July 2023 arXiv papers — page 72

Kadir Saygin

Higher-order predictions through the combined accuracy including next-to-leading order (NLO) electroweak (EW) and next-to-NLO (NNLO) quantum chromodynamics (QCD) corrections in underlying perturbation theories are presented thoroughly for the invisible decay of the $Z$ boson into neutrino pair relative to its decay into charged-lepton pair (leptonic decay).

Yang Wenkai, Ji Ruihang, Yue Yiran, Gu Zhonghan

Agriculture industries often face challenges in manual tasks such as planting, harvesting, fertilizing, and detection, which can be time consuming and prone to errors. The "Agricultural Robotic System" project addresses these issues through a modular design that integrates advanced visual, speech recognition, and robotic technologies. This system is comprise

M. Albaladejo, A. Feijoo, I. Vidaña, J. Nieves

We study here the inverse problem of starting from the femtoscopic correlation functions of related channels and analyze them with an efficient tool to extract the maximum information possible on the interaction of the components of these channels, and the existence of possible bound states tied to this interaction. The method is flexible enough to accommoda

Parisa Rahdan, Elisabeth Zeyen, Cristobal Gallego-Castillo, Marta Victoria

Distributed solar photovoltaic (PV) systems are projected to be a key contributor to future energy landscape, but are often poorly represented in energy models due to their distributed nature. They have higher costs compared to utility PV, but offer additional advantages, e.g., in terms of social acceptance. Here, we model the European power network with a h

Jingru Lin, Xianghu Yue, Junyi Ao, Haizhou Li

Acoustic word embeddings (AWEs) aims to map a variable-length speech segment into a fixed-dimensional representation. High-quality AWEs should be invariant to variations, such as duration, pitch and speaker. In this paper, we introduce a novel self-supervised method to learn robust AWEs from a large-scale unlabelled speech corpus. Our model, named Correspond

Frédéric Juget, Maarten van Dijk, Emilio Andrea Maugeri, Maria Dorothea Schumann

The beta spectrum of the main transition of the beta-decay of 171Tm was measured using a double focalizing spectrometer. The instrument was lately improved in order to reduce its low energy threshold to 34 keV. We used the spectrometer to measure the beta spectrum end-point energy of the main transition of 171Tm decay using the Kurie plot formalism. We repor

Satvir Kaur, Siqi Xu, Chandan Mondal, Xingbo Zhao

The internal image of the proton is unveiled by examining the generalized parton distributions (GPDs) at zero skewness, within the basis light-front quantized environment. Several distributions emerge when a quark is sampled with different currents depending upon the helicity arrangements of the active quark and the proton target. We investigate six of the e

Juan Martínez

Let $G$ be a finite group, let $\pi$ be a set of primes and let $p$ be the smallest prime in $\pi$. In this work, we prove that $G$ possesses a normal and abelian Hall $\pi$-subgroup if and only if the probability that two random $\pi$-elements of $G$ commute is larger than $\frac{p^2+p-1}{p^3}$. We also prove that if $x$ is a $\pi$-element not lying in $O_{

Nita Tamang, Pitu Sarkar

Shuffle algebra has been employed to give a proof of the duality theorem for multiple zeta-star values of height one.

Jinzhu Mao, Liu Cao, Chen Gao, Huandong Wang

Understanding and characterizing the vulnerability of urban infrastructures, which refers to the engineering facilities essential for the regular running of cities and that exist naturally in the form of networks, is of great value to us. Potential applications include protecting fragile facilities and designing robust topologies, etc. Due to the strong corr

J. Hou, P. T. Yang, Z. Y. Liu, J. Y. Li

The recent report of pressure-induced structure transition and signature of superconductivity with Tc = 80 K above 14 GPa in the La3Ni2O7 crystals has garnered considerable attention. To further elaborate this discovery, we carried out comprehensive resistance measurements on the La3Ni2O7 crystals grown with the optical-image floating zone furnace under oxyg

Matteo Barigozzi

This paper investigates the properties of Quasi Maximum Likelihood estimation of an approximate factor model for an $n$-dimensional vector of stationary time series. We prove that the factor loadings estimated by Quasi Maximum Likelihood are asymptotically equivalent, as $n\to\infty$, to those estimated via Principal Components. Both estimators are, in turn,

Zhiyu Lu, Batool Imtiaz, Dongdong Zhang, Yi-Fu Cai

We are studying the effects of Self-Interacting dark radiation (SIdr) on the evolution of the universe. Our main focus is on the cosmic microwave background (CMB) and how SIdr could potentially help resolve the Hubble tension. We are looking into different scenarios by mixing SIdr with Free-Streaming dark radiation (FSdr) or not to determine whether SIdr can

G. Tsialiamanis, N. Dervilis, D. J. Wagg, K. Worden

Machine learning has affected the way in which many phenomena for various domains are modelled, one of these domains being that of structural dynamics. However, because machine-learning algorithms are problem-specific, they often fail to perform efficiently in cases of data scarcity. To deal with such issues, combination of physics-based approaches and machi

Jitao Ma, Weiying Xie, Yunsong Li, Leyuan Fang

Hyperspectral anomaly detection (HAD) is widely used in Earth observation and deep space exploration. A major challenge for HAD is the complex background of the input hyperspectral images (HSIs), resulting in anomalies confused in the background. On the other hand, the lack of labeled samples for HSIs leads to poor generalization of existing HAD methods. Thi

Ke Li, Susanne Schmidt, Tim Rolff, Reinhard Bacher

Large industrial facilities such as particle accelerators and nuclear power plants are critical infrastructures for scientific research and industrial processes. These facilities are complex systems that not only require regular maintenance and upgrades but are often inaccessible to humans due to various safety hazards. Therefore, a virtual reality (VR) syst

Vassilis Daskalogiannis, Petros Galanopoulos, Michael Papadimitrakis

We prove the nontrivial variant \[ \sum\limits_{m,n=1}^{\infty}\Big(\frac{n}{m}\Big)^{\frac{1}{q}-\frac{1}{p}}\frac{a_mb_n}{m+n-1}\leq\frac{\pi}{\sin\frac{\pi}{p}} \Big( \sum\limits_{m=1}^{\infty}a_m^p\Big)^{\frac 1p}\Big( \sum\limits_{n=1}^{\infty}b_n^q\Big)^{\frac 1q} \] of the well known Hilbert's inequality. Then we use this to determine the exact value

Ama Bandara, Fátima Rodríguez-Galán, Elana Pereira de Santana, Peter Haring Bolívar

Wireless Network-on-Chip (WNoC) is a promising paradigm to overcome the versatility and scalability issues of conventional on-chip networks for current processor chips. However, the chip environment suffers from delay spread which leads to intense Inter-Symbol Interference (ISI). This degrades the signal when transmitting and makes it difficult to achieve th

Longfeng Wu, Bowen Lei, Dongkuan Xu, Dawei Zhou

Rare categories abound in a number of real-world networks and play a pivotal role in a variety of high-stakes applications, including financial fraud detection, network intrusion detection, and rare disease diagnosis. Rare category analysis (RCA) refers to the task of detecting, characterizing, and comprehending the behaviors of minority classes in a highly-

Muhammad Azeem Aslam, Xu Wei, Hassan Khalid, Nisar Ahmed

BIQA (Blind Image Quality Assessment) is an important field of study that evaluates images automatically. Although significant progress has been made, blind image quality assessment remains a difficult task since images vary in content and distortions. Most algorithms generate quality without emphasizing the important region of interest. In order to solve th

Lei Wang, Bo Liu, Fangfang Liang, Bincheng Wang

Gait recognition is a biometric technique that identifies individuals by their unique walking styles, which is suitable for unconstrained environments and has a wide range of applications. While current methods focus on exploiting body part-based representations, they often neglect the hierarchical dependencies between local motion patterns. In this paper, w

Martin Sulzmann, Peter Thiemann

The lock set method and the partial order method are two main approaches to guarantee that dynamic data race prediction remains efficient. There are many variations of these ideas. Common to all of them is the assumption that the events in a critical section belong to the same thread. We have evidence that critical sections in the wild do extend across threa

Takuhiro Fujiie, Masahiro Hino, Takuya Hosobata, Go Ichikawa

This study entailed the successful deployment of a novel neutron interferometer that utilizes multilayer mirrors. The apparatus facilitates a precise evaluation of the wavelength dependence of interference fringes utilizing a pulsed neutron source. Our interferometer achieved an impressive precision of 0.02 rad within a 20-min of recording time. Compared to

Andrei M. Raigorodskii, Arsenii Sagdeev

In 1993, Kahn and Kalai famously constructed a sequence of finite sets in $d$-dimensional Euclidean spaces that cannot be partitioned into less than $(1.203\ldots+o(1))^{\sqrt{d}}$ parts of smaller diameter. Their method works not only for the Euclidean, but for all $\ell_p$-spaces as well. In this short note, we observe that the larger the value of $p$, the

Renrong Mao, Ernest X. W. Xia

Beck introduced two partition statistics $NT(r,m,n)$ and $M_{\omega}(r,m,n)$,which denote the total number of parts in the partition of $n$ with rank congruent to $r$ modulo $m$ and the total number of ones in the partition of $n$ with crank congruent to $r$ modulo $m$, respectively. In recent years, a number of congruences and identities on $NT(r,m,n)$ and

Anamari Nakic

A $(G,[k_1,\dots,k_t],\lambda)$ {\it partitioned difference family} (PDF) is a partition $\cal B$ of an additive group $G$ into sets ({\it blocks}) of sizes $k_1$, \dots, $k_t$, such that the list of differences of ${\cal B}$ covers exactly $\lambda$ times every non-zero element of $G$. It is called {\it Hadamard} (HPDF) if the order of $G$ is $2\lambda$. Th

Hongzheng Zhao, Marin Bukov, Markus Heyl, Roderich Moessner

Digital quantum simulation relies on Trotterization to discretize time evolution into elementary quantum gates. On current quantum processors with notable gate imperfections, there is a critical tradeoff between improved accuracy for finer timesteps, and increased error rate on account of the larger circuit depth. We present an adaptive Trotterization algori

Beyza Sütlüoğlu Ege, Ceyhun Bulutay

A closed-loop, lossy optomechanical system consisting of one optical and two degenerate mechanical resonators is computationally investigated. This system constitutes an elementary synthetic plaquette derived from the loop phase of the intercoupling coefficients. In examining a specific quantum attribute, we delve into the control of quadrature variances wit

Mehrdad Pournaderi, Yu Xiang

Consider a star network where each local node possesses a set of test statistics that exhibit a symmetric distribution around zero when their corresponding null hypothesis is true. This paper investigates statistical inference problems in networks concerning the aggregation of this general type of statistics and global error rate control under communication

Yan Liu, Jonathan Dong, Thanh-An Pham, Francois Marelli

Optical projection tomography (OPT) is a powerful tool for biomedical studies. It achieves 3D visualization of mesoscopic biological samples with high spatial resolution using conventional tomographic-reconstruction algorithms. However, various artifacts degrade the quality of the reconstructed images due to experimental imperfections in the OPT instruments.

Ali Ghaffari, Fatemeh Zamiri, Marjan Sheibani

We present new additive results for DMP elements in a Banach algebra with involution. The necessary and sufficient conditions under which the sum of two DMP elements is DMP are investigated.

Anup Mishra, Yijie Mao, Carmen D'Andrea, Stefano Buzzi

This letter focuses on a transmitter or base station (BS) side beyond-diagonal reflecting intelligent surface (BD-RIS) deployment strategy to enhance the spectral efficiency (SE) of a time-division-duplex massive multiple-input multiple-output (MaMIMO) network. In this strategy, the active antenna array utilizes a BD-RIS at the BS to serve multiple users in

Szu-Chi Chung

In single-particle cryo-electron microscopy (cryo-EM), the efficient determination of orientation parameters for 2D projection images poses a significant challenge yet is crucial for reconstructing 3D structures. This task is complicated by the high noise levels present in the cryo-EM datasets, which often include outliers, necessitating several time-consumi

Shaik Ahmed, M. Amazioug, Jia-Xin Peng, S. K. Singh

We theoretically investigate a scheme to entangle two squeezed magnon modes in a double cavitymagnon system, where both cavities are driven by a two-mode squeezed vacuum microwave field. Each cavity contains an optical parametric amplifier as well as a macroscopic yttrium iron garnet (YIG) sphere placed near the maximum bias magnetic fields such that this le

Jiangkun Gong, Jun Yan, Deyong Kong, Deren Li

This paper discusses the challenges of detecting and categorizing small drones with radar automatic target recognition (ATR) technology. The authors suggest integrating ATR capabilities into drone detection radar systems to improve performance and manage emerging threats. The study focuses primarily on drones in Group 1 and 2. The paper highlights the need t

Changyu Lee, Dongha Chung, Jonghwi Kim, Jinwhan Kim

In this paper, we describe the development process of autonomous navigation capabilities of a small cruise boat operating in a canal environment and present the results of a field experiment conducted in the Pohang Canal, South Korea. Nonlinear model predictive control (NMPC) was used for the online trajectory planning and tracking control of the cruise boat

Francesco Mandelli, Marco Pinciroli, Michele Trapletti, Edoardo Vittori

In this paper, we focus on finding the optimal hedging strategy of a credit index option using reinforcement learning. We take a practical approach, where the focus is on realism i.e. discrete time, transaction costs; even testing our policy on real market data. We apply a state of the art algorithm, the Trust Region Volatility Optimization (TRVO) algorithm

Yuan Yao, Linhao Li, Masaki Oshikawa, Chang-Tse Hsieh

We study quantum many-body systems in the presence of an exotic antiunitary translation or inversion symmetry involving time reversal. Based on a symmetry-twisting method and spectrum robustness, we propose that a half-integer spin chain that respects any of these two antiunitary crystalline symmetries in addition to the discrete $\mathbb{Z}_2\times\mathbb{Z

Yoshikazu Giga, Zhongyang Gu

We consider a space of $L^2$ vector fields with bounded mean oscillation whose ``normal'' component to the boundary is well-controlled. In the case when the dimension $n \geq 3$, we establish its Helmholtz decomposition for arbitrary uniformly $C^3$ domain in $\mathbf{R}^n$.

Ajay Gunalan, Marco Castello, Simonluca Piazza, Shunlei Li

We present a novel approach to implement compressive sensing in laser scanning microscopes (LSM), specifically in image scanning microscopy (ISM), using a single-photon avalanche diode (SPAD) array detector. Our method addresses two significant limitations in applying compressive sensing to LSM: the time to compute the sampling matrix and the quality of reco

Chunlin Hao, Hao Xu, Shiquan Lin, Jinmiao He

The topography and the electrical properties are two crucial characteristics in determining roles and functionalities of materials. Conductive atomic force microscopy (CAFM) is widely recognized for its ability to independently measure the topology and conductivity. The increasing trend towards miniaturization in electrical devices and sensors has encouraged

Florian Frohn, Jürgen Giesl

We recently proposed Acceleration Driven Clause Learning (ADCL), a novel calculus to analyze satisfiability of Constrained Horn Clauses (CHCs). Here, we adapt ADCL to disprove termination of transition systems, and we evaluate its implementation in our tool LoAT against the state of the art.

Mauro Mariani, Dario Trevisan

We study the large time behavior of the optimal transportation cost towards the uniform distribution, for the occupation measure of a stationary Brownian motion on the flat torus in $d$ dimensions, where the cost of transporting a unit of mass is given by a power of the flat distance. We establish a global upper bound, in terms of the limit for the analogue

L. Andriambariarijaona, F. Datchi H. Zhang, K. Béneut, B. Baptiste

We report a comprehensive experimental investigation of the phase diagram of ammonia hemihydrate (AHH) in the range of 2-30 GPa and 300-700 K, based on Raman spectroscopy and x-ray diffraction experiments and visual observations. Four solid phases, denoted AHH-II, DIMA, pbcc and qbcc, are present in this domain, one of which, AHH-qbcc was discovered in this

Lucas Wansner, Ned J H Wontner

The axiom of countable choice for reals is one of the most basic fragments of the axiom of choice needed in many parts of mathematics. Descriptive choice principles are a further stratification of this fragment by the descriptive complexity of the sets. In this paper, we provide a separation technique for descriptive choice principles based on Jensen forcing

Guillaume Perez, Laurent Condat, Michel Barlaud

Looking for sparsity is nowadays crucial to speed up the training of large-scale neural networks. Projections onto the $\ell_{1,2}$ and $\ell_{1,\infty}$ are among the most efficient techniques to sparsify and reduce the overall cost of neural networks. In this paper, we introduce a new projection algorithm for the $\ell_{1,\infty}$ norm ball. The worst-case

Christoph Schwab, Andreas Stein, Jakob Zech

We establish universality and expression rate bounds for a class of neural Deep Operator Networks (DON) emulating Lipschitz (or H\"older) continuous maps $\mathcal G:\mathcal X\to\mathcal Y$ between (subsets of) separable Hilbert spaces $\mathcal X$, $\mathcal Y$. The DON architecture considered uses linear encoders $\mathcal E$ and decoders $\mathcal D$ via

Keeley Erhardt, Saurabh Khanna

The Internet has not only digitized but also democratized information access across the globe. This gradual but path-breaking move to online information propagation has resulted in search engines playing an increasingly prominent role in shaping access to human knowledge. When an Internet user enters a query, the search engine sorts through the hundreds of b

Lu Gao, Qiang Cheng, Qing-Feng Sun

We study the Andreev reflections and the quantum transport in the proximitized graphene/superconductor junction. The proximitized graphene possesses the pseudospin staggered potential and the intrinsic spin-orbit coupling induced by substrate, which are responsible for the spin-valley dependent double Andreev reflections and the anomalous transport propertie

Yuichi Masubuchi, Yuya Doi, Takato Ishida, Naoyuki Sakumichi

The influence of node functionality (f) on the fracture of polymer networks remains unclear. While many studies have focused on multi-functional nodes with f>4, recent research suggests that networks with f=3 exhibit superior fracture properties compared to those with f=4. To clarify this discrepancy, we conducted phantom chain simulations for star-polymer n

Junhong Xiang, Jingmin Zhang, Zhixiong Nan

To handle the two shortcomings of existing methods, (i)nearly all models rely on high-definition (HD) maps, yet the map information is not always available in real traffic scenes and HD map-building is expensive and time-consuming and (ii) existing models usually focus on improving prediction accuracy at the expense of reducing computing efficiency, yet the

Mateusz Pielach, Agnieszka Jamrozik, Katarzyna Krupa, Yuriy Stepanenko

Ultrafast all-fiber Yb-doped fiber oscillators are usually associated with all-normal-dispersion cavities, which operate in a dissipative soliton regime, quintessential for pulsed operation at the wavelength of 1 {\mu}m. This work presents an all-polarization-maintaining Yb-doper fiber laser oscillator that operates in a dispersion-managed dissipative solito

Shunxin Wang, Raymond Veldhuis, Christoph Brune, Nicola Strisciuglio

Frequency analysis is useful for understanding the mechanisms of representation learning in neural networks (NNs). Most research in this area focuses on the learning dynamics of NNs for regression tasks, while little for classification. This study empirically investigates the latter and expands the understanding of frequency shortcuts. First, we perform expe

S. Meljanac, S. Mignemi

We discuss exact realizations of the Yang-Poisson model on canonical phase space. The Yang model is an example of noncommutative geometry on a background spacetime of constant curvature and is notable for its duality between position and momentum manifolds. We call Yang-Poisson model its classical limit, with commutators replaced by Poisson brackets. The str

Umberto Michieli, Mete Ozay

Vision systems mounted on home robots need to interact with unseen classes in changing environments. Robots have limited computational resources, labelled data and storage capability. These requirements pose some unique challenges: models should adapt without forgetting past knowledge in a data- and parameter-efficient way. We characterize the problem as few

Chengming Bai, Li Guo, Jianqi Liu, Xiaoyan Wang

Derivations play a fundamental role in the definition of vertex (operator) algebras, sometimes regarded as a generalization of differential commutative algebras. This paper studies the role played by the integral counterpart of the derivations, namely Rota-Baxter operators, in vertex (operator) algebras. The closely related notion of dendriform algebras is a

Kenji Sugisaki

Quantum computers are capable of calculating the energy gap of two electronic states by using the quantum phase difference estimation (QPDE) algorithm. The Bayesian inference based implementations for the QPDE have been reported so far, but this approach is not projective, and the quality of the calculated energy gap depends on the input wave functions being

Junyeon Kim, Jun Uzuhashi, Masafumi Horio, Tomoaki Senoo

The utilization of orbital transport provides a versatile and efficient spin manipulation mechanism. As interest in orbital-mediated spin manipulation grows, we face a new issue to identify the underlying physics that determines the efficiency of orbital torque (OT). In this study, we systematically investigate the variation of OT governed by orbital Rashba-

Yaran Chen, Xueyu Chen, Yu Han, Haoran Li

Non alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease, which can be predicted accurately to prevent advanced fibrosis and cirrhosis. While, a liver biopsy, the gold standard for NAFLD diagnosis, is invasive, expensive, and prone to sampling errors. Therefore, non-invasive studies are extremely promising, yet they are sti

Lydia Abady, Jun Wang, Benedetta Tondi, Mauro Barni

Despite the wide variety of methods developed for synthetic image attribution, most of them can only attribute images generated by models or architectures included in the training set and do not work with unknown architectures, hindering their applicability in real-world scenarios. In this paper, we propose a verification framework that relies on a Siamese N

Zhigang Chang, Weitai Hu, Qing Yang, Shibao Zheng

In dyadic speaker-listener interactions, the listener's head reactions along with the speaker's head movements, constitute an important non-verbal semantic expression together. The listener Head generation task aims to synthesize responsive listener's head videos based on audios of the speaker and reference images of the listener. Compared to the Talking-hea

Tobia Boschi, Jacopo Di Iorio, Lorenzo Testa, Marzia A. Cremona

We use data from 107 Italian provinces to characterize and compare mortality patterns in the first two COVID-19 epidemic waves, which occurred prior to the introduction of vaccines. We also associate these patterns with mobility, timing of government restrictions, and socio-demographic, infrastructural, and environmental covariates. Notwithstanding limitatio

Ilias Dimitriadis, Dimitrios P. Giakatos, Stelios Karamanidis, Pavlos Sermpezis

In this paper, we study the Greek wiretappings scandal, which has been revealed in 2022 and attracted a lot of attention by press and citizens. Specifically, we propose a methodology for collecting data and analyzing patterns of online public discussions on Twitter. We apply our methodology to the Greek wiretappings use case, and present findings related to

Zheng Qin, Xiufan Li, Yang Zhou, Shikun Zhang

Variational quantum algorithms are considered one of the most promising methods for obtaining near-term quantum advantages; however, most of these algorithms are only expressed in the conventional quantum circuit scheme. The roadblock to developing quantum algorithms with the measurement-based quantum computation (MBQC) scheme is resource cost. Recently, we

Yinghao Zhang, Xiaodi Li, Weihang Li, Yue Hu

Deep unrolling networks that utilize sparsity priors have achieved great success in dynamic magnetic resonance (MR) imaging. The convolutional neural network (CNN) is usually utilized to extract the transformed domain, and then the soft thresholding (ST) operator is applied to the CNN-transformed data to enforce the sparsity priors. However, the ST operator

Simeon D. Beinlich, Georg Kastlunger, Karsten Reuter, Nicolas G. Hörmann

How does an electrochemical interface respond to changes in the electrode potential? How does the response affect the key properties of the system - energetics, excess charge, capacitance? Essential questions key to ab-initio simulations of electrochemical systems, which we address in this work on the basis of a rigorous mathematical evaluation of the interf

Stephen Zhang, Gilles Mordant, Tetsuya Matsumoto, Geoffrey Schiebinger

Manifold learning is a central task in modern statistics and data science. Many datasets (cells, documents, images, molecules) can be represented as point clouds embedded in a high dimensional ambient space, however the degrees of freedom intrinsic to the data are usually far fewer than the number of ambient dimensions. The task of detecting a latent manifol

Hao Yang, Liyuan Pan, Yan Yang, Richard Hartley

Recovering sharp images from dual-pixel (DP) pairs with disparity-dependent blur is a challenging task.~Existing blur map-based deblurring methods have demonstrated promising results. In this paper, we propose, to the best of our knowledge, the first framework that introduces the contrastive language-image pre-training framework (CLIP) to accurately estimate

G. Adhikari, N. Carlin, J. J. Choi, S. Choi

We report the results of a search for inelastic scattering of weakly interacting massive particles (WIMPs) off $^{127}$I nuclei using NaI(Tl) crystals with a data exposure of 97.7 kg$\cdot$years from the COSINE-100 experiment. The signature of inelastic WIMP-$^{127}$I scattering is a nuclear recoil accompanied by a 57.6 keV $\gamma$-ray from the prompt deexc

Wei Xiang, Chuanhong Zhan, Bang Wang

Event Causality Identification (ECI) aims at determining whether there is a causal relation between two event mentions. Conventional prompt learning designs a prompt template to first predict an answer word and then maps it to the final decision. Unlike conventional prompts, we argue that predicting an answer word may not be a necessary prerequisite for the

Anubhav Dubey, Kirti Chandra Sahu, Gautam Biswas

The evaporation of a liquid drop of initial diameter (Ddrop) migrating in a tube of diameter (D0) is investigated using the coupled level set and volume of fluid (CLSVOF) method focusing on determining the heat and mass transfer coefficients for a deforming drop. A robust phase change model is developed using an embedded boundary method under a finite differ

Gabriele Santin, Tizian Wenzel, Bernard Haasdonk

Kernel interpolation is a versatile tool for the approximation of functions from data, and it can be proven to have some optimality properties when used with kernels related to certain Sobolev spaces. In the context of interpolation, the selection of optimal function sampling locations is a central problem, both from a practical perspective, and as an intere

Xia Huang, Kai Fong Ernest Chong

Web image datasets curated online inherently contain ambiguous in-distribution (ID) instances and out-of-distribution (OOD) instances, which we collectively call non-conforming (NC) instances. In many recent approaches for mitigating the negative effects of NC instances, the core implicit assumption is that the NC instances can be found via entropy maximizat

Pavel Khrapov, Nikita Volkov

The paper presents a comparative analysis of iterative numerical methods of Jacobi and Gauss-Seidel for solving systems of linear algebraic equations (SLAEs) with complex and real matrices. The ranges of convergence for both methods for SLAEs in two and three unknowns, as well as the interrelationships of these ranges are obtained. An algorithm for determini

W. AfzaL, Z. Yue, Z. Li, M. Fuhrer

We report the observation of anomalous Hall effect in Mn$_3$Sn polycrystalline thin films deposited on Pt coated Al$_2$O$_3$ substrate with a large anomalous Hall conductivity of 65($\Omega$cm)$^{-1}$ at 3K. The Hall and magnetic measurements show a very small hysteresis owing to a weak ferromagnetic moment in this material. The longitudinal resistivity decr

Tianyu Fang, Yijie Mao

Beyond-diagonal reconfigurable intelligent surface (BD-RIS) has been proposed recently as a novel and generalized RIS architecture that offers enhanced wave manipulation flexibility and large coverage expansion. However, the beyond-diagonal mathematical model in BD-RIS inevitably introduces additional optimization challenges in beamforming design. In this le

Hamed Rezaee, Ludovic Renson

Control-based continuation (CBC) is a general and systematic method to explore the dynamic response of a physical system and perform bifurcation analysis directly during experimental tests. Although CBC has been successfully demonstrated on a wide range of systems, rigorous and general approaches to designing a noninvasive controller underpinning the methodo

K. O. Nikolaev, S. R. Lake, G. Schmidt, S. O. Demokritov

Spin-wave based transmission and processing of information is a promising emerging nano-technology that can help overcome limitations of traditional electronics based on the transfer of electrical charge. Among the most important challenges for this technology is the implementation of spin-wave devices that can operate without the need for an external bias m

Julia Grabinski, Steffen Jung, Janis Keuper, Margret Keuper

Convolutional Neural Networks (CNNs) are successful in various computer vision tasks. From an image and signal processing point of view, this success is counter-intuitive, as the inherent spatial pyramid design of most CNNs is apparently violating basic signal processing laws, i.e. the Sampling Theorem in their downsampling operations. This issue has been br

Peng-Zhang He, Hai-Qing Zhang

For a Lorentzian invariant theory, the entanglement entropy should be a function of the domain of dependence of the subregion under consideration. More precisely, it should be a function of the domain of dependence and the appropriate cut-off. In this paper, we refine the concept of cut-off to make it applicable to timelike regions and assume that the usual

W. Afzal, F. Yun, Z. Li, Z. Yue

We report a comprehensive study of magneto-transport properties in MoSi$_2$ bulk and thin films. Textured MoSi$_2$ thin films of around 70 nm were deposited on silicon substrates with different orientations. Giant magnetoresistance of 1000% was observed in sintered bulk samples while MoSi$_2$ single crystals exhibit a magnetoresistance (MR) value of 800% at

Peilin Liu, Yanni Tang, Mingyue Zhang, Wu Chen

Graph learning has a wide range of applications in many scenarios, which require more need for data privacy. Federated learning is an emerging distributed machine learning approach that leverages data from individual devices or data centers to improve the accuracy and generalization of the model, while also protecting the privacy of user data. Graph-federate

S. W. Good, O. K. Rantala, A. -S. M. Jylhä, C. H. K. Chen

Interplanetary coronal mass ejections (ICMEs) have low proton beta across a broad range of heliocentric distances and a magnetic flux rope structure at large scales, making them a unique environment for studying solar wind fluctuations. Power spectra of magnetic field fluctuations in 28 ICMEs observed between 0.25 and 0.95 au by Solar Orbiter and Parker Sola

J. Jurysek, T. Tavernier, V. Novotný, M. Heller

The Single-Mirror Small-Sized Telescope, or SST-1M, was originally developed as a prototype of a small-sized telescope for CTA, designed to form an array for observations of gamma-ray-induced atmospheric showers for energies above 3 TeV. A pair of SST-1M telescopes is currently being commissioned at the Ondrejov Observatory in the Czech Republic, and the tel

Pavlina K. Jordanova, Evelina Veleva

This work defines and investigates the properties of the Max-U-Exp distribution. The method of moments is applied in order to estimate its parameters. Then, by using the previous general theory about Mixed Poisson processes, developed by Grandel (1997), and Karlis and Xekalaki (2005), and analogously to Jordanova et al. (2023), and Jordanova and Stehlik (201

Kin G. Olivares, Geoffrey Négiar, Ruijun Ma, O. Nangba Meetei

Obtaining accurate probabilistic forecasts is an operational challenge in many applications, such as energy management, climate forecasting, supply chain planning, and resource allocation. Many of these applications present a natural hierarchical structure over the forecasted quantities; and forecasting systems that adhere to this hierarchical structure are

Shayan Jawed, Kiran Madhusudhanan, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme

In the early observation period of a time series, there might be only a few historic observations available to learn a model. However, in cases where an existing prior set of datasets is available, Meta learning methods can be applicable. In this paper, we devise a Meta learning method that exploits samples from additional datasets and learns to augment time

Charilaos Papaioannou, Emmanouil Benetos, Alexandros Potamianos

Recent developments in MIR have led to several benchmark deep learning models whose embeddings can be used for a variety of downstream tasks. At the same time, the vast majority of these models have been trained on Western pop/rock music and related styles. This leads to research questions on whether these models can be used to learn representations for diff

Zhenghao Feng, Lu Wen, Peng Wang, Binyu Yan

Currently, deep learning (DL) has achieved the automatic prediction of dose distribution in radiotherapy planning, enhancing its efficiency and quality. However, existing methods suffer from the over-smoothing problem for their commonly used L_1 or L_2 loss with posterior average calculations. To alleviate this limitation, we innovatively introduce a diffusi

Varsha Kishore, Chao Wan, Justin Lovelace, Yoav Artzi

Differentiable Search Index is a recently proposed paradigm for document retrieval, that encodes information about a corpus of documents within the parameters of a neural network and directly maps queries to corresponding documents. These models have achieved state-of-the-art performances for document retrieval across many benchmarks. These kinds of models h

Sarah Gao, Andrew Kean Gao

Since late 2022, Large Language Models (LLMs) have become very prominent with LLMs like ChatGPT and Bard receiving millions of users. Hundreds of new LLMs are announced each week, many of which are deposited to Hugging Face, a repository of machine learning models and datasets. To date, nearly 16,000 Text Generation models have been uploaded to the site. Giv

Pasin Manurangsi

In this short note, we show that the problem of computing the recursive teaching dimension (RTD) for a concept class (given explicitly as input) requires $n^{\Omega(\log n)}$-time, assuming the exponential time hypothesis (ETH). This matches the running time $n^{O(\log n)}$ of the brute-force algorithm for the problem.

Manuel Zahn, Elke Beyreuther, Iuliia Kiseleva, Ahmed Samir Lotfy

Ferroelectric domain wall (DW) conductivity (DWC) can be attributed to two separate mechanisms: (a) the injection/ejection of charge carriers across the Schottky barrier formed at the (metal-) electrode-DW junction and (b) the transport of those charge carriers along the DW. Current-voltage (IU) characteristics, recorded at variable temperatures from LiNbO$_

Si Min Chan, Alexei Andreanov, Sergej Flach, G. George Batrouni

We investigate superconducting transport in the DC field induced Wannier-Stark flat bands in the presence of interactions. Flat bands offer the possibility of unconventional high temperature superconductivity, where the superfluid weight, $D_s$, is enhanced by the density overlap of the localized states. However, construction of flat bands typically requires

Koichi Oyakawa

We prove that for any countable acylidrically hyperbolic group $G$, there exists a generating set $S$ of $G$ such that the corresponding Cayley graph $\Gamma(G,S)$ is hyperbolic, $|\partial\Gamma(G,X)|>2$, the natural action of $G$ on $\Gamma(G,S)$ is acylindrical, and the natural action of $G$ on the Gromov boundary $\partial\Gamma(G,S)$ is hyperfinite. Thi

Vivek Mehta, Sonali Jana, Utpal Roy

We report an algorithm, based on quantum optics formulation, where a coherent state is used as the elementary quantum resource for the image representation. We provide an architecture with constituent optical elements in linear order with respect to the image resolution. The obtained phase-distributed multimode coherent state is fed into an image retrieval s

Quan Liu, Hongzi Zhu, Yunsong Zhou, Hongyang Li

Registration of distant outdoor LiDAR point clouds is crucial to extending the 3D vision of collaborative autonomous vehicles, and yet is challenging due to small overlapping area and a huge disparity between observed point densities. In this paper, we propose Group-wise Contrastive Learning (GCL) scheme to extract density-invariant geometric features to reg

Along He, Kai Wang, Zhihong Wang, Tao Li

Limited labeled data makes it hard to train models from scratch in medical domain, and an important paradigm is pre-training and then fine-tuning. Large pre-trained models contain rich representations, which can be adapted to downstream medical tasks. However, existing methods either tune all the parameters or the task-specific layers of the pre-trained mode

F. A. Chiarello, J. Friedrich, S. GÖttlich

Inthispaper,weintroduceanon-localPDE-ODEtrafficmodeldevotedtothedescriptionof a 1-to-1 junction with buffer. We present an existence result in the free flow case as well as a numerical method to approximate weak solutions in the general case. In addition, we show a maximum principle which is uniform in the non-local interaction range. Further, we exploit the

Takeru Goto, Masayuki Ohzeki

We propose a scheme to calibrate the internal parameters of a quantum annealer to obtain well-approximated samples for training a restricted Boltzmann machine (RBM). Empirically, samples from quantum annealers obey the Boltzmann distribution, making them suitable for RBM training. Quantum annealers utilize physical phenomena to generate a large number of sam