October 2022 arXiv papers — page 8

Minkyu Jung, Ohhyeok Kwon, Seunghyun Seo, Soonshin Seo

Connectionist Temporal Classification (CTC) model is a very efficient method for modeling sequences, especially for speech data. In order to use CTC model as an Automatic Speech Recognition (ASR) task, the beam search decoding with an external language model like n-gram LM is necessary to obtain reasonable results. In this paper we analyze the blank label in

Hongji Wang, Chengdong Liang, Shuai Wang, Zhengyang Chen

Speaker modeling is essential for many related tasks, such as speaker recognition and speaker diarization. The dominant modeling approach is fixed-dimensional vector representation, i.e., speaker embedding. This paper introduces a research and production oriented speaker embedding learning toolkit, Wespeaker. Wespeaker contains the implementation of scalable

Maxime Tchibozo, Donggeun Kim, Zijing Wang, Xiaofu He

The goal of emotional brain state classification on functional MRI (fMRI) data is to recognize brain activity patterns related to specific emotion tasks performed by subjects during an experiment. Distinguishing emotional brain states from other brain states using fMRI data has proven to be challenging due to two factors: a difficulty to generate fast yet ac

Jaime Gonzalo Flor Flores, Talha Yerebakan, Wenting Wang, Mingbin Yu

Recent scientific and technological advances have enabled the detection of gravitational waves, autonomous driving, and the proposal of a communications network on the Moon (Lunar Internet or LunaNet). These efforts are based on the measurement of minute displacements and correspondingly the forces or fields transduction, which translate to acceleration, vel

Imaad Zaffar, Guillaume Jaume, Nasir Rajpoot, Faisal Mahmood

Multiple Instance Learning (MIL) is a widely employed framework for learning on gigapixel whole-slide images (WSIs) from WSI-level annotations. In most MIL based analytical pipelines for WSI-level analysis, the WSIs are often divided into patches and deep features for patches (i.e., patch embeddings) are extracted prior to training to reduce the overall comp

Jiaming Liang, Chao Xu, Shengze Cai

Flow visualization technologies such as particle tracking velocimetry (PTV) are broadly used in understanding the all-pervasiveness three-dimensional (3D) turbulent flow from nature and industrial processes. Despite the advances in 3D acquisition techniques, the developed motion estimation algorithms in particle tracking remain great challenges of large part

Meifan Zhang, Sixin Lin, Lihua Yin

Sketches are widely used for frequency estimation of data with a large domain. However, sketches-based frequency estimation faces more challenges when considering privacy. Local differential privacy (LDP) is a solution to frequency estimation on sensitive data while preserving the privacy. LDP enables each user to perturb its data on the client-side to prote

Rahul Ramesh, Jialin Mao, Itay Griniasty, Rubing Yang

We develop information geometric techniques to understand the representations learned by deep networks when they are trained on different tasks using supervised, meta-, semi-supervised and contrastive learning. We shed light on the following phenomena that relate to the structure of the space of tasks: (1) the manifold of probabilistic models trained on diff

Viorel Barbu, Michael Röckner, Deng Zhang

We review the recent progress on the long-time behavior for a general class of focusing $L^2$-critical nonlinear Schr\"odinger equations (NLS) with lower order perturbations. Two canonical models are the stochastic NLS driven by linear multiplicative noise and the classical deterministic NLS. We show the construction and uniqueness of the corresponding blow-

Ziwei Wang, Reza Arablouei, Jiajun Liu, Paulo Borges

Object classification using LiDAR 3D point cloud data is critical for modern applications such as autonomous driving. However, labeling point cloud data is labor-intensive as it requires human annotators to visualize and inspect the 3D data from different perspectives. In this paper, we propose a semi-supervised cross-domain learning approach that does not r

Robin K. S. Hankin

In this short article I introduce the stokes package which provides functionality for working with tensors, alternating forms, wedge products, and related concepts from the exterior calculus. Notation and spirit follow Spivak. Stokes's generalized integral theorem, viz $\int_{\partial X}\phi=\int_Xd\phi$, is demonstrated here using the package; it is availab

Mihaela Ifrim, Daniel Tataru

This article is concerned with one dimensional dispersive flows with cubic nonlinearities on the real line. In a very recent work, the authors have introduced a broad conjecture for such flows, asserting that in the defocusing case, small initial data yields global, scattering solutions. Then this conjecture was proved in the case of a Schr\"odinger dispersi

Masahiro Sanka, Songling Shan

Let $G$ be a $t$-tough graph on $n\ge 3$ vertices for some $t>0$. It was shown by Bauer et al. in 1995 that if the minimum degree of $G$ is greater than $\frac{n}{t+1}-1$, then $G$ is hamiltonian. In terms of Ore-type hamiltonicity conditions, the problem was only studied when $t$ is between 1 and 2, and recently the author proved a general result. The resul

Yu Hou, Bin Li, Yan Bai, Xiaofeng Hao

High-pressure structural searches of superhydrides CeBeH$_8$ and CeBH$_8$ were performed under ambient pressure up to 300 GPa. We identify $Fm\overline{3}m$-CeBeH$_8$ with a superconducting transition temperature $T_{c}$ of 56 K at 10 GPa. Two more phases with spacegroup $R\overline{3}m$ and $C2/m$, were investigated within the increasing pressures. CeBH$_8$

Aiwei Liu, Honghai Yu, Xuming Hu, Shu'ang Li

We propose the first character-level white-box adversarial attack method against transformer models. The intuition of our method comes from the observation that words are split into subtokens before being fed into the transformer models and the substitution between two close subtokens has a similar effect to the character modification. Our method mainly cont

Lianghai Xiao, Yixing Zhao, Jiwei Chen

The online advertising management platform has become increasingly popular among e-commerce vendors/advertisers, offering a streamlined approach to reach target customers. Despite its advantages, configuring advertising strategies correctly remains a challenge for online vendors, particularly those with limited resources. Ineffective strategies often result

Hongzhi Liao

We establish finite convergence of circumcentered-reflection method (CRM) for the case of intersection of two closed convex cones in a real Hilbert space. We apply this result to prove the finite convergence for two polyhedral sets in R^n.

Rui Li, Xin-Yu Qi

The low-energy effective Hamiltonian of the strong `spin'-orbit coupled one-dimensional hole gas in a cylindrical Ge nanowire in the presence of a strong magnetic field is studied both numerically and analytically. Basing on the Luttinger-Kohn Hamiltonian in the spherical approximation, we show this strong `spin'-orbit coupled one-dimensional hole gas can be

Pierrick Bousseau

We present several expected properties of the holomorphic Floer theory of a holomorphic symplectic manifold. In particular, we propose a conjecture relating holomorphic Floer theory of Hitchin integrable systems and Donaldson-Thomas invariants of non-compact Calabi-Yau 3-folds. More generally, we conjecture that the BPS spectrum of a 4-dimensional $\mathcal{

Maximilian Ramgraber, Ricardo Baptista, Dennis McLaughlin, Youssef Marzouk

Smoothers are algorithms for Bayesian time series re-analysis. Most operational smoothers rely either on affine Kalman-type transformations or on sequential importance sampling. These strategies occupy opposite ends of a spectrum that trades computational efficiency and scalability for statistical generality and consistency: non-Gaussianity renders affine Ka

Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two nega

Lu Chen, Guozhen Lu, Ying Xue, Maochun Zhu

In this paper, we will solve this uniqueness problem of positive solutions to the following equations of exponential growth: \begin{equation*} \begin{cases} -\Delta u =\lambda ue^{u^2},\quad\quad & x\in B_1\subset \mathbb{R}^2,\\ u>0, & x\in B_1,\ \\ u=0,\quad\quad &x\in \partial B_1, \end{cases} \end{equation*} where $ 0<\lambda<\lambda_1(B_1)$ and $\lambda

Ebrahim Fazli, Ali Ebnenasir

This paper presents a novel scalable GPU-based method for Test Paths (TPs) and Prime Paths (PPs) Generation, called TPGen, used in structural testing and in test data generation. TPGen outperforms existing methods for PPs and TPs generation in several orders of magnitude, both in time and space efficiency. Improving both time and space efficiency is made pos

Tianyu Wang, Yasong Feng

We prove convergence rates of Stochastic Zeroth-order Gradient Descent (SZGD) algorithms for Lojasiewicz functions. The SZGD algorithm iterates as \begin{align*} \mathbf{x}_{t+1} = \mathbf{x}_t - \eta_t \widehat{\nabla} f (\mathbf{x}_t), \qquad t = 0,1,2,3,\cdots , \end{align*} where $f$ is the objective function that satisfies the \L ojasiewicz inequality w

Xun Shi

Gravitational collapse of dark matter overdensities leads to the formation of dark matter halos which embed galaxies and galaxy clusters. An intriguing feature of dark matter halos is that their density profiles closely follow a universal form irrespective of the initial condition or the corresponding growth history. This represents a class of dynamical syst

Fatemeh Pourahmadian, Houssem Haddar

This work formally investigates the differential evolution indicators as a tool for ultrasonic tracking of elastic transformation and fracturing in randomly heterogeneous solids. Within the framework of periodic sensing, it is assumed that the background contains (i) a multiply connected set of viscoelastic, anisotropic, and piecewise homogeneous inclusions,

Sarthak Kapoor, Jaber Rezaei Mianroodi, Mohammad Khorrami, Nima S. Siboni

The purpose of this work is the systematic comparison of the application of two artificial neural networks (ANNs) to the surrogate modeling of the stress field in materially heterogeneous periodic polycrystalline microstructures. The first ANN is a UNet-based convolutional neural network (CNN) for periodic data, and the second is based on Fourier neural oper

Chunyu Liu, Jiacai Zhang

Neural decoding is still a challenge and hot topic in neurocomputing science. Recently, many studies have shown that brain network patterns containing rich spatial and temporal structure information, which represents the activation information of brain under external stimuli. %Therefore, the research of decoding stimuli from brain network received extensive

Smriti Nandan Paul, Richard J. Licata, Piyush M. Mehta

For objects in the low Earth orbit region, uncertainty in atmospheric density estimation is an important source of orbit prediction error, which is critical for space situational awareness activities such as the satellite conjunction analysis. This paper investigates the evolution of orbit error distribution in the presence of atmospheric density uncertainti

Yuya Sasaki, Yulong Wang

The conventional cluster-robust (CR) standard errors may not be robust. They are vulnerable to data that contain a small number of large clusters. When a researcher uses the 51 states in the U.S. as clusters, the largest cluster (California) consists of about 10% of the total sample. Such a case in fact violates the assumptions under which the widely used CR

Andri Sharma

The transport of biomolecules across a cell membrane is an important phenomena that plays a pivotal role in the functioning of biological cells. In this paper, we investigate such processes by modeling the translocation of polymers through a conical channel, directed from the wider opening to the narrow end of the conical channel. We use the molecular dynami

Alice Saebom Kwak, Jacob O. Israelsen, Clayton T. Morrison, Derek E. Bambauer

This work introduces a natural language inference (NLI) dataset that focuses on the validity of statements in legal wills. This dataset is unique because: (a) each entailment decision requires three inputs: the statement from the will, the law, and the conditions that hold at the time of the testator's death; and (b) the included texts are longer than the on

Chenghong Bian, Yulin Shao, Haotian Wu, Deniz Gunduz

We propose novel deep joint source-channel coding (DeepJSCC) algorithms for wireless image transmission over multi-input multi-output (MIMO) Rayleigh fading channels, when channel state information (CSI) is available only at the receiver. We consider two different schemes; one exploiting the spatial diversity and the other exploiting the spatial multiplexing

Ratchaphat Nakarachinda, Sirachak Panpanich, Shinji Tsujikawa, Pitayuth Wongjun

In a model of spontaneous scalarization of neutron stars proposed by Damour and Esposite-Farese, a general relativistic branch becomes unstable to trigger tachyonic growth of a scalar field $\phi$ toward a scalarized branch. Applying this scenario to cosmology, there is fatal tachyonic instability of $\phi$ during inflation and matter dominance being incompa

T. M. Dunster, A. Gil, J. Segura

Numerical methods for the computation of the parabolic cylinder $U(a,z)$ for real $a$ and complex $z$ are presented. The main tools are recent asymptotic expansions involving exponential and Airy functions, with slowly varying analytic coefficient functions involving simple coefficients, and stable integral representations; these two main main methods can be

Anwarul Islam Sifat, Fiona J. Stevens McFadden, Ramesh Rayudu, Joseph Bailey

Faults in electricity distribution networks have the potential to ignite fires, cause electrocution, and damage the system itself. High current Low Impedance Faults (LIF) are typically detected and mitigated via over-current, distance, directional relays, fuses, etc. In contrast, while High Impedance Faults (HIF) are equally hazardous, they are much more cha

Benyamin Ghojogh, Smriti Sharma

Due to the effectiveness of using machine learning in physics, it has been widely received increased attention in the literature. However, the notion of applying physics in machine learning has not been given much awareness to. This work is a hybrid of physics and machine learning where concepts of physics are used in machine learning. We propose the supervi

Adam F. Kowalski

White-light stellar flares are now reported by the thousands in long-baseline, high precision, broad-band photometry from missions like Kepler, K2, and TESS. These observations are crucial inputs for assessments of biosignatures in exoplanetary atmospheres and surface ultraviolet radiation dosages for habitable zone planets around low-mass stars. A limitatio

Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu

Graph Neural Networks (GNNs) extend basic Neural Networks (NNs) by additionally making use of graph structure based on the relational inductive bias (edge bias), rather than treating the nodes as collections of independent and identically distributed (i.i.d.) samples. Though GNNs are believed to outperform basic NNs in real-world tasks, it is found that in s

Dong-Ho Lee, Akshen Kadakia, Brihi Joshi, Aaron Chan

NLP models are susceptible to learning spurious biases (i.e., bugs) that work on some datasets but do not properly reflect the underlying task. Explanation-based model debugging aims to resolve spurious biases by showing human users explanations of model behavior, asking users to give feedback on the behavior, then using the feedback to update the model. Whi

Tyler Genao

Given a number field $F_0$ that contains no Hilbert class field of any imaginary quadratic field, we show that under GRH there exists an effectively computable constant $B:=B(F_0)\in\mathbb{Z}^+$ for which the following holds: for any finite extension $L/F_0$ whose degree $[L:F_0]$ is coprime to $B$, one has for all elliptic curves $E_{/F_0}$ that the $L$-ra

Chengzhuo Ni, Yuda Song, Xuezhou Zhang, Chi Jin

We study multi-agent general-sum Markov games with nonlinear function approximation. We focus on low-rank Markov games whose transition matrix admits a hidden low-rank structure on top of an unknown non-linear representation. The goal is to design an algorithm that (1) finds an $\varepsilon$-equilibrium policy sample efficiently without prior knowledge of th

Daniel Grimmer

What, if anything, can help us explain the dynamical behavior of matter? One may be tempted here to appeal to the laws of nature, or to the world's geometric structure, or even to the smooth topological structure of the spacetime manifold itself. Some think, however, that the metaphysics involved in such explanatory strategies is excessively spooky. Indeed,

Vadim Semenov

We study the Discrete Gauss Image Problem, a generalization of Aleksandrov's classical question on the existence of convex bodies with prescribed integral curvature. We introduce a combinatorial problem called the Assignment Problem and show its equivalence to the Discrete Gauss Image Problem. We establish sufficient (and nearly necessary) geometric conditio

Kamil Bulinski, Alexander Fish

A theorem of Glasner from 1979 shows that if $Y \subset \mathbb{T} = \mathbb{R}/\mathbb{Z}$ is infinite then for each $\epsilon > 0$ there exists an integer $n$ such that $nY$ is $\epsilon$-dense. This has been extended in various works by showing that certain irreducible linear semigroup actions on $\mathbb{T}^d$ also satisfy such a \textit{Glasner property

Georges Kesserwani, Janice Lynn Ayog, Mohammad Kazem Sharifian, Domenico Bau

Numerical solvers of the two-dimensional (2D) shallow water equations (2D-SWE) can be an efficient option to predict spatial distribution of velocity fields in quasi-steady flows past or throughout hydraulic engineering structures. A second-order finite volume solver (FV2) spuriously elongates small-scale recirculating eddies within its predictions, unless s

Jacob Fox, Zoe Himwich, Nitya Mani, Yunkun Zhou

We study analogues of Sidorenko's conjecture and the forcing conjecture in oriented graphs, showing that natural variants of these conjectures in directed graphs are equivalent to the asymmetric, undirected analogues of the conjectures.

Qiyang Zhao, Mehdi Bennis, Merouane Debbah, Daniel Benevides da Costa

Semantic communication enables intelligent agents to extract meaning (or semantics) of information via interaction, to carry out collaborative tasks. In this paper, we study semantic communication from a topological space perspective, in which higher-order data semantics live in a simplicial complex. Specifically, a transmitter first maps its data into a $k$

Seema Sangari, Xinyan Zhang

This paper addresses a common problem with hierarchical time series. Time series analysis demands the series for a model to be the sum of multiple series at corresponding sub-levels. Hierarchical Time Series presents a two-fold problem. First, each individual time series model at each level in the hierarchy must be estimated separately. Second, those models

Bo Peng, Amit Vishwas, Gordon Stacey, Thomas Nikola

We report the discovery of two companion sources to a strongly lensed galaxy SPT0418-47 ("ring") at redshift 4.225, targeted by the JWST Early Release Science program. We confirm that these sources are at a similar redshift as the ring based on H$\alpha$ detected in the NIRSpec spectrum, and [C II] 158 $\mu$m line from ALMA. Using multiple spectral lines det

Zheyi Ding, Nicolas Wijsen, Gang Li, Stefaan Poedts

We present the implementation of coupling the EUropean Heliospheric FORcasting Information Asset (EUHFORIA) and the improved Particle Acceleration and Transport in the Heliosphere (iPATH) model and simulate the widespread solar energetic particle (SEP) event of 2020 November 29. We compare the simulated time intensity profiles with measurements at Parker Sol

Siqi Miao, Yunan Luo, Mia Liu, Pan Li

Point cloud data is ubiquitous in scientific fields. Recently, geometric deep learning (GDL) has been widely applied to solve prediction tasks with such data. However, GDL models are often complicated and hardly interpretable, which poses concerns to scientists who are to deploy these models in scientific analysis and experiments. This work proposes a genera

Mohammad Hussein Yoosefian Nooshabadi, Hossein Nejat Pishkenari

In this paper, we present an efficient form of Volterra's equations of motion for both unconstrained and constrained multibody dynamical systems that include ignorable coordinates. The proposed method is applicable for systems with both holonomic and nonholonomic constraints. Firstly, based on the definition of ignorable coordinates, one of the motion consta

Calin Galeriu

After reviewing the algebraic derivation of the Doppler factor in the Lienard-Wiechert potentials of an electrically charged point particle, we conclude that the Dirac delta function used in electrodynamics must be the one obeying the weak definition, non-zero in an infinitesimal neighborhood, and not the one obeying the strong definition, non-zero in a poin

Ali Baran Taşdemir, Tuna Karacan, Emir Kaan Kırmacı, Lale Özkahya

Approximate solutions to various NP-hard combinatorial optimization problems have been found by learned heuristics using complex learning models. In particular, vertex (node) classification in graphs has been a helpful method towards finding the decision boundary to distinguish vertices in an optimal set from the rest. By following this approach, we use a le

Sruthy Poulose, Yara Alvarez-Braña, Lourdes Basabel-Desmonts, Fernando Benito-Lopez

The evaporation of drops of water placed at the center of long poly(methyl methacrylate) microfluidic channels with a rectangular cross section of 0.38 mm2 is studied by simultaneously monitoring the shapes of two samples, one is in a 300 mT magnetic field, the other is in no field. A magnetic enhancement of the evaporation rate of up to 140 % is observed, w

Teresa Monteiro Fernandes

Let $X$ and $S$ be complex analytic manifolds where $S$ plays the role of a parameter space. Using the sheaf $\DXS^{\infty}$ of relative differential operators of infinite order, we construct functorially the regular holonomic $\DXS$-module $\shm_{reg}$ associated to a relative holonomic $\DXS$-module $\shm$, extending to the relative case classical theorems

Khyathi Raghavi Chandu, Alborz Geramifard

Contextualizing language technologies beyond a single language kindled embracing multiple modalities and languages. Individually, each of these directions undoubtedly proliferated into several NLP tasks. Despite this momentum, most of the multimodal research is primarily centered around English and multilingual research is primarily centered around contexts

Yuri Bilu, Florian Luca, Joris Nieuwveld, Jöel Ouaknine

In this paper, we refute some conjectures of Marques and Lengyel concerning the $p$-adic valuations of members of the Tribonacci sequence.

Stefan Backens, Alexander Shnirman, Yuriy Makhlin

We consider Majorana zero modes in a Josephson junction on top of a topological insulator in transverse magnetic field. Majorana zero modes reside at periodically located nodes of Josephson vortices. We find that hybridization of these modes is prohibited by symmetries of the problem at vanishing chemical potential, which ensures better protection of zero mo

Yale Fan, Willy Fischler, Eric Kubischta

We develop finite-dimensional versions of the quantum error-correcting codes proposed by Albert, Covey, and Preskill (ACP) for continuous-variable quantum computation on configuration spaces with nonabelian symmetry groups. Our codes can be realized by a charged particle in a Landau level on a spherical geometry -- in contrast to the planar Landau level real

Kai Zhang, Shan Liu, Momiao Xiong

A coordinate system is a foundation for every quantitative science, engineering, and medicine. Classical physics and statistics are based on the Cartesian coordinate system. The classical probability and hypothesis testing theory can only be applied to Euclidean data. However, modern data in the real world are from natural language processing, mathematical f

Hani Sami, Hadi Otrok, Jamal Bentahar, Azzam Mourad

In this paper, we propose Value Iteration Network for Reward Shaping (VIN-RS), a potential-based reward shaping mechanism using Convolutional Neural Network (CNN). The proposed VIN-RS embeds a CNN trained on computed labels using the message passing mechanism of the Hidden Markov Model. The CNN processes images or graphs of the environment to predict the sha

Rajeev Verma, Daniel Barrejón, Eric Nalisnick

We study the statistical properties of learning to defer (L2D) to multiple experts. In particular, we address the open problems of deriving a consistent surrogate loss, confidence calibration, and principled ensembling of experts. Firstly, we derive two consistent surrogates -- one based on a softmax parameterization, the other on a one-vs-all (OvA) paramete

Karthik Desingu, Mirunalini P., Aravindan Chandrabose

Annotated images and ground truth for the diagnosis of rare and novel diseases are scarce. This is expected to prevail, considering the small number of affected patient population and limited clinical expertise to annotate images. Further, the frequently occurring long-tailed class distributions in skin lesion and other disease classification datasets cause

Yaoyiran Li, Fangyu Liu, Ivan Vulić, Anna Korhonen

Bilingual lexicon induction (BLI) with limited bilingual supervision is a crucial yet challenging task in multilingual NLP. Current state-of-the-art BLI methods rely on the induction of cross-lingual word embeddings (CLWEs) to capture cross-lingual word similarities; such CLWEs are obtained 1) via traditional static models (e.g., VecMap), or 2) by extracting

Roshanak Mirzaee, Parisa Kordjamshidi

Recent research shows synthetic data as a source of supervision helps pretrained language models (PLM) transfer learning to new target tasks/domains. However, this idea is less explored for spatial language. We provide two new data resources on multiple spatial language processing tasks. The first dataset is synthesized for transfer learning on spatial quest

Bram van Berlo, Yang Miao, Rizqi Hersyandika, Nirvana Meratnia

Joint Communication and Sensing (JCAS) is envisioned for 6G cellular networks, where sensing the operation environment, especially in presence of humans, is as important as the high-speed wireless connectivity. Sensing, and subsequently recognizing blockage types, is an initial step towards signal blockage avoidance. In this context, we investigate the feasi

Giovanni Paolo Galdi, Václav Mácha, Šárka Nečasová, Bangwei She

We study the motion of the coupled system, $\mathscr S$, constituted by a physical pendulum, $\mathscr B$, with an interior cavity entirely filled with a viscous, compressible fluid, $\mathscr F$. The presence of the fluid may strongly affect on the motion of $\mathscr B$. In fact, we prove that, under appropriate assumptions, the fluid acts as a damper, nam

Zhan Gao, Yulin Shao, Deniz Gunduz, Amanda Prorok

Wireless local area networks (WLANs) manage multiple access points (APs) and assign scarce radio frequency resources to APs for satisfying traffic demands of associated user devices. This paper considers the channel allocation problem in WLANs that minimizes the mutual interference among APs, and puts forth a learning-based solution that can be implemented i

Ivan I. Vrubel, Evgeniia D. Cherotchenko, Georgii D. Miskovets, Vladislav V. Dudelev

The correct accounting for thermal effects is always a challenge when one needs to make quantitative predictions for any laser applications. In such complicated devices as quantum cascade lasers temperature strongly affects the operational conditions preventing reaching the CW mode as well as efficient lasing in pulsed regime. Rate equations are the most eff

Mohamed Suliman, Douglas Leith

In this paper we present new attacks against federated learning when used to train natural language text models. We illustrate the effectiveness of the attacks against the next word prediction model used in Google's GBoard app, a widely used mobile keyboard app that has been an early adopter of federated learning for production use. We demonstrate that the w

Jaime Gonzalo Flor Flores, Connor Nasseraddin, Jim Solomon, Talha Yerebakan

Recent advances in silicon photonics promise to revolutionize modern technology by improving performance of everyday devices in multiple fields. However, as the industry moves into a mass fabrication phase, the problem of effective testing of integrated silicon photonics devices remains to be solved. A cost-efficient manner that reduces schedule risk needs t

Fatemeh Nassajian Mojarrad, Maria Han Veiga, Jan S. Hesthaven, Philipp Öffner

The choice of the shape parameter highly effects the behaviour of radial basis function (RBF) approximations, as it needs to be selected to balance between ill-condition of the interpolation matrix and high accuracy. In this paper, we demonstrate how to use neural networks to determine the shape parameters in RBFs. In particular, we construct a multilayer pe

Dinesh Krishnamoorthy, Francis J. Doyle

This work considers the problem of personalized dose guidance using Bayesian optimization that learns the optimum drug dose tailored to each individual, thus improving therapeutic outcomes. Safe learning using interior point method ensures patient safety with high probability. This is demonstrated using the problem of learning the optimum bolus insulin dose

Xu Cao, Wenqian Ye, Elena Sizikova, Xue Bai

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder with very high prevalence around the world. Research progress in the field of ASD facial analysis in pediatric patients has been hindered due to a lack of well-established baselines. In this paper, we propose the use of the Vision Transformer (ViT) for the computational analysis of pedi

Andres R. Tejedor, Raquel Carracedo, Jorge Ramírez

In this work, we explore the dynamics of active entangled chains using molecular dynamics simulations of a modified Kremer-Grest model. The active chains are diluted in a mesh of very long passive linear chains, to avoid constraint release effects, and an active force is applied to the monomers in a way that it imparts a constant polar drift velocity along t

Gregor von Laszewski, J. P. Fleischer, Geoffrey C. Fox

In this paper, we summarize our effort to create and utilize a simple framework to coordinate computational analytics tasks with the help of a workflow system. Our design is based on a minimalistic approach while at the same time allowing to access computational resources offered through the owner's computer, HPC computing centers, cloud resources, and distr

Yujia Huang, Ivan Dario Jimenez Rodriguez, Huan Zhang, Yuanyuan Shi

Forward invariance is a long-studied property in control theory that is used to certify that a dynamical system stays within some pre-specified set of states for all time, and also admits robustness guarantees (e.g., the certificate holds under perturbations). We propose a general framework for training and provably certifying robust forward invariance in Ne

Bernd Porr, Lucía Muñoz Bohollo

Brain computer interfaces (BCI) depend on reliable realtime detection of conscious EEG changes for example to control a video game. However, scalp recordings are contaminated with non-stationary noise, such as facial muscle activity and eye movements. This interferes with the detection process making it potentially unreliable or even impossible. We have deve

Yuko Kato, David M. J. Tax, Marco Loog

Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence the quality of uncertainty estimates, one of which is the amount of model misspecification. Model misspecification always exists as models are mere simplifications or approximatio

Luis M. Briceño-Arias, Patrick L. Combettes, Francisco J. Silva

The classical perspective of a function is a construction which transforms a convex function into one that is jointly convex with respect to an auxiliary scaling variable. Motivated by applications in several areas of applied analysis, we investigate an extension of this construct in which the scaling variable is replaced by a nonlinear term. Our constructio

A. G. Nikitin

Quantum mechanical systems with position dependent masses (PDM) admitting for and more dimensional symmetry algebras are classified. Namely, all PDM systems are specified which, in addition to their invariance w.r.t. a three parametric Lie group, admit at least one second order integral of motion. The presented classification is partially extended to the mor

Sunil Pai, Olav Solgaard, Shanhui Fan, David A. B. Miller

Programmable unitary photonic networks that interfere hundreds of modes are emerging as a key technology in energy-efficient sensing, machine learning, cryptography, and linear optical quantum computing applications. In this work, we establish a theoretical framework to quantify error tolerance and scalability in a more general class of "binary tree cascade'

Abdel Ghani Labassi, Didier Chételat, Andrea Lodi

Branch-and-bound approaches in integer programming require ordering portions of the space to explore next, a problem known as node comparison. We propose a new siamese graph neural network model to tackle this problem, where the nodes are represented as bipartite graphs with attributes. Similar to prior work, we train our model to imitate a diving oracle tha

Igor Vozniak, Philipp Mueller, Lorena Hell, Nils Lipp

Effective and flexible allocation of visual attention is key for pedestrians who have to navigate to a desired goal under different conditions of urgency and safety preferences. While automatic modelling of pedestrian attention holds great promise to improve simulations of pedestrian behavior, current saliency prediction approaches mostly focus on generic fr

G. D. Alexeev, M. G. Alexeev, C. Alice, A. Amoroso

We report on a measurement of Spin Density Matrix Elements (SDMEs) in hard exclusive $\rho ^0$ meson muoproduction at COMPASS using 160~GeV/$c$ polarised $ \mu ^{+}$ and $ \mu ^{-}$ beams impinging on a liquid hydrogen target. The measurement covers the kinematic range 5.0~GeV/$c^2$ $< W <$ 17.0~GeV/$c^2$, 1.0 (GeV/$c$)$^2$ $< Q^2 <$ 10.0 (GeV/$c$)$^2$ and 0

E. H. Gomes Tavares, M. A. Jorge Silva, V. Narciso, A. Vicente

We explore the energy decay properties related to a model in extensible beams with the so-called energy damping. We investigate the influence of the nonloncal damping coefficient in the stability of the model. We prove, for the first time, that the corresponding energy functional is squeezed by polynomial-like functions involving the power of the damping coe

Patrick Garcia, Angela Hanson, David Jensen, Noah Owen

Segerman's 15+4 puzzle is a hinged version of the classic 15-puzzle, in which the tiles rotate as they slide around. In 1974, Wilson classified the groups of solutions to sliding block puzzles. We generalize Wilson's result to puzzles like the 15+4 puzzle, where the tiles can rotate, and the sets of solutions are subgroups of the generalized symmetric groups

Aldo Antognini, Sonia Bacca, Andreas Fleischmann, Loredana Gastaldo

Recent progress in laser and x-ray spectroscopy of muonic atoms offers promising long-term possibilities at the intersection of atomic, nuclear and particle physics. In muonic hydrogen, laser spectroscopy measurements will determine the ground-state hyperfine splitting (HFS) and additionally improve the Lamb shift by a factor of 5. Precision spectroscopy wit

Kuba Weimann, Tim O. F. Conrad

Recommender systems suffer from the cold-start problem whenever a new user joins the platform or a new item is added to the catalog. To address item cold-start, we propose to replace the embedding layer in sequential recommenders with a dynamic storage that has no learnable weights and can keep an arbitrary number of representations. In this paper, we presen

Jorge Ananias Neto, Widervan de Deus Morais, Ronaldo Thibes

We contextualize the improved gauge-unfixing (GU) formalism within a rather general prototypical second-class system, obtaining a corresponding first-class equivalent description enjoying gauge invariance which can be applied to several situations. The prototypical system is chosen to represent a considerable class of relevant models in field theory. By cons

Sanne ter Horst, Niels Jakob Laustsen

Schur coupling (SC) and equivalence after extension (EAE) are important relations for bounded operators on Banach spaces. It has been known for 30 years that the former implies the latter, but only recently Ter Horst, Messerschmidt, Ran and Roelands disproved the converse by constructing a pair of Fredholm operators which are EAE, but not SC. Motivated by th

S. Marin, I. Tolstukhin, M. B. Oberling, R. A. Knaack

Recent theoretical and experimental results have brought renewed interest and focus on the topic of fission fragment angular momentum. Measurements of neutrons and $\gamma$ rays in coincidence with fission fragments remain the most valuable tool in the exploration of fission physics. To achieve these scientific goals, we have developed a system that combines

Samyak Prajapati, Amrit Raj, Yash Chaudhari, Akhilesh Nandwal

Remote sensing has become a crucial part of our daily lives, whether it be from triangulating our location using GPS or providing us with a weather forecast. It has multiple applications in domains such as military, socio-economical, commercial, and even in supporting humanitarian efforts. This work proposes a novel technique for the automated geo-location o

Megan Heath, Ali Imran, David St-Onge

Precision agriculture aims to use technological tools for the agro-food sector to increase productivity, cut labor costs, and reduce the use of resources. This work takes inspiration from bees vision to design a remote sensing system tailored to incorporate UV-reflectance into a flower detector. We demonstrate how this approach can provide feature-rich image

Petr Blaschke, František Štampach

We analyze the asymptotic distribution of roots of Charlier polynomials with negative parameter depending linearly on the index. The roots cluster on curves in the complex plane. We determine implicit equations for these curves and deduce the limiting density of the root distribution supported on these curves. The proof is based on a determination of the lim

Christian Eistrup

[Abridged] This review paper discussed which chemical effects may be at play in a planet-forming disk midplane, which effects are relevant under different conditions, and which tools are available for modelling chemical kinetics in a disk midplane. The review goes on to discuss some important efforts in the planet formation modelling community to treat chemi

Oksana Bezushchak, Bogdana Oliynyk

We parameterize countable locally standard measure algebras by pairs of a Steinitz number and a real number greater or equal to 1. This is an analog of the theorems of J.Dixmier and A.A.Baranov.

Fabian Gunnink, Anupam Mazumdar, Martine Schut, Marko Toroš

One of the outstanding questions in modern physics is how to test whether gravity is classical or quantum in a laboratory. Recently there has been a proposal to test the quantum nature of gravity by creating quantum superpositions of two nearby neutral masses, close enough that the quantum nature of gravity can entangle the two quantum systems, but still suf