July 2022 arXiv papers — page 144

Ting-Jui Chang, Shahin Shahrampour

Identification of linear time-invariant (LTI) systems plays an important role in control and reinforcement learning. Both asymptotic and finite-time offline system identification are well-studied in the literature. For online system identification, the idea of stochastic-gradient descent with reverse experience replay (SGD-RER) was recently proposed, where t

Mesut Şahin

Motivated by applications to the theory of error-correcting codes, we give methods for computing a generating set for the ideal generated by $\beta$-graded polynomials vanishing on certain subsets of a simplicial complete toric variety $X$ over a finite field $\mathbb{F}_q$, where $\beta$ is a $d\times r$ matrix whose columns generate a subsemigroup $\mathbb

Uisub Shin, Cong Ding, Virginia Woods, Alik S. Widge

Growing evidence suggests that phase-locked deep brain stimulation (DBS) can effectively regulate abnormal brain connectivity in neurological and psychiatric disorders. This letter therefore presents a low-power SoC with both neural connectivity extraction and phase-locked DBS capabilities. A 16-channel low-noise analog front-end (AFE) records local field po

Matthew Groh

Machine learning models are often brittle on production data despite achieving high accuracy on benchmark datasets. Benchmark datasets have traditionally served dual purposes: first, benchmarks offer a standard on which machine learning researchers can compare different methods, and second, benchmarks provide a model, albeit imperfect, of the real world. The

Weiming Zhuang, Chongjie Ye, Ying Xu, Pengzhi Mao

In this demo, we present Chat-to-Design, a new multimodal interaction system for personalized fashion design. Compared to classic systems that recommend apparel based on keywords, Chat-to-Design enables users to design clothes in two steps: 1) coarse-grained selection via conversation and 2) fine-grained editing via an interactive interface. It encompasses t

András Pálinkás, György Kálvin, Péter Vancsó, Konrád Kandrai

The behavior of single layer van der Waals (vdW) materials is profoundly influenced by the immediate atomic environment at their surface, a prime example being the myriad of emergent properties in artificial heterostructures. Equally significant are adsorbates deposited onto their surface from ambient. While vdW interfaces are well understood, our knowledge

Mohamed Y. Selim, Ahmed E. Kamal, Farid Nait-Abdesselam

5G network operators consider the dense deployment of small base-stations (SBSs) to increase network coverage and capacity. Hence, operators face the challenge of X-hauling, i.e., backhauling or fronthauling, their traffic to the core network. Also, SBSs densification will increase the possibility of failure of these X-haul links. To cope with this problem,

Yi Zhang, Junyang Wang, Jitao Sang

Vision-Language Pre-training (VLP) models have achieved state-of-the-art performance in numerous cross-modal tasks. Since they are optimized to capture the statistical properties of intra- and inter-modality, there remains risk to learn social biases presented in the data as well. In this work, we (1) introduce a counterfactual-based bias measurement \emph{C

Mame Gor Ngom, Ibrahima Faye, Diaraf Seck

Coastal erosion describes the displacement of sand caused by the movement induced by tides, waves or currents. Some of its wave phenomena are modeled by Helmholtz-type equations. Our purposes, in this paper are, first, to study optimal shapes obstacles to mitigate sand transport under the constraint of the Helmholtz equation. And the second side of this work

Kristian Miok, Encarnacion Hidalgo-Tenorio, Petya Osenova, Miguel-Angel Benitez-Castro

Parliamentary and legislative debate transcripts provide informative insight into elected politicians' opinions, positions, and policy preferences. They are interesting for political and social sciences as well as linguistics and natural language processing (NLP) research. While existing research studied individual parliaments, we apply advanced NLP methods

Wanru Zhao, Xinchi Qiu, Javier Fernandez-Marques, Pedro P. B. de Gusmão

Federated Learning (FL) has emerged as a prospective solution that facilitates the training of a high-performing centralised model without compromising the privacy of users. While successful, research is currently limited by the possibility of establishing a realistic large-scale FL system at the early stages of experimentation. Simulation can help accelerat

Dimitrios Stoidis, Andrea Cavallaro

Our voice encodes a uniquely identifiable pattern which can be used to infer private attributes, such as gender or identity, that an individual might wish not to reveal when using a speech recognition service. To prevent attribute inference attacks alongside speech recognition tasks, we present a generative adversarial network, GenGAN, that synthesises voice

Pierre Aboulker, Thomas Bellitto, Frédéric Havet, Clément Rambaud

The dichromatic number $\dic(D)$ of a digraph $D$ is the least integer $k$ such that $D$ can be partitioned into $k$ directed acyclic digraphs. A digraph is $k$-dicritical if $\dic(D) = k$ and each proper subgraph $D'$ of $D$ satisfies $\dic(D') \leq k-1$. An oriented graph is a digraph with no directed cycle of length $2$. For integers $k$ and $n$, we denot

Jinrui Zhang, Teng Wang, Feng Zheng, Ran Cheng

Generic Event Boundary Captioning (GEBC) aims to generate three sentences describing the status change for a given time boundary. Previous methods only process the information of a single boundary at a time, which lacks utilization of video context information. To tackle this issue, we design a model that directly takes the whole video as input and generates

Menglong Zhang, Tao Feng

For a positive integer $d\geq 2$, a family $\mathcal F\subseteq \binom{[n]}{k}$ is said to be d-wise intersecting if $|F_1\cap F_2\cap \dots\cap F_d|\geq 1$ for all $F_1, F_2, \dots ,F_d\in \mathcal F$. A d-wise intersecting family $\mathcal F\subseteq \binom{[n]}{k}$ is called maximal if $\mathcal F\cup\{A\}$ is not d-wise intersecting for any $A\in\binom{[

Benjamin Lion, Farhad Arbab, Carolyn Talcott

Composition is an important feature of a specification language, as it enables the design of a complex system in terms of a product of its parts. Decomposition is equally important in order to reason about structural properties of a system. Usually, however, a system can be decomposed in more than one way, each optimizing for a different set of criteria. We

Negar Hashemi, Amjed Tahir, Shawn Rasheed

Flaky tests (tests with non-deterministic outcomes) can be problematic for testing efficiency and software reliability. Flaky tests in test suites can also significantly delay software releases. There have been several studies that attempt to quantify the impact of test flakiness in different programming languages (e.g., Java and Python) and application doma

CMS Collaboration

A search for a charged Higgs boson H$^\pm$ decaying into a heavy neutral Higgs boson H and a W boson is presented. The analysis targets the H decay into a pair of tau leptons with at least one of them decaying hadronically and with an additional electron or muon present in the event. The search is based on proton-proton collision data recorded by the CMS exp

Karl A. Kalina, Lennart Linden, Jörg Brummund, Markus Kästner

Herein, we present a new data-driven multiscale framework called FE${}^\text{ANN}$ which is based on two main keystones: the usage of physics-constrained artificial neural networks (ANNs) as macroscopic surrogate models and an autonomous data mining process. Our approach allows the efficient simulation of materials with complex underlying microstructures whi

Tat-Bao-Thien Nguyen, Minh-Vuong Ngo, Van-Phong Nguyen

According to some medical imaging techniques, breast histopathology images called Hematoxylin and Eosin are considered as the gold standard for cancer diagnoses. Based on the idea of dividing the pathologic image (WSI) into multiple patches, we used the window [512,512] sliding from left to right and sliding from top to bottom, each sliding step overlapping

Paul Guerrero, Miloš Hašan, Kalyan Sunkavalli, Radomír Měch

Procedural material graphs are a compact, parameteric, and resolution-independent representation that are a popular choice for material authoring. However, designing procedural materials requires significant expertise and publicly accessible libraries contain only a few thousand such graphs. We present MatFormer, a generative model that can produce a diverse

Abed Zabihian-Bisheh, Hadi Rezaei Vandchali, Vahid Kayvanfar, Frank Werner

A significant amount of hazardous waste generated from health sectors and industrial processes has posed a major threat to human health by causing environmental issues and contamination of air, soil, and water resources. This paper presents a multi-objective mixed-integer nonlinear programming (MINLP) formulation for a sustainable hazardous waste location-ro

Taha Yasseri, Patrick Gildersleve, Lea David

The digital transformation of our societies and in particular information and communication technologies have revolutionized how we generate, communicate, and acquire information. Collective memory as a core and unifying force in our societies has not been an exception among many societal concepts which have been revolutionized through digital transformation

Bruno Jartoux, Chaya Keller, Shakhar Smorodinsky, Yelena Yuditsky

We introduce and study conflict-free colourings of $t$-subsets in hypergraphs. In such colourings, one assigns colours to all subsets of vertices of cardinality $t$ such that in any hyperedge of cardinality at least $t$ there is a uniquely coloured $t$-subset. The case $t=1$, i.e., vertex conflict-free colouring, is a well-studied notion. Already the case $t

Ana Mucalica, Dmitry E. Pelinovsky

Rarefactive waves and dispersive shock waves are generated from the step-like initial data in many nonlinear evolution equations including the classical example of the Korteweg-de Vries (KdV) equation. When a solitary wave is injected on the step-like initial data, it is either transmitted over the background or trapped in the rarefactive wave. We show that

Kun Wei, Yike Zhang, Sining Sun, Lei Xie

Leveraging context information is an intuitive idea to improve performance on conversational automatic speech recognition(ASR). Previous works usually adopt recognized hypotheses of historical utterances as preceding context, which may bias the current recognized hypothesis due to the inevitable historicalrecognition errors. To avoid this problem, we propose

Fleurianne Bertrand, Carsten Carstensen, Benedikt Gräßle, Ngoc Tien Tran

The known a posteriori error analysis of hybrid high-order methods (HHO) treats the stabilization contribution as part of the error and as part of the error estimator for an efficient and reliable error control. This paper circumvents the stabilization contribution on simplicial meshes and arrives at a stabilization-free error analysis with an explicit resid

Bertrand Teguia Tabuguia

By holonomic guessing, we denote the process of finding a linear differential equation with polynomial coefficients satisfied by the generating function of a sequence, for which only a few first terms are known. Holonomic guessing has been used in computer algebra for over three decades to demonstrate the value of the guess-and-prove paradigm in intuition pr

Jinze Li, Yan Bai, Yihang Lou, Xiongkun Linghu

Few-shot class-incremental learning(FSCIL) focuses on designing learning algorithms that can continually learn a sequence of new tasks from a few samples without forgetting old ones. The difficulties are that training on a sequence of limited data from new tasks leads to severe overfitting issues and causes the well-known catastrophic forgetting problem. Exi

Qingshuai Feng, You Peng, Wenjie Zhang, Ying Zhang

With the ever-increasing prevalence of graph data in a wide spectrum of applications, it becomes essential to analyze structural trends in dynamic graphs on a continual basis. The shortest cycle is a fundamental pattern in graph analytics. In this paper, we investigate the problem of shortest cycle counting for a given vertex in dynamic graphs in light of it

Luc Guyot, Ihsen Yengui

It is well-known that for any commutative unitary ring $\mathbf{R}$, the Serre conjecture ring $\mathbf{R}\langle X \rangle$, i.e., the localization of the univariate polynomial ring $\mathbf{R}[X]$ at monic polynomials, is a B\'ezout domain of Krull dimension $\leq 1$ if so is $\mathbf{R}$. Consequently, defining by induction $\mathbf{R}\langle X_1,\ldots,X

Alexander Schperberg, Yuki Shirai, Xuan Lin, Yusuke Tanaka

In multi-point contact systems, precise force control is crucial for achieving stable and safe interactions between robots and their environment. Thus, we demonstrate an admittance controller with auto-tuning that can be applied for these systems. The controller's objective is to track the target wrench profiles of each contact point while considering the ad

Masashi Aiko, Shinya Kanemura, Kodai Sakurai

We calculate radiative corrections to decay rates of CP-odd Higgs boson $A$ for various decay modes in the four types of two Higgs doublet models with the softly broken discrete $Z_{2}$ symmetry. The decay branching ratios are evaluated at the next-to-leading order for electroweak corrections and the next-to-next-to-leading order for QCD corrections. We comp

Bertrand Teguia Tabuguia, Wolfram Koepf

Linear recurrence equations with constant coefficients define the power series coefficients of rational functions. However, one usually prefers to have an explicit formula for the sequence of coefficients, provided that such a formula is "simple" enough. Simplicity is related to the compactness of the formula due to the presence of algebraic numbers: "the sm

Wu Zheng, Li Jiang, Fanbin Lu, Yangyang Ye

To boost a detector for single-frame 3D object detection, we present a new approach to train it to simulate features and responses following a detector trained on multi-frame point clouds. Our approach needs multi-frame point clouds only when training the single-frame detector, and once trained, it can detect objects with only single-frame point clouds as in

You Peng, Song Bian, Rui Li, Sibo Wang

Community search is a problem that seeks cohesive and connected subgraphs in a graph that satisfy certain topology constraints, e.g., degree constraints. The majority of existing works focus exclusively on the topology and ignore the nodes' influence in the communities. To tackle this deficiency, influential community search is further proposed to include th

Jingying Ding, Yifan Feng, Ying Rong

How do people navigate the exploration-exploitation (EE) trade-off when making repeated choices with unknown rewards? We study this question through the lens of multi-armed bandit problems and introduce a novel behavioral model, Quantal Choice with Adaptive Reduction of Exploration (QCARE). It generalizes Thompson Sampling, allowing for a principled way to q

Anina Gruica, Alberto Ravagnani, John Sheekey, Ferdinando Zullo

We introduce and explore a new concept of evasive subspace with respect to a collection of subspaces sharing a common dimension, most notably partial spreads. We show that this concept generalises known notions of subspace scatteredness and evasiveness. We establish various upper bounds for the dimension of an evasive subspace with respect to arbitrary parti

Fabio Bergonti, Luca Fiorio, Daniele Pucci

Jumping can be an effective way of locomotion to overcome small terrain gaps or obstacles. In this paper we propose two different approaches to perform jumps with a humanoid robot. Specifically, starting from a pre-defined CoM trajectory we develop the theory for a velocity controller and for a torque controller based on an optimization technique for the eva

Fabio Bergonti, Gabriele Nava, Luca Fiorio, Giuseppe L'Erario

This article takes a step to provide humanoid robots with adaptive morphology abilities. We present a systematic approach for enabling robotic covers to morph their shape, with an overall size fitting the anthropometric dimensions of a humanoid robot. More precisely, we present a cover concept consisting of two main components: a skeleton, which is a repetit

Tatsuya Gima, Takehiro Ito, Yasuaki Kobayashi, Yota Otachi

Given a graph and two vertex sets satisfying a certain feasibility condition, a reconfiguration problem asks whether we can reach one vertex set from the other by repeating prescribed modification steps while maintaining feasibility. In this setting, Mouawad et al. [IPEC 2014] presented an algorithmic meta-theorem for reconfiguration problems that says if th

Mirko Horňák

A vertex colouring $f:V(G)\to C$ of a graph $G$ is complete if for any $c_1,c_2\in C$ with $c_1\ne c_2$ there are in $G$ adjacent vertices $v_1,v_2$ such that $f(v_1)=c_1$ and $f(v_2)=c_2$. The achromatic number of $G$ is the maximum number $\mathrm{achr}(G)$ of colours in a proper complete vertex colouring of $G$. Let $G_1\square G_2$ denote the Cartesian p

Shalev Shaer, Yaniv Romano

The model-X conditional randomization test is a generic framework for conditional independence testing, unlocking new possibilities to discover features that are conditionally associated with a response of interest while controlling type-I error rates. An appealing advantage of this test is that it can work with any machine learning model to design powerful

K. Knakkergaard Nielsen

We investigate a rare instance of an exactly solvable nonequilibrium many-body problem. In particular, we derive an exact solution for the nonequilibrium dynamics of an initially localized single hole in a fully anisotropic antiferromagnetic Bethe lattice, described by the $t$-$J_z$ model. The solvability of the model relies on the fractal self-similarity of

Muhammad Bilal, Hyeok Kim, Muhammad Fayaz, Pravin Pawar

As a result of increasing population and globalization, the demand for energy has greatly risen. Therefore, accurate energy consumption forecasting has become an essential prerequisite for government planning, reducing power wastage and stable operation of the energy management system. In this work we present a comparative analysis of major machine learning

David Coyle, Mark Matthewas

This position paper describes the implementation and initial findings of a game called Personal Investigator (PI). PI is an online 3D detective game that implements a model of Brief Solution Focused Therapy (BSFT). It aims to help teenagers overcome mental health problems and engage with traditional mental health care services. It is predicted that the combi

Marco Virgolin, Solon P. Pissis

Symbolic regression (SR) is the task of learning a model of data in the form of a mathematical expression. By their nature, SR models have the potential to be accurate and human-interpretable at the same time. Unfortunately, finding such models, i.e., performing SR, appears to be a computationally intensive task. Historically, SR has been tackled with heuris

Johannah Sprinz

Members of marginalized communities are often accused of being "too sensitive" when subjected to supposedly harmless acts of microaggression. This paper explores a simulated society consisting of marginalized and non-marginalized agents who interact and may, based on their individually held convictions, commit acts of microaggressions. Agents witnessing a mi

Claudette Pretorius, David Coyle

The application of Self-Determination Theory to understand online help-seeking and the design of online help-seeking technologies presents an interesting avenue for investigation. Improving motivation to engage in the help-seeking process could be achieved using the Basic Psychological Needs Theory as a structure to guide the design of online help-seeking te

Germán Ortiz, Gemma Bel Enguix, Helena Gómez-Adorno, Iqra Ameer

Both policy and research benefit from a better understanding of individuals' jobs. However, as large-scale administrative records are increasingly employed to represent labor market activity, new automatic methods to classify jobs will become necessary. We developed an automatic job offers classifier using a dataset collected from the largest job bank of Mex

Tobias Glasmachers

Support vector machines (SVMs) are a standard method in the machine learning toolbox, in particular for tabular data. Non-linear kernel SVMs often deliver highly accurate predictors, however, at the cost of long training times. That problem is aggravated by the exponential growth of data volumes over time. It was tackled in the past mainly by two types of te

Ahmed Samir Imam Mahmoud, Tapajit Dey, Alexander Nolte, Audris Mockus

Background: Hackathons have become popular events for teams to collaborate on projects and develop software prototypes. Most existing research focuses on activities during an event with limited attention to the evolution of the hackathon code. Aim: We aim to understand the evolution of code used in and created during hackathon events, with a particular focus

Alejandro Ibarra, Merlin Reichard, Ryo Nagai

For Majorana fermions the anapole moment is the only allowed electromagnetic multipole moment. In this work we calculate the anapole moment induced at one-loop by the Yukawa and gauge interactions of a Majorana fermion, using the pinch technique to ensure the finiteness and gauge-invariance of the result. As archetypical example of a Majorana fermion, we cal

L. Aramis de Icaza Astiz, Atahualpa S. Kraemer, Gerardo Odriozola, Mariano López de Haro

For molecular dynamics simulations of hard particles, we define dynamic neighbors as the distinct particles that collide with a given reference one during a specific time interval. This definition allows us to determine the distribution of the number of dynamic neighbors, its average, and its standard deviation. We will show that regardless of the time windo

Iqra Ameer, Muhammad Arif, Grigori Sidorov, Helena Gòmez-Adorno

Given the current social distance restrictions across the world, most individuals now use social media as their major medium of communication. Millions of people suffering from mental diseases have been isolated due to this, and they are unable to get help in person. They have become more reliant on online venues to express themselves and seek advice on deal

Pei-Chun Chang, Yan-Yu Tien, Chia-Lin Chen, Li-Fen Chen

Neuroscience studies have revealed that the brain encodes visual content and embeds information in neural activity. Recently, deep learning techniques have facilitated attempts to address visual reconstructions by mapping brain activity to image stimuli using generative adversarial networks (GANs). However, none of these studies have considered the semantic

Menna Hassan, Nourhan Sakr, Arthur Charpentier

This paper designs a sequential repeated game of a micro-founded society with three types of agents: individuals, insurers, and a government. Nascent to economics literature, we use Reinforcement Learning (RL), closely related to multi-armed bandit problems, to learn the welfare impact of a set of proposed policy interventions per $1 spent on them. The paper

Kevin Ta, David Bruggemann, Tim Brödermann, Christos Sakaridis

As neuromorphic technology is maturing, its application to robotics and autonomous vehicle systems has become an area of active research. In particular, event cameras have emerged as a compelling alternative to frame-based cameras in low-power and latency-demanding applications. To enable event cameras to operate alongside staple sensors like lidar in percep

Jeroen C. Vink

This paper investigates dynamical relaxation to quantum equilibrium in the stochastic de Broglie-Bohm-Bell formulation of quantum mechanics. The time-dependent probability distributions are computed as in a Markov process with slowly varying transition matrices. Numerical simulations, supported by exact results for the large-time behavior of sequences of (sl

Satyabrata Majee, Amit Maji, Atanu Manna

We study various inequalities for numerical radius and Berezin number of a bounded linear operator on a Hilbert space. It is proved that the numerical radius of a pure two-isometry is 1 and the Crawford number of a pure two-isometry is 0. In particular, we show that for any scalar-valuednon-constant inner function $\theta$, the numerical radius and the Crawf

Damien Junger

In this article, we study the De Rham cohomology of the first cover in the Lubin-Tate tower. In particular, we get a purely local proof that the supercuspidal part realizes the local Jacquet-Langlands correspondence for ${\rm GL}_n$ by comparing it to the rigid cohomology of some Deligne-Lusztig varieties. The representations obtained are analogous to the on

Tommaso Favalli, Augusto Smerzi

We consider a global quantum system (the "Universe") satisfying a double constraint, both on total energy and total momentum. Generalizing the Page and Wootters quantum clock formalism, we provide a model of 3+1 dimensional, non-relativistic, quantum spacetime emerging from entanglement among different subsystems in a globally "timeless" and "positionless" U

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using a sample of $4.3\times 10^5$ $\eta'\to\eta\pi^0\pi^0$ events selected from the ten billion $J/\psi$ event dataset collected with the BESIII detector, we study the decay $\eta'\to\eta\pi^0\pi^0$ within the framework of nonrelativistic effective field theory. Evidence for a structure at $\pi^+\pi^-$ mass threshold is observed in the invariant mass spectr

Evgenii Reznichenko

Below, by space we mean a separable metrizable zero-dimensional space. It is studied when the space can be embedded in a Cantor set while maintaining the algebraic structure. Main results of the work: every space is an open retract of a Boolean precompact group; every strongly homogeneous space is rectifiable. In this case, the space can be embedded in the C

Diego Guffanti, Alberto Brunete, Miguel Hernando, David Álvarez

The use of 3D cameras for gait analysis has been highly questioned due to the low accuracy they have demonstrated in the past. The objective of the study presented in this paper is to improve the accuracy of the estimations made by robot-mounted 3D cameras in human gait analysis by applying a supervised learning stage. The 3D camera was mounted in a mobile r

Pierluigi Poggiolini, Mahdi Ranjbar-Zefreh

We present a comprehensive closed-form GN/EGN model supporting ultra-wide-band systems spanning 50 THz of optical bandwidth. We show a case-study of 10x100km of SMF where we gradually increase the number of channels across the C,L,S,U,E bands while optimizing launch power.

Bitang Kwrung Tripura, Sonu Kumar, Vamsi Krishna Reddy Anyam, K. Anki Reddy

We investigate the effect of shape and orientation for a horizontally oscillating intruder on its vertical dynamics in a granular medium via Discrete Element Method (DEM) simulations. Five distinct intruder shapes were considered in this study: a disk (OS1), a square (OS2), two rectangles with aspect ratio > 1 (OS3) and < 1 (OS4) respectively, and an equilat

Yang Yu, Xiuxian Li, Li Li, Lihua Xie

This paper studies the decentralized online convex optimization problem for heterogeneous linear multi-agent systems. Agents have access to their time-varying local cost functions related to their own outputs, and there are also time-varying coupling inequality constraints among them. The goal of each agent is to minimize the global cost function by selectin

A. E. Kyprianou, L. Peñaloza, T. Rogers

We consider a stochastic model, called the replicator coalescent, describing a system of blocks of $k$ different types which undergo pairwise mergers at rates depending on the block types: with rate $C_{i,j}$ blocks of type $i$ and $j$ merge, resulting in a single block of type $i$. The replicator coalescent can be seen as generalisation of Kingman's coalesc

Zhijiang Wan, Shichang Liu, Manyu Li

Object detection on microscopic scenarios is a popular task. As microscopes always have variable magnifications, the object can vary substantially in scale, which burdens the optimization of detectors. Moreover, different situations of camera focusing bring in the blurry images, which leads to great challenge of distinguishing the boundaries between objects

Joel Priestley, Gerard Valentí-Rojas, Ewan M. Wright, Patrik Öhberg

We study the properties of two quantum particles which are confined in a ring. The particles interact via a long-range gauge potential proportional to the distance between the particles. It is found that the two-body ground state corresponds to a state with non-zero angular momentum provided that the interaction between the particles is strong enough. In add

Xing Zhang

Chameleon $f(R)$ gravity is equivalent to a class of scalar-tensor theories of gravity with chameleon screening mechanism allowing the theory to satisfy local tests of gravity. Within the framework of chameleon $f(R)$, we study the impact of the chameleon mechanism on the orbital evolution of binary pulsars, and calculate in detail the post-Keplerian (PK) ef

Miklós Antal Werner, Cătălin Paşcu Moca, Márton Kormos, Örs Legeza

Particle formation represents a central theme in various branches of physics, often associated to confinement. Here we show that dynamical hadron formation can be spectroscopically detected in an ultracold atomic setting within the most paradigmatic and simplest model of condensed matter physics, the repulsive $\textrm{SU}(N)$ Hubbard model. By starting from

Huaying Xue, Xiulian Peng, Xue Jiang, Yan Lu

Neural audio coding has shown very promising results recently in the literature to largely outperform traditional codecs but limited attention has been paid on its error resilience. Neural codecs trained considering only source coding tend to be extremely sensitive to channel noises, especially in wireless channels with high error rate. In this paper, we inv

Shun Sawabe

We consider the potential function of the colored Jones polynomial for a link with arbitrary colors and obtain the cone-manifold structure for the link complement. In addition, we establish a relationship between a saddle point equation and hyperbolicity of the link complement. This provides evidence for the Chen-Yang conjecture on the link complement.

Nilasis Chaudhuri

In this paper, our goal is to define a measure valued solution of compressible Navier--Stokes--Fourier system for a heat conducting fluid with Dirichlet boundary condition for temperature in a bounded domain. The definition is based on the weak formulation of entropy inequality and ballistic energy inequality. Moreover, we obtain the weak(measure valued)-str

Massoud Amini

A discrete group $\Gamma$ is C*-simple if the C*-algebra $C_\lambda^*(\Gamma)$ generated by the range of the left regular representation $\lambda$ on $\ell^2(\Gamma)$ is simple. In this case, $\Gamma$ acts faithfully on the Furstenberg boundary $\partial_F\Gamma$ and there is a unique trace on $C_\lambda^*(\Gamma)$. In this paper we study the unique trace pr

Boulos El Hilany

We study some discrete invariants of Newton non-degenerate polynomial maps $f : \mathbb{K}^n \to \mathbb{K}^n$ defined over an algebraically closed field of Puiseux series $\mathbb{K}$, equipped with a non-trivial valuation. It is known that the set $\mathcal{S}(f)$ of points at which $f$ is not finite forms an algebraic hypersurface in $\mathbb{K}^n$. The c

Lukas Lanza

Output reference tracking of unknown nonlinear systems is considered. The control objective is exact tracking in predefined finite time, while in the transient phase the tracking error evolves within a prescribed boundary. To achieve this, a novel high-gain feedback controller is developed that is similar to, but extends, existing high-gain feedback controll

Xiangning Xie, Yuqiao Liu, Yanan Sun, Mengjie Zhang

Neural Architecture Search (NAS) can automatically design architectures for deep neural networks (DNNs) and has become one of the hottest research topics in the current machine learning community. However, NAS is often computationally expensive because a large number of DNNs require to be trained for obtaining performance during the search process. Performan

Edoardo Cetin, Philip J. Ball, Steve Roberts, Oya Celiktutan

Off-policy reinforcement learning (RL) from pixel observations is notoriously unstable. As a result, many successful algorithms must combine different domain-specific practices and auxiliary losses to learn meaningful behaviors in complex environments. In this work, we provide novel analysis demonstrating that these instabilities arise from performing tempor

Jingyao Wang, Naigong Yu

The quality of industrial components is critical to the production of special equipment such as robots. Defect inspection of these components is an efficient way to ensure quality. In this paper, we propose a hybrid network, SSD-Faster Net, for industrial defect inspection of rails, insulators, commutators etc. SSD-Faster Net is a two-stage network, includin

Dmytro Lande, Volodymyr Yuzefovych, Yevheniia Tsybulska

This paper proposes methods of predicting dynamic time series (including non-stationary ones) based on a linguistic approach, namely, the study of occurrences and repetition of so-called N-grams. This approach is used in computational linguistics to create statistical translators, detect plagiarism and duplicate documents. However, the scope of application c

Chuan Tian, Duo Jiang, Austin Hammer, Thomas Sharpton

Understanding how microbes interact with each other is key to revealing the underlying role that microorganisms play in the host or environment and to identifying microorganisms as an agent that can potentially alter the host or environment. For example, understanding how the microbial interactions associate with parasitic infection can help resolve potentia

Chuan Tian, Duo Jiang, Yuan Jiang

The interactions between microbial taxa in microbiome data has been under great research interest in the science community. In particular, several methods such as SPIEC-EASI, gCoda, and CD-trace have been proposed to model the conditional dependency between microbial taxa, in order to eliminate the detection of spurious correlations. However, all those metho

Mai Ha Vu, Rahmad Akbar, Philippe A. Robert, Bartlomiej Swiatczak

Deep neural-network-based language models (LMs) are increasingly applied to large-scale protein sequence data to predict protein function. However, being largely black-box models and thus challenging to interpret, current protein LM approaches do not contribute to a fundamental understanding of sequence-function mappings, hindering rule-based biotherapeutic

Diego Conti, Alessandro Ghigi, Roberto Pignatelli

We describe an algorithm that constructs a list of all topological types of holomorphic actions of a finite group on a compact Riemann surface $C$ of genus at least $g \geq 2$ with $C/G \cong \mathbb{P}^1$.

Ankur Singha, Dipankar Chakrabarti, Vipul Arora

The cost of Monte Carlo sampling of lattice configurations is very high in the critical region of lattice field theory due to the high correlation between the samples. This paper suggests a Conditional Normalizing Flow (C-NF) model for sampling lattice configurations in the critical region to solve the problem of critical slowing down. We train the C-NF mode

Enhui Shi, Hui Xu

Let $\Gamma$ be a higher rank lattice acting on a nondegenerate dendrite $X$ with no infinite order points. We show that there exists a nondegenerate subdendrite $Y$ which is $\Gamma$-invariant and satisfies the following items: (1) There is an inverse system of finite actions $\{(Y_i, \Gamma):i=1,2,3,\cdots\}$ with monotone bonding maps $\phi_i: Y_{i+1}\rig

Meng Mei, Yuan Jiang

Multiple testing is a fundamental problem in high-dimensional statistical inference. Although many methods have been proposed to control false discoveries, it is still a challenging task when the tests are correlated to each other. To overcome this challenge, various methods have been proposed to estimate the false discovery rate (FDR) and/or the false disco

Julia Tan, Ransalu Senanayake, Fabio Ramos

Deep reinforcement learning (RL) is a promising approach to solving complex robotics problems. However, the process of learning through trial-and-error interactions is often highly time-consuming, despite recent advancements in RL algorithms. Additionally, the success of RL is critically dependent on how well the reward-shaping function suits the task, which

Shaopu Wang, Xiaojun Chen, Mengzhen Kou, Jinqiao Shi

Although deep neural networks have enjoyed remarkable success across a wide variety of tasks, their ever-increasing size also imposes significant overhead on deployment. To compress these models, knowledge distillation was proposed to transfer knowledge from a cumbersome (teacher) network into a lightweight (student) network. However, guidance from a teacher

Smritijit Sen, Houria Kabbour, Haranath Ghosh

We report first principles numerical discovery of hydrostatic pressure driven tetragonal to collapsed tetragonal transition in 1111-type material ThMnAsN accompanied by simultaneous magneto-structural, insulator to metal transition together with complete collapse of Mn moment. We present detailed evolution of various structural parameters, magnetism and elec

Hai-Dang Dau, Nicolas Chopin

In the context of state-space models, skeleton-based smoothing algorithms rely on a backward sampling step which by default has a $\mathcal O(N^2)$ complexity (where $N$ is the number of particles). Existing improvements in the literature are unsatisfactory: a popular rejection sampling -- based approach, as we shall show, might lead to badly behaved executi

Nguyen Ha Thanh

Deep learning is a powerful approach in recovering lost information as well as harder inverse function computation problems. When applied in natural language processing, this approach is essentially making use of context as a mean to recover information through likelihood maximization. Not long ago, a linguistic study called Tieq Viet was controversial among

Youjia Wang, Teng Xu, Yiwen Wu, Minzhang Li

In this work, we propose NARRATE, a novel pipeline that enables simultaneously editing portrait lighting and perspective in a photorealistic manner. As a hybrid neural-physical face model, NARRATE leverages complementary benefits of geometry-aware generative approaches and normal-assisted physical face models. In a nutshell, NARRATE first inverts the input p

Vasileios E. Papageorgiou, George Tsaklidis

The prevalence of COVID-19 has been the most serious health challenge of the 21th century to date, concerning national health systems on a daily basis, since December 2019 when it appeared in Wuhan City. Nevertheless, most of the proposed mathematical methodologies aiming to describe the dynamics of an epidemic, rely on deterministic models that are not able

Lin Li, Jingyi Liu, Shuo Wang, Xunkun Wang

Trichomoniasis is a common infectious disease with high incidence caused by the parasite Trichomonas vaginalis, increasing the risk of getting HIV in humans if left untreated. Automated detection of Trichomonas vaginalis from microscopic images can provide vital information for the diagnosis of trichomoniasis. However, accurate Trichomonas vaginalis segmenta

Zsuzsanna Lipták, Francesco Masillo, Simon J. Puglisi

We introduce a new algorithm for constructing the generalized suffix array of a collection of highly similar strings. As a first step, we construct a compressed representation of the matching statistics of the collection with respect to a reference string. We then use this data structure to distribute suffixes into a partial order, and subsequently to speed

Jingzu Yee, Daichi Igarashi, Akinori Yamanaka, Yoshiyuki Tagawa

This paper reports the features of a splashing drop on a solid surface and the temporal evolution, which are extracted through image-sequence classification using a highly interpretable feedforward neural network (FNN) with zero hidden layer. The image sequences used for training-validation and testing of the FNN show the early-stage deformation of milli-siz