October 2024 arXiv papers — page 43

Marcel Binz, Elif Akata, Matthias Bethge, Franziska Brändle

Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational models, we currently do not have one model that captures the human mind in its entirety. A first step in this direction is to create a model that can predict human behavior in a wide ra

Bojan Derajić, Mohamed-Khalil Bouzidi, Sebastian Bernhard, Wolfgang Hönig

This paper presents a novel learning-based approach for online estimation of maximal safe sets for local trajectory planning in unknown static environments. The neural representation of a set is used as the terminal set constraint for a model predictive control (MPC) local planner, resulting in improved recursive feasibility and safety. To achieve real-time

Annalisa Szymanski, Noah Ziems, Heather A. Eicher-Miller, Toby Jia-Jun Li

The potential of using Large Language Models (LLMs) themselves to evaluate LLM outputs offers a promising method for assessing model performance across various contexts. Previous research indicates that LLM-as-a-judge exhibits a strong correlation with human judges in the context of general instruction following. However, for instructions that require specia

Eric Slyman, Anirudh Kanneganti, Sanghyun Hong, Stefan Lee

We study the impact of a standard practice in compressing foundation vision-language models - quantization - on the models' ability to produce socially-fair outputs. In contrast to prior findings with unimodal models that compression consistently amplifies social biases, our extensive evaluation of four quantization settings across three datasets and three C

Omar Hisham Taha, Marwa Abdullah Salih

All rings are commutative, and all modules are unital. The purpose of this paper is to investigate the characterizations of weakly pseudo primary 2-absorbing sub-module in terms of some types of modules. We provide characterizations for the class of multiplication modules with the help of some types of modules such as faithful, non-singular, Z-regular, and p

Kai Cheng, Zhengyuan Li, Xingpeng Sun, Byung-Cheol Min

Embodied Question Answering (EQA) is an essential yet challenging task for robot assistants. Large vision-language models (VLMs) have shown promise for EQA, but existing approaches either treat it as static video question answering without active exploration or restrict answers to a closed set of choices. These limitations hinder real-world applicability, wh

Jeremy Booher, Rachel Pries

For a prime $p$ congruent to three modulo four, we prove that there exists a smooth curve of genus five in characteristic $p$ that is supersingular. We produce this curve as an unramified double cover of a curve of genus three. We conjecture that the setting of unramified double covers of curves of genus three also produces supersingular curves of genus five

Frank Schmidt, Peter van Loock

Using numerical simulations, we explore generalized fusion measurements, extending them in three key ways. We incorporate ancilla-boosting and code-boosting to optimize fusion measurements, which may, but do not necessarily, correspond to Bell measurements. For ancilla-boosting beyond Bell measurements, we identify a hierarchy of fusion efficiencies involvin

Sathwik Narkedimilli, Amballa Venkata Sriram, Satvik Raghav

This study proposes an advanced Federated Learning (FL) framework designed to enhance data privacy and security in IoT environments by integrating Decentralized Attribute-Based Encryption (DABE), Homomorphic Encryption (HE), Secure Multi-Party Computation (SMPC), and Blockchain technology. Unlike traditional FL, our framework enables secure, decentralized au

Liquan Wang, Ankit Goyal, Haoping Xu, Animesh Garg

Human actions manipulating articulated objects, such as opening and closing a drawer, can be categorized into multiple modalities we define as interaction modes. Traditional robot learning approaches lack discrete representations of these modes, which are crucial for empirical sampling and grounding. In this paper, we present ActAIM2, which learns a discrete

Nico Domschke, Thomas Gatter, Richard Golnik, Peter F. Stadler

The set of relevant cuts in a graph is the union of all minimum weight bases of the cut space. A cut is relevant if and only if it is the a minimum weight cut between two distinct vertices. Moreover, we give a characterization in terms of Picard-Queyranne Directed Acyclic Graphs that can be used to accelerate the enumeration of the relevant cuts. Finally, we

Vidullan Surendran, Alan R. Wagner

Recognising intent in collaborative human robot tasks can improve team performance and human perception of robots. Intent can differ from the observed outcome in the presence of mistakes which are likely in physically dynamic tasks. We created a dataset of 1227 throws of a ball at a target from 10 participants and observed that 47% of throws were mistakes wi

Jiwoong Park, Yang Shen

How can diffusion models process 3D geometries in a coarse-to-fine manner, akin to our multiscale view of the world? In this paper, we address the question by focusing on a fundamental biochemical problem of generating 3D molecular conformers conditioned on molecular graphs in a multiscale manner. Our approach consists of two hierarchical stages: i) generati

Andrew Wagenmaker, Kevin Huang, Liyiming Ke, Byron Boots

In order to mitigate the sample complexity of real-world reinforcement learning, common practice is to first train a policy in a simulator where samples are cheap, and then deploy this policy in the real world, with the hope that it generalizes effectively. Such \emph{direct sim2real} transfer is not guaranteed to succeed, however, and in cases where it fail

Fang Liu, Shaobo Guo, Qianwen Xing, Xinye Sha

Stock trading has always been a key economic indicator in modern society and a primary source of profit for financial giants such as investment banks, quantitative trading firms, and hedge funds. Discovering the underlying patterns within the seemingly volatile yet intrinsically structured economic activities has become a central focus of research for many c

Sullam Jeoung, Goeric Huybrechts, Bhavana Ganesh, Aram Galstyan

Understanding long-form video content presents significant challenges due to its temporal complexity and the substantial computational resources required. In this work, we propose an agent-based approach to enhance both the efficiency and effectiveness of long-form video understanding by utilizing large language models (LLMs) and their tool-harnessing abilit

Anwesha Chattopadhyay

We study a one-dimensional chain of identical atoms with two electronic orbitals and two electrons per atom, subject to an external oscillating pressure that periodically modulates the lattice spacing. This leads to time-dependent intra- and inter-orbital hopping amplitudes. In the tight-binding limit with weak inter-orbital hopping, the system exhibits two

Amir Najafi, Samin Mahdizadeh Sani, Farzan Farnia

We address the challenge of certifying the performance of a federated learning model on an unseen target network using only measurements from the source network that trained the model. Specifically, consider a source network "A" with $K$ clients, each holding private, non-IID datasets drawn from heterogeneous distributions, modeled as samples from a broader

Aleksander Ivanov

We consider metric versions of weak soficity, LEF and residual finiteness. The main results of the paper extend Glebsky and Rivera's characterization of weak soficity to the case of normally finitely generated groups with word metrics. Metric LEF and residual finiteness are also characterized in this class. We deduce that the free group $\mathsf{F}_2$ is not

Christopher Harker, Aditya Bhaskara

Graph embeddings have emerged as a powerful tool for understanding the structure of graphs. Unlike classical spectral methods, recent methods such as DeepWalk, Node2Vec, etc. are based on solving nonlinear optimization problems on the graph, using local information obtained by performing random walks. These techniques have empirically been shown to produce '

Irena Gao, Percy Liang, Carlos Guestrin

Users often interact with large language models through black-box inference APIs, both for closed- and open-weight models (e.g., Llama models are popularly accessed via Amazon Bedrock and Azure AI Studio). In order to cut costs or add functionality, API providers may quantize, watermark, or finetune the underlying model, changing the output distribution -- p

Stephen Mann

We prove that infinite-dimensional highest-weight modules are faithful for Iwasawa algebras corresponding to a simple Lie algebra of type D. We use this to prove that all non-zero two-sided ideals of the Iwasawa algebra have finite codimension in this case.

Vipul Gupta, Candace Ross, David Pantoja, Rebecca J. Passonneau

One of the most challenging problems facing NLP today is evaluation. Some of the most pressing issues pertain to benchmark saturation, data contamination, and diversity in the quality of test examples. To address these concerns, we propose Selection Methodology for Accurate, Reduced, and Targeted (SMART) filtering, a novel approach to select a high-quality s

Talaya Farasat, JongWon Kim, Joachim Posegga

Information and Communication Technologies (ICT) infrastructures are becoming increasingly complex day by day, facing numerous challenges to support the latest networking paradigms. Security is undeniably a critical component for the effective functioning of these advanced ICT infrastructures. By considering the current network security challenges, we propos

Lucas McCullum, Kareem A. Wahid, Barbara Marquez, Clifton D. Fuller

The Dice Similarity Coefficient (DSC) is the current de facto standard to determine agreement between a reference segmentation and one generated by manual / auto-contouring approaches. This metric is useful for non-spatially important images; however, radiation therapy requires consideration of nearby Organs-at-Risk (OARs) and their radiosensitivity which ar

Aurora Esteban, Alberto Cano, Amelia Zafra, Sebastián Ventura

Data stream learning is a very relevant paradigm because of the increasing real-world scenarios generating data at high velocities and in unbounded sequences. Stream learning aims at developing models that can process instances as they arrive, so models constantly adapt to new concepts and the temporal evolution in the stream. In multi-label data stream envi

Tomis Prajapati, Harsh Mehta, Shreya Banerjee, Prasanta K. Panigrahi

Violation of the Bell-type inequalities is necessary to confirm the existence of nonlocality in nonclassical (entangled) states. We have designed a customized operator which is made of the sum of the Pauli matrices ($\sigma_x$, $\sigma_y$, and $\sigma_z$). We theoretically and experimentally investigate the violation of Bell-type inequalities using two- and

Benjamin Côté, Hélène Cossette, Etienne Marceau

Understanding the effects of the choice of the tree on the joint distribution of a tree-structured Markov random field (MRF) is crucial for fully exploiting the intelligibility of such probabilistic graphical models. Tools must be developed in this regard: this is the overarching objective of this paper. Our discussion is two-fold. First, we examine concepts

James Healy, Alessandro Ciarfella, Carlos O. Lousto

We performed a series of 769 full numerical simulations of high energy collision of black holes to search for the maximum gravitational energy emitted $E_{rad}$, during their merger. We consider equal mass binaries with spins pointing along their orbital angular momentum $\vec{L}$ and perform a search over impact parameters $b$ and initial linear momenta $p/

Jonathan Engle, Bryce Morsky

The outcomes of democratic elections rest on individuals' decision-making that is driven by their varying preferences and beliefs. Individuals may prefer consensus to gridlock, or gridlock to consensus, and information may be fractured via echo-chambers. To understand the role of these factors in whether or not elections reach consensus, we develop and explo

Keyur D. Shah, Jun Zhou, Justin Roper, Anees Dhabaan

Photon-counting computed tomography (PCCT) marks a significant advancement over conventional energy-integrating detector (EID) CT systems. This review highlights PCCT's superior spatial and contrast resolution, reduced radiation dose, and multi-energy imaging capabilities, which address key challenges in radiotherapy, such as accurate tumor delineation, prec

Ben Szczesny

In this paper, we generalize the Dunn-Brinkmeier~additivity theorem, which establishes a weak equivalence $\mathcal{C}_n \otimes \mathcal{C}_m \simeq \mathcal{C}_{n+m}$ for the little cubes operad $\mathcal{C}_n$. We introduce equivariant framed little disk operads, a new class of operads that simultaneously generalize the framed little disk operads and the

Akhilbaran Ghosh, Rama Sai Adithya Kalidindi

Optimization is critical for optimal performance in deep neural networks (DNNs). Traditional gradient-based methods often face challenges like local minima entrapment. This paper explores population-based metaheuristic optimization algorithms for image classification networks. We propose a novel approach integrating a two-stage training technique with popula

Cibele Cristina Trinca, Reginaldo Palazzo, J. Carmelo Interlando, Ricardo Augusto Watanabe

Quantum error correction is essential for the development of any scalable quantum computer. In this work we introduce a generalization of a quantum interleaving method for combating clusters of errors in toric quantum error-correcting codes. We present new $n$-dimensional toric quantum codes, where $n\geq 5$, which are featured by lattice codes and apply the

Yassir El Mesbahi, Emmanuel Noutahi

SMILES-based molecular generative models have been pivotal in drug design but face challenges in fragment-constrained tasks. To address this, the Sequential Attachment-based Fragment Embedding (SAFE) representation was recently introduced as an alternative that streamlines those tasks. In this study, we investigate the optimal setups for training SAFE genera

Ishita Harish, Saurav Mishra, Neha Bhadoria, Rithik Kumar

Accurate classification of medical images is critical for detecting abnormalities in the gastrointestinal tract, a domain where misclassification can significantly impact patient outcomes. We propose an ensemble-based approach to improve diagnostic accuracy in analyzing complex image datasets. Using a Convolutional Block Attention Module along with a Deep Ne

Yulin Li, Zhicheng Song, Chunxin Zheng, Zhihai Bi

In this work, we present FRTree planner, a novel robot navigation framework that leverages a tree structure of free regions, specifically designed for navigation in cluttered and unknown environments with narrow passages. The framework continuously incorporates real-time perceptive information to identify distinct navigation options and dynamically expands t

Katsiaryna Bahamazava

We present a theoretical framework assessing the economic implications of bias in AI-powered emergency response systems. Integrating health economics, welfare economics, and artificial intelligence, we analyze how algorithmic bias affects resource allocation, health outcomes, and social welfare. By incorporating a bias function into health production and soc

P. R. Gordoa, A. Pickering, D. Puertas-Centeno, E. V. Toranzo

In this paper we present new solutions of the non-linear Schr\"oodinger equation proposed by Nobre, Rego-Monteiro and Tsallis for the free particle, obtained from different Lie symmetry reductions. Analytical expressions for the wave function, the auxiliary field and the probability density are derived using a variety of approaches. Solutions involving ellip

Michal Šedý, Lukáš Holík

We introduce a novel paradigm for reducing the size of finite automata by compressing repeating sub-graphs. These repeating sub-graphs can be viewed as invocations of a single procedure. Instead of representing each invocation explicitly, they can be replaced by a single procedure that uses a small runtime memory to remember the call context. We elaborate on

Arnau Messegué, Josep Maria Miret

An almost Moore digraph is a diregular digraph of degree $d>1$, diameter $k>1$ and order $d+d^2+ \cdots +d^k$. Their existence has only been shown for $k=2$. It has also been conjectured that there are no more almost Moore digraphs, but so far their nonexistence has only been proven for $k=3,4$ and for $d=2,3$ when $k\geq 3$. In this paper we study the struc

Vishesh Prasad, Brian Kim, Nickvash Kani

Recent advances in natural language processing (NLP), particularly with the emergence of large language models (LLMs), have significantly enhanced the field of textual analysis. However, while these developments have yielded substantial progress in analyzing natural language text, applying analysis to mathematical equations and their relationships within tex

Kelly Kostopoulou, Angelos Charalambidis, Panos Rondogiannis

Modern machine learning systems represent their computations as dataflow graphs. The increasingly complex neural network architectures crave for more powerful yet efficient programming abstractions. In this paper we propose an efficient technique for supporting recursive function definitions in dataflow-based systems such as TensorFlow. The proposed approach

Alkida Balliu, Sebastian Brandt, Fabian Kuhn, Dennis Olivetti

In the past few years, a successful line of research has lead to lower bounds for several fundamental local graph problems in the distributed setting. These results were obtained via a technique called round elimination. On a high level, the round elimination technique can be seen as a recursive application of a function that takes as input a problem $\Pi$ a

Alireza Shavali

In this article, we will generalize an explicit formula proved by Quer for the Brauer class of the endomorphism algebra of abelian varieties associated to modular forms of weight 2 to the case of Hilbert modular forms of parallel weight 2, under the condition that the degree of the base field over Q is an odd number.

Emily Chivers, Shawn Curran

Clear legal language forms the backbone of a contract for numerous reasons. Disputes often arise between contract parties where ambiguous language has been used and parties often disagree on the meaning or effect of the words. Unambiguous language can also be important where there is an imbalance of bargaining strength between the parties, for instance where

Mohammed Q. Shormani

This article aims to characterize Generative linguistics (GL) contribution to artificial intelligence (AI), alluding to the debate among linguists and AI scientists on whether linguistics belongs to humanities or science. In this article, I will try not to be biased as a linguist, studying the phenomenon from an independent scientific perspective. The articl

Muhammad Zubair Irshad, Mauro Comi, Yen-Chen Lin, Nick Heppert

Neural Fields have emerged as a transformative approach for 3D scene representation in computer vision and robotics, enabling accurate inference of geometry, 3D semantics, and dynamics from posed 2D data. Leveraging differentiable rendering, Neural Fields encompass both continuous implicit and explicit neural representations enabling high-fidelity 3D reconst

Yimin Deng, Yuxia Wu, Guoshuai Zhao, Li Zhu

New intent discovery is a crucial capability for task-oriented dialogue systems. Existing methods focus on transferring in-domain (IND) prior knowledge to out-of-domain (OOD) data through pre-training and clustering stages. They either handle the two processes in a pipeline manner, which exhibits a gap between intent representation and clustering process or

Christian Remling, Jie Zeng

We study canonical systems that are reflectionless on an open set. In this situation, the two half line $m$ functions are holomorphic continuations of each other and may thus be combined into a single holomorphic function. This idea was explored in [11], and we continue these investigations here. We focus on Dirac operators and especially their interplay wit

Victor Champain, Simon Zihlmann, Alessandro Chessari, Benoit Bertrand

In this study, we provide a full experimental characterization of the parametric longitudinal coupling between a CMOS charge qubit and an off-chip RF resonator. Following Corrigan et al, Phys. Rev. Applied 20, 064005 (2023), we activate parametric longitudinal coupling by driving the charge qubit at the resonator frequency. Managing the crosstalk between the

Dilip Raghavan

Q-points are cofinal in the RK-ordering under several mild hypotheses.

Xinyu Tang, Xiaolei Wang, Wayne Xin Zhao, Ji-Rong Wen

Zero-shot in-context learning (ZS-ICL) aims to conduct in-context learning (ICL) without using human-annotated demonstrations. Most ZS-ICL methods use large language models (LLMs) to generate (input, label) pairs as pseudo-demonstrations and leverage historical pseudo-demonstrations to help solve the current problem. They assume that problems are from the sa

Hunter Ng

This article investigates whether the Federal Reserve Chair strategically controls facial expressions during FOMC press conferences and how these nonverbal cues affect financial markets. I use facial recognition technology on videos of press conferences from April 2011 to December 2020 to quantify changes in the Chair's nonverbal signals. Results show that f

Somabha Mukherjee

In this paper, we consider the problem of estimating the interaction parameter $p$ of a $p$-spin Curie-Weiss model at inverse temperature $\beta$, given a single observation from this model. We show, by a contiguity argument, that joint estimation of the parameters $\beta$ and $p$ is impossible, which implies that estimation of $p$ is impossible if $\beta$ i

Wei Wang, Tang Paai Wong, Haihui Ruan, Somdatta Goswami

Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving physical systems described by partial differential equations (PDEs). However, their accuracy in dynamical systems, particularly those involving sharp moving boundaries with complex initial morphologies, remains a challenge. This study introduces an approach combining residua

Brian E. Perron, Hui Luan, Zia Qi, Bryan G. Victor

Application Programming Interfaces (APIs) are essential tools for social work researchers aiming to harness advanced technologies like Large Language Models (LLMs) and other AI services. This paper demystifies APIs and illustrates how they can enhance research methodologies. It provides an overview of API functionality and integration into research workflows

Daria Lioubashevski, Tomer Schlank, Gabriel Stanovsky, Ariel Goldstein

Understanding the inner workings of Transformers is crucial for achieving more accurate and efficient predictions. In this work, we analyze the computation performed by Transformers in the layers after the top-1 prediction has become fixed, which has been previously referred to as the "saturation event". We expand the concept of saturation events for top-k t

Shirong Chen, Yi C. Huang, Jian-Yang Zhang

We present a pointwise inequality for adjacent even Legendre polynomials in high dimensional spheres featuring the effect of spectral gaps. This improves a recent result of Imbert, Silvestre and Villani that is crucially used in their study of the Fisher information for the Boltzmann equation.

Manqing Fu

In fields such as sociology, political science, public administration, and business management, particularly in the direction of international relations, Qualitative Comparative Analysis (QCA) has been widely adopted as a research method. This article addresses the limitations of the QCA method in its application, specifically in terms of low coverage, facto

Yifan Zeng, Liang Kairong, Fangzhou Dong, Peijia Zheng

As Large Language Models (LLMs) become more prevalent, concerns about their safety, ethics, and potential biases have risen. Systematically evaluating LLMs' risk decision-making tendencies and attitudes, particularly in the ethical domain, has become crucial. This study innovatively applies the Domain-Specific Risk-Taking (DOSPERT) scale from cognitive scien

Samuel Teuber, Philipp Kern, Marvin Janzen, Bernhard Beckert

When validated neural networks (NNs) are pruned (and retrained) before deployment, it is desirable to prove that the new NN behaves equivalently to the (original) reference NN. To this end, our paper revisits the idea of differential verification which performs reasoning on differences between NNs: On the one hand, our paper proposes a novel abstract domain

Enze Cui, James F. Peters

This paper introduces an axiomatic basis for measuring the energy characteristic of vibrating dynamical systems. The basic approach is to compare non-modulated vs. modulated waveforms in measuring energy during the vibratory motion $m(t)$ at time $t$ of moving object such as off-road vehicle oscillating movements recorded in an infrared (IR) video. Modulatio

Dmitry Svintsov, Alexander Shabanov

An exact solution for electromagnetic wave diffraction at the junction of two-dimensional electron systems (2DES) is obtained and analyzed for electric field polarized along the edge. A special emphasis is paid to the metal-contacted and terminated edges. In the former case, electric field at the edge tends to zero; in the latter case, it tends to a finite v

Matthew Kwiecien, Tesla Jeltema, Alexie Leauthaud, Song Huang

The number density and redshift evolution of optically selected galaxy clusters offer an independent measurement of the amplitude of matter fluctuations, $S_8$. However, recent results have shown that clusters chosen by the redMaPPer algorithm show richness-dependent biases that affect the weak lensing signals and number densities of clusters, increasing unc

Rachael Fleurence, Xiaoyan Wang, Jiang Bian, Mitchell K. Higashi

Objective: This article offers a taxonomy of generative artificial intelligence (AI) for health economics and outcomes research (HEOR), explores its emerging applications, and outlines methods to enhance the accuracy and reliability of AI-generated outputs. Methods: The review defines foundational generative AI concepts and highlights current HEOR applicatio

Xutun Wang, Yuchen Zhang, Zidong Li, Haocheng Wen

This study presents a novel approach for quantificationally reconstructing density fields from shadowgraph images using physics-informed neural networks

Mateus C. P. dos Santos, Wesley B. Cardoso

In this paper we introduce a one-dimensional model of coupled fractional nonlinear Schr\"odinger equations with a double-well potential applied to one component. This study examines ground state (GS) solitons, observing spontaneous symmetry breaking (SSB) in both the actuated field and the partner component due to linear coupling. Numerical simulations revea

Houman Mehrafarin, Arash Eshghi, Ioannis Konstas

Evaluating Large Language Models (LLMs) on reasoning benchmarks demonstrates their ability to solve compositional questions. However, little is known of whether these models engage in genuine logical reasoning or simply rely on implicit cues to generate answers. In this paper, we investigate the transitive reasoning capabilities of two distinct LLM architect

Mohammad Beigi, Sijia Wang, Ying Shen, Zihao Lin

In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating the uncertainty in their predictions has become crucial. Current methods often struggle to accurately identify, measure, and address the true uncertainty, with many focusing primar

Marc-Antoine Allard, Paul Teiletche, Adam Zinebi

This study explores the integration of large language models (LLMs) into classic inflation nowcasting frameworks, particularly in light of high inflation volatility periods such as the COVID-19 pandemic. We propose InflaBERT, a BERT-based LLM fine-tuned to predict inflation-related sentiment in news. We use this model to produce NEWS, an index capturing the

Song Xia, Wenhan Yang, Yi Yu, Xun Lin

The utilization of large foundational models has a dilemma: while fine-tuning downstream tasks from them holds promise for making use of the well-generalized knowledge in practical applications, their open accessibility also poses threats of adverse usage. This paper, for the first time, explores the feasibility of adversarial attacking various downstream mo

Lixin Wang, Qian Wang, He Chen, Shidong Zhou

This paper focuses on optimizing the long-term average age of information (AoI) in device-to-device (D2D) networks through age-aware link scheduling. The problem is naturally formulated as a Markov decision process (MDP). However, finding the optimal policy for the formulated MDP in its original form is challenging due to the intertwined AoI dynamics of all

Isabelle Chalendar, Romain Lebreton

In this paper we study the embedding problem of an operator into a strongly continuous semigroup. We obtain characterizations for some classes of operators, namely composition operators and analytic Toeplitz operators on the Hardy space H^2. In particular, we focus on the isometric ones using the necessary and sufficient condition observed by T. Eisner.

J. L. Campbell, P. G. Martin, S. Song, M. Rahman

We explore red stellar populations toward the W3 giant molecular cloud through the use of optical-to-infrared (IR) photometry and Gaia DR 3 data, simultaneously characterizing stellar content and properties of dust in the molecular medium. We use a Rayleigh-Jeans Color Excess (RJCE) method modified to de-redden stellar observations of both red giants (RGs) a

Yuliang Zhu, Ayush Bhandari

Conventional digitization based on the Shannon-Nyquist method, implemented via analog-to-digital converters (ADCs), faces fundamental limitations. High-dynamic-range (HDR) signals often get clipped or saturated in practice. Given a fixed bit budget, one must choose between minimizing quantization noise or accommodating HDR inputs. The Unlimited Sensing Frame

Deeksha Singh, Swati Yadav, Rajesh K. Pandey

This paper presents a numerical method to solve a time-fractional Burgers equation, achieving order of convergence $(2-\alpha)$ in time, here $\alpha$ represents the order of the time derivative. The fractional derivative is modeled by Caputo-Prabhakar (CP) formulation, which incorporates a kernel defined by the three-parameter Mittag-Leffler function. Finit

Melissa L. Morris

We continue to investigate the diffusive compressible Euler (dcE) model for viscous and heat conducting compressible fluid flow, which has been proposed by M. Sv\"ard as an alternative to the Navier-Stokes-Fourier (NSF) equations. The non-convective contribution to the momentum flux tensor in the dcE model is, with inverted sign, the analog of the viscous st

Nafise Sedighi, Zahra Sharbaf, Ignacio Trujillo, Sepideh Eskandarlou

With the arrival of the next generation of ultra-deep optical imaging surveys reaching $\mu_V$$\sim$30 mag/arcsec$^2$ (3$\sigma$; 10"$\times$10"), the removal of scattered light due to the point spread function (PSF) effect remains a critical step for the scientific exploitation of the low surface brightness information contained in these data. Because virtu

Siyong Tao, Zida Xiao, Huaiqing Zuo

For an ideal of a regular $\cc$-algebra, its Bernstein-Sato polynomial is the monic polynomial of the lowest degree satisfying an Bernstein-Sato functional equation. We generalize the notion of Bernstein-Sato functional equations to the case of ideals in an $F$-finite ring of positive characteristic $p$, and show the relationship between these equations and

Asmaa Abada, Nicolás Bernal, Antonio E. Cárcamo Hernández, Sergey Kovalenko

In this talk, we discuss the phenomenology of radiative 3-loop seesaw models. The 3-loop suppression allows the new particles to have masses at the TeV scale, along with relatively large Yukawa couplings, while retaining consistency with neutrino masses and mixing, as observed in neutrino oscillation experiments. This leads to a rich phenomenology, especiall

Alireza Mohamadi, Hosna Ghahramani, Seyyed Amir Asghari, Mehdi Aminian

The increasing integration of the Internet of Medical Things (IoMT) into healthcare systems has significantly enhanced patient care but has also introduced critical cybersecurity challenges. This paper presents a novel approach based on Convolutional Neural Networks (CNNs) for detecting cyberattacks within IoMT environments. Unlike previous studies that pred

Sam Houliston, Alizée Pace, Alexander Immer, Gunnar Rätsch

Aligning Large Language Models (LLMs) to human preferences in content, style, and presentation is challenging, in part because preferences are varied, context-dependent, and sometimes inherently ambiguous. While successful, Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) are prone to the issue of proxy reward overop

Shiqiao Meng, Ying Zhou, Qinghua Zheng, Bingxu Liao

Accurately predicting the dynamic responses of building structures under seismic loads is essential for ensuring structural safety and minimizing potential damage. This critical aspect of structural analysis allows engineers to evaluate how structures perform under various loading conditions, facilitating informed design and safety decisions. Traditional met

Dehai Liu, Kaishun Wang, Tian Yao

A family $\mathcal{F}$ of $k$-subsets of an $n$-set is called $s$-almost $t$-intersecting if each member is $t$-disjoint with at most $s$ members. In this paper, we prove that, if $\left|\mathcal{F}\right|$ is maximum, then $\mathcal{F}$ consists of all $k$-subsets containing a fixed $t$-subset. Consequently, it is natural to consider the maximum-sized $\mat

Ruyi Zhang, Hongzuo Xu, Songlei Jian, Yusong Tan

Training in unsupervised time series anomaly detection is constantly plagued by the discrimination between harmful `anomaly contaminations' and beneficial `hard normal samples'. These two samples exhibit analogous loss behavior that conventional loss-based methodologies struggle to differentiate. To tackle this problem, we propose a novel approach that suppl

D. V. Alekseevsky, A. Spiro

We propose a differential geometric model of hypercolumns in the primary visual cortex V1 that combines features of the symplectic model of the primary visual cortex by A. Sarti, G. Citti and J. Petitot and of the spherical model of hypercolumns by P. Bressloff and J. Cowan. The model is based on classical results in Conformal Geometry.

Anne-Marie Aubert, Roger Plymen

We consider the depth-zero supercuspidal $L$-packets of $\mathrm{SL}_2(F)$ where $F$ is a non-archimedean local field of characteristic zero. We compare the explicit endoscopic character identities for $\mathrm{SL}_2(F)$ with the classical character formulas of Sally-Shalika. Our main result concerns the supercuspidal $L$-packet of size $4$. For this $L$-pac

Yifan Deng, Spencer S. Ericksen, Anthony Gitter

The development of large language models and multi-modal models has enabled the appealing idea of generating novel molecules from text descriptions. Generative modeling would shift the paradigm from relying on large-scale chemical screening to find molecules with desired properties to directly generating those molecules. However, multi-modal models combining

Dhananjay Singh, Satyajit Puhan, Navpreet Kaur, Manpreet Kaur

The role of asymmetric nuclear medium on the properties of kaon is investigated at zero and finite temperature employing a hybrid approach integrating the light cone quark model (LCQM) and the chiral SU(3) quark mean field (CQMF) model. The in-medium quark masses are calculated within the CQMF model and are used as inputs to study the medium modifications in

Zhuan Shi, Yifei Song, Xiaoli Tang, Lingjuan Lyu

Generative art using Diffusion models has achieved remarkable performance in image generation and text-to-image tasks. However, the increasing demand for training data in generative art raises significant concerns about copyright infringement, as models can produce images highly similar to copyrighted works. Existing solutions attempt to mitigate this by per

Matthieu Astorg, Davoud Cheraghi, Arnaud Chéritat

We prove that cubic polynomial maps with a fixed Siegel disk and a critical orbit eventually landing inside that Siegel disk lie in the support of the bifurcation measure. This answers a question of Dujardin in positive. Our result implies the existence of holomorphic disks in the support of the bifurcation measure, and also implies that the set of rigid par

Jiazuo Yu, Haomiao Xiong, Lu Zhang, Haiwen Diao

Multimodal Large Language Models (MLLMs) have gained significant attention due to their impressive capabilities in multimodal understanding. However, existing methods rely heavily on extensive modal-specific pretraining and joint-modal tuning, leading to significant computational burdens when expanding to new modalities. In this paper, we propose PathWeave,

Yuting Tang, Xin-Qiang Cai, Jing-Cheng Pang, Qiyu Wu

Reinforcement Learning (RL) empowers agents to acquire various skills by learning from reward signals. Unfortunately, designing high-quality instance-level rewards often demands significant effort. An emerging alternative, RL with delayed reward, focuses on learning from rewards presented periodically, which can be obtained from human evaluators assessing th

Wen Teng, Yunpeng Xiao

In this paper, we first define twisted Rota-Baxter family operators on Hom-associative algebras indexed by a semigroup $\Omega$. Then we introduce and study Hom-NS-family algebras as the underlying structures of twisted Rota-Baxter family operators. Meanwhile, We show that a Hom-NS-family algebra induces an ordinary Hom-NS-algebra on the tensor product with

Haoyu Song, Wei-Nan Zhang, Kaiyan Zhang, Ting Liu

With the resurgent interest in building open-domain dialogue systems, the dialogue generation task has attracted increasing attention over the past few years. This task is usually formulated as a conditional generation problem, which aims to generate a natural and meaningful response given dialogue contexts and specific constraints, such as persona. And main

Omar A. M. Abdelraouf, Mengfei Wu, Hong Liu

The development of miniaturized light sources with tunable functionality is crucial for advancing integrated photonic devices, enabling applications in quantum computing, communications, and sensing. Achieving tunable light emission after device fabrication remains a significant challenge, particularly when efficient amplification is required. Hybrid metasur

Alex Glushkovsky

The article addresses the application of unsupervised machine learning to represent variables on the 2D latent space by applying a variational autoencoder (beta-VAE). Representation of variables on low dimensional spaces allows for data visualization, disentanglement of variables based on underlying characteristics, finding of meaningful patterns and outlier

Nandakishore S Menon, Chandramouli Kamanchi, Raghuram Bharadwaj Diddigi

Our work aims to build a model that performs dual tasks of image captioning and image generation while being trained on only one task. The central idea is to train an invertible model that learns a one-to-one mapping between the image and text embeddings. Once the invertible model is efficiently trained on one task, the image captioning, the same model can g

Pinky Pamecha, Chaitya Shah, Divyam Jain, Kashish Gandhi

With the rapid growth of social media usage, a common trend has emerged where users often make sarcastic comments on posts. While sarcasm can sometimes be harmless, it can blur the line with cyberbullying, especially when used in negative or harmful contexts. This growing issue has been exacerbated by the anonymity and vast reach of the internet, making cybe