November 2022 arXiv papers — page 94

Liang Zhang, Cheng Long, Gao Cong

Unsupervised region representation learning aims to extract dense and effective features from unlabeled urban data. While some efforts have been made for solving this problem based on multiple views, existing methods are still insufficient in extracting representations in a view and/or incorporating representations from different views. Motivated by the succ

Feng Hao

Based on the celebrated result on zeros of holomorphic 1-forms on complex varieties of general type by Popa and Schnell, we study holomorphic 1-forms on $n$-dimensional varieties of Kodaira dimension $n-1$. We show that a complex minimal smooth projective variety $X$ of Kodaira dimension $\kappa(X)=\dim X-1$ admits a holomorphic 1-form without zero if and on

Nikolaus Dräger, Yonghao Xu, Pedram Ghamisi

Recent years have witnessed the great success of deep learning algorithms in the geoscience and remote sensing realm. Nevertheless, the security and robustness of deep learning models deserve special attention when addressing safety-critical remote sensing tasks. In this paper, we provide a systematic analysis of backdoor attacks for remote sensing data, whe

Waïss Azizian, Franck Iutzeler, Jérôme Malick, Panayotis Mertikopoulos

We examine the last-iterate convergence rate of Bregman proximal methods - from mirror descent to mirror-prox and its optimistic variants - as a function of the local geometry induced by the prox-mapping defining the method. For generality, we focus on local solutions of constrained, non-monotone variational inequalities, and we show that the convergence rat

Golsa Tahmasebzadeh, Eric Müller-Budack, Sherzod Hakimov, Ralph Ewerth

The consumption of news has changed significantly as the Web has become the most influential medium for information. To analyze and contextualize the large amount of news published every day, the geographic focus of an article is an important aspect in order to enable content-based news retrieval. There are methods and datasets for geolocation estimation fro

Jean-François Le Gall

We consider the model of Brownian motion indexed by the Brownian tree, which has appeared in a variety of different contexts in probability, statistical physics and combinatorics. For this model, the total occupation measure is known to have a continuously differentiable density. Although the density process indexed by nonnegative reals is not Markov, we pro

Mikko Partanen, Bruno Anghinoni, Nelson G. C. Astrath, Jukka Tulkki

Electrostriction, the deformation of dielectric materials under the influence of an electric field, is of continuous interest in optics. The classic experiment by Hakim and Higham [Proc. Phys. Soc. 80, 190 (1962)] for a stationary field supports a different formula of the electrostrictive force density than the recent experiment by Astrath et al. [Light Sci.

Vladimir Mikhailets, Olena Atlasiuk, Tetiana Skorobohach

Systems of linear ordinary differential equations with the most general inhomogeneous boundary conditions in fractional Sobolev spaces on a finite interval are studied. The Fredholm property of such problems in corresponding pairs of Banach spaces is proved, and their indices and dimensions of kernels and cokernels are found. Examples are given that show the

V. Baru, E. Epelbaum, A. A. Filin, C. Hanhart

Heavy-quark spin symmetry (HQSS) implies that in the direct decay of a heavy quarkonium with spin $S$, only lower lying heavy quarkonia with the same spin $S$ can be produced. However, this selection rule, expected to work very well in the $b$-quark sector, can be overcome if multiquark intermediate states are involved in the decay chain, allowing for transi

Hongxing Chen, Ming Fang, Changchang Xi

Given an algebra with an idempotent, we introduce two procedures to construct families of new algebras, termed mirror-reflective algebras and reduced mirror-reflective algebras. We then establish connections among these algebras by recollements of derived module categories. In case of given algebras being gendo-symmetric, we show that the (reduced) mirror-re

Vanessa Hüsken

In a geometrically non-linear Cosserat model for micro-polar elastic solids, we insert dipole pairs of singularities into smooth maps and control the amount of Cosserat energy needed to do so. We use this method to force an arbitrary number of singular points into Cosserat-elastic solids by prescribing smooth boundary data. Throughout this paper, we exploit

Anthony Stephenson, Robert Allison, Edward Pyzer-Knapp

When comparing approximate Gaussian process (GP) models, it can be helpful to be able to generate data from any GP. If we are interested in how approximate methods perform at scale, we may wish to generate very large synthetic datasets to evaluate them. Na\"{i}vely doing so would cost \(\mathcal{O}(n^3)\) flops and \(\mathcal{O}(n^2)\) memory to generate a s

Joshua J. Guanzon, Matthew S. Winnel, Austin P. Lund, Timothy C. Ralph

Noiseless linear amplification (NLA) is useful for a wide variety of quantum protocols. Here we propose a fully scalable amplifier which, for asymptotically large sizes, can perform perfect fidelity NLA on any quantum state. Given finite resources however, it is designed to perform perfect fidelity NLA on coherent states and their arbitrary superpositions. O

P. Elli Stamatopoulou, Sotiris Droulias, Guillermo P. Acuna, N. Asger Mortensen

We introduce and theoretically analyze the concept of manipulating optical chirality via strong coupling of the optical modes of chiral nanostructures with excitonic transitions in molecular layers or semiconductors. With chirality being omnipresent in chemistry and biomedicine, and highly desirable for technological applications related to efficient light m

Reza Aghazadeh Ayoubi, Marouan Mizmizi, Dario Tagliaferri, Danilo De Donno

Network-controlled repeaters (NCR) and reconfigurable intelligent surfaces (RIS) are being considered by the third generation partnership project (3GPP) as valid candidates for range extension in millimeter-wave (mmW, 30-300 GHz frequency) 5G and 6G networks, to counteract large path and penetration losses. Nowadays, there is no definite answer on which of t

Duy H. Nguyen, Tuyen M. Pham, Thien D. Le, Tuan Q. Do

In this paper, we would like to figure out whether a k-inflation model admits the Bianchi type I metric as its inflationary solution under a constant-roll condition in the presence of the supergravity motivated coupling between scalar and vector fields, $f^2(\phi)F_{\mu\nu}F^{\mu\nu}$. As a result, some novel anisotropic inflationary solutions are shown to a

Xinyi Yu, Chuwei Wang, Dingran Yuan, Shaoyuan Li

In this paper, we investigate the problem of Model Predictive Control (MPC) of dynamic systems for high-level specifications described by Signal Temporal Logic (STL) formulae. Recent works show that MPC has the great potential in handling logical tasks in reactive environments. However, existing approaches suffer from the heavy computational burden, especial

Wanrong He, Andrew Mao, Jordan Boyd-Graber

For humans and computers, the first step in answering an open-domain question is retrieving a set of relevant documents from a large corpus. However, the strategies that computers use fundamentally differ from those of humans. To better understand these differences, we design a gamified interface for data collection -- Cheater's Bowl -- where a human answers

Johannes Merkle, Christian Rathgeb, Benjamin Tams, Dhay-Parn Lou

The goal of the project "Facial Metrics for EES" is to develop, implement and publish an open source algorithm for the quality assessment of facial images (OFIQ) for face recognition, in particular for border control scenarios.1 In order to stimulate the harmonization of the requirements and practices applied for QA for facial images, the insights gained and

Amirhossein Abaskohi, Nazanin Sabri, Behnam Bahrak

Emotion recognition is one of the machine learning applications which can be done using text, speech, or image data gathered from social media spaces. Detecting emotion can help us in different fields, including opinion mining. With the spread of social media, different platforms like Twitter have become data sources, and the language used in these platforms

ATLAS Collaboration

A search for supersymmetry involving the pair production of gluinos decaying via off-shell third-generation squarks into the lightest neutralino ($\tilde\chi^0_1$) is reported. It exploits LHC proton$-$proton collision data at a centre-of-mass energy $\sqrt{s} = 13$ TeV with an integrated luminosity of 139 fb$^{-1}$ collected with the ATLAS detector from 201

Andrew Coates, Fethi M. Ramazanoğlu

Self-interacting vectors are seeing a burst of interest where various groups demonstrated that the field evolution ends in finite time. Two nonequivalent criteria have been offered to identify this breakdown: (i) the vector constraint equation cannot be satisfied beyond a point where the breakdown occurs, (ii) the dynamics is governed by an effective metric

Patricia Dietzsch

We study the Floer cohomology of the Dehn twist along a real Lagrangian sphere in a symplectic manifold endowed with an anti-symplectic involution. We prove that there exists a distinguished element in the Floer group that is a fixed point of the automorphism induced by the involution. Our methods of proof are based on Mak-Wu's cobordism and Floer-theoretic

Jinyu Chen, Wenchao Xu, Song Guo, Junxiao Wang

Federated Learning (FL) is an emerging paradigm that enables distributed users to collaboratively and iteratively train machine learning models without sharing their private data. Motivated by the effectiveness and robustness of self-attention-based architectures, researchers are turning to using pre-trained Transformers (i.e., foundation models) instead of

Yun Yi, Haokui Zhang, Wenze Hu, Nannan Wang

With the wide and deep adoption of deep learning models in real applications, there is an increasing need to model and learn the representations of the neural networks themselves. These models can be used to estimate attributes of different neural network architectures such as the accuracy and latency, without running the actual training or inference tasks.

Simona Nistor, Cezar Oniciuc, Nurettin Cenk Turgay, Rüya Yeğin Şen

In this paper, we study biconservative surfaces with parallel normalized mean curvature vector field ($PNMC$) in the $4$-dimensional unit Euclidean sphere $\mathbb{S}^4$. First, we study the existence and uniqueness of such surfaces. We obtain that there exists a $2$-parameter family of non-isometric abstract surfaces that admit a (unique) $PNMC$ biconservat

Jesse Huhtala, Nicola Lo Gullo, Iiro Vilja

Despite the large amount of work done in quantum field theory in curved space-times, there are not great many results available for perturbative calculations of particle processes in these systems. Such processes are expected to be important in the early stages of the universe, as well as near highly relativistic objects like black holes and, recently, in ef

X. Hou, Y. You, L. Ji, R. Soria

Pulsating ultraluminous X-ray sources (PULXs) are accreting pulsars with apparent X-ray luminosity exceeding $10^{39}\, \rm erg\ s^{-1}$. We perform Monte-Carlo simulations to investigate whether high collimation effect (or strong beaming effect) is dominant in the presence of accretion outflows, for the fan beam emission of the accretion column of the neutr

Alshaima Almarzooqi, Jawahir Mahmoud, Bayena Alzaabi, Arsiema Ghebremichael

The Domain Name System (DNS) is a core Internet service that translates domain names into IP addresses. It is a distributed database and protocol with many known weaknesses that subject to countless attacks including spoofing attacks, botnets, and domain name registrations. Still, the debate between security and privacy is continuing, that is DNS over TLS or

Yuval Meir, Ofek Tevet, Yarden Tzach, Shiri Hodassman

The realization of complex classification tasks requires training of deep learning (DL) architectures consisting of tens or even hundreds of convolutional and fully connected hidden layers, which is far from the reality of the human brain. According to the DL rationale, the first convolutional layer reveals localized patterns in the input and large-scale pat

Michela Egidi, Katie Gittins, Georges Habib, Norbert Peyerimhoff

In this paper we introduce the magnetic Hodge Laplacian, which is a generalization of the magnetic Laplacian on functions to differential forms. We consider various spectral results, which are known for the magnetic Laplacian on functions or for the Hodge Laplacian on differential forms, and discuss similarities and differences of this new ``magnetic-type''

Rishi Gadepally, Andrew Gomella, Eric Gingold, Paras Lakhani

The discussions around Artificial Intelligence (AI) and medical imaging are centered around the success of deep learning algorithms. As new algorithms enter the market, it is important for practicing radiologists to understand the pitfalls of various AI algorithms. This entails having a basic understanding of how algorithms are developed, the kind of data th

Shamma Rashed, Tasnim Said, Amal Abdulrahman, Arsiema Yohannes

Recently, light has been shed on the trend of personalization, which comes into play whenever different search results are being tailored for a group of users who have issued the same search query. The unpalatable fact that myriads of search results are being manipulated has perturbed a horde of people. With regards to that, personalization can be instrument

N. Stankevich, E. Volkov

We demonstrate that strongly asymmetric limit cycles can be observed in the system of three identical ring oscillators (3-gene networks known as Repressilators) globally coupled by signal molecule diffusion added to the model in a way like the known bacterial "quorum-sensing" mechanism. These cycles are stable over a wide interval of the coupling strengths w

Adrian N. Bishop

The capability of recurrent neural networks to approximate trajectories of a random dynamical system, with random inputs, on non-compact domains, and over an indefinite or infinite time horizon is considered. The main result states that certain random trajectories over an infinite time horizon may be approximated to any desired accuracy, uniformly in time, b

Bruno Barton-Singer, Bernd J. Schroers

Using a general framework, interaction potentials between chiral magnetic solitons in a planar system with a tilted external magnetic field are calculated analytically in the limit of large separation. The results are compared to previous numerical results for solitons with topological charge $\pm 1$. A key feature of the calculation is the interpretation of

Runji Lin, Ye Li, Xidong Feng, Zhaowei Zhang

The pretrain-finetuning paradigm in large-scale sequence models has made significant progress in natural language processing and computer vision tasks. However, such a paradigm is still hindered by several challenges in Reinforcement Learning (RL), including the lack of self-supervised pretraining algorithms based on offline data and efficient fine-tuning/pr

Sadia Kanwal, Faisal Akram, Bilal Masud, E. S. Swanson

The effects of virtual light quark pairs on the charmonium spectrum are studied. Pair creation is modelled with a ``$^{3}P_{0}$" vertex and intermediate states are summed up to 2S excitations. Quark model parameters are obtained by fitting to 12 well-known charmonium states, allowing for feedback between the decaying particle and the induced mass shifts. Bot

A. Mazheika, M. Geske, M. Muller, S. A. Schunk

The conversion of natural gas (methane) to ethane and ethylene (OCM: oxidative coupling of methane) facilitates its transportation and provides a way to synthesize higher value chemicals. The search for high-performance catalysts to achieve this conversion is the main scope of most corresponding studies in the field of OCM. Here, we present a general data-dr

Samuel Balula, Dominic Liao-McPherson, Stefan Stevšić, Alisa Rupenyan

Volume estimation in large indoor spaces is an important challenge in robotic inspection of industrial warehouses. We propose an approach for volume estimation for autonomous systems using visual features for indoor localization and surface reconstruction from 2D-LiDAR measurements. A Gaussian Process-based model incorporates information collected from measu

Niklas Jost, Dorothee Henke, Ivo Hedtke, Oliver Bredtmann

Utilizing existing transportation networks better and designing (parts of) networks involves routing decisions to minimize transportation costs and maximize consolidation effects. We study the concrete example of hinterland networks for the truck-transportation of less-than-container-load (LCL) ocean freight shipments: A set of LCL shipments is given. They h

Sreelekshmi Mohan, Sarita Vig, Watson P. Varricatt, Anandmayee Tej

The HH80-81 system is one of the most powerful jets driven by a massive protostar. We present new near-infrared (NIR) line imaging observations of the HH80-81 jet in the H$_2$ (2.122 $\mu$m) and [Fe II] (1.644 $\mu$m) lines. These lines trace not only the jet close to the exciting source but also the knots located farther away. We have detected nine groups o

Ezgi Kantarcı Oğuz, Emine Yıldırım

We give two new combinatorial methods for computing cluster expansion formulas for arcs coming from possibly punctured surfaces. The first is by using $T$-walks, an extension of the $T$-path model for unpunctured surfaces to general surfaces. We also introduce a new way of generating $T$-paths. The second method is by using order ideals of labeled posets ass

Peng Xiao, Samuel Cheng

Federated learning is a contemporary machine learning paradigm where locally trained models are distilled into a global model. Due to the intrinsic permutation invariance of neural networks, Probabilistic Federated Neural Matching (PFNM) employs a Bayesian nonparametric framework in the generation process of local neurons, and then creates a linear sum assig

Laura Guislain, Eric Bertin

We propose a mean-field theory for nonequilibrium phase transitions to a periodically oscillating state in spin models. A nonequilibrium generalization of the Landau free energy is obtained from the join distribution of the magnetization and its smoothed stochastic time derivative. The order parameter of the transition is a Hamiltonian, whose nonzero value s

Yunrui Yu, Xitong Gao, Cheng-Zhong Xu

Adversarial attacks can deceive neural networks by adding tiny perturbations to their input data. Ensemble defenses, which are trained to minimize attack transferability among sub-models, offer a promising research direction to improve robustness against such attacks while maintaining a high accuracy on natural inputs. We discover, however, that recent state

Dibyo Fabian Dofadar, Riyo Hayat Khan, Shafqat Hasan, Towshik Anam Taj

This paper discusses various types of constraints, difficulties and solutions to overcome the challenges regarding university course allocation problem. A hybrid evolutionary algorithm has been defined combining Local Repair Algorithm and Modified Genetic Algorithm to generate the best course assignment. After analyzing the collected dataset, all the necessa

Heming Du, Chen Liu, Ming Wang, Lincheng Li

Existing gait recognition methods typically identify individuals based on the similarity between probe and gallery samples. However, these methods often neglect the fact that the gallery may not contain identities corresponding to the probes, leading to incorrect recognition.To identify Out-of-Gallery (OOG) gait queries, we propose an Evidence-based Match-st

Yuru Jing, Zixuan Li

A chest X-ray is one of the most widely available radiological examinations for diagnosing and detecting various lung illnesses. The National Institutes of Health (NIH) provides an extensive database, ChestX-ray8 and ChestXray14, to help establish a deep learning community for analysing and predicting lung diseases. ChestX-ray14 consists of 112,120 frontal-v

Siddhartha Datta

The advent of personalized reality has arrived. Rapid development in AR/MR/VR enables users to augment or diminish their perception of the physical world. Robust tooling for digital interface modification enables users to change how their software operates. As digital realities become an increasingly-impactful aspect of human lives, we investigate the design

Letizia Angeli, Julien Barré, Martin Kolodziejczyk, Michela Ottobre

This paper is composed of two parts. In the first part we consider McKean-Vlasov Partial Differential Equations (PDEs), obtained as thermodynamic limits of interacting particle systems (i.e. in the limit $N\to\infty$, where N is the number of particles). It is well-known that, even when the particle system has a unique invariant measure (stationary solution)

Stefano Longhi

Bloch-Zener oscillations (BZO), i.e. the interplay between Bloch oscillations and Zener tunneling in two-band lattices under an external dc force, are ubiquitous in different areas of wave physics, including photonics. While in Hermitian systems such oscillations are rather generally aperiodic and only accidentally periodic, in non-Hermitian (NH) lattices BZ

Anuradha Gupta, Deepak Jain

It is now widely accepted that the concept of negative absolute temperature is real one and not just theoretical curiosity. In this brief report, by combining the formalism used in the statistical mechanics and thermodynamics, we have explained some aspects of negative temperature ( both mathematically and graphically ) in the two level system. We believe th

Kohei Kikuta, Naoki Koseki, Genki Ouchi

In this paper, we construct a compactification of the space of Bridgeland stability conditions on a smooth projective curve, as an analogue of Thurston compactifications in Teichm\"uller theory. In the case of elliptic curves, we compare our results with the classical one of the torus via homological mirror symmetry and give the Nielsen-Thurston classificati

Michael Hein, Christian Spånslätt

We study transport along interfaced edge segments of fractional quantum Hall states hosting non-Abelian Majorana modes. With an incoherent model approach, we compute, for edge segments based on Pfaffian, anti-Pfaffian, and particle-hole-Pfaffian topological orders, thermal conductances, voltage biased noise, and delta-$T$ noise. We determine how the thermal

Ian Rouse, Vladimir Lobaskin

Predicting the adsorption affinity of a small molecule to a target surface is of importance to a range of fields, from catalysis to drug delivery and human safety, but a complex task to perform computationally when taking into account the effects of the surrounding medium. We present a flexible machine-learning approach to predict potentials of mean force (P

Andreas Demleitner

Hyperelliptic manifolds are natural generalizations of hyperelliptic surfaces in dimensions. We provide a full classification of the groups, which arise as the holonomy group of a 4-dimensional hyperelliptic manifold. The classification is mostly based on group- and representation-theoretic methods.

Fangzhou Wang, Qijing Wang, Bangqi Fu, Shui Jiang

Due to cost benefits, supply chains of integrated circuits (ICs) are largely outsourced nowadays. However, passing ICs through various third-party providers gives rise to many threats, like piracy of IC intellectual property or insertion of hardware Trojans, i.e., malicious circuit modifications. In this work, we proactively and systematically harden the phy

Arvind Ayyer, Shubham Sinha

For a positive integer $t \geq 2$, the $t$-core of a partition plays an important role in modular representation theory and combinatorics. We initiate the study of $t$-cores of partitions contained in an $r \times s$ rectangle. Our main results are as follows. We first give a simple formula for the number of partitions in the rectangle that are themselves $t

Oliver Clarke, Akihiro Higashitani, Francesca Zaffalon

We study restricted chain-order polytopes associated to Young diagrams using combinatorial mutations. These polytopes are obtained by intersecting chain-order polytopes with certain hyperplanes. The family of chain-order polytopes associated to a poset interpolate between the order and chain polytopes of the poset. Each such polytope retains properties of th

Fu-Peng Li, Hong-Liang Lü, Long-Gang Pang, Guang-You Qin

The interactions of quarks and gluons are strong at non-perturbative region. The equation of state (EoS) of a strongly-interacting quantum chromodynamics (QCD) medium can only be studied using the first-principle lattice QCD calculations. However, the complicated QCD EoS can be reproduced using simple statistical formula by treating the medium as a free part

Haoran Li, Cheng Li, Weijian Huang, Xiawu Zheng

Brain tumor segmentation based on multi-modal magnetic resonance imaging (MRI) plays a pivotal role in assisting brain cancer diagnosis, treatment, and postoperative evaluations. Despite the achieved inspiring performance by existing automatic segmentation methods, multi-modal MRI data are still unavailable in real-world clinical applications due to quite a

Matteo Santandrea, Kai-Hong Luo, Michael Stefszky, Jan Sperling

The success of quantum technologies is intimately connected to the possibility of using them in real-world applications. To this aim, we study the sensing capabilities of quantum SU(1,1) interferometers in the single-photon-pair regime and in the presence of losses, a situation highly relevant to practical realistic measurements of extremely photosensitive m

M. Koussour, S. H. Shekh, M. Bennai, T. Ouali

In this paper, we investigate the existence of bulk viscous FLRW cosmological models in a recently proposed extended symmetric teleparallel gravity or $f\left( Q,T\right) $ gravity in which $Q$ is the non-metricity and $T$ is the trace of the energy-momentum tensor. We consider a simple coupling between matter and non-metricity, specifically, $f\left( Q,T\ri

Fang Zhang, Gao-Chan Yong

Based on the transport model isospin-dependent Boltzmann-Uehling-Uhlenbeck coupled with a phase-space coalescence afterburner, we studied the effects of the high-momentum tail (HMT) of nucleon momentum distribution in initialization in 197Au+197Au reactions at a beam energy of 400 MeV/nucleon with different impact parameters.We found remarkable impact parame

A. A. Chernyshev, V. A. Saleev

In the article, we study single and pair $J/\psi$ hadroproduction in the Improved Color Evaporation Model via the Parton Reggeization Approach. The last one is based on $k_T$-factorization of hard processes in multi-Regge kinematics, the Kimber-Martin-Ryskin-Watt model for unintegrated parton distribution functions, and the effective field theory of Reggezie

Renato Cordeiro de Amorim

In this paper we discuss some of the key properties of sum-free subsets of abelian groups. Our discussion has been designed with a broader readership in mind, and is hence not overly technical. We consider answers to questions like: how many sum-free subsets are there in a given abelian group $G$? what are its sum-free subsets of maximum cardinality? what is

R. Szakáts, Cs. Kiss, J. L. Ortiz, N. Morales

The rotational states of the members in the dwarf planet - satellite systems in the transneptunian region are determined by the formation conditions and the tidal interaction between the components, and these rotational characteristics are the prime tracers of their evolution. Previously a number of authors claimed highly diverse values for the rotation peri

Francesco Marino, Gianluca Colò, Xavier Roca-Maza, Enrico Vigezzi

Nuclear matter is studied within the Density Functional Theory (DFT) framework. Our method employs a finite number of nucleons in a box subject to periodic boundary conditions, in order to simulate infinite matter and study its response to an external static potential. We detail both the theoretical formalism and its computational implementation for pure neu

Wentao Yu, Hengtao He, Xianghao Yu, Shenghui Song

Reliability is of paramount importance for the physical layer of wireless systems due to its decisive impact on end-to-end performance. However, the uncertainty of prevailing deep learning (DL)-based physical layer algorithms is hard to quantify due to the black-box nature of neural networks. This limitation is a major obstacle that hinders their practical d

Jean Cardinal, Lionel Pournin, Mario Valencia-Pabon

The associahedron $\mathcal{A}(G)$ of a graph $G$ has the property that its vertices can be thought of as the search trees on $G$ and its edges as the rotations between two search trees. If $G$ is a simple path, then $\mathcal{A}(G)$ is the usual associahedron and the search trees on $G$ are binary search trees. Computing distances in the graph of $\mathcal{

Chu Guo, Yi Fan, Zhiqian Xu, Honghui Shang

Quantum Computing is believed to be the ultimate solution for quantum chemistry problems. Before the advent of large-scale, fully fault-tolerant quantum computers, the variational quantum eigensolver~(VQE) is a promising heuristic quantum algorithm to solve real world quantum chemistry problems on near-term noisy quantum computers. Here we propose a highly p

Matteo Rizzo, Cristina Conati, Daesik Jang, Hui Hu

The opacity of deep learning models constrains their debugging and improvement. Augmenting deep models with saliency-based strategies, such as attention, has been claimed to help get a better understanding of the decision-making process of black-box models. However, some recent works challenged saliency's faithfulness in the field of Natural Language Process

Giulio Magli, Juan Antonio Belmonte

In a recent paper in Antiquity (Darvill 2022), the author has proposed that the project of the <<sarsen>> phase (stage 2) of Stonehenge (c. 2600 BC) was conceived in order to represent a calendar year of 365.25 days, that is, a calendar identical in duration to the Julian calendar. The aim of the present paper is to show that this idea is totally unsubstanti

Ayush Maheshwari, Nikhil Singh, Amrith Krishna, Ganesh Ramakrishnan

Sanskrit is a classical language with about 30 million extant manuscripts fit for digitisation, available in written, printed or scannedimage forms. However, it is still considered to be a low-resource language when it comes to available digital resources. In this work, we release a post-OCR text correction dataset containing around 218,000 sentences, with 1

T. Miener, D. Nieto, V. Gammaldi, D. Kerszberg

One of the most pressing questions for modern physics is the nature of dark matter (DM). Several efforts have been made to model this elusive kind of matter. The largest fraction of DM cannot be made of any of the known particles of the Standard Model (SM). We focus on brane world theory as a prospective framework for DM candidates beyond the SM of particle

Pavel Paták, Martin Tancer

The main goal of this paper is to show that shellability is NP-hard for triangulated d-balls (this also gives hardness for triangulated d-manifolds/d-pseudomanifolds with boundary) as soon as d is at least 3. This extends our earlier work with Goaoc, Pat\'akov\'a and Wagner on hardness of shellability of 2-complexes and answers some questions implicitly rais

Luca Marchionna, Giulio Pugliese, Mauro Martini, Simone Angarano

The game of Jenga represents an inspiring benchmark for developing innovative manipulation solutions for complex tasks. Indeed, it encouraged the study of novel robotics methods to successfully extract blocks from the tower. A Jenga game round undoubtedly embeds many traits of complex industrial or surgical manipulation tasks, requiring a multi-step strategy

László Csató, Kolos Csaba Ágoston, Sándor Bozóki

Incomplete pairwise comparison matrices contain some missing judgements. A natural approach to estimate these values is provided by minimising a reasonable measure of inconsistency after unknown entries are replaced by variables. Two widely used inconsistency indices for this purpose are Saaty's inconsistency index and the geometric inconsistency index, whic

Abdallah Slaoui

Quantum resource theories allow us to quantify a useful quantum phenomenon, to develop new protocols for its detection and determine the exact processes that maximize its use for practical tasks. These theories aim at transforming physical phenomena, such as entanglement and quantum coherence, into useful properties for the execution of concrete tasks relate

Andrei K. Lerner

In this paper we consider weighted Morrey spaces ${\mathcal M}_{\lambda, {\mathcal F}}^p(w)$ adapted to a family of cubes ${\mathcal F}$, with norm $$\|f\|_{{\mathcal M}_{\lambda, {\mathcal F}}^p(w)}:=\sup_{Q\in {\mathcal F}}\left(\frac{1}{|Q|^{\lambda}}\int_Q|f|^pw\right)^{1/p},$$ and the question we deal with is whether a Muckenhoupt-type condition charact

Yanbo Xie, Deli Shi, Wenhui Wang, Ziheng Wang

When channels were scaled down to the size of hydrated ions, ionic Coulomb blockade was discovered. However, the experimental CB phenomenon was rarely reported since Feng et.al., discovered in MoS2 nanopore. By using latent-track membranes with diameter of 0.6 nm, we found the channels are nearly non-conductive in small voltage due to the blockade of cations

Liping Zhang, Xiangnan Zhou, Qingguo Li

The Hofmann-Mislove theorem states that in a sober space, the nonempty Scott open filters of its open set lattice correspond bijectively to its compacts saturated sets. In this paper, the concept of $c$-well-filtered spaces is introduced. We show that a retract of a $c$-well-filtered space is $c$-well-filtered and a locally Lindel\"{o}f and $c$-well-filtered

Charalambos Themistocleous

Neurodegeneration characterizes individuals with different dementia subtypes (e.g., individuals with Alzheimer's Disease, Primary Progressive Aphasia, and Parkinson's Disease), leading to progressive decline in cognitive, linguistic, and social functioning. Speech and language impairments are early symptoms in individuals with focal forms of neurodegenerativ

Gaichao Li, Jinsong Chen, Kun He

By incorporating the graph structural information into Transformers, graph Transformers have exhibited promising performance for graph representation learning in recent years. Existing graph Transformers leverage specific strategies, such as Laplacian eigenvectors and shortest paths of the node pairs, to preserve the structural features of nodes and feed the

Lili Wang, Xuehuai Shi, Yi Liu

Recently, virtual reality (VR) technology has been widely used in medical, military, manufacturing, entertainment, and other fields. These applications must simulate different complex material surfaces, various dynamic objects, and complex physical phenomena, increasing the complexity of VR scenes. Current computing devices cannot efficiently render these co

Kaiwen Jiang, Shu-Yu Chen, Feng-Lin Liu, Hongbo Fu

Recent methods for synthesizing 3D-aware face images have achieved rapid development thanks to neural radiance fields, allowing for high quality and fast inference speed. However, existing solutions for editing facial geometry and appearance independently usually require retraining and are not optimized for the recent work of generation, thus tending to lag

Zihan Guo, Yan Wu, Lifang Wang, Junzhi Zhang

The recent proliferation of the research on multi-agent deep reinforcement learning (MDRL) offers an encouraging way to coordinate multiple connected and automated vehicles (CAVs) to pass the intersection. In this paper, we apply a value decomposition-based MDRL approach (QMIX) to control various CAVs in mixed-autonomy traffic of different densities to effic

Yeqi Wang, Weijian Huang, Cheng Li, Xiawu Zheng

Multi-contrast magnetic resonance imaging (MRI)-based automatic auxiliary glioma diagnosis plays an important role in the clinic. Contrast-enhanced MRI sequences (e.g., contrast-enhanced T1-weighted imaging) were utilized in most of the existing relevant studies, in which remarkable diagnosis results have been reported. Nevertheless, acquiring contrast-enhan

Hao Yang, Xuening Cao, Zhi-Gang Hu, Yimeng Gao

Whispering gallery mode (WGM) microcavities have been widely used for high-sensitivity ultrasound detection, due to their optical and mechanical resonances enhanced sensitivity. The ultrasound sensitivity of the cavity optomechanical system is fundamentally limited by the thermal noise. In this work, we theoretically and experimentally investigate the therma

Johannes Riesselmann, Daniel Balzani

A novel finite element formulation for gradient-regularized damage models is presented which allows for the robust, efficient, and mesh-independent simulation of damage phenomena in engineering and biological materials. The paper presents a Lagrange multiplier based mixed finite element formulation for finite strains. Thereby, no numerical stabilization or p

Calin Ioan Hojbota, Mohammad Mirzaie, Do Yeon Kim, Tae Gyu Pak

Petawatt-class lasers can produce multi-GeV electron beams through laser wakefield electron acceleration. As a by-product, the accelerated electron beams can generate broad synchrotron-like radiation known as betatron radiation. In the present work, we measure the properties of the radiation produced from 2 GeV, 215 pC electron beams, which shows a broad rad

Shiyu Zhou, Weiming An, Siqin Ding, Jianfei Hua

Plasma wakefield acceleration in the nonlinear blowout regime has been shown to provide high acceleration gradients and high energy transfer efficiency while maintaining great beam quality for electron acceleration. In contrast, research on positron acceleration in this regime is still in a preliminary stage. We find that an on-axis electron filament can be

Adam Day, Noam Greenberg, Matthew Harrison-Trainor, Dan Turetsky

We give a new and effective classification of all Borel Wadge classes of subsets of Baire space. This relies on the true stage machinery originally developed by Montalb\'an. We use this machinery to give a new proof of Louveau and Saint-Raymond's separation theorem for Borel Wadge classes. This gives a proof of Borel Wadge determinacy in the subsystem $\text

Atsuhisa Ota, Hee-Jong Seo, Shun Saito, Florian Beutler

The late-time nonlinear Lagrangian displacement field is highly correlated with the initial field, so reconstructing it could enable us to extract primordial cosmological information. Our previous work [1] carefully studied the displacement field reconstructed from the late time density field using the iterative method proposed by Ref. [2] and found that it

Chen Shani, Jonathan Zarecki, Dafna Shahaf

Machine learning (ML) is revolutionizing the world, affecting almost every field of science and industry. Recent algorithms (in particular, deep networks) are increasingly data-hungry, requiring large datasets for training. Thus, the dominant paradigm in ML today involves constructing large, task-specific datasets. However, obtaining quality datasets of such

Adam Day, Noam Greenberg, Matthew Harrison-Trainor, Dan Turetsky

We present the true stages machinery and illustrate its applications to descriptive set theory. We use this machinery to provide new proofs of the Hausdorff-Kuratowski and Wadge theorems on the structure of ${\mathbf \Delta}^0_\xi$, Louveau and Saint-Raymond's separation theorem, and Louveau's separation theorem.

Priyashkumar Mistry, Kamlesh Pathak, Georgios Lekkas, Aniket Prasad

We present here a validation of sub-Saturn exoplanet TOI-181b orbiting a K spectral type star TOI-181 (Mass: 0.822 $\pm$ 0.04 M$_{\odot}$, Radius: 0.745 $\pm$ 0.02 R$_{\odot}$, Temperature: 4994 $\pm$ 50 K) as a part of Validation of Transiting Exoplanets using Statistical Tools (VaTEST) project. TOI-181b is a planet with radius 6.95 $\pm$ 0.08 R$_{\oplus}$,

Youru Li, Zhenfeng Zhu, Xiaobo Guo, Shaoshuai Li

Risk prediction, as a typical time series modeling problem, is usually achieved by learning trends in markers or historical behavior from sequence data, and has been widely applied in healthcare and finance. In recent years, deep learning models, especially Long Short-Term Memory neural networks (LSTMs), have led to superior performances in such sequence rep

Kennard Yanting Chan, Guosheng Lin, Haiyu Zhao, Weisi Lin

We propose IntegratedPIFu, a new pixel aligned implicit model that builds on the foundation set by PIFuHD. IntegratedPIFu shows how depth and human parsing information can be predicted and capitalised upon in a pixel-aligned implicit model. In addition, IntegratedPIFu introduces depth oriented sampling, a novel training scheme that improve any pixel aligned