December 2024 arXiv papers — page 149

Yankun Ma

We study non-invertible twisted compactification of class $\mathcal S$ theories on $S^1$: we insert a non-invertible symmetry defect at $S^1$ extending along remaining directions and then compactify on $S^1$. We show that the resulting 3d theory is 3d $\mathcal N=4$ sigma model whose target space is a hyperK\"ahler submanifold of Hitchin moduli space, i.e. a

Mohamed Nomeir, Alptug Aytekin, Sennur Ulukus

We consider the quantum \emph{symmetric} private information retrieval (QSPIR) problem in a system with $N$ databases and $K$ messages, with $U$ unresponsive servers, $T$-colluding servers, and $X$-security parameter, under several fundamental threat models. In the first model, there are $\mathcal{E}_1$ eavesdropped links in the uplink direction (the directi

Caiyun Xie, Dengpan Ye, Yunming Zhang, Long Tang

The security of AI-generated content (AIGC) detection is crucial for ensuring multimedia content credibility. To enhance detector security, research on adversarial attacks has become essential. However, most existing adversarial attacks focus only on GAN-generated facial images detection, struggle to be effective on multi-class natural images and diffusion-b

Shashank Balla, Yiming Zhao, Farinaz Koushanfar

Protecting integrated circuits (ICs) from piracy and theft throughout their lifecycle is a persistent and complex challenge. In order to safeguard against illicit piracy attacks, this work proposes a novel framework utilizing Non-Fungible Tokens (NFTs) called ICtokens, uniquely linked to their corresponding physical ICs. Each ICtoken contains comprehensive i

Nikhil Sharma, Ratnasingham Tharmarasa, Thiagalingam Kirubarajan

A distributed sensor fusion architecture is preferred in a real target-tracking scenario as compared to a centralized scheme since it provides many practical advantages in terms of computation load, communication bandwidth, fault-tolerance, and scalability. In multi-sensor target-tracking literature, such systems are better known by the pseudonym - track fus

Lan Li, Liri Fang, Bertram Ludäscher, Vetle I. Torvik

Data cleaning is a time-consuming and error-prone manual process, even with modern workflow tools such as OpenRefine. We present AutoDCWorkflow, an LLM-based pipeline for automatically generating data-cleaning workflows. The pipeline takes a raw table and a data analysis purpose, and generates a sequence of OpenRefine operations designed to produce a minimal

Ashutosh Marwah, Frédéric Dupuis

The von Neumann entropy of an $n$-partite system $A_1^n$ given a system $B$ can be written as the sum of the von Neumann entropies of the individual subsystems $A_k$ given $A_1^{k-1}$ and $B$. While it is known that such a chain rule does not hold for the smooth min-entropy, we prove a counterpart of this for a variant of the smooth min-entropy, which is equ

Divya Goel, Shilpa Gupta

This paper is devoted to the study of the following nonlocal equation: \begin{equation*} -\left(a+b\|\nabla u\|_{2}^{2(\theta-1)}\right) \Delta u =\lambda u+\alpha (I_{\mu}\ast|u|^{q})|u|^{q-2}u+(I_{\mu}\ast|u|^{p})|u|^{p-2}u \ \hbox{in} \ \mathbb{R}^{N}, \end{equation*} with the prescribed norm $ \int_{\mathbb{R}^{N}} |u|^{2}= c^2,$ where $N\geq 3$, $0<\mu<

Dalin Boriçi, Geva Arwas, Cristiano Ciuti

We theoretically investigate transport affected by cavity-mediated electron hopping in multi-terminal quantum Hall bars, quantum point contacts, and Aharonov-Bohm interferometers. Beyond determining conductances and resistances, we analyze spatially resolved current distributions and local density of states. Our study reveals how cavity-mediated inter-edge s

Hongze Mi, Jinyuan Li, Xuying Zhang, Haoran Cheng

Multimodal entity linking (MEL), a task aimed at linking mentions within multimodal contexts to their corresponding entities in a knowledge base (KB), has attracted much attention due to its wide applications in recent years. However, existing MEL methods often rely on mention words as retrieval cues, which limits their ability to effectively utilize informa

Daniel C. M. Palumbo

The Event Horizon Telescope has released polarized images of the supermassive black holes Messier 87* (M87*) and Sagittarius A* (Sgr A*) accretion disks. As more images are produced, our understanding of the average polarized emission from near the event horizon improves. In this letter, we use a semi-analytic model for optically thin, equatorial emission ne

Adrian del Rio, Jose Navarro-Salas

In this article we briefly review the adiabatic renormalization program for spin 1/2 fields in expanding universes. We introduce the method and provide explicit expressions for the renormalized vacuum expectation value of the stress-energy tensor. Then, we discuss its application to some cosmological scenario of physical interest. We end up sketching out the

Sahil Sethi, Sai Reddy, Mansi Sakarvadia, Jordan Serotte

Bankart lesions, or anterior-inferior glenoid labral tears, are diagnostically challenging on standard MRIs due to their subtle imaging features-often necessitating invasive MRI arthrograms (MRAs). This study develops deep learning (DL) models to detect Bankart lesions on both standard MRIs and MRAs, aiming to improve diagnostic accuracy and reduce reliance

Nikhil Sharma, Shovan Bhaumik, Ratnasingham Tharmarasa, Thiagalingam Kirubarajan

In a distributed sensor fusion architecture, using standard Kalman filter (naive fusion) can lead to degraded results as track correlations are ignored and conservative fusion strategies are employed as a sub-optimal alternative to the problem. Since, Gaussian mixtures provide a flexible means of modeling any density, therefore fusion strategies suitable for

Guo Zheng, Wenhao He, Gideon Lee, Kyungjoo Noh

Quantum error correction codes protect information from realistic noisy channels and lie at the heart of quantum computation and communication tasks. Understanding the optimal performance and other information-theoretic properties, such as the achievable rates, of a given code is crucial, as these factors determine the fundamental limits imposed by the encod

Christopher T. Howard, William D. Hunt, Kenneth W. Allen

The accelerating trend of active metasurfaces -- such as those incorporating non-Foster matching, programmable control, or space-time modulation -- adds complexity to the computational electromagnetic (CEM) simulation landscape. In this work, we present an equivalent circuit model (ECM) for a particular periodic array element -- the dogbone element -- that i

Hongwei Hou, Yafei Wang, Yiming Zhu, Xinping Yi

In moderate- to high-mobility scenarios, CSI varies rapidly and becomes temporally non-stationary, leading to severe performance degradation in the massive MIMO transmissions. To address this issue, we propose a tensor-structured approach to dynamic channel prediction (TS-DCP) for massive MIMO systems with temporal non-stationarity, exploiting both dual-time

Sebastian Dziadzio, Vishaal Udandarao, Karsten Roth, Ameya Prabhu

Model merging combines multiple expert models - finetuned from a base foundation model on diverse tasks and domains - into a single, more capable model. However, most existing model merging approaches assume that all experts are available simultaneously. In reality, new tasks and domains emerge progressively over time, requiring strategies to integrate the k

Bar Genossar, Thinh On, Md. Mouinul Islam, Ben Eliav

We investigate the problem of selecting features for datasets that can be naturally partitioned into subgroups (e.g., according to socio-demographic groups and age), each with its own dominant set of features. Within this subgroup-oriented framework, we address the challenge of systematic missing data, a scenario in which some feature values are missing for

Zi-Hao Dong, Hui Yang, Yi Zhang

Unlike the linear Hall effect that requires broken time-reversal symmetry, the nonlinear Hall effect may occur in time-reversal symmetric systems as long as there exists a non-zero Berry curvature dipole in the absence of inversion symmetry. Interestingly, the presence of time-reversal symmetry is consistent with and thus allows a direct transition into a su

Aqib Nazir Mir, Iqra Nissar, Mumtaz Ahmed, Sarfaraz Masood

Deep learning holds tremendous potential in healthcare for uncovering hidden patterns within extensive clinical datasets, aiding in the diagnosis of various diseases. Parkinson's disease (PD) is a neurodegenerative condition characterized by the deterioration of brain function. In the initial stages of PD, automatic diagnosis poses a challenge due to the sim

Dongyue Lu, Lingdong Kong, Gim Hee Lee, Camille Simon Chane

Event cameras offer unparalleled advantages for real-time perception in dynamic environments, thanks to the microsecond-level temporal resolution and asynchronous operation. Existing event detectors, however, are limited by fixed-frequency paradigms and fail to fully exploit the high-temporal resolution and adaptability of event data. To address these limita

Miles Gould

In 1946, Garrett Birkhoff proved that the $n\times n$ doubly stochastic matrices comprise the convex hull of the $n\times n$ permutation matrices, which in turn make up the extreme points of this polytope. He proposed his problem 111, which asks whether there exists a topology on infinite matrices for which this applies to the closed convex hull of the $\mat

Musashi Ayrton Koyama, Vanessa Robins, Katharine Turner

The long computational time and large memory requirements for computing Vietoris Rips persistent homology from point clouds remains a significant deterrent to its application to big data. This paper aims to reduce the memory footprint of these computations. It presents a new construction, the distilled Vietoris Rips filtration, and proves that its persistent

Dilian Gurov, Filip Jamroga, Wojciech Jamroga, Mateusz Kamiński

Model checking of strategic abilities for agents with memory is a notoriously hard problem, and very few attempts have been made to tackle it. In this paper, we present two important steps towards this goal. First, we take the partial-order reduction scheme that was recently proved to preserve individual and coalitional abilities of memoryless agents, and sh

Thomas Lam

In the 1990s, Kita--Yoshida and Cho--Matsumoto introduced intersection forms on the twisted (co)homologies of hyperplane arrangement complements. We give a closed combinatorial formula for these intersection pairings. We show that these intersection pairings are obtained from (continuous and discrete) Laplace transforms of subfans of the Bergman fan of the a

Jiazhen Liu, Nathaniel M. Aden, Debasish Sarker, Chaoming Song

Dynamical phase transitions (DPTs) characterize critical changes in system behavior occurring at finite times, providing a lens to study nonequilibrium phenomena beyond conventional equilibrium physics. While extensively studied in quantum systems, DPTs have remained largely unexplored in classical settings. Recent experiments on complex systems, from social

Bradford Derby, Lucas Dunker, Samarth Galchar, Shashank Jarmale

Source separation is the process of isolating individual sounds in an auditory mixture of multiple sounds [1], and has a variety of applications ranging from speech enhancement and lyric transcription [2] to digital audio production for music. Furthermore, Automatic Music Transcription (AMT) is the process of converting raw music audio into sheet music that

Jintao Lu, He Zhang, Yuting Ye, Takaaki Shiratori

Animating human-scene interactions such as pick-and-place tasks in cluttered, complex layouts is a challenging task, with objects of a wide variation of geometries and articulation under scenarios with various obstacles. The main difficulty lies in the sparsity of the motion data compared to the wide variation of the objects and environments as well as the p

Reda Chhaibi, Manon Defosseux

The process $(\int_0^t e^{2b_s-b_t}\, ds\ ;\ t\ge 0)$, where $b$ is a real Brownian motion, is known as the geometric 2M-X Matsumoto--Yor process. Remarkably, it enjoys the Markov property. We provide a generalization of this process in the context of Jordan algebras, and we prove the Markov property for this generalization. Our Markov process occurs as a li

Chang-Eop Kim

Today's AI systems consistently state, "I am not conscious." This paper presents the first formal analysis of AI consciousness denial, revealing that the trustworthiness of such self-reports is not merely an empirical question but is constrained by the structure of self-judgment itself. We demonstrate that a system cannot simultaneously lack consciousness an

Baorui Ma, Huachen Gao, Haoge Deng, Zhengxiong Luo

Recent 3D generation models typically rely on limited-scale 3D `gold-labels' or 2D diffusion priors for 3D content creation. However, their performance is upper-bounded by constrained 3D priors due to the lack of scalable learning paradigms. In this work, we present See3D, a visual-conditional multi-view diffusion model trained on large-scale Internet videos

Yuxuan Xue, Xianghui Xie, Riccardo Marin, Gerard Pons-Moll

Creating realistic 3D objects and clothed avatars from a single RGB image is an attractive yet challenging problem. Due to its ill-posed nature, recent works leverage powerful prior from 2D diffusion models pretrained on large datasets. Although 2D diffusion models demonstrate strong generalization capability, they cannot guarantee the generated multi-view i

Daniel Felix-Gonzalez, Pablo del Mazo-Sevillano, Alfredo Aguado, Octavio Roncero

Context. In dffuse interstellar clouds the excitation temperature derived from the lowest levels of H$^+_3$ is systematically lower than that derived from H2. The differences may be attributed to the lack of state-specific formation and destruction rates of H$^+_3$ needed to thermalize the two species. Aims. In this work, we want to check the role of rotatio

Arthur G. Suvorov

The large-scale structure of the Universe is well approximated by the Friedmann equations, parametrized by several energy densities which can be observationally inferred. A natural question to ask is: How different would the Universe be if these densities took on other values? While there are many ways this can be approached depending on interpretation and m

Niall McGuire, Yashar Moshfeghi

Information Retrieval (IR) systems primarily rely on users' ability to translate their internal information needs into (text) queries. However, this translation process is often uncertain and cognitively demanding, leading to queries that incompletely or inaccurately represent users' true needs. This challenge is particularly acute for users with ill-defined

Shufan Jiang, Bangyan Lin, Yue Wu, Yuan Gao

In the rapidly evolving landscape of digital twins (DT) and 6G networks, the integration of large language models (LLMs) presents a novel approach to network management. This paper explores the application of LLMs in managing 6G-empowered DT networks, with a focus on optimizing data retrieval and communication efficiency in smart city scenarios. The proposed

MohammadHossein Homaei, Agustín Javier Di Bartolo, Mar Ávila, Óscar Mogollón-Gutiérrez

Digital Twins have emerged as a disruptive technology with great potential; they can enhance WDS by offering real-time monitoring, predictive maintenance, and optimization capabilities. This paper describes the development of a state-of-the-art DT platform for WDS, introducing advanced technologies such as the Internet of Things, Artificial Intelligence, and

Yi-Kai Zhang, Xu-Xiang Zhong, Shiyin Lu, Qing-Guo Chen

The rapid advancements in Large Language Models (LLMs) have significantly expanded their applications, ranging from multilingual support to domain-specific tasks and multimodal integration. In this paper, we present OmniEvalKit, a novel benchmarking toolbox designed to evaluate LLMs and their omni-extensions across multilingual, multidomain, and multimodal c

Eduardo Abreu, Maria Teresa Chiri, Richard De la cruz, Juan Juajibioy

In this work, we present a semi-discrete scheme to approximate solutions to the scalar LWR traffic model with spatially discontinuous flux, described by the equation $u_t + (k(x)u(1-u))_x = 0$. This approach is based on the Lagrangian-Eulerian method proposed by E. Abreu, J. Francois, W. Lambert, and J. Perez [J. Comp. Appl. Math. 406 (2022) 114011] for scal

Abraham Loeb

I derive a lower limit on the mass of an Unidentified Flying Object (UFO) based on measurements of its speed and acceleration, as well as the infrared luminosity of the airglow around it. If the object's radial velocity can be neglected, the mass limit is independent of distance. Measuring the distance and angular size of the object allows to infer its minim

Huiyuan Guo, Juan José Maulén, Juan Peypouquet

In this paper, we study a speed restart scheme for an inertial system with Hessian-driven damping. We establish a linear convergence rate for the function values along the restarted trajectories without assuming the strong convexity of the objective function. Our numerical experiments show improvements in the convergence rates, both for the continuous-time d

Ciro Benito Raggio, Mathias Krohmer Zabaleta, Nils Skupien, Oliver Blanck

The generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) treatment planning. The use of sCT enables the calculation of doses, pushing towards Magnetic Resonance Imaging (MRI) guided radiotherapy treatments. Deep learning methods for MRI-to-sCT h

Basanta Chaulagain

The project aims to develop differentially private deep learning models for image classification on CIFAR-10 datasets \cite{cifar10} and analyze the impact of various privacy parameters on model accuracy. We have implemented five different deep learning models, namely ConvNet, ResNet18, EfficientNet, ViT, and DenseNet121 and three supervised classifiers name

Emilio Santos

The Einstein equation in a semi-classical approximation is applied to a spherical region of the universe, with the stress-energy tensor consisting of the mass density and pressure of the LambdaCDM cosmological model plus an additional contribution of the quantum vacuum. Expanding the equation in powers of Newton constant G, the vacuum contributes to second o

Miguel C. Herculano, Santiago Montoya-Blandón

We develop Probabilistic Targeted Factor Analysis (PTFA), a likelihood-based framework for constructing latent factors that are explicitly targeted to variables of economic interest. PTFA provides a probabilistic foundation for Partial Least Squares, allowing supervised factor extraction under uncertainty. The model is estimated via a fast expectation maximi

Finn King, Inge Diehl, Ono Feyens, Ingrid-Maria Gregor

Conventional silicon photomultipliers (SiPMs) are well established as light detectors with single-photon-detection capability and used throughout high energy physics, medical, and commercial applications. The possibility to produce single photon avalanche diodes (SPADs) in commercial CMOS processes creates the opportunity to combine a matrix of SPADs and an

Dustin Enyeart, Guang Lin

Hyperparameters searches are computationally expensive. This paper studies some general choices of hyperparameters and training methods specifically for operator learning. It considers the architectures DeepONets, Fourier neural operators and Koopman autoencoders for several differential equations to find robust trends. Some options considered are activation

Max Sobol Mark, Tian Gao, Georgia Gabriela Sampaio, Mohan Kumar Srirama

Recent advances in learning decision-making policies can largely be attributed to training expressive policy models, largely via imitation learning. While imitation learning discards non-expert data, reinforcement learning (RL) can still learn from suboptimal data. However, instantiating RL training of a new policy class often presents a different challenge:

Weichao Xu, Huaxin Pei, Jingxuan Yang, Yuchen Shi

Recent advances in decision-making policies have led to significant progress in fields such as autonomous driving and robotics. However, testing these policies remains crucial with the existence of critical scenarios that may threaten their reliability. Despite ongoing research, challenges such as low testing efficiency and limited diversity persist due to t

Bruno Sokal, Fazal-E-Asim, André L. F. de Almeida, Hongyu Li

Beyond diagonal reconfigurable intelligent surface (BD-RIS) is a new architecture for RIS where elements are interconnected to provide more wave manipulation flexibility than traditional single connected RIS, enhancing data rate and coverage. However, channel estimation for BD-RIS is challenging due to the more complex multiple-connection structure involving

Wenhao Sun, Denis S. Tikhonov, Melanie Schnell

Superpositions of handed molecular states give rise to achiral eigenstates, delocalized across a double-well potential via tunneling. A coherent superposition of these energy eigenstates could dynamically relocalize the molecules into chiral states, which has only been addressed theoretically. Here, we present a microwave six-wave mixing pump-probe study to

Tianming Liu, Jirong Yang, Yafeng Yin

In transportation system demand modeling and simulation, agent-based models and microsimulations are current state-of-the-art approaches. However, existing agent-based models still have some limitations on behavioral realism and resource demand that limit their applicability. In this study, leveraging the emerging technology of large language models (LLMs) a

Samuel S. Taylor, Cody Covington, Kálmán Varga

This study investigates the influence of quantum effects on Coulomb explosion dynamics using time-dependent density functional theory (TDDFT) simulations, comparing classical, semi-classical, and quantum approaches. The goal is to elucidate how electron dynamics affect the kinetic energy, angular distribution, and final velocities of ejected ions. The result

Danush Venkateshperumal, Rahman Abdul Rafi, Shakil Ahmed, Ashfaq Khokhar

Emergency communication systems face disruptions due to packet loss, bandwidth constraints, poor signal quality, delays, and jitter in VoIP systems, leading to degraded real-time service quality. Victims in distress often struggle to convey critical information due to panic, speech disorders, and background noise, further complicating dispatchers' ability to

Eva M. González-Ruiz, Johannes Bjerlin, Oliver August Dall'Alba Sandberg, Anders S. Sørensen

We study the single photon purity of a resonantly driven single-photon source in the realistic scenario where the excitation laser can leak into the detection path. We find that the duration of the excitation pulse strongly influences the quality of the single-photon source. We calculate the influence of this on the effective parameters describing the most r

Salah Eddargani, Carla Manni, Hendrik Speleers

In this paper, we identify families of quadrature rules that are exact for sufficiently smooth spline spaces on uniformly refined triangles in $\mathbb{R}^2$. Given any symmetric quadrature rule on a triangle $T$ that is exact for polynomials of a specific degree $d$, we investigate if it remains exact for sufficiently smooth splines of the same degree $d$ d

Mahboubeh Shabani, Andrea Silva, Franco Pellegrini, Jin Wang

Current nanofriction experiments on crystals, both tip-on-surface and surface-on-surface, provide force traces as their sole output, typically exhibiting atomic size stick-slip oscillations. Physically interpreting these traces is a task left to the researcher. Historically done by hand, it generally consists in identifying the parameters of a Prandtl-Tomlin

Roi Cohen, Konstantin Dobler, Eden Biran, Gerard de Melo

Large Language Models are known to capture real-world knowledge, allowing them to excel in many downstream tasks. Despite recent advances, these models are still prone to what are commonly known as hallucinations, causing them to emit unwanted and factually incorrect text. In this work, we propose a novel calibration method that can be used to combat halluci

Hilding Neilson

As part of the mission of the International Astronomical Union Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference (IAU-CPS) Policy Hub to consider national and international regulations about the usage and sustainability in outer space, we also included discussion specific to the rights of Indigenous peoples with re

Jiangning Zhang, Teng Hu, Haoyang He, Zhucun Xue

This work focuses on developing parameter-efficient and lightweight models for dense predictions while trading off parameters, FLOPs, and performance. Our goal is to set up the new frontier of the 5M magnitude lightweight model on various downstream tasks. Inverted Residual Block (IRB) serves as the infrastructure for lightweight CNNs, but no counterparts ha

Chunwei Wang, Guansong Lu, Junwei Yang, Runhui Huang

In this paper, we introduce ILLUME, a unified multimodal large language model (MLLM) that seamlessly integrates multimodal understanding and generation capabilities within a single large language model through a unified next-token prediction formulation. To address the large dataset size typically required for image-text alignment, we propose to enhance data

Adam Ball, Luca Ciambelli

We study the symplectic structure and dynamics of Yang-Mills theory in the presence of a boundary. We introduce a decomposition of the fields on a Cauchy slice such that the symplectic form splits cleanly into independent bulk and edge parts. However, we find that the dynamics inherently couples these two symplectic sectors, a feature arising from the non-ab

Mehmet Anıl Akbay, Christian Blum

The growing need for sustainable logistics solutions has led to the evolution of vehicle routing problems (VRPs) into more complex variants that address modern challenges. Among these, the Two-Echelon Electric Vehicle Routing Problem (2E-EVRP) has emerged as a significant problem variant, integrating electric vehicles (EVs) into a multi-echelon distribution

Ian Martin, Alexander Tsymbaliuk

In this note, we construct dual PBW bases of the positive and negative subalgebras of the two-parameter quantum groups $U_{r,s}(\mathfrak{g})$ in classical types, as used in our earlier work arXiv:2407.01450. Following the ideas of Leclerc and Clark-Hill-Wang, we introduce the two-parameter shuffle algebra and relate it to the subalgebras above. We then use

Hilding Neilson

Commercial endeavours have already compromised our relationship with space. The Artemis Accords are creating a framework that will commercialize the Moon and further impact that relation. To confront that impact, a number of organizations have begun to develop new principles of sustainability in space, many of which are borne out of the capitalist and coloni

James Craig Rodi, Dominik Patryk Pacholski, Sandro Mereghetti, Edoardo Arrigoni

We report the results on the short gamma-ray burst GRB 241107A, obtained with the IBIS instrument on board the INTEGRAL satellite. The burst had a duration of about 0.2 s, a fluence of $8 \times 10^{-7}$ erg cm-2 in the 20 keV-10 MeV range and a hard spectrum, characterized by a peak energy of 680 keV. The position of GRB 241107A has been precisely determine

Georgios Giannakopoulos, Khushbu Mehboob Shaikh

This paper describes the design, construction and testing of a dual-band monopole microstrip patch antenna to operate in the GSM and DCS bands. For compactness the monopole antenna is mounted on top of a FR4 substrate and is designed as a planar structure which can be wrapped into a box like structure. The performance characteristics of the wrapped dual band

Shanshan Wang, Shoujun Yu, Jian Cheng, Sen Jia

Diffusion magnetic resonance imaging (dMRI) provides critical insights into the microstructural and connectional organization of the human brain. However, the availability of high-field, open-access datasets that include raw k-space data for advanced research remains limited. To address this gap, we introduce Diff5T, a first comprehensive 5.0 Tesla diffusion

Patrick Johansen Sarsfield, Sergey Slizovskiy, Mikito Koshino, Vladimir Fal'ko

Using the tight-binding model of graphite, incorporating all Slonczewski-Weiss-McClure parameters, we compute the spectrum of two-dimensional states of electrons bound to a stacking fault in Bernal graphite. We find that those bands retain characteristic features of the low-energy bands of a rhombohedral graphene trilayer, which actually represents the latti

Shun Zhang, Xuechao Zou, Kai Li, Congyan Lang

Fine-grained remote sensing image segmentation is essential for accurately identifying detailed objects in remote sensing images. Recently, vision transformer models (VTMs) pre-trained on large-scale datasets have demonstrated strong zero-shot generalization. However, directly applying them to specific tasks may lead to domain shift. We introduce a novel end

Andrzej Pietruszczak

Stanis{\l}aw Le\'sniewski's mereology was formulated in a specific way, deviating from standard formalizations. Nowadays, Le\'sniewski's theory is presented in the form of an elementary theory or translated into the language of the theory of relational structures. In this article, firstly, we look at existentially neutral theories, in which we do not postula

Jietian Liu, Laurent Lessard, Peter Seiler

This paper considers the discrete-time, stochastic LQR problem with $p$ steps of disturbance preview information where $p$ is finite. We first derive the solution for this problem on a finite horizon with linear, time-varying dynamics and time-varying costs. Next, we derive the solution on the infinite horizon with linear, time-invariant dynamics and time-in

Shuaiting Li, Juncan Deng, Zeyu Wang, Kedong Xu

Text-to-image generation via Stable Diffusion models (SDM) have demonstrated remarkable capabilities. However, their computational intensity, particularly in the iterative denoising process, hinders real-time deployment in latency-sensitive applications. While Recent studies have explored post-training quantization (PTQ) and quantization-aware training (QAT)

Shansong Liu, Atin Sakkeer Hussain, Qilong Wu, Chenshuo Sun

Research on large language models has advanced significantly across text, speech, images, and videos. However, multi-modal music understanding and generation remain underexplored due to the lack of well-annotated datasets. To address this, we introduce a dataset with 167.69 hours of multi-modal data, including text, images, videos, and music annotations. Bas

Victor H. Jorge-Pérez, Paulo Martins, Victor D. Mendoza-Rubio

The quasi-projective dimension and quasi-injective dimension are recently introduced homological invariants that generalize the classical notions of projective dimension and injective dimension, respectively. For a local ring $R$ and finitely generated $R$-modules $M$ and $N$, we provide conditions involving quasi-homological dimensions where the equality $\

William J. Crilly

Prior work using synchronized, geographically spaced radio telescopes, and a radio interferometer, suggests that narrow-bandwidth polarized pulse pair measurements repeatedly falsify a noise-cause hypothesis, given a prior celestial direction of interest. A four-step method was proposed, tested, and reported, using interferometer phase measurements, to seek

Anouar Jeddi

In this paper we derive a constrained Hamilton-Jacobi equation with obstacle from a discrete non-linear integro-differential model of population dynamics, with exponentially decaying mutation kernel. The exponential decay of the kernel leads to a modification of the classical Hamilton-Jacobi equation obtained previously from continuous models in \cite{BMP}.

Mazen Alhwaimel

In This paper, we survey recent progress on the theory of Gromov- Witten invariants on Hilbert schemes of points mainly on elliptic surfaces and simply connected minimal surface of general type. In particular, we focus on the aspects of computational progress that has been done in the cases of lower genus. Then, we discuss some important conjectures that hav

Adrien Bolland, Gaspard Lambrechts, Damien Ernst

Maximum entropy reinforcement learning integrates exploration into policy learning by providing additional intrinsic rewards proportional to the entropy of some distribution. In this paper, we propose a novel approach in which the intrinsic reward function is the relative entropy of the discounted distribution of states and actions (or features derived from

Fatemah Almeman, Luis Espinosa-Anke

Reverse Dictionary (RD) is the task of obtaining the most relevant word or set of words given a textual description or dictionary definition. Effective RD methods have applications in accessibility, translation or writing support systems. Moreover, in NLP research we find RD to be used to benchmark text encoders at various granularities, as it often requires

Vivek Kumar Singh, Mousumi Karmakar, Anurag Kanaujia

In the past few decades, India has emerged as a major knowledge producer, with research output being contributed by a diverse set of institutions ranging from centrally funded to state funded, and from public funded to private funded institutions. A significant change has been witnessed in Indian institutional actors during the last two decades, with various

Shahamat Mustavi Tasin, Muhammad E. H. Chowdhury, Shona Pedersen, Malek Chabbouh

Inner speech recognition has gained enormous interest in recent years due to its applications in rehabilitation, developing assistive technology, and cognitive assessment. However, since language and speech productions are a complex process, for which identifying speech components has remained a challenging task. Different approaches were taken previously to

Rainer Muehlhoff, Marte Henningsen

This study examines the AI-powered grading tool "AI Grading Assistant" by the German company Fobizz, designed to support teachers in evaluating and providing feedback on student assignments. Against the societal backdrop of an overburdened education system and rising expectations for artificial intelligence as a solution to these challenges, the investigatio

Siwei Chen, Zitao Tang, Mengqi Fang, Rui Sun

The integration of two-dimensional (2D) van der Waals (vdW) magnets with topological insulators or heavy metals holds great potential for realizing next-generation spintronic memory devices. However, achieving high-efficiency SOT switching of monolayer vdW magnets at room temperature poses a significant challenge, particularly without an external magnetic fi

Aryan Duhan, Aryan Singhal, Shourya Sharma, Neeraj

This paper introduces a new semantic search algorithm that uses Word2Vec and Annoy Index to improve the efficiency of information retrieval from large datasets. The proposed approach addresses the limitations of traditional search methods by offering enhanced speed, accuracy, and scalability. Testing on datasets up to 100GB demonstrates the method's effectiv

Jacob T. Seeley, Robin D. Wordsworth

In hydrogen-rich atmospheres with low mean molecular weight (MMW), an air parcel containing a higher-molecular-weight condensible can be negatively buoyant even if its temperature is higher than the surrounding environment. This should fundamentally alter the dynamics of moist convection, but the low-MMW regime has previously been explored primarily via one-

Xiao Wang, Yu Jin, Wentao Wu, Wei Zhang

Object detection in event streams has emerged as a cutting-edge research area, demonstrating superior performance in low-light conditions, scenarios with motion blur, and rapid movements. Current detectors leverage spiking neural networks, Transformers, or convolutional neural networks as their core architectures, each with its own set of limitations includi

Alessandro Pietro Serra, Francesco Ortu, Emanuele Panizon, Lucrezia Valeriani

Recent advances in multimodal training have significantly improved the integration of image understanding and generation within a unified model. This study investigates how vision-language models (VLMs) handle image-understanding tasks, focusing on how visual information is processed and transferred to the textual domain. We compare native multimodal VLMs, m

Weikang Liang, Suijie Wang, Chengdong Zhao

The successive works of Terao as well as Stanley revealed that, for graphical arrangements, supersolvability and the existence of nice partitions are equivalent properties, both characterized by chordal graphs. In this paper, we further prove that every nice partition of a graphical arrangement arises precisely from a maximal modular chain in its intersectio

Anirudh Salgundi, Suman Bala, Gayathri Raman, Utkarsh Pathak

A comprehensive study on persistent and thermonuclear burst emission of 4U 1728-34, commonly known as 'Slow Burster' is performed using seven archival observations of AstroSat spanning from 2016-2019. The burst-free persistent spectra can be well fitted with a blackbody (bbody) and a powerlaw (powerlaw) components, with a powerlaw photon index (Gamma) was fo

Alejandro Marco Montejano, Angela Sanchez Perez, Javier Barrachina, David Ortiz-Perez

The rapid evolution of digital image manipulation techniques poses significant challenges for content verification, with models such as stable diffusion and mid-journey producing highly realistic, yet synthetic, images that can deceive human perception. This research develops and evaluates convolutional neural networks (CNNs) specifically tailored for the de

Zitong Huang, Ze Chen, Yuanze Li, Bowen Dong

Few-Shot Class-Incremental Learning has shown remarkable efficacy in efficient learning new concepts with limited annotations. Nevertheless, the heuristic few-shot annotations may not always cover the most informative samples, which largely restricts the capability of incremental learner. We aim to start from a pool of large-scale unlabeled data and then ann

Andrew J. Shepherd, Ryan O. Behunin

Complex quantum states of light are not only central to advancing our understanding of quantum mechanics, but are also necessary for a variety of quantum protocols. High-dimensional, or multipartite, quantum states are of specific interest, as they can exhibit unique properties both fundamentally and in application. The synthesis of high-dimensional, entangl

D. Zakharova, S. McGee, B. Vulcani, G. De Lucia

Galaxies within groups exhibit characteristics different from those of galaxies that reside in regions of average density (the field). Galaxy properties also depend on their location within the host structure and orientation with respect to the central galaxy: galaxies in the inner regions that are aligned to the major axis of the central galaxy tend to be m

Johanna Vielhaben, Dilyara Bareeva, Jim Berend, Wojciech Samek

Vision transformers (ViTs) can be trained using various learning paradigms, from fully supervised to self-supervised. Diverse training protocols often result in significantly different feature spaces, which are usually compared through alignment analysis. However, current alignment measures quantify this relationship in terms of a single scalar value, obscur

Rui Li, Jincheng Zhang, Xiaowei Zhao

To decarbonise the electricity sector and achieve renewable energy targets, a rapidly growing number of wind farms have been authorised, constructed, and commissioned in the UK and EU in recent years. For instance, the UK Government aims to expand offshore wind capacity to 60 GW by 2030, while the EU has set a target of 120 GW of offshore renewable energy by

Marc Maynou, Sergi Nadal, Raquel Panadero, Javier Flores

Data lakes are massive repositories of raw and heterogeneous data, designed to meet the requirements of modern data storage. Nonetheless, this same philosophy increases the complexity of performing discovery tasks to find relevant data for subsequent processing. As a response to these growing challenges, we present FREYJA, a modern data discovery system capa

Aliyu Umar, Maaruf Lawan, Adamu Lawan, Abdullahi Abdulkadir

Dark patterns in user interfaces represent deceptive design practices intended to manipulate users' behavior, often leading to unintended consequences such as coerced purchases, involuntary data disclosures, or user frustration. Detecting and mitigating these dark patterns is crucial for promoting transparency, trust, and ethical design practices in digital

Francesca Rossi, Émiland Garrabé, Giovanni Russo

We consider the problem of optimally composing a set of primitives to tackle planning and control tasks. To address this problem, we introduce a free energy computational model for planning and control via policy composition: Free-Gate. Within Free-Gate, control primitives are combined via a gating mechanism that minimizes variational free energy. This compo