May 2022 arXiv papers — page 67

Frida Heskebeck, Carolina Bergeling, Bo Bernhardsson

The multi-armed bandit (MAB) problem models a decision-maker that optimizes its actions based on current and acquired new knowledge to maximize its reward. This type of online decision is prominent in many procedures of Brain-Computer Interfaces (BCIs) and MAB has previously been used to investigate, e.g., what mental commands to use to optimize BCI performa

Christian Alrabbaa, Franz Baader, Stefan Borgwardt, Raimund Dachselt

Explanations for description logic (DL) entailments provide important support for the maintenance of large ontologies. The "justifications" usually employed for this purpose in ontology editors pinpoint the parts of the ontology responsible for a given entailment. Proofs for entailments make the intermediate reasoning steps explicit, and thus explain how a c

Harald Reiss

How exactly can critical temperature be determined from results obtained in resistivity measurements? An unconventional approach using an electrical resistance network is presented in this paper to find an answer to this question. In a first step, a recently suggested, dynamic relaxation model is refined and extended beyond its proper applicability and compe

Sangita Majumdar, Amlan K. Roy

An atom placed inside a cavity of finite dimension offers many interesting features, and thus has been a topic of great current activity. This work proposes a density functional approach to pursue both ground and excited states of a multi-electron atom under a spherically impenetrable enclosure. The radial Kohn-Sham (KS) equation has been solved by invoking

Irene Heinrich, Philine Schiewe, Constantin Seebach

Line planning, i.e. choosing paths which are operated by one vehicle end-to-end, is an important aspect of public transport planning. While there exists heuristic procedures for generating lines from scratch, most theoretical observations consider the problem of choosing lines from a predefined line pool. In this paper, we consider the complexity of the line

Xin Xia, Jiashi Li, Jie Wu, Xing Wang

We revisit the existing excellent Transformers from the perspective of practical application. Most of them are not even as efficient as the basic ResNets series and deviate from the realistic deployment scenario. It may be due to the current criterion to measure computation efficiency, such as FLOPs or parameters is one-sided, sub-optimal, and hardware-insen

Ulugbek Salaev, Elmurod Kuriyozov, Carlos Gómez-Rodríguez

Machine transliteration, as defined in this paper, is a process of automatically transforming written script of words from a source alphabet into words of another target alphabet within the same language, while preserving their meaning, as well as pronunciation. The main goal of this paper is to present a machine transliteration tool between three common scr

Alicia Cantón, José L. Fernández, Pablo Fernández, Víctor J. Maciá

In this paper, we give a simple criterion to verify that functions of the form $e^g$ are in the Hayman class when $g$ is a power series with nonnegative coefficients. Thus, using the Hayman and B\'aez-Duarte formulas, we obtain asymptotics for the coefficients of generating functions that arise in many examples of set construction in analytic combinatorics.

Yiheng Liu, Enjie Ge, Mengshen He, Zhengliang Liu

Using deep learning models to recognize functional brain networks (FBNs) in functional magnetic resonance imaging (fMRI) has been attracting increasing interest recently. However, most existing work focuses on detecting static FBNs from entire fMRI signals, such as correlation-based functional connectivity. Sliding-window is a widely used strategy to capture

Nastaran Naghshineh, W. Cade Reinberger, Nathaniel S. Barlow, Mohamed A. Samaha

We examine the power series solutions of two classical nonlinear ordinary differential equations of fluid mechanics that are mathematically related by their large-distance asymptotic behaviors in semi-infinite domains. The first problem is that of the "Sakiadis" boundary layer over a moving flat wall, for which no exact analytic solution has been put forward

Max Wasserman, Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro

Machine learning frameworks such as graph neural networks typically rely on a given, fixed graph to exploit relational inductive biases and thus effectively learn from network data. However, when said graphs are (partially) unobserved, noisy, or dynamic, the problem of inferring graph structure from data becomes relevant. In this paper, we postulate a graph

Liliaokeawawa Cothren, Gianluca Bianchin, Emiliano Dall'Anese

This paper considers the problem of controlling a dynamical system when the state cannot be directly measured and the control performance metrics are unknown or partially known. In particular, we focus on the design of data-driven controllers to regulate a dynamical system to the solution of a constrained convex optimization problem where: i) the state must

Suryadi, Yew-Soon Ong, Lock Yue Chew

Granger causality is a commonly used method for uncovering information flow and dependencies in a time series. Here we introduce JGC (Jacobian Granger Causality), a neural network-based approach to Granger causality using the Jacobian as a measure of variable importance, and propose a thresholding procedure for inferring Granger causal variables using this m

Nobuchika Okada, Desmond Villalba

We propose a supersymmetric extension of the minimal $U(1)_X$ model, along with a new $Z_2$-parity. One of the salient features of this model relates to how both the $U(1)_X$ gauge symmetry and R-parity are broken radiatively at the TeV scale by the VEV of a $Z_2$-even right handed neutrino. By assigning one right-handed neutrino $Z_2$-odd parity, it can rem

Haoyang Liu, Xiantao Xiao, Liwei Zhang

In this paper, we consider online convex optimization (OCO) with time-varying loss and constraint functions. Specifically, the decision maker chooses sequential decisions based only on past information, meantime the loss and constraint functions are revealed over time. We first develop a class of model-based augmented Lagrangian methods (MALM) for time-varyi

Tiago Tresoldi

This work describes an implementation of the "extended alignment" (or "multitiers") approach for cognate reflex prediction, submitted to "Prediction of Cognate Reflexes" shared task. Similarly to List2022d, the technique involves an automatic extension of sequence alignments with multilayered vectors that encode informational tiers on both site-specific trai

Frank van der Velde

This article presents a neural language architecture of sentence structure in the brain, in which sentences are temporal connection paths that interconnect neural structures underlying their words. Words remain 'in-situ', hence they are always content-addressable. Arbitrary and novel sentences (with novel words) can be created with 'neural blackboards' for w

Yacine Izza, Alexey Ignatiev, Nina Narodytska, Martin C. Cooper

Decision trees (DTs) embody interpretable classifiers. DTs have been advocated for deployment in high-risk applications, but also for explaining other complex classifiers. Nevertheless, recent work has demonstrated that predictions in DTs ought to be explained with rigorous approaches. Although rigorous explanations can be computed in polynomial time for DTs

E. A. Pankrushina, A. V. Ushakov, M. M. Abd-Elmeguid, S. V. Streltsov

Using {\it ab initio} band structure calculations we show that mineral cubanite, CuFe$_2$S$_3$, demonstrates an orbital-selective behavior with some of the electrons occupying molecular orbitals of $x^2-y^2$ symmetry and others localized at atomic orbitals. This is a rare situation for $3d$ transition metal compounds explains experimentally observed absence

Daniel Stilck França, Liubov A. Markovich, V. V. Dobrovitski, Albert H. Werner

Characterizing the interactions and dynamics of quantum mechanical systems is an essential task in the development of quantum technologies. We propose an efficient protocol based on the estimation of the time derivatives of few qubit observables using polynomial interpolation for characterizing the underlying Hamiltonian dynamics and Markovian noise of a mul

Ronaldo Freire de Lima

Given orientable Riemannian manifolds $M^n$ and $\bar M^{n+1},$ we study flows $F_t:M^n\rightarrow\bar M^{n+1},$ called Weingarten flows,in which the hypersurfaces $F_t(M)$ evolve in the direction of their normal vectors with speed given by a function $W$ of their principal curvatures,called a Weingarten function, which is homogeneous, monotonic increasing w

Riku Kuze, Shigeo S. Kimura, Kenji Toma

The origins of the GeV gamma-rays from nearby radio galaxies are unknown. Hadronic emission from magnetically arrested disks (MADs) around central black holes (BHs) is proposed as a possible scenario. Particles are accelerated in the MAD by magnetic reconnection and stochastic turbulence acceleration. We pick out the fifteen brightest radio galaxies in the G

Benjamin Kepecs, Homayoon Beigi

Closed-set spoken language identification is the task of recognizing the language being spoken in a recorded audio clip from a set of known languages. In this study, a language identification system was built and trained to distinguish between Arabic, Spanish, French, and Turkish based on nothing more than recorded speech. A pre-existing multilingual dataset

Pierre-Alexandre Arlove

We prove that some paths of contactomorphisms of $\mathbb{R}^{2n} \times S^1$ endowed with its standard contact structure are geodesics for different norms defined on the identity component of the group of compactly supported contactomorphisms and its universal cover. We characterize these geodesics by giving conditions on the Hamiltonian functions that gene

Enxin Wu, Zhongqiang Yang

It is expected that the $D$-topology makes every diffeological vector space into a topological vector space. We show that it is the case for a large class of diffeological vector spaces via $k_\omega$-space theory, but not so in general. The paper also proposes the study of a class of almost topological vector spaces.

C. Zalinescu

Lemma 1 from the paper [N.E. Gretsky, J.M. Ostroy, W.R. Zame, Subdifferentiability and the duality gap, Positivity 6: 261--274, 2002] asserts that the value function $v$ of an infinite dimensional linear programming problem in standard form is lower semicontinuous whenever $v$ is proper and the involved spaces are normed vector spaces. In this note one shows

Noam Soker

I examine recent fittings of luminous supernovae (LSNe) with extra energy sources of magnetar and helium burning and find that in about half of these LSNe the fitting parameters have some problems. In some LSNe the total energy of these two energy sources is larger than the kinetic energy of the ejecta that the fitting yields. In some others LSNe the total e

Matthew Sutton, Robert Salomone, Augustin Chevallier, Paul Fearnhead

New sampling algorithms based on simulating continuous-time stochastic processes called piece-wise deterministic Markov processes (PDMPs) have shown considerable promise. However, these methods can struggle to sample from multi-modal or heavy-tailed distributions. We show how tempering ideas can improve the mixing of PDMPs in such cases. We introduce an exte

J. A. Barceló, C. Castro, M. C. Vilela

We study the numerical approximation of the inverse scattering problem in the two-dimensional homogeneous isotropic linear elasticity with an unknown linear load given by a square matrix. For both backscattering data and fixed-angle scattering data, we show how to obtain numerical approximations of the so-called Born approximations and propose new iterative

Alexander Osterkorn, Constantin Meyer, Salvatore R. Manmana

Periodically driven quantum many-body systems host unconventional behavior not realized at equilibrium. Here we investigate such a setup for strongly interacting spinless fermions on a chain, which at zero temperature and strong interactions form a charge density wave insulator. Using unbiased numerical matrix product state methods for time-dependent spectra

Remi Desmartin, Grant Passmore, Ekaterina Komendantskaya

The demand for formal verification tools for neural networks has increased as neural networks have been deployed in a growing number of safety-critical applications. Matrices are a data structure essential to formalising neural networks. Functional programming languages encourage diverse approaches to matrix definitions. This feature has already been success

Matthis H. de Lange, Chris Verhoek, Valentin Preda, Roland Tóth

Obtaining models that can be used for control is of utmost importance to ensure the guidance and navigation of spacecraft, like a Generic Parafoil Return Vehicle (GPRV). In this paper, we convert a nonlinear model of the atmospheric flight dynamics of an GPRV to a Linear Parameter-Varying (LPV) description, such that the LPV model is suitable for navigation

Lauren Ansell

The international Maritime Organization (IMO) has set the target of reducing the emissions from the shipping sector to at least 50% of the 2008 levels. One potential method to cut emissions is to convert vessels to battery powered propulsion in a similar manner to that which has been adopted for motor vehicles. Although, battery powered propulsion will not b

Olakunle S Abawonse

MacPherson conjectured that the Grassmannian $\mathrm{Gr}(2, \mathbb{R}^n)$ has the same homeomorphism type as the combinatorial Grassmannian $\|\mbox{MacP}(2,n)\|$, while Babson proved that the spaces $\mathrm{Gr}(2,\mathbb{R}^n)$ and $\mathrm{Gr}(1,2,\mathbb{R}^n)$ are homotopy equivalent to their combinatorial analogs $\|\mathrm{MacP}(2,n)\|$ and $\|\mbox

Nyasha Masamba, Kerstin Eder, Tim Blackmore

Efficient and effective testing for simulation-based hardware verification is challenging. Using constrained random test generation, several millions of tests may be required to achieve coverage goals. The vast majority of tests do not contribute to coverage progress, yet they consume verification resources. In this paper, we propose a hybrid intelligent tes

Xiequan Fan, Haijuan Hu, Hao Wu, Yinna Ye

Let $\{Z_{1,n} , n\geq 0\}$ and $\{Z_{2,n}, n\geq 0\}$ be two supercritical branching processes in different random environments, with criticality parameters $\mu_1$ and $\mu_2$ respectively. It is known that $\frac{1}{n} \ln Z_{1,n} \rightarrow \mu_1$ and $\frac{1}{m} \ln Z_{2,m} \rightarrow \mu_2$ in probability as $m, n \rightarrow \infty.$ In this paper,

Amir Abolfazli, Gregory Palmer, Daniel Kudenko

The success of deep reinforcement learning (DRL) hinges on the availability of training data, which is typically obtained via a large number of environment interactions. In many real-world scenarios, costs and risks are associated with gathering these data. The field of offline reinforcement learning addresses these issues through outsourcing the collection

Ohad Inbar, Joachim Meyer

The 'intuitive' trust people feel when encountering robots in public spaces is a key determinant of their willingness to cooperate with these robots. We conducted four experiments to study this topic in the context of peacekeeping robots. Participants viewed scenarios, presented as static images or animations, involving a robot or a human guard performing an

Lixue Cheng, Ziyi Yang, Changyu Hsieh, Benben Liao

Directed evolution is a versatile technique in protein engineering that mimics the process of natural selection by iteratively alternating between mutagenesis and screening in order to search for sequences that optimize a given property of interest, such as catalytic activity and binding affinity to a specified target. However, the space of possible proteins

Arthur Bik, Orlando Marigliano

We propose a classification of all one-dimensional discrete statistical models with maximum likelihood degree one based on their rational parametrization. We show how all such models can be constructed from members of a smaller class of 'fundamental models' using a finite number of simple operations. We introduce 'chipsplitting games', a class of combinatori

Gianluigi Silvestri, Daan Roos, Luca Ambrogioni

In this work, we provide a deterministic alternative to the stochastic variational training of generative autoencoders. We refer to these new generative autoencoders as AutoEncoders within Flows (AEF), since the encoder and decoder are defined as affine layers of an overall invertible architecture. This results in a deterministic encoding of the data, as opp

Clay Cordova, Thomas T. Dumitrescu, Kenneth Intriligator, Shu-Heng Shao

Symmetry plays a central role in quantum field theory. Recent developments include symmetries that act on defects and other subsystems, and symmetries that are categorical rather than group-like. These generalized notions of symmetry allow for new kinds of anomalies that constrain dynamics. We review some transformative instances of these novel aspects of sy

Volker Branding

We study harmonic and biharmonic maps from gradient Ricci solitons. We derive a number of analytic and geometric conditions under which harmonic maps are constant and which force biharmonic maps to be harmonic. In particular, we show that biharmonic maps of finite energy from the two-dimensional cigar soliton must be harmonic.

Takashi Urushibara, Nicolas Chauvet, Satoshi Kochi, Satoshi Sunada

Accelerating artificial intelligence by photonics is an active field of study aiming to exploit the unique properties of photons. Reinforcement learning is an important branch of machine learning, and photonic decision-making principles have been demonstrated with respect to the multi-armed bandit problems. However, reinforcement learning could involve a mas

Yuxin Zhang, Fan Tang, Weiming Dong, Haibin Huang

In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method. A suitable style representation, as a key component in image stylization tasks, is essential to achieve satisfactory results. Existing deep neural network based approaches achieve reasonable results with the guidance f

Andrew Baker

The notion of $P$-algebra due to Margolis, building on work of Moore and Peterson, was motivated by the case of the Steenrod algebra at a prime and its modules. We develop aspects of this theory further, focusing especially on coherent modules and finite dimensional modules. We also discuss the dual Hopf algebra of $P$-algebra and its comodules. One of our a

Frédéric Naud, Polyxeni Spilioti

We consider Laplacians with non unitary twists acting on sections of flat vector bundles over compact hyperbolic surfaces. These non self-adjoint Laplacians have discrete spectrum inside a parabola in the complex plane. For representations of the fundamental group of the base surface which are of Teichm\"uller type, we investigate the high energy limit and g

Alevizos Bastas, George A. Vouros

With the aim to enhance automation in conflict detection and resolution (CD&R) tasks in the Air Traffic Management domain, in this paper we propose deep learning techniques (DL) that can learn models of Air Traffic Controllers' (ATCO) reactions in resolving conflicts that can violate separation minimum constraints among aircraft trajectories: This implies le

Mark Rouncefield, Rob Procter, Peter Tolmie

In this paper we consider some empirical materials from our ongoing research into forms of everyday detection and diagnosis work in healthcare settings, and how these relate to issues of trust; trust in people, in technology, processes and in data.

Sergei Sakovich

We show that the new third-order complex nonlinear wave equation, introduced recently by M\"{u}ller-Hoissen [arXiv:2202.04512], does not pass the Painlev\'{e} test for integrability. We find two reductions of this equation, one integrable and one non-integrable, whose solutions jointly cover all solutions of the original equation.

Pierre Ricco, Martin Skote

We show that the Fukagata et al. (2002)'s identity for free-stream boundary layers simplifies to the von Karman momentum integral equation relating the skin-friction coefficient and the momentum thickness when the upper bound in the integrals used to obtain the identity is taken to be asymptotically large. If a finite upper bound is used, the terms of the id

Yang Liu, Jinpeng Hu, Xiang Wan, Tsung-Hui Chang

Few-Shot Relation Extraction aims at predicting the relation for a pair of entities in a sentence by training with a few labelled examples in each relation. Some recent works have introduced relation information (i.e., relation labels or descriptions) to assist model learning based on Prototype Network. However, most of them constrain the prototypes of each

Nikolaos S. Papageorgiou, Vicenţiu D. Rădulescu, Dušan D. Repovš

We consider a nonlinear parametric Neumann problem driven by the anisotropic $(p,q)$-Laplacian and a reaction which exhibits the combined effects of a singular term and of a parametric superlinear perturbation. We are looking for positive solutions. Using a combination of topological and variational tools together with suitable truncation and comparison tech

Jonathan Grizou

IFTT-PIN is a self-calibrating version of the PIN-entry method introduced in Roth et al. (2004) [1]. In [1], digits are split into two sets and assigned a color respectively. To communicate their digit, users press the button with the same color that is assigned to their digit, which can thus be identified by elimination after a few iterations. IFTT-PIN uses

Piotr Jarosik, Michal Byra, Marcin Lewandowski, Ziemowit Klimonda

Attenuation coefficient (AC) is a fundamental measure of tissue acoustical properties, which can be used in medical diagnostics. In this work, we investigate the feasibility of using convolutional neural networks (CNNs) to directly estimate AC from radio-frequency (RF) ultrasound signals. To develop the CNNs we used RF signals collected from tissue mimicking

Hui-Huang Chen

In this paper, we consider the time evolution of charge imbalance resolved negativity after a global quench in the 1+1 dimensional complex Klein-Gordon theory. We focus on two types of global quenches which are called boundary state quench and mass quench respectively. We first study the boundary state quench where the post-quench dynamic is governed by a ma

Xinrun Mi, De Hou, Xiao Wang, Sabreen Hammouda

The spin-orbit assisted Mott insulator $\alpha$-RuCl$_3$ is a prime candidate for material realization of the Kitaev quantum spin liquid. While little attention has been paid to charge degrees of freedom, charge effects, such as electric polarization, may arise in this system. Here, we report distortion-induced local electric polarization in $\alpha$-RuCl$_3

Giulia Piccitto, Michele Campisi, Davide Rossini

We study a four-stroke Otto engine whose working fluid is a quantum Ising chain. The thermodynamic cycle consists in sweeps of the transverse magnetic field occurring in thermal isolation, alternated by thermalisation strokes with reservoirs at different temperatures. The system-environment coupling is modelled in a thermodynamically consistent way by means

Tristan Phillips

A conditional bound is given for the average analytic rank of elliptic curves over an arbitrary number field. In particular, under the assumptions that all elliptic curves over a number field $K$ are modular and have $L$-functions which satisfy the Generalized Riemann Hypothesis, it is shown that the average analytic rank of isomorphism classes of elliptic c

Martin Ferianc, Miguel Rodrigues

Considering uncertainty estimation of modern neural networks (NNs) is one of the most important steps towards deploying machine learning systems to meaningful real-world applications such as in medicine, finance or autonomous systems. At the moment, ensembles of different NNs constitute the state-of-the-art in both accuracy and uncertainty estimation in diff

Dongdong Wan, Ming Dong, Mengqi Zhang

This work explains a scaling law of the first Landau coefficient of the derived Ginzburg-Landau equation (GLE) in the weakly nonlinear analysis of axisymmetric viscoelastic pipe flows in the large-Weissenberg-number ($Wi$) limit, recently reported in Wan et al. J. Fluid Mech. (2021), vol. 929, A16. Using an asymptotic method, we derive a reduced system, whic

Wenkai Li, Jiuyang Bu, Xiaoqi Li, Xianyi Chen

Decentralized finance (DeFi) in Ethereum is a financial ecosystem built on the blockchain that has locked over 200 billion USD until April 2022. All transaction information is transparent and open when transacting through the DeFi protocol, which has led to a series of attacks. Several studies have attempted to optimize it from both economic and technical pe

Shaik Jani Babu, Rohit Singh, Siona Menezes Picardo, Nilesh Goel

Neuromorphic computing refers to brain-inspired computers, that differentiate it from von Neumann architecture. Analog VLSI based neuromorphic circuits is a current research interest. Two simpler spiking integrate and fire neuron model namely axon-Hillock (AH) and voltage integrate, and fire (VIF) circuits are commonly used for generating spike events. This

Pengcheng Zeng, Zhixiang Lin

Modern high-throughput sequencing technologies have enabled us to profile multiple molecular modalities from the same single cell, providing unprecedented opportunities to assay celluar heterogeneity from multiple biological layers. However, the datasets generated from these technologies tend to have high level of noise and are highly sparse, bringing challe

Jiawei Li, Jianxiao Wang, Lin Chen, Yang Yu

Adversarial attacks on data-driven algorithms applied in the power system will be a new type of threat to grid security. Literature has demonstrated that the adversarial attack on the deep-neural network can significantly mislead the load fore-cast of a power system. However, it is unclear how the new type of attack impacts the operation of the grid system.

Miguel O'Malley, Sara Kalisnik, Nina Otter

Magnitude is an isometric invariant for metric spaces that was introduced by Leinster around 2010, and is currently the object of intense research, since it has been shown to encode many known invariants of metric spaces. In recent work, Govc and Hepworth introduced persistent magnitude, a numerical invariant of a filtered simplicial complex associated to a

Aadhitiya VS, Jani Babu Shaik, Sonal Singhal, Siona Menezes Picardo

Neuromorphic computing systems emulate the electrophysiological behavior of the biological nervous system using mixed-mode analog or digital VLSI circuits. These systems show superior accuracy and power efficiency in carrying out cognitive tasks. The neural network architecture used in neuromorphic computing systems is spiking neural networks (SNNs) analogou

Xiangyuan Yang, Jie Lin, Hanlin Zhang, Xinyu Yang

Black-box query attacks, which rely only on the output of the victim model, have proven to be effective in attacking deep learning models. However, existing black-box query attacks show low performance in a novel scenario where only a few queries are allowed. To address this issue, we propose gradient aligned attacks (GAA), which use the gradient aligned los

Michael Fellner, Anette Messinger, Kilian Ender, Wolfgang Lechner

We demonstrate the applicability of a universal gate set in the parity encoding, which is a dual to the standard gate model, by exploring several quantum gate algorithms such as the quantum Fourier transform and quantum addition. Embedding these algorithms in the parity encoding reduces the circuit depth compared to conventional gate-based implementations wh

M. Kleptsyna, D. Marushkevych, P. Chigansky

This paper revisits the problem of estimating the fractional Ornstein - Uhlenbeck process observed in a linear channel with white noise of small intensity. We drive the exact asymptotic formulas for the mean square errors of the filtering and interpolation estimators. The asymptotic analysis is based on approximations of the eigenvalues and eigenfunctions of

Carlos Tadeu Pagani Zanini, Helio dos Santos Migon, Ronaldo Dias

We study the implementation of Automatic Differentiation Variational inference (ADVI) for Bayesian inference on regression models with bridge penalization. The bridge approach uses $\ell_{\alpha}$ norm, with $\alpha \in (0, +\infty)$ to define a penalization on large values of the regression coefficients, which includes the Lasso ($\alpha = 1$) and ridge $(\

Alexander Sushchyev, Stefan Wessel

Using finite-temperature determinantal quantum Monte Carlo simulations, we examine the thermodynamic properties of the extended Hubbard model on the half-filled square lattice in the Slater regime at intermediate coupling. We consider both the case of nearest-neighbor interactions and long-range Coulomb interactions, for coupling strengths in which the prese

Yunhao Yuan, Gaurav Verma, Barbara Keller, Talayeh Aledavood

The COVID-19 pandemic has disproportionately impacted the lives of minorities, such as members of the LGBTQ community (lesbian, gay, bisexual, transgender, and queer) due to pre-existing social disadvantages and health disparities. Although extensive research has been carried out on the impact of the COVID-19 pandemic on different aspects of the general popu

Simon Langenscheidt

We study criteria for and properties of boundary-to-boundary holography in a class of spin network states defined by analogy to projected entangled pair states (PEPS). In particular, we consider superpositions of states corresponding to well-defined, discrete geometries on a graph. By applying random tensor averaging techniques, we map entropy calculations t

Michael Fellner, Anette Messinger, Kilian Ender, Wolfgang Lechner

We propose a universal gate set for quantum computing with all-to-all connectivity and intrinsic robustness to bit-flip errors based on parity encoding. We show that logical controlled phase gate and $R_z$ rotations can be implemented in parity encoding with single-qubit operations. Together with logical $R_x$ rotations, implemented via nearest-neighbor cont

Bryce Orloski, Samuel Coward, Theo Drane

Hardware implementations of complex functions regularly deploy piecewise polynomial approximations. This work determines the complete design space of piecewise polynomial approximations meeting a given accuracy specification. Knowledge of this design space determines the minimum number of regions required to approximate the function accurately enough and fac

Masaaki Furusawa, Kazuki Morimoto

We investigate the Gross-Prasad conjecture and its refinement for the Bessel periods in the case of $\left(\mathrm{SO}\left(5\right),\mathrm{SO}\left(2\right)\right)$. In particular, by combining several theta correspondences, we prove the Ichino-Ikeda type formula for any tempered irreducible cuspidal automorphic representations. As a corollary of our formu

Michel Plüss, Manuela Hürlimann, Marc Cuny, Alla Stöckli

We present SDS-200, a corpus of Swiss German dialectal speech with Standard German text translations, annotated with dialect, age, and gender information of the speakers. The dataset allows for training speech translation, dialect recognition, and speech synthesis systems, among others. The data was collected using a web recording tool that is open to the pu

Weizhao Chen, Mingqiang Gu, Jiayu Li, Panshuo Wang

The discovery of hidden spin polarization (HSP) in centrosymmetric nonmagnetic crystals, i.e., spatially distributed spin polarization originated from local symmetry breaking, has promised an expanded material pool for future spintronics. However, the measurements of such exotic effects have been limited to subtle space- and momentum-resolved techniques, unf

Masaki Ogura, Koki Kobayashi, Kenji Sugimoto

We propose a deep unfolding-based approach for stabilization of time-delay linear systems. Deep unfolding is an emerging framework for design and improvement of iterative algorithms and attracting significant attentions in signal processing. In this paper, we propose an algorithm to design a static output feedback gain for stabilizing time-delay linear syste

Zhiling Zhang, Siyuan Chen, Mengyue Wu, Kenny Q. Zhu

Depression is a prominent health challenge to the world, and early risk detection (ERD) of depression from online posts can be a promising technique for combating the threat. Early depression detection faces the challenge of efficiently tackling streaming data, balancing the tradeoff between timeliness, accuracy and explainability. To tackle these challenges

Zhicheng Tong, Yong Li

In this paper, we consider the polynomial and exponential convergence rate of weighted Birkhoff averages of irrational rotations on tori. It is shown that these can be achieved for finite and infinite dimensional tori which correspond to the quasiperiodic and almost periodic dynamical systems respectively, under certain balance between the nonresonant condit

Jin Ding, Xue Zhou

Unsupervised domain adaptive (UDA) person re-identification (ReID) has gained increasing attention for its effectiveness on the target domain without manual annotations. Most fine-tuning based UDA person ReID methods focus on encoding global features for pseudo labels generation, neglecting the local feature that can provide for the fine-grained information.

Andi Han, Bamdev Mishra, Pratik Jawanpuria, Junbin Gao

In this paper, we study the differentially private empirical risk minimization problem where the parameter is constrained to a Riemannian manifold. We introduce a framework of differentially private Riemannian optimization by adding noise to the Riemannian gradient on the tangent space. The noise follows a Gaussian distribution intrinsically defined with res

Daniele Capone, Francesco Caturano, Angelo Delicato, Gaetano Perrone

The best way to train people about security is through Cyber Ranges, i.e., the virtual platform used by cyber-security experts to learn new skills and attack vectors. In order to realize such virtual scenarios, container-based virtualization is commonly adopted, as it provides several benefits in terms of performance, resource usage, and portability. Unfortu

J. -P. Allouche

Following our discovery of a nice identity in a recent preprint of Hu and Kim, we show a link between the Kurokawa multiple trigonometric functions and two functions introduced respectively by Borwein-Dykshoorn and by Adamchik. In particular several identities involving $\zeta(3)$, $\pi$ and the Catalan constant $G$ that are proved in these three papers are

Adrià Labay-Mora, Roberta Zambrini, Gian Luca Giorgi

Algorithms for associative memory typically rely on a network of many connected units. The prototypical example is the Hopfield model, whose generalizations to the quantum realm are mainly based on open quantum Ising models. We propose a realization of associative memory with a single driven-dissipative quantum oscillator exploiting its infinite degrees of f

Denis I. Borisov

We consider a boundary value problem for a general second order linear equation in a domain with a fine perforation. The latter is made by small cavities; both the shapes of the cavities and their distribution are arbitrary. The boundaries of the cavities are subject either to a Dirichlet or a nonlinear Robin condition. On the perforation, certain rather wea

Jiayi Zheng, Ling Yang, Heyuan Wang, Cheng Yang

Graph embedding methods including traditional shallow models and deep Graph Neural Networks (GNNs) have led to promising applications in recommendation. Nevertheless, shallow models especially random-walk-based algorithms fail to adequately exploit neighbor proximity in sampled subgraphs or sequences due to their optimization paradigm. GNN-based algorithms s

Shoki Koyanagi, Yoshitaka Tanimura

We investigate the efficiency of a quantum Carnot engine based on open quantum dynamics theory. The model includes time-dependent external fields for the subsystems controlling the isothermal and isentropic processes and for the system--bath (SB) interactions controlling the transition between these processes. Numerical simulations are conducted in a nonpert

Xiaoming Cai, Yi-Cong Yu

Mobility edge (ME), a critical energy separating localized and extended states in spectrum, is a central concept in understanding the localization physics. However, there are few models with exact MEs. In the paper, we generalize the Aubry-Andr\'{e}-Harper model proposed in [Phys. Rev. Lett. 114, 146601 (2015)] and recently realized in [Phys. Rev. Lett. 126,

Yawen Zhao, Mingzhe Zhang, Chenhao Zhang, Weitong Chen

Positive-unlabeled (PU) learning deals with binary classification problems when only positive (P) and unlabeled (U) data are available. Many recent PU methods are based on neural networks, but little has been done to develop boosting algorithms for PU learning, despite boosting algorithms' strong performance on many fully supervised classification problems.

M. Atzori Corona, M. Cadeddu, N. Cargioli, F. Dordei

Coherent elastic neutrino-nucleus scattering (CE$\nu$NS) represents a powerful tool to investigate key electroweak physics parameters and neutrino properties since its first observation in 2017 by the COHERENT experiment exploiting the spallation neutron source at Oak Ridge National Laboratory. In light of the recent detection of such a process with antineut

Marius Bothe, Luca Cocconi, Zigan Zhen, Gunnar Pruessner

We introduce a procedure to test a theory for point particle entity, that is, whether said theory takes into account the discrete nature of the constituents of the system. We then identify the mechanism whereby particle entity is enforced in the context of two field-theoretic frameworks designed to incorporate the particle nature of the degrees of freedom, n

Sergey V. Streltsov, R. E. Ryltsev, N. M. Chtchelkatchev

In recent years, A-site ordered half-doped double-perovskite manganites $\rm RBaMn_2O_6$ (R=rare earth) have attracted much attention due to their remarkable physical properties and a prospect of application as magnetoresistance, multiferroic, and oxygen storage materials. The nature of the ground state in ${\rm RBaMn_2O_6}$ as well as sequence of phase tran

Yibing Wang, Yong Niu, Hao Wu, Shiwen Mao

To exploit richer spectrum resources for even better service quality, millimeter wave (mmWave) communication has been considered for high-speed railway (HSR) communication systems. In this paper, we focus on scheduling as many flows as possible while satisfying their QoS requirements. Due to interference, eavesdropping, or other problems, some flows may not

Tomasz Linowski, Konrad Schlichtholz, Łukasz Rudnicki

The problem of defining a hermitian quantum phase operator is nearly as old as quantum mechanics itself. Throughout the years, a number of solutions was proposed, ranging from abstract operator formalisms to phase-space methods. In this work, we make an explicit connection between two of the most prominent approaches, by proving that the probability distribu

Randhir Singh, S. C. Patekar

A vertex-degree-based topological index named as Sombor index of a simple graph G with n vertices was recently introduced by I. Gutman. In this paper, we find Sombor index of m-splitting graph and m-shadow graph. Also, we determine relation between energy and Sombor energy of m-splitting graph and m-shadow graph of k-regular graph.

Pawan Goyal, Peter Benner

Measurement noise is an integral part while collecting data of a physical process. Thus, noise removal is necessary to draw conclusions from these data, and it often becomes essential to construct dynamical models using these data. We discuss a methodology to learn differential equation(s) using noisy and irregular sampled measurements. In our methodology, t

Fernando Albiac, Jose L. Ansorena, Miguel Berasategui

This paper is devoted to theoretical aspects on optimality of sparse approximation. We undertake a quantitative study of new types of greedy-like bases that have recently arisen in the context of nonlinear $m$-term approximation in Banach spaces as a generalization of the properties that characterize almost greedy bases, i.e., quasi-greediness and democracy.