November 2024 arXiv papers — page 155

Zachary Schwehr

Cancer is the second leading cause of death, with chemotherapy as one of the primary forms of treatment. As a result, researchers are turning to drug combination therapy to decrease drug resistance and increase efficacy. Current methods of drug combination screening, such as in vivo and in vitro, are inefficient due to stark time and monetary costs. In silic

Jiangshan Wang, Junfu Pu, Zhongang Qi, Jiayi Guo

Rectified-flow-based diffusion transformers like FLUX and OpenSora have demonstrated outstanding performance in the field of image and video generation. Despite their robust generative capabilities, these models often struggle with inversion inaccuracies, which could further limit their effectiveness in downstream tasks such as image and video editing. To ad

Marco Cremaschi, Blerina Spahiu, Matteo Palmonari, Ernesto Jimenez-Ruiz

Tabular data plays a pivotal role in various fields, making it a popular format for data manipulation and exchange, particularly on the web. The interpretation, extraction, and processing of tabular information are invaluable for knowledge-intensive applications. Notably, significant efforts have been invested in annotating tabular data with ontologies and e

Alexander Margolis

Inspired by group cohomology, we define several coarse topological invariants of metric spaces. We define the coarse cohomological dimension of a metric space, and demonstrate that if G is a countable group, then the coarse cohomological dimension of G as a metric space coincides with the cohomological dimension of $G$ as a group whenever the latter is finit

Hanyang Wang, Juergen Branke, Matthias Poloczek

In many real-world optimization problems, we have prior information about what objective function values are achievable. In this paper, we study the scenario that we have either exact knowledge of the minimum value or a, possibly inexact, lower bound on its value. We propose bound-aware Bayesian optimization (BABO), a Bayesian optimization method that uses a

Yonatan Harpaz, Thomas Nikolaus, Victor Saunier

We study algebraic K-theory and topological Hochschild homology in the setting of bimodules over a stable category, a datum we refer to as a laced category. We show that in this setting both K-theory and THH carry universal properties, the former defined in terms of additivity and the latter via trace properties. We then use these universal properties in ord

Zhi Jiang, Danyang Yao, Xu Ran, Yu Gao

On chip acousto-optic (AO) modulation represents a significant advancement in the development of highly integrated information processing systems. However, conventional photonic devices face substantial challenges in achieving efficient conversion due to the limited overlap between acoustic waves and optical waves. In this study, we address this limitation b

Laurine Martinien, François Ménard, Gaspard Duchêne, Ryo Tazaki

PDS 453 is a rare highly inclined disk where the stellar photosphere is seen at grazing incidence on the disk surface. Our goal is take advantage of this geometry to constrain the structure and composition of this disk, in particular the fact that it shows a 3.1 $\mu$m water ice band in absorption that can be related uniquely to the disk. We observed the sys

Xinyi Wang, Shaukat Ali, Paolo Arcaini, Narasimha Raghavan Veeraragavan

The Cancer Registry of Norway (CRN) is a part of the Norwegian Institute of Public Health (NIPH) and is tasked with producing statistics on cancer among the Norwegian population. For this task, CRN develops, tests, and evolves a software system called Cancer Registration Support System (CaReSS). It is a complex socio-technical software system that interacts

Dawn Foster

Many popular open source projects are owned and driven by vendors, and in today's difficult economic climate, those vendors are under increasing pressure from investors to deliver a strong return on their investments. One response to this pressure has been the relicensing of popular open source projects to more restrictive licenses in the hopes of generating

M. Gorsd, S. A. Grigera, R. A. Borzi

In this work, we explore the magnetic behavior of diluted spin-ice systems, where magnetic moments are randomly removed at various concentrations. We concentrate on features in which the effect of long range dipolar interactions (usually masked by self-screening in these systems) is made visible by dilution. Our initial focus is on the configurations reached

Detlev Buchholz, Jakob Yngvason

Some advantages of the algebraic approach to many body physics, based on resolvent algebras, are illustrated by the simple example of non-interacting bosons which are confined in compact regions with soft boundaries. It is shown that the dynamics of these systems converges to the spatially homogeneous dynamics for increasing regions and particle numbers and

Marin Fontaine, Clotilde Busschaert, Yaniss Benkadoum, Isabeau A. Bertrix

This study investigates the influence of Herbig-Haro jets on initiating star formation in dense environments. When molecular clouds are nearing gravitational instability, the impact of a protostellar jet could provide the impetus needed to catalyze star formation. A high-energy-density experiment was carried out at the LULI2000 laser facility, where a supers

Chanyoung Chung, Hyunki Seong, David Hyunchul Shim

Fully autonomous racing demands not only high-speed driving but also fair and courteous maneuvers. In this paper, we propose an autonomous racing framework that learns complex racing behaviors from expert demonstrations using hierarchical policy abstractions. At the trajectory level, our policy model predicts a dense distribution map indicating the likelihoo

Ran Li

Recently, it was shown by Danielson-Satishchandran-Wald (DSW) that for the massive or charged body in a quantum spatial separated superposition state, the presence of a black hole can decohere the superposition inevitably towards capturing the radiation of soft photons or gravitons. In this work, we study the DSW decoherence effect for the static charged bod

Amanda Younes, Randall Putnam, Paul Hamilton, Wesley C. Campbell

A near-minimal instance of optical cooling is experimentally presented wherein the internal-state entropy of a single atom is reduced more than twofold by illuminating it with broadband, incoherent light. Since the rate of optical pumping by a thermal state increases monotonically with its temperature, the cooling power in this scenario increases with higher

Felix Petersen, Hilde Kuehne, Christian Borgelt, Julian Welzel

With the increasing inference cost of machine learning models, there is a growing interest in models with fast and efficient inference. Recently, an approach for learning logic gate networks directly via a differentiable relaxation was proposed. Logic gate networks are faster than conventional neural network approaches because their inference only requires l

Jirô Akahori, Norio Konno, Rikuki Okamoto, Iwao Sato

This paper presents a connection between the quantum walk and the absolute mathematics. The quantum walk is a quantum counterpart of the classical random walk. We especially deal with the Grover walk on a graph. The Grover walk is a typical model of quantum walks. The time evolution of the Grover walk is obtained by a unitary matrix that is called the Grover

Nur Imtiazul Haque, Prabin Mali, Mohammad Zakaria Haider, Mohammad Ashiqur Rahman

Incorporating advanced information and communication technologies into smart grids (SGs) offers substantial operational benefits while increasing vulnerability to cyber threats like false data injection (FDI) attacks. Current SG attack analysis tools predominantly employ formal methods or adversarial machine learning (ML) techniques with rule-based bad data

Andrea Pandolfi, Omiros Papaspiliopoulos, Giacomo Zanella

Generalized linear mixed models (GLMMs) are a widely used tool in statistical analysis. The main bottleneck of many computational approaches lies in the inversion of the high dimensional precision matrices associated with the random effects. Such matrices are typically sparse; however, the sparsity pattern resembles a multi partite random graph, which does n

Dengyu Wu, Jiechen Chen, Bipin Rajendran, H. Vincent Poor

Inspired by biological processes, neuromorphic computing leverages spiking neural networks (SNNs) to perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have shown that embedding a small payload within each spike exchanged between spiking neurons can enhance inferenc

Shintaro Fujiwara, Naoki Ishikawa

Conventional decoding algorithms for polar codes strive to balance achievable performance and computational complexity in classical computing. While maximum likelihood (ML) decoding guarantees optimal performance, its NP-hard nature makes it impractical for real-world systems. In this letter, we propose a novel ML decoding architecture for polar codes based

Ji-Cai Liu, Kong-Lian Liao

Let $S$ be a finite set of pairwise coprime positive integers and $Ax^2+Bx$ be an integer valued polynomial with $A> B\ge 0$. For integers $k\ge 1$ and $n\ge 0$, the coefficients $\gamma_{S,A,B}^k (n)$ are defined as \begin{align*} \prod_{s\in S}\frac{1}{1-q^s}\sum_{j\not\in [-k,k-1]} (-1)^{j+k}q^{Aj^2+Bj}=\sum_{n= 0}^{\infty}\gamma_{S,A,B}^k (n)q^n. \end{al

Nozomu Sekino

Flat virtual links are some variant of links, and semiquandles are counterparts of quandles or biquandles, which axiomize the Reidemeister-like moves. In this paper, we give some example of semiquandle and introduce an invariant for flat virtual knot using it. We also explain that it relates to the u-polynomials for flat virtual knots.

Benito Buchheim, Max Reimann, Jürgen Döllner

We present a methodology for conditional control of human shape and pose in pretrained text-to-image diffusion models using a 3D human parametric model (SMPL). Fine-tuning these diffusion models to adhere to new conditions requires large datasets and high-quality annotations, which can be more cost-effectively acquired through synthetic data generation rathe

Meitner Cadena, Alejandro Yerovi

We propose an approach based on a combination of physical, chemical, and mathematical methods to identify and characterize virulent influenza A viruses (IAVs) through the analysis of the hemagglutinin protein. These methods include the isoelectric point, extreme value theory, and tree-like classification. The characterization process involves molecular and b

Marco Uguccioni, Luca Dell'Anna

We compute the current and the noise power matrix in a quantum dot connected to two metallic reservoirs by using the Keldysh field theory approach, a non-equilibrium quantum field theory language in the functional integral formalism. We first show how this technique allows us to recover rapidly and straightforwardly well-known results in literature, such as

Y. Bliokh, V. Maksimov, A. Haim, A. Kostinskiy

The electron velocity distribution function in the plasma, formed by gas ionization with a sub-nanosecond, hundreds of megawatts power level microwave pulse, is studied by a theoretical model and by numerical 3D simulations, the results of which agree well and show that the distribution varies along the pulse as a decreasing power-law function at the rear of

M. R. Xie, J. G. Li, N. Michel, W. Zuo

The convergence properties of spectroscopic factors in the \textit{ab initio} no-core shell model are hereby investigated. For this, we consider nuclear energies and spectroscopic factors in $A = 6$ and 7 isotopes, using the chiral forces NNLO$_{\rm opt}$ and N$^3$LO. While low-lying spectrum energy demonstrates remarkable convergence with the increase of mo

Omri Ben-Eliezer, Slobodan Mitrović, Pranjal Srivastava

We consider the problem of counting the copies of a length-$k$ pattern $\sigma$ in a sequence $f \colon [n] \to \mathbb{R}$, where a copy is a subset of indices $i_1 < \ldots < i_k \in [n]$ such that $f(i_j) < f(i_\ell)$ if and only if $\sigma(j) < \sigma(\ell)$. This problem is motivated by a range of connections and applications in ranking, nonparametric s

Zheng Zhai, Xiaohui Li

Matrix Factorization has emerged as a widely adopted framework for modeling data exhibiting low-rank structures. To address challenges in manifold learning, this paper presents a subspace-constrained quadratic matrix factorization model. The model is designed to jointly learn key low-dimensional structures, including the tangent space, the normal subspace, a

Todor Tsankov

We prove two theorems in the ergodic theory of infinite permutation groups. First, generalizing a theorem of Nessonov for the infinite symmetric group, we show that every non-singular action of a non-archimedean, Roelcke precompact, Polish group on a measure space $(\Omega, \mu)$ admits an invariant $\sigma$-finite measure equivalent to $\mu$. Second, we pro

Rubin Zhao, Yang Liu, Shiqi Zhang, Zijian Yi

Neurons, with their elongated, tree-like dendritic and axonal structures, enable efficient signal integration and long-range communication across brain regions. By reconstructing individual neurons' morphology, we can gain valuable insights into brain connectivity, revealing the structure basis of cognition, movement, and perception. Despite the accumulation

Xinzheng Zhang, Hui Zhu, Hongqian Zhuang

Recently, an intriguing research trend for automatic target recognition (ATR) from synthetic aperture radar (SAR) imagery has arisen: using simulated data to train ATR models is a feasible solution to the issue of inadequate measured data. To close the domain gap that exists between the real and simulated data, the unsupervised domain adaptation (UDA) techni

Ferdinando Fioretto, Pascal Van Hentenryck, Juba Ziani

This chapter is meant to be part of the book "Differential Privacy in Artificial Intelligence: From Theory to Practice" and provides an introduction to Differential Privacy. It starts by illustrating various attempts to protect data privacy, emphasizing where and why they failed, and providing the key desiderata of a robust privacy definition. It then define

Youssef Boulaimen, Gabriele Fossi, Leila Outemzabet, Nathalie Jeanray

The classification of genetic variants, particularly Variants of Uncertain Significance (VUS), poses a significant challenge in clinical genetics and precision medicine. Large Language Models (LLMs) have emerged as transformative tools in this realm. These models can uncover intricate patterns and predictive insights that traditional methods might miss, thus

Chengxin Hu, Hao Li, Yihe Yuan, Jing Li

Following the milestones in large language models (LLMs) and multimodal models, we have seen a surge in applying LLMs to biochemical tasks. Leveraging graph features and molecular text representations, LLMs can tackle various tasks, such as predicting chemical reaction outcomes and describing molecular properties. However, most current work overlooks the *mu

Fabien Poirier

Deep neural networks are highly effective in solving complex problems but are often viewed as "black boxes," limiting their adoption in contexts where transparency and explainability are essential. This lack of visibility raises ethical and legal concerns, particularly in critical areas like security, where automated decisions can have significant consequenc

Zhihui Zhang, Jinhui Pang, Jianan Li, Xiaoshuai Hao

Multi-Image Super-Resolution (MISR) is a crucial yet challenging research task in the remote sensing community. In this paper, we address the challenging task of Multi-Image Super-Resolution in Remote Sensing (MISR-RS), aiming to generate a High-Resolution (HR) image from multiple Low-Resolution (LR) images obtained by satellites. Recently, the weak temporal

Antonio Lerario

We survey some ideas from the subject of Random Algebraic Geometry, a field that introduces a probabilistic perspective on classical topics in real algebraic geometry. This offers a modern approach to classical problems, such as Hilbert's Sixteenth Problem.

Xiaodan Xu, Chao Ni, Xinrong Guo, Shaoxuan Liu

Large language models (LLMs), such as ChatGPT released by OpenAI, have attracted significant attention from both industry and academia due to their demonstrated ability to generate high-quality content for various tasks. Despite the impressive capabilities of LLMs, there are growing concerns regarding their potential risks in various fields, such as news, ed

Driss Oumbarek Espinos, Alexei Zhidkov, Alexandre Rondepierre, Masafumi Tawada

Particle in cell simulations are widely used in most fields of physics to investigate known and new phenomena which cannot be directly observed or measured yet. However, the computational and time resources needed for PICs make them impractical when high resolution and long time/distance simulations are required. In this work, we present a new PIC simulation

Ashkan Sheikhi, Juan Vidal Alegría, Ove Edfors

Feasibility of the promising large intelligent surface (LIS) concept, as well as its scalability, relies on the use of low-cost hardware components, raising concerns about the effects of hardware distortion. We analyze LIS systems with receive-chain (RX-chain) hardware distortion, showing how it may limit performance gains when scaling up these systems. In p

Zheming Luo, Yang Kuang

In this paper, we introduce a highly accurate and efficient numerical solver for the radial Kohn--Sham equation. The equation is discretized using a high-order finite element method, with its performance further improved by incorporating a parameter-free moving mesh technique. This approach greatly reduces the number of elements required to achieve the desir

Jean-Louis Nicolas

Let $\Delta= \sum_{m=0}^\infty q^{(2m+1)^2} \in \mathbb{F}_2[[q]]$ be the reduction mod 2 of the $\Delta$ series. A modular form $f$ modulo $2$ of level 1 is a polynomial in $\Delta$. If $p$ is an odd prime, then the Hecke operator $T_p$ transforms $f$ in a modular form $T_p(f)$ which is a polynomial in $\Delta$ whose degree is smaller than the degree of $f$

Ashwin Sankar, Sparsh Jain, Nikhil Narasimhan, Devilal Choudhary

Speech translation for Indian languages remains a challenging task due to the scarcity of large-scale, publicly available datasets that capture the linguistic diversity and domain coverage essential for real-world applications. Existing datasets cover a fraction of Indian languages and lack the breadth needed to train robust models that generalize beyond cur

Chao Liu, Ziyang Zhao, Tianjing Guo, Jie Xu

We have theoretically investigated surface magnetoplasmons (SMPs) in a yttrium-iron-garnet (YIG) sandwiched waveguide. The dispersion demonstated that this waveguide can support topological unidirectional SMPs. Based on unidirectional SMPs, magnetically controllable multimode interference (MMI) is verified in both symmetric and asymmetric waveguides. Due to

Yiming Sun, Bing Cao, Pengfei Zhu, Qinghua Hu

Infrared and visible image fusion aim to integrate modality strengths for visually enhanced, informative images. Visible imaging in real-world scenarios is susceptible to dynamic environmental brightness fluctuations, leading to texture degradation. Existing fusion methods lack robustness against such brightness perturbations, significantly compromising the

Samuel J. Bell, Skyler Wang

Learning correlations from data forms the foundation of today's machine learning (ML) and artificial intelligence research. While contemporary methods enable the automatic discovery of complex patterns, they are prone to failure when unintended correlations are captured. This vulnerability has spurred a growing interest in interrogating spuriousness, which i

Coenraad Mouton, Randle Rabe, Daniël G. Haasbroek, Marthinus W. Theunissen

It has been observed that the input space of deep neural network classifiers can exhibit `fragmentation', where the model function rapidly changes class as the input space is traversed. The severity of this fragmentation tends to follow the double descent curve, achieving a maximum at the interpolation regime. We study this phenomenon in the context of image

Melisa Natali Cizmeciyan, Nayce Ilayda Bektas, Narin Derin, Tugberk Denizaltı

Introduction: Circadian rhythm disruption has garnered significant attention for its adverse effects on human health, particularly in reproductive medicine and fetal well-being. Assessing pregnancy health often relies on diagnostic markers such as the labyrinth zone (LZ) proportion within the placenta. This study aimed to investigate the impact of disrupted

Salar Farahmand-Tabar

Metaheuristics are stochastic optimization algorithms that mimic natural processes to find optimal solutions to complex problems. The success of metaheuristics largely depends on the ability to effectively explore and exploit the search space. Memory mechanisms have been introduced in several popular metaheuristic algorithms to enhance their performance. Thi

Xiayang Xiao, Zhuoxuan Li, Ruyi Zhang, Jiacheng Chen

The limitations of existing Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) methods lie in their confinement by the closed-environment assumption, hindering their effective and robust handling of unknown target categories in open environments. Open Set Recognition (OSR), a pivotal facet for algorithmic practicality, intends to categorize kn

Christos Anagnostopoulos, Alexandros Gkillas, Nikos Piperigkos, Aris S. Lalos

In this paper we propose a methodology combining Federated Learning (FL) with Cross-view Image Geo-localization (CVGL) techniques. We address the challenges of data privacy and heterogeneity in autonomous vehicle environments by proposing a personalized Federated Learning scenario that allows selective sharing of model parameters. Our method implements a coa

Tianyi Zhang, Miu Kojima, Simon D'Alfonso

Smartphones, equipped with an array of sensors, have become valuable tools for personal sensing. Particularly in digital health, smartphones facilitate the tracking of health-related behaviors and contexts, contributing significantly to digital phenotyping, a process where data from digital interactions is analyzed to infer behaviors and assess mental health

Mikhail Lifshits, Andrei Podchishchailov

In the article, we find the asymptotic distribution of the derivative of the taut string accompanying a Wiener process in a strip of fixed width on long time intervals. This enables to find explicit expressions for minimal energy (averaged function of the derivative) of an absolutely continuous function in this strip. For example, for kinetic energy which wa

Ashkan Sheikhi, Ove Edfors, Juan Vidal Alegría

Non-linear transceivers and non-reciprocity of downlink and uplink channels are two major challenges in the deployment of massive multiple-input-multiple-output (MIMO) systems. We consider an over-the-air (OTA) approach for digital pre-distortion (DPD) and reciprocity calibration to jointly address these issues. In particular, we consider a memory-less non-l

Varun Upreti, Ulysse Chabaud

Modern quantum devices are highly susceptible to errors, making the verification of their correct operation a critical problem. Usual tomographic methods rapidly become intractable as these devices are scaled up. In this paper, we introduce a general framework for the efficient verification of large quantum systems. Our framework combines robust fidelity wit

Xiaotian Nie, Wei Zheng

In a previous theoretical work [arXiv:2205.01461], T. Esslinger group proposed a scheme to realize a spatial-temporal lattice, which possesses dual periodicity on space and time, in a cavity-boson system pumped by a travelling wave laser. However, the prediction was made under the mean-field approximation. In this work, we investigate the dynamics beyond mea

Jianhua Gao, Jiayuan Shen, Yuxiang Zhang, Weixing Ji

Iterative solvers are frequently used in scientific applications and engineering computations. However, the memory-bound Sparse Matrix-Vector (SpMV) kernel computation hinders the efficiency of iterative algorithms. As modern hardware increasingly supports low-precision computation, the mixed-precision optimization of iterative algorithms has garnered widesp

Md. Kutub Uddin, Md. Saiful Islam, Md Abrar Jahin, Md. Saiful Islam Seam

This paper focuses on the generalized grouping problem in the context of cellular manufacturing systems (CMS), where parts may have more than one process route. A process route lists the machines corresponding to each part of the operation. Inspired by the extensive and widespread use of network flow algorithms, this research formulates the process route fam

Ohad Vilk, Motti Charter, Sivan Toledo, Eli Barkai

Diffusion and anomalous diffusion are widely observed and used to study movement across organisms, resulting in extensive use of the mean and mean-squared displacement (MSD). However, these measures - corresponding to specific displacement moments - do not capture the full complexity of movement behavior. Using high-resolution data from over 70 million local

Robert Babin, Florian Hindenlang, Omar Maj, Robert Köberl

Boundary conforming coordinates are commonly used in plasma physics to describe the geometry of toroidal domains, for example, in three-dimensional magnetohydrodynamic equilibrium solvers. The magnetohydrodynamic equilibrium configuration can be approximated with an inverse map, defining nested surfaces of constant magnetic flux. For equilibrium solvers that

Tamar Klein, Tom Aizenberg, Roi Ronen

Climate studies often rely on remotely sensed images to retrieve two-dimensional maps of cloud properties. To advance volumetric analysis, we focus on recovering the three-dimensional (3D) heterogeneous extinction coefficient field of shallow clouds using multiview remote sensing data. Climate research requires large-scale worldwide statistics. To enable sca

Karol Lynch, Fabio Lorenzi, John Sheehan, Duygu Kabakci-Zorlu

Foundation models show great promise for generative tasks in many domains. Here we discuss the use of foundation models to generate structured documents related to critical assets. A Failure Mode and Effects Analysis (FMEA) captures the composition of an asset or piece of equipment, the ways it may fail and the consequences thereof. Our system uses large lan

Sven Banisch, Dennis Jacob, Tom Willaert, Eckehard Olbrich

In order to truly understand how social media might shape online discourses or contribute to societal polarization, we need refined models of platform choice, that is: models that help us understand why users prefer one social media platform over another. This study develops a dynamic model of platform selection, extending Social Feedback Theory by incorpora

Marlon Tobaben, Talal Alrawajfeh, Marcus Klasson, Mikko Heikkilä

Differential privacy (DP) is a formal privacy framework that enables training machine learning (ML) models while protecting individuals&#39; data. As pointed out by prior work, ML models are part of larger systems, which can lead to so-called privacy side-channels even if the model training itself is DP. We identify the output label space of a classification

Jie Liu, Pan Zhou, Yingjun Du, Ah-Hwee Tan

In this work, we address the cooperation problem among large language model (LLM) based embodied agents, where agents must cooperate to achieve a common goal. Previous methods often execute actions extemporaneously and incoherently, without long-term strategic and cooperative planning, leading to redundant steps, failures, and even serious repercussions in c

Salar Farahmand-Tabar, Payam Ashtari

An improved bilinear fuzzy genetic algorithm (BFGA) is introduced in this chapter for the design optimization of steel structures with semi-rigid connections. Semi-rigid connections provide a compromise between the stiffness of fully rigid connections and the flexibility of fully pinned connections. However, designing such structures is challenging due to th

Giulia d'Addato, Placido Falqueto, Luigi Palopoli, Daniele Fontanelli

When humans move in a shared space, they choose navigation strategies that preserve their mutual safety. At the same time, each human seeks to minimise the number of modifications to her/his path. In order to achieve this result, humans use unwritten rules and reach a consensus on their decisions about the motion direction by exchanging non-verbal messages.

Salar Farahmand-Tabar, Sina Shirgir

Opposition-based learning (OBL) is an effective approach to improve the performance of metaheuristic optimization algorithms, which are commonly used for solving complex engineering problems. This chapter provides a comprehensive review of the literature on the use of opposition strategies in metaheuristic optimization algorithms, discussing the benefits and

Ferdinand Schlatt, Maik Fröbe, Matthias Hagen

A wide range of transformer-based language models have been proposed for information retrieval tasks. However, including transformer-based models in retrieval pipelines is often complex and requires substantial engineering effort. In this paper, we introduce Lightning IR, an easy-to-use PyTorch Lightning-based framework for applying transformer-based languag

Risvan Dirza, Hari Prasad Varadarajan, Vegard Aas, Sigurd Skogestad

This paper considers the problem of steady-state real-time optimization (RTO) of interconnected systems with a common constraint that couples several units, for example, a shared resource. Such problems are often studied under the context of distributed optimization, where decisions are made locally in each subsystem, and are coordinated to optimize the over

Ilja Behnke

This dissertation explores the area of real-time IP networking for embedded devices, especially those with limited computational resources. With the increasing convergence of information and operational technologies in various industries, and the growing complexity of communication requirements in (semi-)autonomous machines, there is a need for more advanced

Xiangyu Li, Bodong Shang

Multi-connectivity (MC) in satellite-terrestrial integrated networks (STINs), included in the Third-Generation Partnership Project (3GPP) standards, is regarded as a promising technology for future networks, especially the non-terrestrial network (NTN). The significant advantages of MC in improving coverage, communication, and sensing through satellite-terre

A. A. Araújo Filho, J. R. Nascimento, A. Yu. Petrov, P. J. Porfírio

In this work, we start by examining a spherically symmetric black hole within the framework of non-commutative geometry and apply a modified Newman-Janis method to obtain a new rotating solution. We then investigate its consequences, focusing on the horizon structure, ergospheres, and the black hole's angular velocity. Following this, a detailed thermodynami

Francesco Antonio Denisi

We study the birational geometry of hypersurfaces in products of weighted projective spaces, extending results previously established by J. C. Ottem. For most cases where these hypersurfaces are Mori dream spaces, we determine all relevant cones and characterise their birational models, along with the small $\mathbf{Q}$-factorial modifications to them. We al

Wenjun Zhang, Qiong Wu, Pingyi Fan, Kezhi Wang

Semantic communication transmits the extracted features of information rather than raw data, significantly reducing redundancy, which is crucial for addressing spectrum and energy challenges in 6G networks. In this paper, we introduce semantic communication into a cellular vehicle-to-everything (C-V2X)- based autonomous vehicle platoon system for the first t

Kadir Burak Buldu, Süleyman Özdel, Ka Hei Carrie Lau, Mengdi Wang

Recent developments in computer graphics, machine learning, and sensor technologies enable numerous opportunities for extended reality (XR) setups for everyday life, from skills training to entertainment. With large corporations offering affordable consumer-grade head-mounted displays (HMDs), XR will likely become pervasive, and HMDs will develop as personal

Menno Demmenie, Paul Kolpakov, Boaz van Casteren, Dirk Bakker

The freezing of water is one of the major causes of mechanical damage in materials during wintertime; surprisingly this happens even in situations where water only partially saturates the material so that the ice has room to grow. Here we perform freezing experiments in cylindrical glass vials of various sizes and wettability properties, using a dye that exc

Xinxing Zhou, Jiaqi Ye, Shubao Zhao, Ming Jin

The exponential growth of multivariate time series data from sensor networks in domains like industrial monitoring and smart cities requires efficient and accurate forecasting models. Current deep learning methods often fail to adequately capture long-range dependencies and complex inter-variable relationships, especially under real-time processing constrain

Simon Brandhorst, Grégoire Menet, Stevell Muller

Nikulin-type orbifolds are certain singular 4-dimensional irreducible holomorphic symplectic varieties. We show that the monodromy group of Nikulin-type orbifolds is maximal and classify finite order symplectic automorphisms up to deformation in terms of their action on the second integral cohomology group.

Ryoichiro Agata, Satoru Baba, Ayako Nakanishi, Yasuyuki Nakamura

Accurate hypocenter determination in the Nankai Trough subduction zone is essential for hazard assessment and advancing our understanding of seismic activity in the region. A handy hypocenter determination tool incorporating a realistic 3D velocity structure, accessible to the scientific community, is beneficial. In this study, we developed HypoNet Nankai, a

Jingguang Chen, Wenzhe Liu, Jiajun Wang, Ruo-Yang Zhang

Topological properties of photonic structures described by Hamiltonian matrices have been extensively studied in recent years. Photonic systems are often open systems, and their coupling with the environment is characterized by scattering matrices, which can exhibit topological features as well. In this work, we uncover that topological singularities can be

Chenlei Li, Hongyan Yu, Tao Shu, Yueyang Zhang

Optical memristors represent a monumental leap in the fusion of photonics and electronics, heralding a new era of applications from neuromorphic computing to artificial intelligence. However, current technologies are hindered by complex fabrication, limited endurance, high optical loss or low modulation depth. For the first time, we reveal optical non-volati

Cheng-Cheng Yu, Zi-Han Chen, Yu-Hao Deng, Ming-Cheng Chen

Programmable neutral atom arrays show great promise for fault-tolerant quantum computing. A dominant physical error on this platform is qubit leakage and loss, notably decay errors from the Rydberg state during two-qubit gates. Such leakage events are particularly detrimental as they propagate, generating correlated errors that severely degrade the effective

Taylor Arnold, Lauren Tilton

Many cultural institutions have made large digitized visual collections available online, often under permissible re-use licences. Creating interfaces for exploring and searching these collections is difficult, particularly in the absence of granular metadata. In this paper, we introduce a method for using state-of-the-art multimodal large language models (L

Benjamin Ng, Chi-en Amy Tai, E. Zhixuan Zeng, Alexander Wong

In the United States, prostate cancer is the second leading cause of deaths in males with a predicted 35,250 deaths in 2024. However, most diagnoses are non-lethal and deemed clinically insignificant which means that the patient will likely not be impacted by the cancer over their lifetime. As a result, numerous research studies have explored the accuracy of

Yang Kuang, Guanghui Hu

In this paper, we propose a novel eigenpair-splitting method, inspired by the divide-and-conquer strategy, for solving the generalized eigenvalue problem arising from the Kohn-Sham equation. Unlike the commonly used domain decomposition approach in divide-and-conquer, which solves the problem on a series of subdomains, our eigenpair-splitting method focuses

P. Sanchez-Puga, J. Tajuelo, J. Carrascosa-Tejedor, M. A. Rubio

The study of the structure and mechanical properties of complex fluid interfaces has gained increasing interest in recent decades as a result of its significant scientific relevance to the understanding of biological systems, drug development, and industrial applications. The in situ combination of molecular-level structural measurements with the assessment

Taylor Arnold, Lauren Tilton

In the 1970s, the United States Environmental Protection Agency sponsored Documerica, a large-scale photography initiative to document environmental subjects nation-wide. While over 15,000 digitized public-domain photographs from the collection are available online, most of the images were scanned from damaged copies of the original prints. We present and ev

Philipp Altmann, Julian Schönberger, Maximilian Zorn, Thomas Gabor

According to the Strong Lottery Ticket Hypothesis, every sufficiently large neural network with randomly initialized weights contains a sub-network which - still with its random weights - already performs as well for a given task as the trained super-network. We present the first approach based on a genetic algorithm to find such strong lottery ticket sub-ne

Richard Hanser, Tobias Röddiger, Till Riedel, Michael Beigl

Earphones can give access to sensitive information via voice assistants which demands security methods that prevent unauthorized use. Therefore, we developed EarCapAuth, an authentication mechanism using 48 capacitive electrodes embedded into the soft silicone eartips of two earables. For evaluation, we gathered capactive ear canal measurements from 20 parti

Xinlei Yu, Ahmed Elazab, Ruiquan Ge, Hui Jin

Intracerebral hemorrhage (ICH) is the most fatal subtype of stroke and is characterized by a high incidence of disability. Accurate segmentation of the ICH region and prognosis prediction are critically important for developing and refining treatment plans for post-ICH patients. However, existing approaches address these two tasks independently and predomina

Yassine Abbahaddou, Fragkiskos D. Malliaros, Johannes F. Lutzeyer, Michalis Vazirgiannis

Graph Shift Operators (GSOs), such as the adjacency and graph Laplacian matrices, play a fundamental role in graph theory and graph representation learning. Traditional GSOs are typically constructed by normalizing the adjacency matrix by the degree matrix, a local centrality metric. In this work, we instead propose and study Centrality GSOs (CGSOs), which n

Noam Soker

I identify a point-symmetrical morphology in the core-collapse supernova remnant (CCSNR) W44 compatible with shaping by three or more pairs of jets in the jittering jet explosion mechanism (JJEM). Motivated by recent identifications of point-symmetrical morphologies in CCSNRs and their match to the JJEM, I revisit the morphological classification of CCSNR W4

Clémence Grislain, Risto Vuorio, Cong Lu, Shimon Whiteson

Developing autonomous vehicles that can navigate complex environments with human-level safety and efficiency is a central goal in self-driving research. A common approach to achieving this is imitation learning, where agents are trained to mimic human expert demonstrations collected from real-world driving scenarios. However, discrepancies between human perc

Ijaz Ahmed, Basit Ali, M. S. Amjad, M. Jamil

This paper reports on the theoretical investigation of charged Higgs bosons and their coupling to fermions within the Two Higgs Doublet Model (THDM). The study focuses on the discovery potential of charged Higgs bosons predicted in Types III and IV at the future Circular Hadron-Hadron Collider (FCC-hh) with a center-of-mass energy of (\sqrt{s} = 100) TeV. By

Marco Bravin, Manuel V. Gnann, Hans Knüpfer, Nader Masmoudi

Well-posedness and higher regularity of the heat equation with Robin boundary conditions in an unbounded two-dimensional wedge is established in an $L^{2}$-setting of monomially weighted spaces. A mathematical framework is developed which allows to obtain arbitrarily high regularity without a smallness assumption on the opening angle of the wedge. The challe

Yuechun Jiao, Yu Zhang, Jingxu Bai, Suotang Jia

Floquet systems are periodically driven systems. In this framework, the system Hamiltonian and associated spectra of interest are modified, giving rise to new quantum phases of matter and nonequilibrium dynamics without static counterparts. Here we experimentally demonstrate a self-induced Floquet system in the interacting Rydberg gas. This originates from t