November 2022 arXiv papers — page 53

Yan-Mei Yu, B. K. Sahoo, Bing-Bing Suo

Attempts are made to unify gravity with the other three fundamental forces of nature. As suggested by higher dimensional models, this unification may require space and time variation of some dimensionless fundamental constants. In this scenario, probing temporal variation of the electromagnetic fine structure constant ($\alpha= \frac{e^2} {\hbar c}$) in low

Igor G. Vladimirov

This paper is concerned with quadratic-exponential moments (QEMs) for dynamic variables of quantum stochastic systems with position-momentum type canonical commutation relations. The QEMs play an important role for statistical ``localisation'' of the quantum dynamics in the form of upper bounds on the tail probability distribution for a positive definite qua

Kurt Girstmair

Let $D,Q$ be natural numbers, $(D,Q)=1$, such that $D/Q>1$ and $D/Q$ is not a square. Let $q$ be the smallest divisor of $Q$ such that $Q|\, q^2$. We show that the units $>1$ of the ring $\mathbb Z[\sqrt{Dq^2/Q}]$ are connected with certain convergents of $\sqrt{D/Q}$. Among these units, the units of $\mathbb Z[\sqrt{DQ}]$ play a special role, inasmuch as th

Panagiotis Typaldos, Markos Papageorgiou

A discrete-time stochastic optimal control problem was recently proposed to address the GLOSA (Green Light Optimal Speed Advisory) problem in cases where the next signal switching time is decided in real time and is therefore uncertain in advance. The corresponding numerical solution via SDP (Stochastic Dynamic Programming) calls for substantial computation

Laura Ciobanu, Alex Levine, Alan D. Logan

Post's Correspondence Problem (the PCP) is a classical decision problem in theoretical computer science that asks whether for pairs of free monoid morphisms $g, h\colon\Sigma^*\to\Delta^*$ there exists any non-trivial $x\in\Sigma^*$ such that $g(x)=h(x)$. Post's Correspondence Problem for a group $\Gamma$ takes pairs of group homomorphisms $g, h\colon F(\Sig

Alapan Kuila, Sudeshan Sarkar

The task of event extraction (EE) aims to find the events and event-related argument information from the text and represent them in a structured format. Most previous works try to solve the problem by separately identifying multiple substructures and aggregating them to get the complete event structure. The problem with the methods is that it fails to ident

Xiaoshan Wu, Weihua He, Man Yao, Ziyang Zhang

Event cameras are considered to have great potential for computer vision and robotics applications because of their high temporal resolution and low power consumption characteristics. However, the event stream output from event cameras has asynchronous, sparse characteristics that existing computer vision algorithms cannot handle. Spiking neural network is a

Alexey Kivel, Julien Laux, Felix Yu

We explore the effective theory of an axion in a gauged baryon number symmetry extension of the Standard Model (SM), where the axion is realized from a Dine-Fischler-Srednicki-Zhitnitsky (DFSZ) model construction. Integrating out the anomalons realizes a pattern of effective Wilson coefficients reflecting the factorization between the anomalous Peccei-Quinn

Fiona Anting Tan, Hansi Hettiarachchi, Ali Hürriyetoğlu, Tommaso Caselli

The Event Causality Identification Shared Task of CASE 2022 involved two subtasks working on the Causal News Corpus. Subtask 1 required participants to predict if a sentence contains a causal relation or not. This is a supervised binary classification task. Subtask 2 required participants to identify the Cause, Effect and Signal spans per causal sentence. Th

Varsha Ramachandran

Winds of massive stars are an important ingredient in determining their evolution, final remnant mass, and feedback to the surrounding interstellar medium. We compare empirical results for OB star winds at low metallicity with theoretical predictions. Observations suggest very weak winds at SMC metallicity, but there are exceptions. We identified promising c

Artur Dróżdż, Paweł Niegodajew, Mathias Romańczyk, Witold Elsner

The convection velocity ($U_C$) of turbulent structures has been studied in adverse-pressure-gradient (APG) turbulent boundary layers (TBLs) for a wide range of Reynolds numbers $Re_\tau = 1400 - 4000$. The study is based on estimation of the convection velocity using decomposed streamwise skewness factor introduced in (Drozdz A., Elsner W., Int. J. of Heat

Dezhi Yang, Guoxian Yu, Jun Wang, Zhengtian Wu

Learning directed acyclic graph (DAG) that describes the causality of observed data is a very challenging but important task. Due to the limited quantity and quality of observed data, and non-identifiability of causal graph, it is almost impossible to infer a single precise DAG. Some methods approximate the posterior distribution of DAGs to explore the DAG s

Vicenç Torra

Non-additive measures, also known as fuzzy measures, capacities, and monotonic games, are increasingly used in different fields. Applications have been built within computer science and artificial intelligence related to e.g. decision making, image processing, machine learning for both classification, and regression. Tools for measure identification have bee

Jon Grantham, P. G. Walsh

In this article we further develop methods for representing integers as a sum of three cubes. In particular, a barrier to solving the case $k=3$, which was outlined in a previous paper of the second author, is overcome. A very recent computation indicates that the method is quite favourable to other methods in terms of time estimates. A hybrid of the method

Fenglin Liu, Xian Wu, Chenyu You, Shen Ge

Training supervised video captioning model requires coupled video-caption pairs. However, for many targeted languages, sufficient paired data are not available. To this end, we introduce the unpaired video captioning task aiming to train models without coupled video-caption pairs in target language. To solve the task, a natural choice is to employ a two-step

Felix Meier, Mathias Steinhuber, Juan Diego Urbina, Daniel Waltner

Fast scrambling, quantified by the exponential initial growth of Out-of-Time-Ordered-Correlators (OTOCs), is the ability to efficiently spread quantum correlations among the degrees of freedom of interacting systems, and constitutes a characteristic signature of local unstable dynamics. As such, it may equally manifest both in systems displaying chaos or in

Dominik Scheck, Jeremy S. Sanders, Veronica Biffi, Klaus Dolag

To assume hydrostatic equilibrium between the intracluster medium and the gravitational potential of galaxy clusters is an extensively used method to investigate their total masses. We want to test hydrostatic masses obtained with an observational code in the context of the SRG/eROSITA survey. We use the hydrostatic modeling code MBProj2 to fit surface-brigh

Florian Fervers, Sebastian Bullinger, Christoph Bodensteiner, Michael Arens

This paper proposes a novel method for vision-based metric cross-view geolocalization (CVGL) that matches the camera images captured from a ground-based vehicle with an aerial image to determine the vehicle's geo-pose. Since aerial images are globally available at low cost, they represent a potential compromise between two established paradigms of autonomous

Vianney Coppé, Xavier Gillard, Pierre Schaus

The branch-and-bound algorithm based on decision diagrams introduced by Bergman et al. in 2016 is a framework for solving discrete optimization problems with a dynamic programming formulation. It works by compiling a series of bounded-width decision diagrams that can provide lower and upper bounds for any given subproblem. Eventually, every part of the searc

Th. K. Mavrogordatos

We find that the phase-space representation of the electromagnetic field inside a driven cavity strongly coupled to a two-level atom can be employed to distinguish photon emissions along a ladder of dressed states sustaining a two-photon resonance. The emissions are told apart by means of the different quantum beats generated by the conditional states they p

Andrea Hrckova, Robert Moro, Ivan Srba, Jakub Simko

To mitigate the negative effects of false information more effectively, the development of Artificial Intelligence (AI) systems to assist fact-checkers is needed. Nevertheless, the lack of focus on the needs of these stakeholders results in their limited acceptance and skepticism toward automating the whole fact-checking process. In this study, we conducted

Bernd Bohnet, Chris Alberti, Michael Collins

Most recent coreference resolution systems use search algorithms over possible spans to identify mentions and resolve coreference. We instead present a coreference resolution system that uses a text-to-text (seq2seq) paradigm to predict mentions and links jointly. We implement the coreference system as a transition system and use multilingual T5 as an underl

Weixuan Xiong, Xiaochen Sun

Anomaly detection of time series, especially multivariate time series(time series with multiple sensors), has been focused on for several years. Though existing method has achieved great progress, there are several challenging problems to be solved. Firstly, existing method including neural network only concentrate on the relationship in terms of timestamp.

Asako Sato, Satoko Takahashi, Shun Ishii, Paul T. P. Ho

We present a high angular resolution ($\sim1"$) and wide-field ($2'.9 \times 1'.9$) image of the 1.3-mm continuum, CO($J$ = 2--1) line, and SiO($J$ = 5--4) line emissions toward an embedded protocluster, FIR3, FIR4, and FIR5, in the Orion Molecular Cloud 2 obtained from the Atacama Large Millimeter/submillimeter Array (ALMA). We identify 51 continuum sources

Emily Muller, Emily Gemmell, Ishmam Choudhury, Ricky Nathvani

The interactions of individuals with city neighbourhoods is determined, in part, by the perceived quality of urban environments. Perceived neighbourhood quality is a core component of urban vitality, influencing social cohesion, sense of community, safety, activity and mental health of residents. Large-scale assessment of perceptions of neighbourhood quality

P. G. Walsh

We examine the ranks of a subfamily of curves in a previous article, which are derived from the existence of solutions to certain Pell equations. We exhibit an abundance of curves of moderately large rank, and prove under mild conditions that these curves have rank at least three.

Seungin Oh, Chang-uk Ahn, Kwanghyun Ahn, Jin-Gyun Kim

The two-field vibroacoustic finite-element (FE) model requires a relatively large number of degrees of freedom compared to the monophysics model, and the conventional force identification method for structural vibration can be adjusted for multiphysics problems. In this study, an effective inverse force identification method for an FE vibroacoustic interacti

Filippo Brunelli, Laurent Viennot

In a temporal graph, each edge is available at specific points in time. Such an availability point is often represented by a ''temporal edge'' that can be traversed from its tail only at a specific departure time, for arriving in its head after a specific travel time. In such a graph, the connectivity from one node to another is naturally captured by the exi

Eyal Poleg

We are standing at the edge of a major transformation in manuscript studies. Digital surrogates, Digital Humanities analyses and the rise of new scientific analytical technologies proliferate across universities, libraries and museums. They change the way we consult, research and disseminate historical manuscripts to reveal hitherto unknown, and unknowable,

Emiliano Ambrosi, Matilde Manzaroli

Let $X\rightarrow C$ be a totally real semistable degeneration over a smooth real curve $C$ with degenerate fiber $X_0$. Assuming that the irreducible components of $X_0$ are simple from a cohomological point of view, we give a bound for the individual Betti numbers of a real smooth fiber near $0$ in terms of the complex geometry of the degeneration. This ge

Matthew Ciesler, Han Wang, Shengbai Zhang, Damien West

Hybrid systems, which take advantage of low material dimensionality, have great potential for designing nanoscale devices. Quantum dots (QDs) -- a 0D nanostructure -- can be combined with 2D monolayers to achieve success in photovoltaics and photocatalytic water splitting. In such colloidal systems, ligand molecules such as cysteine play an important role in

J. Cheraghalizadeh, S. Tizdast, N. Valizadeh, S. Doostdari

In this study, we experimentally study the dried pattern droplets of coffee with and without sugar. We statistically analyze the rough surface formed after the stain becomes dried. The amount of sugar is controlled by the mass $m$. Along with the formation of the coffee ring, we discuss the Marangoni effect, in the system, and also analyzed the statistics of

Shengqu Cai, Eric Ryan Chan, Songyou Peng, Mohamad Shahbazi

Scene extrapolation -- the idea of generating novel views by flying into a given image -- is a promising, yet challenging task. For each predicted frame, a joint inpainting and 3D refinement problem has to be solved, which is ill posed and includes a high level of ambiguity. Moreover, training data for long-range scenes is difficult to obtain and usually lac

Jiangjie Chen, Rui Xu, Wenxuan Zeng, Changzhi Sun

Given a possibly false claim sentence, how can we automatically correct it with minimal editing? Existing methods either require a large number of pairs of false and corrected claims for supervised training or do not handle well errors spanning over multiple tokens within an utterance. In this paper, we propose VENCE, a novel method for factual error correct

Ye Yan, Xiaohuang Hu, Yuheng Wu, Hongxia Huang

Stimulated by the observation of $\Lambda_c(2910)^+$ by the Belle Collaboration, the $S$-wave $qqq\bar{q}c~(q=u~\text{or}~d)$ pentaquark systems with $I$ = 0, $J^P$ = $\frac{1}{2}^-,~\frac{3}{2}^- and~\frac{5}{2}^-$ are investigated in the framework of quark delocalization color screening model(QDCSM). The real-scaling method is utilized to check the bound s

Muzaffar M. Rahmatullaev, Dekhkonov D. Jasur

For the Potts model on Cayley trees, a very wide class of new Gibbs measures is given. We give a review of all known Gibbs measures of the Potts model on trees and compare them with our new measures.

Federico Paolucci, Giorgio De Simoni, Francesco Giazotto

Non-reciprocal charge transport in supercurrent diodes (SDs) polarized growing interest in the last few years for its potential applications in superconducting electronics (SCE). So far, SD effects have been reported in complex hybrid superconductor/semiconductor structures or metallic systems subject to moderate magnetic fields, thus showing a limited poten

G. Bison, B. Blau, W. Chen, P. -J. Chiu

We determined the ultracold neutron (UCN) production rate by superthermal conversion in the solid deuterium (sD$_2$) moderator of the UCN source at the Paul Scherrer Institute (PSI). In particular, we considered low amounts of less than $20\,$mol of D$_2$, deposited on the cooled moderator vessel surfaces in thin films of a few mm thickness. We measured the

Sajad Rezaie, João Morais, Ahmed Alkhateeb, Carles Navarro Manchón

Most research in the area of machine learning-based user beam selection considers a structure where the model proposes appropriate user beams. However, this design requires a specific model for each user-device beam codebook, where a model learned for a device with a particular codebook can not be reused for another device with a different codebook. Moreover

Mael Houbre, Florian Boudin, Beatrice Daille

Keyphrase generation is the task consisting in generating a set of words or phrases that highlight the main topics of a document. There are few datasets for keyphrase generation in the biomedical domain and they do not meet the expectations in terms of size for training generative models. In this paper, we introduce kp-biomed, the first large-scale biomedica

Siyu Xing, Chen Gong, Hewei Guo, Xiao-Yu Zhang

Existing GAN inversion methods work brilliantly in reconstructing high-quality (HQ) images while struggling with more common low-quality (LQ) inputs in practical application. To address this issue, we propose Unsupervised Domain Adaptation (UDA) in the inversion process, namely UDA-inversion, for effective inversion and editing of both HQ and LQ images. Rega

Soichiro Fujii, Stephen Lack

We show that 2-categories of the form $\mathscr{B}\mbox{-}\mathbf{Cat}$ are closed under slicing, provided that we allow $\mathscr{B}$ to range over bicategories (rather than, say, monoidal categories). That is, for any $\mathscr{B}$-category $\mathbb{X}$, we define a bicategory $\mathscr{B}/\mathbb{X}$ such that $\mathscr{B}\mbox{-}\mathbf{Cat}/\mathbb{X}\c

Tapio Helin

Statistical inverse learning aims at recovering an unknown function $f$ from randomly scattered and possibly noisy point evaluations of another function $g$, connected to $f$ via an ill-posed mathematical model. In this paper we blend statistical inverse learning theory with the classical regularization strategy of applying finite-dimensional projections. Ou

Martin Korth

Despite recent breakthroughs in the field of artificial intelligence (AI) - or more specifically machine learning (ML) algorithms for object recognition and natural language processing - it seems to be the majority view that current AI approaches are still no real match for natural intelligence (NI). More importantly, philosophers have collected a long catal

Donát M. Takács, Áron Pozsár, Tamás Fülöp

Symplectic numerical schemes for reversible dynamical systems predict the solution reliably over large times as well, and are a good starting point for extension to schemes for simulating irreversible situations like viscoelastic wave propagation and heat conduction coupled via thermal expansion occuring in rocks, plastics, biological samples etc. Dissipatio

Javier del Pino, Oded Zilberberg

The quantum simulation of dynamical gauge field theories offers the opportunity to study complex high-energy physics with controllable low-energy devices. For quantum computation, bosonic codes promise robust error correction that exploits multi-particle redundancy in bosons. Here, we demonstrate how bosonic codes can be used to simulate dynamical gauge fiel

Seonil Son, Junsoo Park, Jeong-in Hwang, Junghwa Lee

One of the challenges of developing a summarization model arises from the difficulty in measuring the factual inconsistency of the generated text. In this study, we reinterpret the decoder overconfidence-regularizing objective suggested in (Miao et al., 2021) as a hallucination risk measurement to better estimate the quality of generated summaries. We propos

Pengcheng Lei, Faming Fang, Guixu Zhang

Motion-based video frame interpolation (VFI) methods have made remarkable progress with the development of deep convolutional networks over the past years. While their performance is often jeopardized by the inaccuracy of flow map estimation, especially in the case of large motion and occlusion. In this paper, we propose a flow guidance deformable compensati

Miguel A. González Casado, Angel Sánchez, Maxi San Miguel

In this work we assess the role played by the dynamical adaptation of the interactions network, among agents playing Coordination Games, in reaching global coordination and in the equilibrium selection. Specifically, we analyze a coevolution model that couples the changes in agents' actions with the network dynamics, so that while agents play the game, they

Shrihari Gopalakrishna, Rakesh Tibrewala

With a goal toward explaining the observed baryon asymmetry of the Universe, we extend the standard model (SM) by adding a vector-vector dimension-six effective operator coupling a new Dirac fermion $\chi$, uncharged under the SM gauge symmetries but charged under baryon number, to a quark-like up-type fermion and two identical down-type fermions. We introdu

Eknath Ghate

The zig-zag conjecture says that the reductions of two-dimensional crystalline representations of the Galois group of ${\mathbb {Q}}_p$ of large exceptional weights and half-integral slopes up to $\frac{p-1}{2}$ vary through an alternating sequence of irreducible and reducible mod $p$ representations. We prove this conjecture in smoothly varying families of

Prasanna M., V. V. Parkar, V. Jha, A. Parmar

The contribution of one neutron stripping cross section to the total reaction cross section has been studied for $^9$Be projectile incident on $^{169}$Tm, $^{181}$Ta, $^{187}$Re and $^{197}$Au targets around Coulomb barrier energy. The measured one neutron stripping cross sections for these systems have been compared with the coupled channel calculations. Th

Vitali Petsiuk, Alexander E. Siemenn, Saisamrit Surbehera, Zad Chin

We provide a new multi-task benchmark for evaluating text-to-image models. We perform a human evaluation comparing the most common open-source (Stable Diffusion) and commercial (DALL-E 2) models. Twenty computer science AI graduate students evaluated the two models, on three tasks, at three difficulty levels, across ten prompts each, providing 3,600 ratings.

Flavia Sofia Acerbo, Jan Swevers, Tinne Tuytelaars, Tong Duy Son

This work evaluates and analyzes the combination of imitation learning (IL) and differentiable model predictive control (MPC) for the application of human-like autonomous driving. We combine MPC with a hierarchical learning-based policy, and measure its performance in open-loop and closed-loop with metrics related to safety, comfort and similarity to human d

Jiangfan Deng, Dewen Fan, Xiaosong Qiu, Feng Zhou

Crowdedness caused by overlapping among similar objects is a ubiquitous challenge in the field of 2D visual object detection. In this paper, we first underline two main effects of the crowdedness issue: 1) IoU-confidence correlation disturbances (ICD) and 2) confused de-duplication (CDD). Then we explore a pathway of cracking these nuts from the perspective

Stefan Kolek, Robert Windesheim, Hector Andrade Loarca, Gitta Kutyniok

Image classifiers are known to be difficult to interpret and therefore require explanation methods to understand their decisions. We present ShearletX, a novel mask explanation method for image classifiers based on the shearlet transform -- a multiscale directional image representation. Current mask explanation methods are regularized by smoothness constrain

Anish Lakkapragada, Essam Sleiman, Saimourya Surabhi, Dennis P. Wall

Multi-Task Learning (MTL) is a growing subject of interest in deep learning, due to its ability to train models more efficiently on multiple tasks compared to using a group of conventional single-task models. However, MTL can be impractical as certain tasks can dominate training and hurt performance in others, thus making some tasks perform better in a singl

Anastasia Antsiferova, Sergey Lavrushkin, Maksim Smirnov, Alexander Gushchin

Video-quality measurement is a critical task in video processing. Nowadays, many implementations of new encoding standards - such as AV1, VVC, and LCEVC - use deep-learning-based decoding algorithms with perceptual metrics that serve as optimization objectives. But investigations of the performance of modern video- and image-quality metrics commonly employ v

Armin Kirchknopf, Djordje Slijepcevic, Ilkay Wunderlich, Michael Breiter

We investigate the problem of explainability for visual object detectors. Specifically, we demonstrate on the example of the YOLO object detector how to integrate Grad-CAM into the model architecture and analyze the results. We show how to compute attribution-based explanations for individual detections and find that the normalization of the results has a gr

Maitane Muñoz-Basagoiti, Olivier Rivoire, Zorana Zeravcic

Catalysis, the acceleration of chemical reactions by molecules that are not consumed in the process, is essential to living organisms but currently absent in physical systems that aspire to emulate biological functionalities with artificial components. Here we demonstrate how to design a catalyst using spherical building blocks interacting via programmable p

Luca Battaglia, Matteo Cozzi, Antonio J. Fernández, Angela Pistoia

We construct multiple solutions to the nonlocal Liouville equation \begin{equation} \label{eqk} \tag{L} (-\Delta)^{\frac{1}{2}} u = K(x) e^u \quad \mbox{ in } \mathbb{R}. \end{equation} More precisely, for $K$ of the form $K(x) = 1+\varepsilon \kappa(x)$ with $\varepsilon \in (0,1)$ small and $\kappa \in C^{1,\alpha}(\mathbb{R}) \cap L^{\infty}(\mathbb{R})$

Jinyun Li, Huiwen Zheng, Yuanlin Liu, Minfang Lu

Large-scale commercial platforms usually involve numerous business domains for diverse business strategies and expect their recommendation systems to provide click-through rate (CTR) predictions for multiple domains simultaneously. Existing promising and widely-used multi-domain models discover domain relationships by explicitly constructing domain-specific

Matias Martinez, Silverio Martínez-Fernández, Xavier Franch

Automated program repair (APR) aims to automatize the process of repairing software bugs in order to reduce the cost of maintaining software programs. Moreover, the success (given by the accuracy metric) of APR approaches has increased in recent years. However, no previous work has considered the energy impact of repairing bugs automatically using APR. The f

Yiheng Tang, Yongxiong Wang, Xiaoli Zhang, Zhe Wang

The spatial correlations and the temporal contexts are indispensable in Electroencephalogram (EEG)-based emotion recognition. However, the learning of complex spatial correlations among several channels is a challenging problem. Besides, the temporal contexts learning is beneficial to emphasize the critical EEG frames because the subjects only reach the pros

Khadije Rahi Maleki, Karim Ghorbani

We investigate the effect of one loop quantum corrections on the elastic scattering of dark matter off the nucleon in a fermionic dark matter model. The model introduces two new singlet fermions and a singlet scalar. The fermions communicate with the SM particles through a Higgs portal. It is found that some viable regions in the parameter space respecting t

Jingjing Wang, Yanhao Wang, Wenjun Jiang, Yuchen Li

A great variety of complex systems, from user interactions in communication networks to transactions in financial markets, can be modeled as temporal graphs consisting of a set of vertices and a series of timestamped and directed edges. Temporal motifs are generalized from subgraph patterns in static graphs which consider edge orderings and durations in addi

Leo Schwinn, Doina Precup, Bjoern Eskofier, Dario Zanca

Existing models of human visual attention are generally unable to incorporate direct task guidance and therefore cannot model an intent or goal when exploring a scene. To integrate guidance of any downstream visual task into attention modeling, we propose the Neural Visual Attention (NeVA) algorithm. To this end, we impose to neural networks the biological c

Paul Gassiat, Benjamin Gess, Pierre-Louis Lions, Panagiotis E. Souganidis

The long-time behavior of stochastic Hamilton-Jacobi equations is analyzed, including the stochastic mean curvature flow as a special case. In a variety of settings, new and sharpened results are obtained. Among them are (i) a regularization by noise phenomenon for the mean curvature flow with homogeneous noise which establishes that the inclusion of noise s

Stefano Berrone, Tommaso Vanzan

Discrete Fracture Networks (DFNs) are complex three-dimensional structures characterized by the intersections of planar polygonal fractures, and are used to model flows in fractured media. Despite being suitable for Domain Decomposition (DD) techniques, there are relatively few works on the application of DD methods to DFNs. In this manuscript, we present a

Xiaofeng Ge, Jiangyu Han, Haixin Guan, Yanhua Long

Recently, more and more personalized speech enhancement systems (PSE) with excellent performance have been proposed. However, two critical issues still limit the performance and generalization ability of the model: 1) Acoustic environment mismatch between the test noisy speech and target speaker enrollment speech; 2) Hard sample mining and learning. In this

Matteo Tuveri, Daniela Fadda, Viviana Fanti, Walter Bonivento

Gravity is, by far, one of the scientific themes that have most piqued the curiosity of scientists and philosophers over the centuries. The history of science tells us that when the creative effort of physicists and philosophers to solve the main puzzles of the understanding of our universe met, a new conceptual revolution has started. However, since Einstei

Xiaomeng Zhang, Wendun Wang, Xinyu Zhang

This paper provides new insights into the asymptotic properties of the synthetic control method (SCM). We show that the synthetic control (SC) weight converges to a limiting weight that minimizes the mean squared prediction risk of the treatment-effect estimator when the number of pretreatment periods goes to infinity, and we also quantify the rate of conver

Michele Coscia, Christian Borgelt, Michael Szell

Multiplex networks allow us to study a variety of complex systems where nodes connect to each other in multiple ways, for example friend, family, and co-worker relations in social networks. Link prediction is the branch of network analysis allowing us to forecast the future status of a network: which new connections are the most likely to appear in the futur

Victor T. Voronchev

A peculiarity of the stellar CNO cycle caused by MeV alpha-particles and protons generated in exoergic nuclear processes is analyzed. The main parameters of these particles and suprathermal reactions induced by them in a stellar core are calculated. It is shown that these reactions can trigger an abnormal nuclear flow in the second branch of the stellar CNO

Mark Rofin, Nikita Balagansky, Daniil Gavrilov

The simplest way to obtain continuous interpolation between two points in high dimensional space is to draw a line between them. While previous works focused on the general connectivity between model parameters, we explored linear interpolation for parameters of pre-trained models after fine-tuning. Surprisingly, we could perform linear interpolation without

Khurram Yamin, Haoyun Wang, Benoit Montreuil, Yao Xie

In this paper, we attempt to detect an inflection or change-point resulting from the Covid-19 pandemic on supply chain data received from a large furniture company. To accomplish this, we utilize a modified CUSUM (Cumulative Sum) procedure on the company's spatial-temporal order data as well as a GLR (Generalized Likelihood Ratio) based method. We model the

Ross G. Pinsky

For $\eta\in S_3$, let $S_n^{\text{av}(\eta)}$ denote the set of permutations in $S_n$ that avoid the pattern $\eta$, and let $E_n^{\text{av}(\eta)}$ denote the expectation with respect to the uniform probability measure on $S_n^{\text{av}(\eta)}$. For $n\ge k\ge2$ and $\tau\in S_k^{\text{av}(\eta)}$, let $N_n^{(k)}(\sigma)$ denote the number of occurrences

Dongli Wu, Bufan Zhang, Xiao Lin

When trading American and Asian options in the FX derivatives market, banks must calculate prices using a complex mathematical model. It is often observed that different models produce varying prices for the same exotic option, which violates the non-arbitrage requirement of derivative risk management. To address this issue, we have studied a fully parameter

Marco Colussi, Gabriele Civitarese, Dragan Ahmetovic, Claudio Bettini

Joint bleeding is a common condition for people with hemophilia and, if untreated, can result in hemophilic arthropathy. Ultrasound imaging has recently emerged as an effective tool to diagnose joint recess distension caused by joint bleeding. However, no computer-aided diagnosis tool exists to support the practitioner in the diagnosis process. This paper ad

Lukas Braun

We introduce the concept of ODD ('$\mathbf{O}$rthogonally $\mathbf{D}$egenerating on a $\mathbf{D}$ivisor') Riemannian metrics on real analytic manifolds $M$. These semipositive symmetric $2$-tensors may degenerate on a finite collection of submanifolds, while their restrictions to these submanifolds satisfy the inductive compatibility criterion to be an ODD

Wei Sun, Tingjun Chen, Neil Gong

Human-centered wireless sensing (HCWS) aims to understand the fine-grained environment and activities of a human using the diverse wireless signals around him/her. While the sensed information about a human can be used for many good purposes such as enhancing life quality, an adversary can also abuse it to steal private information about the human (e.g., loc

Maciej J. Karcz, Luca Messina, Eiji Kawasaki, Serenah Rajaonson

Machine-learning methods are nowadays of common use in the field of material science. For example, they can aid in optimizing the physicochemical properties of new materials, or help in the characterization of highly complex chemical compounds. An especially challenging problem arises in the modeling of chemically disordered solid solutions, for which some p

Thokchom Premkumar Meitei, Lenin S. Shagolsem

A model polydisperse fluid represents many real fluids such as colloidal suspensions and polymer solutions. In this study, considering a concentrated size-polydisperse hard sphere fluid with size derived from two different distribution functions, namely, uniform and Gaussian and explore the effect of polydispersity and mass scaling on the transport propertie

Vir Karan, A. Maruthi Indresh, Saswata Bhattacharyya

Multiphysics problems such as multicomponent diffusion, phase transformations in multiphase systems and alloy solidification involve numerical solution of a coupled system of nonlinear partial differential equations (PDEs). Numerical solutions of these PDEs using mesh-based methods require spatiotemporal discretization of these equations. Hence, the numerica

Yoshihiko Takase

The 6th wave of COVID-19 in Tokyo continued for the longest period of infection (about 190 days from late Nov. 2021), and the 7th wave, which occurred in mid-May 2022, was the largest wave ever (cumulative 1.7 million people). In order to elucidate their factors, the infection wave was analyzed by using the Avrami equation. The main component of the 6th wave

Ramy Hussein, David Shin, Moss Zhao, Jia Guo

Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of a wide range of neurological diseases. Positron emission tomography (PET) with radiolabeled water (15O-water) is considered the gold-standard for the measurement of CBF in humans. PET imaging, however, is not widely available because of its prohibitive costs

Ran Gu, Guotai Wang, Jiangshan Lu, Jingyang Zhang

Generalization to previously unseen images with potential domain shifts and different styles is essential for clinically applicable medical image segmentation, and the ability to disentangle domain-specific and domain-invariant features is key for achieving Domain Generalization (DG). However, existing DG methods can hardly achieve effective disentanglement

Zhihua Fang, Liang He, Hanhan Ma, Xiaochen Guo

The deep learning models used for speaker verification rely heavily on large amounts of data and correct labeling. However, noisy (incorrect) labels often occur, which degrades the performance of the system. In this paper, we propose a novel two-stage learning method to filter out noisy labels from speaker datasets. Since a DNN will first fit data with clean

Okuto Morikawa, Hiroki Wada, Satoshi Yamaguchi

We consider the phase structure of the linear quiver gauge theory, using the 't Hooft anomaly matching condition. This theory is characterized by the length $K$ of the quiver diagram. When $K$ is even, the symmetry and its anomaly are the same as those of massless QCD. Therefore, one can expect that the spontaneous symmetry breaking similar to the chiral sym

David Loeffler, Sarah Livia Zerbes

Given a Hilbert modular form for a totally real field $F$, and a prime $p$ split completely in $F$, the $f$-eigenspace in $p$-adic de Rham cohomology of the Hilbert modular variety has a family of partial filtrations and partial Frobenius maps, indexed by the primes of $F$ above $p$. The general plectic conjectures of Nekovar and Scholl suggest a "plectic co

Dhruv Patel, Meet Gandhi, Shankaranarayanan H., Anand D. Darji

Agriculture has always remained an integral part of the world. As the human population keeps on rising, the demand for food also increases, and so is the dependency on the agriculture industry. But in today's scenario, because of low yield, less rainfall, etc., a dearth of manpower is created in this agricultural sector, and people are moving to live in the

Jonathan Bader, Nicolas Zunker, Soeren Becker, Odej Kao

Scientific workflows are designed as directed acyclic graphs (DAGs) and consist of multiple dependent task definitions. They are executed over a large amount of data, often resulting in thousands of tasks with heterogeneous compute requirements and long runtimes, even on cluster infrastructures. In order to optimize the workflow performance, enough resources

Lipeng Wan, Zeyang Liu, Xingyu Chen, Xuguang Lan

Due to the representation limitation of the joint Q value function, multi-agent reinforcement learning methods with linear value decomposition (LVD) or monotonic value decomposition (MVD) suffer from relative overgeneralization. As a result, they can not ensure optimal consistency (i.e., the correspondence between individual greedy actions and the maximal tr

Injy Hamed, Amir Hussein, Oumnia Chellah, Shammur Chowdhury

Code-switching poses a number of challenges and opportunities for multilingual automatic speech recognition. In this paper, we focus on the question of robust and fair evaluation metrics. To that end, we develop a reference benchmark data set of code-switching speech recognition hypotheses with human judgments. We define clear guidelines for minimal editing

Ionel-Dumitrel Ghiba, Patrizio Neff

In this paper we linearise the recently introduced geometrically nonlinear constrained Cosserat-shell model. In the framework of the linear constrained Cosserat-shell model, we provide a comparison of our linear models with the classical linear Koiter shell model and the "best" first order shell model. For all proposed linear models we show existence and uni

Peter Lowdon, Owe Philipsen

In quantum field theories at finite temperature spectral functions describe how particle systems behave in the presence of a thermal medium. Although data from lattice simulations can in principle be used to determine spectral function characteristics, existing methods rely on the extraction of these quantities from temporal correlators, which requires one t

Khalid Lodhi, Jayant Chhillar, Sumit J. Darak, Divisha Sharma

5G Cell Search (CS) is the first step for user equipment (UE) to initiate the communication with the 5G node B (gNB) every time it is powered ON. In cellular networks, CS is accomplished via synchronization signals (SS) broadcasted by gNB. 5G 3rd generation partnership project (3GPP) specifications offer a detailed discussion on the SS generation at gNB but

Cheng Tan, Zhangyang Gao, Siyuan Li, Stan Z. Li

Recent years have witnessed remarkable advances in spatiotemporal predictive learning, with methods incorporating auxiliary inputs, complex neural architectures, and sophisticated training strategies. While SimVP has introduced a simpler, CNN-based baseline for this task, it still relies on heavy Unet-like architectures for spatial and temporal modeling, whi

Tobias Boege, René Fritze, Christiane Görgen, Jeroen Hanselman

In this paper we discuss the notion of research data for the field of mathematics and report on the status quo of research-data management and planning. A number of decentralized approaches are presented and compared to needs and challenges faced in three use cases from different mathematical subdisciplines. We highlight the importance of tailoring research-

Anchita Dey, Shubhendu Bhasin

In this paper, an adaptive observer is proposed for multi-input multi-output (MIMO) discrete-time linear time-invariant (LTI) systems. Unlike existing MIMO adaptive observer designs, the proposed approach is applicable to LTI systems in their general form. Further, the proposed method uses recursive least square (RLS) with covariance resetting for adaptation