November 2024 arXiv papers — page 183

Emilien Biré, Anthony Kobanda, Ludovic Denoyer, Rémy Portelas

Developing agents for complex and underspecified tasks, where no clear objective exists, remains challenging but offers many opportunities. This is especially true in video games, where simulated players (bots) need to play realistically, and there is no clear reward to evaluate them. While imitation learning has shown promise in such domains, these methods

Yitong Dong, Yijin Li, Zhaoyang Huang, Weikang Bian

In this paper, we propose a novel multi-view stereo (MVS) framework that gets rid of the depth range prior. Unlike recent prior-free MVS methods that work in a pair-wise manner, our method simultaneously considers all the source images. Specifically, we introduce a Multi-view Disparity Attention (MDA) module to aggregate long-range context information within

G. E. Volovik

The decay of proton in the de Sitter environment is governed by the temperature $T=H/\pi$, where $H$ is the Hubble parameter. This temperature is twice larger than the Gibbons-Hawking temperature $T_{\rm GH}=H/2\pi$. This demonstrates the physical difference of two processes. The temperature $T=H/\pi$ determines the proton decay rate in the local process whi

Denis A. Silantyev, Pavel M. Lushnikov, Michael Siegel, David M. Ambrose

We present exact pole dynamics solutions to the generalized Constantin-Lax-Majda (gCLM) equation in a periodic geometry with dissipation $-\Lambda^\sigma$, where its spatial Fourier transform is $\widehat{\Lambda^\sigma}=|k|^\sigma$. The gCLM equation is a simplified model for singularity formation in the 3D incompressible Euler equations. It includes an adv

A. A. Saharian, V. F. Manukyan, T. A. Petrosyan

We study the finite temperature and edge induced effects on the charge and current densities for a massive spinor field localized on a 2D conical space threaded by a magnetic flux. The field operator is constrained on a circular boundary, concentric with the cone apex, by the bag boundary condition and by the condition with the opposite sign in front of the

Jinyin Chen, Danxin Liao, Sheng Xiang, Haibin Zheng

Since DNN is vulnerable to carefully crafted adversarial examples, adversarial attack on LiDAR sensors have been extensively studied. We introduce a robust black-box attack dubbed LiDAttack. It utilizes a genetic algorithm with a simulated annealing strategy to strictly limit the location and number of perturbation points, achieving a stealthy and effective

Till Düsberg, Benjamin Eltzner

In statistics on manifolds, the notion of the mean of a probability distribution becomes more involved than in a linear space. Several location statistics have been proposed, which reduce to the ordinary mean in Euclidean space. A relatively new family of contenders in this field are Diffusion Means, which are a one parameter family of location statistics mo

Yuxin Xiao, Chaoqun Wan, Yonggang Zhang, Wenxiao Wang

As the development and application of Large Language Models (LLMs) continue to advance rapidly, enhancing their trustworthiness and aligning them with human preferences has become a critical area of research. Traditional methods rely heavily on extensive data for Reinforcement Learning from Human Feedback (RLHF), but representation engineering offers a new,

Klemens Flöge, Mohammed Abdul Moeed, Vincent Fortuin

Deep neural network ensembles are powerful tools for uncertainty quantification, which have recently been re-interpreted from a Bayesian perspective. However, current methods inadequately leverage second-order information of the loss landscape, despite the recent availability of efficient Hessian approximations. We propose a novel approximate Bayesian infere

Marshall Baker, Paolo Cea, Volodymyr Chelnokov, Leonardo Cosmai

A characteristic signature of quark confinement is the concentration of the chromoelectric field between a static quark-antiquark pair in a flux tube. Here we report on lattice measurements of field distributions on smeared Monte Carlo ensembles in QCD with (2+1) HISQ flavors. We measure the field distributions for several distances between static quark-anti

Zhiyao Zhang, Zhijie Li, Yunpeng Wang, Huiyu Yang

The accurate and fast prediction of long-term dynamics of turbulence presents a significant challenge for both traditional numerical simulations and machine learning methods. In recent years, the emergence of neural operators has provided a promising approach to address this issue. The implicit U-Net enhanced Fourier neural operator (IU-FNO) has successfully

Valentin Zulj, Shaobo Jin, Måns Magnusson

Compromise estimation entails using a weighted average of outputs from several candidate models, and is a viable alternative to model selection when the choice of model is not obvious. As such, it is a tool used by both frequentists and Bayesians, and in both cases, the literature is vast and includes studies of performance in simulations and applied example

B. Krasch, A. Bernhard, E. Bründermann, S. Fatehi

Undulators are X-ray sources widely used in synchrotron storage rings and free-electron laser facilities. With the commercial availability of low-temperature superconductors, a new type of undulator was born, the superconducting undulator (SCU). In this context, the industrial cooperation between the Karlsruhe Institute of Technology and Bilfinger Nuclear an

Pinyao Song, Aparna Hebbani, Dhaval Vyas

When refugees arrive in a host country, the form of immediate help and support they receive from various service providers sets the stage for successful settlement, integration, and social cohesion. This paper presents results from an exploratory study that investigated refugees perceptions of initial services received upon migration, in the first six months

Zhiyuan Yan, Wenji Fang, Mengming Li, Min Li

Assertion-based verification (ABV) is a critical method to ensure logic designs comply with their architectural specifications. ABV requires assertions, which are generally converted from specifications through human interpretation by verification engineers. Existing methods for generating assertions from specification documents are limited to sentences extr

Dhruthi, Nithin Nagaraj, Harikrishnan N B

Inferring causal relationships in the decision-making processes of machine learning algorithms is a crucial step toward achieving explainable Artificial Intelligence (AI). In this research, we introduce a novel causality measure and a distance metric derived from Lempel-Ziv (LZ) complexity. We explore how the proposed causality measure can be used in decisio

Marco Fellous-Asiani, Hui Khoon Ng, Robert S. Whitney

Concatenated error-correction schemes are well-understood routes to fault-tolerant quantum computing, and research on such schemes continues, including recent claims that they may be competitive with surface codes, and show potential when combined with high-rate Quantum Low Density Parity Check codes. However, there are few tools to evaluate the qubit resour

Qikai Wei, Mingzhi Yang, Chunlong Han, Jingfu Wei

Retrieval-Augmented Generation (RAG) mitigates the issue of hallucination in Large Language Models (LLMs) by integrating information retrieval techniques. However, in the tourism domain, since the query is usually brief and the content in the database is diverse, existing RAG may contain a significant amount of irrelevant or contradictory information content

Xinnian Kazusa Pan

This paper establishes the equivalence between synchronous and asynchronous coordination mechanisms in dynamic games with strategic complementarities and common interests. Synchronous coordination, characterized by simultaneous commitments, and asynchronous coordination, defined by sequential action timing, are both prevalent in economic contexts such as cro

Sachitha C. Bandara, Peter J. Smith, Erfan Khordad, Robin Evans

Recent developments in Multiple-Input-Multiple-Output (MIMO) technology include packing a large number of antenna elements in a compact array to access the bandwidth benefits provided by higher mutual coupling (MC). The resulting super-wideband (SW) systems require a circuit-theoretic framework to handle the MC and channel models which span extremely large b

Yuheng Su, Qiusong Yang, Yiwei Ci

The IC3 algorithm, also known as PDR, has made a significant impact in the field of safety model checking in recent years due to its high efficiency, scalability, and completeness. The most crucial component of IC3 is inductive generalization, which involves dropping variables one by one and is often the most time-consuming step. In this paper, we propose a

Frédéric Barraquand

Integrated population models (IPMs) combine multiple ecological data types such as capture-mark-recapture histories, reproduction surveys, and population counts into a single statistical framework. In such models, each data type is generated by a probabilistic submodel, and an assumption of independence between the different data types is usually made. The f

Aparna Gupte, Jiahui Liu, Justin Raizes, Bhaskar Roberts

One-time programs (Goldwasser, Kalai and Rothblum, CRYPTO 2008) are functions that can be run on any single input of a user's choice, but not on a second input. Classically, they are unachievable without trusted hardware, but the destructive nature of quantum measurements seems to provide a quantum path to constructing them. Unfortunately, Broadbent, Gutoski

Hibiki Takegami, Takao Morinari

The Kitaev model, renowned for its exact solvability and potential to host non-Abelian anyons, remains a focal point in the study of quantum spin liquids and topological phases. While much of the existing literature has employed Majorana fermion techniques to analyze the model, particularly at zero temperature, its finite-temperature behavior has been less t

Dang Quang A, Dang Quang Long

In this paper we construct high order numerical methods for solving third and fourth orders nonlinear functional differential equations (FDE). They are based on the discretization of iterative methods on continuous level with the use of the trapezoidal quadrature formulas with corrections. Depending on the number of terms in the corrections we obtain methods

Hayato Arai, Masahito Hayashi

It is important problem to clarify the class of implementable quantum measurements from both fundamental and applicable viewpoints. Positive-Operator-Valued Measure (POVM) measurements are implementable by the indirect measurement methods, and the class is the largest class determined by the mathematical structure of Hilbert space. However, if we assume prob

David Smith Sundarsingh, Bhabani Shankar Dey, Pushpak Jagtap

Incremental stability of dynamical systems ensures the convergence of trajectories from different initial conditions towards each other rather than a fixed trajectory or equilibrium point. Here, we introduce and characterize a novel class of incremental Lyapunov functions, an incremental stability notion known as Incremental Input-to-State practical Stabilit

Yu Ge, Ossi Kaltiokallio, Hui Chen, Jukka Talvitie

The concept of 6G distributed integrated sensing and communications (DISAC) builds upon the functionality of integrated sensing and communications (ISAC) by integrating distributed architectures, significantly enhancing both sensing and communication coverage and performance. In 6G DISAC systems, tracking target trajectories requires base stations (BSs) to h

Kezheng Xiong, Haoen Xiang, Qingshan Xu, Chenglu Wen

Point cloud registration, a fundamental task in 3D vision, has achieved remarkable success with learning-based methods in outdoor environments. Unsupervised outdoor point cloud registration methods have recently emerged to circumvent the need for costly pose annotations. However, they fail to establish reliable optimization objectives for unsupervised traini

Simon Riche

Under some technical assumptions, and building on joint work with Bezrukavnikov, we prove a multiplicity formula for indecomposable tilting perverse sheaves on affine flag varieties, with coefficients in a field of characteristic $p$, in terms of $p$-Kazhdan--Lusztig polynomials. Under the same assumptions, we also explain the construction of a "degrading fu

Suprit Singh

We probe the thermodynamic structure of gravity at local scales. In any general curved spacetime, it is possible to transform to a local inertial frame at any point such that the metric is flat up to quadratic order where the curvature at that point comes in when the metric is written in Riemann normal coordinates. We consider local Rindler observers in that

Zheng-Shu Liu, Xu-Liang Chen, Ding-Kun Lian, Ning Li

Due to the SU(3) symmetry breaking effect, the axial-vector kaons $K_1(1270)$ and $K_1(1400)$ are established to be mixtures of two P-wave $K_{1A}\left( {^3{P_1}} \right)$ and $K_{1B}\left( {^1{P_1}} \right)$ states. In QCD sum rules, we propose a new construction of the $K_1$ current operators and calculate the two-point correlation functions by including t

Resul Dagdanov, Milan Andrejevic, Dikai Liu, Chin-Teng Lin

When interacting with each other, humans adjust their behavior based on perceived trust. To achieve similar adaptability, robots must accurately estimate human trust at sufficiently granular timescales while collaborating with humans. Beta reputation is a popular way to formalize a mathematical estimation of human trust. However, it relies on binary performa

Sabrina Baudart, Atila Poro

We presented the first photometric analysis of the V1961 Cyg and V0890 Lyr binary systems. We observed and analyzed these systems at an observatory in France as part of the Binary Systems of South and North (BSN) Project. We extracted and collected the times of minima from the observations and literature and presented a new ephemeris for each system. Due to

Xuehui Dong, Miyu Feng, Chen Shao, Bokai Lai

Electromagnetically programmable information metasurfaces, as dynamically controllable 2D metamaterials, hold significant promise as low-profile hardware enabling passive wave control and signal generation for backscatter systems. However, current metasurface-based transmitters architecture fundamentally suffer from hardware non-modularization, forcing all t

Josefa Caballero, Hanna Okrasińska-Płociniczak, Łukasz Płociniczak, Kishin Sadarangani

We consider a nonlocal functional equation that is a generalization of the mathematical model used in behavioral sciences. The equation is built upon an operator that introduces a convex combination and a nonlinear mixing of the function arguments. We show that, provided some growth conditions of the coefficients, there exists a unique solution in the natura

Chandan Sarma, Praveen C. Srivastava

In this work, we explore the entanglement structure of two $N = Z$ nuclei, $^{20}$Ne and $^{22}$Na using single-orbital entanglement entropy within the No-Core Shell Model (NCSM) framework for two realistic interactions, INOY and N$^3$LO. We begin with the determination of the optimal frequencies based on the variation of ground-state (g.s.) binding energy w

Sandip Mondal, Kedar Khare, Sergey E. Skipetrov, Martin Kamp

We present a thorough study of the complexity of optical localized modes in two-dimensional disordered photonic crystals. Direct experimental measurements of complexity were made using an interferometric setup that allowed for extraction of phases and, hence, complex-valued wavefunctions. The comparison of experimental and theoretical results allows us to pr

Jin Wang, Bocheng Guo, Yijie Li, Junyi Wang

Tractography fiber clustering using diffusion MRI (dMRI) is a crucial strategy for white matter (WM) parcellation. Current methods primarily use the geometric information of fibers (i.e., the spatial trajectories) to group similar fibers into clusters, overlooking the important functional signals present along the fiber tracts. There is increasing evidence t

Liping Meng, Zhaoyi Xu, Meirong Tang

The hairy black hole model provides a new theoretical framework for exploring phenomena in strong gravitational fields. This paper systematically investigates the influence of the hair parameter $\beta$ on the timelike geodesics of the regular hairy black hole, including the radius of the event horizon, the properties of bound orbits, and the characteristics

Cheng Tan, Zhenxiao Cao, Zhangyang Gao, Lirong Wu

Post-translational modifications (PTMs) profoundly expand the complexity and functionality of the proteome, regulating protein attributes and interactions that are crucial for biological processes. Accurately predicting PTM sites and their specific types is therefore essential for elucidating protein function and understanding disease mechanisms. Existing co

Tengxiao Liu, Qipeng Guo, Xiangkun Hu, Cheng Jiayang

Trained on vast corpora of human language, language models demonstrate emergent human-like reasoning abilities. Yet they are still far from true intelligence, which opens up intriguing opportunities to explore the parallels of humans and model behaviors. In this work, we study the ability to skip steps in reasoning - a hallmark of human expertise developed t

Wenxiu Ding, Dan Li, Yu Wang

For $F\subseteq V(G)$, if $G-F$ is a disconnected graph with at least $r$ components and each vertex $v\in V(G)\backslash F$ has at least $g$ neighbors, then $F$ is called a $g$-good $r$-component cut of $G$. The $g$-good $r$-component connectivity of $G$, denoted by $c\kappa_{g,r}(G)$, is the minimum cardinality of $g$-good $r$-component cuts of $G$. Let $\

Gaochao Song, Chong Cheng, Hao Wang

In this paper we present a novel method for efficient and effective 3D surface reconstruction in open scenes. Existing Neural Radiance Fields (NeRF) based works typically require extensive training and rendering time due to the adopted implicit representations. In contrast, 3D Gaussian splatting (3DGS) uses an explicit and discrete representation, hence the

Yian Wang

Image Matching Challenge 2024 is a competition focused on building 3D maps from diverse image sets, requiring participants to solve fundamental computer vision challenges in image matching across varying angles, lighting, and seasonal changes. This project develops a Pipeline method that combines multiple advanced techniques: using pre-trained EfficientNet-B

Hengkai Tan, Xuezhou Xu, Chengyang Ying, Xinyi Mao

Embodied agents require robust spatial intelligence to execute precise real-world manipulations. However, this remains a significant challenge, as current methods often struggle to accurately position objects in space. Collecting extensive data can help address this issue by enhancing the agent's spatial understanding. Nonetheless, obtaining such data with r

Thi-Huong Vu, Hoang-Long Ngo, Duc-Trong Luong, Tran Ngoc Khue

We propose a tamed-adaptive Milstein scheme for stochastic differential equations in which the first-order derivatives of the coefficients are locally H\"older continuous of order $\alpha$. We show that the scheme converges in the $L_2$-norm with a rate of $(1+\alpha)/2$ over both finite intervals $[0, T]$ and the infinite interval $(0, +\infty)$, under cert

Ahmed Mohamed, Mostafa Ali, Shahd Ahmed, Nouran Hani

Student disengagement in online learning has become a critical challenge, particularly post-pandemic. This review explores deep learning techniques used to detect disengagement, emphasizing computer vision and affective computing as effective approaches. We examine recent studies focusing on facial expressions, eye movements, and posture to assess student at

Jiyong Kim, Jangseop Park, Nayong Kim, Younyeol Yu

This study aims to predict the spatio-temporal evolution of physical quantities observed in multi-layered display panels subjected to the drop impact of a ball. To model these complex interactions, graph neural networks have emerged as promising tools, effectively representing objects and their relationships as graph structures. In particular, MeshGraphNets

Yunfeng Chen, Jianliang Zhai, Tusheng Zhang

In this paper, we establish the existence and uniqueness of solutions of elliptic-parabolic stochastic Keller-Segel systems. The solution is obtained through a carefully designed localization procedure together with some a priori estimates. Both noise of linear growth and nonlinear noise are considered. The Lp Ito formula plays an important role.

Jie Yang, Wang Zeng, Sheng Jin, Lumin Xu

Recent advancements in Multimodal Large Language Models (MLLMs) have greatly improved their abilities in image understanding. However, these models often struggle with grasping pixel-level semantic details, e.g., the keypoints of an object. To bridge this gap, we introduce the novel challenge of Semantic Keypoint Comprehension, which aims to comprehend keypo

Valeriia Starichkova

In this note, we generalise two results on prime numbers in short intervals. The first result is Ingham's theorem which connects the zero-density estimates with short intervals where the prime number theorem holds, and the second result is due to Heath-Brown and Iwaniec, which derives the weighted zero-density estimates used for obtaining the lower bound for

Raju Biswas, Rajib Mandal

The primary objective of this paper is to establish several sharp versions of improved Bohr inequality, refined Bohr-type inequality, and refined Bohr-Rogosinski inequality for the class of $K$-quasiconformal sense-preserving harmonic mappings $f=h+\overline{g}$ in the unit disk $\mathbb{D} := \{z\in\mathbb{C} : |z| < 1\}$. In order to achieve these objectiv

Yaqiong Li, Peng Zhang, Hansu Gu, Tun Lu

Although there have been automated approaches and tools supporting toxicity censorship for social posts, most of them focus on detection. Toxicity censorship is a complex process, wherein detection is just an initial task and a user can have further needs such as rationale understanding and content modification. For this problem, we conduct a needfinding stu

Mengmeng Yang, Chi-Hung Chi, Kwok-Yan Lam, Jie Feng

Data sharing is a prerequisite for collaborative innovation, enabling organizations to leverage diverse datasets for deeper insights. In real-world applications like FinTech and Smart Manufacturing, transactional data, often in tabular form, are generated and analyzed for insight generation. However, such datasets typically contain sensitive personal/busines

Dong Li

Recommender systems have become increasingly important with the rise of the web as a medium for electronic and business transactions. One of the key drivers of this technology is the ease with which users can provide feedback about their likes and dislikes through simple clicks of a mouse. This feedback is commonly collected in the form of ratings, but can a

Haneul Yoo, Cheonbok Park, Sangdoo Yun, Alice Oh

Large language models (LLMs) now exhibit near human-level performance in various tasks, but their performance drops drastically after a handful of high-resource languages due to the imbalance in pre-training data. Inspired by the human process of second language acquisition, particularly code-switching$\unicode{x2014}$the practice of language alternation in

Zijun Min, Bingshuai Liu, Liang Zhang, Jia Song

The field of bioinformatics has seen significant progress, making the cross-modal text-molecule retrieval task increasingly vital. This task focuses on accurately retrieving molecule structures based on textual descriptions, by effectively aligning textual descriptions and molecules to assist researchers in identifying suitable molecular candidates. However,

Jiawen Zhang, Shun Zheng, Xumeng Wen, Xiaofang Zhou

Numerous industrial sectors necessitate models capable of providing robust forecasts across various horizons. Despite the recent strides in crafting specific architectures for time-series forecasting and developing pre-trained universal models, a comprehensive examination of their capability in accommodating varied-horizon forecasting during inference is sti

Shi Dong, Xiaobei Niu, Rui Zhong, Zhifeng Wang

Accurate annotation of educational resources is crucial for effective personalized learning and resource recommendation in online education. However, fine-grained knowledge labels often overlap or share similarities, making it difficult for existing multi-label classification methods to differentiate them. The label distribution imbalance due to sparsity of

Henry McNulty

The problem of identifying and reconstructing operators from a diagonal of the Gabor matrix is considered. The framework of Quantum Time--Frequency Analysis is used, wherein this problem is equivalent to the discretisation of the diagonal of the polarised Cohen's class of the operator. Metaplectic geometry allows the generalisation of conditions on appropria

Rina Carines Cabral, Soyeon Caren Han, Areej Alhassan, Riza Batista-Navarro

Discontinuous Named Entity Recognition (DNER) presents a challenging problem where entities may be scattered across multiple non-adjacent tokens, making traditional sequence labelling approaches inadequate. Existing methods predominantly rely on custom tagging schemes to handle these discontinuous entities, resulting in models tightly coupled to specific tag

Pradeep Kayshap, Peter R. Young

The center-to-limb variations (CLV) of transition region line Gaussian fit parameters in solar plage are reported for the first time. The Si iv 1402.77 A line observed by Interface Region Imaging Spectrograph (IRIS) is used. The spectral intensity increases linearly from the disk center to the solar limb. Similarly, the non-thermal velocity also increases li

Raju Biswas, Rajib Mandal

The classical Bohr theorem and its subsequent generalizations have become active areas of research, with investigations conducted in numerous function spaces. Let $\{\psi_n(r)\}_{n=0}^\infty$ be a sequence of non-negative continuous functions defined on $[0,1)$ such that the series $\sum_{n=0}^\infty \psi_n(r)$ converges locally uniformly on the interval $[0

Shuangping Li, Tselil Schramm

We show that the shortest $s$-$t$ path problem has the overlap-gap property in (i) sparse $\mathbf{G}(n,p)$ graphs and (ii) complete graphs with i.i.d. Exponential edge weights. Furthermore, we demonstrate that in sparse $\mathbf{G}(n,p)$ graphs, shortest path is solved by $O(\log n)$-degree polynomial estimators, and a uniform approximate shortest path can

Ben F. Allen, Rod Gover

We define and construct a conformally invariant energy for closed smoothly immersed submanifolds of even dimension, but of arbitrary codimension, in conformally flat Riemannian manifolds. This is a higher dimensional analogue of the Willmore energy for immersed surfaces and is given directly via a coupling of the tractor connection to the (submanifold critic

Guan-Ting Lin, Prashanth Gurunath Shivakumar, Aditya Gourav, Yile Gu

While textless Spoken Language Models (SLMs) have shown potential in end-to-end speech-to-speech modeling, they still lag behind text-based Large Language Models (LLMs) in terms of semantic coherence and relevance. This work introduces the Align-SLM framework, which leverages preference optimization inspired by Reinforcement Learning with AI Feedback (RLAIF)

Shengjie Niu, Lifan Lin, Jian Huang, Chao Wang

Semi-supervised learning (SSL) offers a robust framework for harnessing the potential of unannotated data. Traditionally, SSL mandates that all classes possess labeled instances. However, the emergence of open-world SSL (OwSSL) introduces a more practical challenge, wherein unlabeled data may encompass samples from unseen classes. This scenario leads to misc

Hao Wu, Junxiao Deng, Minchen Yu, Yue Yu

Serverless computing has gained significant traction for machine learning inference applications, which are often deployed as serverless workflows consisting of multiple CPU and GPU functions with data dependency. However, existing data-passing solutions for serverless computing primarily reply on host memory for fast data transfer, mandating substantial dat

Kefan Dong, Arvind Mahankali, Tengyu Ma

Mathematical theorem proving is an important testbed for large language models' deep and abstract reasoning capability. This paper focuses on improving LLMs' ability to write proofs in formal languages that permit automated proof verification/evaluation. Most previous results provide human-written lemmas to the theorem prover, which is an arguably oversimpli

Ying Li, Chao Zhang

We study the well-posedness of solutions to the general nonlinear parabolic equations with merely integrable data in time-dependent Musielak-Orlicz spaces. With the help of a density argument, we establish the existence and uniqueness of both renormalized and entropy solutions. Moreover, we conclude that the entropy and renormalized solutions for this equati

Kaito Ito, Kenji Kashima

This paper introduces the risk-sensitive control as inference (RCaI) that extends CaI by using R\'{e}nyi divergence variational inference. RCaI is shown to be equivalent to log-probability regularized risk-sensitive control, which is an extension of the maximum entropy (MaxEnt) control. We also prove that the risk-sensitive optimal policy can be obtained by

Alex Xinting Wu, Ian R. Petersen, Valery Ugrinovskii, Iman Shames

In this paper, we develop an online optimization algorithm for solving a class of nonconvex optimization problems with a linearly varying optimal point. The global convergence of the algorithm is guaranteed using the circle criterion for the class of functions whose gradient is bounded within a sector. Also, we show that the corresponding Lur\'e-type nonline

Han Liang, Ziwei Zhan, Weijie Liu, Xiaoxi Zhang

Federated Learning (FL) is a distributed machine learning paradigm that achieves a globally robust model through decentralized computation and periodic model synthesis, primarily focusing on the global model's accuracy over aggregated datasets of all participating clients. Personalized Federated Learning (PFL) instead tailors exclusive models for each client

Debjoty Paul, Shivesh Yadav, Shikhar Gupta, Bikash Patra

Topological chiral antiferromagnets, such as Mn$_{3}$Sn, are emerging as promising materials for next-generation spintronic devices due to their intrinsic transport properties linked to exotic magnetic configurations. Here, we demonstrate that anisotropic strain in Mn$_{3}$Sn thin films offers a novel approach to manipulate the magnetic ground state, unlocki

S. Godambe, N. Mankuzhiyil, C. Borwankar, B. Ghosal

The radio galaxy NGC 1275, located at the central region of Perseus cluster, is a well-known very high-energy (VHE) gamma-ray emitter. The Major Atmospheric Cherenkov Experiment Telescope has detected two distinct episodes of VHE (E > 80 GeV) gamma-ray emission from NGC 1275 during 2022 December and 2023 January. The second outburst, observed on 2023 January

Xi He, Feiyu Du, Xiaohan Yu, Yang Zhao

The scarcity of labelled data is specifically an urgent challenge in the field of quantum machine learning (QML). Two transfer fusion frameworks are proposed in this paper to predict the labels of a target domain data by aligning its distribution to a different but related labelled source domain on quantum devices. The frameworks fuses the quantum data from

Lingyi Wang, Wei Wu, Fuhui Zhou, Zhijin Qin

Learning-task oriented semantic communication is pivotal in optimizing transmission efficiency by extracting and conveying essential semantics tailored to specific tasks, such as image reconstruction and classification. Nevertheless, the challenge of eavesdropping poses a formidable threat to semantic privacy due to the open nature of wireless communications

Wenbo Ouyang, Ruiyang Wu, Ning Hao, Hao Helen Zhang

This paper introduces a novel framework for dynamic classification in high dimensional spaces, addressing the evolving nature of class distributions over time or other index variables. Traditional discriminant analysis techniques are adapted to learn dynamic decision rules with respect to the index variable. In particular, we propose and study a new supervis

Bo Gao, Jianhui Wang, Xinyuan Song, Yangfan He

Current semantic segmentation models typically require a substantial amount of manually annotated data, a process that is both time-consuming and resource-intensive. Alternatively, leveraging advanced text-to-image models such as Midjourney and Stable Diffusion has emerged as an efficient strategy, enabling the automatic generation of synthetic data in place

Shashank Nag, Alan T. L. Bacellar, Zachary Susskind, Anshul Jha

Transformers are set to become ubiquitous with applications ranging from chatbots and educational assistants to visual recognition and remote sensing. However, their increasing computational and memory demands is resulting in growing energy consumption. Building models with fast and energy-efficient inference is imperative to enable a variety of transformer-

Shelei Li, Yong Chai Tan, Tai Vincent

Graph Convolutional Network (GCN) are widely used in Graph Anomaly Detection (GAD) due to their natural compatibility with graph structures, resulting in significant performance improvements. However, most researchers approach GAD as a graph node classification task and often rely on low-pass filters or feature aggregation from neighboring nodes. This paper

Thanh Nguyen Canh, Huy-Hoang Ngo, Xiem HoangVan, Nak Young Chong

Localization is one of the most crucial tasks for Unmanned Aerial Vehicle systems (UAVs) directly impacting overall performance, which can be achieved with various sensors and applied to numerous tasks related to search and rescue operations, object tracking, construction, etc. However, due to the negative effects of challenging environments, UAVs may lose s

Bridget Casey, Greg Marston, Dhaval Vyas

Supporting practices around self-care is crucial for enabling older adults to continue living in their own homes and ageing in place. While existing assistive technology and research concerning self-care practices have been centered on a medicalized viewpoint, it neglects a holistic perspective of older adults' preferences in self-care. This paper presents a

Thanh Nguyen Canh, Xiem HoangVan, Nak Young Chong

Navigating safely in dynamic human environments is crucial for mobile service robots, and social navigation is a key aspect of this process. In this paper, we proposed an integrative approach that combines motion prediction and trajectory planning to enable safe and socially-aware robot navigation. The main idea of the proposed method is to leverage the adva

Suvir Mirchandani, Suneel Belkhale, Joey Hejna, Evelyn Choi

A long-standing goal in robot learning is to develop methods for robots to acquire new skills autonomously. While reinforcement learning (RL) comes with the promise of enabling autonomous data collection, it remains challenging to scale in the real-world partly due to the significant effort required for environment design and instrumentation, including the n

Soumyaranjan Dash, Sanjeev Kumar

Using the combination of a new effective Hamiltonian approach and hybrid Monte-Carlo simulations, we unveil a variety of partially magnetically ordered (PMO) phases in the Kondo lattice model. Our approximation is motivated by two crucial features of the Hamiltonian: (i) formation of Kondo singlets leading to vanishing local magnetic moments, and (ii) spatia

Dingyun Yao, Tianning Xiao, Chao Zhang, Youjin Deng

The two-dimensional (2D) XY model plays a crucial role in statistical and condensed matter physics. With the introduction of long-range interactions, the system exhibits a richer set of physical phenomena and a crossover between non-classical and short-range universality classes.In this work, we investigate the 2D XY model with algebraically decaying interac

Ryoga Mahara

We study the problem of fairly and efficiently allocating indivisible goods among agents with additive valuation functions. Envy-freeness up to one good (EF1) is a well-studied fairness notion for indivisible goods, while Pareto optimality (PO) and its stronger variant, fractional Pareto optimality (fPO), are widely recognized efficiency criteria. Although e

Swagat Bordoloi, P. Shalima, Rupjyoti Gogoi, Jayant Murthy

The Ultra Violet Imaging Telescope (UVIT) onboard India's first dedicated multiwavelength satellite \textit{AstroSat} observed a significant fraction of the sky in the ultraviolet with a spatial resolution of 1.4\arcsec. We present a catalog of the point sources observed by UVIT in the far ultraviolet (FUV; 1300-1800 \AA) and near ultraviolet (NUV; 2000-3000

Dan Li, Hye-Bin Shin, Kang Yin, Seong-Whan Lee

The significant inter-subject variability in electroen-cephalogram (EEG) signals often results in substantial changes to neural network weights as data distributions shift. This variability frequently causes catastrophic forgetting in continual EEG decoding tasks, where previously acquired knowledge is overwritten as new subjects are introduced. While retrai

Kapilan Balagopalan, Tuan Ngo Nguyen, Yao Zhao, Kwang-Sung Jun

The best arm identification problem requires identifying the best alternative (i.e., arm) in active experimentation using the smallest number of experiments (i.e., arm pulls), which is crucial for cost-efficient and timely decision-making processes. In the fixed confidence setting, an algorithm must stop data-dependently and return the estimated best arm wit

Yu Pan, Hongfeng Yu, Tianjiao Zhao, Jianxin Sun

Large language models (LLMs) are emerging as few-shot learners capable of handling a variety of tasks, including comprehension, planning, reasoning, question answering, arithmetic calculations, and more. At the core of these capabilities is LLMs' proficiency in representing and understanding structural or semi-structural data, such as tables and graphs. Nume

Hiroto Horiuchi, Yasufumi Araki, Yuki K. Wakabayashi, Jun'ichi Ieda

Spin Berry curvature characterizes the band topology as the spin counterpart of Berry curvature and is crucial in generating novel spintronics functionalities. By breaking the crystalline inversion symmetry, the spin Berry curvature is expected to be significantly enhanced; this enhancement will increase the intrinsic spin Hall effect in ferromagnetic materi

Fuming You, Minghui Fang, Li Tang, Rongjie Huang

Motion-to-music and music-to-motion have been studied separately, each attracting substantial research interest within their respective domains. The interaction between human motion and music is a reflection of advanced human intelligence, and establishing a unified relationship between them is particularly important. However, to date, there has been no work

Luisa Mao, Ryan Soussan, Brian Coltin, Trey Smith

We are interested in long-term deployments of autonomous robots to aid astronauts with maintenance and monitoring operations in settings such as the International Space Station. Unfortunately, such environments tend to be highly dynamic and unstructured, and their frequent reconfiguration poses a challenge for robust long-term localization of robots. Many st

Wenzhi Gao, Ya-Chi Chu, Yinyu Ye, Madeleine Udell

We introduce a framework to accelerate the convergence of gradient-based methods with online learning. The framework learns to scale the gradient at each iteration through an online learning algorithm and provably accelerates gradient-based methods asymptotically. In contrast with previous literature, where convergence is established based on worst-case anal

Oleg Chalykh, Yongchao Lü

In this work, we examine the classical and quantum Seiberg-Witten curves of 5d N = 1 SCFTs and their 4d limits. The 5d theories we consider are Seiberg's theories of type $E_{6,7,8}$, which serve as the UV completions of 5d SU(2) gauge theories with 5, 6, or 7 flavors. Their classical curves can be constructed using the five-brane web construction [1]. We al

Dongwon Kim, Seoyeon Kim, Suha Kwak

Object-centric learning (OCL) aims to learn representations of individual objects within visual scenes without manual supervision, facilitating efficient and effective visual reasoning. Traditional OCL methods primarily employ bottom-up approaches that aggregate homogeneous visual features to represent objects. However, in complex visual environments, these

Shufan Shen, Junshu Sun, Xiangyang Ji, Qingming Huang

Parameter-efficient fine-tuning (PEFT) is an effective method for adapting pre-trained vision models to downstream tasks by tuning a small subset of parameters. Among PEFT methods, sparse tuning achieves superior performance by only adjusting the weights most relevant to downstream tasks, rather than densely tuning the whole weight matrix. However, this perf