November 2024 arXiv papers — page 41

Igor Yanilkin, Amir Gumarov, Bulat Gabbasov, Roman Yusupov

Continuously graded ferromagnetic thin films represent a new class of magnonic materials, where a spin-wave resonance spectrum can be tuned in resonance frequencies/fields, number of modes and even dispersion law by the magnetic properties profiling across the film thickness. In the paper, we demonstrate that temperature is another degree of freedom controll

Patrick Styll, Dowon Kim, Jiook Cha

Brain development in the first few months of human life is a critical phase characterized by rapid structural growth and functional organization. Accurately predicting developmental outcomes during this time is crucial for identifying delays and enabling timely interventions. This study introduces the SwiFT (Swin 4D fMRI Transformer) model, designed to predi

Soumya Adhikari, Bindusar Sahoo

New dilaton Weyl multiplets are constructed in four and five space-time dimensions for $N=4$ and $N=2$ conformal supergravity respectively. They are constructed from a mixture of the old dilaton weyl multiplets with an on-shell vector multiplet. The old dilaton Weyl multiplets have a $USp(4)$ R-symmetry group whereas the new multiplets have $SU(2)\times SU(2

Serge Kernbach

This work demonstrates biological detection of a low concentration of O3 by measuring electrochemical impedances of tissues in tobacco and tomato plants located indoor and outdoor. The lower range of generated ozone in the O3-air mix is about 30 ug/m3 over the atmospheric level, which allows phytosensors to be considered as biodetectors of environmental poll

Jiajing Lin, Zhenzhong Wang, Dejun Xu, Shu Jiang

4D content generation aims to create dynamically evolving 3D content that responds to specific input objects such as images or 3D representations. Current approaches typically incorporate physical priors to animate 3D representations, but these methods suffer from significant limitations: they not only require users lacking physics expertise to manually spec

Edvige Corbelli

When visiting the Museum of Modern Art's special exhibit ''van Gogh and the Colors of the Night (New York, USA, 2008)'' I was impressed by the artist's effort to paint and write about landscapes and life scenes at night. At the same time a clear contrast emerged between the colored Starry Night paint and the darkness or twilight in his earlier production. Si

Leon Sick, Dominik Engel, Sebastian Hartwig, Pedro Hermosilla

Traditionally, algorithms that learn to segment object instances in 2D images have heavily relied on large amounts of human-annotated data. Only recently, novel approaches have emerged tackling this problem in an unsupervised fashion. Generally, these approaches first generate pseudo-masks and then train a class-agnostic detector. While such methods deliver

Duong H. Le, Tuan Pham, Sangho Lee, Christopher Clark

We introduce OneDiffusion, a versatile, large-scale diffusion model that seamlessly supports bidirectional image synthesis and understanding across diverse tasks. It enables conditional generation from inputs such as text, depth, pose, layout, and semantic maps, while also handling tasks like image deblurring, upscaling, and reverse processes such as depth e

Kenichi Umeda, Noriyuki Kodera

Amplitude-modulation atomic force microscopy enables observation of fragile molecules at the nanometer scale. To shorten measurement times and capture dynamic molecules, increasing the frame rate is essential. Traditionally, maximum frame rates were thought to be limited by device bandwidth. However, for fragile molecules, imaging speed is often constrained

Xin He, Haiyun Guo, Kuan Zhu, Bingke Zhu

Lane detection plays an important role in autonomous driving perception systems. As deep learning algorithms gain popularity, monocular lane detection methods based on them have demonstrated superior performance and emerged as a key research direction in autonomous driving perception. The core designs of these algorithmic frameworks can be summarized as foll

Zheng Li, Xichen Guo, Feng Xie, Yan Zeng

Estimating causal effects from nonexperimental data is a fundamental problem in many fields of science. A key component of this task is selecting an appropriate set of covariates for confounding adjustment to avoid bias. Most existing methods for covariate selection often assume the absence of latent variables and rely on learning the global network structur

Frederic Madesta, Lukas Wimmert, Tobias Gauer, René Werner

In lung radiotherapy, the primary objective is to optimize treatment outcomes by minimizing exposure to healthy tissues while delivering the prescribed dose to the target volume. The challenge lies in accounting for lung tissue motion due to breathing, which impacts precise treatment alignment. To address this, the paper proposes a prospective approach that

Duo Wu, Jinghe Wang, Yuan Meng, Yanning Zhang

Utilizing large language models (LLMs) for tool planning has emerged as a promising avenue for developing general AI systems, where LLMs automatically schedule external tools (e.g., vision models) to tackle complex tasks based on task descriptions. To push this paradigm toward practical applications, it is crucial for LLMs to consider tool execution costs (e

Yiying Wei, Hadi Amirpour, Jong Hwan Ko, Christian Timmerer

Leveraging the overfitting property of deep neural networks (DNNs) is trending in video delivery systems to enhance video quality within bandwidth limits. Existing approaches transmit overfitted super-resolution (SR) model streams for low-resolution (LR) bitstreams, which are used to reconstruct high-resolution (HR) videos at the decoder. Although these appr

Emma Skarstein, Leonardo Soares Bastos, Håvard Rue, Stefanie Muff

Misclassified variables used in regression models, either as a covariate or as the response, may lead to biased estimators and incorrect inference. Even though Bayesian models to adjust for misclassification error exist, it has not been shown how these models can be implemented using integrated nested Laplace approximation (INLA), a popular framework for fit

Jaime Corsetti, Francesco Giuliari, Alice Fasoli, Davide Boscaini

Understanding functionalities in 3D scenes involves interpreting natural language descriptions to locate functional interactive objects, such as handles and buttons, in a 3D environment. Functionality understanding is highly challenging, as it requires both world knowledge to interpret language and spatial perception to identify fine-grained objects. For exa

Henning Krause

A notion of support for objects in any Grothendieck category is introduced. This is based on the spectral category of a Grothendieck category and uses its Boolean lattice of localising subcategories. The support provides a classification of all subcategories that are closed under arbitrary coproducts, subobjects, and essential extensions. There is also a not

Wentao Qu, Jing Wang, YongShun Gong, Xiaoshui Huang

Existing conditional Denoising Diffusion Probabilistic Models (DDPMs) with a Noise-Conditional Framework (NCF) remain challenging for 3D scene understanding tasks, as the complex geometric details in scenes increase the difficulty of fitting the gradients of the data distribution (the scores) from semantic labels. This also results in longer training and inf

Mario Galici, Giuseppe Filippone

The concept of Schreier extensions of loops was introduced in the general case in [11] and, more recently, it has been explored in the context of Steiner loops in [6]. In the latter case, it gives a powerful method for constructing Steiner triple systems containing Veblen points. Counting all Steiner triple systems of order v is an open problem for v>21. In

Yan Liu, Ya-Wen Sun, Xin-Meng Wu

We derive the Schwinger-Keldysh effective field theories for diffusion including the lowest non-hydrodynamic degree of freedom from holographic Gubser-Rocha systems. At low temperature the dynamical non-hydrodynamic mode could be either an IR mode or a slow mode, which is related to IR quantum critical excitations or encodes the information of all energy sca

Magdalena Kaiser, Patrick Ernst, György Szarvas

Task-oriented Dialog (ToD) systems have to solve multiple subgoals to accomplish user goals, whereas feedback is often obtained only at the end of the dialog. In this work, we propose SUIT (SUbgoal-aware ITerative Training), an iterative training approach for improving ToD systems. We sample dialogs from the model we aim to improve and determine subgoals tha

Yuchen Xia, Quan Yuan, Guiyang Luo, Xiaoyuan Fu

Collaborative perception in autonomous driving significantly enhances the perception capabilities of individual agents. Immutable heterogeneity, where agents have different and fixed perception networks, presents a major challenge due to the semantic gap in exchanged intermediate features without modifying the perception networks. Most existing methods bridg

Sudhakantha Girmohanta, Yuichiro Nakai, Yu-Cheng Qiu, Zhihao Zhang

The dilaton emerges as a pseudo-Nambu-Goldstone boson (pNGB) associated with the spontaneous breaking of scale invariance in a nearly conformal field theory (CFT). We show the existence of a wiggly dilaton potential that contains multiple vacuum solutions in a five-dimensional (5D) holographic formulation. The wiggly feature originates from boundary potentia

Dun Zeng, Zheshun Wu, Shiyu Liu, Yu Pan

Federated Learning (FL) is a distributed learning approach that trains machine learning models across multiple devices while keeping their local data private. However, FL often faces challenges due to data heterogeneity, leading to inconsistent local optima among clients. These inconsistencies can cause unfavorable convergence behavior and generalization per

Sotiris Pelekis, Evangelos Karakolis, George Lampropoulos, Spiros Mouzakitis

The present study aims to evaluate the current fuzzy landscape of Trustworthy AI (TAI) within the European Union (EU), with a specific focus on the energy sector. The analysis encompasses legal frameworks, directives, initiatives, and standards like the AI Ethics Guidelines for Trustworthy AI (EGTAI), the Assessment List for Trustworthy AI (ALTAI), the AI ac

Peng-Zhang He, Hai-Qing Zhang

We investigate the Krylov complexity of Schr\"odinger field theories, focusing on both bosonic and fermionic systems within the grand canonical ensemble that includes a chemical potential. Krylov complexity measures operator growth in quantum systems by analyzing how operators spread within the Krylov space, a subspace of the Hilbert space spanned by success

Yuxuan Yang, Tao Geng

Interior design is a complex and creative discipline involving aesthetics, functionality, ergonomics, and materials science. Effective solutions must meet diverse requirements, typically producing multiple deliverables such as renderings and design drawings from various perspectives. Consequently, interior design processes are often inefficient and demand si

Nhan Thanh Nguyen, Ly V. Nguyen, Nir Shlezinger, Yonina C. Eldar

In this paper, we propose a low-complexity and fast hybrid beamforming design for joint communications and sensing (JCAS) based on deep unfolding. We first derive closed-form expressions for the gradients of the communications sum rate and sensing beampattern error with respect to the analog and digital precoders. Building on this, we develop a deep neural n

Shaolei Zhang, Kehao Zhang, Qingkai Fang, Shoutao Guo

Large language models (LLMs), with their powerful generative capabilities and vast knowledge, empower various tasks in everyday life. However, these abilities are primarily concentrated in high-resource languages, leaving low-resource languages with weaker generative capabilities and relatively limited knowledge. Enhancing the multilingual capabilities of LL

Jerome Mitard, Luka Kljucar, Nouredine Rassoul, Harold Dekkers

Back and double gate IGZO nFETs have been demonstrated down to 120nm and 70nm respectively leveraging 300mm fab processing. While the passivation of oxygen vacancies in IGZO is challenging with an integration of front side gate, a scaled back gated flow has been optimized by multiplying design of experiments around contacts and material engineering. We then

Valentin Six, Alexandre Chidiac, Arkin Worlikar

This document is an evaluation of the original "Rank-N-Contrast" (arXiv:2210.01189v2) paper published in 2023. This evaluation is done for academic purposes. Deep regression models often fail to capture the continuous nature of sample orders, creating fragmented representations and suboptimal performance. To address this, we reproduced the Rank-N-Contrast (R

João Almeida, Alexandre Francisco, Daniel Santos, José Rui Figueira

A new multi-objective method for the thesis defence scheduling problem is introduced. The problem involves appointing committees to defences and assigning them to a time slot and room. A multi-objective approach is necessary to provide a better understanding of possible solutions and trade-offs to decision-makers. However, this type of approach is often time

Wiktor Wichrowski

We clarify and extend insights from Lavrentiev's seminal paper. We examine the original theorem dealing with the absence of the Lavrentiev phenomenon, a cornerstone issue in the calculus of variations. We point out some inconsistencies in the original proof by providing a counterexample and supply the result with a new, concise, and complete reasoning. In th

Rafael S. Toledo, Cristiano S. Oliveira, Vitor H. T. Oliveira, Eric A. Antonelo

Autonomous driving needs good roads, but 85% of Brazilian roads have damages that deep learning models may not regard as most semantic segmentation datasets for autonomous driving are high-resolution images of well-maintained urban roads. A representative dataset for emerging countries consists of low-resolution images of poorly maintained roads and includes

Emanuel Carneiro, Mithun Kumar Das

In this paper we provide a detailed study on effective versions of the celebrated Bilu's equidistribution theorem for Galois orbits of sequences of points of small height in the $N$-dimensional algebraic torus, identifying the quantitative dependence of the convergence in terms of the regularity of the test functions considered. We develop a general Fourier

Jr-Jun Lin, Cheng-En Tsai, Jung-Ren Huang, Jih-Chiang Tsai

Tightly packed granular particles under shear often exhibit intriguing intermittencies, specifically, sudden stress drops that we refer to as quaking. To probe the nature of this phenomenon, we prototype a circular shear cell that is capable of imposing a uniform and unlimited shear strain under quasi-static cyclic driving. Spherical PDMS(polydimethylsiloxan

Anubhav Dubey, Constantin Habes, Holger Marschall, Sakir Amiroudine

A novel phase field method is proposed to model the continuous transition of binary fluids exhibiting temperature sensitive miscibility gap, from immiscible state to miscible state via partially miscible states. The model is employed to investigate the isothermal single-mode Rayleigh-Taylor (RT) instability for binary fluids as the system temperature is vari

Luca Allodi, Roy Ricaldi, Jai Wientjes, Adriana Radu

The cybercriminal underground consists of hundreds of forum communities that function as marketplaces and information-exchange platforms for both established and wannabe cybercriminals. The ecosystem is continuously evolving, with users migrating between forums and platforms. The emergence of cybercrime communities in Telegram and Discord only highlights the

José D. Guimarães, James Lim, Mikhail I. Vasilevskiy, Susana F. Huelga

Two-dimensional electronic spectroscopy (2DES) is a powerful tool for exploring quantum effects in energy transport within photosynthetic systems and investigating novel material properties. However, simulating the dynamics of these experiments poses significant challenges for classical computers due to the large system sizes, long timescales and numerous ex

Tom Wehrbein, Marco Rudolph, Bodo Rosenhahn, Bastian Wandt

Monocular 3D human pose and shape estimation is an inherently ill-posed problem due to depth ambiguities, occlusions, and truncations. Recent probabilistic approaches learn a distribution over plausible 3D human meshes by maximizing the likelihood of the ground-truth pose given an image. We show that this objective function alone is not sufficient to best ca

Ferdinand Ihringer

We survey results for Cameron-Liebler sets and low degree Boolean functions for Hamming graphs, Johnson graphs and Grassmann graphs from the point of view of association schemes. This survey covers selected results in finite geometry, Boolean function analysis, design theory, coding theory, and cryptography.

Yunda Li, Wei Han, Zengming Meng, Wenxin Yang

When two or more energy bands become degenerate at a singular point in the momentum space, such singularity, or ``Dirac points", gives rise to intriguing quantum phenomena as well as unusual material properties. Systems at the Dirac points can possess topological charges and their unique properties can be probed by various methods, such as transport measurem

Jyoti Turi, Gadadhar Banerjee

The influence of the presence of cosmic fluid on the magnetosonic waves and modulation instabilities in the interstellar medium of spiral galaxies is investigated. The fluid model is developed by modifying the pressure equation in such dissipative rotating magnetoplasmas incorporating thermal ionized gas and cosmic rays. Applying the normal mode analysis, a

Georgios Tzoumanekas, Michail Chatzianastasis, Loukas Ilias, George Kiokes

Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots-automated programs disseminating misinformation and ideologies with tangible real-world consequences. While bot detection in platform X has been the area of many deep learning models with adequate results, most approaches neglect the graph structur

Trevor Jack

A semigroup conjugacy is an equivalence relation that equals group conjugacy when the semigroup is a group. In this note, we answer five open problems related to semigroup conjugacy. (Problem One) We say a conjugacy ~ is partition-covering if for every set X and every partition of the set, there exists a semigroup with universe X such that the partition give

Tomoki Nakanishi

We focus on the $G$-fans associated with cluster patterns whose initial exchange matrices are of infinite type. We study the asymptotic behavior of the $g$-vectors around the initial $G$-cone under the alternating mutations for two indices of infinite type. In the rank 3 case, we classify them into several patterns. As an application, the incompleteness of t

Matthias Beckmann, Robert Beinert, Jonas Bresch

The Radon cumulative distribution transform (R-CDT) exploits one-dimensional Wasserstein transport and the Radon transform to represent prominent features in images. It is closely related to the sliced Wasserstein distance and facilitates classification tasks, especially in the small data regime, like the recognition of watermarks in filigranology. Here, a t

Fei Wang

We develop a thermodynamic framework that couples mass dynamics, described by the Newton- Gibbs-van der Waals formalism, with electromagnetic fields beyond the scope of classical Maxwell theory. Classical Newtonian mechanics does not capture density evolution in the momentum balance, while the standard Maxwell equations neglect the contribution of the curl c

Leonard Tokic

Using a Milnor-Moore argument we show that, $K(2)$-locally at the prime $2$, the spectra $MU\langle 6\rangle$ and $MString$ split as direct sums of Morava $E$-theories after tensoring with a finite Galois extension of the sphere called $E^{hF_{3/2}}$. In the case of $MString$ we are able to refine this splitting in several ways: we show that the projection m

Pol Benítez, Siyu Chen, Ruoshi Jiang, Cibrán López

Silver chalcohalide antiperovskites (CAP), Ag$_{3}$XY (X = S, Se; Y = Br, I), are a family of highly anharmonic inorganic compounds with great potential for energy applications. However, a substantial and unresolved discrepancy exists between the optoelectronic properties predicted by theoretical first-principles methods and those measured experimentally at

Hamed Shirzad, Honghao Lin, Balaji Venkatachalam, Ameya Velingker

Graph Transformers excel in long-range dependency modeling, but generally require quadratic memory complexity in the number of nodes in an input graph, and hence have trouble scaling to large graphs. Sparse attention variants such as Exphormer can help, but may require high-degree augmentations to the input graph for good performance, and do not attempt to s

Eric Hanchen Jiang, Yasi Zhang, Zhi Zhang, Yixin Wan

Text-to-image (T2I) diffusion models have revolutionized generative modeling by producing high-fidelity, diverse, and visually realistic images from textual prompts. Despite these advances, existing models struggle with complex prompts involving multiple objects and attributes, often misaligning modifiers with their corresponding nouns or neglecting certain

Jingfeng Chen, Wanlin Deng, Dangxing Chen, Luyao Zhang

Machine learning is critical for innovation and efficiency in financial markets, offering predictive models and data-driven decision-making. However, challenges such as missing data, lack of transparency, untimely updates, insecurity, and incompatible data sources limit its effectiveness. Blockchain technology, with its transparency, immutability, and real-t

Mohammadreza Molavi, Reza Khodadadi

This paper introduces an efficient and accurate pipeline for text-dependent speaker verification (TDSV), designed to address the need for high-performance biometric systems. The proposed system incorporates a Fast-Conformer-based ASR module to validate speech content, filtering out Target-Wrong (TW) and Impostor-Wrong (IW) trials. For speaker verification, w

I. V. Beznosenko, A. V. Vasyliev, G. V. Sotnikov, G. O. Krivonosov

To support of our experimental studies on dielectric laser acceleration, numerical studies of laser acceleration of nonrelativistic electrons with the initial energy of 33.9 keV in transparent and reflective periodic structures are car-ried out. On the basis of computer simulations, the acceleration rates of electrons and the quality of their beams after acc

Hans A. Weidenmüller

Using the parametric representation of a chaotic many-body quantum system derived earlier, we calculate explicitly the large-time dependence and asymptotic value of the out-of-time correlator (OTOC) of that system. The dependence on time $t$ is determined by $\Delta t / \hbar$. Here $\Delta$ is the energy correlation width within which the Bohigas-Giannoni-S

Silas Ruhrberg Estévez, Josée Mallah, Dominika Kazieczko, Chenyu Tang

Ankle exoskeletons have garnered considerable interest for their potential to enhance mobility and reduce fall risks, particularly among the aging population. The efficacy of these devices relies on accurate real-time prediction of the user's intended movements through sensor-based inputs. This paper presents a novel motion prediction framework that integrat

Boris L. Feigin, Simon D. Lentner

A vertex algebra with an action of a group $G$ comes with a notion of $g$-twisted modules, forming a $G$-crossed braided tensor category. For a Lie group $G$, one might instead wish for a notion of $(\mathrm{d}+A)$-twisted modules for any $\mathfrak{g}$-connection on the formal punctured disc. For connections with a regular singularity, this reduces to $g$-t

Haifeng Wang, Jingwei Sun, Hong Zhang, Xu Qian

In this work, we develop a class of up to third-order energy-stable schemes for the Cahn--Hilliard equation. Building on Lawson's integrating factor Runge--Kutta method, which is widely used for stiff semilinear equations, we discuss its limitations, such as the inability to preserve the equilibrium state and the oversmoothing of interfacial layers in the so

Mischa Elkner

We study Kirillov algebras attached to minuscule highest weight representations of semisimple Lie algebras. They can be viewed as equivariant cohomology algebras of partial flag varieties. Real structures on the varieties then induce involutions of these algebras. We describe how these involutions act on the spectra of minuscule Kirillov algebras, and model

Songning Lai, Mingqian Liao, Zhangyi Hu, Jiayu Yang

Concept Bottleneck Models (CBMs) enhance the interpretability of AI systems, particularly by bridging visual input with human-understandable concepts, effectively acting as a form of multimodal interpretability model. However, existing CBMs typically assume static datasets, which fundamentally limits their adaptability to real-world, continuously evolving mu

Sinya Aoki, Janos Balog, Kengo Shimada

We derive the GKP-Witten relation in terms of correlation functions by symmetry without referring to a Lagrangian or the large $N$ expansion. By constructing bulk operators from boundary operators in conformal field theory (CFT) by the conformal smearing, we first determine bulk-boundary 2-pt functions for an arbitrary spin using both conformal and bulk symm

Antonio Alarcon, Tjasa Vrhovnik

We establish a Mittag-Leffler-type theorem with approximation and interpolation for meromorphic curves $M\to \mathbb{C}^n$ ($n\geq 3$) directed by Oka cones in $\mathbb{C}^n$ on any open Riemann surface $M$. We derive a result of the same type for proper conformal minimal immersions $M\to \mathbb{R}^n$. This includes interpolation in the poles and approximat

Alexander von Rohr, David Stenger, Dominik Scheurenberg, Sebastian Trimpe

Controller tuning is crucial for closed-loop performance but often involves manual adjustments. Although Bayesian optimization (BO) has been established as a data-efficient method for automated tuning, applying it to large and high-dimensional search spaces remains challenging. We extend a recently proposed local variant of BO to include crash constraints, w

Dario Giandinoto

We formulate and partially prove a general conjecture providing necessary and sufficient conditions for the reality of the asymptotic spectrum of an arbitrary real banded block Toeplitz matrix. Additionally we present numerical experiments supporting it. This conjecture is a direct generalization of the already existing one in the case of banded Toeplitz mat

Jingyi Wu, Anton Yu. Bykov, Anastasiia Zaleska, Anatoly V. Zayats

Manipulating intensity, phase and polarization of the electromagnetic fields on ultrafast timescales is essential for all-optical switching, optical information processing and development of novel time-variant media. Noble metal based plasmonics has provided numerous platforms for optical switching and control, enabled by strong local field enhancement, arti

Virginia del Campo, Iker Malaina

This study investigates the impact of changes in body composition and follow-up imaging modalities on recurrence and prognosis in patients with head and neck squamous cell carcinoma (HNSCC). The results indicate that an increase in the adiposity index post-radiotherapy is significantly associated with higher recurrence and mortality rates. Additionally, the

Uzi Pereg

Communication over a fully quantum relay channel is considered. We establish three bounds based on different coding strategies, i.e., partial decode-forward, measure-forward, and assist-forward. Using the partial-decode forward strategy, the relay decodes part of the information, while the other part is decoded without the relay's help. The result by Savov e

Mathis Immertreu, Achim Schilling, Andreas Maier, Patrick Krauss

This study explores the potential for artificial agents to develop core consciousness, as proposed by Antonio Damasio's theory of consciousness. According to Damasio, the emergence of core consciousness relies on the integration of a self model, informed by representations of emotions and feelings, and a world model. We hypothesize that an artificial agent,

Samuel Bronstein

In this paper, we study nonmaximal representations of surface groups in PU(2,1). In genus large enough, we show the existence of convex-cocompact representations of non-maximal Toledo invariant admitting a unique equivariant minimal surface, which is holomorphic and almost totally geodesic. These examples can be obtained for any Toledo invariant of the form

Juan Lucas Rey

This document presents a new C++ Automatic Differentiation (AD) tool, AD-HOC (Automatic Differentiation for High-Order Calculations). This tool aims to have the following features: -Calculation of user specified derivatives of arbitrary order -To be able to run with similar speeds as handwritten code -All derivatives calculations are computed in a single bac

Fu-Chieh Chang, You-Chen Lin, Pei-Yuan Wu

Large language models (LLMs) have demonstrated remarkable mathematical capabilities, largely driven by chain-of-thought (CoT) prompting, which decomposes complex reasoning into step-by-step solutions. This approach has enabled significant advancements, as evidenced by performance on benchmarks like GSM8K and MATH. However, the mechanisms underlying LLMs' abi

Tong You, Johan Bielecki, Filipe R. N. C. Maia

Single-particle imaging (SPI) using X-ray free-electron Lasers (XFELs) offers the potential to determine protein structures at high spatial and temporal resolutions without the need for crystallization or vitrification. However, the technique faces challenges due to weak diffraction signals from single proteins and significant background scattering from gase

N. Dimova, J. S. Barnard, D. Bortoletto, G. Crevatin

We have evaluated the imaging capabilities of the Timepix4 hybrid silicon pixel detector for 100 keV and 200 keV electrons in a Transmission Electron Microscope (TEM). Using the knife-edge method, we have measured the Modulation Transfer Function (MTF) at both energies. Our results show a decrease in MTF response at Nyquist (spatial) frequency, dropping from

Stefano Biagi, Francesco Esposito, Alberto Roncoroni, Eugenio Vecchi

In this paper we consider a quasilinear elliptic and critical problem with Dirichlet boundary conditions in presence of the anisotropic $p$-Laplacian. The critical exponent is the usual $p^{\star}$ such that the embedding $W^{1,p}_{0}(\Omega) \subset L^{p^{\star}}(\Omega)$ is not compact. We prove the existence of a weak positive solution in presence of both

Md Arsalan Ashraf, Pramod Pullarkat

Optical tweezers has emerged as a powerful tool in manipulating microscopic particles and in measuring weak forces of the order of a pico-Newton. As a result, it has found wide applications ranging from material science to biology. Dual-trap optical tweezers (DTOT) are of particular importance as they allow for two point correlation measurements as in molecu

Alireza Amiri-Margavi, Iman Jebellat, Ehsan Jebellat, Seyed Pouyan Mousavi Davoudi

We propose a collaborative framework in which multiple large language models -- including GPT-4-0125-preview, Meta-LLaMA-3-70B-Instruct, Claude-3-Opus, and Gemini-1.5-Flash -- generate and answer complex, PhD-level statistical questions when definitive ground truth is unavailable. Our study examines how inter-model consensus improves both response reliabilit

Demian Hespe, Lukas Hübner, Charel Mercatoris, Peter Sanders

Supercomputers getting ever larger and energy-efficient is at odds with the reliability of the used hardware. Thus, the time intervals between component failures are decreasing. Contrarily, the latencies for individual operations of coarse-grained big-data tools grow with the number of processors. To overcome the resulting scalability limit, we need to go be

Constantin Pestka, Marcus Paradies, Matthias Pohl

The performance of storage hardware has improved vastly recently, leaving the traditional I/O stack incapable of exploiting these gains due to increasingly large relative overheads. Newer asynchronous I/O APIs, such as io_uring, have significantly improved performance by reducing such overheads, but exhibit limited adoption in practice. In this paper, we dis

Zhigang Wang, Yifei Su, Chenhui Li, Dong Wang

Open-vocabulary 3D scene understanding is indispensable for embodied agents. Recent works leverage pretrained vision-language models (VLMs) for object segmentation and project them to point clouds to build 3D maps. Despite progress, a point cloud is a set of unordered coordinates that requires substantial storage space and does not directly convey occupancy

Sina Abbasi, Mohammad Reza Modarres, Mohammad Taher Pilehvar

With new large language models (LLMs) emerging frequently, it is important to consider the potential value of model-agnostic approaches that can provide interpretability across a variety of architectures. While recent advances in LLM interpretability show promise, many rely on complex, model-specific methods with high computational costs. To address these li

Yi Cai, Arthur Zimek, Eirini Ntoutsi, Gerhard Wunder

Recent literature highlights the critical role of neighborhood construction in deriving model-agnostic explanations, with a growing trend toward deploying generative models to improve synthetic instance quality, especially for explaining text classifiers. These approaches overcome the challenges in neighborhood construction posed by the unstructured nature o

Eric Shah, Jay Patel, Mr. Vishal Katheriya, Parth Pataliya

Fundus images are widely used for diagnosing various eye diseases, such as diabetic retinopathy, glaucoma, and age-related macular degeneration. However, manual analysis of fundus images is time-consuming and prone to errors. In this report, we propose a novel method for fundus detection using object detection and machine learning classification techniques.

Ayon Mondal, Arijit Sar, Maitreya Kundu, Ritaban Chatterjee

In 2023 October-November, the blazar BL Lacertae underwent a very large-amplitude submm outburst. The usual single-zone leptonic model with the lower energy peak of the spectral energy distribution (SED) fit by the synchrotron emission from one distribution of relativistic electrons in the jet and inverse-Compton (IC) scattering of lower energy photons from

Evan Tillotson, James McHugh, James Howarth, Teruo Hashimoto

Twisted 2D material heterostructures provide an exciting platform for investigating new fundamental physical phenomena. Many of the most interesting behaviours emerge at small twist angles, where the materials reconstruct to form areas of perfectly stacked crystal separated by partial dislocations. However, understanding the properties of these systems is of

Jianhan Sun, Jianfeng Lv, Shang Tian, Juntao Liu

DC-SRF-II gun, a high-brightness continuous-wave photocathode gun, has greater potential in electron beam irradiation applications. This paper presents the in-vacuum and in-air irradiation dosimetry study of the high repetition rate electron beam from the DC-SRF-II gun with both Monte Carlo simulations and experiments. Especially, high-dose uniform irradiati

Sam Allen, David Ginsbourger, Johanna Ziegel

Probabilistic predictions are probability distributions over the set of possible outcomes. Such predictions quantify the uncertainty in the outcome, making them essential for effective decision making. By combining multiple predictions, the information sources used to generate the predictions are pooled, often resulting in a more informative forecast. Probab

Anastassis Kapetanakis, Aggelos Ferikoglou, George Anagnostopoulos, Sotirios Xydis

Computational Fluid Dynamics (CFD) simulations are essential for analyzing and optimizing fluid flows in a wide range of real-world applications. These simulations involve approximating the solutions of the Navier-Stokes differential equations using numerical methods, which are highly compute- and memory-intensive due to their need for high-precision iterati

Igor Martins, Hedibert Freitas Lopes

This paper expands on stochastic volatility models by proposing a data-driven method to select the macroeconomic events most likely to impact volatility. The paper identifies and quantifies the effects of macroeconomic events across multiple countries on exchange rate volatility using high-frequency currency returns, while accounting for persistent stochasti

Inyong Cho

We introduce the scalar and tensor modes of the gravitational perturbation in the presence of a scalar field which describes inflation. We investigate the back-reaction of the perturbations to the background by studying the effective energy-momentum tensor (2EMT) which is the second order constructed by the quadratic terms of the linear perturbations. 2EMT i

Ruofan Jia, Weiying Xie, Jie Lei, Jitao Ma

While large pre-trained models have achieved impressive performance across AI tasks, their deployment in privacy-sensitive and distributed environments remains challenging. Federated learning (FL) offers a viable solution by enabling decentralized fine-tuning without data sharing, but real-world applications face significant obstacles due to heterogeneous cl

Jean M. Kempf, François Rincon

Context. The magneto-thermal instability (MTI) is one of many possible drivers of stratified turbulence in the intracluster medium (ICM) outskirts of galaxy clusters, where the background temperature gradient is aligned with the gravity. This instability occurs because of the fast anisotropic conduction of heat along magnetic field lines; but to what extent

M. F. Fauzi, H. S. Ramadhan, A. Sulaksono

Regular black hole is one of the bottom-up solutions designed to eliminate the singularity at the center of black holes. Its horizonless solution has gained interest recently to model ultracompact star. Despite interesting, this proposal is problematic due to the absence of a well-defined boundary. In this work, we introduce a novel regular black hole model

S. Finocchiaro, D. Ferraro, M. Sassetti, G. Benenti

We investigate a hybrid thermal machine based on a single closed quantum Hall edge channel forming a quantum dot. It is tunneling coupled with two quantum Hall states at $\nu = 2$ in contact with reservoirs at different temperatures and chemical potentials. One of these edge states is also driven out-of-equilibrium by means of a periodic train of Lorentzian

Zhengmin Yu, Jiutian Zeng, Siyi Chen, Wenhan Xu

Over the past year, there has been a notable rise in the use of large language models (LLMs) for academic research and industrial practices within the cybersecurity field. However, it remains a lack of comprehensive and publicly accessible benchmarks to evaluate the performance of LLMs on cybersecurity tasks. To address this gap, we introduce CS-Eval, a publ

Yuchen Hu, Junhao Ye, Ke Xu, Jialin Sun

Verifying hardware designs in embedded systems is crucial but often labor-intensive and time-consuming. While existing solutions have improved automation, they frequently rely on unrealistic assumptions. To address these challenges, we introduce a novel framework, UVLLM, which combines Large Language Models (LLMs) with the Universal Verification Methodology

Andrzej Puć, Joanna Janczura

In this paper, we develop a new approach to the very short-term point forecasting of electricity prices in the continuous market. It is based on the Support Vector Regression with a kernel correction built on additional forecast of dependent variable. We test the proposed approach on a dataset from the German intraday continuous market and compare its foreca

Hong Liu, Yitong Lu

This paper presents a novel method to improve the robustness of foundation models to group-based biases. We propose a simple yet effective method, called DoubleCCA, that leverages random sentences and Canonical Correlation Analysis (CCA) to enrich the text embeddings of the foundation model. First, we generate various random sentences that augment the origin

Manu Harsu, Eero Hyry

By utilizing domain theory, we generalize the notion of an ephemeral module to the so-called continuous posets. We investigate the quotient category of persistence modules by the Serre subcategory of ephemeral modules and show that it is equivalent to the category of sheaves on the Scott topology. Furthermore, we study the metric properties of persistence mo

Katsuhiro Endo, Kazuaki Z. Takahashi

From weather to neural networks, modeling is not only useful for understanding various phenomena, but also has a wide range of potential applications. Although nonlinear differential equations are extremely useful tools in modeling, their solutions are difficult to obtain. Based on the expectation of quantum transcendence, quantum algorithms for efficiently