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This paper introduces a novel classification for Autonomous Mobile Robots (AMRs), into three phases and five steps, focusing on autonomous collision-free navigation. Additionally, it presents the main methods and widely accepted technologies for each phase of the proposed classification. The purpose of this classification is to facilitate understanding and establish connections between the independent input variables of the system (hardware, software) and autonomous navigation. By analyzing well-established technologies in terms of sensors and methods used for autonomous navigation, this paper aims to provide a foundation of knowledge that can be applied in future projects of mobile robots., Comment: This paper was presented at the JAR Congress in Buenos Aires, Argentina, and published as ID 27 at 9:20 on June 5, 2024. You can find more details on the conference at the following link: https://jar.com.ar/programa.html#programa. Additionally, the content of the presentation was re-recorded and uploaded to YouTube for better understanding: https://www.youtube.com/watch?v=TU6EkT43VfE&t=4s

The highly conducting and transparent inorganic perovskites SrBO$_3$ with V, Nb, Mo, and their mixtures at the B-site have recently attracted the attention of the oxide electronics community as novel alternative transparent conducting oxides. For different applications from solar cells to transparent electronics, it is desirable to tune the optical transmission window in the ultraviolet (UV), visible and infrared (IR) range. The conventional approach is substitutional design at the A- and/or B-site. Here, we suggest a method by engineering the off-stoichiometry of the perovskite, opening new pathways to broaden the range of applications without adding additional elements. For oxide molecular beam epitaxy grown SrNbO$_3$ on GdScO$_3$ substrates, we show that controlled Sr deficiency shifts the plasma edge from about 2 eV in the visible range into the near-infrared region, 1.37 eV (similar to stoichiometric SrVO$_3$). Here, epitaxial growth allows going beyond the limitations of phase stability set by thermodynamics. The suggested approach opens a new design toolbox by including controlled vacancy sites as quasi-substitutional virtual elements., Comment: 19 pages

New experimental methods make it possible to measure the expression levels of many genes, simultaneously, in snapshots from thousands or even millions of individual cells. Current approaches to analyze these experiments involve clustering or low-dimensional projections. Here we use the principle of maximum entropy to obtain a probabilistic description that captures the observed presence or absence of mRNAs from hundreds of genes in cells from the mammalian brain. We construct the Ising model compatible with experimental means and pairwise correlations, and validate it by showing that it gives good predictions for higher-order statistics. We notice that the probability distribution of cell states has many local maxima. By labeling cell states according to the associated maximum, we obtain a cell classification that agrees well with previous results that use traditional clustering techniques. Our results provide quantitative descriptions of gene expression statistics and interpretable criteria for defining cell classes, supporting the hypothesis that cell classes emerge from the collective interaction of gene expression levels., Comment: 14 pages, 21 figures

The acorn is the fruit of the oak and is an important crop in the Spanish dehesa extreme\~na, especially for the value it provides in the Iberian pig food to obtain the "acorn" certification. For this reason, we want to maximise the production of Iberian pigs with the appropriate weight. Hence the need to know the area covered by the crowns of the acorn trees, to determine the covered wooded area (CWA, from the Spanish Superficie Arbolada Cubierta SAC) and thereby estimate the number of Iberian pigs that can be released per hectare, as indicated by the royal decree 4/2014. In this work, we propose the automatic estimation of the CWA, through aerial digital images (orthophotos) of the pastureland of Extremadura, and with this, to offer the possibility of determining the number of Iberian pigs to be released in a specific plot of land. Among the main issues for automatic detection are, first, the correct identification of acorn trees, secondly, correctly discriminating the shades of the acorn trees and, finally, detect the arbuscles (young acorn trees not yet productive, or shrubs that are not oaks). These difficulties represent a real challenge, both for the automatic segmentation process and for manual segmentation. In this work, the proposed method for automatic segmentation is based on the clustering algorithm proposed by Gustafson-Kessel (GK) but the modified version of Babuska (GK-B) and on the use of real orthophotos. The obtained results are promising both in their comparison with the real images and when compared with the images segmented by hand. The whole set of orthophotos used in this work correspond to an approximate area of 142 hectares, and the results are of great interest to producers of certified "acorn" pork., Comment: 22 pages, 15 Figures, 2 Tables

Around 17,000 micromagnetic simulations were performed with a wide variation of geometric and magnetic parameters of different cellular nanostructures in the samarium-cobalt-based 1:7-type (SmCo-1:7) magnets. A forward prediction neural network (NN) model is trained to unveil the influence of these parameters on the coercivity of materials, along with the sensitivity analysis. Results indicate the important role of the 1:5-phase in enhancing coercivity. Moreover, an inverse design NN model is obtained to suggest the nanostructure for a queried coercivity.

We consider a truncation of the BMN matrix model to a configuration of two fuzzy spheres, described by two coupled non-linear oscillators dependent on the mass parameter $\mu$. The classical phase diagram of the system generically ($\mu \neq 0$) contains three equilibrium points: two centers and a center-saddle; as $\mu \to 0$ the system exhibits a pitchfork bifurcation. We demonstrate that the system is exactly integrable in quadratures for $\mu=0$, while for very large values of $\mu$, it approaches another integrable point characterized by two harmonic oscillators. The classical phase space is mixed, containing both integrable islands and chaotic regions, as evidenced by the classical Lyapunov spectrum. At the quantum level, we explore indicators of early and late time chaos. The eigenvalue spacing is best described by a Brody distribution, which interpolates between Poisson and Wigner distributions; it dovetails, at the quantum level, the classical results and reemphasizes the notion that the quantum system is mixed. We also study the spectral form factor and the quantum Lyapunov exponent, as defined by out-of-time-ordered correlators. These two indicators of quantum chaos exhibit weak correlations with the Brody distribution. We speculate that the behavior of the system as $\mu \to 0$ dominates the spectral form factor and the quantum Lyapunov exponent, making these indicators of quantum chaos less effective in the context of a mixed phase space., Comment: 34 pages, 3 tables, 21 figures. V2: References added

We address the conflicting requirements of a multi-agent assignment problem through constrained reinforcement learning, emphasizing the inadequacy of standard regularization techniques for this purpose. Instead, we recur to a state augmentation approach in which the oscillation of dual variables is exploited by agents to alternate between tasks. In addition, we coordinate the actions of the multiple agents acting on their local states through these multipliers, which are gossiped through a communication network, eliminating the need to access other agent states. By these means, we propose a distributed multi-agent assignment protocol with theoretical feasibility guarantees that we corroborate in a monitoring numerical experiment., Comment: 12 pages, 3 figures, 6th Annual Conference on Learning for Dynamics and Control

We revisit the existence of monotonic quantities along renormalization group flows using only the Null Energy Condition and the Ryu-Takayanagi formula for the entanglement entropy of field theories with anti-de Sitter gravity duals. In particular, we consider flows within the same dimension and holographically reprove the $c$-, $F$-, and $a$-theorems in dimensions two, three, and four. We focus on the family of maximally spherical entangling surfaces, define a quasi-constant of motion corresponding to the breaking of conformal invariance, and use a properly defined distance between minimal surfaces to construct a holographic $c$-function that is monotonic along the flow. We then apply our method to the case of flows across dimensions: There, we reprove the monotonicity of flows from $\mathrm{AdS}_{D+1}$ to $\mathrm{AdS}_3$ and prove the novel case of flows from $\mathrm{AdS}_5$ to $\mathrm{AdS}_4$., Comment: 28 pages, 3 figures; references added

Stochastic orbital techniques offer reduced computational scaling and memory requirements to describe ground and excited states at the cost of introducing controlled statistical errors. Such techniques often rely on two basic operations, stochastic trace estimation and stochastic resolution of identity, both of which lead to statistical errors that scale with the number of stochastic realizations ($N_{\xi}$) as $\sqrt{N_{\xi}^{-1}}$. Reducing the statistical errors without significantly increasing $N_{\xi}$ has been challenging and is central to the development of efficient and accurate stochastic algorithms. In this work, we build upon recent progress made to improve stochastic trace estimation based on the ubiquitous Hutchinson's algorithm and propose a two-step approach for the stochastic resolution of identity, in the spirit of the Hutch++ method. Our approach is based on employing a randomized low-rank approximation followed by a residual calculation, resulting in statistical errors that scale much better than $\sqrt{N_{\xi}^{-1}}$. We implement the approach within the second-order Born approximation for the self-energy in the computation of neutral excitations and discuss three different low-rank approximations for the two-body Coulomb integrals. Tests on a series of hydrogen dimer chains with varying lengths demonstrate that the Hutch++-like approximations are computationally more efficient than both deterministic and purely stochastic (Hutchinson) approaches for low error thresholds and intermediate system sizes. Notably, for arbitrarily large systems, the Hutchinson-like approximation outperforms both deterministic and Hutch++-like methods.

As part of the activities of the IEEE-GDL CCD working group of physical infrastructure, this whitepaper is intented to be an initial guide to understand the layers, taxonomy of services and best practices for the development of smart buildings. Open standards are claimed in order to increase interoperability between layers and services. Moreover, two buildings in Guadalajara city, one new and another to renew, are described as a proof of concept under development and being part of the strategy to develop the smart city infrastructure based in a master plan. A discussion will be addressed in order to identify the areas of innovation and opportunities for the smart buildings as the contribution of this document., Comment: 5 pages, 4 figures, 4 Tables

This document describes the development of a video game prototype designed to encourage physical activity among children and older adults. The prototype consists of a laptop, a camera with 3D sensors, and optionally requires an LCD screen or a projector. The programming component of this prototype was developed in Scratch, a programming language geared towards children, which greatly facilitates the creation of a game tailored to the users' preferences. The idea to create such a prototype originated from the desire to offer an option that promotes physical activity among children and adults, given that a lack of physical exercise is a predominant factor in the development of chronic degenerative diseases such as diabetes and hypertension, to name the most common. As a result of this initiative, an active video game prototype was successfully developed, based on a ping-pong game, which allows both children and adults to interact in a fun way while encouraging the performance of physical activities that can positively impact the users' health., Comment: 13 pages, in Spanish language, 11 figures

The superconformal index of half-BPS states in ${\cal N}=4$ supersymmetric Yang-Mills with gauge group $U(N)$ admits an expansion in terms of giant gravitons, ${\cal I}_N(q)={\cal I}_\infty(q) \sum\limits_{m=0}^\infty q^{mN}\hat{\mathcal I}_m(q)$, where $m$ is the number of giant gravitons. We derive this expansion directly in supergravity from the class of half-BPS solutions due to Lin, Lunin, and Maldacena in type IIB supergravity. The moduli space of these configurations can be quantized using covariant quantization methods. We review how this quantization leads to the graviton index, ${\cal I}_\infty(q)$, and present a modification that leads to the precise expression for the expansion in terms of giant gravitons. Our proposal provides a derivation of the giant graviton expansion directly in terms of supergravity degrees of freedom. We also comment on how to derive the expansion in terms of the effective Fermi droplet picture., Comment: 17 pages, 4 figures

Several approaches to graphically representing context-specific relations among jointly distributed categorical variables have been proposed, along with structure learning algorithms. While existing optimization-based methods have limited scalability due to the large number of context-specific models, the constraint-based methods are more prone to error than even constraint-based DAG learning algorithms since more relations must be tested. We present a hybrid algorithm for learning context-specific models that scales to hundreds of variables while testing no more constraints than standard DAG learning algorithms. Scalable learning is achieved through a combination of an order-based MCMC algorithm and sparsity assumptions analogous to those typically invoked for DAG models. To implement the method, we solve a special case of an open problem recently posed by Alon and Balogh. The method is shown to perform well on synthetic data and real world examples, in terms of both accuracy and scalability., Comment: 23 pages, 6 figures

The low-temperature thermodynamics of near-extremal rotating black holes has recently been revisited to incorporate one-loop contributions that are dominant in this regime. We discuss these quantum corrections to the gravitational path integral for asymptotically Anti de-Sitter black holes in four and five dimensions. In four dimensions we explicitly consider Kerr-AdS$_4$, Kerr-Newman-AdS$_4$ and the rotating black hole in ${\cal N}=4$ gauged supergravity with two scalars and two electric charges turned on. In five dimensions we explicitly address the asymptotically flat Myers-Perry black hole and the Kerr-AdS$_5$ black hole. In every case we find that tensor modes contribute $\frac{3}{2} \log T_{\rm Hawking}$ to the low-temperature thermodynamics. We identify the root cause of this universality in two facts: (i) the universal presence of a $SL(2,\mathbb{R})$ subgroup of isometries in the near-horizon geometry and (ii) a set of cancellations in the Lichnerowicz operator. We show that these two conditions hold for near-extremal black holes in asymptotically flat and asymptotically AdS spacetimes of various dimensions., Comment: 42 pages

Attribution scores reflect how important the feature values in an input entity are for the output of a machine learning model. One of the most popular attribution scores is the SHAP score, which is an instantiation of the general Shapley value used in coalition game theory. The definition of this score relies on a probability distribution on the entity population. Since the exact distribution is generally unknown, it needs to be assigned subjectively or be estimated from data, which may lead to misleading feature scores. In this paper, we propose a principled framework for reasoning on SHAP scores under unknown entity population distributions. In our framework, we consider an uncertainty region that contains the potential distributions, and the SHAP score of a feature becomes a function defined over this region. We study the basic problems of finding maxima and minima of this function, which allows us to determine tight ranges for the SHAP scores of all features. In particular, we pinpoint the complexity of these problems, and other related ones, showing them to be NP-complete. Finally, we present experiments on a real-world dataset, showing that our framework may contribute to a more robust feature scoring., Comment: In ECAI 2024 proceedings

Attribution scores can be applied in data management to quantify the contribution of individual items to conclusions from the data, as part of the explanation of what led to these conclusions. In Artificial Intelligence, Machine Learning, and Data Management, some of the common scores are deployments of the Shapley value, a formula for profit sharing in cooperative game theory. Since its invention in the 1950s, the Shapley value has been used for contribution measurement in many fields, from economics to law, with its latest researched applications in modern machine learning. Recent studies investigated the application of the Shapley value to database management. This article gives an overview of recent results on the computational complexity of the Shapley value for measuring the contribution of tuples to query answers and to the extent of inconsistency with respect to integrity constraints. More specifically, the article highlights lower and upper bounds on the complexity of calculating the Shapley value, either exactly or approximately, as well as solutions for realizing the calculation in practice., Comment: 12 pages, including references. This is the authors version of the corresponding SIGMOD Record article

We construct Type IIB string theory setups which, via double holography, realize two gravitational systems in separate AdS spaces which interact with each other and with a non-gravitational bath. We employ top-down string theory solutions with concrete field theory duals in the form of 4d $\mathcal N=4$ SYM BCFTs and a first-principles notion of double holography. The setups are used to realize pairs of `near' and `far' black holes from the perspective of the bath, which exchange Hawking radiation with each other and radiate into the bath. We identify three phases for the entropy in the bath characterized as no island, partial island and full island, and discuss the entropy curves. The setups differ from the black hole binaries observed in gravitational wave experiments but may capture certain aspects., Comment: 28 pages, 10 figures; v2: references added, published version

We study the entanglement entropy associated with a holographic RG flow from $\textrm{AdS}_7$ to $\textrm{AdS}_{4} \times \mathbb{H}_3$, where $\mathbb{H}_3$ is a $3$-dimensional hyperbolic manifold with curvature $\kappa$. The dual six-dimensional RG flow is disconnected from Lorentz-invariant flows. In this context we address various notions of central charges and identify a monotonic candidate $c$-function that captures IR aspects of the flow. The UV behavior of the holographic entanglement entropy and, in particular its universal term, display an interesting dependence on the curvature, $\kappa$. We then contrast our holographic results with existing field theory computations in six dimensions and find a series of new corrections in curvature to the universal term in the entanglement entropy., Comment: 31 pages, 9 figures. v2: References added, matches JHEP version

We present the first results of the Hubble Deep Hydrogen Alpha (HDH$\alpha$) project, which analyzes the space-borne deep H$\alpha$ narrowband imaging data in the GOODS-S region. The HDH$\alpha$ data comprises 72 orbits' images taken with the HST ACS/WFC F658N filter. The exposure time varies across a total area of $\sim$76.1 $\rm{arcmin}^2$, adding up to a total exposure time of 195.7 ks, among which 68.8 ks are spent in the deepest region. These images are aligned, reprojected, and combined to have the same pixel grid as the Hubble Legacy Fields (HLF). The scientific goals of the HDH$\alpha$ include establishing a sample of emission-line galaxies (ELGs) including [O III] emitters at $z\sim$ 0.3, [O II] emitters at $z\sim$ 0.8, and Lyman-$\alpha$ emitters (LAEs) at $z \sim 4.4$, studying the line morphology of ELGs with high resolution imaging data, and statistically analyzing the line luminosity functions and line equivalent-width distributions of ELGs selected with HST. Furthermore, the HDH$\alpha$ project enhances the legacy value of the GOODS-S field by contributing the first HST-based narrowband image to the existing data sets, which includes the HST broadband data and other ancillary data from X-ray to radio taken by other facilities. In this paper, we describe the data reduction process of the HDH$\alpha$, select ELGs based on HST's F658N and broadband data, validate the redshifts of the selected candidates by cross matching with the public spectroscopic catalogs in the GOODS-S, and present a final catalog of the confirmed [O III] emitters at $z\sim$ 0.3, [O II] emitters at $z\sim$ 0.8, and LAEs at $z \sim 4.4$., Comment: 27 pages, 14 figures, 9 tables, accepted by ApJS

We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used - without any model fine-tuning - for a variety of downstream tasks including (1) accurate in-modality and cross-modality semantic similarity search, (2) photometric redshift estimation, (3) galaxy property estimation from both images and spectra, and (4) morphology classification. Our approach to implementing AstroCLIP consists of two parts. First, we embed galaxy images and spectra separately by pretraining separate transformer-based image and spectrum encoders in self-supervised settings. We then align the encoders using a contrastive loss. We apply our method to spectra from the Dark Energy Spectroscopic Instrument and images from its corresponding Legacy Imaging Survey. Overall, we find remarkable performance on all downstream tasks, even relative to supervised baselines. For example, for a task like photometric redshift prediction, we find similar performance to a specifically-trained ResNet18, and for additional tasks like physical property estimation (stellar mass, age, metallicity, and sSFR), we beat this supervised baseline by 19\% in terms of $R^2$. We also compare our results to a state-of-the-art self-supervised single-modal model for galaxy images, and find that our approach outperforms this benchmark by roughly a factor of two on photometric redshift estimation and physical property prediction in terms of $R^2$, while remaining roughly in-line in terms of morphology classification. Ultimately, our approach represents the first cross-modal self-supervised model for galaxies, and the first self-supervised transformer-based architectures for galaxy images and spectra., Comment: 18 pages, accepted in Monthly Notices of the Royal Astronomical Society, Presented at the NeurIPS 2023 AI4Science Workshop