May 2024 arXiv papers — page 33

Timothé Albouy, Emmanuelle Anceaume, Davide Frey, Mathieu Gestin

This paper introduces a new asynchronous Byzantine-tolerant asset transfer system (cryptocurrency) with three noteworthy properties: quasi-anonymity, lightness, and consensus-freedom. Quasi-anonymity means no information is leaked regarding the receivers and amounts of the asset transfers. Lightness means that the underlying cryptographic schemes are \textit

Di Yang, Yihao Huang, Qing Guo, Felix Juefei-Xu

The widespread use of diffusion methods enables the creation of highly realistic images on demand, thereby posing significant risks to the integrity and safety of online information and highlighting the necessity of DeepFake detection. Our analysis of features extracted by traditional image encoders reveals that both low-level and high-level features offer d

Enno Breukelman, Sophie Hall, Giuseppe Belgioioso, Florian Dörfler

Over the past decade, the continuous surge in cloud computing demand has intensified data center workloads, leading to significant carbon emissions and driving the need for improving their efficiency and sustainability. This paper focuses on the optimal allocation problem of batch compute loads with temporal and spatial flexibility across a global network of

Albin Soutif--Cormerais, Simone Magistri, Joost van de Weijer, Andew D. Bagdanov

Broad, open source availability of large pretrained foundation models on the internet through platforms such as HuggingFace has taken the world of practical deep learning by storm. A classical pipeline for neural network training now typically consists of finetuning these pretrained network on a small target dataset instead of training from scratch. In the c

Hind I. Alshbanat, Hafida Benhidour, Said Kerrache

Recommender systems are essential tools in the digital era, providing personalized content to users in areas like e-commerce, entertainment, and social media. Among the many approaches developed to create these systems, latent factor models have proven particularly effective. This survey systematically reviews latent factor models in recommender systems, foc

Kun Shi

In this paper, we give some estimations for Ekeland-Hofer-Zehnder capacities of lagrangian products with special forms through combinatorial formulas. Based on these estimations, we give some interesting corollaries.

Scarlett Gauthier, Thirupathaiah Vasantam, Gayane Vardoyan

To effectively support the execution of quantum network applications for multiple sets of user-controlled quantum nodes, a quantum network must efficiently allocate shared resources. We study traffic models for a type of quantum network hub called an Entanglement Generation Switch (EGS), a device that allocates resources to enable entanglement generation bet

Issar Tzachor, Boaz Lerner, Matan Levy, Michael Green

The task of Visual Place Recognition (VPR) is to predict the location of a query image from a database of geo-tagged images. Recent studies in VPR have highlighted the significant advantage of employing pre-trained foundation models like DINOv2 for the VPR task. However, these models are often deemed inadequate for VPR without further fine-tuning on VPR-spec

Peter J Bentley, Soo Ling Lim, Rajat Mathur, Sid Narang

When data on building features is unavailable, the task of determining how to improve that building in terms of carbon emissions becomes infeasible. We show that from only a set of images, a Large Language Model with appropriate prompt engineering and domain knowledge can successfully estimate a range of building features relevant for sustainability calculat

N. Ahmadiniaz, C. Bähtz, A. Benediktovitch, C. Bömer

Quantum field theory predicts a nonlinear response of the vacuum to strong electromagnetic fields of macroscopic extent. This fundamental tenet has remained experimentally challenging and is yet to be tested in the laboratory. A particularly distinct signature of the resulting optical activity of the quantum vacuum is vacuum birefringence. This offers an exc

Michael Vierhauser, Iris Groher, Tobias Antensteiner, Clemens Sauerwein

Artificial Intelligence (AI) approaches have been incorporated into modern learning environments and software engineering (SE) courses and curricula for several years. However, with the significant rise in popularity of large language models (LLMs) in general, and OpenAI's LLM-powered chatbot ChatGPT in particular in the last year, educators are faced with r

Neha Sharma, Kairit Sirts

Research exploring linguistic markers in individuals with depression has demonstrated that language usage can serve as an indicator of mental health. This study investigates the impact of discussion topic as context on linguistic markers and emotional expression in depression, using a Reddit dataset to explore interaction effects. Contrary to common findings

Mehrimah Amirpour, Reza Azmi

The PRFashion24 dataset is a comprehensive Persian dataset collected from various online fashion stores, spanning from April 2020 to March 2024. With 767,272 reviews, it is the first dataset in its kind that encompasses diverse categories within the fashion industry in the Persian language. The goal of this study is to harness deep learning techniques, speci

Michał Czuba, Piotr Bródka

The problem of selecting an optimal seed set to maximise influence in networks has been a subject of intense research in recent years. However, despite numerous works addressing this area, it remains a topic that requires further elaboration. Most often, it is considered within the scope of classically defined graphs with a spreading model in the form of Ind

Jiayu Li, Hanyu Li, Zhiyu He, Weizhi Ma

With the applications of recommendation systems rapidly expanding, an increasing number of studies have focused on every aspect of recommender systems with different data inputs, models, and task settings. Therefore, a flexible library is needed to help researchers implement the experimental strategies they require. Existing open libraries for recommendation

I. Bailleul, H. Eulry

We study in this short note a counterpart to the quasilinear generalized parabolic Anderson model (gPAM) on the 2-dimensional torus where the coefficients are nonlocal functionals of the solution. Under a positivity assumption on the diffusion coefficient we give a local in time solution theory within the framework of paracontrolled calculus.

Irene Plaza, Nina Melero, Cristina del Pozo, Javier Conde

The evaluation of Large Language Models (LLMs) is a key element in their continuous improvement process and many benchmarks have been developed to assess the performance of LLMs in different tasks and topics. As LLMs become adopted worldwide, evaluating them in languages other than English is increasingly important. However, most LLM benchmarks are simply tr

Pranay Agarwal, Riddhipratim Basu

For exponential last passage percolation on the plane we analyse the probability that the point-to-line geodesic exhibits an atypically large transversal fluctuation at the endpoint as well as the probability that the point-to-point geodesic exhibits an atypically large transversal fluctuation at the halfway point. In particular, we show that $p^*_n(t)$, the

Shogo Nakakita

We provide a novel dimension-free uniform concentration bound for the empirical risk function of constrained logistic regression. Our bound yields a milder sufficient condition for a uniform law of large numbers than conditions derived by the Rademacher complexity argument and McDiarmid's inequality. The derivation is based on the PAC-Bayes approach with sec

S. P. Baranov, A. V. Lipatov, M. A. Malyshev, A. A. Prokhorov

We calculate the cross sections of associated $J/\psi + \psi^\prime$ and $J/\psi + J/\psi$ production in $pp$ collisions at $\sqrt s = 13$ TeV in the forward kinematic region. The High Energy Factorization ($k_T$-factorization) framework supplemented with the Catani-Ciafaloni-Fiorani-Marchesini evolution of gluon densities in a proton is applied. We demonstr

Carlos P. Herrero, Rafael Ramirez

Various condensed phases of water, spanning from the liquid state to multiple ice phases, have been systematically investigated under extreme conditions of pressure and temperature to delineate their stability boundaries. This study focuses on probing the mechanical stability of liquid water through path-integral molecular dynamics simulations, employing the

Okko Makkonen, David Karpuk, Camilla Hollanti

A new framework for interference alignment in secure and private information retrieval (PIR) from colluding servers is proposed, generalizing the original cross-subspace alignment (CSA) codes proposed by Jia, Sun, and Jafar. The general scheme is built on algebraic geometry codes and explicit constructions with replicated storage are given over curves of gen

Maximilian Ferle, Nora Grieb, Markus Kreuz, Uwe Platzbecker

The ability to accurately predict disease progression is paramount for optimizing multiple myeloma patient care. This study introduces a hybrid neural network architecture, combining Long Short-Term Memory networks with a Conditional Restricted Boltzmann Machine, to predict future blood work of affected patients from a series of historical laboratory results

Tim Poštuvan, Claas Grohnfeldt, Michele Russo, Giulio Lovisotto

Anomaly detection in continuous-time dynamic graphs is an emerging field yet under-explored in the context of learning algorithms. In this paper, we pioneer structured analyses of link-level anomalies and graph representation learning for identifying categorically anomalous graph links. First, we introduce a fine-grained taxonomy for edge-level anomalies lev

D. W. Boukhvalov, D. A. Zatsepin, D. Yu. Biryukov, Yu. V. Shchapova

Systematic studies of the gradual fabrication by means of carbon ion-implantation of high-quality 6H-SiC layers on silicon surfaces have been carried out. The fluence of carbon ions varied from 5*10^15 cm-2 to 10^17 cm-2. Results of first-principle calculations, X-ray diffraction (XRD), and Raman spectroscopy demonstrate the amorphization of silicon substrat

Laurent Doyen, Pranshu Gaba, Shibashis Guha

Stochastic two-player games model systems with an environment that is both adversarial and stochastic. In this paper, we study the expected value of bounded quantitative prefix-independent objectives in the context of stochastic games. We show a generic reduction from the expectation problem to linearly many instances of the almost-sure satisfaction problem

Christopher Rae, Joseph K. L. Lee, James Richings

As Deep Neural Networks (DNNs) grow in size and complexity, they often exceed the memory capacity of a single accelerator, necessitating the sharding of model parameters across multiple accelerators. Pipeline parallelism is a commonly used sharding strategy for training large DNNs. However, current implementations of pipeline parallelism are being unintentio

P. D. Grigoriev, N. S. Pavlov, I. A. Nekrasov, I. R. Shein

Layered topologically non-trivial and trivial semimetals with AFM-type ordering of magnetic sublattice are known to exhibit a negative magnetoresistance that is well correlated with AFM magnetization changes in a magnetic field. This effect is reported in several experimental studies with EuFe$_2$As$_2$, EuSn$_2$As$_2$, EuSn$_2$P$_2$, etc., where the resista

Panagiotis Koromilas, Giorgos Bouritsas, Theodoros Giannakopoulos, Mihalis Nicolaou

What do different contrastive learning (CL) losses actually optimize for? Although multiple CL methods have demonstrated remarkable representation learning capabilities, the differences in their inner workings remain largely opaque. In this work, we analyse several CL families and prove that, under certain conditions, they admit the same minimisers when opti

Jiaqi Shao, Tianjun Yuan, Tao Lin, Bing Luo

Cognitive abilities, such as Theory of Mind (ToM), play a vital role in facilitating cooperation in human social interactions. However, our study reveals that agents with higher ToM abilities may not necessarily exhibit better cooperative behavior compared to those with lower ToM abilities. To address this challenge, we propose a novel matching coalition mec

Hélène Rousseau, Steve Ertel, Denis Defrère, Virginie Faramaz

Ground-based large-aperture telescopes, interferometers, and future Extremely Large Telescopes equipped with adaptive-optics systems provide angular resolution and high-contrast performance that are superior to space-based telescopes at thermal-infrared wavelengths. Their sensitivity, however, is critically limited by the high thermal background inherent to

Youngwan Lee, Jeffrey Ryan Willette, Jonghee Kim, Sung Ju Hwang

The Masked autoencoder (MAE) has drawn attention as a representative self-supervised approach for masked image modeling with vision transformers. However, even though MAE shows better generalization capability than fully supervised training from scratch, the reason why has not been explored. In another line of work, the Reconstruction Consistent Masked Auto

Reza Abdolmaleki, Shinya Kumashiro

Let $A$ be a commutative Noetherian local ring with maximal ideal $\mathfrak{m}$, and let $I$ be an ideal. The fiber cone is then an image of the polynomial ring over the residue field $A/\mathfrak{m}$. The kernel of this map is called the defining ideal, and it is natural to ask how to compute it. In this paper, we provide a construction for the defining id

Thanh Trung Huynh, Trong Bang Nguyen, Phi Le Nguyen, Thanh Tam Nguyen

Federated learning (FL) has recently emerged as a compelling machine learning paradigm, prioritizing the protection of privacy for training data. The increasing demand to address issues such as ``the right to be forgotten'' and combat data poisoning attacks highlights the importance of techniques, known as \textit{unlearning}, which facilitate the removal of

Omar Erak, Omar Alhussein, Shimaa Naser, Nouf Alabbasi

This study introduces an innovative framework that employs large language models (LLMs) to automate the design and generation of curricula for reinforcement learning (RL). As mobile networks evolve towards the 6G era, managing their increasing complexity and dynamic nature poses significant challenges. Conventional RL approaches often suffer from slow conver

Aaron Hofer, Ingo Runkel

Given a not necessarily semisimple modular tensor category C, we use the corresponding 3d TFT defined in [arXiv:1912.02063] to explicitly describe a modular functor as a symmetric monoidal 2-functor from a 2-category of oriented bordisms to a 2-category of finite linear categories. This recovers a result by Lyubashenko [arXiv:hep-th/9405168] obtained via gen

Luca Martinazzi, Ali Hyder

Given a half-harmonic map $u\in \dot H^{\frac{1}{2},2}(\mathbb{R},\mathbb{S}^1)$ minimizing the fractional Dirichlet energy under Dirichlet boundary conditions in $\mathbb{R}\setminus I$, we show the existence of a second half-harmonic map, minimizing the fractional Dirichlet energy in a different homotopy class. This is based on the study of the degree of f

Wanlin Cai, Kun Wang, Hao Wu, Xiaoxu Chen

The challenge of effectively learning inter-series correlations for multivariate time series forecasting remains a substantial and unresolved problem. Traditional deep learning models, which are largely dependent on the Transformer paradigm for modeling long sequences, often fail to integrate information from multiple time series into a coherent and universa

Guangmin Zheng, Jin Wang, Liang-Chih Yu, Xuejie Zhang

Aspect-based sentiment analysis (ABSA) identifies sentiment information related to specific aspects and provides deeper market insights to businesses and organizations. With the emergence of large language models (LMs), recent studies have proposed using fixed examples for instruction tuning to reformulate ABSA as a generation task. However, the performance

Matej Benko, Iwona Chlebicka, Jørgen Endal, Błażej Miasojedow

We study the spatially homogeneous granular medium equation \[\partial_t\mu=\rm{div}(\mu\nabla V)+\rm{div}(\mu(\nabla W \ast \mu))+\Delta\mu\,,\] within a large and natural class of the confinement potentials $V$ and interaction potentials $W$. The considered problem do not need to assume that $\nabla V$ or $\nabla W$ are globally Lipschitz. With the aim of

Mihnea-Bogdan Jurca, Remco Royen, Ion Giosan, Adrian Munteanu

Gaussian Splatting has revolutionized the world of novel view synthesis by achieving high rendering performance in real-time. Recently, studies have focused on enriching these 3D representations with semantic information for downstream tasks. In this paper, we introduce RT-GS2, the first generalizable semantic segmentation method employing Gaussian Splatting

Michel Rigo, Manon Stipulanti, Markus A. Whiteland

Parikh-collinear morphisms have the property that all the Parikh vectors of the images of letters are collinear, i.e., the associated adjacency matrix has rank 1. In the conference DLT-WORDS 2023 we showed that fixed points of Parikh-collinear morphisms are automatic. We also showed that the abelian complexity function of a binary fixed point of such a morph

Dmitry Kovalev, Ekaterina Borodich, Alexander Gasnikov, Dmitrii Feoktistov

We consider the task of minimizing the sum of convex functions stored in a decentralized manner across the nodes of a communication network. This problem is relatively well-studied in the scenario when the objective functions are smooth, or the links of the network are fixed in time, or both. In particular, lower bounds on the number of decentralized communi

Giovanni Bambini, Alessandro Ottaviano, Christian Conficoni, Andrea Tilli

The race towards performance increase and computing power has led to chips with heterogeneous and complex designs, integrating an ever-growing number of cores on the same monolithic chip or chiplet silicon die. Higher integration density, compounded with the slowdown of technology-driven power reduction, implies that power and thermal management become incre

Zebin You, Xinyu Zhang, Hanzhong Guo, Jingdong Wang

The ultimate goal of generative models is to perfectly capture the data distribution. For image generation, common metrics of visual quality (e.g., FID) and the perceived truthfulness of generated images seem to suggest that we are nearing this goal. However, through distribution classification tasks, we reveal that, from the perspective of neural network-ba

Aryo Pradipta Gema, Chaeeun Lee, Pasquale Minervini, Luke Daines

The MEDIQA-CORR 2024 shared task aims to assess the ability of Large Language Models (LLMs) to identify and correct medical errors in clinical notes. In this study, we evaluate the capability of general LLMs, specifically GPT-3.5 and GPT-4, to identify and correct medical errors with multiple prompting strategies. Recognising the limitation of LLMs in genera

Jaewoo Ahn, Taehyun Lee, Junyoung Lim, Jin-Hwa Kim

While Large Language Models (LLMs) can serve as agents to simulate human behaviors (i.e., role-playing agents), we emphasize the importance of point-in-time role-playing. This situates characters at specific moments in the narrative progression for three main reasons: (i) enhancing users' narrative immersion, (ii) avoiding spoilers, and (iii) fostering engag

Jonas Spethmann, Nguyen Duy Khanh, Haruto Yoshimochi, Rina Takagi

The two stable surfaces of GdRu2Si2 are studied using spin-polarized scanning tunneling microscopy (SP-STM). Depending on the applied magnetic field different magnetic phases have been found and the presented measurements are in agreement with the respective previously proposed multi-Q spin textures. In particular the multi-Q nature of the zero magnetic fiel

Dvir Samuel, Rami Ben-Ari, Matan Levy, Nir Darshan

Personalized retrieval and segmentation aim to locate specific instances within a dataset based on an input image and a short description of the reference instance. While supervised methods are effective, they require extensive labeled data for training. Recently, self-supervised foundation models have been introduced to these tasks showing comparable result

Shaili Sett, Rahul Debnath, Arup Singha, Shinjan Mandal

At near-parallel orientation, twisted bilayer of transition metal dichalcogenides exhibit inter-layer charge transfer-driven out-of-plane ferroelectricity that may lead to unique electronic device architectures. Here we report detailed electrical transport in a dual-gated graphene field-effect transistor placed on 3R stacked twisted bilayer of WSe2 at a twis

Xue Jia, Fengwei Li, Huan Sun, Qin Yue

Classical Goppa codes are a well-known class of codes with applications in code-based cryptography, which are a special case of alternant codes. Many papers are devoted to the search for Goppa codes with a cyclic extension or with a cyclic parity-check subcode. Let $\Bbb F_q$ be a finite field with $q=2^l$ elements, where $l$ is a positive integer. In this p

Jinhyung Park

This is a survey paper on recent work on syzygies of algebraic varieties. We discuss the gonality conjecture on weight-one syzygies of algebraic curves, syzygies of secant varieties of algebraic curves, syzygies of tangent developable surfaces and Green's conjecture on syzygies of canonical curves, and asymptotic syzygies of algebraic varieties. All results

Mathieu Cocheteux, Julien Moreau, Franck Davoine

Despite the increasing interest in enhancing perception systems for autonomous vehicles, the online calibration between event cameras and LiDAR - two sensors pivotal in capturing comprehensive environmental information - remains unexplored. We introduce MULi-Ev, the first online, deep learning-based framework tailored for the extrinsic calibration of event c

Jens Robben, Katrien Antonio, Torsten Kleinow

Using fine-grained, publicly available data, this paper studies the short-term association between environmental factors, i.e., weather and air pollution characteristics, and weekly mortality rates in small geographical regions in Europe. Hereto, we develop a mortality modeling framework where a baseline model describes a region-specific, seasonal trend obse

Pietro Savazzi, Anna Vizziello, Fabio Dell'Acqua

Wireless spiking neural networks (WSNNs) allow energy-efficient communications, especially when considering edge intelligence and learning for both terrestrial beyond 5G/6G and space networking systems. Recent research work has revealed that distributed wireless SNNs (DWSNNs) show good performance in terms of inference accuracy and low energy consumption of

Felix Seegräber, Mengkun She, Felix Woelk, Kevin Köser

Many underwater applications rely on vision sensors and require proper camera calibration, i.e. knowing the incoming light ray for each pixel in the image. While for the ideal pinhole camera model all viewing rays intersect in a single 3D point, underwater cameras suffer from - possibly multiple - refractions of light rays at the interfaces of water, glass a

Søren Toxvaerd

Feynman gave in 1982 a keynote speech \textit{Simulating Physics with Computers} (Int. J. Theor. Phys. {\bf 21}, 467 (1982)) in which he talked ``...about the possibility...that the computer will do exactly the same as nature". The motivation was that: ``...the physical world is quantum mechanical, and therefore the proper problem is the simulation of quantu

Di Zhang

In this work, we revisit the one-loop renormalization group equations (RGEs) among non-degenerate seesaw scales, i.e., threshold effects in the canonical seesaw mechanism, which have been obtained for more than two decades. Different from the previous work only focusing on the Weinberg operator, we derive the complete one-loop RGEs of all three dimension-fiv

L. B. Soros, Alyssa Adams, Stefano Kalonaris, Olaf Witkowski

Artificial Life (ALife) as an interdisciplinary field draws inspiration and influence from a variety of perspectives. Scientific progress crucially depends, then, on concerted efforts to invite cross-disciplinary dialogue. The goal of this paper is to revitalize discussions of potential connections between the fields of Computational Creativity (CC) and ALif

Xiang Dai, Sarvnaz Karimi, Abeed Sarker, Ben Hachey

Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medical literature, social media and search engine logs. Over the years, many datasets have been created, and shared tasks have been organised to facilitate active adverse event surveillance. However, most - if not all - datase

Wenbing Li, Hang Zhou, Junqing Yu, Zikai Song

The essence of multi-modal fusion lies in exploiting the complementary information inherent in diverse modalities. However, prevalent fusion methods rely on traditional neural architectures and are inadequately equipped to capture the dynamics of interactions across modalities, particularly in presence of complex intra- and inter-modality correlations. Recen

Nikolay Kuznetsov

In the first part of this note, we review results concerning analytic characterization of convexity for planar sets. The second part is devoted to results valid for arbitrary $m \ge 2$.

Muhammad Adi Nugroho, Sangmin Woo, Sumin Lee, Jinyoung Park

Weakly-Supervised Group Activity Recognition (WSGAR) aims to understand the activity performed together by a group of individuals with the video-level label and without actor-level labels. We propose Flow-Assisted Motion Learning Network (Flaming-Net) for WSGAR, which consists of the motion-aware actor encoder to extract actor features and the two-pathways r

Andreas Lizenberger, Ferdinand Pfeifer, Bastian Polewka

Cluster-based algorithm selection deals with selecting recommendation algorithms on clusters of users to obtain performance gains. No studies have been attempted for many combinations of clustering approaches and recommendation algorithms. We want to show that clustering users prior to algorithm selection increases the performance of recommendation algorithm

Lars Kastner

A triangulation of a point configuration is regular if it can be given by a height function, that is every point gets lifted to a certain height and projecting the lower convex hull gives the triangulation. Checking regularity of a triangulation usually is done by solving a linear program. However when checking many flip-connected triangulations for regulari

Zican Dong, Junyi Li, Xin Men, Wayne Xin Zhao

Transformer-based large language models (LLMs) typically have a limited context window, resulting in significant performance degradation when processing text beyond the length of the context window. Extensive studies have been proposed to extend the context window and achieve length extrapolation of LLMs, but there is still a lack of in-depth interpretation

JUNO Collaboration, Angel Abusleme, Thomas Adam, Shakeel Ahmad

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment under construction in South China. This paper presents an updated estimate of JUNO's sensitivity to neutrino mass ordering using the reactor antineutrinos emitted from eight nuclear reactor cores in the Taishan and Yangjiang nuclear power plants. This measurement is p

Chunlin Yang, Zexian Li, Hongmei Yao, Zhaobing Fan

Block encoding severs as an important data input model in quantum algorithms, enabling quantum computers to simulate non-unitary operators effectively. In this paper, we propose an efficient block-encoding protocol for sparse matrices based on a novel data structure, called the dictionary data structure, which classifies all non-zero elements according to th

Lucas Henao, Saúl Ares, Pablo Catalán

We review the construction and evolution of mathematical models of the Arabidopsis circadian clock, structuring the discussion into two distinct historical phases of modeling strategies: extension and reduction. The extension phase explores the bottom-up assembly of regulatory networks introducing as many components and interactions as possible in order to c

Hugo Henneuse

Persistent homology is a tool from Topological Data Analysis (TDA) used to summarize the topology underlying data. It can be conveniently represented through persistence diagrams. Observing a noisy signal, common strategies to infer its persistence diagram involve plug-in estimators, and convergence properties are then derived from sup-norm stability. This d

Yuhao Shen, Liyuan Sun, Yan Xu, Wenbin Liu

With the widespread application of artificial intelligence (AI), particularly deep learning (DL) and vision large language models (VLLMs), in skin disease diagnosis, the need for interpretability becomes crucial. However, existing dermatology datasets are limited in their inclusion of concept-level meta-labels, and none offer rich medical descriptions in nat

Bowen Zhang, Xiaofei Xie, Haotian Lu, Na Ma

Diffusion-based video generation has achieved significant progress, yet generating multiple actions that occur sequentially remains a formidable task. Directly generating a video with sequential actions can be extremely challenging due to the scarcity of fine-grained action annotations and the difficulty in establishing temporal semantic correspondences and

Daniel Le, Bao V. Le Hung, Brandon Levin, Stefano Morra

Let $G_0$ be a reductive group over $\mathbb{F}_p$ with simply connected derived subgroup, (geometrically) connected center and Coxeter number $h+1$. We extend Jantzen's generic decomposition pattern from $(2h-1)$-generic to $h$-generic Deligne--Lusztig representations, which is optimal. We also prove several results on the ``obvious'' Jordan--H\"older facto

Fangyu Zhang, Yuang Chen, Hancheng Lu, Yongsheng Huang

Optimizing microservice placement to enhance the reliability of services is crucial for improving the service level of microservice architecture-based mobile networks and Internet of Things (IoT) networks. Despite extensive research on service reliability, the impact of network load and routing on service reliability remains understudied, leading to suboptim

Lukas Schulthess, Philipp Mayer, Luca Benini, Michele Magno

Establishing reliable data exchange in an underwater domain using energy and power-efficient communication methods is crucial and challenging. Radio frequencies are absorbed by the salty and mineral-rich water and optical signals are obstructed and scattered after short distances. In contrast, acoustic communication benefits from low absorption and enables c

Devang Somawanshi, Souradeep Bhattacharya, Manish Kataria, Chiaki Kobayashi

Edge-on galaxies act as the best laboratories to understand the origin of thin and thick discs in galaxies. Measurement of spatially resolved stellar population properties in such galaxies, particularly age, metallicity and [$\alpha$/Fe], are crucial to understanding the formation and evolution of disc galaxies. Such measurements are made possible from stell

Yuqi Zhou, Sunhao Dai, Liang Pang, Gang Wang

Recommender systems are essential for information access, allowing users to present their content for recommendation. With the rise of large language models (LLMs), AI-generated content (AIGC), primarily in the form of text, has become a central part of the content ecosystem. As AIGC becomes increasingly prevalent, it is important to understand how it affect

Fernando Ballesta Yagüe, Gustavo Garrigós

We show that the cone multiplier satisfies local $L^p$-$L^q$ bounds only in the trivial range $1\leq q\leq 2\leq p\leq\infty$. To do so, we suitably adapt to this setting the proof of Fefferman for the ball multiplier. As a consequence we answer negatively a question by B\'ekoll\'e and Bonami (Colloq. Math. 68, 1995, 81-100), regarding the continuity from $L

J. Fernando Barbero G.

This paper discusses several functional analytic issues relevant for field theories in the context of the Hamiltonian formulation for a free, massless, scalar field defined on a closed interval of the real line. The fields that we use belong to a Sobolev space with a scalar product. As we show this choice is useful because it leads to an explicit representat

Shentong Mo, Sukmin Yun

The joint-embedding predictive architecture (JEPA) recently has shown impressive results in extracting visual representations from unlabeled imagery under a masking strategy. However, we reveal its disadvantages, notably its insufficient understanding of local semantics. This deficiency originates from masked modeling in the embedding space, resulting in a r

YuPing He, Ge Sun, Jing Li, Ya Yang

An oscillating bound state is a phenomenon where excitations mediated by the continuum modes oscillate persistently. Although it is generated by the superposition of two bound states in the continuum (BICs), such phenomenon is said to be unique to giant atoms. We present the phenomenon of an oscillating bound state with an alternative waveguide QED system, w

Ariful Hoque, Tapas Baug, Lokesh Dewangan, Ke Wang

We studied the influence of a massive star on a mid-infrared bubble and its surrounding gas in the IRAS\,16489-4431 star-forming region using multi-wavelength data. The {\it Spitzer} mid-infrared band images revealed the shocked nature of the bubble. Analyses showed that the bubble is developed by a massive star owing to its strong radiation pressure. Eviden

Laurent Bonnasse-Gahot, Christophe Pallier

Over the past decade, studies of naturalistic language processing where participants are scanned while listening to continuous text have flourished. Using word embeddings at first, then large language models, researchers have created encoding models to analyze the brain signals. Presenting these models with the same text as the participants allows to identif

Roy Miles, Pradyumna Reddy, Ismail Elezi, Jiankang Deng

Large language models (LLMs) have recently emerged as powerful tools for tackling many language-processing tasks. Despite their success, training and fine-tuning these models is still far too computationally and memory intensive. In this paper, we identify and characterise the important components needed for effective model convergence using gradient descent

Tommaso Bacchielli, Lorenzo Pucci, Davide Dardari, Andrea Giorgetti

Only the chairs can edit The availability of abundant bandwidth at terahertz (THz) frequencies holds promise for significantly enhancing the sensing performance of integrated sensing and communication (ISAC) systems in the next-generation wireless systems, enabling high accuracy and resolution for precise target localization. In orthogonal frequency-division

Bratati Bhat

Since molecules are ubiquitous in space, the study of the 'Molecular Universe' could unfold the mystery of the existing Interstellar medium. Star formation is linked to the chemical evolution processes. Thus, an analysis of the formation of stars coupled with the chemical evolution would give a clear insight into the entire process. For example, various evol

Tinatin Baratashvili, Benjamin Grison, Brigitte Schmieder, Pascal Demoulin

Coronal Mass Ejections (CMEs) are the main drivers of the disturbances in interplanetary space. Understanding the CME interior magnetic structure is crucial for advancing space weather studies. Assessing the capabilities of a numerical heliospheric model is crucial, as understanding the nature and extent of its limitations can be used for improving the model

Xijia Che, Yi He, Xuewei Feng, Kun Sun

Bluetooth Low Energy (BLE) is a short-range wireless communication technology for resource-constrained IoT devices. Unfortunately, BLE is vulnerable to session-based attacks, where previous packets construct exploitable conditions for subsequent packets to compromise connections. Defending against session-based attacks is challenging because each step in the

Friedrich M. Philipp

It is well known that linear and non-linear dissipative port-Hamiltonian systems in finite dimensions admit an energy balance, relating the energy increase in the system with the supplied energy and the dissipated energy. The integrand in the dissipation term is then a function of the state variable. In this note, we answer the question of when this is possi

Parth Pavaskar, Ka Ho Yuen, Huirong Yan, Sunil Malik

One of the biggest challenges in understanding Magnetohydrodynamic (MHD) turbulence is identifying the plasma mode components from observational data. Previous studies on synchrotron polarization from the interstellar medium (ISM) suggest that the dominant MHD modes can be identified via statistics of Stokes parameters, which would be crucial for studying va

Xiaoting Lyu, Yufei Han, Wei Wang, Hangwei Qian

Graph Prompt Learning (GPL) bridges significant disparities between pretraining and downstream applications to alleviate the knowledge transfer bottleneck in real-world graph learning. While GPL offers superior effectiveness in graph knowledge transfer and computational efficiency, the security risks posed by backdoor poisoning effects embedded in pretrained

Dean Brandner, Sergio Lucia

Model predictive control can optimally deal with nonlinear systems under consideration of constraints. The control performance depends on the model accuracy and the prediction horizon. Recent advances propose to use reinforcement learning applied to a parameterized model predictive controller to recover the optimal control performance even if an imperfect mo

Takaaki Ito

In a previous work, we stated the conjecture that any semiring homomorphism between the Boolean function semirings on 1-dimensional tropical fans has the property called geometric. In this paper, we show that the conjecture is true. As an application, we establish a way to find all the morphisms between given 1-dimensional tropical fans.

Stéphane Louboutin

Let $m\ge 1$ be a rational integer. We give an explicit formula for the mean value $$\frac{2}{\phi(f)}\sum_{\chi (-1)=(-1)^m}\vert L(m,\chi )\vert^2,$$ where $\chi$ ranges over the $\phi (f)/2$ Dirichlet characters modulo $f>2$ with the same parity as $m$. We then adapt our proof to obtain explicit means values for products of the form $L(m_1,\chi_1)\cdots L

Anirudh Phukan, Shwetha Somasundaram, Apoorv Saxena, Koustava Goswami

With the enhancement in the field of generative artificial intelligence (AI), contextual question answering has become extremely relevant. Attributing model generations to the input source document is essential to ensure trustworthiness and reliability. We observe that when large language models (LLMs) are used for contextual question answering, the output a

Dick Maryopi, Daud Al Adumy, Osman Musa, Peter Jung

To efficiently utilize the scarce wireless resource, the random access scheme has been attaining renewed interest primarily in supporting the sporadic traffic of a large number of devices encountered in the Internet of Things (IoT). In this paper we investigate the performance of slotted ALOHA -- a simple and practical random access scheme -- in connection w

Rong Yang, Songxiao Li

A conjecture posed by Chalmoukis in 2020 states that if $T_{g,a}:H^p\to H^q(0<q<p<\infty)$ is bounded, then $g$ must be in $H^{\frac{pq}{p-q}}$. In this article, we provide a positive answer to the aforementioned conjecture. We also consider the compactness of $T_{g,a}:H^p\to H^q(0<q<p<\infty)$.

Xiaobao Wu, Thong Nguyen, Delvin Ce Zhang, William Yang Wang

Topic models have been evolving rapidly over the years, from conventional to recent neural models. However, existing topic models generally struggle with either effectiveness, efficiency, or stability, highly impeding their practical applications. In this paper, we propose FASTopic, a fast, adaptive, stable, and transferable topic model. FASTopic follows a n

Seongyun Lee, Sue Hyun Park, Seungone Kim, Minjoon Seo

Although humans inherently have diverse values, current large language model (LLM) alignment methods often assume that aligning LLMs with the general public's preferences is optimal. A major challenge in adopting a more individualized approach to LLM alignment is its lack of scalability, as it involves repeatedly acquiring preference data and training new re

Shaohua Wu, Jiangang Luo, Xi Chen, Lingjun Li

Yuan 2.0-M32, with a similar base architecture as Yuan-2.0 2B, uses a mixture-of-experts architecture with 32 experts of which 2 experts are active. A new router network, Attention Router, is proposed and adopted for a more efficient selection of experts, which improves the accuracy compared to the model with classical router network. Yuan 2.0-M32 is trained