May 2024 arXiv papers — page 135

D. Miceli, P. Da Vela, E. Prandini

The intergalactic magnetic field (IGMF) present in the voids of large-scale structures is considered to be the weakest magnetic field in the Universe. Gamma-ray observations of blazars in the GeV-TeV domain have led to lower limits on the IGMF strength based on the search for delayed or extended emission. Nevertheless, these results have been obtained with s

Yizun Lin, Yangyu Zhang, Zhao-Rong Lai, Cheng Li

The $\ell_0$-constrained mean-CVaR model poses a significant challenge due to its NP-hard nature, typically tackled through combinatorial methods characterized by high computational demands. From a markedly different perspective, we propose an innovative autonomous sparse mean-CVaR portfolio model, capable of approximating the original $\ell_0$-constrained m

Xin Su, Ruisi He, Peng Zhang, Bo Ai

Terahertz (THz) cell-free massive multiple-input-multiple-output (mMIMO) networks have been envisioned as a prospective technology for achieving higher system capacity, improved performance, and ultra-high reliability in 6G networks. However, due to severe attenuation and limited scattering in THz transmission, as well as high power consumption for increased

Paulo Amorim, Alexander Keimer, Lukas Pflug, Jakob Rodestock

In this contribution, we present a novel approach for solving the obstacle problem for (linear) conservation laws. Usually, given a conservation law with an initial datum, the solution is uniquely determined. How to incorporate obstacles, i.e., inequality constraints on the solution so that the resulting solution is still "physically reasonable" and obeys th

Luiz Vale Silva

I discuss the extraction of the Cabibbo-Kobayashi-Maskawa (CKM) matrix elements under the Standard Model (SM) framework from a global fit combining observables that satisfy the double requirement of being precisely known both experimentally and theoretically. The analysis shown here relies on the CKMfitter package, consisting of a frequentist approach that e

Pratik Gujral, Kshitij Awaldhi, Navya Jain, Bhavuk Bhandula

The study of how social media affects the formation of public opinion and its influence on political results has been a popular field of inquiry. However, current approaches frequently offer a limited comprehension of the complex political phenomena, yielding inconsistent outcomes. In this work, we introduce a new method: harnessing the capabilities of Large

Yuning Huang, Mohamed Abul Hassan, Jiangpeng He, Janine Higgins

Detecting an ingestion environment is an important aspect of monitoring dietary intake. It provides insightful information for dietary assessment. However, it is a challenging problem where human-based reviewing can be tedious, and algorithm-based review suffers from data imbalance and perceptual aliasing problems. To address these issues, we propose a neura

Frank Fagan

While most commentators have focused exclusively on how LLMs will transform day-to-day law practice, a substantial structural change could be afoot within the legal sector as a whole. Large increases in productivity and attendant cost savings could encourage law firms and corporate legal departments to develop large language models in-house. A ten percent in

A. Savaş Arapoğlu, Sermet Çağan, Aybike Çatal-Özer

The massless fields in the universal NS-NS sector of string theory form $O(D, D)$ multiplets of Double Field Theory, which is a theory that provides a T-duality covariant formulation of supergravity, leading to a stringy modification of General Relativity. In this framework, it is possible to write down the extensions of the Einstein field equations and the

Abdelkader Belhenniche, Roman Chertovskih

We apply methods of the fixed point theory to a Lambda policy iteration with a randomization algorithm for weak contractions mappings. This type of mappings covers a broader range than the strong contractions typically considered in the literature, such as \'Ciri\'c contraction. Specifically, we explore the characteristics of reinforcement learning procedure

Olabode T. Ajenifujah, Francis Ogoke, Florian Wirth, Jack Beuth

Laser powder bed fusion (LPBF) has shown promise for wide range of applications due to its ability to fabricate freeform geometries and generate a controlled microstructure. However, components generated by LPBF still possess sub-optimal mechanical properties due to the defects that are created during laser-material interactions. In this work, we investigate

Patricia A. Apellániz, Ana Jiménez, Borja Arroyo Galende, Juan Parras

The ever-increasing use of generative models in various fields where tabular data is used highlights the need for robust and standardized validation metrics to assess the similarity between real and synthetic data. Current methods lack a unified framework and rely on diverse and often inconclusive statistical measures. Divergences, which quantify discrepanci

Richard J Povinelli, Mathew Dupont

Aim: Approximately six million people suffer cardiac arrests worldwide per year with very low survival rates (<1%). Thus, the aim of this study is to estimate the probability of a poor outcome after cardiac arrest. Accurate outcome predictions avoid removing care too soon for patients with potentially good outcomes or continuing care for patients with likely

L. Planquart, C. Paladini, A. Jorissen, A. Escorza

Our purpose is to study the effect of binary companions located within the first 10 stellar radii from the primary AGB star. In this work, we target the mass-losing carbon star V Hydrae (V Hya), looking for signatures of its companion in the dust forming region of the atmosphere. The star was observed in the L- and N-bands with the VLTI/MATISSE instrument at

L. Planquart, A. Jorissen, A. Escorza, O. Verhamme

The well studied carbon star V Hydrae is known to exhibit a complex asymmetric environment made of a dense equatorial wind and high-velocity outflows, hinting at its transition from the AGB phase to the asymmetric planetary nebula phase. In addition, V Hydrae also exhibits a long secondary period of 17 years in its light curve, suggesting the presence of a b

Johannes Blühdorn, Nicolas R. Gauger

In shared-memory parallel automatic differentiation, inputs that are shared among simultaneous thread-local preaccumulations lead to data races if Jacobians are accumulated with a single, shared vector of adjoint variables. In this work, we discuss the benefits and tradeoffs of re-enabling such preaccumulations by a transition to suitable local adjoints. We

Irene Gil Fernández, Jaehoon Kim, Hong Liu, Oleg Pikhurko

We present a new lower bound on the Bowen-Radin maximal density of radius-R ball packings in the m-dimensional hyperbolic space, improving on the basic covering bound by factor \Omega(m(R+\ln m)) as m tends to infinity. This is done by applying the recent theorem of Campos, Jenssen, Michelen and Sahasrabudhe on independent sets in graphs with sparse neighbou

Helen Beierling, Anna-Lisa Vollmer

This study contributes to the evolving field of robot learning in interaction with humans, examining the impact of diverse input modalities on learning outcomes. It introduces the concept of "meta-modalities" which encapsulate additional forms of feedback beyond the traditional preference and scalar feedback mechanisms. Unlike prior research that focused on

Fedor Scholz, Erik Ayari, Johannes Bertram, Martin V. Butz

Infants learn actively in their environments, shaping their own learning curricula. They learn about their environments' affordances, that is, how local circumstances determine how their behavior can affect the environment. Here we model this type of behavior by means of a deep learning architecture. The architecture mediates between global cognitive map exp

Wojciech Broniowski, Enrique Ruiz Arriola

We show that the recent MIT lattice QCD data for the pion's gravitational form factors are, in the covered momentum transfer range, fully consistent with the meson dominance principle. In particular, the $2^{++}$ component can be accurately saturated with the $f_2(1270)$ meson, whereas the $0^{++}$ component with the $\sigma$ meson. To incorporate the large

Matthew Keller, Chi-en Amy Tai, Yuhao Chen, Pengcheng Xi

Many aging individuals encounter challenges in effectively tracking their dietary intake, exacerbating their susceptibility to nutrition-related health complications. Self-reporting methods are often inaccurate and suffer from substantial bias; however, leveraging intelligent prediction methods can automate and enhance precision in this process. Recent work

Ke Wang, Nikolaos Dimitriadis, Guillermo Ortiz-Jimenez, François Fleuret

Model merging and task arithmetic have emerged as promising scalable approaches to merge multiple single-task checkpoints to one multi-task model, but their applicability is reduced by significant performance loss. Previous works have linked these drops to interference in the weight space and erasure of important task-specific features. Instead, in this work

Sergi Abadal, Chong Han, Vitaly Petrov, Laura Galluccio

Emerging from the symbiotic combination of nanotechnology and communications, the field of nanonetworking has come a long way since its inception more than fifteen years ago. Significant progress has been achieved in several key communication technologies as enablers of the paradigm, as well as in the multiple application areas that it opens. In this paper,

Ruiyang Dai, Bruno Després

We analyze why the discretization of linear transport with asymmetric Hermite basis functions can be instable in quadratic norm. The main reason is that the finite truncation of the infinite moment linear system looses the skew-symmetry property with respect to the Gram matrix. Then we propose an original closed formula for the scalar product of any pair of

Rui Han

We establish Anderson localization for Schr\"odinger operators with even analytic potentials on the first supercritical stratum for Liouville frequencies in the sharp regime $\{E: L(\omega,E)>\beta(\omega)>0, \kappa(\omega,E)=1\}$, with $\kappa(\omega,E)$ being Avila's acceleration. This paper builds on the large deviation measure estimate and complexity bou

Theodoros Zafeiriou, Dimitris Kalles

The present document delineates the analysis, design, implementation, and benchmarking of various neural network architectures within a short-term frequency prediction system for the foreign exchange market (FOREX). Our aim is to simulate the judgment of the human expert (technical analyst) using a system that responds promptly to changes in market condition

Khoi Phuong Dao, Juejun Hu, Richard Soref

The on-chip photonic switch is a critical building block for photonic integrated circuits (PICs) and the integration of phase change materials (PCMs) enables non-volatile switch designs that are compact, low-loss, and energy-efficient. Existing switch designs based on these materials typically rely on weak evanescent field interactions, resulting in devices

Yifei Sun, Hang Zou, Chao Zhang, Samson Lasaulce

Conventional data compression schemes aim at implementing a trade-off between the rate required to represent the compressed data and the resulting distortion between the original and reconstructed data. However, in more and more applications, what is desired is not reconstruction accuracy but the quality of the realization of a certain task by the receiver.

Derek Egolf, William Schultz, Stavros Tripakis

We present a novel and efficient method for synthesis of parameterized distributed protocols by sketching. Our method is both syntax-guided and counterexample-guided, and utilizes a fast equivalence reduction technique that enables efficient completion of protocol sketches, often significantly reducing the search space of candidate completions by several ord

Shashikant Ilager, Jakob Fahringer, Alessandro Tundo, Ivona Brandić

Edge computing provides resources for IoT workloads at the network edge. Monitoring systems are vital for efficiently managing resources and application workloads by collecting, storing, and providing relevant information about the state of the resources. However, traditional monitoring systems have a centralized architecture for both data plane and control

J. Meng, Y. P. Wang

The brief history for the prediction of the nuclear chirality is provided. The theoretical and experimental investigations of the nuclear chirality are reviewed, including the verification of chiral doublet bands, the chiral conundrum and its resolution, and the prediction and observation of the multiple chiral doublets (M$\chi$D). Some recent theoretical pr

Cong Wang, Zhongqiu Wang, Jianhua Yang, Miguel A. F. Sanjuán

Ultra-high frequency linear frequency modulation (UHF-LFM) signal, as a kind of typical non-stationary signal, has been widely used in microwave radar and other fields, with advantages such as long transmission distance, strong anti-interference ability, and wide bandwidth. Utilizing optimal dynamics response has unique advantages in weak feature identificat

Hector Zenil, Felipe S. Abrahão, Luan C. S. M. Ozelim

Based on the principles of information theory, measure theory, and theoretical computer science, we introduce a signal deconvolution method with a wide range of applications to coding theory, particularly in zero-knowledge one-way communication channels, such as in deciphering messages (i.e., objects embedded into multidimensional spaces) from unknown genera

P. Grafstrom, A. D. Martin, M. G. Ryskin

We discuss the oscillations in the elastic $pp$ differential cross section seen in the TOTEM data at $\sqrt{s}=13$~TeV on the top of the usual smooth behaviour.

Jian Liu, Wei Sun, Hui Yang, Zhiwen Zeng

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contem

Ruikai Yang, Fan He, Mingzhen He, Kaijie Wang

Data imputation, the process of filling in missing feature elements for incomplete data sets, plays a crucial role in data-driven learning. A fundamental belief is that data imputation is helpful for learning performance, and it follows that the pursuit of better classification can guide the data imputation process. While some works consider using label info

Samratul Fuady, Danesh Tarapore, Shoaib Ehsan, Mohammad D. Soorati

Robot swarms offer the potential to bring several advantages to the real-world applications but deploying them presents challenges in ensuring feasibility across diverse environments. Assessing the feasibility of new tasks for swarms is crucial to ensure the effective utilisation of resources, as well as to provide awareness of the suitability of a swarm sol

Wenhao Wu

This paper undertakes an empirical study to revisit the latest advancements in Multimodal Large Language Models (MLLMs): Video Assistant. This study, namely FreeVA, aims to extend existing image-based MLLM to the video domain in a training-free manner. The study provides an essential, yet must-know baseline, and reveals several surprising findings: 1) FreeVA

Andrea Nava, Leonardo Bernardini, Matteo Biassoni, Tommaso Bradanini

The ASPECT-BET (An sdd-SPECTrometer for BETa decay studies) project aims to develop a novel technique for the precise measurement of forbidden $\beta$ spectra in the 10 keV - 1 MeV range. This technique uses a Silicon Drift Detector (SDD) as the main spectrometer, surrounded, if necessary, by a veto system to reject events with only partial energy deposition

Alix Deleporte, Gaultier Lambert

We obtain a central limit theorem for bulk counting statistics of free fermions in smooth domains of $\mathbb{R}^n$ with an explicit description of the covariance structure. This amounts to a study of the asymptotics of norms of commutators between spectral projectors of semiclassical Schr\"odinger operators and indicator functions supported in the bulk. In

Zirui Yan, Arpan Mukherjee, Burak Varıcı, Ali Tajer

This paper investigates the robustness of causal bandits (CBs) in the face of temporal model fluctuations. This setting deviates from the existing literature's widely-adopted assumption of constant causal models. The focus is on causal systems with linear structural equation models (SEMs). The SEMs and the time-varying pre- and post-interventional statistica

F. Matias, P. L. Grande, N. E. Koval, J. M. B. Shorto

This study provides accurate results for the electronic stopping cross-sections of H, He, N, and Ne in silicon in low to intermediate energy ranges using various non-perturbative theoretical methods, including real-time time-dependent density functional theory, transport cross-section, and induced-density approach. Recent experimental findings [Ntemou \texti

Jianmin Chen, Shiquan Ruan, Jinfeng Zhang

We present a geometric model for the category of vector bundles over the weighted projective line of type (2,2,n). This model is based on the orbit space of an infinite marked strip under a specific group action. We establish a bijection between indecomposable bundles and orbits of line segments on the strip, which yields geometric interpretations for variou

Hanyan Yin, Dongxie Wen, Jiajun Li, Zhewei Wei

Matrix sketching, aimed at approximating a matrix $\boldsymbol{A} \in \mathbb{R}^{N\times d}$ consisting of vector streams of length $N$ with a smaller sketching matrix $\boldsymbol{B} \in \mathbb{R}^{\ell\times d}, \ell \ll N$, has garnered increasing attention in fields such as large-scale data analytics and machine learning. A well-known deterministic mat

Ruikai Yang, Fan He, Mingzhen He, Jie Yang

Random feature (RF) has been widely used for node consistency in decentralized kernel ridge regression (KRR). Currently, the consistency is guaranteed by imposing constraints on coefficients of features, necessitating that the random features on different nodes are identical. However, in many applications, data on different nodes varies significantly on the

Georg Kruse, Rodrigo Coehlo, Andreas Rosskopf, Robert Wille

Advancements in Quantum Computing (QC) and Neural Combinatorial Optimization (NCO) represent promising steps in tackling complex computational challenges. On the one hand, Variational Quantum Algorithms such as QAOA can be used to solve a wide range of combinatorial optimization problems. On the other hand, the same class of problems can be solved by NCO, a

K. Thorwarth, M. Watroba, O. Pshyk, S. Zhuk

Ternary transition metal nitride coatings are promising for many applications as they can offer improved hardness and oxidation resistance compared to binary counterparts. A common challenge in the deposition of functional nitride thin films is oxygen contamination. Even low amounts of oxygen contamination can adversely affect the functional properties of th

Zachary Bamberger, Ofek Glick, Chaim Baskin, Yonatan Belinkov

Language Models (LMs) struggle with linguistic understanding at the discourse level, even though discourse patterns such as coherence, cohesion, and narrative flow are prevalent in their pre-training data. To improve the discourse capabilities of LMs already at the pre-training stage, we introduce DEPTH, an encoder-decoder model that learns latent representa

Roberto Bruno, Ugo Vaccaro

In this paper, we introduce novel characterizations of the classical concept of majorization in terms of upper triangular (resp., lower triangular) row-stochastic matrices, and in terms of sequences of linear transforms on vectors. We used our new characterizations of majorization to derive an improved entropy inequality.

Bojie He

In this paper, we first show that a union of upper-level sets associated to fibrewise Lelong numbers of plurisubharmonic functions is in general a pluripolar subset. Then we obtain analyticity theorems for a union of sub-level sets associated to fibrewise complex singularity exponents of some special (quasi-)plurisubharmonic functions. As a corollary, we con

Yuval Gerzon, Ilan Shomorony, Nir Weinberger

We consider a molecular channel, in which messages are encoded to the frequency of objects (or concentration of molecules) in a pool, and whose output during reading time is a noisy version of the input frequencies, as obtained by sampling with replacement from the pool. We tightly characterize the capacity of this channel using upper and lower bounds, when

Zhi Cen, Huaijin Pi, Sida Peng, Zehong Shen

Generating human motions from textual descriptions has gained growing research interest due to its wide range of applications. However, only a few works consider human-scene interactions together with text conditions, which is crucial for visual and physical realism. This paper focuses on the task of generating human motions in 3D indoor scenes given text de

Lorenzo Roberti, Marco Pignatari, Chris L. Fryer, Maria Lugaro

The $\gamma$-process in core-collapse supernovae (CCSNe) can produce a number of neutron-deficient stable isotopes heavier than iron (p-nuclei). However, current model predictions do to not fully reproduce the solar abundances. We investigate the impact of different explosion energies and parameters on the nucleosynthesis of p-nuclei, by studying stellar mod

Lijun Lyu, Nirmal Roy, Harrie Oosterhuis, Avishek Anand

Neural ranking models have become increasingly popular for real-world search and recommendation systems in recent years. Unlike their tree-based counterparts, neural models are much less interpretable. That is, it is very difficult to understand their inner workings and answer questions like how do they make their ranking decisions? or what document features

Adrian Bajraktari, Michelle Binder, Andreas Vogelsang

Modern science is relying on software more than ever. The behavior and outcomes of this software shape the scientific and public discourse on important topics like climate change, economic growth, or the spread of infections. Most researchers creating software for scientific purposes are not trained in Software Engineering. As a consequence, research softwar

Zhiyong Yang, Qianqian Xu, Sicong Li, Zitai Wang

This paper explores test-agnostic long-tail recognition, a challenging long-tail task where the test label distributions are unknown and arbitrarily imbalanced. We argue that the variation in these distributions can be broken down hierarchically into global and local levels. The global ones reflect a broad range of diversity, while the local ones typically a

Gustavo P. de Brito, Antonio D. Pereira

The Refined Gribov-Zwanziger (RGZ) action in the Landau gauge provides a local and renormalizable framework to account for the existence of infinitesimal Gribov copies in the path integral together with other relevant infrared effects such as the formation of condensates. The properties of the tree-level gluon propagator obtained in this setup has been thoro

Karahan Sarıtaş, Cahid Arda Öz, Tunga Güngör

Word embeddings are fixed-length, dense and distributed word representations that are used in natural language processing (NLP) applications. There are basically two types of word embedding models which are non-contextual (static) models and contextual models. The former method generates a single embedding for a word regardless of its context, while the latt

Xinying Wang, Zhixiong Huang, Sifan Zhang, Jiawen Zhu

Mainstream approaches to spectral reconstruction (SR) primarily focus on designing Convolution- and Transformer-based architectures. However, CNN methods often face challenges in handling long-range dependencies, whereas Transformers are constrained by computational efficiency limitations. Recent breakthroughs in state-space model (e.g., Mamba) has attracted

Denisa Qosja, Simon Wagner, Daniel O'Hagan

In recent years, diffusion models (DMs) have become a popular method for generating synthetic data. By achieving samples of higher quality, they quickly became superior to generative adversarial networks (GANs) and the current state-of-the-art method in generative modeling. However, their potential has not yet been exploited in radar, where the lack of avail

Duy Duong-Tran, Siqing Wei, Li Shen

Nowadays, engineers need to tackle many unprecedented challenges that are often complex, and, most importantly, cannot be exhaustively compartmentalized into a single engineering discipline. In other words, most engineering problems need to be solved from a multidisciplinary approach. However, conventional engineering programs usually adopt pedagogical appro

Titas Chanda, Luca Barbiero, Maciej Lewenstein, Manfred J. Mark

In recent years, the systems comprising of bosonic atoms confined to optical lattices at ultra-cold temperatures have demonstrated tremendous potential to unveil novel quantum mechanical effects appearing in lattice boson models with various kinds of interactions. In this progress report, we aim to provide an exposition to recent advancements in quantum simu

Julien Cohen-Adad

This document explores the advantages of integrating open source software and practices in managing a scientific lab, emphasizing reproducibility and the avoidance of pitfalls. It details practical applications from website management using GitHub Pages to organizing datasets in compliance with BIDS standards, highlights the importance of continuous testing

Silvia Tulli, Stylianos Loukas Vasileiou, Sarath Sreedharan

"Human-aware" has become a popular keyword used to describe a particular class of AI systems that are designed to work and interact with humans. While there exists a surprising level of consistency among the works that use the label human-aware, the term itself mostly remains poorly understood. In this work, we retroactively try to provide an account of what

Samuel Gornard, Pascaline Venon, Florian Lasfont, Thierry Balliau

Background: Two strains of the endoparasitoid Cotesia typhae present a differential parasitism success on the host, Sesamia nonagrioides. One is virulent on both permissive and resistant host populations, and the other only on the permissive host. This interaction provides a very interesting frame for studying virulence factors. Here, we used a combination o

Samuel Gornard, Florence Mougel, Isabelle Germon, Véronique Borday-Birraux

Cotesia typhae is an eastern African endoparasitoid braconid wasp that targets the larval stage of the lepidopteran stem borer, Sesamia nonagrioides, a maize crop pest in Europe. The French host population is partially resistant to the Makindu strain of the wasp, allowing its development in only 40% of the cases. Resistant larvae can encapsulate the parasito

Maja Franz, Tobias Winker, Sven Groppe, Wolfgang Mauerer

Identifying optimal join orders (JOs) stands out as a key challenge in database research and engineering. Owing to the large search space, established classical methods rely on approximations and heuristics. Recent efforts have successfully explored reinforcement learning (RL) for JO. Likewise, quantum versions of RL have received considerable scientific att

Rafael Kourdis, Gabriel Gordon-Hall, Philip John Gorinski

Multitask Learning is a Machine Learning paradigm that aims to train a range of (usually related) tasks with the help of a shared model. While the goal is often to improve the joint performance of all training tasks, another approach is to focus on the performance of a specific target task, while treating the remaining ones as auxiliary data from which to po

Jean-Loup Bertaux, Anastasiia Ivanova, Rosine Lallement

Pipelines of state-of-the-art spectrographs dedicated to planet detection provide, for each exposure, series of Cross-Correlation Functions (CCFs) built with a Binary Mask (BM), and the absolute radial velocity (RV) derived from Gaussian fit of a weighted average CCF$_{tot}$ of the CCFs. Here we tested the benefits of the application of the shift finding alg

Hossein A. Rahmani, Nick Craswell, Emine Yilmaz, Bhaskar Mitra

Test collections play a vital role in evaluation of information retrieval (IR) systems. Obtaining a diverse set of user queries for test collection construction can be challenging, and acquiring relevance judgments, which indicate the appropriateness of retrieved documents to a query, is often costly and resource-intensive. Generating synthetic datasets usin

Jochen L. Leidner, Mark Stevenson

Over the course of the recent decade, tremendous progress has been made in the areas of machine learning and natural language processing, which opened up vast areas of potential application use cases, including hiring and human resource management. We review the use cases for text analytics in the realm of human resources/personnel management, including actu

Mubashara Akhtar, Chenxi Pang, Andreea Marzoca, Yasemin Altun

Language models, potentially augmented with tool usage such as retrieval are becoming the go-to means of answering questions. Understanding and answering questions in real-world settings often requires retrieving information from different sources, processing and aggregating data to extract insights, and presenting complex findings in form of structured arti

Juni Schindler, Sneha Jha, Xixuan Zhang, Kilian Buehling

We present Local Graph-based Dictionary Expansion (LGDE), a method for data-driven discovery of the semantic neighbourhood of words using tools from manifold learning and network science. At the heart of LGDE lies the creation of a word similarity graph from the geometry of word embeddings followed by local community detection based on graph diffusion. The d

Ayush Basu, Vojtech Rodl, Yi Zhao

We study the maximum number of $r$-vertex cliques in $(r-1)$-uniform hypergraphs not containing complete $r$-partite hypergraphs $K_r^{(r-1)}(a_1, \dots, a_r)$. By using the hypergraph removal lemma, we show that this maximum is $o( n^{r - 1/(a_1 \cdots a_{r-1})} )$. This immediately implies the corresponding results of Mubayi and Mukherjee and of Balogh, Ji

Johan Öfverstedt, Elin Lundström, Göran Bergström, Joel Kullberg

The study of associations between an individual's age and imaging and non-imaging data is an active research area that attempts to aid understanding of the effects and patterns of aging. In this work we have conducted a supervoxel-wise association study between both volumetric and tissue density features in coronary computed tomography angiograms and the chr

Javier Alejandro Chávez-Domínguez, Verónica Dimant

We introduce a notion of completely bounded holomorphic functions defined on the open unit ball of an operator space. We endow the set of these functions with an operator space structure, and in the scalar-valued case we identify an operator space predual for it which is a noncommutative version of Mujica's predual for the space of bounded holomorphic functi

Mengge Du, Yuntian Chen, Zhongzheng Wang, Longfeng Nie

Equation discovery is aimed at directly extracting physical laws from data and has emerged as a pivotal research domain. Previous methods based on symbolic mathematics have achieved substantial advancements, but often require the design of implementation of complex algorithms. In this paper, we introduce a new framework that utilizes natural language-based p

Wei-Ting Tang, Joel A. Paulson

Bayesian optimization (BO) is a popular approach for optimizing expensive-to-evaluate black-box objective functions. An important challenge in BO is its application to high-dimensional search spaces due in large part to the curse of dimensionality. One way to overcome this challenge is to focus on local BO methods that aim to efficiently learn gradients, whi

Haopeng Wang, Zijian Long, Haiwei Dong, Abdulmotaleb El Saddik

Over the last few years, 360{\deg} video traffic on the network has grown significantly. A key challenge of 360{\deg} video playback is ensuring a high quality of experience (QoE) with limited network bandwidth. Currently, most studies focus on tile-based adaptive bitrate (ABR) streaming based on single viewport prediction to reduce bandwidth consumption. Ho

Hiroto Imaeda, Reiji Toida, Tsunehiro Takeuchi, Hiroyuki Awano

Heat flux sensors (HFS) have attracted significant interest for their potential in managing waste heat efficiently. A recently proposed HFS, that works on the basis of the anomalous Nernst effect (ANE), offers several advantages in its simple structure leading to easy fabrication, low cost, and reduced thermal resistance. However, enhancing sensitivity throu

Per August Jarval Moen

We study the detection of a change in the covariance matrix of $n$ independent sub-Gaussian random variables of dimension $p$. Our first contribution is to show that $\log\log(8n)$ is the exact minimax testing rate for a change in variance when $p=1$, thereby giving a complete characterization of the problem for univariate data. Our second contribution is to

Arno Geimer, Beltran Fiz, Radu State

Federated learning (FL) is a collaborative and privacy-preserving Machine Learning paradigm, allowing the development of robust models without the need to centralize sensitive data. A critical challenge in FL lies in fairly and accurately allocating contributions from diverse participants. Inaccurate allocation can undermine trust, lead to unfair compensatio

Lucas Oliverio, Damien Rontani, Marc Sciamanna

We numerically investigate a time-delayed reservoir computer architecture based on a single mode laser diode with optical injection and optical feedback. Through a high-resolution parametric analysis, we reveal unforeseen regions of high dynamical consistency. We demonstrate furthermore that the best computing performance is not achieved at the edge of consi

D. Baker, L. van Driel-Gesztelyi, A. W. James, P. Demoulin

Within the coronae of stars, abundances of those elements with low first ionization potential (FIP) often differ from their photospheric values. The coronae of the Sun and solar-type stars mostly show enhancements of low-FIP elements (the FIP effect) while more active stars such as M dwarfs have coronae generally characterized by the inverse-FIP (I-FIP) effe

Ole König, Guglielmo Mastroserio, Thomas Dauser, Mariano Méndez

The Neutron Star Interior Composition Explorer (NICER) monitoring campaign of Cyg X-1 allows us to study its spectral-timing behavior at energies ${<}1$ keV across all states. The hard state power spectrum can be decomposed into two main broad Lorentzians with a transition at around 1 Hz. The lower-frequency Lorentzian is the dominant component at low energi

Shahbaz Khan, Satyajit Sarkar, Nicolas B. Lawler, Ali Akbar

In this work, we investigate the effects of H2 on the physical properties of Fe25Pd75. Broadband ferromagnetic resonance (FMR) spectroscopy revealed a significant FMR peak shift induced by H2 absorption for the FCC phased Fe25Pd75. The peak shifted towards higher applied fields, which is contrary to what was previously observed for CoPd alloys. Additionally,

Bastien Fernandez, Matteo Tanzi

Feedback delay has been identified as a key ingredient in the quorum sensing synchronization of synthetic gene oscillators. While this influence has been evidenced at the theoretical level in a simplified system of degrade-and-fire oscillators coupled via a common activator protein, full mathematical certifications remained to be provided. Here, we prove fro

Paris Papavasileiou, Dimitrios G. Giovanis, Gabriele Pozzetti, Martin Kathrein

This study introduces a machine learning framework tailored to large-scale industrial processes characterized by a plethora of numerical and categorical inputs. The framework aims to (i) discern critical parameters influencing the output and (ii) generate accurate out-of-sample qualitative and quantitative predictions of production outcomes. Specifically, we

Grahame Erskine, Terry S. Griggs

Cycle switching is a particular form of transformation applied to isomorphism classes of a Steiner triple system of a given order $v$ (an $STS(v)$), yielding another $STS(v)$. This relationship may be represented by an undirected graph. An $STS(v)$ admits cycles of lengths $4,6,\ldots,v-7$ and $v-3$. In the particular case of $v=19$, it is known that the ful

Franz Kevin Stehle, Wainer Vandelli, Giuseppe Avolio, Felix Zahn

Anomaly detection in distributed systems such as High-Performance Computing (HPC) clusters is vital for early fault detection, performance optimisation, security monitoring, reliability in general but also operational insights. Deep Neural Networks have seen successful use in detecting long-term anomalies in multidimensional data, originating for instance fr

Dmitry A. Ivanov, Denis A. Larionov, Oleg V. Maslennikov, Vladimir V. Voevodin

In real applications of Reinforcement Learning (RL), such as robotics, low latency and energy efficient inference is very desired. The use of sparsity and pruning for optimizing Neural Network inference, and particularly to improve energy and latency efficiency, is a standard technique. In this work, we perform a systematic investigation of applying these op

V. Diana Rakotonirina, Marco Bragato, Stefan Heinen, O. Anatole von Lilienfeld

We study the applicability of the Hammett-inspired product (HIP) Ansatz to model relative substrate binding within homogenous organometallic catalysis, assigning $\sigma$ and $\rho$ to ligands and metals, respectively. Implementing an additive combination (c) rule for obtaining $\sigma$ constants for any ligand pair combination results in a cHIP model that e

T. Boyer, A. Neronov

Recent evidence for the stochastic gravitational wave backgorund reported by the pulsar timing arrays (PTA) can be interpreted as a signal from the cosmological phase transition. We use up-to-date models of the gravitational wave power spectra to compare constraints on the parameters of the phase transition for the three different available PTA measurements

Cagri Toraman

Despite advancements in English-dominant generative large language models, further development is needed for low-resource languages to enhance global accessibility. The primary methods for representing these languages are monolingual and multilingual pretraining. Monolingual pretraining is expensive due to hardware requirements, and multilingual models often

Pin Ji, Yang Feng, Duo Wu, Lingyue Yan

The rapidly developing deep learning (DL) techniques have been applied in software systems with various application scenarios. However, they could also pose new safety threats with potentially serious consequences, especially in safety-critical domains. DL libraries serve as the underlying foundation for DL systems, and bugs in them can have unpredictable im

Nigel T. Bishop, Vishnu Kakkat, Amos S. Kubeka, Monos Naidoo

It is well-known that gravitational waves undergo no absorption or dissipation when traversing through a perfect fluid. However, in the presence of a viscous fluid, GWs transfer energy to the fluid medium. In this paper, we present a review of our recent series of results regarding the interaction between gravitational waves and surrounding matter. Additiona

František Štampach, Jakub Waclawek

We prove sufficient conditions on a parameter sequence to determine optimal weights in inequalities for an integer power $\ell$ of the discrete Laplacian on the half-line. By a concrete choice of the parameter sequence, we obtain explicit optimal discrete Rellich ($\ell=2$) and Birman ($\ell\geq3$) weights. For $\ell=1$, we rediscover the optimal Hardy weigh

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using 9.0 $\rm fb^{-1}$ of $e^+e^-$ collision data collected at center-of-mass energies from 4.178 to 4.278 GeV with the BESIII detector at the BEPCII collider, we perform the first search for the radiative transition $\chi_{c1}(3872)\to\gamma \psi_2(3823)$. No $\chi_{c1}(3872)\to\gamma \psi_2(3823)$ signal is observed. The upper limit on the ratio of branch

Meng Cao

Let $\mathrm{SLAut}(\mathbb{F}_{q}^{n})$ denote the group of all semilinear isometries on $\mathbb{F}_{q}^{n}$, where $q=p^{e}$ is a prime power. Matrix-product (MP) codes are a class of long classical codes generated by combining several commensurate classical codes with a defining matrix. We give an explicit formula for calculating the dimension of the $\s

Xi Yao, Wei Dai

This paper presents a new approach to the recovery of a spectrally sparse signal (SSS) from partially observed entries, focusing on challenges posed by large-scale data and heavy noise environments. The SSS reconstruction can be formulated as a non-convex low-rank Hankel recovery problem. Traditional formulations for SSS recovery often suffer from reconstruc