February 2024 arXiv papers — page 21

Federico Lozano-Cuadra, Beatriz Soret

This paper introduces a Multi-Agent Deep Reinforcement Learning (MA-DRL) approach for routing in Low Earth Orbit Satellite Constellations (LSatCs). Each satellite is an independent decision-making agent with a partial knowledge of the environment, and supported by feedback received from the nearby agents. Building on our previous work that introduced a Q-rou

Laura Casabella, Michael Joswig, Lars Kastner

The secondary fan $\Sigma(k,n)$ is a polyhedral fan which stratifies the regular subdivisions of the hypersimplices $\Delta(k,n)$. We find new infinite families of rays of $\Sigma(k,n)$, and we compute the fans $\Sigma(2,7)$ and $\Sigma(3,6)$. In the special case $k=2$ the fan $\Sigma(2,n)$ is closely related to the metric fan $\mathop{MF}(n)$, which forms a

Deshan Gong, Ningtao Mao, He Wang

We propose a new method for cloth digitalization. Deviating from existing methods which learn from data captured under relatively casual settings, we propose to learn from data captured in strictly tested measuring protocols, and find plausible physical parameters of the cloths. However, such data is currently absent, so we first propose a new dataset with a

E. I. Kaptsov

The study of the recently constructed group foliation for the geopotential forecast equation is continued. The group foliation consists of two systems, namely the automorphic and resolving systems, the analysis of which facilitates the derivation of invariant solutions for the original equation. As obtaining a general solution to the resolving system (even t

M. Romano, D. Donevski, Junais, A. Nanni

The evolution of dwarf galaxies is dramatically affected by gaseous and dusty outflows, which can easily deprive their interstellar medium of the material needed for the formation of new stars, simultaneously enriching their surrounding circumgalactic medium (CGM). In this letter, we present the first evidence of extended [CII] 158 $\mu$m line and dust conti

Benedetto Militello, Anna Napoli

The Stimulated Raman Adiabatic Passage on a three-state system interacting with a spin bath is considered focusing on the efficiency of the population transfer. Our analysis is based on the perturbation treatment of the interaction term evaluated beyond the Rotating Wave Approximation, thus focusing on the limit of weak system-bath coupling. The analytical e

Raul P. Pelaez, Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi

Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in the TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Ne

Ivan Homoliak, Martin Perešíni

In this work, we review existing cryptocurrency wallet solutions with regard to authentication methods and factors from the user's point of view. In particular, we distinguish between authentication factors that are verified against the blockchain and the ones verified locally (or against a centralized party). With this in mind, we define notions for $k-fact

H. Adami, A. Parvizi, M. M. Sheikh-Jabbari, V. Taghiloo

We study 4 dimensional $(4d$) gravitational waves (GWs) with compact wavefronts, generalizing Robinson-Trautman (RT) solutions in Einstein gravity with an arbitrary cosmological constant. We construct the most general solution of the GWs in the presence of a causal, timelike, or null boundary when the usual tensor modes are turned off. Our solution space bes

Matthias Hecker, Anant Rastogi, Daniel F. Agterberg, Rafael M. Fernandes

Electronic nematic order has been reported in a rich landscape of materials, encompassing not only a range of intertwined correlated and topological phenomena, but also different underlying lattice symmetries. Motivated by these findings, we investigate the behavior of electronic nematicity as the spherical symmetry of three-dimensional (3D) space is systema

Wei Zhang

We survey recent developments on generalizing the Gross--Zagier formula to high dimensional Shimura varieties, with an emphasis on the Arithmetic Gan--Gross--Prasad conjecture and the relative trace formula approach.

Yuang Zhao, Chuhan Wu, Qinglin Jia, Hong Zhu

Accurately predicting the probabilities of user feedback, such as clicks and conversions, is critical for advertisement ranking and bidding. However, there often exist unwanted mismatches between predicted probabilities and true likelihoods due to the rapid shift of data distributions and intrinsic model biases. Calibration aims to address this issue by post

Travis Grigsby, Edward Richmond

In this paper, we give a formula for the number of permutations that avoid the split patterns $3|12$ and $23|1$ with respect to a position $r$. Such permutations count the number of Schubert varieties for which the projection map from the flag variety to a Grassmannian induces a fiber bundle structure. We also study the corresponding bivariate generating fun

David S. W. Williams, Daniele De Martini, Matthew Gadd, Paul Newman

Knowing when a trained segmentation model is encountering data that is different to its training data is important. Understanding and mitigating the effects of this play an important part in their application from a performance and assurance perspective - this being a safety concern in applications such as autonomous vehicles (AVs). This work presents a segm

Yuting Yang, Andrea Merlina, Weijia Song, Tiancheng Yuan

We consider ML query processing in distributed systems where GPU-enabled workers coordinate to execute complex queries: a computing style often seen in applications that interact with users in support of image processing and natural language processing. In such systems, coscheduling of GPU memory management and task placement represents a promising opportuni

A. Abrardo

Reconfigurable Intelligent Surfaces (RIS) will play a pivotal role in next-generation wireless systems. Despite efforts to minimize pilot overhead associated with channel estimation, the necessity of configuring the RIS multiple times before obtaining reliable Channel State Information (CSI) may significantly diminish their benefits. Therefore, we propose a

Mohammad Ghalavand, Javad Hatami, Seyed Kamaledin Setarehdan, Fatimah Nosrati

This study investigates how the human brain differentiates between intentional human agents and artificial intelligence (AI) agents during real-time social interaction. Using functional near-infrared spectroscopy (fNIRS) hyperscanning, we recorded prefrontal brain activity of participants as they played a one-on-one virtual tennis game, once against a human

Tanise Ceron, Neele Falk, Ana Barić, Dmitry Nikolaev

Due to the widespread use of large language models (LLMs), we need to understand whether they embed a specific "worldview" and what these views reflect. Recent studies report that, prompted with political questionnaires, LLMs show left-liberal leanings (Feng et al., 2023; Motoki et al., 2024). However, it is as yet unclear whether these leanings are reliable

Felix Binkowski, Julius Kullig, Fridtjof Betz, Lin Zschiedrich

Exceptional points are spectral degeneracies of non-Hermitian systems where both eigenfrequencies and eigenmodes coalesce. The eigenfrequency sensitivities near an exceptional point are significantly enhanced, whereby they diverge directly at the exceptional point. Capturing this enhanced sensitivity is crucial for the investigation and optimization of excep

Lexy A. L. Andati, Lerato M. Baidoo, Athanaseus J. T. Ramaila, Oleg M. Smirnov

We present the results of a polarimetric study from our new high-sensitivity L-band (0.8--1.7 GHz) observation of Pictor A with the MeerKAT radio telescope. We confirm the presence of the radio jet extending from the nucleus to the western hotspot of this source. Additionally, we show the radio emission expected to be coincident with previously observed X-ra

Matheus Rolim Sales, Serhiy Yanchuk, Jürgen Kurths

Adaptive dynamical networks are network systems in which the structure co-evolves and interacts with the dynamical state of the nodes. We study an adaptive dynamical network in which the structure changes on a slower time scale relative to the fast dynamics of the nodes. We identify a phenomenon we refer to as recurrent adaptive chaotic clustering (RACC), in

Shuangrui Ding, Zihan Liu, Xiaoyi Dong, Pan Zhang

Creating lyrics and melodies for the vocal track in a symbolic format, known as song composition, demands expert musical knowledge of melody, an advanced understanding of lyrics, and precise alignment between them. Despite achievements in sub-tasks such as lyric generation, lyric-to-melody, and melody-to-lyric, etc, a unified model for song composition has n

Xiao Liu, Zirui Wu, Xueqing Wu, Pan Lu

Quantitative reasoning is a critical skill to analyze data, yet the assessment of such ability remains limited. To address this gap, we introduce the Quantitative Reasoning with Data (QRData) benchmark, aiming to evaluate Large Language Models' capability in statistical and causal reasoning with real-world data. The benchmark comprises a carefully constructe

A. N. Madhavanunni, Niya Mariam Benoy, Mahesh Raveendranatha Panicker, Himanshu Shekhar

Ultrafast ultrasound localization microscopy (ULM), which has shown promising results in microvascular imaging, overcomes the typical trade-off between resolution and penetration depth. Combining ultrasound contrast agents and high frame rate imaging enables ULM to visualize microvasculature and quantify flow. However, the quality of the microvascular maps o

Dietmar Pfeifer

In this paper we show that Cardanos formula for the solution of cubic equations can be reduced to expressions involving only square roots if the real root is rational.

Ran Wei, Jinjiong Yu

In this paper, we study a disordered pinning model induced by a random walk whose increments have a finite $(2+\kappa)$-th moment for some $\kappa>0$. It is known that this model is marginally relevant, and moreover, it undergoes a phase transition in an intermediate disorder regime. We show that, in the critical window, the point-to-point partition function

Yuesong Shen, Nico Daheim, Bai Cong, Peter Nickl

We give extensive empirical evidence against the common belief that variational learning is ineffective for large neural networks. We show that an optimizer called Improved Variational Online Newton (IVON) consistently matches or outperforms Adam for training large networks such as GPT-2 and ResNets from scratch. IVON's computational costs are nearly identic

Markus Lohmayer, Owen Lynch, Sigrid Leyendecker

Mathematical modeling of real-world physical systems requires the consistent combination of a multitude of physical laws and phenomenological models. This challenging task can be greatly simplified by hierarchically decomposing systems into ultimately simple components. Moreover, the use of diagrams for expressing the decomposition helps make the process mor

Yin-jie Chen, Jing-tao Lü

Utilizing the non-equilibrium Green's function method, we study the local heat current flow of phonons in nanoscale ballistic graphene nanoribbon, where boundary scattering leads to the formation of atomic-scale current vortices. We further map out the atomic temperature distribution in the ribbon with B\"uttiker's probe approach. From the heat current and t

Daisuke Fujii, Katsumasa Nakayama, Kei Suzuki

The Casimir effect is known to be induced from photon fields confined by a small volume, and also its fermionic counterpart has been predicted in a wide range of quantum systems. Here, we investigate what types of Casimir effects can occur from quark fields in dense and thin quark matter. In particular, in the dual chiral density wave, which is a possible gr

Aurélien Bibaut, Winston Chou, Simon Ejdemyr, Nathan Kallus

When primary objectives are insensitive or delayed, experimenters may instead focus on proxy metrics derived from secondary outcomes. For example, technology companies often infer the long-term impacts of product interventions from their effects on short-term user engagement signals. We consider the meta-analysis of many historical experiments to learn the c

Franciszek Knyszewski

Let $F$ be a number field and $p\geq7$ a rational prime. We obtain a simple descent criterion characterising those projective Galois representations $\overline\rho:G_F\to\mathrm{PGL}_2(\mathbb{F}_p)$ for which the corresponding twist $X_{\overline\rho}(p)$ of the principal modular curve of level $p$ is defined over $\mathbb{Q}$. We also give a more concrete

Emma Giovinazzo, Maxime Trebitsch, Valentin Mauerhofer, Pratika Dayal

Lya emitters (LAEs) are particularly useful objects for the study of the Epoch of Reionization. Lya profiles can be used to estimate the amount of ionizing photons that are able to escape the galaxies, and therefore to understand what objects contributed to reionization. However, Lya is a resonant line and its complex radiative transfer effects make the inte

Todd Elder, Allen H Boozer

A novel form of the current potential, a mathematical tool for the design of stellarators and stellarator coils, is developed. Specifically, these are current potentials with a finite-element-like basis, called \textit{current potential patches}. Current potential patches leverage the relationship between distributions of magnetic dipoles and current potenti

Fabian Retkowski, Alexander Waibel

Text segmentation is a fundamental task in natural language processing, where documents are split into contiguous sections. However, prior research in this area has been constrained by limited datasets, which are either small in scale, synthesized, or only contain well-structured documents. In this paper, we address these limitations by introducing a novel b

Andrew Holliday, Gregory Dudek

Planning a public transit network is a challenging optimization problem, but essential in order to realize the benefits of autonomous buses. We propose a novel algorithm for planning networks of routes for autonomous buses. We first train a graph neural net model as a policy for constructing route networks, and then use the policy as one of several mutation

Ali Mehrban, Mostafa Jani

The disaggregated and multi-vendor nature of OPEN-RAN networks introduces new supply chain security risks, making equipment authenticity and integrity crucial challenges. Robust solutions are needed to mitigate vulnerabilities in manufacturing and integration. This paper puts forth a novel blockchain-based approach to secure OPEN-RAN equipment through its li

Gerth Stølting Brodal, Sebastian Wild

In the multiple-selection problem one is given an unsorted array $S$ of $N$ elements and an array of $q$ query ranks $r_1<\cdots<r_q$, and the task is to return, in sorted order, the $q$ elements in $S$ of rank $r_1, \ldots, r_q$, respectively. The asymptotic deterministic comparison complexity of the problem was settled by Dobkin and Munro [JACM 1981]. In t

Huajian Zhang, Yumo Xu, Laura Perez-Beltrachini

We study existing approaches to leverage off-the-shelf Natural Language Inference (NLI) models for the evaluation of summary faithfulness and argue that these are sub-optimal due to the granularity level considered for premises and hypotheses. That is, the smaller content unit considered as hypothesis is a sentence and premises are made up of a fixed number

P A Horvathy

The quantum mechanically admissible definitions of the factor $\exp\big[(i/\hbar)S(\gamma)\big]$ in the Feynman integral are put in bijection with the prequantisations of Kostant and Souriau. The different allowed expressions of this factor -- the inequivalent prequantisations -- are classified. The theory is illustrated by the Aharonov-Bohm experiment and b

Scott Schmieding, Christopher-Lloyd Simon

The modular group $\operatorname{PSL}_2(\mathbb{Z})$ acts on the upper-half plane $\mathbb{HP}$ with quotient the modular orbifold, uniformized by the function $\mathfrak{j} \colon \mathbb{HP}\to \mathbb{C}$. We first show that second derived subgroup $\operatorname{PSL}_2(\mathbb{Z})''$ corresponds to a $\mathbb{Z}^2\rtimes \mathbb{Z}/6$ Galois cover of the

Raphaël Photopoulos, Antoine Boulet

Introducing low-energy effective Hamiltonians is usual to grasp most correlations in quantum many-body problems. For instance, such effective Hamiltonians can be treated at the mean-field level to reproduce some physical properties of interest. Employing effective Hamiltonians that contain many-body correlations renders the use of perturbative many-body tech

Won Sang Chung, Georg Junker, Hassan Hassanabadi

Most approaches towards a quantum theory of gravitation indicate the existence of a minimal length scale of the order of the Planck length. Quantum mechanical models incorporating such an intrinsic length scale call for a deformation of Heisenberg's algebra resulting in a generalized uncertainty principle and constitute what is called gravitational quantum m

Ha Min Son, Moon-Hyun Kim, Tai-Myoung Chung, Chao Huang

Federated learning is a promising framework to train neural networks with widely distributed data. However, performance degrades heavily with heterogeneously distributed data. Recent work has shown this is due to the final layer of the network being most prone to local bias, some finding success freezing the final layer as an orthogonal classifier. We invest

Maha Shadaydeh, Joachim Denzler, Mirco Migliavacca

Ecosystem respiration (Reco) represents a major component of the global carbon cycle, and accurate characterization of its dynamics is essential for a comprehensive understanding of ecosystem-climate interactions and the impacts of climate extremes on the ecosystem. This paper presents a novel data-driven and physics-aware method for estimating Reco dynamics

Chufeng Xiao, Hongbo Fu

Personalization techniques for large text-to-image (T2I) models allow users to incorporate new concepts from reference images. However, existing methods primarily rely on textual descriptions, leading to limited control over customized images and failing to support fine-grained and local editing (e.g., shape, pose, and details). In this paper, we identify sk

Leonardo Biagetti, Vincenzo Alba

Integrable systems possess stable families of quasiparticles, which are composite objects (bound states) of elementary excitations. Motivated by recent quantum computer experiments, we investigate bound-state transport in the spin-$1/2$ anisotropic Heisenberg chain ($XXZ$ chain). Specifically, we consider the sudden vacuum expansion of a finite region $A$ pr

David S. W. Williams, Matthew Gadd, Paul Newman, Daniele De Martini

This work proposes a semantic segmentation network that produces high-quality uncertainty estimates in a single forward pass. We exploit general representations from foundation models and unlabelled datasets through a Masked Image Modeling (MIM) approach, which is robust to augmentation hyper-parameters and simpler than previous techniques. For neural networ

Natasha K. Dudek, Mariam Chakhvadze, Saba Kobakhidze, Omar Kantidze

Machine learning (ML) is poised to drive innovations in clinical microbiomics, such as in disease diagnostics and prognostics. However, the successful implementation of ML in these domains necessitates the development of reproducible, interpretable models that meet the rigorous performance standards set by regulatory agencies. This study aims to identify key

Federico Fioravanti

We consider the problem where a set of individuals has to classify $m$ objects into $p$ categories and does so by aggregating the individual classifications. We show that if $m\geq 3$, $m\geq p\geq 2$, and classifications are fuzzy, that is, objects belong to a category to a certain degree, then an optimal and independent aggregator rule that satisfies a wea

Oscar Jarrin, Gaston Vergara-Hermosilla

It is known that the Kuramoto-Velarde equation is globally well-posed on Sobolev spaces in the case when the parameters $\gamma_1$ and $\gamma_2$ involved in the non-linear terms verify $ \gamma_1=\frac{\gamma_1}{2}$ or $\gamma_2=0$. In the complementary case of these parameters, the global existence or blow-up of solutions is a completely open (and hard) pr

Uwe Nagel, Sonja Petrović

In this work, we investigate the presence of the weak Lefschetz property (WLP) and Hilbert functions for various types of random standard graded Artinian algebras. If an algebra has the WLP then its Hilbert function is unimodal. Using probabilistic models for random monomial algebras, our results and simulations suggest that in each considered regime the Hil

Mário S. Alvim, Artur Gaspar da Silva, Sophia Knight, Frank Valencia

We generalize the DeGroot model for opinion dynamics to better capture realistic social scenarios. We introduce a model where each agent has their own individual cognitive biases. Society is represented as a directed graph whose edges indicate how much agents influence one another. Biases are represented as the functions in the square region $[-1,1]^2$ and c

Jonas Herzog

Few-shot segmentation performance declines substantially when facing images from a domain different than the training domain, effectively limiting real-world use cases. To alleviate this, recently cross-domain few-shot segmentation (CD-FSS) has emerged. Works that address this task mainly attempted to learn segmentation on a source domain in a manner that ge

Izia Xiaoxiao Wang, Xihan Wu, Edith Coates, Min Zeng

The utilization of technology in second language learning and teaching has become ubiquitous. For the assessment of writing specifically, automated writing evaluation (AWE) and grammatical error correction (GEC) have become immensely popular and effective methods for enhancing writing proficiency and delivering instant and individualized feedback to learners

Vincenzo Petrecca, Maria Teresa Botticella, Enrico Cappellaro, Laura Greggio

The Legacy Survey of Space and Time (LSST) will revolutionize Time Domain Astronomy by detecting millions of transients. In particular, it is expected to increment the number of type Ia supernovae (SNIa) of a factor of 100 compared to existing samples up to z~1.2. Such a high number of events will dramatically reduce statistical uncertainties in the analysis

David Torpey, Lawrence Pratt, Richard Klein

Pretraining has been shown to improve performance in many domains, including semantic segmentation, especially in domains with limited labelled data. In this work, we perform a large-scale evaluation and benchmarking of various pretraining methods for Solar Cell Defect Detection (SCDD) in electroluminescence images, a field with limited labelled datasets. We

Tuyen Vu

The paper deals with the semi-Dirac operator in a half-space arising in the description of quasiparticles in quantum mechanics as well as in semi-metals materials and related structures. It completely shows the self-adjointness, computes the square and the spectrum of the operator. We also set up sufficient conditions for the existence of the point and discr

Giorgio Cipolloni, László Erdős, Joscha Henheik

We consider the time evolution of the out-of-time-ordered correlator (OTOC) of two general observables $A$ and $B$ in a mean field chaotic quantum system described by a random Wigner matrix as its Hamiltonian. We rigorously identify three time regimes separated by the physically relevant scrambling and relaxation times. The main feature of our analysis is th

Alessio Miaschi, Felice Dell'Orletta, Giulia Venturi

In this paper, we explore the impact of augmenting pre-trained Encoder-Decoder models, specifically T5, with linguistic knowledge for the prediction of a target task. In particular, we investigate whether fine-tuning a T5 model on an intermediate task that predicts structural linguistic properties of sentences modifies its performance in the target task of p

Pepijn B. Cox, Wim L. van Rossum

Flexible front-end technology will become available in future multifunction radar systems to improve adaptability to the operational theatre. A potential concept to utilize this flexibility is to subdivide radar tasks spatially over the array, the so-called split-aperture phased array (SAPA) concept. As radars are generally designed for their worst-case scen

Cong Zhang, Zhiguang Cao, Yaoxin Wu, Wen Song

Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs). This paper proposes the topology-aware bidirectional graph attention network (TBGAT), a novel GNN architecture based on the a

A. C. Lehum, J. R. Nascimento, A. Yu. Petrov, P. J. Porfirio

In this paper, we consider the coupling of the metric-affine bumblebee gravity to the Abelian gauge field and obtain the effective model corresponding to the weak gravity limit of this theory. The effective bumblebee theory displays new unconventional couplings between the bumblebee field and its field strength, and the $U(1)$ gauge field along with its resp

Mirjeta Pasha, Silvia Gazzola, Connor Sanderford, Ugochukwu O. Ugwu

In this paper, we describe TRIPs-Py, a new Python package of linear discrete inverse problems solvers and test problems. The goal of the package is two-fold: 1) to provide tools for solving small and large-scale inverse problems, and 2) to introduce test problems arising from a wide range of applications. The solvers available in TRIPs-Py include direct regu

Yang Zhou

We consider the Euler-Lagrange equation of Sobolev trace inequality and prove several classification results. Exploiting the moving sphere method, it has been shown, when $p=2$, positive solutions of Euler-Lagrange equation of Sobolev trace inequality are classified. Since the moving sphere method strongly relies on the symmetries of the equation, in this pa

Matthias Boeker, Vajira Thambawita, Michael Riegler, Pål Halvorsen

Understanding sleep and activity patterns plays a crucial role in physical and mental health. This study introduces a novel approach for sleep detection using weakly supervised learning for scenarios where reliable ground truth labels are unavailable. The proposed method relies on a set of weak labels, derived from the predictions generated by conventional s

Zheng-Zhe Chen, Chiao-Yun Chang, Ya-Ting Tsai, Po-Cheng Tsai

The development of semiconductor optoelectronic devices is moving toward low power consumption and miniaturization, especially for high-efficiency quantum emitters. However, most of these quantum sources work at low carrier density region, where the Shockley-Read-Hall recombination may dominant and seriously reduce the emission efficiency. In order to dimini

Bruno Le Floch, Philippe G. LeFloch

To construct asymptotically-Euclidean Einstein's initial data sets, we introduce the localized seed-to-solution method, which projects from approximate to exact solutions of the Einstein constraints. The method enables us to glue together initial data sets in multiple asymptotically-conical regions, and in particular construct data sets that exhibit the grav

Prairie Wentworth-Nice

In 1962, Jesse MacWilliams published a set of formulas for linear and abelian group codes that among other applications, were incredibly valuable in the study of self-dual codes. Now called the MacWilliams Identities, her results relate the weight enumerator and complete weight enumerator of a code to those of its dual code. A similar set of MacWilliams iden

Gregorio Marchesini, Pedro Roque, Dimos V. Dimarogonas

In this work, we propose an extension of the previously introduced Corridor Model Predictive Control scheme for high-order and distributed systems, with an application for on-orbit inspection. To this end, we leverage high order control barrier function (HOCBF) constraints as a suitable control approach to maintain each agent in the formation within a safe c

Hong T. M. Chu, Subhro Ghosh, Chi Thanh Lam, Soumendu Sundar Mukherjee

The phenomenon of implicit regularization has attracted interest in recent years as a fundamental aspect of the remarkable generalizing ability of neural networks. In a nutshell, it entails that gradient descent dynamics in many neural nets, even without any explicit regularizer in the loss function, converges to the solution of a regularized learning proble

Christian Schröder, Bereket Ghebretinsae, Martin Lonsky, Mohanad Al Mamoori

We present a comprehensive study of small-scale three-dimensional (3D) tetrahedral CoFe nanostructure arrays prepared by focused electron beam-induced deposition (FEBID) and placed in two distinct orientations with respect to the direction of an external magnetic field. Using ultra-sensitive micro-Hall magnetometry we obtain angular-dependent magnetic stray

Ren Zhong, Zhaofeng Tian, Jinghui Liao, Weisong Shi

The increasing shortage of drivers poses a significant threat to vulnerable populations, particularly seniors and disabled individuals who heavily depend on public transportation for accessing healthcare services and social events. Autonomous Vehicles (AVs) emerge as a promising alternative, offering potential improvements in accessibility and independence f

Jan Eberhardt, Melissa J. Hobson, Thomas Henning, Trifon Trifonov

We report the discovery and characterization of three giant exoplanets orbiting solar-analog stars, detected by the \tess space mission and confirmed through ground-based photometry and radial velocity (RV) measurements taken at La Silla observatory with \textit{FEROS}. TOI-2373\,b is a warm Jupiter orbiting its host star every $\sim$ 13.3 days, and is one o

Jori Liesenborgs, Derek Perera, Liliya L. R. Williams

While gravitational lens inversion holds great promise to reveal the structure of the light-deflecting mass distribution, both light and dark, the existence of various kinds of degeneracies implies that care must be taken when interpreting the resulting lens models. This article illustrates how thinking in terms of the projected potential helps to gain insig

C. F. Wethers, S. Aalto, G. C. Privon, F. Stanley

Compact obscured nuclei (CONs) are an extremely obscured (N$_{H2}$ >10$^{25}$ cm$^{-2}$) class of galaxy nuclei thought to exist in 20-40 per cent of nearby (ultra-)luminous infrared galaxies. While they have been proposed to represent a key phase of the active galactic nucleus (AGN) feedback cycle, the nature of these CONs - what powers them, their dynamics

Xiaoyu Liu, Beitong Zhou, Zuogong Yue, Cheng Cheng

Recently, the usage of Contrastive Representation Learning (CRL) as a pre-training technique improves the performance of learning with noisy labels (LNL) methods. However, instead of pre-training, when trivially combining CRL loss with LNL methods as an end-to-end framework, the empirical experiments show severe degeneration of the performance. We verify thr

Hugo da Gião, André Flores, Rui Pereira, Jácome Cunha

DevOps is a combination of methodologies and tools that improves the software development, build, deployment, and monitoring processes by shortening its lifecycle and improving software quality. Part of this process is CI/CD, which embodies mostly the first parts, right up to the deployment. Despite the many benefits of DevOps and CI/CD, it still presents ma

Gregorio Marchesini, Siyuan Liu, Lars Lindemann, Dimos V. Dimarogonas

In this work, we propose a method to decompose signal temporal logic (STL) tasks for multi-agent systems subject to constraints imposed by the communication graph. Specifically, we propose to decompose tasks defined over multiple agents which require multi-hop communication, by a set of sub-tasks defined over the states of agents with 1-hop distance over the

C. W. Xiao, J. M. Dias, L. R. Dai, W. H. Liang

We study the $J/\psi \to \phi \pi^+ a_0(980)^- (a_0^- \to \pi^- \eta)$ decay, evaluating the double mass distribution in terms of the $\pi^- \eta$ and $\pi^+ a^-_0$ invariant masses. We show that the $\pi^- \eta$ mass distribution exhibits the typical cusp structure of the $a_0(980)$ seen in recent high statistics experiments, and the $\pi^+ a^-_0$ spectrum

Bruno Arderucio Costa

I discuss how five reasonably sounding assumptions lead to a dilemma -- the Page-time paradox -- , which appears to challenge a conventional statistical mechanical underpinning of black hole thermodynamics. By inspecting the conceptual subtleties behind each hypothesis, I list questions that require clarification before the puzzle can be deemed paradoxical.

Junjie Huang, Jinyang Liu, Zhuangbin Chen, Zhihan Jiang

Postmortem analysis is essential in the management of incidents within cloud systems, which provides valuable insights to improve system's reliability and robustness. At CloudA, fault pattern profiling is performed during the postmortem phase, which involves the classification of incidents' faults into unique categories, referred to as fault pattern. By aggr

Ed Swartz, Prairie Wentworth-Nice, Alexander Xue

Given a subgroup $\mathcal{H}$ of a product of finite groups $\mathcal{G} = \displaystyle\prod^n_{i=1} \Gamma_i$ and $b>1,$ we define a polymatroid $P(\mathcal{H},b).$ If all of the $\Gamma_i$ are isomorphic to $\mathbb{Z}/p\mathbb{Z},$ $p$ a prime, and $b=p,$ then $P(\mathcal{H},b)$ is the usual matroid associated to any $\mathbb{Z}/p\mathbb{Z}$-matrix whos

Daniel Forero-Sánchez, Francisco-Shu Kitaura, Francesco Sinigaglia, Jose María Coloma-Nadal

Modern galaxy surveys demand extensive survey volumes and resolutions surpassing current dark matter-only simulations' capabilities. To address this, many methods employ effective bias models on the dark matter field to approximate object counts on a grid. However, realistic catalogs necessitate specific coordinates and velocities for a comprehensive underst

Sebastian D. Proell, Julian Brotz, Martin Kronbichler, Wolfgang A. Wall

Fast and efficient simulations of metal additive manufacturing (AM) processes are highly relevant to exploring the full potential of this promising manufacturing technique. The microstructure composition plays an important role in characterizing the part quality and deriving mechanical properties. When complete parts are simulated, one often needs to resort

Alessandro Carones, Mathieu Remazeilles

Upcoming Cosmic Microwave Background (CMB) experiments, aimed at measuring primordial CMB B-modes, require exquisite control of Galactic foreground contamination. Minimum-variance techniques, like the Needlet Internal Linear Combination (NILC), have proven effective in reconstructing the CMB polarization signal and mitigating foregrounds across diverse sky m

Angela A. Albanese, Claudio Mele, Alessandro Oliaro

In this paper we give different estimates between Lebesgue norms of quadratic time-frequency representations. We show that, in some cases, it is not possible to have such bounds in classical $L^p$ spaces, but the Lebesgue norm needs to be suitably weighted. This leads to consider weights of polynomial type, and, more generally, of ultradifferentiable type, a

Himangshu Sekhar Sarmah, Subhradip Ghosh

MXene, the two-dimensional derivatives of MAX compounds, due to their structural and compositional flexibility, is an ideal family of compounds to study a number of structure-property relations. In this work, we have investigated the tunability of magnetic properties in Cr-based MXenes that have an in-plane ordering arising out of alloying Cr with another no

C. Klein, J. -C. Saut

The aim of this paper is to survey and complete, mostly by numerical simulations, results on a remarkable Boussinesq system describing weakly nonlinear, long surface water waves. It is the only member of the so-called (abcd) family of Boussinesq systems known to be completely integrable.

Petr Kurfürst

Massive stars can during their evolution reach the phase of critical (or very rapid, near-critical) rotation when further increase in rotation rate is no longer kinematically allowed. The mass ejection and angular momentum outward transport from such rapidly rotating star's equatorial surface may lead to formation and supports further existence of a circumst

Wenqi Zhang, Ke Tang, Hai Wu, Mengna Wang

Large Language Models (LLMs) exhibit robust problem-solving capabilities for diverse tasks. However, most LLM-based agents are designed as specific task solvers with sophisticated prompt engineering, rather than agents capable of learning and evolving through interactions. These task solvers necessitate manually crafted prompts to inform task rules and regul

Aleksandar Ichkov, Alexander Wietfeld, Marina Petrova, Ljiljana Simić

Hybrid beamforming (HBF) multi-user multiple-input multiple-output (MU-MIMO) is a key technology for unlocking the directional millimeter-wave (mm-wave) nature for spatial multiplexing beyond current codebook-based 5G-NR networks. In order to suppress co-scheduled users' interference, HBF MU-MIMO is predicated on having sufficient radio frequency chains and

Michiel Stock, Dimitri Boeckaerts, Pieter Dewulf, Steff Taelman

Advances in bioinformatics are primarily due to new algorithms for processing diverse biological data sources. While sophisticated alignment algorithms have been pivotal in analyzing biological sequences, deep learning has substantially transformed bioinformatics, addressing sequence, structure, and functional analyses. However, these methods are incredibly

Alain Cartellier, Juan Lasheras

The onset of air entrainment by a smooth vertical liquid jet impacting a pool of the same liquid has been experimentally determined. The ranges of parameters covered complement those considered by Lin & Donnelly (1969). The influence of the jet curvature is clarified. A model based on the viscous stress acting on the interface proves to be in good agreement

Daniel Iong, Matthew McAnear, Yuezhou Qu, Shasha Zou

Gaussian Processes (GP) have become popular machine-learning methods for kernel-based learning on datasets with complicated covariance structures. In this paper, we present a novel extension to the GP framework using a contaminated normal likelihood function to better account for heteroscedastic variance and outlier noise. We propose a scalable inference alg

Jonas Benhamou, Silvère Bonnabel, Camille Chapdelaine

To enhance accuracy of robot state estimation, active sensing (or perception-aware) methods seek trajectories that maximize the information gathered by the sensors. To this aim, one possibility is to seek trajectories that minimize the (estimation error) covariance matrix output by an extended Kalman filter (EKF), w.r.t. its control inputs over a given horiz

Ming Li

In the high energy limit, soft gluons can be approximately described by quasi-classical gluon fields. It is well-known that the gluon field is a pure gauge field on the transverse plane at eikonal order. We derived the complete next-to-eikonal order solutions of the classical Yang-Mills equations for soft gluons in the dense nuclear regime. Utilizing these s

Manfredi Scalici, Moein Naseri, Alexander Streltsov

We explore methods to generate quantum coherence through unitary evolutions, by introducing and studying the coherence generating capacity of Hamiltonians. This quantity is defined as the maximum derivative of coherence that can be achieved by a Hamiltonian. By adopting the relative entropy of coherence as our figure of merit, we evaluate the maximal coheren

Daniel Baratta, Berardino Sciunzi, Domenico Vuono

We consider solutions to $$ - \Delta_{p} u = f(x) \quad \text{in } \Omega\, ,$$ when $p$ approaches the semilinear limiting case $p=2$ and we get third order estimates. As a consequence we deduce improved regularity properties of the stress field.

Vladimir Rovenski

We study new Willmore-type variational problem for a hypersurface $M$ in $\mathbb{R}^{n+1}$ equipped with an $s$-dimensional foliation ${\cal F}$. Its general version is the Reilly-type functional $WF_{n,s}=\int_M F(\sigma^{\cal F}_1,\ldots,\sigma^{\cal F}_s)\,{\rm d}V$, where $\sigma^{\cal F}_i$ are elementary symmetric functions of the eigenvalues of the s