October 2024 arXiv papers — page 9

Congming Li, Meiqing Xu, Hui Yang, Ran Zhuo

This paper works on the direct method of moving spheres and establishes a Liouville-type theorem for the fractional elliptic equation \[ (-\Delta)^{\alpha/2} u =f(u) ~~~~~~ \text{in } \mathbb{R}^{n} \] with general non-linearity. One of the key improvement over the previous work is that we do not require the usual Lipschitz condition. In fact, we only assume

Ryohtaroh T. Ishikawa, Yukio Katsukawa

In the quiet region of the solar photosphere, turbulent convective motions of the granular flows naturally drive the subgranular-scale flows. However, evaluating such small-scale velocities is challenging because of the limited instrumental resolution. Our previous study, Ishikawa et al. (2020), found line broadening events during fading process of granules;

Ting Sun, Yanjin Wang

We consider the free boundary problem for a layer of compressible viscous barotropic fluid lying above a fixed rigid bottom and below the atmosphere of positive constant pressure. The fluid dynamics is governed by the compressible Navier--Stokes equations with gravity, and the effect of surface tension is neglected on the upper free boundary. We prove the gl

Sayaka Ichihara, Masato Yamagishi, Yuta Kurashina, Masanori Ota

In this work, the spatiotemporal pressure field of MHz-focused ultrasound is measured using a background-oriented schlieren technique combined with fast checkerboard demodulation and vector tomography (VT-BOS). Hydrophones have been commonly employed to directly measure the local pressure in underwater ultrasound. However, their limitations include that they

Ismail Belgacem, Roderick Edwards, Etienne Farcot

Glass networks model systems of variables that interact via sharp switching. A body of theory has been developed over several decades that, in principle, allows rigorous proof of dynamical properties in high dimensions that is not normally feasible in nonlinear dynamical systems. Previous work has, however, used examples of dimension no higher than 6 to illu

Shuihua Yang, Chen Jianfeng, Guoqiang Xu, Jiaxin Li

Nonreciprocal thermophotonics, by breaking Lorentz reciprocity, exceeds current theoretical efficiency limits, unlocking opportunities to energy devices and thermal management. However, energy transfer in current systems is highly defect-sensitive. This sensitivity is further amplified at deep subwavelength scales by inevitable multi-source interactions, int

Jinxue Fu, Juan Cheng, Weiming Li, Tao Xiong

An asymptotic-preserving (AP) implicit-explicit PN numerical scheme is proposed for the gray model of the radiative transfer equation, where the first- and second-order numerical schemes are discussed for both the linear and nonlinear models. The AP property of this numerical scheme is proved theoretically and numerically, while the numerical stability of th

Guan-Hua Huang, Wan-Chen Lai, Tai-Been Chen, Chien-Chin Hsu

Parkinson's disease (PD), a degenerative disorder of the central nervous system, is commonly diagnosed using functional medical imaging techniques such as single-photon emission computed tomography (SPECT). In this study, we utilized two SPECT data sets (n = 634 and n = 202) from different hospitals to develop a model capable of accurately predicting PD stag

Matthew Nethercote, Anastasia Kisil, Raphael Assier

Motivated by research in metamaterials, we consider the challenging problem of acoustic wave scattering by a doubly periodic quadrant of sound-soft scatterers arranged in a square formation, which we have dubbed the quarter lattice. This leads to a Wiener--Hopf equation in two complex variables with three unknown functions for which we can reduce and solve e

Daniyaer Paizulamu, Lin Cheng, Yingrui Zhuang, Helin Xu

LiFePO4 batteries are widely used in electric vehicles and energy storage systems due to long cycle life and high safety performance. However, the OCV-SOC curve (OSC) of these batteries features a long plateau region, making state of charge (SOC) estimation highly sensitive to OSC error, which arises due to aging and temperature. To address this, we propose

Charles Cifarelli

Let $B$ be a K\"ahler-Einstein Fano manifold, and $L \to B$ be a suitable root of the canonical bundle. We give a construction of complete Calabi-Yau metrics and gradient shrinking, steady, and expanding K\"ahler-Ricci solitons on the total space $M$, ${\rm dim}_{\mathbb{C}} M = n$ of certain vector bundles $E \to B$, composed of direct sums of powers of $L$

Sanjoy Dasgupta, Geelon So

In the realizable online setting, a learner is tasked with making predictions for a stream of instances, where the correct answer is revealed after each prediction. A learning rule is online consistent if its mistake rate eventually vanishes. The nearest neighbor rule (Fix and Hodges, 1951) is a fundamental prediction strategy, but it is only known to be con

Aditya Agarwal, Gaurav Singh, Bipasha Sen, Tomás Lozano-Pérez

Careful robot manipulation in every-day cluttered environments requires an accurate understanding of the 3D scene, in order to grasp and place objects stably and reliably and to avoid colliding with other objects. In general, we must construct such a 3D interpretation of a complex scene based on limited input, such as a single RGB-D image. We describe SceneC

Ramin Nateghi, Ruoji Zhou, Madeline Saft, Marina Schnauss

Artificial intelligence may assist healthcare systems in meeting increasing demand for pathology services while maintaining diagnostic quality and reducing turnaround time and costs. We aimed to investigate the performance of an institutionally developed system for prostate cancer detection, grading, and workflow optimization and to contrast this with commer

Hanchao Liu, Yujiang Li, Tai-Jiang Mu, Shi-Min Hu

Despite huge progress in skeleton-based action recognition, its generalizability to different domains remains a challenging issue. In this paper, to solve the skeleton action generalization problem, we present a recover-and-resample augmentation framework based on a novel complete action prior. We observe that human daily actions are confronted with temporal

Tomohiro Motoda, Takahide Kitamura, Ryo Hanai, Yukiyasu Domae

The development of large language models and vision-language models (VLMs) has resulted in the increasing use of robotic systems in various fields. However, the effective integration of these models into real-world robotic tasks is a key challenge. We developed a versatile robotic system called SuctionPrompt that utilizes prompting techniques of VLMs combine

Gregory Roudenko, Yurrian Pierre-Boyer

We explore Fermi's Paradox via a system of differential equations and using simulations of dispersal and interactions between competing interplanetary civilizations. To quantify the resources and potentials of these worlds, three different state variables representing population, environment, and technology, are used. When encounters occur between two differ

Yeachan Kim, Junho Kim, SangKeun Lee

Parameter-efficient fine-tuning (PEFT) has enabled the efficient optimization of cumbersome language models in real-world settings. However, as datasets in such environments often contain noisy labels that adversely affect performance, PEFT methods are inevitably exposed to noisy labels. Despite this challenge, the adaptability of PEFT to noisy environments

Sung-Jin Kim, Dae-Hyeok Lee, Hyeon-Taek Han

Electroencephalography (EEG) is a generally used neuroimaging approach in brain-computer interfaces due to its non-invasive characteristics and convenience, making it an effective tool for understanding human intentions. Therefore, recent research has focused on decoding human intentions from EEG signals utilizing deep learning methods. However, since EEG si

Akshay Rane, Kunalkumar Shelar

This paper uses the Modified Projection Method to examine the errors in solving the boundary integral equation from Laplace equation. The analysis uses weighted norms, and parallel algorithms help solve the independent linear systems. By applying the method developed by Kulkarni, the study shows how the approximate solution behaves in polygonal domains. It a

Jeremy McMahan

We extend anytime constraints to the Markov game setting and the corresponding solution concept of an anytime-constrained equilibrium (ACE). Then, we present a comprehensive theory of anytime-constrained equilibria that includes (1) a computational characterization of feasible policies, (2) a fixed-parameter tractable algorithm for computing ACE, and (3) a p

Kate Y. L. Su, Andras Gaspar, George H. Rieke, Renu Malhotra

We present images of the Vega planetary debris disk obtained at 15.5, 23, and 25.5 microns with the Mid-Infrared Instrument (MIRI) on JWST. The debris system is remarkably symmetric and smooth, and centered accurately on the star. There is a broad Kuiper-belt-analog ring at 80 to 170 au that coincides with the planetesimal belt detected with ALMA at 1.34 mm.

Hiroshi Noguchi

Nonequilibrium membrane pattern formation is studied using meshless membrane simulation. We consider that molecules bind to either surface of a bilayer membrane and move to the opposite leaflet by flip--flop. When binding does not modify the membrane properties and the transfer rates among the three states are cyclically symmetric, the membrane exhibits spir

Babak Akbari, Justin Frank, Melissa Greeff

Tiny aerial robots hold great promise for applications such as environmental monitoring and search-and-rescue, yet face significant control challenges due to limited onboard computing power and nonlinear dynamics. Model Predictive Control (MPC) enables agile trajectory tracking and constraint handling but depends on an accurate dynamics model. While existing

Menghua Deng, Wei Li, Kangyi Hu, Fuxiang Li

Non-Hermitian physics provides an effective description of open and nonequilibrium systems and hosts many novel and intriguing phenomena such as exceptional points and non-Hermitian skin effect. Despite extensive theoretical and experimental studies, however, how to properly deal with the nonadiabatic dynamics in driven non-Hermitian quantum system is still

Udaya Ghai, Karan Singh

The theory of boosting provides a computational framework for aggregating approximate weak learning algorithms, which perform marginally better than a random predictor, into an accurate strong learner. In the realizable case, the success of the boosting approach is underscored by a remarkable fact that the resultant sample complexity matches that of a comput

Pouya Asadi, Graham D. Kribs, Chester J. Hamilton Mantel

We identify symmetries in a broad class of vector-like confining dark sectors that forbid the leading electromagnetic moments that would ordinarily mediate dark baryon scattering with the Standard Model. The absence of these operators implies dark baryon dark matter has much smaller cross sections for elastic scattering off nuclei, leading to suppressed dire

Hadassah Harland, Richard Dazeley, Peter Vamplew, Hashini Senaratne

Emerging research in Pluralistic Artificial Intelligence (AI) alignment seeks to address how intelligent systems can be designed and deployed in accordance with diverse human needs and values. We contribute to this pursuit with a dynamic approach for aligning AI with diverse and shifting user preferences through Multi Objective Reinforcement Learning (MORL),

Kyungjin Seo, Junghoon Seo, Hanseok Jeong, Sangpil Kim

We present PiMForce, a novel framework that enhances hand pressure estimation by leveraging 3D hand posture information to augment forearm surface electromyography (sEMG) signals. Our approach utilizes detailed spatial information from 3D hand poses in conjunction with dynamic muscle activity from sEMG to enable accurate and robust whole-hand pressure measur

Gabriel Weiderpass, Mayur Sharma, Savdeep Sethi

A wide range of non-equilibrium phenomena in nature involve non-reciprocal interactions. To understand the novel behaviors that can emerge in such systems, finding tractable models is essential. With this goal, we introduce a non-reciprocal generalization of the kinetic Ising model in one dimension and solve it exactly. Our solution uncovers novel properties

Yucun Hou, Fenglin Zhan, Xin Cheng, Chenxi Li

Positron emission tomography (PET) is a critical tool for diagnosing tumors and neurological disorders but poses radiation risks to patients, particularly to sensitive populations. While reducing injected radiation dose mitigates this risk, it often compromises image quality. To reconstruct full-dose-quality images from low-dose scans, we propose a Cycle-con

Laboni Paul, Rahul Deb Mohalder, Kazi Masudul Alam

The population of the urban areas is increasing daily, and this migration is causing serious environmental pollution. A larger population is creating pressure on the municipality's waste management and the city corporations of developing countries such as Bangladesh, further threatening human health. New generation technologies, such as the Internet of Thing

Liuchuan Yu, Ching-Yu Cheng, William F Ranc, Joshua Dow

Virtual reality is widely adopted for applications such as training, education, and collaboration. The construction industry, known for its complex projects and numerous personnel involved, relies heavily on effective collaboration. Setting up a real-world construction site for experiments can be expensive and time-consuming, whereas conducting experiments i

Akihiro Sakoda, Nobuki Takayama

A holonomic system of linear partial differential equations is, roughly speaking, a system whose solution space is finite dimensional. A distribution that is a solution of a holonomic system is called a holonomic distribution. We give methods to numerically evaluate dual activations of holonomic activator distributions for neural tangent kernels. These metho

Jehan Yang, Maxwell Soh, Vivianna Lieu, Douglas J Weber

This paper introduces the first generalization and adaptation benchmark using machine learning for evaluating out-of-distribution performance of electromyography (EMG) classification algorithms. The ability of an EMG classifier to handle inputs drawn from a different distribution than the training distribution is critical for real-world deployment as a contr

Rui Hong, Hao-Wei Cui, An-Chun Ji, Shi-Ju Ran

Simulating strongly-correlated quantum systems in continuous space belongs to the most challenging and long-concerned issues in quantum physics. This work investigates the quantum entanglement and criticality of the ground-state wave-functions of infinitely-many coupled quantum oscillators (iCQOs). The essential task involves solving a set of partial differe

Xiufeng Song, Xiao Guo, Jiache Zhang, Qirui Li

Large numbers of synthesized videos from diffusion models pose threats to information security and authenticity, leading to an increasing demand for generated content detection. However, existing video-level detection algorithms primarily focus on detecting facial forgeries and often fail to identify diffusion-generated content with a diverse range of semant

Fateh Aliyev, Nikita Gladkov

We establish a lower bound on the forcing numbers of domino tilings computable in polynomial time based on height functions. This lower bound is sharp for a 2n by 2n square as well as other cases.

Ryan Welch, Jiaqi Zhang, Caroline Uhler

Causal disentanglement aims to learn about latent causal factors behind data, holding the promise to augment existing representation learning methods in terms of interpretability and extrapolation. Recent advances establish identifiability results assuming that interventions on (single) latent factors are available; however, it remains debatable whether such

Hrishabh Bharadwaj

We explore the properties of self-conjugate dark matter (DM) particles that predominantly interact with Standard Model electroweak gauge bosons, using an effective field theory approach. The study emphasizes effective contact interactions, invariant under the Standard Model gauge group, between vector DM and SM-neutral electroweak gauge bosons. Focusing on i

Zeph Landau, Yunchao Liu

We give a polynomial time algorithm that, given copies of an unknown quantum state $\vert\psi\rangle=U\vert 0^n\rangle$ that is prepared by an unknown constant depth circuit $U$ on a finite-dimensional lattice, learns a constant depth quantum circuit that prepares $\vert\psi\rangle$. The algorithm extends to the case when the depth of $U$ is $\mathrm{polylog

Virginia Vassilevska Williams, Zoe Xi, Yinzhan Xu, Uri Zwick

Let $G=(V,E,w)$ be a weighted directed graph without negative cycles. For two vertices $s,t\in V$, we let $d_{\le h}(s,t)$ be the minimum, according to the weight function $w$, of a path from $s$ to $t$ that uses at most $h$ edges, or hops. We consider algorithms for computing $d_{\le h}(s,t)$ for every $1\le h\le n$, where $n=|V|$, in various settings. We c

Dmitri Vassilevich

In this paper, we consider a Carroll magnetic limit of a one-loop scalar effective action. We work on general static backgrounds and compute both divergent and finite parts of the effective action in this limit. We show, that the divergent part can be removed by adding local counterterms. The finite part is related to an effective action in a lower dimension

Gabriel Artur Weiderpass, Mayur Sharma, Savdeep Sethi

Non-reciprocal interactions are a generic feature of non-equilibrium systems. We define a non-reciprocal generalization of the kinetic Ising model in one spatial dimension. We solve the model exactly using two different approaches for infinite, semi-infinite and finite systems with either periodic or open boundary conditions. The exact solution allows us to

Aaditya Ramdas, Ruodu Wang

This book is written to offer a humble, but unified, treatment of e-values in hypothesis testing. It is organized into three parts: Fundamental Concepts, Core Ideas, and Advanced Topics. The first part includes four chapters that introduce the basic concepts. The second part includes five chapters of core ideas such as universal inference, log-optimality, e-

Jinyoung Park, Minseong Bae, Dohwan Ko, Hyunwoo J. Kim

Large Language Models (LLMs) have demonstrated remarkable generalization and instruction-following capabilities with instruction tuning. The advancements in LLMs and instruction tuning have led to the development of Large Vision-Language Models (LVLMs). However, the competency of the LLMs and instruction tuning have been less explored in the molecular domain

Mahsa Karimi, Faezeh Kimiaee Asadi, Stephen C. Wein, Christoph Simon

In this paper, we investigate three schemes for implementing Controlled-Z (CZ) gates between individual ytterbium (Yb) rare-earth ions doped into yttrium orthovanadate (YVO$_4$ or YVO). Specifically, we investigate the CZ gates based on magnetic dipolar interactions between Yb ions, photon scattering off a cavity, and a photon interference-based protocol, wi

Yu-Long Yan, Quan Cheng, Xiao-Ping Zheng

The observed timing data, magnetic tilt angle $\chi$, and age of young pulsars could be used to probe some important issues about neutron star (NS) physics, e.g., the NS internal magnetic field configuration, and the number of precession cycles $\xi$. \textbf{Both} quantities are critical in studying the continuous gravitational wave emission from pulsars, a

Xinqi Huang, Chong Shangguan, Xiande Zhang, Yuhao Zhao

Motivated by the study of a variant of sunflowers, Alon and Holzman recently introduced focal-free hypergraphs. In this paper, we show that there is an interesting connection between the maximum size of focal-free hypergraphs and the renowned Erd\H{o}s Matching Conjecture on the maximum number of edges that can be contained in a uniform hypergraph with bound

Cheng Gao, Yuan Cao, Zihao Li, Yihan He

Despite the widespread success of Transformers across various domains, their optimization guarantees in large-scale model settings are not well-understood. This paper rigorously analyzes the convergence properties of gradient flow in training Transformers with weight decay regularization. First, we construct the mean-field limit of large-scale Transformers,

David Wan, Jesse Vig, Mohit Bansal, Shafiq Joty

Large Language Models (LLMs) often exhibit positional bias in long-context settings, under-attending to information in the middle of inputs. We investigate the presence of this bias in long-form summarization, its impact on faithfulness, and various techniques to mitigate this bias. To consistently evaluate faithfulness, we first compile a benchmark of eight

Tianyi Zhang, Baoxin Li, Jae-sun Seo, Yu Cao

In recent years, the long-range attention mechanism of vision transformers has driven significant performance breakthroughs across various computer vision tasks. However, the traditional self-attention mechanism, which processes both informative and non-informative tokens, suffers from inefficiency and inaccuracies. While sparse attention mechanisms have bee

Moeta Tsukamoto, Zhewen Xu, Tomoya Higo, Kouta Kondou

Spin chirality in antiferromagnets offers new opportunities for spintronics. The kagome antiferromagnet Mn3Sn is a paradigmatic material in which the antiferromagnetic order parameter can be detected and controlled by electrical means. However, direct investigation of the magnetic texture of Mn3Sn has been challenging because of the tiny moment hosted in its

Aadarsh Pathak, J. Stuart B. Wyithe, Ralph S. Sutherland, L. J Kewley

We present $[OIII]/H_{\rm \beta}$ emision line flux ratio predictions for galaxies at $z \sim 7-9$ using the MAPPINGS V v5.2.0 photoionization modelling code combined with an analytic galaxy formation model. Properties such as pressure and ionization parameter that determine emission line properties are thought to evolve towards high redshift. In order to de

Shuyang Yu, Runxue Bao, Parminder Bhatia, Taha Kass-Hout

Large language models (LLMs) can learn vast amounts of knowledge from diverse domains during pre-training. However, long-tail knowledge from specialized domains is often scarce and underrepresented, rarely appearing in the models' memorization. Prior work has shown that in-context learning (ICL) with retriever augmentation can help LLMs better capture long-t

Y. Boujakhrout, R. Sammani, E. H Saidi

Using the Chern-Simons formulation of AdS3 gravity as well as the Costello-Witten-Yamazaki (CWY) theory for quantum integrability, we construct a novel topological 4D gravity given by Eq(5.1) with observables based on gravitational gauge field holonomies. The field action $S^{grav}_{4D}$ of this gravity has a gauge symmetry $SL(2,\mathbb{C})$ and reads also

Colin Conwell, Christopher Hamblin, Chelsea Boccagno, David Mayo

When we experience a visual stimulus as beautiful, how much of that experience derives from perceptual computations we cannot describe versus conceptual knowledge we can readily translate into natural language? Disentangling perception from language in visually-evoked affective and aesthetic experiences through behavioral paradigms or neuroimaging is often e

Matthew Werenski, Brendan Mallery, Shuchin Aeron, James M. Murphy

We propose the linear barycentric coding model (LBCM) which utilizes the linear optimal transport (LOT) metric for analysis and synthesis of probability measures. We provide a closed-form solution to the variational problem characterizing the probability measures in the LBCM and establish equivalence of the LBCM to the set of 2-Wasserstein barycenters in the

Skyler Wu, Fred Lu, Edward Raff, James Holt

Online learning methods, like the seminal Passive-Aggressive (PA) classifier, are still highly effective for high-dimensional streaming data, out-of-core processing, and other throughput-sensitive applications. Many such algorithms rely on fast adaptation to individual errors as a key to their convergence. While such algorithms enjoy low theoretical regret,

Mohammedsaid Alhalimi, Tom Hutchcroft, Minghao Pan, Omer Tamuz

Let $\Gamma$ be a finitely generated group, and let $\mu$ be a nondegenerate, finitely supported probability measure on $\Gamma$. We show that every co-compact $\Gamma$ action on a locally compact Hausdorff space admits a nonzero $\mu$-stationary Radon measure. The main ingredient of the proof is a stationary analogue of Tarski's theorem: we show that for ev

Alexander J. White

We present the first calculations of the inelastic part of the dynamical structure factor (DSF) for warm dense matter (WDM) using Time-Dependent Orbital-Free Density Functional Theory (TD-OF-DFT) and Mixed-Stochastic-Deterministic (mixed) Kohn Sham TD-DFT (KS TD-DFT). WDM is an intermediate phase of matter found in planetary cores and laser-driven experiment

Minheng Chen, Chao Cao, Tong Chen, Yan Zhuang

The 3-hinge gyrus (3HG) is a newly defined folding pattern, which is the conjunction of gyri coming from three directions in cortical folding. Many studies demonstrated that 3HGs can be reliable nodes when constructing brain networks or connectome since they simultaneously possess commonality and individuality across different individual brains and populatio

Jinwei Chu

We discuss the transition between black strings and fundamental strings in the presence of a compact dimension, $\mathbb{S}^1_z$. In particular, we study the Horowitz-Polchinski effective field theory in $\mathbb{R}^d\times\mathbb{S}^1_z$, with a reduction on the Euclidean time circle $\mathbb{S}_\tau^1$. The classical solution of this theory describes a bou

Y. Yin

We investigate the wave function of electrons and holes injected by voltage pulses with non-integer flux quantum. We find that the wave function can be delocalized in the time domain, which is measured by using the inverse participation ratio. As the flux approaches an integer multiples of flux quantum, the wave function can either remain delocalized or unde

Jiayu Su, David A. Knowles, Raul Rabadan

The success of machine learning models relies heavily on effectively representing high-dimensional data. However, ensuring data representations capture human-understandable concepts remains difficult, often requiring the incorporation of prior knowledge and decomposition of data into multiple subspaces. Traditional linear methods fall short in modeling more

Weiguo Gao, Ming Li

Real-world data is often assumed to lie within a low-dimensional structure embedded in high-dimensional space. In practical settings, we observe only a finite set of samples, forming what we refer to as the sample data subspace. It serves an essential approximation supporting tasks such as dimensionality reduction and generation. A major challenge lies in wh

Robert B. Murphy, Richard P. Messmer

A model for the bonding of hypervalent molecules containing sulfur is presented using generalized valence bond wavefunctions. To delineate the model from more commonly used DFT and delocalized wavefunctions, we present detailed comparisons to these other methods. The result is a new concept of local transferable bonding in hypervalent molecules.

Binyan Xu, Yufan Dai, Afzal Suleman, Yang Shi

In order to address the nonlinear multi-agent formation tracking control problem with input constraints and unknown communication faults, a novel adaptive distributed observer-based distributed model predictive control method is developed in this paper. This design employs adaptive distributed observers in local control systems to estimate the leader's state

Nick Pant, Rob Armitage, Emmanouil Kioupakis

Significant effort has been devoted to mitigating polarization fields in nitride LEDs, as these fields are traditionally viewed as detrimental to light emission, particularly for red emission. Contrary to this prevailing notion, we demonstrate that strong polarization fields can enhance the optical-transition strength of AlInGaN quantum wells emitting in the

Eric J Tchetgen Tchetgen

The instrumental variable method is a prominent approach to recover under certain conditions, valid inference about a treatment causal effect even when unmeasured confounding might be present. In a groundbreaking paper, Imbens and Angrist (1994) established that a valid instrument nonparametrically identifies the average causal effect among compliers, also k

Paulson Kavalambramalil George, Hanna Terletska, Herbert F Fotso

We investigate how an external driving field can control the amount of extractable work from a quantum emitter, a two-level quantum system (TLS) interacting with a photonic environment. In this scenario, the TLS functions as a quantum battery, interacting with the photonic bath that discharges it while the control field recharges it. Ergotropy serves as our

Rodrigues D. Dikandé Bitha, Andrus Giraldo, Neil G. R. Broderick, Bernd Krauskopf

Optical systems that combine nonlinearity with coupling between various subsystems offer a flexible platform for observing a diverse range of nonlinear dynamics. Furthermore, engineering tolerances are such that the subsystems can be identical to within a fraction of the wavelength of light; hence, such coupled systems inherently have a natural symmetry that

Jinyong Chen, Rui Zhou, Zhaozong Wang, Yunjie Zhang

This paper studies the problem of multi-robot pursuit of how to coordinate a group of defending robots to capture a faster attacker before it enters a protected area. Such operation for defending robots is challenging due to the unknown avoidance strategy and higher speed of the attacker, coupled with the limited communication capabilities of defenders. To s

Yu-Long Yan, Quan Cheng, Xiao-Ping Zheng

The observational data (e.g., the timing data and magnetic tilt angles $\chi$) of young pulsars can be used to probe some critical issues about the internal physics of neutron stars (NSs), for instance, the number of precession cycles $\xi$ and the internal magnetic field configuration (IMFC) of NSs. Evolution of the dipole magnetic field $B_{\rm d}$ of NSs

Andy Lo, Albert Q. Jiang, Wenda Li, Mateja Jamnik

Ontologies are useful for automatic machine processing of domain knowledge as they represent it in a structured format. Yet, constructing ontologies requires substantial manual effort. To automate part of this process, large language models (LLMs) have been applied to solve various subtasks of ontology learning. However, this partial ontology learning does n

Farshad Noravesh

State-of-the-art models for relation extraction (RE) in the biomedical domain consider finetuning BioBERT using classification, but they may suffer from the anisotropy problem. Contrastive learning methods can reduce this anisotropy phenomena, and also help to avoid class collapse in any classification problem. In the present paper, a new training method cal

Shaoyan Pan, Vanessa Su, Junbo Peng, Junyuan Li

We present a new imaging system to support real-time tumor tracking for surface-guided radiotherapy (SGRT). SGRT uses optical surface imaging (OSI) to acquire real-time surface topography images of the patient on the treatment couch. However, OSI cannot visualize internal anatomy. This study proposes an Advanced Surface Imaging (A-SI) framework to address th

Guibin Sun, Yang Xu, Kexin Liu, Jinhu Lü

Different from most of the formation strategies where robots require unique labels to identify topological neighbors to satisfy the predefined shape constraints, we here study the problem of identity-less distributed shape formation in homogeneous swarms, which is rarely studied in the literature. The absence of identities creates a unique challenge: how to

Qiao Wang, Chester Lee Schmaltz, Jeannette Jackson-Thompson, Dongchu Sun

In public health, it is critical for policymakers to assess the relationship between the disease prevalence and associated risk factors or clinical characteristics, facilitating effective resources allocation. However, for diseases like female breast cancer (FBC), reliable prevalence data at specific geographical levels, such as the county-level, are limited

Mukul Rai Choudhuri

Kakeya sets are compact subsets of $\mathbb{R}^n$ that contain a unit line segment pointing in every direction. The Kakeya conjecture states that such sets must have Hausdorff dimension $n$. The property of stickiness was first discovered by Katz-{\L}aba-Tao in their 1999 breakthrough paper on the Kakeya problem. Then Wang-Zahl formalized the definition of a

Janakan Sivaloganathan, Ainaz Jamshidi, Andriy Miranskyy, Lei Zhang

Flaky tests, which pass or fail inconsistently without code changes, are a major challenge in software engineering in general and in quantum software engineering in particular due to their complexity and probabilistic nature, leading to hidden issues and wasted developer effort. We aim to create an automated framework to detect flaky tests in quantum softwar

Ziqi Gao, Wendi Yang, Yujia Li, Lei Xing

Non-semantic context information is crucial for visual recognition, as the human visual perception system first uses global statistics to process scenes rapidly before identifying specific objects. However, while semantic information is increasingly incorporated into computer vision tasks such as image reconstruction, non-semantic information, such as global

Yu-Long Yan, Quan Cheng, Xiao-Ping Zheng, Xia-Xia Ouyang

The initial spin periods of newborn magnetars are \textbf{strongly associated with the origin of their strong magnetic fields, both of which can affect the electromagnetic radiation and gravitational waves (GWs) emitted at their birth.} Combining the upper limit $E_{\rm SNR}\lesssim10^{51}$ erg on the explosion energies of \textbf{the supernova (SN) remnants

Zhang Zhang, Da Li, Geng Wu, Yaoning Li

In this paper, we create "Love in Action" (LIA), a body language-based social game utilizing video cameras installed in public spaces to enhance social relationships in real-world. In the game, participants assume dual roles, i.e., requesters, who issue social requests, and performers, who respond social requests through performing specified body languages.

Ryosuke Kadono, Takashi U. Ito

We extend the previously published model that distinguishes between the diffusive motion of diamagnetic muons and the dynamics of ions around the muon in matter, and propose a generalized model for {\sl paramagnetic muons} (Mu$^0$s, bound states of a muon and an unpaired electron) observed in non-metallic host materials. The new model distinguishes among the

Zhengmiao Wang, Zhi-Wei Liu, Ming Chi, Xiaoling Wang

This paper addresses an online convex optimization problem where the cost function at each step depends on a history of past decisions (i.e., memory), and the decision maker has access to limited predictions of future cost values within a finite window. The goal is to design an algorithm that minimizes the dynamic regret against the optimal sequence of decis

Yuki Osada, Ryo Takagi, Hideki Arimatsu, Takuya Fujima

Poly 3,4-ethylenedioxythiophene (PEDOT) has been attracting attention as a thermoelectric material for room-temperature use due to its flexibility and non-toxicity. However, PEDOT reportedly generates insufficient thermoelectric power for practical use. This work tried to improve the Seebeck coefficient by introducing molecular strain to PEDOT molecules by l

Xiaolei Guo, Xi Liu

With the 15 yrs of Pass 8 data recorded by the {\em Fermi} Large Area Telescope, we report the detection of an extended gigaelectronvolt emission component with a 68\% containment radius of $0^{\circ}\!.85$, which is spatially associated with the newly identified supernova remnant (SNR) G321.3-3.9. The $\gamma$-ray spectrum is best described by a log-parabol

Shaswat Garg, Houman Masnavi, Baris Fidan, Farrokh Janabi-Sharifi

This paper presents a novel reinforcement learning framework for trajectory tracking of unmanned aerial vehicles in cluttered environments using a dual-agent architecture. Traditional optimization methods for trajectory tracking face significant computational challenges and lack robustness in dynamic environments. Our approach employs deep reinforcement lear

Minghong Xie, Mengzhao Wang, Huafeng Li, Yafei Zhang

Visual grounding has attracted wide attention thanks to its broad application in various visual language tasks. Although visual grounding has made significant research progress, existing methods ignore the promotion effect of the association between text and image features at different hierarchies on cross-modal matching. This paper proposes a Phrase Decoupl

Yujie Zhao, Jose Efraim Aguilar Escamill, Weyl Lu, Huazheng Wang

Reinforcement Learning from Human Feedback (RLHF) has recently surged in popularity, particularly for aligning large language models and other AI systems with human intentions. At its core, RLHF can be viewed as a specialized instance of Preference-based Reinforcement Learning (PbRL), where the preferences specifically originate from human judgments rather t

Lingyun Ding, Sarah C. Burnett, Andrea L. Bertozzi

Spiral gravity separators are designed to separate multi-species slurry components based on differences in density and size. Previous studies have investigated steady-state solutions for mixtures of liquids and single particle species in thin-film flows. However, these models are constrained to single-species systems and cannot describe the dynamics of multi

Junichi Takahashi, Hiroshi Ohno, Akio Tomiya

We present spectral functions extracted from Euclidean-time correlation functions by using sparse modeling. Sparse modeling is a method that solves inverse problems by considering only the sparseness of the solution we seek. To check applicability of the method, we firstly test it with mock data which imitate charmonium correlation functions on a fine lattic

Pierre Aboulker, Frédéric Havet, William Lochet, Raul Lopes

A class of acyclic digraphs $\mathscr{C}$ is linearly unavoidable if there exists a constant $c$ such that every digraph $D\in \mathscr{C}$ is contained in all tournaments of order $c\cdot |V(D)|$. The class of all acyclic digraphs is not linearly avoidable, and Fox, He, and Widgerson recently showed that this is not even the case for acyclic digraphs with b

Sang-Eon Han

In the literature of a digital-topological ($DT$-, for brevity) group structure on a digital image $(X,k)$, roughly saying, two kinds of methods are shown. Given a digital image $(X,k)$, the first one, named by a $DT$-$k$-group, was established in 2022 \cite{H10} by using both the $G_{k^\ast}$- or $C_{k^\ast}$-adjacency \cite{H10} for the product $X^2:=X \ti

D. A. Khudaiberdiev, Z. D. Kvon, M. S. Ryzhkov, D. A. Kozlov

We report the experimental observation of Anderson localization in two-dimensional (2D) electrons and holes in the bulk of HgTe quantum wells with a semimetallic spectrum and under strong disorder. In contrast, the one-dimensional (1D) edge channels, arising from the spectrum's inversion, demonstrate remarkable robustness against disorder due to topological

Zheng Che, Meng Shen, Zhehui Tan, Hanbiao Du

With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious transactions is

Tianqi Liu, Jiancheng Lai, Zhenhua Li, Tao Li

Quantum secret sharing (QSS) plays a pivotal role in multiparty quantum communication, ensuring the secure distribution of private information among multiple parties. However, the security of QSS schemes can be compromised by attacks exploiting imperfections in measurement devices. Here, we propose a reconfigurable approach to implement QSS based on measurem

Wing Pok Lee, Jonathan D. Woo, Luke F. Triplett, Yifan Gu

The dynamics of viscous thin-film particle-laden flows down inclined surfaces are commonly modeled with one of two approaches: a diffusive flux model or a suspension balance model. The diffusive flux model assumes that the particles migrate via a diffusive flux induced by gradients in both the particle concentration and the effective suspension viscosity. Th

Lian-Hui Yu, Xiao-Yu Li, Geng Chen, Qin-Sheng Zhu

Quantum architecture search (QAS) represents a fundamental challenge in quantum machine learning. Unlike previous methods that treat it as a static search process, from a perspective on QAS as an item retrieval task in vast search space, we decompose the search process into dynamic alternating phases of coarse and fine-grained knowledge learning. We propose