October 2023 arXiv papers — page 69

Nathan P. Lawrence, Philip D. Loewen, Shuyuan Wang, Michael G. Forbes

We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guarantees provided by using the Youla-Kucera parameterization to define the search domain. Recent advances in behavioral systems allow us to construct a data-driven internal model; th

Simon Michel, Axel Fünfhaus, Robin Quade, Roser Valentí

The existence of bound states induced by local impurities coupled to an insulating host depends decisively on the global topological properties of the host's electronic structure. In this context, we consider magnetic impurities modelled as classical unit-length spins that are exchange-coupled to the spinful Haldane model on the honeycomb lattice. We investi

Anuj P. Lara, Samudra Roy, Govind P. Agrawal

A theoretical model is developed by exploiting the variational technique to investigate the evolution of an optical beam inside an optically pumped graded-index fiber amplifier. The variational analysis is a semi-analytical method that provides us with a set of coupled ordinary differential equations for the beam's four parameters. Numerical solution of thes

Amit K. Mittal, Brian L Babler, Snezana Stanimirovic, Nickolas Pingel

Interstellar turbulence shapes the HI distribution in the Milky Way (MW). How this affects large-scale statistical properties of HI column density across the MW remains largely unconstrained. We use approx 13,000 square-degree GALFA-HI survey to map statistical fluctuations of HI over the 40 km s-1 velocity range. We calculate the spatial power spectrum (SPS

Rohan Sharma, Divakar Vashisth, Bharath Shekar

Rayleigh wave dispersion curves have been widely used in near-surface studies, and are primarily inverted for the shear wave (S-wave) velocity profiles. However, the inverse problem is ill-posed, non-unique and nonlinear. Here, we introduce DispersioNET, a deep learning model based on convolution neural networks (CNN) to perform the joint inversion of Raylei

Aagam Bakliwal, Shubham Manish Gandhi, Yashodhara Haribhakta

Significant challenges are posed in talent acquisition and recruitment by processing and analyzing unstructured data, particularly resumes. This research presents a novel approach for orphan entity allocation in resume processing using knowledge graphs. Techniques of association mining, concept extraction, external knowledge linking, named entity recognition

Yuwei Zeng, Yiqing Xu

Learning reward functions for physical skills are challenging due to the vast spectrum of skills, the high-dimensionality of state and action space, and nuanced sensory feedback. The complexity of these tasks makes acquiring expert demonstration data both costly and time-consuming. Large Language Models (LLMs) contain valuable task-related knowledge that can

Lenny Fukshansky, Pavel Guerzhoy, Tanis Nielsen

Given a lattice $L$ in the plane, we define the affiliated deep hole lattice $H(L)$ to be spanned by a shortest vector of $L$ and a deep hole of $L$ contained in the triangle with sides corresponding to the shortest basis vectors. We study the geometric and arithmetic properties of deep hole lattices. In particular we investigate conditions on $L$ under whic

Peixuan Song, JunKyu Lee, Lev Mukhanov

The advancements in 5G mobile networks and Edge computing offer great potential for services like augmented reality and Cloud gaming, thanks to their low latency and high bandwidth capabilities. However, the practical limitations of achieving optimal latency on real applications remain uncertain. This paper aims to investigate the actual latency and bandwidt

Francesco Di Plinio, A. Walton Green, Brett D. Wick

We quantify the Sobolev space norm of the Beltrami resolvent $(I- \mu \mathcal{B})^{-1}$, where $\mathcal B$ is the Beurling-Ahlfors transform, in terms of the corresponding Sobolev space norm of the dilatation $\mu$ in the critical and supercritical ranges. Our estimate entails as a consequence quantitative self-improvement inequalities of Caccioppoli type

Zexue He, Yu Wang, An Yan, Yao Liu

Curated datasets for healthcare are often limited due to the need of human annotations from experts. In this paper, we present MedEval, a multi-level, multi-task, and multi-domain medical benchmark to facilitate the development of language models for healthcare. MedEval is comprehensive and consists of data from several healthcare systems and spans 35 human

Tianyi Lin, Marco Cuturi, Michael I. Jordan

Kernel-based optimal transport (OT) estimators offer an alternative, functional estimation procedure to address OT problems from samples. Recent works suggest that these estimators are more statistically efficient than plug-in (linear programming-based) OT estimators when comparing probability measures in high-dimensions~\citep{Vacher-2021-Dimension}. Unfort

Adam Teixidó-Bonfill, Joseph Schindler, Dominik Šafránek

We investigate four partial orderings on the space of quantum measurements (i.e on POVMs or positive operator valued measures), describing four notions of coarse/fine-ness of measurement. These are the partial orderings induced by: (1) classical post-processing, (2) measured relative entropy, (3) observational entropy, and (4) linear relation of POVMs. The o

Michael I. Jordan, Tianyi Lin, Zhengyuan Zhou

Online gradient descent (OGD) is well known to be doubly optimal under strong convexity or monotonicity assumptions: (1) in the single-agent setting, it achieves an optimal regret of $\Theta(\log T)$ for strongly convex cost functions; and (2) in the multi-agent setting of strongly monotone games, with each agent employing OGD, we obtain last-iterate converg

Nicholas S. Moore, Eric C. Cyr, Peter Ohm, Christopher M. Siefert

Sparse matrix computations are ubiquitous in scientific computing. With the recent interest in scientific machine learning, it is natural to ask how sparse matrix computations can leverage neural networks (NN). Unfortunately, multi-layer perceptron (MLP) neural networks are typically not natural for either graph or sparse matrix computations. The issue lies

Ian Baughman, John B Rundle, Tianjin Zhang

Earthquake nowcasting has been proposed as a means of tracking the change in large earthquake potential in a seismically active area. The method was developed using observable seismic data, in which probabilities of future large earthquakes can be computed using Receiver Operating Characteristic (ROC) methods. Furthermore, analysis of the Shannon information

Noureddine Mhadhbi, Sameh Gana, Mazen Fawaz Alsaeedi

This article demonstrates how variation of parameters can be successfully implemented in combination with other classical techniques, such as the method of characteristics, to derive novel classes of solutions to nonlinear partial differential equations (NLPDES) by considering specific initial conditions. This innovative approach offers the advantage of gene

Jungpyo Lee, Sebastian D. Lee, Tae Myung Huh, Hannah S. Stuart

Suction cups offer a useful gripping solution, particularly in industrial robotics and warehouse applications. Vision-based grasp algorithms, like Dex-Net, show promise but struggle to accurately perceive dark or reflective objects, sub-resolution features, and occlusions, resulting in suction cup grip failures. In our prior work, we designed the Smart Sucti

Md Aminul Islam, Shabbir Ahmed Shuvo, Mohammad Abu Tareq Rony, M Raihan

The COVID19 pandemic, a unique and devastating respiratory disease outbreak, has affected global populations as the disease spreads rapidly. Recent Deep Learning breakthroughs may improve COVID19 prediction and forecasting as a tool of precise and fast detection, however, current methods are still being examined to achieve higher accuracy and precision. This

Sara Ban, Sanja Rukavina

Extremal Type II $\mathbb{Z}_4$-codes are a class of self-dual $\mathbb{Z}_4$-codes with Euclidean weights divisible by eight and the largest possible minimum Euclidean weight for a given length. A small number of such codes is known for lengths greater than or equal to $48.$ The doubling method is a method for constructing Type II $\mathbb{Z}_4$-codes from

Haw-Shiuan Chang, Nikhil Agarwal, Andrew McCallum

Recent studies suggest that the existing neural models have difficulty handling repeated items in sequential recommendation tasks. However, our understanding of this difficulty is still limited. In this study, we substantially advance this field by identifying a major source of the problem: the single hidden state embedding and static item embeddings in the

Sujoy Bhore, Arnold Filtser, Csaba D. Tóth

Low-distortional metric embeddings are a crucial component in the modern algorithmic toolkit. In an online metric embedding, points arrive sequentially and the goal is to embed them into a simple space irrevocably, while minimizing the distortion. Our first result is a deterministic online embedding of a general metric into Euclidean space with distortion $O

A. Brach, L. Bahmad, S. Benyoussef

In this article, we examine the structure and the electronic, optical, and magnetic properties of ZnTe before and after doping with the transition metal Mn. The ab initio calculations of this compound were performed using the full potential linearized extended full potential planar waveform (FP-LAPW) in the context of density functional theory (DFT) implemen

Yanbang Wang, Jon Kleinberg

In an online social network, link recommendations are a way for users to discover relevant links to people they may know, thereby potentially increasing their engagement on the platform. However, the addition of links to a social network can also have an effect on the level of conflict in the network -- expressed in terms of polarization and disagreement. To

Chaeree Park, Hyunkyu Park, Jung Kim

Soft robotics is a modern robotic paradigm for performing dexterous interactions with the surroundings via morphological flexibility. The desire for autonomous operation requires soft robots to be capable of proprioception and makes it necessary to devise a calibration process. These requirements can be greatly benefited by adopting numerical simulation for

M Tirandari, A Mohammadi

Warm inflation in the framework of locally rotationally symmetric Bianchi type I universe model in the Hamilton-Jacobi formalism is being considered. Matter-Radiation fluid in warm inflation, endowed with and without a viscous pressure. For the tow different cases dynamical equations are obtained under slow-roll approximation and in the strong dissipative re

Bartholomew Andrews, Dominic Reiss, Fenner Harper, Rahul Roy

The obstruction to constructing localized degrees of freedom is a signature of several interesting condensed matter phases. We introduce a localization renormalization procedure that harnesses this property, and apply our method to distinguish between topological and trivial phases in quantum Hall and Chern insulators. By iteratively removing a fraction of m

Anton Glushchenko, Konstantin Lastochkin

Most identification laws of unknown parameters of linear regression equations (LRE) ensure only boundedness of a parametric error in the presence of additive perturbations, which is almost always unacceptable for practical scenarios. In this paper, a new identification law is proposed to overcome this drawback and guarantee asymptotic convergence of the unkn

Umesh Kumar, Seemant Mishra, Anupam Kundu, Abhishek Dhar

We consider a Klein-Gordon chain that is periodically driven at one end and has dissipation at one or both boundaries. An interesting numerical observation in a recent study~[arXiv:2209.03977] was that for driving frequency in the phonon band, there is a range of values of the driving amplitude $F_d\in (F_1, F_2)$ over which the energy current remains consta

Anant Godbole, Hannah Swickheimer

Let $\pi_n$ be a uniformly chosen random permutation on $[n]$. Using an analysis of the probability that two overlapping consecutive $k$-permutations are order isomorphic, the authors of a recent paper showed that the expected number of distinct consecutive patterns of all lengths $k\in\{1,2,\ldots,n\}$ in $\pi_n$ is $\frac{n^2}{2}(1-o(1))$ as $n\to\infty$.

Christopher C. Leon, Abhishek Grewal, Klaus Kuhnke, Klaus Kern

The alkali halides are ionic compounds. Each alkali atom donates an electron to a halogen atom, leading to ions with full shells. The valence band is mainly located on halogen atoms, while, in a traditional picture, the conduction band is mainly located on alkali atoms. Scanning tunnelling microscopy of NaCl at 4 K actually shows that the conduction band is

Ahmed Zidane, Ghada Soliman

This paper proposes an approach of Ladder Bottom-up Convolutional Bidirectional Variational Autoencoder (LCBVAE) architecture for the encoder and decoder, which is trained on the image translation of the dotted Arabic expiration dates by reconstructing the Arabic dotted expiration dates into filled-in expiration dates. We employed a customized and adapted ve

Alireza Khalili Golmankhaneh, Kerri Welch, Cristina Serpa, Ivanka Stamova

In this paper, random and stochastic processes are defined on fractal curves. Fractal calculus is used to define cumulative distribution function, probability density function, moments, variance and correlation function of stochastic process on fractal curve. A new framework which is a generalization of mean square calculus is formulated. Sequence of random

Qidong Liu, Xian Wu, Xiangyu Zhao, Yuanshao Zhu

The recent surge in Large Language Models (LLMs) has garnered significant attention across numerous fields. Fine-tuning is often required to fit general LLMs for a specific domain, like the web-based healthcare system. However, two problems arise during fine-tuning LLMs for medical applications. One is the task variety problem, which involves distinct tasks

Jorge A. Rivero

This paper extends the linear grouped fixed effects (GFE) panel model to allow for heteroskedasticity from a discrete latent group variable. Key features of GFE are preserved, such as individuals belonging to one of a finite number of groups and group membership is unrestricted and estimated. Ignoring group heteroskedasticity may lead to poor classification,

Sonia Rani, Vinod Kumar, Mohammad Rafee

The emergence of generalized square metrics in Finsler geometry can be attributed to various classification concerning ({\alpha}, \beta})-metrics. They have excellent geometric properties in Finsler geometry. Within the scope of this research paper, we have conducted an investigation into the generalized square metric denoted as F(x,y)=({\alpha}(x,y)+\beta}(

Eran Igra

The R\"ossler System is one of the best known chaotic dynamical systems, exhibiting a plethora of complex phenomena - and yet, only a few studies tackled its complexity analytically. Building on previous work by the author, in this paper we characterize the dynamical complexity for the R\"ossler System at parameter values at which the flow satisfies a certai

Durgakant Pushp, Zheng Chen, Chaomin Luo, Jason M. Gregory

Mapless navigation has emerged as a promising approach for enabling autonomous robots to navigate in environments where pre-existing maps may be inaccurate, outdated, or unavailable. In this work, we propose an image-based local representation of the environment immediately around a robot to parse navigability. We further develop a local planning and control

Sohaib Kiani, Jared Barton, Jon Sushinsky, Lynda Heimbach

This research addresses the challenge of conducting interpretable causal inference between a binary treatment and its resulting outcome when not all confounders are known. Confounders are factors that have an influence on both the treatment and the outcome. We relax the requirement of knowing all confounders under desired treatment, which we refer to as Sele

Aditya Kapoor, Vartika Sengar, Nijil George, Vighnesh Vatsal

Tracking of inventory and rearrangement of misplaced items are some of the most labor-intensive tasks in a retail environment. While there have been attempts at using vision-based techniques for these tasks, they mostly use planogram compliance for detection of any anomalies, a technique that has been found lacking in robustness and scalability. Moreover, ex

Zhili Feng, J. Zico Kolter

This work studies the neural tangent kernel (NTK) of the deep equilibrium (DEQ) model, a practical ``infinite-depth'' architecture which directly computes the infinite-depth limit of a weight-tied network via root-finding. Even though the NTK of a fully-connected neural network can be stochastic if its width and depth both tend to infinity simultaneously, we

Mayank Narang, Manoj P., Himanshu Tyagi, Dan M. Watson

Investigating Protostellar Accretion (IPA) is a JWST Cycle~1 GO program that uses NIRSpec IFU and MIRI MRS to obtain 2.9--28~$\mu$m spectral cubes of young, deeply embedded protostars with luminosities of 0.2 to 10,000~L$_{\odot}$ and central masses of 0.15 to 12~M$_{\odot}$. In this Letter, we report the discovery of a highly collimated atomic jet from the

Jonathan H. Rystrøm

Recommender systems can be a helpful tool for recommending content but they can also influence users' preferences. One sociological theory for this influence is that companies are incentivised to influence preferences to make users easier to predict and thus more profitable by making it harder to change preferences. This paper seeks to test that theory empir

Faezeh Khodabandehlou, Christian Maes

We propose an embedding of standard active particle models in terms of two-temperature processes. One temperature refers to an ambient thermal bath, and the other temperature effectively describes ``hot spots,'' i.e., systems with few degrees of freedom showing important population homogenization or even inversion of energy levels as a result of activation.

Yaniv Sadeh, Haim Kaplan

Motivated by the desire to utilize a limited number of configurable optical switches by recent advances in Software Defined Networks (SDNs), we define an online problem which we call the Caching in Matchings problem. This problem has a natural combinatorial structure and therefore may find additional applications in theory and practice. In the Caching in Mat

Jianyu Li, Zhangkai Cao, Jiahao Su, Ruipeng Wang

Fullerene solids doped with alkali metals (A$_3$C$_{60}$, A = K, Rb, Cs) exhibit a superconducting transition temperature ($T_c$) as high as 40 K, and their unconventional superconducting properties have been a subject of debate. With application of high pressure on K$_3$C$_{60}$ and Rb$_3$C$_{60}$, the experiments demonstrate the decrease of $T_c$. In this

Jacques Carette, Chris Heunen, Robin Kaarsgaard, Amr Sabry

Rig groupoids provide a semantic model of \PiLang, a universal classical reversible programming language over finite types. We prove that extending rig groupoids with just two maps and three equations about them results in a model of quantum computing that is computationally universal and equationally sound and complete for a variety of gate sets. The first

Alice Guionnet, Justin Ko, Florent Krzakala, Pierre Mergny

We study the asymptotic behavior of the spectrum of a random matrix where a non-linearity is applied entry-wise to a Wigner matrix perturbed by a rank-one spike with independent and identically distributed entries. In this setting, we show that when the signal-to-noise ratio scale as $N^{\frac{1}{2} (1-1/k_\star)}$, where $k_\star$ is the first non-zero gene

Chrysostomos Mylonas, Maria Stavara, Evangelos Mitsakis

Intelligent Transport Systems are crucial in the digital transformation of transportation. The EC mandates the establishment of National Access Points (NAPs) in each Member State, serving as common national interfaces for ITS data exchange. While progress has been made in standardizing NAP data, integration with operational ITS practices remain limited. This

Marcus J. Min, Yangruibo Ding, Luca Buratti, Saurabh Pujar

Code Large Language Models (Code LLMs) are being increasingly employed in real-life applications, so evaluating them is critical. While the conventional accuracy evaluates the performance of Code LLMs on a set of individual tasks, their self-consistency across different tasks is overlooked. Intuitively, a trustworthy model should be self-consistent when gene

Areti Kotsi, Vasileia Klimi, Evangelos Mitsakis

Fleet management systems have been one of the most important research fields in transportation science. Nowadays the enhancement of fleet management systems with technologies such as the Cooperative Intelligent Transport System (CITS) that allows fleets to communicate with their environment, with other vehicles or with the road infrastructure, resulting in s

Randy Llerena, Paolo Piovano

A variational model for describing the morphology of two-phase continua by allowing for the interplay between coherent and incoherent interfaces is introduced. Coherent interfaces are characterized by the microscopical arrangement of atoms of the two materials in a homogeneous lattice, with deformation being the solely stress relief mechanism, while at incoh

Vivek Iyer, Edoardo Barba, Alexandra Birch, Jeff Z. Pan

Lexical ambiguity is a significant and pervasive challenge in Neural Machine Translation (NMT), with many state-of-the-art (SOTA) NMT systems struggling to handle polysemous words (Campolungo et al., 2022). The same holds for the NMT pretraining paradigm of denoising synthetic "code-switched" text (Pan et al., 2021; Iyer et al., 2023), where word senses are

Xiaohan Gao, Haoyi Zhang, Siyuan Ye, Mingjie Liu

Post-layout simulation provides accurate guidance for analog circuit design, but post-layout performance is hard to be directly optimized at early design stages. Prior work on analog circuit sizing often utilizes pre-layout simulation results as the optimization objective. In this work, we propose a post-layout-simulation-driven (post-simulation-driven for s

Joshua Flynn, Jérôme Vétois

We obtain Liouville-type results for solutions to the CR Yamabe equation in $\mathbb{H}^n$, which extend a result obtained by Jerison and Lee for solutions in $L^{2+2/n}(\mathbb{H}^n)$. We obtain our results under either pointwise conditions or integral conditions at infinity. In particular, our results hold for all bounded solutions when $n=2$ and solutions

Chengwei Dai, Minxuan Lv, Kun Li, Wei Zhou

We study model extraction attacks in natural language processing (NLP) where attackers aim to steal victim models by repeatedly querying the open Application Programming Interfaces (APIs). Recent works focus on limited-query budget settings and adopt random sampling or active learning-based sampling strategies on publicly available, unannotated data sources.

Mohammad Masjed-Jamei

As a result of a rather long-time research started in 2016, this theory whose structure is based on a fixed variable and an algebraic inequality, improves and somehow generalizes the well-known least squares theory. In fact, the fixed variable has a fundamental role in constituting the least p-variances theory. In this sense, some new concepts such as p-cova

Daniel Drayss, Dengyang Fang, Christoph Füllner, Wolfgang Freude

Comb-based optical arbitrary waveform measurement (OAWM) techniques can overcome the bandwidth limitations of conventional coherent detection schemes and may have disruptive impact on a wide range of scientific and industrial applications. Over the previous years, different OAWM schemes have been demonstrated, showing the performance and the application pote

Vsevolod I. Avrutskiy

Derivative training is an established method that can significantly increase the accuracy of neural networks in certain low-dimensional tasks. In this paper, we extend this improvement to an illustrative image analysis problem: reconstructing the vertices of a cube from its image. By training the derivatives with respect to the cube's six degrees of freedom,

Jincheng Zhang, György Fazekas, Charalampos Saitis

Emerging Denoising Diffusion Probabilistic Models (DDPM) have become increasingly utilised because of promising results they have achieved in diverse generative tasks with continuous data, such as image and sound synthesis. Nonetheless, the success of diffusion models has not been fully extended to discrete symbolic music. We propose to combine a vector quan

Lishan Fang, Hua-Lin Huang, Yuechen Li

We study the computational complexity of a diagonalization technique for multivariate homogeneous polynomials, that is, expressing them as sums of powers of independent linear forms. It is based on Harrison's center theory and consists of a criterion and a diagonalization algorithm. Detailed formulations and computational complexity of each component of

Jincheng Zhang, György Fazekas, Charalampos Saitis

Diffusion models have shown promising results for a wide range of generative tasks with continuous data, such as image and audio synthesis. However, little progress has been made on using diffusion models to generate discrete symbolic music because this new class of generative models are not well suited for discrete data while its iterative sampling process

Takeo Nishinou

In this paper, we consider deformations of singular complex curves on complex surfaces. Despite the fundamental nature of the problem, little seems to be known for curves on general surfaces. Let $C\subset S$ be a complete integral curve on a smooth surface. Let $\tilde C$ be a partial normalization of $C$, and $\varphi\colon \tilde C\to S$ be the induced ma

Juan Moreno Nadales, Astghik Hakobyan, David Muñoz de la Peña, Daniel Limon

In the realm of maritime transportation, autonomous vessel navigation in natural inland waterways faces persistent challenges due to unpredictable natural factors. Existing scheduling algorithms fall short in handling these uncertainties, compromising both safety and efficiency. Moreover, these algorithms are primarily designed for non-autonomous vessels, le

Gurusha Juneja, Subhabrata Dutta, Soumen Chakrabarti, Sunny Manchanda

Large Language Models (LLMs) prompted to generate chain-of-thought (CoT) exhibit impressive reasoning capabilities. Recent attempts at prompt decomposition toward solving complex, multi-step reasoning problems depend on the ability of the LLM to simultaneously decompose and solve the problem. A significant disadvantage is that foundational LLMs are typically

Tianshuo Zhou, Sen Mei, Xinze Li, Zhenghao Liu

This paper proposes Multi-modAl Retrieval model via Visual modulE pLugin (MARVEL), which learns an embedding space for queries and multi-modal documents to conduct retrieval. MARVEL encodes queries and multi-modal documents with a unified encoder model, which helps to alleviate the modality gap between images and texts. Specifically, we enable the image unde

Mihaela Claudia Rosca

The deep learning recipe of casting real-world problems as mathematical optimisation and tackling the optimisation by training deep neural networks using gradient-based optimisation has undoubtedly proven to be a fruitful one. The understanding behind why deep learning works, however, has lagged behind its practical significance. We aim to make steps towards

M. S. Shustin, S. V. Aksenov, I. S. Burmistrov

One of the problems concerning topological phases in solid-state systems which still remains urgent is an issue of many-body effects. In this study we address it within perturbative theory framework by considering topological phase transitions related to charge correlations in the extended Kitaev chain model that belongs to the BDI symmetry class. Obtained c

John X. Morris, Chandan Singh, Alexander M. Rush, Jianfeng Gao

Prompting language models (LMs) is the main interface for applying them to new tasks. However, for smaller LMs, prompting provides low accuracy compared to gradient-based finetuning. Tree Prompting is an approach to prompting which builds a decision tree of prompts, linking multiple LM calls together to solve a task. At inference time, each call to the LM is

Ilídio Lopes

Our study investigates the complex interaction between active neutrinos and the ultralight bosonic dark matter halo surrounding the Sun. This halo extends over several solar radii due to the Sun's gravitational field, and we represent it as a coherent oscillating classical field configuration of bosonic dark matter particles that vary in time. Our investigat

Freddy Heppell, Kalina Bontcheva, Carolina Scarton

This paper analyses two hitherto unstudied sites sharing state-backed disinformation, Reliable Recent News (rrn.world) and WarOnFakes (waronfakes.com), which publish content in Arabic, Chinese, English, French, German, and Spanish. We describe our content acquisition methodology and perform cross-site unsupervised topic clustering on the resulting multilingu

Vasileios Angelidakis, Michael Blank, Eric J. R. Parteli, Sudeshna Roy

The packing behavior of powders is significantly influenced by various types of inter-particle attractive forces, including adhesion and non-bonded van der Waals forces [1, 2, 3, 4, 5, 6]. Alongside particle size and shape distributions, the inter-particle interactions, in particular frictional and adhesive forces, play a crucial role in determining the flow

Abdelhakim Amer, Mohit Mehndiratta, Jonas le Fevre Sejersen, Huy Xuan Pham

Automated visual inspection of on-and offshore wind turbines using aerial robots provides several benefits, namely, a safe working environment by circumventing the need for workers to be suspended high above the ground, reduced inspection time, preventive maintenance, and access to hard-to-reach areas. A novel nonlinear model predictive control (NMPC) framew

Andre Niyongabo Rubungo, Craig Arnold, Barry P. Rand, Adji Bousso Dieng

The prediction of crystal properties plays a crucial role in the crystal design process. Current methods for predicting crystal properties focus on modeling crystal structures using graph neural networks (GNNs). Although GNNs are powerful, accurately modeling the complex interactions between atoms and molecules within a crystal remains a challenge. Surprisin

Vibhor Agarwal, Yu Chen, Nishanth Sastry

Online conversation understanding is an important yet challenging NLP problem which has many useful applications (e.g., hate speech detection). However, online conversations typically unfold over a series of posts and replies to those posts, forming a tree structure within which individual posts may refer to semantic context from higher up the tree. Such sem

M. M. Babayev

In this work, a mixed problem for a time-fractional equation with a delayed argument and pseudodifferential operators related to Laplace operators with non-local boundary conditions in Sobolev classes is studied. The solutions to the initial-boundary problem are constructed as a series of the eigenfunctions of a multidimensional spectral problem. The eigenva

Galina L. Klimchitskaya, Vladimir M. Mostepanenko

We consider the Casimir pressure between two metallic plates and calculate the four contributions to it determined by the propagating and evanescent waves and by the transverse magnetic and transverse electric polarizations of the electromagnetic field. The range of interplate separations is considered where nearly the whole pressure has its origin in the el

Anastasia Kritharoula, Maria Lymperaiou, Giorgos Stamou

Visual Word Sense Disambiguation (VWSD) is a novel challenging task with the goal of retrieving an image among a set of candidates, which better represents the meaning of an ambiguous word within a given context. In this paper, we make a substantial step towards unveiling this interesting task by applying a varying set of approaches. Since VWSD is primarily

Xi Li, Shuai Wang, Xiang Luo, Yu-Yang Zhou

The nature of pseudogap lies at the heart of strongly-interacting superconductivity and superfluidity. With known pairing interactions, unitary Fermi gases provide an ideal testbed to verify whether a pseudogap can arise from many-body pairing. Here we report the observation of the long-sought pair-fluctuation-driven pseudogap in homogeneous unitary Fermi ga

CMS Collaboration

The first search for singly produced narrow resonances decaying to three well-separated hadronic jets is presented. The search uses proton-proton collision data corresponding to an integrated luminosity of 138 fb$^{-1}$ at $\sqrt{s}$ = 13 TeV, collected at the CERN LHC. No significant deviations from the background predictions are observed between 1.75-9.00

Cyril Cohen, Enzo Crance, Assia Mahboubi

Libraries of formalized mathematics use a possibly broad range of different representations for a same mathematical concept. Yet light to major manual input from users remains most often required for obtaining the corresponding variants of theorems, when such obvious replacements are typically left implicit on paper. This article presents Trocq, a new proof

James Jie Pan, Jianguo Wang, Guoliang Li

There are now over 20 commercial vector database management systems (VDBMSs), all produced within the past five years. But embedding-based retrieval has been studied for over ten years, and similarity search a staggering half century and more. Driving this shift from algorithms to systems are new data intensive applications, notably large language models, th

Pak-Yeung Chan, Zilu Ma, Yongjia Zhang

We consider noncollapsed steady gradient Ricci solitons with nonnegative sectional curvature. We show that such solitons always dimension reduce at infinity. This generalizes an earlier result in [CDM22] to higher dimensions. In dimension four, we classify possible reductions at infinity, which lays foundation for possible classifications of steady solitons.

Yang He, Lingao Xiao, Joey Tianyi Zhou

Dataset condensation is a crucial tool for enhancing training efficiency by reducing the size of the training dataset, particularly in on-device scenarios. However, these scenarios have two significant challenges: 1) the varying computational resources available on the devices require a dataset size different from the pre-defined condensed dataset, and 2) th

Tatsuki Kobayashi, Yoshiko Maruyama, Isao Nambu, Shohei Yano

Virtual sound synthesis is a technology that allows users to perceive spatial sound through headphones or earphones. However, accurate virtual sound requires an individual head-related transfer function (HRTF), which can be difficult to measure due to the need for a specialized environment. In this study, we proposed a method to generate HRTFs from one direc

Yihe Wang, Yu Han, Haishuai Wang, Xiang Zhang

Contrastive representation learning is crucial in medical time series analysis as it alleviates dependency on labor-intensive, domain-specific, and scarce expert annotations. However, existing contrastive learning methods primarily focus on one single data level, which fails to fully exploit the intricate nature of medical time series. To address this issue,

Weiming Huang, Qinghua Huang, Liyan Ma, Chuan Wang

Sound event localization and detection (SELD) involves sound event detection (SED) and direction of arrival (DoA) estimation tasks. SED mainly relies on temporal dependencies to distinguish different sound classes, while DoA estimation depends on spatial correlations to estimate source directions. This paper addresses the need to simultaneously extract spati

Luciano M. Abreu, Jing Song, Pedro C. S. Brandão, Eulogio Oset

In this note we study the tensor and vector exchange contributions to the elastic reactions involving the pseudoscalars mesons $\pi^+ \pi^-$, $K^{+}K^{-}$ and $D^{+}D^{-}$. In the case of the tensor-exchange contributions we assume that an intermediate tensor $f_2(1270)$ is dynamically generated from the interaction of two virtual $\rho$ mesons, with the use

M. A. May Pech, M. Mondragón, G. Patellis, G. Zoupanos

The idea of reduction of couplings consists in the search for relations between seemingly independent couplings of a renormalizable theory that are renormalization group invariant. In this article, we demonstrate the existence of such 1-loop relations among the top Yukawa, the Higgs quartic and the gauge colour couplings of the Type-II Two Higgs Doublet Mode

Sudeshna Roy, Hongyi Xiao, Vasileios Angelidakis, Thorsten Pöschel

Producing dense and homogeneous powder layers with smooth free surface is challenging in additive manufacturing, as interparticle cohesion can strongly affect the powder packing structure and therefore influence the quality of the end product. We use the Discrete Element Method to simulate the spreading process of spherical powders and examine how cohesion i

Mitchell Harling, Varun A. Kelkar, Kimani C. Toussaint,, Ayman F. Abouraddy

We introduce a taxonomy for partially coherent optical fields spanning multiple degrees of freedom (DoFs) based on the rank of the associated coherence matrix (the number of non-zero eigenvalues). When DoFs comprise two spatial modes and polarization, a fourfold classification emerges, with rank-1 fields corresponding to fully coherent fields. We demonstrate

Emile Jules Beckwée, Maarten Houlleberghs, Radu-George Ciocarlan, C. Vinod Chandran

Confinement of water and methane in mesopores of hydrophobized SBA-15 is demonstrated to promote methane hydrate formation. In comparison to as-synthesized SBA-15, hydrophobization by C8 grafting accelerates the kinetics of methane storage in and delivery from the hydrate. C8 grafting density was determined at 0.5 groups nm-2 based on TGA and quantitative NM

Qingmin Zhang, Weilin Teng, Dong Li, Jun Dai

Hot channels (HCs) are prevalent in the solar corona and play a critical role in driving flares and CMEs. In this paper, we estimate the energy contents of an X1.4 eruptive flare with a fast CME generated by a HC eruption on 2011 September 22. Originating from NOAA AR11302, the HC is the most dramatic feature in 131 and 94 {\AA} images observed by SDO/AIA. T

Yiqin Tan, Ling Pan, Longbo Huang

Deep reinforcement learning has achieved remarkable performance in various domains by leveraging deep neural networks for approximating value functions and policies. However, using neural networks to approximate value functions or policy functions still faces challenges, including low sample efficiency and overfitting. In this paper, we introduce OMNet, a no

J. Wongphechauxsorn, D. J. Champion, M. Bailes, V. Balakrishnan

The HTRU-S Low Latitude survey data within 1$^{\circ}$of the Galactic Centre (GC) were searched for pulsars using the Fast Folding Algorithm (FFA). Unlike traditional Fast Fourier Transform (FFT) pipelines, the FFA optimally folds the data for all possible periods over a given range, which is particularly advantageous for pulsars with low-duty cycle. For the

Pak-Yeung Chan, Zilu Ma, Yongjia Zhang

The metric flow is introduced and extensively studied by Bamler [Bam20b, Bam20c], especially as an $\mathbb{F}$-limit of a sequence of smooth Ricci flows with uniformly bounded Nash entropy, in which case each regular point on the limit is a point of smooth convergence. In this note, we shall consider the $\mathbb{F}$-convergence of a sequence of $\mathbb{F}

Maria Andrade, Benedito Leandro, Thamara Policarpo

This study investigates the topological implications arising from stable (free boundary) minimal surfaces in a static perfect fluid space while ensuring that the fluid satisfies certain energy conditions. Based on the main findings, it has been established the topology of the level set $\{f=c\}$ (the boundary of a stellar model), where $c$ is a positive cons

Haz Sameen Shahgir, Tanjeem Azwad Zaman, Khondker Salman Sayeed, Md. Asif Haider

Optical Coherence Tomography (OCT) scan yields all possible cross-section images of a retina for detecting biomarkers linked to optical defects. Due to the high volume of data generated, an automated and reliable biomarker detection pipeline is necessary as a primary screening stage. We outline our new state-of-the-art pipeline for identifying biomarkers fro

M. M. Babayev

In this work we study traces of spectral decompositions corresponding to self adjoint elliptic pseudo-differential operators of distributions in the classes of Soboloev-Liouville and Nikolskii-Besov

A. K. Hartmann, B. Meerson

We study the probability distribution $P(A)$ of the area $A=\int_0^T x(t) dt$ swept under fractional Brownian motion (fB\ m) $x(t)$ until its first passage time $T$ to the origin. The process starts at $t=0$ from a specified point $x=L$. We show that $P(A)$ obeys exact scaling relation $$ P(A) = \frac{D^\frac{1}{2H}}{L^{1+\frac{1}{H}}}\,\Phi_H\left(\frac{D^\