July 2023 arXiv papers — page 59

Arthur Müller, Matthia Sabatelli

Reinforcement Learning (RL) has been widely explored in Traffic Signal Control (TSC) applications, however, still no such system has been deployed in practice. A key barrier to progress in this area is the reality gap, the discrepancy that results from differences between simulation models and their real-world equivalents. In this paper, we address this chal

Pengcheng An, Chaoyu Zhang, Haichen Gao, Ziqi Zhou

We introduce the preliminary exploration of AniBalloons, a novel form of chat balloon animations aimed at enriching nonverbal affective expression in text-based communications. AniBalloons were designed using extracted motion patterns from affective animations and mapped to six commonly communicated emotions. An evaluation study with 40 participants assessed

Abolfazl Asudeh, Nima Shahbazi, Stavros Sintos

Hashmap is a fundamental data structure in computer science. There has been extensive research on constructing hashmaps that minimize the number of collisions leading to efficient lookup query time. Recently, the data-dependant approaches, construct hashmaps tailored for a target data distribution that guarantee to uniformly distribute data across different

Zepeng Wu, Yang Sun, Artur P. Durajski, Feng Zheng

We present a computational investigation into the effects of chemical doping with 15 different elements on phase stability and superconductivity in the LaH10 structure. Most doping elements were found to induce softening of phonon modes, enhancing electron-phonon coupling and improving critical superconducting temperature while weakening dynamical stability.

Hengyu Fu, Tianyu Guo, Yu Bai, Song Mei

Attention layers -- which map a sequence of inputs to a sequence of outputs -- are core building blocks of the Transformer architecture which has achieved significant breakthroughs in modern artificial intelligence. This paper presents a rigorous theoretical study on the learning and generalization of a single multi-head attention layer, with a sequence of k

Byeongchan Kim, Min-hwan Oh

In this paper, we propose a model-based offline reinforcement learning method that integrates count-based conservatism, named $\texttt{Count-MORL}$. Our method utilizes the count estimates of state-action pairs to quantify model estimation error, marking the first algorithm of demonstrating the efficacy of count-based conservatism in model-based offline deep

Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy, Ichiro Takeuchi

Selective inference (SI) has been actively studied as a promising framework for statistical hypothesis testing for data-driven hypotheses. The basic idea of SI is to make inferences conditional on an event that a hypothesis is selected. In order to perform SI, this event must be characterized in a traceable form. When selection event is too difficult to char

Rafael Hernández-Jiménez, Claudia Moreno

Starting with the Lagrangian formulation of General Relativity, we will conduct an investigation into the production of spacetime waves, due to a geometric boundary term of a closed extended manifold, within the tensor and scalar sectors. This scheme will be studied in an inflationary universe. We explore two distinct scenarios: Cold Inflation and Warm Infla

Sourav Sanyal, Rohan Kumar Manna, Kaushik Roy

Vision-based object tracking is an essential precursor to performing autonomous aerial navigation in order to avoid obstacles. Biologically inspired neuromorphic event cameras are emerging as a powerful alternative to frame-based cameras, due to their ability to asynchronously detect varying intensities (even in poor lighting conditions), high dynamic range,

Alexey Milekhin, Nikolay Sukhov

Scar states are special finite-energy density, but non-thermal states of chaotic Hamiltonians. We argue that all holographic quantum field theories, including $\mathcal{N}=4$ super Yang--Mills, have scar states. Their presence is tied to the existence of non-topological, horizonless soliton solutions in gravity: oscillons and a novel family of excited boson

Arashi Sakai

In a triangulated category equipped with a $t$-structure, we investigate a relation between ICE-closed (=Image-Cokernel-Extension-closed) subcategories of the heart of the $t$-structure and aisles in the triangulated categories. We introduce an ICE sequence, a sequence of ICE-closed subcategories satisfying a certain condition, and establish a bijection betw

Zihan Guan, Zihao Wu, Zhengliang Liu, Dufan Wu

Participant recruitment based on unstructured medical texts such as clinical notes and radiology reports has been a challenging yet important task for the cohort establishment in clinical research. Recently, Large Language Models (LLMs) such as ChatGPT have achieved tremendous success in various downstream tasks thanks to their promising performance in langu

Ching-Hsuan Huang, Edmund Seto

Transportation is one of the most pervasive sources of community noise. In this study, we used a spatially-resolved model of transportation-related noise with established transportation noise exposure-response functions to estimate the population highly annoyed (HA) due to aviation, road, and railway traffic sources in the United States. Additionally, we emp

Xinyi Wang, Zesong Fei, Peng Liu, J. Andrew Zhang

In this paper, we investigate the realization of covert communication in a general radar-communication cooperation system, which includes integrated sensing and communications as a special example. We explore the possibility of utilizing the sensing ability of radar to track and jam the aerial adversary target attempting to detect the transmission. Based on

Ipsita Mohanty

Defect Triage is a time-sensitive and critical process in a large-scale agile software development lifecycle for e-commerce. Inefficiencies arising from human and process dependencies in this domain have motivated research in automated approaches using machine learning to accurately assign defects to qualified teams. This work proposes a novel framework for

Fang Gao, XueTao Li, Jun Yu, Feng Shaung

The advent of Chat-GPT has led to a surge of interest in Embodied AI. However, many existing Embodied AI models heavily rely on massive interactions with training environments, which may not be practical in real-world situations. To this end, the Maniskill2 has introduced a full-physics simulation benchmark for manipulating various 3D objects. This benchmark

Mingze Gao, Qilong Wang, Zhenyi Lin, Pengfei Zhu

Recently, efficient fine-tuning of large-scale pre-trained models has attracted increasing research interests, where linear probing (LP) as a fundamental module is involved in exploiting the final representations for task-dependent classification. However, most of the existing methods focus on how to effectively introduce a few of learnable parameters, and l

Boshen Shi, Yongqing Wang, Fangda Guo, Jiangli Shao

Graph domain adaptation models are widely adopted in cross-network learning tasks, with the aim of transferring labeling or structural knowledge. Currently, there mainly exist two limitations in evaluating graph domain adaptation models. On one side, they are primarily tested for the specific cross-network node classification task, leaving tasks at edge-leve

Ludovic Tangpi, Shichun Wang

In this paper we further extend the optimal bubble riding model proposed by Tangpi and Wang by allowing for price-dependent entry times. Agents are characterized by their individual entry threshold that represents their belief in the strength of the bubble. Conversely, the growth dynamics of the bubble is fueled by the influx of players. Price-dependent entr

Zinuo Cai, Hao Wang, Tao Song, Yang Hua

Deploying deep learning models in cloud clusters provides efficient and prompt inference services to accommodate the widespread application of deep learning. These clusters are usually equipped with host CPUs and accelerators with distinct responsibilities to handle serving requests, i.e. generalpurpose CPUs for input preprocessing and domain-specific GPUs f

Xilin Feng, Qiang Zhang, Zhongyi Zhang, Yuhao Gu

We classify the pairing symmetries of three-dimensional superconductivity in the zinc-blende structure which can support an electronic environment to host unconventional high temperature superconductivity, and calculate the pairing symmetry in the presence of strong electron-electron correlation by the slave boson mean-field approach. We find that the $d_{2z

Zixiang Ren, Yunfei Peng, Xianxin Song, Yuan Fang

This paper investigates the performance tradeoff for a multi-antenna integrated sensing and communication (ISAC) system with simultaneous information multicasting and multi-target sensing, in which a multi-antenna base station (BS) sends the common information messages to a set of single-antenna communication users (CUs) and estimates the parameters of multi

Quang Huy Che, Tung Do Thanh, Cuong Truong Van

Automatic License Plate Recognition (ALPR) is becoming a popular study area and is applied in many fields such as transportation or smart city. However, there are still several limitations when applying many current methods to practical problems due to the variation in real-world situations such as light changes, unclear License Plate (LP) characters, and im

Wenbo Hu, Yuling Wang, Lin Ma, Bangbang Yang

Despite the tremendous progress in neural radiance fields (NeRF), we still face a dilemma of the trade-off between quality and efficiency, e.g., MipNeRF presents fine-detailed and anti-aliased renderings but takes days for training, while Instant-ngp can accomplish the reconstruction in a few minutes but suffers from blurring or aliasing when rendering at va

Qizhang Li, Yiwen Guo, Xiaochen Yang, Wangmeng Zuo

The transferability of adversarial examples allows for the attack on unknown deep neural networks (DNNs), posing a serious threat to many applications and attracting great attention. In this paper, we improve the transferability of adversarial examples by incorporating the Bayesian formulation into both the model parameters and model input, enabling their jo

Faisal Hamman, Sanghamitra Dutta

This work presents an information-theoretic perspective to group fairness trade-offs in federated learning (FL) with respect to sensitive attributes, such as gender, race, etc. Existing works often focus on either $\textit{global fairness}$ (overall disparity of the model across all clients) or $\textit{local fairness}$ (disparity of the model at each client

Reza Sameni

Machine learning (ML) and deep learning models are extensively used for parameter optimization and regression problems. However, not all inverse problems in ML are ``identifiable,'' indicating that model parameters may not be uniquely determined from the available data and the data model's input-output relationship. In this study, we investigate the notion o

Bin-Ni Sun, yufeng Zhao

In 1935, Wright conjectured that ideal solutions to the PTE problem in Diophantine number theory should exist. In this paper, we prove Wright's conjecture holds true based on the the representation theory of the minuscule strongly commuting ring introduced by Kostant in 1975 and the complex coefficient cohomology ring structures of the Grassmannian variey.

Shusen Wang

Recommender system (RS) is an established technology with successful applications in social media, e-commerce, entertainment, and more. RSs are indeed key to the success of many popular APPs, such as YouTube, Tik Tok, Xiaohongshu, Bilibili, and others. This paper explores the methodology for improving modern industrial RSs. It is written for experienced RS e

Shuang Chen, Jinqiao Duan

We study the compactification of nonautonomous systems with autonomous limits and related dynamics. Although the $C^{1}$ extension of the compactification was well established, a great number of problems arising in bifurcation and stability analysis require the compactified systems with high-order smoothness. Inspired by this, we give a criterion for the $C^

Rui-Chang Shen, Jie Li, Yi-Ming Sun, Wei-Jiang Wu

Building hybrid quantum systems is a crucial step for realizing multifunctional quantum technologies, quantum information processing, and hybrid quantum networks. A functional hybrid quantum system requires strong coupling among its components. However, couplings between distinct physical systems are typically very weak. Experimental realization of strong co

Debasmita Mukherjee, Ritwik Singhai, Homayoun Najjaran

Virtual reality has proved to be useful in applications in several fields ranging from gaming, medicine, and training to development of interfaces that enable human-robot collaboration. It empowers designers to explore applications outside of the constraints posed by the real world environment and develop innovative solutions and experiences. Hand gestures r

Zdenek Sekanina

I call attention to extraordinary features displayed by the genetically related long-period comet pair of C/1844 Y1 (Great Comet) and C/2019 Y4 (ATLAS). The issue addressed most extensively is the fragmentation and disintegration of the latter object, itself a thousands-of-years-old fragment. Of the four fragments of C/2019 Y4 recognized by the Minor Planet

Matthew Hines, Gregory Glatzer, Shreya Ghosh, Prasenjit Mitra

The interaction between elephants and their environment has profound implications for both ecology and conservation strategies. This study presents an analytical approach to decipher the intricate patterns of elephant movement in Sub-Saharan Africa, concentrating on key ecological drivers such as seasonal variations and rainfall patterns. Despite the complex

Li Huang, Shuang Liang

In this paper, we propose a new analytic continuation method to extract real frequency spectral functions from imaginary frequency Green's functions of quantum many-body systems. This method is based on the pole representation of Matsubara Green's function and a stochastic sampling procedure is utilized to optimize the amplitudes and locations of poles. In o

Kai Lei, Zhan Chen, Shuman Jia, Xiaoteng Zhang

In the field of autonomous driving, 3D object detection is a very important perception module. Although the current SOTA algorithm combines Camera and Lidar sensors, limited by the high price of Lidar, the current mainstream landing schemes are pure Camera sensors or Camera+Radar sensors. In this study, we propose a new detection algorithm called HVDetFusion

Carllos Eduardo Holanda, Eduardo Santana

We study some notions of cohomology for asymptotically additive sequences and prove a Liv\v{s}ic-type result for almost additive sequences of potentials. As a consequence, we are able to characterize almost additive sequences based on their equilibrium measures and also show the existence of almost (and asymptotically) additive sequences of H\"older continuo

Xiu-Wu Wang, Zhi-Gang Wang

In the present work, considering the conservation of isospin in the strong decays, we investigate the strong decays of the pentaquark molecule candidate $P_c(4312)$ and its possible higher isospin cousin $P_c(4330)$ in the framework of the QCD sum rules. What's more, the pole residue of the $\Delta$ baryon with isospin eigenstate $|II_3\rangle=|\frac{3}{2}\f

Gargi Shaw, Gary Ferland, M. Chatzikos

A two-body gas-phase reaction rate coefficient can be given by the usual Arrhenius-type formula which depends on temperature. The UMIST Database for Astrochemistry is a widely used database for reaction rate coefficients. They provide fittings for coefficients valid over a particular range of temperatures. The permissible upper-temperature limits vary over a

Junyao You, Chengpu Yu

This paper focuses on the identification of graphical autoregressive models with dynamical latent variables. The dynamical structure of latent variables is described by a matrix polynomial transfer function. Taking account of the sparse interactions between the observed variables and the low-rank property of the latent-variable model, a new sparse plus low-r

Yiqing Xu, David Hsu

Tidying up a messy table may appear simple for humans, but articulating clear criteria for tidiness is challenging due to the ambiguous nature of common sense reasoning. Large Language Models (LLMs) have proven capable of capturing common sense knowledge to reason over this vague concept of tidiness. However, they alone may struggle with table tidying due to

Taksu Cheon, Michikazu Kobayashi

A model of the urban agglomeration and segregation is formulated, in which two types of agents move around on the square-lattice aligned cells. The model is shown to exhibit, when the density of agents are varied as the control parameter, various phase transitions representing appearance of urban aggregation, segregation and social disorder.

Karan Shah, Vishruth Veerendranath, Anushka Hebbar, Raghavendra Bhat

Streaming Linear Discriminant Analysis (LDA) while proven in Class-incremental Learning deployments at the edge with limited classes (upto 1000), has not been proven for deployment in extreme classification scenarios. In this paper, we present: (a) XLDA, a framework for Class-IL in edge deployment where LDA classifier is proven to be equivalent to FC layer i

Yangyi Chen, Xingyao Wang, Heng Ji

Pre-trained language models (PLMs) serve as backbones for various real-world systems. For high-stake applications, it's equally essential to have reasonable confidence estimations in predictions. While the vanilla confidence scores of PLMs can already be effectively utilized, PLMs consistently become overconfident in their wrong predictions, which is not des

Yangpeng Huang, Naixing Feng, Yijun Cai

It is well known that the inverse design of terahertz (THz) multi-resonant graphene metasurfaces by using traditional deep neural networks (DNNs) has limited generalization ability. In this paper, we propose improved Transformer and conditional generative adversarial neural networks (CGAN) for the inverse design of graphene metasurfaces based upon THz multi-

Kathleen M. Lewis, Emily Mu, Adrian V. Dalca, John Guttag

Recent vision-language models outperform vision-only models on many image classification tasks. However, because of the absence of paired text/image descriptions, it remains difficult to fine-tune these models for fine-grained image classification. In this work, we propose a method, GIST, for generating image-specific fine-grained text descriptions from imag

Zhenhang Zhang, Jingang Jin, Haowen Fang, Qinru Qiu

Spiking neural networks (SNNs) are bio-plausible computing models with high energy efficiency. The temporal dynamics of neurons and synapses enable them to detect temporal patterns and generate sequences. While Backpropagation Through Time (BPTT) is traditionally used to train SNNs, it is not suitable for online learning of embedded applications due to its h

Hong-Ming Liu, Jin-Biao Wei, Zeng-Hua Li, G. F. Burgio

We study the effects of dark matter on the properties of neutron stars by employing a DM-admixed model. The Brueckner-Hartree-Fock theory with realistic three-body forces and a generic bosonic self-interacting dark matter model describe the equations of state for nuclear matter and DM, respectively. We study the complete set of stable dark neutron stars and

Myong-Hwan Ri

We show that a Leray-Hopf weak solution to the 3D Navier-Stokes Cauchy problem belonging to the space $L^\infty(0,T; B^{-1}_{\infty,\infty}(\mathbb R^3))$ is regular in $(0,T]$. As a consequence, it follows that any Leray-Hopf weak solution to the 3D Navier-Stokes equations is regular while it is temporally bounded in the largest critical space $\dot{B}^{-1}

H. Yao, T. A. Zaki, C. Meneveau

Based on a generalized local Kolmogorov-Hill equation expressing the evolution of kinetic energy integrated over spheres of size $\ell$ in the inertial range of fluid turbulence, we examine a possible definition of entropy and entropy generation for turbulence. Its measurement from direct numerical simulations in isotropic turbulence leads to confirmation of

Seong-Kun Kim, Yonghae Lee

In the field of quantum information theory, the concept of quantum fidelity is employed to quantify the similarity between two quantum states. It has been observed that the fidelity between two states describing a bipartite quantum system $A \otimes B$ is always less than or equal to the quantum fidelity between the states in subsystem $A$ alone. While this

Zhichao Liu, Konstantinos Karydis

Micro Aerial Vehicles (MAVs) often face a high risk of collision during autonomous flight, particularly in cluttered and unstructured environments. To mitigate the collision impact on sensitive onboard devices, resilient MAVs with mechanical protective cages and reinforced frames are commonly used. However, compliant and impact-resilient MAVs offer a promisi

Zezeng Li, ShengHao Li, Zhanpeng Wang, Na Lei

Sampling from diffusion probabilistic models (DPMs) can be viewed as a piecewise distribution transformation, which generally requires hundreds or thousands of steps of the inverse diffusion trajectory to get a high-quality image. Recent progress in designing fast samplers for DPMs achieves a trade-off between sampling speed and sample quality by knowledge d

Ruyi Zha, Xuelian Cheng, Hongdong Li, Mehrtash Harandi

Reconstructing soft tissues from stereo endoscope videos is an essential prerequisite for many medical applications. Previous methods struggle to produce high-quality geometry and appearance due to their inadequate representations of 3D scenes. To address this issue, we propose a novel neural-field-based method, called EndoSurf, which effectively learns to r

Shota Saito

This paper deals with the problem of soft guessing under log-loss distortion (logarithmic loss) that was recently investigated by [Wu and Joudeh, IEEE ISIT, pp. 466--471, 2023]. We extend this problem to soft guessing allowing errors, i.e., at each step, a guesser decides whether to stop the guess or not with some probability and if the guesser stops guessin

Thong Hoang, Hoa Khanh Dam, Tingting Bi, Qinghua Lu

Quantum computing systems depend on the principles of quantum mechanics to perform multiple challenging tasks more efficiently than their classical counterparts. In classical software engineering, the software life cycle is used to document and structure the processes of design, implementation, and maintenance of software applications. It helps stakeholders

Guangzong Xing, Yoshio Miura, Terumasa Tadano

Recent theoretical study has shown that the hexagonal YCo$_5$ is dynamically unstable and distorts into a stable orthorhombic structure. In this study, we show theoretically that the orthorhombic phase is energetically more stable than the hexagonal phase in the low-temperature region, while the phonon entropy stabilizes the hexagonal phase thermodynamically

Naoki Tsukamoto

Vagnozzi et al. constrained the additional parameter of spacetimes with a photon sphere from the observation of the shadow of Sagittarius A* under the assumption that a distance to the Sagittarius A* and its mass parameter was estimated from other observations. They claimed that a Damour-Solodukhin wormhole with an additional parameter $\lambda$ is not an as

Stanislav Srednyak, Vladimir Khachatryan

In this work, we analyze vanishing cycles of Feynman loop integrals by means of the Mayer-Vietoris spectral sequence. A complete classification of possible vanishing geometries are obtained. We employ this result for establishing an asymptotic expansion for the loop integrals near their singularity locus, then give explicit formulas for the coefficients of s

Iwan Setiawan, Binayak Kar, Shan-Hsiang Shen

With the dynamic demands and stringent requirements of various applications, networks need to be high-performance, scalable, and adaptive to changes. Researchers and industries view network softwarization as the best enabler for the evolution of networking to tackle current and prospective challenges. Network softwarization must provide programmability and f

Shengjun Fan, Ying Hu, Shanjian Tang

We establish a general existence and uniqueness of integrable adapted solutions to scalar backward stochastic differential equations with integrable parameters, where the generator $g$ has an iterated-logarithmic uniform continuity in the second unknown variable $z$. The result improves our previous one in \cite{FanHuTang2023SCL}.

Haitao Lin, Yanwei Fu, Xiangyang Xue

Liquid perception is critical for robotic pouring tasks. It usually requires the robust visual detection of flowing liquid. However, while recent works have shown promising results in liquid perception, they typically require labeled data for model training, a process that is both time-consuming and reliant on human labor. To this end, this paper proposes a

Mohammad Mahdi Mohajer, Alvine Boaye Belle, Nima Shiri harzevili, Junjie Wang

The fairness of machine learning (ML) approaches is critical to the reliability of modern artificial intelligence systems. Despite extensive study on this topic, the fairness of ML models in the software engineering (SE) domain has not been well explored yet. As a result, many ML-powered software systems, particularly those utilized in the software engineeri

Rakesh Patibanda, Aryan Saini, Nathalie Overdevest, Maria F. Montoya

Spectating digital games can be exciting. However, due to its vicarious nature, spectators often wish to engage in the gameplay beyond just watching and cheering. To blur the boundaries between spectators and players, we propose a novel approach called "Fused Spectatorship", where spectators watch their hands play games by loaning bodily control to a computa

Akitaka Nakanishi, Shusuke Kasamatsu, Jun Haruyama, Osamu Sugino

Zr oxides and oxynitrides are promising candidates to replace precious metal cathodes in polymer electrolyte fuel cells. Oxygen reduction reaction activity in this class of materials has been correlated with the amount of oxygen vacancies, but a microscopic understanding of this correlation is still lacking. To address this, we simulate a defective Zr$_7$O$_

Sayan Das

In this paper we answer a question of Kaimanovich by characterizing (jointly) bi-harmonic functions on countable, discrete groups with respect to a symmetric, generating measure. We also study the peripheral Poisson boundary of $L(\G)$ with respect to Markov operators arising from symmetric, generating probability measures on a countable, discrete group $\G$

Hugh Roxburgh, Ryan Ridden-Harper, Zachary G. Lane, Armin Rest

Gamma-ray bursts produce afterglows that can be observed across the electromagnetic spectrum and can provide insight into the nature of their progenitors. While most telescopes that observe afterglows are designed to rapidly react to trigger information, the Transiting Exoplanet Survey Satellite (TESS) continuously monitors sections of the sky at cadences be

Emily Siska, G. Alexander Smith, Sergio Villa-Cortes, Lewis J. Conway

Controlling the formation and stoichiometric content of desired phases of materials has become a central interest for the study of a variety of fields, notably high temperature superconductivity under extreme pressures. The further possibility of accessing metastable states by initiating reactions by x-ray triggered mechanisms over ultra-short timescales is

Rakesh Patibanda, Chris Hill, Aryan Saini, Xiang Li

Most digital bodily games focus on the body as they use movement as input. However, they also draw the player's focus away from the body as the output occurs on visual displays, creating a divide between the physical body and the virtual world. We propose a novel approach - the "Body as a Play Material" - where a player uses their body as both input and outp

Hussain Sadiq Abuwala, Bohan Zhang, Mushi Wang

The goal of our research was to investigate the effects of collaboration between academia and industry on Natural Language Processing (NLP). To do this, we created a pipeline to extract affiliations and citations from NLP papers and divided them into three categories: academia, industry, and hybrid (collaborations between academia and industry). Our empirica

Baptiste Goujaud, Adrien Taylor, Aymeric Dieuleveut

In this work, we show that the heavy-ball ($\HB$) method provably does not reach an accelerated convergence rate on smooth strongly convex problems. More specifically, we show that for any condition number and any choice of algorithmic parameters, either the worst-case convergence rate of $\HB$ on the class of $L$-smooth and $\mu$-strongly convex \textit{qua

Ida Hamidah, Doni Fajar Ramadhan, Ramdhani Ramdhani, Budi Mulyanti

Battery pack used in electrical vehicles (EVs) have developed fastly nowadays and are important due to its capability to stores electric charge. However, it is still found that there is an electrical voltage fluctuations due to sudden load changes or faults in the power system. To overcome this problem, the conventional strategy focuses on using voltage stab

Ruisong Gao, Yufeng Wang, Min Yang, Chuanjun Chen

We present a new category of physics-informed neural networks called physics informed variational embedding generative adversarial network (PI-VEGAN), that effectively tackles the forward, inverse, and mixed problems of stochastic differential equations. In these scenarios, the governing equations are known, but only a limited number of sensor measurements o

Viraj Mehta, Ojash Neopane, Vikramjeet Das, Sen Lin

Preference-based feedback is important for many applications where direct evaluation of a reward function is not feasible. A notable recent example arises in reinforcement learning from human feedback on large language models. For many of these applications, the cost of acquiring the human feedback can be substantial or even prohibitive. In this work, we tak

Randall Putnam, Adam D. West, Wesley C. Campbell, Paul Hamilton

We perform entanglement of spin and motional degrees of freedom of a single, ground-state trapped ion through the application of a $16$ ps laser pulse. The duration of the interaction is significantly shorter than both the motional timescale ($30$ $\mu$s) and spin precession timescale ($1$ ns) , demonstrating that neither sets a fundamental speed limit on th

Spencer Killen, Jia-Huai You

A wide variety of nonmonotonic semantics can be expressed as approximators defined under AFT (Approximation Fixpoint Theory). Using traditional AFT theory, it is not possible to define approximators that rely on information computed in previous iterations of stable revision. However, this information is rich for semantics that incorporate classical negation

Mozhgan Mohammadpour, Shayne Waldron

We consider the general question of when all orbits under the unitary action of a finite group give a complex spherical design. Those orbits which have large stabilisers are then good candidates for being optimal complex spherical designs. This is done by developing the general theory of complex designs and associated (harmonic) Molien series for group actio

Weiming Zhuang, Yonggang Wen, Lingjuan Lyu, Shuai Zhang

Federated learning (FL) is an emerging distributed machine learning method that empowers in-situ model training on decentralized edge devices. However, multiple simultaneous FL tasks could overload resource-constrained devices. In this work, we propose the first FL system to effectively coordinate and train multiple simultaneous FL tasks. We first formalize

Wenmeng Zhang, Kening Lu, Weinian Zhang

Smooth linearization is one of the central themes in the study of dynamical systems. The classical Belitskii's $C^1$ linearization theorem has been widely used in the investigation of dynamical behaviors such as bifurcations, mixing, and chaotic behaviors due to its minimal requirement of partial second order non-resonances and low regularity of systems. In

Daniel Shy, Richard S. Woolf, Eric A. Wulf, Clio C. Sleator

Astronomical observations with gamma rays in the range of several hundred keV to hundreds of MeV currently represent the least explored energy range. To address this so-called MeV gap, we designed and built a prototype CsI:Tl calorimeter instrument using a commercial off-the-shelf (COTS) SiPMs and front-ends which may serve as a subsystem for a larger gamma-

Mordecai Waegell, Kelvin J. McQueen, Emily C. Adlam

Recently there has been significant interest in using causal modelling techniques to understand the structure of physical theories. However, the notion of `causation' is limiting - insisting that a physical theory must involve causal structure already places significant constraints on the form that theory may take. Thus in this paper, we aim to set out a mor

Michael Sedlmayer, Dang-Khoa Nguyen, Radu Ioan Bot

We study monotone variational inequalities that can arise as optimality conditions for constrained convex optimisation or convex-concave minimax problems and propose a novel algorithm that uses only one gradient/operator evaluation and one projection onto the constraint set per iteration. The algorithm, which we call fOGDA-VI, achieves a $o \left( \frac{1}{k

Diana M. Negoescu, Humberto Gonzalez, Saad Eddin Al Orjany, Jilei Yang

We introduce Epsilon*, a new privacy metric for measuring the privacy risk of a single model instance prior to, during, or after deployment of privacy mitigation strategies. The metric requires only black-box access to model predictions, does not require training data re-sampling or model re-training, and can be used to measure the privacy risk of models not

T. S. Tavares, G. A. P. Ribeiro

We consider the interaction-round-a-face version of the six-vertex model for arbitrary anisotropy parameter, which allow us to derive an integrable one-dimensional quantum Hamiltonian with three-spin interactions. We apply the quantum transfer matrix approach for the face model version of the six-vertex model. The integrable quantum Hamiltonian shares some t

Abdelrahman Abdallah, Adam Jatowt

Open-domain question answering (QA) tasks usually require the retrieval of relevant information from a large corpus to generate accurate answers. We propose a novel approach called Generator-Retriever-Generator (GRG) that combines document retrieval techniques with a large language model (LLM), by first prompting the model to generate contextual documents ba

Okan Atalar, Amin Arbabian

Polarization modulators have a broad range of applications in optics. The acceptance angle of a free-space polarization modulator is crucial for many applications. Polarization modulators that can achieve a wide acceptance angle are constructed by attaching a piezoelectric transducer to an isotropic material, and utilize a resonant transverse interaction bet

Jiachen Yao, Yikai Zhang, Songzhu Zheng, Mayank Goswami

Noisy labels can significantly affect the performance of deep neural networks (DNNs). In medical image segmentation tasks, annotations are error-prone due to the high demand in annotation time and in the annotators' expertise. Existing methods mostly assume noisy labels in different pixels are \textit{i.i.d}. However, segmentation label noise usually has str

Erick Guzman, Fariborz Kargar, Avani Patel, Saurabh Vishwakarma

We report an investigation of the bulk optical, bulk acoustic, and surface acoustic phonons in thin films of turbostratic boron nitride (t-BN) and cubic boron nitride (c-BN) grown on B-doped polycrystalline and single-crystalline diamond (001) and (111) substrates. The characteristics of different types of phonons were determined using Raman and Brillouin-Ma

Murthy S. Gudipati, Benjamin Fleury, Robert Wagner, Bryana L. Henderson

Ice is a major component of astrophysical environment - from interstellar molecular clouds through protoplanetary disks to evolved solar systems. Ice and complex organic matter coexist in these environments as well, and it is thought primordial ice brought the molecules of life to Earth four billion years ago, which could have kickstarted the origin of life

Debajyoti Chakraborty

Breast cancer is a leading cause of cancer-related deaths, but current programs are expensive and prone to false positives, leading to unnecessary follow-up and patient anxiety. This paper proposes a solution to automated breast cancer detection, to improve the efficiency and accuracy of screening programs. Different methodologies were tested against the RSN

A. Sen, C. Sumnicht, S. Choudhuri, A. Chang

As LEO/VLEO satellites offer many attractive features, such as low transmission delay, they are expected to be an integral part of 6G. Global entanglement distribution over LEO and VLEO satellites network must reckon with satellite movement over time. Current studies do not fully capture the dynamic nature of satellite constellations. We model a dynamic LEO/

Hayato Arai, Masahito Hayashi

It is a key issue to characterize the model of standard quantum theory out of general models by an operational condition. The framework of General Probabilistic Theories (GPTs) is a new information theoretical approach to single out standard quantum theory. It is known that traditional properties, for example, Bell-CHSH inequality are not sufficient to singl

Francesco Gabriele, Riccardo Senese, Claudio Castellani, Lara Benfatto

Transverse plasma polaritons and longitudinal plasmons describe the propagation of light-matter modes in an isotropic metal. However, in a layered metal the anisotropy of the bare electromagnetic response mixes the longitudinal and transverse excitations, making the distinction between polariton and plasmon blurred at small wavevectors, where retardation eff

Amir Mohammadian, Foad Ghaderi

Building detection and change detection using remote sensing images can help urban and rescue planning. Moreover, they can be used for building damage assessment after natural disasters. Currently, most of the existing models for building detection use only one image (pre-disaster image) to detect buildings. This is based on the idea that post-disaster image

Xiyuan Li, Shahram Abbassi, Varenya Upadhyaya, Xiyang Zhang

We investigate the role of r-mode oscillations in pulsar spin-down and their implications for gravitational wave emission and pulsar timing analysis. Using a non-linear differential framework that includes r-mode contributions, we derive time-dependent solutions for rotational frequency and period evolution. These expressions are validated using observationa

Brendan C. Sheehan, Guanchu Chen, Sai Chauhan, Rilla McKeegan

We present a novel design of loop-gap resonator, the loop-zag resonator, for sub-X-band electron-spin resonance spectroscopy. The loop-zag design can achieve improved coupling to small-sample spin systems through the improvement of sample filling factor and RF $B_1$ field. By introducing ``zags'' to the resonator's gap path, the capacitance is increased, acc

Giulio Burgio, Guillaume St-Onge, Laurent Hébert-Dufresne

People organize in groups and contagions spread across them. A simple process, but complex to model due to dynamical correlations within groups and between groups. Groups can also change as agents join and leave them to avoid infection. To study the characteristic levels of group activity required to best model dynamics and for agents to adapt, we develop ma

Yuwen Zhang, Chunfeng Cui, Chaoyu He, Tao Ouyang

Ferroelectricity has a wide range of applications in functional electronics and is extremely important for the development of next-generation information storage technology, but it is difficult to achieve due to its special symmetry requirements. In this letter, based on van derWaals stacking, a generic model is proposed for realizing ferroelectric devices,

Barco Jie You

The human brain is the substrate for human intelligence. By simulating the human brain, artificial intelligence builds computational models that have learning capabilities and perform intelligent tasks approaching the human level. Deep neural networks consist of multiple computation layers to learn representations of data and improve the state-of-the-art in

Hideki Yanaoka, Yoshitomo Hatakeyama

Axis switching of a jet ejected from a rectangular nozzle affects flow mixing characteristics. To elucidate such a mixing mechanism, the axis switching and vortex structure deformation should be investigated in detail. This study performed a numerical analysis of the axis switching of a pulsating jet ejected from a rectangular nozzle at a low Reynolds number