May 2023 arXiv papers — page 96

Sun Runjin, Guo ShiZe, Li Wei, Zhang XingYu

In the field of cybersecurity, the ability to compute similarity scores at the function level is import. Considering that a single binary file may contain an extensive amount of functions, an effective learning framework must exhibit both high accuracy and efficiency when handling substantial volumes of data. Nonetheless, conventional methods encounter sever

Haoxing Chen, Zhuoer Xu, Zhangxuan Gu, Jun Lan

Diffusion model based language-guided image editing has achieved great success recently. However, existing state-of-the-art diffusion models struggle with rendering correct text and text style during generation. To tackle this problem, we propose a universal self-supervised text editing diffusion model (DiffUTE), which aims to replace or modify words in the

Andrea Bacciu, Federico Siciliano, Nicola Tonellotto, Fabrizio Silvestri

Sequential Recommender Systems (SRSs) are a popular type of recommender system that learns from a user's history to predict the next item they are likely to interact with. However, user interactions can be affected by noise stemming from account sharing, inconsistent preferences, or accidental clicks. To address this issue, we (i) propose a new evaluation pr

Won Jang, Dan Lim, Heayoung Park

This paper presents FastFit, a novel neural vocoder architecture that replaces the U-Net encoder with multiple short-time Fourier transforms (STFTs) to achieve faster generation rates without sacrificing sample quality. We replaced each encoder block with an STFT, with parameters equal to the temporal resolution of each decoder block, leading to the skip con

Zihua Si, Zhongxiang Sun, Xiao Zhang, Jun Xu

Modern online service providers such as online shopping platforms often provide both search and recommendation (S&R) services to meet different user needs. Rarely has there been any effective means of incorporating user behavior data from both S&R services. Most existing approaches either simply treat S&R behaviors separately, or jointly optimize them by agg

Yanjie Fu, Meng Ge, Honglong Wang, Nan Li

Recently, stunning improvements on multi-channel speech separation have been achieved by neural beamformers when direction information is available. However, most of them neglect to utilize speaker's 2-dimensional (2D) location cues contained in mixture signal, which limits the performance when two sources come from close directions. In this paper, we propos

Giulio Cerbai

In 2011, Duncan and Steingr\'imsson conjectured that modified ascent sequences avoiding any of the patterns 212, 1212, 2132, 2213, 2231 and 2321 are counted by the Bell numbers. Furthermore, the distribution of the number of ascents is the reverse of the distribution of blocks on set partitions. We solve the conjecture for all the patterns except 2321. We de

Jingheng Ye, Yinghui Li, Qingyu Zhou, Yangning Li

Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity. Designing an evaluation metric that is as objective as possible is crucial to the development of GEC task. However, mainstream evaluation metrics, i.e., reference-based metrics, introduce bias into the multi-reference evaluation by extracti

Sofia Maria Lo Cicero Vaina, Nikita Balagansky, Daniil Gavrilov

Diffusion Language models (DLMs) are a promising avenue for text generation due to their practical properties on tractable controllable generation. They also have the advantage of not having to predict text autoregressively. However, despite these notable features, DLMs have not yet reached the performance levels of their autoregressive counterparts. One of

Vittorio Del Tatto, Gianfranco Fortunato, Domenica Bueti, Alessandro Laio

We introduce an approach which allows detecting causal relationships between variables for which the time evolution is available. Causality is assessed by a variational scheme based on the Information Imbalance of distance ranks, a statistical test capable of inferring the relative information content of different distance measures. We test whether the predi

Kevin Wilkinghoff, Alessia Cornaggia-Urrigshardt

To segment a signal into blocks to be analyzed, few-shot keyword spotting (KWS) systems often utilize a sliding window of fixed size. Because of the varying lengths of different keywords or their spoken instances, choosing the right window size is a problem: A window should be long enough to contain all necessary information needed to recognize a keyword but

M. Mangut, H. Gürsel, S. Kanzi, İ. Sakallı

The ability of bumblebee gravity models to explain dark energy, which is the phenomenon responsible for the universe's observed accelerated expansion, is one of their most significant applications. An effect that causes faster expansion can be linked to how much the Lorentz symmetry of our universe is violated. Moreover, since we do not know what generates d

Daniel R. Mason, Duc Nguyen-Manh, Victor W. Lindblad, Fredric G. Granberg

We describe the parameterization of a tungsten-hydrogen empirical potential designed for use with large-scale molecular dynamics simulations of highly irradiated tungsten containing hydrogen isotope atoms, and report test results. Particular attention has been paid to getting good elastic properties, including the relaxation volumes of small defect clusters,

Vadim Kravtsov, Alexandra Veledina, Andrei V. Berdyugin, Andrzej A. Zdziarski

We present the high-precision optical polarimetric observations of black hole X-ray binary Cyg X-1, spanning several cycles of its 5.6 day orbital period. Week-long observations on two telescopes located in opposite hemispheres allowed us to track the evolution of the polarization within one orbital cycle with the highest temporal resolution to date. Using t

Projesh Kumar Roy, Pinaki Chaudhuri, Satyavani Vemparala

Via large-scale molecular dynamics simulations, we observe the melting of a topological glass of stiff ring polymers by incorporating flexible ring polymers, along an isobaric path. As more flexible ring polymers are introduced, cluster glass-like structures emerge in the stiffer ring polymers with reduced orthogonal threading. This eventually evolves to a s

Chao Zhang, Xuanran Zhu, Bei Zeng

In pure-state tomography, the concept of unique determinedness (UD) -- the ability to uniquely determine pure states from measurement results -- is crucial. This study presents a new variational approach to examining UD, offering a robust solution to the challenges associated with the construction and certification of UD measurement schemes. We put forward a

Kananart Kuwaranancharoen, Shreyas Sundaram

The problem of designing distributed optimization algorithms that are resilient to Byzantine adversaries has received significant attention. For the Byzantine-resilient distributed optimization problem, the goal is to (approximately) minimize the average of the local cost functions held by the regular (non adversarial) agents in the network. In this paper, w

Henry Marichal, Diego Passarella, Gregory Randall

This work describes a Tree Ring Detection method for complete Cross-Sections of Trees (CS-TRD) that detects, processes and connects edges corresponding to the tree's growth rings. The method depends on the parameters for the Canny Devernay edge detector (sigma), a resize factor, the number of rays, and the pith location. The first five are fixed by default.

Yichen Zhang, Jiehong Lin, Ke Chen, Zelin Xu

Domain gap between synthetic and real data in visual regression (e.g. 6D pose estimation) is bridged in this paper via global feature alignment and local refinement on the coarse classification of discretized anchor classes in target space, which imposes a piece-wise target manifold regularization into domain-invariant representation learning. Specifically,

Chia-Hao Kao, Ying-Chieh Weng, Yi-Hsin Chen, Wei-Chen Chiu

This paper proposes a transformer-based learned image compression system. It is capable of achieving variable-rate compression with a single model while supporting the region-of-interest (ROI) functionality. Inspired by prompt tuning, we introduce prompt generation networks to condition the transformer-based autoencoder of compression. Our prompt generation

Quirin Aumann, Steffen W. R. Werner

Interpolation-based methods are well-established and effective approaches for the efficient generation of accurate reduced-order surrogate models. Common challenges for such methods are the automatic selection of good or even optimal interpolation points and the appropriate size of the reduced-order model. An approach that addresses the first problem for lin

Francesco Sigona, Mirko Grimaldi

Different variants of a Forensic Automatic Speaker Recognition (FASR) system based on Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Network (ECAPA-TDNN) are tested under conditions reflecting those of a real forensic voice comparison case, according to the forensic_eval_01 evaluation campaign settings. Using this recent neura

Giuseppe Cuono, Raghottam M. Sattigeri, Carmine Autieri, Tomasz Dietl

An underestimation of the fundamental band gap values by the density functional theory within the local density approximation and associated approaches is a well-known challenge of ab-initio electronic structure computations. Motivated by recent optical experiments [D. Santos-Cottin et al., arXiv:2301.08014], we have revisited first-principle results obtaine

Kisun Lee, Nan Li, Lihong Zhi

We propose a two-step Newton's method for refining an approximation of a singular zero whose deflation process terminates after one step, also known as a deflation-one singularity. Given an isolated singular zero of a square analytic system, our algorithm exploits an invertible linear operator obtained by combining the Jacobian and a projection of the Hessia

Tommaso Ruggeri, Shigeru Taniguchi

We study the shock structure and the sub-shocks formation in a binary mixture of rarefied polyatomic gases, considering the dissipation only due to the dynamic pressure. We classify the regions depending on the concentration and the Mach number for which there may exist the sub-shock in the profile of shock structure in one or both constituents or not for pr

Feng Gao, Jiaxu Leng, Gan Ji, Xinbo Gao

DEtection TRansformer (DETR) and its variants (DETRs) achieved impressive performance in general object detection. However, in crowded pedestrian detection, the performance of DETRs is still unsatisfactory due to the inappropriate sample selection method which results in more false positives. To settle the issue, we propose a simple but effective sample sele

Sifan Liu, Rang Liu, Ming Li, Qian Liu

Owing to the promising ability of saving hardware cost and spectrum resources, integrated sensing and communication (ISAC) is regarded as a revolutionary technology for future sixth-generation (6G) networks. The mono-static ISAC systems considered in most of existing works can only achieve limited sensing performance due to the single observation angle and e

Qiuhui Chen, Xinyue Hu, Zirui Wang, Yi Hong

Vision-language pre-training (VLP) models have been demonstrated to be effective in many computer vision applications. In this paper, we consider developing a VLP model in the medical domain for making computer-aided diagnoses (CAD) based on image scans and text descriptions in electronic health records, as done in practice. To achieve our goal, we present a

Josef Durech, Josef Hanus

Gaia Data Release 3 contains accurate photometric observations of more than 150,000 asteroids covering a time interval of 34 months. With a total of about 3,000,000 measurements, a typical number of observations per asteroid ranges from a few to several tens. We aimed to reconstruct the spin states and shapes of asteroids from this dataset. We computed the v

Jozef Chaber, Roman Pol

The theorem we prove is a slight strengthening of some results by Just, Miller, Scheepers and Szeptycki [JMSS]. We use the Michael technique instead of the combinatorial approach in the literature. Comments by the submitter: This short unpublished paper, written in 2002, is a cornerstone in the study of selection principles and classic covering properties. I

Hibiki Yama, Daisuke Suzuki, Shota Miyazaki, Andrew Rakich

We describe the optical alignment method for the Prime-focus Infrared Microlensing Experiment (PRIME) telescope which is a prime-focus near-infrared (NIR) telescope with a wide field of view for the microlensing planet survey toward the Galactic center that is the major task for the PRIME project. There are three steps for the optical alignment: preliminary

Chong Li, Keping Qiu, Di Li, Hongchi Wang

Using the HI self-absorption data from the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we perform a study of the cold atomic gas in the Cygnus-X North region. The most remarkable HI cloud is characterized by a filamentary structure, associated in space and in velocity with the principle molecular filament in the Cygnus-X North region. We in

Changtao Miao, Qi Chu, Zhentao Tan, Zhenchao Jin

As deepfake content proliferates online, advancing face manipulation forensics has become crucial. To combat this emerging threat, previous methods mainly focus on studying how to distinguish authentic and manipulated face images. Although impressive, image-level classification lacks explainability and is limited to specific application scenarios, spurring r

S. Fijma, N. Castro Segura, N. Degenaar, C. Knigge

Accreting low mass X-ray binaries (LMXBs) are capable of launching powerful outflows such as accretion disc winds. In disc winds, vast amounts of material can be carried away, potentially greatly impacting the binary and its environment. Previous studies have uncovered signatures of disc winds in the X-ray, optical, near-infrared, and recently even the UV ba

V. Voronyuk, E. E. Kolomeitsev, N. S. Tsegelnik

Global polarization of hyperons induced by the local vorticity of the medium created in heavy-ion collisions at energies 2.3\,GeV$\le\sqrt{s_{NN}}\le$11.5 GeV is calculated in the parton-hadron-string dynamic (PHSD) model. The separation of spectator nucleons and the fluidization of the generated particle distributions are performed. The polarization of all

Yu Chen, Yiling He

Fingerprint authentication has been widely adopted on smartphones to complement traditional password authentication, making it a tempting target for attackers. The smartphone industry is fully aware of existing threats, and especially for the presentation attack studied by most prior works, the threats are nearly eliminated by liveness detection and attempt

Yuan Gong, Hongyin Luo, Alexander H. Liu, Leonid Karlinsky

The ability of artificial intelligence (AI) systems to perceive and comprehend audio signals is crucial for many applications. Although significant progress has been made in this area since the development of AudioSet, most existing models are designed to map audio inputs to pre-defined, discrete sound label sets. In contrast, humans possess the ability to n

BESIII Collaboration

The process $e^{+}e^{-}\rightarrow D_{s}^{\ast+}D_{s}^{\ast-}$ is studied with a semi-inclusive method using data samples at center-of-mass energies from threshold to 4.95 GeV collected with the BESIII detector operating at the Beijing Electron Positron Collider. The Born cross sections of the process are measured for the first time with high precision in th

Hang Shao, Bei Liu, Wei Wang, Xun Gong

As a popular multilingual and multitask pre-trained speech model, Whisper has the problem of curse of multilinguality. To enhance multilingual capabilities in small Whisper models, we propose DQ-Whisper, a novel joint distillation and quantization framework to compress Whisper for efficient inference. Firstly, we propose a novel dynamic matching distillation

Jake Lane, Evelina Gersabeck, Jonas Rademacker

We present a novel unbinned method to combine $B^{\pm} \to DK^{\pm}$ and charm threshold data for the amplitude-model unbiased measurement of the CKM angle gamma in cases where the D meson decays to a three-body final state. The new unbinned approach avoids any kind of integration over the D Dalitz plot, to make optimal use the available information. We veri

Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng

Prior studies diagnose the anisotropy problem in sentence representations from pre-trained language models, e.g., BERT, without fine-tuning. Our analysis reveals that the sentence embeddings from BERT suffer from a bias towards uninformative words, limiting the performance in semantic textual similarity (STS) tasks. To address this bias, we propose a simple

Bo Lin, Shangwen Wang, Zhongxin Liu, Yepang Liu

Software is constantly changing, requiring developers to perform several derived tasks in a timely manner, such as writing a description for the intention of the code change, or identifying the defect-prone code changes. Considering that the cost of dealing with these tasks can account for a large proportion (typically around 70 percent) of the total develop

Egor Kornev, Sergey Dolgov, Karan Pinto, Markus Pflitsch

The solution of computational fluid dynamics problems is one of the most computationally hard tasks, especially in the case of complex geometries and turbulent flow regimes. We propose to use Tensor Train (TT) methods, which possess logarithmic complexity in problem size and have great similarities with quantum algorithms in the structure of data representat

Shrestha Mohanty, Negar Arabzadeh, Julia Kiseleva, Artem Zholus

Human intelligence's adaptability is remarkable, allowing us to adjust to new tasks and multi-modal environments swiftly. This skill is evident from a young age as we acquire new abilities and solve problems by imitating others or following natural language instructions. The research community is actively pursuing the development of interactive "embodied age

Raj Sanjay Shah, Vijay Marupudi, Reba Koenen, Khushi Bhardwaj

Large Language Models (LLMs) do not differentially represent numbers, which are pervasive in text. In contrast, neuroscience research has identified distinct neural representations for numbers and words. In this work, we investigate how well popular LLMs capture the magnitudes of numbers (e.g., that $4 < 5$) from a behavioral lens. Prior research on the repr

Arpit Babbar, Sudarshan Kumar Kenettinkara, Praveen Chandrashekar

Lax-Wendroff Flux Reconstruction (LWFR) is a single-stage, high order, quadrature free method for solving hyperbolic conservation laws. We develop a subcell based limiter by blending LWFR with a lower order scheme, either first order finite volume or MUSCL-Hancock scheme. While the blending with a lower order scheme helps to control oscillations, it may not

Ludovica Dieli, Claudio Conti

Theories on the bosonic nature of dark matter are a promising alternative to the cold dark matter model. Here we consider a dark matter halo in the state of a Bose-Einstein condensate, subject to the gravitation of a black hole. In the low energy limit, we bring together the general relativity in the Schwarzschild metric and the quantum description of the Bo

Machiko Fukada, Akihiro Miyata

According to the AdS/CFT dictionary, adding a relevant double-trace deformation $f\int O^2$ to a holographic CFT action is dual to imposing mixed Neumann/Dirichlet boundary conditions for the field dual to $O$ in AdS. We observed similar behaviour in codimension-two flat space holographies. We consider deformations of boundary conditions in flat spacetimes u

Chunxiao Zhang, Jin Zhang, Wenchao Zheng

In this paper, we focus on analyzing the supercloseness property of a two-dimensional singularly perturbed convection-diffusion problem with exponential boundary layers. The local discontinuous Galerkin (LDG) method with piecewise tensor-product polynomials of degree k is applied to Bakhvalov-type mesh. By developing special two-dimensional local Gauss-Radau

Sebastian Zimmermann, Clarissa Schönecker

Analytical expressions for the velocity field and the effective slip length of pressure-driven Stokes flow through slippery pipes and annuli with rotationally symmetrical longitudinal slits are derived. Specifically, the developed models incorporate a finite local slip length or shear stress along the slits and thus go beyond the assumption of perfect slip c

Viola De Renzis

The relativistic spin-precession equations for black-hole binaries have four different equilibrium solutions that correspond to systems where the two individual black hole spins are either aligned or anti-aligned with the orbital angular momentum. Surprisingly, it was demonstrated that only three of these equilibrium solutions are stable. Binary systems in t

Ahmed Adel Attia, Mark Tiede, Carol Y. Espy-Wilson

Accurate analysis of speech articulation is crucial for speech analysis. However, X-Y coordinates of articulators strongly depend on the anatomy of the speakers and the variability of pellet placements, and existing methods for mapping anatomical landmarks in the X-ray Microbeam Dataset (XRMB) fail to capture the entire anatomy of the vocal tract. In this pa

Cecilia González-Tokman, Joshua Peters

This work investigates the stability properties of Lyapunov exponents of transfer operator cocycles from a measure-theoretic perspective. Our results focus on so-called Blaschke product cocycles, a class of random dynamical systems amenable to rigorous analysis. We show that prevalence of stability is related to the dimension of the base system's domain, $\O

Yangshuo He, Guanding Yu, Yunlong Cai

Recently, the ever-increasing demand for bandwidth in multi-modal communication systems requires a paradigm shift. Powered by deep learning, semantic communications are applied to multi-modal scenarios to boost communication efficiency and save communication resources. However, the existing end-to-end neural network (NN) based framework without the channel e

Mengke Li, Yiu-ming Cheung, Juyong Jiang

Deep neural networks frequently suffer from performance degradation when the training data is long-tailed because several majority classes dominate the training, resulting in a biased model. Recent studies have made a great effort in solving this issue by obtaining good representations from data space, but few of them pay attention to the influence of featur

Chenguang Du, Kaichun Yao, Hengshu Zhu, Deqing Wang

Recent years have witnessed the rapid development of heterogeneous graph neural networks (HGNNs) in information retrieval (IR) applications. Many existing HGNNs design a variety of tailor-made graph convolutions to capture structural and semantic information in heterogeneous graphs. However, existing HGNNs usually represent each node as a single vector in th

Ming-Hui Ding, Hongyu Liu, Guang-Hui Zheng

In this paper, we propose and study several inverse problems of identifying/determining unknown coefficients for a class of coupled PDE systems by measuring the average flux data on part of the underlying boundary. In these coupled systems, we mainly consider the non-negative solutions of the coupled equations, which are consistent with realistic settings in

Shitong Shao, Xu Dai, Lujun Li, Huanran Chen

Diffusion Probability Models (DPMs) have made impressive advancements in various machine learning domains. However, achieving high-quality synthetic samples typically involves performing a large number of sampling steps, which impedes the possibility of real-time sample synthesis. Traditional accelerated sampling algorithms via knowledge distillation rely on

Keizo Hasegawa

A Hopf manifold is a compact complex manifold of which the universal covering is C^n\{0}. In this note we show that any Hopf manifold admits a locally conformally Kaehler structure (shortly lcK structure), by constructing a complex analytic family around a Hopf manifold of diagonal type, which admits a lcK potential, and applying a well known fact (due to Or

Takuya Yoshimoto, Satoru Shinoda, Kouji Yamamoto, Kouji Tahata

In oncology, phase II studies are crucial for clinical development plans as such studies identify potent agents with sufficient activity to continue development in the subsequent phase III trials. Traditionally, phase II studies are single-arm studies, with the primary endpoint being short-term treatment efficacy. However, drug safety is also an important co

Chong Yu, Tao Chen, Zhongxue Gan

Adversarial attack is commonly regarded as a huge threat to neural networks because of misleading behavior. This paper presents an opposite perspective: adversarial attacks can be harnessed to improve neural models if amended correctly. Unlike traditional adversarial defense or adversarial training schemes that aim to improve the adversarial robustness, the

Ishita Tikader, Muktish Acharyya

In this article, we briefly review the studies on magnetic relaxation behaviours. The theoretical as well as experimental investigations are reported briefly. A major part of this article is devoted to the recent Monte Carlo investigations into the roles of boundary conditions, dynamics and the Geometrical structures on the relaxation of magnetic monolayers

Minghua Liu, Ruoxi Shi, Kaiming Kuang, Yinhao Zhu

We introduce OpenShape, a method for learning multi-modal joint representations of text, image, and point clouds. We adopt the commonly used multi-modal contrastive learning framework for representation alignment, but with a specific focus on scaling up 3D representations to enable open-world 3D shape understanding. To achieve this, we scale up training data

Zhenhui Ye, Rongjie Huang, Yi Ren, Ziyue Jiang

Improving text representation has attracted much attention to achieve expressive text-to-speech (TTS). However, existing works only implicitly learn the prosody with masked token reconstruction tasks, which leads to low training efficiency and difficulty in prosody modeling. We propose CLAPSpeech, a cross-modal contrastive pre-training framework that explici

Ruiqi Li, Jing Liang, Cheng Cheng, Xiaoyan Zhang

Venture capital (VC) is a relatively newly emergent industry that is still subject to large uncertainties in China. Therefore, building a robust social network with other VC institutions is a good way to share information, various resources, and benefit from skill and knowledge complementarity to against risks. Strong evidences indicate that better networked

Zizheng Zhang, Chen Chen, Hsin-Hung Chen, Xiang Liu

Recently, speech separation (SS) task has achieved remarkable progress driven by deep learning technique. However, it is still challenging to separate target speech from noisy mixture, as the neural model is vulnerable to assign background noise to each speaker. In this paper, we propose a noise-aware SS (NASS) method, which aims to improve the speech qualit

Chen Yang, Zhe Zheng, Jia-Rui Lin

The layout design of pipelines is a critical task in the construction industry. Currently, pipeline layout is designed manually by engineers, which is time-consuming and laborious. Automating and streamlining this process can reduce the burden on engineers and save time. In this paper, we propose a method for generating three-dimensional layout of pipelines

Eung Jin Chun, Tomasz P. Dutka, Tae Hyun Jung, Xander Nagels

We explore the possibility of embedding thermal leptogenesis within a first-order phase transition (FOPT) such that RHNs remain massless until a FOPT arises. Their sudden and violent mass gain allows the neutrinos to become thermally decoupled, and the lepton asymmetry generated from their decay can be, in principle, free from the strong wash-out processes t

Lirong Wu, Haitao Lin, Yufei Huang, Tianyu Fan

Recent years have witnessed the great success of Graph Neural Networks (GNNs) in handling graph-related tasks. However, MLPs remain the primary workhorse for practical industrial applications due to their desirable inference efficiency and scalability. To reduce their gaps, one can directly distill knowledge from a well-designed teacher GNN to a student MLP,

Takalo Jouni Juhani

We study the latitudinal distribution and temporal evolution of the sunspot penumbra-umbra ratio (q) for the even and odd Solar Cycles 12-24 of RGO sunspot groups, SC21-SC24 of Debrecen sunspot groups and Kodaikanal sunspot dataset for SC16-SC24. We find that RGO even (odd) Cycles have q-values 5.20 (4.75), Kodaikanal even (odd) cycles have q-values 5.27 (5.

Revati Gunjal, Sushama Wagh, Syed Shadab Nayyer, Alex Stankovic

Gradient Descent (GD) is a ubiquitous algorithm for finding the optimal solution to an optimization problem. For reduced computational complexity, the optimal solution $\mathrm{x^*}$ of the optimization problem must be attained in a minimum number of iterations. For this objective, the paper proposes a genesis of an accelerated gradient algorithm through the

Xiao-Qiu Cai, Zi-Fan Liu, Tian-Yin Wang

Quantum secret sharing plays an important role in quantum communications and secure multiparty computation. In this paper, we present a new measurement-device-independent quantum secret sharing protocol, which can double the space distance between the dealer and each sharer for quantum transmission compared with prior works. Furthermore, it is experimentally

Qiankun Zuo, Hao Tian, Chi-Man Pun, Hongfei Wang

Effective connectivity can describe the causal patterns among brain regions. These patterns have the potential to reveal the pathological mechanism and promote early diagnosis and effective drug development for cognitive disease. However, the current methods utilize software toolkits to extract empirical features from brain imaging to estimate effective conn

Takuma Nishibuchi, Tetsuo Hyodo

We study the $\Xi(1620)$ resonance near the $\bar{K}\Lambda$ threshold in the light of the recent experimental constraints. The Belle collaboration have found a resonance peak of $\Xi(1620)$ slightly below the $\bar{K}^{0}\Lambda$ threshold in the $\pi^{+}\Xi^{-}$ invariant mass spectrum, and the ALICE collaboration have determined the $K^{-}\Lambda$ scatter

Feng Wang, Chuan-Fu Yang

In this work, we consider Dirac-type operators with a constant delay less than half of the interval and not less than two-fifths of the interval. For our considered Dirac-type operators, two inverse spectral problems are studied. Specifically, reconstruction of two complex $L_{2}$-potentials is studied from complete spectra of two boundary value problems wit

Ivailo Hartarsky, Lyuben Lichev

Brownian snails with removal is a spatial epidemic model defined as follows. Initially, a homogeneous Poisson process of susceptible particles on $\mathbb R^d$ with intensity $\lambda>0$ is deposited and a single infected one is added at the origin. Each particle performs an independent standard Brownian motion. Each susceptible particle is infected immediat

Taesoo Song, Joerg Aichelin, Jiaxing Zhao, Pol Bernard Gossiaux

We study bottomonium $b\bar b$ production in pp collisions as well as in heavy-ion collisions, using a quantal density matrix approach. The initial bottom (anti)quarks are provided by the PYTHIA event generator. We solve the Schr\"odinger equation for the $b\bar b$ pair, identifying the potential with the free energy, calculated with lattice QCD, to obtain t

Mutsunori Banbara, Shin-ichi Minato, Hirotaka Ono, Ryuhei Uehara

In this study, we investigate the computational complexity of some variants of generalized puzzles. We are provided with two sets S_1 and S_2 of polyominoes. The first puzzle asks us to form the same shape using polyominoes in S_1 and S_2. We demonstrate that this is polynomial-time solvable if S_1 and S_2 have constant numbers of polyominoes, and it is stro

Maximilian P. Niroomand, Luke Dicks, Edward O. Pyzer-Knapp, David J. Wales

Prior beliefs about the latent function to shape inductive biases can be incorporated into a Gaussian Process (GP) via the kernel. However, beyond kernel choices, the decision-making process of GP models remains poorly understood. In this work, we contribute an analysis of the loss landscape for GP models using methods from physics. We demonstrate $\nu$-cont

Yanting Ni, Xuyang Lou, Junjie Jiao, Jiajia Jia

The article addresses the problem of strong structural controllability of structured networks with multi-input multi-output (MIMO) node systems. The authors first present necessary and sufficient conditions for strong structural controllability, which involve both algebraic and graph-theoretic aspects. These conditions are computationally expensive, especial

Henrik Junkerkalefeld

We present recent measurements of semileptonic $B$-meson decays using a data sample collected at the $\Upsilon(4S)$ resonance by the Belle II experiment corresponding to an integrated luminosity of $189\,\mathrm{fb}^{-1}$. We determine the CKM-matrix elements $|V_\mathrm{cb}|$ using untagged $B^0 \to D^{*-} \, \ell^{+} \, \nu_\ell$ decays and $|V_\mathrm{ub}

E. S. Kammoun, Z. Igo, J. M. Miller, A. C. Fabian

SMSS\,J114447.77-430859.3 ($z=0.83$) has been identified in the SkyMapper Southern Survey as the most luminous quasar in the last $\sim 9\,\rm Gyr$. In this paper, we report on the eROSITA/Spectrum-Roentgen-Gamma (SRG) observations of the source from the eROSITA All Sky Survey, along with presenting results from recent monitoring performed using Swift, XMM-N

Duksang Lee, William Overman, Dabeen Lee

This paper studies a long-term resource allocation problem over multiple periods where each period requires a multi-stage decision-making process. We formulate the problem as an online allocation problem in an episodic finite-horizon constrained Markov decision process with an unknown non-stationary transition function and stochastic non-stationary reward an

Aiping Liu, Jiawei Liu, Zhanfei Kang, Guang-Jie Chen

A free-space-to-chip pipeline is proposed to efficiently transport single atoms from a magneto-optical trap to an on-chip evanescent field trap. Due to the reflection of the dipole laser on the chip surface, the conventional conveyor belt approach can only transport atoms close to the chip surface but with a distance of about one wavelength, which prevents e

Zihao Li, Huangjun Zhu, Masahito Hayashi

Blind quantum computation (BQC) is a secure quantum computation method that protects the privacy of clients. Measurement-based quantum computation (MBQC) is a promising approach for realizing BQC. To obtain reliable results in blind MBQC, it is crucial to verify whether the resource graph states are accurately prepared in the adversarial scenario. However, p

Krishna Gopal Benerjee, Adrish Banerjee

For any given alphabet of size $q$, a Homopolymer Free code (HF code) refers to an $(n, M, d)_q$ code of length $n$, size $M$ and minimum Hamming distance $d$, where all the codewords are homopolymer free sequences. For any given alphabet, this work provides upper and lower bounds on the maximum size of any HF code using Sphere Packing bound and Gilbert-Vars

Benjamin Hoffman, Maddie Cusimano, Vittorio Baglione, Daniela Canestrari

Animal-borne sensors (`bio-loggers') can record a suite of kinematic and environmental data, which are used to elucidate animal ecophysiology and improve conservation efforts. Machine learning techniques are used for interpreting the large amounts of data recorded by bio-loggers, but there exists no common framework for comparing the different machine learni

Hojin Lee, Sangmin Lee

We initiate the construction of the global Poincar\'e algebra generators in the context of the post-Minkowskian Hamiltonian formulation of gravitating binary dynamics in isotropic coordinates that is partly inspired by scattering amplitudes. At the first post-Minkowskian (1PM) order, we write down the Hamiltonian in a form valid in an arbitrary inertial fram

Meng Liu, Yue Liu, Ke Liang, Wenxuan Tu

Deep graph clustering has recently received significant attention due to its ability to enhance the representation learning capabilities of models in unsupervised scenarios. Nevertheless, deep clustering for temporal graphs, which could capture crucial dynamic interaction information, has not been fully explored. It means that in many clustering-oriented rea

Alberto Bressan, Graziano Guerra

Consider a strictly hyperbolic $n\times n$ system of conservation laws, where each characteristic field is either genuinely nonlinear or linearly degenerate. In this standard setting, it is well known that there exists a Lipschitz semigroup of weak solutions, defined on a domain of functions with small total variation. If the system admits a strictly convex

Chenhe Dong, Yuexiang Xie, Yaliang Li, Ying Shen

Despite substantial progress in abstractive text summarization to generate fluent and informative texts, the factual inconsistency in the generated summaries remains an important yet challenging problem to be solved. In this paper, we construct causal graphs for abstractive text summarization and identify the intrinsic causes of the factual inconsistency, i.

Agnibha De Sarkar, Nayana A. J., Nirupam Roy, Soebur Razzaque

Very high energy (VHE; 100 GeV $<$ E $\leq$ 100 TeV) and high energy (HE; 100 MeV $<$ E $\leq$ 100 GeV) gamma-rays were observed from the symbiotic recurrent nova RS Ophiuchi (RS Oph) during its outburst in August 2021, by various observatories such as High Energy Stereoscopic System (H.E.S.S.), Major Atmospheric Gamma Imaging Cherenkov (MAGIC), and {\it Fer

Hao Shi, Kazuki Shimada, Masato Hirano, Takashi Shibuya

Diffusion-based generative speech enhancement (SE) has recently received attention, but reverse diffusion remains time-consuming. One solution is to initialize the reverse diffusion process with enhanced features estimated by a predictive SE system. However, the pipeline structure currently does not consider for a combined use of generative and predictive de

Md Ragib Shaharear

It has been found that certain biological organisms, such as Erodium seeds and Scincus scincus, are capable of effectively and efficiently burying themselves in soil. Biological Organisms employ various locomotion modes, including coiling and uncoiling motions, asymmetric body twisting, and undulating movements that generate motion waves. The coiling-uncoili

Ibrahim Sitiwaldi, Keping Xie, Alim Ablat, Sayipjamal Dulat

In this study, we examine closely the impact of the post-CT18 LHC Drell-Yan data on parton distribution functions (PDFs) in the general CTEQ-TEA global analysis framework. We compare the two main theoretical predictions, the MCFM fixed order calculations at next-to-next-to-leading order (NNLO) and the ResBos2 NNLO matched to $q_T$ resummation up to next-to-n

Hwihun Jeong, Heejoon Byun, Dong Un Kang, Jongho Lee

In MRI, images of the same contrast (e.g., T$_1$) from the same subject can exhibit noticeable differences when acquired using different hardware, sequences, or scan parameters. These differences in images create a domain gap that needs to be bridged by a step called image harmonization, to process the images successfully using conventional or deep learning-

Jihyung Moon, Dong-Ho Lee, Hyundong Cho, Woojeong Jin

Toxic language, such as hate speech, can deter users from participating in online communities and enjoying popular platforms. Previous approaches to detecting toxic language and norm violations have been primarily concerned with conversations from online forums and social media, such as Reddit and Twitter. These approaches are less effective when applied to

Ming Hu, Zhihao Yue, Xiaofei Xie, Cheng Chen

Although Federated Learning (FL) enables global model training across clients without compromising their raw data, due to the unevenly distributed data among clients, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance. Specifically, different data distributions among clients lead to various optimization d

Tanmay Khandelwal, Rohan Kumar Das

Sound event detection (SED) entails identifying the type of sound and estimating its temporal boundaries from acoustic signals. These events are uniquely characterized by their spatio-temporal features, which are determined by the way they are produced. In this study, we leverage some distinctive high-level acoustic characteristics of various sound events to

Ariel Boyarsky, Hongseok Namkoong, Jean Pouget-Abadie

Experiments on online marketplaces and social networks suffer from interference, where the outcome of a unit is impacted by the treatment status of other units. We propose a framework for modeling interference using a ubiquitous deployment mechanism for experiments, staggered roll-out designs, which slowly increase the fraction of units exposed to the treatm