February 2024 arXiv papers — page 107

Pietro Dona, Hal M. Haggard, Carlo Rovelli, Francesca Vidotto

Quantum gravitational tunneling effects are expected to give rise to a number of interesting observable phenomena, including, in particular, the evolution of black holes at the end of their existence or the emergence of the early universe from a quantum phase. Covariant Loop Quantum Gravity provides a framework to study these phenomena, yet a precise identif

Priyanka Magar-Sawant

In this paper, the concordance structure set of connected sums of complex and quaternionic projective spaces in the real $n$-dimensional range with $8\leq n\leq 16$ is computed. It is demonstrated that the concordance inertia group of a connected sum equals the sum of individual concordance inertia groups. Furthermore, the concordance structure sets of manif

Michele Coscia, Karel Devriendt

Recently, the first author proposed a measure to calculate Pearson correlations for node values expressed in a network, by taking into account distances or metrics defined on the network. In this technical note, we show that using an arbitrary choice of distances might result in imaginary or unbounded correlation values, which is undesired. We prove that thi

Andrian Kuch, Romain Tisserand, François Durand, Tony Monnet

Optimal postural stability is required to perform in archery. Since the dynamic consequences of the string release may disturb the archer's postural equilibrium, they should have integrated them in their motor program to optimize postural stability. This study aimed to characterize the postural strategy archers use to limit the potentially detrimental impact

Farsane Tabataba-Vakili, Lukas Krelle, Lukas Husel, Huy P. G. Nguyen

Metasurfaces allow to manipulate light at the nanoscale. Integrating metasurfaces with transition metal dichalcogenide monolayers provides additional functionality to ultrathin optics, including tunable optical properties with enhanced light-matter interactions. In this work, we demonstrate the realization of a polaritonic metasurface utilizing the sizable l

Tiantian Feng, Daniel Yang, Digbalay Bose, Shrikanth Narayanan

Multi-modal learning has emerged as an increasingly promising avenue in vision recognition, driving innovations across diverse domains ranging from media and education to healthcare and transportation. Despite its success, the robustness of multi-modal learning for visual recognition is often challenged by the unavailability of a subset of modalities, especi

Matteo Tuveri, Arianna Steri, Daniela Fadda

Studies in Physics Education Research show that interdisciplinary approaches in education foster students' motivation, creativity, curiosity, and interest in physics. We discuss their features and potential role in bringing contemporary physics topics to high school, and how to use them to integrate formal educational programs. We make an explicit example of

Barathi Subramanian, Rathinaraja Jeyaraj, Anand Paul

Activation functions govern how recurrent networks regulate and transmit information across temporal dependencies. Despite advances in sequence modelling, gated recurrent units (GRUs) still depend on the standard sigmoid and tanh nonlinearities, which can produce weak gate separation and unstable learning, particularly when training data are limited. We intr

Jeroen Rombouts, Marie Ternes, Ines Wilms

Platform businesses operate on a digital core and their decision making requires high-dimensional accurate forecast streams at different levels of cross-sectional (e.g., geographical regions) and temporal aggregation (e.g., minutes to days). It also necessitates coherent forecasts across all levels of the hierarchy to ensure aligned decision making across di

Sylvain Coly, Pierre Druilhet, Nourddine Azzaoui

Acquiring information on spatial phenomena can be costly and time-consuming. In this context, to obtain reliable global knowledge, the choice of measurement location is a crucial issue. Space-lling designs are often used to control variability uniformly across the whole space. However, in a monitoring context, it is more relevant to focus on crucial regions,

M. Boström, S. Pal, H. R. Gopidi, S. Osella

The Casimir-Lifshitz force is calculated between a heterogeneous gapped metal surface and a silica sphere attached to an AFM cantilever tip. We demonstrate that heterogeneous surface patches with different off-stoichiometry surface properties lead to changes in the predicted distances for a specific force. This can incorrectly be interpreted as occurrences o

Cosimo Della Santina, Carlos Hernandez Corbato, Burak Sisman, Luis A. Leiva

Consciousness has been historically a heavily debated topic in engineering, science, and philosophy. On the contrary, awareness had less success in raising the interest of scholars in the past. However, things are changing as more and more researchers are getting interested in answering questions concerning what awareness is and how it can be artificially ge

Venelin P. Pavlov, Yoana R. Chorbadzhiyska, Charlie Nation, Diego Porras

We theoretically investigate parameter quantum estimation in quantum chaotic systems. Our analysis is based on an effective description of non-integrable quantum systems in terms of a random matrix Hamiltonian. Based on this approach we derive an analytical expression for the time evolution of the quantum Fisher information. We test our random matrix theory

Cangqing Wang, Jiangchuan Gong

This study endeavors to conceptualize and execute a sophisticated agricultural greenhouse control system grounded in the amalgamation of the Internet of Things (IoT) and machine learning. Through meticulous monitoring of intrinsic environmental parameters within the greenhouse and the integration of machine learning algorithms, the conditions within the gree

Tiantian Feng, Shrikanth Narayanan

Medical residency training is often associated with physically intense and emotionally demanding tasks, requiring them to engage in extended working hours providing complex clinical care. Residents are hence susceptible to negative psychological effects, including stress and anxiety, that can lead to decreased well-being, affecting them achieving desired tra

François Morain

Let $\mathcal{E}$ be an elliptic curve over a field $\mathbf{K}$ and $\ell$ a prime. There exists an elliptic curve $\mathcal{E}^*$ related to $\mathcal{E}$ by an isogeny of degree $\ell$ only if $\Phi_\ell^t(X, j(\mathcal{E})) = 0$, where $\Phi_\ell^t(X, Y)$ is the traditional modular polynomial. Moreover, $\Phi_\ell^t$ gives the coefficients of $\mathcal{E

Oleg Andreev

We propose the stringy description of the system consisting of two heavy and four light quarks in the case of two light flavors of equal mass. As an application, we consider the three low-lying Born-Oppenheimer potentials as a function of the heavy quark separation. Our analysis shows that the ground state potential is described in terms of both hadro-quarko

Jiwon Song, Kyungseok Oh, Taesu Kim, Hyungjun Kim

Large language models (LLMs) have proven to be highly effective across various natural language processing tasks. However, their large number of parameters poses significant challenges for practical deployment. Pruning, a technique aimed at reducing the size and complexity of LLMs, offers a potential solution by removing redundant components from the network

Valentin Blot, Gilles Dowek, Thomas Traversié, Théo Winterhalter

The $\lambda$$\Pi$-calculus modulo theory is an extension of simply typed $\lambda$-calculus with dependent types and user-defined rewrite rules. We show that it is possible to replace the rewrite rules of a theory of the $\lambda$$\Pi$-calculus modulo theory by equational axioms, when this theory features the notions of proposition and proof, while maintain

Abdelghani Maddi

This paper aims to analyze the extent to which inventive activity relies on open science. In other words, it investigates whether inventors utilize Open Access (OA) publications more than subscription-based ones, especially given that some inventors may lack institutional access. To achieve this, we utilized the (Marx, 2023) database, which contains citation

Shiyi Yang, Lina Yao, Chen Wang, Xiwei Xu

Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks. Understanding attack tactics helps improve the robustness of RSs. We intend to develop efficient attack methods that use limited resources to generate high-quality fake user profiles to achieve 1) transferability among black-box RSs 2) and imperceptibili

Vincenzo Corso, Leonardo Mariani, Daniela Micucci, Oliviero Riganelli

AI-based code assistants are increasingly popular as a means to enhance productivity and improve code quality. This study compares four AI-based code assistants, GitHub Copilot, Tabnine, ChatGPT, and Google Bard, in method generation tasks, assessing their ability to produce accurate, correct, and efficient code. Results show that code assistants are useful,

Carlos Olarte, Carlos Ramírez, Camilo Rocha, Frank Valencia

Social media platforms have played a key role in weaponizing the polarization of social, political, and democratic processes. This is, mainly, because they are a medium for opinion formation. Opinion dynamic models are a tool for understanding the role of specific social factors on the acceptance/rejection of opinions because they can be used to analyze cert

Rathin Das, Biswabrata Pradhan

This work considers design of Bayesian reliability acceptance sampling plan (RASP) under hybrid censored life test for the products sold under optional warranty. The consumer and manufacturer agree on a common lifetime distribution of the product. However, they differ in the assessment of the prior distributions because of the adversarial nature of the consu

Jean-Christophe Perrin, Assma El Kaddouri, Laouès Guendouz, Christine Mrad

As programs to support efficient and sustainable energy sources are expanding, research into the potential applications of the hydrogen vector is accelerating. Proton exchange membrane fuel cells are electrochemical converters that transform the chemical energy of hydrogen into electrical energy. These devices are used today for low- and medium-power station

Yusuke Tanaka, Takaharu Yaguchi, Tomoharu Iwata, Naonori Ueda

The operator learning has received significant attention in recent years, with the aim of learning a mapping between function spaces. Prior works have proposed deep neural networks (DNNs) for learning such a mapping, enabling the learning of solution operators of partial differential equations (PDEs). However, these works still struggle to learn dynamics tha

Alexander B. Ivanov, Sian Nie

We determine the cohomology of the closed Drinfeld stratum of $p$-Deligne--Lusztig schemes of Coxeter type attached to arbitrary inner forms of unramified groups over a local non-archimedean field. We prove that the corresponding torus weight spaces are supported in exactly one cohomological degree, and are pairwisely non-isomorphic irreducible representatio

Zhuoyuan Wang, Haiqiao Wang, Yi Wang

The advent of deep-learning-based registration networks has addressed the time-consuming challenge in traditional iterative methods.However, the potential of current registration networks for comprehensively capturing spatial relationships has not been fully explored, leading to inadequate performance in large-deformation image registration.The pure convolut

Negar Arabzadeh, Julia Kiseleva, Qingyun Wu, Chi Wang

The rapid development in the field of Large Language Models (LLMs) has led to a surge in applications that facilitate collaboration among multiple agents to assist humans in their daily tasks. However, a significant gap remains in assessing whether LLM-powered applications genuinely enhance user experience and task execution efficiency. This highlights the p

Aleksandr Lobanov, Alexander Gasnikov, Andrei Krasnov

Frequently, the burgeoning field of black-box optimization encounters challenges due to a limited understanding of the mechanisms of the objective function. To address such problems, in this work we focus on the deterministic concept of Order Oracle, which only utilizes order access between function values (possibly with some bounded noise), but without assu

Germain Garreau, Azzurra Bigioli, Romain Laugier, Gert Raskin

Asgard/NOTT (previously Hi-5) is a European Research Council (ERC)-funded project hosted at KU Leuven and a new visitor instrument for the Very Large Telescope Interferometer (VLTI). Its primary goal is to image the snow line region around young stars using nulling interferometry in the L-band (3.5 to 4.0)$\mu$m, where the contrast between exoplanets and the

Indranil Biswas, Sorin Dumitrescu

We prove that any holomorphic geometric structure of affine type on an Oeljeklaus- Toma manifold is locally homogeneous. For locally conformal K\"ahler Oeljeklaus-Toma manifolds we prove that all holomorphic geometric structures, and also all holomorphic Cartan geometries, on them are locally homogeneous.

Yuval Gil

We design new deterministic CONGEST approximation algorithms for \emph{maximum weight independent set (MWIS)} in \emph{sparse graphs}. As our main results, we obtain new $\Delta(1+\epsilon)$-approximation algorithms as well as algorithms whose approximation ratio depend strictly on $\alpha$, in graphs with maximum degree $\Delta$ and arboricity $\alpha$. For

Karri I. I. Koljonen, Sindre S. Lindseth, Manuel Linares, Alice K. Harding

Radio emission from pulsars can be used to map out their distances through dispersion measure (DM), which quantifies the amount of radio pulse dispersion. However, this method relies on accurately modelling the free electron density in the line of sight. Here, we present a detailed study of the multiwavelength emission from PSR J1720$-$0534, a black widow co

Agnes Ngina Mwange, Dimas Maulana Rachman, Rin Suyama, Atsuo Maki

Autonomous ships are essentially designed and equipped to perceive their internal and external environment and subsequently perform appropriate actions depending on the predetermined objective(s) without human intervention. Consequently, trajectory planning algorithms for autonomous berthing must consider factors such as system dynamics, ship actuators, envi

Philipp Seeberger, Korbinian Riedhammer

Automatic summarization of mass-emergency events plays a critical role in disaster management. The second edition of CrisisFACTS aims to advance disaster summarization based on multi-stream fact-finding with a focus on web sources such as Twitter, Reddit, Facebook, and Webnews. Here, participants are asked to develop systems that can extract key facts from s

Hernán Mella, Felipe Galarce, Tetsuro Sekine, Julio Sotelo

Hemodynamic parameters are often estimated assuming a constant Newtonian viscosity, even though blood exhibits shear-thinning behavior. This article investigates the influence of blood rheology and hematocrit (Hct) percentage on the estimation of Wall Shear Stress (WSS), rate of viscous Energy Loss ($\dot{E}_L$) at different points in the cardiac cycle, and

Hao Liu, Jiancheng An, Derrick Wing Kwan Ng, George C. Alexandropoulos

Stacked intelligent metasurfaces (SIM) represents an advanced signal processing paradigm that enables over-the-air processing of electromagnetic waves at the speed of light. Its multi-layer structure exhibits customizable increased computational capability compared to conventional single-layer reconfigurable intelligent surfaces and metasurface lenses. In th

Zhandos A. Moldabekov, Thomas D. Gawne, Sebastian Schwalbe, Thomas R. Preston

Ultrafast heating of solids with modern X-ray free electron lasers (XFELs) leads to a unique set of conditions that is characterized by the simultaneous presence of heated electrons in a cold ionic lattice. In this work, we analyze the effect of electronic heating on the dynamic structure factor (DSF) in bulk Aluminium (Al) with a face-centered cubic lattice

Juhyeon Shin, Yujin Oh, Jonghyun Lee, Saehyung Lee

Test-Time Adaptation (TTA) adapts pre-trained models using only unlabeled test streams, requiring real-time inference and update without access to source data. We propose StructuralTest-time Alignment of Gradients (STAG), a lightweight plug-in enhancer that exploits an always-available structural signal: the classifier's intrinsic geometry. STAG derives clas

N. N. Leonenko, M. D. Ruiz-Medina

The asymptotic behavior of an extended family of integral geometric random functionals, including spatiotemporal Minkowski functionals under moving levels, is analyzed in this paper. Specifically, sojourn measures of spatiotemporal long-range dependence (LRD) Gaussian random fields are considered in this analysis. The limit results derived provide general re

M. Starkov

Take any $d + 3$ points in $\mathbb{R}^d$. It is known that (a) if $d = 2k + 1$, then there are two linked $(k + 1)$-simplices with the vertices at these points; (b) if $d = 2k$, then there are two disjoint $(k + 1)$-tuples of these points such that their convex hulls intersect. The analogue of (b) for $d = 2k + 1$, which is also the analogue of (a) for inte

Xiaoyuan Zhang, Xi Lin, Yichi Zhang, Yifan Chen

Multiobjective optimization (MOO) is prevalent in numerous applications, in which a Pareto front (PF) is constructed to display optima under various preferences. Previous methods commonly utilize the set of Pareto objectives (particles on the PF) to represent the entire PF. However, the empirical distribution of the Pareto objectives on the PF is rarely stud

Matthias Kränzler, Christian Herglotz, André Kaup

Energy and compression efficiency are two essential parts of modern video decoder implementations that have to be considered. This work comprehensively studies the following six video coding formats regarding compression and decoding energy efficiency: AVC, VP9, HEVC, AV1, VVC, and AVM. We first evaluate the energy demand of reference and optimized software

Zhi-Guang Lu, Ying Wu, Xin-You Lü

Based on the scattering matrix method, we theoretically demonstrate that the chiral interaction can induce the almost perfect photon blockade (PB) in the waveguide-cavity quantum electrodynamics (QED) system. The mechanism relies on the multi-photon paths interference within the waveguide, which is clearly shown by the analytic parameter regime for $g^{(2)}(

Ali Haidar, Daniel Al Mouiee, Farhannah Aly, David Thwaites

Standardising structure volume names in radiotherapy (RT) data is necessary to enable data mining and analyses, especially across multi-institutional centres. This process is time and resource intensive, which highlights the need for new automated and efficient approaches to handle the task. Several machine learning-based methods have been proposed and evalu

Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu

We study the Stochastic Shortest Path (SSP) problem with a linear mixture transition kernel, where an agent repeatedly interacts with a stochastic environment and seeks to reach certain goal state while minimizing the cumulative cost. Existing works often assume a strictly positive lower bound of the cost function or an upper bound of the expected length for

Abdelilah Karara, Mohamed Rossafi

In this paper, we introduce a new concept of K-biframes for Hilbert spaces. We then examine several characterizations with the assistance of a biframe operator. Moreover, we investigate their properties from the perspective of operator theory by establishing various relationships and properties.

Taixian Hou, Jiaxin Tu, Xiaofei Gao, Zhiyan Dong

Electric quadruped robots used in outdoor exploration are susceptible to leg-related electrical or mechanical failures. Unexpected joint power loss and joint locking can immediately pose a falling threat. Typically, controllers lack the capability to actively sense the condition of their own joints and take proactive actions. Maintaining the original motion

Jiaying Lu, Bo Pan, Jieyi Chen, Yingchaojie Feng

Recently, Large Language Model based Autonomous system(LLMAS) has gained great popularity for its potential to simulate complicated behaviors of human societies. One of its main challenges is to present and analyze the dynamic events evolution of LLMAS. In this work, we present a visualization approach to explore detailed statuses and agents' behavior within

Qiongyi Zhou, Changde Du, Shengpei Wang, Huiguang He

The study of decoding visual neural information faces challenges in generalizing single-subject decoding models to multiple subjects, due to individual differences. Moreover, the limited availability of data from a single subject has a constraining impact on model performance. Although prior multi-subject decoding methods have made significant progress, they

Boulos El Hilany, Kemal Rose

Two continuous maps $f, g : \mathbb{C}^2\to\mathbb{C}^2$ are said to be topologically equivalent if there exist homeomorphisms $\varphi,\psi:\mathbb{C}^2\to\mathbb{C}^2$ satisfying $\psi\circ f\circ\varphi = g$. It is known that there are finitely many topologically non-equivalent polynomial maps $\mathbb{C}^2\to\mathbb{C}^2$ with any given degree $d$. The n

Jiaming Liang

High-probability guarantees in stochastic optimization are often obtained only under strong noise assumptions such as sub-Gaussian tails. We show that such guarantees can also be achieved under the weaker assumption of bounded variance by developing a stochastic proximal point method. This method combines a proximal subproblem solver, which inherently reduce

Chenlu Ye, Jiafan He, Quanquan Gu, Tong Zhang

This study tackles the challenges of adversarial corruption in model-based reinforcement learning (RL), where the transition dynamics can be corrupted by an adversary. Existing studies on corruption-robust RL mostly focus on the setting of model-free RL, where robust least-square regression is often employed for value function estimation. However, these tech

Haibo Yang, Chitin Hon, Qixiang Yang, Tao Qian

The research on the algorithm of analytic signal has received much attention for a long time. Takenaka-Malmquist (TM) system was introduced to consider analytic functions in 1925. If TM system satisfies hyperbolic inseparability condition, then it is an orthogonal basis. It can form unconditional basis for Hilbert space $\mathbb{H}^{2}(D)$ and Schauder basis

Jean-Michel Coron, Shengquan Xiang

In this paper, we study the global controllability and stabilization problems of the harmonic map heat flow from a circle to a sphere. Combining ideas from control theory, heat flow, differential geometry, and asymptotic analysis, we obtain several important properties, such as small-time local controllability, local quantitative rapid stabilization, obstruc

Tim Seppelt

Two graphs $G$ and $H$ are homomorphism indistinguishable over a family of graphs $\mathcal{F}$ if for all graphs $F \in \mathcal{F}$ the number of homomorphisms from $F$ to $G$ is equal to the number of homomorphism from $F$ to $H$. Many natural equivalence relations comparing graphs such as (quantum) isomorphism, cospectrality, and logical equivalences can

Wenyi Zhang, Zihan Xu, Sangeetha Abdu Jyothi

Low Earth Orbit (LEO) satellite networks are rapidly gaining traction today. Although several real-world deployments exist, our preliminary analysis of LEO topology performance with the soon-to-be operational Inter-Satellite Links (ISLs) reveals several interesting characteristics that are difficult to explain based on our current understanding of topologies

Hong Wu, Juan Fu, Hongsheng Ye, Yuming Zhong

Prostate cancer is the most common noncutaneous cancer in the world. Recently, multi-modality transrectal ultrasound (TRUS) has increasingly become an effective tool for the guidance of prostate biopsies. With the aim of effectively identifying prostate cancer, we propose a framework for the classification of clinically significant prostate cancer (csPCa) fr

Wenwei Zhao, Xiaowen Li, Shangqing Zhao, Jie Xu

Machine learning has been adopted for efficient cooperative spectrum sensing. However, it incurs an additional security risk due to attacks leveraging adversarial machine learning to create malicious spectrum sensing values to deceive the fusion center, called adversarial spectrum attacks. In this paper, we propose an efficient framework for detecting advers

Thomas Lubinski, Joshua J. Goings, Karl Mayer, Sonika Johri

The QED-C suite of Application-Oriented Benchmarks provides the ability to gauge performance characteristics of quantum computers as applied to real-world applications. Its benchmark programs sweep over a range of problem sizes and inputs, capturing key performance metrics related to the quality of results, total time of execution, and quantum gate resources

Pablo Álvarez-Caudevilla, Cristina Brändle, Mónica Molina-Becerra, Antonio Suárez

In this work we consider an interface logistic problem where two populations live in two different regions, separated by a membrane or interface where it happens an interchange of flux. Thus, the two populations only interact or are coupled through such a membrane where we impose the so-called Kedem-Katchalsky boundary conditions. For this particular scenari

Zhangchen Xu, Fengqing Jiang, Luyao Niu, Jinyuan Jia

As large language models (LLMs) become increasingly integrated into real-world applications such as code generation and chatbot assistance, extensive efforts have been made to align LLM behavior with human values, including safety. Jailbreak attacks, aiming to provoke unintended and unsafe behaviors from LLMs, remain a significant/leading LLM safety threat.

Xubin Wang, Haojiong Shangguan, Fengyi Huang, Shangrui Wu

Feature selection is a crucial step in data mining to enhance model performance by reducing data dimensionality. However, the increasing dimensionality of collected data exacerbates the challenge known as the "curse of dimensionality", where computation grows exponentially with the number of dimensions. To tackle this issue, evolutionary computational (EC) a

Seiseki Akibue, Go Kato, Seiichiro Tani

A quantum channel whose image approximates the set of separable states is called a disentangler, which plays a prominent role in the investigation of variants of the computational model called Quantum Merlin Arthur games, and has potential applications in classical and quantum algorithms for the separability testing and NP-complete problems. So far, two type

Bharathi Seshadri, Yongkui Han, Chris Olson, David Pollak

Software supply chain attacks, which exploit the build process or artifacts used in the process of building a software product, are increasingly of concern. To combat these attacks, one must be able to check that every artifact that a software product depends on does not contain vulnerabilities. In this paper, we introduce OmniBOR, (Universal Bill of Receipt

Sihoon Moon, Sanghoon Lee, Kyung-Joon Park

In smart manufacturing systems (SMSs), flexible job-shop scheduling with transportation constraints (FJSPT) is essential to optimize solutions for maximizing productivity, considering production flexibility based on automated guided vehicles (AGVs). Recent developments in deep reinforcement learning (DRL)-based methods for FJSPT have encountered a scale gene

Wong Kam-Kwai, Yan Luo, Xuanwu Yue, Wei Chen

Financial cluster analysis allows investors to discover investment alternatives and avoid undertaking excessive risks. However, this analytical task faces substantial challenges arising from many pairwise comparisons, the dynamic correlations across time spans, and the ambiguity in deriving implications from business relational knowledge. We propose Prismati

Kumari Priyanka, A. Antony Selvan

This paper is concerned with the problem of sampling and interpolation involving derivatives in shift-invariant spaces and the error analysis of the derivative sampling expansions for fundamentally large classes of functions. A new type of polynomials based on derivative samples is introduced, which is different from the Euler-Frobenius polynomials for the m

Haoyang Wang, Jingao Xu, Chenyu Zhao, Zihong Lu

A heterogeneous micro aerial vehicles (MAV) swarm consists of resource-intensive but expensive advanced MAVs (AMAVs) and resource-limited but cost-effective basic MAVs (BMAVs), offering opportunities in diverse fields. Accurate and real-time localization is crucial for MAV swarms, but current practices lack a low-cost, high-precision, and real-time solution,

Chen Wang, Fangxin Wang, Ruocheng Guo, Yueqing Liang

In Sequential Recommendation Systems (SRecsys), traditional training approaches that rely on Cross-Entropy (CE) loss often prioritize accuracy but fail to align well with user satisfaction metrics. CE loss focuses on maximizing the confidence of the ground truth item, which is challenging to achieve universally across all users and sessions. It also overlook

Mingrui Ma, Lansheng Han, Chunjie Zhou

Transformer, as one of the most advanced neural network models in Natural Language Processing (NLP), exhibits diverse applications in the field of anomaly detection. To inspire research on Transformer-based anomaly detection, this review offers a fresh perspective on the concept of anomaly detection. We explore the current challenges of anomaly detection and

Tangila Tanni, Mamtaj Akter, Joshua Anderson, Mary Amon

We collected and analyzed Instagram direct messages (DMs) from 173 youth aged 13-21 (including 86 LGBTQ+ youth). We examined youth's risk-flagged social media trace data with their self-reported mental health outcomes to examine how the differing online experiences of LGBTQ+ youth compare with their heterosexual counterparts. We found that LGBTQ+ youth exper

Jingjing Cai, Ziheng Chen, Yuanling Niu

This work focuses on the numerical approximations of neutral stochastic delay differential equations with their drift and diffusion coefficients growing super-linearly with respect to both delay variables and state variables. Under generalized monotonicity conditions, we prove that the backward Euler method not only converges strongly in the mean square sens

Anurag Kumar Patel

In this paper, we prove that the null space of a weighted composition operator on $\ell_p~ (1 \leq p < \infty)$ is a complemented subspace. We also give a necessary and sufficient condition for a weighted composition operator on $\ell_p$ whose range space is of finite co-dimension. Thereafter, we characterize a class of weighted composition operators whose r

Minho Lee, Junghyun Min, Yerang Kim, Woochul Lee

Modern generative pre-trained language models excel at open-ended text generation, yet continue to underperform on structure-related tasks such as NER, relation extraction, and semantic role labeling, especially when compared to encoder-only models of similar sizes. While this gap has been attributed to limited structure knowledge, we hypothesize this is als

Idris Assani

In this note we would like to correct a comment made by E.H. El Abdaloui about my work [arXiv:1312:5270].

Weijie Du, James P. Vary

We present a novel input scheme for general second-quantized Hamiltonians of relativistic or non-relativistic many-fermion systems. This input scheme incorporates the fermionic anticommutation relations, particle number variations, and respects the symmetries of the Hamiltonian. Based on our input scheme, we propose a hybrid quantum-classical framework for s

Siwon Kim, Shuyang Dai, Mohammad Kachuee, Shayan Ray

Current conversational AI systems based on large language models (LLMs) are known to generate unsafe responses, agreeing to offensive user input or including toxic content. Previous research aimed to alleviate the toxicity, by fine-tuning LLM with manually annotated safe dialogue histories. However, the dependency on additional tuning requires substantial co

Salman Rahman, Lavender Yao Jiang, Saadia Gabriel, Yindalon Aphinyanaphongs

Advances in large language models (LLMs) provide new opportunities in healthcare for improved patient care, clinical decision-making, and enhancement of physician and administrator workflows. However, the potential of these models importantly depends on their ability to generalize effectively across clinical environments and populations, a challenge often un

Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto

GitHub's Copilot for Pull Requests (PRs) is a promising service aiming to automate various developer tasks related to PRs, such as generating summaries of changes or providing complete walkthroughs with links to the relevant code. As this innovative technology gains traction in the Open Source Software (OSS) community, it is crucial to examine its early adop

Yeongjae Cho, Taehee Kim, Heejun Shin, Sungzoon Cho

Difference visual question answering (diff-VQA) is a challenging task that requires answering complex questions based on differences between a pair of images. This task is particularly important in reading chest X-ray images because radiologists often compare multiple images of the same patient taken at different times to track disease progression and change

Susobhan Mandal, S. Shankaranarayanan

The occurrence of singularities at the centers of black holes suggests that general relativity (GR), although a highly successful model of gravity and cosmology, is inapplicable. This is due to the breakdown of the equivalence principle. Gauss-Bonnet (GB) action is the simplest extension of GR as it possesses second-order equations of motion and is devoid of

Saswat Padhi, Sunil K. Bhasin, Udaya K. Ammu, Alex Bergman

Estimating the overall user experience (UX) on a device is a common challenge faced by manufacturers. Today, device makers primarily rely on microbenchmark scores, such as Geekbench, that stress test specific hardware components, such as CPU or RAM, but do not satisfactorily capture consumer workloads. System designers often rely on domain-specific heuristic

Won-Seok Choi, Hyundo Lee, Dong-Sig Han, Junseok Park

Recent machine learning algorithms have been developed using well-curated datasets, which often require substantial cost and resources. On the other hand, the direct use of raw data often leads to overfitting towards frequently occurring class information. To address class imbalances cost-efficiently, we propose an active data filtering process during self-s

Tony J. Puthenpurakal

Let $A$ be the ring of integers of global field $K$. Let $G \subseteq GL_2(A)$ be a finite group. Let $G$ act linearly on $R = A[X,Y]$ (fixing $A$). Let $R^G$ be the ring of invariants. In the equi-characteristic case we prove $R^G$ is Cohen-Macaulay. In mixed characteristic case we prove that if for all primes $p$ dividing $|G|$ the Sylow $p$-subgroup of $G

Zhao Li, Xin Wang, Jun Zhao, Wenbin Guo

Knowledge hypergraph embedding models are usually computationally expensive due to the inherent complex semantic information. However, existing works mainly focus on improving the effectiveness of knowledge hypergraph embedding, making the model architecture more complex and redundant. It is desirable and challenging for knowledge hypergraph embedding to rea

Zhaoqing Wang, Xiaobo Xia, Ziye Chen, Xiao He

Current state-of-the-art open-vocabulary segmentation methods typically rely on image-mask-text triplet annotations for supervision. However, acquiring such detailed annotations is labour-intensive and poses scalability challenges in complex real-world scenarios. While existing weakly-supervised approaches leverage image-text pairs to reduce the expansive an

Shogo Kusano, Masayuki Uchida

We consider a model selection problem for structural equation modeling (SEM) with latent variables for diffusion processes based on high-frequency data. First, we propose the quasi-Akaike information criterion of the SEM and study the asymptotic properties. Next, we consider the situation where the set of competing models includes some misspecified parametri

Junhan Kim, Chungman Lee, Eulrang Cho, Kyungphil Park

With the increasing complexity of generative AI models, post-training quantization (PTQ) has emerged as a promising solution for deploying hyper-scale models on edge devices such as mobile and TVs. Existing PTQ schemes, however, consume considerable time and resources, which could be a bottleneck in real situations where frequent model updates and multiple h

Yinya Huang, Xiaohan Lin, Zhengying Liu, Qingxing Cao

Recent large language models (LLMs) have witnessed significant advancement in various tasks, including mathematical reasoning and theorem proving. As these two tasks require strict and formal multi-step inference, they are appealing domains for exploring the reasoning ability of LLMs but still face important challenges. Previous studies such as Chain-of-Thou

Jaber Daneshamooz, Melody Yu, Sucheer Maddury

The Internet relies on routing protocols to direct traffic efficiently across interconnected networks, with the Border Gateway Protocol (BGP) serving as the core mechanism managing routing between autonomous systems. However, BGP configurations are largely manual, making them susceptible to human errors that can lead to outages or security vulnerabilities. V

Martha Lewis, Melanie Mitchell

Large language models (LLMs) have performed well on several reasoning benchmarks, including ones that test analogical reasoning abilities. However, it has been debated whether they are actually performing humanlike abstract reasoning or instead employing less general processes that rely on similarity to what has been seen in their training data. Here we inve

Charles Frankston, Jonathan Godfrey, Shamsi Brinn, Alison Hofer

In October 2023, arXiv made HTML formatted papers available to readers. This was the exciting outcome of over a year of accessibility research and development with the scientific community. Currently, only 2.4% of research outputs meet accessibility guidelines. Informed by scientists who rely on assistive technology, our analysis demonstrates that offering H

Liuquan Yao, Shuai Yuan

It is a manuscript for results about entropic central limit theorem for independent sum under finite Poincar\'e constant conditions.

Shuixin Xiao, Yuanlong Wang, Jun Zhang, Daoyi Dong

Quantum process tomography is a critical task for characterizing the dynamics of quantum systems and achieving precise quantum control. In this paper, we propose a two-stage solution for both trace-preserving and non-trace-preserving quantum process tomography. Utilizing a tensor structure, our algorithm exhibits a computational complexity of $O(MLd^2)$ wher

Salih Kibaroğlu, Sergei D. Odintsov, Tanmoy Paul

We propose a novel modified gravity: unimodular generalization of the Born-Infeld-$f(R)$ gravity within the framework of cosmology. After formulating the action corresponding to the generalized Born-Infeld-$f(R)$ gravity, we present a reconstruction scheme of this unimodular extension to achieve various cosmological eras of the universe. Interestingly, the u

Joshua H. Davis, Pranav Sivaraman, Joy Kitson, Konstantinos Parasyris

Portability is critical to ensuring high productivity in developing and maintaining scientific software as the diversity in on-node hardware architectures increases. While several programming models provide portability for diverse GPU systems, they don't make any guarantees about performance portability. In this work, we explore several programming models --

Naga Dileep Varikuti, Soumik Bandyopadhyay

Quantum state designs, by enabling an efficient sampling of random quantum states, play a quintessential role in devising and benchmarking various quantum protocols with broad applications ranging from circuit designs to black hole physics. Symmetries, on the other hand, are expected to reduce the randomness of a state. Despite being ubiquitous, the effects

Ziang Chen, Rong Ge

In this work, we study the mean-field flow for learning subspace-sparse polynomials using stochastic gradient descent and two-layer neural networks, where the input distribution is standard Gaussian and the output only depends on the projection of the input onto a low-dimensional subspace. We establish a necessary condition for SGD-learnability, involving bo