November 2024 arXiv papers — page 156

Zijian Zhang, Vinay Setty, Yumeng Wang, Avishek Anand

With the rapid advancement of neural language models, the deployment of over-parameterized models has surged, increasing the need for interpretable explanations comprehensible to human inspectors. Existing post-hoc interpretability methods, which often focus on unigram features of single input textual instances, fail to capture the models' decision-making pr

Constantin Berger, Myeongseop Kim, Lukas Scheel-Platz, Vasilis Ntziachristos

Hyperspectral optoacoustic microscopy (OAM) enables obtaining images with label-free biomolecular contrast, offering excellent perspectives as a diagnostic tool to assess freshly excised and unprocessed tissues. However, time-consuming raster-scanning image formation currently limits the translation potential of OAM into the clinical setting-for instance, in

Himadri Shekhar Chakraborty, Williams Chiari, Tsuyoshi Miezaki, Manabu Oura

In the present paper, we discuss the class of Type III and Type IV codes from the perspectives of neighbors. Our investigation analogously extends the results originally presented by Dougherty [8] concerning the neighbor graph of binary self-dual codes. Moreover, as an application of neighbors in invariant theory, we show that the ring of the weight enumerat

Li Zhao, Zhengmin Lu

This paper introduces DanceFusion, a novel framework for reconstructing and generating dance movements synchronized to music, utilizing a Spatio-Temporal Skeleton Diffusion Transformer. The framework adeptly handles incomplete and noisy skeletal data common in short-form dance videos on social media platforms like TikTok. DanceFusion incorporates a hierarchi

Lisa Bombieri, Zhongda Zeng, Roberto Tricarico, Rui Lin

Quantum adiabatic optimization seeks to solve combinatorial problems using quantum dynamics, requiring the Hamiltonian of the system to align with the problem of interest. However, these Hamiltonians are often incompatible with the native constraints of quantum hardware, necessitating encoding strategies to map the original problem into a hardware-conformant

Jonathan F. Carter, Lionel Tarassenko

Accurate classification of sleep stages from less obtrusive sensor measurements such as the electrocardiogram (ECG) or photoplethysmogram (PPG) could enable important applications in sleep medicine. Existing approaches to this problem have typically used deep learning models designed and trained to operate on one or more specific input signals. However, the

Alessandro Mantelero

What is the context which gave rise to the obligation to carry out a Fundamental Rights Impact Assessment (FRIA) in the AI Act? How has assessment of the impact on fundamental rights been framed by the EU legislator in the AI Act? What methodological criteria should be followed in developing the FRIA? These are the three main research questions that this art

Jingrun Chen, Zheng Ma, Keke Wu

This paper introduces the Asymptotic-Preserving Random Feature Method (APRFM) for the efficient resolution of multiscale radiative transfer equations. The APRFM effectively addresses the challenges posed by stiffness and multiscale characteristics inherent in radiative transfer equations through the application of a micro-macro decomposition strategy. This a

Jonathan Fhima, Elad Ben Avraham, Oren Nuriel, Yair Kittenplon

Vision-Language (VL) models have garnered considerable research interest; however, they still face challenges in effectively handling text within images. To address this limitation, researchers have developed two approaches. The first method involves utilizing external Optical Character Recognition (OCR) tools to extract textual information from images, whic

Joaquin Garcia-Suarez

We study the fluid-mediated approach of a deformable axisymmetric object towards a rigid substrate, focusing on how its shape influences contact formation. For low approach velocities and large Stokes numbers, we show that sharper profiles (e.g., conical) maximize contact at the center and avoid fluid entrapment, while blunter ones form central dimples that

Carles Mulet-Forteza, Berta Ferrer-Rosell, Onofre Martorell Cunill, Salvador Linares-Mustarós

This study aims to explore the impact of expansion strategies and specific attributes of hotel establishments on the performance of international hotel chains, focusing on four key performance indicators: RevPAR, efficiency, occupancy, and asset turnover. Data were collected from 255 hotels across various international hotel chains, providing a comprehensive

Vladimir Lysikov, Michael Walter

Many natural computational problems in computer science, mathematics, physics, and other sciences amount to deciding if two objects are equivalent. Often this equivalence is defined in terms of group actions. A natural question is to ask when two objects can be distinguished by polynomial functions that are invariant under the group action. For finite groups

Nitin Nayak, Manuel Schönberger, Valter Uotila, Zhengtong Yan

Quantum annealing is a meta-heuristic approach tailored to solve combinatorial optimization problems with quantum annealers. In this tutorial, we provide a fundamental and comprehensive introduction to quantum annealing and modern data management systems and show quantum annealing's potential benefits and applications in the realm of database optimization. W

Ekaterina Artemova, Akim Tsvigun, Dominik Schlechtweg, Natalia Fedorova

Training and deploying machine learning models relies on a large amount of human-annotated data. As human labeling becomes increasingly expensive and time-consuming, recent research has developed multiple strategies to speed up annotation and reduce costs and human workload: generating synthetic training data, active learning, and hybrid labeling. This tutor

Md Junayed Hasan, Somasundar Kannan, Ali Rohan, Mohd Asif Shah

This study explores the potential of the Ping 360 sonar device, primarily used for navigation, in detecting complex underwater obstacles. The key motivation behind this research is the device's affordability and open-source nature, offering a cost-effective alternative to more expensive imaging sonar systems. The investigation focuses on understanding the be

Teresa Lüdenbach

An ideal filling is a combinatorial object introduced by Judd that amounts to expressing a dominant weight $\lambda$ of $SL_n$ as a rational sum of the positive roots in a canonical way, such that the coefficients satisfy a $\max$ relation. He proved that whenever an ideal filling has integral coefficients it corresponds to a lattice point in the interior of

Muhammad Al-Zafar Khan, Jamal Al-Karaki, Emad Mahafzah

Inspired by the success of Geographically Weighted Regression and its accounting for spatial variations, we propose GeogGNN -- A graph neural network model that accounts for geographical latitude and longitudinal points. Using a synthetically generated dataset, we apply the algorithm for a 4-class classification problem in cybersecurity with seemingly realis

Billy C. Smith, Christopher Thornton, Rachel E. Stirling, Guillermo M. Besne

Objective: The circadian rhythm synchronizes physiological and behavioural patterns with the 24-hour light-dark cycle. Disruption to the circadian rhythm is linked to various health conditions, though optimal methods to describe these disruptions remain unclear. An emerging approach is to examine the intra-individual variability in measurable properties of t

João Paulo M. Pitelli, Ricardo A. Mosna

We investigate the response function of an inertial Unruh-deWitt detector in an impulsive plane wave spacetime. Through symmetry considerations applied to the Wightman function, we demonstrate that the response function remains invariant for any inertial detector, even for those experiencing a discontinuous lightcone coordinate shift after interacting with t

André Ferreira, Tiago Jesus, Behrus Puladi, Jens Kleesiek

This paper presents the winning solution of task 1 and the third-placed solution of task 3 of the BraTS challenge. The use of automated tools in clinical practice has increased due to the development of more and more sophisticated and reliable algorithms. However, achieving clinical standards and developing tools for real-life scenarios is a major challenge.

Javed Hussain, Abdul Fatah, Saeed Ahmed

This article extends the work on stochastic constrained heat equation in \cite{brzezniak2020global}. We will show the existence of Martingale solutions to the stochastic-constrained heat equations. The proof is based on compactness, tightness of measure, quadratic variations, and Martingale representation theorem.

André Ferreira, Gijs Luijten, Behrus Puladi, Jens Kleesiek

This paper presents the second-placed solution for task 8 and the participation solution for task 7 of BraTS 2024. The adoption of automated brain analysis algorithms to support clinical practice is increasing. However, many of these algorithms struggle with the presence of brain lesions or the absence of certain MRI modalities. The alterations in the brain'

Chandrashekar Radhakrishnan, Yuhang Zheng, Olivier Marin

This work addresses the complexities involved in designing distributed quantum algorithms, highlighting that quantum entanglement does not bypass the Fischer-Lynch-Paterson (FLP) impossibility theorem in asynchronous networks. Although quantum resources such as entanglement offer potential speedups, the inherent constraints of classical communication remain.

Sung Rak Choi, Sungwook Jang, Dae-Won Lee

In this paper, we give a characterization of Fano type varieties in terms of the asymptotic base loci of $-(K_X+\Delta)$. We also show that for a potentially lc pair $(X,\Delta)$, if no plc centers are contained in the augmented base locus $\mathbf{B}_{+}(-(K_X+\Delta))$, then $(X,\Delta)$ has a good $-(K_X+\Delta)$-minimal model. This gives an analogous res

Joel Daou, Prabakaran Rajamanickam

The hydrodynamic instabilities of propagating interfaces in Hele-Shaw channels or porous media under the influence of an imposed flow and gravitational acceleration are investigated within the framework of Darcy's law. The stability analysis pertains to an interface between two fluids with different densities, viscosities, and permeabilities, which can be su

Thomas Raujouan, Nick Schmitt, Jonas Ziefle

We introduce the Loop Weierstrass Representation for minimal surfaces in Euclidean space and constant mean curvature 1 surfaces in hyperbolic space by applying integral system methods to the Weierstrass and Bryant representations. We unify associated families, dual surfaces and Goursat transformations under the same holomorphic data, we introduce a simple fa

Shouvik Datta Choudhury

[1] investigates advanced connotations of Hardy and Rellich-type inequalities on complete noncompact Riemannian manifolds, delving on deriving inequalities that incorporate poignant weight functions. These inequalities prolongate classical results by providing sharper estimates conforming the geometry and structure of the underlying manifold. We in this pape

Heyang Zhao, Chenlu Ye, Quanquan Gu, Tong Zhang

Reverse-Kullback-Leibler (KL) regularization has emerged to be a predominant technique used to enhance policy optimization in reinforcement learning (RL) and reinforcement learning from human feedback (RLHF), which forces the learned policy to stay close to a reference policy. While the effectiveness and necessity of KL-regularization have been empirically d

Xuzhen Cao, Chunyu Jia, Ying Hu, Zhaoxin Liang

In nonlinear topological physics, Thouless pumping of nonlinear excitations is a central topic, often illustrated by scalar solitons. Vector solitons, with the additional spin degree of freedom, exhibit phenomena absent in scalar solitons due to enriched interplay between nonlinearity and topology. Here, we theoretically investigate Thouless pumping of vecto

Rogelio Albarracín, M. Zoccali, J. Olivares Carvajal, Á. Rojas-Arriagada

The structure and kinematics of the Milky Way disk are largely inferred from the solar vicinity. To gain a comprehensive understanding, it is essential to find reliable tracers in less-explored regions like the bulge and the far side of the disk. Mira variables, which are well-studied and bright standard candles, offer an excellent opportunity to trace inter

Prabakaran Rajamanickam, Joel Daou

This paper investigates the theoretical implications of applying Darcy's law to premixed flames, a topic of growing interest in research on flame propagation in porous media and confined geometries. A multiple-scale analysis is carried out treating the flame as a hydrodynamic discontinuity in density, viscosity and permeability. The analysis accounts in part

Yiyang Du, Yanyan Niu

In this paper, we first prove that a compact K\"ahler manifold is projective if it satisfies certain quasi-positive curvature conditions, including quasi-positive $S_2^\perp,\, S_2^+,\,\mbox{Ric}_3^\perp, \,\mbox{Ric}_3^+$ or $2$-quasi-positive $\mbox{Ric}_k$. Subsequently, we prove that a compact K\"ahler manifold with a restricted holonomy group is both pr

L. Csedreki, G. F. Ciani, J. Balibrea-Correa, A. Best

We introduce the LUNA neutron detector array developed for the investigation of the 13C(a,n)16O reaction towards its astrophysical s-process Gamow peak in the low-background environment of the Laboratori Nazionali del Gran Sasso (LNGS). Eighteen 3He counters are arranged in two different configurations (in a vertical and a horizontal orientation) to optimize

Said Harb, Pedro Achanccaray, Mehdi Maboudi, Markus Gerke

Cracks are among the earliest indicators of deterioration in concrete structures. Early automatic detection of these cracks can significantly extend the lifespan of critical infrastructures, such as bridges, buildings, and tunnels, while simultaneously reducing maintenance costs and facilitating efficient structural health monitoring. This study investigates

Prabakaran Rajamanickam

This paper investigates shear flow in a Hele-Shaw cell, driven by varying horizontal buoyancy forces resulting from a horizontal density gradient induced by a scalar field. By employing asymptotic methods and taking the dependence of density and transport coefficients on the scalar field into account, effective two-dimensional hydrodynamic equations coupled

Gábor Hegedüs

A family $\mbox{$\cal F$}=\{F_1,\ldots,F_m\}$ of subsets of $[n]$ is said to be ordered, if there exists an $1\leq r\leq m$ index such that $n\in F_i$ for each $1\leq i\leq r$, $n\notin F_i$ for each $i>r$ and $|F_i|\leq |F_j|$ for each $1\leq i<j\leq m$. Our main result is a new upper bound for the size of ordered $L$-intersecting set systems.

J. P. Linares Fernandez, N. Michel, M. Płoszajczak

The open quantum system eigenstate in the vicinity of low-energy decay channel may mimic its features, in particular the characteristic clustering properties of the decay channel. This generic mechanism of clusterization, the so-called mimicry mechanism of clusterization, is discussed here on example of the ground state wave function of $^8$Be. At higher exc

Etienne Chevalier, Yadh Hafsi, Vathana Ly Vath

We study optimal liquidation strategies under partial information for a single asset within a finite time horizon. We propose a model tailored for high-frequency trading, capturing price formation driven solely by order flow through mutually stimulating marked Hawkes processes. The model assumes a limit order book framework, accounting for both permanent pri

Willem Heijltjes

The Functional Machine Calculus (Heijltjes 2022) is an extension of the lambda-calculus that preserves confluent reduction and typed termination, while enabling both call-by-name and call-by-value reduction behaviour and encoding the computational effects of mutable higher-order store, input/output, and probabilistic computation. In this note the calculus is

Johan Leray, Salim Rivière, Friedrich Wagemann

We introduce a general definition of a $n$-crossed module of $P$-algebras over an algebraic operad $P$, which coincides with historical definitions in the cases of the operads As and Lie and $n = 1$. We establish a natural isomorphism between the abelian group of equivalence classes of $n$-crossed modules over a pair $(A,M)$ for an operad $P$ and the $(n+1)^

Yuto Asano, Kenta Yamamoto, Tatsuki Fushimi, Yoichi Ochiai

A convolutional neural network (CNN) is useful for overcoming the trade-off between generation speed and accuracy in the process of synthesizing computer-generated holograms (CGHs). However, methods using a CNN have limited applicability as they cannot specify the propagation distance when synthesizing a hologram. We developed a distance-adaptive CGH generat

Jai G Singla

With the launch of Carto2S series of satellites, high resolution images (0.6-1.0 meters) are acquired and available for use. High resolution Digital Elevation Model (DEM) with better accuracies can be generated using C2S multi-view and multi date datasets. DEMs are further used as an input to derive Digital terrain models (DTMs) and to extract accurate heigh

Jian Yang, Zihang Song, Han Zhang, Yue Gao

Efficient wideband spectrum sensing (WSS) is essential for managing spectrum scarcity in wireless communications. However, existing compressed sensing (CS)-based WSS methods require high sampling rates and power consumption, particularly with high-precision analog-to-digital converters (ADCs). Although 1-bit CS with low-precision ADCs can mitigate these dema

Jai Singla

Most of the research work in the solar potential analysis is performed utilizing aerial imagery, LiDAR data, and satellite imagery. However, in the existing studies using satellite data, parameters such as trees/ vegetation shadow, adjacent higher architectural structures, and eccentric roof structures in urban areas were not considered, and relatively coars

Xiang Gao, Weidong Yan, Wengen Ouyang, Ze Liu

Van der Waals heterogeneous interfaces are promising candidates for the scaling up of structural superlubricity to meet practical applications. Several factors, however, have been identified that may eliminate superlubricity. Elasticity is one such intrinsic factor, where shear induced lattice reconstruction leads to local interfacial pinning, even at clean

Jai Lalita, Pavithran Iyer, Subhashish Banerjee

This study explores robust entangled states described using the framework of discrete Wigner functions. Notably, these states are known to outperform the Bell state in measures of entanglement in the presence of non-Markovian noise. Our study focuses on methods for preparing these states using quantum circuits that can be implemented on superconducting hardw

Yair Bleiberg, Michael Werman

Coordinate-based Multi-Layer Perceptrons (MLPs) are known to have difficulty reconstructing high frequencies of the training data. A common solution to this problem is Positional Encoding (PE), which has become quite popular. However, PE has drawbacks. It has high-frequency artifacts and adds another hyper-hyperparameter, just like batch normalization and dr

Chong Wang, Fengbei Liu, Yuanhong Chen, Helen Frazer

Recent advances in prototypical learning have shown remarkable potential to provide useful decision interpretations associating activation maps and predictions with class-specific training prototypes. Such prototypical learning has been well-studied for various single-label diseases, but for quite relevant and more challenging multi-label diagnosis, where mu

Yuri P. Panarin, Wanhe Jiang, Neelam Yadav, Mudit Sahai

A set of polar rod-shaped liquid crystalline molecules with large dipole moments (mu > 10.4-14.8 D), their molecular structures based on the ferroelectric nematic prototype DIO, are designed, synthesized, and investigated. When the penultimate fluoro-phenyl ring is replaced by phenylpyrimidine moiety, the molecular dipole moment increases from 9.4 D for DIO

Haiyu Huang, Cheng Chen, Kunyi Chen, Pengfei Chen

Distributed traces contain valuable information but are often massive in volume, posing a core challenge in tracing framework design: balancing the tradeoff between preserving essential trace information and reducing trace volume. To address this tradeoff, previous approaches typically used a '1 or 0' sampling strategy: retaining sampled traces while complet

Amin Abdedaiem, Abdelhalim Hafedh Dahou, Mohamed Amine Cheragui, Brigitte Mathiak

In the context of low-resource languages, the Algerian dialect (AD) faces challenges due to the absence of annotated corpora, hindering its effective processing, notably in Machine Learning (ML) applications reliant on corpora for training and assessment. This study outlines the development process of a specialized corpus for Fake News (FN) detection and sen

Erich Häusler, Harald Luschgy

We establish several features of the stationary solution of purely explosive autoregressions of order d based on nonstandard initial values.

Ruiyang Ren, Yuhao Wang, Kun Zhou, Wayne Xin Zhao

Large language models (LLMs), with advanced linguistic capabilities, have been employed in reranking tasks through a sequence-to-sequence approach. In this paradigm, multiple passages are reranked in a listwise manner and a textual reranked permutation is generated. However, due to the limited context window of LLMs, this reranking paradigm requires a slidin

Margaret Bayer, Marija Jelić Milutinović, Julianne Vega

An outerplanar graph is a planar graph that has a planar drawing with all vertices on the unbounded face. The matching complex of a graph is the simplicial complex whose faces are subsets of disjoint edges of the graph. In this paper we prove that the matching complexes of outerplanar graphs are contractible or homotopy equivalent to a wedge of spheres. This

Amadeu Delshams, Piotr Zgliczynski

In this work we consider a saddle-center equilibrium for general vector fields as well as Hamiltonian systems, and we transform it locally into a polynomial normal form in the saddle variables by a change of coordinates. This problem was first solved by Bronstein and Kopanskii in 1995, as well as by Banyaga, de la Llave and Wayne in 1996 [BLW] in the saddle

Suliang Si

In this paper, we show the increasing stability of the inverse source problems for the acoustic wave equation in the full space R3.The goal is to understand increasing stability for wave equation in the time domain. If the time and spatial variables of the source term can be separated with compact support, the increasing stability estimates of the $L^2$-norm

Luca Scofano, Alessio Sampieri, Edoardo De Matteis, Indro Spinelli

Accurately estimating the 3D pose of the camera wearer in egocentric video sequences is crucial to modeling human behavior in virtual and augmented reality applications. The task presents unique challenges due to the limited visibility of the user's body caused by the front-facing camera mounted on their head. Recent research has explored the utilization of

Adrian Solymos, Dávid Jakab, Zoltán Zimborás

We investigate the extendibility problem for Brauer states, focusing on the symmetric two-sided extendibility and the de Finetti extendibility. By employing the representation theory of the unitary and orthogonal groups, we provide a general recipe for determining the set of $(n,m)$-extendible and $n$-de Finetti-extendible Brauer states. From the concrete fo

Johanna Engman, Karl Åström, Magnus Oskarsson

In this paper we present a method for line segment detection in images, based on a semi-supervised framework. Leveraging the use of a consistency loss based on differently augmented and perturbed unlabeled images with a small amount of labeled data, we show comparable results to fully supervised methods. This opens up application scenarios where annotation i

Xiaoyan Jiang, Zhi Zhou, Hailing Wang, Guozhong Wang

Integrating textual data with imaging in liver tumor segmentation is essential for enhancing diagnostic accuracy. However, current multi-modal medical datasets offer only general text annotations, lacking lesion-specific details critical for extracting nuanced features, especially for fine-grained segmentation of tumor boundaries and small lesions. To addres

Benedikt Brückner, Alessio Lomuscio

We develop a method for the efficient verification of neural networks against convolutional perturbations such as blurring or sharpening. To define input perturbations we use well-known camera shake, box blur and sharpen kernels. We demonstrate that these kernels can be linearly parameterised in a way that allows for a variation of the perturbation strength

Arya Chandran, Vishnu Namboothiri K

Cohen-Ramanujan sum, denoted by $c_r^s(n)$, is an exponential sum similar to the Ramanujan sum $c_r(n):=\sum\limits_{\substack{h=1\\{(h,r)=1}}}^{r}e^{\frac{2\pi i n h}{r}}$. An arithmetical function $f$ is said to admit a Cohen-Ramanujan expansion $ f(n):=\sum\limits_{r}\widehat{f}(r)c_r^s(n)$ if the series on the right hand side converges for suitable compl

Filip Loisel, Geri Zeqo, Andrea Morichetta, Anna Lackinger

The increasing volume and complexity of IoT systems demand a transition from the cloud-centric model to a decentralized IoT architecture in the so-called Computing Continuum, with no or minimal reliance on central servers. This paradigm shift, however, raises novel research concerns for decentralized coordination, calling for accurate policies. However, buil

Petra Suková, Francesco Tombesi, Dheeraj R. Pasham, Michal Zajaček

In the last few years, a mysterious new class of astrophysical objects has been uncovered. These are spatially coincident with the nuclei of external galaxies and show X-ray variations that repeat on timescales of minutes to a month. They manifest in three different ways in the data: stable quasi-periodic oscillations (QPOs), quasi-periodic eruptions (QPEs)

Santiago Badia, Wei Li, Alberto F. Martín

We extend the finite element interpolated neural network (FEINN) framework from partial differential equations (PDEs) with weak solutions in $H^1$ to PDEs with weak solutions in $H(\textbf{curl})$ or $H(\textbf{div})$. To this end, we consider interpolation trial spaces that satisfy the de Rham Hilbert subcomplex, providing stable and structure-preserving ne

Mazyar Ghani Varzaneh, Sebastian Riedel

We study differential equations with a linear, path dependent drift and discrete delay in the diffusion term driven by a $\gamma$-H\"older rough path for $\gamma > \frac{1}{3}$. We prove well-posedness of these systems and establish a priori bounds for their solutions. Applying these results to an equation driven by a multidimensional fractional Brownian mot

Igor Kuzmenko, Y. B. Band, Yshai Avishai

It is demonstrated that the Aharonov-Casher (AC) phase is a geometric phase that, in general, depends on the details of the closed path taken by a particle with a magnetic moment that is subject to an electric field. Consequently, it is not a topological phase. The proof of this statement is obtained by developing a counterexample that elucidates the depende

Ahlam Alrehili, Areej Alhothali

Natural language processing (NLP) utilizes text data augmentation to overcome sample size constraints. Increasing the sample size is a natural and widely used strategy for alleviating these challenges. In this study, we chose Arabic to increase the sample size and correct grammatical errors. Arabic is considered one of the languages with limited resources fo

Ushnanshu Dutta, Moumita Bhowmik, Anupam Hazra, Suryachandra. A. Rao

The Indian summer monsoon (ISM) and associated monsoon intraseasonal oscillations (MISOs) influence the billions of people living in the Indian subcontinent. This study explores the role of autoconversion parameterization in microphysical schemes for the simulation of MISO with the coupled climate model, e.g., the Climate Forecast System version 2 (CFSv2), b

Aitor Martinez-Seras, Javier Del Ser, Aitzol Olivares-Rad, Alain Andres

Robustness is a fundamental aspect for developing safe and trustworthy models, particularly when they are deployed in the open world. In this work we analyze the inherent capability of one-stage object detectors to robustly operate in the presence of out-of-distribution (OoD) data. Specifically, we propose a novel detection algorithm for detecting unknown ob

Noam Dahan, Gabriel Stanovsky

Automatic summarization has consistently attracted attention due to its versatility and wide application in various downstream tasks. Despite its popularity, we find that annotation efforts have largely been disjointed, and have lacked common terminology. Consequently, it is challenging to discover existing resources or identify coherent research directions.

Philippe Gottfrois, Fabian Gröger, Faly Herizo Andriambololoniaina, Ludovic Amruthalingam

Africa faces a huge shortage of dermatologists, with less than one per million people. This is in stark contrast to the high demand for dermatologic care, with 80% of the paediatric population suffering from largely untreated skin conditions. The integration of AI into healthcare sparks significant hope for treatment accessibility, especially through the dev

R A Beeston, H L Gomez, L Dunne, S Maddox

The amount of evolution in the dust content of galaxies over the past five billion years of cosmic history is contested in the literature. Here we present a far-infrared census of dust based on a sample of 29,241 galaxies with redshifts ranging from 0 < z < 0.5 using data from the Herschel Astrophysical Terahertz Survey (H-ATLAS). We use the spectral energy

Amir Khan, Nicolas Sylvester Beermann, Shalini Sharma, Tiago de Oliveira Schneider

We investigate the performance of state-of-the-art spintronic THz emitters (W or Ta)/CoFeB/Pt with non-magnetic underlayer deposited using oblique angle deposition. The THz emission amplitude in the presence or absence of an external magnetic field remains the same and remarkably stable over time. This stability is attributed to the enhanced uniaxial magneti

Eduard Jorswieck, Mohammad Soleymani, Ignacio Santamaria, Jesús Gutiérrez

The challenges in dense ultra-reliable low-latency communication networks to deliver the required service to multiple devices are addressed by three main technologies: multiple antennas at the base station (MISO), rate splitting multiple access (RSMA) with private and common message encoding, and simultaneously transmitting and reflecting reconfigurable inte

Hung Guei, Yan-Ru Ju, Wei-Yu Chen, Ti-Rong Wu

MuZero has achieved superhuman performance in various games by using a dynamics network to predict the environment dynamics for planning, without relying on simulators. However, the latent states learned by the dynamics network make its planning process opaque. This paper aims to demystify MuZero's model by interpreting the learned latent states. We incorpor

Mary Scott, Graham Cormode, Carsten Maple

Statistical heterogeneity is a measure of how skewed the samples of a dataset are. It is a common problem in the study of differential privacy that the usage of a statistically heterogeneous dataset results in a significant loss of accuracy. In federated scenarios, statistical heterogeneity is more likely to happen, and so the above problem is even more pres

Tianqi Song, Yugin Tan, Zicheng Zhu, Yibin Feng

Multi-agent systems - systems with multiple independent AI agents working together to achieve a common goal - are becoming increasingly prevalent in daily life. Drawing inspiration from the phenomenon of human group social influence, we investigate whether a group of AI agents can create social pressure on users to agree with them, potentially changing their

M. Scisciò, G. Petringa, Z. Zhu, M. R. D. Rodrigues

Driving the nuclear fusion reaction p+11B -> 3 alpha + 8.7 MeV in laboratory conditions, by interaction between high-power laser pulses and matter, has become a popular field of research, due to numerous applications that it can potentially allow: an alternative to deuterium-tritium (DT) for fusion energy production, astrophysics studies and alpha-particle g

Siying Hu, Piaohong Wang, Ka I Chan, Yaxing Yao

The rapid advancement of Large Language Models (LLMs) has transformed knowledge-intensive has led to its widespread usage by knowledge workers to enhance their productivity. As these professionals handle sensitive information, and the training of text-based GenAI models involves the use of extensive data, there are thus concerns about privacy, security, and

Chunmei Xu, Mahdi Boloursaz Mashhadi, Yi Ma, Rahim Tafazolli

Generative foundation models can revolutionize the design of semantic communication (SemCom) systems allowing high fidelity exchange of semantic information at ultra low rates. In this work, a generative SemCom framework with pretrained foundation models is proposed, where both uncoded forward-with-error and coded discard-with-error schemes are developed for

Aritra Basu, Soumya P. Dash, Sonia Aissa

Reconfigurable intelligent surfaces (RIS) and index modulation (IM) represent key technologies for enabling reliable wireless communication with high energy efficiency. However, to fully take advantage of these technologies in practical deployments, comprehending the impact of the non-ideal nature of the underlying transceivers is paramount. In this context,

Leena G Pillai, Kavya Manohar, Basil K Raju, Elizabeth Sherly

This paper presents a novel multistage fine-tuning strategy designed to enhance automatic speech recognition (ASR) performance in low-resource languages using OpenAI's Whisper model. In this approach we aim to build ASR model for languages with limited digital resources by sequentially adapting the model across linguistically similar languages. We experiment

Thomas Chaplin, Heather A. Harrington, Ulrike Tillmann

Directed graphs can be studied by their associated directed flag complex. The homology of this complex has been successful in applications as a topological invariant for digraphs. Through comparison with path homology theory, we derive a homotopy-like equivalence relation on digraph maps such that equivalent maps induce identical maps on the homology of the

Yuxuan Duan, Yan Hong, Bo Zhang, Jun Lan

The recent progress in text-to-image models pretrained on large-scale datasets has enabled us to generate various images as long as we provide a text prompt describing what we want. Nevertheless, the availability of these models is still limited when we expect to generate images that fall into a specific domain either hard to describe or just unseen to the m

Jhony H. Giraldo, Aref Einizade, Andjela Todorovic, Jhon A. Castro-Correa

Graph Neural Networks (GNNs) have shown great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-scale networks. Previous studies have primarily attempted to utilize the information from higher-order neighbors in the graph, involving the incorporation of powers of the shift opera

Rahul Vashisht, P. Krishna Kumar, Harsha Vardhan Govind, Harish G. Ramaswamy

Neural networks trained with stochastic gradient descent exhibit an inductive bias towards simpler decision boundaries, typically converging to a narrow family of functions, and often fail to capture more complex features. This phenomenon raises concerns about the capacity of deep models to adequately learn and represent real-world datasets. Traditional appr

Xinke Shen, Runmin Gan, Kaixuan Wang, Shuyi Yang

Electroencephalogram (EEG)-based emotion decoding can objectively quantify people's emotional state and has broad application prospects in human-computer interaction and early detection of emotional disorders. Recently emerging deep learning architectures have significantly improved the performance of EEG emotion decoding. However, existing methods still fal

Ali Talebi, Mahsa Bagherikalhor, Behrouz Askari, G. Reza Jafari

The Sherrington-Kirkpatrick spin-glass model used the replica symmetry method to find the phase transition of the system. In 1979-1980, Parisi proposed a solution based on replica symmetry breaking (RSB), which allowed him to identify the underlying phases of complex systems such as spin-glasses. Regardless of the method used for detection, the intrinsic pha

Adam Bouland, Ishaun Datta, Bill Fefferman, Felipe Hernandez

BosonSampling and Random Circuit Sampling are important both as a theoretical tool for separating quantum and classical computation, and as an experimental means of demonstrating quantum speedups. Prior works have shown that average-case hardness of sampling follows from certain unproven conjectures about the hardness of computing output probabilities, such

Emre Gurkanli, Serdar Spor

We explore the sensitivity of future muon colliders to CP-violating interactions in the Higgs sector, specifically focusing on the process $\mu^- \mu^+ \to h \bar{\nu_{l}} \nu_{l} \to b\bar{b} \bar{\nu_{l}}\nu_{l}$. Using a model-independent approach within the framework of the Standard Model Effective Field Theory (SMEFT), we analyze the contribution of dim

Abhiram Manohara, Ahad N. Zehmakan

Consider an undirected graph G, representing a social network, where each node is blue or red, corresponding to positive or negative opinion on a topic. In the voter model, in discrete time rounds, each node picks a neighbour uniformly at random and adopts its colour. Despite its significant popularity, this model does not capture some fundamental real-world

Clément Pellouin, Irina Dvorkin, Léonard Lehoucq

Recent discoveries of gravitational wave sources have advanced our knowledge about the formation of compact object binaries. At present, many questions about the stellar origins of binary neutron stars remain open. We explore the evolution of binary neutron star progenitors with the population synthesis code COSMIC. We identify three dominant evolutionary tr

Marvin Alles, Philip Becker-Ehmck, Patrick van der Smagt, Maximilian Karl

In offline reinforcement learning, a policy is learned using a static dataset in the absence of costly feedback from the environment. In contrast to the online setting, only using static datasets poses additional challenges, such as policies generating out-of-distribution samples. Model-based offline reinforcement learning methods try to overcome these by le

Sulaiman Muhammad Rashid, Ibrahim Aliyu, Abubakar Isah, Jihoon Lee

This paper presents an approach to joint wireless and computing resource management in slice-enabled metaverse networks, addressing the challenges of inter-slice and intra-slice resource allocation in the presence of in-network computing. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and derive an optimal solution using st

Zoltán L. Blázsik, Leila Vivien Nagy

In a directed graph $D$, a vertex subset $S\subseteq V$ is a total dominating set if every vertex of $D$ has an in-neighbor from $S$. A total dominating set exists if and only if every vertex has at least one in-neighbor. We call the orientation of such directed graphs valid. The total domination number of $D$, denoted by $\gamma_t(D)$, is the size of the sm

Andrew Graham, Rob Rockwood

We construct four-variable $p$-adic $L$-functions for the spin Galois representation of a Siegel modular form of genus 2 twisted by the Galois representation of a cuspidal modular form as the modular forms vary in Coleman families. The main ingredient is the construction of a space of nearly overconvergent modular forms in the coherent cohomology of the Sieg

Kai-Yi Zhang, An-Jing Huang, Kun Tu, Ming-Han Li

Secure multiparty computation enables collaborative computations across multiple users while preserving individual privacy, which has a wide range of applications in finance, machine learning and healthcare. Secure multiparty computation can be realized using oblivious transfer as a primitive function. In this paper, we present an experimental implementation

Rohan Kumar Yadav, Bimal Bhattarai, Abhik Jana, Lei Jiao

Designing an explainable model becomes crucial now for Natural Language Processing(NLP) since most of the state-of-the-art machine learning models provide a limited explanation for the prediction. In the spectrum of an explainable model, Tsetlin Machine(TM) is promising because of its capability of providing word-level explanation using proposition logic. Ho

Dasheng Fan, Xihan Mu, Yongkang Lai, Donghui Xie

For the retrieval of large-scale vegetation biophysical parameters, the inversion of radiative transfer models (RTMs) is the most commonly used approach. In recent years, Artificial Neural Network (ANN)-based methods have become the mainstream for inverting RTMs due to their high accuracy and computational efficiency. It has been widely used in the retrieval