October 2024 arXiv papers — page 74

Oleg Sautenkov, Selamawit Asfaw, Yasheerah Yaqoot, Muhammad Ahsan Mustafa

The swift advancement of unmanned aerial vehicle (UAV) technologies necessitates new standards for developing human-drone interaction (HDI) interfaces. Most interfaces for HDI, especially first-person view (FPV) goggles, limit the operator's ability to obtain information from the environment. This paper presents a novel interface, FlightAR, that integrates a

Haowei Zhu, Dehua Tang, Ji Liu, Mingjie Lu

Diffusion models have achieved remarkable progress in the field of image generation due to their outstanding capabilities. However, these models require substantial computing resources because of the multi-step denoising process during inference. While traditional pruning methods have been employed to optimize these models, the retraining process necessitate

Moorad Alexanian

A recently introduced recurrence-relation ansatz applied to the Bose-Hubbard model is here used in the Fermi-Hubbard model. The resulting modified Fermi-Hubbard model is soluble and exhibits a continuous phase transition (second order) reminiscent of the integer quantum Hall resistance and a ground-state, first-order phase transition.

Orlenys López-Pintado, Marlon Dumas

In business process simulation, resource availability is typically modeled by assigning a calendar to each resource, e.g., Monday-Friday, 9:00-18:00. Resources are assumed to be always available during each time slot in their availability calendar. This assumption often becomes invalid due to interruptions, breaks, or time-sharing across processes. In other

Rajat Gupta, Aditi Savalia

In the spirit of the work of Hardy-Littlewood and Lavrik, we study the Dirichlet series associated to the generalized divisor function $\sigma_{\alpha}(n):=\sum_{d|n}d^{\alpha}$. We obtain an exact identity relating the Dirichlet series $\zeta(s)\zeta(s-\alpha)$ and a segment of the Euler product attached to it. Specifically, our main theorems are valid in t

Lena Heinemann, Alexander Jaus, Zdravko Marinov, Moon Kim

Within this work, we introduce LIMIS: The first purely language-based interactive medical image segmentation model. We achieve this by adapting Grounded SAM to the medical domain and designing a language-based model interaction strategy that allows radiologists to incorporate their knowledge into the segmentation process. LIMIS produces high-quality initial

Julian Schneider, Balint Varga, Sören Hohmann

This paper explores cooperative trajectory planning approaches within the context of human-machine shared control. In shared control research, it is typically assumed that the human and the automation use the same reference trajectory to stabilize the coupled system. However, this assumption is often incorrect, as they usually follow different trajectories,

Annika Ofenloch, Jan Sören Schwarz, Deborah Tolk, Tobias Brandt

Co-simulation is commonly used for the analysis of complex cyber-physical energy systems (CPES). Different domain-specific simulation tools and modeling approaches are used to simulate all or parts of the system. The co-simulation framework mosaik is a powerful tool to couple these simulation tools and models. This paper identifies the limitations of mosaik

Christian Lafforgue, Boris Zabelich, Camille-Sophie Brès

Electro-optic (EO) modulation is a key functionality to have on-chip. However, achieving a notable linear EO effect in stoichiometric silicon nitride has been a persistent challenge due to the material's intrinsic properties. Recent advancements revealed that the displacement of thermally excited charge carriers under a high electric field induces a second-o

Dominik Fuchsgruber, Tim Poštuvan, Stephan Günnemann, Simon Geisler

Many applications in traffic, civil engineering, or electrical engineering revolve around edge-level signals. Such signals can be categorized as inherently directed, for example, the water flow in a pipe network, and undirected, like the diameter of a pipe. Topological methods model edge signals with inherent direction by representing them relative to a so-c

Huned Materwala, Shraddha M. Naik, Aya Taha, Tala Abdulrahman Abed

Decentralized Finance (DeFi) leverages blockchain-enabled smart contracts to deliver automated and trustless financial services without the need for intermediaries. However, the public visibility of financial transactions on the blockchain can be exploited, as participants can reorder, insert, or remove transactions to extract value, often at the expense of

Kamińska Barbara, Sznajd-Weron Katarzyna

The growing interest in models of opinion dynamics, particularly those that distinguish between private beliefs and publicly expressed opinions, spans several academic disciplines, from the social sciences to the hard sciences. They have been developed to study decision-making mechanisms and applied to many social phenomena, such as pluralistic ignorance, th

Alessandro Fortunati, Massimiliano d'Aquino, Claudio Serpico

Here we consider the magnetization dynamics in ferromagnetic nanoparticles or films driven by external magnetic field pulses directed transverse to the initial equilibrium state. The excitation pulse drives large-angle ultra-fast magnetization dynamics that may eventually end up in the reversed equilibrium realizing successful precessional switching. We cons

Chihaya Jibiki, Shuhei Maruyama

In this paper, we construct countably many isolated circular orders on the free products $G = F_{2n} \ast \mathbb{Z}_{m_1} \ast \cdots \ast \mathbb{Z}_{m_k}$ of cyclic groups. Moreover, we prove that these isolated circular orders are not the automorphic images of the others. By using these isolated circular orders, we also construct countably many isolated

Jean-Yves Degos

Let $p$ be a primer number, $n \geq 3$ and integer. Let $f(X) = X^n + a_{n-1}X^{n-1} + \cdots +a_1 X + a_0 \in \mathbb{F}_p[X]$ be a primitive polynomial of degree $n$. Let $C_f$ be the companion matrix of $f(X)$, and $G$ the companion matrix of the polynomial $X^n-1$. Define $G_1 := C_f$ and $G_{k+1} = G G_k G^{-1}$ for $0 \leq k \leq n-1$. The so called ``

Bryan R. Christ, Zack Gottesman, Jonathan Kropko, Thomas Hartvigsen

Math reasoning is an active area of Large Language Model (LLM) research because it is a hallmark of artificial intelligence and has implications in several domains, including math education. However, few works have explored how math reasoning is encoded within LLM parameters and if it is a skill that can be isolated within models. Doing so could allow target

Junchang Wang, Manos Athanassoulis

Bitmap indexes are widely used for read-intensive analytical workloads because they are clustered and offer efficient reads with a small memory footprint. However, they are notoriously inefficient to update. As analytical applications are increasingly fused with transactional applications, leading to the emergence of hybrid transactional/analytical processin

Biswanath Dutta, Debanjali Bain

Artificial intelligence (AI) has become indispensable for managing and processing the vast amounts of data generated during the COVID-19 pandemic. Ontology, which formalizes knowledge within a domain using standardized vocabularies and relationships, plays a crucial role in AI by enabling automated reasoning, data integration, semantic interoperability, and

Maurice Kraus, Felix Divo, Devendra Singh Dhami, Kristian Kersting

Time series data is prevalent across numerous fields, necessitating the development of robust and accurate forecasting models. Capturing patterns both within and between temporal and multivariate components is crucial for reliable predictions. We introduce xLSTM-Mixer, a model designed to effectively integrate temporal sequences, joint time-variate informati

Django Beatty, Kritsada Masanthia, Teepakorn Kaphol, Niphan Sethi

As large language models (LLMs) become integral to recruitment processes, concerns about AI-induced bias have intensified. This study examines biases in candidate interview reports generated by Claude 3.5 Sonnet, GPT-4o, Gemini 1.5, and Llama 3.1 405B, focusing on characteristics such as gender, race, and age. We evaluate the effectiveness of LLM-based anony

Tycho F. A. van der Ouderaa, Maximilian L. Croci, Agrin Hilmkil, James Hensman

Recent works on compression of large language models (LLM) using quantization considered reparameterizing the architecture such that weights are distributed on the sphere. This demonstratively improves the ability to quantize by increasing the mathematical notion of coherence, resulting in fewer weight outliers without affecting the network output. In this w

Babak Jabbar Nezhad

In this paper, we present a paradox arising from the acceptance of the Law of Excluded Middle (LEM) within classical mathematics. Specifically, we construct a nonzero analytic function on a connected open subset of the complex plane whose zeros are not isolated. This contradicts a fundamental theorem in complex analysis, thereby revealing an inconsistency ti

Huimin Zheng, Xiaofeng Xing, Xiangmin Xu

We present a novel approach to personalized sleep health management using few-shot Chain-of-Thought (CoT) distillation, enabling small-scale language models (> 2B parameters) to rival the performance of large language models (LLMs) in specialized health domains. Our method simultaneously distills problem-solving strategies, long-tail expert knowledge, and pe

Jan Sören Schwarz, Leonard Enrique Ramos Perez, Minh Cong Pham, Kai Heussen

In context of highly complex energy system experiments, sensitivity analysis is gaining more and more importance to investigate the effects changing parameterization has on the outcome. Thus, it is crucial how to design an experiment to efficiently use the available resources. This paper describes the functionality of a toolbox designed to support the users

Mengxin Xu, Weiwei Wan, Hesheng Wang, Kensuke Harada

This paper proposes a control method to address the physical Human-Robot Interaction (pHRI) challenge in the context of hierarchical tasks. A common approach to managing hierarchical tasks is Hierarchical Quadratic Programming (HQP), which, however, cannot be directly applied to human interaction due to its allowance of arbitrary velocity direction adjustmen

Chung-Hang Kwan, Wing Hong Leung

We present a "beyond-endoscopic" treatment of the functional equation for the standard $L$-function of a holomorphic cusp form with level and nebentypus. We use Petersson's formula and methods from Venkatesh's thesis and "spectral reciprocity".

Guang-Zai Ye, Chong-Ye Chen, GuoYang Fu, Chao Niu

We investigate spontaneous vectorization in the Einstein-Maxwell-Vector (EMV) model, introducing a novel mechanism driven by the interplay between electromagnetic and vector fields. A key innovation in our work is the resolution of an apparent divergence in the vector field near the event horizon, achieved by employing a generalized coordinate transformation

Tomoyuki Kagaya, Yuxuan Lou, Thong Jing Yuan, Subramanian Lakshmi

In recent years, Large Language Models (LLMs) have demonstrated high reasoning capabilities, drawing attention for their applications as agents in various decision-making processes. One notably promising application of LLM agents is robotic manipulation. Recent research has shown that LLMs can generate text planning or control code for robots, providing subs

Yutaka Yoshii

We describe the structure of projective indecomposable modules for the subalgebra consisting of the elements of degree 0 in the hyperalgebra of the $r$-th Frobenius kernel for the algebraic group ${\rm SL}_2(k)$, using the primitive idempotents which were constructed before by the author.

Wang Liang

There are already many DNA large language models, but most of them still follow traditional uses, such as extracting sequence features for classification tasks. More innovative applications of large language models, such as prompt engineering, RAG, and zero-shot or few-shot prediction, remain challenging for DNA-based models. The key issue lies in the fact t

Keita Omiya

Quantum many-body scars (QMBS) represent a weak ergodicity-breaking phenomenon that defies the common scenario of thermalization in closed quantum systems. They are often regarded as a many-body analog of quantum scars (QS) -- a single-particle phenomenon in quantum chaos -- due to their superficial similarities. However, unlike QS, a clear connection betwee

Bolin Zhang, Yu Liu, Tianqi Gao, Jialiang Yin

Probabilistic Bit (P-Bit) device serves as the core hardware for implementing Ising computation. However, the severe intrinsic variations of stochastic P-Bit devices hinder the large-scale expansion of the P-Bit array, significantly limiting the practical usage of Ising computation. In this work, a behavioral model which attributes P-Bit variations to two pa

S M Rafi-Ul-Islam, Zhuo Bin Siu, Md. Saddam Hossain Razo, Mansoor B. A. Jalil

One of the most fascinating phenomena in non-Hermitian systems is the extensive accumulation of the bulk eigenstates under open-boundary conditions which is known as the non-Hermitian skin effect (NSHE). Here, we propose a switchable NHSE in a topolectrical (TE) set-up which can be turned on or off simply by varying the driving frequency without any modifica

Till Huckemann, Pascal Muster, Wolfram Langheinrich, Varvara Brackmann

This paper reports the compatibility of heterostructure-based spin qubit devices with industrial CMOS technology. It features Si/Si-Ge quantum dot devices fabricated using Infineon's 200 mm production line within a restricted thermal budget. The devices exhibit state-of-the-art charge sensing, charge noise and valley splitting characteristics, showing that i

Wakutaka Nakano

The Migdal transition probabilities for dark matter scattering are compared to the total single-electron inelastic cross sections of electron-atom scattering for isolated Ar and Xe. The comparison is done by expressing the electron-atom scattering cross section by connecting the Migdal probability. The resultant differences are around $30 \ \%$ for Ar and $8

Jorge da Silva Gonçalves, Laura Manduchi, Moritz Vandenhirtz, Julia E. Vogt

Generative modeling and clustering are conventionally distinct tasks in machine learning. Variational Autoencoders (VAEs) have been widely explored for their ability to integrate both, providing a framework for generative clustering. However, while VAEs can learn meaningful cluster representations in latent space, they often struggle to generate high-quality

Jan Zubáč, Miloslav Surýnek, Kamil Olejník, Andrej Farkaš

Solving complex tasks in a modern information-driven society requires novel materials and concepts for energy-efficient hardware. Antiferromagnets offer a promising platform for seeking such approaches due to their exceptional features: low power consumption and possible high integration density are desirable for information storage and processing or applica

Siddharth Sahu, Abdulrahman Altahhan

Capsule Networks outperform Convolutional Neural Networks in learning the part-whole relationships with viewpoint invariance, and the credit goes to their multidimensional capsules. It was assumed that increasing the number of capsule layers in the capsule networks would enhance the model performance. However, recent studies found that Capsule Networks lack

Marco Cattaneo

Quantum maps are fundamental to quantum information theory and open quantum systems. Covariant or weakly symmetric quantum maps, in particular, play a key role in defining quantum evolutions that respect thermodynamics, establish free operations in resource theories, and are consistent with transformations of quantum reference frames. To implement quantum ma

Farhang Loran, Ali Mostafazadeh

The transfer matrix of scattering theory in one dimension can be expressed in terms of the time-evolution operator for an effective non-unitary quantum system. In particular, it admits a Dyson series expansion which turns out to facilitate the construction of the low-frequency series expansion of the scattering data. In two and three dimensions, there is a s

Jingsheng Gao, Linxu Li, Weiyuan Li, Yuzhuo Fu

RAG systems consist of multiple modules to work together. However, these modules are usually separately trained. We argue that a system like RAG that incorporates multiple modules should be jointly optimized to achieve optimal performance. To demonstrate this, we design a specific pipeline called \textbf{SmartRAG} that includes a policy network and a retriev

Fatemeh Davoodi

Topological plasmonics offers new ways to manipulate light by combining concepts from topology and plasmonics, similar to topological edge states in photonics. However, designing such topological states remains challenging due to the complexity of the high dimensional design space. We present a novel method that uses supervised, physics informed deep learnin

Bob Knighton

We revisit the path integral formulation of bosonic string theory in locally-AdS$_3$ spacetimes. Through a careful analysis of the worldsheet sigma model, we write down an effective theory of long strings living near the boundary of AdS$_3$. By directly computing the partition function of the long-string sector, we find that the worldsheet path integral natu

Mayank Nagda, Phil Ostheimer, Sophie Fellenz

Topic models are a popular approach for extracting semantic information from large document collections. However, recent studies suggest that the topics generated by these models often do not align well with human intentions. Although metadata such as labels and authorship information are available, it has not yet been effectively incorporated into neural to

Matthias Eckardt, Farnaz Ghorbanpour, Aila Särkkä

The immense progress in data collection and storage capacities have yielded rather complex, challenging spatial event-type data, where each event location is augmented by a non-simple mark. Despite the growing interest in analysing such complex event patterns, the methodology for such analysis is not embedded well in the literature. In particular, the litera

You-You Lin, Ji-Ying Wang, Ailin Zhang

Motivated by the first observation of a doubly charmed tetraquark candidate $T_{cc}(3875)^+$, we perform a systematic calculation of the mass spectra of doubly charmed tetraquark states from $1S$ to $2P$ excitations in a nonrelativistic constituent quark potential model. In terms of the quark-quark potential, the mass of the charmed spin-$1$ diquark is predi

Marco Miani, Hrittik Roy, Søren Hauberg

Bayesian deep learning all too often underfits so that the Bayesian prediction is less accurate than a simple point estimate. Uncertainty quantification then comes at the cost of accuracy. For linearized models, the null space of the generalized Gauss-Newton matrix corresponds to parameters that preserve the training predictions of the point estimate. We pro

Daisuke Kusuda

Gompf conjectured that the elliptic surface $E(n)_{p,q}$ has no handle decomposition without 1- and 3-handles. We prove that each of the elliptic surfaces $E(n)_{5,6}$, $E(n)_{6,7}$, $E(n)_{7,8}$ and $E(n)_{8,9}$ has a handle decomposition without 1-handles for $n\geq4$, $n\geq 5$, $n\geq 9$ and $n\geq 24$, respectively.

Samuel Lerbet

Given a smooth variety $X$ over the field $\mathbb{R}$ of real numbers and a line bundle $\mathcal{L}$ on $X$ with associated topological line bundle $L=\mathcal{L}(\mathbb{R})$, we study the quadratic real cycle class map $\widetilde{\gamma}_{\mathbb{R}}^c:\widetilde{\mathrm{CH}}^c(X,\mathcal{L})\rightarrow\mathrm{H}^c(X(\mathbb{R}),\mathbb{Z}(L))$ from the

Jakub Jurek, Andrzej Materka, Kamil Ludwisiak, Agata Majos

We propose a novel approach to denoising diffusion magnetic resonance images (dMRI) using convolutional neural networks, that exploits the benefits of data acquired at multiple b-values to offset the need for many redundant observations. Denoising is especially relevant in dMRI since noise can have a deleterious impact on both quantification accuracy and ima

Christoph Linse, Beatrice Brückner, Thomas Martinetz

This paper proposes a novel regularization approach to bias Convolutional Neural Networks (CNNs) toward utilizing edge and line features in their hidden layers. Rather than learning arbitrary kernels, we constrain the convolution layers to edge and line detection kernels. This intentional bias regularizes the models, improving generalization performance, esp

Alexandra L. Lysenko, Dmitry S. Svinkin, Dmitry D. Frederiks, Anna V. Ridnaia

In this catalog, we present the results of a systematic study of 199 short gamma-ray bursts (GRBs) detected by Konus-Wind between 2011 January 1 and 2021 August 31. The catalog extends the Second Catalog of short gamma-ray bursts covering the period 1994-2010 by ten years of data. The resulting Konus-Wind short GRB sample includes 494 bursts. From temporal a

Horst Lenske

In-medium interactions of omega-mesons in infinite nuclear matter and finite nuclei are investigated in a microscopic approach with nucleon-nucleon and nucleon-resonance particle-hole polarization modes. The nuclear polarization tensor formalism is used. Covariant mean-field self-energies are taken into account. Longitudinal and transversal self-energies are

Yutaka Yoshii

In this paper we construct primitive idempotents of the hyperalgebra for the $r$-th Frobenius kernel of the algebraic group ${\rm SL}(2,k)$.

Yilin Xie, Shiqiang Zhang, Joel A. Paulson, Calvin Tsay

Bayesian optimization relies on iteratively constructing and optimizing an acquisition function. The latter turns out to be a challenging, non-convex optimization problem itself. Despite the relative importance of this step, most algorithms employ sampling- or gradient-based methods, which do not provably converge to global optima. This work investigates mix

Haiping Wang, Yuan Liu, Ziwei Liu, Wenping Wang

In this paper, we propose VistaDream a novel framework to reconstruct a 3D scene from a single-view image. Recent diffusion models enable generating high-quality novel-view images from a single-view input image. Most existing methods only concentrate on building the consistency between the input image and the generated images while losing the consistency bet

Clara Klink, Moritz Schlaich, Jonas Fischer, Alexandre Obertelli

In nuclear and atomic physics experiments, charged ion beams often need to be guided from the ion production to the experimental site. In the PUMA experiment, an ion source beamline was developed, which can be operated with up to \SI{5}{\kilo\electronvolt} beam energy at a base pressure of $10^{-9}$\,mbar or better. In this paper, a low-energy pulsed drift t

Shaoyang Cui, Shanglin Wu, Nikolai Madlener

Based on the former work Conscious Turing Machine, in this paper, we attempt to talk about the consciousness of CTM, dig deeper into the self-consciousness in CTM, offer a clear definition of it, and design a possible model of the Model-of-the-World processor. To prove the consciousness of CTM does exist, we chose two definitions of human consciousness and e

Gerard t Hooft

A mechanism is found that explains how matter falling into the future event horizon of a black hole leaves information there, which it sends to the past event horizon, and there it determines how particles are emitted. This way information must be conserved. The mechanism is a calculable gravitational effect. We also show how it is avoided that the "hidden r

Indranil Chattopadhyay, Raj Kishor Joshi, Sanjit Debnath, Priyesh Kumar Tripathi

Matter falling onto black holes, {also called} accretion discs, emit intense high-energy radiation. Accretion discs during {hard to hard intermediate} spectral states also emit bipolar outflows. Radiation drag was supposed to impose the upper limit on the terminal speed. It was later shown that a radiation field around an advective accretion disc imposes no

Mario Abundo

We address some inverse problems for the first-passage place and the first-passage time of a one-dimensional diffusion process $\mathcal X(t)$ with stochastic resetting, starting from an initial position $\mathcal X(0)= \eta ;$ this type of diffusion $\mathcal X(t)$ is characterized by the fact that a reset to the position $x_R $ can occur according to a hom

Kai Zhao, Zhihao Zhuang, Chenjuan Guo, Hao Miao

Time series anomaly prediction plays an essential role in many real-world scenarios, such as environmental prevention and prompt maintenance of cyber-physical systems. However, existing time series anomaly prediction methods mainly require supervised training with plenty of manually labeled data, which are difficult to obtain in practice. Besides, unseen ano

Huaxi, Zhang, Melanie Fontaine, Marianne Huchard

The ethical issues concerning the AI-based exoskeletons used in healthcare have already been studied literally rather than technically. How the ethical guidelines can be integrated into the development process has not been widely studied. However, this is one of the most important topics which should be studied more in real-life applications. Therefore, in t

Félix Cabello Sánchez, Jesús M. F. Castillo, Yolanda Moreno

We show that if $p>1$ every subspace of $\ell_p(\Gamma)$ is an $\ell_p$-sum of separable subspaces of $\ell_p$, and we provide examples of subspaces of $\ell_p(\Gamma)$ for $0<p\leq 1$ that are not even isomorphic to any $\ell_p$-sum of separable spaces, notably the kernel of any quotient map $\ell_p(\Gamma)\to L_1(2^{\Gamma})$ with $\Gamma$ uncountable. We

Andrei V. Zavarnitsine

We find an explicit form of the inverse isomorphism from Shapiro's lemma in terms of inhomogeneous cocycles and apply it to construct special nonsplit coverings of groups with a unique conjugacy class of involutions.

Pirzada Suhail, Amit Sethi

Machine Learning models are often trained on proprietary and private data that cannot be shared, though the trained models themselves are distributed openly assuming that sharing model weights is privacy preserving, as training data is not expected to be inferred from the model weights. In this paper, we present Training-Like Data Reconstruction (TLDR), a ne

Arpad Hegedus

In the repulsive regime of the sine-Gordon model, we work out a method, that enables one to formulate the UV limit of finite volume expectation values in terms of the integrable description of the UV limit of the corresponding spectral problem. Since these expectation values are related to 3-point couplings containing at least two identical operators, our co

Leyao Wang, Yu Wang, Bo Ni, Yuying Zhao

Real-world graph data often follows long-tailed distributions, making it difficult for Graph Neural Networks (GNNs) to generalize well across both head and tail classes. Recent advances in Vicinal Risk Minimization (VRM) have shown promise in mitigating class imbalance with numeric interpolation; however, existing approaches largely rely on embedding-space a

Ahmed Ala Eddine Benali, Massimo Cafaro, Italo Epicoco, Marco Pulimeno

Precise energy load forecasting in residential households is crucial for mitigating carbon emissions and enhancing energy efficiency; indeed, accurate forecasting enables utility companies and policymakers, who advocate sustainable energy practices, to optimize resource utilization. Moreover, smart meters provide valuable information by allowing for granular

Humayun Ahmed, Luca Biancofiore

Lubricant viscoelasticity arises due to a finite polymer relaxation time ($\lambda$) and can provide beneficial effects. In applications, such as bearings, gears, biological joints, etc., where the height-to-length ratio is small ($H_0 / \ell_x$) and the shear due to the wall velocity ($U_0$) is high, a simplified two-dimensional computational analysis acros

Lukas Hughes-Noehrer, Leda Channer, Gabriel Strain, Gregory Yates

Objectives: To investigate clinicians' attitudes towards current automated interpretation of ECG and novel AI technologies and their perception of computer-assisted interpretation. Materials and Methods: We conducted a series of interviews with clinicians in the UK. Our study: (i) explores the potential for AI, specifically future 'human-like' computing appr

I-Kang Liu, Andrew W. Baggaley, Carlo F. Barenghi, Toby S. Wood

We simulate the dynamics of about 600 quantum vortices in a spinning-down cylindrical container using a Gross--Pitaevskii model. For the first time, we find convincing spatial-temporal evidence of avalanching behaviour resulting from vortex depinning and collective motion. During a typical avalanche, about 10 to 20 vortices exit the container in a short peri

Xufen Zhang, Shan Wu, Rui-Hong Yue, Ming Zhang

In this paper, we investigate massive charged scalar perturbations in four-dimensional charged Lifshitz-AdS black holes with scalar hair, within the framework of Einstein--Maxwell--Dilaton (EMD) gravity. Using the improved asymptotic iteration method (AIM), we compute the quasinormal modes (QNMs) and explore their dependence on key parameters, including the

Konstantinos Kalimeris, Leonidas Mindrinos

Originating from the mathematical modelling of rainfall infiltration, we derive the solution of an initial-boundary value problem of a linear evolution partial differential equation, by using the Fokas method. We present numerical examples which correspond to specific physical rainfall problems. Based on this formalism we present an effective algorithm for t

Yuqing Ren, Leyu Zhang, Yifei Shen, Wenqing Song

Next-generation channel coding has stringent demands on throughput, energy consumption, and error rate performance while maintaining key features of 5G New Radio (NR) standard codes such as rate compatibility, which is a significant challenge. Due to excellent capacity-achieving performance, spatially-coupled low-density parity-check (SC-LDPC) codes are cons

Yuzhe Chen, Dayu Qin, Ercan Engin Kuruoglu

Autism Spectrum Disorder (ASD) is a prevalent neurological disorder. However, the multi-faceted symptoms and large individual differences among ASD patients are hindering the diagnosis process, which largely relies on subject descriptions and lacks quantitative biomarkers. To remediate such problems, this paper explores the use of graph theory and topologica

Zhao-Sai Jia, Zhen-Hua Zhang, Feng-Kun Guo, Gang Li

The possible existence of nucleon-antinucleon bound states has been studied for decades. We investigate the $e^+e^-\to p\bar{p}$ and $e^+e^-\to n\bar{n}$ cross sections in the nonrelativistic effective field theory framework. The proton-antiproton and neutron-antineutron coupled-channel final state interactions are considered and found responsible for near-t

Nicholas I-Hsien Kuo, Blanca Gallego, Louisa Jorm

Access to real clinical data is heavily restricted by privacy regulations, hindering both healthcare research and education. These constraints slow progress in developing new treatments and data-driven healthcare solutions, while also limiting students' access to real-world datasets, leaving them without essential practical skills. High-utility synthetic dat

Sarit Khirirat, Abdurakhmon Sadiev, Artem Riabinin, Eduard Gorbunov

We provide the first proof of convergence for normalized error feedback algorithms across a wide range of machine learning problems. Despite their popularity and efficiency in training deep neural networks, traditional analyses of error feedback algorithms rely on the smoothness assumption that does not capture the properties of objective functions in these

Rémi Khellaf, Aurélien Bellet, Julie Josse

We study Federated Causal Inference, an approach to estimate treatment effects from decentralized data across centers. We compare three classes of Average Treatment Effect (ATE) estimators derived from the Plug-in G-Formula, ranging from simple meta-analysis to one-shot and multi-shot federated learning, the latter leveraging the full data to learn the outco

Hyunmoon Kim

We develop a correspondence between the orbits of the group of linear symplectomorphisms of a real finite dimensional symplectic vector space in the complex Lagrangian Grassmannian and the Grassmannians of linear subspaces of the real symplectic vector space. Under this correspondence, orbit fibration maps whose fibers are holomorphic arc components correspo

Julius Martinetz, Christoph Linse, Thomas Martinetz

We investigate the learning dynamics of classifiers in scenarios where classes are separable or classifiers are over-parameterized. In both cases, Empirical Risk Minimization (ERM) results in zero training error. However, there are many global minima with a training error of zero, some of which generalize well and some of which do not. We show that in separa

Serkan Sulun, Paula Viana, Matthew E. P. Davies

We introduce VEMOCLAP: Video EMOtion Classifier using Pretrained features, the first readily available and open-source web application that analyzes the emotional content of any user-provided video. We improve our previous work, which exploits open-source pretrained models that work on video frames and audio, and then efficiently fuse the resulting pretraine

Alfio Bonanno, Mariano Cadoni, Mirko Pitzalis, Andrea Pierfrancesco Sanna

Using the Functional Renormalization Group approach we construct effective quantum spacetime geometries by self-consistently deforming the classical Schwarzschild-de Sitter black-hole solution. This involves studying how quantum corrections, driven by the running of the Newton's and cosmological constants modify the solution across the infrared and ultraviol

Maxim Snoep, Bettina Speckmann, Kevin Verbeek

In this paper we study polycubes: orthogonal polyhedra with axis-aligned quadrilateral faces. We present a complete characterization of polycubes of any genus based on their dual structure: a collection of oriented loops which run in each of the axis directions and capture polycubes via their intersection patterns. A polycube loop structure uniquely correspo

Dmytro Zabolotnii, Yar Muhammad, Naveed Muhammad

Pedestrian motion prediction is a key part of the modular-based autonomous driving pipeline, ensuring safe, accurate, and timely awareness of human agents' possible future trajectories. The autonomous vehicle can use this information to prevent any possible accidents and create a comfortable and pleasant driving experience for the passengers and pedestrians.

Wenyang Qian

Heavy quark thermalization in the quark-gluon plasma (QGP) is one of the most promising phenomena for understanding the strong interaction, where their energy loss and momentum broadening at low momentum can be well described by a stochastic process with drag and diffusion terms. We propose an accelerated quantum circuit Monte-Carlo (aQCMC) framework that ul

Ladislaus Alexander Bányai

According to Quantum Mechanics a narrow wave-packet of the center of mass of any macroscopic object should smear out after some time. The problem is usually waved out by assuming that due to their heavy masses this occurs over astronomical times. Without a clear definition of macroscopic objects this remains ambiguous. On the other hand, Quantum Mechanics al

Patrick B. Warren, Richard P. Sear

A gradient of a single salt in a solution generates an electric field, but not a current. Recent theoretical work by one of us [Phys. Rev. Lett. 24, 248004 (2020)] showed that the Nernst-Planck equations imply that crossed gradients of two or more different salts generate ion currents. These currents in solution have associated non-local electric fields. Par

Rosario R. Riso, Matteo Castagnola, Enrico Ronca, Henrik Koch

Separation of the two mirror images of a chiral molecule, the enantiomers, is a historically complicated problem of major relevance for biological systems. Since chiral molecules are optically active, it has been speculated that strong coupling to circularly polarized fields may be used as a general procedure to unlock enantiospecific reactions. In this work

Eddy Keming Chen, Roderich Tumulka

We establish three impossibility results regarding our knowledge of the quantum state of the universe. Suppose the universal quantum state is a typical unit vector in a high-dimensional subspace $\mathscr{H}_0$ of Hilbert space $\mathscr{H}$, such as the low-entropy subspace defined by the Past Hypothesis. We show that: (1) Any particular observation is inca

Lorenzo Pompili, Alessandra Buonanno, Michael Pürrer

The increasing sensitivity of current and upcoming gravitational-wave (GW) detectors poses stringent requirements on the accuracy of the GW models used for data analysis. If these requirements are not met, systematic errors could dominate over statistical uncertainties, hindering our ability to extract astrophysical and cosmological information, and conduct

Pulikandala Nithish Kumar, Nneka Umeorah, Alex Alochukwu

Volatility forecasting is essential for risk management and decision-making in financial markets. Traditional models like Generalized Autoregressive Conditional Heteroskedasticity (GARCH) effectively capture volatility clustering but often fail to model complex, non-linear interdependencies between multiple indices. This paper proposes a novel approach using

Marina Khoroshiltseva, Luca Palmieri, Sinem Aslan, Sebastiano Vascon

Jigsaw puzzle solving is a challenging task for computer vision since it requires high-level spatial and semantic reasoning. To solve the problem, existing approaches invariably use color and/or shape information but in many real-world scenarios, such as in archaeological fresco reconstruction, this kind of clues is often unreliable due to severe physical an

Vu Thi Huong, Hong-Kun Xu, Nguyen Dong Yen

In this paper, for the first time in the literature, we study the stability of solutions of two classes of feasibility (i.e., split equality and split feasibility) problems by set-valued and variational analysis techniques. Our idea is to equivalently reformulate the feasibility problems as parametric generalized equations to which set-valued and variational

Michael Zichert, Adrian Wüthrich

Virtual particles are peculiar objects. They figure prominently in much of theoretical and experimental research in elementary particle physics. But exactly what they are is far from obvious. In particular, to what extent they should be considered "real" remains a matter of controversy in philosophy of science. Also their origin and development has only rece

Arvind Kumar, Prabhat Kumar Mishra

Let $k \ge 2$ be an even integer, $ \ell \ge \max\{5, k-1\} $ be a prime, and $N$ be a squarefree positive integer. It is known that if the $\rm{mod}\,\ell$ Galois representation $\overline{\rho}_f$ associated with a newform $f$ of weight $k$, level $N$, and trivial nebentypus is reducible, then $\overline{\rho}_f \simeq 1 \oplus \overline{\chi}_\ell^{k-1}$,

Xiang Ma, Xuemei Li, Lexin Fang, Caiming Zhang

Many contrastive learning based models have achieved advanced performance in image-text matching tasks. The key of these models lies in analyzing the correlation between image-text pairs, which involves cross-modal interaction of embeddings in corresponding dimensions. However, the embeddings of different modalities are from different models or modules, and

Gerald K. Cooray, Vernon Cooray, Karl J. Friston

Macroscopic studies of cortical tissue reveal a prevalence of oscillatory activity, that reflect a fine tuning of neural interactions. This research extends neural field theories by incorporating generalized oscillatory dynamics into previous work on conservative or semi-conservative neural field dynamics. Prior studies have largely assumed isotropic connect

Tianyu Zhang, Tao Liu, Neelotpal Dutta, Yongxue Chen

While multi-axis 3D printing can align continuous fibers along principal stresses in continuous fiber-reinforced thermoplastic (CFRTP) composites to enhance mechanical strength, existing methods have difficulty generating toolpaths with high fiber coverage. This is mainly due to the orientation consistency constraints imposed by vector-field-based methods an