November 2024 arXiv papers — page 102

Calum McCartney, Eric Chen, Subhayan Roy Moulik

The butterfly velocity is commonly used to understand information transport properties in quantum dynamical systems and is related to growth of operators. Here we utilise a quantum teleportation based protocol and Riemannian Trust-Region method to estimate the butterfly velocity via the operator averaged out-of-time-order correlation function. We particularl

C. C. Finlay, J. Velímský, C. Kloss, R. M. Blangsbøll

Satellite magnetic field observations have the potential to provide valuable information on dynamics, heat content and salinity throughout the ocean. Here we present the expected spatio-temporal characteristics of the ocean-induced magnetic field at satellite altitude on periods of months to decades. We compare these to the characteristics of other sources o

Michael Wilson, Tom Needham, Anuj Srivastava

Wasserstein distances form a family of metrics on spaces of probability measures that have recently seen many applications. However, statistical analysis in these spaces is complex due to the nonlinearity of Wasserstein spaces. One potential solution to this problem is Linear Optimal Transport (LOT). This method allows one to find a Euclidean embedding, call

Yu Ren, Yang Cong, Ronghan Chen, Jiahao Long

Learning robust and generalizable manipulation skills from demonstrations remains a key challenge in robotics, with broad applications in industrial automation and service robotics. While recent imitation learning methods have achieved impressive results, they often require large amounts of demonstration data and struggle to generalize across different spati

Manuel Dias, David Tewodrose

The symmetrized Asymptotic Mean Value Laplacian $\tilde{\Delta}$, obtained as limit of approximating operators $\tilde{\Delta}_r$, is an extension of the classical Euclidean Laplace operator to the realm of metric measure spaces. We show that, as $r \downarrow 0$, the operators $\tilde{\Delta}_r$ eventually admit isolated eigenvalues defined via min-max proc

Raul Hindov, Shahaf Nitzan, Jan-Fredrik Olsen, Eskil Rydhe

We obtain sharp embeddings from the Sobolev space $W^{k,2}_0(-1,1)$ into the space $L^1(-1,1)$ and determine the extremal functions. This improves on a previous estimate of the sharp constants of these embeddings due to Kalyabin.

Kosuke Iwama, Ryugo Morita, Jinjia Zhou

Compressive sensing (CS), acquiring and reconstructing signals below the Nyquist rate, has great potential in image and video acquisition to exploit data redundancy and greatly reduce the amount of sampled data. To further reduce the sampled data while keeping the video quality, this paper explores the temporal redundancy in video CS and proposes a block bas

Chun Kit Dennis Law, József Zsolt Bernád

The formalism of quantum estimation theory with a specific focus on classical data postprocessing is applied to a two-level system driven by an external gyrating magnetic field. We employed both Bayesian and frequentist approaches to estimate the unknown transition frequency. In the frequentist approach, we have shown that only reducing the distance between

Andrea Alfarano, Alberto Alfarano, Linda Friso, Andrea Bacciu

Spatio-Temporal predictive Learning is a self-supervised learning paradigm that enables models to identify spatial and temporal patterns by predicting future frames based on past frames. Traditional methods, which use recurrent neural networks to capture temporal patterns, have proven their effectiveness but come with high system complexity and computational

Nico Roos

In many situations humans have to reason with inconsistent knowledge. These inconsistencies may occur due to not fully reliable sources of information. In order to reason with inconsistent knowledge, it is not possible to view a set of premisses as absolute truths as is done in predicate logic. Viewing the set of premisses as a set of assumptions, however, i

Anna Blin, Alexander Kolar, Andrew Kamen, Qian Lin

The obtention of quantum-grade rare-earth doped oxide thin films that can be integrated with optical cavities and microwave resonators is of great interest for the development of scalable quantum devices. Among the different growth methods, Chemical Vapour Deposition (CVD) offers high flexibility and has demonstrated the ability to produce oxide films hostin

Yufei Wei, Sha Lu, Fuzhang Han, Rong Xiong

Monocular visual odometry (MVO) is vital in autonomous navigation and robotics, providing a cost-effective and flexible motion tracking solution, but the inherent scale ambiguity in monocular setups often leads to cumulative errors over time. In this paper, we present BEV-ODOM, a novel MVO framework leveraging the Bird's Eye View (BEV) Representation to addr

Zhe Chen, Yushan Pan

Recently, by studying an explicit basis, K\"ock and Laurent give the decomposition of the $\overline{\mathbb{F}}_q[\mathrm{SL}_2(\mathbb{F}_q)]$-module of holomorphic forms on the Drinfeld curve. We present a crystalline cohomological proof of a weaker version of this result, without specifying a basis. As a by-product we observe a similar decomposition for

Rishit Kapoor, Jesher Joshua, Muralidharan Vijayarangan, Natarajan B

This research work introduces a novel approach to the classification of Alzheimer's disease by using the advanced deep learning techniques combined with secure data processing methods. This research work primary uses transfer learning models such as ResNet, ImageNet, and VNet to extract high-level features from medical image data. Thereafter, these pre-train

Christos Koutlis, Symeon Papadopoulos

Deepfake technology has rapidly advanced and poses significant threats to information integrity and trust in online multimedia. While significant progress has been made in detecting deepfakes, the simultaneous manipulation of audio and visual modalities, sometimes at small parts or in subtle ways, presents highly challenging detection scenarios. To address t

Wo Meijer, Jacky Bourgeois, Tilman Dingler, Gerd Kortuem

Designers often engage with video to gain rich, temporal insights about the context of users, collaboratively analyzing it to gather ideas, challenge assumptions, and foster empathy. To capture the full visual context of users and their situations, designers are adopting 360$^\circ$ video, providing richer, more multi-layered insights. Unfortunately, the sph

Fenghua Ling, Kang Chen, Jiye Wu, Tao Han

Seamless forecasting that produces warning information at continuum timescales based on only one system is a long-standing pursuit for weather-climate service. While the rapid advancement of deep learning has induced revolutionary changes in classical forecasting field, current efforts are still focused on building separate AI models for weather and climate

Jack Burkart, Alastair N. Fletcher, Daniel A. Nicks

We construct a quasiregular mapping in $\mathbb{R}^3$ that is the first to illustrate several important dynamical properties: the quasi-Fatou set contains wandering components; these quasi-Fatou components are bounded and hollow; and the Julia set has components that are genuine round spheres. The key tool in this construction is a new quasiregular interpola

Chenhao Li, Taishi Ono, Takeshi Uemori, Sho Nitta

Recent inverse rendering methods have greatly improved shape, material, and illumination reconstruction by utilizing polarization cues. However, existing methods only support dielectrics, ignoring conductors that are found everywhere in life. Since conductors and dielectrics have different reflection properties, using previous conductor methods will lead to

Dhruv Nevatia, Si Liu, David Basin

The high complexity of DNS poses unique challenges for ensuring its security and reliability. Despite continuous advances in DNS testing, monitoring, and verification, protocol-level defects still give rise to numerous bugs and attacks. In this paper, we provide the first decision procedure for the DNS verification problem, establishing its complexity as $\m

Hanzhong Guo, Jianfeng Zhang, Cheng Zou, Jun Li

Image-based virtual try-on, widely used in online shopping, aims to generate images of a naturally dressed person conditioned on certain garments, providing significant research and commercial potential. A key challenge of try-on is to generate realistic images of the model wearing the garments while preserving the details of the garments. Previous methods f

ATLAS Collaboration

Top-quark pair production is observed in lead-lead (Pb+Pb) collisions at $\sqrt{s_\mathrm{NN}}=5.02$ TeV at the Large Hadron Collider with the ATLAS detector. The data sample was recorded in 2015 and 2018, amounting to an integrated luminosity of 1.9 nb$^{-1}$. Events with exactly one electron and one muon and at least two jets are selected. Top-quark pair p

Alberto Presta, Enzo Tartaglione, Attilio Fiandrotti, Marco Grangetto

Learned progressive image compression is gaining momentum as it allows improved image reconstruction as more bits are decoded at the receiver. We propose a progressive image compression method in which an image is first represented as a pair of base-quality and top-quality latent representations. Next, a residual latent representation is encoded as the eleme

Valeria Jannelli, Stefan Schoepf, Matthias Bickel, Torbjørn Netland

This paper explores how Large Language Models (LLMs) can automate consensus-seeking in supply chain management (SCM), where frequent decisions on problems such as inventory levels and delivery times require coordination among companies. Traditional SCM relies on human consensus in decision-making to avoid emergent problems like the bullwhip effect. Some rout

Mizuki Miyamoto, Ryugo Morita, Jinjia Zhou

Text-to-image generation and text-guided image manipulation have received considerable attention in the field of image generation tasks. However, the mainstream evaluation methods for these tasks have difficulty in evaluating whether all the information from the input text is accurately reflected in the generated images, and they mainly focus on evaluating t

Martin Grohe

We give an overview of different approaches to measuring the similarity of, or the distance between, two graphs, highlighting connections between these approaches. We also discuss the complexity of computing the distances.

Kai Li, Yi Ling, Peng Liu, Meng-He Wu

We construct a holographic model to study the striped superconductor on ionic lattices. This model features a phase diagram with three distinct phases, namely the charge density wave (CDW) phase, ordinary superconducting phase (SC) and the striped superconducting phase (SSC). The effect of the ionic lattices on the phase diagram is investigated in detail. Fi

Siddharth Roheda, Rohit Chowdhury, Aniruddha Bala, Rohan Jaiswal

We propose a novel Auto-Regressive (AR) image generation approach that models images as hierarchical compositions of interpretable visual layers. While AR models have achieved transformative success in language modeling, replicating this success in vision tasks remains challenging due to inherent spatial dependencies in images. Addressing the unique challeng

Anurag Bishnoi, István Tomon

A strong $s$-blocking set in a projective space is a set of points that intersects each codimension-$s$ subspace in a spanning set of the subspace. We present an explicit construction of such sets in a $(k - 1)$-dimensional projective space over $\mathbb{F}_q$ of size $O_s(q^s k)$, which is optimal up to the constant factor depending on $s$. This also yields

Liang Zhang, Danlan Huang, Xinyi Zhou, Feng Ding

Recent developments in Deep learning based Joint Source-Channel Coding (DeepJSCC) have demonstrated impressive capabilities within wireless semantic communications system. However, existing DeepJSCC methodologies exhibit limited generalization ability across varying channel conditions, necessitating the preparation of multiple models. Optimal performance is

Marziogiuseppe Gentile, Marius Gerlach, Robert Richter, Michiel J. van Setten

The dissociative photoionization of \textit{tert}-butyl methyl methacrylate, a monomer unit found in many ESCAP resists, was investigated in a gas phase photoelectron photoion coincidence experiment employing extreme ultraviolet (EUV) synchrotron radiation at 13.5 nm. It was found that the interaction of EUV photons with the molecules leads almost exclusivel

Marco Matarese, Francesco Rea, Katharina J. Rohlfing, Alessandra Sciutti

Collaborative decision-making with artificial intelligence (AI) agents presents opportunities and challenges. While human-AI performance often surpasses that of individuals, the impact of such technology on human behavior remains insufficiently understood, primarily when AI agents can provide justifiable explanations for their suggestions. This study compare

Moritz Schneider, Robert Krug, Narunas Vaskevicius, Luigi Palmieri

Visual Reinforcement Learning (RL) methods often require extensive amounts of data. As opposed to model-free RL, model-based RL (MBRL) offers a potential solution with efficient data utilization through planning. Additionally, RL lacks generalization capabilities for real-world tasks. Prior work has shown that incorporating pre-trained visual representations

Benoit Coqueret, Mathieu Carbone, Olivier Sentieys, Gabriel Zaid

During the past decade, Deep Neural Networks (DNNs) proved their value on a large variety of subjects. However despite their high value and public accessibility, the protection of the intellectual property of DNNs is still an issue and an emerging research field. Recent works have successfully extracted fully-connected DNNs using cryptanalytic methods in har

Rotem Ben Zion, Boaz Carmeli, Orr Paradise, Yonatan Belinkov

When artificial agents are jointly trained to perform collaborative tasks using a communication channel, they develop opaque goal-oriented communication protocols. Good task performance is often considered sufficient evidence that meaningful communication is taking place, but existing empirical results show that communication strategies induced by common obj

Houssam Razouk, Leonie Benischke, Daniel Garber, Roman Kern

The extraction of causal information from textual data is crucial in the industry for identifying and mitigating potential failures, enhancing process efficiency, prompting quality improvements, and addressing various operational challenges. This paper presents a study on the development of automated methods for causal information extraction from actual indu

Anant Garg, K Madhava Krishna

World Model-based Reinforcement Learning (WMRL) enables sample efficient policy learning by reducing the need for online interactions which can potentially be costly and unsafe, especially for autonomous driving. However, existing world models often suffer from low prediction fidelity and compounding one-step errors, leading to policy degradation over long h

Piotr Spieker, Nick Le Large, Martin Lauer

This paper introduces an extension to the arbitration graph framework designed to enhance the safety and robustness of autonomous systems in complex, dynamic environments. Building on the flexibility and scalability of arbitration graphs, the proposed method incorporates a verification step and structured fallback layers in the decision-making process. This

Zewei Lin, Jiachi Chen, Jiajing Wu, Weizhe Zhang

In recent years, security incidents stemming from centralization defects in smart contracts have led to substantial financial losses. A centralization defect refers to any error, flaw, or fault in a smart contract's design or development stage that introduces a single point of failure. Such defects allow a specific account or user to disrupt the normal opera

Saskia Redgate, Andrew M. Bean, Adam Mahdi

The growing capabilities of large language models (LLMs) have led to their use as substitutes for human feedback for training and assessing other LLMs. These methods often rely on `constitutions', written guidelines which a critic model uses to provide feedback and improve generations. We investigate how the choice of constitution affects feedback quality by

Haiyan Wang

Stochastic models play an essential role in accounting for the variability and unpredictability seen in real-world. This paper focuses on the application of the gamma distribution to analysis of the stationary distributions of populations governed by the discrete stochastic logistic equation at equilibrium. It is well known that the population dynamics of de

R. Camassa, G. Falqui, G. Ortenzi, M. Pedroni

By means of the Hamiltonian approach to two-dimensional wave motions in heterogeneous fluids proposed by Benjamin, we derive a natural Hamiltonian structure for ideal fluids, density stratified in four homogenous layers, constrained in a channel of fixed total height and infinite lateral length. We derive the Hamiltonian and the equations of motion in the di

Zhisheng Xiong, Bo Zeng, Peter Palensky, Pedro P. Vergara

This paper presents a confidence level-based distributionally information gap decision theory (CL-DIGDT) framework for the two-stage robust optimal operation of distribution networks, aiming at deriving an optimal operational scheme capable of addressing asymmetric uncertainties related to renewable energy and load demands. Building on conventional IGDT, the

Yudong Liu, Chenglong Ma, Xiecheng Nie, Xiaoyu Qu

Let $\overline X$ be a smooth rigid variety over $C=\mathbb C_p$ admitting a lift $X$ over $B_{dR}^+$. In this paper, we use the stacky language to prove a nilpotent $p$-adic Riemann-Hilbert correspondence. After introducing the moduli stack of $\mathbb B^+_{dR}$-local systems and $t$-connections, we prove that there is an equivalence of the nilpotent locus

Thomas Lips, Francis wyffels

Building generic robotic manipulation systems often requires large amounts of real-world data, which can be dificult to collect. Synthetic data generation offers a promising alternative, but limiting the sim-to-real gap requires significant engineering efforts. To reduce this engineering effort, we investigate the use of pretrained text-to-image diffusion mo

Yutao Hou, Yajing Luo, Zhiwen Ruan, Hongru Wang

Large language models (LLMs) demonstrate remarkable performance across various tasks, prompting researchers to develop diverse evaluation benchmarks. However, most benchmarks typically measure the ability of LLMs to respond to individual questions, neglecting the complex interactions in real-world applications. We introduce Compound Question Synthesis (CQ-Sy

Martin Leskovec, Sagar Zade, Mehdi Niazi, Pedro Costa

Suspensions of finite-size solid particles in a turbulent pipe flow are found in many industrial and technical flows. Due to the ample parameter space consisting of particle size, concentration, density and Reynolds number, a complete picture of the particle-fluid interaction is still lacking. Pressure drop predictions are often made using viscosity models o

Zewen Chen, Juan Wang, Wen Wang, Sunhan Xu

Existing Image Quality Assessment (IQA) methods achieve remarkable success in analyzing quality for overall image, but few works explore quality analysis for Regions of Interest (ROIs). The quality analysis of ROIs can provide fine-grained guidance for image quality improvement and is crucial for scenarios focusing on region-level quality. This paper propose

Esmaeil Shahryari, Maria Cecilia Poletti, Dalibor Preisler, Petr Harcuba

Near-beta titanium alloys have shown low Young's modulus and good strength, making them excellent implant candidates. However, their processing using thermomechanical routes in single phase beta region results in heterogeneous microstructures due to high content alloying elements and consequent slow diffusion-controlled processes such as dynamic recovery. Th

Loïc Bourdin, Fabien Caubet, Aymeric Jacob de Cordemoy

The aim of this work is to analyse a shape optimization problem in a mechanical friction context. Precisely we perform a shape sensitivity analysis of a Tresca friction problem, that is, a boundary value problem involving the usual linear elasticity equations together with the (nonsmooth) Tresca friction law on a part of the boundary. We prove that the solut

Libo Wang

To address the sycophancy problem caused by reinforcement learning from human feedback in large language models, this research applies synthetic data intervention technology to the decoder-only transformer architecture. Based on the research gaps in the existing literature, the researcher designed an experimental process to reduce the tendency of models to c

G. Thomas Hudson, Dean Slack, Thomas Winterbottom, Jamie Sterling

Multimodal learning, which involves integrating information from various modalities such as text, images, audio, and video, is pivotal for numerous complex tasks like visual question answering, cross-modal retrieval, and caption generation. Traditional approaches rely on modality-specific encoders and late fusion techniques, which can hinder scalability and

Stefan Forste, Saurabh Natu

We identify half-wormhole contributions to the non averaged $\mathscr{N} =1$ supersymmetric SYK model in which time has been reduced to a point. As in previously studied examples, the inclusion of half-wormholes restores factorisation in the large $N$ limit. Wormholes as well as half-wormholes break supersymmetry.

Anish Dhir, Ruby Sedgwick, Avinash Kori, Ben Glocker

Current causal discovery approaches require restrictive model assumptions in the absence of interventional data to ensure structure identifiability. These assumptions often do not hold in real-world applications leading to a loss of guarantees and poor performance in practice. Recent work has shown that, in the bivariate case, Bayesian model selection can gr

Gerardo Duran-Martin, Leandro Sánchez-Betancourt, Alexander Y. Shestopaloff, Kevin Murphy

We propose a unifying framework for methods that perform probabilistic online learning in non-stationary environments. We call the framework BONE, which stands for generalised (B)ayesian (O)nline learning in (N)on-stationary (E)nvironments. BONE provides a common structure to tackle a variety of problems, including online continual learning, prequential fore

Michael Mayr, Georgios C. Chasparis, Josef Küng

The process industry's high expectations for Digital Twins require modeling approaches that can generalize across tasks and diverse domains with potentially different data dimensions and distributional shifts i.e., Foundational Models. Despite success in natural language processing and computer vision, transfer learning with (self-) supervised signals for pr

Qingwei Jiang, Mingqing Liu, Mengyuan Xu, Wen Fang

Due to power attenuation, improving transmission efficiency in the radio-frequency (RF) band remains a significant challenge, which hinders advancements in various fields of the Internet of Things (IoT), such as wireless power transfer (WPT) and wireless communication. Array design and retro-directive beamforming (RD-BF) techniques offer simple and effective

Anton Sergeev, Victor Minchenkov, Aleksei Soldatov, Vasiliy Kakurin

Various technologies, including computer vision models, are employed for the automatic monitoring of manual assembly processes in production. These models detect and classify events such as the presence of components in an assembly area or the connection of components. A major challenge with detection and classification algorithms is their susceptibility to

Keiko I. Nagao, Tatsuhiro Naka, Takaaki Nomura

This study explores a two-component dark matter model in which one component, heavier dark matter, annihilates into a lighter dark matter. The lighter dark matter is expected to generate detectable signals in detectors due to its enhanced momentum, enabling direct detection even for MeV-scale dark matter. We investigate the effectiveness of directional direc

Zijian Ge, Jingjing Jiang, Matthew Coombes

The application of Multiple Unmanned Aerial Vehicles (Multi-UAV) in Wilderness Search and Rescue (WiSAR) significantly enhances mission success due to their rapid coverage of search areas from high altitudes and their adaptability to complex terrains. This capability is particularly crucial because time is a critical factor in searching for a lost person in

Rui-Chun Xiao, Hui Li, Hui Han, Wei Gan

Recently, the altermagnets, a new kind of collinear antiferromagnet with nearly zero net magnetization and momentum-dependent spin-splitting of bands, have sparked great interest. Despite simple magnetic structures, these altermagnets exhibit intriguing and intricate dependence of anomalous Hall effect (AHE) on the N\'eel vector, in contrast to the conventio

Alex V. Plyukhin

The heat exchange fluctuation theorem (XFT) by Jarzynski and W\'ojcik [Phys. Rev. Lett. 92, 230602 (2004)] addresses the setting where two systems with different temperatures are brought in thermal contact at time $t=0$ and then disconnected at later time $\tau$. The theorem asserts that the probability of an anomalous heat flux (from cold to hot), while non

Yijiong Yu

Large Language Models (LLMs) have demonstrated remarkable capabilities in handling long texts and have almost perfect performance in traditional retrieval tasks. However, their performance significantly degrades when it comes to numerical calculations in the long-context. Numeric-involved long-context tasks typically cannot be addressed by current LLMs in no

Xujun Wei, Feng Zhang, Renhe Zhang, Wenwen Li

In the past few years, Artificial Intelligence (AI)-based weather forecasting methods have widely demonstrated strong competitiveness among the weather forecasting systems. However, these methods are insufficient for high-spatial-resolution short-term nowcasting within 6 hours, which is crucial for warning short-duration, mesoscale and small-scale weather ev

Jianhua Gao, Bingjie Liu, Yizhuo Wang, Weixing Ji

Owing to the diverse scales and varying distributions of sparse matrices arising from practical problems, a multitude of choices are present in the design and implementation of sparse matrix-vector multiplication (SpMV). Researchers have proposed many machine learning-based optimization methods for SpMV. However, these efforts only support one area of sparse

Fernando Luis Neves, Jose Nuno Oliveira

Computer scientists Jeannette Wing and Simon Peyton Jones have catalyzed a pivotal discussion on the need to introduce computing in K-12 mandatory education. In Wing's own words, computing 'represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.'' The crux of this educational endeavo

Marvin Kahra, Michael Breuß, Andreas Kleefeld, Martin Welk

Mathematical morphology is a part of image processing that uses a window that moves across the image to change certain pixels according to certain operations. The concepts of supremum and infimum play a crucial role here, but it proves challenging to define them generally for higher-dimensional data, such as colour representations. Numerous approaches have t

Hermann Stolte, Jonas Sinapius, Iftach Sadeh, Elisa Pueschel

Blazars are a subclass of active galactic nuclei with relativistic jets pointing toward the observer. They are notable for their flux variability at all observed wavelengths and timescales. Together with simultaneous measurements at lower energies, the very-high-energy (VHE) emission observed during blazar flares may be used to probe the population of accele

Alfred Müller

The basic principle of any version of insurance is the paradigm that exchanging risk by sharing it in a pool is beneficial for the participants. In case of independent risks with a finite mean this is the case for risk averse decision makers. The situation may be very different in case of infinite mean models. In that case it is known that risk sharing may h

Peter Gersing, Mark Doll, Joerg Huschke, Jan Plachy

Integrating sensing functionality into 6G communication networks requires some changes to existing components as well as new entities processing the radar sensing signals received by the communication antennas. This whitepaper provides a comprehensive overview of the 6G design proposal for ISaC (Integrated Sensing and Communication). The whitepaper has been

Jiaqi Wang, Huan Zhao, Zhenyuan Yang, Peng Shu

In this paper, we review legal testing methods based on Large Language Models (LLMs), using the OPENAI o1 model as a case study to evaluate the performance of large models in applying legal provisions. We compare current state-of-the-art LLMs, including open-source, closed-source, and legal-specific models trained specifically for the legal domain. Systemati

Yihang Fu, Ziyang Chen, Yiwen Ye, Xingliang Lei

Medical images often exhibit distribution shifts due to variations in imaging protocols and scanners across different medical centers. Domain Generalization (DG) methods aim to train models on source domains that can generalize to unseen target domains. Recently, the segment anything model (SAM) has demonstrated strong generalization capabilities due to its

José A. Adell, Daniel Cárdenas-Morales, Antonio J. López-Moreno

Let $f$ be a real function defined on the interval $[0,1]$ which is constant on $(a,b)\subset [0,1]$, and let $B_nf$ be its associated $n$th Bernstein polynomial. We prove that, for any $x\in (a,b)$, $|B_nf(x)-f(x)|$ converges to $0$ as $n\rightarrow \infty $ at an exponential rate of decay. Moreover, we show that this property is no longer true at the bound

J. C. Naranjo, A. Ortega, G. P. Pirola, I. Spelta

We study an explicit $(2g-1)$-dimensional family of Jacobian varieties of dimension $\frac{d-1}2(g-1)$, arising from quotient curves of unramified cyclic coverings of prime degree $d$ of hyperelliptic curves of genus $g\ge 2$. By using a deformation argument, we prove that the generic element of the family is simple. Furthermore, we completely describe their

Xiaobin Deng, Changyu Diao, Min Li, Ruohan Yu

3D Gaussian Splatting (3DGS) has demonstrated outstanding performance in novel view synthesis, achieving a balance between rendering quality and real-time performance. 3DGS employs Adaptive Density Control (ADC) to increase the number of Gaussians. However, the clone and split operations within ADC are not sufficiently efficient, impacting optimization speed

Hardik Gajera, Akhil Reddy, Bhagath Reddy

The Web3 ecosystem is highly fragmented, making seamless integration difficult for over a billion Web2 businesses, enterprises, and AI protocols. As blockchains, rollups, and app-specific chains expand, cross-chain interactions remain inefficient, and liquidity is deeply fragmented. AI systems lack standardized blockchain access, limiting autonomous function

Ben Andrews

In this paper, we analyse a misere tree searching game, where players take turns to guess vertices in a tree with a secret `poisoned' vertex. After each turn, the guessed vertex is removed from the tree and the game continues on the component containing the poisoned vertex, and as soon as a player guesses the poisoned vertex, they lose. We describe and prove

Jiaojian Shi, Christian Heide, Haowei Xu, Yuejun Shen

Important advances have recently been made in the search for materials with complex multi-phase landscapes that host photoinduced metastable collective states with exotic functionalities. In almost all cases so far, the desired phases are accessed by exploiting light-matter interactions via the imaginary part of the dielectric function through above-bandgap

Yushen Zuo, Jun Xiao, Kin-Chung Chan, Rongkang Dong

The stylization of 3D scenes is an increasingly attractive topic in 3D vision. Although image style transfer has been extensively researched with promising results, directly applying 2D style transfer methods to 3D scenes often fails to preserve the structural and multi-view properties of 3D environments, resulting in unpleasant distortions in images from di

Md. Asif Haider, Ayesha Binte Mostofa, Sk. Sabit Bin Mosaddek, Anindya Iqbal

Generating accurate code review comments remains a significant challenge due to the inherently diverse and non-unique nature of the task output. Large language models pretrained on both programming and natural language data tend to perform well in code-oriented tasks. However, large-scale pretraining is not always feasible due to its environmental impact and

Sagar Ghosh, Kushal Bose, Swagatam Das

The emergence of Deep Convolutional Neural Networks (DCNNs) has been a pervasive tool for accomplishing widespread applications in computer vision. Despite its potential capability to capture intricate patterns inside the data, the underlying embedding space remains Euclidean and primarily pursues contractive convolution. Several instances can serve as a pre

Sonja Gombar, Milica Rutonjski, Petar Mali, Slobodan Radošević

In this paper certain classes of infinite sums involving special functions are evaluated analytically by application of basic quantum mechanical principles to simple models of half harmonic oscillator and a particle trapped inside an infinite potential well. The infinite sums $\sum^{\infty}_{n=0}\frac{2^{2n}}{(2n+1)!}\Gamma^{2}\left(n+\frac{3}{2}\right)\left

Paolo De Angelis, Giovanni Trezza, Giulio Barletta, Pietro Asinari

Artificial Intelligence (AI) in materials science is driving significant advancements in the discovery of advanced materials for energy applications. The recent GNoME protocol identifies over 380,000 novel stable crystals. From this, we identify over 33,000 materials with potential as energy materials forming the Energy-GNoME database. Leveraging Machine Lea

S. Giardiello, A. J. Duivenvoorden, E. Calabrese, G. Galloni

We investigate the impact of beam chromaticity, i.e., the frequency dependence of the beam window function, on cosmological and astrophysical parameter constraints from CMB power spectrum observations. We show that for future high-resolution CMB measurements it is necessary to include a color-corrected beam for each sky component with a distinct spectral ene

Benoît Blossier, Jochen Heitger, Jan Neuendorf, Teseo San José

Estimating decay parameters in lattice simulations is a computationally demanding problem, requiring several volumes and momenta. We explore an alternative approach, where the transition amplitude can be extracted from the spectral decomposition of particular ratios built from correlation functions. This so-called ratio method has the advantage of not needin

Bo Thomsen, Yuki Nagai, Keita Kobayashi, Ikutaro Hamada

The introduction of machine learned potentials (MLPs) has greatly expanded the space available for studying Nuclear Quantum Effects computationally with ab initio path integral (PI) accuracy, with the MLPs' promise of an accuracy comparable to that of ab initio at a fraction of the cost. One of the challenges in development of MLPs is the need for a large an

Paavo Sattler, Markus Pauly, Merle Munko

Comparing the mean vectors across different groups is a cornerstone in the realm of multivariate statistics, with quadratic forms commonly serving as test statistics. However, when the overall hypothesis is rejected, identifying specific vector components or determining the groups among which differences exist requires additional investigations. Conversely,

Boris Chorny, David White

Building on work of Marta Bunge in the one-categorical case, we characterize when a given model category is Quillen equivalent to a presheaf category with the projective model structure. This involves introducing a notion of homotopy atoms, generalizing the orbits of Dwyer and Kan. Apart from the orbit model structures of Dwyer and Kan, our examples include

Antonio Iannizzotto, Vasile Staicu, Vincenzo Vespri

We study a Dirichlet problem driven by the (degenerate or singular) fractional $p$-Laplacian and involving a $(p-1)$-superlinear reaction at infinity, not necessarily satisfying the Ambrosetti-Rabinowitz condition. Using critical point theory, truncation, and Morse theory, we prove the existence of at least three nontrivial solutions to the problem.

Surender K. Jain, André Leroy, Ajit Iqbal Singh

Let $X$ be a Banach space and $\mathcal A$ be the Banach algebra $B(X)$ of bounded (i.e. continuous) linear transformations (to be called operators) on $X$ to itself. Let $\mathcal E$ be the set of idempotents in $\mathcal A$ and $\mathcal S$ be the semigroup generated by $\mathcal E$ under composition as multiplication. If $T\in \mathcal S$ with $0\ne T\ne

Venelin P. Pavlov, Peter A. Ivanov

We propose a spin-motion state for high-precision quantum metrology with super-Heisenberg scaling of the parameter estimation uncertainty using a trapped ion system. Such a highly entangled state can be created using the Tavis-Cummings Hamiltonian which describes the interaction between a collective spin system and a single vibrational mode. Our method relie

Qiyang Sun, Alican Akman, Björn W. Schuller

The continuous development of artificial intelligence (AI) theory has propelled this field to unprecedented heights, owing to the relentless efforts of scholars and researchers. In the medical realm, AI takes a pivotal role, leveraging robust machine learning (ML) algorithms. AI technology in medical imaging aids physicians in X-ray, computed tomography (CT)

Aurélie Lebrun, Michel Bottlaender, Julien Lagarde, Marie Sarazin

Longitudinal analyses are increasingly used in clinical studies as they allow the study of subtle changes over time within the same subjects. In most of these studies, it is necessary to align all the images studied to a common reference by registering them to a template. In the study of white matter using the recently developed fixel-based analysis (FBA) me

Léo Dana, Muni Sreenivas Pydi, Yann Chevaleyre

Recent research has explored the memorization capacity of multi-head attention, but these findings are constrained by unrealistic limitations on the context size. We present a novel proof for language-based Transformers that extends the current hypothesis to any context size. Our approach improves upon the state-of-the-art by achieving more effective exact m

Darius Jurčiukonis, Luís Lavoura

We study the extension of the Standard Model (SM) by introducing a scalar multiplet with arbitrary isospin $J$ and hypercharge $Y$. We explicitly consider various possible values of the weak isospin $J$, up to and including $J=7/2$. The mass differences among the components of the multiplet originate from its coupling to the Higgs doublet of the SM, as prese

Conrado da Costa, Debleena Thacker, Andrew Wade

We study internal diffusion limited aggregation on $\mathbb{Z}$, where a cluster is grown incrementally by adding, for each random walk dispatched from the origin, the first site it reaches outside the cluster. We assume that the increment distribution $X$ of the driving random walks has $\mathbb{E} X =0$, but need neither be simple nor symmetric, and can ha

Semyon Churilov

Long-distance transmission of energy by waves is a key mechanism for many natural processes. It becomes possible when the inhomogeneous medium is arranged in such a manner that it enables a specific type of waves to propagate with virtually no reflection or scattering. If the corresponding wave equation admits factorization, at least one of the waves it desc

Mathis Koroglu, Hugo Caselles-Dupré, Guillaume Jeanneret Sanmiguel, Matthieu Cord

We consider the problem of text-to-video generation tasks with precise control for various applications such as camera movement control and video-to-video editing. Most methods tacking this problem rely on providing user-defined controls, such as binary masks or camera movement embeddings. In our approach we propose OnlyFlow, an approach leveraging the optic

Frédéric Déglise, Rakesh Pawar

With the aim of understanding Morel's result on the $\mathbb{A}^1$-homotopy sheaves over a field, we extend the theory of unstable spectral sequences of Bousfield and Kan in the $\infty$-categorical setting. With this natural extension, parallel to the classical formalism of cohomology theory with supports, we introduce the notion of cohomotopy theory with s

K. Koleva, R. Chandra, P. Duchlev, P. Devi

We present the observations of a quiescent filament eruption and its deflection from the radial direction. The event occurred in the southern solar hemisphere on 2021 May 9 and was observed by the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO), by the STEREO A Observatory and GONG. Part of the filament erupted in the west di