April 2024 arXiv papers — page 124

Zhe Yu

We are concerned with the study of the twin non-local inequalities featuring non-homogeneous differential operators $$\displaystyle -\Delta^2 u + \lambda\Delta u \geq (K_{\alpha, \beta} * u^p)u^q \quad\text{ in } \mathbb{R}^N (N\geq 1),$$ and $$\displaystyle \Delta^2 u - \lambda\Delta u \geq (K_{\alpha, \beta} * u^p)u^q \quad\text{ in } \mathbb{R}^N (N\geq 1

Masao Jinzenji, Ken Kuwata

In this paper, we propose a method for computing genus 1 Gromov-Witten invariants of Calabi-Yau and Fano projective hypersurfaces using the B-model. Our formalism is applicable to both Calabi-Yau and Fano cases. In the Calabi-Yau case, significant cancellation of terms within our formalism occurs, resulting in an alternative representation of the BCOV-Zinger

Kasra Arnavaz, Kenny Erleben

Soft robots have gained increased popularity in recent years due to their adaptability and compliance. In this paper, we use a digital twin model of cable-driven soft robots to learn control parameters in simulation. In doing so, we take advantage of differentiable rendering as a way to instruct robots to complete tasks such as point reach, gripping an objec

Davide Pedrotti, Sunny Vagnozzi

Eikonal quasinormal modes (QNMs) of black holes (BHs) and parameters of null geodesics, ultimately tied to the appearance of BHs to external observers, are known to be related, and the eikonal QNM-BH shadow radii correspondence has been extensively studied for spherically symmetric BHs. The extension to rotating BHs is non-trivial, and has been worked out on

Robert Fleischer, Eleftheria Malami, Anders Rehult, K. Keri Vos

Experimental data on rare $B$-meson decays indicate deviations from Standard Model predictions. In studies of these decays, possible new sources of CP violation are often neglected. We discuss CP violation in the rare $B$-meson decays $B\to K\ell^+\ell^- (\ell = \mu,e)$ and point to two phenomena that arise when new sources of CP violation are included. Firs

Peter Eichelsbacher

In this paper, we study a mean-field spin model with three- and two-body interactions. In a recent paper by Contucci, Mingione and Osabutey, the equilibrium measure for large volumes was shown to have three pure states, two with opposite magnetization and an unpolarized one with zero magnetization, merging at the critical point. The authors proved a central

Daichi Hiraki, Yasuyuki Hamura, Kaoru Irie, Shonosuke Sugasawa

Functional time series data frequently appears in econometric analyses, where the functions of interest are subject to some shape constraints, including monotonicity and convexity, as typical of the estimation of the Lorenz curve. This paper proposes a state-space model for time-varying functions to extract trends and serial dependence from functional time s

J. Cernicharo, B. Tercero, M. Agúndez, C. Cabezas

We present a study of CH$_3$CH$_2$CCH, CH$_3$CH$_2$CN, CH$_2$CHCCH, and CH$_2$CHCN in TMC-1 using the QUIJOTE$^1$ line survey. We confirm the presence of CH$_3$CH$_2$CCH in TMC-1, which was previously reported as tentative by our group. From a detailed study of the ethynyl and cyanide derivatives of CH$_2$CH$_2$ and CH$_3$CH$_3$ in TMC-1, we found that the C

Chaoqun He, Renjie Luo, Shengding Hu, Yuanqian Zhao

Evaluation is pivotal for refining Large Language Models (LLMs), pinpointing their capabilities, and guiding enhancements. The rapid development of LLMs calls for a lightweight and easy-to-use framework for swift evaluation deployment. However, considering various implementation details, developing a comprehensive evaluation platform is never easy. Existing

Yuki Hirano, Martin Kalck, Genki Ouchi

We introduce the notion of composition series of triangulated categories, which generalizes full exceptional sequences. The lengths of composition series yield invariants for triangulated categories. We study composition series of derived categories for some classes of projective varieties and finite-dimensional algebras. We prove that certain negative ratio

I. Kostylev, M. Hatifi, D. Konstantinov, A. D Chepelianskii

The properties of a two-dimensional (2D) electron system can be drastically altered by a magnetic field applied perpendicular to the 2D plane. In particular, the frequency of its bulk collective excitations becomes gaped at the cyclotron frequency, while the low-frequency localized excitations, the edge magnetoplasmon (EMP), appear near the system's edge. A

Jiachen Zhu, Yichao Wang, Jianghao Lin, Jiarui Qin

We primarily focus on the field of multi-scenario recommendation, which poses a significant challenge in effectively leveraging data from different scenarios to enhance predictions in scenarios with limited data. Current mainstream efforts mainly center around innovative model network architectures, with the aim of enabling the network to implicitly acquire

Jinhong Wang, Yi Cheng, Jintai Chen, Hongxia Xu

Multi-rater annotations commonly occur when medical images are independently annotated by multiple experts (raters). In this paper, we tackle two challenges arisen in multi-rater annotations for medical image segmentation (called ambiguous medical image segmentation): (1) How to train a deep learning model when a group of raters produces a set of diverse but

Uyoata E. Uyoata, Abolfazl Amiri, Enric Juan, Guillermo Pocovi

Mechanisms for data recovery and packet reliability are essential components of the upcoming 6th generation (6G) communication system. In this paper, we evaluate the interaction between a fast hybrid automatic repeat request (HARQ) scheme, present in the physical and medium access control layers, and a higher layer automatic repeat request (ARQ) scheme which

O. T. Whaites, T. S. Monteiro

For applications of solid state quantum computing and quantum simulations, high fidelity initialisation of thermally mixed electronic and nuclear spin qubits is essential. Whereas electronic spins can readily be initialised optically to high fidelity, initialisation of the nuclear spins requires alternative approaches, such as dynamic nuclear polarisation (D

Lidang Jiang, Changyan Hu, Sibei Ji, Hang Zhao

In optimizing performance and extending the lifespan of lithium batteries, accurate state prediction is pivotal. Traditional regression and classification methods have achieved some success in battery state prediction. However, the efficacy of these data-driven approaches heavily relies on the availability and quality of public datasets. Additionally, genera

Mathias S. Feinler, Bernadette N. Hahn

Magnetic Resonance Imaging allows high resolution data acquisition with the downside of motion sensitivity due to relatively long acquisition times. Even during the acquisition of a single 2D slice, motion can severely corrupt the image. Retrospective motion correction strategies do not interfere during acquisition time but operate on the motion affected dat

Zeyu Zhang, Yuanshen Zhao, Jingxian Duan, Yaou Liu

The diagnosis and prognosis of cancer are typically based on multi-modal clinical data, including histology images and genomic data, due to the complex pathogenesis and high heterogeneity. Despite the advancements in digital pathology and high-throughput genome sequencing, establishing effective multi-modal fusion models for survival prediction and revealing

Tien-Hong Lo, Fu-An Chao, Tzu-I Wu, Yao-Ting Sung

Automated speaking assessment (ASA) typically involves automatic speech recognition (ASR) and hand-crafted feature extraction from the ASR transcript of a learner's speech. Recently, self-supervised learning (SSL) has shown stellar performance compared to traditional methods. However, SSL-based ASA systems are faced with at least three data-related challenge

Reza Hafezi, David A. Wood, Firouzeh Rosa Taghikhah

Climate change and environmental concerns represent a global crisis accompanied by significant economic challenges. Regular international conferences held to address these issues, such as in the UK (2021) and Egypt (2022), spark debate about the effectiveness and practicality of international commitments. This study examines international treaties from a dif

Alessandro Candido, Luigi Del Debbio, Tommaso Giani, Giacomo Petrillo

We discuss a Bayesian methodology for the solution of the inverse problem underlying the determination of parton distribution functions (PDFs). In our approach, Gaussian Processes (GPs) are used to model the PDF prior, while Bayes theorem is used in order to determine the posterior distribution of the PDFs given a set of data. We discuss the general formalis

ZhenZhe Gao, Zhenjun Tang, Zhaoxia Yin, Baoyuan Wu

Neural networks have increasingly influenced people's lives. Ensuring the faithful deployment of neural networks as designed by their model owners is crucial, as they may be susceptible to various malicious or unintentional modifications, such as backdooring and poisoning attacks. Fragile model watermarks aim to prevent unexpected tampering that could lead D

Xiao Tan, Changxin Liu, Karl H. Johansson, Dimos V. Dimarogonas

In this work, we propose a continuous-time distributed optimization algorithm with guaranteed zero coupling constraint violation and apply it to safe distributed control in the presence of multiple control barrier functions (CBF). The optimization problem is defined over a network that collectively minimizes a separable cost function with coupled linear cons

Alex Camilo Gormaz-Matamala, Jorge Cuadra, Sylvia Ekström, Georges Meynet

We use Geneva-evolution-code to run evolutionary tracks for stellar masses ranging from $20$ to $85$ $M_\odot$ at SMC metallicity ($Z=0.002$). We upgrade the recipe for stellar winds by adopting our self-consistent m-CAK prescription, which reduces the value of mass-loss rate by a factor between 2 and 6 depending on the mass range. The impact of our new wind

Marcel Hallgarten, Julian Zapata, Martin Stoll, Katrin Renz

Real-world autonomous driving systems must make safe decisions in the face of rare and diverse traffic scenarios. Current state-of-the-art planners are mostly evaluated on real-world datasets like nuScenes (open-loop) or nuPlan (closed-loop). In particular, nuPlan seems to be an expressive evaluation method since it is based on real-world data and closed-loo

Masataka Iwai, Shin-ichi Matsumura, Niklas Müller

In this paper, we establish a structure theorem for minimal projective klt varieties $X$ that satisfiy Miyaoka's equality $3c_2(X) = c_1(X)^2$. Specifically, we prove that the canonical divisor $K_X$ is semi-ample and that the Kodaira dimension $\kappa(K_X)$ is either $0$, $1$, or $2$. Furthermore, based on this abundance result, we show that a maximally qua

Shahin Tavakoli, Beatrice Matteo, Davide Pigoli, Eleanor Chodroff

Phonetics is the scientific field concerned with the study of how speech is produced, heard and perceived. It abounds with data, such as acoustic speech recordings, neuroimaging data, or articulatory data. In this paper, we provide an introduction to different areas of phonetics (acoustic phonetics, sociophonetics, speech perception, articulatory phonetics,

Grzegorz Świderski

We study Nevai's condition from the theory of orthogonal polynomials on the real line. We prove that a large class of measures with unbounded Jacobi parameters satisfies Nevai's condition locally uniformly on the support of the measure away from a finite explicit set. This allows us to give applications to relative uniform and weak asymptotics of Christoffel

Claudia Toci, Simone Ceppi, Nicolás Cuello, Gaspard Duchêne

GG Tau is one of the most studied multiple young stellar systems: GG Tau A is a hierarchical triple surrounded by a massive disc and its companion, GG Tau B, is also a binary. Despite numerous observational attempts, an understanding of the geometry of the GG Tau A system is still elusive. We provide new astrometric measures of the system and we run a set of

Stanislav Frolov, Brian B. Moser, Sebastian Palacio, Andreas Dengel

We present ObjBlur, a novel curriculum learning approach to improve layout-to-image generation models, where the task is to produce realistic images from layouts composed of boxes and labels. Our method is based on progressive object-level blurring, which effectively stabilizes training and enhances the quality of generated images. This curriculum learning s

Janik Schüttler, Robert L. Jack, Michael E. Cates

We study the effect of phase separating diffusive dynamics on the mean time to extinction in several reaction-diffusion models with slow reactions. We consider a continuum theory similar to model AB, and a simple model where individual particles on two sites undergo on-site reactions and hopping between the sites. In the slow-reaction limit, we project the m

Prosenjit Bose, Guillermo Esteban, David Orden, Rodrigo I. Silveira

Continuous 2-dimensional space is often discretized by considering a mesh of weighted cells. In this work we study how well a weighted mesh approximates the space, with respect to shortest paths. We consider a shortest path $ \mathit{SP_w}(s,t) $ from $ s $ to $ t $ in the continuous 2-dimensional space, a shortest vertex path $ \mathit{SVP_w}(s,t) $ (or any

Maryam Massouras, Salvatore Perna, Massimiliano d'Aquino, Claudio Serpico

We present a computational study of the parametric excitation of spin waves in thin film disks with a mode-resolved approach. The method involves projecting out the time-dependent magnetization, computed using micromagnetics simulations, onto the spatial profile of the eigenmodes that are obtained from the linearization of the equations of motion. Unlike spe

Xavier Alameda-Pineda, Angus Addlesee, Daniel Hernández García, Chris Reinke

Despite the many recent achievements in developing and deploying social robotics, there are still many underexplored environments and applications for which systematic evaluation of such systems by end-users is necessary. While several robotic platforms have been used in gerontological healthcare, the question of whether or not a social interactive robot wit

Dan Qiao, Yu-Xiang Wang

We study the problem of multi-agent reinforcement learning (multi-agent RL) with differential privacy (DP) constraints. This is well-motivated by various real-world applications involving sensitive data, where it is critical to protect users' private information. We first extend the definitions of Joint DP (JDP) and Local DP (LDP) to two-player zero-sum epis

Xingzhi Wang

Statistical independence is a notion ubiquitous in various fields such as in statistics, probability, number theory and physics. We establish the stability of independence for any pair of random variables by their corresponding Brockwell transforms (Brockwell, 2007) beyond the non-atomic condition that is naturally imposed on their distributions, thereby gen

Pavlo Mykytyn, Marcin Brzozowski, Zoya Dyka, Peter Langendörfer

In the era of cutting-edge autonomous systems, Unmanned Aerial Vehicles (UAVs) are becoming an essential part of the solutions for numerous complex challenges. This paper evaluates UAV peer-to-peer telemetry communication, highlighting its security vulnerabilities and explores a transition to a het-erogeneous multi-hop mesh all-to-all communication architect

Ziteng Liu, Jiahua Zhu, Bainan Liu, Hao Liu

This paper presents a novel method of smoke removal from the laparoscopic images. Due to the heterogeneous nature of surgical smoke, a two-stage network is proposed to estimate the smoke distribution and reconstruct a clear, smoke-free surgical scene. The utilization of the lightness channel plays a pivotal role in providing vital information pertaining to s

Qingxiang Zhang, Feng Huang, Zhongxiang Wang, Taotao Fang

Polarimetric features during the prompt phase of Gamma-ray Bursts (GRBs) have been essential for elucidating the debated emission mechanisms and gaining insight into the inner structure of GRBs. However, the potential impact of photon-Axion-Like-Particle (ALP) mixing in extragalactic magnetic fields, leading to significant modifications to the initial polari

Jae Wan Park, Sang Hyun Park, Jun Young Koh, Junha Lee

The emergence of various adapters, including Low-Rank Adaptation (LoRA) applied from the field of natural language processing, has allowed diffusion models to personalize image generation at a low cost. However, due to the various challenges including limited datasets and shortage of regularization and computation resources, adapter training often results in

M. M. Morsali, Z. Sharifi, F. Fallah, S. Hashembeiki

This paper introduces SFSORT, the world's fastest multi-object tracking system based on experiments conducted on MOT Challenge datasets. To achieve an accurate and computationally efficient tracker, this paper employs a tracking-by-detection method, following the online real-time tracking approach established in prior literature. By introducing a novel cost

Bo Zhang, Jinli Suo, Qionghai Dai

Video snapshot compressive imaging (SCI) encodes the target dynamic scene compactly into a snapshot and reconstructs its high-speed frame sequence afterward, greatly reducing the required data footprint and transmission bandwidth as well as enabling high-speed imaging with a low frame rate intensity camera. In implementation, high-speed dynamics are encoded

François Brunault

Borisov and Gunnells have proved that certain linear combinations of products of Eisenstein series are Eisenstein series themselves, in analogy with the Manin relations for modular symbols. We devise a new method for determining and proving such relations, by differentiating with respect to the parameters of the Eisenstein series.

Yijie Chen, Yijin Liu, Fandong Meng, Yufeng Chen

Code generation aims to understand the problem description and generate corresponding code snippets, where existing works generally decompose such complex tasks into intermediate steps by prompting strategies, such as Chain-of-Thought and its variants. While these studies have achieved some success, their effectiveness is highly dependent on the capabilities

Domenico Cotroneo, Roberta De Luca, Pietro Liguori

Context: AI code generators are revolutionizing code writing and software development, but their training on large datasets, including potentially untrusted source code, raises security concerns. Furthermore, these generators can produce incomplete code snippets that are challenging to evaluate using current solutions. Objective: This research work introduce

Karl Schrab, Moritz Schweppenhäuser, Robert Protzmann, Kay Massow

Ride-hailing services enjoy a large popularity in the sector of individualized mobility. Due to broad availability, ease of use, and competitive pricing strategies, these services have established themselves throughout the last decades. With the increased popularity, ride-hailing providers aimed to consistently improve the efficiency of their services, leadi

Quanyu Long, Yin Wu, Wenya Wang, Sinno Jialin Pan

In-context Learning (ICL) has emerged as a powerful capability alongside the development of scaled-up large language models (LLMs). By instructing LLMs using few-shot demonstrative examples, ICL enables them to perform a wide range of tasks without updating millions of parameters. However, the precise contributions of demonstrations towards improving end-tas

Ang Li, Anning Hu, Wei Xi, Wenxian Yu

Accurate and dense depth estimation with stereo cameras and LiDAR is an important task for automatic driving and robotic perception. While sparse hints from LiDAR points have improved cost aggregation in stereo matching, their effectiveness is limited by the low density and non-uniform distribution. To address this issue, we propose a novel stereo-LiDAR dept

Robert Vacareanu, Vlad-Andrei Negru, Vasile Suciu, Mihai Surdeanu

We analyze how well pre-trained large language models (e.g., Llama2, GPT-4, Claude 3, etc) can do linear and non-linear regression when given in-context examples, without any additional training or gradient updates. Our findings reveal that several large language models (e.g., GPT-4, Claude 3) are able to perform regression tasks with a performance rivaling

Yule Duan, Xiao Wu, Haoyu Deng, Liang-Jian Deng

Currently, machine learning-based methods for remote sensing pansharpening have progressed rapidly. However, existing pansharpening methods often do not fully exploit differentiating regional information in non-local spaces, thereby limiting the effectiveness of the methods and resulting in redundant learning parameters. In this paper, we introduce a so-call

ALICE Collaboration

The intense photon fluxes from relativistic nuclei provide an opportunity to study photonuclear interactions in ultraperipheral collisions. In particular, it allows for the investigations of excited, light-flavour vector mesons. The measurement of coherently photoproduced $ \pi^+ \pi^- \pi^+ \pi^-$ final states in ultraperipheral Pb$-$Pb collisions at $\sqrt

Shejie Qin, Yu Yang, Yongxiang Huang, Xinyu Mei

Traditionally, results given by the direct numerical simulation (DNS) of Navier-Stokes equations are widely regarded as reliable benchmark solutions of turbulence, as long as grid spacing is fine enough (i.e. less than the minimum Kolmogorov scale) and time-step is small enough, say, satisfying the Courant-Friedrichs-Lewy condition. Is this really true? In t

Thomas Serre, Mathieu Fontaine, Éric Benhaim, Geoffroy Dutour

Isolating the desired speaker's voice amidst multiplespeakers in a noisy acoustic context is a challenging task. Per-sonalized speech enhancement (PSE) endeavours to achievethis by leveraging prior knowledge of the speaker's voice.Recent research efforts have yielded promising PSE mod-els, albeit often accompanied by computationally intensivearchitectures, u

Caroline Hillairet, Thomas Peyrat, Anthony Réveillac

In this paper we develop a representation formula of Clark-Ocone type for any integrable Poisson functionals, which extends the Poisson imbedding for point processes. This representation formula differs from the classical Clark-Ocone formula on three accounts. First the representation holds with respect to the Poisson measure instead of the compensated one;

Nathan Szwarcberg, Tom Colinot, Christophe Vergez, Michaël Jousserand

This study investigates the role of localized nonlinear losses in the register hole on the production of second-register notes. First, an experiment is conducted to study the ability of a register hole to produce second register. A cylindrical tube is drilled with holes of increasing diameter. Five are at the same level as the register hole of a B-flat clari

Konstantin Dietrich, Diederick Vermetten, Carola Doerr, Pascal Kerschke

The recently proposed MA-BBOB function generator provides a way to create numerical black-box benchmark problems based on the well-established BBOB suite. Initial studies on this generator highlighted its ability to smoothly transition between the component functions, both from a low-level landscape feature perspective, as well as with regard to algorithm pe

Taras Mel'nyk, Christian Rohde

The aim of the paper is to construct and justify asymptotic approximations for solutions to quasilinear convection-diffusion problems with a predominance of nonlinear convective flow in a thin cylinder, where an inhomogeneous nonlinear Robin-type boundary condition involving convective and diffusive fluxes is imposed on the lateral surface. The limit problem

Yinan Sun, Xiongkuo Min, Huiyu Duan, Guangtao Zhai

The analysis and prediction of visual attention have long been crucial tasks in the fields of computer vision and image processing. In practical applications, images are generally accompanied by various text descriptions, however, few studies have explored the influence of text descriptions on visual attention, let alone developed visual saliency prediction

Luca Rosafalco, Paolo Conti, Andrea Manzoni, Stefano Mariani

Measured data from a dynamical system can be assimilated into a predictive model by means of Kalman filters. Nonlinear extensions of the Kalman filter, such as the Extended Kalman Filter (EKF), are required to enable the joint estimation of (possibly nonlinear) system dynamics and of input parameters. To construct the evolution model used in the prediction p

Fausto Ferrari, Nicolò Forcillo, Davide Giovagnoli, David Jesus

In this paper, we prove that flat free boundaries of solutions to inhomogeneous one-phase Stefan problem are $C^{1,\alpha}$. The method consists of employing a hodograph transform and deriving the regularity via a linearization technique, following the approach introduced by De Silva, Forcillo, and Savin in \cite{DFS23}.

Tianyun Wei, Yasunobu Arikawa, Seyed Reza Mirfayzi, Yanjun Gu

Generation of quasi-monoenergetic ion pulse by laser-driven acceleration is one of the hot topics in laser plasma physics. In this study, we present a new method for the \textit{In-situ} deposition of an ultra-thin D$_2$O layer on the surface of an aluminum foil target utilizing a spherical D$_2$O capsule. Employing a 10$^{19}$ W/cm$^2$ laser, we achieve the

Dipika Jha, Ankit K. Bhagat, Raju Halder, Rajendra N. Paramanik

This paper presents a comprehensive Decentraland parcels dataset, called IITP-VDLand, sourced from diverse platforms such as Decentraland, OpenSea, Etherscan, Google BigQuery, and various Social Media Platforms. Unlike existing datasets which have limited attributes and records, IITP-VDLand offers a rich array of attributes, encompassing parcel characteristi

Chengyu Xia, Danny H. K. Tsang, Vincent K. N. Lau

In this paper, we investigate Bayesian model compression in federated learning (FL) to construct sparse models that can achieve both communication and computation efficiencies. We propose a decentralized Turbo variational Bayesian inference (D-Turbo-VBI) FL framework where we firstly propose a hierarchical sparse prior to promote a clustered sparse structure

Sana Benhafsia, Rejeb Hadiji

Recently, great attention has been focused on the study of fractional and non-local operators of elliptic type, both for pure mathematical research and in view of concrete real-world applications. Our problem is related to the fractional Yamabe problem. First, we study a non-local problem involving the fractional laplacian, a critical nonlinearity with a non

Yao Meng, Alex McAvoy, Aming Li

Collective cooperation drives the dynamics of many natural, social, and economic phenomena, making understanding the evolution of cooperation with evolutionary game theory a central question of modern science. Although human interactions are best described as complex networks, current explorations are limited to static networks where interactions represented

Y. Liu, V. F. Dal Poggetto, A. S. Gliozzi, N. M. Pugno

Bioinspiration has widely been demonstrated to be a powerful approach for the design of innovative structures and devices. Recently, this concept has been extended to the field of elasticity, dynamics, and metamaterials. In this paper, we propose a seashell-inspired metasensor that can simultaneously perform spatial frequency mapping and act as a polarizer.

Rosalia Ferraro, Jasmin Di Franco, Sergio Caserta, Stefano Guido

Cell spheroids are a widely used model to investigate cell-cell and cell-matrix interactions in a 3D microenvironment in vitro. Most research on cell spheroids has been focused on their response to various stimuli under static conditions. Recently, the effect of flow on cell spheroids has been investigated in the context of tumor invasion in interstitial spa

Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar

Cyber-physical systems are at the intersection of digital technology and engineering domains, rendering them high-value targets of sophisticated and well-funded cybersecurity threat actors. Prominent cybersecurity attacks on CPS have brought attention to the vulnerability of these systems and the inherent weaknesses of critical infrastructure reliant on them

Marcella Bonazzoli, Houssem Haddar, Tuan Anh Vu

When an inverse problem is solved by a gradient-based optimization algorithm, the corresponding forward and adjoint problems, which are introduced to compute the gradient, can be also solved iteratively. The idea of iterating at the same time on the inverse problem unknown and on the forward and adjoint problem solutions yields the concept of one-shot invers

Agasthya Gangavarapu

Addressing the imminent shortfall of 10 million health workers by 2030, predominantly in Low- and Middle-Income Countries (LMICs), this paper introduces an innovative approach that harnesses the power of Large Language Models (LLMs) integrated with machine translation models. This solution is engineered to meet the unique needs of Community Health Workers (C

Yunxiang Li, Rui Yuan, Chen Fan, Mark Schmidt

Policy gradient is a widely utilized and foundational algorithm in the field of reinforcement learning (RL). Renowned for its convergence guarantees and stability compared to other RL algorithms, its practical application is often hindered by sensitivity to hyper-parameters, particularly the step-size. In this paper, we introduce the integration of the Polya

Sander M. Vermeulen, Torrey Cullen, Daniel Grass, Ian A. O. MacMillan

The GQuEST (Gravity from the Quantum Entanglement of Space-Time) experiment uses tabletop-scale Michelson laser interferometers to probe for fluctuations in space-time. We present a practicable interferometer design featuring a novel photon counting readout method that provides unprecedented sensitivity, as it is not subject to the interferometric standard q

Hyung-il Ahn, Young Chol Song, Santiago Olivar, Hershel Mehta

Successful supply chain optimization must mitigate imbalances between supply and demand over time. While accurate demand prediction is essential for supply planning, it alone does not suffice. The key to successful supply planning for optimal and viable execution lies in maximizing predictability for both demand and supply throughout an execution horizon. Th

Athanasios Bakopoulos, Nikos Chatzifotis, Thanasis Karakasis

In this work, we embark on the thermodynamic investigation concerning a family of primary charged black holes within the context of shift and parity symmetric Beyond Horndeski gravity. Employing the Euclidean approach, we derive the functional expression for the free energy and derive the first thermodynamic law, offering a methodology to address the challen

Simone Taioli, Maurizio Dapor

Over the past decade, experimental microscopy and spectroscopy have made significant progress in the study of the morphological, optical, electronic and transport properties of materials. These developments include higher spatial resolution, shorter acquisition times, more efficient monochromators and electron analysers, improved contrast imaging and advance

Anant Khandelwal

The potential for zero-shot generalization in vision-language (V-L) models such as CLIP has spurred their widespread adoption in addressing numerous downstream tasks. Previous methods have employed test-time prompt tuning to adapt the model to unseen domains, but they overlooked the issue of imbalanced class distributions. In this study, we explicitly addres

Jiing-Ping Wang, Ming-Guang Lin, An-Yeu, Wu

With the rise of Transformer models in NLP and CV domain, Multi-Head Attention has been proven to be a game-changer. However, its expensive computation poses challenges to the model throughput and efficiency, especially for the long sequence tasks. Exploiting the sparsity in attention has been proven to be an effective way to reduce computation. Nevertheless

Yuwei Sun, Ippei Fujisawa, Arthur Juliani, Jun Sakuma

Neural networks encounter the challenge of Catastrophic Forgetting (CF) in continual learning, where new task learning interferes with previously learned knowledge. Existing data fine-tuning and regularization methods necessitate task identity information during inference and cannot eliminate interference among different tasks, while soft parameter sharing a

Xin Zhou, Chuang Lin, Can Wang, Xiaojiang Peng

Surface electromyography (sEMG) has demonstrated significant potential in simultaneous and proportional control (SPC). However, existing algorithms for predicting joint angles based onsEMGoften suffer fromhigh inference costs or are limited to specific subjects rather than multi-subject scenarios. To address these challenges, we introduced a hierarchical Spi

Naonori Kakimura, Ildikó Schlotter

This paper studies the computational complexity of a robust variant of a two-stage submodular minimization problem that we call Robust Submodular Minimizer. In this problem, we are given $k$ submodular functions~$f_1,\dots,f_k$ over a set family~$2^V$, which represent $k$ possible scenarios in the future when we will need to find an optimal solution for one

Yu Xia, Zhiqiang Xu, Zili Xu

In this paper, we primarily focus on analyzing the stability property of phase retrieval by examining the bi-Lipschitz property of the map $\Phi_{\boldsymbol{A}}(\boldsymbol{x})=|\boldsymbol{A}\boldsymbol{x}|\in \mathbb{R}_+^m$, where $\boldsymbol{x}\in \mathbb{H}^d$ and $\boldsymbol{A}\in \mathbb{H}^{m\times d}$ is the measurement matrix for $\mathbb{H}\in\

Avinash Anand, Janak Kapuriya, Apoorv Singh, Jay Saraf

While Large Language Models (LLMs) can achieve human-level performance in various tasks, they continue to face challenges when it comes to effectively tackling multi-step physics reasoning tasks. To identify the shortcomings of existing models and facilitate further research in this area, we curated a novel dataset, MM-PhyQA, which comprises well-constructed

Jianqiang Xiao, Weiwen Guo, Junfeng Liu, Mengze Li

In the field of computer vision, data augmentation is widely used to enrich the feature complexity of training datasets with deep learning techniques. However, regarding the generalization capabilities of models, the difference in artificial features generated by data augmentation and natural visual features has not been fully revealed. This study introduces

A. V. Ivanov, N. V. Kharuk

The paper studies the quantum action for the five-dimensional real $\phi^3$-theory in the case of a general formulation using the background field method. The three-loop renormalization is performed with the usage of a cutoff regularization in the coordinate representation. The explicit form of the first three coefficients for the renormalization constants i

Mauro Cainelli, Reo Baba, Yuki Kurashige

We evaluate the accuracy of the quantum inverse (Q-Inv) algorithm in which the multiplication of $\hat{H}^{-k}$ to the reference wavefunction is replaced by the Fourier Transformed multiplication of $e^{-i\lambda \hat{H}}$, as a function of the integration parameters ($\lambda$) and the power $k$ for various systems, including H$_2$, LiH, BeH$_2$ and the not

Hyung-il Ahn, Santiago Olivar, Hershel Mehta, Young Chol Song

Supply chain networks in enterprises are typically composed of complex topological graphs involving various types of nodes and edges, accommodating numerous products with considerable demand and supply variability. However, as supply chain networks expand in size and complexity, traditional supply chain planning methods (e.g., those found in heuristic rule-b

Takaaki Nishimoto, Yasuo Tabei

Big data, encompassing extensive datasets, has seen rapid expansion, notably with a considerable portion being textual data, including strings and texts. Simple compression methods and standard data structures prove inadequate for processing these datasets, as they require decompression for usage or consume extensive memory resources. Consequently, this moti

Baihong Li, Qi-qi Li, Zhuo-zhuo Wang, Penglong Wang

We theoretically propose a multiparameter cascaded quantum interferometer in which a two-input and two-output setup is obtained by concatenating 50:50 beam splitters with $n$ independent and adjustable time delays. A general method for deriving the coincidence probability of such an interferometer is given based on the linear transformation of the matrix of

Raphaël Gass, Zhongliang Li, Rachid Outbib, Samir Jemei

A one-dimensional, dynamic, two-phase, isothermal model of proton exchange membrane fuel cell systems using a finite-difference approach has been developed. This model balances the simplicity of lumped-parameter models with the detailed accuracy of computational fluid dynamics models, offering precise internal state descriptions with low computational demand

Justin Yang, Zhihao Duan, Jiangpeng He, Fengqing Zhu

Food image classification systems play a crucial role in health monitoring and diet tracking through image-based dietary assessment techniques. However, existing food recognition systems rely on static datasets characterized by a pre-defined fixed number of food classes. This contrasts drastically with the reality of food consumption, which features constant

Zhen Ning, Junjie Zeng, Da-Shuai Ma, Dong-Hui Xu

Recent studies have attracted widespread attention on magnet-superconductor hybrid systems with emergent topological superconductivity. Here, we present the Floquet engineering of realistic two-dimensional topological nodal-point superconductors that are composed of antiferromagnetic monolayers in proximity to an s-wave superconductor. We show that Floquet c

Jie Hu, Jean-Marc Matin, Paul Nicaise, Faouzi Boussaha

In this paper, we investigate the quasi-particle (QP) relaxation of strongly disordered superconducting resonators under optical illumination at different bath temperatures with the Rothwarf and Taylor equations and the gap-broadening theory described by the Usadal equation. The analysis is validated with various single-photon responses of Titanium Nitride (

Thies Oelerich, Christian Hartl-Nesic, Andreas Kugi

This work develops a novel trajectory planner for human-robot handovers. The handover requirements can naturally be handled by a path-following-based model predictive controller, where the path progress serves as a progress measure of the handover. Moreover, the deviations from the path are used to follow human motion by adapting the path deviation bounds wi

Yanhao Wu, Tong Zhang, Wei Ke, Congpei Qiu

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can create a tendency for neural networks to exploit these strong dependencies, bypassing the individual object p

Ruibo Liu, Jerry Wei, Fangyu Liu, Chenglei Si

The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and high costs. Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns. This paper provides an overview of synthetic data researc

Rubén Ruiz-Torrubiano

Providing explanations about how machine learning algorithms work and/or make particular predictions is one of the main tools that can be used to improve their trusworthiness, fairness and robustness. Among the most intuitive type of explanations are counterfactuals, which are examples that differ from a given point only in the prediction target and some set

Julian Neuberger, Leonie Doll, Benedict Engelmann, Lars Ackermann

Business Process Modeling projects often require formal process models as a central component. High costs associated with the creation of such formal process models motivated many different fields of research aimed at automated generation of process models from readily available data. These include process mining on event logs, and generating business proces

Xian Gong, Paul X. McCarthy, Marian-Andrei Rizoiu, Paolo Boldi

In this paper we use for the first time a systematic approach in the study of harmonic centrality at a Web domain level, and gather a number of significant new findings about the Australian web. In particular, we explore the relationship between economic diversity at the firm level and the structure of the Web within the Australian domain space, using harmon

M. Archita

Given a finite group $G$ of order $n.$ Denote the sum of the inverse-power of element orders in $G$ by $m(G).$ Let $\mathbb{Z}_n$ be the cyclic group of order $n.$ Suppose $G$ is a non-cyclic group of order $n$ then we show that $m(G)\geq \frac{5}{4}m(\mathbb{Z}_n).$ Our result improves the inequality $m(G)>m(\mathbb{Z}_n)$ obtained by Baniasad Azad, M., and

Yu Tokutake, Kazushi Okamoto

Serendipity-oriented recommender systems aim to counteract over-specialization in user preferences. However, evaluating a user's serendipitous response towards a recommended item can be challenging because of its emotional nature. In this study, we address this issue by leveraging the rich knowledge of large language models (LLMs), which can perform a variet