August 2024 arXiv papers — page 6

Ole Løseth Elvetun, Kim Knudsen, Bjørn Fredrik Nielsen

For linear ill-posed problems with nontrivial null spaces, Tikhonov regularization and truncated singular value decomposition (TSVD) typically yield solutions that are close to the minimum norm solution. Such a bias is not always desirable, and we have therefore in a series of papers developed a weighting procedure which produces solutions with a different a

Yujing Wang, Hainan Zhang, Liang Pang, Binghui Guo

In a real-world RAG system, the current query often involves spoken ellipses and ambiguous references from dialogue contexts, necessitating query rewriting to better describe user's information needs. However, traditional context-based rewriting has minimal enhancement on downstream generation tasks due to the lengthy process from query rewriting to resp

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $(2712.4 \pm 14.3) \times 10^6~ψ$(3686) events collected with the BESIII detector operating at the BEPCII collider, we search for the hadronic transition $h_c \to π^+π^-J/ψ$ via $ψ(3686)\to π^0 h_c$. No significant signal is observed. We set the most stringent upper limits to date on the branching fractions $\mathcal{B}(ψ(3686)\to π^0 h_c)\times\mathca

Yusheng Tian, Junbin Liu, Tan Lee

This paper describes a human-in-the-loop approach to personalized voice synthesis in the absence of reference speech data from the target speaker. It is intended to help vocally disabled individuals restore their lost voices without requiring any prior recordings. The proposed approach leverages a learned speaker embedding space. Starting from an initial voi

Alexander V. Karzanov

We consider a model of stable edge sets (``matchings'') in a bipartite graph $G=(V,E)$ in which the preferences for vertices of one side (``firms'') are given via choice functions subject to standard axioms of consistency, substitutability and cardinal monotonicity, whereas the preferences for the vertices of the other side (``workers'&#3

Zhiyuan Yan, Yandan Zhao, Shen Chen, Mingyi Guo

Three key challenges hinder the development of current deepfake video detection: (1) Temporal features can be complex and diverse: how can we identify general temporal artifacts to enhance model generalization? (2) Spatiotemporal models often lean heavily on one type of artifact and ignore the other: how can we ensure balanced learning from both? (3) Videos

Chun Tong Lei, Hon Ming Yam, Zhongliang Guo, Yifei Qian

Neural networks have revolutionized numerous fields with their exceptional performance, yet they remain susceptible to adversarial attacks through subtle perturbations. While diffusion-based purification methods like DiffPure offer promising defense mechanisms, their computational overhead presents a significant practical limitation. In this paper, we introd

Asifullah Khan, Anabia Sohail, Mustansar Fiaz, Mehdi Hassan

Advances in deep learning are re-defining how visual data is processed and understand by the machines. Vision Transformers (ViTs) have recently demonstrated prominent performance in computer vision related tasks. However, their performance improves with increasing numbers of labeled data, indicating reliance on labeled data. Humanly annotated data are diffic

Nasim Jamshidi Avanaki, Abhijay Ghildyal, Nabajeet Barman, Saman Zadtootaghaj

Recent advancements in the field of No-Reference Image Quality Assessment (NR-IQA) using deep learning techniques demonstrate high performance across multiple open-source datasets. However, such models are typically very large and complex making them not so suitable for real-world deployment, especially on resource- and battery-constrained mobile devices. To

Pengfei Shi, Wei Zhong, Xinyu Zhang, Ningtao Wang

Estimating the conditional average treatment effects (CATE) is very important in causal inference and has a wide range of applications across many fields. In the estimation process of CATE, the unconfoundedness assumption is typically required to ensure the identifiability of the regression problems. When estimating CATE using high-dimensional data, there ha

Zhengru Fang, Senkang Hu, Jingjing Wang, Yiqin Deng

Collaborative perception systems leverage multiple edge devices, such surveillance cameras or autonomous cars, to enhance sensing quality and eliminate blind spots. Despite their advantages, challenges such as limited channel capacity and data redundancy impede their effectiveness. To address these issues, we introduce the Prioritized Information Bottleneck

Roy Ganz, Michael Elad

Joint Energy Models (JEMs), while drawing significant research attention, have not been successfully scaled to real-world, high-resolution datasets. We present CLIP-JEM, a novel approach extending JEMs to the multimodal vision-language domain using CLIP, integrating both generative and discriminative objectives. For the generative one, we introduce an image-

Glenn Sun, Yihong Zhang, Haobin Ni

We demonstrate a new connection between e-graphs and Boolean circuits. This allows us to adapt existing literature on circuits to easily arrive at an algorithm for optimal e-graph extraction, parameterized by treewidth, which runs in $2^{O(w^2)}\text{poly}(w, n)$ time, where $w$ is the treewidth of the e-graph. Additionally, we show how the circuit view of e

George P. Harabin, Amir Mirzendehdel, Morad Behandish

One of the main concerns in design and process planning for multi-axis additive and subtractive manufacturing is collision avoidance between moving objects (e.g., tool assemblies) and stationary objects (e.g., a part unified with fixtures). The collision measure for various pairs of relative rigid translations and rotations between the two pointsets can be c

Takeshi Omori, Yasutaka Yamaguchi

Through the decomposition of the pressure into the kinetic and the intermolecular contributions, we show that the pressure anisotropy in the fluid interface, which is the source of the interfacial tension, comes solely from the latter contribution. The pressure anisotropy due to the intermolecular force between the fluid particles in the same or the differen

Kumar Gautam

This thesis explores the concept of realizing quantum gates using physical systems like atoms and oscillators perturbed by electric and magnetic fields. The basic idea is that if a time-independent Hamiltonian $H_0$ is perturbed by a time-varying Hamiltonian of the form $f(t)V$, where $f(t)$ is a scalar function of time and $V$ is a Hermitian operator that d

Sitaram Ramakrishnan, Tatsuya Yamakawa, Ryohei Oishi, Soichiro Yamane

We report the crystal structures and superconductivity (SC) of LaPt$_{x}$Si$_{2-x}$ ($0.5 \leq x \leq 1.0$) that are solid solutions of LaSi$_{2}$ and LaPtSi with centrosymmetric tetragonal ($I4_{1}/amd$, $D_{4h}^{19}$, \#141) and non-centrosymmetric tetragonal ($I4_{1}md$, $C_{4v}^{11}$, \#109) structures, respectively. It was found that at $0.86 \leq x \le

Tongtong Feng, Qi Qi, Lingqi Guo, Jingyu Wang

Accuracy anomaly detection in user-level network traffic is crucial for network security. Compared with existing models that passively detect specific anomaly classes with large labeled training samples, user-level network traffic contains sizeable new anomaly classes with few labeled samples and has an imbalance, self-similar, and data-hungry nature. Motiva

Lei Shen, Akira Ono, Yu-Gang Ma

Molecular dynamics approaches use wave packets as nucleon wave functions to simulate the time evolution of nuclear reactions. It is crucial to activate the momentum fluctuation inherent in each wave packet so that it properly affects the time evolution. In the antisymmetrized molecular dynamics (AMD) model, this has traditionally been done by splitting the w

Hunter Swan, Andrii Torchylo, Michael J. Van de Graaff, Jan Rudolph

A phase-only spatial light modulator (SLM) provides a powerful way to shape laser beams into arbitrary intensity patterns, but at the cost of a hard computational problem of determining an appropriate SLM phase. Here we show that optimal transport methods can generate approximate solutions to this problem that serve as excellent initializations for iterative

Atnafu Lambebo Tonja, Bonaventure F. P. Dossou, Jessica Ojo, Jenalea Rajab

High-resource language models often fall short in the African context, where there is a critical need for models that are efficient, accessible, and locally relevant, even amidst significant computing and data constraints. This paper introduces InkubaLM, a small language model with 0.4 billion parameters, which achieves performance comparable to models with

Ashish Kumar Meena

This paper studies gravitational lensing degeneracies in the wave-optics regime, focusing on lensed gravitational waves (GWs). Considering lensing degeneracies as re-scaling (or transformations) of arrival time delay surface, we can divide them into local and global types. Local degeneracies only affect the time delay surface in localized regions, whereas gl

Philipp Adämmer, Philipp Wittenberg, Christian H. Weiß, Murat Caner Testik

In process monitoring, it is common for measurements to be taken regularly or randomly from different spatial locations in two or three dimensions. While there are nonparametric methods for process monitoring with such spatial data to detect changes in the mean, there is a gap in the literature for nonparametric control charting methods developed to monitor

Guangya Wan, Yuqi Wu, Jie Chen, Sheng Li

Self-Consistency mitigates hallucinations in Large Language Models (LLMs) by sampling multiple reasoning paths,but it lacks a systematic approach to determine the optimal number of samples or select the most faithful rationale. To address this limitation, we introduce Reasoning-Aware Self-Consistency (RASC), a novel framework that enhances sampling efficienc

Winston Chen, Yifan Jiang, William Stafford Noble, Yang Young Lu

Machine learning (ML) models are powerful tools for detecting complex patterns within data, yet their "black box" nature limits their interpretability, hindering their use in critical domains like healthcare and finance. To address this challenge, interpretable ML methods have been developed to explain how features influence model predictions. Howeve

Emma K. Laird, Brendan C. Mulkerin, Jia Wang, Matthew J. Davis

Pairing lies at the heart of superfluidity in fermionic systems. Motivated by recent experiments in mesoscopic Fermi gases, we study up to six fermionic atoms with equal masses and equal populations in two different spin states, confined in a quasi-two-dimensional harmonic trap. We couple a stochastic variational approach with the use of an explicitly correl

Hyun Min Lee, Adriana G. Menkara, Myeong-Jung Seong, Jun-Ho Song

We propose a consistent framework with the $U(1)$ Peccei-Quinn (PQ) symmetry for obtaining the initial condition for axion kinetic misalignment from inflation. We introduce a PQ complex scalar field and an extra Higgs doublet, which are conformally coupled to gravity, and three right-handed neutrinos for the seesaw mechanism. In the DFSZ type scenarios for t

Jutika Borah, Kumaresh Sarmah, Hidam Kumarjit Singh

Imaging techniques such as Chest X-rays, whole slide images, and optical coherence tomography serve as the initial screening and detection for a wide variety of medical pulmonary and ophthalmic conditions respectively. This paper investigates the intricacies of using pretrained deep convolutional neural networks with transfer learning across diverse medical

Yimei Li, Philippe Souplet

We prove that the Dirichlet problem for the Lane-Emden system in a half-space has no positive classical solution that is bounded on finite strips. Such a nonexistence result was previously available only for bounded solutions or under a restriction on the powers in the nonlinearities.

Shuyang Zhang, Jinhao He, Yilong Zhu, Jin Wu

The stability of visual odometry (VO) systems is undermined by degraded image quality, especially in environments with significant illumination changes. This study employs a deep reinforcement learning (DRL) framework to train agents for exposure control, aiming to enhance imaging performance in challenging conditions. A lightweight image simulator is develo

Zhun-Yong Ong

The use of structurally complex lattice defects, such as functional groups, embedded nanoparticles, and nanopillars, to generate phonon scattering is a popular approach in phonon engineering for thermoelectric applications. However, the theoretical treatment of this scattering phenomenon remains a formidable challenge, especially with regard to the determina

Shen Li, Liuyi Yao, Lan Zhang, Yaliang Li

Aligned LLMs are secure, capable of recognizing and refusing to answer malicious questions. However, the role of internal parameters in maintaining such security is not well understood yet, further these models can be vulnerable to security degradation when subjected to fine-tuning attacks. To address these challenges, our work uncovers the mechanism behind

D. T. Hoai, J. M. Winters, P. T. Nhung, P. Darriulat

New observations are presented of millimetre line emissions of the circumstellar envelope (CSE) of AGB star $χ$ Cygni using the recently upgraded NOEMA array. $χ$ Cygni is an S-type Mira variable, at the border between oxygen-rich and carbon-rich, that has been observed for over 40 years to display features giving evidence for the strong role played by pulsa

Allen Fraiman, Lawrence Ziegler

There is an urgent need for the development of novel and truly rapid (equal or less than 1 hour) antibiotic susceptibility testing (AST) platforms in order to provide best antimicrobial prescribing practices and to help reduce the increasing global threat of antibiotic resistance. A 785 nm surface enhanced Raman spectroscopy (SERS) based phenotypic methodolo

B V Rajarama Bhat, Neeru Bala

Let $A$ be an $m\times m$ complex matrix and let $λ_1, λ_2, \ldots , λ_m$ be the eigenvalues of $A$ arranged such that $|λ_1|\geq |λ_2|\geq \cdots \geq |λ_m|$ and for $n\geq 1,$ let $s^{(n)}_1\geq s^{(n)}_2\geq \cdots \geq s^{(n)}_m$ be the singular values of $A^n$. Then a famous theorem of Yamamoto (1967) states that $$\lim _{n\to \infty}(s^{(n)}_j )^{\frac

Huang Chenan, Narumasa Tsutsumida

Unsupervised clustering has emerged as a critical tool for uncovering hidden patterns in vast, unlabeled datasets. However, traditional methods, such as Partitioning Around Medoids (PAM), struggle with scalability owing to their quadratic computational complexity. To address this limitation, we introduce WOA-kMedoids, a novel unsupervised clustering method t

Yanmeng Xing, Ye Sun, Tongxin Pan, Giacomo Livan

Establishing an independent academic identity is a central yet insufficiently understood challenge for early-career researchers. However, limited resources and mentor-driven research agendas often constrain early efforts toward autonomy. To provide large-scale quantitative evidence on how junior researchers develop independence, we introduce a framework that

Zijie Xin, Minquan Wang, Jingyu Liu, Ye Ma

Adding proper background music helps complete a short video to be shared. Previous work tackles the task by video-to-music retrieval (V2MR), aiming to find the most suitable music track from a collection to match the content of a given query video. In practice, however, music tracks are typically much longer than the query video, necessitating (manual) trimm

Tim J. Boonen, Engel John C. Dela Vega

This paper considers an insurance company that faces two key constraints: a ratcheting dividend constraint and an irreversible reinsurance constraint. The company allocates part of its reserve to pay dividends to its shareholders while strategically purchasing reinsurance for its claims. The ratcheting dividend constraint ensures that dividend cuts are prohi

Guoyang Xu, Zhenxi Song, Junqi Xue, Yuxin Liu

Achieving consistent sentiment representation across diverse modalities remains a key challenge in multimodal sentiment analysis. However, rapid emotional fluctuations over time often introduce instability, leading to compromised prediction performance. To address this challenge, we propose a robust sentiment representation dual enhancement strategy that sim

Tong Wang, Ronilo Ragodos, Lu Feng, Yu

Post hoc explainers such as SHAP and LIME are used widely in business research to interpret complex machine learning models. Although they were designed to explain model predictions, there has been an increasing trend in which the explanations they generate are treated as evidence about underlying data relationships. Based on a systematic review of 181 studi

Tan Zhi-Xuan, Micah Carroll, Matija Franklin, Hal Ashton

The dominant practice of AI alignment assumes (1) that preferences are an adequate representation of human values, (2) that human rationality can be understood in terms of maximizing the satisfaction of preferences, and (3) that AI systems should be aligned with the preferences of one or more humans to ensure that they behave safely and in accordance with ou

Sudeep Salgia, Yuejie Chi

We consider the problem of federated Q-learning, where $M$ agents aim to collaboratively learn the optimal Q-function of an unknown infinite-horizon Markov decision process with finite state and action spaces. We investigate the trade-off between sample and communication complexities for the widely used class of intermittent communication algorithms. We firs

Jinghan Yao, Sam Ade Jacobs, Masahiro Tanaka, Olatunji Ruwase

Large Language Models (LLMs) with long context capabilities are integral to complex tasks in natural language processing and computational biology, such as text generation and protein sequence analysis. However, training LLMs directly on extremely long contexts demands considerable GPU resources and increased memory, leading to higher costs and greater compl

Lihang Liu, Shanzhuo Zhang, Yang Xue, Xianbin Ye

The AlphaFold series has transformed protein structure prediction with remarkable accuracy, often matching experimental methods. AlphaFold2, AlphaFold-Multimer, and the latest AlphaFold3 represent significant strides in predicting single protein chains, protein complexes, and biomolecular structures. While AlphaFold2 and AlphaFold-Multimer are open-sourced,

Zhenyu Xiao, Tomi Ohtsuki, Kohei Kawabata

We investigate the monitored quantum dynamics of Gaussian mixed states and derive the universal Fokker-Planck equations that govern the stochastic time evolution of entire density-matrix spectra, obtaining their exact solutions. From these equations, we reveal an even-odd effect in purification dynamics: whereas entropy exhibits exponential decay for an even

Ioan Bejenaru, Mohandas Pillai, Daniel Tataru

We consider equivariant solutions for the Schrödinger Map equation in $2+1$ dimensions, with values into $\mathbb{S}^2$. Within each equivariance class $m \in \mathbb{Z}$ this admits a lowest energy nontrivial steady state $Q^m$, which extends to a two dimensional family of steady states by scaling and rotation. If $|m| \geq 3$ then these ground states are k

Zhe Wang, Haixia Fan, Jiyuan Zhang, Xiao-Yun Wang

Nonlinear circuits serve as crucial carriers and physical models for investigating nonlinear dynamics and chaotic behavior, particularly in the simulation of biological neurons. In this study, Chua's circuit and Lorenz circuit are systematically explored for the first time through machine learning correlation algorithms. Specifically, the upgraded and op

Niccoló Loret

In this paper I would like to show how the Deformed Special Relativity family of models - developed to approach spacetime quantization - can actually be applied to the description of classical cosmology. I use the bicrossproduct basis of $κ$-Poincaré algebra to describe photon propagation in deSitter classical General Relativity. I show the Hopf algebraic as

Hanwen Bi, Thushara D. Abhayapala

Machine learning and neural networks have advanced numerous research domains, but challenges such as large training data requirements and inconsistent model performance hinder their application in certain scientific problems. To overcome these challenges, researchers have investigated integrating physics principles into machine learning models, mainly throug

Chao Wang, Neo Wu, Lin Ning, Jiaxing Wu

Large language models (LLMs) have shown remarkable capabilities in generating user summaries from a long list of raw user activity data. These summaries capture essential user information such as preferences and interests, and therefore are invaluable for LLM-based personalization applications, such as explainable recommender systems. However, the developmen

Dewen Zeng, Yawen Wu, Xinrong Hu, Xiaowei Xu

Contrastive learning with the nearest neighbor has proved to be one of the most efficient self-supervised learning (SSL) techniques by utilizing the similarity of multiple instances within the same class. However, its efficacy is constrained as the nearest neighbor algorithm primarily identifies "easy" positive pairs, where the representations are al

Kunal Das, Shan Yu, Guannan Wang, Li Wang

Accurately estimating data density is crucial for making informed decisions and modeling in various fields. This paper presents a novel nonparametric density estimation procedure that utilizes bivariate penalized spline smoothing over triangulation for data scattered over irregular spatial domains. The approach is likelihood-based with a regularization term

Rahul Yadav, Maria D. Kazachenko, Andrey N. Afanasyev, Gianna Cauzzi

We present high-resolution observations of a C4.1-class solar flare (SOL2023-05-03T20:53) in AR 13293 from the ViSP and VBI instruments at the DKIST. The fast cadence, good resolution, and high polarimetric sensitivity of ViSP data provide a unique opportunity to explore the photospheric magnetic fields before and during the flare. We infer the magnetic fiel

Alexis Kagan

We consider a null-recurrent randomly biased walk $\mathbb{X}$ on a Galton-Watson tree in the (sub)-diffusive regime and we prove that properly renormalized, the local time in a critical generation converges in law towards some function of a stable continuous-state branching process. We also provide an explicit equivalent of the probability that critical gen

Fabiola Fortuna, Juan Manuel Márquez, Pablo Roig

In a previous work \cite{Buttazzo:2020ibd}, it has been pointed out that heavy new physics (NP) contributions to lepton dipole moments and high-energy cross-sections of lepton pairs into Higgs and $γ/Z$ bosons are connected, if the electroweak symmetry breaking is realized linearly. As a consequence, a multi-TeV muon collider would provide a unique test of N

Zhenhao Li

We consider a frictionless system coupled to an external Markovian environment. The quantum and classical evolution of such systems are described by the Lindblad and the Fokker-Planck equation respectively. We show that when such a system is given by an at most quadratically growing Hamiltonian and at most linearly growing real jump functions, the quantum an

Rafid Bendimerad, Elaine Petro

This study introduces a machine learning framework to predict the suitability of ionic liquids with unknown physical properties as propellants for electrospray thrusters based on their molecular structure. We construct a training dataset by labeling ionic liquids as suitable (+1) or unsuitable (-1) for electrospray thrusters based on their density, viscosity

Fairuz Shadmani Shishir, Md Mashfiq Rizvee, Tanvir Hossain, Tamzidul Hoque

Detecting counterfeit integrated circuits (ICs) in unreliable supply chains demands robust tracking and authentication. Physical Unclonable Functions (PUFs) offer unique IC identifiers, but noise undermines their utility. This study introduces the Persistent Hierarchical Bloom Filter (PHBF) framework, ensuring swift and accurate IC authentication with an acc

Thomas Kindred

We introduce new numerical invariants of a spanning surface $F\subset S^3$, called the \emph{algebraic essence} and \emph{geometric essence} of $F$, which measure how far $F$ is from being compressible. We extend a theorem of Ozawa by showing that algebraic essence behaves well under Murasugi sum. We also introduce a ``twisted'' generalization of Mur

Sina Saadati, Mohammadreza Razzazi

Motion disorders pose a significant global health concern and are often managed with pharmacological treatments that may lead to undesirable long-term effects. Current therapeutic strategies lack differentiation between healthy and unhealthy muscles in a patient, necessitating a targeted approach to distinguish between musculature. There is still no motion a

Ernesto de León, Mykola Makhortykh, Aleksandra Urman, Roberto Ulloa

The conspiracy theory that the US 2020 presidential election was fraudulent - the Big Lie - remained a prominent part of the media agenda months after the election. Whether and how search engines prioritized news stories that sought to thoroughly debunk the claims, provide a simple negation, or support the conspiracy is crucial for understanding information

Leandro Julian Mele, Muhammad Ashraful Alam, Pierpaolo Palestri

A model for the current-voltage characteristic of the junction between an Ion-Sensitive-Membrane and an electrolyte solution is derived and compared with numerical simulations of the Poisson-Nernst-Planck model for ion transport. The expression resembles that of a semiconductor pn junction with a non-ideality factor of 2. The non-ideality correlated to the v

Andrew Hamara, Pablo Rivas

This study investigates ImageBind's ability to generate meaningful fused multimodal embeddings for online auto parts listings. We propose a simplistic embedding fusion workflow that aims to capture the overlapping information of image/text pairs, ultimately combining the semantics of a post into a joint embedding. After storing such fused embeddings in a

Xiaoyu Zhang, Ruobing Xie, Yougang Lyu, Xin Xin

Conversational recommender systems (CRSs) are able to elicit user preferences through multi-turn dialogues. They typically incorporate external knowledge and pre-trained language models to capture the dialogue context. Most CRS approaches, trained on benchmark datasets, assume that the standard items and responses in these benchmarks are optimal. However, th

Anna L. Trella, Susobhan Ghosh, Erin E. Bonar, Lara Coughlin

Online AI decision-making algorithms are increasingly used by digital interventions to dynamically personalize treatment to individuals. These algorithms determine, in real-time, the delivery of treatment based on accruing data. The objective of this paper is to provide guidelines for enabling effective monitoring of online decision-making algorithms with th

Dan Bohus, Sean Andrist, Yuwei Bao, Eric Horvitz

We report initial work towards constructing ecologically valid benchmarks to assess the capabilities of large multimodal models for engaging in situated collaboration. In contrast to existing benchmarks, in which question-answer pairs are generated post hoc over preexisting or synthetic datasets via templates, human annotators, or large language models (LLMs

Matúš Čávojský, Eugen Šlapak, Matúš Dopiriak, Gabriel Bugár

We present the CARLA corner case simulation (3CSim) for evaluating autonomous driving (AD) systems within the CARLA simulator. This framework is designed to address the limitations of traditional AD model training by focusing on non-standard, rare, and cognitively challenging scenarios. These corner cases are crucial for ensuring vehicle safety and reliabili

Paul Fergus, Carl Chalmers, Steve Longmore, Serge Wich

The rapid decline in global biodiversity demands innovative conservation strategies. This paper examines the use of artificial intelligence (AI) in wildlife conservation, focusing on the Conservation AI platform. Leveraging machine learning and computer vision, Conservation AI detects and classifies animals, humans, and poaching-related objects using visual

Wenjia Xie, Rui Zhou, Hao Wang, Tingjia Shen

Sequential recommendation has attracted increasing attention due to its ability to accurately capture the dynamic changes in user interests. We have noticed that generative models, especially diffusion models, which have achieved significant results in fields like image and audio, hold considerable promise in the field of sequential recommendation. However,

Houssem Gasmi, Jannik Laval, Abdelaziz Bouras

The automated and timely conversion of cybersecurity information from unstructured online sources, such as blogs and articles to more formal representations has become a necessity for many applications in the domain nowadays. Named Entity Recognition (NER) is one of the early phases towards this goal. It involves the detection of the relevant domain entities

Harsha Perera, Sung Une Lee, Yue Liu, Boming Xia

As Artificial Intelligence (AI) becomes integral to business operations, integrating Responsible AI (RAI) within Environmental, Social, and Governance (ESG) frameworks is essential for ethical and sustainable AI deployment. This study examines how leading companies align RAI with their ESG goals. Through interviews with 28 industry leaders, we identified a s

Masoud Tafavvoghi, Anders Sildnes, Mehrdad Rakaee, Nikita Shvetsov

Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is costly and not widely accessible in many regions. Previous approaches have highlighted the potential application o

Youzhu Jin, Yichen Zhang

Multimodal large language models (MLLMs) have achieved significant success in the general field of image processing. Their emerging task generalization and freeform conversational capabilities can greatly facilitate medical diagnostic assistance, helping patients better understand their conditions and enhancing doctor-patient trust. Computed Tomography (CT)

Giovanna Angelis Schmidt

This thesis investigates the mechanically controlled break junctions, with a particular emphasis on elucidating the behaviour of molecular currents at room temperature. The core of this experimental investigation involves a detailed analysis of conductance, examining how it varies over time and with changes in the gap between electrodes. Additionally, this s

Wen-Xiang Chen

This paper explores the advanced mathematical frameworks used to analyze symmetry breaking in high-dimensional field theories, emphasizing the roles of Laurent series, residues, and winding numbers. Symmetry breaking is fundamental in various physical contexts, such as high-energy physics, condensed matter physics, and cosmology. The study addresses how thes

Peter Mohr, David Newell, Barry Taylor, Eite Tiesinga

We report the 2022 self-consistent values of constants and conversion factors of physics and chemistry recommended by the Committee on Data of the International Science Council (CODATA). The recommended values can also be found at physics.nist.gov/constants. The values are based on a least-squares adjustment that takes into account all theoretical and experi

Alex Linden, Betül Gül

Postselection is an operation that allows the selection of specific measurement outcomes. It serves as a powerful theoretical tool for enhancing the performance of existing quantum algorithms. Despite recent developments such as time reversal in quantum measurements and IBM's mid-circuit measurements, postselection continues to face significant challenge

Omar Eidous, Hala Maqableh

Studying overlapping coefficients has recently become of great benefit, especially after its use in goodness-of-fit tests. These coefficients are defined as the amount of similarity between two statistical distributions. This research examines the estimation of one of these overlapping coefficients, which is the Weitzman coefficient Δ, assuming two Weibull d

Glenn Bruda

Working from definitions and an elementarily obtained integral formula for the Euler-Mascheroni constant, we give an alternative proof of the classical Puiseux representation of the exponential integral.

Samhita Pal, Xinge Jessie Jeng

Understanding the genetic underpinnings of complex traits and diseases has been greatly advanced by genome-wide association studies (GWAS). However, a significant portion of trait heritability remains unexplained, known as ``missing heritability". Most GWAS loci reside in non-coding regions, posing challenges in understanding their functional impact. Int

Shounak Sural, Naren, Ragunathan Rajkumar

In recent years, there has been a notable increase in the development of autonomous vehicle (AV) technologies aimed at improving safety in transportation systems. While AVs have been deployed in the real-world to some extent, a full-scale deployment requires AVs to robustly navigate through challenges like heavy rain, snow, low lighting, construction zones a

Amandine Pierrot, Pierre Pinson

The risk of missing data and subsequent incomplete data records at wind farms increases with the number of turbines and sensors. We propose here an imputation method that blends data-driven concepts with expert knowledge, by using the geometry of the wind farm in order to provide better estimates when performing Nearest Neighbor imputation. Our method relies

Shuhao Zeng, Hongliang Zhang, Boya Di, Zhu Han

Holographic multiple-input-multiple output (HMIMO), which is enabled by large-scale antenna arrays with quasi-continuous apertures, is expected to be an important technology in the forthcoming 6G wireless network. Reconfigurable intelligent surface (RIS)-based antennas provide an energy-efficient solution for implementing HMIMO. Most existing works in this a

Stefania Albanesi, Domonkos F. Vamossy

Credit scores are critical for allocating consumer debt in the United States, yet little evidence is available on their performance. We benchmark a widely used credit score against a machine learning model of consumer default and find significant misclassification of borrowers, especially those with low scores. Our model improves predictive accuracy for youn

Jiatong Li, Kalliopi Basioti, Vladimir Pavlovic

A large amount of procedural videos on the web show how to complete various tasks. These tasks can often be accomplished in different ways and step orderings, with some steps able to be performed simultaneously, while others are constrained to be completed in a specific order. Flow graphs can be used to illustrate the step relationships of a task. Current ta

Sounak Bhowmik, Himanshu Thapliyal

Anomaly detection is a crucial task in cyber security. Technological advancement brings new cyber-physical threats like network intrusion, financial fraud, identity theft, and property invasion. In the rapidly changing world, with frequently emerging new types of anomalies, classical machine learning models are insufficient to prevent all the threats. Quantu

Song-Ying Li, Sujuan Long, Jie Lao

Let $D$ be a bounded domain in the complex plane with Lipschitz boundary. In the paper, we construct an integral solution operator $T[f]$ for any $\overline{\partial}$ closed $(0,1)$-form $f\in L^p_{(0,1)}(D^n)$ solving the Cauchy-Riemain equation $\overline{\partial} u=f$ on the product domains $D^n$ and obtain the $L^p$-estimates for all $1<p\le \infty$.

Christian Chan, Fatemeh Mahmoudi, Chel Hee Lee, Quan Long

We introduce a novel method to simultaneously perform variable selection and estimation in the joint frailty model of recurrent and terminal events using the Broken Adaptive Ridge Regression penalty. The BAR penalty can be summarized as an iteratively reweighted squared $L_2$-penalized regression, which approximates the $L_0$-regularization method. Our metho

Gene Abrams, Roozbeh Hazrat

Leavitt path algebras, which are algebras associated to directed graphs, were first introduced about 20 years ago. They have strong connections to such topics as symbolic dynamics, operator algebras, non-commutative geometry, representation theory, and even chip firing. In this article we invite the reader to sneak a peek at these fascinating algebras and th

A. A. Shchepkin, G. I. Vasilyev, V. M. Ostryakov, A. K. Pavlov

The work considers the modelling of nearby supernova (SN) effects on Earth&#39;s biosphere via cosmic rays (CRs) accelerated by shockwaves. The rise of the radiation background on Earth resulted from the external irradiation by CR high-energy particles and internal radiation in organisms by the decay of cosmogenic ${}^{14}$C is evaluated. We have taken into

Zexin Chen, Chengxi Li, Xiangyu Xie, Parijat Dube

This paper explores the potential of a small, domain-specific language model trained exclusively on sports-related data. We investigate whether extensive training data with specially designed small model structures can overcome model size constraints. The study introduces the OnlySports collection, comprising OnlySportsLM, OnlySports Dataset, and OnlySports

Leonardo Ramos Thomas

The Real Face Dataset is a pedestrian face detection benchmark dataset in the wild, comprising over 11,000 images and over 55,000 detected faces in various ambient conditions. The dataset aims to provide a comprehensive and diverse collection of real-world face images for the evaluation and development of face detection and recognition algorithms. The Real F

Jiří Milička

This paper revisits Menzerath&#39;s Law, also known as the Menzerath-Altmann Law, which models a relationship between the length of a linguistic construct and the average length of its constituents. Recent findings indicate that simple stochastic processes can display Menzerathian behaviour, though existing models fail to accurately reflect real-world data.

Asmad Bin Abdul Razzaque, Andrea Baiocchi

Data collection in an IoT environment requires simple and effective communication solutions to address resource constraints, ensure network efficiency, while achieving scalability. Efficiency is evaluated based on the timeliness of collected data (Age of Information), the energy spent per delivered unit of data, and the effectiveness in utilizing spectrum re

Patrycja Strycharczuk, Sam Kirkham, Emily Gorman, Takayuki Nagamine

Diphthong vowels exhibit a degree of inherent dynamic change, the extent of which can vary synchronically and diachronically, such that diphthong vowels can become monophthongs and vice versa. Modelling this type of change requires defining diphthongs in opposition to monophthongs. However, formulating an explicit definition has proven elusive in acoustics a

Andrea Vezzosi, Andrea Bertoni, Marco Gibertini, Guido Goldoni

We predict that in InAs/GaSb nanowires with an inverted band alignment a transverse electric field induces a collapse of the hybridization gap, and a semimetal phase occurs. We use a self-consistent k.p approach and an adapted Bernevig-Hughes-Zhang model to show that massless Dirac points result from exact cancellation between the kinetic electron-hole coupl

Vihang Jumle, Mykola Makhortykh, Maryna Sydorova, Victoria Vziatysheva

Framing is among the most extensively used concepts in the field of communication science. The availability of digital data offers new possibilities for studying how specific aspects of social reality are made more salient in online communication but also raises challenges related to the scaling of framing analysis and its adoption to new research areas (e.g

Abouzeid M. Shalaby

The perturbation series for the renormalization group functions of the $O(N)-$symmetric $ϕ^4$ field theory are divergent but asymptotic. They are usually followed by Resummation calculations to extract reliable results. Although the same features exist for QED series, their partial sums can return accurate results because the perturbation parameter is small.

Di Wu

The year 2024 witnessed a major development in the cryptocurrency industry with the long-awaited approval of spot Bitcoin exchange-traded funds (ETFs). This innovation provides investors with a new, regulated path to gain exposure to Bitcoin through a familiar investment vehicle (Kumar et al., 2024). However, unlike traditional ETFs that directly hold underl