May 2023 arXiv papers — page 157

Matteo Pagin, Sandra Lagén, Biljana Bojović, Michele Polese

Channel modeling is a fundamental task for the design and evaluation of wireless technologies and networks, before actual prototyping, commercial product development and real deployments. The recent trends of current and future mobile networks, which include large antenna systems, massive deployments, and high-frequency bands, require complex channel models

Justin Salez

We prove that the absolute spectral gap of any monotone Markov chain coincides with its optimal Ollivier-Ricci curvature, where the word `optimal' refers to the choice of the underlying metric. Moreover, we provide a new expression in terms of local variations of increasing functions, which has several practical advantages over the traditional variational fo

Simona Diaconu

Traces of large powers of real-valued Wigner matrices are known to have Gaussian fluctuations: for $A=\frac{1}{\sqrt{n}}(a_{ij})_{1 \leq i,j \leq n}\in \mathbb{R}^{n \times n}, A=A^T$ with $(a_{ij})_{1 \leq i \leq j \leq n}$ i.i.d., symmetric, subgaussian, $\mathbb{E}[a^{2}_{11}]=1,$ and $p=o(n^{2/3}),$ as $n,p \to \infty,$ $\frac{\sqrt{\pi}}{2^{p}}(tr(A^p)-

Jasmin Fiedler, Michael Gnewuch, Christian Weiß

According to Aistleitner and Weimar, there exist two-dimensional (double) infinite matrices whose star-discrepancy $D_N^{*s}$ of the first $N$ rows and $s$ columns, interpreted as $N$ points in $[0,1]^s$, satisfies an inequality of the form $$D_N^{*s} \leq \sqrt{\alpha} \sqrt{A+B\frac{\ln(\log_2(N))}{s}}\sqrt{\frac{s}{N}}$$ with $\alpha = \zeta^{-1}(2) \appr

Chelsea Zou, Kishan Chandan, Yan Ding, Shiqi Zhang

Human-robot collaboration (HRC) has become increasingly relevant in industrial, household, and commercial settings. However, the effectiveness of such collaborations is highly dependent on the human and robots' situational awareness of the environment. Improving this awareness includes not only aligning perceptions in a shared workspace, but also bidirection

Kazuki Osawa, Satoki Ishikawa, Rio Yokota, Shigang Li

Gradient preconditioning is a key technique to integrate the second-order information into gradients for improving and extending gradient-based learning algorithms. In deep learning, stochasticity, nonconvexity, and high dimensionality lead to a wide variety of gradient preconditioning methods, with implementation complexity and inconsistent performance and

Changjiang Yi, Xiaolong Feng, Ning Mao, Premakumar Yanda

Compounds with kagome lattice structure are known to exhibit Dirac cones, flat bands, and van Hove singularities, which host numerous versatile quantum phenomena. Inspired by these intriguing properties, we investigate the temperature and magnetic field dependent electrical transports along with the theoretical calculations of ScV6Sn6, a nonmagnetic charge d

Alexander Dobler, Martin Nöllenburg

Tanglegrams are drawings of two rooted binary phylogenetic trees and a matching between their leaf sets. The trees are drawn crossing-free on opposite sides with their leaf sets facing each other on two vertical lines. Instead of minimizing the number of pairwise edge crossings, we consider the problem of minimizing the number of block crossings, that is, tw

Duong Van Loi, Cao H. Nam, Phung Van Dong

We consider a gauge symmetry extension of the standard model given by $SU(3)_C\otimes SU(2)_L\otimes U(1)_X\otimes U(1)_N\otimes Z_2$ with minimal particle content, where $X$ and $N$ are family dependent but determining the hypercharge as $Y=X+N$, while $Z_2$ is an exact discrete symmetry. In our scenario, $X$ (while $N$ is followed by $X-Y$) and $Z_2$ charg

Simone Brivio, Stefania Fresca, Nicola Rares Franco, Andrea Manzoni

POD-DL-ROMs have been recently proposed as an extremely versatile strategy to build accurate and reliable reduced order models (ROMs) for nonlinear parametrized partial differential equations, combining (i) a preliminary dimensionality reduction obtained through proper orthogonal decomposition (POD) for the sake of efficiency, (ii) an autoencoder architectur

Andrea Braides, Gianni Dal Maso

We prove an integral-representation result for limits of non-local quadratic forms on $H^1_0(\Omega)$, with $\Omega$ a bounded open subset of $\mathbb R^d$, extending the representation on $C^\infty_c(\Omega)$ given by the Beurling-Deny formula in the theory of Dirichlet forms. We give a counterexample showing that a corresponding representation may not hold

Giulia Cavagnari, Giuseppe Savaré, Giacomo Enrico Sodini

We study monotone operators in reflexive Banach spaces that are invariant with respect to a group of suitable isometric isomorphisms and we show that they always admit a maximal extension which preserves the same invariance. A similar result applies to Lipschitz maps in Hilbert spaces, thus providing an invariant version of Kirzsbraun-Valentine extension The

Chiara Calascibetta, Luca Biferale, Francesco Borra, Antonio Celani

Pursuing a drifting target in a turbulent flow is an extremely difficult task whenever the searcher has limited propulsion and maneuvering capabilities. Even in the case when the relative distance between pursuer and target stays below the turbulent dissipative scale, the chaotic nature of the trajectory of the target represents a formidable challenge. Here,

Milena Trajanoska, Riste Stojanov, Dimitar Trajanov

The growing trend of Large Language Models (LLM) development has attracted significant attention, with models for various applications emerging consistently. However, the combined application of Large Language Models with semantic technologies for reasoning and inference is still a challenging task. This paper analyzes how the current advances in foundationa

Babette Dellen, Uwe Jaekel, Paulo S. A. Freitas, John W. Clark

Accurate estimation of nuclear masses and their prediction beyond the experimentally explored domains of the nuclear landscape are crucial to an understanding of the fundamental origin of nuclear properties and to many applications of nuclear science, most notably in quantifying the $r$-process of stellar nucleosynthesis. Neural networks have been applied wi

Philipe De Fabritiis, Fillipe M. Guedes, Giovani Peruzzo, Silvio P. Sorella

Three classes of entangled coherent states are employed to study the Bell-CHSH inequality. By using pseudospin operators in infinite dimensional Hilbert spaces, four dichotomic operators $(A,A',B,B')$ entering the inequality are constructed. For each class of coherent states, we compute the correlator $\langle \psi \vert A B + A' B + A B' - A' B' \vert \psi

Leonardo Ranaldi, Elena Sofia Ruzzetti, Fabio Massimo Zanzotto

Pre-trained Language Models such as BERT are impressive machines with the ability to memorize, possibly generalized learning examples. We present here a small, focused contribution to the analysis of the interplay between memorization and performance of BERT in downstream tasks. We propose PreCog, a measure for evaluating memorization from pre-training, and

Orestis Papakyriakopoulos, Anna Seo Gyeong Choi, Jerone Andrews, Rebecca Bourke

Speech datasets are crucial for training Speech Language Technologies (SLT); however, the lack of diversity of the underlying training data can lead to serious limitations in building equitable and robust SLT products, especially along dimensions of language, accent, dialect, variety, and speech impairment - and the intersectionality of speech features with

Shen Li, Xiaoyang Lin, Pingzhi Li, Suteng Zhao

Magnetic racetrack memory has significantly evolved and developed since its first experimental verification and is considered as one of the most promising candidates for future high-density on-chip solid state memory. However, the lack of a fast and precise magnetic domain wall (DW) shifting mechanism and the required extremely high DW motion (DWM) driving c

Arman Mohammadi, Theodor Westny, Daniel Jung, Mattias Krysander

Data-driven modeling and machine learning are widely used to model the behavior of dynamic systems. One application is the residual evaluation of technical systems where model predictions are compared with measurement data to create residuals for fault diagnosis applications. While recurrent neural network models have been shown capable of modeling complex n

Xiangzhi Cao

In this paper, we proved the existence of Symphonic map from ellipsoid to ellipsoid. We also geive give Hopf construction of Symphonic map from ellipsoid to ellipsoid.

Rohit Dilip Holkar, Md Amir Hossain, Dheeraj Kulkarni

For a path connected, locally path connected and semilocally simply connected space $X$, let $\Pi_1(X)$ denote its topologised fundamental groupoid as established in the first article of this series. Let $\mathcal{E}$ be the category of $\Pi_1(X)$-spaces in which the momentum maps are local homeomorphisms. We show that this category is isomorphic to that of

Ken Shiozaki

We present a discrete formulation of the Kane-Mele $\mathbb{Z}_2$ invariant that is manifestly gauge-independent and quantized.

Gizem Ozler, Holger Grosshans

The globally supported social distancing rules to prevent airborne transmission of COVID-19 assume small saliva droplets evaporate fast and large ones, which contain most viral copies, fall fast to the ground. However, during evaporation, solutes distribute non-uniformly within the droplets. We developed a numerical model to predict saliva droplet drying in

Tin Barisin, Katja Schladitz, Claudia Redenbach

Scale invariance of an algorithm refers to its ability to treat objects equally independently of their size. For neural networks, scale invariance is typically achieved by data augmentation. However, when presented with a scale far outside the range covered by the training set, neural networks may fail to generalize. Here, we introduce the Riesz network, a n

Francesco De Anna, Joshua Kortum, Stefano Scrobogna

We address a physically-meaningful extension of the Prandtl system, also known as hyperbolic Prandtl equations. We show that the linearised model around a non-monotonic shear flow is ill-posed in any Sobolev spaces. Indeed, shortly in time, we generate solutions that experience a dispersion relation of order k^(1/3) in the frequencies of the tangential direc

Sebastian Trojanowski

A new physics program has been initiated as part of the ongoing LHC physics run in the far-forward region, where dedicated FASER and SND@LHC experiments are currently taking data. We discuss the possible discovery prospects of this program in the search for signatures of beyond the Standard Model physics. We focus on both the present period and the proposed

Arianna S. Long, Jacqueline Antwi-Danso, Erini L. Lambrides, Christopher C. Lovell

Substantial populations of massive quiescent galaxies at $z\ge3$ challenge our understanding of rapid galaxy growth and quenching over short timescales. In order to piece together this evolutionary puzzle, more statistical samples of these objects are required. Established techniques for identifying massive quiescent galaxies are increasingly inefficient and

Jiguang He, Aymen Fakhreddine, Arthur S. de Sena, Yu Tian

Reconfigurable intelligent surfaces (RISs) bring various benefits to the current and upcoming wireless networks, including enhanced spectrum and energy efficiency, soft handover, transmission reliability, and even localization accuracy. These remarkable improvements result from the reconfigurability, programmability, and adaptation capabilities of RISs for f

Julio Castaño-Amoros, Pablo Gil

Adding tactile sensors to a robotic system is becoming a common practice to achieve more complex manipulation skills than those robotics systems that only use external cameras to manipulate objects. The key of tactile sensors is that they provide extra information about the physical properties of the grasping. In this paper, we implemented a system to predic

Andrea Sciandra

Recently it was shown that the category of cocommutative Hopf algebras over an arbitrary field $\Bbbk$ is semi-abelian. We extend this result to the category of cocommutative color Hopf algebras, i.e. of cocommutative Hopf monoids in the symmetric monoidal category of $G$-graded vector spaces with $G$ an abelian group, given an arbitrary skew-symmetric bicha

Han Chen, Ziwen Zhao, Yuhua Li, Yixiong Zou

Graph Contrastive Learning (GCL) is an effective way to learn generalized graph representations in a self-supervised manner, and has grown rapidly in recent years. However, the underlying community semantics has not been well explored by most previous GCL methods. Research that attempts to leverage communities in GCL regards them as having the same influence

Marco Baiesi, Tomohiro Nishiyama, Gianmaria Falasco

Observing stochastic trajectories with rare transitions between states, practically undetectable on time scales accessible to experiments, makes it impossible to directly quantify the entropy production and thus infer whether and how far systems are from equilibrium. To solve this issue for Markovian jump dynamics, we show a lower bound that outperforms any

Bart Bogaerts, Balder ten Cate, Brett McLean, Jan Van den Bussche

We investigate a number of semantically defined fragments of Tarski's algebra of binary relations, including the function-preserving fragment. We address the question whether they are generated by a finite set of operations. We obtain several positive and negative results along these lines. Specifically, the homomorphism-safe fragment is finitely generated (

O. J. Wahab, E. Daviddi, B. Xin, P. Z. Sun

Defect-free graphene is impermeable to all atoms and ions at ambient conditions. Experiments that can resolve gas flows of a few atoms per hour through micrometre-sized membranes found that monocrystalline graphene is completely impermeable to helium, the smallest of atoms. Such membranes were also shown to be impermeable to all ions, including the smallest

Sirui Chen, Albert Wu, C. Karen Liu

Daily objects embedded in a contextual environment are often ungraspable initially. Whether it is a book sandwiched by other books on a fully packed bookshelf or a piece of paper lying flat on the desk, a series of nonprehensile pregrasp maneuvers is required to manipulate the object into a graspable state. Humans are proficient at utilizing environmental co

Jeong-Wan Park, Kwang-Je Kim, Ryan Lindberg

We present a Hamiltonian-based, 3D theory in paraxial approximation for X-ray amplified spontaneous emission (or superfluorescence) pumped by X-ray free-electron laser. The seed field is included. The ensemble-averaged Heisenberg equations become Maxwell-Bloch equations if factorization of operator products is assumed and are adequate when the stimulated emi

Arnaldo Spalvieri

The paper analyzes the entropy of a system composed by non-interacting and indistinguishable particles whose quantum state numbers are modelled as independent and identically distributed classical random variables. The crucial observation is that, under this assumption, whichever is the number of particles that constitute the system, the occupancy numbers of

Yupei Lin, Sen Zhang, Xiaojun Yang, Xiao Wang

Large-scale text-to-image models have demonstrated amazing ability to synthesize diverse and high-fidelity images. However, these models are often violated by several limitations. Firstly, they require the user to provide precise and contextually relevant descriptions for the desired image modifications. Secondly, current models can impose significant change

Pardeep Kumar Tanwar, Mujeeb Ahmad, Md Shahin Alam, Xiaohan Yao

Quantum anomalies are the breakdowns of classical conservation laws that occur in quantum-field theory description of a physical system. They appear in relativistic field theories of chiral fermions and are expected to lead to anomalous transport properties in Weyl semimetals. This includes a chiral anomaly, which is a violation of the chiral current conserv

Patrick Dorin, Mustafa Khan, K. W. Wang

Topological metamaterials unlock confined and robust elastic wave control in mechanical structures. Recent breakthroughs have precipitated the development of 3D topological mechanical metamaterials, which extend beyond the conventional 1D and 2D metamaterials to facilitate extraordinary wave manipulation along 2D planar and layer-dependent elastic waveguides

Uliana E. Khodaeva, Dmitry L. Kovrizhin, Johannes Knolle

The Fermi-Hubbard model is one of the central paradigms in the physics of strongly-correlated quantum many-body systems. Here we propose a quantum circuit algorithm based on the $\mathrm{Z}_2$ lattice gauge theory (LGT) representation of the one-dimensional Fermi-Hubbard model, which is suitable for implementation on current NISQ quantum computers. Within th

Zita Abreu, Julia Lieb, Raquel Pinto, Joachim Rosenthal

Maximum-distance separable (MDS) convolutional codes are characterized by the property that their free distance reaches the generalized Singleton bound. In this paper, new criteria to construct MDS convolutional codes are presented. Additionally, the obtained convolutional codes have optimal first (reverse) column distances and the criteria allow to relate t

Debajyoti Sengupta, Samuel Klein, John Andrew Raine, Tobias Golling

Model independent techniques for constructing background data templates using generative models have shown great promise for use in searches for new physics processes at the LHC. We introduce a major improvement to the CURTAINs method by training the conditional normalizing flow between two side-band regions using maximum likelihood estimation instead of an

Tiankai Zhao, Hongyan Yuan

Epithelial cells can assemble into cohesive colonies and collectively interact with substrates by generating extracellular forces through focal adhesions. Recently, a molecularly based thermodynamic model, which integrates both the monolayer elasticity and force-mediated focal adhesion formation, has been developed to elucidate the regulation of the cellular

Gabriel Sattig, László Székelyhidi

We use a convex integration construction from \cite{ModenaSattig2020} in a Baire category argument to show that weak solutions to the transport equation with incompressible vector fields with Sobolev regularity are generic in the Baire category sense. Using the construction of \cite{BurczakModenaSzekelyhidi20} we prove an analog statement for the 3D Navier-S

Ángel L. Corps, Pedro Pérez-Fernández, Armando Relaño

We explore a full dynamical phase diagram by means of a double quench protocol that depends on a relaxation time as the only control parameter. The protocol comprises two fixed quenches and an intermediate relaxation time that determines the phase in which the quantum state is placed after the final quench. We apply it to an anharmonic Lipkin-Meshkov-Glick m

Nancy Guelman, Isabelle Liousse

H\"older's theorem states that any group acting freely by circle homeomorphisms is abelian, this is no longer true for interval exchange transformations: we first give examples of free actions of non abelian groups. Then after noting that finitely generated groups acting freely by IET are virtually abelian, we classify the free actions of groups containing a

Huaifeng Zhang, Mohannad Alhanahnah, Philipp Leitner, Ahmed Ali-Eldin

Containers have become a standard for deploying applications due to their convenience, but they often suffer from significant software bloat-unused files that inflate image sizes, increase provisioning times, and waste resources. These inefficiencies are particularly problematic in serverless and edge computing scenarios, where resources are constrained, and

Victor Linroth

This paper outlines an approach for proving existence of hyperbolic quasi-periodic invariant tori using computer assisted methods based on an a posteriori KAM-like theorem. We implement it for the dissipative standard map following the approach and give computer assisted proofs of existence of invariant circles for some parameter choices.

Arda Inceoglu, Eren Erdal Aksoy, Sanem Sariel

An autonomous service robot should be able to interact with its environment safely and robustly without requiring human assistance. Unstructured environments are challenging for robots since the exact prediction of outcomes is not always possible. Even when the robot behaviors are well-designed, the unpredictable nature of physical robot-object interaction m

Ayush Sawarni, Rahul Madhavan, Gaurav Sinha, Siddharth Barman

We study the causal bandit problem that entails identifying a near-optimal intervention from a specified set $A$ of (possibly non-atomic) interventions over a given causal graph. Here, an optimal intervention in ${A}$ is one that maximizes the expected value for a designated reward variable in the graph, and we use the standard notion of simple regret to qua

Yan-Song Ma, Shao-Jun Li, Chen-Sheng Gu, Jian-Xia Jiang

Based on the spectral decomposition through a code of PrepSpec, the light curves (spanning 6.5 years in the observed frame) of the broad-line Balmer decrement, i.e., the flux ratio of the broad \ha to the broad \hb line, are calculated for a sample of 44 Sloan Digital Sky Survey reverberation-mapped quasars ($z<0.53$). It is found that the logarithm of the m

Heming Xia, Peiyi Wang, Tianyu Liu, Binghuai Lin

Continual relation extraction (CRE) models aim at handling emerging new relations while avoiding catastrophically forgetting old ones in the streaming data. Though improvements have been shown by previous CRE studies, most of them only adopt a vanilla strategy when models first learn representations of new relations. In this work, we point out that there exi

Felix Liu, Albin Fredriksson, Stefano Markidis

Cholesky factorization is a widely used method for solving linear systems involving symmetric, positive-definite matrices, and can be an attractive choice in applications where a high degree of numerical stability is needed. One such application is numerical optimization, where direct methods for solving linear systems are widely used and often a significant

Julia Walchessen, Amanda Lenzi, Mikael Kuusela

In spatial statistics, fast and accurate parameter estimation, coupled with a reliable means of uncertainty quantification, can be challenging when fitting a spatial process to real-world data because the likelihood function might be slow to evaluate or wholly intractable. In this work, we propose using convolutional neural networks to learn the likelihood f

Airton Deppman, Eugenio Megias, Roman Pasechnik

This work presents an analysis of fractional derivatives and fractal derivatives, discussing their differences and similarities. The fractal derivative is closely connected to Haussdorff's concepts of fractional dimension geometry. The paper distinguishes between the derivative of a function on a fractal domain and the derivative of a fractal function, where

B. D. Goddard, M. Ottobre, K. J. Painter, I. Souttar

Motivated by applications to mathematical biology, we study the averaging problem for slow-fast systems, {\em in the case in which the fast dynamics is a stochastic process with multiple invariant measures}. We consider both the case in which the fast process is decoupled from the slow process and the case in which the two components are fully coupled. We wo

Bojan Evkoski, Senja Pollak

The work covers the development and explainability of machine learning models for predicting political leanings through parliamentary transcriptions. We concentrate on the Slovenian parliament and the heated debate on the European migrant crisis, with transcriptions from 2014 to 2020. We develop both classical machine learning and transformer language models

Halil Yigit Oksuz, Fabio Molinari, Henning Sprekeler, Jörg Raisch

In a multi-agent system, agents can cooperatively learn a model from data by exchanging their estimated model parameters, without the need to exchange the locally available data used by the agents. This strategy, often called federated learning, is mainly employed for two reasons: (i) improving resource-efficiency by avoiding to share potentially large datas

João Henrique Inacio de Souza, Victor Croisfelt, Radosław Kotaba, Taufik Abrão

This letter proposes a scheme assisted by a reconfigurable intelligent surface (RIS) for efficient uplink traffic multiplexing between enhanced mobile broadband (eMBB) and ultra-reliable-low-latency communication (URLLC). The scheme determines two RIS configurations based only on the eMBB channel state information (CSI) available at the base station (BS). Th

Julio Ivan Davila Carrazco, Suvarna Kishorkumar Kadam, Pietro Morerio, Alessio Del Bue

In this paper, we introduce a novel framework for the challenging problem of One-Shot Unsupervised Domain Adaptation (OSUDA), which aims to adapt to a target domain with only a single unlabeled target sample. Unlike existing approaches that rely on large labeled source and unlabeled target data, our Target-driven One-Shot UDA (TOS-UDA) approach employs a lea

Aitor Azemar

Consider a closed surface $S$ with negative Euler characteristic, and an admissible probability measure on the fundamental group of $S$ with finite first moment with respect to some hyperbolic metric on $S$. Corresponding to each point in Teichm\"uller space there is an associated random walk on the hyperbolic plane. Azemar--Gadre--Gou\"ezel--Haettel--Lessa-

Michela Ascolese, Andrea Frosini

In [2], while studying a relevant class of polyominoes that tile the plane by translation, i.e., double square polyominoes, the authors found that their boundary words, encoded by the Freeman chain coding on a four letters alphabet, have specific interesting properties that involve notions of combinatorics on words such as palindromicity, periodicity and sym

RB Yadav, Rinkila Bhutia, Namita Behera

In this paper we study a cohomology theory of compatible Leibniz algebra. We construct a graded Lie algebra whose Maurer-Cartan elements characterize the structure of compatible Leibniz algebras. Using this, we study cohomology, infinitisimal deformations, Nijenhuis operator and their relation for compatible leibniz algebras. Finally using cohomology of comp

Darrick Lee, Harald Oberhauser

The signature kernel is a positive definite kernel for sequential data. It inherits theoretical guarantees from stochastic analysis, has efficient algorithms for computation, and shows strong empirical performance. In this short survey paper for a forthcoming Springer handbook, we give an elementary introduction to the signature kernel and highlight these th

Arnab Patra, Jyoti Rani, Sanjay Kumar Mahto

In this article, a comprehensive study is made on the continuity, compactness, and spectrum of the lower triangular terraced matrix, introduced by H. C. Rhaly, Jr. [Houston J. Math. 15(1): 137-146, 1989], acting on the weighted null sequence spaces with bounded, strictly positive weights. Several spectral subdivisions such as point spectrum, residual spectru

Li-Jun Jia, Zhuang Li, Fei Wang

We propose to embed the General NMSSM (Next-to-Minimal Supersymmetric Standard Model) into the deflected AMSB (Anomaly Mediated Supersymmetry Breaking) mechanism with Yukawa/gauge deflection contributions. After integrating out the heavy messenger fields, the analytical expressions of the relevant soft SUSY breaking spectrum for General NMSSM at the messenge

Kostiantyn Cherkashyn

There are many approaches to the classification of Morse functions and their gradient fields (Morse Fields) on 2-surfaces. This paper studies the gluings of quadrilaterals and the classification of topological surfaces obtained by gluing together sides of quadrilateral graphs created by trajectories on gradient fields with focal critical points as vertices a

Gareth D. Smith, Edward Gillen, Simon T. Hodgkin, Douglas R. Alves

We present a study of rotation across 30 square degrees of the Orion Star-forming Complex, following a $\sim$200 d photometric monitoring campaign by the Next Generation Transit Survey (NGTS). From 5749 light curves of Orion members, we report periodic signatures for 2268 objects and analyse rotation period distributions as a function of colour for 1789 star

Fang-Stars Wei, Kang Zhou

We provide a new derivation of the fundamental BCJ relation among double color ordered tree amplitudes of bi-adjoint scalar theory, based on the leading soft theorem for external scalars. Then, we generalize the fundamental BCJ relation to $1$-loop Feynman integrands. We also use the fundamental BCJ relation to understand the Adler's zero for tree amplitudes

Yaowen Ye, Lianghao Xia, Chao Huang

While some powerful neural network architectures (e.g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dependency modeling, they may suffer from poor representation capability in label scarcity scenarios. To address the issue of insufficient labels, Contrastive Learning (CL) has attrac

Yiming Bian

The issue of over-limit during passenger aircraft flights has drawn increasing attention in civil aviation due to its potential safety risks. To address this issue, real-time automated warning systems are essential. In this study, a real-time warning model for civil aviation over-limit is proposed based on QAR data monitoring. Firstly, highly correlated attr

Po-Ning Chen, Mu-Tao Wang, Ye-Kai Wang, Shing-Tung Yau

In this article, we present the definitive transformation formulae of the mass aspect and angular momentum aspect under BMS transformations. Two different approaches that lead to the same formulae are taken. In the first approach, the formulae are derived by reading off the aspect functions from the curvature tensor. While in the second and more traditional

Jaime Arias, Carlos Olarte, Laure Petrucci, Łukasz Maśko

Expressing attack-defence trees in a multi-agent setting allows for studying a new aspect of security scenarios, namely how the number of agents and their task assignment impact the performance, e.g. attack time, of strategies executed by opposing coalitions. Optimal scheduling of agents' actions, a non-trivial problem, is thus vital. We discuss associated c

Junkai Zhang, Tharmalingam Ratnarajah

This paper analyzes the performance of the 3rd Generation Partnership Project (3GPP)-inspired multi-cell wideband single-hop backhaul millimeter-wave-in-band-full-duplex (IBFD)-integrated access and backhaul (IAB) networks by using stochastic geometry. We model the wired-connected Next Generation NodeBs (gNBs) as the Mat\'ern hard-core point process (MHCPP)

Aditya Shirwatkar, Aman Singh, Jana Ravi Kiran

Autonomous agents face the challenge of coordinating multiple tasks (perception, motion planning, controller) which are computationally expensive on a single onboard computer. To utilize the onboard processing capacity optimally, it is imperative to arrive at computationally efficient algorithms for global path planning. In this work, it is attempted to redu

Kyu-Hyun Chae

A gravitational anomaly is found at weak gravitational acceleration $g_{\rm{N}} < 10^{-9}$ m s$^{-2}$ from analyses of the dynamics of wide binary stars selected from the Gaia DR3 database that have accurate distances, proper motions, and reliably inferred stellar masses. Implicit high-order multiplicities are required and the multiplicity fraction is calibr

Lamprini Mitsiou, Stylianos Trevlakis, Argiris Tsiolas, Dimitrios J. Vergados

Until recently, researchers used machine learning methods to compensate for hardware imperfections at the symbol level, indicating that optimum radio-frequency transceiver performance is possible. Nevertheless, such approaches neglect the error correcting codes used in wireless networks, which inspires machine learning (ML)-approaches that learn and minimise

David San Andrés, Laura Colzi, Víctor M. Rivilla, Juan García de la Concepción

The ${\rm H_2NC}$ radical is the high-energy metastable isomer of ${\rm H_2CN}$ radical, which has been recently detected for the first time in the interstellar medium towards a handful of cold galactic sources, besides a warm galaxy in front of the PKS 1830-211 quasar. These detections have shown that the ${\rm H_2CN}$/${\rm H_2NC}$ isomeric ratio, likewise

Sitara Afzal, Haseeb Ali Khan, Imran Ullah Khan, Md. Jalil Piran

As the name suggests, affective computing aims to recognize human emotions, sentiments, and feelings. There is a wide range of fields that study affective computing, including languages, sociology, psychology, computer science, and physiology. However, no research has ever been done to determine how machine learning (ML) and mixed reality (XR) interact toget

Patrick J. Strohbeen, Aurelia M. Brook, Wendy L. Sarney, Javad Shabani

Superconducting germanium films are an intriguing material for possible applications in fields such as cryogenic electronics and quantum bits. Recently, there has been great deal of progress in hyperdoping of Ga doped Ge using ion implantation. The thin film growths would be advantageous allowing homoepitaxy of doped and undoped Ge films opening possibilitie

Ayan Banerjee, Sanket Biswas, Josep Lladós, Umapada Pal

Instance-level segmentation of documents consists in assigning a class-aware and instance-aware label to each pixel of the image. It is a key step in document parsing for their understanding. In this paper, we present a unified transformer encoder-decoder architecture for en-to-end instance segmentation of complex layouts in document images. The method adapt

Babak Maboudi Afkham, Nicolai André Brogaard Riis, Yiqiu Dong, Per Christian Hansen

This work describes a Bayesian framework for reconstructing the boundaries that represent targeted features in an image, as well as the regularity (i.e., roughness vs. smoothness) of these boundaries.This regularity often carries crucial information in many inverse problem applications, e.g., for identifying malignant tissues in medical imaging. We represent

Lothar Sebastian Krapp, Salma Kuhlmann

We develop a first-order theory of ordered transexponential fields in the language $\{+,\cdot,0,1,<,e,T\}$, where $e$ and $T$ stand for unary function symbols. While the archimedean models of this theory are readily described, the study of the non-archimedean models leads to a systematic examination of the induced structure on the residue field and the value

Srikar Kale, Keshav Agarwal, Prasad Krishnan

We present a new class of private information retrieval (PIR) schemes that keep the identity of the file requested private in the presence of at most $t$ colluding servers, based on the recent framework developed for such $t$-PIR schemes using star products of transitive codes. These $t$-PIR schemes employ the class of Berman codes as the storage-retrieval c

Hoang-Anh Phan, Duy Nam Bui, Tuan Nguyen Dinh, Bao-Anh Hoang

Pick-and-place robots are commonly used in modern industrial manufacturing. For complex devices/parts like camera modules used in smartphones, which contain optical parts, electrical components and interfacing connectors, the placement operation may not absolutely accurate, which may cause damage in the device under test during the mechanical movement to mak

Giuseppe Serra, Photios A. Stavrou, Marios Kountouris

In this paper, we study the computation of the rate-distortion-perception function (RDPF) for discrete memoryless sources subject to a single-letter average distortion constraint and a perception constraint that belongs to the family of f-divergences. For that, we leverage the fact that RDPF, assuming mild regularity conditions on the perception constraint,

Kunal Chelani, Torsten Sattler, Fredrik Kahl, Zuzana Kukelova

Visual localization is the task of estimating the camera pose from which a given image was taken and is central to several 3D computer vision applications. With the rapid growth in the popularity of AR/VR/MR devices and cloud-based applications, privacy issues are becoming a very important aspect of the localization process. Existing work on privacy-preservi

Tong Wei, Linlong Wu, Kumar Vijay Mishra, M. R. Bhavani Shankar

To enable non-line-of-sight (NLoS) sensing and communications, dual-function radar-communications (DFRC) systems have recently proposed employing reconfigurable intelligent surface (RIS) as a reflector in wireless media. However, in the dense environment and higher frequencies, severe propagation and attenuation losses are a hindrance for RIS-aided DFRC syst

Filip Bár

This paper presents new research in infinitesimal algebra by introducing the concept of an infinitesimal group and exploring its properties and ramifications. The author investigates first- and second-order subgroups of Lie groups and demonstrates the use of the second-order infinitesimal group structure to define a Lie bracket of points intrinsic to the Lie

Hirofumi Nishi, Koki Hamada, Yusuke Nishiya, Taichi Kosugi

Ground-state preparation is an important task in quantum computation. The probabilistic imaginary-time evolution (PITE) method is a promising candidate for preparing the ground state of the Hamiltonian, which comprises a single ancilla qubit and forward- and backward-controlled real-time evolution operators. The ground state preparation is a challenging task

Runcong Zhao, Lin Gui, Yulan He

Contrastive opinion extraction aims to extract a structured summary or key points organised as positive and negative viewpoints towards a common aspect or topic. Most recent works for unsupervised key point extraction is largely built on sentence clustering or opinion summarisation based on the popularity of opinions expressed in text. However, these methods

Alejandro Bandera Moreno, Cristina Caravaca García, Tomás Chacón Rebollo, Enrique Delgado Ávila

In this work, we introduce an a posteriori error indicator for the reduced basis modelling of turbulent flows. It is based upon the $k^{-5/3}$ Kolmogorov turbulence theory, thus it may be applied to any numerical discretisation of LES turbulence models. The main idea of this indicator is that if the full-order solution and the Reduced Order solution are clos

Shubhransh Singhvi, Avital Boruchovsky, Han Mao Kiah, Eitan Yaakobi

We study a data-driven approach to the bee identification problem for DNA strands. The bee-identification problem, introduced by Tandon et al. (2019), requires one to identify $M$ bees, each tagged by a unique barcode, via a set of $M$ noisy measurements. Later, Chrisnata et al. (2022) extended the model to case where one observes $N$ noisy measurements of e

Akash Kumar, Pankhuri Gupta, Niru Chowdhury, Kacho Imtiyaz Ali Khan

Angle-resolved spin-torque ferromagnetic resonance measurements are carried out in heterostructures consisting of Py (Ni$_{81}$Fe$_{19}$) and a noncollinear antiferromagnetic quantum material $\gamma-$IrMn$_{3}$. The structural characterization reveals that $\gamma-$IrMn$_{3}$ is polycrystalline in nature. A large exchange bias of 158~Oe is found in Py/$\gam

Chiara Crociati, Elena Valenti, Francesco R. Ferraro, Cristina Pallanca

In the context of a project aimed at characterizing the properties of the so-called Bulge Fossil Fragments (the fossil remnants of the bulge formation epoch), here we present the first determination of the metallicity distribution of Liller 1. For a sample of 64 individual member stars we used ESO- MUSE spectra to measure the equivalent width of the CaII tri

Hoang-Anh Phan, Phuc Vinh Nguyen, Thu Hang Thi Khuat, Hieu Dang Van

In recent years, 3D mapping for indoor environments has undergone considerable research and improvement because of its effective applications in various fields, including robotics, autonomous navigation, and virtual reality. Building an accurate 3D map for indoor environment is challenging due to the complex nature of the indoor space, the problem of real-ti

Nick Heesters, Oliver Müller, Francine R. Marleau, Pierre-Alain Duc

Dwarf galaxies have been extensively studied in the Local Group, in nearby groups, and selected clusters, giving us a robust picture of their global stellar and dynamical properties in particular locations in the Universe. Intense study of these properties has revealed correlations between them, including the well known universal stellar mass-metallicity rel

Matteo De Berardinis, Silvio Ghilardi

Taking inspiration from the monadicity of complete atomic Boolean algebras, we prove that profinite modal algebras are monadic over Set. While analyzing the monadic functor, we recover the universal model construction - a construction widely used in the modal logic literature for describing finitely generated free modal algebras and the essentially finite su