November 2024 arXiv papers — page 172

Farzaneh Taleb, Miguel Vasco, Antônio H. Ribeiro, Mårten Björkman

The human brain encodes stimuli from the environment into representations that form a sensory perception of the world. Despite recent advances in understanding visual and auditory perception, olfactory perception remains an under-explored topic in the machine learning community due to the lack of large-scale datasets annotated with labels of human olfactory

Waseem Akram, Sanjeev Saxena

We investigate a weighted variant of the interval stabbing problem, where the goal is to design an efficient data structure for a given set $\mathcal{I}$ of weighted intervals such that, for a query point $q$ and an integer $k>0$, we can report the $k$ intervals with largest weights among those stabbed by $q$. In this paper, we present a linear space solutio

Julian Schwab, Florian Mangold, Bettina Frank, Timothy J. Davis

Twistronics is studied intensively in twisted 2D heterostructures and its extension to trilayer moir\'e structures has proven beneficial for the tunability of unconventional correlated states and superconductivity in twisted trilayer graphene. Just recently, the concept of twistronics has been applied to plasmonic lattices with nontrivial topology, demonstra

Quechen Yang

With the increasing maturity and expansion of the cryptocurrency market, understanding and predicting its price fluctuations has become an important issue in the field of financial engineering. This article introduces an innovative Genetic Algorithm-generated Alpha Sentiment (GAS) blending ensemble model specifically designed to predict Bitcoin market trends

Dawei Dai, Xu Long, Li Yutang, Zhang Yuanhui

Human-scene vision-language tasks are increasingly prevalent in diverse social applications, yet recent advancements predominantly rely on models specifically tailored to individual tasks. Emerging research indicates that large vision-language models (VLMs) can enhance performance across various downstream vision-language understanding tasks. However, genera

Xintian Sun, Benji Peng, Charles Zhang, Fei Jin

Remote sensing has evolved from simple image acquisition to complex systems capable of integrating and processing visual and textual data. This review examines the development and application of multi-modal language models (MLLMs) in remote sensing, focusing on their ability to interpret and describe satellite imagery using natural language. We cover the tec

Qishuai Wen, Chun-Guang Li

State-of-the-art methods for Transformer-based semantic segmentation typically adopt Transformer decoders that are used to extract additional embeddings from image embeddings via cross-attention, refine either or both types of embeddings via self-attention, and project image embeddings onto the additional embeddings via dot-product. Despite their remarkable

Julian Schwab, Alexander Neuhaus, Pascal Dreher, Shai Tsesses

The study of van der Waals heterostructures with an interlayer twist, known as "twistronics", has been instrumental in advancing contemporary condensed matter research. Most importantly, it has underpinned the emergence of a multitude of strongly-correlated phases, many of which derive from the topology of the physical system. Here, we explore the applicatio

Jean-Pierre Gazeau, Mariano A. del Olmo, Hamed Pejhan

This paper obtains the matrix elements and characters of the discrete series unitary irreducible representations (UIRs) of the Sp$(4,\mathbb{R})$ group. With an isomorphic relationship to the two-fold covering of SO$_0(2,3)$ (Sp$(4,\mathbb{R}) \sim$ SO$_0(2,3)\times \mathbb{Z}_2$), this group holds particular importance as the kinematical/relativity group wi

Juan C. Petit, Saswati Ganguly, Matthias Sperl

We investigate the vibrational properties of polycrystalline monodisperse and disordered bidisperse granular packings during jamming and unjamming using discrete element method simulations. Both systems deviate from Debye scaling at low frequencies $(\omega)$, but only bidisperse packings exhibit a low-$\omega$ plateau. The low $\omega$ exponent ($\alpha$) i

P. Michael Kielstra, Tianyi Shi, Hengrui Luo, Jianliang Qian

This paper presents a multilevel tensor compression algorithm called tensor butterfly algorithm for efficiently representing large-scale and high-dimensional oscillatory integral operators, including Green's functions for wave equations and integral transforms such as Radon transforms and Fourier transforms. The proposed algorithm leverages a tensor extensio

Sumantrak Mukherjee, Mengyan Zhang, Seth Flaxman, Sebastian Josef Vollmer

We study the problem of globally optimising a target variable of an unknown causal graph on which a sequence of soft or hard interventions can be performed. The problem of optimising the target variable associated with a causal graph is formalised as Causal Bayesian Optimisation (CBO). We study the CBO problem under the cumulative regret objective with unkno

Weian Guo, Ruizhi Sha, Li Li, Lun Zhang

To alleviate computational load on RSUs and cloud platforms, reduce communication bandwidth requirements, and provide a more stable vehicular network service, this paper proposes an optimized pinning control approach for heterogeneous multi-network vehicular ad-hoc networks (VANETs). In such networks, vehicles participate in multiple task-specific networks w

Lu Wang-Nöth, Philipp Heiler, Hai Huang, Daniel Lichtenstern

Objective. Electroencephalography (EEG) is a widely used neuroimaging technique known for its cost-effectiveness and user-friendliness. However, various artifacts, particularly biological artifacts like Electromyography (EMG) signals, lead to a poor signal-to-noise ratio, limiting the precision of analyses and applications. The currently reported EEG data cl

Zelin Yao, Chuang Liu, Xianke Meng, Yibing Zhan

Graph neural networks (GNNs) are gaining popularity for processing graph-structured data. In real-world scenarios, graph data within the same dataset can vary significantly in scale. This variability leads to depth-sensitivity, where the optimal depth of GNN layers depends on the scale of the graph data. Empirically, fewer layers are sufficient for message p

Nilasis Chaudhuri, Tomasz Piasecki, Ewelina Zatorska

In this paper we prove the local-in-time existence of regular solutions to dissipative Aw-Rascle system with the offset equal to gradient of some increasing and regular function of density. It is a mixed degenerate parabolic-hyperbolic hydrodynamic model, and we extend the techniques previously developed for compressible Navier-Stokes equations to show the w

CMS Collaboration

Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a GEANT-based simulation of the detectors are used to produce large samples of simulated events for analysis by the LHC experiments. These sim

Alexandru-Victor Andrei, Georg Velev, Filip-Mihai Toma, Daniel Traian Pele

Energy is a critical driver of modern economic systems. Accurate energy price forecasting plays an important role in supporting decision-making at various levels, from operational purchasing decisions at individual business organizations to policy-making. A significant body of literature has looked into energy price forecasting, investigating a wide range of

Roberto Riaño, Gorka Abad, Stjepan Picek, Aitor Urbieta

While security vulnerabilities in traditional Deep Neural Networks (DNNs) have been extensively studied, the susceptibility of Spiking Neural Networks (SNNs) to adversarial attacks remains mostly underexplored. Until now, the mechanisms to inject backdoors into SNN models have been limited to digital scenarios; thus, we present the first evaluation of backdo

Daniel de Vassimon Manela, Linying Yang, Robin J. Evans

Ensuring robust model performance in diverse real-world scenarios requires addressing generalizability across domains with covariate shifts. However, no formal procedure exists for statistically evaluating generalizability in machine learning algorithms. Existing predictive metrics like mean squared error (MSE) help to quantify the relative performance betwe

Stefanie Lenzer, Laura Pannullo, Andreas Nehring, Lisa Stinken-Roesner

Scientific literacy, a central goal of modern science education, should be accessible to all students regardless of their backgrounds. Despite international education reforms focused on diversity, equity and inclusion many teachers struggle to create inclusive science lessons. One reason may be, that creating inclusive science lessons is challenging: it requ

Isaac Baglin, Xiatian Zhu, Simon Hadfield

Federated Learning is a privacy preserving decentralized machine learning paradigm designed to collaboratively train models across multiple clients by exchanging gradients to the server and keeping private data local. Nevertheless, recent research has revealed that the security of Federated Learning is compromised, as private ground truth data can be recover

Zhiou Zhang, Weian Guo, Li Li, Dongyang Li

This letter presents a blockchain-based multi-path mobile access point (MAP) selection strategy for secure 5G vehicular ad-hoc networks (VANETs). The proposed method leverages blockchain technology for decentralized, transparent, and secure MAP selection, while the multi-path transmission strategy enhances network reliability and reduces communication delays

David S. Pereira, João Ferraz, Francisco S. N. Lobo, José P. Mimoso

This review delves into the pivotal primordial stage of the universe, a period that holds the key to understanding its current state. To fully grasp this epoch, it is essential to consider three fundamental domains of physics: gravity, particle physics, and thermodynamics. The thermal history of the universe recreates the extreme high-energy conditions that

Łukasz Kułacz, Adrian Kliks

Spectrum occupancy detection is a key enabler for dynamic spectrum access, where machine learning algorithms are successfully utilized for detection improvement. However, the main challenge is limited access to labeled data about users transmission presence needed in supervised learning models. We present a distributed federated learning approach that addres

Thomas Hackl, Markus Ankenbrand, Bart van Adrichem, David Wilkins

The effective visualization of genomic data is crucial for exploring and interpreting complex relationships within and across genes and genomes. Despite advances in developing dedicated bioinformatics software, common visualization tools often fail to efficiently integrate the diverse datasets produced in comparative genomics, lack intuitive interfaces to co

Bikalpa Gautam, Anmol Guragain, Sarthak Giri

The construction industry faces high risks due to frequent accidents, often leaving workers in perilous situations where rapid response is critical. Traditional safety monitoring methods, including wearable sensors and GPS, often fail under obstructive or indoor conditions. This research introduces a novel real-time scream detection and localization system t

Laura Engelhardt, Renate Sachse, Rainer Burgkart, Wolfgang A. Wall

The shoulder joint is one of the functionally and anatomically most sophisticated articular systems in the human body. Both complex movement patterns and the stabilization of the highly mobile joint rely on intricate three-dimensional interactions among various components. Continuum-based finite element models can capture such complexity, and are thus partic

David Huk, Mark Steel, Ritabrata Dutta

We propose reinterpreting copula density estimation as a discriminative task. Under this novel estimation scheme, we train a classifier to distinguish samples from the joint density from those of the product of independent marginals, recovering the copula density in the process. We derive equivalences between well-known copula classes and classification prob

Jisong Kim, Minjae Seong, Jun Won Choi

Accurate and robust 3D object detection is a critical component in autonomous vehicles and robotics. While recent radar-camera fusion methods have made significant progress by fusing information in the bird's-eye view (BEV) representation, they often struggle to effectively capture the motion of dynamic objects, leading to limited performance in real-world s

Georgy Andryushchenko, Vladimir Ivanov, Vladimir Makharev, Elizaveta Tukhtina

Question answering over source code provides software engineers and project managers with helpful information about the implemented features of a software product. This paper presents a work devoted to using large language models for question answering over source code in Python. The proposed method for implementing a source code question answering system in

Michal Bujak, Rafal Kucharski

In a ride-pooling system, travellers experience discomfort associated with a detour and a longer travel time, which is compensated with a sharing discount. Most studies assume travellers receive either a flat discount or, in rare cases, a proportional to the inconvenience. We show the system benefits from individually tailored fares. We argue that fares that

A. Tsolakis, L. Ferranti, V. Reppa

This paper introduces a Fault Diagnosis (Detection, Isolation, and Estimation) method using Set-Membership Estimation (SME) designed for a class of nonlinear systems that are linear to the fault parameters. The methodology advances fault diagnosis by continuously evaluating an estimate of the fault parameter and a feasible parameter set where the true fault

Ahmadreza Sezavar, Catarina Brites, Joao Ascenso

Emerging event cameras acquire visual information by detecting time domain brightness changes asynchronously at the pixel level and, unlike conventional cameras, are able to provide high temporal resolution, very high dynamic range, low latency, and low power consumption. Considering the huge amount of data involved, efficient compression solutions are very

Minjin Choi, Hoon Ryu

We design a variational quantum algorithm to solve multi-dimensional Poisson equations with mixed boundary conditions that are typically required in various fields of computational science. Employing an objective function that is formulated with the concept of the minimal potential energy, we not only present in-depth discussion on the cost-efficient & noise

Thomas P Cannon, Özgür Simsek

Continual reinforcement learning poses a major challenge due to the tendency of agents to experience catastrophic forgetting when learning sequential tasks. In this paper, we introduce a modularity-based approach, called Hierarchical Orchestra of Policies (HOP), designed to mitigate catastrophic forgetting in lifelong reinforcement learning. HOP dynamically

Sijie Dong, Soror Sahri, Themis Palpanas

Artificial intelligence (AI) has transformed various fields, significantly impacting our daily lives. A major factor in AI success is high-quality data. In this paper, we present a comprehensive review of the evolution of data quality (DQ) awareness from traditional data management systems to modern data-driven AI systems, which are integral to data science.

Michal Kaufman, Jaroslav Vlcek, Jiri Houska, Sadoon Farrukh

The reversible semiconductor-to-metal transition of vanadium dioxide (VO2) makes VO2-based coatings a promising candidate for thermochromic smart windows, reducing the energy consumption of buildings. We report low-temperature (320 degC) depositions of thermochromic V1-x-yWxSryO2 films with a thickness of 71-73 nm onto 170-175 nm thick Y-stabilized ZrO2 laye

Rohit Kumar Mishra, Anamika Purohit, Indrani Zamindar

In this article, we study the problem of recovering symmetric $m$-tensor fields (including vector fields) supported in a unit disk $\mathbb{D}$ from a set of generalized V-line transforms, namely longitudinal, transverse, and mixed V-line transforms, and their integral moments. We work in a circular geometric setup, where the V-lines have vertices on a circl

Samuel Lee, Zach Wood-Doughty

Causal understanding is a fundamental goal of evidence-based medicine. When randomization is impossible, causal inference methods allow the estimation of treatment effects from retrospective analysis of observational data. However, such analyses rely on a number of assumptions, often including that of no unobserved confounding. In many practical settings, th

Timo Brand, Stephan Held

Recently, Van Hoeve proposed an algorithm for graph coloring based on an integer flow formulation on decision diagrams for stable sets. We prove that the solution to the linear flow relaxation on exact decision diagrams determines the fractional chromatic number of a graph. This settles the question whether the decision diagram formulation or the fractional

David Immel, Ralf Drautz, Godehard Sutmann

Large-scale atomistic simulations rely on interatomic potentials providing an efficient representation of atomic energies and forces. Modern machine-learning (ML) potentials provide the most precise representation compared to electronic structure calculations while traditional potentials provide a less precise, but computationally much faster representation

Mark Mineev-Weinstein, Oleg Alekseev

We address pattern selection problems in nonlinear interface dynamics by maximizing the entropy of the most probable (classical) scenario associated with the processes. This variational principle we applied to well-known selection problems in a Hele-Shaw cell: stationary Saffman-Taylor finger in a channel and self-similar finger in a wedge. The obtained resu

Sean Richards, Jan Eldridge, Sohan Ghodla, Max Briel

The recent discovery of examples of intermediate-mass helium stars have offered new insights into interacting binaries. These observations will allow significant improvements in our understanding of helium stars. However, in the creation of these stars their companions may accrete a significant amount of helium-rich stellar material. These creates stars with

Bin Huang, Siyu Wang, Yuanpeng Chen, Yidan Wu

This technical report outlines the methodologies we applied for the PRCV Challenge, focusing on cognition and decision-making in driving scenarios. We employed InternVL-2.0, a pioneering open-source multi-modal model, and enhanced it by refining both the model input and training methodologies. For the input data, we strategically concatenated and formatted t

Thomas P Cannon, Özgür Simsek

Creating reinforcement learning agents that generalise effectively to new tasks is a key challenge in AI research. This paper introduces Fracture Cluster Options (FraCOs), a multi-level hierarchical reinforcement learning method that achieves state-of-the-art performance on difficult generalisation tasks. FraCOs identifies patterns in agent behaviour and for

Eiffat E Zaman, Rahima Khanam

The global shift towards renewable energy has pushed PV cell manufacturing as a pivotal point as they are the fundamental building block of green energy. However, the manufacturing process is complex enough to lose its purpose due to probable defects experienced during the time impacting the overall efficiency. However, at the moment, manual inspection is be

Che Chen Tho, Yee Sin Ang

Band alignment of metal contacts to 2D semiconductors often deviate from the ideal Shottky-Mott (SM) rule due to the non-ideal factors such as the formation of interface dipole and metal-induced gap states (MIGS). Although MIGS can be strongly suppressed using van der Waals (vdW) contact engineering, the interface dipole is hard to eliminate due to the elect

Christofer Fellicious, Lorenz Wendlinger, Mario Gancarski, Jelena Mitrovic

Supervised machine learning often encounters concept drift, where the data distribution changes over time, degrading model performance. Existing drift detection methods focus on identifying these shifts but often overlook the challenge of acquiring labeled data for model retraining after a shift occurs. We present the Strategy for Drift Sampling (SUDS), a no

Yongjie Pan, Jiatong Yan, Sansheng Yang, Baocheng Zhang

We explore the entanglement dynamics of two detectors undergoing uniform acceleration and circular motion within a massive scalar field, while also investigating the influence of the anti-Unruh effect on entanglement harvesting. Contrary to the conventional understanding of the weak anti-Unruh effect, where entanglement typically increases, we observe that t

Snehasish Paul, Shivali Chauhan, Atul Kumar Pal

This study investigated the integration of cutting-edge technologies and methodologies for creating dynamic, user-centered library environments. In creative strategies for engagement and innovation, library users must be empowered to undertake the new role of modernizing library services and enhancing user experiences. It also enhances the information manage

Junchen Fu, Xuri Ge, Xin Xin, Alexandros Karatzoglou

Multimodal foundation models (MFMs) have revolutionized sequential recommender systems through advanced representation learning. While Parameter-efficient Fine-tuning (PEFT) is commonly used to adapt these models, studies often prioritize parameter efficiency, neglecting GPU memory and training speed. To address this, we introduced the IISAN framework, signi

Madhukrishna Chakraborty, Subenoy Chakraborty

The paper deals with the modified Raychaudhuri equation (RE) within the framework of homogeneous and isotropic Fractal Universe. Focusing of a congruence of time-like geodesics has been examined for three generic choices of the fractal function. Finally, comments on the existence and possible avoidance of the initial big-bang singularity have been made by ex

Xin-Yue Liu, Chun-Jie Yang, Jun-Hong An

As an ideal platform for exploring strong quantized light-matter interactions, surface plasmon polariton (SPP) has inspired many applications in quantum technologies. Recent experiments discovered that quantum surface effects (QSEs) of the metal, including nonlocal optical response, electron spill-out, and Landau damping, invalidate the classical electromagn

Adrien LeCoz, Stéphane Herbin, Faouzi Adjed

For classification models based on neural networks, the maximum predicted class probability is often used as a confidence score. This score rarely predicts well the probability of making a correct prediction and requires a post-processing calibration step. However, many confidence calibration methods fail for problems with many classes. To address this issue

Qian Li, Qianchuan Wang, Junji Jia

We investigate the absorption and scattering of a charged massive scalar field by a charged Horndeski black hole using both the approximation or classical geometric method and the partial wave method and compare the numerical and analytical results, which are found to agree with each other very well. We observe that an increase in either the BH charge $Q$ or

Ignas Lukosiunas, Kestutis Staliunas

We consider a scheme of thin films, deposited on periodically modulated amplifying materials. We show that the reflection from such meta-interface can undergo substantial amplification, due to Fano resonances in the thin films. The amplification strongly increases when the Fano waveguiding modes approach the edge of the continuum and degenerate into the leak

S. Howard, N. Weisse, J. Schroeder, C. Barbero

Wavefront reconstruction is a critical component in various optical systems, including adaptive optics, interferometry, and phase contrast imaging. Traditional reconstruction methods often employ either the Cartesian (pixel) basis or the Zernike polynomial basis. While the Cartesian basis is adept at capturing high-frequency features, it is susceptible to ov

Yang Zhao, Zidong Nie, Kangsheng Dong, Qinghua Huang

The application of intelligent decision-making in unmanned aerial vehicle (UAV) is increasing, and with the development of UAV 1v1 pursuit-evasion game, multi-UAV cooperative game has emerged as a new challenge. This paper proposes a deep reinforcement learning-based model for decision-making in multi-role UAV cooperative pursuit-evasion game, to address the

Rikima Kuwada, Shuto Ito, Yuta Shimoda, Haruka Fukunishi

Materials with J-shaped stress-strain behavior under uniaxial stretching, where strength increases as deformation progresses, have been developed through various materials designs. On the other hand, polymer materials that progressively stiffen under bending remain unrealized. To address this gap, this study drew inspiration from membrane tensegrity structur

Walter D. van Suijlekom

We extend our previous definition of K-theoretic invariants for operator systems based on hermitian forms to higher K-theoretical invariants. We realize the need for a positive parameter $\delta$ as a measure for the spectral gap of the representatives for the K-theory classes. For each $\delta$ and integer $p \geq 0$ this gives operator system invariants $\

Thomas I. Strasser, Edmund Widl, René A. Kuchenbuch, Laura Lázaro-Elorriaga

Interoperability is the key to implementing a wide range of energy systems applications. It involves the seamless cooperation of different methods and components. With smart energy systems, interoperability faces challenges due to integrating differ-ent approaches and technologies. This includes dealing with heterogeneous approaches with various communicatio

Zongchen Chen, Aditya Lonkar, Chunyang Wang, Kuan Yang

We present efficient counting and sampling algorithms for random $k$-SAT when the clause density satisfies $\alpha \le \frac{2^k}{\mathrm{poly}(k)}.$ In particular, the exponential term $2^k$ matches the satisfiability threshold $\Theta(2^k)$ for the existence of a solution and the (conjectured) algorithmic threshold $2^k (\ln k) / k$ for efficiently finding

Xin Wen, Xuening Zhu, Renjiao Yi, Zhifeng Wang

Reconstructing from multi-view images is a longstanding problem in 3D vision, where neural radiance fields (NeRFs) have shown great potential and get realistic rendered images of novel views. Currently, most NeRF methods either require accurate camera poses or a large number of input images, or even both. Reconstructing NeRF from few-view images without pose

Nayandeep Deka Baruah, Abhishek Sarma

A partition is said to be $\ell$-regular if none of its parts is a multiple of $\ell$. Let $b^\prime_5(n)$ denote the number of 5-regular partitions into distinct parts (equivalently, into odd parts) of $n$. This function has also close connections to representation theory and combinatorics. In this paper, we study arithmetic properties of $b^\prime_5(n)$. W

Keivan Navaie

As 6G evolves into an AI-native technology, the integration of artificial intelligence (AI) and Generative AI into cellular communication systems presents unparalleled opportunities for enhancing connectivity, network optimization, and personalized services. However, these advancements also introduce significant data protection challenges, as AI models incre

Jurriaan Rot, Sebastian Junges, Harsh Beohar

Geuvers and Jacobs (LMCS 2021) formulated the notion of apartness relation on state-based systems modelled as coalgebras. In this context apartness is formally dual to bisimilarity, and gives an explicit proof system for showing that certain states are not bisimilar. In the current paper, we relate apartness to another classical element of the theory of beha

Wei Hao, M. Atif Sultan, En Wang

The mass spectrum of the charmed mesons is investigated by considering the coupled channel effects within the nonrelativistic potential model. The predicted masses of the charmed mesons are in agreement with experimental data. The strong decay properties are further analyzed within the $^3P_0$ model by using numerical wave functions obtained from nonrelativi

Matheus Puime Pedra, Josune Hernantes, Leire Casals, Leire Labaka

Climate change-associated disasters have become a significant concern, principally when affecting urban areas. Assessing these regions' resilience to strengthen their disaster management is crucial, especially in the areas vulnerable to windstorms, one of Spain's most critical disasters. Smart cities and machine learning offer promising solutions to manage d

Francisco Giral, Ignacio Gómez, Ricardo Vinuesa, Soledad Le Clainche

This study presents a transformer-based approach for fault-tolerant control in fixed-wing Unmanned Aerial Vehicles (UAVs), designed to adapt in real time to dynamic changes caused by structural damage or actuator failures. Unlike traditional Flight Control Systems (FCSs) that rely on classical control theory and struggle under severe alterations in dynamics,

Xiaoliang Liu, Furao Shen, Jian Zhao

The Segment Anything Model (SAM) is a cornerstone of image segmentation, demonstrating exceptional performance across various applications, particularly in autonomous driving and medical imaging, where precise segmentation is crucial. However, SAM is vulnerable to adversarial attacks that can significantly impair its functionality through minor input perturb

Maren Pielka, Tobias Schneider, Jan Terheyden, Rafet Sifa

We present an outline of the first large language model (LLM) based chatbot application in the context of patient-reported outcome measures (PROMs) for diabetic retinopathy. By utilizing the capabilities of current LLMs, we enable patients to provide feedback about their quality of life and treatment progress via an interactive application. The proposed fram

Liv Kåreborn, Erica Ingerstad, Amanda Berg, Justus Karlsson

In this work, the seasonal predictive capabilities of Neural Radiance Fields (NeRF) applied to satellite images are investigated. Focusing on the utilization of satellite data, the study explores how Sat-NeRF, a novel approach in computer vision, performs in predicting seasonal variations across different months. Through comprehensive analysis and visualizat

Dylan Laplace Mermoud

This paper provides formulae and algorithms to compute the projection onto the core of a preimputation outside it. The core of a game is described using an exponential number of linear constraints, and we cannot know beforehand which are redundant or defining the polytope. We apply these new results to market games, a class of games in which every game has a

S. A. Mohideen, A. J. Howard, C. Cheng, I. Gabalski

We investigate the orientation dependence of Enhanced Ionization (EI) during strong-field-driven nuclear motion in acetylene (C$_2$H$_2$). Here, we both initiate and probe molecular dynamics in acetylene with intense 6-fs cross-polarized pulse pairs, separated by a variable delay. Following multiple ionization by the first pulse, acetylene undergoes simultan

Yanru Chen, Houshan Fu, Suijie Wang, Jinxing Yang

This paper primarily investigates a specific type of deformation of the braid arrangement $\mathcal{B}_n$ in $\mathbb{R}^n$, denoted by $\mathcal{B}_n^A$ and defined in (1.2). Let $r_l(\mathcal{B}_n^A)$ be the number of regions of level $l$ in $\mathcal{B}_n^A$ with the corresponding exponential generating function $R_l(A;x)$. Using the weighted digraph mode

Dingxin Zhang

We show that if V is a subvariety of the affine N-space defined by polynomials of degree at most d, then the sum of its $\ell$-adic Betti numbers does not exceed $2(N + 1)^{2N +1}(d+ 1)^N$. This answers a question of Katz (FFA 2001).

Xavier Timoneda, Markus Herb, Fabian Duerr, Daniel Goehring

LiDAR Semantic Segmentation is a fundamental task in autonomous driving perception consisting of associating each LiDAR point to a semantic label. Fully-supervised models have widely tackled this task, but they require labels for each scan, which either limits their domain or requires impractical amounts of expensive annotations. Camera images, which are gen

D. A. Green

A revised catalogue of 310 Galactic supernova remnants (SNRs) is presented, along with some statistics of their properties. 21 SNRs have been added to the catalogue since the previous published version from 2019, and 5 entries have been removed, as they have been identified as HII regions. Also discussed are some basics statistics of the remnants in the cata

Manish Chaudhary, Zhiyuan Lin, Shuang Li, Mohan Zhang

We develop methods for performing quantum teleportation of the total spin variables of an unknown state, using quantum nondemolition measurements, spin projection measurements, and classical communication. While theoretically teleportation of high-dimensional states can be attained with the assumption of generalized Bell measurements, this is typically exper

Zdzislaw Burda, Desmond A. Johnston, Mario Kieburg

Using the electrostatic analogy, we derive an exact formula for the limiting Yang-Lee zero distribution in the random allocation model of general weights. This exhibits a real-space condensation phase transition, which is induced by a pressure change. The exact solution allows one to read off the scaling of the density of zeros at the critical point and the

Carlotta Accettura, Simon Adrian, Rohit Agarwal, Claudia Ahdida

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that hav

A. A. Vorobyova, A. I. Boltalin, D. M. Tsymbarenko, I. V. Morozov

The Re5+(5d2) compounds possess large spin-orbital interaction which urges for large anisotropy, non-collinear structures and other phenomena. Here we present ReCl5 composed by separate Re2Cl10 units formed by edge-shared chlorine octahedra. It demonstrates the formation of antiferromagnetically ordered state in two steps at TN1 = 35.5 K and TN2 = 13.2 K see

Pourya Jafarzadeh, Amir Mohammad Rostami, Padideh Choobdar

Speech is the most natural way of expressing ourselves as humans. Identifying emotion from speech is a nontrivial task due to the ambiguous definition of emotion itself. Speaker Emotion Recognition (SER) is essential for understanding human emotional behavior. The SER task is challenging due to the variety of speakers, background noise, complexity of emotion

Sakshi Bhayana, Biswajit Nag

This study explores whether the Global Value Chain(GVC) participation of 16 emerging market economies (EMEs) from 1995 to 2018 in the manufacturing sector leads to a rise in carbon emissions embodied in trade. The study covers the ecological dimension of the Global Value Chain and validates the Pollution Haven Hypothesis in developing nations.To address the

Ashish Kujur, Md Ramiz Reza

A well known result of Brown and Halmos shows that the Toeplitz operators induced by $L^{\infty}(\mathbb T)$ symbols on the Hardy space of the unit disc $\mathbb D$ are characterized by the operator identity $T_{\bar{z}}AT_z=A,$ where $T_z, T_{\bar{z}}$ are the Toeplitz operators induced by the function $z$ and $\bar{z}$ on the unit circle $\mathbb T$ respec

Xiangyu Wang

We revisit the Ou-Wang's approach to the cone restriction problem via polynomial partitioning. By recasting their inductive scheme as a recursive algorithm and incorporating the nested polynomial Wolff axioms, we obtain improved bounds for cone restriction estimates in higher dimensions.

Hongkui Wang, Xinmin Hou

Let $k$, $t$ and $m$ be positive integers. A $k$-multiset of $[m]$ is a collection of $k$ elements of $[m]$ with repetition and without ordering. We use $\left(\binom {[m]}{k}\right)$ to denote all the $k$-multisets of $[m]$. Two multiset families $\mathcal{F}$ and $\mathcal{G}$ in $\left(\binom {[m]}{k}\right)$ are called cross $t$-intersecting if $|F\cap G

Jiejun Tan, Zhicheng Dou, Wen Wang, Mang Wang

Retrieval-Augmented Generation (RAG) has been shown to improve knowledge capabilities and alleviate the hallucination problem of LLMs. The Web is a major source of external knowledge used in RAG systems, and many commercial RAG systems have used Web search engines as their major retrieval systems. Typically, such RAG systems retrieve search results, download

Keisuke Fujii, Yuto Ashida

We consider two-dimensional continuum fluids with odd viscosity under a chiral body force. The chiral body force makes the low-energy excitation spectrum of the fluids gapped, and the odd viscosity allows us to introduce the first Chern number of each energy band in the fluids. Employing a mapping between hydrodynamic variables and U(1) gauge-field strengths

Nikola Milosevic, Johannes Müller, Nico Scherf

Reinforcement Learning (RL) agents can solve diverse tasks but often exhibit unsafe behavior. Constrained Markov Decision Processes (CMDPs) address this by enforcing safety constraints, yet existing methods either sacrifice reward maximization or allow unsafe training. We introduce Constrained Trust Region Policy Optimization (C-TRPO), which reshapes the pol

Anup B. Rao, Peng Zhang

We investigate experimental design for randomized controlled trials (RCTs) with both equal and unequal treatment-control assignment probabilities. Our work makes progress on the connection between the distributional discrepancy minimization (DDM) problem introduced by Harshaw et al. (2024) and the design of RCTs. We make two main contributions: First, we pro

Rajesh Kumar, Rajesh Kumar Yadav, Avinash Khare

This paper presents the first-order supersymmetric rational extension of the quantum anisotropic harmonic oscillator (QAHO) in multiple dimensions, including full-line, half-line, and their combinations. The exact solutions are in terms of the exceptional orthogonal polynomials. The rationally extended potentials are isospectral to the conventional QAHOs.

Heiko Oppel, Michael Munz

Kinematic sensors are often used to analyze movement behaviors in sports and daily activities due to their ease of use and lack of spatial restrictions, unlike video-based motion capturing systems. Still, the generation, and especially the labeling of motion data for specific activities can be time-consuming and costly. Additionally, many models struggle wit

Vahid Khandan, Ryan C. Chiechi, Elisabeth Verpoorte, Klaus Mathwig

Diffusion-based microfluidic gradient generators (DMGGs) are essential for various in-vitro studies due to their ability to provide a convection-free concentration gradient. However, these systems, often referred to as membrane-based DMGGs, exhibit delayed gradient formation due to the incorporated flow-resistant membrane. This limitation substantially hinde

Shuonan Wu, Hao Zhou

This paper presents a novel stabilized nonconforming finite element method for solving the surface biharmonic problem. The method extends the New-Zienkiewicz-type (NZT) element to polyhedral (approximated) surfaces by employing the Piola transform to establish the connection of vertex gradients across adjacent elements. Key features of the surface NZT finite

Xingjian Tang, Jingwei Guan, Linge Li, Ran Shi

Diffusion models, as powerful generative models, have found a wide range of applications and shown great potential in solving image reconstruction problems. Some works attempted to solve MRI reconstruction with diffusion models, but these methods operate directly in pixel space, leading to higher computational costs for optimization and inference. Latent dif

Gihwan Kim, Andreas Butler, Vinicius S. Ferreira, Xueyue Zhang

Rapid and accurate initialization of qubits, reset, is a crucial building block for various tasks in quantum information processing, such as quantum error-correction and estimation of statistics of noisy quantum devices with many qubits. We demonstrate unconditional reset of a frequency-tunable transmon qubit that simultaneously resets multiple excited state

Eric Volkmann, Alena Brändle, Daniel Durstewitz, Georgia Koppe

Data-driven inference of the generative dynamics underlying a set of observed time series is of growing interest in machine learning and the natural sciences. In neuroscience, such methods promise to alleviate the need to handcraft models based on biophysical principles and allow to automatize the inference of inter-individual differences in brain dynamics.

Taras Panov

Exponential actions defined by vector configurations provide a universal framework for several constructions of holomorphic dynamics, non-Kaehler complex geometry, toric geometry and topology. These include leaf spaces of holomorphic foliations, intersections of real and Hermitian quadrics, the quotient construction of simplicial toric varieties, LVM and LVM