November 2024 arXiv papers — page 39

Julian Wechs, Ognyan Oreshkov

One can theoretically conceive of processes where the causal order between quantum operations is no longer well-defined. Certain such causally indefinite processes have an operational interpretation in terms of quantum operations on time-delocalised subsystems -- that is, they can take place as part of standard quantum mechanical evolutions on quantum system

Hong Cai Chen, Longchang Wu, Ming Gao, Lingrui Shen

Efficient and accurate extraction of electrical parameters from circuit datasheets and design documents is critical for accelerating circuit design in Electronic Design Automation (EDA). Traditional workflows often rely on engineers manually searching and extracting these parameters, which is time-consuming, and prone to human error. To address these challen

Boming Miao, Chunxiao Li, Xiaoxiao Wang, Andi Zhang

Diffusion models have achieved impressive success in generating photorealistic images, but challenges remain in ensuring precise semantic alignment with input prompts. Optimizing the initial noisy latent offers a more efficient alternative to modifying model architectures or prompt engineering for improving semantic alignment. A latest approach, InitNo, refi

Junqi Jiang, Tom Bewley, Saumitra Mishra, Freddy Lecue

Reward models (RMs) are a crucial component in the alignment of large language models' (LLMs) outputs with human values. RMs approximate human preferences over possible LLM responses to the same prompt by predicting and comparing reward scores. However, as they are typically modified versions of LLMs with scalar output heads, RMs are large black boxes whose

Thodoris Spanos, Fran Fabra, José A. López-Salcedo, Gonzalo Seco-Granados

This paper presents a comprehensive exploration of Angle of Arrival (AoA) estimation techniques in 5G environments, using the Sounding Reference Signal (SRS) in Uplink scenarios both in simulations and with actual measurements. Leveraging 5G capabilities, we investigate AoA algorithms for single-base station positioning. The study includes simulations and pr

Alex Gough

The cosmic large scale structure encodes the formation and evolution of a weblike network of dark matter and galaxies within the Universe. The cosmological information is wrapped up in non-Gaussian statistics requiring characterisation beyond two-point correlations. Accurate modelling of these non-Gaussian statistics and the underlying non-linear dynamics of

Jacques Giacomoni, Tuhina Mukherjee, Lovelesh Sharma

In this paper, we study a class of eigenvalue problems involving both local as well as nonlocal operators, precisely the classical Laplace operator and the fractional Laplace operator in the presence of mixed boundary conditions, that is \begin{equation} \label{1} \left\{\begin{split} \mathcal{L}u\: &= \lambda u,~~u>0~ \text{in} ~\Omega, u&=0~~\text{in} ~~{U

David Eduardo Moreno-Villamarín, Anna Hilsmann, Peter Eisert

We present a generative model that learns to synthesize human motion from limited training sequences. Our framework provides conditional generation and blending across multiple temporal resolutions. The model adeptly captures human motion patterns by integrating skeletal convolution layers and a multi-scale architecture. Our model contains a set of generativ

Daniel T. Larson, Daniel Bennett, Abduhla Ali, Anderson S. Chaves

Twistronics has received much attention as a new method to manipulate the properties of 2D van der Waals structures by introducing moir\'e patterns through a relative rotation between two layers. Here we begin a theoretical exploration of twistronics beyond the realm of van der Waals materials by developing a first-principles description of the electronic st

Thodoris Spanos, Fran Fabra, José A. López-Salcedo, Gonzalo Seco-Granados

This paper presents a novel testbed designed for 5th-Generation (5G) positioning using Universal Software Radio Peripherals (USRPs). The testbed integrates multiple units: an Operation Unit for test management, a User Unit equipped with an Ettus E312 USRP, and a Station Unit featuring an Ettus N310 USRP equipped with a three-element Uniform Linear Array for

Amy Xin, Jinxin Liu, Zijun Yao, Zhicheng Lee

Despite the outstanding capabilities of large language models (LLMs), knowledge-intensive reasoning still remains a challenging task due to LLMs' limitations in compositional reasoning and the hallucination problem. A prevalent solution is to employ chain-of-thought (CoT) with retrieval-augmented generation (RAG), which first formulates a reasoning plan by d

A. Balmaseda, D. Krejcirik, J. M. Pérez-Pardo

We introduce a relativistic version of the non-self-adjoint operator obtained by a dilation analytic transformation of the quantum harmonic oscillator. While the spectrum is real and discrete, we show that the eigenfunctions do not form a basis and that the pseudospectra are highly non-trivial.

Shenghe Zheng, Hongzhi Wang

With the rapid growth of deep learning, there is an increasing availability of open-source models for various tasks. However, single fine-tuned models often fall short of meeting the diverse needs of users. Model merging has thus emerged as an efficient method to integrate the capabilities of existing models into a unified model. Nevertheless, existing model

Jonas Luther, Patrick Jenny

Accurate simulation of turbulent flows remains a challenge due to the high computational cost of direct numerical simulations (DNS) and the limitations of traditional turbulence models. This paper explores a novel approach to augmenting standard models for Reynolds-Averaged Navier-Stokes (RANS) simulations using a Non-Linear Super-Stencil (NLSS). The propose

E. G. Santos

We derive recurrences and closed-form expressions for counting nonattacking placements of two types of chess pieces with unbounded straight-line moves, namely the bishop (two diagonal moves) and the anassa (one horizontal or vertical move and one diagonal move), placed on a standard square chessboard. Additionally, we obtain explicit expressions for the corr

Manoel Horta Ribeiro, Robert West, Ryan Lewis, Sanjay Kairam

Effective content moderation in online communities is often a delicate balance between maintaining content quality and fostering user participation. In this paper, we introduce post guidance, a novel approach to community moderation that proactively guides users' contributions using rules that trigger interventions as users draft a post to be submitted. For

Franco Flandoli, Giuseppina Guatteri, Umberto Pappalettera, Gianmario Tessitore

The paper is devoted to the optimal control of a system with two time-scales, in a regime when the limit equation is not of averaging type but, in the spirit of Wong-Zakai principle, it is a stochastic differential equation for the slow variable, with noise emerging from the fast one. It proves that it is possible to control the slow variable by acting only

Marek G. Mikulicz, Paweł Mrowiński, Paweł Holewa, Kresten Yvind

Fiber-based long-haul quantum communication would greatly benefit from a robust and deterministically integrated source of quantum state. Here, we report the design, fabrication, and optical characterization of InAs/InP quantum dots in the InP H1 point defect 2D photonic crystal cavity, integrated with the standard single-mode fiber using a micro-transfer pr

Zhen Huang, Haoyang Zou, Xuefeng Li, Yixiu Liu

This paper presents a critical examination of current approaches to replicating OpenAI's O1 model capabilities, with particular focus on the widespread but often undisclosed use of knowledge distillation techniques. While our previous work explored the fundamental technical path to O1 replication, this study reveals how simple distillation from O1's API, com

Svetlana Churina, Kokil Jaidka

Incivility on platforms such as Twitter (now X) and Reddit complicates the development of AI systems that can support productive, rhetorically sound political argumentation. We present experiments with \textit{GPT-3.5 Turbo} fine-tuned on two contrasting datasets of political discourse: high-incivility Twitter replies to U.S. Congress and low-incivility post

Yuejia Zhang, Nicholas J. Moore, Jinzi Mac Huang

Heat transfer in a fluid can be greatly enhanced by natural convection, giving rise to the nuanced relationship between the Nusselt number and Rayleigh number that has been a focus of modern fluid dynamics. Our work explores convection in an annular domain, where the geometry reinforces the large-scale circulatory flow pattern that is characteristic of natur

Srikrishna Iyer

We present our submission to the BabyLM challenge, aiming to push the boundaries of data-efficient language model pretraining. Our method builds upon deep mutual learning, introducing a student model search for diverse initialization. We address the limitation of treating students equally by formulating weighted mutual learning as a bi-level optimization pro

Halil Mutuk

The magnetic moment of a hadron is an important spectroscopic parameter that encodes valuable information about its internal structure. In this work, we systematically investigate the magnetic moments of hidden-charm pentaquark states, including the experimentally observed $P_c(4457)$ and related configurations with and without strangeness. The analysis is p

Samrith Ram

We give a new proof of a theorem of Bender, Coley, Robbins and Rumsey on counting subspaces with a given profile with respect to a simple operator. Counting such subspaces is equivalent to the problem of determining the $q$-Whittaker coefficients in the expansion of the power sum symmetric function. As a consequence we obtain a result of Chen and Tseng which

Arno Vanthieghem, Amir Levinson

In this paper, the first in a series, we present a new theoretical model for the global structure and dissipation of relativistically magnetized collisionless shock waves. Quite remarkably, we find that in contrast to unmagnetized shocks, the leading energy dissipation channel does not involve collective plasma interactions. Rather, it is a consequence of no

Weiyi Gong, Tao Sun, Hexin Bai, Jeng-Yuan Tsai

Predicting electronic band structures from crystal structures is crucial for understanding structure-property correlations in materials science. First-principles approaches are accurate but computationally intensive. Recent years, machine learning (ML) has been extensively applied to this field, while existing ML models predominantly focus on band gap predic

Jie Hu, Junwei Zheng, Jiale Wei, Jiaming Zhang

Recent advancements in the Mamba architecture, with its linear computational complexity, being a promising alternative to transformer architectures suffering from quadratic complexity. While existing works primarily focus on adapting Mamba as vision encoders, the critical role of task-specific Mamba decoders remains under-explored, particularly for distortio

Surajit Sen, Tushar Kanti Dey

We present a novel method to study the Bloch space of the qutrit system by examining the Bloch trajectories in it. Since such system is inherently a three-level quantum system, therefore we use the SU(3) group as the basis group to obtain the Bloch vectors of different configurations of it. The norm of the Bloch space is evaluated from the geometric consider

Max H. Cohen, Noel Csomay-Shanklin, William D. Compton, Tamas G. Molnar

Reduced-order models (ROMs) provide lower dimensional representations of complex systems, capturing their salient features while simplifying control design. Building on previous work, this paper presents an overarching framework for the integration of ROMs and control barrier functions, enabling the use of simplified models to construct safety-critical contr

Nesrin Akel, Guillaume Stoltz, Antoine Wautier, François Nicot

One of the challenge that face the effectiveness of Polyvinyl Chloride geomembranes (PVC GMs) as a hydraulic barrier is the capacity to withstand unexpected mechanical actions, particularly tensile forces, during installation and throughout their lifespan. These forces pose risks of premature failure and impermeability degradation. In this study, the charact

Sayan Biswas, Graham Cormode, Carsten Maple, Mary Scott

Measuring distribution drifts is a key task in managing distributed, sensitive data, as it underpins a wide range of federated learning and analytics applications. In many practical settings, however, directly sharing such information is either undesirable (e.g., due to privacy concerns) or infeasible (e.g., due to high communication costs). In this work, we

Juliette Achddou, Nicolò Cesa-Bianchi, Hao Qiu

Motivated by practical federated learning settings where clients may not be always available, we investigate a variant of distributed online optimization where agents are active with a known probability $p$ at each time step, and communication between neighboring agents can only take place if they are both active. We introduce a distributed variant of the FT

M. McCann, L. P. Mulholland, Z. Xiong, C. A. Ramsbottom

A large number of R-matrix calculations of electron impact excitation for heavy elements (Z > 70) have been performed in recent years for applications in fusion and astrophysics research. With the expanding interest in heavy ions due to kilonova (KN) events such as AT2017gfo and AT2023vfi, this new data can be utilised for the diagnosis and study of observed

Vinicius Oliveira Cavalcanti, Jérôme Roques, Denis Horlait, Eric Gilabert

A known issue for future nuclear reactors is helium accumulation inside the steel structure materials, responsible for structural issues such as embrittlement and cracking. One possible solution is using new types of reinforced steel, such as oxide dispersion strengthened (ODS) steel. It consists of adding oxide nanoparticles to the Fe-based material, especi

Rajintha Gunawardena, Zi-Qiang Lang, Fei He

System identification involves constructing mathematical models of dynamic systems using input-output data, enabling analysis and prediction of system behaviour in both time and frequency domains. This approach can model the entire system or capture specific dynamics within it. For meaningful analysis, it is essential for the model to accurately reflect the

Vladimir Kulagin, Rémi Delaporte-Mathurin, Etienne A. Hodille, Mikhail Zibrov

The open-source code FESTIM (Finite Element Simulation of Tritium In Materials) is a powerful user-friendly software for hydrogen transport simulations. Up to now, it was capable of addressing various hydrogen transport problems with surface processes dependent on the concentration of solute species. However, the kinetics of surface hydrogen concentration sh

Cedric Holme Qvistgaard, Luise Theil Kuhn, Morten Sales, Takenao Shinohara

In this paper we showcase the strengths of polarized neutron imaging as a magnetic imaging technique through a case study on field-cooled multifilamentary YBCO tape carrying a transport current while containing a trapped magnetic field. The measurements were done at J-PARC's RADEN beamline, measuring a radiograph of a single polarization component, to showca

Giuseppe Cuono, Filomena Forte, Alfonso Romano, Canio Noce

The Mott insulator Ca2RuO4 is a paradigmatic example among transition metal oxides, where the interplay of charge, spin, orbital, and lattice degrees of freedom leads to competing quantum phases. In this paper, we focus on and review some key aspects, from the underlying physical framework and its basic properties, to recent theoretical efforts that aim to t

Naomi Vogel, Andreas Zmija, Frederik Wohlleben, Gisela Anton

In recent years, intensity interferometry has been successfully applied to the Imaging Atmospheric Cherenkov Telescopes H.E.S.S. , MAGIC, and VERITAS. All three telescope systems have proven the feasibility and capability of this method. After our first campaign in 2022, when two of the H.E.S.S. telescopes in Namibia were equipped with our external setup and

Sergey Karpov

We present a simple web-based tool, STDWeb, for a quick-look photometry and transient detection in astronomical images. It tries to implement a self-consistent and mostly automatic data analysis workflow that would work on any image uploaded to it, allowing to perform basic interactive masking, do object detection, astrometrically calibrate the image, and bu

Stefan Neuwirth, Henri Lombardi, Thierry Coquand

In 1955, Paul Lorenzen is a mathematician who devotes all his research to foundations of mathematics, on a par with Hans Hermes, but his academic background is algebra in the tradition of Helmut Hasse and Wolfgang Krull. This shift from algebra to logic goes along with his discovery that his ``algebraic works [...] have been concerned with a problem that has

Yutong Wang, Jiajie Teng, Jiajiong Cao, Yuming Li

As a very common type of video, face videos often appear in movies, talk shows, live broadcasts, and other scenes. Real-world online videos are often plagued by degradations such as blurring and quantization noise, due to the high compression ratio caused by high communication costs and limited transmission bandwidth. These degradations have a particularly s

T. Felipe, S. J. González Manrique, D. Martínez-Gómez, M. M. Gómez-Míguez

In sunspot umbrae, the core of some chromospheric lines exhibits periodic brightness enhancements known as umbral flashes. The consensus is that they are produced by the upward propagation of shock waves. This view has recently been challenged by the detection of downflowing umbral flashes and the confirmation of the existence of a resonant cavity above suns

Martin Engilberge, F. Wilke Grosche, Pascal Fua

Tracking-by-detection has become the de facto standard approach to people tracking. To increase robustness, some approaches incorporate re-identification using appearance models and regressing motion offset, which requires costly identity annotations. In this paper, we propose exploiting motion clues while providing supervision only for the detections, which

Raphael Steiner

Given a graph $G$, its Hall ratio $\rho(G)=\max_{H\subseteq G}\frac{|V(H)|}{\alpha(H)}$ forms a natural lower bound on its fractional chromatic number $\chi_f(G)$. A recent line of research studied the fundamental question of whether $\chi_f(G)$ can be bounded in terms of a (linear) function of $\rho(G)$. In a breakthrough-result, Dvo\v{r}\'{a}k, Ossona de M

Aldric Labarthe, Yann Kerzreho

In this paper, we introduce a conceptual framework that model human social networks as an undirected dot-product graph of independent individuals. Their relationships are only determined by a cost-benefit analysis, i.e. by maximizing an objective function at the scale of the individual or of the whole network. On this framework, we build a new artificial net

Xia Chen, Xi Zhang, Wenjie He, Yu Li

Despite the tremendous progress in spintronic studies of van der Waals (vdW) room-temperature ferromagnet Fe3GaTe2, much less effort has been spent on its lattice dynamics and possible interaction with spintronic degrees of freedom. In this work, by combining Raman spectroscopy in a wide range of pressure (atmospheric pressure~19.5 GPa) and temperature (80~6

Satoki Ishikawa, Tal Ben-Nun, Brian Van Essen, Rio Yokota

Communication overhead is a key challenge in distributed deep learning, especially on slower Ethernet interconnects, and given current hardware trends, communication is likely to become a major bottleneck. While gradient compression techniques have been explored for SGD and Adam, the Lion optimizer has the distinct advantage that its update vectors are the o

Jonathan Louvet, Eduardo Serrano-Ensástiga, Thierry Bastin, John Martin

The equivalence between absolutely separable states and absolutely positive partial transposed (PPT) states in general remains an open problem in quantum entanglement theory. In this work, we study an analogous question for symmetric multiqubit states. We show that symmetric absolutely PPT (SAPPT) states (symmetric states that remain PPT after any symmetry-p

Shengeng Tang, Jiayi He, Lechao Cheng, Jingjing Wu

Generating continuous sign language videos from discrete segments is challenging due to the need for smooth transitions that preserve natural flow and meaning. Traditional approaches that simply concatenate isolated signs often result in abrupt transitions, disrupting video coherence. To address this, we propose a novel framework, Sign-D2C, that employs a co

Stefan Neuwirth, Henri Lombardi, Ihsen Yengui

Let M be a finitely generated submodule of a free module over a multivariate polynomial ring with coefficients in a discrete coherent ring. We prove that its module MLT(M ) of leading terms is countably generated and provide an algorithm for computing explicitly a generating set. This result is also useful when MLT(M ) is not finitely generated. Suppose that

G. Cosentino, I. Jiménez-Serra, A. T. Barnes, J. C. Tan

How Supernova Remnant (SNR) shocks impact nearby molecular clouds is still poorly observationally constrained. It is unclear if SNRs can positively or negatively affect clouds star formation potential. We have studied the dense gas morphology and kinematics toward the Infrared Dark Cloud (IRDC) G034.77-00.55, shock-interacting with the SNR W44, to identify e

Ran Elbaz, Gilad Yehudai, Meirav Galun, Haggai Maron

Reconstructing training data from trained neural networks is an active area of research with significant implications for privacy and explainability. Recent advances have demonstrated the feasibility of this process for several data types. However, reconstructing data from group-invariant neural networks poses distinct challenges that remain largely unexplor

Haoming Li

In this paper, we present a novel trajectory prediction model for autonomous driving, combining a Characterized Diffusion Module and a Spatial-Temporal Interaction Network to address the challenges posed by dynamic and heterogeneous traffic environments. Our model enhances the accuracy and reliability of trajectory predictions by incorporating uncertainty es

Evaldas Drasutis

This paper introduces a novel architecture for a distributed ledger, commonly referred to as a "blockchain", which is organized in the form of directed acyclic graph (DAG) with UTXO transactions as vertices, rather than as a chain of blocks. Consensus on the state of ledger assets is achieved through the cooperative consensus: an profit-driven behavior of to

I. Marini, P. Popesso, K. Dolag, M. Bravo

Wide-field cosmological surveys provide hundreds of thousands of spectroscopically confirmed galaxy groups and clusters, valuable for tracing baryonic matter distribution. However, controlling systematics in identifying host dark matter halos and estimating their properties is crucial. We evaluate three group detection methods on a simulated dataset replicat

Xiaocong Yang, Jiacheng Lin, Ziqi Wang, Chengxiang Zhai

Large language models (LLMs) are known to struggle with complicated reasoning tasks such as math word problems (MWPs). In this paper, we present how analogy from similarly structured questions can improve LLMs' problem-solving capabilities for MWPs. Specifically, we rely on the retrieval of problems with similar computational graphs to the given question to

Rafael A Jara-Toro, Martín Taccone, Jordan Dezalay, Jennifer Noble

Dipole-bound states in anions exist when a polar neutral core binds an electron in a diffuse orbital through charge--dipole interaction. Electronically excited polar neutral cores can also bind an electron in a diffuse orbital to form Core-Excited Dipole-Bound States (CE-DBSs), which are difficult to observe because they usually lie above the electron detach

Léonie Papon

We prove that the interface separating $+1$ and $-1$ spins in the critical planar Ising model with Dobrushin boundary conditions perturbed by an external magnetic field has a scaling limit. This result holds when the Ising model is defined on a bounded and simply connected subgraph of $\delta \mathbb{Z}^2$, with $\delta >0$. We show that if the scaling of th

Cynthia Marcelino, Stefan Nastic

Serverless computing promises a scalable, reliable, and cost-effective solution for running data-intensive applications and workflows in the heterogeneous and limited-resource environment of the Edge-Cloud Continuum. However, building and running data-intensive serverless workflows also brings new challenges that can significantly degrade the application per

Hana Haseljić, Robert Frysch, Vojtěch Kulvait, Thomas Werncke

The success of embolisation, a minimally invasive treatment of liver cancer, could be evaluated in the operational room with cone-beam CT by acquiring a dynamic perfusion scan. The reconstruction algorithm must address the issues of low temporal sampling and higher noise levels inherent in cone-beam CT systems, compared to conventional CT. Therefore, a model

Huda Mahdi, Jan Sieber, Krasimira Tsaneva-Atanasova

The Jansen-Rit model of a cortical column in the cerebral cortex is widely used to simulate spontaneous brain activity (EEG) and event-related potentials. It couples a pyramidal cell population with two interneuron populations, of which one is fast and excitatory and the other slow and inhibitory. Our paper studies the transition between alpha and delta osci

Jin Hu

We provide an exact analytical solution to the nonlinear relativistic Boltzmann equation for a homogeneous, anisotropically scattering massless gas. Utilizing a BKW-like trial solution, we cast the Boltzmann equation into a set of nonlinear coupled equations for scalar moments, based on which the analytical solution is derived. One remarkable feature of our

Jianghao Gong, Peiqi Yan, Yue Zhang, Hongli An

In the domain of large language models, considerable advancements have been attained in multimodal large language models and explainability research, propelled by the continuous technological progress and innovation. Nonetheless, security and privacy concerns continue to pose as prominent challenges in this field. The emergence of blockchain technology, mark

A. Potnis, M. Macier, T. Leusmann, D. Anton

Chloride-induced corrosion significantly contributes to the degradation of reinforced concrete structures, making accurate predictions of chloride migration and its effects on material durability critical. This paper explores two modeling approaches to estimate the effective diffusion coefficient for chloride transport. The first approach follows Gehlen's in

Jiawei Wang, Zhiming Cui, Changjian Li

This paper presents VQ-SGen, a novel algorithm for high-quality creative sketch generation. Recent approaches have framed the task as pixel-based generation either as a whole or part-by-part, neglecting the intrinsic and contextual relationships among individual strokes, such as the shape and spatial positioning of both proximal and distant strokes. To overc

Raphael Sarfati

What constitutes a "physics of firefly swarms"? In response to a Comment in Nature Reviews Physics, I offer a brief scientific perspective.

Jannik Luboeinski, Sebastian Schmitt, Shirin Shafiee, Thorsten Hater

Arbor is a software library designed for efficient simulation of large-scale networks of biological neurons with detailed morphological structures. It combines customizable neuronal and synaptic mechanisms with high-performance computing, supporting multi-core CPU and GPU systems. In humans and other animals, synaptic plasticity processes play a vital role i

R. Pearce-Casey, B. C. Nagam, J. Wilde, V. Busillo

The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg^2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen scientists alone is infeasible. Machine learning algorithms, particularly convolutional neural networks (CNNs),

B. Gyamfi

This paper statistically analysed pensioner longevity in Ghana. It fundamentally sought to ascertain the significant determinants of longevity amongst Ghanaian pensioners, specifically, SSNIT pensioners by estimating the mortality rate of SSNIT pensioners, determining the factors that significantly affect the longevity of the average SSNIT pensioner and cons

Iuliia Vetik, Nikita Žoglo, Akmal Kosimov, Ritums Cepitis

Addressing climate change calls for action to control CO$_2$ pollution. Direct air and ocean capture offer a solution to this challenge. Making carbon capture competitive with alternatives, such as forestation and mineralisation, requires fundamentally novel approaches and ideas. One such approach is electrosorption, which is currently limited by the availab

Yanfei Xiang, Weixin Jin, Haiyu Dong, Mingliang Bai

The forecasting skill of numerical weather prediction (NWP) models critically depends on the accurate initial conditions, also known as analysis, provided by data assimilation (DA). Traditional DA methods often face a trade-off between computational cost and accuracy due to complex linear algebra computations and the high dimensionality of the model, especia

Hyojun Go, Byeongjun Park, Jiho Jang, Jin-Young Kim

Text-based generation and editing of 3D scenes hold significant potential for streamlining content creation through intuitive user interactions. While recent advances leverage 3D Gaussian Splatting (3DGS) for high-fidelity and real-time rendering, existing methods are often specialized and task-focused, lacking a unified framework for both generation and edi

Kunming Shao, Fengshi Tian, Xiaomeng Wang, Jiakun Zheng

Digital Computing-in-Memory (DCIM) is an innovative technology that integrates multiply-accumulation (MAC) logic directly into memory arrays to enhance the performance of modern AI computing. However, the need for customized memory cells and logic components currently necessitates significant manual effort in DCIM design. Existing tools for facilitating DCIM

Luca Colombo, Alessandro Falcetta, Manuel Roveri

Training machine and deep learning models directly on extremely resource-constrained devices is the next challenge in the field of tiny machine learning. The related literature in this field is very limited, since most of the solutions focus only on on-device inference or model adaptation through online learning, leaving the training to be carried out on ext

Katharina Bendig, René Schuster, Nicole Thiemer, Karen Joisten

The increasing capabilities of deep neural networks for re-identification, combined with the rise in public surveillance in recent years, pose a substantial threat to individual privacy. Event cameras were initially considered as a promising solution since their output is sparse and therefore difficult for humans to interpret. However, recent advances in dee

Shyam Kumar, Jiarong Hong

Despite its potential for label-free particle diagnostics, holographic microscopy is limited by specialized processing methods that struggle to generalize across diverse settings. We introduce a deep learning architecture leveraging human perception of longitudinal variation of diffracted patterns of particles, which enables highly generalizable analysis of

Nicolas Urbani, Sylvain Rousseau, Yves Grandvalet, Leonardo Tanzi

In many large-scale classification problems, classes are organized in a known hierarchy, typically represented as a tree expressing the inclusion of classes in superclasses. We introduce a loss for this type of supervised hierarchical classification. It utilizes the knowledge of the hierarchy to assign each example not only to a class but also to all encompa

Xide Xu, Muhammad Atif Butt, Sandesh Kamath, Bogdan Raducanu

The growing demand for customized visual content has led to the rise of personalized text-to-image (T2I) diffusion models. Despite their remarkable potential, they pose significant privacy risk when misused for malicious purposes. In this paper, we propose a novel and efficient adversarial attack method, Concept Protection by Selective Attention Manipulation

R. Watteaux, J. A. Redford, A. Llor

The distribution functions of field fluctuations of the turbulent mixing layer produced by a Rayleigh-Taylor instability (RTI) have long been hypothesized to involve bimodal effects. The present work reviews existing quantitative and qualitative evidence in support this conjecture, provides an associated theoretical framework, and measures the corresponding

Lei Li, Sen Jia, Jianhao Wang, Zhongyu Jiang

This paper presents LLaMo (Large Language and Human Motion Assistant), a multimodal framework for human motion instruction tuning. In contrast to conventional instruction-tuning approaches that convert non-linguistic inputs, such as video or motion sequences, into language tokens, LLaMo retains motion in its native form for instruction tuning. This method pr

Matthias Deiml, Daniel Peterseim

This paper presents a novel framework for high-dimensional nonlinear quantum computation that exploits tensor products of amplified vector and matrix encodings to efficiently evaluate multivariate polynomials. The approach enables the solution of nonlinear equations by quantum implementations of the fixed-point iteration and Newton's method, with quantitativ

S. Fegan

This paper presents Taylor expansions for the imaging and timing characteristics of spherical optical systems with infinitesimal mirror facets, sometimes referred to as ''modified Davies-Cotton'' telescopes. Such a system comprises a discontinuous spherical mirror surface whose curvature radius is different from its focal length, and whose mirrors are aligne

Jaap Jumelet

When we speak, write or listen, we continuously make predictions based on our knowledge of a language's grammar. Remarkably, children acquire this grammatical knowledge within just a few years, enabling them to understand and generalise to novel constructions that have never been uttered before. Language models are powerful tools that create representations

V. K. Dobrev

Langlands duality is one of the most influential topics in mathematical research. It has many different appearances and influential subtopics. Yet there is a topic that until now seems unrelated to the Langlands program. That is the topic of invariant differential operators. That is strange since both items are deeply rooted in Harish-Chandra's representatio

Anna Maria Dziubyna, Tomasz Śmierzchalski, Bartłomiej Gardas, Marek M. Rams

Optimization problems pose challenges across various fields. In recent years, quantum annealers have emerged as a promising platform for tackling such challenges. To provide a new perspective, we develop a heuristic tensor network (TN) based algorithm to reveal the low-energy spectrum of Ising spin-glass systems with interaction graphs relevant to present-da

Wolfgang Flachberger, Thomas Antretter, Swaroop Gaddikere-Nagaraja, Silvia Leitner

There are various methods for modeling phase transformations in materials science, including general classes of phase-field methods and reactive diffusion methodologies, which most importantly differ in their treatment of interface energy. These methodologies appear mutually exclusive since the respective numerical schemes only allow for their primary use ca

Younes Boulaguiem, Luca Insolia, Maria-Pia Victoria-Feser, Dominique-Laurent Couturier

Multivariate (average) equivalence testing is widely used to assess whether the means of two conditions of interest are `equivalent' for different outcomes simultaneously. The multivariate Two One-Sided Tests (TOST) procedure is typically used in this context by checking if, outcome by outcome, the marginal $100(1-2\alpha$)\% confidence intervals for the dif

Christian Beck, Constantino Tsallis

Recent work [Wild et al., Nature 615, 425 (2023)] has provided an experimental break-through in the realization of a quantum-tunneling reaction involving a proton transfer. The reaction $D^-+H_2 \to H^-+HD$ has an extremely slow reaction rate as it can happen only via quantum tunneling, thus requiring an extremely large density of the reactants in the ion tr

Somjit Nath, Yik Chau Lui, Siqi Liu

Event sequence data record the occurrences of events in continuous time. Event sequence forecasting based on temporal point processes (TPPs) has been extensively studied, but outlier or anomaly detection, especially without any supervision from humans, is still underexplored. In this work, we develop, to the best our knowledge, the first unsupervised outlier

Linqing Zhong, Chen Gao, Zihan Ding, Yue Liao

The Zero-Shot Object Navigation (ZSON) task requires embodied agents to find a previously unseen object by navigating in unfamiliar environments. Such a goal-oriented exploration heavily relies on the ability to perceive, understand, and reason based on the spatial information of the environment. However, current LLM-based approaches convert visual observati

Zuhao Liu, Aleksandar Yanev, Ahmad Mahmood, Ivan Nikolov

Advances in video generation have significantly improved the realism and quality of created scenes. This has fueled interest in developing intuitive tools that let users leverage video generation as world simulators. Text-to-video (T2V) generation is one such approach, enabling video creation from text descriptions only. Yet, due to the inherent ambiguity in

Hardy Chan, Marco A. Fontelos, María del Mar González

Hermite polynomials, which are associated to a Gaussian weight and solve the Laplace equation with a drift term of linear growth, are classical in analysis and well-understood via ODE techniques. Our main contribution is to give explicit Euclidean formulae of the fractional analogue of Hermite polynomials, which appear as eigenfunctions of a L\'evy Fokker-Pl

Subhadip Basu, Oded Farago

Many ternary mixtures composed of saturated and unsaturated lipids with cholesterol (Chol) exhibit a region of coexistence between liquid-disordered $(L_d)$ and liquid-ordered $(L_o)$ domains, bearing some similarities to lipid rafts in biological membranes. However, biological rafts also contain many proteins that interact with the lipids and modify the dis

Abedin Sherifi

The aviation industry is rapidly evolving, driven by advancements in technology. Turbofan engines used in commercial aerospace are very complex systems. The majority of turbofan engine components are susceptible to degradation over the life of their operation. Turbofan engine degradation has an impact to engine performance, operability, and reliability. Pred

Asanobu Kitamoto, Erwan Dzik, Gaspar Faure

This paper presents the Digital Typhoon Dataset V2, a new version of the longest typhoon satellite image dataset for 40+ years aimed at benchmarking machine learning models for long-term spatio-temporal data. The new addition in Dataset V2 is tropical cyclone data from the southern hemisphere, in addition to the northern hemisphere data in Dataset V1. Having

Yirun Wang, Yongqing Wang, Yuyao Shen, Gongpu Wang

Channel parameter recovery is critical for the next-generation reconfigurable intelligent surface (RIS)-empowered communications and sensing. Tensor-based mechanisms are particularly effective, inherently capturing the multi-dimensional nature of wireless channels. However, existing studies assume either a line-of-sight (LOS) scenario or a blocked TX-RX chan

C. Gouiffés, C. Ng, I. Cognard, M. Dennefeld

The origin(s) of Fast Radio Bursts (FRBs), mysterious radio bursts coming from extragalactic distances, remains unknown. Multi-wavelength observations are arguably the only way to answer this question unambiguously. We attempt to detect hard X-ray/soft gamma-ray counterparts to one of the most active FRB sources, FRB20121102, as well as improve understanding

Qing Han, Jiongduo Xie

In this survey paper, we study the optimal regularity of solutions to uniformly degenerate elliptic equations in bounded domains and establish the H\"older continuity of solutions and their derivatives up to the boundary.

Claudia Drygala, Edmund Ross, Mohammad Sharifi Ghazijahani, Christian Cierpka

Numerical simulations of turbulent flows present significant challenges in fluid dynamics due to their complexity and high computational cost. High resolution techniques such as Direct Numerical Simulation (DNS) and Large Eddy Simulation (LES) are generally not computationally affordable, particularly for technologically relevant problems. Recent advances in