November 2024 arXiv papers — page 78

Jan Goedgebeur, Jarne Renders, Steven Van Overberghe

We present an algorithm for the efficient generation of all pairwise non-isomorphic cycle permutation graphs, i.e. cubic graphs with a $2$-factor consisting of two chordless cycles, non-hamiltonian cycle permutation graphs and permutation snarks, i.e. cycle permutation graphs that do not admit a $3$-edge-colouring. This allows us to generate all cycle permut

Wei Zhong, Zhen-Tai Zhang, Hui-Sheng Zhong, Bo Ma

Observations have revealed unique temperature profiles in hot Jupiter atmospheres. We propose that the energy transport by vertical mixing could lead to such thermal features. In our new scenario, strong absorbers, TiO and VO are not necessary. Vertical mixing could be naturally excited by atmospheric circulation or internal gravity wave breaking. We perform

Zhiwen Shao, Yichen Yuan, Lizhuang Ma, Xiaojia Zhu

Curvature angle is a quantitative measurement of a curve, in which Cobb angle is customized for spinal curvature. Automatic Cobb angle measurement from X-ray images is crucial for scoliosis screening and diagnosis. However, most existing regression-based and segmentation-based methods struggle with inaccurate spine representations or mask connectivity and fr

Mark Schöne, Yash Bhisikar, Karan Bania, Khaleelulla Khan Nazeer

Handling sparse and unstructured geometric data, such as point clouds or event-based vision, is a pressing challenge in the field of machine vision. Recently, sequence models such as Transformers and state-space models entered the domain of geometric data. These methods require specialized preprocessing to create a sequential view of a set of points. Further

Ron Keuth, Lasse Hansen, Maren Balks, Ronja Jäger

Semantic segmentation is a crucial task in medical imaging. Although supervised learning techniques have proven to be effective in performing this task, they heavily depend on large amounts of annotated training data. The recently introduced Segment Anything Model (SAM) enables prompt-based segmentation and offers zero-shot generalization to unfamiliar objec

Kehan Shi, Martin Burger

Hypergraph learning with $p$-Laplacian regularization has attracted a lot of attention due to its flexibility in modeling higher-order relationships in data. This paper focuses on its fast numerical implementation, which is challenging due to the non-differentiability of the objective function and the non-uniqueness of the minimizer. We derive a hypergraph $

Stanley Wei, Sadhika Malladi, Sanjeev Arora, Amartya Sanyal

Machine unlearning algorithms are increasingly important as legal concerns arise around the provenance of training data, but verifying the success of unlearning is often difficult. Provable guarantees for unlearning are often limited to supervised learning settings. In this paper, we provide the first theoretical guarantees for unlearning in the pre-training

Anjitha Ashokan, Chithra A

The eccentricity matrix of a simple connected graph is derived from its distance matrix by preserving the largest non-zero distance in each row and column, while the other entries are set to zero. This article examines the $\epsilon$-spectrum, $\epsilon$-energy, $\epsilon$-inertia and irreducibility of the central graph (respectively complement of the centra

Shipeng Liu, Liang Zhao, Dengfeng Chen

As a fundamental visual attribute, image complexity significantly influences both human perception and the performance of computer vision models. However, accurately assessing and quantifying image complexity remains a challenging task. (1) Traditional metrics such as information entropy and compression ratio often yield coarse and unreliable estimates. (2)

Theo Glauch, Kristian Tchiorniy

Gamma rays measured by the Fermi-LAT satellite tell us a lot about the processes taking place in high-energetic astrophysical objects. The fluxes coming from these objects are, however, extremely variable. Hence, gamma-ray light curves optimally use adaptive bin sizes in order to retrieve most information about the source dynamics and to combine gamma-ray ob

Yiliang Yuan, Mustafa Misir

Molecular docking is a major element in drug discovery and design. It enables the prediction of ligand-protein interactions by simulating the binding of small molecules to proteins. Despite the availability of numerous docking algorithms, there is no single algorithm consistently outperforms the others across a diverse set of docking scenarios. This paper in

Kay Brandner

The visible dynamics of small-scale systems are strongly affected by unobservable degrees of freedom, which can belong either to external environments or internal subsystems and almost inevitably induce memory effects. Formally, such inaccessible degrees of freedom can be systematically eliminated from essentially any microscopic model through projection ope

Kay Brandner

Memory effects are ubiquitous in small-scale systems. They emerge from interactions between accessible and inaccessible degrees of freedom and give rise to evolution equations that are non-local in time. If the characteristic time scales of accessible and inaccessible degrees of freedom are sharply separated, locality can be restored through the standard Mar

Fuyu Dong, Pengjie Zhang, Haojie Xu, Jian Qin

The gravitational potential decay rate (DR) is caused by the cosmic acceleration of the universe, providing a direct probe into the existence of dark energy (DE). We present measurements of DR and explore its implications for DE models using the Data Release 9 galaxy catalog of DESI imaging surveys and the Planck cosmic microwave background maps. Our analysi

Congbo Ma, Hu Wang, Zitai Qiu, Shan Xue

Social media data is inherently rich, as it includes not only text content, but also users, geolocation, entities, temporal information, and their relationships. This data richness can be effectively modeled using heterogeneous information networks (HINs) as it can handle multiple types of nodes and relationships, allowing for a comprehensive representation

Sanjay Rijal, Shital Adhikari, Manish Dahal, Manish Awale

Despite the growing advancements in Automatic Speech Recognition (ASR) models, the development of robust models for underrepresented languages, such as Nepali, remains a challenge. This research focuses on making an exhaustive and generalized dataset followed by fine-tuning OpenAI's Whisper models of different sizes to improve transcription (speech-to-text)

Benny Ren, Ian Barnett, Haochang Shou, Jeremy Rubin

In the age of digital healthcare, passively collected physical activity profiles from wearable sensors are a preeminent tool for evaluating health outcomes. In order to fully leverage the vast amounts of data collected through wearable accelerometers, we propose to use quantile functional regression to model activity profiles as distributional outcomes throu

Jan Matyáš Křišťan, Jakub Svoboda

In reconfiguration, we are given two solutions to a graph problem, such as Vertex Cover or Dominating Set, with each solu tion represented by a placement of tokens on vertices of the graph. Our task is to reconfigure one into the other using small steps while ensuring the intermediate configurations of tokens are also valid solutions. The two commonly studie

E. Siri, N. Sadooghi

We study the Bose-Einstein condensation (BEC) of a free Bose gas under rigid rotation. The aim is to explore the impact of rotation on the thermodynamic quantities associated with BEC, including the Bose-Einstein (BE) transition temperature and condensate fraction. We begin by introducing the rotation in the Lagrangian density of free charged Klein-Gordon fi

Laura Ruis, Maximilian Mozes, Juhan Bae, Siddhartha Rao Kamalakara

The capabilities and limitations of Large Language Models have been sketched out in great detail in recent years, providing an intriguing yet conflicting picture. On the one hand, LLMs demonstrate a general ability to solve problems. On the other hand, they show surprising reasoning gaps when compared to humans, casting doubt on the robustness of their gener

Andreas Defant, Daniel Galicer, Martín Mansilla, Mieczysław Mastyło

We investigate projection constants for spaces of bihomogeneous harmonic and bihomogeneous polynomials on the unit sphere in finite-dimensional complex Hilbert spaces. Using averaging techniques, we demonstrate that the minimal norm projection aligns with the natural orthogonal projection. This result enables us to establish a connection between these consta

Yilin Zhang, Songshan Yang, Yunan Wu, Lan Wang

This paper introduces the partial Gini covariance, a novel dependence measure that addresses the challenges of high-dimensional inference with heavy-tailed errors, often encountered in fields like finance, insurance, climate, and biology. Conventional high-dimensional regression inference methods suffer from inaccurate type I errors and reduced power in heav

Juntao Huang, Kun Ding, Jiangping Hu, Zhesen Yang

We introduce the complex frequency fingerprint (CFF), an experimentally accessible method for detecting the complex frequency Green's function (GF). Unlike the real frequency GF, where $\omega$ is real, this complex frequency GF is shown to play a necessary role in both non-Hermitian and quantum many-body systems. For non-Hermitian systems, we will prove tha

Ekaterina Shumitskaya, Mikhail Pautov, Dmitriy Vatolin, Anastasia Antsiferova

Most modern No-Reference Image-Quality Assessment (NR-IQA) metrics are based on neural networks vulnerable to adversarial attacks. Attacks on such metrics lead to incorrect image/video quality predictions, which poses significant risks, especially in public benchmarks. Developers of image processing algorithms may unfairly increase the score of a target IQA

Gabriel Finneran, Antonio Martin-Carrillo

The velocities of Ic-BL supernovae can be determined using two techniques (spline fitting and template fitting), sometimes resulting in different velocities for the same event. This work compares and contrasts both methods, identifying sources of error which are not accounted for by most authors and quantifying their impact on the final velocity measurement.

Andrea Dal Prete, Zeynep Özge Orhan, Anastasia Bolotnikova, Marta Gandolla

Accurate detection of locomotion transitions, such as walk to sit, walk to stair ascent, and descent, is crucial to effectively control robotic assistive devices, such as lower-limb exoskeletons, as each locomotion mode requires specific assistance. Variability in collected sensor data introduced by user- or system-specific characteristics makes it challengi

Leonardo Pierattelli, Fabio Taddei, Alessandro Braggio

The study of charge current fluctuations (noise) can give useful insights into the properties of nanoscale systems. In this work, the peculiar properties of noise in multiterminal hybrid normal-superconducting systems are explored in the thermal out-of-equilibrium regime, i.e., when temperature biases are present ($\Delta T$-noise). Using the Landauer-B\"utt

Avner Seror

As large language models (LLMs) increasingly participate in tasks with ethical and societal stakes, a critical question arises: do they exhibit an emergent "moral mind" - a consistent structure of moral preferences guiding their decisions - and to what extent is this structure shared across models? To investigate this, we applied tools from revealed preferen

Shuo Jiang, Min Xie, Jianxi Luo

Combinatorial optimization (CO) is essential for improving efficiency and performance in engineering applications. As complexity increases with larger problem sizes and more intricate dependencies, identifying the optimal solution become challenging. When it comes to real-world engineering problems, algorithms based on pure mathematical reasoning are limited

Simone Martino, Domiziano Doria, Chiara Lionello, Matteo Becchi

Reconstructing the physical complexity of many-body dynamical systems can be challenging. Starting from the trajectories of their constitutive units (raw data), typical approaches require selecting appropriate descriptors to convert them into time-series, which are then analyzed to extract interpretable information. However, identifying the most effective de

Peter Hintz

On a class of dynamical spacetimes which are asymptotic as $t\to\infty$ to a stationary spacetime containing a horizon $\mathcal{H}_0$, we show the existence of a unique null hypersurface $\mathcal{H}$ which is asymptotic to $\mathcal{H}_0$. This is a special case of a general unstable manifold theorem for perturbations of flows which translate in time and h

Marios Voskou

Let $\Gamma$ be a cocompact Fuchsian group, and $l$ a fixed closed geodesic. We study the counting of those images of $l$ that have a distance from $l$ less than or equal to $R$. We prove an $\Omega$-result for the error term in the asymptotic expansion of the counting function. More specifically, we prove that the error term is equal to $\Omega_{\delta}\lef

Daniel Bennett, Michele Pizzochero, Javier Junquera, Efthimios Kaxiras

First-principles density functional theory (DFT) codes which employ a localized basis offer advantages over those which use plane-wave bases, such as better scaling with system size and better suitability to low-dimensional systems. The trade-off is that care must be taken in order to generate a good localized basis set which is efficient and accurate in a v

Zi-Hang Zhu, Ying Liu, Gianluca Lagnese, Federica Maria Surace

In the context of quantum electrodynamics, the decay of false vacuum leads to the production of electron-positron pair, a phenomenon known as the Schwinger effect. In practical experimental scenarios, producing a pair requires an extremely strong electric field, thus suppressing the production rate and making this process very challenging to observe. Here we

Jianghui Xu, Yang Yang, Jiang-Tao Li, Guilin Liu

Radio halos of edge-on galaxies are crucial for investigating cosmic ray propagation and magnetic field structures in galactic environments. We present VLA C-configuration S-band (2--4 GHz) observations of the spiral galaxy NGC 3556, a target from the Continuum Halos in Nearby Galaxies - an EVLA Survey (CHANG-ES). We estimate the thermal contribution to the

Christian Capezza, Antonio Lepore, Kamran Paynabar

Statistical process monitoring (SPM) methods are essential tools in quality management to check the stability of industrial processes, i.e., to dynamically classify the process state as in control (IC), under normal operating conditions, or out of control (OC), otherwise. Traditional SPM methods are based on unsupervised approaches, which are popular because

Joseph Natal

Wolfram's hypergraph dynamics should replace outmoded models in physics. This should even more so be the case if experimental evidence for the theory is found (which I believe is probable). However, due to the breadth and depth of the theory, it may be difficult to produce experimental evidence which falsifies it. Some of Wolfram's personal work relating to

Yu. A. Fadeyev

Consistent stellar evolution and nonlinear radial stellar pulsation calculations were carried out for models of asymptotic giant branch stars with initial masses $1.5M_\odot\le M_\mathrm{ZAMS}\le 3M_\odot$ and initial metal abundance $Z=0.006$. All the models are shown to be either the fundamental mode or the first overtone pulsators. The lower limit of the

Zeyu Liang, Hailun Xia, Naichuan Zheng, Huan Xu

Skeleton-based action recognition has achieved remarkable performance with the development of graph convolutional networks (GCNs). However, most of these methods tend to construct complex topology learning mechanisms while neglecting the inherent symmetry of the human body. Additionally, the use of temporal convolutions with certain fixed receptive fields li

Jananga Kalawana, Malith Dilshan, Kaveesha Dinamidu, Kalana Wijethunga

The necessity for complex calculations in high-energy physics and large-scale data analysis has led to the development of computing grids, such as the ALICE computing grid at CERN. These grids outperform traditional supercomputers but present challenges in directly evaluating new features, as changes can disrupt production operations and require comprehensiv

Ismail Nejjar, Hao Dong, Olga Fink

Open-set Domain Adaptation (OSDA) aims to adapt a model from a labeled source domain to an unlabeled target domain, where novel classes - also referred to as target-private unknown classes - are present. Source-free Open-set Domain Adaptation (SF-OSDA) methods address OSDA without accessing labeled source data, making them particularly relevant under privacy

Hamid Reza Hashempour, Gilberto Berardinelli, Ramoni Adeogun, Eduard A. Jorswieck

The forthcoming sixth-generation (6G) industrial Internet-of-Things (IIoT) subnetworks are expected to support ultra-fast control communication cycles for numerous IoT devices. However, meeting the stringent requirements for low latency and high reliability poses significant challenges, particularly due to signal fading and physical obstructions. In this pap

Xiang Li, Jianpeng Qi, Zhongying Zhao, Guanjie Zheng

Graph anomaly detection (GAD) is a critical task in graph machine learning, with the primary objective of identifying anomalous nodes that deviate significantly from the majority. This task is widely applied in various real-world scenarios, including fraud detection and social network analysis. However, existing GAD methods still face two major challenges: (

Jimmy Hickey, Jonathan P. Williams, Brian J. Reich, Emily C. Hector

Untreated periodontitis causes inflammation within the supporting tissue of the teeth and can ultimately lead to tooth loss. Modeling periodontal outcomes is beneficial as they are difficult and time consuming to measure, but disparities in representation between demographic groups must be considered. There may not be enough participants to build group speci

Min-Huan Chu, Haoyang Bai, Jun Hua, Jian Liang

We calculate the leading-twist light-cone distribution amplitudes of the light $\Lambda$ baryon using lattice methods within the framework of large momentum effective theory. Our numerical computations are conducted employing $N_f=2+1$ stout smeared clover fermions and a Symanzik gauge action on a lattice with spacing $a=0.077\;\rm{fm}$, and a pion mass of 3

André Chailloux, Jean-Pierre Tillich

In the last years, Regev's reduction has been used as a quantum algorithmic tool for providing a quantum advantage for variants of the decoding problem. Following this line of work, the authors of [JSW+24] have recently come up with a quantum algorithm called Decoded Quantum Interferometry that is able to solve in polynomial time several optimization problem

Patrizio Neff, Nina J. Husemann, Aurélien S. Nguetcho Tchakoutio, Sergey N. Korobeynikov

In isotropic nonlinear elasticity the corotational stability postulate (CSP) is the requirement that \begin{equation*} \langle\frac{\mathrm{D}^{\circ}}{\mathrm{D} t}[\sigma] , D \rangle > 0 \quad \forall \ D \in \text{Sym}(3)\setminus \{0\} \, , \end{equation*} where $\frac{\mathrm{D}^{\circ}}{\mathrm{D} t}$ is any corotational stress rate, $\sigma$ is the C

Ivan Contreras, Diego Martinez, Nicolas Martinez, Diego Rodriguez

Symplectic and Poisson geometry emerged as a tool to understand the mathematical structure behind classical mechanics. However, due to its huge development over the past century, it has become an independent field of research in differential geometry. In this lecture notes, we will introduce the essential objects and techniques in symplectic geometry (e.g Da

Hanwool Lee, Kieran Flatt, Joonwoo Bae

Sequential quantum information processing may lie in the peaceful coexistence of no-go theorems on quantum operations, such as the no-cloning theorem, the monogamy of correlations, and the no-signalling principle. In this work, we investigate a sequential scenario of quantum state discrimination with maximum confidence, called maximum-confidence discriminati

Qiaoqiao Ren, Tony Belpaeme

Tactile interaction plays an essential role in human-to-human interaction. People gain comfort and support from tactile interactions with others and touch is an important predictor for trust. While touch has been explored as a communicative modality in HCI and HRI, we here report on two studies in which touching a social robot is used to regulate people's st

Jan Mandrysch, Miguel Navascués

The Fewster-Verch (FV) framework provides a local and covariant approach for defining measurements in quantum field theory (QFT). Within this framework, a probe QFT represents the measurement device, which, after interacting with the target QFT, undergoes an arbitrary local measurement. Remarkably, the FV framework is free from Sorkin-like causal paradoxes a

Fernando Lledó, Carlos Palazuelos, Julio I. de Vicente

Quantum networks are promising venues for quantum information processing. This motivates the study of the entanglement properties of the particular multipartite quantum states that underpin these structures. In particular, it has been recently shown that when the links are noisy two drastically different behaviors can occur regarding the global entanglement

Baoliang Chen, Siyi Pan, Dongxu Wu, Liang Xie

Despite the impressive performance of large multimodal models (LMMs) in high-level visual tasks, their capacity for image quality assessment (IQA) remains limited. One main reason is that LMMs are primarily trained for high-level tasks (e.g., image captioning), emphasizing unified image semantics extraction under varied quality. Such semantic-aware yet quali

Yuke Wu, Xiang Liu, Yunyu Shi, Xinyi Chen

The irregular and challenging characteristics of lung adenocarcinoma nodules in computed tomography (CT) images complicate staging diagnosis, making accurate segmentation critical for clinicians to extract detailed lesion information. In this study, we propose a segmentation model, S3TU-Net, which integrates multi-dimensional spatial connectors and a superpi

Aislan Leal Fontes, Maxwell Paixão

The goal of this paper is to construct the Hilbert scheme of complete intersections in the biprojective space $X=\mathbb{P}^m\times\mathbb{P}^n$ and for this, we define a partial order on the bidegrees of the bihomogeneous forms. As a consequence of this construction, we computer explicitly the Hilbert scheme for curves of genus 7 and 8 listed in \cite{MUK95

Dietmar Paschek, Johanna Busch, Angel Mary Chiramel Tony, Ralf Ludwig

In this contribution, we compute the $^1$H nuclear magnetic resonance (NMR) relaxation rate of liquid water at ambient conditions. We are using structural and dynamical information from Coupled Cluster Molecular Dynamics (CCMD) trajectories generated at CCSD(T) electronic structure accuracy while considering also nuclear quantum effects in addition to consul

Weizhao Chen, Yu Zhang, Yi Liu, Zhe Yuan

Based upon first-principles calculations, we report ultralow Gilbert damping in two-dimensional (2D) van derWaals (vdW) ferromagnets. The low damping occurs at weak scattering because mirror symmetry prohibits intraband transitions. The monotonic dependence on the electronic scattering rate suggests the absent lower limit, in contrast to conventional ferroma

Zhen Zeng, Leijiang Gu, Xun Yang, Zhangling Duan

Knowledge editing aims to efficiently and cost-effectively correct inaccuracies and update outdated information. Recently, there has been growing interest in extending knowledge editing from Large Language Models (LLMs) to Multimodal Large Language Models (MLLMs), which integrate both textual and visual information, introducing additional editing complexitie

Christopher Mantzaris, Ajda Fošner

Tax administrative cost reduction is an economically and socially desirable goal for public policy. This article proposes total administrative cost as percentage of total tax revenue as a vivid measurand, also useful for cross-jurisdiction comparisons. Statistical data, surveys and a novel approach demonstrate: Germany's 2021 tax administrative costs likely

Mario De Lucas Garcia, Mark Roman Miller

Physical rehabilitation plays a crucial role in restoring functional abilities, but traditional approaches often face challenges in terms of cost, accessibility, and personalized monitoring. Asynchronous physical rehabilitation has gained traction as a cost-effective and convenient alternative, but it lacks real-time monitoring and assessment capabilities. T

Mostafa Naseri, Eli De Poorter, Ingrid Moerman, H. Vincent Poor

Co-channel interference cancellation (CCI) is the process used to reduce interference from other signals using the same frequency channel, thereby enhancing the performance of wireless communication systems. An improvement to this approach is blind CCI, which reduces interference without relying on prior knowledge of the interfering signal characteristics. R

Yue-Mei Sun, Xin-Yu Wang, Liang-Jun Zhai

The driven dynamics of localization transitions in a non-Hermitian Disordered Aubry-Andr\'{e} (DAA) model are examined under both open boundary conditions (OBC) and periodic boundary conditions (PBC). Through an analysis of the static properties of observables, including the localization length ($\xi$), inverse participation ratio ($\rm IPR$), and energy gap

Etienne Manderscheid, Matthias Lee

For many call centers, customer satisfaction (CSAT) is a key performance indicator (KPI). However, only a fraction of customers take the CSAT survey after the call, leading to a biased and inaccurate average CSAT value, and missed opportunities for coaching, follow-up, and rectification. Therefore, call centers can benefit from a model predicting customer sa

Carsten Henkel

A review of the nonlocal electromagnetic response functions for the degenerate electron gas, computed within standard perturbation theory, is given. These expressions due to Lindhard, Klimontovich and Silin are used to re-analyze the Casimir interaction between two thick conducting plates in the leading order at high temperatures (zero'th term of Matsubara s

Riccardo Grazzi, Julien Siems, Arber Zela, Jörg K. H. Franke

Linear Recurrent Neural Networks (LRNNs) such as Mamba, RWKV, GLA, mLSTM, and DeltaNet have emerged as efficient alternatives to Transformers for long sequences. However, both Transformers and LRNNs struggle to perform state-tracking, which may impair performance in tasks such as code evaluation. In one forward pass, current architectures are unable to solve

Bhaskar Shukla, Owais Riyaz, Subhash Mahapatra

We discuss the classical and quantum chaos of closed strings on a recently constructed charged confining holographic background. The confining background corresponds to the charged soliton, which is a solution of minimal $d=5$ gauged supergravity. The solution has a compact spacelike direction with a Wilson line on a circle and asymptotes to $AdS_5$ with a p

Jhair S. Gallego, Ricardo E. Ramirez

This work describes the process of integrating a depth camera into the navigation system of a self-driving ground vehicle (SDV) and the implementation of a multilayer costmap that enhances the vehicle's obstacle identification process by expanding its two-dimensional field of view, based on 2D LIDAR, to a three-dimensional perception system using an RGB-D ca

Finn L. Sebastian, Simon Settele, Han Li, Benjamin S. Flavel

Semiconducting single-wall carbon nanotubes (SWCNTs) are a promising material platform for near-infrared in-vivo imaging, optical sensing, and single-photon emission at telecommunication wavelengths. The functionalization of SWCNTs with luminescent defects can lead to significantly enhanced photoluminescence (PL) properties due to efficient trapping of highl

Nadia Guiñazú, Noelia Juarez, Pablo Neme, Jorge Oviedo

This paper presents weakened notions of corewise stability and setwise stability for matching markets where agents have substitutable choice functions. We introduce the concepts of worker-quasi-core, firm-quasi-core, and worker-quasisetwise stability. We also examine their relationship to established notions in the literature, such as worker-quasi and firm-q

Ming-Tien Tsai

Under a multinormal distribution with an arbitrary unknown covariance matrix, the main purpose of this paper is to propose a framework to achieve the goal of reconciliation of Bayesian, frequentist, and Fisher's reporting $p$-values, Neyman-Pearson's optimal theory and Wald's decision theory for the problems of testing mean against restricted alternatives (c

Felisia Angela Chiarello, Simone Fagioli, Massimiliano Daniele Rosini

This paper is concerned with one-dimensional 2 x 2 systems of conservation laws with a flux f=f(x, U) that is discontinuous with respect to the spatial variable. No monotonicity assumption is imposed on the mapping x \to f(x,U). We introduce a Kruzhkov-type entropy condition and establish the global existence of entropy solutions for large data. The proof re

Yang Zou, Zhixin Chen, Zhipeng Zhang, Xingyuan Li

Image super-resolution (SR) is a classical yet still active low-level vision problem that aims to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts, serving as a key technique for image enhancement. Current approaches to address SR tasks, such as transformer-based and diffusion-based methods, are either dedicated to extracti

Winfried Hochstättler, Sophia Keip

This work builds on Varchenko et al's introduction of bilinear forms for hyperplane arrangements, where the determinant of the associated matrices factorizes into simple components. While one of the determinant formula developed by Varchenko has been generalized to complexes of oriented matroids (COMs) already, this question was open for another, distinct fo

Xiao-Xiong Zeng, Li-Fang Li, Pan Li, Bo Liang

Using the AdS/CFT correspondence, this paper investigates the holographic images of a charged black hole within the context of Lorentz symmetry breaking massive gravity. The photon rings, luminosity-deformed rings, or light points from various observational perspectives are obtained. We also study the influences of both the chemical potential and temperature

Jennifer Haase, Sebastian Pokutta

Human-AI co-creativity represents a transformative shift in how humans and generative AI tools collaborate in creative processes. This chapter explores the synergies between human ingenuity and AI capabilities across four levels of interaction: Digital Pen, AI Task Specialist, AI Assistant, and AI Co-Creator. While earlier digital tools primarily facilitated

Takol Tangphati, Grigoris Panotopoulos, Ayan Banerjee, Anirudh Pradhan

We investigate the properties of anisotropic white dwarf stars within the rainbow gravity adopting for matter content the Chandrasekhar model based on an ideal Fermi gas at zero temperature. We study in detail the effects of the anisotropic factor on stellar mass and radius, the speed of sound, and the relativistic adiabatic index in both radial and tangenti

Quang Vinh Nguyen, Vo Hoang Thanh Son, Chau Truong Vinh Hoang, Duc Duy Nguyen

Naturalistic driving action localization task aims to recognize and comprehend human behaviors and actions from video data captured during real-world driving scenarios. Previous studies have shown great action localization performance by applying a recognition model followed by probability-based post-processing. Nevertheless, the probabilities provided by th

Parosh Aziz Abdulla, Mohamed Faouzi Atig, Julie Cailler, Chencheng Liang

This paper proposes a Graph Neural Network-guided algorithm for solving word equations, based on the well-known Nielsen transformation for splitting equations. The algorithm iteratively rewrites the first terms of each side of an equation, giving rise to a tree-like search space. The choice of path at each split point of the tree significantly impacts solvin

Rania Briq, Jiangtao Wang, Stefan Kesselheim

Data pruning is the problem of identifying a core subset that is most beneficial to training and discarding the remainder. While pruning strategies are well studied for discriminative models like those used in classification, little research has gone into their application to generative models. Generative models aim to estimate the underlying distribution of

Marcus C. Christiansen, Christian Furrer

Thiele's differential equation explains the change in prospective reserve and plays a fundamental role in safe-side calculations and other types of actuarial model comparisons. This paper presents a `model lean' version of Thiele's equation with the novel feature that it supports any canonical insurance model, irrespective of the model's intertemporal depend

Yi Chen

Relativistic full weak-neutral axial-vector four-current distributions inside a general spin-$\frac{1}{2}$ hadron are systematically studied for the first time, where the second-class current contribution associated with the induced pseudotensor form factor (FF) is included. We clearly demonstrate that the 3D axial charge distribution, being parity-odd in th

Yuhao Jin, Qizhong Gao, Xiaohui Zhu, Yong Yue

While deep learning-based robotic grasping technology has demonstrated strong adaptability, its computational complexity has also significantly increased, making it unsuitable for scenarios with high real-time requirements. Therefore, we propose a low computational complexity and high accuracy model named VMGNet for robotic grasping. For the first time, we i

Panagiotis Dorlis, Nick E. Mavromatos, Sotirios-Neilos Vlachos

In this work, we elaborate further on a 4D cosmological Running-Vacuum-type Model (RVM) of inflation that characterises string-inspired Chern-Simons (CS) gravity. It has been shown that inflation in such models is caused by a condensation of the gravitational CS (gCS) terms induced by primordial gravitational waves (GW), which leads to a linear-axion potenti

Leyun Gao, Alim Ruzi, Qite Li, Chen Zhou

Entanglement is a fundamental pillar of quantum mechanics. Probing quantum entanglement and testing Bell inequality with muons can be a significant leap forward, as muon is arguably the only massive elementary particle that can be manipulated and detected over a wide range of energies, e.g., from approximately 0.3 to $10^2$ GeV, corresponding to velocities f

Remy Demichelis

This article aims to demonstrate how the approach to computing is being disrupted by deep learning (artificial neural networks), not only in terms of techniques but also in our interactions with machines. It also addresses the philosophical tradition of hermeneutics (Don Ihde, Wilhelm Dilthey) to highlight a parallel with this movement and to demystify the i

Anantha S. Rao, Donovan Buterakos, Barnaby van Straaten, Valentin John

Arrays of gate-defined semiconductor quantum dots are among the leading candidates for building scalable quantum processors. High-fidelity initialization, control, and readout of spin qubit registers require exquisite and targeted control over key Hamiltonian parameters that define the electrostatic environment. However, due to the tight gate pitch, capaciti

Steven J. Chisolm, Emily Guo, Vignesh Subramaniam, Kyle D. Schulze

Transitions between solid-like and fluid-like states in living tissues have been found in steps of embryonic development and in stages of disease progression. Our current understanding of these transitions has been guided by experimental and theoretical investigations focused on how motion becomes arrested with increased mechanical coupling between cells, ty

Hanbeom Chang, Jongseong Brad Choi, Chul Min Yeum

Indoor SLAM often suffers from issues such as scene drifting, double walls, and blind spots, particularly in confined spaces with objects close to the sensors (e.g. LiDAR and cameras) in reconstruction tasks. Real-time visualization of point cloud registration during data collection may help mitigate these issues, but a significant limitation remains in the

Gabriel Arenas-Henriquez, Felipe Diaz, David Rivera-Betancour

In the framework of AdS/CFT correspondence, the Fefferman--Graham (FG) gauge offers a useful way to express asymptotically anti-de Sitter spaces, allowing a clear identification of their boundary structure. A known feature of this approach is that choosing a particular conformal representative for the boundary metric breaks explicitly the boundary scaling sy

Kiril Bangachev, Guy Bresler, Stefan Tiegel, Vinod Vaikuntanathan

We present a polynomial-time reduction from solving noisy linear equations over $\mathbb{Z}/q\mathbb{Z}$ in dimension $\Theta(k\log n/\mathsf{poly}(\log k,\log q,\log\log n))$ with a uniformly random coefficient matrix to noisy linear equations over $\mathbb{Z}/q\mathbb{Z}$ in dimension $n$ where each row of the coefficient matrix has uniformly random suppor

Alberto Enciso, Carlos Valero

On a $3$-dimensional Riemannian manifold with boundary, we define an analogue of the Dirichlet-to-Neumann map for Beltrami fields, which are the eigenvectors of the curl operator and play a major role in fluid mechanics. This map sends the normal component of a Beltrami field to its tangential component on the boundary. In this paper we establish two results

Joanna Kaleta, Weronika Smolak-Dyżewska, Dawid Malarz, Diego Dall'Alba

Endoluminal endoscopic procedures are essential for diagnosing colorectal cancer and other severe conditions in the digestive tract, urogenital system, and airways. 3D reconstruction and novel-view synthesis from endoscopic images are promising tools for enhancing diagnosis. Moreover, integrating physiological deformations and interaction with the endoscope

Thorsten Schank, Vivien Voigt, Christian Orthey

It is well-established that the home advantage (HA), the phenomenon that on average the local team performs better than the visiting team, exists in many sports. In response to the COVID-19 outbreak, spectators were banned from football stadiums, which we leverage as a natural experiment to examine the impact of stadium spectators on HA. Using data from the

Ruyi Ding, Tong Zhou, Lili Su, Aidong Adam Ding

Adapting pre-trained deep learning models to customized tasks has become a popular choice for developers to cope with limited computational resources and data volume. More specifically, probing--training a downstream head on a pre-trained encoder--has been widely adopted in transfer learning, which helps to prevent overfitting and catastrophic forgetting. Ho

Jiachen Jiang

Around 50 years ago, the famous bet between Stephen Hawking and Kip Thorne on whether Cyg X-1 hosts a stellar-mass black hole became a well-known story in the history of black hole science. Today, Cyg X-1 is widely recognised as hosting a stellar-mass black hole with a mass of approximately 20 solar masses. With the advancement of X-ray telescopes, Cyg X-1 h

Hao Fu, Da Yang

How a cloud ensemble responds to external forcing is a puzzle in tropical convection research. Convectively coupled gravity waves (CCGWs) in a finite domain have controllable wavelengths, providing a convenient simulation setup for studying the cloud ensemble. A multiscale analysis shows that the growth of CCGWs in a finite-domain involves not only the ampli

Giulio Schimperna

We consider an evolutionary PDE system coupling the Cahn-Hilliard equation with singular potential, mass source and transport effects, to a Brinkman-type relation for the macroscopic velocity field and to a further equation describing the evolution of the concentration of a chemical substance affecting the phase separation process. The main application we ha

Jiyang He, Benjamin Favier, Stéphane Le Dizès

In rotating fluids, the viscous smoothing of inviscid singular inertial waves leads to the formation of internal shear layers. In previous works, we analysed the internal shear layers excited by a viscous forcing (longitudinal libration) in a spherical shell geometry (He \textit{et al.}, \textit{J. Fluid Mech.} {\bf 939}, A3, 2022; {\bf 974}, A3, 2023). We n

Chuanyu Li, Renjun Dang, Xiang Li, Zhiyuan Wu

This article introduces the ManiSkill-ViTac Challenge 2025, which focuses on learning contact-rich manipulation skills using both tactile and visual sensing. Expanding upon the 2024 challenge, ManiSkill-ViTac 2025 includes 3 independent tracks: tactile manipulation, tactile-vision fusion manipulation, and tactile sensor structure design. The challenge aims t

Daniel Jenson, Jhonathan Navott, Mengyan Zhang, Makkunda Sharma

Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic processes. Originally developed as a scalable alternative to Gaussian Processes (GPs), which are limited by $O(n^3)$ runtime complexity, the most accurate modern NPs can often rival GPs but still suffer from an $O(n^2)$