February 2024 arXiv papers — page 126

Parisa Elahidoost, Michael Unterkalmsteiner, Davide Fucci, Peter Liljenberg

Context and motivation: In this industry-academia collaborative project, a team of researchers, supported by a software architect, business analyst, and test engineer explored the challenges of requirement variability in a large business software development company. Question/problem: Following the design science paradigm, we studied the problem of requireme

Wataru Nakanishi, Noriko Kaneko

Electric road system (ERS) is expected to make electric vehicles (EVs) more popular as EVs with Dynamic Wireless Power Transfer (DWPT) system can be charged while driving on ERS. Although some studies dealt with ERS implementation, its toll system has not been explored yet. This paper aims at a fundamental analysis on impact of ERS toll system on a traffic a

Sebastien N. Abadi, Ke-Jun Xu, Eder G. Lomeli, Pascal Puphal

Recent studies of La$_3$Ni$_2$O$_7$ have identified a bilayer (2222) structure and an unexpected alternating monolayer-trilayer (1313) structure, both of which feature signatures of superconductivity near 80 K under high pressures. Using angle-resolved photoemission spectroscopy, we measure the electronic structure of 1313 samples. In contrast to the previou

Kacper Topolnicki, Tomasz Bold

This paper describes a procedure for a realistic estimation of the number of iterations in the main loop of a recent particle detection algorithm from [1]. The calculations are based on a Monte Carlo simulation of the ATLAS inner detector. The resulting estimates of numerical complexity suggest that using the procedure from [1] for online triggering is not f

Alexander Demin, Fabrice Rouillier, Joao Ruiz

In this contribution, we consider a zero-dimensional polynomial system in $n$ variables defined over a field $\mathbb{K}$. In the context of computing a Rational Univariate Representation (RUR) of its solutions, we address the problem of certifying a separating linear form and, once certified, calculating the RUR that comes from it, without any condition on

Yuyao Ge, Shenghua Liu, Baolong Bi, Yiwei Wang

Large language models (LLMs) have achieved significant success in reasoning tasks, including mathematical reasoning and logical deduction. Among these reasoning tasks, graph problems stand out due to their complexity and unique structural characteristics, attracting considerable attention from researchers. Previous studies have explored LLMs' graph reasoning

Muhammad Bilal Shahid, Cody Fleming

The selection of the target variable is important while learning parameters of the classical car following models like GIPPS, IDM, etc. There is a vast body of literature on which target variable is optimal for classical car following models, but there is no study that empirically evaluates the selection of optimal target variables for black-box models, such

Malinda Dilhara, Abhiram Bellur, Timofey Bryksin, Danny Dig

Software developers often repeat code changes, known as "code change patterns" (CPATs), within and across projects. Automating these CPATs accelerates development, but current Transformation by Example (TBE) techniques are limited by the input examples' quality and quantity, missing variations with different syntax or flow yet semantically similar. Large Lan

Tarakanta Nayak, Soumen Pal

By a symmetry of the Julia set of a polynomial, also referred as polynomial Julia set, we mean an Euclidean isometry preserving the Julia set. Each such symmetry is in fact a rotation about the centroid of the polynomial. In this article, a survey of the symmetries of polynomial Julia sets is made. Then the Euclidean isometries preserving the Julia set of ra

Yudai Suzuki, Keita Yokoyama

In this paper, we introduce a hierarchy dividing the set $\{\sigma \in \Pi^1_2 : \Pi^1_1$-$\mathsf{CA}_0 \vdash \sigma\}$. Then, we give some characterizations of this set using weaker variants of some principles equivalent to $\Pi^1_1$-$\mathsf{CA}_0$: leftmost path principle, Ramsey's theorem for $\Sigma^0_n$ classes of $[\mathbb{N}]^{\mathbb{N}}$ and dete

Yuta Takaya

We establish a representability criterion of $v$-sheaf theoretic modifications of formal schemes and apply this criterion to moduli spaces of parahoric level structures on local shtukas. In the proof, we introduce nice classes of equivariant profinite perfectoid covers and study geometric quotients of perfectoid formal schemes by profinite groups. As a corol

Cathy W. S. Chen, Takaaki Koike, Wei-Hsuan Shau

This research incorporates realized volatility and overnight information into risk models, wherein the overnight return often contributes significantly to the total return volatility. Extending a semi-parametric regression model based on asymmetric Laplace distribution, we propose a family of RES-CAViaR-oc models by adding overnight return and realized measu

Harald Schmid

This paper presents some new results on the eigenvalues of the spheroidal wave equation. We study the angular and Coulomb spheroidal wave equation as a special case of a more general linear Hamiltonian system depending on three parameters. We prove that the eigenvalues of this system satisfy a first-order quasilinear partial differential equation with respec

Shaojian Qiu, Huihao Huang, Jianxiang Luo, Yingjie Kuang

Software defect prediction aims to identify defect-prone code, aiding developers in optimizing testing resource allocation. Most defect prediction approaches primarily focus on coarse-grained, file-level defect prediction, which fails to provide developers with the precision required to locate defective code. Recently, some researchers have proposed fine-gra

Karan Chadha, John Duchi, Rohith Kuditipudi

We consider the task of constructing confidence intervals with differential privacy. We propose two private variants of the non-parametric bootstrap, which privately compute the median of the results of multiple "little" bootstraps run on partitions of the data and give asymptotic bounds on the coverage error of the resulting confidence intervals. For a fixe

Nilasis Chaudhuri, Young-Pil Choi, Oliver Tse, Ewelina Zatorska

We consider a one-dimensional hydrodynamic model featuring nonlocal attraction-repulsion interactions and singular velocity alignment. We introduce a two-velocity reformulation and the corresponding energy-type inequality, in the spirit of the Bresch-Desjardins estimate. We identify a dependence between the communication weight and interaction kernel and bet

Hongjun Zhang

Use denoising diffusion implicit model for bridge-type innovation. The process of adding noise and denoising to an image can be likened to the process of a corpse rotting and a detective restoring the scene of a victim being killed, to help beginners understand. Through an easy-to-understand algebraic method, derive the function formulas for adding noise and

Qiong Qin, Yi-feng Yang

Can room temperature superconductivity be achieved in correlated materials under ambient pressure? Our answer to this billion-dollar question is probably no, at least for realistic models within the current theoretical framework. This is shown by our systematic simulations on the pairing instability of some effective models for two-dimensional superconductiv

Chrisantus Eze, Christopher Crick

Robot learning of manipulation skills is hindered by the scarcity of diverse, unbiased datasets. While curated datasets can help, challenges remain in generalizability and real-world transfer. Meanwhile, large-scale "in-the-wild" video datasets have driven progress in computer vision through self-supervised techniques. Translating this to robotics, recent wo

Yun Gao

Huang's Lemma is an important tool in CR geometry to study rigidity problems. This paper introduces a generalization of Huang's Lemma based on the rigidity properties of holomorphic mappings preserving certain orthogonality on projective spaces, which is optimal for the case of partial linearity. By exploring the intricate relationship between rigidity and H

Frederico A. Turolla, Moises D. Vassallo, Alessandro V. M. Oliveira

This paper presents a test of intermodal interaction between coaches and airlines in Brazil in order to check for the efficacy of recent liberalization measures designed to promote competition in both industries. Interstate travel service in the country is heavily provided by coaches, and the system is fully operated by the private sector under public delega

Helena Povoa, Alessandro V. M. Oliveira

The number of air transportation passengers during the holidays in Brazil has grown notably since the late nineties. One of the reasons is greater competition in airfares made possible by economic liberalization. This paper presents an econometric model of airline pricing aiming at estimating the impacts of holiday periods on fares, with special emphasis on

Chansreynich Huot, Kimleang Kea, Tae-Kyung Kim, Youngsun Han

Portfolio optimization is a primary component of the decision-making process in finance, aiming to tactfully allocate assets to achieve optimal returns while considering various constraints. Herein, we proposed a method that uses the knapsack-based portfolio optimization problem and incorporates the quantum computing capabilities of the quantum walk mixer wi

Minghuan Zeng, Xiang Li, Yongjun Wang, Shiping Feng

The quasiparticle scattering interference (QSI) is intimately related to the nature of the quasiparticle and of its interplay with a variety of electronic orders and superconductivity. Here starting from the microscopic octet scattering model, the nature of QSI in cuprate superconductors is studied in the $T$-matrix approach. In particular, a new method of t

A. A. Saharian, S. B. Dabagov, H. F. Khachatryan, L. Sh. Grigoryan

We investigate the Cherenkov radiation by a charge uniformly moving inside a dielectric cylindrical channel in a homogeneous medium. The expressions for the Fourier components of the electric and magnetic fields are derived by using the electromagnetic field Green tensor. The spectral distribution of the Cherenkov radiation intensity in the exterior medium i

Arijit Mahapatra, Sage A. Malingen, Padmini Rangamani

The formation of microparticles requires the bending of the plasma membrane away from the cytosol. The capcity of the cell membrane to form a microparticle, and the rate of membrane deformation, are controlled by multiple factors, including loss of lipid asymmetry (primarily the exposure of phosphatidylserine on the outer leaflet), detachment of the membrane

Binyan Hu, A. K. Qin

Medical image segmentation has been significantly advanced by deep learning (DL) techniques, though the data scarcity inherent in medical applications poses a great challenge to DL-based segmentation methods. Self-supervised learning offers a solution by creating auxiliary learning tasks from the available dataset and then leveraging the knowledge acquired f

Dhruv Srikanth, Jayang Gurung, N Satya Deepika, Vineet Joshi

Blindness and other eye diseases are a global health concern, particularly in low- and middle-income countries like India. In this regard, during the COVID-19 pandemic, teleophthalmology became a lifeline, and the Grabi attachment for smartphone-based eye imaging gained in use. However, quality of user-captured image often remained inadequate, requiring clin

Seyedmohammadhossein Hosseinian, Andrew J. Schaefer

An integer program (IP) with a finite number of feasible solutions may have an unbounded linear programming relaxation if it contains irrational parameters, due to implicit constraints enforced by the irrational numbers. We show that those constraints can be obtained if the irrational parameters are polynomials of roots of integers over the field of rational

Zicheng Zhang, Haoning Wu, Erli Zhang, Guangtao Zhai

The rapid development of Multi-modality Large Language Models (MLLMs) has navigated a paradigm shift in computer vision, moving towards versatile foundational models. However, evaluating MLLMs in low-level visual perception and understanding remains a yet-to-explore domain. To this end, we design benchmark settings to emulate human language responses related

Gergő Nemes

Recently, there has been renewed interest in studying the asymptotic properties of the integer partition function $p(n)$. Hardy, Ramanujan, and Rademacher provided detailed asymptotic analysis for $p(n)$. Presently, attention has shifted towards Poincar\'e-type asymptotic expansions, characterised by their simplicity albeit reduced accuracy compared to the e

Rudrajit Das, Naman Agarwal, Sujay Sanghavi, Inderjit S. Dhillon

There is a notable dearth of results characterizing the preconditioning effect of Adam and showing how it may alleviate the curse of ill-conditioning -- an issue plaguing gradient descent (GD). In this work, we perform a detailed analysis of Adam's preconditioning effect for quadratic functions and quantify to what extent Adam can mitigate the dependence on

Kai Nakaishi, Koji Hukushima

Probabilistic context-free grammars (PCFGs), which are commonly used to generate trees randomly, have been well analyzed theoretically, leading to applications in various domains. Despite their utility, the distributions that the grammar can express are limited to those in which the distribution of a subtree depends only on its root and not on its context. T

Muhammad Abdullah, George Ilhwan Park

We perform a detailed numerical study of modal and non-modal stability in oblique Couette-Poiseuille profiles, which are among the simplest examples of three-dimensional boundary layers. Through a comparison with the Orr-Sommerfeld operator for the aligned case, we show how an effective wall speed succinctly characterizes modal stability. Large-scale paramet

Serik Sagitov, Lotta Eriksson, Marija Cvijovic

We employ the framework of multitype Galton-Watson processes to model a population of dividing cells. The cellular type is represented by its biological age, defined as the count of harmful proteins hosted by the cell. The stochastic evolution of the biological age of a cell is modeled as a discrete Markov chain with a finite state space $\{0,1,\ldots,n\}$,

Anmol Desai, Emma V. Turtelboom, Caleb K. Harada, Courtney D. Dressing

We present and confirm TOI-1751 b, a transiting sub-Neptune orbiting a slightly evolved, solar-type, metal-poor star ($T_{eff} = 5996 \pm 110$ K, $log(g) = 4.2 \pm 0.1$, V = 9.3 mag, [Fe/H] = $-0.40 \pm 0.06$ dex) every 37.47 d. We use TESS photometry to measure a planet radius of $2.77_{-0.07}^{+0.15}~\rm{R_\oplus}$. We also use both Keck/HIRES and APF/Levy

Ban Lin, Mauricio Romo

We study autoequivalences of $D^{b}Coh(X)$ associated to B-brane transport around loops in the stringy K\"ahler moduli of $X$. We consider the case of $X$ being certain resolutions of determinantal varieties embedded in $\mathbb{P}^{d}\times G(k,n)$. Such resolutions have been modeled, in general, by nonabelian gauged linear sigma models (GLSM). We use the G

Wenzhi Gao, Chunlin Sun, Chenyu Xue, Dongdong Ge

Online linear programming plays an important role in both revenue management and resource allocation, and recent research has focused on developing efficient first-order online learning algorithms. Despite the empirical success of first-order methods, they typically achieve a regret no better than $\mathcal{O}(\sqrt{T})$, which is suboptimal compared to the

Alex Christopher Stutts, Danilo Erricolo, Theja Tulabandhula, Amit Ranjan Trivedi

We present a novel statistical approach to incorporating uncertainty awareness in model-free distributional reinforcement learning involving quantile regression-based deep Q networks. The proposed algorithm, $\textit{Calibrated Evidential Quantile Regression in Deep Q Networks (CEQR-DQN)}$, aims to address key challenges associated with separately estimating

Qinglan Xia, Haotian Sun

This article generalizes the study of branched/ramified optimal transportation to those with capacity constraints. Each admissible transport network studied here is represented by a transport multi-path between measures, with a capacity constraint on each of its components. The associated transport cost is given by the sum of the $\textbf{M}_{\alpha}$-cost o

Nagaraj Nagalingam, Vikram Korede, Daniel Irimia, Jerry Westerweel

Oscillatory flow in confined spaces is central to understanding physiological flows and rational design of synthetic periodic-actuation based micromachines. Using theory and experiments on oscillating flows generated through a laser-induced cavitation bubble, we associate the dynamic bubble size (fluid velocity) and bubble lifetime to the laser energy suppli

Gonzalo Ramos, Rick Barraza, Victor Dibia, Sharon Lo

In this position paper, we reflect on fictional stories dealing with the infinite and how they connect with the current, fast-evolving field of image generation models. We draw attention to how some of these literary constructs can serve as powerful metaphors for guiding human-centered design and technical thinking in the space of these emerging technologies

Victor Tänzel, Miriam Jäger, Steffen Wolf

Understanding the dynamics of biomolecular complexes, e.g., of protein-ligand (un)binding, requires the understanding of paths such systems take between metastable states. In MD simulation data, paths are usually not observable per se, but need to be inferred from simulation trajectories. Here we present a novel approach to cluster trajectories based on a co

Jeongyeol Kwon, Liu Yang, Robert Nowak, Josiah Hanna

Learning good representations of historical contexts is one of the core challenges of reinforcement learning (RL) in partially observable environments. While self-predictive auxiliary tasks have been shown to improve performance in fully observed settings, their role in partial observability remains underexplored. In this empirical study, we examine the effe

Jeongyeol Kwon, Dohyun Kwon, Hanbaek Lyu

We consider the problem of finding stationary points in Bilevel optimization when the lower-level problem is unconstrained and strongly convex. The problem has been extensively studied in recent years; the main technical challenge is to keep track of lower-level solutions $y^*(x)$ in response to the changes in the upper-level variables $x$. Subsequently, all

Scott E. Smart, Prineha Narang

Quantum computing offers several new pathways toward finding many-body eigenstates, with variational approaches being some of the most flexible and near-term oriented. These require particular parameterizations of the state, and for solving multiple eigenstates must incorporate orthogonality. In this work, we use techniques from manifold optimization to arri

Ziang Chen, Jialin Liu, Xiaohan Chen, Xinshang Wang

Graph neural networks (GNNs) have been widely used to predict properties and heuristics of mixed-integer linear programs (MILPs) and hence accelerate MILP solvers. This paper investigates the capacity of GNNs to represent strong branching (SB), the most effective yet computationally expensive heuristic employed in the branch-and-bound algorithm. In the liter

Henry Gann, Josiah Bull, Trevor Gee, Mahla Nejati

The use of synthetic data in machine learning saves a significant amount of time when implementing an effective object detector. However, there is limited research in this domain. This study aims to improve upon previously applied implementations in the task of instance segmentation of pallets in a warehouse environment. This study proposes using synthetical

Kaito Kobayashi, Yukitoshi Motome

Quantum phase transitions are highly remarkable phenomena manifesting in quantum many-body systems. However, their precise identifications in equilibrium systems pose significant theoretical and experimental challenges. Thus far, dynamical detection protocols employing global quantum quenches have been proposed, wherein transitions are discerned via global n

Pedro Rizzo, Joel Torres del Valle, Alexander Torres-Gomez

In the realm of supercommutative superrings, this article investigates the unique factorization of elements. We build upon recent findings by Naser et. al. concerning similar results in noncommutative symmetric rings with zerodivisors, delving deeper into the ramifications. Strikingly, we demonstrate that any unique factorization superdomain necessarily take

Xiaohui Chen, Katherine Luo, Trevor Gee, Mahla Nejati

Humanoid robots are designed to be relatable to humans for applications such as customer support and helpdesk services. However, many such systems, including Softbank's Pepper, fall short because they fail to communicate effectively with humans. The advent of Large Language Models (LLMs) shows the potential to solve the communication barrier for humanoid rob

Shu Nakamura

We show that the free massless staggered fermion (or the KS-fermion) Hamiltonian is equivalent to a discrete Hodge-Dirac operator on the $d$-dimensional square lattice $h\mathbb{Z}^d$. In fact, they are identical operator valued matrices under suitable choices of their representations on $\ell^2(2h\mathbb{Z}^d)\otimes\mathbb{C}^{2^d}$. We employ the formulat

Brody Johnson, Simon McCreary-Ellis

This paper examines the stability of the \`a trous algorithm under arbitrary iteration in the context of a more general study of shift-invariant filter banks. The main results describe sufficient conditions on the associated filters under which an infinitely iterated shift-invariant filter bank is stable. Moreover, it is shown that the stability of an infini

Haonan Chen, Zhicheng Dou, Kelong Mao, Jiongnan Liu

Conversational search utilizes muli-turn natural language contexts to retrieve relevant passages. Existing conversational dense retrieval models mostly view a conversation as a fixed sequence of questions and responses, overlooking the severe data sparsity problem -- that is, users can perform a conversation in various ways, and these alternate conversations

Luwei Zeng, T. Donna Chen, John S. Miller, Faria Tuz Zahura

Accelerated sea level rise has resulted in recurrent flooding in coastal regions, increasingly impacting both transportation systems and local populations. Using the Hampton Roads region in Virginia as a case study, this study a. identifies hotspots with frequent, significant accessibility reduction for work and nonwork travel utilizing crowdsourced WAZE flo

A. A. Wood, D. J. McCloskey, N. Dontschuk, A. Lozovoi

Characterising charge transport in a material is central to the understanding of its electrical properties, and can usually only be inferred from bulk measurements of derived quantities such as current flow. Establishing connections between host material impurities and transport properties in emerging electronics materials, such as wide bandgap semiconductor

Jun Yan Leea, Duo Wu, Xuanrui Guoc, Mohammad Mahdi Ariannejad

Nanometer scale power amplifiers (PA) at sub-THz suffer from severe parasitic effects that lead to experience limited maximum frequency and reduced power performance at the device transceiver front end. The integrated circuits researchers proposed different PA design architecture combinations at scaled down technologies to overcome these limitations. Althoug

Yu Yang, Haidong Yuan, Fuli Li

In quantum multiparameter estimation, multiple to-be-estimated parameters are encoded in a quantum dynamics system by a unitary evolution. As the parameters vary, the system may undergo a topological phase transition (TPT). In this paper, we investigate two SU(2) TPT models and propose the singular behavior of the quantum metric tensor around the TPT point a

Shizhe Feng, Xiaodong Zheng, Pengjie Shi, Thuc Hue Ly

The pattern development of multiple cracks in extremely anisotropic solids such as bilayer or multilayer two-dimensional (2D) crystals contains rich physics, which, however, remains largely unexplored. We studied crack interaction across neighboring 2D layers by transmission electron microscopy and molecular dynamics simulations. Parallel and anti-parallel (

Nate Gillman, Michael Freeman, Daksh Aggarwal, Chia-Hong Hsu

As synthetic data becomes higher quality and proliferates on the internet, machine learning models are increasingly trained on a mix of human- and machine-generated data. Despite the successful stories of using synthetic data for representation learning, using synthetic data for generative model training creates "self-consuming loops" which may lead to train

Daniel T. Yimam, Dennis van der Veen, Teodor Zaharia, Maria Loi

The non-linear and tunable optical constants of phase-change materials associated with their phase-switching have been utilized in reconfigurable optical devices. For example, one possible application of phase-change thin films is for tunable perfect absorption designs, where p- polarized light reflectance vanishes at a specific incidence angle known as the

Kenichi Fujita, Atsushi Ando, Yusuke Ijima

This paper proposes a speech rhythm-based method for speaker embeddings to model phoneme duration using a few utterances by the target speaker. Speech rhythm is one of the essential factors among speaker characteristics, along with acoustic features such as F0, for reproducing individual utterances in speech synthesis. A novel feature of the proposed method

Philippe G. LeFloch, Jean-Marc Mercier, Shohruh Miryusupov

This monograph develops a unified, application-driven framework for kernel methods grounded in reproducing kernel Hilbert spaces (RKHS) and optimal transport (OT). Part I lays the theoretical and numerical foundations on positive-definite kernels; discrete and continuous RKHS; kernel engineering and scaling maps; error assessment via kernel discrepancy/maxim

Shaojie Zhang, Yinghui Wang, Peixuan Liu, Wei Li

The images captured by Wireless Capsule Endoscopy (WCE) always exhibit specular reflections, and removing highlights while preserving the color and texture in the region remains a challenge. To address this issue, this paper proposes a highlight removal method for capsule endoscopy images. Firstly, the confidence and feature terms of the highlight region's e

Yan Dai, Qiwen Cui, Simon S. Du

Markov Games (MG) is an important model for Multi-Agent Reinforcement Learning (MARL). It was long believed that the "curse of multi-agents" (i.e., the algorithmic performance drops exponentially with the number of agents) is unavoidable until several recent works (Daskalakis et al., 2023; Cui et al., 2023; Wang et al., 2023). While these works resolved the

Nischal Ashok Kumar, Andrew Lan

In computer science education, test cases are an integral part of programming assignments since they can be used as assessment items to test students' programming knowledge and provide personalized feedback on student-written code. The goal of our work is to propose a fully automated approach for test case generation that can accurately measure student knowl

Tao Ren, Ruihan Zhou, Jinyang Jiang, Jiafeng Liang

The formulaic alphas are mathematical formulas that transform raw stock data into indicated signals. In the industry, a collection of formulaic alphas is combined to enhance modeling accuracy. Existing alpha mining only employs the neural network agent, unable to utilize the structural information of the solution space. Moreover, they didn't consider the cor

Albert Belenguer-Llorens, Carlos Sevilla-Salcedo, Emilio Parrado-Hernández, Vanessa Gómez-Verdejo

This paper presents the Relevance Feature and Vector Machine (RFVM), a novel model that addresses the challenges of the fat-data problem when dealing with clinical prospective studies. The fat-data problem refers to the limitations of Machine Learning (ML) algorithms when working with databases in which the number of features is much larger than the number o

Hiranmoy Pal, Sarojini Mohapatra

The evolution of certain pair state in a quantum network with isomorphic branches, governed by the Heisenberg $XY$ Hamiltonian, depends solely on the local structure, and it remains unaffected even if the global structure is altered. All graphs which enable high-fidelity vertex state transfer can be considered as isomorphic branches of a quantum network to e

Zhouzhe Wang, Xu Zhang

In this paper, by modifying significantly the Friedrichs-Gross mollifier technique and/or using the Lasry-Lions regularization technique together with some carefully chosen cut-off functions, for the first time we construct explicitly smooth exhaustion functions on any open subset and smooth plurisubharmonic exhaustion functions on any pseudo-convex domain i

Haonan Chen, Zhicheng Dou, Xuetong Hao, Yunhao Tao

Cloud solutions have gained significant popularity in the technology industry as they offer a combination of services and tools to tackle specific problems. However, despite their widespread use, the task of identifying appropriate company customers for a specific target solution to the sales team of a solution provider remains a complex business problem tha

Ish Gupta

The precise measurement of neutron star (NS) spins can provide important insight into the formation and evolution of compact binaries containing NS. While traditional methods of NS spin measurement rely on pulsar observations, gravitational wave detections offer a complementary avenue. However, determining component spins with gravitational waves is hindered

Michael B. Marcus, Jay Rosen

Let $u(s,t)$ be a continuous potential density of a symmetric L\'evy process or diffusion with state space $T$ killed at $T_{0}$, the first hitting time of $0$, or at $\lambda \wedge T_{0}$, where $\lambda$ is an independent exponential time. Let \[ f(t)=\int_{T} u(t,v)\,d\mu(v), \] where $\mu$ is a finite positive measure on $T$. Let $X_{\alpha}=\{X_{\alpha

Igor Frenkel, Matvei Libine

We study a new class of functions that arise naturally in quaternionic analysis, we call them "quasi regular functions". Like the well-known quaternionic regular functions, these functions provide representations of the quaternionic conformal group. However, unlike the regular functions, the quasi regular ones do not admit an invariant unitary structure but

Zhongjian Zhu, Tian Jin

In this paper, we develop the new method to compute the homotopy groups of the mapping cone $C_f=Y\cup_{f}CX$ beyond the metastable range by analysing the homotopy of the $n$-th filtration of the relative James construction $J(X,A)$ for CW-pair $A\stackrel{i}\hookrightarrow X$, defined by B. Gray, which is homotopy equivalent to the homotopy fiber of the pin

Arpita Vats, Vinija Jain, Rahul Raja, Aman Chadha

The paper underscores the significance of Large Language Models (LLMs) in reshaping recommender systems, attributing their value to unique reasoning abilities absent in traditional recommenders. Unlike conventional systems lacking direct user interaction data, LLMs exhibit exceptional proficiency in recommending items, showcasing their adeptness in comprehen

Ana Herrera-Garcia, Sergio Fortes, Eduardo Baena, Jessica Mendoza

Thanks to evolving cellular telecommunication networks, providers can deploy a wide range of services. Soon, 5G mobile networks will be available to handle all types of services and applications for vast numbers of users through their mobile equipment. To effectively manage new 5G systems, end-to-end (E2E) performance analysis and optimization will be key fe

Lisang Ding, Ziang Chen, Xinshang Wang, Wotao Yin

In this work, we propose a novel optimization model termed "sum-of-minimum" optimization. This model seeks to minimize the sum or average of $N$ objective functions over $k$ parameters, where each objective takes the minimum value of a predefined sub-function with respect to the $k$ parameters. This universal framework encompasses numerous clustering applica

Shayan Meshkat Alsadat, Jean-Raphael Gaglione, Daniel Neider, Ufuk Topcu

We present LARL-RM (Large language model-generated Automaton for Reinforcement Learning with Reward Machine) algorithm in order to encode high-level knowledge into reinforcement learning using automaton to expedite the reinforcement learning. Our method uses Large Language Models (LLM) to obtain high-level domain-specific knowledge using prompt engineering i

Klaus Schiefermayr, Olivier Sète

We consider Walsh's conformal map from the complement of a compact set $E = \cup_{j=1}^\ell E_j$ with $\ell$ components onto a lemniscatic domain $\widehat{\mathbb{C}} \setminus L$, where $L$ has the form $L = \{ w \in \mathbb{C} : \prod_{j=1}^\ell \lvert w - a_j \rvert^{m_j} \leq \operatorname{cap}(E) \}$. We prove that the exponents $m_j$ appearing in

Tim Browning, Jakob Glas, Victor Y. Wang

We use a function field version of the circle method to prove that a positive proportion of elements in $\mathbb{F}_q[t]$ are representable as a sum of three cubes of minimal degree from $\mathbb{F}_q[t]$, assuming a suitable form of the Ratios Conjecture and that the characteristic is greater than 3. The analogue of this conjecture for quadratic Dirichlet $

Metin Gurses, Cetin Senturk, Bayram Tekin

We show that there is a phenomenologically and theoretically consistent limit of the generic Einstein-Aether theory in which the Einstein-Aether field equations reduce to Einstein field equations with a perfect fluid distribution sourced by the aether field. This limit is obtained by taking three of the coupling constants of the theory to be zero but keeping

Nam Phuong Tran, The Anh Ta, Shuqing Shi, Debmalya Mandal

Reward allocation, also known as the credit assignment problem, has been an important topic in economics, engineering, and machine learning. An important concept in reward allocation is the core, which is the set of stable allocations where no agent has the motivation to deviate from the grand coalition. In previous works, computing the core requires either

Prathamesh Dharangutte, Jie Gao, Ruobin Gong, Guanyang Wang

This work proposes a class of differentially private mechanisms for linear queries, in particular range queries, that leverages correlated input perturbation to simultaneously achieve unbiasedness, consistency, statistical transparency, and control over utility requirements in terms of accuracy targets expressed either in certain query margins or as implied

Anuj Pokhrel, Aniket Datar, Mohammad Nazeri, Xuesu Xiao

While the workspace of traditional ground vehicles is usually assumed to be in a 2D plane, i.e., SE(2), such an assumption may not hold when they drive at high speeds on unstructured off-road terrain: High-speed sharp turns on high-friction surfaces may lead to vehicle rollover; Turning aggressively on loose gravel or grass may violate the non-holonomic cons

Queenie Yingkun Huang, Vaithilingam Jeyakumar, Guoyin Li

This paper presents exact Semi-Definite Program (SDP) reformulations for infinite-dimensional moment optimization problems involving a new class of piecewise Sum-of-Squares (SOS)-convex functions and projected spectrahedral support sets. These reformulations show that solving a single SDP finds the optimal value and an optimal probability measure of the orig

Hyeong Soo Chang

A recent theoretical analysis of a Monte-Carlo tree search (MCTS) method properly modified from the ``upper confidence bound applied to trees" (UCT) algorithm established a surprising result, due to a great deal of empirical successes reported from heuristic usage of UCT with relevant adjustments for various problem domains in the literature, that its rate o

Yuriy Dorn, Aleksandr Katrutsa, Ilgam Latypov, Andrey Pudovikov

In this study, we propose a new method for constructing UCB-type algorithms for stochastic multi-armed bandits based on general convex optimization methods with an inexact oracle. We derive the regret bounds corresponding to the convergence rates of the optimization methods. We propose a new algorithm Clipped-SGD-UCB and show, both theoretically and empirica

Sophia Heimann, Hung P. Hoang, Stefan Hougardy

The $k$-Opt algorithm is a local search algorithm for the Traveling Salesman Problem. Starting with an initial tour, it iteratively replaces at most $k$ edges in the tour with the same number of edges to obtain a better tour. Krentel (FOCS 1989) showed that the Traveling Salesman Problem with the $k$-Opt neighborhood is complete for the class PLS (polynomial

Liu Liu, Kunlun Qi

In this paper, we study the Boltzmann equation with uncertainties and prove that the spectral convergence of the semi-discretized numerical system holds in a combined velocity and random space, where the Fourier-spectral method is applied for approximation in the velocity space whereas the generalized polynomial chaos (gPC)-based stochastic Galerkin (SG) met

Raza Imam, Muhammad Huzaifa, Nabil Mansour, Shaher Bano Mirza

In this study, we propose an automated framework for camel farm monitoring, introducing two key contributions: the Unified Auto-Annotation framework and the Fine-Tune Distillation framework. The Unified Auto-Annotation approach combines two models, GroundingDINO (GD), and Segment-Anything-Model (SAM), to automatically annotate raw datasets extracted from sur

Moïse Blanchard, Doron Cohen, Aryeh Kontorovich

Cohen and Kontorovich (COLT 2023) initiated the study of what we call here the Binomial Empirical Process: the maximal absolute value of a sequence of inhomogeneous normalized and centered binomials. They almost fully analyzed the case where the binomials are independent, and the remaining gap was closed by Blanchard and Vor\'{a}\v{c}ek (ALT 2024). In this w

Angeliki Katsenou, Xinyi Wang, Daniel Schien, David Bull

Adaptive video streaming is a key enabler for optimising the delivery of offline encoded video content. The research focus to date has been on optimisation, based solely on rate-quality curves. This paper adds an additional dimension, the energy expenditure, and explores construction of bitrate ladders based on decoding energy-quality curves rather than the

Ruaridh Forbes, Paul Hockett, Quentin Leterrier, Rune Lausten

We report the generation of the 5th harmonic of Ti:sapphire, at 160 nm, with more than 4~$\mu$J of pulse energy, and a pulse length of 37 fs with a 1 kHz repetition rate. The VUV pulses are produced using Four-Wave Difference Frequency Mixing (FWDFM) in a helium-filled stretched Hollow-Core Fiber (HCF), driven by a pump at 267 nm and seeded at 800 nm. Guided

Ralf Köhl

This note establishes that homotopy groups of topological split real Kac-Moody groups are countable and, hence, concludes the existence of Whitehead towers consisting of topological groups for these groups and their maximal compact subgroups. Moreover, this note proposes a construction for string groups of the $E_n$-series.

Ihsan Kanbaz, Okan Yurduseven, Michail Matthaiou

Due to the low impedance and high feeding currents, it is naturally challenging to design super-directive antenna arrays that perfectly match the feed line, and this becomes almost impossible as the number of elements increases. In this paper, we assert that it is crucial to consider the trade-off between directivity and overall efficiency (to achieve high r

Naresh Kshetri, Rahul Mishra, Mir Mehedi Rahman, Tanja Steigner

In the ever-evolving healthcare sector, the widespread adoption of Internet of Things and wearable technologies facilitates remote patient monitoring. However, the existing client/server infrastructure poses significant security and privacy challenges, necessitating strict adherence to healthcare data regulations. To combat these issues, a decentralized appr

Timothy Duff, Kisun Lee

We revisit the problem of certifying the correctness of approximate solution paths computed by numerical homotopy continuation methods. We propose a conceptually simple approach based on a parametric variant of the Krawczyk method from interval arithmetic. Unlike most previous methods for certified path-tracking, our approach is applicable in the general set

Rudrajit Das, Xi Chen, Bertram Ieong, Parikshit Bansal

It is well known that selecting samples with large losses/gradients can significantly reduce the number of training steps. However, the selection overhead is often too high to yield any meaningful gains in terms of overall training time. In this work, we focus on the greedy approach of selecting samples with large \textit{approximate losses} instead of exact

Marcell Vazquez-Chanlatte, Karim Elmaaroufi, Stefan J. Witwicki, Matei Zaharia

Expert demonstrations have proven an easy way to indirectly specify complex tasks. Recent algorithms even support extracting unambiguous formal specifications, e.g. deterministic finite automata (DFA), from demonstrations. Unfortunately, these techniques are generally not sample efficient. In this work, we introduce $L^*LM$, an algorithm for learning DFAs fr