October 2023 arXiv papers — page 144

Stephane Vannitsem

Teleconnections between the tropical and the extratropical climates are often considered as a potential source of long-term predictability at seasonal to decadal time scales in the extratropics. This claim is taken up in the present work by investigating the predictability of a coupled ocean-atmosphere extratropical model under a one-way forcing generated by

Alexander D. Popov

Geometrically, quantum mechanics is defined by a complex line bundle $L_\hbar$ over the classical particle phase space $T^*{R}^3\cong{R}^6$ with coordinates $x^a$ and momenta $p_a$, $a,...=1,2,3$. This quantum bundle $L_\hbar$ is endowed with a connection $A_\hbar$, and its sections are standard wave functions $\psi$ obeying the Schr\"odinger equation. The c

Konstantin Dmitriev, Johann Schumann, Islam Bostanov, Mostafa Abdelhamid

In recent years, the remarkable progress of Machine Learning (ML) technologies within the domain of Artificial Intelligence (AI) systems has presented unprecedented opportunities for the aviation industry, paving the way for further advancements in automation, including the potential for single pilot or fully autonomous operation of large commercial airplane

Su-Youn Yoon, Eva Miszoglad, Lisa R. Pierce

Since its launch in November 2022, ChatGPT has had a transformative effect on education where students are using it to help with homework assignments and teachers are actively employing it in their teaching practices. This includes using ChatGPT as a tool for writing teachers to grade and generate feedback on students' essays. In this study, we evaluated the

Guanting Dong, Jinxu Zhao, Tingfeng Hui, Daichi Guo

With the increasing capabilities of large language models (LLMs), these high-performance models have achieved state-of-the-art results on a wide range of natural language processing (NLP) tasks. However, the models' performance on commonly-used benchmark datasets often fails to accurately reflect their reliability and robustness when applied to real-world no

G. Polychrou

In this paper, we address the following question: Which hyperbolic or elliptic PDEs admit functional separable solutions. We shall focus on the study of a sinh-Gordon type equation. We construct solutions to this equation via the method of functional separation. We prove that these are the only families that have the property of functional separation and so

Xiancai Xu, Jia-Dong Zhang, Rongchang Xiao, Lei Xiong

Recently, ChatGPT has attracted great attention from both industry and academia due to its surprising abilities in natural language understanding and generation. We are particularly curious about whether it can achieve promising performance on one of the most complex tasks in aspect-based sentiment analysis, i.e., extracting aspect-category-opinion-sentiment

M. Querejeta, J. Pety, A. Schruba, A. K. Leroy

We present a new wide-field 10.75 x 10.75 arcmin^2 (~11x11 kpc^2), high-resolution (theta = 3.6" ~ 60 pc) NOEMA CO(1-0) survey of the very nearby (d=3.45 Mpc) spiral galaxy IC 342. The survey spans out to about 1.5 effective radii and covers most of the region where molecular gas dominates the cold interstellar medium. We resolved the CO emission into >600 i

Yuan Li, Lichao Sun, Yixuan Zhang

Significant advancements have occurred in the application of Large Language Models (LLMs) for social simulations. Despite this, their abilities to perform teaming in task-oriented social events are underexplored. Such capabilities are crucial if LLMs are to effectively mimic human-like social behaviors and form efficient teams to solve tasks. To bridge this

Rafael González-Hernández, Philipp Ritzinger, Karel Výborný, Jakub Železný

The Edelstein effect is the origin of the spin-orbit torque: a current-induced torque that is used for the electrical control of ferromagnetic and antiferromagnetic materials. This effect originates from the relativistic spin-orbit coupling, which necessitates utilizing materials with heavy elements. Here we show that in magnetic materials with non-collinear

Shiping Yang, Renliang Sun, Xiaojun Wan

Large Language Models (LLMs) have shown their ability to collaborate effectively with humans in real-world scenarios. However, LLMs are apt to generate hallucinations, i.e., makeup incorrect text and unverified information, which can cause significant damage when deployed for mission-critical tasks. In this paper, we propose a self-check approach based on re

Kaito Kobayashi, Yukitoshi Motome

Nonlinear spin dynamics in magnetic materials offers a promising avenue for implementing physical reservoir computing, one of the most accomplished brain-inspired frameworks for information processing. In this study, we investigate the practical utility of magnetic physical reservoirs by assessing their performance in a video recognition task. Leveraging a r

Alan R Bowman, Jacob J Leaver, Kyle Frohna, Samuel D Stranks

CdTe is the most commercially successful thin-film photovoltaic technology to date. The recent development of Se-alloyed CdSe$_{x}$Te$_{1-x}$ layers in CdTe solar cells has led to higher device efficiencies, due to a lowered bandgap improving the photocurrent, improved voltage characteristics and longer carrier lifetimes. Evidence from cross-sectional electr

Kamal N. Soltanov

In this article, the existence of the spectrum (the eigenvalues) for the nonlinear continuous operators acting in the Banach spaces is investigated. For the study, this question is used a different approach that allows the studying of all eigenvalues of the nonlinear operator relative to another nonlinear operator. Here shows that in nonlinear operators, cas

Katherine Slattery, Joshua Eby, Lauren Street, Rohana Wijewardhana

Previous work has shown that optomechanical force sensing can be used for efficient detection of ultralight (sub-eV) dark matter candidates. We propose to extend the reach of this method to the search for ultralight dark matter in gravitationally-bound configurations in the Milky Way. We consider three scenarios, each strongly-motivated by previous studies:

Deniz Yılmaz

Let $k$ be an algebraically closed field of characteristic $p>0$ and let $\mathbb{F}$ be an algebraically closed field of characteristic $0$. Recently, together with Bouc, we introduced the notion of functorial equivalences between blocks of finite groups and proved that given a $p$-group $D$, there is only a finite number of pairs $(G,b)$ of a finite group

Sohan Kumar Jha, Anisur Rahaman

We consider a static and spherically symmetric black hole metric that emerges from the vacuum solution of the traceless metric-affine bumblebee model. Our study focuses on the possible implications of the modifications induced by the model on various astrophysical observables that include quasinormal modes, ringdown waveforms, Hawking radiation spectrum, spa

Xinyu Lin, Wenjie Wang, Yongqi Li, Fuli Feng

Harnessing Large Language Models (LLMs) for recommendation is rapidly emerging, which relies on two fundamental steps to bridge the recommendation item space and the language space: 1) item indexing utilizes identifiers to represent items in the language space, and 2) generation grounding associates LLMs' generated token sequences to in-corpus items. However

Songbai Chen, Jiliang Jing

We have investigated the motion for photons in the Kerr black hole spacetime under the axion-photon coupling. The birefringence phenomena arising from the axion-photon coupling can be negligible in the weak coupling approximation because the leading-order contributions to the equations of motion come from the square term of the coupling parameter. We find th

Julien Paulet, Axel Molina, Benjamin Beltzung, Takafumi Suzumura

Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for animals and time-intensive for researchers. In recent years, the integration of deep learning in research offe

Changze Lv, Tianlong Li, Wenhao Liu, Yufei Gu

Spiking Neural Networks (SNNs) have emerged as a promising alternative to conventional Artificial Neural Networks (ANNs), demonstrating comparable performance in both visual and linguistic tasks while offering the advantage of improved energy efficiency. Despite these advancements, the integration of linguistic and visual features into a unified representati

Debarshi Majumder, Binod Sreenivasan

The secular variation of the geomagnetic field suggests that there are anticyclonic polar vortices in the Earth's core. Under the influence of a magnetic field, the polar azimuthal flow is thought to be produced by one or more coherent upwellings within the tangent cylinder, offset from the rotation axis. In this study, convection within the tangent cylinder

Guoyuan An, Juhyung Seon, Inkyu An, Yuchi Huo

This paper presents an innovative approach to enhancing explainable image retrieval, particularly in situations where a fine-tuning set is unavailable. The widely-used SPatial verification (SP) method, despite its efficacy, relies on a spatial model and the hypothesis-testing strategy for instance recognition, leading to inherent limitations, including the a

Joni Virta, Andreas Artemiou

We propose MNPCA, a novel non-linear generalization of (2D)$^2${PCA}, a classical linear method for the simultaneous dimension reduction of both rows and columns of a set of matrix-valued data. MNPCA is based on optimizing over separate non-linear mappings on the left and right singular spaces of the observations, essentially amounting to the decoupling of t

Xuan Luo, Mingqing Huang, Rui Lv, Hui Zhao

Sequential location recommendation plays a huge role in modern life, which can enhance user experience, bring more profit to businesses and assist in government administration. Although methods for location recommendation have evolved significantly thanks to the development of recommendation systems, there is still limited utilization of geographic informati

Hilal AlQuabeh, Bhaskar Mukhoty, Bin Gu

Pairwise learning is essential in machine learning, especially for problems involving loss functions defined on pairs of training examples. Online gradient descent (OGD) algorithms have been proposed to handle online pairwise learning, where data arrives sequentially. However, the pairwise nature of the problem makes scalability challenging, as the gradient

Vladimiro Benedetti, Michele Bolognesi, Daniele Faenzi, Laurent Manivel

Given a smooth genus two curve $C$, the moduli space SU$_C(3)$ of rank three semi-stable vector bundles on $C$ with trivial determinant is a double cover in $\mathbb{P}^8$ branched over a sextic hypersurface, whose projective dual is the famous Coble cubic, the unique cubic hypersurface that is singular along the Jacobian of $C$. In this paper we continue ou

Jingbo Jia, Peng Wu, Hussain Dawood

To address the problem of insufficient failure data generated by disks and the imbalance between the number of normal and failure data. The existing Conditional Tabular Generative Adversarial Networks (CTGAN) deep learning methods have been proven to be effective in solving imbalance disk failure data. But CTGAN cannot learn the internal information of disk

Alfredo Luis

We examine the satisfaction of Bell criteria for single realizations of quantum systems. This is possible via the joint noisy measurement of all observables involved in the Bell test.We readily find that every outcome violates Bell bounds for local hidden variables models. This agrees with the idea that to reveal nonclassical effects a necessary condition is

Somesh Bhattacharya, Chrysanthos Charalambous, Anja Banjac, Zoran Miletic

The frequency stability of a power system is of paramount importance, as a fast frequency swings in the system can lead to oscillatory instability, and thereby blackouts. A grid-connected microgrid, that can operate in the islanded mode can also possess such deteriorating effect due to the higher share of converter-based sources. In this paper, a coordinated

Kamal N. Soltanov, Ugur Sert

In this article, we study the properties of a class of functional spaces which arise from the investigation of nonlinear differential equations. We establish some integral inequalities then by applying these inequalities, we prove some lemmas and theorems, which indicate the relation of these spaces (pn-spaces) with the Lebesgue and Sobolev spaces in the cas

Yunhyung Cho, Naoki Fujita, Akihiro Higashitani, Eunjeong Lee

A flag variety is a smooth projective homogeneous variety. In this paper, we study Newton-Okounkov polytopes of the flag variety $Fl(\mathbb{C}^4)$ arising from its cluster structure. More precisely, we present defining inequalities of such Newton-Okounkov polytopes of $Fl(\mathbb{C}^4)$. Moreover, we classify these polytopes, establishing their equivalence

Jalal Smiri, Oguz Umut Salman, Matteo Ghidelli, Ioan R. Ionescu

Compression experiments are widely used to study the mechanical properties of materials at micro- and nanoscale. However, the conventional engineering stress measurement method used in these experiments neglects to account for the alterations in the material's shape during loading. This can lead to inaccurate stress values and potentially misleading conclusi

Saba Esnaashari, Jonathan Bright, John Francis, Youmna Hashem

Allocation of scarce resources is a recurring challenge for the public sector: something that emerges in areas as diverse as healthcare, disaster recovery, and social welfare. The complexity of these policy domains and the need for meeting multiple and sometimes conflicting criteria has led to increased focus on the use of algorithms in this type of decision

Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, Lidong Bing

While large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, they pose potential safety concerns, such as the ``jailbreak'' problem, wherein malicious instructions can manipulate LLMs to exhibit undesirable behavior. Although several preventive measures have been developed to mitigate the potential risks associated with LL

H. Abe, S. Abe, V. A. Acciari, I. Agudo

Gamma-ray bursts (GRBs) are explosive transient events occurring at cosmological distances, releasing a large amount of energy as electromagnetic radiation over several energy bands. We report the detection of the long GRB~201216C by the MAGIC telescopes. The source is located at $z=1.1$ and thus it is the farthest one detected at very high energies. The emi

Said Pashayev, Romain Lhermerout, Christophe Roblin, Eric Alibert

Despite several decades of development, microfluidics lacks a sealing material that can be readily fabricated, leak-tight under high liquid water pressure, stable over a long time, and vacuum compatible. In this paper, we report the performances of a micro-scale processable sealing material for nanofluidic/microfluidics chip fabrication, which enables us to

Zhaogang Dong, Sergey Gorelik, Ramón Paniagua-Dominguez, Johnathan Yik

Dielectric nanostructures have demonstrated optical antenna effects due to Mie resonances. Preliminary investigations on dielectric nanoantennas have been carried out for a trifecta of enhancements, i.e., simultaneous enhancements in absorption, emission directionality and radiative decay rates of quantum emitters. However, these investigations are limited b

Hongbin Xu, Yamei Xia, Shuai Zhao, Bo Cheng

Query-based methods have garnered significant attention in object detection since the advent of DETR, the pioneering query-based detector. However, these methods face challenges like slow convergence and suboptimal performance. Notably, self-attention in object detection often hampers convergence due to its global focus. To address these issues, we propose F

Prerit Pramod

This paper explains the phenomenon of current circulation and the resulting electromagnetic torque generation in electric machines employing delta windings. The description entails a systematic assessment of the electrical and magnetic behavior of the machine to develop mathematical models, followed by intuitive explanations of the derived analytical forms.

Thomas Starck, Tanguy Fardet, Fabien Esculier

Nitrogen (N) is essential for plant growth and protein synthesis but global reactive N losses, mainly from food systems, induce strong environmental impacts.N losses after human excretion are often overlooked because, in Western societies, they partly occur as inert N2, following denitrification in wastewater treatment plants (WWTP), and losses in waters are

Yang Wang, Bo Dong, Ke Xu, Haiyin Piao

Deep Neural Networks (DNNs) are widely used for computer vision tasks. However, it has been shown that deep models are vulnerable to adversarial attacks, i.e., their performances drop when imperceptible perturbations are made to the original inputs, which may further degrade the following visual tasks or introduce new problems such as data and privacy securi

Nicoletta D'Angelo

We consider the problem of feature detection in the presence of clutter in spatial point processes. Classification methods have been developed in previous studies. Among these, Byers and Raftery (1998) models the observed Kth nearest neighbour distances as a mixture distribution and classifies the clutter and feature points consequently. In this paper, we en

Pham Truong Xuan, Tran Thi Ngoc

In this paper we establish the asymptotic stability of steady solutions for the Boussinesq systems in the framework of Cartesian product of critical weak-Morrey spaces on $\mathbb{R}^n$, where $n \geqslant 3$. In our strategy, we first establish the continuity for the long time of the bilinear terms associated with the mild solutions of the Boussinesq system

Reo Ohkura, Takaya Inukai, Shun Takahashi, Hitoshi Mizuno

Spin-orbit coupling (SOC) of light plays a fundamental photophysics that is important for various fields such as materials science, optics, and quantum technology, contributing to the elucidation of new physical phenomena and the development of innovative applications. In this study, we investigate the impact of SOC in a microcavity system using the highly o

Meiqiao Zhang, Fengming Dong, Ruixue Zhang

A {\it mixed hypergraph} ${\cal H}=({\cal V},{\cal C},{\cal D})$ consists of the vertex set ${\cal V}$ and two families of subsets of $2^{{\cal V}}$: the family ${\cal C}$ of co-edges and the family ${\cal D}$ of edges. ${\cal H}$ is said to be colorable if there is a mapping $f$ from ${\cal V}$ to the set of positive integers such that $|\{f(v):v\in e\}|<|e

L. Pantoni, R. Adam, P. Ade, H. Ajeddig

We present the first preliminary results of the project \textit{ICED}, focusing on the face-on galaxy NGC4254. We use the millimetre maps observed with NIKA2 at IRAM-30m, as part of the IMEGIN Guaranteed Time Large Program, and of a wide collection of ancillary data (multi-wavelength photometry and gas phase spectral lines) that are publicly available. We de

Kalun Bedingfield, Benjamin Yuen, Angela Demetriadou

Plasmonic nanocavities are known for their extreme field enhancement and sub-wavelength light confinement in gaps of just a few nanometers. Pairing this with the ability to host quantum emitters, they form highly promising platforms to control or engineer quantum states at room temperature. Here, we use the lossy nature of plasmonic nanocavities to form sub-

Thomas Starck, Tanguy Fardet, Fabien Esculier

Phosphorus (P) is an essential constituent of life but large P losses from agroecosystems and sanitation systems are a major source of eutrophication in water bodies.These losses are doubly detrimental as P in human excretions can be used for crop fertilization.Through a unique dataset of 20,000 French WasteWater Treatment Plants (WWTPs) operational measurem

Pascal Auscher, Pierre Portal

We establish well-posedness and maximal regularity estimates for linear parabolic SPDE in divergence form involving random coefficients that are merely bounded and measurable in the time, space, and probability variables. To reach this level of generality, and avoid any of the smoothness assumptions used in the literature, we introduce a notion of pathwise w

Tejhas Kapoor

In this work, we investigate the time-dependent angular analysis of $B_s^0 \rightarrow \phi \phi$ decay to search for new physics signals via CP-violating observables. We work with a new physics Hamiltonian containing both left- and right-handed Chromomagnetic dipole operators. The hierarchy of the helicity amplitudes in this model gives us a new scheme of e

Li Zhou, Antonia Karamolegkou, Wenyu Chen, Daniel Hershcovich

The increasing ubiquity of language technology necessitates a shift towards considering cultural diversity in the machine learning realm, particularly for subjective tasks that rely heavily on cultural nuances, such as Offensive Language Detection (OLD). Current understanding underscores that these tasks are substantially influenced by cultural values, howev

Sulav Ghimire, Kanakesh V. Kkuni, Emerson D. Guest, Kim H. Jensen

Synchronous condensers (SCs) have been reported to improve the overall stability and short-circuit power of a power system. SCs are also being integrated into offshore wind power plants (WPPs) for the same reason. This paper, investigates the effect of synchronous condensers on an offshore wind power plant with grid-following (GFL) and grid-forming (GFM) con

Sonja Zweng, Gabriel Mendoza-Rojas, Florian Altegoer

Recombinant protein production is pivotal in molecular biology, enabling profound insights into cellular processes through biophysical, biochemical, and structural analyses of the purified samples. The demand for substantial biomolecule quantities often presents challenges, particularly for eukaryotic proteins. Escherichia coli expression systems have evolve

Kamal N. Soltanov

Here we consider a perturbation of continuous mappings on Banach spaces and investigate their image under various conditions. Consequently, we study the solvability of some classes of equations and inclusions. For these, we start by the investigating of local properties of the considered mapping and local comparing this mapping with certain smooth mappings.

Angel Rodriguez Palacios

We prove unit-free versions of both the associative and the non-associative Vidav-Palmer theorems. Then these results are applied to prove a unit-free version of the Blecher-Ruan-Sinclair non-associative characterization of unital $C^*$-algebras.

Nathaël Alibaud, Jørgen Endal, Espen Jakobsen, Ola Mæhlen

We study well-posedness of degenerate mixed-type parabolic-hyperbolic equations $$ \partial_tu+\text{div}\big(f(u)\big)=\mathcal{L}[b(u)] $$ on bounded domains with general Dirichlet boundary/exterior conditions. The nonlocal diffusion operator $\mathcal{L}$ can be any symmetric L{\'e}vy operator (e.g. fractional Laplacians) and $b$ is nondecreasing and allo

Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina

Large language models (LLMs) fine-tuned with reinforcement learning from human feedback (RLHF) have been used in some of the most widely deployed AI models to date, such as OpenAI's ChatGPT or Anthropic's Claude. While there has been significant work developing these methods, our understanding of the benefits and downsides of each stage in RLHF is still limi

Petra Raussi, Jirapa Kamsamrong, Alexandros Paspatis, Kai Heussen

Smart energy systems comprise multiple domains like power, thermal, control, information, and communication technology, which increases the complexity of research and development studies. This expansion also requires larger and ever so complex experimental pilot environments driving the demand for geographically distributed multi-research infrastructure test

Tianshu Yu, Ting-En Lin, Yuchuan Wu, Min Yang

In recent research on large language models (LLMs), there has been a growing emphasis on aligning these models with human values to reduce the impact of harmful content. However, current alignment methods often rely solely on singular forms of human feedback, such as preferences, annotated labels, or natural language critiques, overlooking the potential adva

Mohamed Ghattassi, Nader Masmoudi, Eliot Pacherie

We investigate the existence and stability of small perturbations of constant states of the generalized Hughes model for pedestrian flow in an infinitely large corridor. We show that constant flows are stable under a condition on the density. Our findings indicates that when the density is less than half of the maximum density $\rho_{m}/2$, which is the Lasr

Danni Yang, Yun Ji, Zhoubin Kou, Xiaoxiong Zhong

To address the challenges posed by the heterogeneity inherent in federated learning (FL) and to attract high-quality clients, various incentive mechanisms have been employed. However, existing incentive mechanisms are typically utilized in conventional synchronous aggregation, resulting in significant straggler issues. In this study, we propose a novel async

Joaquín Medina Dueñas, José H. García, Stephan Roche

We report a theoretical description of novel spin-orbit torque components emerging in two-dimensional Dirac materials with broken inversion symmetry. In contrast to usual metallic interfaces where field-like and damping-like torque components are competing, we find that an intrinsic damping-like torque which derives from all Fermi-sea electrons can be simult

Hirofumi Suzuki, Hiroaki Iwashita, Takuya Takagi, Yuta Fujishige

Machine learning models need to be continually updated or corrected to ensure that the prediction accuracy remains consistently high. In this study, we consider scenarios where developers should be careful to change the prediction results by the model correction, such as when the model is part of a complex system or software. In such scenarios, the developer

David Fellner, Thomas I Strasser, Wolfgang Kastner

Modeling and simulation of electrical power systems are becoming increasingly important approaches for the development and operation of novel smart grid functionalities -- especially with regard to data-driven applications as data of certain operational states or misconfigurations can be next to impossible to obtain. The DeMaDs framework allows for the simul

Tong Guo, Xuanping Li, Haitao Yang, Xiao Liang

Historical behaviors have shown great effect and potential in various prediction tasks, including recommendation and information retrieval. The overall historical behaviors are various but noisy while search behaviors are always sparse. Most existing approaches in personalized search ranking adopt the sparse search behaviors to learn representation with bott

Hao-Tian Ma, Peng-Fei Tian, Da-Liang Guo, Yu Liu

In many Ce-based superconductors, superconducting (SC) phases emerge or can be tuned in proximity to the antiferromagnetic (AF) quantum critical point (QCP), but so far the explicit phase evolution near the QCP lack theoretical understanding. Here, by combing the density functional theory plus dynamical mean-field theory (DFT+DMFT) with effective-model calcu

Enzo Vitillaro

The paper deals with the existence and multiplicity of nontrivial solutions for the doubly elliptic problem $$\begin{cases} \Delta u=0 \qquad &\text{in $\Omega$,}\\ u=0 &\text{on $\Gamma_0$,}\\ -\Delta_\Gamma u +\partial_\nu u =|u|^{p-2}u\qquad &\text{on $\Gamma_1$,} \end{cases} $$ where $\Omega$ is a bounded open subset of $\mathbb{R}^N$ ($N\ge 2$) with $C^

Jérôme Euzenat

Relational concept analysis (RCA) is an extension of formal concept analysis allowing to deal with several related contexts simultaneously. It has been designed for learning description logic theories from data and used within various applications. A puzzling observation about RCA is that it returns a single family of concept lattices although, when the data

Xiangyu Wu, Yang Yang, Shengdong Xu, Yifeng Wu

In this paper, we present our solution to a Multi-modal Algorithmic Reasoning Task: SMART-101 Challenge. Different from the traditional visual question-answering datasets, this challenge evaluates the abstraction, deduction, and generalization abilities of neural networks in solving visuolinguistic puzzles designed specifically for children in the 6-8 age gr

Takashi Ono

Let $X$ be a compact K\"ahler manifold and $(E,\overline\partial_E,\theta)$ be a Higgs bundle over it. We study the structure of the Kuranishi space for the pair $(X, E,\theta)$ when the Higgs bundle admits a harmonic metric or equivalently when the Higgs bundle is polystable and the Chern classes are 0. Under such assumptions, we show that the Kuranishi spa

Xiangyu Wu, Yi Gao, Hailiang Zhang, Yang Yang

In this paper, we present our solution to the New frontiers for Zero-shot Image Captioning Challenge. Different from the traditional image captioning datasets, this challenge includes a larger new variety of visual concepts from many domains (such as COVID-19) as well as various image types (photographs, illustrations, graphics). For the data level, we colle

Suman Das, Arnab Kundu

In this article, building on our recent investigations and motivated by the fuzzball-paradigm, we explore normal modes of a probe massless scalar field in the rotating BTZ-geometry in an asymptotically AdS spacetime and correspondingly obtain the Spectral Form Factor (SFF) of the scalar field. In particular, we analyze the SFF obtained from the single-partic

Cong Yang, Bipin Indurkhya, John See, Bo Gao

Skeleton Ground Truth (GT) is critical to the success of supervised skeleton extraction methods, especially with the popularity of deep learning techniques. Furthermore, we see skeleton GTs used not only for training skeleton detectors with Convolutional Neural Networks (CNN) but also for evaluating skeleton-related pruning and matching algorithms. However,

Nianlong Gu, Yingqiang Gao, Richard H. R. Hahnloser

We introduce MemSum-DQA, an efficient system for document question answering (DQA) that leverages MemSum, a long document extractive summarizer. By prefixing each text block in the parsed document with the provided question and question type, MemSum-DQA selectively extracts text blocks as answers from documents. On full-document answering tasks, this approac

Yue Jin, Yibin Xu, Chengyuan Yang, Han Wang

In the existing software development ecosystem, security issues introduced by third-party code cannot be overlooked. Among these security concerns, memory access vulnerabilities stand out prominently, leading to risks such as the theft or tampering of sensitive data. To address this issue, software-based defense mechanisms have been established at the progra

Srijith Radhakrishnan, Chao-Han Huck Yang, Sumeer Ahmad Khan, Rohit Kumar

We introduce a new cross-modal fusion technique designed for generative error correction in automatic speech recognition (ASR). Our methodology leverages both acoustic information and external linguistic representations to generate accurate speech transcription contexts. This marks a step towards a fresh paradigm in generative error correction within the rea

Boxi Yu, Qiuyang Mang, Qingshuo Guo, Pinjia He

A test oracle serves as a criterion or mechanism to assess the correspondence between software output and the anticipated behavior for a given input set. In automated testing, black-box techniques, known for their non-intrusive nature in test oracle construction, are widely used, including notable methodologies like differential testing and metamorphic testi

Alessandro Fassò

Intensive farming is known to significantly impact air quality, particularly fine particulate matter (PM$_{2.5}$). Understanding in detial their relation is important for scientific reasons and policy making. Ammonia emissions convey the impact of farming, but are not directly observed. They are computed through emission inventories based on administrative d

Hammadi Abidi, Guilong Gui, Xueli Ke

Consideration in this paper is the global well-posedness for the 3D axisymmetric MHD equations with only vertical dissipation and vertical magnetic diffusion. The existence of unique low-regularity global solutions of the system with initial data in Lorentz spaces is established by using higher-order energy estimates and real interpolation method.

Jie Xu, Yun You, Fengwen Kang, Sanshui Xiao

All-optical computing has recently emerged as a vibrant research field in response to the energy crisis and the growing demand for information processing. However, the efficiency of subwavelength-scale all-optical devices remains relatively low due to challenges such as back-scattering reflections and strict surface roughness. Furthermore, achieving multifun

Hui Zhao, Jia Hao, Jing Li, Shao-Ming Fei

We study genuine tripartite entanglement and multipartite entanglement in arbitrary $n$-partite quantum systems based on complete orthogonal basis (COB). While the usual Bloch representation of a density matrix uses three types of generators, the density matrix with COB operators has one uniformed type of generators which may simplify related computations. W

Jianguo Huang, Huajun Xi, Linjun Zhang, Huaxiu Yao

Conformal prediction is a statistical framework that generates prediction sets containing ground-truth labels with a desired coverage guarantee. The predicted probabilities produced by machine learning models are generally miscalibrated, leading to large prediction sets in conformal prediction. To address this issue, we propose a novel algorithm named $\text

Richard Kenyon, István Prause

We discuss how to construct limit shapes for the domino tiling model (square lattice dimer model) and $5$-vertex model, in appropriate polygonal domains. Our methods are based on the harmonic extension method of [R. Kenyon and I. Prause, Gradient variational problems in $\mathbb{R}^2$, Duke Math J. 2022].

Nick Bryan-Kinns, Corey Ford, Alan Chamberlain, Steven David Benford

This first international workshop on explainable AI for the Arts (XAIxArts) brought together a community of researchers in HCI, Interaction Design, AI, explainable AI (XAI), and digital arts to explore the role of XAI for the Arts. Workshop held at the 15th ACM Conference on Creativity and Cognition (C&C 2023).

Zijie Huang, Wanjia Zhao, Jingdong Gao, Ziniu Hu

Learning complex multi-agent system dynamics from data is crucial across many domains, such as in physical simulations and material modeling. Extended from purely data-driven approaches, existing physics-informed approaches such as Hamiltonian Neural Network strictly follow energy conservation law to introduce inductive bias, making their learning more sampl

Gregory Boyle, Nathan Garland, Bob McEachran, Kalpani Mirihana

The theory for electron transport in simple liquids developed by Cohen and Lekner is extended to simple liquid mixtures. The focus is on developing benchmark models for binary mixtures of hard-spheres, using the Percus-Yevick model to represent the density structure effects. A multi-term solution of the Boltzmann equation is employed to investigate the effec

Zhuangzhuang Cui, Peize Zhang, Sofie Pollin

The sixth generation (6G) wireless communication nowadays is seeking a new spectrum to inherit the pros and discard the cons of sub-6 GHz, millimeter-wave (mmWave), and sub-terahertz (THz) bands. To this end, an upper mid-band with a Frequency Range (FR) spanning from 7 GHz to 24 GHz, also known as FR3, has emerged as a focal point in 6G communications. Thus

Finn Süberkrüb, Rita Laezza, Yiannis Karayiannidis

Humans are able to manipulate Deformable Linear Objects (DLOs) such as cables and wires, with little or no visual information, relying mostly on force sensing. In this work, we propose a reduced DLO model which enables such blind manipulation by keeping the object under tension. Further, an online model estimation procedure is also proposed. A set of element

Katsunori Arai

A spatial surface is a compact surface embedded in the $3$-sphere. We assume that a spatial surface is oriented and that each connected component of a spatial surface is neither a disk nor without a boundary. A diagram of a spatial surface is a diagram of a spatial trivalent graph that is a spine of the spatial surface. In this paper, we introduce the notion

Kilian Sprenkamp, Daniel Gordon Jones, Liudmila Zavolokina

The prevalence of propaganda in our digital society poses a challenge to societal harmony and the dissemination of truth. Detecting propaganda through NLP in text is challenging due to subtle manipulation techniques and contextual dependencies. To address this issue, we investigate the effectiveness of modern Large Language Models (LLMs) such as GPT-3 and GP

O. Fedchenko, Y. -J. Song, O. Tkach, Y. Lytvynenko

We present results of hard X-ray angle-resolved photoemission spectroscopy and photoemission diffraction measurements performed on high-quality single crystals of the valence transition compound EuPd$_2$Si$_2$ for temperatures 25~K $\leq$ T $\leq$ 300~K. At low temperatures we observe a Eu $4f$ valence $v=2.5$, % occupation number $n=6.5$, which decreases to

Xianyao Hu, Congming Jin

Anomaly detection is the process of identifying atypical data samples that significantly deviate from the majority of the dataset. In the realm of clinical screening and diagnosis, detecting abnormalities in medical images holds great importance. Typically, clinical practice provides access to a vast collection of normal images, while abnormal images are rel

Wei Zhang, C. N. Kuo, S. T. Kuo, Chun Wa So

The Lifshitz-Kosevich (LK) theory is the pillar of magnetic quantum oscillations, which have been extensively applied to characterize a wide range of metallic states. In this study, we focus on the Shubnikov-de Haas (SdH) effect observed in SmSb, a rare-earth monopnictide. We observed a significant departure from the expected LK theory near $T_N=2.4$~K: both

Meng Cao, Xiantao Deng

Symmetric informationally complete positive operator-valued measures (SIC-POVMs) in finite dimension $d$ are a particularly attractive case of informationally complete POVMs (IC-POVMs), which consist of $d^{2}$ subnormalized projectors with equal pairwise fidelity. However, it is difficult to construct SIC-POVMs, and it is not even clear whether there exists

Qitian Wu, Chenxiao Yang, Kaipeng Zeng, Michael Bronstein

The capability of generalization is a cornerstone for the success of modern learning systems. For non-Euclidean data, e.g., graphs, that particularly involves topological structures, one important aspect neglected by prior studies is how machine learning models generalize under topological shifts. This paper proposes Advective Diffusion Transformer (AdvDIFFo

Marcin Magdziarz, Kacper Taźbierski

In this paper we introduce a general stochastic representation for an important class of processes with resetting. It allows to describe any stochastic process intermittently terminated and restarted from a predefined random or non-random point. Our approach is based on stochastic differential equations called jump-diffusion models. It allows to analyze proc

Quirin Göttl, Jonathan Pirnay, Jakob Burger, Dominik G. Grimm

Process synthesis in chemical engineering is a complex planning problem due to vast search spaces, continuous parameters and the need for generalization. Deep reinforcement learning agents, trained without prior knowledge, have shown to outperform humans in various complex planning problems in recent years. Existing work on reinforcement learning for flowshe

Chen Zhuang, Hongbo Zhao

The development of vehicle-to-vehicle (V2V) communication facil-itates the study of cooperative positioning (CP) techniques for vehicular applications. The CP methods can improve the posi-tioning availability and accuracy by inter-vehicle ranging and data exchange between vehicles. However, the inter-vehicle rang-ing can be easily interrupted due to many fac

Emanuele Pelucchi

Epitaxial semiconductor quantum dots have been, in the last 40 years or so, at the center of the research effort of a large community. The focus being on semiconductor physics and devices, in view of the broad applications and potential, e.g., for efficient temperature insensitive lasers at telecom wavelengths, or as artificial atoms for quantum information