October 2022 arXiv papers — page 72

Oleg Antipin, Alexander Bednyakov, Jahmall Bersini, Pantelis Panopoulos

Quantum field theories with global symmetries simplify considerably in the large-charge limit allowing to compute correlators via a semiclassical expansion in the inverse powers of the conserved charges. A generalization of the approach to gauge symmetries has faced the problem of defining gauge-independent observables and, therefore, has not been developed

Jared Moore

Some claim language models understand us. Others won't hear it. To clarify, I investigate three views of human language understanding: as-mapping, as-reliability and as-representation. I argue that while behavioral reliability is necessary for understanding, internal representations are sufficient; they climb the right hill. I review state-of-the-art languag

Weilong Guan, Kaihan Yang, Yinsheng Chen, Zhong Guan

Physics-informed neural networks (PINNs) have been widely applied in different fields due to their effectiveness in solving partial differential equations (PDEs). However, the accuracy and efficiency of PINNs need to be considerably improved for scientific and commercial use. To address this issue, we systematically propose a novel dimension-augmented physic

Yangyi Chen, Hongcheng Gao, Ganqu Cui, Fanchao Qi

Textual adversarial samples play important roles in multiple subfields of NLP research, including security, evaluation, explainability, and data augmentation. However, most work mixes all these roles, obscuring the problem definitions and research goals of the security role that aims to reveal the practical concerns of NLP models. In this paper, we rethink t

T. Bloom, L. Bos, N. Levenberg

The notion of asymptotic Fekete arrays, arrays of points in a compact set $K\subset {\bf C}^d$ which behave asymptotically like Fekete arrays, has been well-studied, albeit much more recently in dimensions $d>1$. Here we show that one can allow a more flexible definition where the points in the array need not lie in $K$. Our results, which work in the genera

Zachary P. Adams, James MacLaurin

We study the dynamics of waves, oscillations, and other spatio-temporal patterns in stochastic evolution systems, including SPDE and stochastic integral equations. Representing a given pattern as a smooth, stable invariant manifold of the deterministic dynamics, we reduce the stochastic dynamics to a finite dimensional SDE on this manifold using the isochron

Soraya Caixeiro, Casper Kunstmann-Olsen, Marcel Schubert, Joseph Hill

Multiplexed, specific and sensitive detection of antigens is critical for the rapid and accurate diagnosis of disease and the informed development of personalized treatment plans. Here, we show that polymer microsphere lasers can be used as photonic sensors to monitor and quantify direct surface binding of biomolecules via changes in the refractive index. Th

Tomasz Woźniak, Umm-e-hani Asghar, Paulo E. Faria Junior, Muhammad S. Ramzan

MA2Z4 monolayers form a new class of hexagonal non-centrosymmetric materials hosting extraordinary spin-valley physics. While only two compounds (MoSi2N4 and WSi2N4) were recently synthesized, theory predicts interesting (opto)electronic properties of a whole new family of such two-dimensional materials. Here, the chemical trends of band gaps and spin-orbit

Xin Xu, Xiang Chen, Ningyu Zhang, Xin Xie

This paper presents an empirical study to build relation extraction systems in low-resource settings. Based upon recent pre-trained language models, we comprehensively investigate three schemes to evaluate the performance in low-resource settings: (i) different types of prompt-based methods with few-shot labeled data; (ii) diverse balancing methods to addres

Barış Can Esmer, Jacob Focke, Dániel Marx, Paweł Rzążewski

The goal of this paper is to investigate a family of optimization problems arising from list homomorphisms, and to understand what the best possible algorithms are if we restrict the problem to bounded-treewidth graphs. For a fixed $H$, the input of the optimization problem LHomVD($H$) is a graph $G$ with lists $L(v)$, and the task is to find a set $X$ of ve

Lars Prehn, Pawel Foremski, Oliver Gasser

The Internet is a critical resource in the day-to-day life of billions of users. To support the growing number of users and their increasing demands, operators have to continuously scale their network footprint -- e.g., by joining Internet Exchange Points -- and adopt relevant technologies -- such as IPv6. IPv6, however, has a vastly larger address space com

Aryan Pedawi, Pawel Gniewek, Chaoyi Chang, Brandon M. Anderson

Virtual, make-on-demand chemical libraries have transformed early-stage drug discovery by unlocking vast, synthetically accessible regions of chemical space. Recent years have witnessed rapid growth in these libraries from millions to trillions of compounds, hiding undiscovered, potent hits for a variety of therapeutic targets. However, they are quickly appr

Avery Eddins, Kyung-hwan Lee, Alessandra Corsi, Imre Bartos

The multi-messenger detection of GW170817 showed that binary neutron star (BNS) mergers are progenitors of (at least some) short gamma-ray bursts (GRBs), and that short GRB jets (and their afterglows) can have structures (and observational properties) more complex than predicted by the standard top-hat jet scenario. Indeed, the emission from the structured j

M. Hukkanen, W. Ryssens, P. Ascher, M. Bender

Precision mass measurements of neutron-rich rhodium isotopes have been performed at the JYFLTRAP Penning trap mass spectrometer at the Ion Guide Isotope Separator On-Line (IGISOL) facility. We report results on ground- and isomeric-state masses in $^{110,112,114,116,118}$Rh and the very first mass measurement of $^{120}$Rh. The isomeric states were separated

Jia Ren, Yun Wang, Lu-Lu Zhang, Zi-Gao Dai

In this paper, we model the dynamics and radiation physics of the rarity event GRB 221009A afterglow in detail. By introducing a top-hat jet that propagates in an environment dominated by stellar winds, we explain the publicly available observations of afterglow associated with GRB 221009A over the first week. It is predicted that GRB 221009A emits a luminou

Reem Hazim, Hind Saddiki, Bashar Alhafni, Muhamed Al Khalil

This demo paper presents a Google Docs add-on for automatic Arabic word-level readability visualization. The add-on includes a lemmatization component that is connected to a five-level readability lexicon and Arabic WordNet-based substitution suggestions. The add-on can be used for assessing the reading difficulty of a text and identifying difficult words as

Jeppe R. Andersen, Hitham Hassan, Andreas Maier, Jérémy Paltrinieri

We present all-order predictions for Higgs boson production plus at least one jet which are accurate to leading logarithm in $\hat s/|p_\perp|^2$. Our calculation includes full top and bottom quark mass dependence at all orders in the logarithmic part, and to highest available order in the tree-level matching. The calculation is implemented in the framework

Pravendra Singh, Pratik Mazumder, Mohammed Asad Karim

In order to address real-world problems, deep learning models are jointly trained on many classes. However, in the future, some classes may become restricted due to privacy/ethical concerns, and the restricted class knowledge has to be removed from the models that have been trained on them. The available data may also be limited due to privacy/ethical concer

Tunazzina Islam, Shamik Roy, Dan Goldwasser

Social media platforms are currently the main channel for political messaging, allowing politicians to target specific demographics and adapt based on their reactions. However, making this communication transparent is challenging, as the messaging is tightly coupled with its intended audience and often echoed by multiple stakeholders interested in advancing

Lu Zeng, Sree Hari Krishnan Parthasarathi, Dilek Hakkani-Tur

Text-to-SQL task maps natural language utterances to structured queries that can be issued to a database. State-of-the-art (SOTA) systems rely on finetuning large, pre-trained language models in conjunction with constrained decoding applying a SQL parser. On the well established Spider dataset, we begin with Oracle studies: specifically, choosing an Oracle h

B. Eslam Panah

Two-dimensional ($2D$) Lifshitz-like black holes in special $F(R)$ gravity cases are extracted. We indicate an essential singularity at $r=0$, covered with an event horizon. Then conserved and thermodynamic quantities such as temperature, mass, entropy, and the heat capacity of $2D$ Lifshitz-like black holes in $F(R)$ gravity are evaluated. Our analysis show

Joshua S. North, Christopher K. Wikle, Erin M. Schliep

Many real-world scientific processes are governed by complex nonlinear dynamic systems that can be represented by differential equations. Recently, there has been increased interest in learning, or discovering, the forms of the equations driving these complex nonlinear dynamic system using data-driven approaches. In this paper we review the current literatur

S. Fathi Hafshejani, D. Gaur, S. Hossain, R. Benkoczi

We propose a method for computing binary orthogonal non-negative matrix factorization (BONMF) for clustering and classification. The method is tested on several representative real-world data sets. The numerical results confirm that the method has improved accuracy compared to the related techniques. The proposed method is fast for training and classificatio

Julian Todt, Simon Hanisch, Thorsten Strufe

Face images are a rich source of information that can be used to identify individuals and infer private information about them. To mitigate this privacy risk, anonymizations employ transformations on clear images to obfuscate sensitive information, all while retaining some utility. Albeit published with impressive claims, they sometimes are not evaluated wit

Shiva T. Konakanchi, Jukka I. Väyrynen, Yong P. Chen, Pramey Upadhyaya

After a decade of intense theoretical and experimental efforts, demonstrating braiding of Majorana modes remains an unsolved problem in condensed matter physics due to platform specific challenges. In this work, we propose topological superconductor -- magnetic multilayer heterostructures with on-chip microwave cavity readout as a novel platform for initiali

Sarika Karanth, Shobha M. Erappa

Three papers describing different methods to solve the inverse scattering problem of the reconstruction of the shape and/or impedance of an obstacle have been chosen for analysis. This literature review consists of an evaluation of these methods in which comparison of the assumptions, conditions, advantages, disadvantages, accuracy, and applicability has bee

Pradeep R. Varadwaj, Arpita Varadwaj, Helder M. Marques, Koichi Yamashita

This paper proposes a definition of the term "tetrel bond" based on the evidence documented in the current literature. It lists the donors, acceptors, as well as related characteristic features of tetrel bonds that are commonly observed in the crystalline phase and/or that emerge from first principles calculations in the solid-state and gas phases. These may

Ning Wei, Shuangqing Tang, Zeyuan Zhang

Since the orthogonality of the line-of-sight multiple input multiple output (LoS MIMO) channel is only available within the Rayleigh distance, coverage of communication systems is restricted due to the finite implementation spacing of antennas. However, media with different permittivity in the transmission path are likely to loosen the requirement for antenn

Goro Miyano, Kotaro Matsuo, Tomohiro, Kawatsu

In recent years, artificial intelligence has developed in a variety of fields and is now integrated into many aspects of our daily lives. This includes AI speakers,communication robots, and other interactive systems. We are interested in AI speaker like systems in which the robot responds to the user's needs and speech synthesis. We participated in an intera

Wei Peng, Wen Yao, Weien Zhou, Xiaoya Zhang

Physics-informed neural networks (PINNs) have been proposed to solve two main classes of problems: data-driven solutions and data-driven discovery of partial differential equations. This task becomes prohibitive when such data is highly corrupted due to the possible sensor mechanism failing. We propose the Least Absolute Deviation based PINN (LAD-PINN) to re

Werner M. J. van Weerdenburg, Anand Kamlapure, Eirik Holm Fyhn, Xiaochun Huang

BCS theory has been widely successful at describing elemental bulk superconductors. Yet, as the length scales of such superconductors approach the atomic limit, dimensionality as well as the environment of the superconductor can lead to drastically different and unpredictable superconducting behavior. Here, we report a threefold enhancement of the supercondu

Alberto Cabezas, Christopher Nemeth

We propose a new framework for efficiently sampling from complex probability distributions using a combination of normalizing flows and elliptical slice sampling (Murray et al., 2010). The central idea is to learn a diffeomorphism, through normalizing flows, that maps the non-Gaussian structure of the target distribution to an approximately Gaussian distribu

Linfeng Liu, Xu Han, Dawei Zhou, Li-Ping Liu

Subgraph similarity search, one of the core problems in graph search, concerns whether a target graph approximately contains a query graph. The problem is recently touched by neural methods. However, current neural methods do not consider pruning the target graph, though pruning is critically important in traditional calculations of subgraph similarities. On

Chowdhury Mohammad Sakib Anwar, Alexander Matros, Sonali SenGupta

We study a public good game with N citizens and a Governor who allocates resources from a common fund. Citizens may voluntarily contribute or be compelled to do so if audited, in which case shirkers face a penalty. The Governor decides how much of the fund to devote to public good provision, with the remainder embezzled. Crucially, the Governor's utility com

João Gustavo Atkinson Amorim, André Victória Matias, Allan Cerentini, Luiz Antonio Buschetto Macarini

Cervical cancer is a public health problem, where the treatment has a better chance of success if detected early. The analysis is a manual process which is subject to a human error, so this paper provides a way to analyze argyrophilic nucleolar organizer regions (AgNOR) stained slide using deep learning approaches. Also, this paper compares models for instan

Peipei Liu, Gaosheng Wang, Hong Li, Jie Liu

Named Entity Recognition (NER) on social media refers to discovering and classifying entities from unstructured free-form content, and it plays an important role for various applications such as intention understanding and user recommendation. With social media posts tending to be multimodal, Multimodal Named Entity Recognition (MNER) for the text with its a

Ding Li, Qichao Zhang, Shuai Lu, Yifeng Pan

This paper aims to tackle the interactive behavior prediction task, and proposes a novel Conditional Goal-oriented Trajectory Prediction (CGTP) framework to jointly generate scene-compliant trajectories of two interacting agents. Our CGTP framework is an end to end and interpretable model, including three main stages: context encoding, goal interactive predi

Bingyang Hu, Zhenghui Huo, Loredana Lanzani, Kevin Palencia

Consider a bounded, strongly pseudoconvex domain $D\subset \mathbb C^n$ with minimal smoothness (namely, the class $C^2$) and let $b$ be a locally integrable function on $D$. We characterize boundedness (resp., compactness) in $L^p(D), p > 1$, of the commutator $[b, P]$ of the Bergman projection $P$ in terms of an appropriate bounded (resp. vanishing) mean o

Longyuan Zhang, Ziyue Hou, Ji Wang, Ziang Liu

In this paper, we propose a novel reinforcement learning (RL) based path generation (RL-PG) approach for mobile robot navigation without a prior exploration of an unknown environment. Multiple predictive path points are dynamically generated by a deep Markov model optimized using RL approach for robot to track. To ensure the safety when tracking the predicti

Marin Biloš, Emanuel Ramneantu, Stephan Günnemann

Observations made in continuous time are often irregular and contain the missing values across different channels. One approach to handle the missing data is imputing it using splines, by fitting the piecewise polynomials to the observed values. We propose using the splines as an input to a neural network, in particular, applying the transformations on the i

Yunsheng Zhang, Jianguo Yao, Ruixiang Zhang, Siyang Chen

Deep Neural Network (DNN) based point cloud semantic segmentation has presented significant achievements on large-scale labeled aerial laser point cloud datasets. However, annotating such large-scaled point clouds is time-consuming. Due to density variations and spatial heterogeneity of the Airborne Laser Scanning (ALS) point clouds, DNNs lack generalization

Amit Milstein, Haoran Deng, Guy Revach, Hai Morgenstern

Pairs trading is a family of trading techniques that determine their policies based on monitoring the relationships between pairs of assets. A common pairs trading approach relies on describing the pair-wise relationship as a linear Space State (SS) model with Gaussian noise. This representation facilitates extracting financial indicators with low complexity

Henglin Huang, Chen Tang, Tyler Loakman, Frank Guerin

Story generation aims to generate a long narrative conditioned on a given input. In spite of the success of prior works with the application of pre-trained models, current neural models for Chinese stories still struggle to generate high-quality long text narratives. We hypothesise that this stems from ambiguity in syntactically parsing the Chinese language,

Sibaprasad Barik, Bappa Bisai

Given a bounded operator $Q$ on a Hilbert space $\mathcal{H}$, a pair of bounded operators $(T_1, T_2)$ on $\mathcal{H}$ is said to be $Q$-commuting if one of the following holds: \[ T_1T_2=QT_2T_1 \text{ or }T_1T_2=T_2QT_1 \text{ or }T_1T_2=T_2T_1Q. \] We give an explicit construction of isometric dilations for pairs of $Q$-commuting contractions for unitar

Fabio Briscese

We consider the effect of inter-atom interactions on the condensation temperature $T_c$ of an atomic Bose-Einstein condensate. We find an analytic expression of the shift in $T_c$ induced by interactions with respect the ideal non-interacting case, in the mean-field and semi-classical approximations. Such a shift is expressed in terms of the ratio $a/\lambda

Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye

Masked image modeling has demonstrated great potential to eliminate the label-hungry problem of training large-scale vision Transformers, achieving impressive performance on various downstream tasks. In this work, we propose a unified view of masked image modeling after revisiting existing methods. Under the unified view, we introduce a simple yet effective

Chao Hang, Zhengyang Bai, Weibin Li, Anatoly M. Kamchatnov

Dispersive shock waves (DSWs) are fascinating wave phenomena occurring in media when nonlinearity overwhelms dispersion (or diffraction). Creating DSWs with low generation power and realizing their active controls is desirable but remains a longstanding challenge. Here, we propose a scheme to generate weak-light DSWs and realize their manipulations in an ato

Yang Guan, Liye Tang, Chuanxiao Li, Shengbo Eben Li

Self-evolution is indispensable to realize full autonomous driving. This paper presents a self-evolving decision-making system based on the Integrated Decision and Control (IDC), an advanced framework built on reinforcement learning (RL). First, an RL algorithm called constrained mixed policy gradient (CMPG) is proposed to consistently upgrade the driving po

Michael Dillane, Evgeny A. Viktorov, Bryan Kelleher

Excitable photonic systems show promise for ultrafast analog computation, several orders of magnitude faster than biological neurons. Optically injected quantum dot lasers display several excitable mechanisms with dual state quantum lasers recently emerging as true all or none excitable artificial neurons. For use in applications, deterministic triggering is

Abhishek Mall, Kartik Ayyer

Single particle imaging (SPI) at X-ray free electron lasers (XFELs) is a technique to determine the 3D structure of nanoscale objects like biomolecules from a large number of diffraction patterns of copies of these objects in random orientations. Millions of low signal-to-noise diffraction patterns with unknown orientation are collected during an X-ray SPI e

Slimane Thabet, Romain Fouilland, Loic Henriet

Recently, efforts have been made in the community to design new Graph Neural Networks (GNN), as limitations of Message Passing Neural Networks became more apparent. This led to the appearance of Graph Transformers using global graph features such as Laplacian Eigenmaps. In our paper, we introduce a GNN architecture where the aggregation weights are computed

Hazan Daglayan, Simon Vary, Faustine Cantalloube, P. -A. Absil

Direct imaging of exoplanets is a challenging task due to the small angular distance and high contrast relative to their host star, and the presence of quasi-static noise. We propose a new statistical method for direct imaging of exoplanets based on a likelihood ratio detection map, which assumes that the noise after the background subtraction step obeys a L

Sang-Hyun Chun, Sung-Chul Yoon, Heeyoung Oh, Byeong-Gon Park

We report a spectroscopic study on red supergiant stars (RSGs) in the irregular dwarf galaxy IC 1613 in the Local Group. We derive the effective temperatures ($T_\mathrm{eff}$) and metallicities of 14 RSGs by synthetic spectral fitting to the spectra observed with the MMIRS instrument on the MMT telescope for a wavelength range from 1.16 $\mu$m to 1.23 $\mu$

Nicolaus Heuer, Dawid Kielak

We introduce the notion of quasi-BNS invariants, where we replace homomorphism to $\mathbb R$ by homogenous quasimorphisms to $\mathbb R$ in the theory of Bieri-Neumann-Strebel invariants. We prove that the quasi-BNS invariant $Q\Sigma(G)$ of a finitely generated group $G$ is open; we connect it to approximate finite generation of almost kernels of homogenou

Royi Rassin, Shauli Ravfogel, Yoav Goldberg

We study the way DALLE-2 maps symbols (words) in the prompt to their references (entities or properties of entities in the generated image). We show that in stark contrast to the way human process language, DALLE-2 does not follow the constraint that each word has a single role in the interpretation, and sometimes re-use the same symbol for different purpose

Charles Laroche, Andrés Almansa, Eva Coupeté, Matias Tassano

Plug & Play methods combine proximal algorithms with denoiser priors to solve inverse problems. These methods rely on the computability of the proximal operator of the data fidelity term. In this paper, we propose a Plug & Play framework based on linearized ADMM that allows us to bypass the computation of intractable proximal operators. We demonstrate the co

Alessandro Fassò, Jacopo Rodeschini, Alessandro Fusta Moro, Qendrim Shaboviq

The air in the Lombardy region, Italy, is one of the most polluted in Europe because of limited air circulation and high emission levels. There is a large scientific consensus that the agricultural sector has a significant impact on air quality. To support studies quantifying the role of the agricultural and livestock sectors on the Lombardy air quality, thi

Fernanda Torres, Sujoy K. Modak, Alfredo Aranda

We argue, in light of Collapse Model interpretation of quantum theory, that the fundamental division between the quantum and classical behaviors is analogous to the division of thermodynamic phases. A specific relationship between the collapse parameter $(\lambda)$ and the collapse length scale ($r_C$) plays the role of the coexistence curve in usual thermod

Chen Tang, Zhihao Zhang, Tyler Loakman, Chenghua Lin

To improve the performance of long text generation, recent studies have leveraged automatically planned event structures (i.e. storylines) to guide story generation. Such prior works mostly employ end-to-end neural generation models to predict event sequences for a story. However, such generation models struggle to guarantee the narrative coherence of separa

Conor McMenamin, Vanesa Daza, Bruno Mazorra

The always-available liquidity of automated market makers (AMMs) has been one of the most important catalysts in early cryptocurrency adoption. However, it has become increasingly evident that AMMs in their current form are not viable investment options for passive liquidity providers. This is large part due to the cost incurred by AMMs providing stale price

Elie Abdo, Nathan Glatt-Holtz, Mihaela Ignatova

We consider a stochastic electroconvection model describing the nonlinear evolution of a surface charge density in a two-dimensional fluid with additive stochastic forcing. We prove the existence and uniqueness of solutions, we define the corresponding Markov semigroup, and we study its Feller properties. When the noise forces enough modes in phase space, we

Jiuzhou Han, Ehsan Shareghi

Large-scale pre-trained language models (PLMs) have advanced Graph-to-Text (G2T) generation by processing the linearised version of a graph. However, the linearisation is known to ignore the structural information. Additionally, PLMs are typically pre-trained on free text which introduces domain mismatch between pre-training and downstream G2T generation tas

N. Arakawa, A. C. Fabian, G. J. Ferland, W. Ishibashi

Radiation pressure-driven outflows from luminous accreting supermassive black holes are an important part of active galactic nucleus (AGN) feedback. The effective Eddington limit, based on absorption of radiation by dust, not electron scattering, is revealed in the plane of AGN absorption column density $N_{\mathrm{H}}$ as a function of Eddington fraction $\

Yi-Ming Wu, Gang Fan, Fang-Fang Du

High-efficiency quantum information processing is equivalent to the fewest quantum resources and the simplest operations by means of logic qubit gates. Based on the reflection geometry of a single photon interacting with a three-level $\Lambda$-typle atom-cavity system, we present some refined protocols for realizing controlled-not (CNOT), Fredkin, and Toffo

Adam Black, Tal Malinovitch

We study the scattering properties of Schr\"{o}dinger operators with potentials that have short-range decay along a collection of rays in $\bbR^d$. This generalizes the classical setting of short-range scattering in which the potential is assumed to decay along \emph{all} rays. For these operators, we show that any state decomposes into an asymptotically fre

Lei Zhang, Xiaoke Wang, Michael Rawson, Radu Balan

Purpose To develop and evaluate a deep learning-based method (MC-Net) to suppress motion artifacts in brain magnetic resonance imaging (MRI). Methods MC-Net was derived from a UNet combined with a two-stage multi-loss function. T1-weighted axial brain images contaminated with synthetic motions were used to train the network. Evaluation used simulated T1 and

Alessandro Palmas

The recent advances in reinforcement learning have led to effective methods able to obtain above human-level performances in very complex environments. However, once solved, these environments become less valuable, and new challenges with different or more complex scenarios are needed to support research advances. This work presents DIAMBRA Arena, a new plat

Andrei I. Davydychev, York Schröder

We investigate the structure of a particular class of massive vacuum Feynman integrals at two loops. This class enjoys the linear relation $m_1+m_2=m_3$ between its three propagator masses, corresponding to zeros of the associated K\"all\'en function. Apart from having applications in thermal field theory, the integrals can be mapped onto one-loop three-poin

Kaike Zhang, Qi Cao, Gaolin Fang, Bingbing Xu

Unsupervised representation learning for dynamic graphs has attracted a lot of research attention in recent years. Compared with static graph, the dynamic graph is a comprehensive embodiment of both the intrinsic stable characteristics of nodes and the time-related dynamic preference. However, existing methods generally mix these two types of information int

Calum Buchanan, Brandon Du Preez, K. E. Perry, Puck Rombach

A simple graph $G=(V,E)$ on $n$ vertices is said to be recursively partitionable (RP) if $G \simeq K_1$, or if $G$ is connected and satisfies the following recursive property: for every integer partition $a_1, a_2, \dots, a_k$ of $n$, there is a partition $\{A_1, A_2, \dots, A_k\}$ of $V$ such that each $|A_i|=a_i$, and each induced subgraph $G[A_i]$ is RP (

Donghwa Kang, Seunghoon Lee, Hoon Sung Chwa, Seung-Hwan Bae

Different from existing MOT (Multi-Object Tracking) techniques that usually aim at improving tracking accuracy and average FPS, real-time systems such as autonomous vehicles necessitate new requirements of MOT under limited computing resources: (R1) guarantee of timely execution and (R2) high tracking accuracy. In this paper, we propose RT-MOT, a novel syste

Dušan D. Repovš, Mikhail V. Zaicev

We study polynomial identities of algebras with involution of nonassociative algebras over a field of characteristic zero. We prove that the growth of the sequence of $*$-codimensions of a finite-dimensional algebra is exponentially bounded. We construct a series of finite-dimensional algebras with fractional $*$-PI-exponent. We also construct a family of in

Antti Haimi, José Luis Romero

We consider smooth linear statistics of determinantal point processes on the complex plane, and their large scale asymptotics. We prove asymptotic normality in the finite variance case, where Soshnikov's theorem is not applicable. The setting is similar to that of Rider and Vir\'ag [Electron. J. Probab., 12, no. 45, 1238--1257, (2007)] for the complex plane,

Zhan-Feng Mai, Run-Qiu Yang

This paper proposes physical processes to use a Schwarzschild black hole as a rechargeable battery and nuclear reactor. As a rechargeable battery, it can at most transform 25\% of input mass into available electric energy in a controllable and slow way. We study its internal resistance, efficiency of discharging, maximum output power, cycle life and totally

Thomas Frick, Diego Antognini, Mattia Rigotti, Ioana Giurgiu

Aging civil infrastructures are closely monitored by engineers for damage and critical defects. As the manual inspection of such large structures is costly and time-consuming, we are working towards fully automating the visual inspections to support the prioritization of maintenance activities. To that end we combine recent advances in drone technology and d

Iiro Kumpulainen, Nikolaj Tatti

Finding densely connected groups of nodes in networks is a widely used tool for analysis in graph mining. A popular choice for finding such groups is to find subgraphs with a high average degree. While useful, interpreting such subgraphs may be difficult. On the other hand, many real-world networks have additional information, and we are specifically interes

Peipei Liu, Hong Li, Zhiyu Wang, Yimo Ren

Enterprise relation extraction aims to detect pairs of enterprise entities and identify the business relations between them from unstructured or semi-structured text data, and it is crucial for several real-world applications such as risk analysis, rating research and supply chain security. However, previous work mainly focuses on getting attribute informati

Ahmed Abbes, Michel Gros

Faltings initiated in 2005 a p-adic analogue of the (complex) Simpson correspondence whose construction has been taken up by various authors, according to several approaches. Following the one we initiated previously, we develop in this new monograph new features of the p-adic Simpson correspondence, inspired by our construction of the relative Hodge-Tate sp

Saleem Khan, S. Pirzada

Let $G$ be a simple connected simple graph of order $n$. The distance Laplacian matrix $D^{L}(G)$ is defined as $D^L(G)=Diag(Tr)-D(G)$, where $Diag(Tr)$ is the diagonal matrix of vertex transmissions and $D(G)$ is the distance matrix of $G$. The eigenvalues of $D^{L}(G)$ are the distance Laplacian eigenvalues of $G$ and are denoted by $\partial_{1}^{L}(G), \

Jordi Mill, Josquin Harrison, Benoit Legghe, Andy L. Olivares

Atrial fibrillation (AF) is considered the most common human arrhythmia. Around 99\% of thrombi in non-valvular AF are formed in the left atrial appendage (LAA). Studies suggest that abnormal LAA haemodynamics and the subsequently stagnated flow are the factors triggering clot formation. However, the relation between LAA morphology, the blood pattern and the

Ricardo Kleinlein, Cristina Luna-Jiménez, Fernando Fernández-Martínez

The impressive capacity shown by recent text-to-image diffusion models to generate high-quality pictures from textual input prompts has leveraged the debate about the very definition of art. Nonetheless, these models have been trained using text data collected from content-based labelling protocols that focus on describing the items and actions in an image b

Guilherme Zeus Dantas e Moura, Andrew Keisling, Astrid Lilly, Annika Mauro

Zeckendorf's Theorem implies that the Fibonacci number $F_n$ is the smallest positive integer that cannot be written as a sum of non-consecutive previous Fibonacci numbers. Catral et al. studied a variation of the Fibonacci sequence, the Fibonacci Quilt sequence: the plane is tiled using the Fibonacci spiral, and integers are assigned to the squares of the s

Liz Kneale, Michael Smy, Matthew Malek

A reconstruction algorithm has been developed to capitalize on advances in Cherenkov technology for reactor antineutrino detection. Large gadolinium-doped water (Gd-H$_2$O) Cherenkov detectors are a developing technology which use Gd loading to increase the visibility of the neutrons produced in inverse beta decay (IBD) interactions, which produce positron-n

Marija Bliznac Trebješanin, Sanda Bujačić Babić

A set $\{a, b, c, d\}$ of four non-zero distinct polynomials in $\mathbb{Z}[i][X]$ is said to be a Diophantine $D(4)$-quadruple if the product of any two of its distinct elements increased by 4 is a square of some polynomial in $\mathbb{Z}[i][X]$. In this paper we prove that every $D(4)$-quadruple in $\mathbb{Z}[i][X]$ is regular, or equivalently that the eq

Xian Xu

We revisit the standard bisimulation equalities in process models free of the restriction operator. As is well-known, in general the weak bisimilarity is coarser than the strong bisimilarity because it abstracts from internal actions. In absence of restriction, those internal actions become somewhat visible, so one might wonder if the weak bisimilarity is st

P. -A. Gourdain, M. B. Adams, M. Evans, H. R. Hasson

Radial basis functions are typically used when discretization sche-mes require inhomogeneous node distributions. While spawning from a desire to interpolate functions on a random set of nodes, they have found successful applications in solving many types of differential equations. However, the weights of the interpolated solution, used in the linear superpos

Itzhak Fouxon, Boris Rubinstein, Alexander M. Leshansky

In the present paper we theoretically study the shear force exerted on an infinite horizontal plane undergoing fast lateral oscillations in presence of a rigid particle suspended in the viscous liquid above the plate. The study is largely motivated by Quartz Crystal Microbalance (QCM-D) technique which relies on analyzing response (complex impedance) of fast

Daniel Montero Hernández, Jorge Peña Queralta, Tomi Westerlund

The increased use of Internet of Things (IoT) devices -- from basic sensors to robust embedded computers -- has boosted the demand for information processing and storing solutions closer to these devices. Edge computing has been established as a standard architecture for developing IoT solutions, since it can optimize the workload and capacity of systems tha

Nicholas Loutrel, Paolo Pani, Nicolás Yunes

In general relativity, isolated black holes obey the no hair theorems, which fix the multipolar structure of their exterior spacetime. However, in modified gravity, or when the compact objects are not black holes, the exterior spacetime may have a different multipolar structure. When two black holes are in a binary, this multipolar structure determines the m

Xin Wang, Junichi Yamagishi

A good training set for speech spoofing countermeasures requires diverse TTS and VC spoofing attacks, but generating TTS and VC spoofed trials for a target speaker may be technically demanding. Instead of using full-fledged TTS and VC systems, this study uses neural-network-based vocoders to do copy-synthesis on bona fide utterances. The output data can be u

Thomas E. Cecil

This is a survey of local and global classification results concerning Dupin hypersurfaces in $S^n$ (or ${\bf R}^n$) that have been obtained in the context of Lie sphere geometry. The emphasis is on results that relate Dupin hypersurfaces to isoparametric hypersurfaces in spheres. Along with these classification results, many important concepts from Lie sphe

Olan Hall, Francis Dompae, Ibrahim Wahab, Fred Mawunyo Dzanku

This paper reviews the state of the art in satellite and machine learning based poverty estimates and finds some interesting results. The most important factors correlated to the predictive power of welfare in the reviewed studies are the number of pre-processing steps employed, the number of datasets used, the type of welfare indicator targeted, and the cho

Federico D'Onofrio, Giorgio Grani, Marta Monaci, Laura Palagi

In recent years, there has been growing attention to interpretable machine learning models which can give explanatory insights on their behaviour. Thanks to their interpretability, decision trees have been intensively studied for classification tasks and, due to the remarkable advances in mixed integer programming (MIP), various approaches have been proposed

Linda S. L. Tan

In stochastic variational inference, use of the reparametrization trick for the multivariate Gaussian gives rise to efficient updates for the mean and Cholesky factor of the covariance matrix, which depend on the first order derivative of the log joint model density. In this article, we show that an alternative unbiased gradient estimate for the Cholesky fac

Margherita Ronchini, Yasser Rezaeiyan, Milad Zamani, Gabriella Panuccio

Therapeutic intervention in neurological disorders still relies heavily on pharmacological solutions, while the treatment of patients with drug resistance remains an open challenge. This is particularly true for patients with epilepsy, 30% of whom are refractory to medications. Implantable devices for chronic recording and electrical modulation of brain acti

Valentin Hernandez, Benjamin Schraen

If $\bar\rho$ is an automorphic modulo $p$ Galois representation, it is natural to wonder if automorphic points are Zariski dense in the deformation space of $\bar\rho$. We prove new results in this direction in the case of a unitary group split (and unramified) at $p$. Namely, if $\bar\rho$ is associated to an automorphic form for a unitary group (which con

Xabier Morales, Jordi Mill, Guillem Simeon, Kristine A. Juhl

The assessment of left atrial appendage (LAA) thrombogenesis has experienced major advances with the adoption of patient-specific computational fluid dynamics (CFD) simulations. Nonetheless, due to the vast computational resources and long execution times required by fluid dynamics solvers, there is an ever-growing body of work aiming to develop surrogate mo

Shixin Zhu, Ruhao Wan

Let $q=p^m$ be a prime power and $e$ be an integer with $0\leq e\leq m-1$. $e$-Galois self-dual codes are generalizations of Euclidean $(e=0)$ and Hermitian ($e=\frac{m}{2}$ with even $m$) self-dual codes. In this paper, for a linear code $\C$ and a nonzero vector $\bm{u}\in \F_q^n$, we give a sufficient and necessary condition for the dual extended code $\u

Philipp Richter, Oliver Gasser, Arthur Berger

While scans of the IPv4 space are ubiquitous, today little is known about scanning activity in the IPv6 Internet. In this work, we present a longitudinal and detailed empirical study on large-scale IPv6 scanning behavior in the Internet, based on firewall logs captured at some 230,000 hosts of a major Content Distribution Network (CDN). We develop methods to

Mengchu Cai, Tianbo Liu, Bo-Qiang Ma

We study the lepton pair production in the photodisintegration of the deuteron process. The complete seven-fold differential cross section is calculated via the Bethe-Heitler mechanism with final state interactions taken into account. The deuteron bound state is described by a relativistic covariant deuteron-nucleon vertex. With numerical results, we find th