April 2023 arXiv papers — page 40

Milad Abolpour, MohammadJavad Salehi, Antti Tölli

A practical barrier to the implementation of cache-aided networks is dynamic and unpredictable user behavior. In dynamic setups, users can freely depart and enter the network at any moment. The shared caching concept has the potential to handle this issue by assigning $K$ users to $P$ caching profiles, where all $\eta_{p}$ users assigned to profile $p$ store

Nikhil Malviya, Navaneetha K. Ravichandran

Callaway's simplified heat flow model is often used to confirm experimental realizations of unconventional, hydrodynamic and Poiseuille phonon transport in ultrahigh thermal conductivity ($\kappa$) materials, due to its simplicity and low computational cost. Here, we show that the Callaway model works exceptionally well for most ultrahigh-$\kappa$ materials

Liliana M. Cantú, Martín Figallo

In \cite{LC, LCMF}, it was introduced a logic (called \Six ) associated to a class of algebraic structures known as {\em involutive Stone algebras}. This class of algebras, denoted by \Sto , was considered by the first time in \cite{CS1} as a tool for the study of certain problems connected to the theory of finite--valued \L ukasiewicz--Moisil algebras. In f

I. N. Belov, A. V. Berezhnoy, E. A. Leshchenko, A. K. Likhoded

Rare decays of the Higgs boson into quarkonia-pairs are studied within the framework of NRQCD approach. The main decay mechanisms and their interference are studied in detail. One-loop corrections to the widths of these decays are taken into account for the first time.

Jonathan Roberts, Kai Han, Samuel Albanie

Interpreting remote sensing imagery enables numerous downstream applications ranging from land-use planning to deforestation monitoring. Robustly classifying this data is challenging due to the Earth's geographic diversity. While many distinct satellite and aerial image classification datasets exist, there is yet to be a benchmark curated that suitably cover

Yichi Zhang, Mingyang Chen, Wen Zhang

Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims to generate negative triples to make a positive-negative contrast during training. However, existing NS methods are unsuitable when multi-modal information is considered in KGE models. They are also inefficient due to their complex design. In this paper, we propose Modality-

Kyeongsu Choi, Minhyun Kim, Taehun Lee

We establish curvature estimates for anisotropic Gauss curvature flows. By using this, we show that given a measure $μ$ with a positive smooth density $f$, any solution to the $L_p$ Minkowski problem in $\mathbb{R}^{n+1}$ with $p \le -n+2$ is a hypersurface of class $C^{1,1}$. This is a sharp result because for each $p\in [-n+2,1)$ there exists a convex hype

Anirudh Pradhan, Gopikant Goswami, Aroonkumar Beesham

In this paper, an attempt is made to construct a Friedmann-Lemaitre-Robertson-Walker model in $f(R,T)$ gravity with a perfect fluid that yields acceleration at late times. We take $f(R,T)$ as $R$ + $8\pi \mu T$. As in the $\Lambda$CDM model, we take the matter to consist of two components, viz., $\Omega_m$ and $\Omega_{\mu}$ such that $\Omega_m$ + $\Omega_{\

Marko Maljkovic, Gustav Nilsson, Nikolas Geroliminis

This paper analyzes a class of Stackelberg games where different actors compete for shared resources and a central authority tries to balance the demand through a pricing mechanism. Situations like this can for instance occur when fleet owners of electric taxi services compete about charging spots. In this paper, we model the competition between the follower

Vincent Nguyen

In this paper, we evaluate in closed form several different series involving the harmonic numbers and skew-harmonic numbers. We consider two classes of series involving these sequences. One class of series involves the product of the $n$th harmonic or skew-harmonic number and a tail. We provide the solution to two open problems concerning these harmonic seri

Massimo Benerecetti, Laura Bozzelli, Fabio Mogavero, Adriano Peron

Monadic Second-Order Logic (MSO) extends First-Order Logic (FO) with variables ranging over sets and quantifications over those variables. We introduce and study Monadic Tree Logic (MTL), a fragment of MSO interpreted on infinite-tree models, where the sets over which the variables range are arbitrary subtrees of the original model. We analyse the expressive

Rima chetouane, Brahim Dridi, Rached Jaidane

The main purpose of this paper is to study the existence of least energy sign-changing solutions for Logarithmic weighted $(N,p)$-Laplacian problem in the unit ball $B$ of $\mathbb{R}^{N},$ $N>2$. The non-linearity of the equation is assumed to have exponential growth in view of Trudinger-Moser type inequalities. In order to obtain our existence result, we u

Anamika Tiwari, Abheejeet Mohapatra, Soumya Ranjan Sahoo

With uncertain injections from Renewable Energy Sources (RESs) and loads, deterministic AC Optimal Power Flow (OPF) often fails to provide optimal setpoints of conventional generators. A computationally time-efficient, economical, and robust solution is essential for ACOPF with short-term injection uncertainties. Usually, applying Robust Optimization (RO) fo

Saidakhmat N. Lakaev, Mukhayyo O. Akhmadova

We study the Schr\"odinger operators $H_{\gamma \lambda \mu}(K)$, $K\in\T$ being a fixed (quasi)momentum of the particles pair, associated with a system of two identical bosons on the one-dimensional lattice $\mathbb{Z}$, where the real quantities $\gamma$, $\lambda$ and $\mu$ describe the interactions between pairs of particles on one site, two nearest neig

Cilin Yan, Haochen Wang, Jie Liu, Xiaolong Jiang

Click-based interactive segmentation aims to generate target masks via human clicking, which facilitates efficient pixel-level annotation and image editing. In such a task, target ambiguity remains a problem hindering the accuracy and efficiency of segmentation. That is, in scenes with rich context, one click may correspond to multiple potential targets, whi

Prathmesh Deshmukh, Sitakanta Satapathy, Evripidis Michail, Andrew H. Olsson

Exciton-polaritons (EP), half-light half-matter quasiparticles that form in optical cavities, are attractive platforms for creating macroscopic coherent states like BECs. EPs based on organic molecules are of particular interest for realizing such states at room temperature while offering the promise of synthetic tunability. However, the demonstrations of su

Kouèssi Norbert Adédji, Marija Bliznac Trebješanin

Let $(P_n)_{n\ge 0}$ and $(Q_n )_{n\ge 0}$ be the Pell and Pell-Lucas sequences. Let $b$ be a positive integer such that $b\ge 2.$ In this paper, we prove that the following two Diophantine equations $P_{n}=b^{d}P_{m}+Q_{k}$ and $P_{n}=b^{d}Q_{m}+P_{k}$ with $d,$ the number of digits of $P_k$ or $Q_k$ in base $b,$ have only finitely many solutions in nonegat

Maciej Klein, Krzysztof Blecharz, Bryan Wei Hao Cheng, Annalisa Bruno

Unbalanced mobility and injection of charge carriers in metal-halide perovskite light-emitting devices pose severe limitations to the efficiency and response time of the electroluminescence. Modulation of gate bias in methylammonium lead iodide light-emitting transistors has proven effective to increase the brightness of light emission, up to MHz frequencies

Rui Xu, Zhiyang Dou, Ningna Wang, Shiqing Xin

Estimating normals with globally consistent orientations for a raw point cloud has many downstream geometry processing applications. Despite tremendous efforts in the past decades, it remains challenging to deal with an unoriented point cloud with various imperfections, particularly in the presence of data sparsity coupled with nearby gaps or thin-walled str

NanNan Cai, JinLin Han, WeiCong Jing, ZeKai Zhang

Artificial intelligence methods are indispensable to identifying pulsars from large amounts of candidates. We develop a new pulsar identification system that utilizes the CoAtNet to score two-dimensional features of candidates, uses a multilayer perceptron to score one-dimensional features, and uses logistic regression to judge the scores above. In the data

Yaosi Hu, Zhenzhong Chen, Chong Luo

The video generation field has witnessed rapid improvements with the introduction of recent diffusion models. While these models have successfully enhanced appearance quality, they still face challenges in generating coherent and natural movements while efficiently sampling videos. In this paper, we propose to condense video generation into a problem of moti

Marco Fabris

Regular ring lattices (RRLs) are defined as peculiar undirected circulant graphs constructed from a cycle graph, wherein each node is connected to pairs of neighbors that are spaced progressively in terms of vertex degree. This kind of network topology is extensively adopted in several graph-based distributed scalable protocols and their spectral properties

Binni Sun, Yufeng Zhao

Let $\mathfrak{g}$ be a complex simple Lie algebra and $Z(\mathfrak{g})$ be the center of the universal enveloping algebra $U(\mathfrak{g})$. Denote by $V_\lambda$ the finite-dimensional irreducible $\mathfrak{g}$-module with highest weight $\lambda$. Lehrer and Zhang defined the notion of strongly multiplicity free representations for simple Lie algebras mo

Vahid Hamdipoor, Nader Meskin, Christos G. Cassandras

In this paper, we study a safe control design for dynamical systems in the presence of uncertainty in a dynamical environment. The worst-case error approach is considered to formulate robust Control Barrier Functions (CBFs) in an optimization-based control synthesis framework. It is first shown that environmentally robust CBF formulations result in second-or

Simon Göppel, Jürgen Frikel, Markus Haltmeier

In a number of tomographic applications, data cannot be fully acquired, resulting in a severely underdetermined image reconstruction. In such cases, conventional methods lead to reconstructions with significant artifacts. To overcome these artifacts, regularization methods are applied that incorporate additional information. An important example is TV recons

Hao Zhu, Piotr Koniusz

Few-shot learning (FSL) is popular due to its ability to adapt to novel classes. Compared with inductive few-shot learning, transductive models typically perform better as they leverage all samples of the query set. The two existing classes of methods, prototype-based and graph-based, have the disadvantages of inaccurate prototype estimation and sub-optimal

Saimunur Rahman, Piotr Koniusz, Lei Wang, Luping Zhou

Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolutional feature maps. However, pairwise correlation will become misleading once there is another channel correlating with both channels of interest, resulting in the ``confounding''

Qing Yang, Yingzhi Tian

Luo, Tian and Wu conjectured in 2022 that for any tree $T$ with bipartition $X$ and $Y$, every $k$-connected bipartite graph $G$ with $\delta(G) \geq k + t$, where $t = \max\{|X|,|Y |\}$, contains a subtree $T' \cong T$ such that $G-V(T')$ remains $k$-connected. This conjecture has been proved for caterpillars and spiders when $k\leq 3$; and for paths with o

Yongcheng Jing, Xinchao Wang, Dacheng Tao

The recent work known as Segment Anything (SA) has made significant strides in pushing the boundaries of semantic segmentation into the era of foundation models. The impact of SA has sparked extremely active discussions and ushered in an encouraging new wave of developing foundation models for the diverse tasks in the Euclidean domain, such as object detecti

Bryan S. Hernandez, Patrick Vincent N. Lubenia

Steady states are frequently used to investigate the long-term behaviors of (bio)-chemical systems. Recently, there has been a growing interest in network-based approaches due to their efficiency in deriving parametrizations of positive steady states in systems with mass-action kinetics. In this study, we extend this approach to derive positive steady states

Jian Ma

Identifying differential equation governing dynamical system is an important problem with wide applications. Copula Entropy (CE) is a mathematical concept for measuring statistical independence in information theory. In this paper we propose a method for identifying differential equation of dynamical systems with CE. The problem is considered as a variable s

Fazl Barez, Hosien Hasanbieg, Alesandro Abbate

Reinforcement learning agents naturally learn from extensive exploration. Exploration is costly and can be unsafe in $\textit{safety-critical}$ domains. This paper proposes a novel framework for incorporating domain knowledge to help guide safe exploration and boost sample efficiency. Previous approaches impose constraints, such as regularisation parameters

Mohsen Ebrahimi

Application of electronic railway systems as well as the implication of Automatic Train Control (ATC) System has increased the safety of rail transportation. However, one of the most important causes of accidents on the railway is rail damage and breakage. In this paper, we have proposed a method that the rail region is first recognized from the observation

Zhizhang Wang, Xueyun Li, Jitao Ji, Zhenxing Sun

Fast scanning-speed and low-power-consumption are becoming progressively more and more important in realizing high-performance chiplet optical phased arrays (OPAs). Here, we establish an integrated OPA based on thin-film lithium niobate-on-insulator (LNOI) platform to access these outstanding performances. Significantly, a lithium niobate (LN) OPA chip is im

V. E. Adler, M. P. Kolesnikov

We study reductions of the Korteweg--de Vries equation corresponding to stationary equations for symmetries from the noncommutative subalgebra. An equivalent system of $n$ second-order equations is obtained, which reduces to the Painlev\'e equation P$_{34}$ for $n=1$. On the singular line $t=0$, a subclass of special solutions is described by a system of $n-

D. Bénisti, O. Morice, C. Rouseaux, A. Debayle

In this Letter, we introduce a new inline model for stimulated Raman scattering (SRS), which runs on our radiation hydrodynamics code TROLL. The modeling follows from a simplified version of a rigorous theory for SRS, which we describe, and accounts for nonlinear kinetic effects. It also accounts for the SRS feedback on the plasma hydrodynamics. We dubbed it

Rahim Kargar, Oona Rainio

The modulus metric between two points in a subdomain of $\mathbb{R}^n, n\ge 2,$ is defined in terms of moduli of curve families joining the boundary of the domain with a continuum connecting the two points. This metric is one of the conformally invariant hyperbolic type metrics, which have become a standard tool in geometric function theory. We prove that th

Antoine Caradot, Cuipo Jiang, Zongzhu Lin

In this paper, we define differential graded vertex operator algebras and the algebraic structures on the associated Zhu algebras and $C_2$-algebras. We also introduce the corresponding notions of modules, and investigate the relations between the different module categories.

Ha Nguyen

Predicting corporate default risk has long been a crucial topic in the finance field, as bankruptcies impose enormous costs on market participants as well as the economy as a whole. This paper aims to forecast frailty correlated default models with subjective judgements on a sample of U.S. public non-financial firms spanning January 1980-June 2019. We consid

Ji Lin, Liangang Peng

Let $t$ be a positive integer and $\mathcal{A}$ a hereditary abelian category satisfying some finiteness conditions. We define the semi-derived Ringel-Hall algebra of $\mathcal{A}$ from the category $\mathcal{C}_{\mathbb{Z}/t}(\mathcal{A})$ of $\mathbb{Z}/t$-graded complexes and obtain a natural basis of the semi-derived Ringel-Hall algebra. Moreover, we des

Jiahao Nie, Zhiwei He, Yuxiang Yang, Zhengyi Bao

Two-stage point-to-box network acts as a critical role in the recent popular 3D Siamese tracking paradigm, which first generates proposals and then predicts corresponding proposal-wise scores. However, such a network suffers from tedious hyper-parameter tuning and task misalignment, limiting the tracking performance. Towards these concerns, we propose a simp

Bui Tien Thanh, Dinh Van Tuan, Tuan Anh Chi, Nguyen Van Dai

In the digital transformation era, integrating digital technology into every aspect of banking operations improves process automation, cost efficiency, and service level improvement. Although logistics for ATM cash is a crucial task that impacts operating costs and consumer satisfaction, there has been little effort to enhance it. Specifically, in Vietnam, w

Aleksandra M. Tucholska, Marcin Modrzejewski, Robert Moszynski

We present the expansion to the expectation value coupled cluster theory (XCC) to the wavefunctions that include the inter electronic distances $r_{12}$ explicitly. We have extended our algebraic manipulation code \paldus to deal with the rems arising in the CC-F12 theory. We present the full working expressions for the one-electron density matrix (1RDM) and

Yuanshao Zhu, Yongchao Ye, Shiyao Zhang, Xiangyu Zhao

Pervasive integration of GPS-enabled devices and data acquisition technologies has led to an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal data mining research. Nonetheless, GPS trajectories contain personal geolocation information, rendering serious privacy concerns when working with raw data. A promising approach t

Masafumi Noguchi

We investigate how the diverse star formation histories observed across galaxy masses emerged using models that evolve under gas accretion from host halos. They also include ejection of interstellar matter by supernova feedback, recycling of ejected matter and preventive feedback that partially hinders gas accretion. We consider three schemes of gas accretio

Yixiao Wang, Canlin Zhou, Xingyang Qi, Hui Li

The quick and accurate retrieval of an object height from a single fringe pattern in Fringe Projection Profilometry has been a topic of ongoing research. While a single shot fringe to depth CNN based method can restore height map directly from a single pattern, its accuracy is currently inferior to the traditional phase shifting technique. To improve this me

Michael Schlosser, Daniel König, Michael Teutsch

Object detection is one of the key tasks in many applications of computer vision. Deep Neural Networks (DNNs) are undoubtedly a well-suited approach for object detection. However, such DNNs need highly adapted hardware together with hardware-specific optimization to guarantee high efficiency during inference. This is especially the case when aiming for effic

Johan F. Hoorn, Ella-Jenna Oosterglorenwoud

Currently, there is a trend for the wider public to rely on LLMs for financial or legal consultation, medical and mental support (Chatterji et al., 2025), often accepting the advice provided without necessarily seeking logical verification or empirical validation. While one might be fortunate enough to encounter a model with a particularly solid 'ground trut

Kaisheng Liang, Bin Xiao

Adversarial attacks can mislead deep neural networks (DNNs) by adding imperceptible perturbations to benign examples. The attack transferability enables adversarial examples to attack black-box DNNs with unknown architectures or parameters, which poses threats to many real-world applications. We find that existing transferable attacks do not distinguish betw

J. K. Singh, Shaily, Akanksha Singh, Aroonkumar Beesham

We investigate a bounce realization in the framework of higher order curvature in $ f(R,T) $ modified theory of gravity. We perform a detailed analysis of the cosmological parameters to explain the contraction phase, the bounce phase, and the expansion phase. Furthermore, we observe a violation of the null energy condition, instability of the model, and a si

Ali Lazrak, Hanxiao Wang, Jiongmin Yong

We investigate a linear quadratic stochastic zero-sum game where two players lobby a political representative to invest in a wind turbine farm. Players are time-inconsistent because they discount performance with a non-constant rate. Our objective is to identify a consistent planning equilibrium in which the players are aware of their inconsistency and canno

Daniel Arnström, David Broman, Daniel Axehill

We propose the first method that determines the exact worst-case execution time (WCET) for implicit linear model predictive control (MPC). Such WCET bounds are imperative when MPC is used in real time to control safety-critical systems. The proposed method applies when the quadratic programming solver in the MPC controller belongs to a family of well-establi

Paolo Galeazzi, Johannes Marti

Following the decision-theoretic approach to game theory, we extend the analysis of Epstein & Wang and of Di Tillio from hierarchies of preference relations to hierarchies of choice functions. We then construct the universal choice structure containing all these choice hierarchies, and show how the universal preference structure of Di Tillio is embedded in i

Steven Zvi Lapp, Eli David, Nathan S. Netanyahu

In this paper, we introduce PathRTM, a novel deep neural network detector based on RTMDet, for automated KI-67 proliferation and tumor-infiltrated lymphocyte estimation. KI-67 proliferation and tumor-infiltrated lymphocyte estimation play a crucial role in cancer diagnosis and treatment. PathRTM is an extension of the PathoNet work, which uses single pixel k

Chao Li, Hao Xu, Kun He

Meta-structures are widely used to define which subset of neighbors to aggregate information in heterogeneous information networks (HINs). In this work, we investigate existing meta-structures, including meta-path and meta-graph, and observe that they are initially designed manually with fixed patterns and hence are insufficient to encode various rich semant

Kumar Rao, Saurabh D. Rindani, Priyanka Sarmah, Balbeer Singh

The production of a Higgs boson in association with a $Z$ at an electron-positron collider is one of the cleanest methods for the measurement of the couplings of the Higgs boson. In view of the large production cross section at energies a little above the threshold, it seems feasible to make a more detailed study of the process by measuring the cross section

Ruiqi Wang, Yiming Yang, Behrooz Makki, Atif Shamim

Despite the growing interest in reconfigurable intelligent surfaces (RISs) for millimeter-wave (mm-wave) bands, and the considerable theoretical work reported by the communication community, there is a limited number of published works demonstrating practical implementations and experimental results. To the authors' knowledge, no published literature has rep

Hari Mohan Srivastava, Pishtiwan Othman Sabir, Sevtap Sümer Eker, Abbas Kareem Wanas

The Ruscheweyh derivative operator is used in this paper to introduce and investigate interesting general subclasses of the function class $\Sigma_{\mathrm{m}}$ of $m$-fold symmetric bi-univalent analytic functions. Estimates of the initial Taylor-Maclaurin coefficients $\left|a_{m+1}\right|$ and $\left|a_{2 m+1}\right|$ are obtained for functions of the sub

S. Mazevet, A. Affholder, B. Sauterey, A. Bixel

With thousands of exoplanets now identified, the characterization of habitable planets and the potential identification of inhabited ones is a major challenge for the coming decades. We review the current working definition of habitable planets, the upcoming observational prospects for their characterization and present an innovative approach to assess habit

Guizhen Xu, Hongyang Xing, Zhanqiang Xue, Dan Lu

Recent advancements in photonic bound states in the continuum (BICs) have opened up exciting new possibilities for the design of optoelectronic devices with improved performance. In this perspective article, we provide an overview of recent progress in photonic BICs based on metamaterials and photonic crystals, focusing on both the underlying physics and the

Alessandro Calvia, Fausto Gozzi, Marta Leocata, Georgios I. Papayiannis

We introduce a spatial economic growth model where space is described as a network of interconnected geographic locations and we study a corresponding finite-dimensional optimal control problem on a graph with state constraints. Economic growth models on networks are motivated by the nature of spatial economic data, which naturally possess a graph-like struc

Wenxiong Liao, Zhengliang Liu, Haixing Dai, Shaochen Xu

Background: Large language models such as ChatGPT are capable of generating grammatically perfect and human-like text content, and a large number of ChatGPT-generated texts have appeared on the Internet. However, medical texts such as clinical notes and diagnoses require rigorous validation, and erroneous medical content generated by ChatGPT could potentiall

Jun Hu, Yizhou Liang, Ting Lin

This paper discusses the construction of local bounded commuting projections for discrete subcomplexes of the gradgrad complexes in two and three dimensions, which play an important role in the finite element theory of elasticity (2D) and general relativity (3D). The construction first extends the local bounded commuting projections to the discrete de Rham c

Hayeong Song, Jennifer Healey, Alexa Siu, Curtis Wigington

Multi-media increases engagement and is increasingly prevalent in online content including news, web blogs, and social media, however, it may not always be beneficial to users. To determine what types of media users actually wanted, we conducted an exploratory study where users got to choose their own media augmentation. Our findings showed that users desire

Vivek Nigam, Carolyn Talcott

Cyber-physical systems (CPSes), such as autonomous vehicles, use sophisticated components like ML-based controllers. It is difficult to provide evidence about the safe functioning of such components. To overcome this problem, Runtime Assurance Architecture (RTA) solutions have been proposed. The \RAP's decision component evaluates the system's safety risk an

Laurent Doyen, Pranshu Gaba, Shibashis Guha

Stochastic two-player games model systems with an environment that is both adversarial and stochastic. The adversarial part of the environment is modeled by a player (Player 2) who tries to prevent the system (Player 1) from achieving its objective. We consider finitary versions of the traditional mean-payoff objective, replacing the long-run average of the

Luisa Ferrari, Giancarlo Manzi, Alessandra Micheletti, Federica Nicolussi

When pandemics like COVID-19 spread around the world, the rapidly evolving situation compels officials and executives to take prompt decisions and adapt policies depending on the current state of the disease. In this context, it is crucial for policymakers to have always a firm grasp on what is the current state of the pandemic, and to envision how the numbe

Hayeong Song, Zhengyang Qi, John Stasko, Diyi Yang

Social media (i.e., Reddit) users are overloaded with people's opinions when viewing discourses about divisive topics. Traditional user interfaces in such media present those opinions in a linear structure, which can limit users in viewing diverse social opinions at scale. Prior work has recognized this limitation, that the linear structure can reinforce bia

Chakka Sai Pradeep, Neelam Sinha

In this work, we report an autoencoder-based 2D representation to classify a time-series as stochastic or non-stochastic, to understand the underlying physical process. Content-aware conversion of 1D time-series to 2D representation, that simultaneously utilizes time- and frequency-domain characteristics, is proposed. An autoencoder is trained with a loss fu

Jing-Sheng Tan, Shaoshi Yang, Kuo Meng, Jianhua Zhang

The flexible duplex (FD) technique, including dynamic time-division duplex (D-TDD) and dynamic frequency-division duplex (D-FDD), is regarded as a promising solution to achieving a more flexible uplink/downlink transmission in 5G-Advanced or 6G mobile communication systems. However, it may introduce serious cross-link interference (CLI). For better mitigatin

Yutaka Jitsumatsu

The error exponent of fixed-length lossy source coding was established by Marton. Ahlswede showed that this exponent can be discontinuous at a rate $R$, depending on the probability distribution $P$ of the given information source and the distortion measure $d(x,y)$. The reason for the discontinuity in the error exponent is that there exists $(d,\Delta)$ suc

Weiyi Yu, Yiming Lei, Hongming Shan

Since stroke is the main cause of various cerebrovascular diseases, deep learning-based stroke lesion segmentation on magnetic resonance (MR) images has attracted considerable attention. However, the existing methods often neglect the domain shift among MR images collected from different sites, which has limited performance improvement. To address this probl

Xiping Liu, Zhao Tan

Chain-of-thought (CoT) prompting combined with large language models (LLMs) have achieved encouraging results on complex reasoning tasks. Text-to-SQL is a critical semantic parsing task that converts natural language questions into SQL statements, involving a complex reasoning process. However, there is little work about using CoT prompting to activate LLM's

Camillo De Lellis, Anna Skorobogatova

We consider an area-minimizing integral current $T$ of codimension higher than $1$ in a smooth Riemannian manifold $\Sigma$. In a previous paper we have subdivided the set of interior singular points with at least one flat tangent cone according to a real parameter, which we refer to as ``singularity degree''. This parameter determines the infinitesimal orde

Shirong Jiang, Jiahao Wang, Chunfang Xia, Xiang Li

Polarization-adjusted convolutional (PAC) codes can approach the theoretical bound for block error rate (BLER) performance at short-to-medium codeword length. PAC codes have excellent BLER performance using Monte Carlo (MC) rate-profiles and Weighted Sum (WS) rate-profiles, but the BLER performances of the constructed codes still fall away from the dispersio

Camillo De Lellis, Paul Minter, Anna Skorobogatova

We consider an area-minimizing integral current $T$ of codimension higher than 1 ins a smooth Riemannian manifold $\Sigma$. We prove that $T$ has a unique tangent cone, which is a superposition of planes, at $\mathcal{H}^{m-2}$-a.e. point in its support. In combination with works of the first and third authors, we conclude that the singular set of $T$ is cou

Camillo De Lellis, Anna Skorobogatova

We consider an area-minimizing integral current of dimension $m$ and codimension at least $2$ and fix an arbitrary interior singular point $q$ where at least one tangent cone is flat. For any vanishing sequence of scales around $q$ along which the rescaled currents converge to a flat cone, we define a suitable singularity degree of the rescalings, which is a

Daniel Alpay, Fabrizio Colombo, Kamal Diki, Irene Sabadini

In this paper we use techniques in Fock spaces theory and compute how the Segal-Bargmann transform acts on special wave functions obtained by multiplying superoscillating sequences with normalized Hermite functions. It turns out that these special wave functions can be constructed also by computing the approximating sequence of the normalized Hermite functio

Jianqiang Ding, Taoran Wu, Yuping Qian, Lijun Zhang

In this paper, we propose an approach for synthesizing provable reach-avoid controllers, which drive a deterministic system operating in an unknown environment to safely reach a desired target set. The approach falls within the reachability analysis framework and is based on the computation of inner-approximations of controlled reach-avoid sets(CRSs). Given

Grigory Solomatov, Derya Akkaynak

A number of problems in computer vision and related fields would be mitigated if camera spectral sensitivities were known. As consumer cameras are not designed for high-precision visual tasks, manufacturers do not disclose spectral sensitivities. Their estimation requires a costly optical setup, which triggered researchers to come up with numerous indirect m

Kamal Tripathi, Satyavani Vemparala

Conformations of a crowded neutral semiflexible polymer under confinement near an attractive wall are studied via coarse-grained simulations. We study the effects of the interplay of the length of the polymer, bending rigidity, and the repulsive crowder density on such equilibrium semiflexible polymer conformations. The length of the polymer dictates the num

Jianzong Wang, Xulong Zhang, Haobin Tang, Aolan Sun

In recent Text-to-Speech (TTS) systems, a neural vocoder often generates speech samples by solely conditioning on acoustic features predicted from an acoustic model. However, there are always distortions existing in the predicted acoustic features, compared to those of the groundtruth, especially in the common case of poor acoustic modeling due to low-qualit

Anton Svirsky, Corentin Herbert, Anna Frishman

Two-dimensional turbulence self-organizes through a process of energy accumulation at large scales, forming a coherent flow termed a condensate. We study the condensate in a model with local dynamics, the large-scale quasi-geostrophic equation, observed here for the first time. We obtain analytical results for the mean flow and the two-point, second-order co

Zhongyu Yang, Chen Shen, Wei Shao, Tengfei Xing

Lane detection is challenging due to the complicated on road scenarios and line deformation from different camera perspectives. Lots of solutions were proposed, but can not deal with corner lanes well. To address this problem, this paper proposes a new top-down deep learning lane detection approach, CANET. A lane instance is first responded by the heat-map o

Giuseppe Mulone

We study the monotone energy stability of ``Poiseuille flow" in a plane-parallel channel with a saturated porous medium modeled by the Brinkman equation, on the basis of an analogy with a magneto-hydrodynamic problem (Hartmann flow) (cf. \cite{Hill.Straughan.2010}, \cite{Nield.2003}). We prove that the least stabilizing perturbations, in the energy norm, are

Wolfgang Herfort, Karl H. Hofmann, Francesco G. Russo

The notion of conditional coproduct of a family of abelian pro-Lie groups in the category of abelian pro-Lie groups is introduced. It is shown that the cartesian product of an arbitrary family of abelian pro-Lie groups can be characterized by the universal property of the conditional coproduct.

Nicolo Galvani, Marina Pasquet, Arnab Mukherjee, Alice Requier

Coarsening of two-phase systems is crucial for the stability of dense particle packings such as alloys, foams, emulsions or supersaturated solutions. Mean field theories predict an asymptotic scaling state with a broad particle size distribution. Aqueous foams are good model systems for investigations of coarsening-induced structures, because the continuous

Nikolaos Zioulis, James F. O'Brien

KBody is a method for fitting a low-dimensional body model to an image. It follows a predict-and-optimize approach, relying on data-driven model estimates for the constraints that will be used to solve for the body's parameters. Acknowledging the importance of high quality correspondences, it leverages ``virtual joints" to improve fitting performance, disent

Tiziana Di Matteo, Daniel Angles-Alcazar, Francesco Shankar

Massive black holes are fundamental constituents of our cosmos, from the Big Bang to today. Understanding their formation from cosmic dawn, their growth, and the emergence of the first, rare quasars in the early Universe remains one of our greatest theoretical and observational challenges. Hydrodynamic cosmological simulations self-consistently combine the p

Shuren Zhou, Pengjie Zhang, Ziyang Chen

Ongoing and upcoming galaxy surveys are providing precision measurements of galaxy clustering. However a major obstacle in its cosmological application is the stochasticity in the galaxy bias. We explore whether the principal component analysis (PCA) of galaxy correlation matrix in hyperspace of galaxy properties (e.g. magnitude and color) can reveal further

Rui Chen, Tao Chen, Qiong Wang, Yazhou Yao

Semi-supervised semantic segmentation aims to learn from a small amount of labeled data and plenty of unlabeled ones for the segmentation task. The most common approach is to generate pseudo-labels for unlabeled images to augment the training data. However, the noisy pseudo-labels will lead to cumulative classification errors and aggravate the local inconsis

Ruiyu Han, Dejan Slepčev, Yunan Yang

In order to compare and interpolate signals, we investigate a Riemannian geometry on the space of signals. The metric allows discontinuous signals and measures both horizontal (thus providing many benefits of the Wasserstein metric) and vertical deformations. Moreover, it allows for signed signals, which overcomes the main deficiency of optimal transportatio

Yunfang Tang, Xuli Qi, Douglas B. West

The \emph{eccentricity} of a vertex $u$ in a graph $G$, denoted by $e_G(u)$, is the maximum distance from $u$ to other vertices in $G$. We study extremal problems for the average eccentricity and the first and second Zagreb eccentricity indices, denoted by $\sigma_0(G)$, $\sigma_1(G)$, and $\sigma_2(G)$, respectively. These are defined by $\sigma_0(G)=\frac{

Hong Cai, Geng Chen, Yannan Shen

In this paper, we study the Lipschitz continuous dependence of conservative H\"older continuous weak solutions to a variational wave system derived from a model for nematic liquid crystals. Since the solution of this system generally forms finite time cusp singularity, the solution flow is not Lipschitz continuous under the Sobolev metric used in the existen

Kunze Wang, Yihao Ding, Soyeon Caren Han

Text Classification is the most essential and fundamental problem in Natural Language Processing. While numerous recent text classification models applied the sequential deep learning technique, graph neural network-based models can directly deal with complex structured text data and exploit global information. Many real text classification applications can

Roshni G. Iyer, Wei Wang, Yizhou Sun

Recent graph neural networks (GNNs) with the attention mechanism have historically been limited to small-scale homogeneous graphs (HoGs). However, GNNs handling heterogeneous graphs (HeGs), which contain several entity and relation types, all have shortcomings in handling attention. Most GNNs that learn graph attention for HeGs learn either node-level or rel

Yunzhe Zheng, Keita Kanno

Quantum error correcting code can diagnose potential errors and correct them based on measured outcomes by leveraging syndrome measurement. However, mid-circuit measurement has been technically challenging for early fault-tolerant quantum computers and the readout-induced noise acts as a main contributor to the logical infidelity. We present a different meth

Hirokuni Iiboshi, Daikuke Ozaki, Yui Yoshii

This study explores the impact of gender differences in preferences and productivity in home production on the time allocation in married couples, particularly in relation to childcare responsibilities. Using aggregated data from Japan, we estimate a life-cycle model that tracks the development of a child from infancy to adulthood by extending the work of Bl

Debesh Jha, Gorkem Durak, Vanshali Sharma, Elif Keles

Artificial Intelligence (AI) is poised to transform healthcare delivery through revolutionary advances in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains, paving the way for a new

Smriti Regmi, Aliza Subedi, Ulas Bagci, Debesh Jha

Medical image analysis is a hot research topic because of its usefulness in different clinical applications, such as early disease diagnosis and treatment. Convolutional neural networks (CNNs) have become the de-facto standard in medical image analysis tasks because of their ability to learn complex features from the available datasets, which makes them surp