May 2023 arXiv papers — page 22

Tullio Traverso, Thomas Abadie, Omar K. Matar, Luca Magri

The prediction of the drop size distribution (DSD) resulting from liquid atomization is key to the optimization of multi-phase flows, from gas-turbine propulsion, through agriculture, to healthcare. Obtaining high-fidelity data of liquid atomization, either experimentally or numerically, is expensive, which makes the exploration of the design space difficult

Diganta Bandopadhyay, Christopher J. Moore

This paper considers the problem of searching for quiet, long-duration and broadband gravitational wave signals, such as stellar-mass binary black hole binaries, in mock LISA data. We propose a method that combines a semi-coherent likelihood with the use of a particle swarm optimizer capable of efficiently exploring a large parameter space. The semi-coherent

Attia Qammar, Hongmei Wang, Jianguo Ding, Abdenacer Naouri

Chatbots shifted from rule-based to artificial intelligence techniques and gained traction in medicine, shopping, customer services, food delivery, education, and research. OpenAI developed ChatGPT blizzard on the Internet as it crossed one million users within five days of its launch. However, with the enhanced popularity, chatbots experienced cybersecurity

Qian Wang, Biao Zhang, Michael Birsak, Peter Wonka

Recent works have explored text-guided image editing using diffusion models and generated edited images based on text prompts. However, the models struggle to accurately locate the regions to be edited and faithfully perform precise edits. In this work, we propose a framework termed InstructEdit that can do fine-grained editing based on user instructions. Ou

Mathias Schreiner, Ole Winther, Simon Olsson

Computing properties of molecular systems rely on estimating expectations of the (unnormalized) Boltzmann distribution. Molecular dynamics (MD) is a broadly adopted technique to approximate such quantities. However, stable simulations rely on very small integration time-steps ($10^{-15}\,\mathrm{s}$), whereas convergence of some moments, e.g. binding free en

Wei Xie, Yanxiong Li, Qianhua He, Wenchang Cao

New classes of sounds constantly emerge with a few samples, making it challenging for models to adapt to dynamic acoustic environments. This challenge motivates us to address the new problem of few-shot class-incremental audio classification. This study aims to enable a model to continuously recognize new classes of sounds with a few training samples of new

Hyoshin Kim, Sujit K. Ghosh, Adriana Di Martino, Emily C. Hector

Motivated by the need to model the dependence between regions of interest in functional neuroconnectivity for efficient inference, we propose a new sampling-based Bayesian clustering approach for covariance structures of high-dimensional Gaussian outcomes. The key technique is based on a Dirichlet process that clusters covariance sub-matrices into independen

Chunxue Li, Hongchi Wang, Yuehui Ma, Lianghao Lin

In this paper we present the distribution of molecular gas in the Milky Way Galactic plane from $l$ = [59.75, 74.75]$^{\circ}$ and $b$ = [${-}$5.25, +5.25]$^{\circ}$, using the MWISP $^{12}$CO/$^{13}$CO/$\rm {C}^{18}{O}$ emission line data. The molecular gas in this region can be mainly attributed to the Local spur, Local arm, Perseus arm, and Outer arm. Sta

Puneet Pareek, Monoj Adhikari, Chandan Dasgupta, Saroj Kumar Nandi

Despite the enormous theoretical and application interests, a fundamental understanding of the glassy dynamics remains elusive. The static properties of glassy and ordinary liquids are similar, but their dynamics are dramatically different. What leads to this difference is the central puzzle of the field. Even the primary defining glassy characteristics, the

Mikhail A. Bugaev, Igor D. Novikov, Serge V. Repin, Polina S. Samorodskaya

The problem of the possibility of observing a uniform sky glow through the throat of a Morris--Thorne wormhole by an observer located in another asymptotically flat space-time is considered. It is shown that an individual star has multiple images, and the image of a luminous sky has a complex structure and contains ring structures. The reasons for the emerge

Victor Prokhorov, Ivan Titov, N. Siddharth

Conditional neural processes (CNPs) are a flexible and efficient family of models that learn to learn a stochastic process from data. They have seen particular application in contextual image completion - observing pixel values at some locations to predict a distribution over values at other unobserved locations. However, the choice of pixels in learning CNP

Rayhana Darwich

Polarization sets were introduced by Dencker (1982) as a refinement of wavefront sets to the vector-valued case. He also clarified the propagation of polarization sets when the characteristic variety of the pseudodifferential system under study consists of two hypersurfaces intersecting tangentially (1992), or transversally (1995). In this paper, we consider

Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato

Symmetry learning has proven to be an effective approach for extracting the hidden structure of data, with the concept of equivariance relation playing the central role. However, most of the current studies are built on architectural theory and corresponding assumptions on the form of data. We propose Neural Fourier Transform (NFT), a general framework of le

Jacob Lindbäck, Zesen Wang, Mikael Johansson

We present an efficient algorithm for regularized optimal transport. In contrast to previous methods, we use the Douglas-Rachford splitting technique to develop an efficient solver that can handle a broad class of regularizers. The algorithm has strong global convergence guarantees, low per-iteration cost, and can exploit GPU parallelization, making it consi

Mikołaj Bojańczyk

MSO transductions are binary relations between structures which are defined using monadic second-order logic. MSO transductions form a category, since they are closed under composition. We show that many notions from language theory, such as recognizability or tree decompositions, can be defined in an abstract way that only refers to MSO transductions and th

Yuwen Li, Ludmil T. Zikatanov, Cheng Zuo

This work is on a fast and accurate reduced basis method for solving discretized fractional elliptic partial differential equations (PDEs) of the form $\mathcal{A}^su=f$ by rational approximation. A direct computation of the action of such an approximation would require solving multiple (20$\sim$30) large-scale sparse linear systems. Our method constructs th

ATLAS Collaboration

Semi-visible jets, with a significant contribution to the event's missing transverse momentum, can arise in strongly interacting dark sectors. This results in an event topology where one of the jets can be aligned with the direction of the missing transverse momentum. The first search for semi-visible jets produced via a $t$-channel mediator exchange is pres

Krishna Ronanki, Beatriz Cabrero-Daniel, Jennifer Horkoff, Christian Berger

Complying with the EU AI Act (AIA) guidelines while developing and implementing AI systems will soon be mandatory within the EU. However, practitioners lack actionable instructions to operationalise ethics during AI systems development. A literature review of different ethical guidelines revealed inconsistencies in the principles addressed and the terminolog

Andreas Petridis, Mirela Popa, Filipa Peleja, Dario Dotti

Fashion is one of the largest world's industries and computer vision techniques have been becoming more popular in recent years, in particular, for tasks such as object detection and apparel segmentation. Even with the rapid growth in computer vision solutions, specifically for the fashion industry, many problems are far for being resolved. Therefore, not at

Ludovic Thomas, Jean-Yves Le Boudec

The interleaved regulator (implemented by IEEE TSN Asynchronous Traffic Shaping) is used in time-sensitive networks for reshaping the flows with per-flow contracts. When applied to an aggregate of flows that come from a FIFO system, an interleaved regulator that reshapes the flows with their initial contracts does not increase the worst-case delay of the agg

Chayan Banerjee, Kien Nguyen, Clinton Fookes, George Karniadakis

The incorporation of physical information in machine learning frameworks is opening and transforming many application domains. Here the learning process is augmented through the induction of fundamental knowledge and governing physical laws. In this work, we explore their utility for computer vision tasks in interpreting and understanding visual data. We pre

Francesco Antici, Andrea Galassi, Federico Ruggeri, Katerina Korre

We develop novel annotation guidelines for sentence-level subjectivity detection, which are not limited to language-specific cues. We use our guidelines to collect NewsSD-ENG, a corpus of 638 objective and 411 subjective sentences extracted from English news articles on controversial topics. Our corpus paves the way for subjectivity detection in English and

Philippe Weitz, Masi Valkonen, Leslie Solorzano, Circe Carr

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge

Suhyung Jang, Ghang Lee

This paper presents an enhanced building information modeling (BIM) logger that captures building element geometry and attributes to accurately represent the BIM authoring process. The authors developed the logger and reproducing algorithm using the Revit C# API based on the analysis of information required to define building elements and associated attribut

Panagiotis Dorlis, Nick E. Mavromatos, Sotirios-Neilos Vlachos

In this work we establish in a rigorous manner, and a model independent way, the conditions for bypassing Bekenstein's no-scalar-hair theorem for static, spherically symmetric, and asymptotically flat black holes, while maintaining the validity of the energy conditions. Specifically, we argue that a hidden assumption in the theorem, namely the vanishing of t

Peiyuan Si, Liangxin Qian, Jun Zhao, Kwok-Yan Lam

Unmanned aerial vehicles (UAVs) are promising for providing communication services due to their advantages in cost and mobility, especially in the context of the emerging Metaverse and Internet of Things (IoT). This paper considers a UAV-assisted Metaverse network, in which UAVs extend the coverage of the base station (BS) to collect the Metaverse data gener

Cameron Trotter, Filipa Peleja, Dario Dotti, Alberto de Santos

There is high demand for online fashion recommender systems that incorporate the needs of the consumer's body shape. As such, we present a methodology to classify human body shape from a single image. This is achieved through the use of instance segmentation and keypoint estimation models, trained only on open-source benchmarking datasets. The system is capa

Pepa Atanasova, Oana-Maria Camburu, Christina Lioma, Thomas Lukasiewicz

Explanations of neural models aim to reveal a model's decision-making process for its predictions. However, recent work shows that current methods giving explanations such as saliency maps or counterfactuals can be misleading, as they are prone to present reasons that are unfaithful to the model's inner workings. This work explores the challenging question o

Ambuj Mehrish, Abhinav Ramesh Kashyap, Li Yingting, Navonil Majumder

There are significant challenges for speaker adaptation in text-to-speech for languages that are not widely spoken or for speakers with accents or dialects that are not well-represented in the training data. To address this issue, we propose the use of the "mixture of adapters" method. This approach involves adding multiple adapters within a backbone-model l

Anastasiya V. Dolmatova, Irina V. Tyulkina, Denis S. Goldobin

We employ the circular cumulant approach to construct a low dimensional description of the macroscopic dynamics of populations of phase oscillators (elements) subject to non-Gaussian white noise. Two-cumulant reduction equations for alpha-stable noises are derived. The implementation of the approach is demonstrated for the case of the Kuramoto ensemble with

Jinan Zou, Maihao Guo, Yu Tian, Yuhao Lin

Identifying unexpected domain-shifted instances in natural language processing is crucial in real-world applications. Previous works identify the out-of-distribution (OOD) instance by leveraging a single global feature embedding to represent the sentence, which cannot characterize subtle OOD patterns well. Another major challenge current OOD methods face is

Yotam M. Y. Feldman, Barak Hirshberg

Bosonic exchange symmetry leads to fascinating quantum phenomena, from exciton condensation in quantum materials to the superfluidity of liquid Helium-4. Unfortunately, path integral molecular dynamics (PIMD) simulations of bosons are computationally prohibitive beyond $\mathord{\sim} 100$ particles, due to a cubic scaling with the system size. We present an

Ido Kahana, Noam Hazon

In many situations, several agents need to make a sequence of decisions. For example, a group of workers that needs to decide where their weekly meeting should take place. In such situations, a decision-making mechanism must consider fairness notions. In this paper, we analyze the fairness of three known mechanisms: round-robin, maximum Nash welfare, and lex

Janko Vidaković, Filip Karlo Došilović, Domagoj Pluščec

Transformer-based models have consistently produced substantial performance gains across a variety of NLP tasks, compared to shallow models. However, deep models are orders of magnitude more computationally expensive than shallow models, especially on tasks with large sequence lengths, such as document-level event detection. In this work, we attempt to bridg

Ruiyang Li, Fujun Yang, Xianjie Liu, Hongwei Shi

Prosthetic Joint Infection (PJI) is a prevalent and severe complication characterized by high diagnostic challenges. Currently, a unified diagnostic standard incorporating both computed tomography (CT) images and numerical text data for PJI remains unestablished, owing to the substantial noise in CT images and the disparity in data volume between CT images a

Maximilian Engel, Guillermo Olicón-Méndez

We consider a stochastic version of the so-called Brusselator - a mathematical model for a two-dimensional chemical reaction network - in which one of its parameters is assumed to vary randomly. It has been suggested via numerical explorations that the system exhibits noise-induced synchronization when time goes to infinity. Complementing this perspective, i

Qianjun Lyu, Wing Suen, Yimeng Zhang

We study an information design problem with continuous state and discrete signal space. Under convex and S-shaped value functions, the optimal information structure is interval-partitional and exhibits a dual expectations property: each induced signal is the conditional mean (taken under the prior density) of each interval; and each interval cutoff is the ba

Alvaro J. Magdaleno, Mercy Cutler, Jesse J. Suurmond, Marc Meléndez

Transient Photoluminescence Microscopy (TPLM) allows for the direct visualization of carrier transport in semiconductor materials with sub nanosecond and few nanometer resolution. The technique is based on measuring changes in the spatial distribution of a diffraction limited population of carriers using spatiotemporal detection of the radiative decay of the

Shu-Ichiro Suzuki, Alexander A. Golubov

The robustness of the chiral surface current of chiral superconductors against surface roughness is studied utilizing the quasiclassical Eilenberger theory. We consider the general chiral superconductors where the pair potential is given by the spherical harmonics $Y_l^m$ such that $(l,m)=(1, \pm1)$ state corresponds to an ($p_x \pm ip_y$)-wave superconducto

Naso Evangelou-Oost, Larissa Meinicke, Callum Bannister, Ian J. Hayes

This paper introduces Concurrent Valuation Algebras (CVAs), a novel extension of ordered valuation algebras (OVAs). CVAs include two combine operators representing parallel and sequential products, adhering to a weak exchange law. This development offers theoretical and practical benefits for the specification and modelling of concurrent and distributed syst

Petros Toupas, Christos-Savvas Bouganis, Dimitrios Tzovaras

3D Convolutional Neural Networks are gaining increasing attention from researchers and practitioners and have found applications in many domains, such as surveillance systems, autonomous vehicles, human monitoring systems, and video retrieval. However, their widespread adoption is hindered by their high computational and memory requirements, especially when

Bazarkan N. Biyarov

The authors Matsaev and Mogulskii singled out a wide class of weak perturbation of a positive compact operator $H$, of the form $H(I+S)$, where $S$ is such a compact operator that $I+S$ is continuously invertible, which does not have a nonzero eigenvalue, i.e., is Volterra. On the other hand, such weak perturbations have a complete system of root vectors if

David Tena Cucala, Bernardo Cuenca Grau, Boris Motik, Egor V. Kostylev

Although there has been significant interest in applying machine learning techniques to structured data, the expressivity (i.e., a description of what can be learned) of such techniques is still poorly understood. In this paper, we study data transformations based on graph neural networks (GNNs). First, we note that the choice of how a dataset is encoded int

Paul Dubois

Radiotherapy dosimetry plays a crucial role in optimizing treatment plans for cancer patients. In this study, we investigate the performance of a dozen standard state-of-the-art open-source optimizers for radiotherapy dosimetry. Our evaluation includes the use of TGG119 benchmark cases as well as one real case obtained from the Institute du Cancer de Montpel

Tiago Barros, Luís Garrote, Martin Aleksandrov, Cristiano Premebida

Autonomous driving systems often require reliable loop closure detection to guarantee reduced localization drift. Recently, 3D LiDAR-based localization methods have used retrieval-based place recognition to find revisited places efficiently. However, when deployed in challenging real-world scenarios, the place recognition models become more complex, which co

Noé Brucy, Patrick Hennebelle, Tine Colman, Simon Iteanu

The observed star formation rate (SFR) in galaxies is well below what it should be if gravitational collapse alone were at play. It has recently been shown that one candidate that might regulate star formation, the feedback from massive stars, is suitable only if the mean column density at the kiloparsec scale is lower than $\approx 20 M_\odot\cdot\mathrm{pc

Haotian Jiang, Qianxiao Li

We present a theoretical analysis of the approximation properties of convolutional architectures when applied to the modeling of temporal sequences. Specifically, we prove an approximation rate estimate (Jackson-type result) and an inverse approximation theorem (Bernstein-type result), which together provide a comprehensive characterization of the types of s

Alan Pedrassoli Chitayat, Florian Block, James Walker, Anders Drachen

Esport games comprise a sizeable fraction of the global games market, and is the fastest growing segment in games. This has given rise to the domain of esports analytics, which uses telemetry data from games to inform players, coaches, broadcasters and other stakeholders. Compared to traditional sports, esport titles change rapidly, in terms of mechanics as

Weihuang Liu, Xi Shen, Chi-Man Pun, Xiaodong Cun

Foreground segmentation is a fundamental problem in computer vision, which includes salient object detection, forgery detection, defocus blur detection, shadow detection, and camouflage object detection. Previous works have typically relied on domain-specific solutions to address accuracy and robustness issues in those applications. In this paper, we present

Xiaoliang Wu, Peter Bell, Ajitha Rajan

Explainable AI (XAI) techniques have been widely used to help explain and understand the output of deep learning models in fields such as image classification and Natural Language Processing. Interest in using XAI techniques to explain deep learning-based automatic speech recognition (ASR) is emerging. but there is not enough evidence on whether these explan

Shuai Zhao, Xiaohan Wang, Linchao Zhu, Yi Yang

One fascinating aspect of pre-trained vision-language models~(VLMs) learning under language supervision is their impressive zero-shot generalization capability. However, this ability is hindered by distribution shifts between the training and testing data. Previous test time adaptation~(TTA) methods for VLMs in zero-shot classification rely on minimizing the

Mengtian Li, Yi Dong, Minxuan Lin, Haibin Huang

In this work, we introduce a new approach for face stylization. Despite existing methods achieving impressive results in this task, there is still room for improvement in generating high-quality artistic faces with diverse styles and accurate facial reconstruction. Our proposed framework, MMFS, supports multi-modal face stylization by leveraging the strength

Piotr Wzorek, Tomasz Kryjak

Despite the dynamic development of computer vision algorithms, the implementation of perception and control systems for autonomous vehicles such as drones and self-driving cars still poses many challenges. A video stream captured by traditional cameras is often prone to problems such as motion blur or degraded image quality due to challenging lighting condit

Haotian Jiang, Qianxiao Li

The Transformer architecture is widely applied in sequence modeling applications, yet the theoretical understanding of its working principles remains limited. In this work, we investigate the approximation rate for single-layer Transformers with one head. We consider a class of non-linear relationships and identify a novel notion of complexity measures to es

Hyunsoo Lee, Minsoo Kang, Bohyung Han

We present a novel algorithm for text-driven image-to-image translation based on a pretrained text-to-image diffusion model. Our method aims to generate a target image by selectively editing the regions of interest in a source image, defined by a modifying text, while preserving the remaining parts. In contrast to existing techniques that solely rely on a ta

Matt Young, Marco Lucamarini, Stefano Pirandola

As Quantum Key Distribution technologies mature, it is pertinent to consider these systems in contexts beyond lab settings, and how these systems may have to operate autonomously. To begin, an abstract definition of a type of error that can occur with regard to the ratio of bit values in the raw key is presented, and how this has an impact on the security an

Jiawei Huang, Yi Ren, Rongjie Huang, Dongchao Yang

Large diffusion models have been successful in text-to-audio (T2A) synthesis tasks, but they often suffer from common issues such as semantic misalignment and poor temporal consistency due to limited natural language understanding and data scarcity. Additionally, 2D spatial structures widely used in T2A works lead to unsatisfactory audio quality when generat

Alexey E. Rastegin

Entropic uncertainty relations are interesting in their own rights as well as for a lot of applications. Keeping this in mind, we try to make the corresponding inequalities as tight as possible. The use of parametrized entropies also allows one to improve relations between various information measures. Measurements of special types are widely used in quantum

Toygar Tanyel

The aim of this study is to determine the perceived stress levels of 150 individuals and analyze the responses given to adapted questions in Turkish using machine learning. The test consists of 14 questions, each scored on a scale of 0 to 4, resulting in a total score range of 0-56. Out of these questions, 7 are formulated in a negative context and scored ac

Miu Nicole Takagi

In developed nations assassinations are rare and thus the impact of such acts on the electoral and political landscape is understudied. In this paper, we focus on Twitter data to examine the effects of Japan's former Primer Minister Abe's assassination on the Japanese House of Councillors elections in 2022. We utilize sentiment analysis and emotion detection

Matteo Peron, Sarah Libanore, Andrea Ravenni, Michele Liguori

We perform a detailed study of the cosmological bias of gravitational gave (GW) events produced by binary black hole mergers (BBHM). We start from a BBHM distribution modeled inside the EAGLE hydrodyamical simulation using the population synthesis code MOBSE. We then compare our findings with predictions from different Halo Occupation Distribution (HOD) pres

K. U. Kristiansen, A. H. Sarantaris

Recently, concepts from the emerging field of tropical geometry have been used to identify different scaling regimes in chemical reaction networks where dimension reduction may take place. In this paper, we try to formalize these ideas further in the context of planar polynomial ODEs. In particular, we develop a theory of a tropical dynamical system, based u

A. Shepherd, T. Patton, A. Pimazzoni, B. Pouradier-Duteil

For negative ion beam sources there are several methods of measuring the accelerated beam current, most commonly electrical measurements at the power supply and calorimetric measurements. On SPIDER, the ITER Heating Neutral Beam full-scale beam source prototype, electrical measurements at the acceleration grid power supply (AGPS) are complemented by polarizi

Tan Van Vu, Van Tuan Vo, Keiji Saito

Recent studies have revealed a deep connection between the asymmetry of cross-correlations and thermodynamic quantities in the short-time limit. In this study, we address the finite-time domain of the asymmetry for both open classical and quantum systems. Focusing on Markovian dynamics, we show that the asymmetry observed in finite-time cross-correlations is

K. Ramesh Babu, Guang-Yu Guo

We perform a systematic \textit{ab initio} density functional study of the superconductivity, electronic and phononic band structures, electron-phonon coupling and elastic constants of all four possible structures of niobium nitride $\beta$-Nb$_2$N as well as Nb-rich $\gamma$-Nb$_4$N$_3$ and N-rich $\beta^\prime$-Nb$_4$N$_5$. First of all, we find that all f

Nicanor Carrasco-Vargas

The Erd\H{o}s, Gr\"unwald, and Weiszfeld theorem is a characterization of those infinite graphs which are Eulerian. That is, infinite graphs that admit infinite Eulerian paths. In this article we prove an effective version of the Erd\H{o}s, Gr\"unwald, and Weiszfeld theorem for a class of graphs where vertices of infinite degree are allowed, generalizing a t

Senhui Qiu, Saugat Bhattacharyya, Damien Coyle, Shirin Dora

This paper presents a new learning algorithm, termed Deep Bi-directional Predictive Coding (DBPC) that allows developing networks to simultaneously perform classification and reconstruction tasks using the same weights. Predictive Coding (PC) has emerged as a prominent theory underlying information processing in the brain. The general concept for learning in

Mengzhao Chen, Wenqi Shao, Peng Xu, Mingbao Lin

Token compression aims to speed up large-scale vision transformers (e.g. ViTs) by pruning (dropping) or merging tokens. It is an important but challenging task. Although recent advanced approaches achieved great success, they need to carefully handcraft a compression rate (i.e. number of tokens to remove), which is tedious and leads to sub-optimal performanc

I T Habibullin, K I Faizulina, A R Khakimova

It is well known that the Laplace cascade method is an effective tool for constructing solutions to linear equations of hyperbolic type, as well as nonlinear equations of the Liouville type. The connection between the Laplace method and soliton equations of hyperbolic type remains less studied. The article shows that the Laplace cascade also has important ap

D. Q. Fang, H. Zhang, D. W. Wang

Two-dimensional materials provide remarkable platforms to uncover intriguing quantum phenomena and develop nanoscale devices of versatile applications. Recently, AlSb in the double-layer honeycomb (DLHC) structure was successfully synthesized exhibiting a semiconducting nature [ACS Nano 15, 8184 (2021)], which corroborates the preceding theoretical predictio

Lukas Einkemmer, Qin Li, Li Wang, Yunan Yang

Fusion energy offers the potential for the generation of clean, safe, and nearly inexhaustible energy. While notable progress has been made in recent years, significant challenges persist in achieving net energy gain. Improving plasma confinement and stability stands as a crucial task in this regard and requires optimization and control of the plasma system.

Guangyao Li, Yixin Xu, Di Hu

Audio question answering (AQA), acting as a widely used proxy task to explore scene understanding, has got more attention. The AQA is challenging for it requires comprehensive temporal reasoning from different scales' events of an audio scene. However, existing methods mostly extend the structures of visual question answering task to audio ones in a simple p

Antonio Capolupo, Aniello Quaranta, Raoul Serao

We analyze several phenomenological implications of a nonlocal generalization of quantum electrodynamics (QED). We compute the nonlocal corrections to the photon propagator up to one loop, and we show that nonlocality leads to a change of the Coulomb potential. We then investigate the ensuing modifications to the Lamb shift and to the electrostatic forces an

Tianheng Wei, Yanzhao Liu, Wei Ren, Ziqiang Wang

Symmetry-breaking electronic phase in unconventional high-temperature (high-Tc) superconductors is a fascinating issue in condensed-matter physics, among which the most attractive phases are charge density wave (CDW) phase with four unit-cell periodicity in cuprates and nematic phase breaking the C4 rotational symmetry in iron-based superconductors (FeSCs).

Marek Kryspin, Janusz Mierczynski, Sylvia Novo, Rafael Obaya

This paper deals with the exponential separation of type II, an important concept for random systems of differential equations with delay, introduced in \JM\ et al.~\cite{MiNoOb1}. Two different approaches to its existence are presented. The state space $X$ will be a separable ordered Banach space with $\dim X\geq 2$, dual space $X^{*}$ and positive cone $X^

Robin Vassantlal, Hasan Heydari, Alysson Bessani

Byzantine Consensus is fundamental for building consistent and fault-tolerant distributed systems. In traditional quorum-based consensus protocols, quorums are defined using globally known assumptions shared among all participants. Motivated by decentralized applications on open networks, the Stellar blockchain relaxes these global assumptions by allowing ea

S. T. Ogunjo, A. B. Rabiu

Previous studies on the impact and influence of solar activity on terrestrial weather has yielded contradictory results in literature. Present study presents, on a global scale, the correlation between surface air temperature and two solar activity indices (Sunspot number, 'Rz', and solar radio flux at 10.7, 'F10.7' ) at different time scales during solar cy

Shraddha Agrawal, Sai Naga Manoj Paladugu, Bryce Gadway

Building on the development of momentum state lattices (MSLs) over the past decade, we introduce a simple extension of this technique to higher dimensions. Based on the selective addressing of unique Bragg resonances in matter-wave systems, MSLs have enabled the realization of tight-binding models with tunable disorder, gauge fields, non-Hermiticity, and oth

O. A. Veliev

We study the spectrum of the differential operator T generated by the differential expression of order n>2 with the m by m PT-symmetric periodic matrix coefficients. The case when m and n are the odd numbers was investigated in [8]. In this paper, we consider the all remained cases: (a) n is an odd number and m is an even number, (b) n is an even number and

Maximilian Schumacher, Gernot Alber

Recently proposed correlation-matrix based sufficient conditions for bipartite steerability from Alice to Bob are applied to local informationally complete positive operator valued measures (POVMs) of the $(N,M)$-type. These POVMs allow for a unified description of a large class of local generalized measurements of current interest. It is shown that this suf

Animesh Chaturvedi, Rajesh Sharma

Hate speech classification has become an important problem due to the spread of hate speech on social media platforms. For a given set of Hate Terms lists (HTs-lists) and Hate Speech data (HS-data), it is challenging to understand which hate term contributes the most for hate speech classification. This paper contributes two approaches to quantitatively meas

Xiao-Fei Dong, Liang-Duan Liu, He Gao, Sheng Yang

Recent observations indicate that hydrogen-poor superluminous supernovae often display bumpy declining light curves. However, the cause of these undulations remains unclear. In this paper, we have improved the magnetar model, which includes flare activities. We present a systematic analysis of a well-observed SLSNe-I sample with bumpy light curves in the lat

Avner Offer, Ofer Lahav

Astrophysics is a social enterprise exemplified here by the Dark Energy Survey (DES) which completed its fieldwork in 2019 after 16 years of preparation and observation, while data analysis continues. Society funds astrophysics on a grand scale. For human capital and for governance the discipline draws on a self-governing "republic of science", while the fun

Giampiero Lovat, Walter Fuscaldo, Massimo Moccia, Giuseppe Castaldi

A rigorous full-wave modal analysis based on the method of moments in the spectral domain is presented for line waveguides constituted by two-part impedance planes with arbitrary anisotropic surface impedances. An integral equation is formulated by introducing an auxiliary current sheet on one of the two half planes and extending the impedance boundary condi

R. Flaschmann, C. Schmid, L. Zugliani, S. Strohauer

We investigate the growth conditions for thin (less than 200 nm) sputtered aluminum (Al) films. These coatings are needed for various applications, e.g. for advanced manufacturing processes in the aerospace industry or for nanostructures for quantum devices. Obtaining high-quality films, with low roughness, requires precise optimization of the deposition pro

BAQIS Quafu Group

We present Quafu-Qcover, an open-source cloud-based software package designed for combinatorial optimization problems that support both quantum simulators and hardware backends. Quafu-Qcover provides a standardized and complete workflow for solving combinatorial optimization problems using the Quantum Approximate Optimization Algorithm (QAOA). It enables the

Rafał Bistroń, Wojciech Śmiałek, Karol Życzkowski

The notion of convolution of two probability vectors, corresponding to a coincidence experiment can be extended for a family of binary operations determined by (tri)stochastic tensors, to describe Markov chains of a higher order. The problem of associativity, commutativity and the existence of neutral elements and inverses is analyzed for such operations. Fo

Marco Nurisso, Alexis Arnaudon, Maxime Lucas, Robert L. Peach

Simplicial Kuramoto models have emerged as a diverse and intriguing class of models describing oscillators on simplices rather than nodes. In this paper, we present a unified framework to describe different variants of these models, categorized into three main groups: "simple" models, "Hodge-coupled" models, and "order-coupled" (Dirac) models. Our framework

J. Zs Mezei, M. A. Ayouz, A. Orbán, A. Abdoulanziz

Dissociative recombination of N$_2$H$^+$ is explored in a two-step theoretical study. In a first step, a diatomic (1D) rough model with frozen NN bond and frozen angles is adopted, in the framework of the multichannel quantum defect theory (MQDT). The importance of the indirect mechanism and of the bending mode is revealed, in spite of the disagreement betwe

Jiaxin Lu, Yifan Sun, Qixing Huang

Automated assembly of 3D fractures is essential in orthopedics, archaeology, and our daily life. This paper presents Jigsaw, a novel framework for assembling physically broken 3D objects from multiple pieces. Our approach leverages hierarchical features of global and local geometry to match and align the fracture surfaces. Our framework consists of four comp

Folkert Müller-Hoissen

Polygon equations generalize the prominent pentagon equation in very much the same way as simplex equations generalize the famous Yang-Baxter equation. In particular, they appeared as ''cocycle equations'' in Street's category theory associated with oriented simplices. Whereas the $(N-1)$-simplex equation can be regarded as a realization of the higher Bruhat

Bohan Wang, Huishuai Zhang, Zhi-Ming Ma, Wei Chen

We provide a simple convergence proof for AdaGrad optimizing non-convex objectives under only affine noise variance and bounded smoothness assumptions. The proof is essentially based on a novel auxiliary function $\xi$ that helps eliminate the complexity of handling the correlation between the numerator and denominator of AdaGrad's update. Leveraging simple

Simone Mastrogiovanni, Grégoire Pierra, Stéphane Perriès, Danny Laghi

We present icarogw 2.0, a pure CPU/GPU python code developed to infer astrophysical and cosmological population properties of noisy, heterogeneous, and incomplete observations. icarogw 2.0 is mainly developed for compact binary coalescence (CBC) population inference with gravitational wave (GW) observations. The code contains several models for masses, spins

Issa Mouawad, Nikolas Brasch, Fabian Manhardt, Federico Tombari

For autonomous vehicles, driving safely is highly dependent on the capability to correctly perceive the environment in 3D space, hence the task of 3D object detection represents a fundamental aspect of perception. While 3D sensors deliver accurate metric perception, monocular approaches enjoy cost and availability advantages that are valuable in a wide range

Erik Ekstedt, Siyang Wang, Éva Székely, Joakim Gustafson

Turn-taking is a fundamental aspect of human communication where speakers convey their intention to either hold, or yield, their turn through prosodic cues. Using the recently proposed Voice Activity Projection model, we propose an automatic evaluation approach to measure these aspects for conversational speech synthesis. We investigate the ability of three

Qinglong Cao, Yuntian Chen, Chao Ma, Xiaokang Yang

Few-shot aerial image segmentation is a challenging task that involves precisely parsing objects in query aerial images with limited annotated support. Conventional matching methods without consideration of varying object orientations can fail to activate same-category objects with different orientations. Moreover, conventional algorithms can lead to false r

Roland Szakacs, Céline Péroux, Dylan Nelson, Martin A. Zwaan

The upcoming ByCycle project on the VISTA/4MOST multi-object spectrograph will offer new prospects of using a massive sample of $\sim 1$ million high spectral resolution ($R$ = 20,000) background quasars to map the circumgalactic metal content of foreground galaxies (observed at $R$ = 4000 - 7000), as traced by metal absorption. Such large surveys require sp

Luca Nannini

Large Language Models (LLMs) have emerged as transformative forces in the realm of natural language processing, wielding the power to generate human-like text. However, despite their potential for content creation, they carry the risk of eroding our Semantic Capital (SC) - the collective knowledge within our digital ecosystem - thereby posing diverse social

Artem G. Volosniev, Giacomo Bighin, Luis Santos, Luis A. Peña Ardila

We study the out-of-equilibrium quantum dynamics of dipolar polarons, i.e., impurities immersed in a dipolar Bose-Einstein condensate, after a quench of the impurity-boson interaction. We show that the dipolar nature of the condensate and of the impurity results in anisotropic relaxation dynamics, in particular, anisotropic dressing of the polaron. More rele

Krishna Garg, Jishnu Ray Chowdhury, Cornelia Caragea

Keyphrase generation is the task of summarizing the contents of any given article into a few salient phrases (or keyphrases). Existing works for the task mostly rely on large-scale annotated datasets, which are not easy to acquire. Very few works address the problem of keyphrase generation in low-resource settings, but they still rely on a lot of additional