November 2022 arXiv papers — page 21

Jipeng Lv, Heng Guo, Guanying Chen, Jinxiu Liang

Multispectral photometric stereo(MPS) aims at recovering the surface normal of a scene from a single-shot multispectral image captured under multispectral illuminations. Existing MPS methods adopt the Lambertian reflectance model to make the problem tractable, but it greatly limits their application to real-world surfaces. In this paper, we propose a deep ne

Minda Zhao, Zehua Lai, Lek-Heng Lim

Is it possible for a first-order method, i.e., only first derivatives allowed, to be quadratically convergent? For univariate loss functions, the answer is yes -- the Steffensen method avoids second derivatives and is still quadratically convergent like Newton method. By incorporating an optimal step size we can even push its convergence order beyond quadrat

Gábor Gévay, Piotr Pokora

In this paper we construct several arrangements of lines and/or conics that are derived from the geometry of the Klein arrangement of $21$ lines in the complex projective plane.

Miroslav Kuchta

Operators with fractional perturbations are crucial components for robust preconditioning of interface-coupled multiphysics systems. However, in case the perturbation is strong, standard approaches can fail to provide scalable approximation of the inverse, thus compromising efficiency of the entire multiphysics solver. In this work, we develop efficient and

Xiuheng Wang, Jie Chen, Cédric Richard

Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images~(HSI) generated by the acquisition devices. This issue is usually addressed by solving an ill-posed inverse problem. While investigating proper image priors can enhance the deconvolution performance, it is not trivial to handcraft a powerful regular

Ulrich Bauer, Luis Scoccola

Algebraic persistence studies persistence modules (typically, linear representations of the poset $\mathbf{R}^n$ with $n \geq 1$) and the algebraic relationships between persistence modules that are interleaved. The notion of $\varepsilon$-interleaving between persistence modules is a generalization of the notion of isomorphism (recovering isomorphism when $

Aydin Deger, Matthew D. Horner, Jiannis K. Pachos

One of the key applications of AdS/CFT correspondence is the duality it dictates between the entanglement entropy of Anti-de Sitter (AdS) black holes and lower-dimensional conformal field theories (CFTs). Here we employ a square lattice of fermions with inhomogeneous tunneling couplings that simulate the effect rotationally symmetric 3D black holes have on D

Kimyeong Lee, Kaiwen Sun

Recently, Harvey and Wu proposed a suitable Hecke operator for vector-valued $SL(2,\mathbb{Z})$ modular forms to connect the characters of different 2d rational conformal field theories (RCFTs). We generalize such an operator to the 2d fermionic RCFTs and call it fermionic Hecke operator. The new Hecke operator naturally maps the Neveu-Schwarz (NS) character

Renato Cardoso, Sofia Vallecorsa, Edoardo Nemni

Image generation and image completion are rapidly evolving fields, thanks to machine learning algorithms that are able to realistically replace missing pixels. However, generating large high resolution images, with a large level of details, presents important computational challenges. In this work, we formulate the image generation task as completion of an i

Yongcun Song, Xiaoming Yuan, Hangrui Yue

The optimal control of thermally convective flows is usually modeled by an optimization problem with constraints of Boussinesq equations that consist of the Navier-Stokes equation and an advection-diffusion equation. This optimal control problem is challenging from both theoretical analysis and algorithmic design perspectives. For example, the nonlinearity a

Hancheng Min, Enrique Mallada

We propose a structure-preserving model-reduction methodology for large-scale dynamic networks with tightly-connected components. First, the coherent groups are identified by a spectral clustering algorithm on the graph Laplacian matrix that models the network feedback. Then, a reduced network is built, where each node represents the aggregate dynamics of ea

Fabian Ruffy, Jed Liu, Prathima Kotikalapudi, Vojtěch Havel

We present P4Testgen, a test oracle for the P4$_{16}$ language. P4Testgen supports automatic test generation for any P4 target and is designed to be extensible to many P4 targets. It models the complete semantics of the target's packet-processing pipeline including the P4 language, architectures and externs, and target-specific extensions. To handle non-dete

Charantej Reddy P, Aditya Siripuram, Brad Osgood

Suppose an $N-$length signal has known frequency support of size $k$. Given sample access to this signal, how fast can we compute the DFT? The answer to this question depends on the structure of the frequency support. We first identify some frequency supports for which (an ideal) $O(k \log k)$ complexity is achievable, referred to as homogeneous sets. We giv

Clayton Barnes, Leonid Mytnik, Zhenyao Sun

Consider a system of Brownian particles on the real line where each pair of particles coalesces at a certain rate according to their intersection local time. Assume that there are infinitely many initial particles in the system. We give a necessary and sufficient condition for the number of particles to come down from infinity. We also identify the rate of t

Luiz C. B. da Silva, Rafael López

We introduce the concept of extrinsic catenary in the hyperbolic plane. Working in the hyperboloid model, we define an extrinsic catenary as the shape of a curve hanging under its weight as seen from the ambient space. In other words, an extrinsic catenary is a critical point of the potential functional, where we calculate the potential with the extrinsic di

Alexander Ziesen, Alexander Altland, Reinhold Egger, Fabian Hassler

Tunnel spectroscopy data for the detection of Majorana bound states (MBS) is often criticized for its proneness to misinterpretation of genuine MBS with low-lying Andreev bound states. Here, we suggest a protocol removing this ambiguity by extending single shot measurements to sequences performed at varying system parameters. We demonstrate how such sampling

Romuald Kilianski, Robert Bennett

Superposing multiple plane waves can generate helicity lattices in which the optical helicity varies regularly in space. Here we propose an inverse design method for constructing arbitrary helicity structures based on placing a digital object of dielectric inclusions in three-dimensional space. We apply the method to design structures that reproduce two-dime

Fabian Göttsch, Noboru Osawa, Takeo Ohseki, Yoshiaki Amano

We consider a user-centric scalable cell-free massive MIMO network with a total of $LM$ distributed remote radio unit antennas serving $K$ user equipments (UEs). Many works in the current literature assume $LM\gg K$, enabling high UE data rates but also leading to a system not operating at its maximum performance in terms of sum throughput. We provide a new

Johan Kopra

We extend previously known two-dimensional multiplication tiling systems that simulate multiplication by two natural numbers $p$ and $q$ in base $pq$ to higher dimensional multiplication tessellation systems. We develop the theory of these systems and link different multiplication tessellation systems with each other via macrotile operations that glue cubes

Abhishek Nag, Yiran Peng, Jiemin Li, Stefano Agrestini

Among condensed matter systems, Mott insulators exhibit diverse properties that emerge from electronic correlations. In itinerant metals, correlations are usually weak, but can also be enhanced via geometrical confinement of electrons, that manifest as `flat' dispersionless electronic bands. In the fast developing field of topological materials, which includ

Robert Laterveer

We exhibit a 2-dimensional family of non-hyperelliptic curves of genus 5, called Humbert curves, for which the tautological ring injects into cohomology. In particular, Humbert curves have a multiplicative Chow-K\"unneth decomposition (in the sense of Shen-Vial), and their Ceresa cycle is torsion.

Kuwari Mahanta

Let $S_g$ denote a closed, orientable surface of genus $g \geq 2$ and $\mathcal{C}(S_g)$ be the associated curve graph. Let $d$ be the path metric on $\mathcal{C}(S_g)$ and $a_0$ and $a_4$ be a pair of curves on $S_g$ with $d(a_0, a_4) = 4$. In this article, we fix the vertex $a_0$ and apply the Dehn twist about $a_4$, $T_{a_4}$, to it in an attempt to creat

A. Moradpouri, S. A. Jafari, Mahdi Torabian

We present a gravity dual to a quantum material with tilted Dirac cone in 2+1 dimensional spacetime. In this many-body system the electronics degrees of freedom are strongly-coupled, constitute a Dirac fluid and admit an effective hydrodynamic description. The holographic techniques are applied to compute the thermodynamic variables and hydrodynamic transpor

J. Leigh Wojno, Karoline M. Gilbert, Evan N. Kirby, Ivanna Escala

We present spectroscopic chemical abundances of red giant branch (RGB) stars in Andromeda (M31), using medium resolution ($R\sim6000$) spectra obtained via the Spectroscopic and Photometric Landscape of Andromeda's Stellar Halo (SPLASH) survey. In addition to individual chemical abundances, we coadd low signal-to-noise ratio (S/N) spectra of stars to obtain

Yagmur Yigit, Khayal Huseynov, Hamed Ahmadi, Berk Canberk

With the development of industrialization, air pollution is also steadily on the rise since both industrial and daily activities generate a massive amount of air pollution. Since decreasing air pollution is critical for citizens' health and well-being, air pollution monitoring is becoming an essential topic. Industrial Internet of Things (IIoT) research focu

G. Russo, F. Cerutti, L. S. Esposito, G. Franchetti

Crossing a horizontal nonlinear resonance is the approach that can be used to split a beam in several beamlets with the goal to perform multi-turn extraction from a circular particle accelerator. Such an approach has been successfully implemented in the CERN Proton Synchrotron and is used routinely for the production of high-intensity proton beams for fixed-

Bastian Sander, Gerhard Hensler

We numerically investigate the internal evolution of multiphase clouds, which are at rest with respect to an ambient, highly ionized medium (HIM) representing the hot component of the circumgalactic medium (CGM). Time-dependent saturated thermal conduction and its implications like condensation rates and mixing efficiency are assessed in multiphase clouds. O

Tuan Minh Pham, Kunihiko Kaneko

The relationship between genotype and phenotype plays a crucial role in determining the function and robustness of biological systems. Here the evolution progresses through the change in genotype, whereas the selection is based on the phenotype, and genotype-phenotype relation also evolves. Theory for such phenotypic evolution remains poorly-developed, in co

Miguel Luis, Catia Vaz

The increasing risk of epidemics and a fast-growing world population has contributed to a great investment in phylogenetic analysis, in order to track numerous diseases and conceive effective medication and treatments. Phylogenetic analysis requires large quantities of information to be analyzed and processed for knowledge extraction, using suitable techniqu

Kunjal Panchal, Sunav Choudhary, Nisarg Parikh, Lijun Zhang

Personalization in Federated Learning (FL) aims to modify a collaboratively trained global model according to each client. Current approaches to personalization in FL are at a coarse granularity, i.e. all the input instances of a client use the same personalized model. This ignores the fact that some instances are more accurately handled by the global model

Stefano Marseglia, Caleb Springer

We study the groups of rational points of abelian varieties defined over a finite field $ \mathbb{F}_q$ whose endomorphism rings are commutative, or, equivalently, whose isogeny classes are determined by squarefree characteristic polynomials. When $\mathrm{End}(A)$ is locally Gorenstein, we show that the group structure of $A(\mathbb{F}_q)$ is determined by

Ricardo Chacón, Pedro J. Martínez

This manuscript is a comment about a published article in PRL 129, 053201 (2022) by J. Minguzzi et al.

Pengwei Yang, Chongyangzi Teng, Jack George Mangos

Label noise is a significant obstacle in deep learning model training. It can have a considerable impact on the performance of image classification models, particularly deep neural networks, which are especially susceptible because they have a strong propensity to memorise noisy labels. In this paper, we have examined the fundamental concept underlying relat

Chia-Ying Lin, Mei Ian Sam, Yi-Ching Tsai, Hsiu-Hau Lin

In the past few decades, there have been intense debates whether the brain operates at a critical state. To verify the criticality hypothesis in the neuronal networks is challenging and the accumulating experimental and theoretical results remain controversial at this point. Here we simulate how visual information of a nature image is processed by the finite

Hiranya Kishore Dey, Umesh Shankar, Sivaramakrishnan Sivasubramanian

Carlitz and Scoville in 1973 considered a four variable polynomial that enumerates permutations in $\mathfrak{S}_n$ with respect to the parity of its descents and ascents. In recent work, Pan and Zeng proved a $q$-analogue of Carlitz-Scoville's generating function by enumerating permutations with the above four statistice along with the inversion number. Fur

Junho Kim, Jongho Lee, Chang-Seong Moon, Aurore Savoy-Navarro

The experiments at LHC are implementing novel and challenging detector upgrades for the High Luminosity LHC, among which the tracking systems. This paper reports on performance studies, illustrated by an electron trigger, using a simplified pixel tracker. To achieve a real-time trigger (e.g. processing HL-LHC collision events at 40 MHz), simple algorithms ar

Joaquim Ortiz-Haro, Erez Karpas, Michael Katz, Marc Toussaint

Robotic planning in real-world scenarios typically requires joint optimization of logic and continuous variables. A core challenge to combine the strengths of logic planners and continuous solvers is the design of an efficient interface that informs the logical search about continuous infeasibilities. In this paper we present a novel iterative algorithm that

Min Jun Jo

We study upper bounds on the box-counting dimension of the set of potential singular points in suitable weak solutions to the 3D incompressible hyperdissipative Navier-Stokes system \begin{equation*} \partial_t u + (-\Delta)^{\alpha}u+(u\cdot \nabla)u+\nabla p = 0, \qquad \operatorname{div} u = 0, \end{equation*} for $\alpha\in(1,5/4)$. Our main observation

Krunal Raval, Carla Manni, Hendrik Speleers

Tchebycheffian splines are smooth piecewise functions whose pieces are drawn from (possibly different) Tchebycheff spaces, a natural generalization of algebraic polynomial spaces. They enjoy most of the properties known in the polynomial spline case. In particular, under suitable assumptions, Tchebycheffian splines admit a representation in terms of basis fu

Nico Stucki, Johannes C. Paetzold, Suprosanna Shit, Bjoern Menze

Image segmentation is a largely researched field where neural networks find vast applications in many facets of technology. Some of the most popular approaches to train segmentation networks employ loss functions optimizing pixel-overlap, an objective that is insufficient for many segmentation tasks. In recent years, their limitations fueled a growing intere

Eva Cetinic

The paper discusses the potential of large vision-language models as objects of interest for empirical cultural studies. Focusing on the comparative analysis of outputs from two popular text-to-image synthesis models, DALL-E 2 and Stable Diffusion, the paper tries to tackle the pros and cons of striving towards culturally agnostic vs. culturally specific AI

Belle II Collaboration, F. Abudinén, I. Adachi, K. Adamczyk

We present results on the semileptonic decays $B^0 \to \rho^- \ell^+ \nu_\ell$ and $B^+ \to \rho^0 \ell^+ \nu_\ell$ in a sample corresponding to 189.9/fb of Belle II data at the SuperKEKB $e^- e^+$ collider. Signal decays are identified using full reconstruction of the recoil $B$ meson in hadronic final states. We determine the total branching fractions via

Yoshihiro Michishita

Machine learning with neural networks is now becoming a more and more powerful tool for various tasks, such as natural language processing, image recognition, winning the game, and even for the issues of physics. Although there are many studies on the application of machine learning to numerical calculation and the assistance of experimental detection, the m

Tamara Czinczoll, Helen Yannakoudakis, Pushkar Mishra, Ekaterina Shutova

This paper examines the encoding of analogy in large-scale pretrained language models, such as BERT and GPT-2. Existing analogy datasets typically focus on a limited set of analogical relations, with a high similarity of the two domains between which the analogy holds. As a more realistic setup, we introduce the Scientific and Creative Analogy dataset (SCAN)

Jingke Xu, Yaqian Zhang, Libo Wang

In this paper, due to the important value in practical applications, we consider the coded distributed matrix multiplication problem of computing $AA^\top$ in a distributed computing system with $N$ worker nodes and a master node, where the input matrices $A$ and $A^\top$ are partitioned into $m$-by-$p$ and $p$-by-$m$ blocks of equal-size sub-matrices respec

Sha-Sha Wang, Hai-Ling Liu, Su-Juan Qin, Fei Gao

The Quantum Alternating Operator Ansatz (QAOA+) is an extension of the Quantum Approximate Optimization Algorithm (QAOA), where the search space is smaller in solving constrained combinatorial optimization problems. However, QAOA+ requires a trivial feasible solution as the initial state, so it cannot be used for problems that are difficult to find a trivial

Žiga Babnik, Vitomir Štruc

Face image quality assessment (FIQA) attempts to improve face recognition (FR) performance by providing additional information about sample quality. Because FIQA methods attempt to estimate the utility of a sample for face recognition, it is reasonable to assume that these methods are heavily influenced by the underlying face recognition system. Although mod

Andrea Amorese, Philipp Hansmann, Andrea Marino, Peter Korner

We investigated the electronic structure of the enigmatic CeRh$_3$B$_2$ using resonant inelastic scattering and x-ray absorption spectroscopy in combination with $ab$ $initio$ density functional calculations. We find that the Rh 4$d$ states are irrelevant for the high-temperature ferromagnetism and the Kondo effect. We also find that the Ce 4$f$ crystal-fiel

S. Bhuvaneswari, R. Muthuganesan, R. Radha

In this paper, we investigate the quantum correlations and coherence of two accelerating Unruh-deWitt detectors coupled to a scalar field in 3 + 1 Minkowski space-time. We show that the entanglement is completely destroyed in the limit of infinite acceleration while the local quantum uncertainty and l1-norm of coherence remain nonzero. In addition, we also h

Saminu Mohammad Aliyu, Gregory Maksha Wajiga, Muhammad Murtala, Shamsuddeen Hassan Muhammad

Social media platforms allow users to freely share their opinions about issues or anything they feel like. However, they also make it easier to spread hate and abusive content. The Fulani ethnic group has been the victim of this unfortunate phenomenon. This paper introduces the HERDPhobia - the first annotated hate speech dataset on Fulani herders in Nigeria

Tobias Runge, Tabea Bordis, Alex Potanin, Thomas Thüm

Correctness-by-Construction (CbC) is an incremental program construction process to construct functionally correct programs. The programs are constructed stepwise along with a specification that is inherently guaranteed to be satisfied. CbC is complex to use without specialized tool support, since it needs a set of predefined refinement rules of fixed granul

Konstantin Häusler

Investments in cryptocurrencies (CCs) remain risky due to high volatility. Exchange Traded Funds (ETFs) are a suitable tool to diversify risk and to benefit from the growth of the whole CC sector. We construct an ETF on the CRIX, the CRyptocurrency IndeX that maps the non-stationary CC dynamics closely by adapting its constituents weights dynamically. The sc

Paul F. Jaeger, Carsten T. Lüth, Lukas Klein, Till J. Bungert

Reliable application of machine learning-based decision systems in the wild is one of the major challenges currently investigated by the field. A large portion of established approaches aims to detect erroneous predictions by means of assigning confidence scores. This confidence may be obtained by either quantifying the model's predictive uncertainty, learni

Zihan Li, Ziye Luo, Yifan Sun

In many biomedical problems, data are often heterogeneous, with samples spanning multiple patient subgroups, where different subgroups may have different disease subtypes, stages, or other medical contexts. These subgroups may be related, but they are also expected to have differences with respect to the underlying biology. The heterogeneous data presents a

Artem Velikzhanin, Benjie Wang, Marta Kwiatkowska

This report summarises the outcomes of a systematic literature search to identify Bayesian network models used to support decision making in healthcare. After describing the search methodology, the selected research papers are briefly reviewed, with the view to identify publicly available models and datasets that are well suited to analysis using the causal

Shreyas Hampali, Tomas Hodan, Luan Tran, Lingni Ma

We propose a method for in-hand 3D scanning of an unknown object with a monocular camera. Our method relies on a neural implicit surface representation that captures both the geometry and the appearance of the object, however, by contrast with most NeRF-based methods, we do not assume that the camera-object relative poses are known. Instead, we simultaneousl

ATLAS Collaboration

This paper presents measurements of charged-hadron spectra obtained in $pp$, $p$+Pb, and Pb+Pb collisions at $\sqrt{s}$ or $\sqrt{s_{_\text{NN}}}=5.02$ TeV, and in Xe+Xe collisions at $\sqrt{s_{_\text{NN}}}=5.44$ TeV. The data recorded by the ATLAS detector at the LHC have total integrated luminosities of 25 pb${}^{-1}$, 28 nb${}^{-1}$, 0.50 nb${}^{-1}$, and

Michela Eleuteri, Petteri Harjulehto, Peter Hästö

Motivated by the image denoising problem and the undesirable stair-casing effect of the total variation method, we introduce bounded variation spaces with generalized Orlicz growth. Our setup covers earlier variable exponent and double phase models. We study the norm and modular of the new space and derive a formula for the modular in terms of the Lebesgue d

Jingcan Duan, Bin Xiao, Siwei Wang, Haifang Zhou

Recently, graph anomaly detection on attributed networks has attracted growing attention in data mining and machine learning communities. Apart from attribute anomalies, graph anomaly detection also aims at suspicious topological-abnormal nodes that exhibit collective anomalous behavior. Closely connected uncorrelated node groups form uncommonly dense substr

Yinghao Ma, Richard M. Stern

While end-to-end systems are becoming popular in auditory signal processing including automatic music tagging, models using raw audio as input needs a large amount of data and computational resources without domain knowledge. Inspired by the fact that temporal modulation is regarded as an essential component in auditory perception, we introduce the Temporal

Patricia Pauli, Dennis Gramlich, Frank Allgöwer

In this work, we propose a dissipativity-based method for Lipschitz constant estimation of 1D convolutional neural networks (CNNs). In particular, we analyze the dissipativity properties of convolutional, pooling, and fully connected layers making use of incremental quadratic constraints for nonlinear activation functions and pooling operations. The Lipschit

Sridhar P. Narayanan, Digjoy Paul, Amritanshu Prasad, Shraddha Srivastava

We use character polynomials to obtain a positive combinatorial interpretation of the multiplicity of the sign representation in irreducible polynomial representations of $GL_n(\mathbb{C})$ indexed by two-column and hook partitions. Our method also yields a positive combinatorial interpretation for the multiplicity of the trivial representation of $S_n$ in a

Avinash Kumar, Sujit Kumar Sahoo

The linear inverse problem emerges from various real-world applications such as Image deblurring, inpainting, etc., which are still thrust research areas for image quality improvement. In this paper, we have introduced a new algorithm called the Enhanced fast iterative shrinkage thresholding algorithm (EFISTA) for linear inverse problems. This algorithm uses

Luis L. Bonilla, Manuel Carretero, Emanuel Mompó

Weakly coupled semiconductor superlattices under dc voltage bias are nonlinear systems with many degrees of freedom whose nonlinearity is due to sequential tunneling of electrons. They may exhibit spontaneous chaos at room temperature and act as fast physical random number generator devices. Here we present a general sequential transport model with different

Henry Bradford

We introduce a notion of "local stability in permutations" for finitely generated groups. If a group is sofic and locally stable in our sense, then it is also locally embeddable into finite groups (LEF). Our notion is weaker than the "permutation stability" introduced by Glebsky-Rivera and Arzhantseva-Paunescu, which allows one to upgrade soficity to residua

Yifan Sun, Zhengyang Sun

How to observe the state of a network from a limited number of measurements has become an important issue in complex networks, engineering, communication, epidemiology, etc. Under some scenarios, it is neither unfeasible nor unnecessary to observe the entire network. Therefore, we investigate the target observation of a network in this paper. We propose a ta

Carsten Lutz, Marcin Przybylko

We study the enumeration of answers to ontology-mediated queries when the ontology is formulated in a description logic that supports functional roles and the query is a CQ. In particular, we show that enumeration is possible with linear preprocessing and constant delay when a certain extension of the CQ (pertaining to functional roles) is acyclic and free-c

Bergfinnur Durhuus, Thordur Jonsson, John Wheater

In this article we study two related models of quantum geometry: generic random trees and two-dimensional causal triangulations. The Hausdorff and spectral dimensions that arise in these models are calculated and their relationship with the structure of the underlying random geometry is explored. Modifications due to interactions with matter fields are also

Murat Akman, Agnid Banerjee, Isidro H. Munive

In this paper, we prove gradient continuity estimates for viscosity solutions to $\Delta_{p}^N u- u_t= f$ in terms of the scaling critical $L(n+2,1 )$ norm of $f$, where $\Delta_{p}^N$ is the game theoretic normalized $p-$Laplacian operator defined in (1.2) below. Our main result, Theorem 2.5 constitutes borderline gradient continuity estimate for $u$ in ter

Pedro L. Luque-Escamilla, Josep Martí, Enrique Mestre, Jorge A. Combi

The Fermi source 4FGL J1848.7-0129 has been historically related to the globular cluster GLIMPSE-C01 since its very first detection. Although this association is widely accepted, as it appears in the most recent Fermi catalog, it deserves to be revisited given the multi-wavelength evidences and the recent discovery of variable X-ray sources in the Fermi sour

Ramonika Sengupta, Brijesh Tripathi, Asha Adhiya

This paper describes a method for the explicit reconstruction and visualization of various polarization ellipses from the intensities measured after a rotating polarizer. The state of polarization of a light beam is represented by the variation of the electric field amplitude vector with polar angle in the laboratory coordinate system defined by the experime

Arash Rabbani, Chenhao Sun, Masoud Babaei, Vahid J. Niasar

DeepAngle is a machine learning-based method to determine the contact angles of different phases in the tomography images of porous materials. Measurement of angles in 3--D needs to be done within the surface perpendicular to the angle planes, and it could become inaccurate when dealing with the discretized space of the image voxels. A computationally intens

Qian Yu, Yury Polyanskiy

In the study of Ising models on large locally tree-like graphs, in both rigorous and non-rigorous methods one is often led to understanding the so-called belief propagation distributional recursions and its fixed points. We prove that there is at most one non-trivial fixed point for Ising models with zero or certain random external fields. Previously this wa

Yiping Lu, Jiajin Li, Lexing Ying, Jose Blanchet

The optimal design of experiments typically involves solving an NP-hard combinatorial optimization problem. In this paper, we aim to develop a globally convergent and practically efficient optimization algorithm. Specifically, we consider a setting where the pre-treatment outcome data is available and the synthetic control estimator is invoked. The average t

Frits Beukers

Many interesting combinatorial sequences, such as Ap\'ery numbers and Franel numbers, enjoy the so-called Lucas property modulo almost all primes $p$. Modulo prime powers $p^r$ such sequences have a more complicated behaviour which can be described by matrix versions of the Lucas property called $p$-linear schemes. They are examples of finite $p$-automata. I

Yun Sun, Bing Li, Yiming Ding

We focus on the relationships between matching and subshift of finite type for intermediate $\beta$-transformations $T_{\beta,\alpha}(x)=\beta x+\alpha $ ($\bmod$ 1), where $x\in[0,1]$ and $(\beta,\alpha) \in \Delta:= \{ (\beta, \alpha) \in \mathbb{R}^{2}:\beta \in (1, 2) \; \rm{and} \; 0 < \alpha <2 - \beta\}$. We prove that if the kneading space $\Omega_{\

Sudipta Sarkar, Sahil Kalra, Niraj K. Shukla

In this article, we describe the supremum cosine angle between two multiplication invariant (MI) spaces and its connection with the closedness of the sum of those spaces. The results obtained for MI spaces are preserved by the corresponding fiber spaces almost everywhere. Employing the Zak transform, we obtain the results for translation invariant spaces on

Jiang-Tian Zhai, Qi Zhang, Tong Wu, Xing-Yu Chen

Learning fine-grained interplay between vision and language allows to a more accurate understanding for VisionLanguage tasks. However, it remains challenging to extract key image regions according to the texts for semantic alignments. Most existing works are either limited by textagnostic and redundant regions obtained with the frozen detectors, or failing t

Edward Crane, Stanislav Volkov

We study a stochastic model of consensus formation, introduced in 2015 by Grinfeld, Volkov and Wade, who called it a multidimensional randomized Keynesian beauty contest. The model was generalized by Kennerberg and Volkov, who called their generalization the Jante's law process. We consider a version of the model where the space of possible opinions is a con

Milon Kundar, Sahil Bhandari, Sein Chung, Kilwon Cho

Perovskite solar cells (PSCs) with superior performance have been recognized as a potential candidate in photovoltaic technologies. However, the defects in active perovskite layer induce non-radiative recombination which restricts the performance and stability of the PSCs. The construction of thiophene-based 2D structure is one of the significant approaches

Shaolei Liu, Siqi Yin, Linhao Qu, Manning Wang

Unsupervised domain adaptation (UDA) aims to learn a model trained on source domain and performs well on unlabeled target domain. In medical image segmentation field, most existing UDA methods depend on adversarial learning to address the domain gap between different image modalities, which is ineffective due to its complicated training process. In this pape

Jingwei Li, Ruitian Wu, Tzu-liang Huang, Zian Pan

Parkinson's Disease (PD) is a progressive nervous system disorder that has affected more than 5.8 million people, especially the elderly. Due to the complexity of its symptoms and its similarity to other neurological disorders, early detection requires neurologists or PD specialists to be involved, which is not accessible to most old people. Therefore, we in

Mingqi Yuan, Xin Jin, Bo Li, Wenjun Zeng

We present MEM: Multi-view Exploration Maximization for tackling complex visual control tasks. To the best of our knowledge, MEM is the first approach that combines multi-view representation learning and intrinsic reward-driven exploration in reinforcement learning (RL). More specifically, MEM first extracts the specific and shared information of multi-view

Timothée Bénard

We prove limit theorems for the homological winding of geodesic rays distributed via a harmonic measure on a Gromov hyperbolic space. We obtain applications to the inverse problem for the harmonic measure, and winding statistics for Patterson-Sullivan measures.

Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson

Deep neural networks are susceptible to shortcut learning, using simple features to achieve low training loss without discovering essential semantic structure. Contrary to prior belief, we show that generative models alone are not sufficient to prevent shortcut learning, despite an incentive to recover a more comprehensive representation of the data than dis

Dawei Shen

In 1993, the global stability of Minkowski spacetime has been proven in the celebrated work of Christodoulou and Klainerman \cite{Ch-Kl} in a maximal foliation. In 2003, Klainerman and Nic\`olo \cite{Kl-Ni} gave a second proof of the stability of Minkowski in the case of the exterior of an outgoing null cone. In this paper, we give a new proof of \cite{Kl-Ni

Lampros Bouranis, Nikolaos Demiris, Konstantinos Kalogeropoulos, Ioannis Ntzoufras

We consider a flexible Bayesian evidence synthesis approach to model the age-specific transmission dynamics of COVID-19 based on daily mortality counts. The temporal evolution of transmission rates in populations containing multiple types of individuals is reconstructed via an appropriate dimension-reduction formulation driven by independent diffusion proces

Jack Hau Yung Lo, Yuan Liu, Sze Yi Mak, Zhuo Xu

We study the breakup of a liquid thread inside another liquid at different surface tensions. In general, the pinch-off of a liquid thread is governed by the dynamics of fluid flow. However, when the interfacial tension is ultralow (2 to 3 orders lower than normal liquids), we find that the pinch-off dynamics can be governed by bulk diffusion. By studying the

Qingzhe Liu, Jack Hau Yung Lo, Ye Li, Yuan Liu

The impact and splash of liquid drops on solid substrates are ubiquitous in many important fields. However, previous studies have mainly focused on spherical drops while the non-spherical situations, such as raindrops, charged drops, oscillating drops, and drops affected by electromagnetic field, remain largely unexplored. Using ferrofluid, we realize variou

Alessandro Ottino, Joshua Benjamin, Georgios Zervas

Distributed deep learning (DDL) systems strongly depend on network performance. Current electronic packet switched (EPS) network architectures and technologies suffer from variable diameter topologies, low-bisection bandwidth and over-subscription affecting completion time of communication and collective operations. We introduce a near-exascale, full-bisecti

Klemen Grm, Berk Kemal Özata, Vitomir Štruc, Hazım Kemal Ekenel

In this paper, we aim to address the large domain gap between high-resolution face images, e.g., from professional portrait photography, and low-quality surveillance images, e.g., from security cameras. Establishing an identity match between disparate sources like this is a classical surveillance face identification scenario, which continues to be a challeng

He Zhao, Mathias Schultheis, Anke Arentsen, Georges Kordopatis

Although diffuse interstellar bands (DIBs) were discovered over 100 years ago, for most of them, their origins are still unknown. Investigation on the correlations between different DIBs is an important way to study the behavior and distributions of their carriers. Based on stacking thousands of spectra from the Pristine Inner Galaxy Survey, we study the cor

Zakariya Chaouai, Mohamed Tamaazousti

We consider the following problem $ -\Delta_{p}u= h(x,u) \mbox{ in }\Omega$, $u\in W^{1,p}_{0}(\Omega)$, where $\Omega$ is a bounded domain in $\mathbb{R}^{N}$, $1<p<N$, with a smooth boundary. In this paper we assume that $h(x,u)=a(x)f(u)+b(x)g(u)$ such that $f$ is regularly varying of index $p-1$ and superlinear at infinity. The function $g$ is a $p$-subli

Francesca Bonizzoni, Moritz Hauck, Daniel Peterseim

This paper presents a method for the numerical treatment of reaction-convection-diffusion problems with parameter-dependent coefficients that are arbitrary rough and possibly varying at a very fine scale. The presented technique combines the reduced basis (RB) framework with the recently proposed super-localized orthogonal decomposition (SLOD). More specific

Xiaoqing Chen, Dongrui Wu

Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI research focused on improving its accuracy, but few had considered its security. Recent studies, however, have shown that EEG-based BCIs are vulnerable to adversarial attacks, where small perturbations added to the input can cau

Vasileios Perifanis, Nikolaos Pavlidis, Remous-Aris Koutsiamanis, Pavlos S. Efraimidis

Cellular traffic prediction is of great importance on the path of enabling 5G mobile networks to perform intelligent and efficient infrastructure planning and management. However, available data are limited to base station logging information. Hence, training methods for generating high-quality predictions that can generalize to new observations across diver

M. Zubkins, J. Gabrusenoks, G. Chikvaidze, I. Aulika

Crystalline wurtzite zinc oxide (w-ZnO) can be used as a wide band gap semiconductor for light emitting devices and for transparent or high temperature electronics. The use of amorphous zinc oxide (a-ZnO) can be an advantage in these applications. In this paper we report on X-ray amorphous a-ZnOx thin films (~500 nm) deposited at cryogenic temperatures by re

Aleksandr N. Grekov, Yurii E. Shishkin, Sergei S. Peliushenko, Aleksandr S. Mavrin

The efficiency of using the YOLOV5 machine learning model for solving the problem of automatic de-tection and recognition of micro-objects in the marine environment is studied. Samples of microplankton and microplastics were prepared, according to which a database of classified images was collected for training an image recognition neural network. The result

Micaela Jara Ten Kathen, Princy Johnson, Isabel Jurado Flores, Daniel Guti errez Reina

The preservation, monitoring, and control of water resources has been a major challenge in recent decades. Water resources must be constantly monitored to know the contamination levels of water. To meet this objective, this paper proposes a water monitoring system using autonomous surface vehicles, equipped with water quality sensors, based on a multimodal p

Yifan Sun, Ziye Luo, Xinyan Fan

Revealing relationships between genes and disease phenotypes is a critical problem in biomedical studies. This problem has been challenged by the heterogeneity of diseases. Patients of a perceived same disease may form multiple subgroups, and different subgroups have distinct sets of important genes. It is hence imperative to discover the latent subgroups an