May 2023 arXiv papers — page 151

Inge S. Helland

The Bell experiment is discussed in the light of a new approach to the foundation of quantum mechanics. It is concluded from the basic model that the mind of any observer must be limited in some way: In certain contexts, he is simply not able to keep enough variables in his mind when making decisions. This has consequences for Bell's theorem, but it also see

Auguste Tetenoire, J. Iñaki Juaristi, Maite Alducin

The photo-induced desorption and oxidation of CO on Ru(0001) is simulated using ab initio molecular dynamics with electronic friction that accounts for the non-equilibrated excited electrons and phonons. Different (O,CO) coverages are considered, the experimental room temperature coverage consisting in 0.5ML-O+0.25ML- CO (low coverage), the saturation covera

Djouher Bedrane, Arnaud Houel, Anne Delobbe, Mehdi Lagaize

We investigated the pressure dependence of gas flow and field ion intensity of a coaxial ion source operating at room temperature over a wide pressure range, testing various gases and ionisation voltages. Flow conductance measurements taking into account the different gases' viscosity and molecular mass consistently exhibit a generic pattern. Three different

Rupesh Kumar, Ashutosh Bajpai, Ayush Sinha

This research paper describes a realtime system for identifying American Sign Language (ASL) movements that employs modern computer vision and machine learning approaches. The suggested method makes use of the Mediapipe library for feature extraction and a Convolutional Neural Network (CNN) for ASL gesture classification. The testing results show that the su

Dung Nguyen Manh, Nam Le Hai, Anh T. V. Dau, Anh Minh Nguyen

We present The Vault, a dataset of high-quality code-text pairs in multiple programming languages for training large language models to understand and generate code. We present methods for thoroughly extracting samples that use both rule-based and deep learning-based methods to ensure that they contain high-quality pairs of code and text, resulting in a data

Robert Litschko, Ekaterina Artemova, Barbara Plank

Transferring information retrieval (IR) models from a high-resource language (typically English) to other languages in a zero-shot fashion has become a widely adopted approach. In this work, we show that the effectiveness of zero-shot rankers diminishes when queries and documents are present in different languages. Motivated by this, we propose to train rank

Adrian Wiltz, Xiao Tan, Dimos V. Dimarogonas

In this paper, we show that under mild controllability assumptions a time-invariant Control Barrier Function (CBF) can be constructed based on predictions with a finite horizon. As a starting point, we require only a known subset of a control-invariant set where the latter set does not need to be explicitly known. We show that, based on ideas similar to the

David Pape, Sina Däubener, Thorsten Eisenhofer, Antonio Emanuele Cinà

Model stealing aims at inferring a victim model's functionality at a fraction of the original training cost. While the goal is clear, in practice the model's architecture, weight dimension, and original training data can not be determined exactly, leading to mutual uncertainty during stealing. In this work, we explicitly tackle this uncertainty by generating

Shengdong Zhao, Felicia Tan, Katherine Fennedy

This article introduces our vision for a new interaction paradigm: Heads-Up Computing, a concept involving the provision of seamless computing support for daily activities. Its synergistic and user-centric approach frees humans from common constraints caused by existing interactions (e.g. smartphone zombies), made possible by matching input and output channe

Alba Crescente, Dario Ferraro, Matteo Carrega, Maura Sassetti

The coherent energy transfer between two identical two-level systems is investigated. Here, the first quantum system plays the role of a charger, while the second can be seen as a quantum battery. Firstly, a direct energy transfer between the two objects is considered and then compared to a transfer mediated by an additional intermediate two-level system. In

Jian Wang, Dongding Lin, Wenjie Li

Goal-directed dialogue systems aim to proactively reach a pre-determined target through multi-turn conversations. The key to achieving this task lies in planning dialogue paths that smoothly and coherently direct conversations towards the target. However, this is a challenging and under-explored task. In this work, we propose a coherent dialogue planning app

Tian-Wei Liu, Jiang-Bo Bai, Hao-Tian Xi, Nicholas Fantuzzi

The bistable deployable composite boom (Bi-DCB) can realize the bistable function by storing and releasing strain energy, which has a good application prospect in the aerospace field. In this paper, the folding stable state of the Bi-DCB was investigated using experimental and numerical approaches. Using the vacuum bag method, six Bi-DCB specimens were prepa

Jens Deussen, Jonathan Hüser, Uwe Naumann

We are considering differential-algebraic equations with embedded optimization criteria (DAEOs) in which the embedded optimization problem is solved by global optimization. This actually leads to differential inclusions for cases in which there are multiple global optimizer at the same time. Jump events from one global optimum to another result in nonsmooth

Kostas Kanellopulos, Robert G. West, Silvan Schmid

Understanding light-matter interaction at the nanoscale requires probing the optical properties of matter at the individual nano-absorber level. To this end, we have developed a nanomechanical photothermal sensing platform that can be used as a full spectromicroscopy tool for single molecule and single particle analysis. As a demonstration, the absorption cr

Grégoire Barrué, Tony Quertier

In a context of malicious software detection, machine learning (ML) is widely used to generalize to new malware. However, it has been demonstrated that ML models can be fooled or may have generalization problems on malware that has never been seen. We investigate the possible benefits of quantum algorithms for classification tasks. We implement two models of

Wu Guan, Liping Liang

Variant belief propagation (BP) algorithms are applied to low-density parity-check (LDPC) codes. However, conventional decoders suffer from a large resource consumption due to gathering messages from all the neighbour variable-nodes and/or check-nodes through cumulative calculations. In this paper, a check-belief propagation (CBP) decoding algorithm is propo

Saeif Alhazbi, Savio Sciancalepore, Gabriele Oligeri

The performance of Radio Frequency (RF) Fingerprinting (RFF) techniques is negatively impacted when the training data is not temporally close to the testing data. This can limit the practical implementation of physical-layer authentication solutions. To circumvent this problem, current solutions involve collecting training and testing datasets at close time

Vladimir Vovk

The topic of this paper is testing exchangeability using e-values in the batch mode, with the Markov model as alternative. The null hypothesis of exchangeability is formalized as a Kolmogorov-type compression model, and the Bayes mixture of the Markov model w.r. to the uniform prior is taken as simple alternative hypothesis. Using e-values instead of p-value

Guangyuan Shen, Shengjie Sun, Dehong Gao, Libin Yang

We present a new encoder-decoder generative network dubbed EdgeNet, which introduces a novel encoder-decoder framework for data-driven auction design in online e-commerce advertising. We break the neural auction paradigm of Generalized-Second-Price(GSP), and improve the utilization efficiency of data while ensuring the economic characteristics of the auction

Anthony Gauvan, Samuel Bronstein, Romain Branchereau

We characterize the boundedness properties on the spaces $L^p(\mathbb{H}^2)$ of the maximal operator $M_\mathcal{B}$ where $\mathcal{B}$ is an arbitrary family of hyperbolic triangles stable by isometries.

Arian Beckmann, Anna Hilsmann, Peter Eisert

Due to the rising threat of deepfakes to security and privacy, it is most important to develop robust and reliable detectors. In this paper, we examine the need for high-quality samples in the training datasets of such detectors. Accordingly, we show that deepfake detectors proven to generalize well on multiple research datasets still struggle in real-world

Mingzhou Liu, Xinwei Sun, Yu Qiao, Yizhou Wang

Distinguishing causal connections from correlations is important in many scenarios. However, the presence of unobserved variables, such as the latent confounder, can introduce bias in conditional independence testing commonly employed in constraint-based causal discovery for identifying causal relations. To address this issue, existing methods introduced pro

Han Wu, Mingjie Zhan, Haochen Tan, Zhaohui Hou

Compared to news and chat summarization, the development of meeting summarization is hugely decelerated by the limited data. To this end, we introduce a versatile Chinese meeting summarization dataset, dubbed VCSum, consisting of 239 real-life meetings, with a total duration of over 230 hours. We claim our dataset is versatile because we provide the annotati

Xin Shen, Jiaying Shi, Sungro Yoon, Jon Katzur

Understanding the customers' high level shopping intent, such as their desire to go camping or hold a birthday party, is critically important for an E-commerce platform; it can help boost the quality of shopping experience by enabling provision of more relevant, explainable, and diversified recommendations. However, such high level shopping intent has been o

Fang-Cheng Wang, Qi-Jun Ye, Yu-Cheng Zhu, Xin-Zheng Li

The exploration of solid-solid phase transition suffers from the uncertainty of how atoms in two crystal structures match. We devised a theoretical framework to describe and classify crystal-structure matches (CSM). Such description fully exploits the translational and rotational symmetries and is independent of the choice of supercells. This is enabled by t

Zeyan Zhuang, Xin Zhang, Dongfang Xu, Shenghui Song

Intelligent reflecting surfaces (IRSs) have emerged as a promising technology to improve the efficiency of wireless communication systems. However, passive IRSs suffer from the ``multiplicative fading" effect, because the transmit signal will go through two fading hops. With the ability to amplify and reflect signals, active IRSs offer a potential way to tac

Mingzhou Liu, Xinwei Sun, Lingjing Hu, Yizhou Wang

Inferring causal structures from time series data is the central interest of many scientific inquiries. A major barrier to such inference is the problem of subsampling, i.e., the frequency of measurement is much lower than that of causal influence. To overcome this problem, numerous methods have been proposed, yet either was limited to the linear case or fai

Guangyuan Shen, Shenjie Sun, Dehong Gao, Shaolei Li

In online advertising (Ad), advertisers are always eager to know how to globally optimize their budget allocation strategies across different channels for more conversions such as orders, payments, etc. Ignoring competition among different advertisers causes objective inconsistency, that is, a single advertiser locally optimizes the conversions only based on

Svante Linusson, Petter Restadh

The purpose of this paper is twofold. We investigate a simple necessary condition, called the rhombus criterion, for two vertices in a polytope not to form an edge and show that in many examples of $0/1$-polytopes it is also sufficient. We explain how also when this is not the case, the criterion can give a good algorithm for determining the edges of high-di

Swarnava Dey, Pallab Dasgupta, Partha P Chakrabarti

The rising demand for networked embedded systems with machine intelligence has been a catalyst for sustained attempts by the research community to implement Convolutional Neural Networks (CNN) based inferencing on embedded resource-limited devices. Redesigning a CNN by removing costly multiplication operations has already shown promising results in terms of

Terufumi Yamaguchi, Kazuki Nakazawa, Ai Yamakage

Electric current flows parallel to the outer product of an applied electric field and temperature gradient, a phenomenon we call the nonlinear chiral thermo-electric (NCTE) Hall effect. We present a general microscopic formulation of this effect and demonstrate its existence in a chiral crystal. We show that the contribution of the orbital magnetic moment, w

Yanlin Liu, Ping Zhang

In this paper, we first prove the global existence of strong solutions to 3-D incompressible Navier-Stokes equations with solenoidal initial data, which writes in the cylindrical coordinates is of the form: $A(r,z)\cos N\theta +B(r,z)\sin N\theta,$ provided that $N$ is large enough. In particular, we prove that the corresponding solution has almost the same

Xuandi Fu, Kanthashree Mysore Sathyendra, Ankur Gandhe, Jing Liu

Attention-based contextual biasing approaches have shown significant improvements in the recognition of generic and/or personal rare-words in End-to-End Automatic Speech Recognition (E2E ASR) systems like neural transducers. These approaches employ cross-attention to bias the model towards specific contextual entities injected as bias-phrases to the model. P

J. Krticka, A. Kawka, Z. Mikulasek, L. Fossati

Very precise satellite photometry has revealed a large number of variable stars whose variability is caused either by surface spots or by binarity. Detailed studies of such variables provide insights into the physics of these objects. We study the nature of the periodic light variability of the white dwarf EPIC 206197016 that was observed by the K2 mission.

Jakob D. Asmussen, Keshav Sishodia, Björn Bastian, Abdul R. Abid

Helium nanodroplets are ideal model systems to unravel the complex interaction of condensed matter with ionizing radiation. Here we study the effect of purely elastic electron scattering on angular and energy distributions of photoelectrons emitted from He nanodroplets of variable size ($10$-$10^9$ atoms per droplets). For large droplets, photoelectrons deve

Changyu Zeng, Wei Wang, Anh Nguyen, Yutao Yue

3D point clouds are a crucial type of data collected by LiDAR sensors and widely used in transportation applications due to its concise descriptions and accurate localization. Deep neural networks (DNNs) have achieved remarkable success in processing large amount of disordered and sparse 3D point clouds, especially in various computer vision tasks, such as p

Gk Tejus, Giacomo Zara, Paolo Rota, Andrea Fusiello

In this paper we address the rotation synchronization problem, where the objective is to recover absolute rotations starting from pairwise ones, where the unknowns and the measures are represented as nodes and edges of a graph, respectively. This problem is an essential task for structure from motion and simultaneous localization and mapping. We focus on the

Xin Shen, Yan Zhao, Sujan Perera, Yujia Liu

On E-commerce stores, there are rich recommendation content to help shoppers shopping more efficiently. However given numerous products, it's crucial to select most relevant content to reduce the burden of information overload. We introduced a content ranking service powered by a linear causal bandit algorithm to rank and select content for each shopper unde

Chen-Te Ma, Hui Zhang

We focus on the behavior of (2+1)d $\lambda\phi^4$ and (5+1)d $\lambda\phi^3$ or $\lambda|\phi|^3$ theories in different regimes and compare the results obtained from the adaptive perturbation method with those obtained from lattice simulation. These theories are simple models that exhibit asymptotic freedom, which is a property that is also observed in more

Yiming Xu, Dongfang Xu, Lei Xie, Shenghui Song

Different from conventional radar, the cellular network in the integrated sensing and communication (ISAC) system enables collaborative sensing by multiple sensing nodes, e.g., base stations (BSs). However, existing works normally assume designated BSs as the sensing nodes, and thus can't fully exploit the macro-diversity gain. In the paper, we propose a joi

Yi C. Huang

About ten years ago, Schmuckenschl\"ager proved that the lowest eigenvalue of Dirichlet Laplacian for the intersection of two balls (i.e., convex, symmetric and compact subsets of $\mathbb{R}^n$ with non-empty interior) is less than the sum of the lowest eigenvalue for each. His arguments rely on Kac's formula, the log-concavity of Gaussian measures, the sym

Shifeng Jin, Munan Hao, Zhongnan Guo, Bohao Yin

The presence and distribution of water on the Moon are fundamental to our understanding of the Earth-Moon system. Despite extensive research and remote detection, the origin and chemical form of lunar water (H2O) have remained elusive. In this study, we present the discovery of a hydrated mineral, (NH4)MgCl3*6H2O, in lunar soil samples returned by the Chang'

Ivan Rojkov, Paul Moser Röggla, Martin Wagener, Moritz Fontboté-Schmidt

We present techniques for performing two-qubit gates on Gottesman-Kitaev-Preskill (GKP) codes with finite energy, and find that operations designed for ideal infinite-energy codes create undesired entanglement when applied to physically realistic states. We demonstrate that this can be mitigated using recently developed local error-correction protocols, and

B. Shaw, B. W. Stappers, P. Weltevrede, C. A. Jordan

We undertake the first targeted search at 1.5 GHz for radio emission from the variable $\gamma$-ray pulsar PSR J2021$+$4026. This radio-quiet pulsar assumes one of two stable $\gamma$-ray emission states, between which it transitions on a timescale of years. These transitions, in both $\gamma$-ray flux and pulse profile shape, are accompanied by contemporane

Bo Yuan, Yao Jiang, Keren Fu, Qijun Zhao

Light field salient object detection (SOD) is an emerging research direction attributed to the richness of light field data. However, most existing methods lack effective handling of focal stacks, therefore making the latter involved in a lot of interfering information and degrade the performance of SOD. To address this limitation, we propose to utilize mult

Xing Gao, Thijs J. Roskamp, Timm Swoboda, Carlos M. M. Rosario

Vanadium dioxide (VO2) exhibits a hysteretic insulator-to-metal transition near room temperature, forming the foundation for various forms of resistive switching devices. Usually, these are realized in the form of two-terminal bridge-like structures. We show here that by incorporating multiple, parallel VO2 bridges in a single two-terminal device, a wider ra

Dario De Angelis, Emiliano Principi, Filippo Bencivenga, Daniele Fausti

Time-resolved X-ray Emission/Absorption Spectroscopy (Tr-XES/XAS) is an informative experimental tool sensitive to electronic dynamics in materials, widely exploited in diverse research fields. Typically, Tr-XES/XAS requires X-ray pulses with both a narrow bandwidth and sub-picosecond pulse duration, a combination that in principle finds its optimum with Fou

Yacine Belal, Sonia Ben Mokhtar, Hamed Haddadi, Jaron Wang

Federated learning involves training statistical models over edge devices such as mobile phones such that the training data is kept local. Federated Learning (FL) can serve as an ideal candidate for training spatial temporal models that rely on heterogeneous and potentially massive numbers of participants while preserving the privacy of highly sensitive loca

Bruno Arcanjo, Bruno Ferrarini, Michael Milford, Klaus D. McDonald-Maier

Visual place recognition (VPR) enables autonomous systems to localize themselves within an environment using image information. While Convolution Neural Networks (CNNs) currently dominate state-of-the-art VPR performance, their high computational requirements make them unsuitable for platforms with budget or size constraints. This has spurred the development

Shreya Ghosh, Zhixi Cai, Parul Gupta, Garima Sharma

Automatic group emotion recognition plays an important role in understanding complex human-human interaction. This paper introduces, Emolysis, a Python-based, standalone open-source group emotion analysis toolkit for use in different social situations upon getting consent from the users. Given any input video, Emolysis processes synchronized multimodal input

S. W. Lovesey

Newly published diffraction data on hexagonal YMnO3 at a temperature of 10 K are shown to be consistent with a trusted expression of the magnetic symmetry, although the data alone are not definitive [M. Ramakrishnan et al., Phys. Rev. Research 5, 013203 (2023)]. Howard et al. conclude from an exhaustive review of experimental data that the symmetry of the an

Yifei Yang, Hongqiu Wu, Hai Zhao

Named Entity Recognition (NER) is a cornerstone NLP task while its robustness has been given little attention. This paper rethinks the principles of NER attacks derived from sentence classification, as they can easily violate the label consistency between the original and adversarial NER examples. This is due to the fine-grained nature of NER, as even minor

Siyu Yuan, Jiangjie Chen, Ziquan Fu, Xuyang Ge

In everyday life, humans often plan their actions by following step-by-step instructions in the form of goal-oriented scripts. Previous work has exploited language models (LMs) to plan for abstract goals of stereotypical activities (e.g., "make a cake"), but leaves more specific goals with multi-facet constraints understudied (e.g., "make a cake for diabetic

Cosmin Burtea, Nicolas Meunier, Clément Mouhot

We consider a drift-diffusion model, with an unknown function depending on the spatial variable and an additional structural variable, the amount of ingested lipid. The diffusion coefficient depends on this additional variable. The drift acts on this additional variable, with a power-law coefficient of the additional variable and a localization function in s

Jia Xu, Longbing Cao

We address an important yet challenging problem - modeling high-dimensional dependencies across multivariates such as financial indicators in heterogeneous markets. In reality, a market couples and influences others over time, and the financial variables of a market are also coupled. We make the first attempt to integrate variational sequential neural learni

Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff

The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion toke

Z. S. Yuan, J. L. Han, H. Böhringer, Z. L. Wen

During cluster mergers, the intracluster gas and member galaxies undergo dynamic evolution, but at different timescales and reach different states. We collect 24 galaxy clusters in quasi-equilibrium state as indicated by the X-ray image, and calculate the cluster orientations and three kinds of dynamical parameters, i.e., the normalized centroid offset, the

Junseo Lee

With the advancement of quantum technologies, there is a potential threat to traditional encryption systems based on integer factorization. Therefore, developing techniques for accurately measuring the performance of associated quantum algorithms is crucial, as it can provide insights into the practical feasibility from the current perspective. In this chapt

Guoqiang Wu, Chongxuan Li, Yilong Yin

Macro-AUC is the arithmetic mean of the class-wise AUCs in multi-label learning and is commonly used in practice. However, its theoretical understanding is far lacking. Toward solving it, we characterize the generalization properties of various learning algorithms based on the corresponding surrogate losses w.r.t. Macro-AUC. We theoretically identify a criti

Aryan Jadon, Shashank Kumar

The widespread adoption of electronic health records and digital healthcare data has created a demand for data-driven insights to enhance patient outcomes, diagnostics, and treatments. However, using real patient data presents privacy and regulatory challenges, including compliance with HIPAA and GDPR. Synthetic data generation, using generative AI models li

Zs. Bognár, Á. Sódor, I. R. Clark, S. D. Kawaler

Context. In 2020, a publication presented the first-light results for 18 known ZZ Ceti stars observed by the TESS space telescope during the first survey observations of the southern ecliptic hemisphere. However, in the meantime, new measurements have become available from this field, in many cases with the new, 20s ultrashort cadence mode. Aims. We investig

Tomoyuki Tokuue, Tomoaki Ishiyama

Sorting is one of the most basic algorithms, and developing highly parallel sorting programs is becoming increasingly important in high-performance computing because the number of CPU cores per node in modern supercomputers tends to increase. In this study, we have implemented two multi-threaded sorting algorithms based on samplesort and compared their perfo

A. Loi, R. Mossa

We prove two rigidity results on holomorphic isometries into homogeneous K\"{a}hler manifolds. The first shows that a K\"{a}hler-Ricci soliton induced by the homogeneous metric of the K\"{a}hler product of a special flag manifold (i.e. a flag of classical type or integral type) with a bounded homogeneous domain is trivial, i.e. K\"{a}hler-Einstein. In the se

Heiko Dietrich, Bettina Eick

In 1980, Leedham-Green and Newman introduced the invariant coclass to the theory of groups of prime-power order and they proposed five far-reaching conjectures related to it. Their work has initiated a deep and fruitful research project in group theory that is still ongoing today. We outline the main results of this celebrated article, we describe the histor

Thomas Fischbacher

For some research questions that involve Spin(p, q) representation theory, using symbolic algebra based techniques might be an attractive option for simplifying and manipulating expressions. Yet, for some such problems, especially as they arise in the study of various limits of M-theory (such as dimensional reductions), the complexity of the resulting expres

T. Zabelotsky, S. Singh, G. Haim, R. Malkinson

Boron vacancies (VB${^-}$) in hexagonal boron-nitride (hBN) have sparked great interest in recent years, due to their electronic spin properties. Since hBN can be readily integrated into devices where it interfaces a huge variety of other 2D materials, boron vacancies may serve as a precise sensor which can be deployed at very close proximity to many importa

Thomas Benz, Michael Rogenmoser, Paul Scheffler, Samuel Riedel

Data transfers are essential in today's computing systems as latency and complex memory access patterns are increasingly challenging to manage. Direct memory access engines (DMAEs) are critically needed to transfer data independently of the processing elements, hiding latency and achieving high throughput even for complex access patterns to high-latency memo

Jiajun Fan, Yuzheng Zhuang, Yuecheng Liu, Jianye Hao

The exploration problem is one of the main challenges in deep reinforcement learning (RL). Recent promising works tried to handle the problem with population-based methods, which collect samples with diverse behaviors derived from a population of different exploratory policies. Adaptive policy selection has been adopted for behavior control. However, the beh

Alireza Furutanpey, Johanna Barzen, Marvin Bechtold, Schahram Dustdar

Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs is soon possible everywhere. Existing work has yet to draw from research in edge computing to explore systems exploiting mobile QPUs, or how hybrid applications can benefit from distributed heterogeneous resources. Hence, th

Arian Prabowo, Hao Xue, Wei Shao, Piotr Koniusz

New roads are being constructed all the time. However, the capabilities of previous deep forecasting models to generalize to new roads not seen in the training data (unseen roads) are rarely explored. In this paper, we introduce a novel setup called a spatio-temporal (ST) split to evaluate the models' capabilities to generalize to unseen roads. In this setup

Joackim Bernier, Benoît Grébert, Tristan Robert

In this paper, we succeed in integrating Strichartz estimates (encoding the dispersive effects of the equations) in Birkhoff normal form techniques. As a consequence, we deduce a result on the long time behavior of quintic NLS solutions on the circle for small but very irregular initial data (in $H^s$ for $s > 2/5$). Note that since $2/5 < 1$, we cannot clai

Ciprian A Tudor, Jérémy Zurcher

Let F ($\nu$) be the centered Gamma law with parameter $\nu$ > 0 and let us denote by P Y the probability distribution of a random vector Y. We develop a multidimensional variant of the Stein's method for Gamma approximation that allows to obtain bounds for the second Wasserstein distance between the probability distribution of an arbitrary random vector (X,

Minju Jo, Seungji Kook, Noseong Park

Hawkes processes are a popular framework to model the occurrence of sequential events, i.e., occurrence dynamics, in several fields such as social diffusion. In real-world scenarios, the inter-arrival time among events is irregular. However, existing neural network-based Hawkes process models not only i) fail to capture such complicated irregular dynamics, b

Jiahui Zhu, Wei Liu, Jianliang Zhai

In this work we establish a Freidlin-Wentzell type large deviation principle for stochastic nonlinear Schr\"{o}dinger equation, with either focusing or defocusing nonlinearity, driven by nonlinear multiplicative L\'evy noise in the Marcus canonical form. This task is challenging in the current setting due to the presence of the power-type nonlinear term, the

Songling Zhu, Ronghua Shang, Bo Yuan, Weitong Zhang

The knowledge distillation uses a high-performance teacher network to guide the student network. However, the performance gap between the teacher and student networks can affect the student's training. This paper proposes a novel knowledge distillation algorithm based on dynamic entropy correction to reduce the gap by adjusting the student instead of the tea

M. -R. Babaa, E. Mcrae, Sandrine Delpeux, J. Ghanbaja

Physisorption studies and transmission electron microscopy have been used to characterise multi-walled carbon nanotubes (MWNTs) made by a template-synthesis technique. Microscopic investigations revealed formation of 'branched nanotubes' with significant irregularities in diameters and with structural defects on the external surfaces of the tubes. Krypton ad

Zhexin Wang, Sihem Mesnager, Nian Li, Xiangyong Zeng

This paper deals with Niho functions which are one of the most important classes of functions thanks to their close connections with a wide variety of objects from mathematics, such as spreads and oval polynomials or from applied areas, such as symmetric cryptography, coding theory and sequences. In this paper, we investigate specifically the $c$-differentia

Nannan Wu, Li Yu, Xuefeng Jiang, Kwang-Ting Cheng

Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning. Existing research, relying on the assumption of class-balanced global data, might be incapable to model complicated label noise, especially in medical scenarios. In this paper, we first formulate a new and more realistic federated

Herman L. dos Santos, Cristian J. Vaca-Rubio, Radosław Kotaba, Yi Song

This paper proposes a method for reducing {third-party} exposure to electromagnetic fields (EMF) by exploiting the capability of a reconfigurable intelligent surfaces' (RIS) to manipulate the electromagnetic environment. We consider users capable of multi-beam communication, such that a user can use a set of different propagation paths enabled by the RIS. Th

Xin Shen, Xiaonan Zhao, Rui Luo

Fine-grained multi-label classification models have broad applications in e-commerce, such as visual based label predictions ranging from fashion attribute detection to brand recognition. One challenge to achieve satisfactory performance for those classification tasks in real world is the wild visual background signal that contains irrelevant pixels which co

Tom Bäckström

Speech technology for communication, accessing information, and services has rapidly improved in quality. It is convenient and appealing because speech is the primary mode of communication for humans. Such technology, however, also presents proven threats to privacy. Speech is a tool for communication and it will thus inherently contain private information.

Cong Ma, Yaping Zhang, Mei Tu, Yang Zhao

Text image machine translation (TIMT) has been widely used in various real-world applications, which translates source language texts in images into another target language sentence. Existing methods on TIMT are mainly divided into two categories: the recognition-then-translation pipeline model and the end-to-end model. However, how to transfer knowledge fro

Alessandro Zenesini, Anna Berti, Riccardo Cominotti, Chiara Rogora

In quantum field theory, the decay of an extended metastable state into the real ground state is known as ``false vacuum decay'' and it takes place via the nucleation of spatially localized bubbles. Despite the large theoretical effort to estimate the nucleation rate, experimental observations were still missing. Here, we observe bubble nucleation in isolate

Hakim Boumaza

In this paper we review results of Anderson localization for different random families of operators which enter in the framework of random quasi-one-dimensional models. We first recall what is Anderson localization from both physical and mathematical point of views. From the Anderson-Bernoulli conjecture in dimension 2 we justify the introduction of quasi-on

Eugenio Tufino, Luigi Gratton, Stefano Oss

We propose two experiments suited for high school and/or undergraduate physics laboratory which are aimed to the discovery of the practical meaning and usefulness of the FFT analysis as a mathematical and graphical instrument, intended to discriminate different periodic signals in various situations, ranging from basic, demonstrative measurements to very com

Xin Shen, Kyungdon Joo, Jean Oh

We propose an end-to-end deep learning approach to rectify fisheye images and simultaneously calibrate camera intrinsic and distortion parameters. Our method consists of two parts: a Quick Image Rectification Module developed with a Pix2Pix GAN and Wasserstein GAN (W-Pix2PixGAN), and a Calibration Module with a CNN architecture. Our Quick Rectification Netwo

Yunchao Yang, Yipeng Zhou, Miao Hu, Di Wu

Federated learning (FL) is a prospective distributed machine learning framework that can preserve data privacy. In particular, cross-silo FL can complete model training by making isolated data islands of different organizations collaborate with a parameter server (PS) via exchanging model parameters for multiple communication rounds. In cross-silo FL, an inc

Tyler Carbin, Xinshu Zhang, Adrian B. Culver, Hengdi Zhao

Upon intense femtosecond photo-excitation, a many-body system can undergo a phase transition through a non-equilibrium route, but understanding these pathways remains an outstanding challenge. Here, we use time-resolved second harmonic generation to investigate a photo-induced phase transition in Ca$_3$Ru$_2$O$_7$ and show that mesoscale inhomogeneity profou

Philippe Moustrou, Cordian Riener, Hugues Verdure

This chapter investigates how symmetries can be used to reduce the computational complexity in polynomial optimization problems. A focus will be specifically given on the Moment-SOS hierarchy in polynomial optimization, where results from representation theory and invariant theory of groups can be used. In addition, symmetry reduction techniques which are mo

Yongjin Choi, Krishna Kumar

Accurate simulation of granular flow dynamics is crucial for assessing various geotechnical risks, including landslides and debris flows. Granular flows involve a dynamic rearrangement of particles exhibiting complex transitions from solid-like to fluid-like responses. Traditional continuum and discrete numerical methods are limited by their computational co

Orhan Erece, Irek Khamitov, Murat Kaplan, Yucel Kilic

In this study, we carried out photometric, spectroscopic, and for the first time, polarimetric observations of the Amor-type near-Earth asteroid (2059) Baboquivari. Our findings represent the first reliable determination of Baboquivari's physical properties. We used data from a 1m-class telescope (T100) along with ALCDEF data for photometric analyses and a 1

Jens Decke, Olaf Wünsch, Bernhard Sick

This dataset contains 10,000 fluid flow and heat transfer simulations in U-bend shapes. Each of them is described by 28 design parameters, which are processed with the help of Computational Fluid Dynamics methods. The dataset provides a comprehensive benchmark for investigating various problems and methods from the field of design optimization. For these inv

Lukáš Gajdošech, Peter Kravár

Application of neural networks in industrial settings, such as automated factories with bin-picking solutions requires costly production of large labeled data-sets. This paper presents an automatic data generation tool with a procedural model of a cardboard box. We briefly demonstrate the capabilities of the system, its various parameters and empirically pro

Kaushal Kumar Maurya, Rahul Kejriwal, Maunendra Sankar Desarkar, Anoop Kunchukuttan

We address the task of machine translation (MT) from extremely low-resource language (ELRL) to English by leveraging cross-lingual transfer from 'closely-related' high-resource language (HRL). The development of an MT system for ELRL is challenging because these languages typically lack parallel corpora and monolingual corpora, and their representations are

Sophia N. Wilson, Kasper E. Heintz, Páll Jakobsson, Suzanne C. Madden

The assembly and build-up of neutral atomic hydrogen (HI) in galaxies is one of the most fundamental processes in galaxy formation and evolution. Studying this process directly in the early universe is hindered by the weakness of the hyperfine 21-cm HI line transition, impeding direct detections and measurements of the HI gas masses ($M_{\rm HI}$). Here we p

Yassine El Kheir, Fouad Khnaisser, Shammur Absar Chowdhury, Hamdy Mubarak

This paper introduces a novel Arabic pronunciation learning application QVoice, powered with end-to-end mispronunciation detection and feedback generator module. The application is designed to support non-native Arabic speakers in enhancing their pronunciation skills, while also helping native speakers mitigate any potential influence from regional dialects

Rui Zhang, Jack Holligan, Xiangdong Ji, Yushan Su

In lattice-QCD calculations of parton distribution functions (PDFs) via large-momentum effective theory, the leading power (twist-three) correction appears as ${\cal O}(\Lambda_{\rm QCD}/P^z)$ due to the linear-divergent self-energy of Wilson line in quasi-PDF operators. For lattice data with hadron momentum $P^z$ of a few GeV, this correction is dominant in

Giulia Cavagnari, Giuseppe Savaré, Giacomo Enrico Sodini

We introduce and study the class of totally dissipative multivalued probability vector fields (MPVF) $\boldsymbol{\mathrm F}$ on the Wasserstein space $(\mathcal{P}_2(\mathsf{X}),W_2)$ of Euclidean or Hilbertian probability measures. We show that such class of MPVFs is in one to one correspondence with law-invariant dissipative operators in a Hilbert space $

Richard Vale

Many large and small companies in the tech and startup sector have been laying off an unusually high number of workers in 2022 and 2023. We are interested in predicting when this period of layoffs might end, without resorting to economic forecasts. We observe that a sample of layoffs up to March 31, 2023 follow the pattern of noisy observations from an SIR (

Boshuai Cai, Cenxi Yuan

How nuclides decay in the superheavy region is key information for investigating new elements beyond oganesson and the island of stability. The Random Forest algorithm is applied to study the competition between different decay modes in the superheavy region, including $\alpha$ decay, $\beta^-$ decay, $\beta^+$ decay, electron capture and spontaneous fission