November 2024 arXiv papers — page 139

Yiqing Lin, Jianheng Tang, Chenyi Zi, H. Vicky Zhao

Graph Anomaly Detection (GAD) aims to identify uncommon, deviated, or suspicious objects within graph-structured data. Existing methods generally focus on a single graph object type (node, edge, graph, etc.) and often overlook the inherent connections among different object types of graph anomalies. For instance, a money laundering transaction might involve

Bijoy Ahmed Saiem, MD Sadik Hossain Shanto, Rakib Ahsan, Md Rafi ur Rashid

As the integration of the Large Language Models (LLMs) into various applications increases, so does their susceptibility to misuse, raising significant security concerns. Numerous jailbreak attacks have been proposed to assess the security defense of LLMs. Current jailbreak attacks mainly rely on scenario camouflage, prompt obfuscation, prompt optimization,

Niklas Kochdumper, Youran Wang, Johannes Betz, Matthias Althoff

In recent years, different approaches for motion planning of autonomous vehicles have been proposed that can handle complex traffic situations. However, these approaches are rarely compared on the same set of benchmarks. To address this issue, we present the results of a large-scale motion planning competition for autonomous vehicles based on the CommonRoad

Yushi Yang, Filip Sondej, Harry Mayne, Andrew Lee

Safety fine-tuning algorithms reduce harmful outputs in language models, yet their mechanisms remain under-explored. Direct Preference Optimization (DPO) is a popular choice of algorithm, but prior explanations, attributing its effects solely to dampened toxic neurons in the MLP layers, are incomplete. In this study, we analyse four language models (Llama-3.

Yong He, Yujie Hou, Haixia Liu, Yalin Wang

Matrix factor models have been growing popular dimension reduction tools for large-dimensional matrix time series. However, the heteroscedasticity of the idiosyncratic components has barely received any attention. Starting from the pseudo likelihood function, this paper introduces a Generalized Principal Component Analysis (GPCA) method for matrix factor mod

Mathys Rennela, Harold Ollivier

Noise remains one of the most significant challenges in the development of reliable and scalable quantum processors. While quantum error correction and mitigation techniques offer potential solutions, they are often limited by the substantial overhead required. To address this, tailored approaches that exploit specific hardware characteristics have emerged.

Mirek Giersz, Abbas Askar, Arkadiusz Hypki, Jongsuk Hong

Using the MOCCA code, we study the evolution of globular clusters (GCs) with multiple stellar populations. For this purpose, the MOCCA code has been significantly extended to take into account the formation of an enriched population of stars from re-accreted gas with a time delay after the formation of the pristine population of stars. The possibility of clu

Abhishek Kaushik, Kayla Rush

Music is a potent form of expression that can communicate, accentuate or even create the emotions of an individual or a collective. Both historically and in contemporary experiences, musical expression was and is commonly instrumentalized for social, political and/or economic purposes. Generative artificial intelligence provides a wealth of both opportunitie

Frank Trujillo

We prove that for almost every irreducible interval exchange transformation $T$ and for any vector $\omega$ in its associated central-stable space (with respect to the Kontsevich-Zorich cocycle) there exists a unique AIET, up to normalization of its domain, semi-conjugated to $T$ and whose log-slope vector equals $\omega$. This provides a partial answer to a

Andreas Vollmer

We show that two natural and a priori unrelated structures encapsulate the same data, namely certain commutative and associative product structures and a class of superintegrable Hamiltonian systems. More precisely, consider a Euclidean space of dimension at least three, equipped with a commutative and associative product structure that satisfies the conditi

Gabriele Oligeri, Savio Sciancalepore

Radio Frequency Fingerprinting (RFF) techniques allow a receiver to authenticate a transmitter by analyzing the physical layer of the radio spectrum. Although the vast majority of scientific contributions focus on improving the performance of RFF considering different parameters and scenarios, in this work, we consider RFF as an attack vector to identify a t

Lena Verscht, Benjamin Lucien Kaminski

We study Hoare-like logics, including partial and total correctness Hoare logic, incorrectness logic, Lisbon logic, and many others through the lens of predicate transformers \`a la Dijkstra and through the lens of Kleene algebra with top and tests (TopKAT). Our main goal is to give an overview - a taxonomy - of how these program logics relate, in particular

Xiangqian Yan, Yongsheng Li, Wei Yan, Xin Liu

This paper is devoted to studying the maximal-in-time estimates and Strichartz estimates for orthonormal functions and convergence problem of density functions related to Boussinesq operator on manifolds. Firstly, we present the pointwise convergence of density function related to Boussinesq operator with $\gamma_{0}\in\mathfrak{S}^{\beta}(\dot{H}^{\frac{1}{

Sreejita Bhaduri, Mohammed Ibrahim Sugarno, Ashoke De

Cavities exhibit inherent self-sustaining oscillations driven by the coupling between their hydrodynamic and acoustic properties. In practical applications, cavities are often placed within confinements that introduce compression waves, significantly influencing their primary flow characteristics. The oscillations in cavities have widespread applications, su

Chi-Sheng Chen, Wei-Sheng Wang

The Psycho Frame, a sophisticated system primarily used in Universal Century (U.C.) series mobile suits for NEWTYPE pilots, has evolved as an integral component in harnessing the latent potential of mental energy. Its ability to amplify and resonate with the pilot's psyche enables real-time mental control, creating unique applications such as psychomagnetic

Dmitry Berdinsky, Ivan Rybnikov

We describe a method for constructing $n$-orthogonal coordinate systems in constant curvature spaces. The construction proposed is a modification of Krichever's method for producing orthogonal curvilinear coordinate systems in the $n$-dimensional Euclidean space. To demonstrate how this method works, we construct examples of orthogonal coordinate systems on

H. P. Zhang, Z. Song

The Wannier-Stark ladder (WSL) is a basic concept, supporting periodic oscillation, widely used in many areas of physics. In this paper, we investigate the formations of WSL in generalized systems, including strongly correlated and non-Hermitian systems. We present a theorem on the existence of WSL for a set of general systems that are translationally symmet

Atul Dixit, Gaurav Kumar

Page 27 of Ramanujan's Lost Notebook contains a beautiful identity which not only gives, as a special case, a famous modular relation between the Rogers-Ramanujan functions $G(q)$ and $H(q)$ but also a relation between two fifth order mock theta functions and $G(q)$ and $H(q)$. We generalize Ramanujan's relation with the help of a parameter $s$ to get an inf

Steven R. Costenoble, Thomas Hudson

We calculate the ordinary $C_2$-cohomology, with Burnside ring coefficients, of $BU(2)$, the classifying space for $C_2$-equivariant complex 2-plane bundles, using an extended grading that allows us to capture a more natural set of generators. This allows us to define characteristic classes for such bundles. Combined with earlier calculations, it also allows

Netanel Blumenfeld, Inna Stainvas, Igal Bilik

"This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible." Driver's interaction with a vehicle via automatic gesture recognition is expected to enhance driving safety by decreasing driver's distraction. Optical and infrared-based gesture recognition s

Liao Zhang, Fabian Mitterwallner, Jan Jakubuv, Cezary Kaliszyk

Term rewriting plays a crucial role in software verification and compiler optimization. With dozens of highly parameterizable techniques developed to prove various system properties, automatic term rewriting tools work in an extensive parameter space. This complexity exceeds human capacity for parameter selection, motivating an investigation into automated s

Osher Azulay, Dhruv Metha Ramesh, Nimrod Curtis, Avishai Sintov

Compared to rigid hands, underactuated compliant hands offer greater adaptability to object shapes, provide stable grasps, and are often more cost-effective. However, they introduce uncertainties in hand-object interactions due to their inherent compliance and lack of precise finger proprioception as in rigid hands. These limitations become particularly sign

Zhi-Cheng He, Zheng-Yuan Xue

Quantum error correction represents a significant milestone in large-scale quantum computing, with the surface code being a prominent strategy due to its high error threshold and experimental feasibility. However, it is challenging to implement non-Clifford logical gates in a fault-tolerant way with low overhead, through the conventional magic state distilla

Sepehr Nourmohammadi, Arda Sarp Yenicesu, Shervin Rahimzadeh Arashloo, Ozgur S. Oguz

This paper presents a novel approach to one-class classifier fusion through locally adaptive learning with dynamic $\ell$p-norm constraints. We introduce a framework that dynamically adjusts fusion weights based on local data characteristics, addressing fundamental challenges in ensemble-based anomaly detection. Our method incorporates an interior-point opti

Chen Chen, Jingya Qian, Hui Luo, Yongye Li

The k-truss model is one of the most important models in cohesive subgraph analysis. The k-truss decomposition problem is to compute the trussness of each edge in a given graph, and has been extensively studied. However, the conventional k-truss model is difficult to characterize the fine-grained hierarchical structures in networks due to the neglect of high

Yining Ma, Qadeer Khan, Daniel Cremers

In this paper we propose MA-DV2F: Multi-Agent Dynamic Velocity Vector Field. It is a framework for simultaneously controlling a group of vehicles in challenging environments. DV2F is generated for each vehicle independently and provides a map of reference orientation and speed that a vehicle must attain at any point on the navigation grid such that it safely

Søren Riis

We study impartial games under fixed-latency, fixed-scale quantised inference (FSQI). In this fixed-scale, bounded-range regime, we prove that inference is simulable by constant-depth polynomial-size Boolean circuits (AC0). This yields a worst-case representational barrier: single-frame agents in the FSQI/AC0 regime cannot strongly master NIM, because optima

Sultan Alrashed, Dmitrii Khizbullin, David R. Pugh

As large language models (LLMs) grow and develop, so do their data demands. This is especially true for multilingual LLMs, where the scarcity of high-quality and readily available data online has led to a multitude of synthetic dataset generation approaches. A key technique in this space is machine translation (MT), where high-quality English text is adapted

Barbara Baumeister, Patrick Wegener

In this paper, we study in detail the hyperbolic covers $\tilde{W}$ and $\hat{W}$ of an elliptic Weyl system introduced by Saito. We show that they are isomorphic and also isomorphic to an extended Coxeter system of star type. For $\tilde{c}$ a Coxeter transformation in $\tilde{W}$ we can conclude the Hurwitz transitivity of the braid group action on the set

Jinbo Hu, Yin Cao, Ming Wu, Fang Kang

Sound event localization and detection (SELD) has seen substantial advancements through learning-based methods. These systems, typically trained from scratch on specific datasets, have shown considerable generalization capabilities. Recently, deep neural networks trained on large-scale datasets have achieved remarkable success in the sound event classificati

Katharine Beaumont, Rem Collier

We demonstrate the integration of Transfer Learning into a hypermedia Multi-Agent System using the Multi-Agent MicroServices (MAMS) architectural style. Agents use RDF knowledge stores to reason over information and apply Reinforcement Learning techniques to learn how to interact with a Tic-Tac-Toe API. Agents form advisor-advisee relationships in order to s

Sumera Rounaq, Shahid Munir Shah, Mahmoud Aljawarneh

COVID-19 is extremely contagious and its rapid growth has drawn attention towards its early diagnosis. Early diagnosis of COVID-19 enables healthcare professionals and government authorities to break the chain of transition and flatten the epidemic curve. With the number of cases accelerating across the developed world, COVID-19 induced Viral Pneumonia cases

Xingguo Chen, Yu Gong, Shangdong Yang, Wenhao Wang

Fast-converging algorithms are a contemporary requirement in reinforcement learning. In the context of linear function approximation, the magnitude of the smallest eigenvalue of the key matrix is a major factor reflecting the convergence speed. Traditional value-based RL algorithms focus on minimizing errors. This paper introduces a variance minimization (VM

Jianyu Ou, Yuanyuan Qu, Guoqiang Wu

Suppose $(M^n, g, f)$ is a complete shrinking gradient Ricci soliton. We give several rigidity results under some natural conditions, generalizing the results in \cite{Petersen-Wylie,Guan-Lu-Xu}. Using maximum principle, we prove that shrinking gradient Ricci soliton with constant scalar curvature $R=1$ is isometric to a finite quotient of $\mathbb{R}^2\time

Zhao Yingjie, Mahdi Abolghasemi

Hierarchical time series forecasting plays a crucial role in decision-making in various domains while presenting significant challenges for modelling as they involve multiple levels of aggregation, constraints, and availability of information. This study explores the influence of distinct information utilisation on the accuracy of hierarchical forecasts, pro

Jicheng Ma, Yunyan Yang

On a connected finite graph, we propose an evolution of weights including Ollivier's Ricci flow as a special case. During the evolution process, on each edge, the speed of change of weight is exactly the difference between the Wasserstein distance related to two probability measures and certain graph distance. Here the probability measure may be chosen as an

Song Yu, Shufeng Gong, Qian Tao, Sijie Shen

The growing volume of graph data may exhaust the main memory. It is crucial to design a disk-based graph storage system to ingest updates and analyze graphs efficiently. However, existing dynamic graph storage systems suffer from read or write amplification and face the challenge of optimizing both read and write performance simultaneously. To address this c

Shuqi Li, Yuebo Sun, Yuxin Lin, Xin Gao

There are two issues in news-driven multi-stock movement prediction tasks that are not well solved in the existing works. On the one hand, "relation discovery" is a pivotal part when leveraging the price information of other stocks to achieve accurate stock movement prediction. Given that stock relations are often unidirectional, such as the "supplier-consum

Yutong Chen, Marko Mihajlovic, Xiyi Chen, Yiming Wang

3D Gaussian Splatting (3DGS) has recently transformed photorealistic reconstruction, achieving high visual fidelity and real-time performance. However, rendering quality significantly deteriorates when test views deviate from the camera angles used during training, posing a major challenge for applications in immersive free-viewpoint rendering and navigation

Yadh Hafsi, Edoardo Vittori

This study investigates the development of an optimal execution strategy through reinforcement learning, aiming to determine the most effective approach for traders to buy and sell inventory within a finite time horizon. Our proposed model leverages input features derived from the current state of the limit order book and operates at a high frequency to maxi

Agustina Felipe, Ruben Sevilla, Oubay Hassan

Purpose: This study aims to assess the accuracy of degree adaptive strategies in the context of incompressible Navier-Stokes flows using the high order hybridisable discontinuous Galerkin (HDG) method. Design/methodology/approach: The work presents a series of numerical examples to show the inability of standard degree adaptive processes to accurate capture

Jaehyeok Lee, Keisuke Sakaguchi, JinYeong Bak

Self-training approach for large language models (LLMs) improves reasoning abilities by training the models on their self-generated rationales. Previous approaches have labeled rationales that produce correct answers for a given question as appropriate for training. However, a single measure risks misjudging rationale quality, leading the models to learn fla

Priya, Monu Kadyan

Let $Z$ be an abelian group, $ x \in Z$, and $[x] = \{ y : \langle x \rangle = \langle y \rangle \}$. A graph is called integral if all its eigenvalues are integers. It is known that a Cayley graph is integral if and only if its connection set can be express as union of the sets $[x] $. In this paper, we determine an algebraic formula for eigenvalues of the

Sumin Seo, Heeseon Cheon, Hyunho Kim

To effectively manage and utilize knowledge graphs, it is crucial to have metrics that can assess the quality of knowledge graphs from various perspectives. While there have been studies on knowledge graph quality metrics, there has been a lack of research on metrics that measure how richly ontologies, which form the backbone of knowledge graphs, are defined

Hu-Wei Jia, Wen-Jun Liu, Yue-Hong Wu, Kou-Han Ma

Thermal broadening of the quasi-particle peak in the spectral function is an important physical feature in many statistical systems, but it is difficult to calculate. To tackle this problem, we propose the $H$-expanded basis within the projective truncation approximation (PTA) of the Green's function equation of motion. A zeros-removing technique is introduc

Giovanna Kobus Conrado, Adam Husted Kjelstrøm, Andreas Pavlogiannis, Jaco van de Pol

Context-free language (CFL) reachability is a standard approach in static analyses, where the analysis question is phrased as a language reachability problem on a graph $G$ wrt a CFL L. While CFLs lack the expressiveness needed for high precision, common formalisms for context-sensitive languages are such that the corresponding reachability problem is undeci

Aryan Naveen, Jalil Morris, Christian Chan, Daniel Mhrous

Autonomous flapping-wing micro-aerial vehicles (FWMAV) have a host of potential applications such as environmental monitoring, artificial pollination, and search and rescue operations. One of the challenges for achieving these applications is the implementation of an onboard sensor suite due to the small size and limited payload capacity of FWMAVs. The curre

Junyi Zhang, Chang Liu, Chun Yang

In text recognition, complex glyphs and tail classes have always been factors affecting model performance. Specifically for Chinese text recognition, the lack of shape-awareness can lead to confusion among close complex characters. Since such characters are often tail classes that appear less frequently in the training-set, making it harder for the model to

Zhongyao Hu, Bo Chen, Jianzheng Wang, Daniel W. C. Ho

This paper studies a distributed estimation problem for time-varying/time-invariant large-scale interconnected systems (LISs). A fully distributed estimator is presented by recursively solving a distributed modified Riccati equation (DMRE) with decoupling variables. By partitioning the LIS based on the transition matrix's block structure, it turns out that t

Yixiao Qiao, Zhengde Xu, Zhuo Xu, Yumeng Yang

The electrical switching of antiferromagnet (AFM) is very important for the development of ultrafast magnetic random-access memory (MRAM). This task becomes more difficult in antiferromagnetic oxide NiO which has complex anisotropy. We show that by utilizing two spin-orbit torques (SOT) from orthogonal currents, one can deterministically switch the magnetic

Hanwen Cao, George J. Pappas, Nikolay Atanasov

In this paper, we derive a new Kalman filter with probabilistic data association between measurements and states. We formulate a variational inference problem to approximate the posterior density of the state conditioned on the measurement data. We view the unknown data association as a latent variable and apply Expectation Maximization (EM) to obtain a filt

Jieji Ren, Heng Guo, Zaiyan Yang, Jinnuo Zhang

Camera-based tactile sensors can provide high-density surface geometry and force information for robots in the interaction process with the target. However, most existing methods cannot achieve accurate reconstruction with high efficiency, impeding the applications in robots. To address these problems, we propose an efficient two-shot photometric stereo meth

Zhibai Huang, Chen Chen, James Yen, Yihan Shen

Peripheral Component Interconnect Express (PCIe) is the de facto interconnect standard for high-speed peripherals and CPUs. The development of PCIe devices for emerging applications requires realistic Transaction Layer Packet (TLP) traces that accurately simulate device-CPU interactions. While generative AI offers a promising avenue for synthesizing complex

Jeongjin Shin, Sangdon Park

Deep learning models are vulnerable to backdoor attacks, where adversaries inject malicious functionality during training that activates on trigger inputs at inference time. Extensive research has focused on developing stealthy backdoor attacks to evade detection and defense mechanisms. However, these approaches still have limitations that leave the door ope

Stephen Cantrell, Eduardo Reyes, Cagri Sert

We define and study geometric versions of the Benoist limit cone and matrix joint spectrum, which we call the translation cone and the joint translation spectrum, respectively. These new notions allow us to generalize the study of embeddings into products of rank-one simple Lie groups and to compare group actions on different metric spaces, quasi-morphisms,

Yuanshuai Luo, Rui Wang, Yaxin Liang, Ankai Liang

With the rapid growth of digital information, personalized recommendation systems have become an indispensable part of Internet services, especially in the fields of e-commerce, social media, and online entertainment. However, traditional collaborative filtering and content-based recommendation methods have limitations in dealing with data sparsity and cold

D. C. Liu, X. Liu, L. Wang, X. F. Zheng

The scaling of scrape-off layer (SOL) power width ({\lambda}q) is essential for advancing the understanding of particle and heat transport in the SOL. Due to the sparse layout of divertor Langmuir probes (Div-LPs) and probe erosion during long-pulse, high-performance operations on EAST, estimating SOL particle flux width ({\lambda}js, used to approximate {\l

Ming-Yang Zhuang, Feige Wang, Fengwu Sun, Yue Shen

We present the Early Data Release of the Multi-Cycle JWST-NEXUS Treasury program (2024-2028), which includes NIRCam imaging and WFSS observations from the first (partial) NEXUS-Wide epoch covering the central 100 ${\rm arcmin^2}$ of the NEXUS field, located near the North Ecliptic Pole and within the Euclid Ultra-Deep Field. We release reduced NIRCam mosaics

Sreeram Vennam, Anish Joishy, Ponnurangam Kumaraguru

We present a method to compress the final linear layer of language models, reducing memory usage by up to 3.4x without significant performance loss. By grouping tokens based on Byte Pair Encoding (BPE) merges, we prevent materialization of the memory-intensive logits tensor. Evaluations on the TinyStories dataset show that our method performs on par with GPT

Edith Cohen, Jelani Nelson, Tamás Sarlós, Mihir Singhal

Cardinality sketches are compact data structures for representing sets or vectors. These sketches are space-efficient, typically requiring only logarithmic storage in the input size, and enable approximation of cardinality (or the number of nonzero entries). A crucial property in applications is \emph{composability}, meaning that the sketch of a union of set

Ru-Yu Lai, Gunther Uhlmann, Lili Yan

In this paper, we study the partial data inverse problem for nonlinear magnetic Schr\"odinger equations. We show that the knowledge of the Dirichlet-to-Neumann map, measured on an arbitrary part of the boundary, determines the time-dependent linear coefficients, electric and magnetic potentials, and nonlinear coefficients, provided that the divergence of the

Joshua A. Howie, Dionne Ibarra, Daniel V. Mathews, Lecheng Su

In the complete hyperbolic structure on the complement of the figure eight knot, we determine the set of lambda lengths from the maximal cusp to itself. Using the correspondence between spinors and spin-decorated horospheres, we show that these lambda lengths are precisely the Eisenstein integers, up to multiplication by a unit. We also show that the inter-c

Hoki Kim, Jinseong Park, Yujin Choi, Seungyun Lee

Neural additive model (NAM) is a recently proposed explainable artificial intelligence (XAI) method that utilizes neural network-based architectures. Given the advantages of neural networks, NAMs provide intuitive explanations for their predictions with high model performance. In this paper, we analyze a critical yet overlooked phenomenon: NAMs often produce

Samapti Pratihar, K. C. Sivakumar

Two new matrix classes are introduced; inverse cyclic matrices and bi-diagonal south-west matrices. An interesting relation is established between these classes. Applications to two classes of inverse $Z$-matrices are provided.

Guowei Ren, Mouyuan Sun, Nan Ding, Xing Yang

Studying the periodic flux-variation behavior of blazars is vital for probing supermassive black hole binaries and the kinematics of relativistic jets. In this work, we report the detection of the multi-band possible periodic variations of the blazar PKS J2134-0153, including the infrared ($1.6(\pm0.4)\times 10^3$ days) and optical ($1.8(\pm1)\times 10^3$ da

Liuyue Xie, Jiancong Guo, Laszlo A. Jeni, Zhiheng Jia

Recent extended reality headsets and field robots have adopted covers to protect the front-facing cameras from environmental hazards and falls. The surface irregularities on the cover can lead to optical aberrations like blurring and non-parametric distortions. Novel view synthesis methods like NeRF and 3D Gaussian Splatting are ill-equipped to synthesize fr

Chee Kian Yap

We develop a finite-dimensional, symmetric matrix framework associated with the Riemann zeta function for complex arguments s with Real(s) unequal 1/2.

Haiying Shen, Tanmoy Sen

As Large Language Models (LLMs) continue to grow, reducing costs and alleviating GPU demands has become increasingly critical. However, existing schedulers primarily target either GPU compute or Key-Value Cache (KVC) utilization, failing to fully optimize both GPU compute and KVC usage during each iteration or guarantee timely KVC allocations when needed. To

Hao Tang, Junhao Lu, Guoheng Huang, Ming Li

In Few-Shot Learning (FSL), traditional metric-based approaches often rely on global metrics to compute similarity. However, in natural scenes, the spatial arrangement of key instances is often inconsistent across images. This spatial misalignment can result in mismatched semantic pixels, leading to inaccurate similarity measurements. To address this issue,

Abraham Itzhak Weinberg, Pythagoras Petratos, Alessio Faccia

This paper explores the coexistence possibilities of Central Bank Digital Currencies (CBDCs) and blockchain-based cryptocurrencies within a post-quantum computing landscape. It examines the implications of emerging quantum algorithms and cryptographic techniques such as Multi-Party Computation (MPC) and Oblivious Transfer (OT). While exploring how CBDCs and

Mingshuai Xu, Haocheng Zhong

A huge progress in studying holographic theories is that holography can be interpreted via the quantum error correction, which makes equal the entanglement wedge reconstruction, the Jafferis-Lewkowycz-Maldacena-Suh formula, the radial commutativity and the Ryu-Takayanagi formula. We call the equivalence the reconstruction theorem, whose infinite-dimensional

Mohsen Dehghankar, Mahdi Erfanian, Abolfazl Asudeh

Despite their tremendous success and versatility, Deep Neural Networks (DNNs) such as Large Language Models (LLMs) suffer from inference inefficiency and rely on advanced computational infrastructure. To address these challenges and make these models more accessible and cost-effective, in this paper, we propose algorithms to improve the inference time and me

Vishwani Singh, Rohit Verma, Shaurya Shriyam, Tapan K. Gandhi

Transcranial direct current stimulation (tDCS) has emerged as a promising non-invasive therapeutic intervention for major depressive disorder (MDD), yet its effects on neural mechanisms remain incompletely understood. This study investigates the impact of tDCS in individuals with MDD using resting-state EEG data and network neuroscience to analyze functional

Ryuya Hora

Both topos theory and automata theory are known for their multi-faceted nature and relationship with topology, algebra, logic, and category theory. This paper aims to clarify the topos-theoretic aspects of automata theory, particularly demonstrating through two main theorems how regular (and non-regular) languages arise in topos-theoretic calculation. First,

Hongkun Cao, Xin Jin, Junjie Wei, Yihui Fan

Scattering imaging is often hindered by extremely low signal-to-noise ratios (SNRs) due to the prevalence of scattering noise. Light field imaging has been shown to be effective in suppressing noise and collect more ballistic photons as signals. However, to overcome the SNR limit in super-strong scattering environments, even with light field framework, only

Peng-Ju Wu, Xin Zhang

The cosmic curvature $\Omega_{K}$ is an important parameter related to the inflationary cosmology and the ultimate fate of the universe. In this work, we adopt the non-CMB observations to constrain $\Omega_{K}$ in the $\Lambda$CDM model and its extensions. The DESI baryon acoustic oscillation, DES type Ia supernova, cosmic chronometer, and strong gravitation

Huaisong Yong

We theoretically study the reentrant condensation of a polyelectrolyte in the presence of an oppositely-charged surfactant,a phenomenon whose phase-transition mechanism remains under discussion. We focus on the adsorption and attraction effects of surfactant near/on polymer chains, and ignore their own non-essential mixing effects if surfactant molecules are

Xin Liao, Hua Chen

In this paper, we establish a Liouville theorem for solutions to the Lane Emden equation involving Baouendi Grushin operators. We focus on solutions that are stable outside a compact set. Specifically, we prove that when p is smaller than the Joseph Lundgren exponent and differs from the Sobolev exponent, 0 is the unique solution stable outside a compact set

Tian Xie, Jifan Zhang, Haoyue Bai, Robert Nowak

Machine learning models deployed in open-world scenarios often encounter unfamiliar conditions and perform poorly in unanticipated situations. As AI systems advance and find application in safety-critical domains, effectively handling out-of-distribution (OOD) data is crucial to building open-world learning systems. In this work, we introduce ALOE, a novel a

Mayank Kumar Kundalwal, Anurag Saraswat, Ishan Mishra, Deepak Mishra

Mobile devices, including smartphones and laptops, generate decentralized and heterogeneous data, presenting significant challenges for traditional centralized machine learning models due to substantial communication costs and privacy risks. Federated Learning (FL) offers a promising alternative by enabling collaborative training of a global model across dec

Maokai Hu, Lifan Wang, Xiaofeng Wang

Red supergiants may experience a short-lived period of episodic mass loss rather than steady winds before their core collapses, leading to dense circumstellar matter (CSM) close to core-collapse supernovae (SNe). Interaction of SN ejecta with such nearby CSM can generate additional radiation, appending to the cooling radiation from the shock breakout of the

Anees Ahmed, Nojan Sheybani, Davi Moreno, Nges Brian Njungle

Collision-resistant, cryptographic hash (CRH) functions have long been an integral part of providing security and privacy in modern systems. Certain constructions of zero-knowledge proof (ZKP) protocols aim to utilize CRH functions to perform cryptographic hashing. Standard CRH functions, such as SHA2, are inefficient when employed in the ZKP domain, thus ca

Tian Lin, Mattia Bruno, Xu Feng, Lu-Chang Jin

We develop a method to compute the pion transition form factor directly at arbitrary space-like photon momenta and use it to determine the $\pi^0$-pole contribution to the hadronic light-by-light scattering in the anomalous magnetic moment of the muon. The calculation is performed using eight gauge ensembles generated with 2+1 flavor domain wall fermions, in

Wenxin Li

The efficient scheduling of multi-task jobs across multiprocessor systems has become increasingly critical with the rapid expansion of computational systems. This challenge, known as Multiprocessor Multitask Scheduling (MPMS), is essential for optimizing the performance and scalability of applications in fields such as cloud computing and deep learning. In t

Yui Tatsumi, Shoko Tanaka, Shunsuke Akamatsu, Takahiro Shindo

Sign language is a visual language expressed through hand movements and non-manual markers. Non-manual markers include facial expressions and head movements. These expressions vary across different nations. Therefore, specialized analysis methods for each sign language are necessary. However, research on Japanese Sign Language (JSL) recognition is limited du

Le-Trung Nguyen, Aël Quélennec, Enzo Tartaglione, Samuel Tardieu

Internet of Things and Deep Learning are synergetically and exponentially growing industrial fields with a massive call for their unification into a common framework called Edge AI. While on-device inference is a well-explored topic in recent research, backpropagation remains an open challenge due to its prohibitive computational and memory costs compared to

Zhi Wang, Feiyu Deng

We propose a holographic dual of boundary conformal field theory (BCFT) with $T\bar T$ deformation, i.e. of $T\bar T$ BCFT. Our holographic proposal distinguishes two types of $T\bar T$ BCFTs, depending on whether the $T\bar T$ deformation deforms the boundary. For the boundary-deformed case, we find that boundary entropy serves as an effective measure to qu

Parth Parag Kulkarni, Gaurav Kumar Nayak, Mubarak Shah

Video geolocalization is a crucial problem in current times. Given just a video, ascertaining where it was captured from can have a plethora of advantages. The problem of worldwide geolocalization has been tackled before, but only using the image modality. Its video counterpart remains relatively unexplored. Meanwhile, video geolocalization has also garnered

Hailin Xu, Hongxia Wang, Huanshui Zhang

The Bundle Adjustment (BA) model is commonly optimized using a nonlinear least squares method, with the Levenberg-Marquardt (L-M) algorithm being a typical choice. However, despite the L-M algorithm's effectiveness, its sensitivity to initial conditions often results in slower convergence when applied to poorly conditioned datasets, motivating the exploratio

Lars van der Laan, Ziming Lin, Marco Carone, Alex Luedtke

Inverse weighting with an estimated propensity score is widely used by estimation methods in causal inference to adjust for confounding bias. However, directly inverting propensity score estimates can lead to instability, bias, and excessive variability due to large inverse weights, especially when treatment overlap is limited. In this work, we propose a pos

Pham Truong Xuan, Nguyen Thi Van, Tran Minh Nguyet, Nguyen Thi Loan

In this paper we investigate the existence, uniqueness and exponential stability of pseudo almost periodic (PAP-) mild solutions of the parabolic-elliptic (P-E) Keller-Segel system on a bounded domain $\Omega\in \mathbb{R}^n$ with smooth boundary. First, the well-posedness of the corresponding linear system is established by using the smoothing estimates of

Yiyue Huang, Philipp D. Loesel, David M. Paganin, Andrew M. Kingston

Ghost imaging (GI) is an unconventional technique that combines information from two correlated patterned light fields to compute an image of the object of interest. GI can be performed with visible light as well as penetrating radiation such as x-rays, electrons, etc. Penetrating radiation is usually ionizing and damages biological specimens; therefore, min

Li Shen, Yongpeng Wu, Peihong Yuan, Chengshan Xiao

A wiretap channel is served as the fundamental model of physical layer security techniques, where the secrecy capacity of the Gaussian wiretap channel is proven to be achieved by Gaussian input. However, there remains a gap between the Gaussian secrecy capacity and the secrecy rate with conventional uniformly distributed discrete constellation input, e.g. am

Junda Wang, Weijian Li, Han Wang, Hanjia Lyu

Causal inference and model interpretability are gaining increasing attention, particularly in the biomedical domain. Despite recent advance, decorrelating features in nonlinear environments with human-interpretable representations remains underexplored. In this study, we introduce a novel method called causal rule generation with target trial emulation frame

Chris McElroy, Daniel W. O'Neill

We use multi-regional input-output analysis to calculate the paid labour, energy, emissions, and material use required to provide basic needs for all people. We calculate two different low-consumption scenarios, using the UK as a case study: (1) a "decent living" scenario, which includes only the bare necessities, and (2) a "good life" scenario, based on the

Mst Rafia Islam, Azmine Toushik Wasi

AI has made significant strides recently, leading to various applications in both civilian and military sectors. The military sees AI as a solution for developing more effective and faster technologies. While AI offers benefits like improved operational efficiency and precision targeting, it also raises serious ethical and legal concerns, particularly regard

Kuntal Banerjee, Steven Rayan

We explore very stable and wobbly bundles, twisted in a particular sense by a line bundle, over complex algebraic curves of genus $1$. We verify that twisted stable bundles on an elliptic curve are not very stable for any positive twist. We utilize semistability of trivially twisted very stable bundles to prove that the wobbly locus is always a divisor in th

Senxin Wu, Jinlong Hu, Ling Zhang

Video courses have become a significant component of modern education. However, the increasing demand for live streaming video courses places considerable strain on the service capabilities of campus networks. The challenges associated with live streaming course videos in campus network environments exhibit distinct spatial distribution characteristics. The

Yunmei Chen, Lezhi Liu, Lei Zhang

This work proposes a general learned proximal alternating minimization algorithm, LPAM, for solving learnable two-block nonsmooth and nonconvex optimization problems. We tackle the nonsmoothness by an appropriate smoothing technique with automatic diminishing smoothing effect. For smoothed nonconvex problems we modify the proximal alternating linearized mini

Xuyang Huang, Han-Ze Li, Yu-Jun Zhao, Shuo Liu

In recent years, measurement induced entanglement transitions (MIETs) have attracted significant attention. However, the dynamical transition associated with the feedback induced skin effect, which exhibits a wealth of intriguing phenomena, has not been fully understood. In this work, we investigate a dynamical phase transition in a tilted free-fermion chain