April 2024 arXiv papers — page 70

Zarif Ahmed, Chowdhury Nur E Alam Siddiqi, Fardifa Fathmiul Alam, Tasnim Ahmed

Nuclei instance segmentation is crucial in oncological diagnosis and cancer pathology research. H&E stained images are commonly used for medical diagnosis, but pre-processing is necessary before using them for image processing tasks. Two principal pre-processing methods are formalin-fixed paraffin-embedded samples (FFPE) and frozen tissue samples (FS). While

Matthias Rakotomalala

In this paper, we establish a result for existence and uniqueness of stochastic differential equations on Riemannian manifolds, for regular inhomogeneous tensor coefficients with stochastic drift, under geometrical hypothesis on the manifold, so-called manifolds of bounded geometry. Furthermore, we provide stochastic flow estimates for the solutions.

Mateusz Daniol, Daria Hemmerling, Jakub Sikora, Pawel Jemiolo

Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally. This research aims to develop a system leveraging Mixed Reality capabilities for tracking and assessing eye movements. In this paper, we present a medical scenario and outline the development of an application designed to capture eye-tracking signals through Mixed Rea

Ayush Chopra, Arnau Quera-Bofarull, Nurullah Giray-Kuru, Michael Wooldridge

The practical utility of agent-based models in decision-making relies on their capacity to accurately replicate populations while seamlessly integrating real-world data streams. Yet, the incorporation of such data poses significant challenges due to privacy concerns. To address this issue, we introduce a paradigm for private agent-based modeling wherein the

Petru Constantinescu, Asbjørn Christian Nordentoft

We prove that one hundred percent of the closed geodesic periods of a Hecke--Maa{\ss} cusp form for the modular group are non-vanishing when ordered by length. We present applications to the non-vanishing of central values of Rankin--Selberg $L$-functions. Similar results for holomorphic forms for general Fuchsian groups of finite covolume with a cusp are al

Nigel Hitchin

We investigate the geometry of holomorphic vector bundles $E$ over a Riemann surface $C$ together with a section of the endomorphism bundle tensored with $K^{1/2}$ -- a square root of the canonical bundle $K$. These parallel to some extent the various features of usual Higgs bundles, such as spectral curve constructions, but some features are radically diffe

Marek Wodzinski, Niccolò Marini, Manfredo Atzori, Henning Müller

The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful to quickly transfer annotations between consecutive or restained slides, thus significantly reducing the annotation time and associated c

Tianhong Catherine Yu, Guilin Hu, Ruidong Zhang, Hyunchul Lim

We present Ring-a-Pose, a single untethered ring that tracks continuous 3D hand poses. Located in the center of the hand, the ring emits an inaudible acoustic signal that each hand pose reflects differently. Ring-a-Pose imposes minimal obtrusions on the hand, unlike multi-ring or glove systems. It is not affected by the choice of clothing that may cover wris

Chengxin Chen, Pengyuan Zhang

One persistent challenge in Speech Emotion Recognition (SER) is the ubiquitous environmental noise, which frequently results in deteriorating SER performance in practice. In this paper, we introduce a Two-level Refinement Network, dubbed TRNet, to address this challenge. Specifically, a pre-trained speech enhancement module is employed for front-end noise re

Tasnuba Binte Jamal, Samiul Hasan, Omar I. Abdul-Aziz, Pallab Mozumder

This study presents an agent-based model (ABM) developed to simulate the resilience of a community to hurricane-induced infrastructure disruptions, focusing on the interdependencies between electric power and transportation networks. In this ABM approach, agents represent the components of a system, where interactions within a system shape intra-dependency o

Juliana O. Costa, Isabelle Aguiar, Jadna L. Barauna, Eugenio Megías

Particle production yields measured in central Au-Au collision at RHIC are obtained with free Fermi and Bose gases and also with a replacement of these statistics by non-extensive statistics. For the latter calculation, a set of different parameters was used with values of the Tsallis parameter $q$ chosen between 1.01 and 1.25, with 1.16 generating the best

Ruijing Tang, Dahai Yan, Haiyun Zhang, Qingchang Zhao

Gaussian processes method is employed to analyze the light curves of bursts detected by Insight-HXMT, NICER, and GECAM from SGR 1935+2154 between 2020 to 2022. It is found that a stochastically driven damped simple harmonic oscillator (SHO) is necessary to capture the characteristics of the X-ray bursts. Variability timescale of the X-ray bursts, correspondi

Xiaokun Zhang, Bo Xu, Youlin Wu, Yuan Zhong

Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items, provide the potentials to capture user preferences and item characteristics at a fine-grained level. To this end, we propose a novel framework FineRec that explores the at

Felix Frischmuth, Mattis Berghoff, Martin Braun, Philipp Haertel

Climate neutrality paradigms put electricity systems at the core of a clean energy supply. At the same time, indirect electrification, with a potential uptake of hydrogen or derived fuel economy, plays a crucial role in decarbonising the energy supply and industrial processes. Besides energy markets coordinating the transition, climate and energy policy targ

Xiaofei Wang, Xingxu Huang, Stephen J. Price, Chao Li

The recent advancement of spatial transcriptomics (ST) allows to characterize spatial gene expression within tissue for discovery research. However, current ST platforms suffer from low resolution, hindering in-depth understanding of spatial gene expression. Super-resolution approaches promise to enhance ST maps by integrating histology images with gene expr

Majid Jafari, Fatemeh Yadollahi Farsani, Norbert H. Menzler, Christian Lenser

La$_{0.58}$Ca$_{0.4}$Co$_{0.2}$Fe$_{0.8}$O$_{3-\delta}$ (L58CCF) was synthesized and evaluated as a cathode electrode for intermediate temperature solid oxide fuel cells (IT-SOFC) based on the Y$_2$O$_3$-stabilized ZrO$_2$ (YSZ) electrolyte. The effect of sintering temperature on the L58CCF performance was investigated. The best Area specific resistances (AS

Ryan R. Martin, Balázs Patkós

The Erd\H os Matching Conjecture states that the maximum size $f(n,k,s)$ of a family $\mathcal{F}\subseteq \binom{[n]}{k}$ that does not contain $s$ pairwise disjoint sets is $\max\{|\mathcal{A}_{k,s}|,|\mathcal{B}_{n,k,s}|\}$, where $\mathcal{A}_{k,s}=\binom{[sk-1]}{k}$ and $\mathcal{B}_{n,k,s}=\{B\in \binom{[n]}{k}:B\cap [s-1]\neq \emptyset\}$. The case $s

Maria Dronova, Vladislav Cheremnykh, Alexey Kotcov, Aleksey Fedoseev

Current methods for 3D reconstruction and environmental mapping frequently face challenges in achieving high precision, highlighting the need for practical and effective solutions. In response to this issue, our study introduces FlyNeRF, a system integrating Neural Radiance Fields (NeRF) with drone-based data acquisition for high-quality 3D reconstruction. U

Xiaokun Zhang, Bo Xu, Zhaochun Ren, Xiaochen Wang

Session-based recommendation aims to predict intents of anonymous users based on their limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence patterns reflected by item IDs, and fine-grained preferences represented by item modalities (e.g., text and images). However, existing methods typically entangle these causes, leadin

Oscar Key, So Takao, Daniel Giles, Marc Peter Deisenroth

Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation requires the computation to be distributed across increasingly many compute nodes, yet existing approaches suffer from synchronisation overhead in this setting. In this paper, we exploit the formulation of data assimilation

Vinícius Litvinoff Justus, Vitor Batista Rodrigues, Alex Rodrigo dos Santos Sousa

Bootstrap is a widely used technique that allows estimating the properties of a given estimator, such as its bias and standard error. In this paper, we evaluate and compare five bootstrap-based methods for making confidence intervals: two of them (Normal and Studentized) based on the bootstrap estimate of the standard error; another two (Quantile and Better)

Viola Gattus, Apostolos Pilaftsis

We review a recently proposed SuperGeometric (SG) approach to Quantum Field Theories (QFTs) that allow for scalar-fermion field transformations in a manifestly reparameterisation covariant manner. By adopting natural choices for the field-space supermetric, we show how a non-trivial field-space Riemannian curvature can be introduced in the fermionic sector,

Yian Li, Wentao Tian, Yang Jiao, Jingjing Chen

Recently, Multimodal Large Language Models (MLLMs) have achieved significant success across multiple disciplines due to their exceptional instruction-following capabilities and extensive world knowledge. However, whether these MLLMs possess human-like compositional reasoning abilities remains an open problem. To unveil their reasoning behaviors, we first cur

Tarik Dzanic

In (Dzanic, J. Comp. Phys., 508:113010, 2024), a limiting approach for high-order discontinuous Galerkin schemes was introduced which allowed for imposing constraints on the solution continuously (i.e., everywhere within the element). While exact for linear constraint functionals, this approach only imposed a sufficient (but not the minimum necessary) amount

Julien Claisse, Jiazhi Kang, Xiaolu Tan

We study a nonlinear branching diffusion process in the sense of McKean, i.e., where particles are subjected to a mean-field interaction. We consider first a strong formulation of the problem and we provide an existence and uniqueness result by using contraction arguments. Then we consider the notion of weak solution and its equivalent martingale problem for

David Rapado-Rincon, Akshay K. Burusa, Eldert J. van Henten, Gert Kootstra

With the current demand for automation in the agro-food industry, accurately detecting and localizing relevant objects in 3D is essential for successful robotic operations. However, this is a challenge due the presence of occlusions. Multi-view perception approaches allow robots to overcome occlusions, but a tracking component is needed to associate the obje

George Gkountoumis

We construct freely acting asymmetric $\mathbb{Z}_4$ orbifolds of type IIB string theory on $T^5$ preserving 24,16 or 8 supercharges in five dimensions. We show that these models are well-defined if the SO(8) lattice is chosen, instead of the SU(2)$^4$ lattice, which was previously considered in the literature.

Modesto Orozco-Ruiz, Wasim Rehman, Florian Mintert

We present an entangling gate scheme for trapped-ion chains that achieves high-fidelity operations with excited motional states despite multiple error sources. Our approach incorporates all relevant motional modes and exhibits enhanced robustness against both motional heating effects and detuning errors, critical features for building robust and scalable tra

Bruno Cucco, Joshua Leveillee, Viet-Anh Ha, Jacky Even

Layered halide perovskites have emerged as potential alternatives to three-dimensional halide perovskites due to their improved stability and larger material phase space, allowing fine-tuning of structural, electronic, and optical properties. However, their charge carrier mobilities are significantly smaller than that of three-dimensional halide perovskites,

Stephen M. Barnett, James D. Cresser, Sarah Croke

We investigate the effects of the electromagnetic vacuum field on a harmonically bound electron. We show that in the electric-dipole approximation the model atom couples only to an effective one-dimensional electric field. In a simplified form, in which the problem is reduced to a single spatial dimension, we determine, analytically, the form of the ground s

Mohammad Zunaed, Anwarul Hasan, Taufiq Hasan

Despite the advancement of deep learning-based computer-aided diagnosis (CAD) methods for pneumonia from adult chest x-ray (CXR) images, the performance of CAD methods applied to pediatric images remains suboptimal, mainly due to the lack of large-scale annotated pediatric imaging datasets. Establishing a proper framework to leverage existing adult large-sca

Qinyuan Wu, Mohammad Aflah Khan, Soumi Das, Vedant Nanda

In this paper, we focus on the challenging task of reliably estimating factual knowledge that is embedded inside large language models (LLMs). To avoid reliability concerns with prior approaches, we propose to eliminate prompt engineering when probing LLMs for factual knowledge. Our approach, called Zero-Prompt Latent Knowledge Estimator (ZP-LKE), leverages

Thomas Führer, Diego Paredes

For a reaction-dominated diffusion problem we study a primal and a dual hybrid finite element method where weak continuity conditions are enforced by Lagrange multipliers. Uniform robustness of the discrete methods is achieved by enriching the local discretization spaces with modified face bubble functions which decay exponentially in the interior of an elem

Jaco J. Geuchies, Johan Klarbring, Lucia Di Virgillio, Shuai Fu

Two-dimensional hybrid organic-inorganic metal halide perovskites offer enhanced stability for perovskite-based applications. Their crystal structure's soft and ionic nature gives rise to strong interactions between charge carriers and ionic rearrangements. Here, we investigate the interaction of photo-generated electrons and ionic polarizations in single-cr

Simon Saunders

I show that frequentism, as an explanation of probability in classical statistical mechanics, can be extended in a natural way to a decoherent quantum history space, the analogue of a classical phase space. The result is a form of finite frequentism, in which the Gibbs concept of an infinite ensemble of gases is replaced by the quantum state expressed as a s

Yves Baumann, Tal Ben-Nun, Maciej Besta, Lukas Gianinazzi

Contemporary accelerator designs exhibit a high degree of spatial localization, wherein two-dimensional physical distance determines communication costs between processing elements. This situation presents considerable algorithmic challenges, particularly when managing sparse data, a pivotal component in progressing data science. The spatial computer model q

Grace Guo, John Stasko, Alex Endert

Visual augmentations are commonly added to charts and graphs in order to convey richer and more nuanced information about relationships in the data. However, many design spaces proposed for categorizing augmentations were defined in a top-down manner, based on expert heuristics or from surveys of published visualizations. Less well understood are user prefer

Victor Arrial, Giulio Guerrieri, Delia Kesner

This paper studies the strength of embedding Call-by-Name ({\tt dCBN}) and Call-by-Value ({\tt dCBV}) into a unifying framework called the Bang Calculus ({\tt dBANG}). These embeddings enable establishing (static and dynamic) properties of {\tt dCBN} and {\tt dCBV} through their respective counterparts in {\tt dBANG}. While some specific static properties ha

Alex T. Bregazzi, Jabir A. Ouassou, Arthur G. Coveney, Nadia A. Stelmashenko

In a superconductor/ferromagnet hybrid, a magnetically controlled singlet-to-triplet Cooper pair conversion can modulate the superconducting critical temperature. In these triplet superconducting spin valves, such control usually requires inhomogeneous magnetism. However, in the presence of spin-orbit coupling from an interfacial heavy-metal layer, the singl

Shakhnaz Akhmedova, Nils Körber

Neural networks are trained by minimizing a loss function that defines the discrepancy between the predicted model output and the target value. The selection of the loss function is crucial to achieve task-specific behaviour and highly influences the capability of the model. A variety of loss functions have been proposed for a wide range of tasks affecting t

Marek Wodzinski, Niccolò Marini, Manfredo Atzori, Henning Müller

DeeperHistReg is a software framework dedicated to registering whole slide images (WSIs) acquired using multiple stains. It allows one to perform the preprocessing, initial alignment, and nonrigid registration of WSIs acquired using multiple stains (e.g. hematoxylin \& eosin, immunochemistry). The framework implements several state-of-the-art registration al

Axel Halgaard Kristensen, Thomas A. Ryttov

We study Quantum Chromodynamics in the chiral large $N_c$ limit which contains a left-handed Weyl fermion in the fundamental representation, a left-handed Weyl fermion in the two index antisymmetric representation and $(N_c-3)$ left-handed Weyl fermions in the antifundamental representation of the $SU(N_c)$ gauge group. We construct gauge singlet composite o

Alejandro Mahillo, Silvia Rueda

A new condition is introduced by generalizing the Ritt and Kreiss operators named $(\alpha, \beta)$-RK condition. Geometrical properties of the spectrum for the case $\beta < 1$ are studied, moreover it is shown that in that case if $\alpha + \beta = 1$ the operator is Ritt. Estimates for the power and power differences norms for this type of operators are a

Roman Pico, Alejandro Rebola, Jorge Lasave, Paula Abufager

We investigate the magnetic properties of Fe Phthalocyanines (FePc) that are experimentally arranged in quasi one-dimensional chains when they are grown in thin films or powders. By means of DFT calculations we reproduce the structural parameters found in experiments, and then we build a generalized Heisenberg magnetic model with single ion anisotropy, and c

Grace Guo, Aishwarya Mudgal Sunil Kumar, Adit Gupta, Adam Coscia

Intelligent tutoring systems leverage AI models of expert learning and student knowledge to deliver personalized tutoring to students. While these intelligent tutors have demonstrated improved student learning outcomes, it is still unclear how teachers might integrate them into curriculum and course planning to support responsive pedagogy. In this paper, we

Louis G. Christie, John A. D. Aston

In this paper we present the framework of symmetry in nonparametric regression. This generalises the framework of covariate sparsity, where the regression function depends only on at most $s < d$ of the covariates, which is a special case of translation symmetry with linear orbits. In general this extends to other types of functions that capture lower dimens

Nicolas Ugrinovic, Thomas Lucas, Fabien Baradel, Philippe Weinzaepfel

We present a novel method to generate human motion to populate 3D indoor scenes. It can be controlled with various combinations of conditioning signals such as a path in a scene, target poses, past motions, and scenes represented as 3D point clouds. State-of-the-art methods are either models specialized to one single setting, require vast amounts of high-qua

Tim Kräuter

Behavioral models play an essential role in Model-driven engineering (MDE). Keeping inter-related behavioral models consistent is critical to use them successfully in MDE. However, consistency checking for behavioral models, especially in a heterogeneous scenario, is limited. We propose a methodology to integrate heterogeneous behavioral models to achieve co

Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth

Conventional diffusion models typically relies on a fixed forward process, which implicitly defines complex marginal distributions over latent variables. This can often complicate the reverse process' task in learning generative trajectories, and results in costly inference for diffusion models. To address these limitations, we introduce Neural Flow Diffusio

Janne Ruostekoski

Atomic ensembles strongly interacting with light constitute rich quantum-optical many-body systems, with the potential for observing cooperative effects and dissipative nonequilibrium phase transitions. We theoretically analyze the conditions under which a driven atomic ensemble in free space, characterized by strong dipole-dipole interactions and large spat

Oana Ignat, Xiaomeng Xu, Rada Mihalcea

Deceptive reviews are becoming increasingly common, especially given the increase in performance and the prevalence of LLMs. While work to date has addressed the development of models to differentiate between truthful and deceptive human reviews, much less is known about the distinction between real reviews and AI-authored fake reviews. Moreover, most of the

Agnaldo A. da Silva, Mario Garcia-Fernandez, Jason D. Lotay, Henrique N. Sá Earp

We introduce the coupled instanton equations for a metric, a spinor, a three-form, and a connection on a bundle, over a spin manifold. Special solutions in dimensions $6$ and $7$ arise, respectively, from the Hull--Strominger and the heterotic $\operatorname{G}_2$ system. The equations are motivated by recent developments in theoretical physics and can be re

Isabella Negrini

We construct a Shintani lift for rigid analytic cocycles of higher weight, attaching modular forms of half-integral weight to such cocycles. The expression for the Fourier coefficients of the modular form $\mathcal{RS}(J)$ attached to a cocycle $J$ is given in terms of the residues of $J$, and shares a striking similarity with the expression for the coeffici

Sheeba Samuel, Daniel Mietchen

The way in which data are shared can affect their utility and reusability. Here, we demonstrate how data that we had previously shared in bulk can be mobilized further through a knowledge graph that allows for much more granular exploration and interrogation. The original dataset is about the computational reproducibility of GitHub-hosted Jupyter notebooks a

Tom T. P. Franken, Thomas Neele

AuDaLa is a recently introduced programming language that follows the new data autonomous paradigm. In this paradigm, small pieces of data execute functions autonomously. Considering the paradigm and the design choices of AuDaLa, it is interesting to determine the expressiveness of the language and to create verification methods for it. In this paper, we tak

Oana Ignat, Gayathri Ganesh Lakshmy, Rada Mihalcea

Inspiration is linked to various positive outcomes, such as increased creativity, productivity, and happiness. Although inspiration has great potential, there has been limited effort toward identifying content that is inspiring, as opposed to just engaging or positive. Additionally, most research has concentrated on Western data, with little attention paid t

Tim Kräuter, Harald König, Adrian Rutle, Yngve Lamo

Debugging is an essential part of software maintenance and evolution since it allows software developers to analyze program execution step by step. Understanding a program is required to fix potential flaws, alleviate bottlenecks, and implement new desired features. Thus, software developers spend a large percentage of their time validating and debugging sof

Giacomo Mazza, Costantino Budroni

We investigate entanglement detection in quantum materials through criteria based on the simultaneous suppression of collective matter excitations. Unlike other detection schemes, these criteria can be applied to continuous and unbounded variables. By considering a system of interacting dipoles on a lattice, we show the detection of collective entanglement a

Shashwat Chandra, Yi-Jun Chang, Michal Dory, Mohsen Ghaffari

We revisit the classic broadcast problem, wherein we have $k$ messages, each composed of $O(\log{n})$ bits, distributed arbitrarily across a network. The objective is to broadcast these messages to all nodes in the network. In the distributed CONGEST model, a textbook algorithm solves this problem in $O(D+k)$ rounds, where $D$ is the diameter of the graph. W

Galor Geva, Tamir Admon, Maayan Levin, Yael Roichman

We study the relaxation process of two driven colloidal suspensions in diffusive contact to a steady state, similar to thermalization. We start by studying a single suspension, subjecting it to random driving forces via holographic optical tweezers, which agitate it to a higher effective temperature. Interestingly, the effective temperature of the suspension

Luís Carvalho, João L. Costa, José Mourão, Gonçalo Oliveira

The Neural Tangent Kernel (NTK) has emerged as a fundamental concept in the study of wide Neural Networks. In particular, it is known that the positivity of the NTK is directly related to the memorization capacity of sufficiently wide networks, i.e., to the possibility of reaching zero loss in training, via gradient descent. Here we will improve on previous

Abhishek Mitra, Ruhee D'Cunha, Qiaohong Wang, Matthew R. Hermes

We introduce a hybrid quantum-classical algorithm, the localized active space unitary selective coupled cluster singles and doubles (LAS-USCCSD) method. Derived from the localized active space unitary coupled cluster (LAS-UCCSD) method, LAS-USCCSD first performs a classical LASSCF calculation, then selectively identifies the most important parameters (cluste

Janak Kapuriya, Chhavi Kirtani, Apoorv Singh, Jay Saraf

Recent advancements in LLMs have shown their significant potential in tasks like text summarization and generation. Yet, they often encounter difficulty while solving complex physics problems that require arithmetic calculation and a good understanding of concepts. Moreover, many physics problems include images that contain important details required to unde

Yang Ye, Shihao Ji

As point cloud provides a natural and flexible representation usable in myriad applications (e.g., robotics and self-driving cars), the ability to synthesize point clouds for analysis becomes crucial. Recently, Xie et al. propose a generative model for unordered point sets in the form of an energy-based model (EBM). Despite the model achieving an impressive

Calin Tataru

We show that the category of partially ordered sets $\mathsf{Pos}$ is equivalent to the free conservative cocompletion of the category of finite non-empty totally ordered sets $\Delta$, which is also known as the simplex category.

Joe D. Longbottom, Max D. Champneys, Timothy J. Rogers

In engineering, accurately modeling nonlinear dynamic systems from data contaminated by noise is both essential and complex. Established Sequential Monte Carlo (SMC) methods, used for the Bayesian identification of these systems, facilitate the quantification of uncertainty in the parameter identification process. A significant challenge in this context is t

Jacopo Bonato, Marco Cotogni, Luigi Sabetta

In this paper, we introduce Selective-distillation for Class and Architecture-agnostic unleaRning (SCAR), a novel approximate unlearning method. SCAR efficiently eliminates specific information while preserving the model's test accuracy without using a retain set, which is a key component in state-of-the-art approximate unlearning algorithms. Our approach ut

Keisuke Fujii, Tilman Enss

The hydrodynamic attractor is a concept that describes universal equilibration behavior in which systems lose microscopic details before hydrodynamics becomes applicable. We propose a setup to observe hydrodynamic attractors in ultracold atomic gases, taking advantage of the fact that driving the two-body $s$-wave scattering length causes phenomena equivalen

Konstantinos Vilouras, Pedro Sanchez, Alison Q. O'Neil, Sotirios A. Tsaftaris

Localizing the exact pathological regions in a given medical scan is an important imaging problem that traditionally requires a large amount of bounding box ground truth annotations to be accurately solved. However, there exist alternative, potentially weaker, forms of supervision, such as accompanying free-text reports, which are readily available. The task

Marek Wodzinski, Daria Hemmerling, Mateusz Daniol

Thousands of people suffer from cranial injuries every year. They require personalized implants that need to be designed and manufactured before the reconstruction surgery. The manual design is expensive and time-consuming leading to searching for algorithms whose goal is to automatize the process. The problem can be formulated as volumetric shape completion

Beat Zurbuchen

Based on a suggestion by Katz, we determine the monodromy group of a certain hypergeometric sum to be $G_2$. Our approach is based on the uniformity results by Katz on the Fourier transform to deduce uniformity for the Tannakian monodromy groups.

Jonathan Colen, Alexis Poncet, Denis Bartolo, Vincenzo Vitelli

We present a data-driven pipeline for model building that combines interpretable machine learning, hydrodynamic theories, and microscopic models. The goal is to uncover the underlying processes governing nonlinear dynamics experiments. We exemplify our method with data from microfluidic experiments where crystals of streaming droplets support the propagation

Antonio Pio Ricciardi, Valentino Maiorca, Luca Moschella, Riccardo Marin

Visual Reinforcement Learning is a popular and powerful framework that takes full advantage of the Deep Learning breakthrough. It is known that variations in input domains (e.g., different panorama colors due to seasonal changes) or task domains (e.g., altering the target speed of a car) can disrupt agent performance, necessitating new training for each vari

Zhenyang Ni, Rui Ye, Yuxi Wei, Zhen Xiang

Vision-Large-Language-models(VLMs) have great application prospects in autonomous driving. Despite the ability of VLMs to comprehend and make decisions in complex scenarios, their integration into safety-critical autonomous driving systems poses serious security risks. In this paper, we propose BadVLMDriver, the first backdoor attack against VLMs for autonom

ATLAS Collaboration

A search for the resonant production of a heavy scalar $X$ decaying into a Higgs boson and a new lighter scalar $S$, through the process $X \to S(\to bb) H(\to \gamma\gamma)$, where the two photons are consistent with the Higgs boson decay, is performed. The search is conducted using an integrated luminosity of 140 fb$^{-1}$ of proton-proton collision data a

G. R. Boroun, B. Rezaei

The nuclear reduced cross section $\sigma^{A}_{r}$, in the kinematic range of the electron-Ion collider with center-of-mass energy $\sqrt{s}=140~\mathrm{GeV}$ and $y{\leq}1$, is discussed. The importance of the nuclear longitudinal structure function $F^{A}_{L}$ and its behavior owing to the impact parameter for the heavy and light nucleus of Pb-208 and C-12

Dildar Ali, Suman Banerjee, Yamuna Prasad

Given billboard and trajectory database, finding a limited number of billboard slots for maximizing the influence is an important problem in the context of billboard advertisement. Most of the existing literature focused on the influential slot selection problem without considering any specific zonal influence constraint. To bridge this gap in this paper, we

Daigo Mugita, Kazuyoshi Souno, Hiroaki Koyama, Taisei Nakamura

In nonequilibrium statistical physics, quantifying the nearest (and higher-order) neighbors and free volumes of particles in many-body systems is crucial to elucidating the origin of macroscopic collective phenomena, such as glass/granular jamming transitions and various aspects of the behavior of active matter. However, conventional techniques (based on a f

Qi Zheng, XueGuang Zhang, QiRong Yuan, Paola Severgnini

In this paper, we presented a detailed analysis of the Sloan Digital Sky Survey optical spectrum of a new sub-kpc scale dual AGN candidate SDSS J222428.53+261423.2 (=SDSS J2224). The target is one of the few AGNs with all the optical narrow emission lines characterized by double-peaked profiles and with peak separations in velocity units of about 930 km/s. I

Nicolò Piccione, Maria Maffei, Andrew N. Jordan, Kater W. Murch

A (target) quantum system is often measured through observations performed on a second (meter) system to which the target is coupled. In the presence of global conservation laws holding on the joint meter-target system, the Wigner-Araki-Yanase theorem and its generalizations predict a lower-bound on the measurement's error (Ozawa's bound). While practically

Arthur Niedźwiecki, Sascha Jongebloed, Yanxiang Zhan, Michaela Kümpel

The paper presents a novel cloud-based digital twin learning platform for teaching and training concepts of cognitive robotics. Instead of forcing interested learners or students to install a new operating system and bulky, fragile software onto their personal laptops just to solve tutorials or coding assignments of a single lecture on robotics, it would be

Santosh, Li Lin, Irene Amerini, Xin Wang

Diffusion models (DMs) have revolutionized image generation, producing high-quality images with applications spanning various fields. However, their ability to create hyper-realistic images poses significant challenges in distinguishing between real and synthetic content, raising concerns about digital authenticity and potential misuse in creating deepfakes.

Anna Zych-Pawlewicz, Marek Żochowski

In this paper we present the first dynamic algorithms for the problem of Feedback Arc Set in Tournaments (FAST) and the problem of Feedback Vertex Set in Tournaments (FVST). Our algorithms maintain a dynamic tournament on n vertices altered by redirecting the arcs, and answer if the tournament admits a feedback arc set (or respectively feedback vertex set) o

Djamel Himane

In this paper, show that the Diophantine equation $ x^2+(x+1)^2=w^4 $ has only two solutions $ (0,1) $ and $ (119,13)$ in non-negative integers $ x $ and $ w $. This equation concerned a classic problem posed by Pierre de Fermat, wonders about finding a Pythagorean triangle in which the hypotenuse and the sum of the arms are square. We review the method of f

Jan Tóth, Ondřej Kuželka

We study the time complexity of the weighted first-order model counting (WFOMC) over the logical language with two variables and counting quantifiers. The problem is known to be solvable in time polynomial in the domain size. However, the degree of the polynomial, which turns out to be relatively high for most practical applications, has never been properly

K. Paschalidis, F. Lucco Castello, S. Ratynskaia, P. Tolias

The MEMENTO (MEtallic Melt Evolution in Next-step TOkamaks) code is a new numerical implementation of the physics model originally developed for the MEMOS-U code with the objective to self-consistently describe the generation of melt and its subsequent large scale dynamics in fusion devices and to assess the damage of metallic reactor armor under powerful no

Dingming Liu, Shaowei Li, Ruoyan Zhou, Lili Liang

Chinese landscape painting is a gem of Chinese cultural and artistic heritage that showcases the splendor of nature through the deep observations and imaginations of its painters. Limited by traditional techniques, these artworks were confined to static imagery in ancient times, leaving the dynamism of landscapes and the subtleties of artistic sentiment to t

Jacopo Moi, Gherardo Chirici, Leonardo Chiesi, Saverio Francini

Green areas are a crucial element in evolution of a city, contributing to improve citizens' life, to reduce effects of climate change, and to make possible the survival of other species in urban areas. Unfortunately, the above effects are difficult to assess quantitatively for regulators, stakeholders and experts, making troublesome the planning of city deve

Chang Liu, Xiaohui Xie, Xinggong Zhang, Yong Cui

The networking field is characterized by its high complexity and rapid iteration, requiring extensive expertise to accomplish network tasks, ranging from network design, configuration, diagnosis and security. The inherent complexity of these tasks, coupled with the ever-changing landscape of networking technologies and protocols, poses significant hurdles fo

Teng-Fang Hsiao, Bo-Kai Ruan, Hong-Han Shuai

Painterly image harmonization aims at seamlessly blending disparate visual elements within a single image. However, previous approaches often struggle due to limitations in training data or reliance on additional prompts, leading to inharmonious and content-disrupted output. To surmount these hurdles, we design a Training-and-prompt-Free General Painterly Ha

Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Rama K. Vasudevan

Scientific advancement is universally based on the dynamic interplay between theoretical insights, modelling, and experimental discoveries. However, this feedback loop is often slow, including delayed community interactions and the gradual integration of experimental data into theoretical frameworks. This challenge is particularly exacerbated in domains deal

Luke Wernert, Bastián Pradenas, Oleg Tchernyshyov, Hua Chen

Noncollinear antiferromagnets (AFMs) have recently attracted attention in the emerging field of antiferromagnetic spintronics because of their various interesting properties. Due to the noncollinear magnetic order, the localized electron spins on different magnetic sublattices are not conserved even when spin-orbit coupling is neglected, making it difficult

Arthur Aubret, Timothy Schaumlöffel, Gemma Roig, Jochen Triesch

Humans judge the similarity of two objects not just based on their visual appearance but also based on their semantic relatedness. However, it remains unclear how humans learn about semantic relationships between objects and categories. One important source of semantic knowledge is that semantically related objects frequently co-occur in the same context. Fo

Ahmed Elshabrawy, Yongxin Huang, Iryna Gurevych, Alham Fikri Aji

While Large Language Models (LLMs) exhibit remarkable capabilities in zero-shot and few-shot scenarios, they often require computationally prohibitive sizes. Conversely, smaller Masked Language Models (MLMs) like BERT and RoBERTa achieve state-of-the-art results through fine-tuning but struggle with extending to few-shot and zero-shot settings due to their a

Daria Poul, Ankita Samal, Amanda Rodriguez Betancourt, Carole Quesada

Periodontal diseases affect 45.9\% of adults aged 30 or older in the United States. Current diagnostic methods for clinical assessment of these diseases are visual examination and bleeding on probing that are subjective, qualitative, and/or invasive. Thus, there is a critical need for research on noninvasive modalities for periodontal tissue characterization

Jan Erik Bellingrath

The self-simulational theory of temporal extension describes an information-theoretically formalized mechanism by which the width of subjective temporality emerges from the architecture of self-modelling. In this paper, the perspective of the free energy principle will be assumed to cast the emergence of subjective temporal extension from first principles of

Eric V. Woods, Tim M. Schwarz, Mahander P. Singh, Shuo Zhang

The understanding of protein structure, folding, and interaction with other proteins remains one of the grand challenges of modern biology. Tremendous progress has been made thanks to X-ray- or electron-based techniques that have provided atomic configurations of proteins, and their solvation shell. These techniques though require a large number of similar m

Pietro Liguori, Christian Marescalco, Roberto Natella, Vittorio Orbinato

As the Windows OS stands out as one of the most targeted systems, the PowerShell language has become a key tool for malicious actors and cybersecurity professionals (e.g., for penetration testing). This work explores an uncharted domain in AI code generation by automatically generating offensive PowerShell code from natural language descriptions using Neural

Asmar Muqeet, Shaukat Ali, Tao Yue, Paolo Arcaini

Quantum computers have the potential to outperform classical computers for some complex computational problems. However, current quantum computers (e.g., from IBM and Google) have inherent noise that results in errors in the outputs of quantum software executing on the quantum computers, affecting the reliability of quantum software development. The industry

Eloisa Detomi, Marta Morigi, Pavel Shumyatsky

For subsets X,Y of a finite group G, we write Pr(X,Y) for the probability that two random elements x in X and y in Y commute. This paper addresses the relation between the structure of an approximate subgroup A of G and the probabilities Pr(A,G) and Pr(A,A).