November 2024 arXiv papers — page 149

Ankita Singh, Shiv Prakash, Sudhakar Singh

Wireless networking is encouraged by the constant enhancement of sensors' ability and wireless communication. To provide service quality support for multimedia viz. audio and video streams, the IEEE 802.11e MAC (Media Access Control) improves basic 802.11 MAC. IEEE 802.11 standard series such as IEEE 802.11a, b, g, n, p, and ac have been promoted and specifi

Yujia An, Rafael de la Llave, Xifeng Su, Donghua Wang

We consider one dimensional chains of interacting particles subjected to one dimensional almost-periodic media. We formulate and prove two KAM type theorems corresponding to both short-range and long-range interactions respectively. Both theorems presented have an a posteriori format and establish the existence of almost-periodic equilibria. The new part her

Krishnan Rajkumar, Shubham

We investigate the variation in the total number of points in a random $p\times p$ square in $\mathbb{Z}^2$ where the $p$-adic valuation of a given polynomial in two variables is precisely $1$. We establish that this quantity follows a Poisson distribution as $p\rightarrow\infty$ under a certain conjecture. We also relate this conjecture to certain uniform d

Rafik Aramyan

It is known that the Funk transform (the Funk-Radon transform) is invertible in the class of even (symmetric) continuous functions defined on the unit 2-sphere S^2. In this article, for the reconstruction of f from C(S^2) (can be non-even), an additional condition (to reconstruct an odd function) is found, and the injectivity of the so-called two data Funk t

Langxuan Chen, Ning Sun, Pengfei Zhang

When interacting with an environment, the entanglement within quantum many-body systems is rapidly transferred to the entanglement between the system and the bath. For systems with a large local Hilbert space dimension, this leads to a first-order entanglement transition for the reduced density matrix of the system. On the other hand, recent studies have int

Tianfeng Wu, Fang Yang, Fei Li, Renzhi Yuan

This paper tackles limitations in existing non-line-of-sight (NLoS) ultraviolet (UV) channel models, where conventional approaches assume obstacle-free propagation or uniform radiation intensity. In this paper, we develop a path loss model incorporating scattering and reflection, and then propose an obstacle-boundary approximation method to achieve computati

Haoran Zhang, Junkai Deng, Xuhui Chen, Fei Hou

Traditional 3D shape reconstruction techniques from multi-view images, such as structure from motion and multi-view stereo, face challenges in reconstructing transparent objects. Recent advances in neural radiance fields and its variants primarily address opaque or transparent objects, encountering difficulties to reconstruct both transparent and opaque obje

Zhiwei Li, Tingzhen Zhang, Meihua Zhou, Dandan Tang

During the process of driving, humans usually rely on multiple senses to gather information and make decisions. Analogously, in order to achieve embodied intelligence in autonomous driving, it is essential to integrate multidimensional sensory information in order to facilitate interaction with the environment. However, the current multi-modal fusion sensing

Chien-yu Huang, Wei-Chih Chen, Shu-wen Yang, Andy T. Liu

Multimodal foundation models, such as Gemini and ChatGPT, have revolutionized human-machine interactions by seamlessly integrating various forms of data. Developing a universal spoken language model that comprehends a wide range of natural language instructions is critical for bridging communication gaps and facilitating more intuitive interactions. However,

Alessandro Chiesa, Marcel Dall Agnol, Zijing Di, Ziyi Guan

We analyze the post-quantum security of succinct interactive arguments constructed from interactive oracle proofs (IOPs) and vector commitment schemes. We prove that an interactive variant of the BCS transformation is secure in the standard model against quantum adversaries when the vector commitment scheme is collapsing. Our proof builds on and extends prio

Tianfeng Wu, Fang Yang, Tian Cao, Ling Cheng

Existing studies on ultraviolet (UV) non-line-of-sight (NLoS) channel modeling primarily focus on scenarios without any obstacle, which makes them unsuitable for small transceiver elevation angles in most cases. To address this issue, a UV NLoS channel model incorporating an obstacle was investigated in this paper, where the impacts of atmospheric scattering

Yu Yuan

We survey quadratic Hessian equations: definition, background, rigidity of entire solutions, regularity of viscosity solutions, a priori Hessian estimates, and open problems.

Hankyeol Lee, Gawon Seo, Wonseok Choi, Geunyoung Jung

The performance of vision-language models (VLMs), such as CLIP, in visual classification tasks, has been enhanced by leveraging semantic knowledge from large language models (LLMs), including GPT. Recent studies have shown that in zero-shot classification tasks, descriptors incorporating additional cues, high-level concepts, or even random characters often o

Xi Wang, Hailou Yao, Lei Shen

We study the $\lambda$-pure global dimension of a Grothendieck category $\cal A$, and provide two different applications about this dimension. We obtain that if the $\lambda$-pure global dimension $\plgldA<\infty$, then (1) The ordinary bounded derived category (where $\cal A$ has enough projective objects) and the bounded $\lambda$-pure one differ only by a

Marlon J. Ares-Milian, Gregory Provan, Marcos Quinones-Grueiro

Control auto-tuning for industrial and robotic systems, when framed as an optimization problem, provides an excellent means to tune these systems. However, most optimization methods are computationally costly, and this is problematic for high-dimension control parameter spaces. In this paper, we present a multi-stage control tuning framework that decomposes

Xingyu Ai, Bin Huang, Fang Chen, Liu Shi

Recent advances in diffusion models have demonstrated exceptional performance in generative tasks across vari-ous fields. In positron emission tomography (PET), the reduction in tracer dose leads to information loss in sino-grams. Using diffusion models to reconstruct missing in-formation can improve imaging quality. Traditional diffu-sion models effectively

Ahmed Salah, David Yevick

This paper demonstrates that grokking behavior in modular arithmetic with a modulus P in a neural network can be controlled by modifying the profile of the activation function as well as the depth and width of the model. Plotting the even PCA projections of the weights of the last NN layer against their odd projections further yields patterns which become si

Manju Perumbil, Matthew J. Blacker, Stuart S. Szigeti, Simon A. Haine

We investigate the use of an atomic Fabry-Perot interferometer (FPI) with a pulsed non-interacting Bose-Einstein condensate (BEC) source as a space-based acceleration sensor. We derive an analytic approximation for the device's transmission under a uniform acceleration, which we use to compute the device's attainable acceleration sensitivity using the classi

Zhaohui Dong, Xianfeng Chen, Luqi Yuan

The Moir\'e superlattice has attracted growing interest in the electromagnetic and optical communities. Here, we extend this concept to time-varying photonic systems by superposing two binary modulations on the refractive index with different modulation periods, i.e., the Moir\'e photonic time crystal (PTC). Such a Moir\'e PTC leads to extreme narrow bands i

Jock McOrist, Martin Sticka, Eirik Eik Svanes

We study the moduli of the universal geometry of $d=4$ $N=1$ heterotic vacua. Universal geometry refers to a family of heterotic vacua fibered over the moduli space. The universal geometry mimics aspects of the original heterotic vacua, in particular holomorphic data such as F-terms, as well as the Green-Schwarz Bianchi identity. Here we study first order de

Honghao Shi, Longkai Cheng, Wenli Wu, Yuhang Wang

Recent advancements in Large Language Models (LLMs) and related technologies such as Retrieval-Augmented Generation (RAG) and Diagram of Thought (DoT) have enabled the creation of autonomous intelligent systems capable of performing cluster diagnostics and troubleshooting. By integrating these technologies with self-play methodologies, we have developed an L

Dongguo Zhang, Fei Sun, Yichao Liu

To address the increasingly serious issue of noise pollution, we propose an ultra-broadband and wide-angle acoustic absorber based on a periodic acoustic rigid-metaporous composite array. Numerical simulation results verify the broadband good acoustic absorption performance of the proposed absorber, which can achieve an average absorption coefficient of appr

Zongyuan Li, Yanan Ni, Runnan Qi, Lumin Jiang

The tremendous potential has been demonstrated by large language models (LLMs) in intelligent decision-making problems, with unprecedented capabilities shown across diverse applications ranging from gaming AI systems to complex strategic planning frameworks. However, the StarCraft II platform, which has been widely adopted for validating decision-making algo

Minsu Yi, Dongju Lee, Panayotis Benetatos

The freely jointed chain model with reversible hinges (rFJC) is the simplest theoretical model that captures reversible transitions of the local bending stiffness along the polymer chain backbone, e.g. helix-coil-type of local conformational changes or changes due to the binding/unbinding of ligands). In this work, we analyze the bending fluctuations and the

Pochun Li, Yuyang Xiao, Jinghua Yan, Xuan Li

This study presents a novel computer system performance optimization and adaptive workload management scheduling algorithm based on Q-learning. In modern computing environments, characterized by increasing data volumes, task complexity, and dynamic workloads, traditional static scheduling methods such as Round-Robin and Priority Scheduling fail to meet the d

Esther Gan, Yiran Zhao, Liying Cheng, Yancan Mao

Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning using Chain-of-Thought (CoT) prompting. However, CoT can be biased by users' instruction. In this work, we study the reasoning robustness of LLMs to typographical errors, which can naturally occur in users' queries. We design an Adversarial Typo Attack ($\texttt{ATA}$) algori

Quang Truong Nguyen, Thanh Nguyen Canh, Xiem HoangVan

Depth information plays a crucial role in autonomous systems for environmental perception and robot state estimation. With the rapid development of deep neural network technology, depth estimation has been extensively studied and shown potential for practical applications. However, in particularly challenging environments such as low-light and noisy underwat

Joshua Enwright, Fernando Figueroa

In this article, we give a characterization of log Calabi--Yau pairs of complexity zero and arbitrary index. As an application, we show that a log Calabi--Yau pair of birational complexity zero admits a crepant birational model which is a generalized Bott tower.

Thanh Nguyen Canh, Ba Phuong Nguyen, Hong Quan Tran, Xiem HoangVan

In this paper, we propose the development of an interactive platform between humans and a dual-arm robotic system based on the Robot Operating System (ROS) and a multimodal artificial intelligence model. Our proposed platform consists of two main components: a dual-arm robotic hardware system and software that includes image processing tasks and natural lang

M. Kosuda, M. Oura, Sarbaini

There are two permutation groups that they share the same character table of order 1344. We take up natural representations on 8 and 14 letters respectively. The purpose of this paper is to examine the semi-simple structure of centralizing algebras in the tensor representation.

Dharmendra Prajapat, Durga Toshniwal

Task-oriented dialogue (TOD) system is designed to accomplish user-defined tasks through dialogues. The TOD system has progressed towards end-to-end modeling by leveraging pre-trained large language models. Fine-tuning the pre-trained language models using only supervised learning leads to the exposure bias and token loss problem and it deviates the models f

Rohin E. McIntosh, Arthur Goetschy, Nicholas Bender, Alexey Yamilov

The maximum deposition eigenchannel provides the largest possible power delivery to a target region inside a diffusive medium by optimizing the incident wavefront of a monochromatic beam. It originates from constructive interference of scattered waves, which is frequency sensitive. We investigate the spectral width of maximum deposition eigenchannels over a

Shruti Singh, Nandan Sarkar, Arman Cohan

Scientific literature is typically dense, requiring significant background knowledge and deep comprehension for effective engagement. We introduce SciDQA, a new dataset for reading comprehension that challenges LLMs for a deep understanding of scientific articles, consisting of 2,937 QA pairs. Unlike other scientific QA datasets, SciDQA sources questions fro

Thanh Nguyen Canh, Duc Manh Do, Xiem HoangVan

Localization and navigation are two crucial issues for mobile robots. In this paper, we propose an approach for localization and navigation systems for a differential-drive robot based on monocular SLAM. The system is implemented on the Robot Operating System (ROS). The hardware includes a differential-drive robot with an embedded computing platform (Jetson

Mohammed Aledhari, Mohamed Rahouti, Ali Alfatemi

Autism Spectrum Disorder (ASD) is often underdiagnosed in females due to gender-specific symptom differences overlooked by conventional diagnostics. This study evaluates machine learning models, particularly Random Forest and convolutional neural networks, for enhancing ASD diagnosis through structured data and facial image analysis. Random Forest achieved 1

Awanish Pandey, Alex Krasnok

Backscattering in micro-ring cavities induces mode mixing and limits device performance. Existing methods to mitigate backscattering often involve complex fabrication processes or are insufficient for complete suppression. In this work, we introduce a novel method to eliminate backscattering by operating the cavity at an exceptional point (EP). By engineerin

Wentang Song, Zhiyuan Yan, Yuzhen Lin, Taiping Yao

Detecting AI-generated images, particularly deepfakes, has become increasingly crucial, with the primary challenge being the generalization to previously unseen manipulation methods. This paper tackles this issue by leveraging the forgery quality of training data to improve the generalization performance of existing deepfake detectors. Generally, the forgery

Tian-Xiao He

In this paper, we define double almost-Riordan arrays and find that the set of all double almost-Riordan arrays forms a group, called the double almost-Riordan group. We also obtain the sequence characteristics of double almost-Riordan arrays and give the production matrices for double almost-Riordan arrays. We define the compression of double almost-Riordan

Xuan Wu, Qian Gong, Jieyang Chen, Qing Liu

The unprecedented amount of scientific data has introduced heavy pressure on the current data storage and transmission systems. Progressive compression has been proposed to mitigate this problem, which offers data access with on-demand precision. However, existing approaches only consider precision control on primary data, leaving uncertainties on the quanti

Yuqing He, Guanyi Wang, Yu Yang

Sparse Principal Component Analysis (sparse PCA) is a fundamental dimension-reduction tool that enhances interpretability in various high-dimensional settings. An important variant of sparse PCA studies the scenario when samples are adversarially perturbed. Notably, most existing statistical studies on this variant focus on recovering the ground truth and ve

Kun Wang, Sumanth Varambally, Duncan Watson-Parris, Yi-An Ma

Many important phenomena in scientific fields like climate, neuroscience, and epidemiology are naturally represented as spatiotemporal gridded data with complex interactions. Inferring causal relationships from these data is a challenging problem compounded by the high dimensionality of such data and the correlations between spatially proximate points. We pr

Jaewon Chu, Jinyoung Park, Seunghun Lee, Hyunwoo J. Kim

Latent Bayesian optimization (LBO) approaches have successfully adopted Bayesian optimization over a continuous latent space by employing an encoder-decoder architecture to address the challenge of optimization in a high dimensional or discrete input space. LBO learns a surrogate model to approximate the black-box objective function in the latent space. Howe

Rahul Gulati, Shiva Rudraraju

Electrical signaling in the brain plays a vital role to our existence but at the same time, the fundamental mechanism of this propagation is undeciphered. Notable advancements have been made in the numerical modeling supplementing the related experimental findings. Cable theory based models provided a significant breakthrough in understanding the mechanism o

Teng Ye, Hanson Yan, Xuhuan Huang, Connor Grogan

With the rise of social media and peer-to-peer networks, users increasingly rely on crowdsourced responses for information and assistance. However, the mechanisms used to rank and promote responses often prioritize and end up biasing in favor of timeliness over quality, which may result in suboptimal support for help-seekers. We analyze millions of responses

Bingjie Hao, Elijah Platnick, István A. Kovács

Social networks inherently exhibit complex relationships that can be positive or negative, as well as directional. Understanding balance in these networks is crucial for unraveling social dynamics, yet traditional theories struggle to incorporate directed interactions. This perspective presents a comprehensive roadmap for understanding balance in signed dire

Kenichiro Takagi, Naoki Moriya, Shiori Aoki, Katsuhiro Endo

We investigate the possibility and current limitations of flow computations using quantum annealers by solving a fundamental flow problem on Ising machines. As a fundamental problem, we consider the one-dimensional advection-diffusion equation. We formulate it in a form suited to Ising machines (i.e., both classical and quantum annealers), perform extensive

Zhifeng Wang, Jiaqin Wan, Yang Yang, Chunyan Zeng

With the continuous deepening and development of the concept of smart education, learners' comprehensive development and individual needs have received increasing attention. However, traditional educational evaluation systems tend to assess learners' cognitive abilities solely through general test scores, failing to comprehensively consider their actual know

Yuanyuan Liu, Lin Wei, Kejun Liu, Yibing Zhan

Emotion Recognition (ER) is the process of identifying human emotions from given data. Currently, the field heavily relies on facial expression recognition (FER) because facial expressions contain rich emotional cues. However, it is important to note that facial expressions may not always precisely reflect genuine emotions and FER-based results may yield mis

Weijie Chen, Alan McMillan

This paper introduces an efficient sub-model ensemble framework aimed at enhancing the interpretability of medical deep learning models, thus increasing their clinical applicability. By generating uncertainty maps, this framework enables end-users to evaluate the reliability of model outputs. We developed a strategy to develop diverse models from a single we

Ming Chen, Muhammed Tawfiqul Islam, Maria Rodriguez Read, Rajkumar Buyya

The transition from monolithic architecture to microservices has enhanced flexibility in application design and its scalable execution. This approach typically uses a computing cluster managed by a container orchestration platform to deploy microservices. However, this shift introduces significant challenges, particularly in the efficient scheduling of conta

Zhiyu Zhang, Guo Lu, Huanxiong Liang, Zhengxue Cheng

The neural radiance fields (NeRF) have advanced the development of 3D volumetric video technology, but the large data volumes they involve pose significant challenges for storage and transmission. To address these problems, the existing solutions typically compress these NeRF representations after the training stage, leading to a separation between represent

Aaron Holman, Yuan Xu, Ximo Sun, Jiahao Wu

Optical tweezer arrays have emerged as a key experimental platform for quantum computation, quantum simulation, and quantum metrology, enabling unprecedented levels of control over single atoms and molecules. However, existing tweezer platforms have fundamental limitations in array geometry, size, and scalability. Here we demonstrate the trapping of single s

Zexin Fang, Bin Han, Wenwen Chen, Hans D. Schotten

Unauthorized sensing activities pose an increasing threat to individual privacy, yet effective countermeasures remain underdeveloped. This paper presents a novel methodology to characterize and counter such unauthorized surveillance. We model pedestrian trajectories as a random process and leverage the Cramer-Rao bound (CRB) to evaluate sensing performance,

Morgan Hedges, Ankit Papneja, Karun Paul, Ben C Buchler

Alkali-noble-gas comagnetometers have become an essential tool for tests of fundamental physics and offer a compact platform for precision gyroscopy. They are, however, limited by technical noise at low frequencies, commonly due to their limited suppression of magnetic noise. Here we investigate a new method for co-magnetometry between a single noble gas and

Yanhui Zhu, Samik Basu, A. Pavan

Submodular optimization has become increasingly prominent in machine learning and fairness has drawn much attention. In this paper, we propose to study the fair $k$-submodular maximization problem and develop a $\frac{1}{3}$-approximation greedy algorithm with a running time of $\mathcal{O}(knB)$. To the best of our knowledge, our work is the first to incorp

Yanxin Zheng, Wensheng Gan, Zefeng Chen, Pinlyu Zhou

In recent years, data mining technologies have been well applied to many domains, including e-commerce. In customer relationship management (CRM), the RFM analysis model is one of the most effective approaches to increase the profits of major enterprises. However, with the rapid development of e-commerce, the diversity and abundance of e-commerce data pose a

Dong Shu, Bingbing Duan, Kai Guo, Kaixiong Zhou

Latent representation alignment has become a foundational technique for constructing multimodal large language models (MLLM) by mapping embeddings from different modalities into a shared space, often aligned with the embedding space of large language models (LLMs) to enable effective cross-modal understanding. While preliminary protein-focused MLLMs have eme

Ziwei Su, Diego Klabjan

Stochastic simulation models are generative models that mimic complex systems to help with decision-making. The reliability of these models heavily depends on well-calibrated input model parameters. However, in many practical scenarios, only output-level data are available to learn the input model parameters, which is challenging due to the often intractable

T. Nordlander, M. Baratella, L. Spina, V. D'Orazi

Spectroscopic analyses of young late-type stars suffer from systematic inaccuracies, typically under-estimating metallicities but over-estimating abundances of certain elements including oxygen and barium. Effects are stronger in younger and cooler stars, and recent evidence specifically indicates a connection to the level of chromospheric activity. We prese

Hamza Ahmed, Paul-Konstantin Oehlmann, Fabian Ruehle

In this work, we significantly expand the web of T-dualities among heterotic NS5-brane theories with eight supercharges. This is achieved by introducing twists involving outer automorphisms of discrete gauge/flavor factors and tensor multiplet permutations along the compactification circle. We assemble field theory data that we propose as invariants across T

Abdullah Al Asif, Farhana Chowdhury Tisha

One of the deadliest pandemics is now happening in the current world due to COVID-19. This contagious virus is spreading like wildfire around the whole world. To minimize the spreading of this virus, World Health Organization (WHO) has made protocols mandatory for wearing face masks and maintaining 6 feet physical distance. In this paper, we have developed a

Tao Ma, Hongbin Zhou, Qiusheng Huang, Xuemeng Yang

Offboard perception aims to automatically generate high-quality 3D labels for autonomous driving (AD) scenes. Existing offboard methods focus on 3D object detection with closed-set taxonomy and fail to match human-level recognition capability on the rapidly evolving perception tasks. Due to heavy reliance on human labels and the prevalence of data imbalance

Issei Suzuki, Kako Washizu, Daiki Motai, Masao Kita

This study introduces a new method for synthesizing Cu+-containing metastable phases through ion exchange. Traditionally, CuCl has been used as a Cu+ ion source for solid-state ion exchanges; however, its thermodynamic driving force is often insufficient for complete ion exchange with Li+-containing precursors. First-principles calculations have identified C

Nurlan Nazaraliyev, Elaheh Sadredini, Nael Abu-Ghazaleh

Graphics Processing Units (GPUs) leverage massive parallelism and large memory bandwidth to support high-performance computing applications, such as multimedia rendering, crypto-mining, deep learning, and natural language processing. These applications require models and datasets that are getting bigger in size and currently challenge the memory capacity of

Fan Yang, Zhida Zhou, Chaolong Jiang

In this paper, a novel high-order, mass and energy-conserving scheme is proposed for the regularized logarithmic Schr\"{o}dinger equation(RLogSE). Based on the idea of the supplementary variable method (SVM), we firstly reformulate the original system into an equivalent form by introducing two supplementary variables, and the resulting SVM reformulation is t

Sien Li, Tao Wang, Ruizhe Hu, Wenxi Liu

In semi-supervised semantic segmentation (SSS), weak-to-strong consistency regularization techniques are widely utilized in recent works, typically combined with input-level and feature-level perturbations. However, the integration between weak-to-strong consistency regularization and network perturbation has been relatively rare. We note several problems wi

Chen Ling, Chenjian Pan, Liqun Qi

Solving dual quaternion equations is an important issue in many fields such as scientific computing and engineering applications. In this paper, we first introduce a new metric function for dual quaternion matrices. Then, we reformulate dual quaternion overdetermined equations as a least squares problem, which is further converted into a bi-level optimizatio

Pengzhe Xin, Yang Cao, Yue Wu, Dongming Wang

Cell-free massive multiple-input-multiple-output (CF-mMIMO) is regarded as one of the promising technologies for next-generation wireless networks. However, due to its distributed architecture, geographically separated access points (APs) jointly serve a large number of user-equipments (UEs), there will inevitably be a discrepancies in the arrival time of tr

Tianfeng Wu, Fang Yang, Tian Cao, Ling Cheng

As transceiver elevation angles increase from small to large, existing ultraviolet (UV) non-line-of-sight (NLoS) models encounter two challenges: i) cannot estimate the channel characteristics of UV NLoS communication scenarios when there exists an obstacle in the overlap volume between the transmitter beam and the receiver field-of-view (FoV), and ii) canno

Yuxiang Liu, Ligong Wang

A graph $G$ is $F$-free if $G$ does not contain $F$ as a subgraph. Let $\rho(G)$ be the spectral radius of a graph $G$. Let $\theta(1,p,q)$ denote the theta graph, which is obtained by connecting two distinct vertices with three internally disjoint paths with lengths $1, p, q$, where $p\leq q$. Let $S_{n,k}$ denote the graph obtained by joining every vertex

Malik Wagih, Yannick Naunheim, Tianjiao Lei, Christopher A. Schuh

Tailoring the nanoscale distribution of chemical species at grain boundaries is a powerful method to dramatically influence the properties of polycrystalline materials. However, classical approaches to the problem have tacitly assumed that only competition is possible between solute species. In this paper, we show that solute elements can cooperate in the wa

Boxiao Yu, Kuang Gong

Positron Emission Tomography (PET) is a vital imaging modality widely used in clinical diagnosis and preclinical research but faces limitations in image resolution and signal-to-noise ratio due to inherent physical degradation factors. Current deep learning-based denoising methods face challenges in adapting to the variability of clinical settings, influence

Michael Hite, Yannick Meurice

We introduce an approach for approximate real-time evolution of quantum systems using Tensor Renormalization Group (TRG) methods originally developed for imaginary time. We use Higher- Order TRG (HOTRG) to generate a coarse-grained time evolution operator for a 1+1D Transverse Ising Model with a longitudinal field. We show that it is effective and efficient

Saikatul Haque, Rowan Killip, Monica Visan, Yunfeng Zhang

We establish global well-posedness for both the defocusing and focusing complex-valued modified Korteweg--de Vries equations on the real line in modulation spaces $M_p^{s,2}(\mathbb{R})$, for all $1\leq p<\infty$ and $0\leq s<3/2-1/p$. We will also show that such solutions admit global-in-time bounds in these spaces and that equicontinuous sets of initial da

Chul Chung, Seunghyun Park, Junhyuk Son, Hyejeon Cho

Type Ia supernovae (SNe Ia) serve as the most crucial standardizable candles in cosmology, providing direct measurements of the universe's expansion history. However, it is well-known that the post-standardization brightness of SNe Ia is influenced by the properties of their host galaxies, such as mass and star formation rate, both of which are closely relat

Dongjun Li, Qiuhao Hu, Weiran Jiang, Haoxuan Dong

Effective power and thermal management are essential for ensuring battery efficiency, safety, and longevity in Connected and Automated Electric Vehicles (CAEVs). However, real-time implementation is challenging due to the multi-timescale dynamics and complex trade-offs between energy consumption, battery degradation, traffic efficiency, and thermal regulatio

Ekaterina Bogdanova

We define the stack of $G$-local systems with restricted variation on the formal puntured disc and study its properties. We embed sheaves of categories over this stack into the category of factorization module categories over $\operatorname{Rep}(G)$. Along the way we develop a theory of factorization structures in families and study functorialities of such u

Shairoz Sohail

Kolmogorov-Arnold Networks have recently been introduced as a flexible alternative to multi-layer Perceptron architectures. In this paper, we examine the training dynamics of different KAN architectures and compare them with corresponding MLP formulations. We train with a variety of different initialization schemes, optimizers, and learning rates, as well as

Elijah Pelofske

The Quantum Alternating Operator Ansatz, a generalization of the Quantum Approximate Optimization Algorithm (QAOA), is a quantum algorithm used for approximately solving combinatorial optimization problems. QAOA typically uses the Transverse field mixer as the driving Hamiltonian. One of the interesting properties of the Transverse-field driving Hamiltonian

Xunwu Hu, Dao-Xin Yao, Kun Cao

Controlling magnetic anisotropy energy (MAE) in two-dimensional (2D) ferromagnetic materials is crucial for designing novel spintronic devices. Using first-principles calculations, we systematically investigate the magnetic properties of monolayer Fe$_5$GeTe$_2$ (F5GT) under two scenarios: (I) Co and Ni doping, and (II) compressive and tensile strains. Our r

Yun Zhao, Zhan Gong, Peiru Zheng, Hong Zhu

More and more research works fuse the LiDAR and camera information to improve the 3D object detection of the autonomous driving system. Recently, a simple yet effective fusion framework has achieved an excellent detection performance, fusing the LiDAR and camera features in a unified bird's-eye-view (BEV) space. In this paper, we propose a LiDAR-camera fusio

Zhiyu Zhou, Ziwen Wang, Xiyu Chen, Jia-Yi Lu

Narrow-gap rare-earth Zintl phases frequently exhibit fascinating physical phenomena due to their various crystal structures, complex magnetic properties, and tunable transport behaviors. Here we report the synthesis, magnetic, thermodynamic, and transport properties of a Eu-containing Zintl arsenide, Eu$_{11}$Zn$_6$As$_{12}$, which consists of infinite chai

Jaegeun Park, Chanho Kim, Hongjun An, Zorawar Wadiasingh

Spectrally hard X-ray emission with double-peak light curves (LCs) and orbitally modulated gamma rays have been observed in some millisecond pulsar binaries, phenomena attributed to intrabinary shocks (IBSs). While the existing IBS model by Sim, An, and Wadiasingh (2024) successfully explains these high-energy features observed in three pulsar binaries, it n

Ryan Sun, Tianyi Zhou, Xun Chen, Lichao Sun

Large Language Models (LLMs) have become essential in advancing natural language processing (NLP) tasks, but their sequential token generation limits inference speed. Multi-Draft Speculative Decoding (MDSD) offers a promising solution by using a smaller draft model to generate multiple token sequences, which the target LLM verifies in parallel. However, curr

Man Tsung Yeung, Penghui Qi, Min Lin, Xinyi Wan

Pipeline parallelism is widely used to scale the training of transformer-based large language models, various works have been done to improve its throughput and memory footprint. In this paper, we address a frequently overlooked issue: the vocabulary layers can cause imbalanced computation and memory usage across pipeline stages, worsening pipeline bubbles a

Parmita Mondal, Allison Shields, Mohammad Mahdi Shiraz Bhurwani, Kyle A Williams

In Angiographic Parametric Imaging (API), accurate estimation of parameters from Time Density Curves (TDC) is crucial. However, these estimations are often marred by errors arising from factors such as patient motion, procedural preferences, image noise, and injection variability. While fitting methods like gamma-variate fitting offer a solution to recover i

Hamidreza Samadi, Md Manjurul Ahsan, Shivakumar Raman

Metrology, the science of measurement, plays a key role in Advanced Manufacturing (AM) to ensure quality control, process optimization, and predictive maintenance. However, it has often been overlooked in AM domains due to the current focus on automation and the complexity of integrated precise measurement systems. Over the years, Digital Twin (DT) technolog

Liming Dong, Qinghua Lu, Liming Zhu

Large language model (LLM) agents have demonstrated remarkable capabilities across various domains, gaining extensive attention from academia and industry. However, these agents raise significant concerns on AI safety due to their autonomous and non-deterministic behavior, as well as continuous evolving nature . From a DevOps perspective, enabling observabil

Parmita Mondal, Mohammed Mahdi Shiraz Bhurwani, Kyle A Williams, Ciprian N Ionita

During intracranial aneurysm (IA) treatment with Diverters (FDs), the device/parent artery diameters ratio may influence the ability of the device to induce aneurysm healing response. Oversized FDs are safer to deploy but may not induce enough hemodynamic resistance to ensure aneurysm occlusion. Methods based on Computational Fluid Dynamics (CFD) could allow

Binhan Lu, Zhaoyun Chen, Yuchun Wu

Quantum cloud platforms, which rely on Noisy Intermediate-Scale Quantum (NISQ) devices, face significant challenges in efficiently managing quantum programs. This paper proposes a QPU Scheduling and Resource Allocation (QSRA) approach to address these challenges. QSRA enhances qubit utilization and reduces turnaround time by adapting CPU scheduling technique

Akshat Ramachandran, Souvik Kundu, Tushar Krishna

Quantization of foundational models (FMs) is significantly more challenging than traditional DNNs due to the emergence of large magnitude values called outliers. Existing outlier-aware algorithm-architecture co-design techniques either use mixed-precision, retaining outliers at high precision but compromise hardware efficiency, or quantize inliers and outlie

Zijian Hu, Jipeng Zhang, Rui Pan, Zhaozhuo Xu

We present Fox-1, a series of small language models (SLMs) consisting of Fox-1-1.6B and Fox-1-1.6B-Instruct-v0.1. These models are pre-trained on 3 trillion tokens of web-scraped document data and fine-tuned with 5 billion tokens of instruction-following and multi-turn conversation data. Aiming to improve the pre-training efficiency, Fox-1-1.6B model introdu

T. Taniguchi, K. Osato, H. Okabe, T. Kitazawa

YbCu4Au is a unique material exhibiting multiple quantum fluctuations simultaneously. In this study, we investigated the field-induced criticality in YbCu4Au, based on comprehensive micro and macro measurements, including powder X-ray diffraction (XRD), neutron powder diffraction (NPD), nuclear magnetic resonance, magnetization, resistivity, specific heat, m

Lin Yang, Ganesh Iyer, Baichuan Lou, Sri Harsha Turlapati

Path planning in narrow passages is a challenging problem in various applications. Traditional planning algorithms often face challenges in complex environments like mazes and traps, where narrow entrances require special orientation control for successful navigation. In this work, we present a novel approach that combines superquadrics (SQ) representation a

Zhen Gao, Xingyu Zhou, Boyu Ning, Yu Su

The advent of ultra-massive multiple-input-multiple output systems holds great promise for next-generation communications, yet their channels exhibit hybrid far- and near- field beam-squint (HFBS) effect. In this paper, we not only overcome but also harness the HFBS effect to propose an integrated location sensing and communication (ILSC) framework. During t

Saksham Rastogi, Danish Pruthi

Amidst rising concerns about the internet being proliferated with content generated from language models (LMs), watermarking is seen as a principled way to certify whether text was generated from a model. Many recent watermarking techniques slightly modify the output probabilities of LMs to embed a signal in the generated output that can later be detected. S

Sajal Regmi, Chetan Phakami Pun

Large Language Models (LLMs), such as GPT, have revolutionized artificial intelligence by enabling nuanced understanding and generation of human-like text across a wide range of applications. However, the high computational and financial costs associated with frequent API calls to these models present a substantial bottleneck, especially for applications lik

Yasra Chandio, Victoria Interrante, Fatima M. Anwar

Distractions in mixed reality (MR) environments can significantly influence user experience, affecting key factors such as presence, reaction time, cognitive load, and Break in Presence (BIP). Presence measures immersion, reaction time captures user responsiveness, cognitive load reflects mental effort, and BIP represents moments when attention shifts from t

Kai Zhao, Xuhao Li, Qiyu Kang, Feng Ji

We introduce the Distributed-order fRActional Graph Operating Network (DRAGON), a novel continuous Graph Neural Network (GNN) framework that incorporates distributed-order fractional calculus. Unlike traditional continuous GNNs that utilize integer-order or single fractional-order differential equations, DRAGON uses a learnable probability distribution over

Shuchang Lyu, Qi Zhao, Guangliang Cheng, Yiwei He

Unsupervised Domain Adaptation for Remote Sensing Semantic Segmentation (UDA-RSSeg) addresses the challenge of adapting a model trained on source domain data to target domain samples, thereby minimizing the need for annotated data across diverse remote sensing scenes. This task presents two principal challenges: (1) severe inconsistencies in feature represen