October 2024 arXiv papers — page 56

Alexandros Graikos, Nebojsa Jojic, Dimitris Samaras

Large denoising diffusion models, such as Stable Diffusion, have been trained on billions of image-caption pairs to perform text-conditioned image generation. As a byproduct of this training, these models have acquired general knowledge about image statistics, which can be useful for other inference tasks. However, when confronted with sampling an image unde

Jacopo D'Ignazi, Andreas Kaltenbrunner, Yelena Mejova, Michele Tizzani

Over the last few years, verifying the credibility of information sources has become a fundamental need to combat disinformation. Here, we present a language-agnostic model designed to assess the reliability of web domains as sources in references across multiple language editions of Wikipedia. Utilizing editing activity data, the model evaluates domain reli

Steven A. H. de Rooij, Remko Fermin, Kevin Kouwenhoven, Tonny Coppens

Disordered superconductors offer new impedance regimes for quantum circuits, enable a pathway to protected qubits, and can improve superconducting detectors due to their high kinetic inductance and sheet resistance. The performance of these devices can be limited, however, by quasiparticles - the fundamental excitations of a superconductor. While experiments

Shihan Li, Yu Dai, Mingde Ding, Jinhan Guo

We present observations and analysis of an X1.8 non-eruptive solar flare on 2012 October 23, which is characterized by an extremely large late-phase peak seen in the warm coronal extreme-ultraviolet (EUV) emissions ($\sim$ 3 MK), with the peak intensity over 1.4 times that of main flare peak. The flare is driven by a failed eruption of a magnetic flux rope (

Balázs Gyenes, Nikolai Franke, Philipp Becker, Gerhard Neumann

Perceiving the environment via cameras is crucial for Reinforcement Learning (RL) in robotics. While images are a convenient form of representation, they often complicate extracting important geometric details, especially with varying geometries or deformable objects. In contrast, point clouds naturally represent this geometry and easily integrate color and

François Laroussinie, Nicolas Markey

We introduce a new class of automata (which we coin EU-automata) running on infininte trees of arbitrary (finite) arity. We develop and study several algorithms to perform classical operations (union, intersection, complement, projection, alternation removal) for those automata, and precisely characterise their complexities. We also develop algorithms for so

Wei He, Zhiheng Xi, Wanxu Zhao, Xiaoran Fan

Solving complex chart Q&A tasks requires advanced visual reasoning abilities in multimodal large language models (MLLMs), including recognizing key information from visual inputs and conducting reasoning over it. While fine-tuning MLLMs for reasoning is critical, collecting and annotating charts and questions is expensive, hard to scale, and often results in

Nian Wu, Miaomiao Zhang

This paper presents a novel method, named geodesic deformable networks (GDN), that for the first time enables the learning of geodesic flows of deformation fields derived from images. In particular, the capability of our proposed GDN being able to predict geodesics is important for quantifying and comparing deformable shape presented in images. The geodesic

Robert Bland, Kevin McGoff

Given a countable group $G$ and two subshifts $X$ and $Y$ over $G$, a continuous, shift-commuting map $\phi : X \to Y$ is called a homomorphism. Our main result states that if every finitely generated subgroup of $G$ has polynomial growth, $X$ is aperiodic, and $Y$ has the finite extension property (FEP), then there exists a homomorphism $\phi : X \to Y$. By

Geoffrey Kasenbacher, Felix Ehret, Gerrit Ecke, Sebastian Otte

The locally competitive algorithm (LCA) can solve sparse coding problems across a wide range of use cases. Recently, convolution-based LCA approaches have been shown to be highly effective for enhancing robustness for image recognition tasks in vision pipelines. To additionally maximize representational sparsity, LCA with hard-thresholding can be applied. Wh

Peizheng Li, Ioannis Mavromatis, Tim Farnham, Adnan Aijaz

Seamless integration of artificial intelligence (AI) and machine learning (ML) techniques with wireless systems is a crucial step for 6G AInization. However, such integration faces challenges in terms of model functionality and lifecycle management. ML operations (MLOps) offer a systematic approach to tackle these challenges. Existing approaches toward imple

Yuxing Chen, Weijie Wang, Sylvain Lobry, Camille Kurtz

Large language models (LLMs) are being used in data science code generation tasks, but they often struggle with complex sequential tasks, leading to logical errors. Their application to geospatial data processing is particularly challenging due to difficulties in incorporating complex data structures and spatial constraints, effectively utilizing diverse fun

Pedro Nogarolli, Gabriel S. Denicol, Eduardo S. Fraga

We investigate the first-order transport coefficients of a fluid made of quasiparticles with a temperature-dependent mass extracted from chiral models. We describe this system using an effective kinetic theory, given by the relativistic Boltzmann equation coupled to a temperature-dependent background field determined from the thermal masses. We then simplify

Ali Harandi, Hooman Danesh, Kevin Linka, Stefanie Reese

A novel physics-informed operator learning technique based on spectral methods is introduced to model the complex behavior of heterogeneous materials. The Lippmann-Schwinger operator in Fourier space is employed to construct physical constraints with minimal computational overhead, effectively eliminating the need for automatic differentiation. The introduce

Peizheng Li, Adrián Sánchez-Mompó, Tim Farnham, Aftab Khan

Generative artificial intelligence (GAI) has emerged as a pivotal technology for content generation, reasoning, and decision-making, making it a promising solution on the 6G stage characterized by openness, connected intelligence, and service democratization. This article explores strategies for integrating and monetizing GAI within future open 6G networks,

Arturo Martin Jimenez, Marc Baltes, Jackson Cornelius, Neset Akozbek

Free-space optical communication (FSOC) systems offer high-bandwidth and secure communication with minimal capital costs. Adaptive optics (AO) are typically added to these systems to decrease atmospheric channel losses; however, the performance of traditional AO wavefront sensors degrades in long-range, deep turbulence conditions. Alternative wavefront senso

Gaëtan Chenevier

We develop a method initiated by Bacher and Venkov, and based on a study of the Kneser neighbors of the standard lattice Z^n, which allows to classify the integral unimodular Euclidean lattices of rank n. As an application, of computational flavour, we determine the isometry classes of unimodular lattices of rank 26 and 27.

Luca Haardt, Patrick Tolksdorf

We establish the Kato square root property for the generalized Stokes operator on $\mathbb{R}^d$ with bounded measurable coefficients. More precisely, we identify the domain of the square root of $Au := - \operatorname{div}(\mu \nabla u) + \nabla \phi$, $\operatorname{div}(u) = 0$, with the space of divergence-free $\mathrm{H}^1$-vector fields and further pr

Yucheng Shi, Wenlong Wang, Xiaowen Tao, Ivana Dusparic

Dynamic scheduling of access to shared resources by autonomous systems is a challenging problem, characterized as being NP-hard. The complexity of this task leads to a combinatorial explosion of possibilities in highly dynamic systems where arriving requests must be continuously scheduled subject to strong safety and time constraints. An example of such a sy

Qi Li, Xiang Liu, Zhenheng Tang, Peijie Dong

Model editing has become an increasingly popular alternative for efficiently updating knowledge within language models. Current methods mainly focus on reliability, generalization, and locality, with many methods excelling across these criteria. Some recent works disclose the pitfalls of these editing methods such as knowledge distortion or conflict. However

Zhihan Huang, Yuting Wei, Yuxin Chen

The denoising diffusion probabilistic model (DDPM) has emerged as a mainstream generative model in generative AI. While sharp convergence guarantees have been established for the DDPM, the iteration complexity is, in general, proportional to the ambient data dimension, resulting in overly conservative theory that fails to explain its practical efficiency. Th

Léa Bresque, Debraj Das, Édgar Roldán

We introduce a single-qubit quantum measurement engine fuelled by backaction energy input. To reduce energetic costs associated with information processing, the measurement outcomes are only used with a prescribed laziness probability in the feedback step. As a result, we show that the work extracted over consecutive cycles is a second-order Markov process,

Raúl Fuentes-Azcatl

Specifically, we examine concentrations from [NaCl]=1m to [NaCl]=6m, where 6.1m represents the solubility threshold of NaCl in H2O. For the water model, we employ the flexible TIP4P/$\epsilon_{Flex}$ model, which offers an enhanced reproduction of various properties compared to other flexible models and non-polarisable rigid models. This includes improvement

Fabiano F. Santos, Behnam Pourhassan, Emmanuel N. Saridakis, Oleksii Sokoliuk

We investigate entanglement islands and the Page curve in the framework of Horndeski gravity on a Karch-Randall braneworld background. In particular, treating the holographic boundary conformal field theory analytically we find that the Horndeski parameters significantly alter the behavior of the Page curve compared to standard general relativity, a feature

Harbir Antil, Sören Bartels, Alex Kaltenbach, Rohit Khandelwal

In this paper, on the basis of a (Fenchel) duality theory on the continuous level, we derive an $\textit{a posteriori}$ error identity for arbitrary conforming approximations of a primal formulation and a dual formulation of variational problems involving gradient constraints. In addition, on the basis of a (Fenchel) duality theory on the discrete level, we

Ankit Singh Rawat, Veeranjaneyulu Sadhanala, Afshin Rostamizadeh, Ayan Chakrabarti

A primary challenge in large language model (LLM) development is their onerous pre-training cost. Typically, such pre-training involves optimizing a self-supervised objective (such as next-token prediction) over a large corpus. This paper explores a promising paradigm to improve LLM pre-training efficiency and quality by suitably leveraging a small language

Oliver L. A. Monti, Yonatan Dubi

The spinterface mechanism was suggested as a possible origin for the chirality induced spin-selectivity (CISS) effect, and was used to explain and reproduce, with remarkable accuracy, experimental data from transport experiments showing the CISS effect. Here, we apply the spinterface mechanism to explain the appearance of magnetization at the interface betwe

Hiba Hmede, Luc Paquet, Gerd Wachsmuth

Additive manufacturing by laser fusion on a metal oxides powder bed has developed considerably in the last few years and allows to produce a wide range of complex parts. The mathematical models correspond to initial boundary value problems for the heat equation with moving heat sources according to the laser trajectories. The main questions concern the optim

Shilin Lu, Zihan Zhou, Jiayou Lu, Yuanzhi Zhu

Current image watermarking methods are vulnerable to advanced image editing techniques enabled by large-scale text-to-image models. These models can distort embedded watermarks during editing, posing significant challenges to copyright protection. In this work, we introduce W-Bench, the first comprehensive benchmark designed to evaluate the robustness of wat

Chung-Yiu Yau, Haoming Liu, Hoi-To Wai

A challenging problem in decentralized optimization is to develop algorithms with fast convergence on random and time varying topologies under unreliable and bandwidth-constrained communication network. This paper studies a stochastic approximation approach with a Fully Stochastic Primal Dual Algorithm (FSPDA) framework. Our framework relies on a novel obser

Desen Liang, E. M. M., Kivits, Maarten Schoukens

This paper addresses the problem of consistently estimating a continuous-time (CT) diffusively coupled network (DCN) to identify physical components in a physical network. We develop a three-step frequency-domain identification method for linear CT DCNs that allows to accurately recover all the physical component values of the network while exploiting the pa

V. A. Melent'ev

The search is based on the preliminary transformation of matrices or adjacency lists traditionally used in the study of graphs into projections cleared of redundant information (refined) followed by the selection of the desired shortest paths. Each projection contains complete information about all the shortest paths from its base (angle vertex) and is based

Antonino Flachi, Muneto Nitta, Satoshi Takada, Ryosuke Yoshii

This paper explores how magnetic fields affect the Casimir effect within the context of a simple quasi-1D interacting fermionic system. A novel phenomenon emerges, resulting from the interaction between external magnetic fields and boundary conditions, which alters the ground state in complex ways and leads to first-order phase transitions among various grou

Giordano Fausti, Cesare Nardini, Michael E Cates

In phase-separated active fluids, the Ostwald process can go into reverse leading to either microphase separation or bubbly phase separation. We show that the latter is formed of two macroscopic regions that are occupied by the homogeneous fluid and by the microphase separated one. Within the microphase separated fluid, the relative rate of the Ostwald proce

Erling A. T. Svela

Daubechies-type theorems for localization operators are established in the multi-variate setting, where Hagedorn wavepackets are identified as the proper substitute of the Hermite functions. The class of Reinhardt domains is shown to be the natural class of masks that allow for a Daubechies-type result. Daubechies' classical theorem is a consequence of doubl

Johanna L. Mathieu, Gregor Verbič, Thomas Morstyn, Mads Almassalkhi

Demand response is a concept that has been around since the very first electric power systems. However, we have seen an explosion of research on demand response and demand-side technologies in the past 30 years, coinciding with the shift towards liberalized/deregulated electricity markets and efforts to decarbonize the power sector. Now we are also seeing a

Yu Liu, Gaojie Chen, Yun Wen, Qu Luo

Traditional self-interference cancellation (SIC) methods are common in full-duplex (FD) integrated sensing and communication (ISAC) systems. However, exploring new SIC schemes is important due to the limitations of traditional approaches. With the challenging limitations of traditional SIC approaches, this paper proposes a novel simultaneous transmitting and

Haoxuan Kuang, Kunxiang Deng, Linlin You, Jun Li

Electric vehicle charging demand prediction is important for vacant charging pile recommendation and charging infrastructure planning, thus facilitating vehicle electrification and green energy development. The performance of previous spatio-temporal studies is still far from satisfactory nowadays because urban region attributes and multivariate temporal inf

Antonio Vecchio, Milan Maksimovic, Nicolina Chrysaphi, Eduard P. Kontar

Radio observations from space allow to characterize solar radio bursts below the ionospheric cutoff, which are otherwise inaccessible, but suffer from low, insufficient temporal resolution. In this Letter we present novel, high-temporal resolution observations of type III solar radio bursts in the range $3-13$ MHz. A dedicated configuration of the Radio and

Yingjie Li, Yun Luo, Xiaotian Xie, Yue Zhang

Large language models (LLMs) have exhibited impressive zero-shot performance on inference tasks. However, LLMs may suffer from spurious correlations between input texts and output labels, which limits LLMs' ability to reason based purely on general language understanding. In other words, LLMs may make predictions primarily based on premise or hypothesis, rat

Lorenzo Napolitano, Marco Castellano, Laura Pentericci, Cristian Vignali

We present James Webb Space Telescope (JWST)/NIRSpec PRISM spectroscopic characterization of GHZ9 at z= 10.145 $\pm$ 0.010, currently the most distant source detected by the Chandra X-ray Observatory. The spectrum reveals several UV high-ionization lines, including CII, SiIV, NIV], CIV, HeII, OIII], NIII], and CIII]. The prominent rest-frame equivalent width

Janan Arslan, Sepinoud Azimi, Lina Sami, Farah Ajili

There has been a long history of women innovators producing outstanding contributions to society and public benefit yet having their work passed over or sidelined or attributed to male colleagues. This phenomenon has been coined the Matilda Effect. The amendments to the record of human achievements are now taking place, with an increasing pace in recent time

Savita Mathur, Ângela R. G. Santos

With the new discoveries enabled thanks to the recent space missions, stellar physics is going through a revolution. However, these discoveries opened the door to many new questions that require more observations. The European Space Agency's Human and Robotic Exploration programme provides an excellent opportunity to push forward the limits of our knowledge

Maxime Perdriat, Alrik Durand, Louis Chambard, Julien Voisin

We report the observation of spin-dependent force induced by Nitrogen Vacancy (NV) centers embedded in a diamond crystal attached to a tethered oscillator. This result was obtained using a spin-dependent torque generated by a micro-diamond containing billions of NV centers, placed at the end of a commercially available silicon cantilever. %We demonstrate tha

Yuanjiu Lyu, Bin Xu

All hyperK\"ahler ALE 4-manifolds with a given non-trivial finite group $\Gamma$ in $SU(2)$ at infinity are parameterized by an open dense subset of a real linear space of dimension $3$rank$\Phi$. Here, $\Phi$ denotes the root system associated with $\Gamma$ via the McKay correspondence. Such manifolds are diffeomorphic to the minimal resolution of a Kleinia

Connor Heimig, Alexander A. Antonov, Dmytro Gryb, Thomas Possmayer

In the strong-coupling regime, the interaction between light and matter reaches a hybridization state where the photonic and material components are inseparably linked. Using tailored states of light to break symmetries in such systems can facilitate the development of novel non-equilibrium quantum materials. Chiral optical cavities offer a promising approac

S. Hartl, L. Freund, M. Kühn, J. Ziegler

We study the quantum Hall effect (QHE) in the three-dimensional topological insulator HgTe, which features topological Dirac-type surface states in a bulk gap opened by strain. Despite the co-existence of multiple carrier subsystems, the system exhibits perfectly quantized Hall plateaus at high magnetic fields. Here we study the system using three different

Chithra H. Sharma, Appanna Parvangada, Lars Tiemann, Kai Rossnagel

MoS2 has recently emerged as a promising material for enabling quantum devices and spintronic applications. In this context, an improved physical understanding of the g-factor of MoS2 depending on device geometry is of great importance. Resistively detected electron spin resonance (RD-ESR) could be employed to and the determine the g-factor in micron-scale d

Neil Irwin Bernardo

This study introduces a short-time Fourier transform-based method for reconstructing signals encoded using modulo analog-to-digital converters with 1-bit folding information. In contrast to existing Fourier-based reconstruction approaches that require complete access to the entire observation, the proposed technique performs reconstruction over short, overla

Alessandro Ragano, Helard Becerra Martinez, Andrew Hines

This study investigates the evaluation of multimedia quality models, focusing on the inherent uncertainties in subjective Mean Opinion Score (MOS) ratings due to factors like rater inconsistency and bias. Traditional statistical measures such as Pearson's Correlation Coefficient (PCC), Spearman's Rank Correlation Coefficient (SRCC), and Kendall's Tau (KTAU)

Haonan Lin, Mengmeng Wang, Jiahao Wang, Wenbin An

Text-guided diffusion models have significantly advanced image editing, enabling high-quality and diverse modifications driven by text prompts. However, effective editing requires inverting the source image into a latent space, a process often hindered by prediction errors inherent in DDIM inversion. These errors accumulate during the diffusion process, resu

Pradipta Kr. Das, Venkat R. Bhethanabotla

We examined theoretically, experimentally and numerically the origin of the acoustothermal effect using a standing surface acoustic wave actuated sessile water droplet system. Despite a wealth of experimental studies and a few recent theoretical explorations, a profound understanding of the acoustothermal mechanism remains elusive. This study bridges the exi

Arpan Bhattacharyya, Suddhasattwa Brahma, Satyaki Chowdhury, Xiancong Luo

Recent studies have shown that there is a strong interplay between quantum complexity and quantum chaos. In this work, we consider a new method to study geometric complexity for interacting non-Gaussian quantum mechanical systems to benchmark the quantum chaos in a well-known oscillator model. In particular, we study the circuit complexity for the unitary ti

Alex Evetts, Maarten Lathouwers

We initiate the study of the \emph{twisted conjugacy growth series} of a finitely generated group, the formal power series associated to the twisted conjugacy growth function. Our main result is that, for a virtually abelian group, this series is always an explicitly computable $\mathbb{N}$-rational function. As a corollary, we obtain a similar result for th

Korosh Mahmoodi, Scott E. Kerick, Piotr J. Franaszczuk, Paolo Grigolini

We introduce a dynamic model for complexity control (CC) between systems, represented by time series characterized by different temporal complexity measures, as indicated by their respective inverse power law (IPL) indices. Given the apparent straightforward character of the model and the generality of the result, we formulate a hypothesis based on the close

Mohit Garg, N. Raja, Suneel Sarswat, Abhishek Kr Singh

Double auctions are widely used in financial markets, such as those for stocks, derivatives, currencies, and commodities, to match demand and supply. Once all buyers and sellers have placed their trade requests, the exchange determines how these requests are to be matched. The two most common objectives for determining the matching are maximizing trade volum

Bernard Field, Sinéad M. Griffin

As "2D" materials (i.e. materials just a few atoms thick) continue to gain prominence, understanding their symmetries is critical for unlocking their full potential. In this work, we present comprehensive tables that tabulate the rod group symmetries of all crystallographic lines in all 80 layer groups, which describe the symmetries of 2D materials. These ta

Md. Khairul Islam, Andrew Wang, Tianhao Wang, Yangfeng Ji

Differential privacy (DP) is applied when fine-tuning pre-trained large language models (LLMs) to limit leakage of training examples. While most DP research has focused on improving a model's privacy-utility tradeoff, some find that DP can be unfair to or biased against underrepresented groups. In this work, we show the impact of DP on bias in LLMs through e

Luping Wang, Sheng Chen, Linnan Jiang, Shu Pan

The large models, as predicted by scaling raw forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks, where they have approached or even surpassed human levels. However, the unprecedented scale of their parameters brings significant computational and storage costs. These large models require substantial

Kurt G Schilling, Amy FD Howard, Francesco Grussu, Andrada Ianus

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has severa

Balázs Gulácsi, Guido Burkard

The ubiquitous effects of the environment on quantum-mechanical systems generally cause temporally correlated fluctuations. This particularly holds for systems of interest for quantum computation where such effects lead to correlated errors. The Markovian approximation neglects these correlations and thus fails to accurately describe open-system dynamics whe

Sasank Budaraju, Alberto Parola, Yasir Iqbal, Federico Becca

The $U(1)$ Dirac spin liquid might realize an exotic phase of matter whose low-energy properties are described by quantum electrodynamics in $2+1$ dimensions, where gapless modes exists but spinons and gauge fields are strongly coupled. Its existence has been proposed in frustrated Heisenberg models in presence of frustrating super-exchange interactions, by

Christoffer Hindlycke, Jakov Krnic, Jan-Åke Larsson

The Toffoli gate is an important universal quantum gate, and will alongside the Clifford gates be available in future fault-tolerant quantum computing hardware. Many quantum algorithms rely on performing arbitrarily small single-qubit rotations for their function, and these rotations may also be used to construct any unitary from a limited (but universal) ga

Chenxin An, Jun Zhang, Ming Zhong, Lei Li

Advancements in distributed training and efficient attention mechanisms have significantly expanded the context window sizes of large language models (LLMs). However, recent work reveals that the effective context lengths of open-source LLMs often fall short, typically not exceeding half of their training lengths. In this work, we attribute this limitation t

H. Dawczak-Dębicki, M. Victoria Ale Crivillero, M. S. Cook, S. M. Thomas

Materials exhibiting electronic inhomogeneities at the nanometer scale have enormous potential for applications. Magnetic polarons are one such type of inhomogeneity which link the electronic, magnetic and lattice degrees of freedom in correlated matter and often give rise to colossal magnetoresistance. Here, we investigate single crystals of Eu$_5$In$_2$Sb$

Gao-Xiang Fang, Ye-Ling Zhou

We apply a universal two-zero texture (UTZT) to all mass matrices for matters in their flavour space in SO(10) GUT framework. This texture can be realised by assigning different charge for each family in a $Z_6$ symmetry. By fixing charged fermion masses at their best-fit values, we fit the rest 9 precisely measured observables (three angles and one CP-viola

Songbo Yang, Ziwei Zhao, Zihang Chen, Haotian Zhang

Popularity prediction for information cascades has significant applications across various domains, including opinion monitoring and advertising recommendations. While most existing methods consider this as a discrete problem, popularity actually evolves continuously, exhibiting rich dynamic properties such as change rates and growth patterns. In this paper,

Sara Dal Cengio, Romain Mari, Eric Bertin

Systems driven far from equilibrium may exhibit anomalous density fluctuations: active matter with orientational order display giant density fluctuations at large scale, while systems of interacting particles close to an absorbing phase transition may exhibit hyperuniformity, suppressing large-scale density fluctuations. We show that these seemingly incompat

Jonas Vinther, Michael James Kastoryano

The continuous variable quantum computing platform constitutes a promising candidate for realizing quantum advantage, as exemplified in Gaussian Boson Sampling. While noise in the experiments makes the computation attainable for classical simulations, it has been suggested that the addition of non-linear elements to the experiment will help retain the quantu

Adnan Aijaz

The IEEE 802.1 time-sensitive networking (TSN) standards improve real-time capabilities of the standard Ethernet. TSN and local/private 5G systems are envisaged to co-exist in industrial environments. The IEEE 802.1CB standard provides fault tolerance to TSN systems via frame replication and elimination for reliability (FRER) capabilities. This paper present

Israel A. Huaman, Fares D. E. Ghorabe, Sofya S. Chumakova, Alexandra A. Pisarenko

Advanced image segmentation and processing tools present an opportunity to study cell processes and their dynamics. However, image analysis is often routine and time-consuming. Nowadays, alternative data-driven approaches using deep learning are potentially offering automatized, accurate, and fast image analysis. In this paper, we extend the applications of

Mengfei Xia, Nan Xue, Yujun Shen, Ran Yi

Classifier-Free Guidance (CFG), which combines the conditional and unconditional score functions with two coefficients summing to one, serves as a practical technique for diffusion model sampling. Theoretically, however, denoising with CFG \textit{cannot} be expressed as a reciprocal diffusion process, which may consequently leave some hidden risks during us

Matias Ginzburg, Ugo Marzolino

We analyze the performance of the Harrow-Hassidim-Lloyd algorithm (HHL algorithm) for solving linear problems and of a variant of this algorithm (HHL variant) commonly encountered in literature. This variant relieves the algorithm of preparing an entangled initial state of an auxiliary register. We prove that the computational error of the variant algorithm

Ämin Baumeler, Stefan Wolf

Causal models capture cause-effect relations both qualitatively - via the graphical causal structure - and quantitatively - via the model parameters. They offer a powerful framework for analyzing and constructing processes. Here, we introduce a tool - the flow of causal structures - to visualize and explore the dynamical aspect of classical-deterministic pro

Luzia A. Trinca, Steven G. Gilmour

Response surface designs are usually described as being run under complete randomization of the treatment combinations to the experimental units. In practice, however, it is often necessary or beneficial to run them under some kind of restriction to the randomization, leading to multi-stratum designs. In particular, some factors are often hard to set, so the

Wojciech M. Zabołotny, David Emschermann, Marek Gumiński, Michał Kruszewski

The STS detector in the CBM experiment delivers data via multiple e-links connected to GBTX ASICs. In the process of data aggregation, that data must be received, combined into a smaller number of streams, and packed into so-called microslices containing data from specific periods. The aggregation must consider data randomization due to amplitude-dependent p

Steven A. Frank

Natural selection acts on traits at different scales, often with opposing consequences. This article identifies the particular forces that act at each scale and how those forces combine to determine the overall evolutionary outcome. A series of extended models derive from the tragedy of the commons, illustrating opposing forces at different scales. Examples

A. Cemmi, B. D'Orsi, E. Di Meco, I. Di Sarcina

The Crilin calorimeter is a semi-homogeneous calorimetric system based on Lead Fluoride (PbF$_2$) crystals with UV-extended Silicon Photomultipliers (SiPMs) proposed for the Muon Collider. This study investigates the radiation resistance of crystals and SiPMs, subjected to 10 kGy gamma irradiation, equivalent to a 10-year service life in the Muon Collider. O

Hend Gabr, Brian H Willis, Mohammed Baragilly

This paper introduces a novel nonparametric criterion for determining the appropriate number of clusters, which is derived from the spatial median. The method is constructed to reconcile two competing objectives of cluster analysis: the preservation of internal homogeneity within clusters and the maximization of heterogeneity across clusters. To this end, th

Jia Tian, Tengzhou Lai, Farzad Omidi

In this work, we study and generalize the spacetime banana proposal for computing correlation functions of huge operators in the context of the AdS$_3$/CFT$_2$ correspondence. First, we introduce time-like and space-like EOW branes into the proposal and demonstrate that: 1) a holographic dual of the one-point function in a BCFT can be obtained and its modifi

Joseph Cho, Masaya Hara

We give a comprehensive account of zero mean curvature surfaces in isotropic 3-space with planar curvature lines. After giving a complete classification all such surfaces, we show that they belong to a 1-parameter family of surfaces. We then investigate their relationship to Thomsen-type surfaces in isotropic 3-space, those zero mean curvature surfaces in is

Florent P Audonnet, Andrew Hamilton, Yakiyasu Domae, Ixchel G Ramirez-Alpizar

Robots are being created each year with the goal of integrating them into our daily lives. As such, there is an interest in research in evaluating the trust of humans toward robots. In addition, teleoperating robotic arms can be challenging for non-experts. To reduce the strain put on the user, we created TELESIM, a modular and plug-and-play framework that e

Alexander Schnurr, Angelika Silbernagel, Manuel Ruiz Marin

The symbolic correlation integral provides a way to measure the complexity of time series and dynamical systems. In the present article we prove limit results for an estimator of this quantity which is based on U-statistics under the assumption of short-range dependence. To this end, we slightly generalize classical limit results in the framework of 1-approx

Akshat Dubey, Zewen Yang, Georges Hattab

Artificial Intelligence is rapidly advancing and radically impacting everyday life, driven by the increasing availability of computing power. Despite this trend, the adoption of AI in real-world healthcare is still limited. One of the main reasons is the trustworthiness of AI models and the potential hesitation of domain experts with model predictions. Expla

Daniel Bermuth, Alexander Poeppel, Wolfgang Reif

In the rapidly evolving field of computer vision, the task of accurately estimating the poses of multiple individuals from various viewpoints presents a formidable challenge, especially if the estimations should be reliable as well. This work presents an extensive evaluation of the generalization capabilities of multi-view multi-person pose estimators to uns

Yibo Wu, Luca Sanguinetti, Musa Furkan Keskin, Ulf Gustavsson

Cell-free massive multiple-input multiple-output (mMIMO) networks enhance coverage and spectral efficiency (SE) by distributing antennas across access points (APs) with phase coherence between APs. However, the use of cost-efficient local oscillators (LOs) introduces phase noise (PN) that compromises phase coherence, even with centralized processing. Sharing

Marco P. M. de Souza, Sidnei P. Oliveira, Valdenice L. Luiz

In this work, we present the Electric Motor simulator, an application from the SimuF\'isica\textsuperscript{\textregistered} platform designed for classroom use. We briefly describe the technologies behind the application, the equations that govern its operation, some studies showing the dynamics of the electric motor, and, finally, the use of the applicatio

Steffen Schotthöfer, Emanuele Zangrando, Gianluca Ceruti, Francesco Tudisco

Low-Rank Adaptation (LoRA) has become a widely used method for parameter-efficient fine-tuning of large-scale, pre-trained neural networks. However, LoRA and its extensions face several challenges, including the need for rank adaptivity, robustness, and computational efficiency during the fine-tuning process. We introduce GeoLoRA, a novel approach that addre

Andrei Bud, Dawei Chen, Martin Möller

The even spin components of the strata of Abelian differentials are difficult to handle from a birational geometry perspective due to the fact that their spin line bundles have more sections than expected. Nevertheless, in this paper, we prove that for large genus, the minimal even spin components are of general type. This result complements the previous wor

Dayu Qin, Yi Yan, Ercan Engin Kuruoglu

In this paper, we propose a novel framework that leverages large language models (LLMs) for predicting missing values in time-varying graph signals by exploiting spatial and temporal smoothness. We leverage the power of LLM to achieve a message-passing scheme. For each missing node, its neighbors and previous estimates are fed into and processed by LLM to in

Mulugeta Weldezgina Asres, Lei Jiao, Christian Walter Omlin

Recent advancements in artificial intelligence hold ample potential for monitoring applications using surveillance cameras. However, concerns about privacy and model bias have made it challenging to utilize them in public. Although de-identification approaches have been proposed in the literature, aiming to achieve a certain level of anonymization (AN), most

Itay M. Bloch, Ana M. Botti, Mariano Cababie, Gustavo Cancelo

We present results from data acquired by the SENSEI experiment at SNOLAB after a major upgrade in May 2023, which includes deploying 16 new sensors and replacing the copper trays that house the CCDs with a new light-tight design. We observe a single-electron event rate of $(1.39 \pm 0.11) \times 10^{-5}$ e$^-$/pix/day, corresponding to $(39.8 \pm 3.1)$ e$^-$

Zijia Zhao, Longteng Guo, Tongtian Yue, Erdong Hu

In this paper, we investigate the task of general conversational image retrieval on open-domain images. The objective is to search for images based on interactive conversations between humans and computers. To advance this task, we curate a dataset called ChatSearch. This dataset includes a multi-round multimodal conversational context query for each target

Yu. D. Fomin, V. V. Brazhkin

We perform a molecular dynamic study of collective excitations of carbon tetrachloride and compare the results with experimental data from the literature. The data of simulations are in good argeement with the experimental ones. The results of the simulations confirm the presence of large positive sound dispersion (PSD) in carbon tetrachloride, which should

Yixu Wang, Yijia Xu, Zi-Wen Liu

We utilize the symmetry groups of regular tessellations on two-dimensional surfaces of different constant curvatures, including spheres, Euclidean planes and hyperbolic planes, to encode a qubit or qudit into the physical degrees of freedom on these surfaces, which we call tessellation codes. We show that tessellation codes exhibit decent error correction pr

Jingwei Liu, Ling Yang, Hongyan Li, Shenda Hong

While time series diffusion models have received considerable focus from many recent works, the performance of existing models remains highly unstable. Factors limiting time series diffusion models include insufficient time series datasets and the absence of guidance. To address these limitations, we propose a Retrieval- Augmented Time series Diffusion model

Shen Shen, Liangzhong Lin, Linfu Li, Jiang-Tao Liu

High-frequency gravitational waves are crucial for understanding the very early universe and distinguishing between various cosmological models, but detecting them remains a significant challenge. We investigated the effects of high-frequency gravitational waves on photonic-like electronic transport in graphene. The results show that, unlike the influence of

Yu Han, Vittorio Murino, Xiaofeng Liu, Xiang Zhang

Electrocardiogram (ECG) is widely used in healthcare applications, such as arrhythmia detection and sleep monitoring, making accurate ECG analysis critically essential. Traditional deep learning models for ECG are task-specific, with limited generalization and narrow functionality. Foundation models (FMs), or large pre-training models, have recently advanced

Jorge M. Mtz-Vera, Andrea Beraudo, Miguel Ángel Escobedo, Paolo Parotto

QTRAJ is a computer code that simulates the propagation of quarkonium in the quark-gluon plasma (QGP) based on the quantum-trajectory algorithm. This algorithm solves a master equation in which the quarkonium is treated as an open quantum system (OQS). A major advantage of this approach is that it turns a 3D spatial evolution for a density matrix into a 1D S

Naoufal Bouchareb

We study the classification of affine holomorphic bundles over a compact complex manifold $X$ in general, and we apply the general theory to the case $X=\mathbb{P}^1_\mathbb{C}$. We study the moduli space of framed, non-degenerate rank 2 affine bundles over $\mathbb{P}^1_\mathbb{C}$ whose linearisation, viewed as locally free sheaf, is isomorphic to $ {\math