November 2024 arXiv papers — page 143

Rachel Bailey, Sara Costa, Maxim Derevyagin, Caleb Findley

In this paper we show how to construct 1D Hamiltonians, that is, Jacobi matrices, that realize perfect quantum state transfer and also have the property that the overlap of the time evolved state with the initial state is zero for some time before the transfer time. If the latter takes place we call it an early exclusion state. We also show that in some case

Zheng Li, Kai Zhange

In the past two years, large language models (LLMs) have achieved rapid development and demonstrated remarkable emerging capabilities. Concurrently, with powerful semantic understanding and reasoning capabilities, LLMs have significantly empowered the rapid advancement of the recommendation system field. Specifically, in news recommendation (NR), systems mus

Xilong Liang, Suk-Jin Yoon, Jingkun Zhao

With Gaia, APOGEE, GALAH, and LAMOST data, we investigate the positional, kinematic, chemical, and age properties of nine moving groups in the solar neighborhood. We find that each moving group has a distinct distribution in the velocity space in terms of its metallicity, $\alpha$ abundance, and age. Comparison of the moving groups with their underlying back

Juan F. R. Archilla, Jānis Bajārs, Sergej Flach

In this article, we explore the lifetime of localized excitations in nonlinear lattices, called breathers, when a thermalized lattice is perturbed with localized energy delivered to a single site. We develop a method to measure the time it takes for the system to approach equilibrium based on a single scalar quantity, the participation number, and deduce the

Takayuki Kihara, Keng Meng Ng

In this article, we introduce a notion of reducibility for partial functions on the natural numbers, which we call subTuring reducibility. One important aspect is that the subTuring degrees correspond to the structure of the realizability subtoposes of the effective topos. We show that the subTuring degrees (that is, the realizability subtoposes of the effec

Tetsuhiro S. Hatakeyama

Increasing the enzyme concentration generally speeds up enzymatic reactions. However, in this Letter, we show that increasing the enzyme concentration can also slow down the enzymatic reaction. We consider a simple allosteric protein with multiple modification sites, catalyzed by two enzymes with the same catalytic activity, but slightly different affinities

Md-Ferdous Pervej, Andreas F. Molisch

Extreme resource constraints make large-scale machine learning (ML) with distributed clients challenging in wireless networks. On the one hand, large-scale ML requires massive information exchange between clients and server(s). On the other hand, these clients have limited battery and computation powers that are often dedicated to operational computations. S

Yun Liu, Peng Li, Xuefeng Yan, Liangliang Nan

The core of self-supervised point cloud learning lies in setting up appropriate pretext tasks, to construct a pre-training framework that enables the encoder to perceive 3D objects effectively. In this paper, we integrate two prevalent methods, masked point modeling (MPM) and 3D-to-2D generation, as pretext tasks within a pre-training framework. We leverage

Jawad Chowdhury, Gabriel Terejanu

Improving generalization and achieving highly predictive, robust machine learning models necessitates learning the underlying causal structure of the variables of interest. A prominent and effective method for this is learning invariant predictors across multiple environments. In this work, we introduce a simple yet powerful approach, CGLearn, which relies o

Yoav Yoscovich, Amir Schreiber, Nir Hadar, Reuth Mirsky

Despite obesity being widely discussed in the social sciences, the effect of a robot's perceived obesity level on trust is not covered by the field of HRI. While in research regarding humans, Body Mass Index (BMI) is commonly used as an indicator of obesity, this scale is completely irrelevant in the context of robots, so it is challenging to operational

Aiichi Iwazaki

Axion dark matter inevitably generates electromagnetic radiation in quantum Hall effect experiments that use strong magnetic fields. Although these emissions are very weak, we have shown using a QCD axion model that they influence the plateau-plateau transition at low temperatures (below $100$ mK) in a system with a large surface area (greater than $10^{-3}\

Hailey Joren, Jianyi Zhang, Chun-Sung Ferng, Da-Cheng Juan

Augmenting LLMs with context leads to improved performance across many applications. Despite much research on Retrieval Augmented Generation (RAG) systems, an open question is whether errors arise because LLMs fail to utilize the context from retrieval or the context itself is insufficient to answer the query. To shed light on this, we develop a new notion o

Raushan Singh, Jaroslaw Glowacki, Marius Beaud, Federica Padovano

DNA minicircles are closed double-stranded DNA (dsDNA) fragments that have been demonstrated to be an important experimental tool to understand supercoiled, or stressed, DNA mechanics, such as nucleosome positioning and DNA-protein interactions. Specific minicircles can be simulated using Molecular Dynamics (MD) simulation. However, the enormous sequence spa

Shannon Quinn, Amr Abbadi, Seyed Alireza Vaezi, Russell K. Karls

The goal of this study is to develop a computational model of the progression of changes in mitochondrial phenotype resulting from infection with pathogenic mycobacteria. This ultimately will enable a large-scale virulence screen of mutant bacterial libraries. Mycobacterium tuberculosis (Mtb) is an intracellular pathogen, but only a small number of its genes

Jing Wu, Zhixin Lai, Shengjie Liu, Suiyao Chen

Exploring the optimal management strategy for nitrogen and irrigation has a significant impact on crop yield, economic profit, and the environment. To tackle this optimization challenge, this paper introduces a deployable \textbf{CR}op Management system \textbf{O}ver all \textbf{P}ossible \textbf{S}tate availabilities (CROPS). CROPS employs a language model

Gowtham Premananth, Carol Espy-Wilson

Speech-based assessment of the schizophrenia spectrum has been widely researched over in the recent past. In this study, we develop a deep learning framework to estimate schizophrenia severity scores from speech using a feature fusion approach that fuses articulatory features with different self-supervised speech features extracted from pre-trained audio mod

Elise Karinshak, Amanda Hu, Kewen Kong, Vishwanatha Rao

Immense effort has been dedicated to minimizing the presence of harmful or biased generative content and better aligning AI output to human intention; however, research investigating the cultural values of LLMs is still in very early stages. Cultural values underpin how societies operate, providing profound insights into the norms, priorities, and decision m

Chungen Liu, Zhigao Zhang, Jiabin Zuo

This paper is concerned with a nonlinear fractional Sch\"ordinger system in $\mathbb{{R}}$ with intraspecies interactions $a_{i}>0 \ (i=1,2)$ and interspecies interactions $\beta \in\mathbb{{R}}$. We study this system by solving an associated constrained minimization problem (i.e., $L^2-$norm constrains). Under certain assumptions on the trapping potentials

Won-Kwang Park

We apply MUltiple SIgnal Classification (MUSIC) algorithm for the location reconstruction of a set of {two-dimensional circle-like} small inhomogeneities in the limited-aperture inverse scattering problem. Compared with the full- or limited-view inverse scattering problem, the collected multi-static response (MSR) matrix is no more symmetric (thus not Hermit

Zan Chen, Yungeng Liu, Yu Guang Wang, Yiqing Shen

Recent advancements in Large Language Models (LLMs) have enhanced efficiency across various domains, including protein engineering, where they offer promising opportunities for dry lab and wet lab experiment workflow automation. Previous work, namely TourSynbio-Agent, integrates a protein-specialized multimodal LLM (i.e. TourSynbio-7B) with domain-specific d

Youssef Maghrebi, Mohamed Elhattab, Chadi Assi, Ali Ghrayeb

The emergence of movable antenna (MA) technology has marked a significant advancement in the field of wireless communication research, paving the way for enhanced connectivity, improved signal quality, and adaptability across diverse environments. By allowing antennas to adjust positions dynamically within a finite area at transceivers, this technology enabl

Cailean Osborne, Paul Sharratt, Dawn Foster, Mirko Boehm

Governments are increasingly employing funding for open source software (OSS) development as a policy lever to support the security of software supply chains, digital sovereignty, economic growth, and national competitiveness in science and innovation, among others. However, the impacts of public funding on OSS development remain poorly understood, with a la

Ethan Clements, Felix W. Knollmann, Sabrina Corsetti, Zhaoyi Li

Trapped ions provide a highly controlled platform for quantum sensors, clocks, simulators, and computers, all of which depend on cooling ions close to their motional ground state. Existing methods like Doppler, resolved sideband, and dark resonance cooling balance trade-offs between the final temperature and cooling rate. A traveling polarization gradient ha

Sabrina M. Corsetti, Ashton Hattori, Ethan R. Clements, Felix W. Knollmann

Trapped ions are a promising modality for quantum systems, with demonstrated utility as the basis for quantum processors and optical clocks. However, traditional trapped-ion systems are implemented using complex free-space optical configurations, whose large size and susceptibility to vibrations and drift inhibit scaling to large numbers of qubits. In recent

Hongyu Chen, Bingliang Jiao, Wenxuan Wang, Peng Wang

Lifelong person re-identification attempts to recognize people across cameras and integrate new knowledge from continuous data streams. Key challenges involve addressing catastrophic forgetting caused by parameter updating and domain shift, and maintaining performance in seen and unseen domains. Many previous works rely on data memories to retain prior sampl

Jeanfranco D. Farfan-Escobedo, Julio C. Dos Reis

Conversational systems have a Natural Language Understanding (NLU) module. In this module, there is a task known as an intent classification that aims at identifying what a user is attempting to achieve from an utterance. Previous works use only the current utterance to predict the intent of a given query and they do not consider the role of the context (one

Reza Aghazadeh Ayoubi, Marouan Mizmizi, Eugenio Moro, Ilario Filippini

The growing demand for high-speed, reliable wireless connectivity in 6G networks necessitates innovative approaches to overcome the limitations of traditional Radio Access Network (RAN). Reconfigurable Intelligent Surface (RIS) and Network-Controlled Repeater (NCR) have emerged as promising technologies to address coverage challenges in high-frequency millim

Ayoub Jadouli, Chaker El Amrani

Traditional Feed-Forward Neural Networks (FFNN) and one-dimensional Convolutional Neural Networks (1D CNN) often encounter difficulties when dealing with long, columnar datasets that contain numerous features. The challenge arises from two primary factors: the large volume of data and the potential absence of meaningful relationships between features. In con

Yangming Zhang, Wenqi Jia, Wei Niu, Miao Yin

3D Gaussian Splatting (3DGS) has emerged as a mainstream for novel view synthesis, leveraging continuous aggregations of Gaussian functions to model scene geometry. However, 3DGS suffers from substantial memory requirements to store the multitude of Gaussians, hindering its practicality. To address this challenge, we introduce GaussianSpa, an optimization-ba

Haoxin Liu, Chenghao Liu, B. Aditya Prakash

Large language models (LLMs), with demonstrated reasoning abilities across multiple domains, are largely underexplored for time-series reasoning (TsR), which is ubiquitous in the real world. In this work, we propose TimerBed, the first comprehensive testbed for evaluating LLMs' TsR performance. Specifically, TimerBed includes stratified reasoning patterns wi

Zoe Kahn, Nitin Kohli

Social impact evaluations are emerging as a useful tool to understand, document, and evaluate the societal impacts of generative AI. In this provocation, we begin to think carefully about the types of experts and expertise that are needed to conduct robust social impact evaluations of generative AI. We suggest that doing so will require thoughtfully elicitin

Ik Kyeong Jin, Joseph Hillier, Scott D. Liles, Zhanning Wang

In this work, we probe the sensitivity of hole-spin properties to hole occupation number in a planar silicon double-quantum dot device fabricated on a 300 mm integrated platform. Using DC transport measurements, we investigate the g-tensor and spin-relaxation induced leakage current within the Pauli spin-blockade regime as a function of magnetic-field orient

Kai Xiong, Hanqing Yu, Supeng Leng, Chongwen Huang

Urban Air Mobility (UAM), powered by flying cars, is poised to revolutionize urban transportation by expanding vehicle travel from the ground to the air. This advancement promises to alleviate congestion and enable faster commutes. However, the fast travel speeds mean vehicles will encounter vastly different environments during a single journey. As a result,

Bilal Tüdes, Luca Amendola

We derive the one-loop perturbation kernels for a minimal modified gravity model in which dark energy is coupled to dark matter via a constant coupling. We derive the time-dependent kernels via analytical and numerical solutions and provide accurate fitting functions. These kernels can be directly employed to test for modified gravity in forthcoming large-sc

Yungeng Liu, Zan Chen, Yu Guang Wang, Yiqing Shen

The exponential growth in protein-related databases and scientific literature, combined with increasing demands for efficient biological information retrieval, has created an urgent need for unified and accessible search methods in protein engineering research. We present TourSynbio-Search, a novel bioinformatics search agent framework powered by the TourSyn

Anna Gallo, Diego Garlaschelli, Tiziano Squartini

According to the so-called strong version of structural balance theory, actors in signed social networks avoid establishing triads with an odd number of negative links. Generalising, the weak version of balance theory allows for nodes to be partitioned into any number of blocks with positive internal links, mutually connected by negative links. If this presc

Fabio Calderón, Hongdi Huang, Elizabeth Wicks, Robert Won

In this paper, we present a generalization of well-established results regarding symmetries of $\Bbbk$-algebras, where $\Bbbk$ is a field. Traditionally, for a $\Bbbk$-algebra $A$, the group $\Bbbk$-algebra automorphisms of $A$ captures the symmetries of $A$ via group actions. Similarly, the Lie algebra of derivations of $A$ captures the symmetries of $A$ vi

Balaji Shesharao Ingole, Vishnu Ramineni, Nikhil Kumar Pulipeta, Manoj Jayntilal Kathiriya

The synchronic and diachronic study of the evolution of Artificial Intelligence (AI) unveils one prominent fact that its effect can be traced in almost all fields such as healthcare industry. The growth is perceived holistically in software, hardware implementation, or application in these various fields. As the title suggests, the review will highlight the

Jin-Min Liang, Satoya Imai, Shuheng Liu, Shao-Ming Fei

Randomized measurements are useful for analyzing quantum systems especially when quantum control is not fully perfect. However, their practical realization typically requires multiple rotations in the complex space due to the adoption of random unitaries. Here, we introduce two simplified randomized measurements that limit rotations in a subspace of the comp

Shane Rankin

We explore the natural analogues of the Brylinksi condition, Strong Lefschetz condition, and $d\delta$-lemma in Symplectic Geometry originally explored by Brylinksi, Mathieu, Yan, and Guillemin in the Symplectic Lie Algebroid case. The equivalence of the three conditions is re-established as a purely algebraic statement along with a primitive notion of the $

Al Saqib Majumder

Conventional economic and socio-behavioural models assume perfect symmetric access to information and rational behaviour among interacting agents in a social system. However, real-world events and observations appear to contradict such assumptions, leading to the possibility of other, more complex interaction rules existing between such agents. We investigat

David Wichner, Jeffrey Wishart, Jason Sergent, Sunder Swaminathan

Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for an AV industry SMS have been proposed o

Khartik Uppalapati, Eeshan Dandamudi, S. Nick Ice, Gaurav Chandra

Traditional drug discovery is a long, expensive, and complex process. Advances in Artificial Intelligence (AI) and Machine Learning (ML) are beginning to change this narrative. Here, we provide a comprehensive overview of different AI and ML tools that can be used to streamline and accelerate the drug discovery process. By using data sets to train ML algorit

Wiktoria Mieleszczenko-Kowszewicz, Dawid Płudowski, Filip Kołodziejczyk, Jakub Świstak

This study explores how the Large Language Models (LLMs) adjust linguistic features to create personalized persuasive outputs. While research showed that LLMs personalize outputs, a gap remains in understanding the linguistic features of their persuasive capabilities. We identified 13 linguistic features crucial for influencing personalities across different

Milena Skvortsova

Grey-body factors and quasinormal modes are two distinct characteristics of radiation near black holes, each associated with different boundary conditions. Nevertheless, a correspondence exists between them, which we use to calculate the grey-body factors of three recently constructed quantum-corrected black hole models. Our findings demonstrate that the gre

Olena Blumberg, Ben Morris, Alto Senda

We prove a theorem that reduces bounding the mixing time of a card shuffle to verifying a condition that involves only triplets of cards. Then we use it to analyze a classic model of card shuffling. In 1988, Diaconis introduced the following Markov chain. Cards are arranged in an $n$ by $n$ grid. Each step, choose a row or column, uniformly at random, and cy

José Cantarero

This article contains a basic introduction to the local study of finite groups, including a brief perspective on the theory of fusion systems and $p$-local finite groups. -- Este art\'iculo contiene una introducci\'on b\'asica al estudio local de grupos finitos, incluyendo una breve perspectiva de la teor\'ia de sistemas de fusi\'on y grupos $p$-locales fini

Brian Chang, Jeffrey C. Mogul, Rui Wang, Mingyang Zhang

Applications that run in large-scale data center networks (DCNs) rely on the DCN's ability to deliver application requests in a performant manner. DCNs expose a complex design and operational space, and network designers and operators care how different options along this space affect application performance. One might run controlled experiments and measure

Briley Lewis, Yiting Li, Aidan Gibbs, Michael P. Fitzgerald

Brown dwarfs with measured dynamical masses and spectra from direct imaging are benchmarks that anchor substellar atmosphere cooling and evolution models. We present Subaru SCExAO/CHARIS infrared spectroscopy of HIP 93398 B, a brown dwarf companion recently discovered by Li et al. 2023 as part of an informed survey using the Hipparcos-Gaia Catalog of Acceler

Andreas Lietz, Jeroen Winkel

Picture countably many logicians all wearing a hat in one of $\kappa$-many colours. They each get to look at finitely many other hats and afterwards make finitely many guesses for their own hat's colour. For which $\kappa$ can the logicians guarantee that at least one of them guesses correctly? This will be the archetypical hat problem we analyse and solve h

Bokgyeong Kang, Erin M. Schliep, Alan E. Gelfand, Christopher W. Clark

North Atlantic right whales are an endangered species; their entire population numbers approximately 372 individuals, and they are subject to major anthropogenic threats. They feed on zooplankton species whose distribution shifts in a dynamic and warming oceanic environment. Because right whales in turn follow their shifting food resource, it is necessary to

F. Ge, M. Millea, E. Camphuis, C. Daley

From CMB polarization data alone we reconstruct the CMB lensing power spectrum, comparable in overall constraining power to previous temperature-based reconstructions, and an unlensed E-mode power spectrum. The observations, taken in 2019 and 2020 with the South Pole Telescope (SPT) and the SPT-3G camera, cover 1500 deg$^2$ at 95, 150, and 220 GHz with arcmi

Ghadeer Shaaban, Hassen Fourati, Alain Kibangou, Christophe Prieur

Modern vehicles have evolved from mechanical systems to complex and connected ones controlled by numerous digital computers interconnected through internal networks. While this development has improved their efficiency and safety, it also brings new potential risks, particularly cyber-attacks. Several studies have explored the security of vehicle dynamics ag

Achintya Gopal

Missing data is a common problem in finance and often requires methods to fill in the gaps, or in other words, imputation. In this work, we focused on the imputation of missing implied volatilities for FX options. Prior work has used variational autoencoders (VAEs), a neural network-based approach, to solve this problem; however, using stronger classical bas

Pär Kurlberg, Alina Ostafe, Zeev Rudnick, Igor E. Shparlinski

We study eigenfunction localization for higher dimensional cat maps, a popular model of quantum chaos. These maps are given by linear symplectic maps in ${\mathrm{Sp}}(2g,\mathbb Z)$, which we take to be ergodic. Under some natural assumptions, we show that there is a density one sequence of integers $N$ so that as $N$ tends to infinity along this sequence,

Dylan Smith, Aishik Ghosh, Junze Liu, Pierre Baldi

The simulation of detector response is a vital aspect of data analysis in particle physics, but current Monte Carlo methods are computationally expensive. Machine learning methods, which learn a mapping from incident particle to detector response, are much faster but require a model for every detector element with unique geometry. Complex geometries may requ

Sai Krishna Reddy Sathi

This paper aims to make a mark in the future of sustainable robotics, where efficient algorithms are required to carry out tasks like environmental monitoring and precision agriculture efficiently. We proposed a hybrid algorithm that combines Artificial Bee Colony (ABC) with Levy flight to optimize adaptive sensor placement alongside an important notion of h

Theodore Nye-Matthew, Xinhua Wang

This paper explores the mathematical modelling and 3D design of a tilt-rotor quadrotor aircraft. The aircraft is a VTOL design and has capacity for one pilot. The design incorporates a part manual part automatic computerised flight control system and hybrid powertrain providing energy to eight ducted contrarotating propellers. Analysis of controllability was

Magauiya Zhussip, Iaroslav Koshelev, Stamatis Lefkimmiatis

Diffusion Probabilistic Models (DPMs) have been recently utilized to deal with various blind image restoration (IR) tasks, where they have demonstrated outstanding performance in terms of perceptual quality. However, the task-specific nature of existing solutions and the excessive computational costs related to their training, make such models impractical an

Luke Munn, Liam Magee

Neoliberalism has become orthodoxy in the present, erasing competing paradigms and alternative imaginings. Chile's radical Cybersyn project from 1971 to 1973 offers a departure point for an alternative path, albeit one that was abruptly and violently extinguished. We revisit this moment by fine-tuning AI language models on the words and writing of Salvador A

Priya Mishra, Suraj Racha, Kaustubh Ponkshe, Adit Akarsh

Question Answering (QA) is an important part of tasks like text classification through information gathering. These are finding increasing use in sectors like healthcare, customer support, legal services, etc., to collect and classify responses into actionable categories. LLMs, although can support QA systems, they face a significant challenge of insufficien

Wenyue Hua, Ollie Liu, Lingyao Li, Alfonso Amayuelas

This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory. We evaluate several state-of-the-art LLMs across a spectrum of complete-information and incomplete-information games. Our findings reveal that LLMs frequently deviate from rational strategies, partic

Brian Nelson, Hussein Moradi, Behrouz Farhang-Boroujeny

Recently, filter-bank multicarrier spread spectrum (FBMC-SS) has been proposed as a candidate waveform for ultra-wideband (UWB) communications, sensing, and localization. It has been noted that FBMC-SS is a perfect match to this application, leading to a trivial method of matching to the required spectral mask at different regions of the world. FBMC-SS also

Remi A. Chou, Matthieu R. Bloch

We consider multi-user commitment models that capture the problem of enabling multiple bidders to simultaneously submit auctions to verifiers while ensuring that i) verifiers do not obtain information on the auctions until bidders reveal them at a later stage; and, ii) bidders cannot change their auction once committed. Specifically, we assume that bidders a

Miguel Moura Ramos, Tomás Almeida, Daniel Vareta, Filipe Azevedo

Reinforcement learning (RL) has been proven to be an effective and robust method for training neural machine translation systems, especially when paired with powerful reward models that accurately assess translation quality. However, most research has focused on RL methods that use sentence-level feedback, leading to inefficient learning signals due to the r

Zaid Haddadin, Anna My Nguyen, Lisa V. Poulikakos

All-dielectric metasurfaces can produce structural colors, but the most advantageous design criteria are still being investigated. This work numerically studies how the two-dimensional shape of nanoparticles affects the colorimetric response under circularly polarized light (CPL) to develop a sensor distinguishing CPL orientations. Using lossless dielectric

Taemin Heo, Ruaridh Macdonald

The increasing need for energy storage solutions to balance variable renewable energy sources has highlighted the potential of Pumped Thermal Electricity Storage (PTES). In this paper, we investigate the trade-offs between model accuracy and computational efficiency in PTES systems. We evaluate a range of PTES models, from physically detailed to simplified v

Aviad Susman, Rupak Krishnamurthy, Yan Chak Li, Mohammad Olaimat

Effectively modeling multimodal longitudinal data is a pressing need in various application areas, especially biomedicine. Despite this, few approaches exist in the literature for this problem, with most not adequately taking into account the multimodality of the data. In this study, we developed multiple configurations of a novel multimodal and longitudinal

Haizhou Wang, Nanqing Luo, Xusheng Li, Peng LIu

Sandboxes and other dynamic analysis processes are prevalent in malware detection systems nowadays to enhance the capability of detecting 0-day malware. Therefore, techniques of anti-dynamic analysis (TADA) are prevalent in modern malware samples, and sandboxes can suffer from false negatives and analysis failures when analyzing the samples with TADAs. In su

S. Huyan, J. Schmidt, A. Valadkhani, H. Wang

We present high pressure electrical transport, magnetization, and single crystal X-ray diffraction data on SrCo2P2 single crystals. X-ray diffraction data show that there is a transition to a collapsed tetragonal structure for p ~> 10 GPa and measurements of resistance show that above ~ 10 GPa, a clear transition-like feature can be observed at temperatures

Kushan Mitra, Dan Zhang, Sajjadur Rahman, Estevam Hruschka

Large Language Models (LLMs) have shown impressive capability in language generation and understanding, but their tendency to hallucinate and produce factually incorrect information remains a key limitation. To verify LLM-generated contents and claims from other sources, traditional verification approaches often rely on holistic models that assign a single f

Ha Manh Bui, Enrique Mallada, Anqi Liu

By leveraging the representation power of deep neural networks, neural upper confidence bound (UCB) algorithms have shown success in contextual bandits. To further balance the exploration and exploitation, we propose Neural-$\sigma^2$-LinearUCB, a variance-aware algorithm that utilizes $\sigma^2_t$, i.e., an upper bound of the reward noise variance at round

Anwesan Pal, Radhika Bhargava, Kyle Hinsz, Jacques Esterhuizen

Data sanitization in the context of language modeling involves identifying sensitive content, such as personally identifiable information (PII), and redacting them from a dataset corpus. It is a common practice used in natural language processing (NLP) to maintain privacy. Nevertheless, the impact of data sanitization on the language understanding capability

Nina V. Gusinskaia, Amruta D. Jaodand, Jason W. T. Hessels, Slavko Bogdanov

Transitional millisecond pulsars (tMSPs) are neutron-star systems that alternate between a rotation-powered radio millisecond pulsar state and an accretion-disk-dominated low-mass X-ray binary (LMXB)-like state on multi-year timescales. During the LMXB-like state, the X-ray emission from tMSPs switches between "low" and "high" X-ray brightness modes on a tim

Houari Benammar Ammar, Louis Massonnet, Chenxi Yin

Consider $E$ a vector bundle over a smooth curve $C$. We compute the $\delta$-invariant of all ample ($\mathbb{Q}$-) line bundles on $\mathbb{P}(E)$ when $E$ is strictly Mumford semistable. We also investigate the case when one assumes that the Harder-Narasimhan filtration of $E$ has only one step.

Lantian Zhang, Lei Guo

This paper considers real-time control and learning problems for finite-dimensional linear systems under binary-valued and randomly disturbed output observations. This has long been regarded as an open problem because the exact values of the traditional regression vectors used in the construction of adaptive algorithms are unavailable, as one only has binary

G. Pancheri, Y. N. Srivastava, O. Shekhovtsova

We recall a resummation procedure in QED to extract the zero momentum mode in soft photon emission and present an ansatz about a possible mechanism for the forward peak characterizing elastic proton proton scattering.

Catarina Avelino, Hoi Ping Luk, Altino Santos

We classify the special families of dihedral folding tilings of the sphere derived from the M\"obius triangle $(2,3,4)$. Our study emerges from the study of isometric foldings in the Riemann sphere and meets at the juncture of the triangle group $\Delta(2,3,4)$. The juxtaposition enables us to apply the classification theorem of edge-to-edge tilings of the s

Michael Allen, Olivia Beckwith, Vaishavi Sharma

We study the holomorphic projection of mixed mock modular forms involving sesquiharmonic Maass forms. As a special case, we numerically express the holomorphic projection of a function involving real quadratic class numbers multiplied by a certain theta function in terms of eta quotients. We also analyze certain shifted convolution $L$-series involving mock

Shalin Shah, Srikanth Ryali, Ramasubbu Venkatesh

We propose two new methods for multi-document financial question answering. First, a method that uses semantic tagging, and then, queries the index to get the context (RAG_SEM). And second, a Knowledge Graph (KG_RAG) based method that uses semantic tagging, and, retrieves knowledge graph triples from a graph database, as context. KG_RAG uses knowledge graphs

Hugo T. Carvalho, Min S. Li, Massimiliano di Luca, Alan M. Wing

The synchronization of motor responses to rhythmic auditory cues is a fundamental biological phenomenon observed across various species. While the importance of temporal alignment varies across different contexts, achieving precise temporal synchronization is a prominent goal in musical performances. Musicians often incorporate expressive timing variations,

Adrian Clingher, Andreas Malmendier, Brandon Williams

We determine explicit generators for the ring of modular forms associated with the moduli spaces of K3 surfaces with automorphism group $(\mathbb{Z}/2\mathbb{Z})^2$ and of Picard rank 13 and higher. The K3 surfaces in question carry a canonical Jacobian elliptic fibration and the modular form generators appear as coefficients in the Weierstrass-type equation

Vandana P. Janeja, Christine Mallinson

This perspective calls for scholars across disciplines to address the challenge of audio deepfake detection and discernment through an interdisciplinary lens across Artificial Intelligence methods and linguistics. With an avalanche of tools for the generation of realistic-sounding fake speech on one side, the detection of deepfakes is lagging on the other. P

Noelymar Farinacci

We provide an overview of an optimal control problem within a stochastic model of tumor growth, which includes drug application. The model comprises two stochastic differential equations (SDE) representing the diffusion of nutrient and drug concentrations. To account for various uncertainties, stochastic terms are incorporated into the deterministic framewor

Nathaniel Bannister

Let M be a transitive model of set theory and X be a space in the sense of M. Is there a reasonable way to interpret X as a space in V? A general theory due to Zapletal provides a natural candidate which behaves well on sufficiently complete spaces (for instance \v{C}ech complete spaces) but behaves poorly on more general spaces - for instance, the Zapletal

Aayush Shah, Shankar Jayaratnam

Large language models (LLMs) have demonstrated significant success in natural language processing (NLP) tasks and have shown promising results in other domains such as protein sequence generation. However, there remain salient differences between LLMs used for NLP, which effectively handle multiple tasks and are available in small sizes, and protein language

Tilek Zhumabek, Azamat Mukhamediya, Hrishikesh Chakrabarty, Daniele Malafarina

We consider a modified gravity model with a running gravitational constant coupled to a varying dark energy fluid and test its imprint on the growth of structure in the universe. Using Redshift Space Distortion (RSD) measurement results, we show a tension at the $3 \sigma$ level between the best fit $\Lambda$CDM and the corresponding parameters obtained from

Mateusz Wasala, Krzysztof Blachut, Hubert Szolc, Marcin Kowalczyk

Nowadays, the increasing demand for maintaining high cleanliness standards in public spaces results in the search for innovative solutions. The deployment of CCTV systems equipped with modern cameras and software enables not only real-time monitoring of the cleanliness status but also automatic detection of impurities and optimisation of cleaning schedules.

Mehri Mehrnia, Mohamed Elbayumi, Mohammed S. M. Elbaz

Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with heart failure and stroke. Accurate segmentation of the left atrium (LA) in 3D late gadolinium-enhanced (LGE) MRI is helpful for evaluating AF, as fibrotic remodeling in the LA myocardium contributes to arrhythmia and serves as a key determinant of therapeutic strategies. However

S. I. Ipatov

This study analyzes the motion of bodies ejected from the Earth or the Moon. We studied the ejection of bodies from several points on the Earth's surface, as well as from the most far point of the Moon from the Sun. Different velocities and angles of ejection of bodies were considered. The dynamical lifetimes of bodies reached a few hundred million years. Ov

Xiao Liu, Lijun Zhang, Deepak Ganesan, Hui Guan

Vision Language Models (VLMs) are central to Visual Question Answering (VQA) systems and are typically deployed in the cloud due to their high computational demands. However, this cloud-only approach underutilizes edge computational resources and requires significant bandwidth for transmitting raw images. In this paper, we introduce an edge-cloud collaborati

Raúl Santoveña, Carlos Dafonte, Minia Manteiga

Data compression techniques focused on information preservation have become essential in the modern era of big data. In this work, an encoder-decoder architecture has been designed, where adversarial training, a modification of the traditional autoencoder, is used in the context of astrophysical spectral analysis. The goal of this proposal is to obtain an in

Cassandre Notton, Vasudev Sharma, Vincent Quoc-Huy Trinh, Lina Chen

Colorectal cancer (CRC) is one of the few cancers that have an established dysplasia-carcinoma sequence that benefits from screening. Everyone over 50 years of age in Canada is eligible for CRC screening. About 20\% of those people will undergo a biopsy for a pre-neoplastic polyp and, in many cases, multiple polyps. As such, these polyp biopsies make up the

Arvapalli Sai Susmitha, Pradeep Pujari

Social media has become an integral part of modern life, but it has also brought with it the pervasive issue of cyberbullying a serious menace in today's digital age. Cyberbullying, a form of harassment that occurs on social networks, has escalated alongside the growth of these platforms. Sentiment analysis holds significant potential not only for detecting

Jun Cui

This study investigates the influence of generative artificial intelligence (AI) on the brand construction of cross-border e-commerce companies in the manufacturing industry in Tianjin, China. We examine the direct effects of generative AI on productivity, the mediating role of productivity in the relationship between generative AI and brand building, and th

Joanne Choi, Sam Clark, Ranjan Jaiswal, Peter Kirk

Many workers in cities across the world, who have been teleworking because of the COVID-19 pandemic, are expected to be back to their commutes. As this process is believed to be gradual and telecommuting is likely to remain an option for many workers, hybrid model and flexible schedules might become the norm in the future. This variable work schedules allows

Murad Almadani, Umair bin Waheed, Mudassir Masood, Yangkang Chen

Seismic data inevitably suffers from random noise and missing traces in field acquisition. This limits the utilization of seismic data for subsequent imaging or inversion applications. Recently, dictionary learning has gained remarkable success in seismic data denoising and interpolation. Variants of the patch-based learning technique, such as the K-SVD algo

Loredana Daria Mang, Francisco David Gonzalez Martinez, Damian Martinez Munoz, Sebastian Garcia Galan

Early identification of respiratory irregularities is critical for improving lung health and reducing global mortality rates. The analysis of respiratory sounds plays a significant role in characterizing the respiratory system's condition and identifying abnormalities. The main contribution of this study is to investigate the performance when the input data,

Chathuri Weragama, Joonas Kokkoniemi, Mar Francis De Guzman, Katsuyuki Haneda

Millimeter-Wave (mmWave) (30-300 GHz) and D band (110-170 GHz) frequencies are poised to play a pivotal role in the advancement of sixth-generation (6G) systems and beyond with increased demand for greater bandwidth and capacity. This paper focuses on deriving a generalized channel impulse response for mmWave communications, considering both outdoor and indo

S. I. Ipatov

The motion of planetesimals was studied in the Proxima Centauri and TRAPPIST 1 exoplanetary systems. The size of the feeding zone of planet Proxima Centauri c is discussed. It was noted that after hundreds of Myrs, some planetesimals could still move in elliptical resonant orbits inside the feeding zone of this planet that had been mainly cleared from planet