March 2024 arXiv papers — page 27

Xurong Liang, Tong Chen, Lizhen Cui, Yang Wang

Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods. Meanwhile, owing to the use of an embedding table to represent each user/item as a distinct vector, GNN-based recommenders have inherited the long-standing defect of parameter inefficiency. As a common practice for scalable embeddings, parameter sharing ena

Marlies Pirner

We propose a general multi-species Fokker-Planck model. We prove consistency of our model: conservation properties, positivity of all temperatures, H-Theorem and the shape of equilibrium as Maxwell distributions with the same mean velocity and temperature. Moreover, we derive the usual macroscopic equations from the kinetic two-species BGK model and compute

Yiting Mao, Peigeng Zhong, Haiqing Lin, Xiaoqun Wang

The application of the eigenstate thermalization hypothesis to non-Hermitian quantum systems has become one of the most important topics in dissipative quantum chaos, recently giving rise to intense debates. The process of thermalization is intricate, involving many time-evolution trajectories in the reduced Hilbert space of the system. By considering two di

Luca Savant, Diego Valsesia, Enrico Magli

We present Stochastic Gaussian Splatting (SGS): the first framework for uncertainty estimation using Gaussian Splatting (GS). GS recently advanced the novel-view synthesis field by achieving impressive reconstruction quality at a fraction of the computational cost of Neural Radiance Fields (NeRF). However, contrary to the latter, it still lacks the ability t

Jalaja M A, Shubham Kumar Parate, Binoy Krishna De, Sai Dutt K

On-chip refrigeration at cryogenic temperatures is becoming an important requirement in the context of quantum technologies and nanoelectronics. Ferroic materials with enhanced electrocaloric effects at phase transitions are good material candidates for the same. By exploiting the Mott metal-insulator transition (MIT) of TiOx(Ny), the bottom electrode, we en

Jiro Shimoda, Katsuaki Asano

The Fermi and eROSITA bubbles, large diffuse structures in our Galaxy, can be the by-products of the steady star formation activity. To simultaneously explain the star formation history of the Milky Way and the metallicity of $\sim$ Z$_\odot$ at the Galactic disk, a steady Galactic wind driven by cosmic-rays is required. For tenuous gases with a density of $

Jakub Višňák, Jan Brandejs, Mihály Máté, Lucas Visscher

Heavy atom compounds represent a challenge for computational chemistry, due to the need for simultaneous treatment of relativistic and correlation effects. Often such systems exhibit also strong correlation which hampers the application of perturbation theory or single-reference coupled cluster (CC) methods. As a viable alternative, we have proposed to exter

P. N. Vabishchevich

Stable computational algorithms for the approximate solution of the Cauchy problem for nonstationary problems are based on implicit time approximations. Computational costs for boundary value problems for systems of coupled multidimensional equations can be reduced by additive decomposition of the problem operator(s) and composition of the approximate soluti

Zhongxi Chen, Ke Sun, Ziyin Zhou, Xianming Lin

The rapid progress in deep learning has given rise to hyper-realistic facial forgery methods, leading to concerns related to misinformation and security risks. Existing face forgery datasets have limitations in generating high-quality facial images and addressing the challenges posed by evolving generative techniques. To combat this, we present DiffusionFace

Anthony Salib, Georg Weiss

Global solutions to the obstacle problem were first completely classified in two dimensions by Sakai using complex analysis techniques. Although the complex analysis approach produced a very succinct proof in two dimensions, it left the higher dimensional cases, and even closely related problems in two dimensions, unresolved. A complete classification in dim

Xiang Ji, Xiaosen Yang

We present a pedagogical review of the periodically driven non-Hermitian systems, particularly on the rich interplay between the non-Hermitian skin effect and the topology. We start by reviewing the non-Bloch band theory of the static non-Hermitian systems and discuss the establishment of its generalized bulk-boundary correspondence. Ultimately, we focus on

Zhimin Yuan, Wankang Zeng, Yanfei Su, Weiquan Liu

3D synthetic-to-real unsupervised domain adaptive segmentation is crucial to annotating new domains. Self-training is a competitive approach for this task, but its performance is limited by different sensor sampling patterns (i.e., variations in point density) and incomplete training strategies. In this work, we propose a density-guided translator (DGT), whi

Mohamed Elmanna, Ahmed Elsafty, Yomna Ahmed, Muhammad Rushdi

Digital pathology has recently been revolutionized by advancements in artificial intelligence, deep learning, and high-performance computing. With its advanced tools, digital pathology can help improve and speed up the diagnostic process, reduce human errors, and streamline the reporting step. In this paper, we report a new large red blood cell (RBC) image d

Javier Falco, Daniel Isert

In this paper, we explore various equivalences of Ekeland's variational principle within the framework of group-invariant mappings. We introduce and analyze several key theorems, including the Drop theorem, the Petal theorem, Caristi-Kirk fxed-point theorem, and Takahashi's theorem, all of them within this context. Moreover, we extend the classical Drop theo

Giulio Ortali, Alessandro Gabbana, Nicola Demo, Gianluigi Rozza

Numerical simulations of turbulent flows are well known to pose extreme computational challenges due to the huge number of dynamical degrees of freedom required to correctly describe the complex multi-scale statistical correlations of the velocity. On the other hand, kinetic mesoscale approaches based on the Boltzmann equation, have the potential to describe

Iván Angiono, Emiliano Campagnolo

We classify graded pre-Nichols algebras of diagonal type with finite Gelfand-Kirillov dimension. The characterization is made through an isomorphism of posets with the family of appropriate subsets of the set of positive roots coming from central extensions of Nichols algebras of diagonal type, generalizing the corresponding extensions for small quantum grou

Malka Gorfine, David M. Zucker, Shoval Shoham

Many countries have established population-based biobanks, which are being used increasingly in epidemiolgical and clinical research. These biobanks offer opportunities for large-scale studies addressing questions beyond the scope of traditional clinical trials or cohort studies. However, using biobank data poses new challenges. Typically, biobank data is co

Irina Bobrova

A non-abelian generalisation of a birational representation of affine Weyl groups and their application to the discrete dynamical systems is presented. By using this generalisation, non-commutative analogs for the discrete systems of $A_n^{(1)}$, $n \geq 2$ type and of $d$-Painlev\'e equations with an additive dynamic were derived. A coalescence cascade of t

Nuo Chen, Jiqun Liu, Hanpei Fang, Yuankai Luo

This study examines the decoy effect's underexplored influence on user search interactions and methods for measuring information retrieval (IR) systems' vulnerability to this effect. It explores how decoy results alter users' interactions on search engine result pages, focusing on metrics like click-through likelihood, browsing time, and perceived document u

Shaoxu Li, Ye Pan

Despite significant advancements in image generation using advanced generative frameworks, cross-image integration of content and style remains a key challenge. Current generative models, while powerful, frequently depend on vague textual prompts to define styles--creating difficulties in balancing content semantics and style preservation. We propose a novel

Soham Sen, Sunandan Gangopadhyay

The effect of noise induced by gravitons has been investigated using a Bose-Einstein condensate. The gravitational wave perturbation is then considerd as a sum of discrete Fourier modes in the momentum space. Coming to an operatorial representation and quantizing the phase space variables via appropriately introduced canonincal commutation relations between

Marina Ionova, Jan Kristof Behrens

Assembly processes involving humans and robots are challenging scenarios because the individual activities and access to shared workspace have to be coordinated. Fixed robot programs leave no room to diverge from a fixed protocol. Working on such a process can be stressful for the user and lead to ineffective behavior or failure. We propose a novel approach

Kwan Lin Kristy Fu, Christopher J. Conselice, Leonardo Ferreira, Thomas Harvey

We present the results of a machine learning study to measure the dust content of galaxies observed with JWST at z > 6 through the use of trained neural networks based on high-resolution IllustrisTNG simulations. Dust is an important unknown in the evolution and observability of distant galaxies and is degenerate with other stellar population features throug

A. Ya. Maltsev

The work examines a special behavior of the magnetic conductivity of metals that arises when chaotic electron trajectories appear on the Fermi surface. This behavior is due to the scattering of electrons at singular points of the dynamic system describing the dynamics of electrons in $\, {\bf p}$-space, and caused by small-angle scattering of electrons on ph

Alok Ranjan Sahoo, Pavan Chakraborty

Data driven control of a continuum manipulator requires a lot of data for training but generating sufficient amount of real time data is not cost efficient. Random actuation of the manipulator can also be unsafe sometimes. Meta learning has been used successfully to adapt to a new environment. Hence, this paper tries to solve the above mentioned problem usin

M. García-Rivas, J. Jurčák, N. Bello González, J. M. Borrero

Context. Fully fledged penumbrae have been widely studied both observationally and theoretically. Yet the relatively fast process of penumbra formation has not been studied closely with high spatial resolution. Aims. We investigate the stages previous to and during the formation of penumbral filaments in a developing sunspot. Methods. We analysed Milne-Eddin

Valay Bundele, Mahesh Bhupati, Biplab Banerjee, Aditya Grover

The study of vision-and-language navigation (VLN) has typically relied on expert trajectories, which may not always be available in real-world situations due to the significant effort required to collect them. On the other hand, existing approaches to training VLN agents that go beyond available expert data involve data augmentations or online exploration wh

Wenji Fang, Shang Liu, Hongce Zhang, Zhiyao Xie

In digital IC design, compared with post-synthesis netlists or layouts, the early register-transfer level (RTL) stage offers greater optimization flexibility for both designers and EDA tools. However, timing information is typically unavailable at this early stage. Some recent machine learning (ML) solutions propose to predict the total negative slack (TNS)

Inhwan Bae, Young-Jae Park, Hae-Gon Jeon

There are five types of trajectory prediction tasks: deterministic, stochastic, domain adaptation, momentary observation, and few-shot. These associated tasks are defined by various factors, such as the length of input paths, data split and pre-processing methods. Interestingly, even though they commonly take sequential coordinates of observations as input a

Yi Hu, Jinhang Zuo, Alanis Zhao, Bob Iannucci

Foundation models (FMs) emerge as a promising solution to harness distributed and diverse environmental data by leveraging prior knowledge to understand the complicated temporal and spatial correlations within heterogeneous datasets. Unlike distributed learning frameworks such as federated learning, which often struggle with multimodal data, FMs can transfor

Sagnik Chatterjee, Kamal Krishna Nath, Ritam Mallick

Recycled millisecond pulsars are susceptible to starquakes as they are continuously accreting matter from their binary companion. A starquake happens when the rotational frequency of the star crosses its breaking frequency. In this study, we perform a model analysis of an accreting neutron star suffering a starquake. We analyze two models: a spherical star w

Fedor Vylegzhanin

We develop a general homological approach to presentations of connected graded associative algebras, and apply it to the loop homology of moment-angle complexes $Z_K$ that correspond to flag simplicial complexes $K$. For arbitrary coefficient ring, we describe generators of the Pontryagin algebra $H_*(\Omega Z_K)$ and defining relations between them. We prov

M. V. Lawson, A. Vdovina

We generalize free monoids by defining $k$-monoids. These are nothing other than the one-vertex higher-rank graphs used in $C^{\ast}$-algebra theory with the cardinality requirement waived. The $1$-monoids are precisely the free monoids. We then take the next step and generalize $k$-monoids in such a way that self-similar group actions yield monoids of this

Antonio Mecozzi

In this paper, we establish the theoretical framework for understanding the sensing capabilities of optical fibers. We show the distinct advantage of polarization over phase in detecting subhertz environmental processes. Subsequently, we propose a scheme capable of extracting the spectrum of perturbations affecting a specific section at any position along an

Inhwan Bae, Junoh Lee, Hae-Gon Jeon

Language models have demonstrated impressive ability in context understanding and generative performance. Inspired by the recent success of language foundation models, in this paper, we propose LMTraj (Language-based Multimodal Trajectory predictor), which recasts the trajectory prediction task into a sort of question-answering problem. Departing from tradit

Emir Syahreza Fadhilla, Bobby Eka Gunara, Agus Suroso, Ardian Nata Atmaja

In this work, we explore the inflationary dynamics induced by small fluctuations on the Skyrme brane, characterized by a time-dependent perturbative function $\tilde{\phi}$. In the low-energy regime, the model successfully reproduces standard inflation, with a potential term dictated by the Skyrmion at the brane. Gravity localization is achieved at the brane

Marc Vilà-Insa, Jaume Riba

This paper extends various theoretical results from stationary data processing to cyclostationary (CS) processes under a unified framework. We first derive their asymptotic eigenbasis, which provides a link between their Fourier and Karhunen-Lo\`eve (KL) expansions, through a unitary transformation dictated by the cyclic spectrum. By exploiting this connecti

Nicolas Mauricio Cuadrado, Roberto Alejandro Gutierrez, Martin Takáč

The rise in renewable energy is creating new dynamics in the energy grid that promise to create a cleaner and more participative energy grid, where technology plays a crucial part in making the required flexibility to achieve the vision of the next-generation grid. This work presents FRESCO, a framework that aims to ease the implementation of energy markets

Xiaotong Guo, Huijie Zhao, Shuwei Shao, Xudong Li

Self-supervised monocular depth estimation methods have been increasingly given much attention due to the benefit of not requiring large, labelled datasets. Such self-supervised methods require high-quality salient features and consequently suffer from severe performance drop for indoor scenes, where low-textured regions dominant in the scenes are almost ind

Yanshuo Wang, Ali Cheraghian, Zeeshan Hayder, Jie Hong

Real-world systems often encounter new data over time, which leads to experiencing target domain shifts. Existing Test-Time Adaptation (TTA) methods tend to apply computationally heavy and memory-intensive backpropagation-based approaches to handle this. Here, we propose a novel method that uses a backpropagation-free approach for TTA for the specific case o

Rachit Dhar, Ankur Krishna, Bilal Muhammed

Predicting residual stresses has always been a topic of significance due to its implications in the development of enhanced materials and better processing conditions. In this work, an analytical model for prediction of residual stresses is developed for orthogonal machining. It consists of three component models for force, temperature and stress computation

Iván Caamaño, Estíbalitz Durand-Cartagena, Jesús Á. Jaramillo, Ángeles Prieto

We combine Kirchheim's metric differentials with Cheeger charts in order to establish a non-embeddability principle for any collection $\mathcal C$ of Banach (or metric) spaces: if a metric measure space $X$ bi-Lipschitz embeds in some element in $\mathcal C$, and if every Lipschitz map $X\to Y\in \mathcal C$ is differentiable, then $X$ is rectifiable. This

Yunxiang Li, Nicolas Mauricio Cuadrado, Samuel Horváth, Martin Takáč

The smart grid domain requires bolstering the capabilities of existing energy management systems; Federated Learning (FL) aligns with this goal as it demonstrates a remarkable ability to train models on heterogeneous datasets while maintaining data privacy, making it suitable for smart grid applications, which often involve disparate data distributions and i

Young Seok Jeon, Hongfei Yang, Huazhu Fu, Mengling Feng

Imposing key anatomical features, such as the number of organs, their shapes and relative positions, is crucial for building a robust multi-organ segmentation model. Current attempts to incorporate anatomical features include broadening the effective receptive field (ERF) size with data-intensive modules, or introducing anatomical constraints that scales poo

Pascal Janetzky, Florian Gallusser, Simon Hentschel, Andreas Hotho

Accurate vegetation models can produce further insights into the complex interaction between vegetation activity and ecosystem processes. Previous research has established that long-term trends and short-term variability of temperature and precipitation affect vegetation activity. Motivated by the recent success of Transformer-based Deep Learning models for

Lucas Foppa, Matthias Scheffler

Useful materials are often statistically exceptional and they might be overlooked by AI models that attempt to describe all materials simultaneously. These global models perform well for the majority of (useless) materials, but they do not necessarily capture the useful ones. Subgroup discovery (SGD) identifies rules describing subsets of materials (SGs) ass

Zan-Kai Chong, Hiroyuki Ohsaki, Bryan Ng

In this paper, we investigate collaborative active learning, a paradigm in which multiple collaborators explore a new domain by leveraging their combined machine learning capabilities without disclosing their existing data and models. Instead, the collaborators share prediction results from the new domain and newly acquired labels. This collaboration offers

Haitao Li, Qingyao Ai, Xinyan Han, Jia Chen

Recent research demonstrates the effectiveness of using pre-trained language models for legal case retrieval. Most of the existing works focus on improving the representation ability for the contextualized embedding of the [CLS] token and calculate relevance using textual semantic similarity. However, in the legal domain, textual semantic similarity does not

Grigore Calugareanu, Andrey Chekhlov

As a special case of perspective R-modules, an Abelian goup is called perspective if isomorphic summands have a common complement. In this paper we describe many classes of such groups.

Mengxi Liu, Hymalai Bello, Bo Zhou, Paul Lukowicz

Hand-over-face gestures can provide important implicit interactions during conversations, such as frustration or excitement. However, in situations where interlocutors are not visible, such as phone calls or textual communication, the potential meaning contained in the hand-over-face gestures is lost. In this work, we present iFace, an unobtrusive, wearable

Torsten Reuter, Rainer Schwabe

The goal of subsampling is to select an informative subset of all observations, when using the full data for statistical analysis is not viable. We construct locally $ D $-optimal subsampling designs under a Poisson regression model with a log link in one covariate. A Representation of the support of locally $ D $-optimal subsampling designs is established.

Larry Guth, Dominique Maldague, Changkeun Oh

We identify a new way to divide the $\delta$-neighborhood of surfaces $\mathcal{M}\subset\mathbb{R}^3$ into a finitely-overlapping collection of rectangular boxes $S$. We obtain a sharp $(l^2,L^p)$ decoupling estimate using this decomposition, for the sharp range of exponents $2\leq p\leq 4$. Our decoupling inequality leads to new exponential sum estimates w

Juan De Gregorio, Raúl Toral, David Sánchez

Languages are grouped into families that share common linguistic traits. While this approach has been successful in understanding genetic relations between diverse languages, more analyses are needed to accurately quantify their relatedness, especially in less studied linguistic levels such as syntax. Here, we explore linguistic distances using series of par

Mohammad Ghebleh, Salem Al-Yakoob, Ali Kanso, Dragan Stevanovic

We reimplement here the recent approach of Adam Zsolt Wagner [arXiv:2104.14516], which applies reinforcement learning to construct (counter)examples in graph theory, in order to make it more readable, more stable and much faster. The presented concepts are illustrated by constructing counterexamples for a number of published conjectured bounds for the Laplac

John W. Barrett

Fermion functional integrals are calculated for the Dirac operator of a finite real spectral triple. Complex, real and chiral functional integrals are considered for each KO-dimension where they are non-trivial, and phase ambiguities in the definition are noted.

Eugene N. Ivanov, Michael E. Tobar

We show that fast technical fluctuations of microwave signals can be strongly suppressed by cryogenic resonators. The experiments were carried out with sapphire resonators cooled to approximately 6 K at frequencies around 11 GHz. Each sapphire crystal was shaped like a spindle with the rotational and crystal axes aligned within a degree. Noise suppression fa

Jamshid Mozafari, Anubhav Jangra, Adam Jatowt

Nowadays, individuals tend to engage in dialogues with Large Language Models, seeking answers to their questions. In times when such answers are readily accessible to anyone, the stimulation and preservation of human's cognitive abilities, as well as the assurance of maintaining good reasoning skills by humans becomes crucial. This study addresses such needs

Ilias Mitsouras, Eleftherios Tsonis, Paraskevi Tzouveli, Athanasios Voulodimos

Diffusion models have demonstrated remarkable performance in text-to-image synthesis, producing realistic and high resolution images that faithfully adhere to the corresponding text-prompts. Despite their great success, they still fall behind in sketch-to-image synthesis tasks, where in addition to text-prompts, the spatial layout of the generated images has

Stefano Biagi, Eugenio Vecchi

We prove the existence of a second positive weak solution for mixed local-nonlocal critical semilinear elliptic problems with a sublinear perturbation in the spirit of [Ambrosetti, Brezis, Cerami, 1994].

Brian Formento, Wenjie Feng, Chuan Sheng Foo, Luu Anh Tuan

Language models (LMs) are indispensable tools for natural language processing tasks, but their vulnerability to adversarial attacks remains a concern. While current research has explored adversarial training techniques, their improvements to defend against word-level attacks have been limited. In this work, we propose a novel approach called Semantic Robust

Shreyas N. B., David Martin Diego, Ravi Banavar

Mechanical systems are most often described by a set of continuous-time, nonlinear, second-order differential equations (SODEs) of a particular structure governed by the covariant derivative. The digital implementation of controllers for such systems requires a discrete model of the system and hence requires numerical discretization schemes. Feedback lineari

Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, David Hall

Models such as GPT-4 and Med-PaLM 2 have demonstrated impressive performance on a wide variety of biomedical NLP tasks. However, these models have hundreds of billions of parameters, are computationally expensive to run, require users to send their input data over the internet, and are trained on unknown data sources. Can smaller, more targeted models compet

Wen-Hao Jiang, Yun-Song Piao

The cosmological event horizons are observer-dependent, which might bring a paradox. As an example, in dS$_{2}$ multiverse model there are entanglement islands in crunching regions encoding the information of regions near future infinity of inflating or Minkowski bubbles, however, for two observers in different bubbles, since their island regions overlap, bo

Eugene N. Ivanov, Michael E. Tobar

We show that state-of-the-art phase noise and high frequency stability could be simultaneously achieved in a microwave oscillator based on the sapphire-loaded cavity resonator. The 9 GHz sapphire oscillator was constructed with the SSB phase noise close to -170 dBc/Hz at an offset frequency of 10 kHz and fractional frequency instability less than 2 10$^{-13}

Lucas B. T. de Kam, Thomas L. Maier, Katharina Krischer

This paper introduces the combination of an advanced double-layer model with electrochemical kinetics to explain electrolyte effects on the alkaline hydrogen evolution reaction. It is known from experimental studies that the alkaline hydrogen evolution current shows a strong dependence on the concentration and identity of cations in the electrolyte, but is i

Sicheng Li, Keqiang Sun, Zhixin Lai, Xiaoshi Wu

The conditional text-to-image diffusion models have garnered significant attention in recent years. However, the precision of these models is often compromised mainly for two reasons, ambiguous condition input and inadequate condition guidance over single denoising loss. To address the challenges, we introduce two innovative solutions. Firstly, we propose a

Thomas Leyssens, Michel Henry, Jonathan Lambrechts, Jean-Francois Remacle

This paper proposes two contributions to the calculation of free surface flows using the particle finite element method (PFEM). The PFEM is based on a Lagrangian approach: a set of particles defines the fluid. Then, unlike a pure Lagrangian method, all the particles are connected by a triangular mesh. The difficulty lies in locating the free surface from thi

Mattia Jacopo Villani, Peter McBurney

The transformer neural network has significantly out-shined all other neural network architectures as the engine behind large language models. We provide a theoretical analysis of the expressivity of the transformer architecture through the lens of topos theory. From this viewpoint, we show that many common neural network architectures, such as the convoluti

M. I. Gromyak, O. Ya. Radchenko, S. Ya. Yanchenko

We obtain the exact order estimates of the approximation of the functions of many variables from the generalized Nikol'skii-Besov classes $B^{\Omega}_{p,\theta}(\mathbb{R}^d)$ by sums of de la Vallee Poussin type in the metrics space $L_{\infty}(\mathbb{R}^d)$ and $L_{1}(\mathbb{R}^d)$. These classes of functions for some given $\Omega$ coincide with the wel

Nan Wang, Ricardo G. Sanfelice

This paper proposes a bidirectional rapidly-exploring random trees (RRT) algorithm to solve the motion planning problem for hybrid systems. The proposed algorithm, called HyRRT-Connect, propagates in both forward and backward directions in hybrid time until an overlap between the forward and backward propagation results is detected. Then, HyRRT-Connect const

Savo Glisic

Post Quantum and Quantum Cryptography schemes are feasible quantum computer applications for 7G networks. These schemes could possibly replace existing schemes. These algorithms have been compromised by advances in quantum search algorithms run on quantum computers like Shor algorithm. Shor algorithm is a quantum algorithm for finding the prime factors of an

Mangesh Bhendale, Jayant K. Singh, Alessio Zaccone

The Taylor-Aris theory of shear diffusion predicts that the effective diffusivity of a tracer molecule in a sheared liquid is enhanced by a term quadratic in the shear rate. In sheared supercooled liquids, instead, the observed enhancement is linear in the shear rate. This is a fundamental observation for the physics of nonequilibrium liquids. Here, we deriv

Hungchong Kim, K. S. Kim

Multiquarks can have two-hadron components and hidden-color components in their wave functions. The presence of two-hadron components in multiquarks introduces a potential source of confusion, particularly with respect to their resemblance to hadronic molecules. On the other hand, hidden-color components are essential for distinguishing between multiquarks a

Isaac H. Kim, Xiang Li, Ting-Chun Lin, John McGreevy

Chiral edges of 2+1D systems can have very robust emergent conformal symmetry. When the edge is purely chiral, the Hilbert space of low-energy edge excitations can form a representation of a single Virasoro algebra. We propose a method to systematically extract the generators of the Virasoro algebra from a single ground state wavefunction, using entanglement

Masato Matsuoka, Takeru K. Suzuki, Takato Tokuno, Kensuke Kakiuchi

We investigate non-ideal magnetohydrodynamical (MHD) effects in the chromosphere on the solar wind by performing MHD simulations for Alfv\'en-wave driven winds with explicitly including Ohmic and ambipolar diffusion. We find that MHD waves are significantly damped in the chromosphere by ambipolar diffusion so that the Alfv\'enic Poynting flux that reaches th

Roumita Roy, Sudipta Kanungo

We employ first-principles density-functional theory, to perform a comparative investigation of the effect of the spin-orbit coupling (SOC) on the electronic and magnetic properties of three experimentally synthesized and characterized hexagonal perovskites Sr$_3$MIrO$_6$(M=Mg, Zn, Cd). The electronic structure calculations show that in all the compounds, Ir

Jiaqi Wu, Junbiao Pang, Baochang Zhang, Qingming Huang

Semi-supervised learning (SSL) is a practical challenge in computer vision. Pseudo-label (PL) methods, e.g., FixMatch and FreeMatch, obtain the State Of The Art (SOTA) performances in SSL. These approaches employ a threshold-to-pseudo-label (T2L) process to generate PLs by truncating the confidence scores of unlabeled data predicted by the self-training meth

Wonkyun Kim, Changin Choi, Wonseok Lee, Wonjong Rhee

Stimulated by the sophisticated reasoning capabilities of recent Large Language Models (LLMs), a variety of strategies for bridging video modality have been devised. A prominent strategy involves Video Language Models (VideoLMs), which train a learnable interface with video data to connect advanced vision encoders with LLMs. Recently, an alternative strategy

Shengjie Ma, Qi Chu, Jiaxin Mao, Xuhui Jiang

Determining which legal cases are relevant to a given query involves navigating lengthy texts and applying nuanced legal reasoning. Traditionally, this task has demanded significant time and domain expertise to identify key Legal Facts and reach sound juridical conclusions. In addition, existing data with legal case similarities often lack interpretability,

Apurva Mudgal

A subset $S$ of the unit sphere $\mathbb{S}^2$ is called orthogonal-pair-free if and only if there do not exist two distinct points $u, v \in S$ at distance $\frac{\pi}{2}$ from each other. Witsenhausen \cite{witsenhausen} asked the following question: {\it What is the least upper bound $\alpha_3$ on the Lesbegue measure of any measurable orthogonal-pair-fre

Xiuwei Shang, Guoqiang Chen, Shaoyin Cheng, Shikai Guo

Analyzing the behavior of cryptographic functions in stripped binaries is a challenging but essential task. Cryptographic algorithms exhibit greater logical complexity compared to typical code, yet their analysis is unavoidable in areas such as virus analysis and legacy code inspection. Existing methods often rely on data or structural pattern matching, lead

Georgios Tzolopoulos, Christos Korgialas, Constantine Kotropoulos

The Electric Network Frequency (ENF) serves as a unique signature inherent to power distribution systems. Here, a novel approach for power grid classification is developed, leveraging ENF. Spectrograms are generated from audio and power recordings across different grids, revealing distinctive ENF patterns that aid in grid classification through a fusion of c

Wei-Yuan Kong, Antonio Mosciatti Jofré, Manon Quiros, Marie-Béatrice Bogeat-Triboulot

Turgor is the driving force of plant growth, making possible for roots to overcome soil resistance or for stems to counteract gravity. Maintaining a constant growth rate while avoiding the cell content dilution, which would progressively stop the inward water flux, imposes the production or import of osmolytes in proportion to the increase of volume. We coin

Zhenzhi Qin, Liping Zhang

Robust Principal Component Analysis (RPCA) and its associated non-convex relaxation methods constitute a significant component of matrix completion problems, wherein matrix factorization strategies effectively reduce dimensionality and enhance computational speed. However, some non-convex factorization forms lack theoretical guarantees. This paper proposes a

Thomas Willwacher

We describe a way to compute mapping spaces of cyclic operads through modules. As an application we compute the homotopy automorphism space of the cyclic Batalin-Vilkovisky (Hopf co-)operad.

Maximilian Degner, Raffaele Soloperto, Melanie N. Zeilinger, John Lygeros

We present a model predictive control (MPC) formulation to directly optimize economic criteria for linear constrained systems subject to disturbances and uncertain model parameters. The proposed formulation combines a certainty equivalent economic MPC with a simple least-squares parameter adaptation. For the resulting adaptive economic MPC scheme, we derive

Srinitish Srinivasan, Varenya Pathak, Abirami S

Abstract Art is an immensely popular, discussed form of art that often has the ability to depict the emotions of an artist. Many researchers have made attempts to study abstract art in the form of edge detection, brush stroke and emotion recognition algorithms using machine and deep learning. This papers describes the study of a wide distribution of abstract

Zhengyuan Du, Kangning Liu, Wei Song

In IIB string theory on AdS$_3$ background with NS-NS fluxes, we show that Brown-Henneaux asymptotic Killing vectors can be derived by requiring both the worldsheet equations of motion and Virasoro constraints are preserved near the asymptotic boundary of the target spacetime. The charges on the worldsheet that generate the corresponding transformations can

Maximilian Hess, Lilly Palackal, Abhishek Awasthi, Karen Wintersperger

In variational quantum algorithms, constraints are usually added to the problem objective via penalty terms. For linear inequality constraints, this procedure requires additional slack qubits. Those extra qubits tend to blow up the search space and complicate the parameter landscapes to be navigated by the classical optimizers. In this work, we explore appro

Veljko Janković, Petar Mitrić, Darko Tanasković, Nenad Vukmirović

The optical-conductivity profile carries information on electronic dynamics in interacting quantum many-body systems. Its computation is a formidable task that is usually approached by invoking the single-particle (bubble) approximation and neglecting the vertex corrections, the importance of which remains elusive even in model Hamiltonian calculations. Here

Long Shi, Lei Cao, Yunshan Ye, Yu Zhao

In the context of multi-view clustering, graph learning is recognized as a crucial technique, which generally involves constructing an adaptive neighbor graph based on probabilistic neighbors, and then learning a consensus graph for clustering. However, it is worth noting that these graph learning methods encounter two significant limitations. Firstly, they

Laurent Bugnon, Christian Bernhard, Premysl Marsik

We present a broad band (THz to UV) ellipsometry study of the anisotropic dielectric response of the orthorhombic perovskite YAlO3. The ellipsometric measurements have been performed on YAlO3 crystals with three different surface cuts and for six high symmetry configurations of the crystal axes with respect to the plane of incidence of the photons. The obtai

Steffen Schultze, Helmut Grubmüller

Single molecule X-ray scattering experiments using free electron lasers hold the potential to resolve both single structures and structural ensembles of biomolecules. However, molecular electron density determination has so far not been achieved due to low photon counts, high noise levels and low hit rates. Most analysis approaches therefore focus on large s

Vítězslav Kala, Siu Hang Man

We establish a new connection between sails, a key notion in the geometric theory of generalised continued fractions, and arithmetic of totally real number fields, specifically, universal quadratic forms and additively indecomposable integers. Our main application is to biquadratic fields, for which we show that if their signature rank is at least 3, then ra

Yuri Prokhorov

We investigate the rationality problem for $\mathbf{Q}$-Fano threefolds of Fano index $\ge 2$.

Xiaofeng Wu, Velibor Bojkovic, Bin Gu, Kun Suo

Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing compared with Artificial Neural Networks (ANNs), closely mirroring biological neural processes. However, this potential comes with inherent challenges in directly training SNNs through spatio-temporal backpropagation -- stemming from the temporal dynamics of spiking neuron

Banhirup Sengupta, Swarnendu Sil

We prove up to the boundary regularity estimates in Morrey-Lorentz spaces for weak solutions of the linear system of differential forms with regular anisotropic coefficients \begin{equation*} d^{\ast} \left( A d\omega \right) + B^{\intercal}d d^{\ast} \left( B\omega \right) = \lambda B\omega + f \text{ in } \Omega, \end{equation*} with either $ \nu\wedge \om

Amir Mehrnoosh, Gianluca Bianchin

Feedback optimization is an increasingly popular control paradigm to optimize dynamical systems, accounting for control objectives that concern the system operation at steady-state. Existing feedback optimization techniques heavily rely on centralized systems and controller architectures, and thus suffer from scalability and privacy issues when systems becom

Xuecheng Wang

We prove the global stability of small perturbation near the constant equilibrium for the two dimensional simplified Ericksen-Leslie's hyperbolic system for incompressible liquid crystal model, where the direction function of liquid crystal molecules satisfies a wave map equation with an acoustical metric. This improves the almost global existence result by

Cheng-Chen Tsai

A graph is hypohamiltonian if it is non-Hamiltonian, but the deletion of every single vertex gives a Hamiltonian graph. Until now, the smallest known planar hypohamiltonian graph had 40 vertices, a result due to Jooyandeh, McKay, \"Osterg{\aa}rd, Pettersson, and Zamfirescu. That result is here improved upon by two planar hypohamiltonian graphs on 34 vertices