May 2024 arXiv papers — page 100

Daofei Zhang

In this paper, we study mixing rates for $\mathbb{T}^{d}$-extensions of hyperbolic flows. Given three closed orbits with their holonomies, we can relate them to a point in $\mathbb{R}^{d+1}$. We prove that the extension flow enjoys rapid mixing, if the associated point is an inhomogeneously Diophantine number. Under the same assumption, we also obtain the su

Hélène Eynard-Bontemps, Andrés Navas

We study the problem of conjugating a diffeomorphism of the interval to (positive) powers of itself. Although this is always possible for homeomorphisms, the smooth setting is rather interesting. Besides the obvious obstruction given by hyperbolic fixed points, several other aspects need to be considered. As concrete results we show that, in class $C^1$, if

Alexander Brown, Greg Droge, Jacob Gunther

Robust charging schedules in a growing market of battery electric bus (BEB) fleets are a critical component to successful adoption. In this paper, a BEB charging scheduling framework that considers spatiotemporal schedule constraints, route schedules, fast and slow charging, and battery dynamics is modeled as a mixed integer linear program (MILP). The MILP i

Amirhossein Akbar Tabatabai, Majid Alizadeh, Masoud Memarzadeh

$\nabla$-algebra is a natural generalization of Heyting algebra, unifying many algebraic structures including bounded lattices, Heyting algebras, temporal Heyting algebras and the algebraic presentation of the dynamic topological systems. In a series of two papers, we will systematically study the algebro-topological properties of different varieties of $\na

K. Yu. Osipenko

The paper concerns problems of the recovery of linear operators defined on sets of functions from information of these functions given with stochastic errors. The constructed optimal recovery methods, in general, do not use all the available information. As a consequence, optimal methods are obtained for recovering derivatives of functions from Sobolev class

Ajay Vallabh, John G Tsavalas

Bonding between polymers below the glass transition temperature through molecular-scale dilatation (or densification)-based interdiffusion of macromolecules has recently been introduced. In this mechanism, short timeframe plastic deformation enables polymer chains to interdiffuse and form entanglements at the interface, facilitating rapid bonding below the g

Stephen Mell, Konstantinos Kallas, Steve Zdancewic, Osbert Bastani

Scripting languages are widely used to compose external calls such as native libraries and network services. In such scripts, execution time is often dominated by waiting for these external calls, rendering traditional single-language optimizations ineffective. To address this, we propose a novel opportunistic evaluation strategy for scripting languages base

Stav Marcus, Ari M. Turner, Guy Bunin

Species coexistence is a complex, multifaceted problem. At an equilibrium, coexistence requires two conditions: stability under small perturbations; and feasibility, meaning all species abundances are positive. Which of these two conditions is more restrictive has been debated for many years, with many works focusing on statistical arguments for systems with

Haojie Yan, Yuedong Xu, Lianggui Dai

Small cells with edge computing are densely deployed in 5G mobile networks to provide high throughput communication and low-latency computation. The flexibility of edge computation is empowered by the deployment of lightweight container-based microservices. In this paper, we take the first step toward optimizing the microservice management in small-cell netw

Mingrui Liang, Matthew D. Koslovsky, Emily T. Hebert, Darla E. Kendzor

Functional concurrent, or varying-coefficient, regression models are commonly used in biomedical and clinical settings to investigate how the relation between an outcome and observed covariate varies as a function of another covariate. In this work, we propose a Bayesian nonparametric approach to investigate how clusters of these functional relations evolve

Andrew Shin, Kunitake Kaneko

Large language models (LLMs) have demonstrated remarkable performances on a wide range of natural language tasks. Yet, LLMs' successes have been largely restricted to tasks concerning words, sentences, or documents, and it remains questionable how much they understand the minimal units of text, namely characters. In this paper, we examine contemporary LLMs r

Bashir Mojaveri, Rasoul Jafarzadeh Bahrbeig, Mohammad Ali Fasihi

We study the effect of parity deformation of the environmental field modes on the wireless charging performance of a qubit-based open quantum battery (QB) consisting of a qubit-battery and a qubit-charger, where there is no direct interaction between the qubits and battery is charged by the mediation of the environment. The parity deformation introduces fiel

Daniel Abode, Pedro Maia de Sant Ana, Alexander Artemenko, Ramoni Adeogun

In this paper, we develop a novel power control solution for subnetworks-enabled distributed control systems in factory settings. We propose a channel-independent control-aware (CICA) policy based on the logistic model and learn the parameters using Bayesian optimization with a multi-objective tree-structured Parzen estimator. The objective is to minimize th

Jeck Lim, Stefan Steinerberger

We prove that the existence of infinitely many $(m_k, n_k) \in \mathbb{N}^2$ such that the difference of harmonic numbers $H_{m_k} - H_{n_k}$ approximates 1 well $$ \lim_{k \rightarrow \infty} \left| \sum_{\ell = n}^{m_k} \frac{1}{\ell} - 1 \right|\cdot n_k^2 = 0.$$ This answers a question of Erd\H{o}s and Graham. The construction uses asymptotics for harmon

Juran Ding, Yuanzhe Liu, Lingbin Sun, Brandon Reagen

Privacy concerns have thrust privacy-preserving computation into the spotlight. Homomorphic encryption (HE) is a cryptographic system that enables computation to occur directly on encrypted data, providing users with strong privacy (and security) guarantees while using the same services they enjoy today unprotected. While promising, HE has seen little adopti

Xinyu You, Haojie Yan, Yuedong Xu, Lifeng Wang

Edge computing plays an essential role in the vehicle-to-infrastructure (V2I) networks, where vehicles offload their intensive computation tasks to the road-side units for saving energy and reduce the latency. This paper designs the optimal task offloading policy to address the concerns involving processing delay, energy consumption and edge computing cost.

Yuanzhe Liu, Yixiang Mao, Yao Wang

This work applies an encoder-decoder-based machine learning network to detect and track the motion and growth of the flowering stem apex of Arabidopsis Thaliana. Based on the CenterTrack, a machine learning back-end network, we trained a model based on ten time-lapsed labeled videos and tested against three videos.

Fabian Süß, Marco Freimuth, Andreas Aßmuth, George R. S. Weir

In recent years, Cloud Computing has transformed local businesses and created new business models on the Internet- and Cloud services are still flourishing. But after the emphatic hype in the early years, a more realistic perception of Cloud services has emerged. One reason for this surely is that today, Cloud Computing is considered as an established and we

Xingyu Wu, Yan Zhong, Jibin Wu, Yuxiao Huang

In the algorithm selection research, the discussion surrounding algorithm features has been significantly overshadowed by the emphasis on problem features. Although a few empirical studies have yielded evidence regarding the effectiveness of algorithm features, the potential benefits of incorporating algorithm features into algorithm selection models and the

Mendel Nguyen, Hersh Singh

We consider the Atiyah-Patodi-Singer (APS) index theorem corresponding to the chiral symmetry of a continuum formulation of staggered fermions called K\"ahler-Dirac fermions, which have been recently investigated as an ingredient in lattice constructions of chiral gauge theories. We point out that there are two notions of chiral symmetry for K\"ahler-Dirac f

Samira Shirzadeh-hajimahmood, I. S. W. B. Prasteya, Mehdi Dastani, Frank Dignum

Automated testing of computer games is a challenging problem, especially when lengthy scenarios have to be tested. Automating such a scenario boils down to finding the right sequence of interactions given an abstract description of the scenario. Recent works have shown that an agent-based approach works well for the purpose, e.g. due to agents' reactivity, h

Chin-Yi Cheng, Ruiqi Gao, Forrest Huang, Yang Li

Layout design generation has recently gained significant attention due to its potential applications in various fields, including UI, graphic, and floor plan design. However, existing models face two main challenges that limits their adoption in practice. Firstly, the limited expressiveness of individual condition types used in previous works restricts desig

Ritesh Chandra, Shashi Shekhar Kumar, Rushil Patra, Sonali Agarwal

Forests are crucial for ecological balance, but wildfires, a major cause of forest loss, pose significant risks. Fire weather indices, which assess wildfire risk and predict resource demands, are vital. With the rise of sensor networks in fields like healthcare and environmental monitoring, semantic sensor networks are increasingly used to gather climatic da

Yuqiang Lin, Sam Lockyer, Nic Zhang

Multi-Target Multi-Camera Tracking (MTMCT) has broad applications and forms the basis for numerous future city-wide systems (e.g. traffic management, crash detection, etc.). However, the challenge of matching vehicle trajectories across different cameras based solely on feature extraction poses significant difficulties. This article introduces an innovative

Sudarshan Srinivasa Ramanujam, Akanksha Bindal, Yu Jiang, Timothy J. Hazen

A post embedding (representation of text in embedding space that effectively captures semantic meaning) is a foundational component of LinkedIn that is consumed by product surfaces in retrieval and ranking (e.g., ranking posts in the feed or video tab). This paper presents the post embeddings used at LinkedIn, where a pre-trained transformer-based large lang

Yuhan Yao, Muryel Guolo, Francesco Tombesi, Ruancun Li

We present the tidal disruption event (TDE) AT2022lri, hosted in a nearby ($\approx\!144$ Mpc) quiescent galaxy with a low-mass massive black hole ($10^4\,M_\odot < M_{\rm BH} < 10^6\,M_\odot$). AT2022lri belongs to the TDE-H+He subtype. More than 1 Ms of X-ray data were collected with NICER, Swift, and XMM-Newton from 187 d to 672 d after peak. The X-ray lu

L. Pastur, V. Slavin

We consider a quantum system of large size $N$ and its subsystem of size $L$ assuming that $N$ is much larger than $L$, which can also be sufficiently large, i.e., $1 \ll L \lesssim N $. A widely accepted mathematical version of this heuristic inequality is the asymptotic regime of successive limits: first the macroscopic limit $N \to \infty$, then an asympt

Andreas Aßmuth, Robert Duncan, Simon Liebl, Matthias Söllner

Finding a robust security mechanism for audit trail logging has long been a poorly satisfied goal. There are many reasons for this. The most significant of these is that the audit trail is a highly sought after goal of attackers to ensure that they do not get caught. Thus they have an incredibly strong incentive to prevent companies from succeeding in this w

Niklas Houba, Stefan H. Strub, Luigi Ferraioli, Domenico Giardini

We present a novel machine learning framework tailored to detect massive black hole binaries observed by spaceborne gravitational wave detectors like the Laser Interferometer Space Antenna (LISA) and predict their future merger times. The detection is performed via convolutional neural networks that analyze time-evolving Time-Delay Interferometry (TDI) spect

Shantiram Mahata, Neela Nataraj, Jean-Pierre Raymond

In this work, we consider an initial-boundary value problem for a time-fractional biharmonic equation in a bounded polygonal domain with a Lipschitz continuous boundary in $\mathbb{R}^2$ with clamped boundary conditions. After establishing the well-posedness, we focus on some regularity results of the solution with respect to the regularity of the problem da

Danli Shi, Weiyi Zhang, Xiaolan Chen, Yexin Liu

Artificial intelligence (AI) is vital in ophthalmology, tackling tasks like diagnosis, classification, and visual question answering (VQA). However, existing AI models in this domain often require extensive annotation and are task-specific, limiting their clinical utility. While recent developments have brought about foundation models for ophthalmology, they

Sebastian Schmidt, Leonard Schenk, Leo Schwinn, Stephan Günnemann

When applying deep learning models in open-world scenarios, active learning (AL) strategies are crucial for identifying label candidates from a nearly infinite amount of unlabeled data. In this context, robust out-of-distribution (OOD) detection mechanisms are essential for handling data outside the target distribution of the application. However, current wo

Duo Peng, Qiuhong Ke, Jun Liu

Text-to-Image (T2I) models have raised security concerns due to their potential to generate inappropriate or harmful images. In this paper, we propose UPAM, a novel framework that investigates the robustness of T2I models from the attack perspective. Unlike most existing attack methods that focus on deceiving textual defenses, UPAM aims to deceive both textu

Wei Liu, Peng Gao, Haotian Zhang, Ke Li

Complex multi-step attacks have caused significant damage to numerous critical infrastructures. To detect such attacks, graph neural network based methods have shown promising results by modeling the system's events as a graph. However, existing methods still face several challenges when deployed in practice. First, there is a lack of sufficient real attack

R. R. S. Oliveira

In this paper, we determine the relativistic and nonrelativistic energy levels for Dirac fermions in a spinning conical G\"odel-type spacetime in $(2+1)$-dimensions, where we work with the curved Dirac equation in polar coordinates and we use the tetrads formalism. Solving a second-order differential equation for the two components of the Dirac spinor, we ob

Chengqing Yu, Fei Wang, Zezhi Shao, Tangwen Qian

Multivariate time series forecasting (MTSF) is crucial for decision-making to precisely forecast the future values/trends, based on the complex relationships identified from historical observations of multiple sequences. Recently, Spatial-Temporal Graph Neural Networks (STGNNs) have gradually become the theme of MTSF model as their powerful capability in min

Ronald Orozco López

In this paper, we find solutions to first-order linear proportional difference equations via the $(s,t)$-integration factor method. The $(s,t)$-integration factor involves the $(s,t)$-Pantograph function, which is a generalization of the partial Theta function. Other equations are solved including the $(s,t)$-analog of the Bernoulli equation.

Zijiang Yan, Hina Tabassum

We develop a novel multi-objective reinforcement learning (MORL) framework to jointly optimize wireless network selection and autonomous driving policies in a multi-band vehicular network operating on conventional sub-6GHz spectrum and Terahertz frequencies. The proposed framework is designed to 1. maximize the traffic flow and minimize collisions by control

Muhammad Rehman, Haji Muhammad, Orlando Panella, Mario E. Gomez

Extensions of the Standard Model featuring excited fermions present an interesting framework that motivates the search for exotic particles at the LHC. Additionally, these extensions offer potential explanations for the muon's anomalous magnetic moment and other precision observables, shedding light on the energy scale and key parameters of the new theory. O

Qiang Liu, Shuxin Guo

On April 22, 2020, the CME Group switched to Bachelier pricing for a group of oil futures options. The Bachelier model, or more generally the arithmetic Brownian motion (ABM), is not so widely used in finance, though. This paper provides the first comprehensive survey of options pricing under ABM. Using the risk-neutral valuation, we derive formulas for Euro

T. Sperling, J. Eislöffel, C. F. Manara, J. Campbell-White

The main goal of this study is to screen the PENELLOPE/UVES targets for outflow activity and find microjets via spectro-astrometry in, e.g., the [OI]$\lambda$6300 line. In total, 34 T\,Tauri stars of the PENELLOPE survey have been observed with the high resolution slit spectrograph UVES in three different slit positions rotated by $120^\text{o}$. Our spectro

Michele Chiari, Luca Geatti, Nicola Gigante, Matteo Pradella

Operator Precedence Languages (OPL) have been recently identified as a suitable formalism for model checking recursive procedural programs, thanks to their ability of modeling the program stack. OPL requirements can be expressed in the Precedence Oriented Temporal Logic (POTL), which features modalities to reason on the natural matching between function call

Defang Chen, Zhenyu Zhou, Can Wang, Chunhua Shen

Diffusion-based generative models use stochastic differential equations (SDEs) and their equivalent ordinary differential equations (ODEs) to establish a smooth connection between a complex data distribution and a tractable prior distribution. In this paper, we identify several intriguing trajectory properties in the ODE-based sampling process of diffusion m

Hans J. Haubold, Dilip Kumar, Ashik A. Kabeer

Needed for cosmological and stellar nucleosynthesis, we are studying the closed-form analytic evaluation of thermonuclear reaction rates. In this context, we undertake a comprehensive analysis of three distinct velocity distributions, namely the Maxwell-Boltzmann distribution, the pathway distribution, and the Mittag-Leffer distribution. We emphasize the uti

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A measurement of the transverse polarization of the $\Lambda$ and $\bar{\Lambda}$ hyperons in $p$Ne fixed-target collisions at $\sqrt{s_{NN}}$ = 68.4 GeV is presented using data collected by the LHCb detector. The polarization is studied using the decay $\Lambda \rightarrow p \pi^-$ together with its charge conjugated process, the integrated values measured

Quan Wang, Mingliang Pan, Lucas Kreiss, Saeed Samaei

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already complex DCS theoretical framework but also by the broad range of co

J. Diez-Merida, I. Das, G. Di Battista, A. Diez-Carlon

Magic-angle twisted bilayer graphene (MATBG) stands as one of the most versatile materials in condensed-matter physics due to its hosting of a wide variety of exotic phases while also offering convenient tunability. However, the fabrication of MATBG is still manual, and remains to be a challenging and inefficient process, with devices being highly dependent

Madhushri Roy Chowdhury, Subir Ghosh

The present work deals with quantum Uncertainty Relations (UR) subjected to the Standard Deviations (SD) of the relevant dynamical variables for a particle constrained to move on a torus knot. It is important to note that these variables have to obey the two distinct periodicities of the knotted paths embedded on the torus. We compute generalized forms of th

B. Riaz, W. -F. Thi, M. N. Machida

The detection of Keplerian rotation is rare among Class 0 protostellar systems. We have investigated the high-density tracer HCN as a probe of the inner disk in a Class 0 proto-brown dwarf candidate. Our ALMA high angular resolution observations show the peak in the HCN (3-2) line emission arises from a compact component near the proto-brown dwarf with a sma

Emmanouil Maragkoudakis, Symeon Papadopoulos, Iraklis Varlamis, Christos Diou

Synthetically generated images can be used to create media content or to complement datasets for training image analysis models. Several methods have recently been proposed for the synthesis of high-fidelity face images; however, the potential biases introduced by such methods have not been sufficiently addressed. This paper examines the bias introduced by t

Joydip Saha, Indranath Sengupta, Pranjal Srivastava

This paper investigates the projective closure of simplicial affine semigroups in $\mathbb{N}^{d}$, $d \geq 2$. We present a characterization of the Cohen-Macaulay property for the projective closure of these semigroups using Gr\"{o}bner bases. Additionally, we establish a criterion, based on Gr\"{o}bner bases, for determining the Buchsbaum property of non-C

Moein E. Samadi, Younes Müller, Andreas Schuppert

Kolmogorov-Arnold Networks (KANs) offer an efficient and interpretable alternative to traditional multi-layer perceptron (MLP) architectures due to their finite network topology. However, according to the results of Kolmogorov and Vitushkin, the representation of generic smooth functions by KAN implementations using analytic functions constrained to a finite

Yunfan Lu, Yuchen Ma, David Hsu, Panpan Cai

Robot motion planning has made vast advances over the past decades, but the challenge remains: robot mobile manipulators struggle to plan long-range whole-body motion in common household environments in real time, because of high-dimensional robot configuration space and complex environment geometry. To tackle the challenge, this paper proposes Neural Random

Christian Binkowski, Stefan Appel, Andreas Aßmuth

Open software ecosystems are beneficial for customers; they benefit from 3rd party services and applications, e.g. analysis of data using apps, developed and deployed by other companies or open-source communities. One significant advantage of this approach is that other customers may benefit from these newly developed applications as well. Especially softwar

Ximiao Zhang, Min Xu, Dehui Qiu, Ruixin Yan

In the field of medical decision-making, precise anomaly detection in medical imaging plays a pivotal role in aiding clinicians. However, previous work is reliant on large-scale datasets for training anomaly detection models, which increases the development cost. This paper first focuses on the task of medical image anomaly detection in the few-shot setting,

Yvain Bruned, Yingtong Hou

In this work, we introduce explicit formulae for the coproducts at play for multi-indices in ODEs and in singular SPDEs. The two coproducts described correspond to versions of the Butcher-Connes-Kreimer and extraction/contraction coproducts with multi-indices. The main idea is to use the fact that these coproducts are the adjoints of dual products for which

Yuan-Lin Lyu, Qu-Zhi Li, Zhiguang Xiao, Han-Qing Zheng

The effective potential of the $O(N)$ model at large $N$ limit is reinvestigated with varying pion mass and temperature. For large pion masses and high temperatures, we find the phenomenologically favored vacuum, located on the upper branch of the double-branched effective potential for physical $m_\pi$, moves to the lower branch and becomes no longer a loca

Miguel A. Cardona, Adam Marton, Jaroslav Supina

Inspired by Bartoszy\'nski's work on small sets, we introduce a new ideal defined by interval partitions on natural numbers and summable sequences of positive reals. Similarly, we present another ideal that relies on Bartoszy\'nski's and Shelah's representation of $F_\sigma$ measure zero sets. We show they are $\sigma$-ideals characterizing all small sets an

Tianxin Zhou, Xiang Li, Haibing Lu

Considering the attacks against the power grid, one of the most effective approaches could be the attack to the transmission lines that leads to large cascading failures. Hence, the problem of locating the most critical or vulnerable transmission lines for a Power Grid Cascading Failure (PGCF) has drawn much attention from the research society. There exists

Claire E Plunkett, Sean D Lawley

A wide variety of physical, chemical, and biological processes involve diffusive particles interacting with surfaces containing reactive patches. The theory of boundary homogenization seeks to encapsulate the effective reactivity of such a patchy surface by a single trapping rate parameter. In this paper, we derive the trapping rate for partially reactive pa

George A. McCarthy, Tanmoy Laskar

Multi-wavelength modeling of the synchrotron radiation from relativistic transients such as Gamma-ray Burst (GRB) afterglows is a powerful means of exploring the physics of relativistic shocks and of deriving properties of the explosion, such as the kinetic energy of the associated relativistic outflows. Capturing the location and evolution of the synchrotro

Bharat Adsul, Paul Gastin, Shantanu Kulkarni, Pascal Weil

We propose a local, past-oriented fragment of propositional dynamic logic to reason about concurrent scenarios modelled as Mazurkiewicz traces, and prove it to be expressively complete with respect to regular trace languages. Because of locality, specifications in this logic are efficiently translated into asynchronous automata, in a way that reflects the st

Koustav Roy, Saurabh Basu

We perform systematic analyses of single and multi-frequency driving protocols on a Rashba nanowire with superconducting correlations induced by proximity effects. The results for the single-mode drive reveal interesting frequency dependencies of the Majorana modes, in the sense that the parameters corresponding to the trivial and topological limits of the u

Hosein Zarini, Maryam Farajzadeh Dehkordi, Armin Farhadi, Mohammad Robat Mili

Unmanned aerial vehicles (UAVs) with multiple antennas have recently been explored to improve capacity in wireless networks. However, the strict energy constraint of UAVs, given their simultaneous flying and communication tasks, renders the exploration of energy-efficient multi-antenna techniques indispensable for UAVs. Meanwhile, lens antenna subarray (LAS)

Irumi Sugimori, Katsumi Inoue, Hidetomo Nabeshima, Torsten Schaub

We propose Large Neighborhood Prioritized Search (LNPS) for solving combinatorial optimization problems in Answer Set Programming (ASP). LNPS is a metaheuristic that starts with an initial solution and then iteratively tries to find better solutions by alternately destroying and prioritized searching for a current solution. Due to the variability of neighbor

Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin, Jason Gemsun Young

We introduces the Quantum-Train(QT) framework, a novel approach that integrates quantum computing with classical machine learning algorithms to address significant challenges in data encoding, model compression, and inference hardware requirements. Even with a slight decrease in accuracy, QT achieves remarkable results by employing a quantum neural network a

Bappaditya Bhowmik, Souvik Biswas

Let $\mathcal{A}$ denote the class of all analytic functions $f$ defined in the open unit disc $\mathbb{D}$ with the normalization $f(0)=0=f'(0)-1$ and let $P'$ be the class of functions $f\in\mathcal{A}$ such that ${\rm{Re}}\,f'(z)>0$, $z\in\mathbb{D}$. In this article, we obtain radii of concavity of $P'$ and for the class $P'$ with the fixed second coeffi

Michael Eischer, Tobias Distler

Traditionally, Byzantine fault tolerance (BFT) in geo-replicated systems is achieved by executing complex agreement protocols over large-distance communication links, and therefore typically incurs high response times. In this paper we address this problem with Spider, a resilient and modular BFT replication architecture for geo-distributed systems that leve

Pietro Monti-Guarnieri, Laura Bandiera, Nicola Canale, Stefano Carsi

Recent studies have shown that the electromagnetic shower induced by a high-energy electron, positron or photon incident along the axis of an oriented crystal develops in a space more compact than the ordinary. On the other hand, the properties of the hadronic interactions are not affected by the lattice structure. This means that, inside an oriented crystal

Canshi Wei

Fine-grained image classification, particularly in zero/few-shot scenarios, presents a significant challenge for vision-language models (VLMs), such as CLIP. These models often struggle with the nuanced task of distinguishing between semantically similar classes due to limitations in their pre-trained recipe, which lacks supervision signals for fine-grained

Kaj Munhoz Arfvidsson, Frank J. Jiang, Karl H. Johansson, Jonas Mårtensson

In this work, we propose an approach for ensuring the safety of vehicles passing through an intelligent intersection. There are many proposals for the design of intelligent intersections that introduce central decision-makers to intersections for enhancing the efficiency and safety of the vehicles. To guarantee the safety of such designs, we develop a safety

Kamalkumar Rathinasamy, Jayarama Nettar, Amit Kumar, Vishal Manchanda

Enterprises grapple with the significant challenge of managing proprietary unstructured data, hindering efficient information retrieval. This has led to the emergence of AI-driven information retrieval solutions, designed to adeptly extract relevant insights to address employee inquiries. These solutions often leverage pre-trained embedding models and genera

Pai Zeng, Zhenyu Ning, Jieru Zhao, Weihao Cui

We survey the large language model (LLM) serving area to understand the intricate dynamics between cost-efficiency and accuracy, which is magnified by the growing need for longer contextual understanding when deploying models at a massive scale. Our findings reveal that works in this space optimize along three distinct but conflicting goals: improving servin

Jack Vice, Natalie Ruiz-Sanchez, Pamela K. Douglas, Gita Sukthankar

In humans, intrinsic motivation is an important mechanism for open-ended cognitive development; in robots, it has been shown to be valuable for exploration. An important aspect of human cognitive development is $\textit{episodic memory}$ which enables both the recollection of events from the past and the projection of subjective future. This paper explores t

Euna Jung, Jaeill Kim, Jungmin Ko, Jinwoo Park

The latest advancements in unsupervised learning of sentence embeddings predominantly involve employing contrastive learning-based (CL-based) fine-tuning over pre-trained language models. In this study, we analyze the latest sentence embedding methods by adopting representation rank as the primary tool of analysis. We first define Phase 1 and Phase 2 of fine

Viqar Husain, Irfan Javed, Sanjeev S. Seahra, Nomaan X

In quantum cosmology, it is expected that the Big Bang singularity is resolved and the universe undergoes a bounce. We find that for Gaussian initial states, matter-gravity entanglement entropy rises rapidly during the bounce, declines, and then approaches a steady-state value following the bounce. These observations suggest that matter-gravity entanglement

Nand Lal Yadav, Satyendra Singh, Rajesh Kumar, Sudhakar Singh

X-ray is one of the prevalent image modalities for the detection and diagnosis of the human body. X-ray provides an actual anatomical structure of an organ present with disease or absence of disease. Segmentation of disease in chest X-ray images is essential for the diagnosis and treatment. In this paper, a framework for the segmentation of X-ray images usin

Julian Wachter, Deepika Tiwari, Martin Monperrus, Benoit Baudry

In managed languages, serialization of objects is typically done in bespoke binary formats such as Protobuf, or markup languages such as XML or JSON. The major limitation of these formats is readability. Human developers cannot read binary code, and in most cases, suffer from the syntax of XML or JSON. This is a major issue when objects are meant to be embed

Wuzhou Li, Jiawei Zhou, Xiang Li, Yi Cao

Recently, the field of few-shot detection within remote sensing imagery has witnessed significant advancements. Despite these progresses, the capacity for continuous conceptual learning still poses a significant challenge to existing methodologies. In this paper, we explore the intricate task of incremental few-shot object detection in remote sensing images.

R. S. Bagge, C. Foster, F. D'Eugenio, A. Battisti

We present a study of kinematic asymmetries from the integral field spectroscopic surveys MAGPI and SAMI. By comparing the asymmetries in the ionsied gas and stars, we aim to disentangle the physical processes that contribute to kinematic disturbances. We normalise deviations from circular motion by $S_{05}$, allowing us to study kinematic asymmetries in the

Yuichi Shiozawa, Jian Wang

We establish explicit integral tests for spatial asymptotic behaviors of fractional stochastic heat equations driven by additive L\'evy white noise. Our results indicate that fractional stochastic heat equations enjoy the so-called additive physical intermittent property in all dimensions when the driven L\'evy white noise is sufficiently light-tailed. The p

Shaina Raza, Ananya Raval, Veronica Chatrath

The deployment of Large Language Models (LLMs) in diverse applications necessitates an assurance of safety without compromising the contextual integrity of the generated content. Traditional approaches, including safety-specific fine-tuning or adversarial testing, often yield safe outputs at the expense of contextual meaning. This can result in a diminished

Ming Hu, Siyuan Yan, Peng Xia, Feilong Tang

Deep learning-based diagnostic systems have demonstrated potential in skin disease diagnosis. However, their performance can easily degrade on test domains due to distribution shifts caused by input-level corruptions, such as imaging equipment variability, brightness changes, and image blur. This will reduce the reliability of model deployment in real-world

Xing Gao, Li Guo, Zongjian Han

Rota-Baxter groups with weights $\pm 1$ have attracted quite much attention since their recent introduction, thanks to their connections with Rota-Baxter Lie algebras, factorizations of Lie groups, post- and pre-Lie algebras, braces and set-theoretic solutions of the Yang-Baxter equation. Despite their expected importance from integrals on groups to pre-grou

Tobia Arcangeli, Tatjana Škrbić, Somiealo Azote, Davide Marcato

Using Langevin dynamics complemented by Wang-Landau Monte Carlo simulations, we study the phase behavior of single and multiple semiflexible polymer chains in solution under poor-solvent conditions. In the case of a single chain, we obtain the full phase diagram in the temperature-bending rigidity (stiffness) plane and we provide connections with a classical

Zeyu Zhang, Yiran Wang, Biao Wu, Shuo Chen

In recent years, there has been significant interest in creating 3D avatars and motions, driven by their diverse applications in areas like film-making, video games, AR/VR, and human-robot interaction. However, current efforts primarily concentrate on either generating the 3D avatar mesh alone or producing motion sequences, with integrating these two aspects

Daisuke Ito, Hidetoshi Sano, Kazuhiro Nakazawa, Ikuyuki Mitsuishi

We present a new CO observation toward the Type Ia supernova remnant (SNR) 3C 397 using the Nobeyama 45-m radio telescope at an unprecedent angular resolution of $\sim$18''. We newly found that the CO cloud at $V_{\mathrm{LSR}}$ = 55.7-62.2 km s$^{-1}$ (60 km s$^{-1}$ cloud) shows a good spatial correspondence with the radio continuum shell. We also found an

Hanti Lin

The 2021 Nobel Prize in Economics recognized an epistemology of causal inference based on the Rubin causal model (Rubin 1974), which merits broader attention in philosophy. This model, in fact, presupposes a logical principle of counterfactuals, Conditional Excluded Middle (CEM), the locus of a pivotal debate between Stalnaker (1968) and Lewis (1973) on the

F. C. Sobrinho, I. Danhoni, C. A. Bertulani, L. M. Abreu

One of the hot topics in hadron physics is the study of the new exotic charmonium states and the determination of their internal structure. Another important topic is the search for effects of the magnetic field created in high energy nuclear collisions. In this note we show that we can use ultra-peripheral collisions to address both issues. We compute the c

Amr Keleg, Walid Magdy, Sharon Goldwater

On annotating multi-dialect Arabic datasets, it is common to randomly assign the samples across a pool of native Arabic speakers. Recent analyses recommended routing dialectal samples to native speakers of their respective dialects to build higher-quality datasets. However, automatically identifying the dialect of samples is hard. Moreover, the pool of annot

Ziye Jia, Jiahao You, Chao Dong, Qihui Wu

As the demands for immediate and effective responses increase in both civilian and military domains, the unmanned aerial vehicle (UAV) swarms emerge as effective solutions, in which multiple cooperative UAVs can work together to achieve specific goals. However, how to manage such complex systems to ensure real-time adaptability lack sufficient researches. He

Marianne Arriola, Weishen Pan, Manqi Zhou, Qiannan Zhang

Joint analysis of multi-omic single-cell data across cohorts has significantly enhanced the comprehensive analysis of cellular processes. However, most of the existing approaches for this purpose require access to samples with complete modality availability, which is impractical in many real-world scenarios. In this paper, we propose (Single-Cell Cross-Cohor

Jian-Dong Liu, Wen-Biao Han, Qianyun Yun, Shu-Cheng Yang

The gravitational wave (GW) signal from the merger of two black holes can serve as a standard sirens for cosmological inference. However, a degeneracy exists between the luminosity distance and the inclination angle between the binary system's orbital angular momentum and the observer's line of sight, limiting the precise measurement of the luminosity distan

Michio Amano

Let $\widehat{\mathcal{G}}^{(\lambda)}$ be a formal group scheme which deforms $\widehat{\mathbb{G}}_a$ to $\widehat{\mathbb{G}}_m$. And let $\psi^{(l)}:\widehat{\mathcal{G}}^{(\lambda)}\rightarrow\widehat{\mathcal{G}}^{(\lambda^{p^l})}$ be the $l$-th Frobenius-type homomorphism determined by $\lambda$. We show that the homomorphism $(\psi^{(l)})^\ast:H^2_0(

Ning Shi, Zijun Wu

Recent studies have demonstrated the potential to control paraphrase generation, such as through syntax, which has broad applications in various downstream tasks. However, these methods often require detailed parse trees or syntactic exemplars, countering human-like paraphrasing behavior in language use. Furthermore, an inference gap exists, as control speci

Bing Cao, Haiyu Yao, Pengfei Zhu, Qinghua Hu

Tiny object detection is one of the key challenges in the field of object detection. The performance of most generic detectors dramatically decreases in tiny object detection tasks. The main challenge lies in extracting effective features of tiny objects. Existing methods usually perform generation-based feature enhancement, which is seriously affected by sp

Qiqi Su, Eleftheria Iliadou

It is essential to understand the personal, behavioral, environmental, and other factors that correlate with optimal hearing aid fitting and hearing aid users' experiences in order to improve hearing loss patient satisfaction and quality of life, as well as reduce societal and financial burdens. This work proposes a novel framework that uses Encoder-decoder

Noy Soffer Aranov, Taehyeong Kim

We compute the Hausdorff dimension of the set of singular vectors in function fields and bound the Hausdorff dimension of the set of $\varepsilon$-Dirichlet improvable vectors in this setting. This is a function field analogue of the results of Cheung and Chevallier [Duke Math. J. 165 (2016), 2273--2329].

Yunxin Li, Shenyuan Jiang, Baotian Hu, Longyue Wang

Recent advancements in Multimodal Large Language Models (MLLMs) underscore the significance of scalable models and data to boost performance, yet this often incurs substantial computational costs. Although the Mixture of Experts (MoE) architecture has been employed to efficiently scale large language and image-text models, these efforts typically involve few

Zhuangzhuang He, Yifan Wang, Yonghui Yang, Peijie Sun

As its availability and generality in online services, implicit feedback is more commonly used in recommender systems. However, implicit feedback usually presents noisy samples in real-world recommendation scenarios (such as misclicks or non-preferential behaviors), which will affect precise user preference learning. To overcome the noisy samples problem, a