December 2024 arXiv papers — page 155

Rui Wang, Chengrui Qiu, Chuan-Shen Hu, Zhiming Li

Large density fluctuations of conserved charges have been proposed as a promising signature for exploring the QCD critical point in heavy-ion collisions. These fluctuations are expected to exhibit a fractal or scale-invariant behavior, which can be probed by intermittency analysis. Recent high-energy experimental studies reveal that the signal of critical fl

Kexin Chen, Noelle Lim, Claire Lee, Michael Guerzhoy

We propose a pipeline for gaining insights into complex diseases by training LLMs on challenging social media text data classification tasks, obtaining explanations for the classification outputs, and performing qualitative and quantitative analysis on the explanations. We report initial results on predicting, explaining, and systematizing the explanations o

Ching-Kai Chiu, Yueh-Ting Yao, Tay-Rong Chang, Guang Bian

Second-order topological superconductors host Majorana corner modes (MCMs), which are confined to specific corners of the system. This spatial restriction presents challenges for manipulating and relocating MCMs. We propose a novel protocol for dynamically controlling the movement of time-reversal symmetric MCMs in a heterostructure consisting of a 2D Weyl s

Xueluan Gong, Bowei Tian, Meng Xue, Yuan Wu

Recent studies have revealed the vulnerability of Deep Neural Network (DNN) models to backdoor attacks. However, existing backdoor attacks arbitrarily set the trigger mask or use a randomly selected trigger, which restricts the effectiveness and robustness of the generated backdoor triggers. In this paper, we propose a novel attention-based mask generation m

Yifang Chen, Xiaoyu Li, Yingyu Liang, Zhenmei Shi

In this paper, we analyze the computational limitations of Mamba and State-space Models (SSMs) by using the circuit complexity framework. Despite Mamba's stateful design and recent attention as a strong candidate to outperform Transformers, we have demonstrated that both Mamba and SSMs with $\mathrm{poly}(n)$-precision and constant-depth layers reside within

Xinpeng Liu, Junxuan Liang, Chenshuo Zhang, Zixuan Cai

Analyses of human motion kinematics have achieved tremendous advances. However, the production mechanism, known as human dynamics, is still undercovered. In this paper, we aim to push data-driven human dynamics understanding forward. We identify a major obstacle to this as the heterogeneity of existing human motion understanding efforts. Specifically, hetero

Valentine Nyirahafashimana, Nurisya Mohd Shah, Umair Abdul Halim, Mohamed Othman

This study explores the qubit mapping through the integration of Quasi-Orthogonal Space-Time Block Codes (QOSTBCs) with Quaternion Orthogonal Designs (QODs) in quantum error correction (QEC) frameworks. QOSTBCs have gained prominence for enhancing performance and reliability in quantum computing and communication systems. These codes draw on stabilizer group

Katerina Korre, John Pavlopoulos, Paolo Gajo, Alberto Barrón-Cedeño

The issue of hate speech extends beyond the confines of the online realm. It is a problem with real-life repercussions, prompting most nations to formulate legal frameworks that classify hate speech as a punishable offence. These legal frameworks differ from one country to another, contributing to the big chaos that online platforms have to face when address

Yuan Wang, Ouxiang Li, Tingting Mu, Yanbin Hao

Recent success of text-to-image (T2I) generation and its increasing practical applications, enabled by diffusion models, require urgent consideration of erasing unwanted concepts, e.g., copyrighted, offensive, and unsafe ones, from the pre-trained models in a precise, timely, and low-cost manner. The twofold demand of concept erasure includes not only a prec

Zonglin Meng, Yun Zhang, Zhaoliang Zheng, Zhihao Zhao

Cooperative perception has attracted wide attention given its capability to leverage shared information across connected automated vehicles (CAVs) and smart infrastructures to address sensing occlusion and range limitation issues. However, existing research overlooks the fragile multi-sensor correlations in multi-agent settings, as the heterogeneous agent se

Kangyu Zhu, Peng Xia, Yun Li, Hongtu Zhu

The advancement of Large Vision-Language Models (LVLMs) has propelled their application in the medical field. However, Medical LVLMs (Med-LVLMs) encounter factuality challenges due to modality misalignment, where the models prioritize textual knowledge over visual input, leading to hallucinations that contradict information in medical images. Previous attemp

Jiaxiang Huang, Licheng Jiao

Recent advances in learnable evolutionary algorithms have demonstrated the importance of leveraging population distribution information and historical evolutionary trajectories. While significant progress has been made in continuous optimization domains, combinatorial optimization problems remain challenging due to their discrete nature and complex solution

Ting Qiao, Henry Williams, David Valencia, Bruce MacDonald

One of the bottlenecks preventing Deep Reinforcement Learning algorithms (DRL) from real-world applications is how to explore the environment and collect informative transitions efficiently. The present paper describes bounded exploration, a novel exploration method that integrates both 'soft' and intrinsic motivation exploration. Bounded exploration notably

Qiyu Liao, Xin Yuan, Min Xu, Dadong Wang

In Fine-Grained Visual Classification (FGVC), distinguishing highly similar subcategories remains a formidable challenge, often necessitating datasets with extensive variability. The acquisition and annotation of such FGVC datasets are notably difficult and costly, demanding specialized knowledge to identify subtle distinctions among closely related categori

Tianjun Gan, Christopher A. Theissen, Sharon X. Wang, Adam J. Burgasser

We investigate the stellar metallicity ([Fe/H] and [M/H]) dependence of giant planets around M dwarfs by comparing the metallicity distribution of 746 field M dwarfs without known giant planets with a sample of 22 M dwarfs hosting confirmed giant planets. All metallicity measurements are homogeneously obtained through the same methodology based on the near-i

Jiayu Li, Xuan Zhu, Fang Liu, Yanjun Qi

Fine-tuning large language models (LLMs) for specific tasks requires diverse, high-quality training data. However, obtaining sufficient relevant data remains a significant challenge. Existing data synthesis methods either depend on extensive seed datasets or struggle to balance task relevance and data diversity. To address these challenges, we propose Attrib

Mengyu Sun, Dimeng Xia, Shusen Zhao, Weibin Zhang

In order to improve image quality of projection in industrial applications, generally, a standard method is to increase the current or exposure time, which might cause overexposure of detector units in areas of thin objects or backgrounds. Increasing the projection sampling is a better method to address the issue, but it also leads to significant noise in th

Mohamed R. Ibrahim, Josef Musil, Irene Gallou

In the domain of architectural design, the foundational essence of creativity and human intelligence lies in the mastery of solving floorplans, a skill demanding distinctive expertise and years of experience. Traditionally, the architectural design process of creating floorplans often requires substantial manual labour and architectural expertise. Even when

Zhao Liu, Tian Xie, Xueru Zhang

Current social bias benchmarks for Large Language Models (LLMs) primarily rely on predefined question formats like multiple-choice, limiting their ability to reflect the complexity and open-ended nature of real-world interactions. To close this gap, we extend an existing dataset BBQ (Parrish et al., 2022) to Open-BBQ, a comprehensive framework to evaluate th

Arth Shukla, Stone Tao, Hao Su

High-quality benchmarks are the foundation for embodied AI research, enabling significant advancements in long-horizon navigation, manipulation and rearrangement tasks. However, as frontier tasks in robotics get more advanced, they require faster simulation speed, more intricate test environments, and larger demonstration datasets. To this end, we present MS

Sarah E. Dean, Josephine Munro, Neuton Li, Robert Sharp

Polarisation imaging is used to distinguish objects and surface characteristics that are otherwise not visible with black-and-white or colour imaging. Full-Stokes polarisation imaging allows complex image processing like water glint filtering, which is particularly useful for remote Earth observations. The relatively low cost of small-satellites makes their

Jikang Wang

We consider a RCD$(-(N-1),N)$ space $(X,d,\mathcal{H}^N)$ with local bounded covering geometry. The first result is related to Gromov's almost flat manifold theorem. Specifically, if for every point $\tilde{p}$ in the universal cover $\widetilde{X}$, we have $\mathcal{H}^N(B_1(\tilde{p})) \ge v > 0$ and the diameter of $X$ is sufficiently small, then $X$ is

Ajla Trumic, Aneya Sobalkar, Efe Tandirli, Nishka Yadav

In 2023, Rowan et al. reported the discovery of a black hole (BH) companion to J0946, following the misidentification of V723 Mon by Jayasinghe et al. as containing a "mass-gap" BH. This article reproduced Rowan and Jayasinghe's results on these systems by estimating stellar parameters via Markov Chain Monte Carlo solvers. We implemented Bayesian statistical

Lei Su, Yang Du

We focus on tertiary lymphoid structure (TLS) semantic segmentation in whole slide image (WSI). Unlike TLS binary segmentation, TLS semantic segmentation identifies boundaries and maturity, which requires integrating contextual information to discover discriminative features. Due to the extensive scale of WSI (e.g., 100,000 \times 100,000 pixels), the segmen

Mahgam Tabatabaei, Reza Golshan Khavas, Ali Tavakoli Kashani

The increase in population and economic growth, coupled with accelerated urbanization and suburbanization, has exacerbated traffic congestion and environmental challenges in urban areas. To address these issues, a comprehensive traffic management program has been introduced, aimed at enhancing the regulation and control of traffic flow, thereby ensuring fast

Calvin Glisson, Qiuxiao Chen

Autonomous driving has garnered significant attention in recent research, and Bird's-Eye-View (BEV) map segmentation plays a vital role in the field, providing the basis for safe and reliable operation. While data augmentation is a commonly used technique for improving BEV map segmentation networks, existing approaches predominantly focus on manipulating spa

Qiyang Han, Koulik Khamaru, Cun-Hui Zhang

Upper Confidence Bound (UCB) algorithms are a widely-used class of sequential algorithms for the $K$-armed bandit problem. Despite extensive research over the past decades aimed at understanding their asymptotic and (near) minimax optimality properties, a precise understanding of their regret behavior remains elusive. This gap has not only hindered the evalu

S. W. Jang, E. -S. Kim, T. Tauchi, N. Terunuma

The nano-meter beam size in future linear colliders requires very high resolution beam position monitor since higher resolution allows more accurate position measurement in the interaction point. We developed and tested a low-Q C-band beam position monitor with position resolution of nanometer. The C-band BPM was tested for the fast beam feedback system at t

Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun, Richard Dodson

Spiking Neural Networks (SNNs) promise efficient and dynamic spatio-temporal data processing. This paper reformulates a significant challenge in radio astronomy, Radio Frequency Interference (RFI) detection, as a time-series segmentation task suited for SNN execution. Automated systems capable of real-time operation with minimal energy consumption are increa

M. A. Khrystik

Let $\mathcal A$ be an $\mathbb F$-algebra and let $\mathcal S$ be its generating set. The length of $\mathcal S$ is the smallest number $k$ such that $\mathcal A$ equals the $\mathbb F$-linear span of all products of length at most $k$ of elements from $\mathcal S$. The length of $\mathcal A$, denoted by $l(\mathcal A)$, is defined to be the maximal length

Zhen-Meng Xu, Da-Zhu Ma, Wen-Fu Cao, Kai Li

The Kerr-MOG black hole has recently attracted significant research attention and has been extensively applied in various fields. To accurately characterize the long-term dynamical evolution of charged particles around Kerr-MOG black hole, it is essential to utilize numerical algorithms that are high-precision, stable, and capable of preserving the inherent

Ahmed Shokry, Amr Elmasry, Ayman Khalafallah, Amr Aly

In this paper we propose a linear-time certifying algorithm for the single-source shortest-path problem capable of verifying graphs with positive, negative, and zero arc weights. Previously proposed linear-time approaches only work for graphs with positive arc weights.

Shu Hong, Xiaojun Lin, Lingjie Duan

Federated learning (FL) enables collaborative model training through model parameter exchanges instead of raw data. To avoid potential inference attacks from exchanged parameters, differential privacy (DP) offers rigorous guarantee against various attacks. However, conventional methods of ensuring DP by adding local noise alone often result in low training a

Elliot H. Young, Rajen D. Shah

Generalized linear models are a popular tool in applied statistics, with their maximum likelihood estimators enjoying asymptotic Gaussianity and efficiency. As all models are wrong, it is desirable to understand these estimators' behaviours under model misspecification. We study semiparametric multilevel generalized linear models, where only the conditional

Tongzhang Qu, Nicolas Brantut, David Wallis, Christopher Harbord

Fifteen marble samples were subjected to semi-brittle deformation through triaxial compression experiments, reaching axial strains of 0.5%, 1.0%, 2.0%, 4.0%, or 7.5% at temperatures of 20C, 200C, or 350C, under a confining pressure of 400 MPa. Deformation twins, lattice curvature, and intragranular microfractures in the samples were quantitatively characteri

Shirin Qiam, Saipraneeth Devunuri, Lewis J. Lehe

Discussions of minimum parking requirement policies often include maps of parking lots, which are time consuming to construct manually. Open source datasets for such parking lots are scarce, particularly for US cities. This paper introduces the idea of using Near-Infrared (NIR) channels as input and several post-processing techniques to improve the predictio

Marcelo E. Coniglio, Martin Figallo, Rafael R. Testa

We further develop the formal foundations of Paraconsistent Belief Revision (PBR) by introducing Logics of Formal Inconsistency (LFIs) specifically designed to support the development of epistemic entrenchment-based models for belief change. The interpretation of formal consistency -- and, more broadly, of paraconsistency -- in terms of the epistemic attitud

Adam Banaszczyk, Mikołaj Łysakowski, Michał R. Nowicki, Piotr Skrzypczyński

In this paper, we introduce a new methodology for assessing the positioning accuracy of virtual reality (VR) headsets, utilizing a cooperative industrial robot to simulate user head trajectories in a reproducible manner. We conduct a comprehensive evaluation of two popular VR headsets, i.e., Meta Quest 2 and Meta Quest Pro. Using head movement trajectories c

Barnabas Gall, Sacha B. Pulsford, Dana Matthews, Matthew A. Spence

Protein evolution underpins life, and understanding its behavior as a system is of great importance. However, our current models of protein evolution are arguably too simplistic to allow quantitative interpretation and prediction of evolutionary trajectories. Viewing protein evolution as a complex system has the potential to advance our understanding and abi

Guoshenghui Zhao, Eric Song

The rapid advancement of large language models (LLMs) has revolutionized natural language processing, enabling applications in diverse domains such as healthcare, finance and education. However, the growing reliance on extensive data for training and inference has raised significant privacy concerns, ranging from data leakage to adversarial attacks. This sur

Ali Menati, Fatemeh Doudi, Dileep Kalathil, Le Xie

The electricity sector is undergoing substantial transformations due to the rising electrification of demand, enhanced integration of renewable energy resources, and the emergence of new technologies. These changes are rendering the electric grid more volatile and unpredictable, making it difficult to maintain reliable operations. In order to address these i

Hanxun Jin, William Goldberg, Zhenqin Wang, Huiyong Li

Renewable and biodegradable plastics derived from soy protein isolate (SPI) offer a promising alternative to conventional petroleum-based plastics, particularly for film-grade bioplastics applications such as plastic bags. However, even with reinforcement from cellulose nanocrystals (CNCs), their mechanical properties including stiffness lag behind those of

Alberto Bucci, Gianfranco Verzella

In this work, we present the tree tensor network Nystr\"om (TTNN), an algorithm that extends recent research on streamable tensor approximation, such as for Tucker and tensor-train formats, to the more general tree tensor network format, enabling a unified treatment of various existing methods. Our method retains the key features of the generalized Nystr\"om

Jian Huang, Bin Luo, W. N. Brandt, Ying Chen

We present photometric selection of type 1 quasars in the $\approx5.3~{\rm deg}^{2}$ XMM-Large Scale Structure (XMM-LSS) survey field with machine learning. We constructed our training and \hbox{blind-test} samples using spectroscopically identified SDSS quasars, galaxies, and stars. We utilized the XGBoost machine learning method to select a total of 1\,591

Youcheng Zhang, Stefano Pecorario, Xian Wei Chua, Xinglong Ren

Recent reports highlight the potential of tin-based perovskite semiconductors for high-performance p-type field-effect transistors (FETs) with mobilities exceeding 20 cm2V-1s-1. However, these high mobilities--often obtained via two-probe (2P) methods on devices with small channel length-to-width ratios (L/W < 0.5) operating in the saturation regime at high

Mahmoud A. A. Abouelatta, Sergejs Boroviks, Olivier J. F. Martin, Karim Achouri

Metasurface research has shown significant potential for controlling the polarization, amplitude, phase and propagation direction of light. Nevertheless, control over the angular response of incident light still remains a long-standing problem. In this work, we show the potential of diffractive systems for obtaining a step function in momentum space where th

Yufeng Liu, Zonglin Li, Shudan Jiang, Min Li

Rhombohedral graphene (RG) has emerged as a promising platform for exploring exotic quantum phenomena, such as quantum magnetism, unconventional superconductivity, and fractional quantum anomalous Hall effects. Despite its potential, atomic-scale investigations of RG remain limited, hindering a detailed microscopic understanding of the origins of these corre

Xuan Ji, Qiang Qi, Yueqi Chen, Chen Zhou

The Non-Equilibrium Green&#39;s Function (NEGF) method combined with ab initio calculations has been widely used to study charge transport in molecular junctions. However, the significant computational demands of high-resolution calculations for all device components pose challenges in simulating junctions with complex molecular structures and understanding

Yin-Long Liu, Rui Feng, Jia-Hong Yuan, Zhen-Hua Ling

Compared to other clinical screening techniques, speech-and-language-based automated Alzheimer&#39;s disease (AD) detection methods are characterized by their non-invasiveness, cost-effectiveness, and convenience. Previous studies have demonstrated the efficacy of fine-tuning pre-trained language models (PLMs) for AD detection. However, the objective of this

Ming Zhong, Boris A. Malomed, Jin Song, Zhenya Yan

We introduce physically relevant new models of two-dimensional (2D) fractional lattice media accounting for the interplay of fractional intersite coupling and onsite self-focusing. Our approach features novel discrete fractional operators based on an appropriately modified definition of the continuous Riesz fractional derivative. The model of the 2D isotropi

Yuki Nishida

Max-plus algebra is a semiring with addition $a\oplus b = \max(a,b)$ and multiplication $a\otimes b = a+b$. It is applied in cases, such as combinatorial optimization and discrete event systems. We consider the power of max-plus square matrices, which is equivalent to obtaining the all-pair maximum weight paths with a fixed length in the corresponding weight

Nicole Aretz, Max Gunzburger, Mathieu Morlighem, Karen Willcox

Ice sheet simulations suffer from vast parametric uncertainties, such as the basal sliding boundary condition or geothermal heat flux. Quantifying the resulting uncertainties in predictions is of utmost importance to support judicious decision-making, but high-fidelity simulations are too expensive to embed within uncertainty quantification (UQ) computations

Tim Boykett

Permutation clones generalise permutation groups and clone theory. We investigate permutation clones defined by relations, or equivalently, the automorphism groups of powers of relations. We find many structural results on the lattice of all relationally defined permutation clones on a finite set. We find all relationally defined permutation clones on two el

J. Posada-Loaiza, J. D. Mazo-Vásquez, E. Reyes-Gómez

The possibility of exciting stationary solitons due to the Brewster effect is investigated. The analysis is performed by computing the transmission coefficient of an electromagnetic monochromatic wave obliquely incident on the optical system and linearly polarized with the transverse-electric polarization. The optical system is supposed to be surrounded by a

Anton Bulle Labate, Fabio Gagliardi Cozman

Despite impressive success, language models often generate outputs with flawed linguistic structure. We analyze the effect of directly infusing various kinds of syntactic and semantic information into large language models. To demonstrate the value of our proposals, we focus on the translation of natural language queries to SQL, in particular dealing with la

Kaleel Mahmood, Shaoyi Huang

One of the key challenges in Transformer architectures is the quadratic complexity of the attention mechanism, which limits the efficient processing of long sequences. Many recent research works have attempted to provide a reduction from the $O(n^2)$ time complexity of attention to semi-linear complexity. However, it remains an unsolved problem in the sense

Rostyslav Olshevskyi, Zhongyuan Zhao, Kevin Chan, Gunjan Verma

Graph neural networks (GNNs) are powerful tools for developing scalable, decentralized artificial intelligence in large-scale networked systems, such as wireless networks, power grids, and transportation networks. Currently, GNNs in networked systems mostly follow a paradigm of `centralized training, distributed execution', which limits their adaptability an

Stéphane Vinet, Wilson Wu, Yujie Zhang, Thomas Jennewein

Frequency-bin quantum encoding shows great promise for quantum communication given its high-dimensional scaling, compatibility with photonic integrated circuits and synergy with classical optical communication technology. However, to date all demonstrations have been performed over single-mode and static channels, while the transmission over fluctuating and

Charlotte Aspinwall, Tobias Clark, Yuanan Diao

In this paper, we tabulate the set of alternating pretzel links. Specifically, for any given crossing number $c$, we derive a closed formula that would allow us to compute $\mathcal{P}(c)$, the total number of alternating pretzel links with crossing number $c$. Numerical computation suggests that $\mathcal{P}(c)\approx 0.155e^{0.588c}$. That is, the number o

Qikang Liu, Yang He, Yanwen Cai, Byeongguk Kwak

Document databases are increasingly popular in various applications, but their queries are challenging to write due to the flexible and complex data model underlying document databases. This paper presents a synthesis technique that aims to generate document database queries from input-output examples automatically. A new domain-specific language is designed

Vincent Gherold, Ioannis Mandralis, Eric Sihite, Adarsh Salagame

Autonomous robots operating in real environments are often faced with decisions on how best to navigate their surroundings. In this work, we address a particular instance of this problem: how can a robot autonomously decide on the energetically optimal path to follow given a high-level objective and information about the surroundings? To tackle this problem

Keith Pritchett, Shardha Jogee, Yuchen Guo

Studying barred galaxies at early epochs can shed light on the early evolution of stellar bars, their impact on secular evolution and the star formation activity of young galaxies, and the origins of present-day barred galaxies like the Milky Way. We analyze data from the James Webb Space Telescope (JWST) Cosmic Evolution Early Release Science (CEERS) Survey

Yiwen Zhu, Mathieu Demarne, Kai Deng, Wenjing Wang

Software engineers frequently grapple with the challenge of accessing disparate documentation and telemetry data, including TroubleShooting Guides (TSGs), incident reports, code repositories, and various internal tools developed by multiple stakeholders. While on-call duties are inevitable, incident resolution becomes even more daunting due to the obscurity

Xuetao Lu, J. Jack Lee

The use of external data in clinical trials offers numerous advantages, such as reducing the number of patients, increasing study power, and shortening trial durations. In Bayesian inference, information in external data can be transferred into an informative prior for future borrowing (i.e., prior synthesis). However, multisource external data often exhibit

Eric Dolores-Cuenca, Aldo Guzman-Saenz, Sangil Kim, Susana Lopez-Moreno

The paper ``Tropical Geometry of Deep Neural Networks'' by L. Zhang et al. introduces an equivalence between integer-valued neural networks (IVNN) with $\text{ReLU}_{t}$ and tropical rational functions, which come with a map to polytopes. Here, IVNN refers to a network with integer weights but real biases, and $\text{ReLU}_{t}$ is defined as $\text{ReLU}_{t}

Sébastien Boucksom, Mattias Jonsson, Antonio Trusiani

Generalizing previous results of Arezzo-Pacard-Singer, Seyyedali-Sz\'ekelyhidi and Hallam, we prove the invariance under smooth blowups of the class of weighted extremal K\"ahler manifolds, modulo a log-concavity assumption on the first weight. Through recent work of Di Nezza-Jubert-Lahdili and Han-Liu, this is obtained as a consequence of a general uniform

T. D. Sandnes, V. R. Eke, J. A. Kegerreis, R. J. Massey

A giant impact has been proposed as a possible formation mechanism for Jupiter's dilute core -- the planet's inferred internal structure in which the transition between its core of heavy elements and its predominantly hydrogen-helium envelope is gradual rather than a discrete interface. A past simulation suggested that a head-on impact of a $10~M_\oplus$ pla

Vivekananda Bal, Jacqueline M. Wolfrum, Paul W. Barone, Stacy L. Springs

Gene therapies using recombinant adeno-associated virus (rAAV) have been developed to treat monogenic and acquired diseases but are currently the most expensive drugs due, in part, to high manufacturing costs. The cells producing rAAV generate substantial quantities of empty (50-90%) and partially filled capsids that must be removed prior to final formulatio

Matteo Mogliani, Florens Odendahl

The popular choice of using a $direct$ forecasting scheme implies that the individual predictions do not contain information on cross-horizon dependence. However, this dependence is needed if the forecaster has to construct, based on $direct$ density forecasts, predictive objects that are functions of several horizons ($e.g.$ when constructing annual-average

Kenta Takahashi, Wataru Nakamura

Fuzzy Extractor (FE) and Fuzzy Signature (FS) are useful schemes for generating cryptographic keys from fuzzy data such as biometric features. Several techniques have been proposed to implement FE and FS for fuzzy data in an Euclidean space, such as facial feature vectors, that use triangular lattice-based error correction. In these techniques, solving the c

Johann Rehberger

The CIA security triad - Confidentiality, Integrity, and Availability - is a cornerstone of data and cybersecurity. With the emergence of large language model (LLM) applications, a new class of threat, known as prompt injection, was first identified in 2022. Since then, numerous real-world vulnerabilities and exploits have been documented in production LLM s

Ashish Goswami, Satyam Kumar Modi, Santhosh Rishi Deshineni, Harman Singh

Text-to-image (T2I) generation has seen significant progress with diffusion models, enabling generation of photo-realistic images from text prompts. Despite this progress, existing methods still face challenges in following complex text prompts, especially those requiring compositional and multi-step reasoning. Given such complex instructions, SOTA models of

Ruoxin Wang, Tianyi Tang, Haiming Du, Yuxuan Cheng

Brain tumor segmentation models have aided diagnosis in recent years. However, they face MRI complexity and variability challenges, including irregular shapes and unclear boundaries, leading to noise, misclassification, and incomplete segmentation, thereby limiting accuracy. To address these issues, we adhere to an outstanding Convolutional Neural Networks (

Zhuofan Li, Corey M. Abramson

Ethnography (social scientific methods that illuminate how people understand, navigate and shape the real world contexts in which they live their lives) and machine learning (computational techniques that use big data and statistical learning models to perform quantifiable tasks) are each core to contemporary social science. Yet these tools have remained lar

Yan V. Fyodorov, Boris A. Khoruzhenko, Thomas Prellberg

We investigate radial statistics of zeros of hyperbolic Gaussian Analytic Functions (GAF) of the form $\varphi (z) = \sum_{k\ge 0} c_k z^k$ given that $|\varphi (0)|^2=t$ and assuming coefficients $c_k$ to be independent standard complex normals. We obtain the full conditional distribution of $N_q$, the number of zeros of $\varphi (z)$ within a disk of radiu

Albrecht Kurze, Karola Köpferl

We relate our previous as well as ongoing research in the domain of smart homes to the concept of HabiTech. HabiTech can benefit from existing approaches and findings in a broader context of whole buildings or communities within. Along with data comes context of data capture and data interpretation in different dimensions (spatial, temporal, social). For def

Marco Usula

Let $X$ be a manifold with boundary, and let $L$ be a 0-elliptic operator on X which is semi-Fredholm essentially surjective with infinite-dimensional kernel. Examples include Hodge Laplacians and Dirac operators on conformally compact manifolds. We construct left and right parametrices for L when supplemented with appropriate elliptic boundary conditions. T

Ane Rahbek Vierø, Michael Szell

Research on cycling conditions focuses on cities, because cycling is commonly considered an urban phenomenon. People outside of cities should, however, also have access to the benefits of active mobility. To bridge the gap between urban and rural cycling research, we analyze the bicycle network of Denmark, covering around 43,000 km2 and nearly 6 mio. inhabit

Leo Fillioux, Julio Silva-Rodríguez, Ismail Ben Ayed, Paul-Henry Cournède

Recent advances in self-supervision and contrastive learning have brought the performance of foundation models to unprecedented levels in a variety of tasks. Fueled by this progress, these models are becoming the prevailing approach for a wide array of real-world vision problems, including risk-sensitive and high-stakes applications. However, ensuring safe d

Kiyoshi Sogo

For an arbitrary positive integer $p$, Landen's formula is extended to express theta function with modulus $p\tau$ by $p$ product of theta functions with $\tau$, which is applied to several examples. Next it is shown that double product of theta functions of genus $g=1$ is written by a sum of $g=2$ theta functions, which is a subset having a special period m

Karlo Koledić, Luka Petrović, Ivan Marković, Ivan Petrović

Generalizing metric monocular depth estimation presents a significant challenge due to its ill-posed nature, while the entanglement between camera parameters and depth amplifies issues further, hindering multi-dataset training and zero-shot accuracy. This challenge is particularly evident in autonomous vehicles and mobile robotics, where data is collected wi

Pramith Devulapalli, Steve Hanneke

Imagine a smart camera trap selectively clicking pictures to understand animal movement patterns within a particular habitat. These "snapshots", or pieces of data captured from a data stream at adaptively chosen times, provide a glimpse of different animal movements unfolding through time. Learning a continuous-time process through snapshots, such as smart c

Nicholas Alonso, Beren Millidge

Recent advances have extended the context window of frontier LLMs dramatically, from a few thousand tokens up to millions, enabling entire books and codebases to fit into context. However, the compute costs of inferencing long-context LLMs are massive and often prohibitive in practice. RAG offers an efficient and effective alternative: retrieve and process o

Martin Thomas Horsch, Fadi Al Machot, Jadran Vrabec

Data and metadata documentation requirements for explainable-AI-ready (XAIR) models and data in physics-based simulation technology are discussed by analysing different perspectives from the literature on two core aspects: First, the scope of the simulation; this category is taken to include subject matter, the objective with which the simulation is conducte

Kiyoshi Sogo

Jacobi's theta relations among quartic products of theta functions are generalized to those of arbitrary $n$ products. Igusa's procedure of derivation is extended to prove such general theta relations, from which we obtain general addition formulas and theta constants identities. To complete the proof, the concept of {\it cycle number $\lambda$} is essential

Yuemei Ren, Liang Liao, Stephen John Maybank, Yanning Zhang

This paper addresses the challenge of spectral-spatial feature extraction for hyperspectral image classification by introducing a novel tensor-based framework. The proposed approach incorporates circular convolution into a tensor structure to effectively capture and integrate both spectral and spatial information. Building upon this framework, the traditiona

William W. Symes, Huiyi Chen, Susan E. Minkoff

Iterative inversion of seismic, ultrasonic, and other wave data by local gradient-based optimization of mean-square data prediction error (Full Waveform Inversion or FWI) can fail to converge to useful model estimates if started from an initial model predicting wave arrival times in error by more than half a wavelength (a phenomenon known as cycle skipping).

Dongwei Wang, Huanrui Yang

Quantization is a critical step to enable efficient LLM serving under limited resource. However, previous research observes that certain weights in the LLM, known as outliers, are significantly sensitive to quantization noises. Existing quantization methods leave these outliers as floating points or higher precisions to retain performance, posting challenges

Gabriele Russo Russo, Romolo Marotta, Flavio Cordari, Francesco Quaglia

The flexibility and the variety of computing resources offered by the cloud make it particularly attractive for executing user workloads. However, IaaS cloud environments pose non-trivial challenges in the case of workflow scheduling under deadlines and monetary cost constraints. Indeed, given the typical uncertain performance behavior of cloud resources, sc

Mohsen Moradi, Hessam Mahdavifar

Recently, a novel variation of polar codes known as polarization-adjusted convolutional (PAC) codes has been introduced by Ar{\i}kan. These codes significantly outperform conventional polar and convolutional codes, particularly for short codeword lengths, and are shown to operate very close to the optimal bounds. It has also been shown that if the rate profi

Fan Wang, Juyong Jiang, Chansung Park, Sunghun Kim

The increasing sizes of large language models (LLMs) result in significant computational overhead and memory usage when adapting these models to specific tasks or domains. Various parameter-efficient fine-tuning (PEFT) methods have been devised to mitigate these challenges by training a small set of parameters for the task-specific updates of the model weigh

Azar Louzi

Stochastic gradient descent (SGD) has been a go-to algorithm for nonconvex stochastic optimization problems arising in machine learning. Its theory however often requires a strong framework to guarantee convergence properties. We hereby present a full scope convergence study of biased nonconvex SGD, including weak convergence, function-value convergence and

Mohammadreza Jamalifard, Javier Andreu-Perez, Hani Hagras, Luis Martínez López

As the data resources grow, providing recommendations that best meet the demands has become a vital requirement in business and life to overcome the information overload problem. However, building a system suggesting relevant recommendations has always been a point of debate. One of the most cost-efficient techniques in terms of producing relevant recommenda

Alexander Clifton, Dániel G. Simon

We investigate two notions of saturation for partial planar embeddings of maximal planar graphs. Let $G = (V, E) $ be a vertex-labeled maximal planar graph on $ n $ vertices, which by definition has $3n - 6$ edges. We say that a labeled plane graph $H = (V, E')$ with $E' \subseteq E$ is a \emph{labeled plane-saturated subgraph} of $G$ if no edge in $E \setmi

Irene Anello, François Alouges, Antonio De Simone

The modeling of the beating of cilia and flagella in fluids is a particularly active field of study, given the biological relevance of these organelles. Various mathematical models have been proposed to represent the nonlinear dynamics of flagella, whose motion is powered by the work of molecular motors attached to filaments composing the axoneme. Here, we f

Kai Smith

If a link $L$ can be decomposed into the union of two tangles $T\cup_{S^2} S$ along a 2-sphere intersecting $L$ in 4 points, then the intersections of perturbed traceless SU(2) character varieties of tangles in a space called the pillowcase form a set of generators for Kronheimer and Mrowka's reduced singular instanton homology, $I^\natural$. It is conjectur

Xingyi Yang, Xinchao Wang

In this paper, we present Language Model as Visual Explainer LVX, a systematic approach for interpreting the internal workings of vision models using a tree-structured linguistic explanation, without the need for model training. Central to our strategy is the collaboration between vision models and LLM to craft explanations. On one hand, the LLM is harnessed

Artem V. Korzhimanov, Sergey A. Koryagin, Andrey D. Sladkov, Mikhail E. Viktorov

Numerical modeling of the interaction process of two counter-streaming supersonic plasma flows with an arched magnetic field configuration in the regime of a magnetic Mach number of the order of unity $M_m \sim 1$ is carried out. The flows were launched from the bases of the arch along the direction of the magnetic field. It is shown that the interaction has

Stephan Thaler, Cristian Gabellini, Nikhil Shenoy, Prudencio Tossou

Neural network potentials (NNPs) offer a fast and accurate alternative to ab-initio methods for molecular dynamics (MD) simulations but are hindered by the high cost of training data from high-fidelity Quantum Mechanics (QM) methods. Our work introduces the Implicit Delta Learning (IDLe) method, which reduces the need for high-fidelity QM data by leveraging

Zhao Song, Ali Vakilian, David P. Woodruff, Samson Zhou

Low-rank approximation and column subset selection are two fundamental and related problems that are applied across a wealth of machine learning applications. In this paper, we study the question of socially fair low-rank approximation and socially fair column subset selection, where the goal is to minimize the loss over all sub-populations of the data. We s