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For typical properly ordered and minimal Bratteli diagrams $(B,\leq_r)$, it is shown that there are finitely many invariant distributions $\mathcal{D}_i$ which are the only obstructions to solving the cohomological equation $f = u-u\circ \phi$ for the corresponding adic transformation $\phi:X_B\rightarrow X_B$ and for $\alpha$-H\"older $f$ with $\alpha$ large enough. These invariant distributions are then used to define cyclic cocycles, a.k.a. traces $\tau:K_0(\mathcal{A}_\phi)\rightarrow \mathbb{R}$ for the crossed product algebra $\mathcal{A}_\phi$., Comment: Comments welcome

Recent spacecraft mission concepts propose larger payloads that have lighter, less rigid structures. For large lightweight structures, the natural frequencies of their vibration modes may fall within the attitude controller bandwidth, threatening the stability and settling time of the controller and compromising performance. This work tackles this issue by proposing an attitude control design paradigm of distributing momentum actuators throughout the structure to have more control authority over vibration modes. The issue of jitter disturbances introduced by these actuators is addressed by expanding the bandwidth of the attitude controller to suppress excess vibrations. Numerical simulation results show that, at the expense of more control action, a distributed configuration can achieve lower settling times and reduce structural deformation compared to a more standard centralized configuration., Comment: 10 pages, 9 figures

Ultra-light bosons, naturally appearing in well-motivated extensions to the Standard Model, can constitute all the dark matter. Models with particle mass close to the smallest phenomenologically allowed exhibit coherent field configurations at (sub)galactic scales, oscillating at a frequency corresponding to the fundamental mass of the dark matter particle. The gravitational field of these structures inherits the dark matter field's coherent oscillations, leaving an imprint on gravitational (and electromagnetic) waves sourced close to (or in) such overdensities. This happens via a heterodyning frequency modulation, which can later be decoded in a gravitational-wave detector. An analogous effect occurs in models with universal (conformal) couplings of ultra-light bosons with ordinary matter, generated by the direct interaction with the oscillating field. In this work, we explore this phenomenon in detail and assess the capability of near-future interferometers to probe ultra-light dark matter and its potential conformal couplings to matter. Using astrophysical population models, together with results from cosmological simulations, we find that the observation of gravitational waves from spinning neutron stars at the Galactic Centre with the Einstein Telescope/Cosmic Explorer would be particularly effective in constraining ultra-light dark matter., Comment: 23 pages, 10 figures

Language models are now capable of solving tasks that require dealing with long sequences consisting of hundreds of thousands of tokens. However, they often fail on tasks that require repetitive use of simple rules, even on sequences that are much shorter than those seen during training. For example, state-of-the-art LLMs can find common items in two lists with up to 20 items but fail when lists have 80 items. In this paper, we introduce MLissard, a multilingual benchmark designed to evaluate models' abilities to process and generate texts of varied lengths and offers a mechanism for controlling sequence complexity. Our evaluation of open-source and proprietary models show a consistent decline in performance across all models and languages as the complexity of the sequence increases. Surprisingly, the use of in-context examples in languages other than English helps increase extrapolation performance significantly. The datasets and code are available at https://github.com/unicamp-dl/Lissard, Comment: GenBench Workshop by EMNLP 2024: Camera-ready version

Multiview systems have become a key technology in modern computer vision, offering advanced capabilities in scene understanding and analysis. However, these systems face critical challenges in bandwidth limitations and computational constraints, particularly for resource-limited camera nodes like drones. This paper presents a novel approach for communication-efficient distributed multiview detection and tracking using masked autoencoders (MAEs). We introduce a semantic-guided masking strategy that leverages pre-trained segmentation models and a tunable power function to prioritize informative image regions. This approach, combined with an MAE, reduces communication overhead while preserving essential visual information. We evaluate our method on both virtual and real-world multiview datasets, demonstrating comparable performance in terms of detection and tracking performance metrics compared to state-of-the-art techniques, even at high masking ratios. Our selective masking algorithm outperforms random masking, maintaining higher accuracy and precision as the masking ratio increases. Furthermore, our approach achieves a significant reduction in transmission data volume compared to baseline methods, thereby balancing multiview tracking performance with communication efficiency., Comment: 10 pages, conference

Explaining Artificial Intelligence (AI) decisions is a major challenge nowadays in AI, in particular when applied to sensitive scenarios like medicine and law. However, the need to explain the rationale behind decisions is a main issue also for human-based deliberation as it is important to justify \textit{why} a certain decision has been taken. Resident medical doctors for instance are required not only to provide a (possibly correct) diagnosis, but also to explain how they reached a certain conclusion. Developing new tools to aid residents to train their explanation skills is therefore a central objective of AI in education. In this paper, we follow this direction, and we present, to the best of our knowledge, the first multilingual dataset for Medical Question Answering where correct and incorrect diagnoses for a clinical case are enriched with a natural language explanation written by doctors. These explanations have been manually annotated with argument components (i.e., premise, claim) and argument relations (i.e., attack, support), resulting in the Multilingual CasiMedicos-Arg dataset which consists of 558 clinical cases in four languages (English, Spanish, French, Italian) with explanations, where we annotated 5021 claims, 2313 premises, 2431 support relations, and 1106 attack relations. We conclude by showing how competitive baselines perform over this challenging dataset for the argument mining task., Comment: 9 pages

We investigate the dispersion of solutes in active nematic fluids confined in narrow channels based on simulations of nematohydrodynamics. The study focuses on two pre-turbulent regimes: oscillatory flow, with net mass flux, and dancing flow, without net flux. Our findings reveal that the longitudinal dispersion of solutes, in both regimes, is determined by the second moments of the longitudinal and transverse components of the velocity field and proportional to the activity, suggesting a common mechanism for dispersion in these distinct flows. Finally, we found that the dispersion coefficient may increase up to one order of magnitude with respect to molecular diffusion in the dancing flow regime and that it is also enhanced in oscillatory flows, with potential for applications in natural biological environments and engineered devices.

Network Slicing (NS) has transformed the landscape of resource sharing in networks, offering flexibility to support services and applications with highly variable requirements in areas such as the next-generation 5G/6G mobile networks (NGMN), vehicular networks, industrial Internet of Things (IoT), and verticals. Although significant research and experimentation have driven the development of network slicing, existing architectures often fall short in intrinsic architectural intelligent security capabilities. This paper proposes an architecture-intelligent security mechanism to improve the NS solutions. We idealized a security-native architecture that deploys intelligent microservices as federated agents based on machine learning, providing intra-slice and architectural operation security for the Slicing Future Internet Infrastructures (SFI2) reference architecture. It is noteworthy that federated learning approaches match the highly distributed modern microservice-based architectures, thus providing a unifying and scalable design choice for NS platforms addressing both service and security. Using ML-Agents and Security Agents, our approach identified Distributed Denial-of-Service (DDoS) and intrusion attacks within the slice using generic and non-intrusive telemetry records, achieving an average accuracy of approximately $95.60\%$ in the network slicing architecture and $99.99\%$ for the deployed slice -- intra-slice. This result demonstrates the potential for leveraging architectural operational security and introduces a promising new research direction for network slicing architectures., Comment: 18 pages, 12 figures, Future Generation Computer Systems (FGCS)

We study a geometric facility location problem under imprecision. Given $n$ unit intervals in the real line, each with one of $k$ colors, the goal is to place one point in each interval such that the resulting \emph{minimum color-spanning interval} is as large as possible. A minimum color-spanning interval is an interval of minimum size that contains at least one point from a given interval of each color. We prove that if the input intervals are pairwise disjoint, the problem can be solved in $O(n)$ time, even for intervals of arbitrary length. For overlapping intervals, the problem becomes much more difficult. Nevertheless, we show that it can be solved in $O(n \log^2 n)$ time when $k=2$, by exploiting several structural properties of candidate solutions, combined with a number of advanced algorithmic techniques. Interestingly, this shows a sharp contrast with the 2-dimensional version of the problem, recently shown to be NP-hard.

It is well-known that the standard bulk-boundary correspondence does not hold for non-Hermitian systems in which also new phenomena such as exceptional points do occur. Here we study by analytical and numerical means a paradigmatic one-dimensional non-Hermitian model with dimerization, asymmetric hopping, and imaginary staggered potentials. We present analytical solutions for the eigenspectrum of this model with both open and closed boundary conditions as well as for the singular-value spectrum. We explicitly demonstrate the proper bulk-boundary correspondence between topological winding numbers in the periodic case and singular values in the open case. We also show that a non-trivial topology leads to protected eigenvalues in the entanglement spectrum. In the $\mathcal{PT}$-symmetric case, we find that the model has a phase where exceptional points become dense in the thermodynamic limit., Comment: 23 pages, 15 figures

Using the group structure of the state space of $q-$state models and a new definition of contour for long-range spin-systems in $\mathbb{Z}^d$, with $d\geq 2$, a multidimensional version of Fr\"{o}hlich-Spencer contours, we prove the phase transition for a class of ferromagnetic long-range systems which includes the Clock and Potts models. Our arguments work for the entire region of exponents of regular power-law interactions, namely $\alpha > d$, and for any $q \geq 2$. As an application, we prove the phase transition for Potts models with decaying fields when the field decays fast enough., Comment: Preliminary version. Comments are welcome. 21 pages

We present the Merian Survey, an optical imaging survey optimized for studying the physical properties of bright star-forming dwarf galaxies. Merian is carried out with two medium-band filters ($N708$ and $N540$, centered at $708$ and $540$ nm), custom-built for the Dark Energy Camera (DECam) on the Blanco telescope. Merian covers $\sim 750\,\mathrm{deg}^2$ of equatorial fields, overlapping with the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) wide, deep, and ultra-deep fields. When combined with the HSC-SSP imaging data ($grizy$), the new Merian DECam medium-band imaging allows for photometric redshift measurements via the detection of H$\rm\alpha$ and [OIII] line emission flux excess in the $N708$ and $N540$ filters, respectively, at $0.06

This paper proposes a class of asymmetric priors to perform Bayesian wavelet shrinkage in the standard nonparametric regression model with Gaussian error. The priors are composed by mixtures of a point mass function at zero and one of the following distributions: asymmetric beta, Kumaraswamy, asymmetric triangular or skew normal. Statistical properties of the associated shrinkage rules such as squared bias, variance and risks are obtained numerically and discussed. Monte Carlo simulation studies are described to evaluate the performances of the rules against standard techniques. An application of the asymmetric rules to a stock market index time series is also illustrated.

The Polynomial Learning With Errors problem (PLWE) serves as the background of two of the three cryptosystems standardized in August 2024 by the National Institute of Standards and Technology to replace non-quantum resistant current primitives like those based on RSA, Diffie-Hellman or its elliptic curve analogue. Although PLWE is highly believed to be quantum resistant, this fact has not yet been established, contrariwise to other post-quantum proposals like multivariate and some code based ones. Moreover, several vulnerabilities have been encountered for a number of specific instances. In a search for more flexibility, it becomes fully relevant to study the robustness of PLWE based on other polynomials, not necessarily cyclotomic. In 2015, Elias et al found a good number of attacks based on different features of the roots of the polynomial. In the present work we present an overview of the approximations made against PLWE derived from this and subsequent works, along with several new attacks which refine those by Elias et al. exploiting the order of the trace of roots over finite extensions of the finite field under the three scenarios laid out by Elias et al., allowing to generalize the setting in which the attacks can be carried out.

An exciting recent development for deep-tissue imaging with cellular resolution is three-photon fluorescence microscopy (3PM) with excitation at long wavelengths (1300 and 1700 nm). In the last few years, long-wavelength 3PM has driven rapid progress in deep-tissue imaging beyond the depth limit of two-photon microscopy, with impacts in neuroscience, immunology, and cancer biology. However, wide adoption of 3PM faces challenges. Three-photon excitation (3PE) is naturally weaker than two-photon excitation, which places a premium on ultrashort pulses with high peak power. The inefficiency, complexity, and cost of current sources of these pulses present major barriers to the use of 3PM in typical biomedical research labs. Here, we describe a fiber-based source of femtosecond pulses with multi-megawatt peak power, tunable from 850 nm to 1700 nm. Compressed pulses from a fiber amplifier at 1030~nm are launched into an antiresonant hollow-core fiber filled with argon. By varying only the gas pressure, pulses with hundreds of nanojoules of energy and sub-100 fs duration are obtained at wavelengths between 850 and 1700 nm. This approach is a new route to an efficient, robust, and potentially low-cost source for multiphoton deep-tissue imaging. In particular, 960-nJ and 50-fs pulses are generated at 1300 nm with a conversion efficiency of 10\%. The nearly 20-MW peak power is an order of magnitude higher than the previous best from femtosecond fiber source at 1300~nm. As an example of the capabilities of the source, these pulses are used to image structure and neuronal activity in mouse brain as deep as 1.1 mm below the dura., Comment: Supplement appended at end

When we want to simulate the realization of a symmetric simple random walk on $\mathbb Z^d$, we use $(2d)$-side fair dice to decide to which neighbor it jumps at each step if $d\geq 2$ or we simply use a fair coin when $d=1$. Assume that instead of using a dice or a coin we want to do a simulation using a well shuffled deck with $K$ cards of each of the $2d$ suits. In the first step the probability of jumping to each neighbor is $(2d)^{-1}$, but from the second step it becomes biased. Of course if we continue performing this simulation, the total variation distance between its law and the law of the random walk will increase until all cards are used. In this paper we investigate the minimum number of cards $N=2d K$ that a deck must contain so that the total variation distance between the law of a $n$-step simulation and the law of a $n$-step realization of the random walk is smaller than a chosen threshold $\varepsilon \in (0,1)$. More generally, we prove that when $N=cn$ this distance converges, as $n \to \infty$, to a Gaussian profile which depends on $c\geq 2d$. Furthermore, our analysis shows that this Gaussian profile vanishes as $c \to \infty$, proving the convergence of a multivariate hypergeometric distribution to a multinomial distribution in total variation., Comment: 12 pages, 2 figures

Multi-agent systems in biology, society, and engineering are capable of making decisions through the dynamic interaction of their elements. Nonlinearity of the interactions is key for the speed, robustness, and flexibility of multi-agent decision-making. In this work we introduce modulatory, that is, multiplicative, in contrast to additive, interactions in a nonlinear opinion dynamics model of fast-and-flexible decision-making. The original model is nonlinear because network interactions, although additive, are saturated. Modulatory interactions introduce an extra source of nonlinearity that greatly enriches the model decision-making behavior in a mathematically tractable way. Modulatory interactions are widespread in both biological and social decision-making networks; our model provides new tools to understand the role of these interactions in networked decision-making and to engineer them in artificial systems., Comment: 6 pages, 4 figures, submitted to American Control Conference 2025

We prove the equivalence between the Ring Learning With Errors (RLWE) and the Polynomial Learning With Errors (PLWE) problems for the maximal totally real subfield of the $2^r 3^s$-th cyclotomic field for $r \geq 3$ and $s \geq 1$. Moreover, we describe a fast algorithm for computing the product of two elements in the ring of integers of these subfields. This multiplication algorithm has quasilinear complexity in the dimension of the field, as it makes use of the fast Discrete Cosine Transform (DCT). Our approach assumes that the two input polynomials are given in a basis of Chebyshev-like polynomials, in contrast to the customary power basis. To validate this assumption, we prove that the change of basis from the power basis to the Chebyshev-like basis can be computed with $\mathcal{O}(n \log n)$ arithmetic operations, where $n$ is the problem dimension. Finally, we provide a heuristic and theoretical comparison of the vulnerability to some attacks for the $p$-th cyclotomic field versus the maximal totally real subextension of the $4p$-th cyclotomic field for a reasonable set of parameters of cryptographic size.

Neurological disorders that affect speech production, such as Alzheimer's Disease (AD), significantly impact the lives of both patients and caregivers, whether through social, psycho-emotional effects or other aspects not yet fully understood. Recent advancements in Large Language Model (LLM) architectures have developed many tools to identify representative features of neurological disorders through spontaneous speech. However, LLMs typically lack interpretability, meaning they do not provide clear and specific reasons for their decisions. Therefore, there is a need for methods capable of identifying the representative features of neurological disorders in speech and explaining clearly why these features are relevant. This paper presents an explainable LLM method, named SLIME (Statistical and Linguistic Insights for Model Explanation), capable of identifying lexical components representative of AD and indicating which components are most important for the LLM's decision. In developing this method, we used an English-language dataset consisting of transcriptions from the Cookie Theft picture description task. The LLM Bidirectional Encoder Representations from Transformers (BERT) classified the textual descriptions as either AD or control groups. To identify representative lexical features and determine which are most relevant to the model's decision, we used a pipeline involving Integrated Gradients (IG), Linguistic Inquiry and Word Count (LIWC), and statistical analysis. Our method demonstrates that BERT leverages lexical components that reflect a reduction in social references in AD and identifies which further improve the LLM's accuracy. Thus, we provide an explainability tool that enhances confidence in applying LLMs to neurological clinical contexts, particularly in the study of neurodegeneration., Comment: 27 pages, 6 figures, authors Marina Ribeiro and B\'arbara Malcorra have equal contribution, C\'esar Renn\'o-Costa is the corresponding author

Human language, while aimed at conveying meaning, inherently carries ambiguity. It poses challenges for speech and language processing, but also serves crucial communicative functions. Efficiently solve ambiguity is both a desired and a necessary characteristic. The lexical meaning of a word in context can be determined automatically by Word Sense Disambiguation (WSD) algorithms that rely on external knowledge often limited and biased toward English. When adapting content to other languages, automated translations are frequently inaccurate and a high degree of expert human validation is necessary to ensure both accuracy and understanding. The current study addresses previous limitations by introducing a new resource for Spanish WSD. It includes a sense inventory and a lexical dataset sourced from the Diccionario de la Lengua Espa\~nola which is maintained by the Real Academia Espa\~nola. We also review current resources for Spanish and report metrics on them by a state-of-the-art system., Comment: 5 pages, 4 tables

Random Number Generators are critical components of modern cryptosystems. Quantum Random Number Generators (QRNG) have emerged to provide high-quality randomness for these applications. Here we describe a scheme to extract random numbers using balanced detection of shot noise from an LED in a commercially available off-the-shelf package. The balanced detection minimizes classical noise contributions from the optical field, improving the isolation of the quantum noise. We present a detailed description and analyze the performance of a QRNG that can be easily integrated into existing systems without the requirement of custom components. The design is optimised for manufacturability, cost, and size., Comment: 5 pages, 6 figures

We review a conjecture by Ruppeiner that relates the nature of interparticle interactions to the sign of the thermodynamic curvature scalar $R$, paying special attention to the case of zero curvature. We highlight the underappreciated fact that there are two Ruppeiner metrics that are equally viable in principle, which are obtained by restricting to systems of constant volume and constant particle number, respectively. We then demonstrate the existence of thermodynamic systems with vanishing curvature scalar but nontrivial interactions. Information about interactions in these systems is obtained by carrying out an inversion procedure on the virial coefficients. Finally, we show using the virial expansion that the ideal gas is the unique physical system for which both curvature scalars vanish. This leads us to propose an extension to Ruppeiner's conjecture., Comment: 15 pages, 3 figures

The vacuum entanglement entropy of a general conformal field theory (CFT) in $d=5$ spacetime dimensions contains a universal term, $F(A)$, which has a complicated and non-local dependence on the geometric details of the region $A$ and the theory. Analogously to the previously known $d=3$ case, we prove that for CFTs in $d=5$ which are holographically dual to Einstein gravity, $F(A)$ is equal to a four-dimensional version of the ``Willmore energy'' associated to a doubled and closed version of the Ryu-Takayanagi (RT) surface of $A$ embedded in $\mathbb{R}^5$. This generalized Willmore energy is shown to arise from a conformal-invariant codimension-two functional obtained by evaluating six-dimensional Conformal Gravity on the conically-singular orbifold of the replica trick. The new functional involves an integral over the doubled RT surface of a linear combination of three quartic terms in extrinsic curvatures and is free from ultraviolet divergences by construction. We verify explicitly the validity of our new formula for various entangling regions and argue that, as opposed to the $d=3$ case, $F(A)$ is not globally minimized by a round ball $A=\mathbb{B}^4$. Rather, $F(A)$ can take arbitrarily positive and negative values as a function of $A$. Hence, we conclude that the round ball is not a global minimizer of $F(A)$ for general five-dimensional CFTs., Comment: 48 pages, 5 figures

Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the high-dimensional interactions that cancer genes have within cancer networks. This study presents a novel method using Persistent Homology to analyze the role of driver genes in higher-order structures within Cancer Consensus Networks derived from main cellular pathways. We integrate mutation data from six cancer types and three biological functions: DNA Repair, Chromatin Organization, and Programmed Cell Death. We systematically evaluated the impact of gene removal on topological voids ($\beta_2$ structures) within the Cancer Consensus Networks. Our results reveal that only known driver genes and cancer-associated genes influence these structures, while passenger genes do not. Although centrality measures alone proved insufficient to fully characterize impact genes, combining higher-order topological analysis with traditional network metrics can improve the precision of distinguishing between drivers and passengers. This work shows that cancer genes play an important role in higher-order structures, going beyond pairwise measures, and provides an approach to distinguish drivers and cancer-associated genes from passenger genes.

Accurate identification and categorization of suicidal events can yield better suicide precautions, reducing operational burden, and improving care quality in high-acuity psychiatric settings. Pre-trained language models offer promise for identifying suicidality from unstructured clinical narratives. We evaluated the performance of four BERT-based models using two fine-tuning strategies (multiple single-label and single multi-label) for detecting coexisting suicidal events from 500 annotated psychiatric evaluation notes. The notes were labeled for suicidal ideation (SI), suicide attempts (SA), exposure to suicide (ES), and non-suicidal self-injury (NSSI). RoBERTa outperformed other models using multiple single-label classification strategy (acc=0.86, F1=0.78). MentalBERT (acc=0.83, F1=0.74) also exceeded BioClinicalBERT (acc=0.82, F1=0.72) which outperformed BERT (acc=0.80, F1=0.70). RoBERTa fine-tuned with single multi-label classification further improved the model performance (acc=0.88, F1=0.81). The findings highlight that the model optimization, pretraining with domain-relevant data, and the single multi-label classification strategy enhance the model performance of suicide phenotyping. Keywords: EHR-based Phenotyping; Natural Language Processing; Secondary Use of EHR Data; Suicide Classification; BERT-based Model; Psychiatry; Mental Health, Comment: submitted to AMIA Informatics Summit 2025 as a conference paper

We study the cohomology rings of tiling spaces $\Omega$ given by cubical substitutions. While there have been many calculations before of cohomology groups of such tiling spaces, the innovation here is that we use computer-assisted methods to compute the cup-product structure. This leads to examples of substitution tilings with isomorphic cohomology groups but different cohomology rings. Part of the interest in studying the cup product comes from Bellissard's gap-labeling conjecture, which is known to hold in dimensions $\le 3$, but where a proof is known in dimensions $\ge 4$ only when the Chern character from $K^0(\Omega)$ to $H^*(\Omega,\mathbb{Q})$ lands in $H^*(\Omega,\mathbb{Z})$. Computation of the cup product on cohomology often makes it possible to compute the Chern character. We introduce a natural generalization of the gap-labeling conjecture, called the equivariant gap-labeling conjecture, which applies to tilings with a finite symmetry group. Again this holds in dimensions $\le 3$, but we are able to show that it fails in general in dimensions $\ge 4$. This, plus some of our cup product calculations, makes it plausible that the gap-labeling conjecture might fail in high dimensions., Comment: Fixed mistake in the first pair of examples, added references

Motivation Network visualization is critical for effective communication in various fields of knowledge. Currently, a gap separates network manipulation from network visualization in programming environments. Users often export network data to be laid out in external interactive software, like Cytoscape and Gephi. We argue the current R package ecosystem lacks an interactive layout engine well integrated with common data analysis workflows. Results We present easylayout, an R package that bridges network manipulation and visualization by leveraging interactive force simulations within the IDE itself (e.g., RStudio, VSCode). It is not yet another visualization library, but instead aims to interconnect existing libraries and streamline their usage into the R ecosystem. easylayout takes an igraph object and serializes it into a web application integrated with the IDE's interface through a Shiny server. The app lays out the network by simulating attraction and repulsion forces. Simulation parameters can be adjusted in real-time. An editing mode allows moving and rotating nodes. The implementation aims for performance, so that even lower-end devices are able to work with relatively large networks. Once the user finishes tweaking the layout, it is sent back to the R session to be plotted through popular libraries like ggraph, igraph or even the base package itself. The current implementation focuses on the R ecosystem, but using web technologies makes it easily portable to similar environments, like Python/Jupyter Notebooks. We expect this tool to reduce the time spent searching for suitable network layouts, ultimately allowing researchers to generate more compelling figures. Availability and implementation easylayout is freely available under an MIT license on GitHub (https://github.com/dalmolingroup/easylayout). The package is implemented in R/Shiny and JavaScript/Svelte.

The integration of Artificial Intelligence (AI) and Augmented Reality (AR) is set to transform satellite Assembly, Integration, and Testing (AIT) processes by enhancing precision, minimizing human error, and improving operational efficiency in cleanroom environments. This paper presents a technical description of the European Space Agency's (ESA) project "AI for AR in Satellite AIT," which combines real-time computer vision and AR systems to assist technicians during satellite assembly. Leveraging Microsoft HoloLens 2 as the AR interface, the system delivers context-aware instructions and real-time feedback, tackling the complexities of object recognition and 6D pose estimation in AIT workflows. All AI models demonstrated over 70% accuracy, with the detection model exceeding 95% accuracy, indicating a high level of performance and reliability. A key contribution of this work lies in the effective use of synthetic data for training AI models in AR applications, addressing the significant challenges of obtaining real-world datasets in highly dynamic satellite environments, as well as the creation of the Segmented Anything Model for Automatic Labelling (SAMAL), which facilitates the automatic annotation of real data, achieving speeds up to 20 times faster than manual human annotation. The findings demonstrate the efficacy of AI-driven AR systems in automating critical satellite assembly tasks, setting a foundation for future innovations in the space industry.

Hot subdwarfs are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. Most models agree on a common envelope binary evolution scenario in the Red Giant phase. However, the binarity rate for these objects is yet unsolved. We aim to develop a novel classification method for identifying hot subdwarf binaries within large datasets using Artificial Intelligence methods and Gaia DR3 data. The results will be compared with those obtained previously using VOSA (Virtual Observatory Sed Analyzer) on coincident samples. The methods include several machine learning techniques. We used Support Vector Machines (SVM) to classify 3084 hot subdwarf stars based on their color-magnitude properties. Of these, 2815 objects have Gaia Data Release 3 BP/RP spectra, which were classified using Self-Organizing Maps (SOM) and Convolutional Neural Networks (CNN). The findings demonstrate a high agreement level (70-90%) with VOSA's classification, indicating that machine learning methods effectively classify sources with an accuracy comparable to human inspection or non-AI techniques. SVM in a radial basis function achieves 70.97% reproducibility for binary targets using photometry. CNN reaches 84.94% for binary detection using spectroscopy. We also found that the single-binary differences are especially observable on the infrared flux in our GDR3 BP/BR spectra, at wavelengths larger than 700 nm. We found that all our methods are effective in discerning between single and binary systems and are consistent with the results previously obtained with VOSA. In global terms, considering all quality metrics, CNN is the method that provides the best accuracy. The methods are also effective for detecting peculiarities in the spectra. Further research is needed to refine our techniques and enhance automated classification reliability, especially for large-scale surveys.

Quantum Reservoir Computing (QRC) offers potential advantages over classical reservoir computing, including inherent processing of quantum inputs and a vast Hilbert space for state exploration. Yet, the relation between the performance of reservoirs based on complex and many-body quantum systems and non-classical state features is not established. Through an extensive analysis of QRC based on a transverse-field Ising model we show how different quantum effects, such as quantum coherence and correlations, contribute to improving the performance in temporal tasks, as measured by the Information Processing Capacity. Additionally, we critically assess the impact of finite measurement resources and noise on the reservoir's dynamics in different regimes, quantifying the limited ability to exploit quantum effects for increasing damping and noise strengths. Our results reveal a monotonic relationship between reservoir performance and coherence, along with the importance of quantum effects in the ergodic regime., Comment: 19 pages (9 main article + 10 references and appendix), 17 figures (7 main article + 10 appendix)

Metallic alloys are routinely subjected to nonequilibrium processes during manufacturing, such as rapid solidification and thermomechanical processing. It has been suggested in the high-entropy alloy literature that chemical short-range order (SRO) could offer a "new knob" to tailor materials properties. While evidence of the effect of SRO on materials properties accumulates, the state of SRO evolution during alloy manufacturing remains obscure. Here we employ high-fidelity atomistic simulations to track SRO evolution during solidification and thermomechanical processing of alloys. This approach reveals that alloy processing leads to nonequilibrium steady-states of SRO that are less ordered than any equilibrium state. We show that the mechanism behind nonequilibrium SRO formation is an inherent ordering bias present in nonequilibrium events. These results demonstrate that conventional manufacturing processes provide pathways for tuning SRO that lead to a broad nonequilibrium spectrum of SRO states that are beyond the equilibrium design space of alloys., Comment: 9 pages, 4 figures

This paper presents chemical abundances of twelve elements (C, Na, Mg, Al, Si, K, Ca, Ti, V, Cr, Mn, and Fe) for 80 FGK dwarfs in the Pleiades open cluster, which span a temperature range of $\sim$2000 K in T$_{\rm eff}$, using the high-resolution (R$\sim$22,500) near-infrared SDSS-IV/APOGEE spectra ($\lambda$1.51--1.69 \micron). Using a 1D LTE abundance analysis, we determine an overall metallicity of [Fe/H]=+0.03$\pm$0.04 dex, with the elemental ratios [$\alpha$/Fe]=+0.01$\pm$0.05, [odd-z/Fe]=-0.04$\pm$0.08, and [iron peak/Fe]=-0.02$\pm$0.08. These abundances for the Pleiades are in line with the abundances of other open clusters at similar galactocentric distances as presented in the literature. Examination of the abundances derived from each individual spectral line revealed that several of the stronger lines displayed trends of decreasing abundance with decreasing $T_{\rm eff}$. The list of spectral lines that yield abundances that are independent of $T_{\rm eff}$ are presented and used for deriving the final abundances. An investigation into possible causes of the temperature-dependent abundances derived from the stronger lines suggests that the radiative codes and the APOGEE line list we employ may inadequately model van der Waals broadening, in particular in the cooler K dwarfs., Comment: 16 pages, 7 figures, and 4 tables. Accepted for publication in MNRAS

Few-shot learning (FSL) aims to enable models to recognize novel objects or classes with limited labelled data. Feature generators, which synthesize new data points to augment limited datasets, have emerged as a promising solution to this challenge. This paper investigates the effectiveness of feature generators in enhancing the embedding process for FSL tasks. To address the issue of inaccurate embeddings due to the scarcity of images per class, we introduce a feature generator that creates visual features from class-level textual descriptions. By training the generator with a combination of classifier loss, discriminator loss, and distance loss between the generated features and true class embeddings, we ensure the generation of accurate same-class features and enhance the overall feature representation. Our results show a significant improvement in accuracy over baseline methods, with our approach outperforming the baseline model by 10% in 1-shot and around 5% in 5-shot approaches. Additionally, both visual-only and visual + textual generators have also been tested in this paper. The code is publicly available at https://github.com/heethanjan/Feature-Generator-for-FSL., Comment: 16 pages, Accepted to ACCV 2024

A mixture of particles with perception-dependent motility and opposite misaligned visual perception shows to spontaneously self-organize into a self-propelling bean-shaped cluster. The two species initially rotate in opposite directions, which together with the steric interactions, makes them segregate into two main counter-rotating domains forming a cohesive and persistently propelling single cluster. Mixtures of particles with misaligned perception and discontinuous motility are therefore a promising pathway for the design of programmable active matter., Comment: 5 pages, 4 figures

We classify all solution triples with $k$-Fibonacci components to the equation $x^2+y^2+z^2=3xyz+m,$ where $m$ is a positive integer and $k\geq 2$. As a result, for $m=8$, we have the Markoff triples with Pell components $(F_2(2), F_2(2n), F_2(2n+2))$, for $n\geq 1$. For all other $m$ there exists at most one such ordered triple, except when $k=3,$ $a$ is odd, $b$ is even and $b\geq a+3$, where $(F_3(a),F_3(b),F_3(a+b))$ and $(F_3(a+1),F_3(b-1),F_3(a+b))$ share the same $m$., Comment: 15 pages, 1 figure

Accurate field boundary delineation is a critical challenge in digital agriculture, impacting everything from crop monitoring to resource management. Existing methods often struggle with noise and fail to generalize across varied landscapes, particularly when dealing with cloud cover in optical remote sensing. In response, this study presents a new approach that leverages time series data from Sentinel-2 (S2) and Sentinel-1 (S1) imagery to improve performance under diverse cloud conditions, without the need for manual cloud filtering. We introduce a 3D Vision Transformer architecture specifically designed for satellite image time series, incorporating a memory-efficient attention mechanism. Two models are proposed: PTAViT3D, which handles either S2 or S1 data independently, and PTAViT3D-CA, which fuses both datasets to enhance accuracy. Both models are evaluated under sparse and dense cloud coverage by exploiting spatio-temporal correlations. Our results demonstrate that the models can effectively delineate field boundaries, even with partial (S2 or S2 and S1 data fusion) or dense cloud cover (S1), with the S1-based model providing performance comparable to S2 imagery in terms of spatial resolution. A key strength of this approach lies in its capacity to directly process cloud-contaminated imagery by leveraging spatio-temporal correlations in a memory-efficient manner. This methodology, used in the ePaddocks product to map Australia's national field boundaries, offers a robust, scalable solution adaptable to varying agricultural environments, delivering precision and reliability where existing methods falter. Our code is available at https://github.com/feevos/tfcl., Comment: under review

Dissipative tunneling remains a cornerstone effect in quantum mechanics. In chemistry, it plays a crucial role in governing the rates of chemical reactions, often modeled as the motion along the reaction coordinate from one potential well to another. The relative positions of energy levels in these wells strongly influences the reaction dynamics. Chemical research will benefit from a fully controllable, asymmetric double-well equipped with precise measurement capabilities of the tunneling rates. In this paper, we show that a continuously driven Kerr parametric oscillator with a third order non-linearity can be operated in the quantum regime to create a fully tunable asymmetric double-well. Our experiment leverages a low-noise, all-microwave control system with a high-efficiency readout of the which-well information. We explore the reaction rates across the landscape of tunneling resonances in parameter space. We uncover two new and counter-intuitive effects: (i) a weak asymmetry can significantly decrease the activation rates, even though the well in which the system is initialized is made shallower, and (ii) the width of the tunneling resonances alternates between narrow and broad lines as a function of the well depth and asymmetry. We predict by numerical simulations that both effects will also manifest themselves in ordinary chemical double-well systems in the quantum regime. Our work paves the way for analog molecule simulators based on quantum superconducting circuits.

Chemical reactions are commonly described by the reactive flux transferring population from reactants to products across a double-well free energy barrier. Dynamics often involves barrier recrossing and quantum effects like tunneling, zero-point energy motion and interference, which traditional rate theories, such as transition-state theory, do not consider. In this study, we investigate the feasibility of simulating reaction dynamics using a parametrically driven bosonic superconducting Kerr-cat device. This approach provides control over parameters defining the double-well free energy profile, as well as external factors like temperature and the coupling strength between the reaction coordinate and the thermal bath of non-reactive degrees of freedom. We demonstrate the effectiveness of this protocol by showing that the dynamics of proton transfer reactions in prototypical benchmark model systems, such as hydrogen bonded dimers of malonaldehyde and DNA base pairs, could be accurately simulated on currently accessible Kerr-cat devices.

Reconfigurable intelligent surfaces (RIS) can actively perform beamforming and have become a crucial enabler for wireless systems in the future. The direction-of-arrival (DOA) estimates of RIS received signals can help design the reflection control matrix and improve communication quality. In this paper, we design a RIS-assisted system and propose a robust Lawson norm-based multiple-signal-classification (LN-MUSIC) DOA estimation algorithm for impulsive noise, which is divided into two parts. The first one, the non-convex Lawson norm is used as the error criterion along with a regularization constraint to formulate the optimization problem. Then, a Bregman distance based alternating direction method of multipliers is used to solve the problem and recover the desired signal. The second part is to use the multiple signal classification (MUSIC) to find out the DOAs of targets based on their sparsity in the spatial domain. In addition, we also propose a RIS control matrix optimization strategy that requires no channel state information, which effectively enhances the desired signals and improves the performance of the LN-MUSIC algorithm. A Cramer-Rao-lower-bound (CRLB) of the proposed DOA estimation algorithm is presented and verifies its feasibility. Simulated results show that the proposed robust DOA estimation algorithm based on the Lawson norm can effectively suppress the impact of large outliers caused by impulsive noise on the estimation results, outperforming existing methods., Comment: 10 figures, 28 pages

We present the first quantum computation of a total decay rate in high-energy physics at second order in perturbative quantum field theory. This work underscores the confluence of two recent cutting-edge advances. On the one hand, the quantum integration algorithm Quantum Fourier Iterative Amplitude Estimation (QFIAE), which efficiently decomposes the target function into its Fourier series through a quantum neural network before quantumly integrating the corresponding Fourier components. On the other hand, causal unitary in the loop-tree duality (LTD), which exploits the causal properties of vacuum amplitudes in LTD to coherently generate all contributions with different numbers of final-state particles to a scattering or decay process, leading to singularity-free integrands that are well suited for Fourier decomposition. We test the performance of the quantum algorithm with benchmark decay rates in a quantum simulator and in quantum hardware, and find accurate theoretical predictions in both settings., Comment: 6 pages (5+1), 5 figures

orbitize! is a package for Bayesian modeling of the orbital parameters of resolved binary objects from time series measurements. It was developed with the needs of the high-contrast imaging community in mind, and has since also become widely used in the binary star community. A generic orbitize! use case involves translating relative astrometric time series, optionally combined with radial velocity or astrometric time series, into a set of derived orbital posteriors. This paper is published alongside the release of orbitize! version 3.0, which has seen significant enhancements in functionality and accessibility since the release of version 1.0 (Blunt et al., 2020)., Comment: Published in JOSS

Oscillations of black hole spacetimes exhibit divergent behavior toward the bifurcation sphere and spatial infinity. This divergence can be understood as a consequence of the geometry in these spacetime regions. In contrast, black-hole oscillations are regular when evaluated toward the event horizon and null infinity. Hyperboloidal surfaces naturally connect these regions, providing a geometric regularization of time-harmonic oscillations called quasinormal modes (QNMs). This review traces the historical development of the hyperboloidal approach to QNMs. We discuss the physical motivation for the hyperboloidal approach and highlight current developments in the field., Comment: Mini-Review to topic collection "Quasi-Normal Modes, Non-Selfadjoint Operators and Pseudospectrum: an Interdisciplinary Approach", Frontiers in Physics

Using a combination of HST, JWST, and ALMA data, we perform spatially resolved spectral energy distributions (SED) fitting of fourteen 4

A recently proposed node-based uniform strain virtual element method (NVEM) is here extended to small strain elastoplastic solids. In the proposed method, the strain is averaged at the nodes from the strain of surrounding linearly-precise virtual elements using a generalization to virtual elements of the node-based uniform strain approach for finite elements. The averaged strain is then used to sample the weak form at the nodes of the mesh leading to a method in which all the field variables, including state and history-dependent variables, are related to the nodes and thus they are tracked only at these locations during the nonlinear computations. Through various elastoplastic benchmark problems, we demonstrate that the NVEM is locking-free while enabling linearly-precise virtual elements to solve elastoplastic solids with accuracy.

We derive the equations of motion for relativistic elastic membranes, that is, two-dimensional elastic bodies whose internal energy depends only on their stretching, starting from a variational principle. We show how to obtain conserved quantities for the membrane's motion in the presence of spacetime symmetries, determine the membrane's longitudinal and transverse speeds of sound in isotropic states, and compute the coefficients of linear elasticity with respect to the relaxed configuration. We then use this formalism to discuss two physically interesting systems: a rigidly rotating elastic disk, widely discussed in the context of Ehrenfest's paradox, and a Dyson sphere, that is, a spherical membrane in equilibrium in Schwarzschild's spacetime, with the isotropic tangential pressure balancing the gravitational attraction. Surprisingly, although spherically symmetric perturbations of this system are linearly stable, the axi-symmetric dipolar mode is already unstable. This may be taken as a cautionary tale against misconstruing radial stability as true stability., Comment: 18 pages, 1 figure. arXiv admin note: text overlap with arXiv:1712.05416

This paper investigates uniqueness results for perturbed periodic Schr\"odinger operators on $\mathbb{Z}^d$. Specifically, we consider operators of the form $H = -\Delta + V + v$, where $\Delta$ is the discrete Laplacian, $V: \mathbb{Z}^d \rightarrow \mathbb{R}$ is a periodic potential, and $v: \mathbb{Z}^d \rightarrow \mathbb{C}$ represents a decaying impurity. We establish quantitative conditions under which the equation $-\Delta u + V u + v u = \lambda u$, for $\lambda \in \mathbb{C}$, admits only the trivial solution $u \equiv 0$. Key applications include the absence of embedded eigenvalues for operators with impurities decaying faster than any exponential function and the determination of sharp decay rates for eigenfunctions. Our findings extend previous works by providing precise decay conditions for impurities and analyzing different spectral regimes of $\lambda$., Comment: 21 pages

Understanding faint dwarf galaxies is fundamental to the development of a robust theory of galaxy formation on small scales. Since the discovery of a population of ultra diffuse galaxies (UDGs) rich in globular clusters (GCs) in Coma, an increasing number of studies on low surface brightness dwarf galaxies (LSBds) have been published in recent years. The most massive LSBds have been observed predominantly in groups and clusters, with properties displaying dependence on the environment. In this work, we use deep DECam imaging to systematically identify LSBds and their GC populations around the low-density environment of NGC 3115. We carefully analyse the structure and morphology of 24 candidates, 18 of which are reported for the first time. Most candidates exhibit red colours suggesting a connection between their colour and distance to NGC 3115. We followed up with Gemini GMOS imaging 9 LSBds to properly identify their GC populations. We derive lower limits for the number of GCs associated with each galaxy. Our analysis reveals that they occur around of the same loci of Fornax LSB dwarf GC systems. The relationship between the number of GCs and total mass provides a tool in which, by counting the GCs in these galaxies, we estimate an upper limit for the total mass of these LSB dwarfs, obtaining the mean value of $\sim 3.3\times10^{10}$ M$_{\odot}$. Our results align with expectations for dwarf-sized galaxies, particularly regarding the distribution and specific frequency of their GC systems., Comment: 25 pages, 22 figures, accepted for publication in MNRAS

Let X_o be a weighted-homogeneous complete intersection germ in (R^N,o) or (C^N,o), with arbitrary singularities, possibly non-reduced. Take the foliation of the ambient space by weighted-homogeneous real arcs, \ga_s. Take a deformation of X_o by higher order terms, X_t. Does the foliation \ga_s deform compatibly with X_t? We identify the ``obstruction locus", \Sigma in X_o, outside of which such a deformation does exist, and possesses exceptionally nice properties. Using this deformed foliation we construct a contact trivialization of the family of defining equations by a homeomorphism that is real analytic (resp. Nash) off the origin, differentiable at the origin, whose presentation in weighted-polar coordinates is globally real-analytic (resp. globally Nash), and with controlled Lipschitz/C^1-properties., Comment: arXiv admin note: text overlap with arXiv:2311.13423