Eric Bernier, Lora Ramunno

We present the design, modeling, and optimization of high-performance plasmonic electro-optic modulators leveraging voltage-gated carrier density in indium tin oxide (ITO) where the gated carrier density is modeled using both the Classical Drift-Diffusion (CDD) and Schr\"odinger-Poisson Coupling (SPC) methods. The latter ensures a more detailed and precise d

Youbang Sun, Shixiang Chen, Alfredo Garcia, Shahin Shahrampour

Many classical and modern machine learning algorithms require solving optimization tasks under orthogonality constraints. Solving these tasks with feasible methods requires a gradient descent update followed by a retraction operation on the Stiefel manifold, which can be computationally expensive. Recently, an infeasible retraction-free approach, termed the

Biju Issac, Kyle Fryer, Seibu Mary Jacob

Botnets could autonomously infect, propagate, communicate and coordinate with other members in the botnet, enabling cybercriminals to exploit the cumulative computing and bandwidth of its bots to facilitate cybercrime. Traditional detection methods are becoming increasingly unsuitable against various network-based detection evasion methods. These techniques

Deepshikha Bhati, Fnu Neha, Md Amiruzzaman, Angela Guercio

Interpreting complex neural networks is crucial for understanding their decision-making processes, particularly in applications where transparency and accountability are essential. This proposed method addresses this need by focusing on layer-wise Relevance Propagation (LRP), a technique used in explainable artificial intelligence (XAI) to attribute neural n

Zihao Zhu, Hongbao Zhang, Guanzong Wu, Siwei Lyu

Visual-textual inconsistency (VTI) evaluation plays a crucial role in cleansing vision-language data. Its main challenges stem from the high variety of image captioning datasets, where differences in content can create a range of inconsistencies (\eg, inconsistencies in scene, entities, entity attributes, entity numbers, entity interactions). Moreover, varia