May 2022 arXiv papers — page 18

P. Navez, A. G. Balanov, S. E. Savel'ev, A. M. Zagoskin

By consistently applying the formalism of quantum electrodynamics we developed a comprehensive theoretical framework describing the interaction of single microwave photons with an array of superconducting transmon qubits in a wave guide cavity resonator. In particular, we analyze the effects of microwave photons on the arrays response to a weak probe signal

Ángel D. Martínez

The eigenfunctions of the Laplacian are a central object from the realms of analytic number theory to geometric analysis. We prove that H\"ormander $L^2$-$L^{\infty}$ estimates are equivalent to restriction estimates to small geodesic spheres for a certain class of manifolds.

Ilya Chevyrev, Tadahiro Oh, Yuzhao Wang

We consider the ill-posedness issue for the cubic nonlinear heat equation and prove norm inflation with infinite loss of regularity in the H\"older-Besov space $\mathcal C^s = B^{s}_{\infty, \infty}$ for $ s \le -\frac 23$. In particular, our result includes the subcritical range $-1< s \le -\frac 23$, which is above the scaling critical regularity $s = -1$

Xinyao Fan, Harry Joe

Factor models are a parsimonious way to explain the dependence of variables using several latent variables. In Gaussian 1-factor and structural factor models (such as bi-factor, oblique factor) and their factor copula counterparts, factor scores or proxies are defined as conditional expectations of latent variables given the observed variables. With mild ass

Tania Robens

In this whitepaper, I briefly review the Benchmark Planes in the Two-Real-Singlet Model (TRSM), a model that enhances the Standard Model (SM) scalar sector by two real singlets that obey a $\mathbb{Z}_2\,\otimes\,\mathbb{Z}_2'$ symmetry. In this model, all fields acquire a vacuum expectation value, such that the model contains in total 3 CP-even neutral scal

Ossi Räisä, Joonas Jälkö, Samuel Kaski, Antti Honkela

While generation of synthetic data under differential privacy (DP) has received a lot of attention in the data privacy community, analysis of synthetic data has received much less. Existing work has shown that simply analysing DP synthetic data as if it were real does not produce valid inferences of population-level quantities. For example, confidence interv

Hans W. A. Hanley, Deepak Kumar, Zakir Durumeric

In the buildup to and in the weeks following the Russian Federation's invasion of Ukraine, Russian state media outlets output torrents of misleading and outright false information. In this work, we study this coordinated information campaign in order to understand the most prominent state media narratives touted by the Russian government to English-speaking

Visakan Balakumar, Rafael P. Bernar, Elizabeth Winstanley

We study the canonical quantization of a massless charged scalar field on a Reissner-Nordstrom black hole background. Our aim is to construct analogues of the standard Boulware, Unruh and Hartle-Hawking quantum states which can be defined for a neutral scalar field, and to explore their physical properties by computing differences in expectation values of th

Lipeng Duan, Monica Musso, Suting Wei

We consider the prescribed scalar curvature problem on $ {\mathbb{S}}^N $ $$ \Delta_{{\mathbb S}^N} v-\frac{N(N-2)}{2} v+\tilde{K}(y) v^{\frac{N+2}{N-2}}=0 \quad \mbox{on} \ {\mathbb S}^N, \qquad v >0 \quad \mbox{on} \ {\mathbb S}^N, $$ under the assumptions that the scalar curvature $\tilde K$ is rotationally symmetric, and has a positive local maximum poin

Pavel Ievlev

This paper investigates the Parisian ruin probability for processes with power-asymmetric behavior of the variance near the unique optimal point. We derive the exact asymptotics as the ruin boundary tends to infinity and extend the previous result arXiv:1504.07061 to the case when the length of Parisian interval is of Pickands scale. As a primary application

Jiazhen Liu, Shengda Huang, Nathan Aden, Neil Johnson

Polarization is a ubiquitous phenomenon in social systems. Empirical studies document substantial evidence for opinion polarization across social media, showing a typical bipolarized pattern devising individuals into two groups with opposite opinions. While coevolving network models have been proposed to understand polarization, existing works cannot generat

Hsin-I Cindy Liu, Marius Brehler, Mahesh Ravishankar, Nicolas Vasilache

Machine learning model deployment for training and execution has been an important topic for industry and academic research in the last decade. Much of the attention has been focused on developing specific toolchains to support acceleration hardware. In this paper, we present IREE, a unified compiler and runtime stack with the explicit goal to scale down mac

Magnús M. Halldórsson, Alexandre Nolin, Tigran Tonoyan

We present a new technique to efficiently sample and communicate a large number of elements from a distributed sampling space. When used in the context of a recent LOCAL algorithm for $(\operatorname{degree}+1)$-list-coloring (D1LC), this allows us to solve D1LC in $O(\log^5 \log n)$ CONGEST rounds, and in only $O(\log^* n)$ rounds when the graph has minimum

Tian Lv, Chongyang Bai, Chaojie Wang

The attention mechanism has become a go-to technique for natural language processing and computer vision tasks. Recently, the MLP-Mixer and other MLP-based architectures, based simply on multi-layer perceptrons (MLPs), are also powerful compared to CNNs and attention techniques and raises a new research direction. However, the high capability of the MLP-base

Cheng-Lin Hong, Ting Tsai, Jyh-Pin Chou, Peng-Jen Chen

Although quantum computation (QC) is regarded as a promising numerical method for computational quantum chemistry, current applications of quantum-chemistry calculations on quantum computers are limited to small molecules. This limitation can be ascribed to technical problems in building and manipulating more qubits and the associated complicated operations

Soo-Min Kim, Yeon-Geun Lim, Linglong Dai, Chan-Byoung Chae

Massive multiple-input multiple-output (MIMO) and full-duplex (FD) are promising candidates for achieving the spectral efficiency to meet the needs of 5G communications. One essential key to realizing practical FD massive MIMO systems is how to effectively mitigate the self-interference (SI). Conventionally, however, the performance comparison of different S

Alexander Avery, Andreas Savakis

Precise 6D pose estimation of rigid objects from RGB images is a critical but challenging task in robotics, augmented reality and human-computer interaction. To address this problem, we propose DeepRM, a novel recurrent network architecture for 6D pose refinement. DeepRM leverages initial coarse pose estimates to render synthetic images of target objects. Th

Congliang Chen, Li Shen, Wei Liu, Zhi-Quan Luo

Distributed adaptive stochastic gradient methods have been widely used for large-scale nonconvex optimization, such as training deep learning models. However, their communication complexity on finding $\varepsilon$-stationary points has rarely been analyzed in the nonconvex setting. In this work, we present a novel communication-efficient distributed Adam in

Subhrajyoti Maji, John Dingliana

We propose an approach, called the Equilibrium Distribution Model (EDM), for automatically selecting colors with optimum perceptual contrast for scientific visualization. Given any number of features that need to be emphasized in a visualization task, our approach derives evenly distributed points in the CIELAB color space to assign colors to the features so

Jun Rao, Xv Meng, Liang Ding, Shuhan Qi

Knowledge distillation (KD) has been extensively employed to transfer the knowledge from a large teacher model to the smaller students, where the parameters of the teacher are fixed (or partially) during training. Recent studies show that this mode may cause difficulties in knowledge transfer due to the mismatched model capacities. To alleviate the mismatch

István Dékány, Eva K. Grebel

RR Lyrae stars are useful chemical tracers thanks to the empirical relationship between their heavy-element abundance and the shape of their light curves. However, the consistent and accurate calibration of this relation across multiple photometric wavebands has been lacking. We have devised a new method for the metallicity estimation of fundamental-mode RR

Brendan Hassett, Yuri Tschinkel

We study arithmetic properties of derived equivalent K3 surfaces over the field of Laurent power series, using the equivariant geometry of K3 surfaces with cyclic groups actions.

Sushant K. Singh, Jan-e Alam

The space-time evolution of the hot and dense fireball of quarks and gluons produced in ultra-relativistic heavy-ion collisions at non-zero baryonic chemical potential and temperature has been studied by using relativistic viscous causal hydrodynamics. For this purpose a numerical code has been developed to solve the relativistic viscous causal hydrodynamics

Rita Maji, Eleonora Luppi, Elena Degoli, Julia Contreras-García

The bonding properties of tilt boundary in poly-silicon and the effect of interstitial impurities are investigated by first-principles. In order to obtain thorough information on the nature of chemical bondings in these solid systems, an accurate topological analysis is performed, through partitioning of the electron localization function. Although the mecha

Jianfei Yang, Xiangyu Peng, Kai Wang, Zheng Zhu

Domain Adaptation of Black-box Predictors (DABP) aims to learn a model on an unlabeled target domain supervised by a black-box predictor trained on a source domain. It does not require access to both the source-domain data and the predictor parameters, thus addressing the data privacy and portability issues of standard domain adaptation. Existing DABP approa

Shuya Chiba, Michitaka Furuya

Gy\'{a}rf\'{a}s and Sumner independently conjectured that for every tree $T$, there exists a function $f_{T}:\mathbb{N}\rightarrow \mathbb{N}$ such that every $T$-free graph $G$ satisfies $\chi (G)\leq f_{T}(\omega (G))$, where $\chi (G)$ and $\omega (G)$ are the {\it chromatic number} and the {\it clique number} of $G$, respectively. This conjecture gives a

Zhuang Wang, Haibin Lin, Yibo Zhu, T. S. Eugene Ng

Gradient compression (GC) is a promising approach to addressing the communication bottleneck in distributed deep learning (DDL). However, it is challenging to find the optimal compression strategy for applying GC to DDL because of the intricate interactions among tensors. To fully unleash the benefits of GC, two questions must be addressed: 1) How to express

Satyanarayana G. Manyam, David W. Casbeer, Swaroop Darbha, Isaac E. Weintraub

A novel coupled path planning and energy management problem for a hybrid unmanned air vehicle is considered, where the hybrid vehicle is powered by a dual gas/electric system. Such an aerial robot is envisioned for use in an urban setting where noise restrictions are in place in certain zones necessitating battery only operation. We consider the discrete ver

Peter Breiding, Sarah Burke-Spolaor, Tao An, Karishma Bansal

Previous Ku-band (15 GHz) imaging with data obtained from the Very Long Baseline Array (VLBA) had shown two compact, sub-pc components at the location of a presumed kpc-scale radio core in the Seyfert galaxy NGC 7674. It was then presumed that these two unresolved and compact components were dual radio cores corresponding to two supermassive black holes (SMB

Farid Ghareh Mohammadi, Cheng Chen, Farzan Shenavarmasouleh, M. Hadi Amini

Understanding 3D point cloud models for learning purposes has become an imperative challenge for real-world identification such as autonomous driving systems. A wide variety of solutions using deep learning have been proposed for point cloud segmentation, object detection, and classification. These methods, however, often require a considerable number of mod

Mohammad Faiyaz Khan, S. M. Sadiq-Ur-Rahman Shifath, Md Saiful Islam

As computers have become efficient at understanding visual information and transforming it into a written representation, research interest in tasks like automatic image captioning has seen a significant leap over the last few years. While most of the research attention is given to the English language in a monolingual setting, resource-constrained languages

Alejandro de la Concha, Nicolas Vayatis, Argyris Kalogeratos

Assuming we have iid observations from two unknown probability density functions (pdfs), $p$ and $q$, the likelihood-ratio estimation (LRE) is an elegant approach to compare the two pdfs only by relying on the available data. In this paper, we introduce the first -to the best of our knowledge-graph-based extension of this problem, which reads as follows: Sup

Chaofeng Wang, Sarah Elizabeth Antos, Jessica Grayson Gosling Goldsmith, Luis Miguel Triveno

In developing countries, building codes often are outdated or not enforced. As a result, a large portion of the housing stock is substandard and vulnerable to natural hazards and climate related events. Assessing housing quality is key to inform public policies and private investments. Standard assessment methods are typically carried out only on a sample /

Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan A. Rossi

Recent advances in contrastive representation learning over paired image-text data have led to models such as CLIP that achieve state-of-the-art performance for zero-shot classification and distributional robustness. Such models typically require joint reasoning in the image and text representation spaces for downstream inference tasks. Contrary to prior bel

Longzhen Yang, Yihang Liu, Yitao Peng, Lianghua He

Accuracy and Diversity are two essential metrizable manifestations in generating natural and semantically correct captions. Many efforts have been made to enhance one of them with another decayed due to the trade-off gap. In this work, we will show that the inferior standard of accuracy draws from human annotations (leave-one-out) are not appropriate for mac

Berk Guler, Pouya P. Niaz, Alireza Madani, Yusuf Aydin

In this paper, we propose a supervised learning approach based on an Artificial Neural Network (ANN) model for real-time classification of subtasks in a physical human-robot interaction (pHRI) task involving contact with a stiff environment. In this regard, we consider three subtasks for a given pHRI task: Idle, Driving, and Contact. Based on this classifica

Nayan Myerson-Jain, Kaixiang Su, Cenke Xu

Recently a ``Pascal's triangle model" constructed with $\text{U}(1)$ rotor degrees of freedom was introduced, and it was shown that ($\textit{i}$.) this model possesses an infinite series of fractal symmetries; and ($\textit{ii}$.) it is the parent model of a series of $Z_p$ fractal models each with its own distinct fractal symmetry. In this work we discuss

Elena Yu. Bannikova, Nina A. Akerman, Massimo Capaccioli, Peter P. Berczik

The recent ALMA maps together with observations of H$_2$O maser emission seem to suggest the presence of a counter-rotation in the obscuring torus of NGC 1068. We propose to explain this phenomenon as due to the influence of a wind, considered as radiation pressure, and the effects of torus orientation. In order to test this idea: 1. we make $N$-body simulat

Yuzhe Zhang, Xu Zhang, Hong Zhang, Aiyi Liu

Motivated by diagnosing the COVID-19 disease using 2D image biomarkers from computed tomography (CT) scans, we propose a novel latent matrix-factor regression model to predict responses that may come from an exponential distribution family, where covariates include high-dimensional matrix-variate biomarkers. A latent generalized matrix regression (LaGMaR) is

Nobumitsu Yokoi, Steven M. Tobias

In strongly compressible magnetohydrodynamic turbulence, obliqueness between the large-scale density gradient and magnetic field gives an electromotive force mediated by density variance (intensity of density fluctuation). This effect is named ``magnetoclinicity'', and is expected to play an important role in large-scale magnetic-field generation in astrophy

Chhavi Sharma, Vishnu Narayanan, P. Balamurugan

We develop two compression based stochastic gradient algorithms to solve a class of non-smooth strongly convex-strongly concave saddle-point problems in a decentralized setting (without a central server). Our first algorithm is a Restart-based Decentralized Proximal Stochastic Gradient method with Compression (C-RDPSG) for general stochastic settings. We pro

Harshit Chawla, Shwetabh Yadav, Hrayer Aprahamian, Dinakar Sagapuram

We present a novel approach to determine the constitutive properties of metals under large plastic strains and strain rates that otherwise are difficult to access using conventional materials testing methods. The approach exploits large-strain plastic flow past a sharp wedge, coupled with high-speed photography and image velocimetry to capture the underlying

Bo Huang, Dongming Wang

This paper studies the number of limit cycles that may bifurcate from an equilibrium of an autonomous system of differential equations. The system in question is assumed to be of dimension $n$, have a zero-Hopf equilibrium at the origin, and consist only of homogeneous terms of order $m$. Denote by $H_k(n,m)$ the maximum number of limit cycles of the system

Efe C. Balta, Mohammad H. Mamduhi, John Lygeros, Alisa Rupenyan

In this paper, we consider a cyber-physical manufacturing system (CPMS) scenario containing physical components (robots, sensors, and actuators), operating in a digitally connected, constrained environment to perform industrial tasks. The CPMS has a centralized control plane with digital twins (DTs) of the physical resources, computational resources, and a n

Maja Petrovic, Branko Malesevic

In this paper we consider Hugelschaffer cubic curves which are generated using appropriate geometric constructions. The main result of this work is the mode of explicitly calculating the area of the egg-shaped part of the cubic curve using elliptic integrals. In this paper, we also analyze the Hugelschaffer surface of cubic curves for which we provide new fo

Rachid Benbrik, Mohamed Krab, Mohamed Ouchemhou

The searches for charged Higgs bosons can be used to probe new physics at the LHC. In the current study, we concentrate on the associated production of the charged Higgs boson with the bottom quark and the jet in Two-Higgs Doublet Model (2HDM) type-I as promising mode for a light $H^\pm$, i.e. $m_{H^\pm}<m_t$. For this we consider the two situations where $h

Nguyen Tien Dung

We develop a general method to study the Fisher information distance in central limit theorem for nonlinear statistics. We first construct completely new representations for the score function. We then use these representations to derive quantitative estimates for the Fisher information distance. To illustrate the applicability of our approach, explicit rate

Hiroto Tanaka, Hikaru Watanabe, Youichi Yanase

The unique nonreciprocal responses of superconductors, which stem from the Cooper pairs' quantum condensation, have been attracting attention. Recently, theories of the second-order nonlinear response in noncentrosymmetric superconductors were formulated based on the Bogoliubov-de Gennes theory. In this paper, we study the mechanism and condition for second-

Qi Zheng, Chaoyue Wang, Dadong Wang, Dacheng Tao

Concept learning constructs visual representations that are connected to linguistic semantics, which is fundamental to vision-language tasks. Although promising progress has been made, existing concept learners are still vulnerable to attribute perturbations and out-of-distribution compositions during inference. We ascribe the bottleneck to a failure of expl

Shaoru Wang, Jin Gao, Zeming Li, Xiaoqin Zhang

Self-supervised learning on large-scale Vision Transformers (ViTs) as pre-training methods has achieved promising downstream performance. Yet, how much these pre-training paradigms promote lightweight ViTs' performance is considerably less studied. In this work, we develop and benchmark several self-supervised pre-training methods on image classification tas

Matheus K. Venturelli, Pedro H. Gomes, Jônatas Wehrmann

In this paper, we propose MAGICSTYLEGAN and MAGICSTYLEGAN-ADA - both incarnations of the state-of-the-art models StyleGan2 and StyleGan2 ADA - to experiment with their capacity of transfer learning into a rather different domain: creating new illustrations for the vast universe of the game "Magic: The Gathering" cards. This is a challenging task especially d

Evangelos Matsinos

This short technical note addresses a number of issues regarding the estimates of the 2004 paper by Ericson, Loiseau, and Wycech for the corrections $\delta_\epsilon$ and $\delta_\Gamma$, which aim at the removal of the effects of electromagnetic origin from the measurements of the strong-interaction shift $\epsilon_{1s}$ and of the total decay width $\Gamma

Xiao Han, Leye Wang, Junjie Wu, Yuncong Yang

Network embedding represents network nodes by a low-dimensional informative vector. While it is generally effective for various downstream tasks, it may leak some private information of networks, such as hidden private links. In this work, we address a novel problem of privacy-preserving network embedding against private link inference attacks. Basically, we

Muhammad Izzatullah, Isa Eren Yildirim, Umair Bin Waheed, Tariq Alkhalifah

Several techniques have been proposed over the years for automatic hypocenter localization. While those techniques have pros and cons that trade-off computational efficiency and the susceptibility of getting trapped in local minima, an alternate approach is needed that allows robust localization performance and holds the potential to make the elusive goal of

Niels Lubbes

We classify the topological types of surfaces in the 3-dimensional unit sphere that contain both a great and a small circle through each point. In particular, these surfaces are homeomorphic to one of five normal forms and are either the pointwise product of circles in the unit quaternions or contain five concurrent circles. We classify the real singular loc

Jamie J. Alnasir, Thomas Heinis, Louis Carteron

DNA has many valuable characteristics that make it suitable for a long-term storage medium, in particular its durability and high information density. DNA can be stored safely for hundreds of years with virtually no degradation, in contrast to hard disk drives which typically last for about 5 years. Furthermore, the duration of DNA-Storage can be extended to

Ralf Fröberg

For a graph $G=(V,E)$ the edge ring $k[G]$ is $k[x_1,\ldots,x_n]/I(G)$, where $n=|V|$ and $I(G)$ is generated by $\{ x_ix_j;\{ i,j\}\in E\}$. The conjecture we treat is the following. If $k[G]$ has a 2-linear resolution, then the projective dimension of $K[G]$, pd$(k[G])$, equals the maximal degree of a vertex in $G$. As far as we know, this conjecture is fi

Mehmet Öncü, Mohsen Izadyari, Özgür E. Müstecaplıoğlu, Kadir Durak

Optical cavities operating in the near-concentric regime are the fundamental tools to perform high precision experiments like cavity QED applications. A strong focusing regime unfortunately is prone to excite higher-order modes. Higher-order mode excitation is challenging to avoid for the realistic strong focusing cavities, and if these modes are closely spa

Raveena, Krishnendra Shekhawat

Existing graph theoretic approaches are mainly restricted to floor-plans with rectangular boundary. In this paper, we introduce floor-plans with $L$-shaped boundary (boundary with only one concave corner). To ensure the L-shaped boundary, we introduce the concept of non-triviality of a floor-plan. A floor-plan with a rectilinear boundary with at least one co

Christian Amsüss

Constrained RESTful Environments tolerate and even benefit from proxy services. We explore the concept of proxies installed at entry points to constrained networks without any unified management. We sketch proxies of different levels of intrusiveness into applications, their announcement and discovery, and compare their theoretical capabilities in mitigating

Yu Pan, Zeyong Su, Ao Liu, Jingquan Wang

Tensorial Convolutional Neural Networks (TCNNs) have attracted much research attention for their power in reducing model parameters or enhancing the generalization ability. However, exploration of TCNNs is hindered even from weight initialization methods. To be specific, general initialization methods, such as Xavier or Kaiming initialization, usually fail t

Cheng Zhang, Li-Tuo Shen, Jie Song, Yan Xia

In this work, we propose a comprehensive design for narrowband and passband composite pulse sequences by involving the dynamics of all states in the three-state system. The design is quite universal as all pulse parameters can be freely employed to modify the coefficients of error terms. Two modulation techniques, the strength and phase modulations, are used

Bendong Lou, Lixia Yuan

We study a generalized mean curvature flow involving a positive power of the mean curvature and a driving force. In this paper, we first construct all kinds of radially symmetric translating solutions, and then select one of them to satisfy a prescribed boundary angle in a cylinder. We then consider the flow starting at an initial hypersurface: showing the a

Kaiyi Zhang, Liang Zhou, Lu Chen, Shitong He

We present angle-uniform parallel coordinates, a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates plot. Despite being a common method for visualizing multidimensional data, parallel coordina

Timur Isaev, Dmitrii Makinski, Andrei Zaitsevski

Recently a new wave of interest to spectroscopy of radioactive compounds has raised due to successful applications of the new experimental ISOL/CRIS technique to optical spectroscopy of radium monofluoride molecules. This opens great prospects to searches of the effects connected with ``new physics'' which point on deviations of the physical laws from those

Linpeng Jin

Deep convolutional neural networks (CNNs) have brought breakthroughs in processing clinical electrocardiograms (ECGs), speaker-independent speech and complex images. However, typical CNNs require a fixed input size while it is common to process variable-size data in practical use. Recurrent networks such as long short-term memory (LSTM) are capable of elimin

Gowthama K K, Manu Kurian, Vinod Chandra

The thermal response of the hot QCD matter has been studied in the presence of a time-varying magnetic field. The impact of magnetic field, its time dependence, and the collision aspects of the medium on thermal transport have been studied within the relativistic kinetic theory. The decay time of the magnetic field in the medium seems to have a strong depend

Ilaria Cardinali, Hans Cuypers, Luca Giuzzi, Antonio Pasini

A polar space S is said to be symplectic if it admits an embedding e in a projective geometry PG(V) such that the e-image e(S) of S is defined by an alternating form of V. In this paper we characterize symplectic polar spaces in terms of their incidence properties, with no mention of peculiar properties of their embeddings. This is relevant especially when S

Bruno P. Zimmermann

The classification of finite group-actions on closed surfaces of small genus is well-known. In the present paper we are interested in the question of which of these group-actions are bounding (extend to a compact 3-manifold with the surface as its unique boundary component, e.g. to a handlebody) or geometrically bounding (extend to a hyperbolic 3-manifold wi

Anders Björner, Mark Goresky, Robert MacPherson

We discuss ways in which tools from topology can be used to derive lower bounds for the circuit complexity of Boolean functions.

Hung-Yuan Fan, Chun-Yueh Chiang

In this paper we consider a class of conjugate discrete-time Riccati equations, arising originally from the linear quadratic regulation problem for discrete-time antilinear systems. Under some mild assumptions and the framework of the fixed-point iteration, a constructive proof is given for the existence of the maximal solution to the conjugate discrete-time

Swagata Acharya, Mikhail I. Katsnelson, Mark van Schilfgaarde

Bulk FeSe becomes superconducting below 9\,K, but the critical temperature (T$_{c}$) is enhanced almost universally by a factor of $\sim$4-5 when it is intercalated with alkali elements. How intercalation modifies the structure is known from in-situ X-ray and neutron scattering techniques, but why T$_{c}$ changes so dramatically is not known. Here we show th

Yahong Yang, Yang Xiang

In this paper, we establish a neural network to approximate functionals, which are maps from infinite dimensional spaces to finite dimensional spaces. The approximation error of the neural network is $O(1/\sqrt{m})$ where $m$ is the size of networks, which overcomes the curse of dimensionality. The key idea of the approximation is to define a Barron spectral

Niccolò Cavagnero, Fernando Dos Santos, Marco Ciccone, Giuseppe Averta

Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving. However, recent findings indicate that transient hardware faults may corrupt the models prediction dramatically. For instance, the radiation-induced misprediction probability can be so hig

Rui Qi, Jin-Bao Wang, Gang Li, Chun-Sheng An

We have investigated the axial charges of the ground octet baryons within the extended chiral constituent quark model, where all the possible compact five-quark Fock components $qqq(q\bar{q}) (q=u, d, s)$ in the baryons are considered. The transition couplings between the three- and five-quark components in the baryons are assumed to be via the $^{3}P_{0}$ m

Sarun Gulyanon, Wasit Limprasert, Pokpong Songmuang, Rachada Kongkachandra

Supervised deep neural networks are the-state-of-the-art for many tasks in the remote sensing domain, against the fact that such techniques require the dataset consisting of pairs of input and label, which are rare and expensive to collect in term of both manpower and resources. On the other hand, there are abundance of raw satellite images available both fo

Saee Dhawalikar, Christoph Federrath, Seth Davidovits, Romain Teyssier

Turbulence in the interstellar medium (ISM) is crucial in the process of star formation. Shocks produced by supernova explosions, jets, radiation from massive stars, or galactic spiral-arm dynamics are amongst the most common drivers of turbulence in the ISM. However, it is not fully understood how shocks drive turbulence, in particular whether shock driving

William Bennett, Ryan G. McClarren

The widely used AZURV1 transport benchmarks package provides a suite of solutions to isotropic scattering transport problems with a variety of initial conditions (Ganapol 2001). Most of these solutions have an initial condition that is a Dirac delta function in space; as a result these benchmarks are challenging problems to use for verification tests in comp

David Bang, Jorge Ignacio González Cázares, Aleksandar Mijatović

We characterise, in terms of their transition laws, the class of one-dimensional L\'evy processes whose graph has a continuously differentiable (planar) convex hull. We show that this phenomenon is exhibited by a broad class of infinite variation L\'evy processes and depends subtly on the behaviour of the L\'evy measure at zero. We introduce a class of stron

Yong Liu, Haixu Wu, Jianmin Wang, Mingsheng Long

Transformers have shown great power in time series forecasting due to their global-range modeling ability. However, their performance can degenerate terribly on non-stationary real-world data in which the joint distribution changes over time. Previous studies primarily adopt stationarization to attenuate the non-stationarity of original series for better pre

Mei-Heng Yueh

The stretch energy is a fully nonlinear energy functional that has been applied to the numerical computation of area-preserving mappings. However, this approach lacks theoretical support and the analysis is complicated due to the full nonlinearity of the functional. In this paper, we provide a theoretical foundation of the stretch energy minimization for the

Lu Wang, Wei Gu, Shuai Lu, Haifeng Qiu

This paper proposes a doubled-sided auction mechanism with price discrimination for social welfare (SW) maximization in the electricity and heating market. In this mechanism, energy service providers (ESPs) submit offers and load aggregators (LAs) submit bids to an energy trading center (ETC) to maximize their utility; in turn, the selfless ETC as an auction

Yuepeng Qian, Shuaishuai Han, Gabriel Aguirre-Ollinger, Chenglong Fu

In assistive robots, compliant actuator is a key component in establishing safe and satisfactory physical human-robot interaction (pHRI). The performance of compliant actuators largely depends on the stiffness of the elastic element. Generally, low stiffness is desirable to achieve low impedance, high fidelity of force control and safe pHRI, while high stiff

Liguang Zhou, Yuhongze Zhou, Xiaonan Qi, Junjie Hu

Environmental sound classification (ESC) is a challenging problem due to the unstructured spatial-temporal relations that exist in the sound signals. Recently, many studies have focused on abstracting features from convolutional neural networks while the learning of semantically relevant frames of sound signals has been overlooked. To this end, we present an

Remo Sasso, Matthia Sabatelli, Marco A. Wiering

A crucial challenge in reinforcement learning is to reduce the number of interactions with the environment that an agent requires to master a given task. Transfer learning proposes to address this issue by re-using knowledge from previously learned tasks. However, determining which source task qualifies as the most appropriate for knowledge extraction, as we

Qi Zhou, Jiahao Weng, Haoran Xie

Autonomous sensory meridian response (ASMR) is a type of video contents designed to help people relax and feel comfortable. Users usually retrieve ASMR contents from various video websites using only keywords. However, it is challenging to examine satisfactory contents to reflect users' needs for ASMR videos using keywords or content-based retrieval. To solv

Kareem Marzouk, Antony Lewis, Julien Carron

We update constraints on the amplitude of the primordial trispectrum, using the final Planck mission temperature and polarization data. In the squeezed limit, a cosmological local trispectrum would be observed as a spatial modulation of small-scale power on the CMB sky. We reconstruct this signal as a source of statistical anisotropy via quadratic estimator

Pedro R. Dieguez, Vinicius F. Lisboa, Roberto M. Serra

A quantum-controlled device may produce a scenario in which two general quantum operations can be performed in such a way that it is not possible to associate a definite order for the operations application. Such an indefinite causal order can be explored to produce nontrivial effects in quantum thermal devices. We investigate a measurement-powered thermal d

Zhenyue Qin, Pan Ji, Dongwoo Kim, Yang Liu

Skeleton sequences are compact and lightweight. Numerous skeleton-based action recognizers have been proposed to classify human behaviors. In this work, we aim to incorporate components that are compatible with existing models and further improve their accuracy. To this end, we design two temporal accessories: discrete cosine encoding (DCE) and chronological

Zhe Ying, Bo Chen, Chunfeng Li, Boyuan Wei

Igniting interface magnetic ordering of magnetic topological insulators by building a van der Waals heterostructure can help to reveal novel quantum states and design functional devices. Here, we observe an interesting exchange bias effect, indicating successful interfacial magnetic coupling, in CrI3/MnBi2Te4 ferromagnetic insulator/antiferromagnetic topolog

Ziang Li, Ming Ding, Weikai Li, Zihan Wang

We argue that the present setting of semisupervised learning on graphs may result in unfair comparisons, due to its potential risk of over-tuning hyper-parameters for models. In this paper, we highlight the significant influence of tuning hyper-parameters, which leverages the label information in the validation set to improve the performance. To explore the

Vladimir Dzhunushaliev, Vladimir Folomeev

The model of nonperturbative vacuum in SU(2) Yang-Mills theory coupled to a nonlinear spinor field is suggested. By analogy with Abelian magnetic monopole dominance in quantum chromodynamics, it is assumed that the dominant contribution to such vacuum is coming from quasiparticles described by dipolelike solutions existing in this theory. Using an assumption

Renrui Zhang, Ziyu Guo, Rongyao Fang, Bin Zhao

Masked Autoencoders (MAE) have shown great potentials in self-supervised pre-training for language and 2D image transformers. However, it still remains an open question on how to exploit masked autoencoding for learning 3D representations of irregular point clouds. In this paper, we propose Point-M2AE, a strong Multi-scale MAE pre-training framework for hier

Adway Mitra

In district-based elections, electors cast votes in their respective districts. In each district, the party with maximum votes wins the corresponding seat in the governing body. The election result is based on the number of seats won by different parties. In this system, locations of electors across the districts may severely affect the election result even

Hassan Ali, Doreen Jirak, Stefan Wermter

As robots are expected to get more involved in people's everyday lives, frameworks that enable intuitive user interfaces are in demand. Hand gesture recognition systems provide a natural way of communication and, thus, are an integral part of seamless Human-Robot Interaction (HRI). Recent years have witnessed an immense evolution of computational models powe

Ye Liu, Chen Shen, Zhaojian Wang, Feng Liu

In multi-infeed hybrid AC-DC (MIDC) systems, the emergency frequency control (EFC) with LCC-HVDC systems participating is of vital importance for system frequency stability. Nevertheless, when regional power systems are operated by different decision-makers, the LCC-HVDC systems and their connected AC systems might be unwilling to participate in the EFC due

Petru A. Cioica-Licht, Martin Hutzenthaler, P. Tobias Werner

We prove that deep neural networks are capable of approximating solutions of semilinear Kolmogorov PDE in the case of gradient-independent, Lipschitz-continuous nonlinearities, while the required number of parameters in the networks grow at most polynomially in both dimension $d \in \mathbb{N}$ and prescribed reciprocal accuracy $\varepsilon$. Previously, th

Moritz Gerlach, Jochen Glück

We show that a positive operator between $L^p$-spaces is given by integration against a kernel function if and only if the image of each positive function has a lower semi-continuous representative with respect to a suitable topology. This is a consequence of a new characterization of kernel operators on general Banach lattices as those operators whose range

Varun Madhavan, Adway Mitra, Partha Pratim Chakrabarti

Agent-Based Models are very useful for simulation of physical or social processes, such as the spreading of a pandemic in a city. Such models proceed by specifying the behavior of individuals (agents) and their interactions, and parameterizing the process of infection based on such interactions based on the geography and demography of the city. However, such

Yan Luo

Tourists tend to visit multiple destinations out of their variety-seeking motivations in their trips. Thus, it is critical to discover travel patterns involving multi-destinations in tourism research. Existing relevant research most relied on survey data or focused on citizens due to the lack of large-scale, fine-grained tourism datasets. Several scholars ha