October 2024 arXiv papers — page 65

Ruicheng Zhang, Guoheng Huang, Yejing Huo, Xiaochen Yuan

Generative Adversarial Networks (GANs) have emerged as a prominent research focus for image editing tasks, leveraging the powerful image generation capabilities of the GAN framework to produce remarkable results.However, prevailing approaches are contingent upon extensive training datasets and explicit supervision, presenting a significant challenge in manip

Umar Sohail Qureshi, Andres Flórez, Alfredo Gurrola, Cristian Rodriguez

A model based on a $U(1)_{T^3_R}$ extension of the Standard Model can address the mass hierarchy between generations of fermions, explain thermal dark matter abundance, and the muon $g - 2$, $R_{(D)}$, and $R_{(D^*)}$ anomalies. The model contains a light scalar boson $\phi'$ and a heavy vector-like quark $\chi_\mathrm{u}$ that can be probed at CERN's Large

P. Amedo, S. Leardini, A. Saá-Hernández, D. González-Díaz

We report time and band-resolved scintillation from $\alpha$-particles in Ar/CF$_4$ mixtures at a pressure of 9.5 bar. Our results show that %-level addition of CF$_4$ enables strong wavelength-shifted scintillation in the visible range, with yields at the level of 1000 ph/MeV, scintillation decay times of 9-25 ns and formation times well below 10 ns. Such a

Abhishek Banerjee, Surjeet Kour

We study module like objects over categorical quotients of algebras by the action of coalgebras with several objects. These take the form of ``entwined comodules'' and ``entwined contramodules'' over a triple $(\mathscr C,A,\psi)$, where $A$ is an algebra, $\mathscr C$ is a coalgebra with several objects and $\psi$ is a collection of maps that ``entwines'' $

K. Darshana Abeyrathna, Sara El Mekkaoui, Andreas Hafver, Christian Agrell

Tsetlin Machines (TMs) have emerged as a compelling alternative to conventional deep learning methods, offering notable advantages such as smaller memory footprint, faster inference, fault-tolerant properties, and interpretability. Although various adaptations of TMs have expanded their applicability across diverse domains, a fundamental gap remains in under

Xiaoli Han, Jiayu Li, Jun Sun

It is known that there is no a Type I singularity for the Lagrangian mean curvature flow with zero Maslov class. In this paper, we study translating solitons which are important models of Type II singularities. A necessary condition for a blow-up limit arising at a Type II singularity of a Lagrangian mean curvature flow with zero Maslov class is provided. As

W. A. Rojas C., A. Zamora

We present an analysis of Bogot'{a}'s sports sector through thermostatistical models applied to economic systems. The study investigates the cross-price elasticity of income ($\lambda$) to determine whether sports services in Bogot'{a} are normal or inferior goods. Analyzing data from the Sports Satellite Account of Bogot'{a} (CSDB) from 2018 to 2022, we fin

Anja Metzner

When studying software engineering, learning to create UML diagrams is crucial. Similar to how an architect would never build a house without a building plan, designing software architectures is important for developing high-quality software. UML diagrams are a standardized notation for the visualization of software architectures and software behavior. The r

Stefano Baranzini, Gian Marco Canneori, Susanna Terracini

We seek periodic trajectories of a system of multiple mutually repelling electrons on a half-line, with an attractive nucleus sitting at the origin. We adopt a variational viewpoint and study critical points of the associated Lagrange-action functional, by means of a modified Lusternik-Schnirelmann theory for manifolds with boundary. Additionally, when the c

Dagur Asgeirsson

Condensed mathematics, developed by Clausen and Scholze over the last few years, proposes a generalization of topology with better categorical properties. It replaces the concept of a topological space by that of a condensed set, which can be defined as a sheaf on a certain site of compact Hausdorff spaces. Since condensed sets are supposed to be a generaliz

Philipp Gersing

We show that the common component of the Generalised Dynamic Factor Model (GDFM) can be represented using only current and past observations basically whenever it is purely non-deterministic.

May Abdallah, Mohamad Darwich, Luc Molinet

We prove the asymptotic stability of the high speed solitary waves to the Benjamin equation. This is done by establishing a Liouville property for the nonlinear evolution of the Benjamin equation around these solitary waves. To do this, inspired by Kenig-Martel-Robbiano 2011, we make use of the KdV limit of the Benjamin equation together with known rigidity

Cristian López, Aryan Singh, Ángel Naranjo, Keegan J. Moore

Determining the underlying equations of motion and parameter values for vibrating structures is of great concern in science and engineering. This work introduces a new data-driven approach called the energy-based dual-phase dynamics identification (EDDI) method for identifying the nonlinear dynamics of single-degree-of-freedom oscillators. The EDDI method le

Natália V. N. Rodrigues, Natalí S. M. de Santi, L. Raul Abramo, Antonio D. Montero-Dorta

The connection between galaxies and dark matter halos encompasses a range of processes and play a pivotal role in our understanding of galaxy formation and evolution. Traditionally, this link has been established through physical or empirical models. Machine learning techniques are adaptable tools that handle high-dimensional data and grasp associations betw

Arno Förster, Fabien Bruneval

Hedin's $GW$ approximation to the electronic self-energy has been impressively successful to calculate quasiparticle energies, such as ionization potentials, electron affinities, or electronic band structures. The success of this fairly simple approximation has been ascribed to the cancellation of the so-called vertex corrections that go beyond $GW$. This cl

Xiao-Yun Wang, Yuan Gao, Xiang Liu

Focusing on the recent measurements of the scattering processes $\bar{\Lambda} p \rightarrow \bar{\Lambda} p$ and $\Lambda p \rightarrow \Lambda p$ reported by BESIII, we offer a dynamical interpretation of the differences in the measured total and differential cross sections for both processes. This discrepancy arises because the $u$-channel contribution is

Huiguang Zhang, Baoguo Liu

The emergence of ultra-wideband (UWB) and high-throughput signals has necessitated advancements in data sampling technologies1. Sub-Nyquist sampling methods, such as the modulated wideband converter (MWC) and compressed auto-correlation spectrum sensing (CCS), address the limitations of traditional analog-to-digital converters (ADCs) by capturing signals bel

Ferdi Kossmann, Bruce Fontaine, Daya Khudia, Michael Cafarella

Serving systems for Large Language Models (LLMs) improve throughput by processing several requests concurrently. However, multiplexing hardware resources between concurrent requests involves non-trivial scheduling decisions. Practical serving systems typically implement these decisions at two levels: First, a load balancer routes requests to different server

Qingshan Xu, Xuanyu Yi, Jianyao Xu, Wenbing Tao

Novel view synthesis with sparse inputs poses great challenges to Neural Radiance Field (NeRF). Recent works demonstrate that the frequency regularization of Positional Encoding (PE) can achieve promising results for few-shot NeRF. In this work, we reveal that there exists an inconsistency between the frequency regularization of PE and rendering loss. This p

Cristian López, Ángel Naranjo, Diego Salazar, Keegan J. Moore

Identifying nonlinear dynamics and characterizing noise from data is critical across science and engineering for understanding and modeling the behavior of the systems accurately. The modified sparse identification of nonlinear dynamics (mSINDy) has emerged as an effective framework for identifying systems embedded in heavy noise; however, further improvemen

Songnian Xu

For a connected graph $G$ with order $n$, let $e(G)$ represent the number of its distinct eigenvalues, and let $d$ denote its diameter. We denote the eigenvalue multiplicity of $\mu$ in $G$ by $m_G(\mu)$. It is well established that the inequality $e(G) \geq d + 1$ implies that when $\mu$ is an eigenvalue of $P_{d+1}$, it follows that $m_G(\mu) \leq n - d$;

Francisco Javier Escoto, José Luis Velasco, Iván Calvo, Edilberto Sánchez

Stellarator magnetic fields that are perfectly optimized for neoclassical transport (with levels of radial neoclassical transport comparable to tokamaks) are called omnigenous. Quasi-isodynamic magnetic fields are a subset of omnigenous magnetic fields in which the isolines of the magnetic field strength close poloidally, which grants them the additional pro

Tianyuan Jin, Keke Huang, Jing Tang, Xiaokui Xiao

This paper studies two variants of the best arm identification (BAI) problem under the streaming model, where we have a stream of $n$ arms with reward distributions supported on $[0,1]$ with unknown means. The arms in the stream are arriving one by one, and the algorithm cannot access an arm unless it is stored in a limited size memory. We first study the st

Danilo de Oliveira, Julius Richter, Jean-Marie Lemercier, Simon Welker

Diffusion models have found great success in generating high quality, natural samples of speech, but their potential for density estimation for speech has so far remained largely unexplored. In this work, we leverage an unconditional diffusion model trained only on clean speech for the assessment of speech quality. We show that the quality of a speech uttera

Yiming Huang, Jingyu Xiao, Lian Tao, Shuang-Nan Zhang

The Chasing All Transients Constellation Hunters (CATCH) space mission is focused on exploring the dynamic universe via X-ray follow-up observations of various transients. The first pathfinder of the CATCH mission, CATCH-1, was launched on June 22, 2024, alongside the Space-based multiband astronomical Variable Objects Monitor (SVOM) mission. CATCH-1 is equi

Laines Schmalwasser, Jakob Gawlikowski, Joachim Denzler, Julia Niebling

Concept Activation Vectors (CAVs) offer insights into neural network decision-making by linking human friendly concepts to the model's internal feature extraction process. However, when a new set of CAVs is discovered, they must still be translated into a human understandable description. For image-based neural networks, this is typically done by visualizing

Monisha Mushtary Uttsha, Cedric Le Gentil, Lan Wu, Teresa Vidal-Calleja

Navigating cluttered environments is a challenging task for any mobile system. Existing approaches for ground-based mobile systems primarily focus on small wheeled robots, which face minimal constraints with overhanging obstacles and cannot manage steps or stairs, making the problem effectively 2D. However, navigation for legged robots (or even humans) has t

Patrick Hippold, Gleb Kleyman, Lukas Woiwode, Tong Wei

Sinusoidal excitation is particularly popular for testing structures in the nonlinear regime. Due to the nonlinear behavior and the inevitable feedback of the structure on the exciter, higher harmonics in the applied excitation are generated. This is undesired, because the acquired response may deviate substantially from that of the structure under purely si

Andrea Kubin, Valerio Pagliari, Antonio Tribuzio

We study the second-order asymptotic expansion of the $s$-fractional Gagliardo seminorm as $s\to1^-$ in terms of a higher order nonlocal functional. We prove a Mosco-convergence result for the energy functionals and that the $L^2$-gradient flows of the energies converge to the $L^2$-gradient flows of the Mosco-limit.

Marston Conder, Gabriel Verret, Darius Young

We prove that the set of orders of finite quotients of a finitely generated group has natural density 0, 1/2 or 1, and characterise when each of these cases occurs. We apply this to show that the sets of orders of various families of symmetric graphs have natural density 0.

Marco Mistretta, Andrew D. Bagdanov

In this paper we introduce RE-tune, a novel approach for fine-tuning pre-trained Multimodal Biomedical Vision-Language models (VLMs) in Incremental Learning scenarios for multi-label chest disease diagnosis. RE-tune freezes the backbones and only trains simple adaptors on top of the Image and Text encoders of the VLM. By engineering positive and negative tex

Mostafa Meliani, Belkacem Said-Houari

In this paper, we consider the Jordan--Moore--Gibson--Thompson with a time-fractional damping term of the type $\delta \textup{D}_t^{1-\alpha} \Delta \psit$ where we allow the challenging so-called critical case ($\delta=0$). This equation arises in the context of acoustic propagation through thermally relaxed media. We tackle the question of long-time exist

Ulf-G. Meißner, Fabian Müller, Akaki Rusetsky

We derive a systematic perturbative expansion for the finite-volume energy spectrum of the non-linear $O(N)$ $\sigma$-model in the $\delta$-regime. The violation of the power-counting rules that emerges after the separation of the fast and slow modes is dealt with to all orders by use of the threshold expansion. The known result for the rest-frame energy spe

ATLAS Collaboration

The paper presents a search for supersymmetric particles produced in proton-proton collisions at $\sqrt{s}=$ 13 TeV and decaying into final states with missing transverse momentum and jets originating from charm quarks. The data were taken with the ATLAS detector at the Large Hadron Collider at CERN from 2015 to 2018 and correspond to an integrated luminosit

Kai Liu, Kang You, Pan Gao, Manoranjan Paul

With the great progress of 3D sensing and acquisition technology, the volume of point cloud data has grown dramatically, which urges the development of efficient point cloud compression methods. In this paper, we focus on the task of learned lossy point cloud attribute compression (PCAC). We propose an efficient attention-based method for lossy compression o

Qingpeng Li, Yuxin Zhang, Leyuan Fang, Yuhan Kang

Object detection algorithms are pivotal components of unmanned aerial vehicle (UAV) imaging systems, extensively employed in complex fields. However, images captured by high-mobility UAVs often suffer from motion blur cases, which significantly impedes the performance of advanced object detection algorithms. To address these challenges, we propose an innovat

C. A. Middelburg

Previous papers give accounts of quests for satisfactory formalizations of the classical informal notion of an algorithm and the contemporary informal notion of an interactive algoritm. In this paper, an attempt is made to generalize the results of the former quest to the contemporary informal notion of a concurrent algorithm. The notion of a concurrent prot

Qiqi Chen, Xinpeng Wang, Philipp Mondorf, Michael A. Hedderich

Tree of Thoughts (ToT) is a reasoning strategy for Large Language Models (LLMs) that employs a generator to suggest reasoning steps and a discriminator to decide which steps to implement. ToT demonstrates strong performance on reasoning tasks, often surpassing simple methods such as Input-Output (IO) prompting and Chain-of-Thought (CoT) reasoning. However, T

Raphaël Picard, Matthew W. Davies

If primordial scalar or tensor perturbations are enhanced on short scales, it may lead to the production of observable gravitational wave signals. These waves may be sourced by scalar-scalar, scalar-tensor or tensor-tensor interactions. Typically, models of inflation capable of producing large peaks in the scalar primordial power spectrum also generate sizea

Antonio Campillo, Raquel Melgar

Many invariants of finitely generated positive cancelative commutative semigroups can be studied from their Poincar\'e series. We offer and present several closed formulas for them. Moreover, those formulas have elementary proofs and are presented in two distinct forms: one characterized in terms of simplicial complexes and the other by a purely set theoreti

Naomi Andrew, Paige Hillen, Robert Alonzo Lyman, Catherine Eva Pfaff

We show that the group $\langle a,b,c,t : a^t=b,b^t=c,c^t=ca^{-1} \rangle$ is profinitely rigid amongst free-by-cyclic groups, providing the first example of a hyperbolic free-by-cyclic group with this property.

Edoardo Legnaro, Sabrina Guastavino, Michele Piana, Anna Maria Massone

A solar active region can significantly disrupt the Sun Earth space environment, often leading to severe space weather events such as solar flares and coronal mass ejections. As a consequence, the automatic classification of active region groups is the crucial starting point for accurately and promptly predicting solar activity. This study presents our resul

Bingqing Han, Yipeng Zhang, Longlong Li, Xinqi Gong

Even with the significant advances of AlphaFold-Multimer (AF-Multimer) and AlphaFold3 (AF3) in protein complex structure prediction, their accuracy is still not comparable with monomer structure prediction. Efficient quality assessment (QA) or estimation of model accuracy (EMA) models that can evaluate the quality of the predicted protein-complexes without k

Kai Wang, Yuanchao Bai, Daxin Li, Deming Zhai

Recent advances in learning-based methods have markedly enhanced the capabilities of image compression. However, these methods struggle with high bit-depth volumetric medical images, facing issues such as degraded performance, increased memory demand, and reduced processing speed. To address these challenges, this paper presents the Bit-Division based Lossle

Rik Banerjee, Manish Kumar, Anisur Rahaman Molla

Dispersion of mobile robots over the nodes of an anonymous graph is an important problem and turns out to be a crucial subroutine for designing efficient algorithms for many fundamental graph problems via mobile robots. In this problem, starting from an arbitrary initial distribution of $n$ robots across the $n$ nodes, the goal is to achieve a final configur

Feiyan Feng, Tianyu Liu, Hong Wang, Jun Zhao

Early detection through imaging and accurate diagnosis is crucial in mitigating the high mortality rate associated with breast cancer. However, locating tumors from low-resolution and high-noise medical images is extremely challenging. Therefore, this paper proposes a novel PGDiffSeg (Prior-Guided Diffusion Denoising Model with Parameter-Shared Attention) th

Dan I. Florentin, Tomer Milo

Assume that $rB_{2}^{n} \subset P$ for some polytope $P \subset \mathbb{R}^n$, where $r \in (\frac{1}{2},1]$. Denote by $\mathcal{F}$ the set of facets of $P$, and by $N=N(P,B_2^n)$ the covering number of $P$ by the Euclidean unit ball $B_2^n$. We prove that if $\log N \le\frac{n}{8}$, then \[ |\mathcal{F}| \ge \left( \frac{1}{ 2\left(1 - r \sqrt{1-\frac{4\l

Yaxiong Wang, Yujiao Wu, Lianwei Wu, Lechao Cheng

Recent advancements in image-text matching have been notable, yet prevailing models predominantly cater to broad queries and struggle with accommodating fine-grained query intention. In this paper, we work towards the \textbf{E}ntity-centric \textbf{I}mage-\textbf{T}ext \textbf{M}atching (EITM), a task that the text and image involve specific entity-related

Kaiwen Zhu, Jinjin Gu, Zhiyuan You, Yu Qiao

Real-world image restoration (IR) is inherently complex and often requires combining multiple specialized models to address diverse degradations. Inspired by human problem-solving, we propose AgenticIR, an agentic system that mimics the human approach to image processing by following five key stages: Perception, Scheduling, Execution, Reflection, and Resched

F. den Hollander

The present paper is a brief overview of random opinion dynamics on random graphs based on the Ising Lecture given by the author at the World Congress in Probability and Statistics, 12--16 August 2024, Bochum, Germany. The content is a snapshot of an interesting area of research that is developing rapidly.

Jakob Metson

Allosteric interactions occur when binding at one part of a complex affects the interactions at another part. Allostery offers a high degree of control in multi-species processes, and these interactions play a crucial role in many biological and synthetic contexts. Leveraging allosteric principles in synthetic systems holds great potential for designing mate

M. Cano-González, R. Schödel, A. Alberdi, J. Moldón

The Arches cluster, one of the most massive clusters in the Milky Way, is located about 30 pc in projection from the central massive black hole. With its high mass, young age, and location in the Galaxy's most extreme star forming environment, the Arches is an extraordinary laboratory to study massive stars and clusters. Our objective is to improve our knowl

Sergio Hernandez, Christophe Peucheret, Francesco Da Ros, Darko Zibar

We experimentally demonstrate the joint optimization of transmitter and receiver parameters in directly modulated laser systems, showing superior performance compared to nonlinear receiver-only equalization while using fewer memory taps, less bandwidth, and lower radiofrequency power.

Pablo Sanchez-Puertas, Enrique Ruiz Arriola

In this work, we employ a dispersion relation that allows to recover the electromagnetic form factor of the pion from its modulus along the unitarity cut. Unlike the standard dispersive approach, this method extends in a straightforward manner above the inelastic region, allowing us to obtain the form factor's phase, as well as its real and imaginary parts.

Kerry He, James Saunderson, Hamza Fawzi

We introduce QICS (Quantum Information Conic Solver), an open-source primal-dual interior point solver fully implemented in Python, which is focused on solving optimization problems arising in quantum information theory. QICS has the ability to solve optimization problems involving the quantum relative entropy, noncommutative perspectives of operator convex

Ruowei Wang, Jiaqi Li, Dan Zeng, Xueqi Ma

Generating high-quality meshes with complex structures and realistic surfaces is the primary goal of 3D generative models. Existing methods typically employ sequence data or deformable tetrahedral grids for mesh generation. However, sequence-based methods have difficulty producing complex structures with many faces due to memory limits. The deformable tetrah

Can Firtina, Maximilian Mordig, Harun Mustafa, Sayan Goswami

Raw nanopore signal analysis is a common approach in genomics to provide fast and resource-efficient analysis without translating the signals to bases (i.e., without basecalling). However, existing solutions cannot interpret raw signals directly if a reference genome is unknown due to a lack of accurate mechanisms to handle increased noise in pairwise raw si

Fabian Backhaus, Karoline Brucke, Peter Ruckdeschel, Sunke Schlüters

With the growing number of forecasting techniques and the increasing significance of forecast-based operation - particularly in the rapidly evolving energy sector - selecting the most effective forecasting model has become a critical task. Given the dynamic nature of energy forecasting, it is highly advantageous to assess the superiority of forecasting model

Qinglin Zhang, Luyao Cheng, Chong Deng, Qian Chen

Full-duplex spoken dialogue systems significantly surpass traditional turn-based dialogue systems, as they allow simultaneous bidirectional communication, closely mirroring human-human interactions. However, achieving low latency and natural interactions in full-duplex dialogue systems remains a significant challenge, especially considering human conversatio

Jiaju Zhang, M. A. Rajabpour, Markus Heyl, Reyhaneh Khasseh

In studying the time evolution of isolated many-body quantum systems, a key focus is determining whether the system undergoes relaxation and reaches a steady state at a given point in time. Traditional approaches often rely on specific local operators or a detailed understanding of the stationary state. In this letter, we introduce an alternative method that

Helena Iuele, Stefania Forciniti, Valentina Onesto, Francesco Colella

Fluorescence imaging allows for non-invasively visualizing and measuring key physiological parameters like pH and dissolved oxygen. In our work, we created two ratiometric fluorescent microsensors designed for accurately tracking dissolved oxygen levels in 3D cell cultures. We developed a simple and cost-effective method to produce hybrid core-shell silica m

Tin Sum Cheng, Aurelien Lucchi, Anastasis Kratsios, David Belius

This paper conducts a comprehensive study of the learning curves of kernel ridge regression (KRR) under minimal assumptions. Our contributions are three-fold: 1) we analyze the role of key properties of the kernel, such as its spectral eigen-decay, the characteristics of the eigenfunctions, and the smoothness of the kernel; 2) we demonstrate the validity of

Francesco Colella, Stefania Forciniti, Valentina Onesto, Giuliana Grasso

Potassium cations play many important roles in living organisms, especially in electro-physiology, since they are involved in neurotransmission and muscle contractions. We report the synthesis of a ratiometric fluorescent microsensor for potassium (K+) detection, based on the fluorescent probe ION potassium green 4. Potassium-sensitive fluorescent microparti

Shanshan Li, Jiaru Lv, Rongli Huang

In this paper, we consider the mean curvature flow of entire Lagrangian graphs with initial data in the pseudo-Euclidean space, which is related to the special Lagrangian parabolic equation. We show that the parabolic equation \eqref{11} has a smooth solution $u(x,t)$ for three corresponding nonlinear equations between the Monge-Amp$\grave{e}$re type equatio

Justo Puerto, Alberto Torrejon

Regression analysis is an important instrument to determine the effect of the explanatory variables on response variables. When outliers and bias errors are present, the standard weighted least squares estimator may perform poorly. For this reason, many alternative robust techniques have been studied in literature. In these terms, the Least Squares Quantile

Charuka Herath, Xiaolan Liu, Sangarapillai Lambotharan, Yogachandran Rahulamathavan

Federated Learning (FL) is a decentralized approach for collaborative model training on edge devices. This distributed method of model training offers advantages in privacy, security, regulatory compliance, and cost-efficiency. Our emphasis in this research lies in addressing statistical complexity in FL, especially when the data stored locally across device

Sumana Bhattacharyya, Uttam Singh, Anupam Sengupta

Predicting fitness of biologically-active populations, communities or systems in fluctuating environments is a long-standing challenge. Phenotypic plasticity and bet-hedging strategy, two key evolutionary traits living systems harness to optimize fitness in dynamic environments, have been widely reported yet how interplays therein could mediate fitness lands

Ondřej Mokrý, Pavel Rajmic

Autoregressive (AR) modeling is invaluable in signal processing, in particular in speech and audio fields. Attempts in the literature can be found that regularize or constrain either the time-domain signal values or the AR coefficients, which is done for various reasons, including the incorporation of prior information or numerical stabilization. Although th

Qijun Zhang, Mengming Li, Yao lu, Zhiyao Xie

Power efficiency is a critical design objective in modern processor design. A high-fidelity architecture-level power modeling method is greatly needed by CPU architects for guiding early optimizations. However, traditional architecture-level power models can not meet the accuracy requirement, largely due to the discrepancy between the power model and actual

Dan Beilis, Sara Beck, John Lacy, Jean L. Turner

II Zw 40 is a starburst dwarf and merger product, and holds a radio-infrared supernebula excited by thousands of embedded OB stars. We present here observations of three aspects of the supernebula: maps of the K and KU radio continuum that trace dense ionized gas with spatial resolution $\sim0.1^{\prime\prime}$, a spectral data cube of the [S IV]$10.5\mu$m e

Jaris Küken, Lennart Purucker, Frank Hutter

Tabular machine learning problems often require time-consuming and labor-intensive feature engineering. Recent efforts have focused on using large language models (LLMs) to capitalize on their potential domain knowledge. At the same time, researchers have observed ethically concerning negative biases in other LLM-related use cases, such as text generation. T

Elisabeth Richter, Michael Barth, Dmitriy A. Kozlov, Angelika Knothe

We explore electrical transport through a point contact in strained HgTe, a three-dimensional topological insulator. In the absence of a magnetic field $B$, there is no quantization. However, under higher magnetic fields, we observe distinct non-integer conductance steps. Based on numerical tight-binding calculations and a phenomenological Landauer-B\"uttike

Guillem Capellera, Luis Ferraz, Antonio Rubio, Antonio Agudo

Understanding trajectories in multi-agent scenarios requires addressing various tasks, including predicting future movements, imputing missing observations, inferring the status of unseen agents, and classifying different global states. Traditional data-driven approaches often handle these tasks separately with specialized models. We introduce TranSPORTmer,

Muhammad Ashfaq, Ahmed R. Sadik, Tommi Mikkonen, Muhammad Waseem

As Systems of Systems evolve into increasingly complex networks, harnessing their collective potential becomes paramount. Traditional SoS engineering approaches lack the necessary programmability to develop third party SoS level behaviors. To address this challenge, we propose a software defined approach to enable flexible and adaptive programming of SoS. We

Salman Rakin, Md. A. R. Shibly, Zahin M. Hossain, Zeeshan Khan

While ongoing advancements in Large Language Models have demonstrated remarkable success across various NLP tasks, Retrieval Augmented Generation Model stands out to be highly effective on downstream applications like Question Answering. Recently, RAG-end2end model further optimized the architecture and achieved notable performance improvements on domain ada

Qijun Zhang, Zhiyao Xie

Point cloud is an important data structure for a wide range of applications, including robotics, AR/VR, and autonomous driving. To process the point cloud, many deep-learning-based point cloud recognition algorithms have been proposed. However, to meet the requirement of applications like autonomous driving, the algorithm must be fast enough, rendering accel

Francesco Bombassei De Bona, Gabriele Dominici, Tim Miller, Marc Langheinrich

As AI becomes fundamental in sectors like healthcare, explainable AI (XAI) tools are essential for trust and transparency. However, traditional user studies used to evaluate these tools are often costly, time consuming, and difficult to scale. In this paper, we explore the use of Large Language Models (LLMs) to replicate human participants to help streamline

Anna Franziska Frigge, Elena Jiltsova, Fredrik Olsson, Dag Nyholm

Deep Brain Stimulation (DBS) is a therapy widely used for treating the symptoms of neurological disorders. Electrical pulses are chronically delivered in DBS to a disease-specific brain target via a surgically implanted electrode. The stimulating contact configuration, stimulation polarity, as well as amplitude, frequency, and pulse width of the DBS pulse se

Zhiwei Hao, Jianyuan Guo, Li Shen, Yong Luo

Recent advancements in multimodal fusion have witnessed the remarkable success of vision-language (VL) models, which excel in various multimodal applications such as image captioning and visual question answering. However, building VL models requires substantial hardware resources, where efficiency is restricted by two key factors: the extended input sequenc

Ayaka Shimizu, Yoshiro Yaguchi

In this paper, we define the OU matrix of a braid diagram and discuss how the OU matrix reflects the warping degree or the layeredness of the braid diagram, and show that the determinant of the OU matrix of a layered braid diagram is the product of the determinants of the layers. We also introduce invariants of positive braids which are derived from the OU m

Harald Räcke, Stefan Schmid, Ruslan Zabrodin

Resource allocation in distributed and networked systems such as the Cloud is becoming increasingly flexible, allowing these systems to dynamically adjust toward the workloads they serve, in a demand-aware manner. Online balanced partitioning is a fundamental optimization problem underlying such self-adjusting systems. We are given a set of $\ell$ servers. O

Xi Geng, Mihai Gradinaru, Samy Tindel

It is known that a properly rescaled version of Sinai's random walk converges in distribution to Brox's diffusion. In this article we quantify this convergence by considering a specific coupling between Sinai's walk and Brox's diffusion. Our method relies on convergence results for martingale problems considered in the rough path setting.

Osamu Hirota, Masaki Sohma

The purpose of this paper is to celebrate Sir D. Payne's 80th birthday by dedicating our latest results as a continuation of his work in optical communications. One of the important issues in optical communications is to protect the transmission information data passing through optical fiber channels. Many ideas have been proposed and are being actively deve

Jiayi Kong, Chen Zong, Jun Luo, Shiqing Xin

The medial axis, a lower-dimensional descriptor that captures the extrinsic structure of a shape, plays an important role in digital geometry processing. Despite its importance, computing the medial axis transform robustly from diverse inputs, especially point clouds with defects, remains a challenging problem. In this paper, we propose a new implicit method

Matthias Scharitzer

We show that the $q$-dilogarithm identities associated to the unidirectional $A_n$-quiver lift to the HOMFLYPT-skein algebra of a genus $n$ handlebody.

Nils Blank, Moritz Reuss, Marcel Rühle, Ömer Erdinç Yağmurlu

A central challenge towards developing robots that can relate human language to their perception and actions is the scarcity of natural language annotations in diverse robot datasets. Moreover, robot policies that follow natural language instructions are typically trained on either templated language or expensive human-labeled instructions, hindering their s

ALICE Collaboration

ALICE is a large experiment at the CERN Large Hadron Collider. Located 52 meters underground, its detectors are suitable to measure muons produced by cosmic-ray interactions in the atmosphere. In this paper, the studies of the cosmic muons registered by ALICE during Run 2 (2015--2018) are described. The analysis is limited to multimuon events defined as even

Max Staats, Matthias Thamm, Bernd Rosenow

This work analyzes singular-value spectra of weight matrices in pretrained transformer models to understand how information is stored at both ends of the spectrum. Using Random Matrix Theory (RMT) as a zero information hypothesis, we associate agreement with RMT as evidence of randomness and deviations as evidence for learning. Surprisingly, we observe prono

ALICE Collaboration

In this Letter, the first evidence of the $^4_{\bar{\Lambda}}\overline{\rm He}$ antihypernucleus is presented, along with the first measurement at the LHC of the production of (anti)hypernuclei with mass number $A=4$, specifically (anti)$^4_{\Lambda}\mathrm{H}$ and (anti)$^4_{\Lambda}\mathrm{He}$. In addition, the antiparticle-to-particle ratios for both hyp

Jakob Shack, Katarina Petrovic, Olga Saukh

Adversarial attacks pose a significant threat to the robustness and reliability of machine learning systems, particularly in computer vision applications. This study investigates the performance of adversarial patches for the YOLO object detection network in the physical world. Two attacks were tested: a patch designed to be placed anywhere within the scene

Sixtine Dromigny, Daniel Bowden, Sebastian Noe, Dominik Husmann

Similar to Distributed Acoustic Sensing (DAS), phase transmission fibre optics allows for large bandwidth seismic data measurements using fibre-optic cables. However, while the application range of DAS is limited to tens of kilometres, phase transmission fibre optics has an application range that can go up to thousands of kilometres. This new method has been

Lars Davidson

Machine Learning (ML) is used for developing wall functions for Improved Delayed Detached Eddy Simulations (IDDES). The ML model is based on KDtree which essentially is a fast look-up table. It searches the nearest target datapoint(s) for which y+ and U+ are closest to the CFD y+ and U+ cells. The target y+ value gives the friction velocity which is used for

F. Capannoli, F. den Hollander

The present overview of interacting particle systems on random graphs collects the notes of a mini-course given by the authors at the Brazilian School of Probability, 5--9 August 2024, in Salvador, Bahia, Brazil. The content is a personal snapshot of an interesting area of research at the interface between probability theory, combinatorics, statistical physi

Artem Basharin, Andrei Chertkov, Ivan Oseledets

We propose a new model for multi-token prediction in transformers, aiming to enhance sampling efficiency without compromising accuracy. Motivated by recent work that predicts the probabilities of subsequent tokens using multiple heads, we connect this approach to rank-$1$ canonical tensor decomposition. By generalizing it to a rank-$r$ canonical probability

Katharina Flügel, Daniel Coquelin, Marie Weiel, Charlotte Debus

The gradients used to train neural networks are typically computed using backpropagation. While an efficient way to obtain exact gradients, backpropagation is computationally expensive, hinders parallelization, and is biologically implausible. Forward gradients are an approach to approximate the gradients from directional derivatives along random tangents co

Jordan Gué, Aurélien Hees, Peter Wolf

We propose a simple modification of space-based gravitational wave (GW) detector optical benches which would enable the measurement of vacuum birefringence of light induced by axion dark matterthrough its coupling to electromagnetism. Specifically, we propose to change a half-wave plate by a circular polarizer. While marginally affecting the sensitivity to G

Suraj Kumar, Soumi Chattopadhyay, Chandranath Adak

Quality-of-Service (QoS) prediction is a critical task in the service lifecycle, enabling precise and adaptive service recommendations by anticipating performance variations over time in response to evolving network uncertainties and user preferences. However, contemporary QoS prediction methods frequently encounter data sparsity and cold-start issues, which

Jianquan Ge, Ya Tao, Yi Zhou

In this paper, we establish new normal scalar curvature inequalities on a class of austere submanifolds by proving sharper DDVV-type inequalities on associated austere subspaces. We also provide some examples of austere submanifolds in this class and point out one of them achieves the equality in our normal scalar curvature inequality everywhere. As a byprod

Bastian Rieck

This overview article makes the case for how topological concepts can enrich research in machine learning. Using the Euler Characteristic Transform (ECT), a geometrical-topological invariant, as a running example, I present different use cases that result in more efficient models for analyzing point clouds, graphs, and meshes. Moreover, I outline a vision fo

Jean Barré

Intertextuality is a key concept in literary theory that challenges traditional notions of text, signification or authorship. It views texts as part of a vast intertextual network that is constantly evolving and being reconfigured. This paper argues that the field of computational literary studies is the ideal place to conduct a study of intertextuality sinc