November 2022 arXiv papers — page 170

Mark J. Panaggio, Mike Fang, Hyunseung Bang, Paige A. Armstrong

In this study, learning modalities offered by public schools across the United States were investigated to track changes in the proportion of schools offering fully in-person, hybrid and fully remote learning over time. Learning modalities from 14,688 unique school districts from September 2020 to June 2021 were reported by Burbio, MCH Strategic Data, the Am

Alexander Braun, Thomas Kesselheim

We consider prophet inequalities for XOS and MPH-$k$ combinatorial auctions and give a simplified proof for the existence of static and anonymous item prices which recover the state-of-the-art competitive ratios. Our proofs make use of a linear programming formulation which has a non-negative objective value if there are prices which admit a given competitiv

Didong Li, Phuc Nguyen, Zhengwu Zhang, David B Dunson

The brain structural connectome is generated by a collection of white matter fiber bundles constructed from diffusion weighted MRI (dMRI), acting as highways for neural activity. There has been abundant interest in studying how the structural connectome varies across individuals in relation to their traits, ranging from age and gender to neuropsychiatric out

Sandra May, Ferdinand Thein

Two phase flows that include phase transition, especially phase creation, with a sharp interface remain a challenging task for numerics. We consider the isothermal Euler equations with phase transition between a liquid and a vapor phase. The phase interface is modeled as a sharp interface and the mass transfer across the phase boundary is modeled by a kineti

James Sikora, Jason Rowe, Daniel Jontof-Hutter, Jack J. Lissauer

Kepler-33 hosts five validated transiting planets ranging in period from 5 to 41 days. The planets are in nearly co-planar orbits and exhibit remarkably similar (appropriately scaled) transit durations indicative of similar impact parameters. The outer three planets have radii of $3.5\lesssim R_{\rm p}/R_\oplus\lesssim4.7$ and are closely-packed dynamically,

Svetlana Kiriushechkina, Anton Vakulenko, Daria Smirnova, Sriram Guddala

The Dirac equation is a paradigmatic model that describes a range of intriguing properties of relativistic spin-1/2 particles, from the existence of antiparticles to Klein tunneling. However, the Dirac-like equations have found application far beyond its original scope, and has been used to comprehend the properties of graphene and topological phases of matt

Hongyang He, Feng Ziliang, Yuanhang Zheng, Shudong Huang

In the field of medical CT image processing, convolutional neural networks (CNNs) have been the dominant technique.Encoder-decoder CNNs utilise locality for efficiency, but they cannot simulate distant pixel interactions properly.Recent research indicates that self-attention or transformer layers can be stacked to efficiently learn long-range dependencies.By

Matteo Croci, Judit Muñoz-Matute

In this article, we propose an algorithm for approximating the action of $φ-$functions of matrices against vectors, which is a key operation in exponential time integrators. In particular, we consider matrices with Kronecker sum structure, which arise from problems admitting a tensor product representation. The method is based on quadrature approximations of

Oliver Predelli

This paper describes a simple method for estimating the strength of a thermal updraft from a Temp (i.e., a SkewT, Emagram, or similar) showing the temperature and dew point profile of the lower atmosphere. The data of the Temp can come from relevant weather models or from radiosondes.

Sayam Singla, Sajid Mannan, Mohd Zaki, N. M. Anoop Krishnan

Chalcogenide glasses possess several outstanding properties that enable several ground breaking applications, such as optical discs, infrared cameras, and thermal imaging systems. Despite the ubiquitous usage of these glasses, the composition property relationships in these materials remain poorly understood. Here, we use a large experimental dataset compris

Zhaodong Shi, P. A. Muñoz, J. Büchner, Siming Liu

How ions are energized and heated is a fundamental problem in the study of energy dissipation in magnetized plasmas. In particular, the heating of heavy ions (including ${}^{4}\mathrm{He}^{2+}$, ${}^{3}\mathrm{He}^{2+}$ and others) has been a constant concern for understanding the microphysics of impulsive solar flares. In this article, via two-dimensional h

Z. Feng, Y. Liu, R. Ferrari, J. W. van. Wingerden

Wind turbines may experience local weather perturbation, which is not taken into account by the commonly-used wind turbine simulation packages. Without this information, it is extremely challenging to evaluate the controller performance with regard to the effect of the variation of local atmospheric conditions. On the other side, it is too late and costly to

Alexey Skrynnik, Zoya Volovikova, Marc-Alexandre Côté, Anton Voronov

The adoption of pre-trained language models to generate action plans for embodied agents is a promising research strategy. However, execution of instructions in real or simulated environments requires verification of the feasibility of actions as well as their relevance to the completion of a goal. We propose a new method that combines a language model and r

Ruby A. Duncan, Alexander J. van der Horst, Paz Beniamini

Studies of gamma-ray bursts (GRBs) and their multi-wavelength afterglows have led to insights in electron acceleration and emission properties from relativistic, high-energy astrophysical sources. Broadband modeling across the electromagnetic spectrum has been the primary means of investigating the physics behind these sources, although independent diagnosti

Thomas C. Fraser

The quantum marginal problem is concerned with characterizing which collections of quantum states on different subsystems are compatible in the sense that they are the marginals of some multipartite quantum state. Presented here is a countable family of inequalities, each of which is necessarily satisfied by any compatible collection of quantum states. Addit

Jiang Liu, Donghong Ji, Jingye Li, Dongdong Xie

So far, discontinuous named entity recognition (NER) has received increasing research attention and many related methods have surged such as hypergraph-based methods, span-based methods, and sequence-to-sequence (Seq2Seq) methods, etc. However, these methods more or less suffer from some problems such as decoding ambiguity and efficiency, which limit their p

Cody Blakeney, Jessica Zosa Forde, Jonathan Frankle, Ziliang Zong

Methods for improving the efficiency of deep network training (i.e. the resources required to achieve a given level of model quality) are of immediate benefit to deep learning practitioners. Distillation is typically used to compress models or improve model quality, but it's unclear if distillation actually improves training efficiency. Can the quality i

Sayed Ali Akbar Ghorashi, Jennifer Cano, Enrico Rossi, Taylor L. Hughes

Doped strong topological insulators are one of the most promising candidates to realize a fully gapped three-dimensional topological superconductor (TSC). In this letter, we revisit this system and reveal a possibility for higher-order topology which was previously missed. We find that over a finite-range of doping, the Fu-Berg superconducting pairing can gi

Albert Zhang, Stephen Millmore, Nikolaos Nikiforakis

This work is concerned with the numerical simulation of ablation of geological materials using a millimetre wave source. To this end, a new mathematical model is developed for a thermal approach to the problem, allowing for large scale simulations, whilst being able to include the strong temperature dependence of material parameters to ensure accurate modell

Riccardo Corvi, Davide Cozzolino, Giada Zingarini, Giovanni Poggi

Over the past decade, there has been tremendous progress in creating synthetic media, mainly thanks to the development of powerful methods based on generative adversarial networks (GAN). Very recently, methods based on diffusion models (DM) have been gaining the spotlight. In addition to providing an impressive level of photorealism, they enable the creation

Priyam Patel, Samuel J. Taylor

We give general conditions to produce endperiodic homeomorphisms that act loxodromically on various arc graphs of infinite-type surfaces.

Anvesh Rao Vijjini, Faeze Brahman, Snigdha Chaturvedi

In this paper, we introduce the task of modeling interpersonal relationships for story generation. For addressing this task, we propose Relationships as Latent Variables for Story Generation, (ReLiSt). ReLiSt generates stories sentence by sentence and has two major components - a relationship selector and a story continuer. The relationship selector specifie

A. N. Wallbank, B. J. Maughan, F. Gastaldello, C. Potter

Temperature measurements of galaxy clusters are used to determine their masses, which in turn are used to determine cosmological parameters. However, systematic differences between the temperatures measured by different telescopes imply a significant source of systematic uncertainty on such mass estimates. We perform the first systematic comparison between c

Zi-Xiang Li, Zhou-Quan Wan, Hong Yao

As an intrinsically-unbiased approach, quantum Monte Carlo (QMC) is of vital importance in understanding correlated phases of matter. Unfortunately, it often suffers notorious sign problem when simulating interacting fermion models. Here, we show for the first time that there exist interacting fermion models whose sign problem becomes less severe for larger

Ferruccio Feruglio

We analyze a large set of modular invariant models of lepton masses and mixing angles, pointing out that many of them prefer to live close to the self-dual point $τ=i$. We show that in the vicinity of this point a universal behavior naturally emerges, independently from details of the theory such as the finite modular group acting on the lepton multiplets, t

Qiang Gao, Junkai Dong, Patrick Ledwith, Daniel Parker

Moiré systems have emerged in recent years as a rich platform to study strong correlations. Here, we will discuss a simple, experimentally feasible setup based on periodically strained graphene that reproduces several key aspects of twisted moiré heterostructures -- but without introducing a twist. We consider a monolayer graphene sheet subject to a $C_2$-br

S. Jang, A. P. Milone, M. V. Legnardi, A. F. Marino

Hubble Space Telescope (HST) photometry is providing an extensive analysis of globular clusters (GCs). In particular, the pseudo two-colour diagram dubbed 'chromosome map (ChM)' allowed to detect and characterize their multiple populations with unprecedented detail. The main limitation of these studies is the small field of view of HST, which makes i

Billy Edwards, Quentin Changeat, Angelos Tsiaras, Kai Hou Yip

We present the analysis of the atmospheres of 70 gaseous extrasolar planets via transit spectroscopy with Hubble's Wide Field Camera 3 (WFC3). For over half of these, we statistically detect spectral modulation which our retrievals attribute to molecular species. Among these, we use Bayesian Hierarchical Modelling to search for chemical trends with bulk

Bo You, F. Li

The main objective of this paper is to establish the null controllability for the fourth order semilinear parabolic equations with the nonlinearities involving the state and its gradient up to second order. First of all, based on optimal control theory of partial differential equations and global Carleman estimates obtained in \cite{gs} for fourth order para

Alexander C. Nwala, Alessandro Flammini, Filippo Menczer

Malicious actors exploit social media to inflate stock prices, sway elections, spread misinformation, and sow discord. To these ends, they employ tactics that include the use of inauthentic accounts and campaigns. Methods to detect these abuses currently rely on features specifically designed to target suspicious behaviors. However, the effectiveness of thes

Matthew Elpers, Rayan Ibrahim, Allison H. Moore

A theta curve is a spatial embedding of the $θ$-graph in the three-sphere, taken up to ambient isotopy. We define the determinant of a theta curve as an integer-valued invariant arising from the first homology of its Klein cover. When a theta curve is simple, containing a constituent unknot, we prove that the determinant of the theta curve is the product of

Sara Malvar, Anvita Bhagavathula, Maria Angels de Luis Balaguer, Swati Sharma

Food protein digestibility and bioavailability are critical aspects in addressing human nutritional demands, particularly when seeking sustainable alternatives to animal-based proteins. In this study, we propose a machine learning approach to predict the true ileal digestibility coefficient of food items. The model makes use of a unique curated dataset that

Frederic Heihoff

We prove two new functional inequalities of the forms\[ \int_G φ(ψ- \overlineψ) \leq \frac{1}{a}\int_G ψ\ln \left(\frac{\;ψ\;}{ \overlineψ}\right) + \frac{a}{4β_0} \left\{ \int_G ψ\right\}\int_G|\nabla φ|^2 \] and \[ \int_G ψ\ln \left(\frac{\;ψ\;}{ \overlineψ}\right) \leq \frac{1}{β_0}\left\{ \int_G ψ\right\}\int_G |\nabla \ln(ψ)|^2 \] for any finitely conne

Khoa Doan, Shulong Tan, Weijie Zhao, Ping Li

Fast item ranking is an important task in recommender systems. In previous works, graph-based Approximate Nearest Neighbor (ANN) approaches have demonstrated good performance on item ranking tasks with generic searching/matching measures (including complex measures such as neural network measures). However, since these ANN approaches must go through the neur

Gabriele Benomio

We present a new proof of linear stability of the Schwarzschild solution to gravitational perturbations. Our approach employs the system of linearised gravity in the new geometric gauge of \cite{benomio_kerr}, specialised to the $|a|=0$ case. The proof fundamentally relies on the novel structure of the transport equations in the system. Indeed, while exploit

Touheed Anwar Atif, Uchenna Chukwu, Jesse Berwald, Raouf Dridi

We consider the Quantum Natural Gradient Descent (QNGD) scheme which was recently proposed to train variational quantum algorithms. QNGD is Steepest Gradient Descent (SGD) operating on the complex projective space equipped with the Fubini-Study metric. Here we present an adaptive implementation of QNGD based on Armijo's rule, which is an efficient backtr

Zayne Sprague, Kaj Bostrom, Swarat Chaudhuri, Greg Durrett

A growing body of work studies how to answer a question or verify a claim by generating a natural language "proof": a chain of deductive inferences yielding the answer based on a set of premises. However, these methods can only make sound deductions when they follow from evidence that is given. We propose a new system that can handle the underspecifi

Kyle Luh, Ryan Vogel, Alan Yu

The current work applies some recent combinatorial tools due to Jain to control the eigenvalue gaps of a matrix $M_n = M + N_n$ where $M$ is deterministic, symmetric with large operator norm and $N_n$ is a random symmetric matrix with subgaussian entries. One consequence of our tail bounds is that $M_n$ has simple spectrum with probability at least $1 - \exp

Pierre Laforgue, Stephan Clémençon, Patrice Bertail

Tournament procedures, recently introduced in Lugosi & Mendelson (2016), offer an appealing alternative, from a theoretical perspective at least, to the principle of Empirical Risk Minimization in machine learning. Statistical learning by Median-of-Means (MoM) basically consists in segmenting the training data into blocks of equal size and comparing the stat

Gabriele Benomio

We propose a new geometric framework to address the stability of the Kerr solution to gravitational perturbations in the full sub-extremal range $|a|<M$. Central to our framework is a new formulation of nonlinear gravitational perturbations of Kerr, whose two novel ingredients are the choice of a geometric gauge and non-integrable null frames both tailored t

Z. Y. Wang, W. W. Zhang

In recent years, machine learning methods represented by deep neural networks (DNN) have been a new paradigm of turbulence modeling. However, in the scenario of high Reynolds numbers, there are still some bottlenecks, including the lack of high-fidelity data and the convergence and stability problem in the coupling process of turbulence models and the RANS s

Daniel Devine, Gurpreet Singh

We are concerned with the existence and boundary behaviour of positive radial solutions for the system \begin{equation*} \left\{ \begin{aligned} Δu&=g(|x|,v(x)) &&\quad\mbox{in}\ Ω, \\ Δv&=f(|x|,|\nabla u(x)|) &&\quad\mbox{in}\ Ω, \end{aligned} \right. \end{equation*} where $Ω\subset \mathbb{R}^N$ is either a ball centered at the origin or the whole space $\

Ernesto Gomez, Keith E. Schubert, Khalil Dajani

We have previously defined synchronization (Gomez, E. and K. Schubert 2011) as a relation between the times at which a pair of events can happen, and introduced an algebra that covers all possible relations for such pairs. In this work we introduce the synchronization matrix, to make it easier to calculate the properties and results of $N$ event synchronizat

Kevin Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris

Research in mechanistic interpretability seeks to explain behaviors of machine learning models in terms of their internal components. However, most previous work either focuses on simple behaviors in small models, or describes complicated behaviors in larger models with broad strokes. In this work, we bridge this gap by presenting an explanation for how GPT-

Yang Liu, Yangyang Shi, Yun Li, Kaustubh Kalgaonkar

End-to-End deep learning has shown promising results for speech enhancement tasks, such as noise suppression, dereverberation, and speech separation. However, most state-of-the-art methods for echo cancellation are either classical DSP-based or hybrid DSP-ML algorithms. Components such as the delay estimator and adaptive linear filter are based on traditiona

Karla Garcia

We describe the topology of the moduli spaces of flat metrics for all the 3-dimensional closed manifolds. We give an algebraic description of the moduli spaces for the 4-dimensional closed flat manifolds with a single generator in their holonomy and, in some cases, also study their topology.

Chan-Jan Hsu, Ho-Lam Chung, Hung-yi Lee, Yu Tsao

In Spoken language understanding (SLU), a natural solution is concatenating pre-trained speech models (e.g. HuBERT) and pretrained language models (PLM, e.g. T5). Most previous works use pretrained language models with subword-based tokenization. However, the granularity of input units affects the alignment of speech model outputs and language model inputs,

Cheng-Ping Hsieh, Subhankar Ghosh, Boris Ginsburg

Fine-tuning is a popular method for adapting text-to-speech (TTS) models to new speakers. However this approach has some challenges. Usually fine-tuning requires several hours of high quality speech per speaker. There is also that fine-tuning will negatively affect the quality of speech synthesis for previously learnt speakers. In this paper we propose an al

Gil Semo, Dor Bernsohn, Ben Hagag, Gila Hayat

The research field of Legal Natural Language Processing (NLP) has been very active recently, with Legal Judgment Prediction (LJP) becoming one of the most extensively studied tasks. To date, most publicly released LJP datasets originate from countries with civil law. In this work, we release, for the first time, a challenging LJP dataset focused on class act

Tobias Dornheim, Jan Vorberger, Zhandos Moldabekov, Maximilian Böhme

The rigorous diagnostics of experiments with warm dense matter (WDM) is notoriously difficult. A key method is given by X-ray Thomson scattering (XRTS), but the interpretation of XRTS measurements is usually based on theoretical models that entail various approximations. Recently, Dornheim et al. [arXiv:2206.12805] have introduced a new framework for tempera

Aziz Kharoof, Cihan Okay

The data of a physical experiment can be represented as a presheaf of probability distributions. A striking feature of quantum theory is that those probability distributions obtained in quantum mechanical experiments do not always admit a joint probability distribution, a celebrated observation due to Bell. Such distributions are called contextual. Simplicia

Honghuai Fang

We study the alternating subspace of holomorphic sections of a special prequantum line bundle over SU(2)-character variety of torus, and show that it is isomorphic to the projective representation of mapping class group of peripheral torus given by the SO(3) Witten-Chern-Simons theory. We conjecture that the large r asymptotics of $L^2$-norm of SO(3)-knot st

Edward W He, Daniel Tolessa, Ashley Suh, Remco Chang

The VAST Challenges have been shown to be an effective tool in visual analytics education, encouraging student learning while enforcing good visualization design and development practices. However, research has observed that students often struggle at identifying a good &#34;starting point&#34; when tackling the VAST Challenge. Consequently, students who cou

Hana Zupan, Frederick Heinz, Bettina G. Keller

Binding processes are difficult to sample with molecular-dynamics (MD) simulations. In particular, the state space exploration is often incomplete. Evaluating the molecular interaction energy on a grid circumvents this problem but is heavily limited by state space dimensionality. Here, we make the first steps towards a low-dimensional grid-based model of mol

Francesco Giuliari, Geri Skenderi, Marco Cristani, Alessio Del Bue

We propose an end-to-end solution to address the problem of object localisation in partial scenes, where we aim to estimate the position of an object in an unknown area given only a partial 3D scan of the scene. We propose a novel scene representation to facilitate the geometric reasoning, Directed Spatial Commonsense Graph (D-SCG), a spatial scene graph tha

Francisco Souza, Tim Offermans, Ruud Barendse, Geert Postma

This work proposes a new data-driven model devised to integrate process knowledge into its structure to increase the human-machine synergy in the process industry. The proposed Contextual Mixture of Experts (cMoE) explicitly uses process knowledge along the model learning stage to mold the historical data to represent operators&#39; context related to the pr

John Paul P. Miranda, Julieta M. Umali, Aileen P. de Leon

The fire and crime incident datasets were requested and collected from two Philippine regional agencies (i.e., the Bureau of Fire Protection and the Philippine National Police). The datasets were used to initially analyze and map both fire and crime incidents within the province of Pampanga for a specific time frame. Several data preparation, normalization,

Daniela De Silva, Ovidiu Savin

We investigate the rigidity of global minimizers $u \ge 0$ of the Alt-Phillips functional involving negative power potentials $$\int_Ω\left(|\nabla u|^2 + u^{-γ} χ_{\{u>0\}}\right) \, dx, \quad \quad γ\in (0,2),$$ when the exponent $γ$ is close to the extremes of the admissible values. In particular we show that global minimizers in $\mathbb{R}^n$ are one-di

Simone Saitta, Ludovica Maga, Chloe Armour, Emiliano Votta

Numerical simulations of blood flow are a valuable tool to investigate the pathophysiology of ascending thoracic aortic aneurysms (ATAA). To accurately reproduce hemodynamics, computational fluid dynamics (CFD) models must employ realistic inflow boundary conditions (BCs). However, the limited availability of in vivo velocity measurements still makes researc

Jose Vargas-Quiros, Laura Cabrera-Quiros, Hayley Hung

Recognizing who is speaking in a crowded scene is a key challenge towards the understanding of the social interactions going on within. Detecting speaking status from body movement alone opens the door for the analysis of social scenes in which personal audio is not obtainable. Video and wearable sensors make it possible recognize speaking in an unobtrusive,

Loïc Van Hoorebeeck, P. -A. Absil

Quadratic hypersurfaces are a natural generalization of affine subspaces, and projections are elementary blocks of algorithms in optimization and machine learning. It is therefore intriguing that no proper studies and tools have been developed to tackle this nonconvex optimization problem. The quadproj package is a user-friendly and documented software that

Xiao Xiang Zhu, Yuanyuan Wang, Mrinalini Kochupillai, Martin Werner

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is

Eric V. Linder

A novel, interesting class of scalar-tensor gravity theories is those with a limit on the field motion, where the scalar field either goes to a constant acceleration or stops accelerating and goes to a constant velocity. We combine these with the ability to dynamically cancel a high energy cosmological constant, e.g. through the well tempered or self tuning

Swasti Bhatia, Aldrin Antony, Pradeep R. Nair

Selective extraction of photo-generated carriers is a fundamental challenge in solar cells which is usually achieved through junctions with the associated doping as well as band offset differences. In this context, here we propose a new paradigm for selective extraction for majority carriers through novel usage of the programmable charge trap which comprises

Irfan Durmić, Alex Han, Pamela E. Harris, Rodrigo Ribeiro

We consider the notion of classical parking functions by introducing randomness and a new parking protocol, as inspired by the work presented in the paper ``Parking Functions: Choose your own adventure,&#39;&#39; (arXiv:2001.04817) by Carlson, Christensen, Harris, Jones, and Rodríguez. Among our results, we prove that the probability of obtaining a parking f

David Omogbhe, Kamran Sadiq

We consider in a two dimensional absorbing and scattering medium, an inverse source problem in the stationary radiative transport, where the source is linearly anisotropic. The medium has an anisotropic scattering property that is neither negligible nor large enough for the diffusion approximation to hold. The attenuating and scattering properties of the med

Xinmeng Huang, Kun Yuan

Decentralized optimization with time-varying networks is an emerging paradigm in machine learning. It saves remarkable communication overhead in large-scale deep training and is more robust in wireless scenarios especially when nodes are moving. Federated learning can also be regarded as decentralized optimization with time-varying communication patterns alt

Junhao Hu, Shirin Shoushtari, Zihao Zou, Jiaming Liu

Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors. Plug-and-play priors (PnP) and deep equilibrium models (DEQ) are two DMBA frameworks that have received significant attention. The key difference between the two is that the image prior in DEQ is trained by usi

Suchetana Sadhukhan, Shiv Manjaree Gopaliya, Pushpdant Jain

Volatility prediction in the financial market helps to understand the profit and involved risks in investment. However, due to irregularities, high fluctuations, and noise in the time series, predicting volatility poses a challenging task. In the recent Covid-19 pandemic situation, volatility prediction using complex intelligence techniques has attracted eno

Paul F. R. Wilson, Mahdi Gilany, Amoon Jamzad, Fahimeh Fooladgar

Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data shows great promise for prostate cancer detection. Previous approaches to analysis of ultrasound data largely follow a supervised learning paradigm. Ground truth labels for ultrasound images used for training deep networks often include coarse annotations generated from the

Jiangbin Zheng, Siyuan Li, Cheng Tan, Chong Wu

Sign Language (SL), as the mother tongue of the deaf community, is a special visual language that most hearing people cannot understand. In recent years, neural Sign Language Translation (SLT), as a possible way for bridging communication gap between the deaf and the hearing people, has attracted widespread academic attention. We found that the current mains

Junhao Dong, Seyed-Mohsen Moosavi-Dezfooli, Jianhuang Lai, Xiaohua Xie

Although current deep learning techniques have yielded superior performance on various computer vision tasks, yet they are still vulnerable to adversarial examples. Adversarial training and its variants have been shown to be the most effective approaches to defend against adversarial examples. These methods usually regularize the difference between output pr

Karolos Nikitaras, Konstantinos Klapsas, Nikolaos Ellinas, Georgia Maniati

This paper proposes an Expressive Speech Synthesis model that utilizes token-level latent prosodic variables in order to capture and control utterance-level attributes, such as character acting voice and speaking style. Current works aim to explicitly factorize such fine-grained and utterance-level speech attributes into different representations extracted b

Lucas Delage

We compute the deformation of the spinor fields of the super-Polyakov action leading to the so-called tensionless super-string theory. This allows us to obtain a satisfying Majorana condition in the tensionless limit, which is the main result of this paper. Our tensionless limit is shown to possess previously computed features of tensionless super-string the

Hamza El-Kebir, Junren Ran, Martin Ostoja-Starzewski, Richard Berlin

We present a novel 3D adaptive observer framework for use in the determination of subsurface organic tissue temperatures in electrosurgery. The observer structure leverages pointwise 2D surface temperature readings obtained from a real-time infrared thermographer for both parameter estimation and temperature field observation. We introduce a novel approach t

Keisuke Toyama, Katsuhito Sudoh, Satoshi Nakamura

Although the well-known MR-to-text E2E dataset has been used by many researchers, its MR-text pairs include many deletion/insertion/substitution errors. Since such errors affect the quality of MR-to-text systems, they must be fixed as much as possible. Therefore, we developed a refined dataset and some python programs that convert the original E2E dataset in

Jinjin Gu, Jinan Zhou, Ringo Sai Wo Chu, Yan Chen

Event cameras are novel bio-inspired vision sensors that output pixel-level intensity changes in microsecond accuracy with a high dynamic range and low power consumption. Despite these advantages, event cameras cannot be directly applied to computational imaging tasks due to the inability to obtain high-quality intensity and events simultaneously. This paper

Miodrag Iovanov, Emre Sen, Alexander Sistko, Shijie Zhu

We classify pointed Hopf algebras of discrete corepresentation type over an algebraically closed field K with characteristic zero. For such algebras $H$, we explicitly determine the algebra structure up to isomorphism for the link indecomposable component $B$ containing the unit. It turns out that $H$ is a crossed product of $B$ and a certain group algebra.

Trond Steihaug

The Fibonacci numbers are familiar to all of us. They appear unexpectedly often in mathematics, so much there is an entire journal and a sequence of conferences dedicated to their study. However, there is also another sequence of numbers associated with Fibonacci. In The On-Line Encyclopedia of Integer Sequences, a sequence of numbers which is an approximati

Raysa M. Benatti, Camila M. L. Villarroel, Sandra Avila, Esther L. Colombini

Natural language processing techniques have helped domain experts solve legal problems. Digital availability of court documents increases possibilities for researchers, who can access them as a source for building datasets -- whose disclosure is aligned with good reproducibility practices in computational research. Large and digitized court systems, such as

Bingyan Han

In a semi-realistic market simulator, independent reinforcement learning algorithms may facilitate market makers to maintain wide spreads even without communication. This unexpected outcome challenges the current antitrust law framework. We study the effectiveness of maker-taker fee models in preventing cooperation via algorithms. After modeling market makin

Tomasz Szemberg, Justyna Szpond

The purpose of this note is to report, in narrative rather than rigorous style, about the nice geometry of $6$-division points on the Fermat cubic $F$ and various conics naturally attached to them. Most facts presented here were derived by symbolic algebra programs and the idea of the note is to propose a research direction for searching for conceptual proof

O. Melchert, S. Kinnewig, F. Dencker, D. Perevoznik

We numerically explore synthetic crystal diamond for realizing novel light sources in ranges which are up to now difficult to achieve with other materials, such as sub-10-fs pulse durations and challenging spectral ranges. We assess the performance of on-chip diamond waveguides for controlling light generation by means of nonlinear soliton dynamics. Tailorin

Carolin Schlosser, Sonja Köhler, Marc Wagner

We present SU(3) lattice Yang-Mills data for hybrid static potentials from five ensembles with different small lattice spacings and the corresponding parametrizations for quark-antiquark separations $0.08\,\text{fm} \le r \le 1.12\,\text{fm}$. We remove lattice discretization errors at tree level of perturbation theory and partly at order $a^2$ as well as th

Andrea Montanari, Yuchen Wu

We consider the problem of estimating the factors of a low-rank $n \times d$ matrix, when this is corrupted by additive Gaussian noise. A special example of our setting corresponds to clustering mixtures of Gaussians with equal (known) covariances. Simple spectral methods do not take into account the distribution of the entries of these factors and are there

Zili Huang, Desh Raj, Paola García, Sanjeev Khudanpur

Self-supervised learning (SSL) methods which learn representations of data without explicit supervision have gained popularity in speech-processing tasks, particularly for single-talker applications. However, these models often have degraded performance for multi-talker scenarios -- possibly due to the domain mismatch -- which severely limits their use for s

Yulan Gao, Ziqiang Ye, Han Yu, Zehui Xiong

This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system. Each mobile device in the system engages the model training process within the specified area and allocates its computation and communication resources for deriving and u

Yulan Gao, Yue Xiao, Xianfu Lei, Qiaonan Zhu

Owing to the recent advancements of meta-materials and meta-surfaces, the concept of reconfigurable intelligent surface (RIS) has been embraced to meet the spectral- and energy-efficient, and yet cost-effective solutions for the sixth-generation (6G) wireless networks. From an operational standpoint, RISs can be easily deployed on the facades of buildings an

Stephanie Stacy, Alfredo Gabaldon, John Karigiannis, James Kubrich

We present an architecture and system for understanding novel behaviors of an observed agent. The two main features of our approach are the adoption of Dennett&#39;s intentional stance and analogical reasoning as one of the main computational mechanisms for understanding unforeseen experiences. Our approach uses analogy with past experiences to construct hyp

Klichchupong Dabsamut, Adisak Boonchun, Walter R. L. Lambrecht

Various defects in ZnO, focused on substitutional N$_O$ and N$_2$ in various sites, O-site, interstitial and Zn-site are studied using first-principles calculations with the goal of understanding the electron paramagnetic resonance (EPR) center reported for N$_2$ in ZnO and substitutional N on the O-site. The $g$ tensors are calculated using the gauge includ

Klaas-Jan Tielrooij, Alessandro Principi, David Saleta Reig, Alexander Block

Achieving efficient, high-power harmonic generation in the terahertz spectral domain has technological applications, for example in sixth generation (6G) communication networks. Massless Dirac fermions possess extremely large terahertz nonlinear susceptibilities and harmonic conversion efficiencies. However, the observed maximum generated harmonic power is l

Yufei Chen, Chao Shen, Yun Shen, Cong Wang

As in-the-wild data are increasingly involved in the training stage, machine learning applications become more susceptible to data poisoning attacks. Such attacks typically lead to test-time accuracy degradation or controlled misprediction. In this paper, we investigate the third type of exploitation of data poisoning - increasing the risks of privacy leakag

Atli Fannar Franklín, Robert K. Moniot

The Taxman game has proven to be hard to solve optimally, so efforts have been made to find heuristic strategies that do well in practice. We present results on the NP-hardness of a variant of the game via an equivalence to a particular kind of graph matching problem. Furthermore this equivalence is used to derive a winning strategy for all $n$ along with ef

Guillaume Bellegarda, Auke Ijspeert

In this letter, we present a method for integrating central pattern generators (CPGs), i.e. systems of coupled oscillators, into the deep reinforcement learning (DRL) framework to produce robust and omnidirectional quadruped locomotion. The agent learns to directly modulate the intrinsic oscillator setpoints (amplitude and frequency) and coordinate rhythmic

Michael Xevgenis, Dimitrios Kogias, Ioannis Christidis, Charalampos Patrikakis

A new era in ICT has begun with the evolution of Next Generation Networks (NGNs) and the development of human-centric applications. Ultra-low latency, high throughput, and high availability are a few of the main characteristics of modern networks. Network Providers (NPs) are responsible for the development and maintenance of network infrastructures ready to

Virat Shejwalkar, Lingjuan Lyu, Amir Houmansadr

Semi-supervised machine learning (SSL) is gaining popularity as it reduces the cost of training ML models. It does so by using very small amounts of (expensive, well-inspected) labeled data and large amounts of (cheap, non-inspected) unlabeled data. SSL has shown comparable or even superior performances compared to conventional fully-supervised ML techniques

Youngjoon Jang, Youngtaek Oh, Jae Won Cho, Dong-Jin Kim

The goal of this work is background-robust continuous sign language recognition. Most existing Continuous Sign Language Recognition (CSLR) benchmarks have fixed backgrounds and are filmed in studios with a static monochromatic background. However, signing is not limited only to studios in the real world. In order to analyze the robustness of CSLR models unde

Hasna Nhaila, Elkebir Sarhrouni, Ahmed Hammouch

Dimensionality reduction is an important preprocessing step of the hyperspectral images classification (HSI), it is inevitable task. Some methods use feature selection or extraction algorithms based on spectral and spatial information. In this paper, we introduce a new methodology for dimensionality reduction and classification of HSI taking into account bot

Subham Sarkar, Ramesh Sreekantan

In this paper we construct extensions of mixed Hodge structures coming from the mixed Hodge structure on the graded quotients of the group ring of the fundamental group of a smooth, projective, pointed curve. These extensions correspond to the regulators of certain motivic cycles in the Jacobian of the curve which were constructed by Beilinson and Bloch. Thi

Bouazza Kacimi, Amina Alem, Mustafa Özkan

This article deals with the interpolating sesqui-harmonicity of a vector field $X$ viewed as a map from a Riemannian manifold $(M,g)$ to its tangent bundle $TM$ endowed with the Sasaki metric $g_{S}$. We show characterization theorem for $X$ to be interpolating sesqui-harmonic map. We give also the critical point condition which characterizes interpolating s