November 2024 arXiv papers — page 120

Huiyu Kang, Anran Zhou, Ying Zhang, Xiuqi Wu

We reveal a new scenario for the transition of solitons to chaos in a mode-locked fiber laser: the modulated subharmonic route. Its universality is confirmed in two different laser configurations, namely, a figure-of-eight and a ring laser. Numerical simulations of the laser models agree well with the experiments. The modulated subharmonic route to chaos cou

X. T. Yan, S. Kannan, Y. S. Athreya, A. G. Truscott

We propose a Bell test experiment using momentum-entangled atom pairs of different masses, specifically metastable helium isotopes 3He* and 4He*, though the method extends to other atom species. Entanglement is generated via collisions, after which the quantum states are manipulated using two independent atom interferometers, enabling precise phase control o

Neelkamal Bhuyan, Debankur Mukherjee, Adam Wierman

We study the multi-agent Smoothed Online Convex Optimization (SOCO) problem, where $N$ agents interact through a communication graph. In each round, each agent $i$ receives a strongly convex hitting cost function $f^i_t$ in an online fashion and selects an action $x^i_t \in \mathbb{R}^d$. The objective is to minimize the global cumulative cost, which include

Agnese Marcato, Javier E. Santos, Aleksandra Pachalieva, Kai Gao

Understanding material failure is critical for designing stronger and lighter structures by identifying weaknesses that could be mitigated. Existing full-physics numerical simulation techniques involve trade-offs between speed, accuracy, and the ability to handle complex features like varying boundary conditions, grid types, resolution, and physical models.

Prajnyajit Mohanty, Umesh C. Pati, Kamalakanta Mahapatra, Saraju P. Mohanty

Consumer lighting plays a significant role in the development of smart cities and smart villages. With the advancement of (IoT) technology, smart lighting solutions have become more prevalent in residential areas as well. These solutions provide consumers with increased energy efficiency, added convenience, and improved security. On the other hand, the growi

Tingxuan Han, Ke Zhu, Hanzhong Liu, Ke Deng

The presence of interference renders classic Fisher randomization tests infeasible due to nuisance unknowns. To address this issue, we propose imputing the nuisance unknowns and computing Fisher randomization p-values multiple times, then averaging them. We term this approach the imputation-based randomization test and provide theoretical results on its asym

Daniel R. Hawtin

A code ${\mathcal C}$ is a subset of the vertex set of a Hamming graph $H(n,q)$, and ${\mathcal C}$ is $2$-neighbour-transitive if the automorphism group $G={\rm Aut}({\mathcal C})$ acts transitively on each of the sets ${\mathcal C}$, ${\mathcal C}_1$ and ${\mathcal C}_2$, where ${\mathcal C}_1$ and ${\mathcal C}_2$ are the (non-empty) sets of vertices that

Liliana Harding, Ciprian Panzaru

This study explores how refugees' destination preferences evolve during transit, with a focus on Central and Eastern Europe, particularly Romania. Using a mixed-methods approach, we analyse data from the International Organization for Migration's (IOM) Flow Monitoring Surveys and complement it with qualitative insights from focus group discussions with refug

Tianshi Feng, Jiedong Wang, Ethan Sun, Antonio Di Buono

With the increasing frequency, intensity, and duration of extreme heat events due to climate change, heat-related diseases or even mortality have become more prevalent. An efficient personal cooling strategy can mitigate heat stress by regulating the skin temperature within the thermal comfort zone. However, lightweight, wearable, and sustainable cooling gar

Shangfeng Chen, Xiayang Shi, Pu Li, Yinlin Li

Large language models (LLMs) have demonstrated remarkable proficiency in machine translation (MT), even without specific training on the languages in question. However, translating rare words in low-resource or domain-specific contexts remains challenging for LLMs. To address this issue, we propose a multi-step prompt chain that enhances translation faithful

Jingyi Zhou, Senlin Luo, Haofan Chen

Emotion and personality are central elements in understanding human psychological states. Emotions reflect an individual subjective experiences, while personality reveals relatively stable behavioral and cognitive patterns. Existing affective computing datasets often annotate emotion and personality traits separately, lacking fine-grained labeling of micro-e

Dhananjaya Mahapatra, Abu Bakkar Miah, HareKrishna Bhunia, Soumik Aon

We investigate the effect of orbital current arising from orbital Hall effect in thin films of Nb and Ti in ohmic contact with ferromagnetic Ni in the second harmonic longitudinal and transverse voltages in response to an a.c. current applied to the bilayer structures. Our experiments were analogous to those on Heavy Metal-Ferromagnet bilayers and we extract

Fan Chen, Xiying Yuan

A graph is said to be $F$-free if it does not contain $F$ as a subgraph. Brualdi-Hoffman-Tur\'{a}n problem seeks to determine the maximum spectral radius of an $F$-free graph with given size. The gem consists of a path on $4$ vertices, along with an additional vertex that is adjacent to every vertex of the path. Concerning Brualdi-Hoffman-Tur\'{a}n problem o

Shayekh Bin Islam, Ridwanul Hasan Tanvir, Sihat Afnan

Bangla is the seventh most spoken language by a total number of speakers in the world, and yet the development of an automated grammar checker in this language is an understudied problem. Bangla grammatical error detection is a task of detecting sub-strings of a Bangla text that contain grammatical, punctuation, or spelling errors, which is crucial for devel

Christopher Wang, Adam Uri Yaari, Aaditya K Singh, Vighnesh Subramaniam

We present the Brain Treebank, a large-scale dataset of electrophysiological neural responses, recorded from intracranial probes while 10 subjects watched one or more Hollywood movies. Subjects watched on average 2.6 Hollywood movies, for an average viewing time of 4.3 hours, and a total of 43 hours. The audio track for each movie was transcribed with manual

Shidong Jiang, Hai Zhu

A high-order quadrature scheme is constructed for the evaluation of Laplace single and double layer potentials and their normal derivatives on smooth surfaces in three dimensions. The construction begins with a harmonic approximation of the density on each patch, which allows for a natural harmonic polynomial extension in a volumetric neighborhood of the pat

Jinbo Wen, Jiawen Kang, Dusit Niyato, Yang Zhang

Data augmentation as a technique can mitigate data scarcity in machine learning. However, owing to fundamental differences in wireless data structures, traditional data augmentation techniques may not be suitable for wireless data. Fortunately, Generative Artificial Intelligence (GenAI) can be an effective solution to wireless data augmentation due to its ex

Zhimin Chen, Bing Li

Semi-supervised learning (SSL) leverages limited labeled and abundant unlabeled data but often faces challenges with data imbalance, especially in 3D contexts. This study investigates class-level confidence as an indicator of learning status in 3D SSL, proposing a novel method that utilizes dynamic thresholding to better use unlabeled data, particularly from

Neely Lovvorn, Oscar Murillo-Espinoza, Adam Sheffer

We prove that, for every set of $n$ points $\mathcal{P}$ in $\mathbb{R}^2$, a random plane graph drawn on $\mathcal{P}$ is expected to contain less than $n/10.18$ isolated vertices. In the other direction, we construct a point set where the expected number of isolated vertices in a random plane graph is about $n/23.32$. For $i\ge 1$, we prove that the expect

Yuto Miyatake, Kaoru Irie, Takeru Matsuda

This paper presents a new Bayesian framework for quantifying discretization errors in numerical solutions of ordinary differential equations. By modelling the errors as random variables, we impose a monotonicity constraint on the variances, referred to as discretization error variances. The key to our approach is the use of a shrinkage prior for the variance

Tian-Ze Li, Lu-Meng Liu, Jun Xu, Zhong-Zhou Ren

Inspired by various studies on extracting the density distributions of nuclei from their collisions at ultrarelativistic energies, in the present work we investigate the possibility of extracting the neutron-skin thickness $\Delta r_{np}$ in nuclei by their collisions at intermediate energies. We have analyzed the free neutron-to-proton yield ratio $n/p$ as

Yingjie Meng, Zhiqiang Wei, Chuankai Zhou

Building on techniques from complex analysis and topology, we establish a remarkable property of branched covers and formulate a complete criterion for the existence of specific types of branched covers between 2-spheres. Our results extend and unify previous work by Jiang (2004), Pervova-Petronio (2006), Zhu (2019), and Wei-Wu-Xu (2024). As applications of

Muttahirul Islam, Nazmul Haque, Md. Hadiuzzaman

This paper presents DEEGITS (Deep Learning Based Heterogeneous Traffic State Measurement), a comprehensive framework that leverages state-of-the-art convolutional neural network (CNN) techniques to accurately and rapidly detect vehicles and pedestrians, as well as to measure traffic states in challenging scenarios (i.e., congestion, occlusion). In this study

Yeong-Joon Ju, Ho-Joong Kim, Seong-Whan Lee

Recent multimodal retrieval methods have endowed text-based retrievers with multimodal capabilities by utilizing pre-training strategies for visual-text alignment. They often directly fuse the two modalities for cross-reference during the alignment to understand multimodal queries. However, existing methods often overlook crucial visual information due to a

Hanming Wang, Yunlong Li, Zijun Wu, Huifen Wang

In the past few years, channel-wise and spatial-wise attention blocks have been widely adopted as supplementary modules in deep neural networks, enhancing network representational abilities while introducing low complexity. Most attention modules follow a squeeze-and-excitation paradigm. However, to design such attention modules, requires a substantial amoun

Elena Grigorescu, Young-San Lin, Maoyuan Song

Learning-augmented algorithms have been extensively studied across the computer science community in the recent years, driven by advances in machine learning predictors, which can provide additional information to augment classical algorithms. Such predictions are especially powerful in the context of online problems, where decisions have to be made without

Dirk Wulferding, Jongho Park, Takami Tohyama, Seung Ryong Park

Understanding how symmetry-breaking processes generate order out of disorder is among the most fundamental problems of nature. The scalar Higgs mode - a massive (quasi-) particle - is a key ingredient in these processes and emerges with the spontaneous breaking of a continuous symmetry. Its related exotic and elusive axial counterpart, a Boson with vector ch

W. L. Ribeiro, C. A. Moura

We develop a collisional framework for neutrino propagation within open quantum systems, termed the \emph{Collisional Approach for Open Neutrino Systems} (CAONS). A Born-Markov equation is derived, linking decoherence, dissipation, decay rates, and scattering cross sections. Perturbation theory is not required and the resultant master equation is applied to

Tong Su, Junbo Zhao

Existing machine learning-based surrogate modeling methods for transient stability constrained-optimal power flow (TSC-OPF) lack certifications in the presence of unseen disturbances or uncertainties. This may lead to divergence of TSC-OPF or insecure control strategies. This paper proposes a neural network certification-informed power system transient stabi

Qiang Zhou, Shaofeng Zhang, Nianzu Yang, Ye Qian

Existing text-to-video (T2V) models often struggle with generating videos with sufficiently pronounced or complex actions. A key limitation lies in the text prompt's inability to precisely convey intricate motion details. To address this, we propose a novel framework, MVideo, designed to produce long-duration videos with precise, fluid actions. MVideo overco

Kaan Öcal, Michael P. H. Stumpf

Cells actively regulate their size during the cell cycle to maintain volume homeostasis across generations. While various mathematical models of cell size regulation have been proposed to explain how this is achieved, relating these models to experimentally observed cell size distributions has proved challenging. In this paper we present a simple formula for

Arthur Bizzi, Lucas Nissenbaum, João M. Pereira

We introduce Neural Conjugate Flows (NCF), a class of neural network architectures equipped with exact flow structure. By leveraging topological conjugation, we prove that these networks are not only naturally isomorphic to a continuous group, but are also universal approximators for flows of ordinary differential equation (ODEs). Furthermore, topological pr

Yongjiang Wu, Yongtao Li, Lihua Feng, Jiuqiang Liu

Let $\mathcal{F}$ be a family of subsets of $[n]$. The diameter of $\mathcal{F}$ is the maximum size of symmetric differences among pairs of its members. Resolving a conjecture of Erd\H{o}s, Kleitman determined the maximum size of a family with fixed diameter, which states that a family with diameter $s$ has cardinality at most that of a Hamming ball of radi

Hui Dai, Ryan Teehan, Mengye Ren

Many existing evaluation benchmarks for Large Language Models (LLMs) quickly become outdated due to the emergence of new models and training data. These benchmarks also fall short in assessing how LLM performance changes over time, as they consist of a static set of questions without a temporal dimension. To address these limitations, we propose using future

Chengkun Tian, Xiaohui Gao, Yongguang Liu

We propose a robust and efficient framework to generate global trajectories for ground robots in complex 3D environments. The proposed method takes point cloud as input and efficiently constructs a multi-level map using triangular patches as the basic elements. A kinematic path search is adopted on the patches, where motion primitives on different patches co

Birender Singh, Grant McNamara, Kyung-Mo Kim, Saif Siddique

The recent discovery of an axial amplitude (Higgs) mode in the long-studied charge density wave (CDW) systems GdTe$_3$ and LaTe$_3$ suggests a heretofore unidentified hidden order. A theoretical study proposed that the axial Higgs results from a hidden ferroaxial component of the CDW, which could arise from non-trivial orbital texture. Here, we report extens

Jeffrey Hatley, Debanjana Kundu

Let $\mathsf{E}/\mathbb{Q}$ be an elliptic curve. By the modularity theorem, it admits a surjection from a modular curve $X_0(N) \to \mathsf{E}$, and the minimal degree among such maps is called the modular degree of $\mathsf{E}$. By the Mordell--Weil Theorem, $\mathsf{E}(\mathbb{Q})\simeq \mathbb{Z}^r \oplus T$ for some nonnegative integer $r$ and some fini

Farouq Sammour, Jia Xu, Xi Wang, Mo Hu

Construction remains one of the most hazardous sectors. Recent advancements in AI, particularly Large Language Models (LLMs), offer promising opportunities for enhancing workplace safety. However, responsible integration of LLMs requires systematic evaluation, as deploying them without understanding their capabilities and limitations risks generating inaccur

Ryoya Kai, Hiroshi Tamaru

A quandle is an algebraic system whose axioms generalize the algebraic structure of the point symmetries of symmetric spaces. In this paper, we give a definition of Euler characteristics for quandles. In particular, the quandle Euler characteristic of a compact connected Riemannian symmetric space coincides with the topological Euler characteristic. Addition

Yasutoshi Makino, Hsin-Ni Ho, Seokhee Jeon

The sixth international conference AsiaHaptics 2024 took place at Sunway University, Malaysia on 28-30 October 2024. AsiaHaptics is an exhibition type of international conference dedicated to the haptics domain, engaging presentations accompanied by hands-on demonstrations. It presents the state-of-the-art of the diverse haptics (touch)-related research, inc

Jonguk Yang

We develop a renormalization theory of non-perturbative dissipative H\'enon-like maps with combinatorics of bounded type. The main novelty of our approach is the incorporation of Pesin theoretic ideas to the renormalization method, which enables us to control the small-scale geometry of dynamics in the higher-dimensional setting. In a prequel to this paper,

Zhengxian He, Ashish Kundu, Mustaque Ahamad

Recent advances in voice synthesis, coupled with the ease with which speech can be harvested for millions of people, introduce new threats to applications that are enabled by devices such as voice assistants (e.g., Amazon Alexa, Google Home etc.). We explore if unrelated and limited amount of speech from a target can be used to synthesize commands for a voic

Christina W. Zhou, Nikki L. B. Freeman, Katharine L. McGinigle, Michael R. Kosorok

Precision medicine leverages patient heterogeneity to estimate individualized treatment regimens, formalized, data-driven approaches designed to match patients with optimal treatments. In the presence of competing events, where multiple causes of failure can occur and one cause precludes others, it is crucial to assess the risk of the specific outcome of int

Aditi Jaiswal, Dennis P. Wall, Peter Washington

Autism and Attention-Deficit Hyperactivity Disorder (ADHD) are two of the most commonly observed neurodevelopmental conditions in childhood. Providing a specific computational assessment to distinguish between the two can prove difficult and time intensive. Given the high prevalence of their co-occurrence, there is a need for scalable and accessible methods

S. S. Ren, R. X. Zhou, Y. G. Zheng, S. J. Kang

We investigate the evolution of a changing-look blazar (CLB) on long timescales and expect to trace the state change of a CLB. Three morphological types, including a flat spectrum radio quasar (FSRQ) state, transition state, and BL Lacertae (BL Lac) state are classified according to the criteria proposed by analyzing the relationship between the equivalent w

Jiayu Huo, Sebastien Ourselin, Rachel Sparks

Medical image translation is crucial for reducing the need for redundant and expensive multi-modal imaging in clinical field. However, current approaches based on Convolutional Neural Networks (CNNs) and Transformers often fail to capture fine-grain semantic features, resulting in suboptimal image quality. To address this challenge, we propose SAM-I2I, a nov

Yuda An, Shushu Yi, Bo Mao, Qiao Li

Compute Express Link (CXL) serves as a rising industry standard, delivering high-speed cache-coherent links to a variety of devices, including host CPUs, computational accelerators, and memory devices. It is designed to promote system scalability, enable peer-to-peer exchanges, and accelerate data transmissions. To achieve these objectives, the most recent C

Xiangting Li, Tom Chou

By decoupling forward and backward stochastic trajectories, we construct a family of martingales and work theorems for both overdamped and underdamped Langevin dynamics. Our results are made possible by an alternative derivation of work theorems that uses tools from stochastic calculus instead of path-integration. We further strengthen the equality in work t

Yuhang Gao, Hui Tian, Tom Van Doorsselaere, Zihao Yang

Recent observations have demonstrated the capability of mapping the solar coronal magnetic field using the technique of coronal seismology based on the ubiquitous propagating Alfvenic/kink waves through imaging spectroscopy. We established a magnetohydrodynamic (MHD) model of a gravitationally stratified open magnetic flux tube, exciting kink waves propagati

Rosa Aghdam, Claudia Solis-Lemus

Understanding complex interactions within microbiomes is essential for exploring their roles in health and disease. However, constructing reliable microbiome networks often poses a challenge due to variations in the output of different network inference algorithms. To address this issue, we present CMiNet, an R package designed to generate a consensus microb

Youngsun Kong, Farnoush Baghestani, William D'Angelo, I-Ping Chen

Assessment of the sympathetic nervous system (SNS) is one of the major approaches for studying affective states. Skin nerve activity (SKNA) derived from high-frequency components of electrocardiogram (ECG) signals has been a promising surrogate for assessing the SNS. However, current SKNA analysis tools have shown high variability across study protocols and

Saikat Barua, Mostafizur Rahman, Md Jafor Sadek, Rafiul Islam

The principles of automation and innovation serve as foundational elements for advancement in contemporary science and technology. Here, we introduce Pygen, an automation platform designed to empower researchers, technologists, and hobbyists to bring abstract ideas to life as core, usable software tools written in Python. Pygen leverages the immense power of

Yungang Yi, Weihua Li, Matthew Kuo, Quan Bai

AI-based music generation has made significant progress in recent years. However, generating symbolic music that is both long-structured and expressive remains a significant challenge. In this paper, we propose PerceiverS (Segmentation and Scale), a novel architecture designed to address this issue by leveraging both Effective Segmentation and Multi-Scale at

Songtao Liu, Dandan Zhang, Zhengkai Tu, Hanjun Dai

A significant challenge in wet lab experiments with current drug design generative models is the trade-off between pharmacological properties and synthesizability. Molecules predicted to have highly desirable properties are often difficult to synthesize, while those that are easily synthesizable tend to exhibit less favorable properties. As a result, evaluat

Runze Cheng, Zhongao Sun, Ye Zhang, Chun Li

Multimodal Magnetic Resonance Imaging (MRI) provides essential complementary information for analyzing brain tumor subregions. While methods using four common MRI modalities for automatic segmentation have shown success, they often face challenges with missing modalities due to image quality issues, inconsistent protocols, allergic reactions, or cost factors

Meng Han, Ming-Chang Chen, Ming-Shian Tsai, Hao Liang

Attosecond science and frequency metrology rely on the precise measurement and control of the laser pulse waveform, a feat traditionally achieved using optoelectronic techniques. In this study, we conducted a laser-induced acoustic experiment in air ionized by carrier-envelope phase (CEP)-stabilized sub-4 femtosecond pulses. Our results reveal that the acous

Siwei Li, Jiangwen Chen, Hua Lin, Wei Wang

This article focuses on the faults of important mechanical components such as pumps, valves, and pipelines in the reactor coolant system, main steam system, condensate system, and main feedwater system of nuclear power plants (NPPs). It proposes a composite multi-fault diagnosis model based on Bayesian algorithm and EfficientNet large model using data-driven

Zijun Chen, Yiming Chen, Chengfu Wei

We propose a novel coupling inequality of the min-max type for two random matrices with finite absolute third moments, which generalizes the quantitative versions of the well-known inequalities by Gordon. Previous results have calculated the quantitative bounds for pairs of Gaussian random matrices. Through integrating the methods utilized by Chatterjee-Meck

Jiahui Li, Lin Li, Tai-wei Chang, Kun Kuang

Reinforcement learning from human feedback (RLHF) offers a promising approach to aligning large language models (LLMs) with human preferences. Typically, a reward model is trained or supplied to act as a proxy for humans in evaluating generated responses during the reinforcement training phase. However, current reward models operate as sequence-to-one models

Roghieh Mahdavihaji, Alexandra Duel-Hallen, Hans Hallen

The performance of millimeter-wave (mmWave) and sub-terahertz (sub-THz) communication systems is significantly impaired by sensitivity to sudden blockages. In this work, we employ machine learning (ML) and our physics-based simulation tool to warn about the upcoming blockage tens of 5G frames ahead for highway speeds, providing a sufficient time for a proact

Hojun Jang, Young Min Kim

We present Reusable Motion prior (ReMP), an effective motion prior that can accurately track the temporal evolution of motion in various downstream tasks. Inspired by the success of foundation models, we argue that a robust spatio-temporal motion prior can encapsulate underlying 3D dynamics applicable to various sensor modalities. We learn the rich motion pr

Weigao Su, Vishal Shrivastav

Achieving low remote memory access latency remains the primary challenge in realizing memory disaggregation over Ethernet within the datacenters. We present EDM that attempts to overcome this challenge using two key ideas. First, while existing network protocols for remote memory access over the Ethernet, such as TCP/IP and RDMA, are implemented on top of th

Xin Tang, Qian Chen, Wenjie Weng, Binhan Liao

Unmanned Aerial Vehicles (UAVs) possess high mobility and flexible deployment capabilities, prompting the development of UAVs for various application scenarios within the Internet of Things (IoT). The unique capabilities of UAVs give rise to increasingly critical and complex tasks in uncertain and potentially harsh environments. The substantial amount of dat

Huimin Li, James P. Donley, David T. Wu, John G. Curro

Two-molecule theory refers to a class of microscopic, self-consistent field theories for the radial distribution function in classical molecular liquids. The version examined here can be considered as one of the very few formally derived closures to the reference interaction site model (RISM) equation. The theory is applied to polyethylene liquids, computing

Norman Matloff, Aditya Mittal

Decision-making processes have increasingly come to rely on sophisticated machine learning tools, raising critical concerns about the fairness of their predictions with respect to sensitive groups. The widespread adoption of commercial "black-box" models necessitates careful consideration of their legal and ethical implications for consumers. When users inte

V. N. Krishnachandran

This paper examines how the mathematicians and astronomers of the Kerala school tackled the problem of computing the values of the arcsin function. Four different approaches are discussed all of which are found in Nilakantha Somayaji's (1444 - 1545 CE) Tantrasangraha and the roots of all of which can be traced to ideas originally articulated by Sangamagrama

Michael C. H. Choi, Max Hird, Youjia Wang

This paper aims at improving the convergence to equilibrium of finite ergodic Markov chains via permutations and projections. First, we prove that a specific mixture of permuted Markov chains arises naturally as a projection under the KL divergence or the squared-Frobenius norm. We then compare various mixing properties of the mixture with other competing Ma

Xinjie Zhao, Shyaman Maduranga Sriwarnasinghe, Jiacheng Tang, Shiyun Wang

The integration of artificial intelligence into development research methodologies presents unprecedented opportunities for addressing persistent challenges in participatory research, particularly in linguistically diverse regions like South Asia. Drawing from an empirical implementation in Sri Lanka's Sinhala-speaking communities, this paper presents an emp

Jerome Gilles

These last years, algorithms allowing to decompose an image into its structures and textures components have emerged. In this paper, we present an application of this type of decomposition to the problem road network detection in aerial or satelite imagery. The algorithmic procedure involves the image decomposition (using a unique property), an alignment det

Jerome Gilles

These last few years, image decomposition algorithms have been proposed to split an image into two parts: the structures and the textures. These algorithms are not adapted to the case of noisy images because the textures are corrupted by noise. In this paper, we propose a new model which decomposes an image into three parts (structures, textures and noise) b

Jerome Gilles

This paper deals with two fields related to active imaging system. First, we begin to explore image processing algorithms to restore the artefacts like speckle, scintillation and image dancing caused by atmospheric turbulence. Next, we examine how to evaluate the performance of this kind of systems. To do this task, we propose a modified version of the germa

Mohamed Mejri, Chandramouli Amarnath, Abhijit Chatterjee

Modern transformer-based encoder-decoder architectures struggle with reasoning tasks due to their inability to effectively extract relational information between input objects (data/tokens). Recent work introduced the Abstractor module, embedded between transformer layers, to address this gap. However, the Abstractor layer while excelling at capturing relati

Shota Ono, Hideo Yoshioka

A recent experiment reports a creation of goldene, which is two-dimensional gold with hexagonal structure. By rolling up the goldene, gold nanotubes (GNT) should exist, but their structural and electronic properties are not understood well. Based on first-principles calculations, we demonstrate a breakdown of inverse square law, wherein the curvature energy

Wenxiang Ying, Benjamin X. K. Chng, Milan Delor, Pengfei Huo

Cavity exciton-polaritons exhibit ballistic transport and can achieve a distance of 100 $\mu $m in one picosecond. This ballistic transport significantly enhances mobility compared to that of bare excitons, which often move diffusively and become the bottleneck for energy conversion and transfer devices. Despite being robustly reproduced in experiments and s

Wei Li, Yong Yang

In this work, we study the resonant tunneling (RT) of electrons and H atoms in double-barrier (DB) systems. Our numerical calculations directly verify the correspondence between the resonant tunneling energies and the energy levels of quasi-bound states (QBS) within the double barriers. Based on this, in-depth analyses are carried out on the modulation of QB

Jin Han, Wu-Jun Li

Protein structure similarity search (PSSS), which tries to search proteins with similar structures, plays a crucial role across diverse domains from drug design to protein function prediction and molecular evolution. Traditional alignment-based PSSS methods, which directly calculate alignment on the protein structures, are highly time-consuming with high mem

Jiaqi Li, Yanfeng Zhang, Jinjie Zeng, Siyuan Yu

On-chip nonlinear photonic conversion functions with wide and precise tunability as well as high conversion efficiency are highly desirable for a wide range of applications. Photonic crystal micro-ring resonators (PhCR) facilitate efficient nonlinear conversion and enable wavenumber-accurate selection of converted optical modes, but do not support post-fabri

Zhong-Feng Sun, Yun-Bin Zhao, Jin-Chuan Zhou, Zheng-Hai Huang

The relaxed optimal $k$-thresholding pursuit (ROTP) is a recent algorithm for linear inverse problems. This algorithm is based on the optimal $k$-thresholding technique which performs vector thresholding and error metric reduction simultaneously. Although ROTP can be used to solve small to medium-sized linear inverse problems, the computational cost of this

Julie Belleville, Prachi Thureja, Harry A. Atwater

We introduce a conceptual framework for a lensless imaging system which employs an active metasurface as a high-frequency, continuously tunable amplitude and phase modulation aperture, coupled to a discrete single-pixel detector. Using an array factor formalism, we first study fundamental limits in information collection, offering a comparison to existing te

Troi Williams, Kasra Torshizi, Pratap Tokekar

Robots often localize to lower navigational errors and facilitate downstream, high-level tasks. However, a robot may want to selectively localize when localization is costly (such as with resource-constrained robots) or inefficient (for example, submersibles that need to surface), especially when navigating in environments with variable numbers of hazards su

Bahareh Marzban, Lucius Miller, Alexander Dikopoltsev, Mathieu Bertrand

We demonstrate a quantum walk comb in synthetic frequency space formed by externally modulating a semiconductor optical amplifier operating in the telecommunication wavelength range in a unidirectional ring cavity. The ultrafast gain saturation dynamics of the gain medium and its operation at high current injections is responsible for the stabilization of th

Peng Wang, Lingzhe Zhao, Yin Zhang, Shiyu Zhao

Emerging 3D scene representations, such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have demonstrated their effectiveness in Simultaneous Localization and Mapping (SLAM) for photo-realistic rendering, particularly when using high-quality video sequences as input. However, existing methods struggle with motion-blurred frames, which are

Yihe Zhang, Nabin Pakka, Nian-Feng Tzeng

Large Language Models (LLMs) have received considerable interest in wide applications lately. During pre-training via massive datasets, such a model implicitly memorizes the factual knowledge of trained datasets in its hidden parameters. However, knowledge held implicitly in parameters often makes its use by downstream applications ineffective due to the lac

Joseph J. Williams, Zachary G. Nicolaou, J. Nathan Kutz, Steven L. Brunton

Vortex shedding is an important physical phenomenon observed across many spatial and temporal scales in fluids. Previous experimental and theoretical studies have established a hierarchy of local and global reduced-order models for vortex shedding based on the Stuart-Landau and Ginzburg-Landau equations. In this work, we employ data-driven methods to discove

Yoshiharu Kawamura

We explore a wider theoretical framework that has quantum field theory built-in, taking the fact that quantum mechanics is reconstructed from quantum field theory as a hint. We formulate a quantum theory with an embedded structure by introducing functional operators, and we find that it could describe the level II multiverse. Topics related to a beginning of

Gustav Kjærbye Bagger

Let $x$ and $n$ be positive integers. We prove a non-trivial lower bound for $x$, dependant only on $\omega_n$, the number of distinct prime factors of $x^n-1$. By considering the divisibility of $\varphi \mid x^n-1$ for $\varphi \mid n$, we obtain a further refinement. This bound has applications for existence problems relating to primitive elements in fini

Shivani Upadhyay, Ronak Pradeep, Nandan Thakur, Daniel Campos

The application of large language models to provide relevance assessments presents exciting opportunities to advance information retrieval, natural language processing, and beyond, but to date many unknowns remain. This paper reports on the results of a large-scale evaluation (the TREC 2024 RAG Track) where four different relevance assessment approaches were

Shadaj Laddad, Alvin Cheung, Joseph M. Hellerstein, Mae Milano

Streaming systems are present throughout modern applications, processing continuous data in real-time. Existing streaming languages have a variety of semantic models and guarantees that are often incompatible. Yet all these languages are considered "streaming" -- what do they have in common? In this paper, we identify two general yet precise semantic propert

Edriss S. Titi, Collin Victor

In this work, we study the applicability of the Azouani-Olson-Titi (AOT) nudging algorithm for continuous data assimilation to evolutionary dynamical systems that are not dissipative. Specifically, we apply the AOT algorithm to a partially dissipative variant of the Lorenz 1963 system, the Korteweg-de Vries equation (KdV) in 1D, and the 2D incompressible Eul

Oguzhan Yigit, Richard C. Wilson

The Laplace-Beltrami operator has established itself in the field of non-rigid shape analysis due to its many useful properties such as being invariant under isometric transformation, having a countable eigensystem forming an orthornormal basis, and fully characterizing geodesic distances of the manifold. However, this invariancy only applies under isometric

Jingye Yan, Hong Zhang, Yabing Wei, Xu Qian

The non-differentiability of the singular nonlinearity (such as $f=\ln|u|^2$) at $u=0$ presents significant challenges in devising accurate and efficient numerical schemes for the logarithmic Schr\"{o}dinger equation (LogSE). To address this singularity, we propose an energy regularization technique for the LogSE. For the regularized model, we utilize Implic

S. P. Glasby, Alice C. Niemeyer, Cheryl E. Praeger, A. E. Zalesski

This paper is concerned with absolutely irreducible quasisimple subgroups $G$ of a finite general linear group $GL_d(\mathbb{F}_q)$ for which some element $g\in G$ of prime order $r$, in its action on the natural module $V=(\mathbb{F}_q)^d$, is irreducible on a subspace of the form $V(1-g)$ of dimension $d/2$. We classify $G,d,r$, the characteristic $p$ of t

Adam Logan

The modularity of an elliptic curve $E/\mathbb Q$ can be expressed either as an analytic statement that the $L$-function is the Mellin transform of a modular form, or as a geometric statement that $E$ is a quotient of a modular curve $X_0(N)$. For elliptic curves over number fields these notions diverge; a conjecture of Hamahata asserts that for every ellipt

Caio Bueno

The main question of this paper is the following: how much cancellation can the partial sums restricted to the $k$-free integers up to $x$ of a $\pm 1$ multiplicative function $f$ be in terms of $x$? Building upon the recent paper by Q. Liu, Acta Math. Sin. (Engl. Ser.) 39 (2023), no. 12, 2316-2328, we prove that under the Riemann Hypothesis for quadratic Di

Zachary Yetman Van Egmond, Luis Rodrigues

This paper provides a least squares formulation for the training of a 2-layer convolutional neural network using quadratic activation functions, a 2-norm loss function, and no regularization term. Using this method, an analytic expression for the globally optimal weights is obtained alongside a quadratic input-output equation for the network. These propertie

Matthias Salzger, John H. Selby

There has been a recent surge of interest within the field of quantum foundations regarding incorporating ideas from general relativity and quantum gravity. However, many quantum information tools remain agnostic to the underlying spacetime. For instance, whenever we draw a quantum circuit the effective spacetime imposed by the connectivity of the physical q

Michael Larsen

Let $S_n$ denote a symmetric group, $\chi$ an irreducible character of $S_n$, and $g\in S_n$ an element which decomposes into $k$ disjoint cycles, where $1$-cycles are included. Then $|\chi(g)|\le k!$, and this upper bound is sharp for fixed $k$ and varying $n$, $\chi$, and $g$. This implies a sharp upper bound of $k!$ for unipotent character values of $SL_n

Wuhan Chen, Yuheng Fan, Chuang Yang, Mugen Peng

Terahertz (THz) communications, with their substantial bandwidth, are essential for meeting the ultra-high data rate demands of emerging high-mobility scenarios such as vehicular-to-everything (V2X) networks. In these contexts, beamwidth adaptation has been explored to address the problem that high-mobility targets frequently move out of the narrow THz beam

Mikhail Freer, Hassan Nosratabadi

In a two-stage model of choice a decision maker first shortlists a given menu and then applies her preferences. We show that a sizeable class of these models run into significant issues in terms of identification of preferences (welfare-relevance) and thus cannot be used for welfare analysis. We classify these models by their revealed preference principles a

Santiago Marin, Bronwyn Loong, Anton H. Westveld

Modern approaches to perform Bayesian variable selection rely mostly on the use of shrinkage priors. That said, an ideal shrinkage prior should be adaptive to different signal levels, ensuring that small effects are ruled out, while keeping relatively intact the important ones. With this task in mind, we develop the nonparametric Bayesian Lasso, an adaptive