April 2024 arXiv papers — page 94

Nicholas Kluge Corrêa, James William Santos, Camila Galvão, Marcelo Pasetti

The past years have presented a surge in (AI) development, fueled by breakthroughs in deep learning, increased computational power, and substantial investments in the field. Given the generative capabilities of more recent AI systems, the era of large-scale AI models has transformed various domains that intersect our daily lives. However, this progress raise

Xiangming Du, Shuowen Zhang, Liang Liu

In this paper, we study the trajectory optimization of a cellular-connected unmanned aerial vehicle (UAV) which aims to sense the location of a target while maintaining satisfactory communication quality with the ground base stations (GBSs). In contrast to most existing works which assumed the target's location is known, we focus on a more challenging scenar

Eduard Feireisl, Wladimir Neves

We consider the vanishing dissipation limit of the compressible Navier-Stokes-Fourier system, where the initial data approach a profile generating a planar rarefaction wave for the limit Euler system. We show that the associated weak solutions converge unconditionally to the planar rarefaction wave strongly in the energy norm.

Seungwook Kim, Kejie Li, Xueqing Deng, Yichun Shi

Leveraging multi-view diffusion models as priors for 3D optimization have alleviated the problem of 3D consistency, e.g., the Janus face problem or the content drift problem, in zero-shot text-to-3D models. However, the 3D geometric fidelity of the output remains an unresolved issue; albeit the rendered 2D views are realistic, the underlying geometry may con

Sanzida Hoque, Abdullah Aydeger, Engin Zeydan

The proliferation of mobile networks and their increasing importance to modern life, combined with the emerging threat of quantum computing, present new challenges and opportunities for cybersecurity. This paper addresses the complexity of protecting these critical infrastructures against future quantum attacks while considering operational sustainability. W

Shutaro Shimamura

We study the mixed 't Hooft anomaly of the subsystem symmetries in the exotic $BF$ theory and the foliated $BF$ theory in 2+1 dimensions, both of which are fractonic quantum field theories describing the equivalent physics. In the anomaly inflow mechanism, the 't Hooft anomaly of the subsystem symmetries can be canceled by combining a subsystem symmetry-prot

Wei-Chung Shia, Yu-Len Huang, Yi-Chun Chen, Hwa-Koon Wu

A positive margin may result in an increased risk of local recurrences after breast retention surgery for any malignant tumour. In order to reduce the number of positive margins would offer surgeon real-time intra-operative information on the presence of positive resection margins. This study aims to design an intra-operative tumour margin evaluation scheme

Zhichao Zhu, Yang Qi, Wenlian Lu, Zhigang Wang

The energy-efficient and brain-like information processing abilities of Spiking Neural Networks (SNNs) have attracted considerable attention, establishing them as a crucial element of brain-inspired computing. One prevalent challenge encountered by SNNs is the trade-off between inference speed and accuracy, which requires sufficient time to achieve the desir

Vlad C. Andrei, Aladin Djuhera, Xinyang Li, Ullrich J. Mönich

Native jamming mitigation is essential for addressing security and resilience in future 6G wireless networks. In this paper a resilient-by-design framework for effective anti-jamming in MIMO-OFDM wireless communications is introduced. A novel approach that integrates information from wireless sensing services to develop anti-jamming strategies, which do not

Alberto Patino-Saucedo, Roy Meijer, Amirreza Yousefzadeh, Manil-Dev Gomony

Configurable synaptic delays are a basic feature in many neuromorphic neural network hardware accelerators. However, they have been rarely used in model implementations, despite their promising impact on performance and efficiency in tasks that exhibit complex (temporal) dynamics, as it has been unclear how to optimize them. In this work, we propose a framew

Jin-Ping Zhu, Rui-Chong Hu, Yacheng Kang, Bing Zhang

In this {\em{Letter}}, we explore the formation of the mass-gap black hole-neutron star (mgBHNS) merger detected in gravitational wave (GW) event, i.e., GW230529, from the isolated binary evolution channel, and study potential signatures of its electromagnetic counterparts. By adopting the `delayed' supernova prescription and reasonable model realizations, o

Kai Chen, Yanze Li, Wenhua Zhang, Yanxin Liu

Large Vision-Language Models (LVLMs) have received widespread attention for advancing the interpretable self-driving. Existing evaluations of LVLMs primarily focus on multi-faceted capabilities in natural circumstances, lacking automated and quantifiable assessment for self-driving, let alone the severe road corner cases. In this work, we propose CODA-LM, th

Chiara Fend, Claudia Redenbach

In spatial statistics, point processes are often assumed to be isotropic meaning that their distribution is invariant under rotations. Statistical tests for the null hypothesis of isotropy found in the literature are based either on asymptotics or on Monte Carlo simulation of a parametric null model. Here, we present a nonparametric test based on resampling

Nida Itrat Abbasi, Guy Laban, Tamsin Ford, Peter B. Jones

Socially Assistive Robots are studied in different Child-Robot Interaction settings. However, logistical constraints limit accessibility, particularly affecting timely support for mental wellbeing. In this work, we have investigated whether online interactions with a robot can be used for the assessment of mental wellbeing in children. The children (N=40, 20

Holger Brenner

Let G be a finite group acting linearly on the polynomial ring with invariant ring R. If the action is small, then a classical result of Auslander gives in dimension two a correspondence between linear representations of G and maximal Cohen-Macaulay R-modules. We establish a correspondence for all linear actions between representations and objects over the i

Luca Buoncompagni, Fulvio Mastrogiovanni

We present a symbolic learning framework inspired by cognitive-like memory functionalities (i.e., storing, retrieving, consolidating and forgetting) to generate task representations to support high-level task planning and knowledge bootstrapping. We address a scenario involving a non-expert human, who performs a single task demonstration, and a robot, which

Grégory Gougeon, Frédéric Munoz, Yoann Corre, Raffaele D'Errico

New-generation communication and sensing systems are gaining strong interest in the context of Industry 4.0 e.g., related to mapping techniques, environmental sensing, automation or hyper-vision. The radio propagation in confined, cluttered and heavily metalized factory environments is a critical challenge; thus an evaluation by accurate propagation channel

Eric Yeats, Cameron Darwin, Eduardo Ortega, Frank Liu

We leverage diffusion models to study the robustness-performance tradeoff of robust classifiers. Our approach introduces a simple, pretrained diffusion method to generate low-norm counterfactual examples (CEs): semantically altered data which results in different true class membership. We report that the confidence and accuracy of robust models on their clea

Kun Wu, Feng Zhang, Wenwen Li, Fengzi Bao

The polarization characteristics of atmospheric scattering are important and should not be ignored in radiative transfer simulations. In this study, a new vector radiative transfer model called the polarized adding method of discrete ordinate approximation (POLDDA) is proposed for use in remote sensing applications for ultraviolet-visible and near-infrared s

Jialin Cheng, Shaocong Liang, Jiliang Qin, Jiatong Li

Random numbers are a basic ingredient of simulation algorithms and cryptography, and play a significant part in computer simulation and information processing. One prominent feature of a squeezed light is its lower fluctuation and more randomness in a pair

Tian-Shuai Shang, Jian Li, Zhong-Ming Niu

The nuclear charge density distribution plays an important role in nuclear physics and atomic physics. As one of the most frequently used models to obtain charge density distribution, the two-parameter fermi (2pF) model has been widely applied in both nuclear physics and atomic physics. Currently, the feedforward neural network has been employed to study the

Chunli Peng, Xuan Dong, Tiantian Cao, Zhengqing Li

The fusion of images from dual camera systems featuring a wide-angle and a telephoto camera has become a hotspot problem recently. By integrating simultaneously captured wide-angle and telephoto images from these systems, the resulting fused image achieves a wide field of view (FOV) coupled with high-definition quality. Existing approaches are mostly deep le

Georgios Mylonopoulos, Behrooz Makki, Stefano Buzzi, Gábor Fodor

6G wireless networks will integrate communication, computing, localization, and sensing capabilities while meeting the needs of high reliability and trustworthiness. In this paper, we develop similar techniques as those used by communication modules of previous generations for the sensing functionality of 6G networks. Specifically, this paper introduces the

Davide Soranzio, Elsa Abreu, Sarah Houver, Janine Dössegger

Materials with linear electronic dispersion often feature high carrier mobilities and unusually strong nonlinear optical interactions. In this work, we investigate the THz nonlinear dynamics of one such material, HgCdTe, with an electronic band dispersion heavily dependent on both temperature and stoichiometry. We show how the band gap, carrier concentration

Saurav G. Varma, Argha Mitra, Sumantra Sarkar

Molecular transport maintains cellular structures and functions. For example, lipid and protein diffusion sculpts the dynamic shapes and structures on the cell membrane that perform essential cellular functions, such as cell signaling. Temperature variations in thermal equilibrium rapidly change molecular transport properties. The coefficient of lipid self-d

Christof Naumzik, Alice Kongsted, Werner Vach, Stefan Feuerriegel

Clinical data informs the personalization of health care with a potential for more effective disease management. In practice, this is achieved by subgrouping, whereby clusters with similar patient characteristics are identified and then receive customized treatment plans with the goal of targeting subgroup-specific disease dynamics. In this paper, we propose

Keqin Li, Peng Xirui, Jintong Song, Bo Hong

With the rapid advancement of technology, Augmented Reality (AR) technology, known for its ability to deeply integrate virtual information with the real world, is gradually transforming traditional work modes and teaching methods. Particularly in the realms of remote work and online education, AR technology demonstrates a broad spectrum of application prospe

Mateo Fayet

Which visual descriptors are suitable for multi-modal interaction and how to integrate them via real-time video data analysis into a corpus-based concatenative synthesis sound system.

E. Petrova, T. Van Doorsselaere, D. Berghmans, S. Parenti

Torsional Alfv\'en waves do not produce any intensity variation and are, therefore, challenging to observe with imaging instruments. Previously, Alfv\'en wave observations were reported throughout all the layers of the solar atmosphere using spectral imaging. We present an observation of a torsional Alfv\'en wave detected in an inverted y-shape structure obs

Hao-Long Zhang, Han-Ze Li, Sheng Yang, Xue-Jia Yu

The phase transition between gapped topological phases represents a class of unconventional criticality beyond the Landau paradigm. However, recent research has shifted attention to topological phases without a bulk gap, where the phase transitions between them are still elusive. In this work, based on large-scale density matrix renormalization group techniq

Chung-Yiu Yau, Hoi-To Wai, Parameswaran Raman, Soumajyoti Sarkar

A key challenge in contrastive learning is to generate negative samples from a large sample set to contrast with positive samples, for learning better encoding of the data. These negative samples often follow a softmax distribution which are dynamically updated during the training process. However, sampling from this distribution is non-trivial due to the hi

Mattia Litrico, Davide Talon, Sebastiano Battiato, Alessio Del Bue

Standard Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target but usually requires simultaneous access to both source and target data. Moreover, UDA approaches commonly assume that source and target domains share the same labels space. Yet, these two assumptions are hardly satisfied in real-world

Lijun Liu, Jiali Yang, Jianfei Song, Xinglin Yang

Recombinant adeno-associated virus (rAAV) vectors have revolutionized gene therapy, but their broad tropism and suboptimal transduction efficiency limit their clinical applications. To overcome these limitations, researchers have focused on designing and screening capsid libraries to identify improved vectors. However, the large sequence space and limited re

Aaron Kujawa, Reuben Dorent, Sebastien Ourselin, Tom Vercauteren

Whole brain parcellation requires inferring hundreds of segmentation labels in large image volumes and thus presents significant practical challenges for deep learning approaches. We introduce label merge-and-split, a method that first greatly reduces the effective number of labels required for learning-based whole brain parcellation and then recovers origin

Jingze Chen, Junfeng Yao, Qiqin Lin, Lei Li

Occlusions hinder point cloud frame alignment in LiDAR data, a challenge inadequately addressed by scene flow models tested mainly on occlusion-free datasets. Attempts to integrate occlusion handling within networks often suffer accuracy issues due to two main limitations: a) the inadequate use of occlusion information, often merging it with flow estimation

Moritz Plenz, Philipp Heinisch, Anette Frank, Philipp Cimiano

Deliberative processes play a vital role in shaping opinions, decisions and policies in our society. In contrast to persuasive debates, deliberation aims to foster understanding of conflicting perspectives among interested parties. The exchange of arguments in deliberation serves to elucidate viewpoints, to raise awareness of conflicting interests, and to fi

Samuele Grossi, Riccardo Torre

We generalize previous studies on constraining operators of the Standard Model Effective Field Theory using Drell-Yan (DY) measurements to include at the same time all relevant operators and uncertainties. It has been shown that fully differential measurements (triple differential for neutral and double differential for charged) are more sensitive to EFT eff

G. Lorenzon, D. Donevski, K. Lisiecki, C. Lovell

Recent discoveries of copious amounts of dust in quiescent galaxies (QGs) at high redshifts ($z\gtrsim 1-2$) challenge the conventional view that these objects have poor interstellar medium (ISM) in proportion to their stellar mass. We use the SIMBA cosmological simulation to explore the evolution of dust and cold gas content in QGs in relation to the quench

Emma Boniface, Karel Devriendt, Serkan Hoşten

We consider toric maximum likelihood estimation over the field of Puiseux series and study critical points of the likelihood function using tropical methods. This problem translates to finding the intersection points of a tropical affine space with a classical linear subspace. We derive new structural results on tropical affine spaces and use these to give a

Ziqin Feng, Guanghui Wang

We discuss the topological properties of the independence complex of Kneser graphs, Ind(KG$(n, k))$, with $n\geq 3$ and $k\geq 1$. By identifying one kind of maximal simplices through projective planes, we obtain homology generators for the $6$-dimensional homology of the complex Ind(KG$(3, k))$. Using cross-polytopal generators, we provide lower bounds for

Trung Nghia Nguyên Lê, Kirill Kondratenko, Imane Arbouch, Alain Moréac

The attachment of redox active molecules to transition metal dichalcogenides (TMDs), such as MoS2, constitutes a promising approach for designing electrochemically switchable devices through the control of the material charge/spin transport properties by the redox state of the grafted molecule and thus the applied electrical potential. In this work, defectiv

Ioannis Tsilikas, Aris Tsirigotis, George Sarantoglou, Stavros Deligiannidis

In this work, we present experimental results of a high-speed label-free imaging cytometry system that seamlessly merges the high-capturing rate and data sparsity of an event-based CMOS camera with lightweight photonic neuromorphic processing. This combination offers high classification accuracy and a massive reduction in the number of trainable parameters o

Muxin Han, Hongguang Liu, Dongxue Qu

This work develops a comprehensive algorithm and a Mathematica program to construct boundary data and compute real and complex critical points in spinfoam amplitudes. Our approach covers both spacelike tetrahedra and triangles in the EPRL model and timelike tetrahedra and triangles in the Conrady-Hnybida extension, aiming at addressing a wide range of physic

Folkert Müller-Hoissen

In 2005 Lorenzoni and Magri showed that a hydrodynamic-type hierarchy determined by the powers of a type (1,1) tensor field (on a smooth manifold) with vanishing Nijenhuis torsion can be deformed to a more general hierarchy, with the help of a chain of conservation laws of the new hierarchy. We review this construction. The (1,1) tensor fields of the resulti

Benjamin J. Livesey, Mihaly Badonyi, Mafalda Dias, Jonathan Frazer

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algo

Bin Liu, Siqi Wu, Jin Wang, Xin Deng

The discovery of drug-target interactions (DTIs) plays a crucial role in pharmaceutical development. The deep learning model achieves more accurate results in DTI prediction due to its ability to extract robust and expressive features from drug and target chemical structures. However, existing deep learning methods typically generate drug features via aggreg

Dmitri B. Horoshko, Mikhail I. Kolobov, Valentina Parigi, Nicolas Treps

Frequency-degenerate pulsed type-I parametric downconversion is a widely used source of squeezed light for numerous quantum optical applications. However, this source is typically spectrally multimode and the generated squeezing is distributed between many spectral modes with a limited degree of squeezing per mode. We show that in a nonlinear crystal, where

Mingyang Wu, Zhixia Yang, Junyou Ye

For the binary classification problem, a novel nonlinear kernel-free quadratic hyper-surface support vector machine with 0-1 loss function (QSSVM$_{0/1}$) is proposed. Specifically, the task of QSSVM$_{0/1}$ is to seek a quadratic separating hyper-surface to divide the samples into two categories. And it has better interpretability than the methods using ker

Pingzhu Gong, Jiachen Guo, Niteesh Bharadwaj Vangipurapu, Kenle Chen

This paper reports a first-ever decade-bandwidth pseudo-Doherty load-modulated balanced amplifier (PD-LMBA), designed for emerging 4G/5G communications and multi-band operations. By revisiting the LMBA theory using the signal-flow graph, a frequency-agnostic phase-alignment condition is found that is critical for ensuring intrinsically broadband load modulat

Samuel J. Magorrian, Nicholas D. M. Hine

The low symmetry and anistropic lattice of 1T$'$ WTe$_2$ is responsible for the existence of parallel one-dimensional channels in the moir\'e patterns of twisted bilayers. This gives the opportunity to explore moir\'e physics of a different nature to that widely observed in twisted bilayers of materials with hexagonal symmetries. Here, we combine plane-wave

Geng Sun, Wenwen Xie, Dusit Niyato, Hongyang Du

With the impressive achievements of chatGPT and Sora, generative artificial intelligence (GAI) has received increasing attention. Not limited to the field of content generation, GAI is also widely used to solve the problems in wireless communication scenarios due to its powerful learning and generalization capabilities. Therefore, we discuss key applications

Huan-Huan Fang, Yong-Cong Chen, Ze-Fei Liu, Xiao-Mei Zhu

The widespread application of nanomaterials in polymerase chain reaction (PCR) technology has opened new avenues for improving detection methods in the biomedical field. Recent experiments (Chem. Eur. J. 2023, e202203513) have revealed oscillatory behavior between PCR efficiency and the concentration of gold nanoparticles in the pM range, potentially linked

Masanori Hirano, Kentaro Imajo

Large language models (LLMs) are now widely used in various fields, including finance. However, Japanese financial-specific LLMs have not been proposed yet. Hence, this study aims to construct a Japanese financial-specific LLM through continual pre-training. Before tuning, we constructed Japanese financial-focused datasets for continual pre-training. As a ba

Ivan S. Maksymov

On the surface, behavioural science and physics seem to be two disparate fields of research. However, a closer examination of problems solved by them reveals that they are uniquely related to one another. Exemplified by the theories of quantum mind, cognition and decision-making, this unique relationship serves as the topic of this chapter. Surveying the cur

Kim H. Parker, Alun D. Hughes

The separation of measured arterial pressure into a reservoir pressure and an excess pressure was introduced nearly 20 years ago as an heuristic hypothesis. We demonstrate that a two-time asymptotic analysis of the 1-D conservation equations in each artery coupled with the separation of the smaller arteries into inviscid and resistance arteries, based on the

Andrés R. Tejedor, Jorge Ramírez, Marisol Ripoll

Polar activity is shown here to induce a progressive local deformation of linear polymer chains, making a clear distinction between head and tail, while the overall chain conformation gets more compact. This breakdown of self-similarity, provoked by the accumulated tension on the polymer backbone induced by the activity, is shown to occur both in the absence

Xiao Wang, Tianze Chen, Xianjun Yang, Qi Zhang

The open-sourcing of large language models (LLMs) accelerates application development, innovation, and scientific progress. This includes both base models, which are pre-trained on extensive datasets without alignment, and aligned models, deliberately designed to align with ethical standards and human values. Contrary to the prevalent assumption that the inh

Stefanie Krause, Bhumi Hitesh Panchal, Nikhil Ubhe

Generative Artificial Intelligence (GAI) models such as ChatGPT have experienced a surge in popularity, attracting 100 million active users in 2 months and generating an estimated 10 million daily queries. Despite this remarkable adoption, there remains a limited understanding to which extent this innovative technology influences higher education. This resea

Roumen Nikolaev Popov

We propose an analytical solution for approximating the gradient of the Evidence Lower Bound (ELBO) in variational inference problems where the statistical model is a Bayesian network consisting of observations drawn from a mixture of a Gaussian distribution embedded in unrelated clutter, known as the clutter problem. The method employs the reparameterizatio

Daniel Bennett, Philippe Ghosez

Ferroelectricity is commonly understood in terms of dynamical charges, which represent the dipole moments generated by atomic displacements or the forces induced by electric fields. In ferroelectrics with a high degree of symmetry, the dynamical charges are typically symmetric tensors, and can be visualized as ellipsoids. In van der Waals (vdW) materials whi

Malte Rippa, Ruben Schulze, Marian Himstedt, Felice Burn

Prostate cancer is a commonly diagnosed cancerous disease among men world-wide. Even with modern technology such as multi-parametric magnetic resonance tomography and guided biopsies, the process for diagnosing prostate cancer remains time consuming and requires highly trained professionals. In this paper, different convolutional neural networks (CNN) are ev

Shiv Shankar, Ritwik Sinha, Yash Chandak, Saayan Mitra

A/B tests are often required to be conducted on subjects that might have social connections. For e.g., experiments on social media, or medical and social interventions to control the spread of an epidemic. In such settings, the SUTVA assumption for randomized-controlled trials is violated due to network interference, or spill-over effects, as treatments to g

Nico Meyer, Jakob Murauer, Alexander Popov, Christian Ufrecht

Reinforcement learning is a powerful framework aiming to determine optimal behavior in highly complex decision-making scenarios. This objective can be achieved using policy iteration, which requires to solve a typically large linear system of equations. We propose the variational quantum policy iteration (VarQPI) algorithm, realizing this step with a NISQ-co

Kazeem Oluwakemi Oseni

E-Government services offer a great deal of potential to improve government activities and citizen support. However, there is a lack of research covering E-Government services at the local government level, particularly in developing countries. However, implementing successful E-Service technology in this part of the world will not come without its barriers

Bjørn Cattell-Ravdal, Erin Delargy, Akash Ganguly, Sean Guan

We consider the polynomial ring in finitely many variables over an algebraically closed field of positive characteristic, and initiate the systematic study of ideals preserved by the action of the general linear group by changes of coordinates. We show that these ideals are classified by sets of carry patterns, which are finite sequences of integers introduc

Maurantonio Caprolu, Roberto Di Pietro, Flavio Lombardi, Elia Onofri

The growth potential of a crypto(currency) project can be measured by the use cases spurred by the underlying technology. However, these projects are usually distributed, with a weak feedback schemes. Hence, a metric that is widely used as a proxy for their healthiness is the number of transactions and related volumes. Nevertheless, such a metric can be subj

H. W. Xu, R. S. Zhao, Erbil Gugercinoglu, H. Liu

This study presents a comprehensive analysis of the spectral properties of 886 pulsars across a wide frequency range from 20MHz to 343.5GHz, including a total of 86 millisecond pulsars. The majority of the pulsars exhibit power-law behavior in their spectra, although some exceptions are observed. Five different spectral models, namely simple power-law, broke

Zhiyou Ji, Yujie Wan, Guoliang Li, Shuai Wang

Robotic sensor network (RSN) is an emerging paradigm that harvests data from remote sensors adopting mobile robots. However, communication and control functionalities in RSNs are interdependent, for which existing approaches become inefficient, as they plan robot trajectories merely based on unidirectional impact between communication and control. This paper

Jaemin Kim, Jiyoung Yun, Joonwoo Bae

We consider entanglement distillation with noisy operations in which quantum measurements that constitute a general quantum operation are particularly noisy. We present a protocol for purifying noisy measurements and show that imperfect local operations can distill entanglement. The protocol works for arbitrary noisy measurements in general and is cost-effec

Mathis Guéneau, Léo Touzo

We consider a generic one-dimensional stochastic process $x(t)$, or a random walk $X_n$, which describes the position of a particle evolving inside an interval $[a,b]$, with absorbing walls located at $a$ and $b$. In continuous time, $x(t)$ is driven by some equilibrium process $\mathbf{\theta}(t)$, while in discrete time, the jumps of $X_n$ follow a station

Christopher Rae, Joseph K. L. Lee, James Richings, Michele Weiland

With the rapid increase in machine learning workloads performed on HPC systems, it is beneficial to regularly perform machine learning specific benchmarks to monitor performance and identify issues. Furthermore, as part of the Edinburgh International Data Facility, EPCC currently hosts a wide range of machine learning accelerators including Nvidia GPUs, the

Hajime Fukushima, Hidenobu Yajima

We study metal enrichment originating from stellar wind and supernovae in low-metallicity clouds by performing three-dimensional radiation hydrodynamics simulations. We find that metals ejected from stellar wind are accumulated, leading to subsequent star formation in the nitrogen-enriched gas. During this early phase, the N/O ratios are similar to observed

Constanza Muñoz López, Davor Krajnović, B. Epinat, Y. Herrero-Alonso

We quantify the stellar rotation of galaxies by computing the $\lambda_{R}$ parameter, a proxy for the stellar angular momentum in a sample of 106 galaxies with redshift 0.1 $<$ z $<$ 0.8 and stellar masses from $\sim$10$^{7.5}$ to 10$^{11.8}$ M$_{\odot}$. The sample is located in the CANDELS/GOODS-S and COSMOS fields, and it was observed by various MUSE sur

Cédric Lecouvey, David Wahiche

The Nekrasov-Okounkov formula gives an expression for the Fourier coefficients of the Euler functions as a sum of hook length products. This formula can be deduced from a specialization in a renormalization of the affine type $A$ Weyl denominator formula and the use of a polynomial argument. In this paper, we rephrase the renormalized Weyl-Kac denominator fo

Youssef Aziz Alaoui, Sean R. Muleady, Edwin Chaparro, Youssef Trifa

We demonstrate a bipartition technique using a super-lattice architecture to access correlations between alternating planes of a mesoscopic array of spin-3 chromium atoms trapped in a 3D optical lattice. Using this method, we observe that out-of-equilibrium dynamics driven by long-range dipolar interactions lead to spin anti-correlations between the two spat

Finn Hughes, Manuel Marschall, Gerd Wübbeler, Gertjan Kok

Virtual experiments (VEs), a modern tool in metrology, can be used to help perform an uncertainty evaluation for the measurand. Current guidelines in metrology do not cover the many possibilities to incorporate VEs into an uncertainty evaluation, and it is often difficult to assess if the intended use of a VE complies with said guidelines. In recent work, it

Ayush Noori, Iñaki Arango, William E. Byrd, Nada Amin

The strict selectivity of the blood-brain barrier (BBB) represents one of the most formidable challenges to successful central nervous system (CNS) drug delivery. Computational methods to generate BBB permeable drugs in silico may be valuable tools in the CNS drug design pipeline. However, in real-world applications, BBB penetration alone is insufficient; ra

Arash Yavari, Dimitris Sfyris

Universal displacements are those displacements that can be maintained for any member of a specific class of linear elastic materials in the absence of body forces, solely by applying boundary tractions. For linear hyperelastic (Green elastic) solids, it is known that the space of universal displacements explicitly depends on the symmetry group of the materi

Chia-Yen Chiang, Ruikang Zhong, Jennifer Ding, Joseph Wood

Active travel is an essential component in intelligent transportation systems. Cycling, as a form of active travel, shares the road space with motorised traffic which often affects the cyclists' safety and comfort and therefore peoples' propensity to uptake cycling instead of driving. This paper presents a unique dataset, collected by cyclists across London,

Niklas Gard, Anna Hilsmann, Peter Eisert

In this paper, we present SPVLoc, a global indoor localization method that accurately determines the six-dimensional (6D) camera pose of a query image and requires minimal scene-specific prior knowledge and no scene-specific training. Our approach employs a novel matching procedure to localize the perspective camera's viewport, given as an RGB image, within

Pawan Kumar, Bidya Binay Karak, Anu Sreedevi

Decay and dispersal of the tilted Bipolar Magnetic Regions (BMRs) on the solar surface are observed to produce large-scale poloidal field, which acts as the seed for the toroidal field and, thus, the next sunspot cycle. However, various properties of BMR, namely, the tilt, time delay between successive emergences, location, and flux, all have irregular varia

Tongjun Liu, Venugopal Raskatla, Jinxiang Li, Kevin F. MacDonald

Nonreciprocal nonequilibrium process are attracting growing interest in sociology, animal behaviour, chemistry, and nanotechnology, and may have played a role in the origin of life. It is less widely recognized, however, that in open systems light can induce nonreciprocal predator-prey like forces between nanoparticles. Such forces provide access to the cont

Gaspard Poulot, Elsa M. Teixeira, Carsten van de Bruck, Nelson J. Nunes

We propose a new model of scalar field dark matter interacting with dark energy. Adopting a fluid description of the dark matter field in the regime of rapid oscillations, we find that the equation of state for dark matter is non-zero and even becomes increasingly negative at late times during dark energy domination. Furthermore, the speed of sound of dark m

Alexander Sikorski, Robert Julian Rabben, Surahit Chewle, Marcus Weber

Markov processes serve as foundational models in many scientific disciplines, such as molecular dynamics, and their simulation forms a common basis for analysis. While simulations produce useful trajectories, obtaining macroscopic information directly from microstate data presents significant challenges. This paper addresses this gap by introducing the conce

Gael Chauvin

Understanding how giant and terrestrial planets form and evolve, what is their internal structure and that of their atmosphere, represents one of the major challenges of modern astronomy, which is directly connected to the ultimate search for life at the horizon 2040. However, several astrophysical, biological and technological obstacles must be overcome. Fr

Flavien Mabilat

A $\lambda$-quiddity of size $n$ is an $n$-tuple of elements from a fixed set, which is a solution to a matrix equation that arises in the study of Coxeter's friezes. The study of these solutions involves in particular the use of a notion of irreducibility. The main objective of this text is to demonstrate that there is a finite number of irreducible $\lambd

Sajjad Maleki, Subhash Lakshminarayana, E. Veronica Belmega, Carsten Maple

Phasor Measurement Units (PMUs) are used in the measurement, control and protection of power grids. However, deploying PMUs at every bus in a power system is prohibitively expensive, necessitating partial PMU placement that can ensure system observability with minimal units. One consequence of this economic approach is increased system vulnerability to False

Niccolò Sellati, Jacopo Fiore, Claudio Castellani, Lara Benfatto

Electromagnetic waves propagating in a layered superconductor with arbitrary momentum with respect to the main crystallographic directions display an unavoidable mixing between longitudinal and transverse degrees of freedom. Here we show that this basic physical mechanism explains the emergence of a well-defined absorption peak in the in-plane optical conduc

Danfeng Qin, Chas Leichner, Manolis Delakis, Marco Fornoni

We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices. At its core, we introduce the Universal Inverted Bottleneck (UIB) search block, a unified and flexible structure that merges Inverted Bottleneck (IB), ConvNext, Feed Forward Network (FFN), and a novel Extra Dep

Marcelo López Fuentes, Mariano Poisson, Cristina H. Mandrini

We analyze the magnetic evolution of solar active region (AR) NOAA 11476 that, between 9 and 10 May 2012, produced a series of surge-type eruptions accompanied by GOES X-ray class M flares. Using force-free models of the AR coronal structure and observations in several wavelengths, in previous works we studied the detailed evolution of those eruptions, relat

Olga Martynova

The number of states and stack symbols needed to determinize nondeterministic input-driven pushdown automata (NIDPDA) working over a fixed alphabet is determined precisely. It is proved that in the worst case exactly 2^{n^2} states are needed to determinize an n-state NIDPDA, and the proof uses witness automata with a stack alphabet \Gamma = {0,1} working on

Maojiang Tian, Mingke Chen, Wei Du, Yang Tang

Large-scale overlapping problems are prevalent in practical engineering applications, and the optimization challenge is significantly amplified due to the existence of shared variables. Decomposition-based cooperative coevolution (CC) algorithms have demonstrated promising performance in addressing large-scale overlapping problems. However, current CC framew

Ruixi Luo, Kai Jin, Zelin Ye

In project management, a project is typically described as an activity-on-edge network (AOE network), where each activity / job is represented as an edge of some network $N$ (which is a DAG). Some jobs must be finished before others can be started, as described by the topology structure of $N$. It is known that job $j_i$ in normal speed would require $b_i$ d

Moshe Berchansky, Daniel Fleischer, Moshe Wasserblat, Peter Izsak

State-of-the-art performance in QA tasks is currently achieved by systems employing Large Language Models (LLMs), however these models tend to hallucinate information in their responses. One approach focuses on enhancing the generation process by incorporating attribution from the given input to the output. However, the challenge of identifying appropriate a

Kuai Dai, Xutao Li, Junying Fang, Yunming Ye

Convection (thunderstorm) develops rapidly within hours and is highly destructive, posing a significant challenge for nowcasting and resulting in substantial losses to infrastructure and society. After the emergence of artificial intelligence (AI)-based methods, convection nowcasting has experienced rapid advancements, with its performance surpassing that of

Burkhard Schmidt, Peter Thalmeier

The tetragonal heavy fermion compound CeRh2As2 has intriguing low temperature symmetry breaking phases whose nature is unclear. The unconventional superconducting phase is complemented by other normal state phases which presumably involve ordering of 4f electron multipoles supported by the Kramers doublets split by the tetragonal crystal electric field (CEF)

Mohammadreza Amel Solouki, Shaahin Angizi, Massimo Violante

Fault tolerance is a critical aspect of modern computing systems, ensuring correct functionality in the presence of faults. This paper presents a comprehensive survey of fault tolerance methods and software-based mitigation techniques in embedded systems. The focus is on real-time embedded systems, considering their resource constraints and the increasing in

Yixin Wan, Kai-Wei Chang

Social biases can manifest in language agency. However, very limited research has investigated such biases in Large Language Model (LLM)-generated content. In addition, previous works often rely on string-matching techniques to identify agentic and communal words within texts, falling short of accurately classifying language agency. We introduce the Language

Daniel Neves, João G. Rosa

We study the dynamical evolution of superradiant instabilities of rotating black holes for multiple axion fields with comparable masses, motivated by string theory constructions, which typically exhibit a large number of light axions, with a broad range of masses. We show, in particular, that even though superradiant clouds for the heavier axion species grow

Sophie Carneiro-Esteves, Antoine Vacavant, Odyssée Merveille

Accurate segmentation of vascular networks is essential for computer-aided tools designed to address cardiovascular diseases. Despite more than thirty years of research, it remains a challenge to obtain vascular segmentation results that preserve the connectivity of the underlying vascular network. Yet connectivity is one of the key feature of these tools. I