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Compact Ultramassive Antenna Array (CUMA) is a pioneering paradigm that leverages the flexibility of the Fluid Antenna System (FAS) to enable a simple multiple access scheme for massive connectivity without the need for precoding, power control at the base station or interference mitigation in each user's equipment. In order to overcome the mathematical intricacy required to analyze their performance, we use an asymptotic matching approach to relax such complexity with a remarkable accuracy. First, we analyze the performance of the CUMA network in terms of the outage probability (OP) and the ergodic rate (ER), deriving simple and highly accurate closed-form approximations to the channel statistics. Then, we evaluate the potential of the CUMA scheme to provide secure multi-user communications from a physical layer security perspective. Leveraging a tight approximation to the signal-to-interference-ratio (SIR) distribution, we derive closed-form expressions for the secrecy outage probability (SOP). We observe that the baseline CUMA (without side information processing) exhibits limited performance when eavesdroppers are equipped with a CUMA of the same type. To improve their secure performance, we suggest that a simple imperfect interference cancellation mechanism at the legitimate receiver may substantially increase the secrecy performance. Monte Carlo simulations validate our approximations and demonstrate their accuracy under different CUMA-based scenarios.

Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare, as MCC significantly impacts patient outcomes and healthcare costs. Graph neural networks (GNNs) are effective methods for modeling complex graph data, such as those found in MCC. However, a significant challenge with GNNs is their reliance on an existing graph structure, which is not readily available for MCC. To address this challenge, we propose a novel generative framework for GNNs that constructs a representative underlying graph structure by utilizing the distribution of the data to enhance predictive analytics for MCC. Our framework employs a graph variational autoencoder (GVAE) to capture the complex relationships in patient data. This allows for a comprehensive understanding of individual health trajectories and facilitates the creation of diverse patient stochastic similarity graphs while preserving the original feature set. These variations of patient stochastic similarity graphs, generated from the GVAE decoder, are then processed by a GNN using a novel Laplacian regularization technique to refine the graph structure over time and improves the prediction accuracy of MCC. A contextual Bandit is designed to evaluate the stochastically generated graphs and identify the best-performing graph for the GNN model iteratively until model convergence. We validate the performance of the proposed contextual Bandit algorithm against $\varepsilon$-Greedy and multi-armed Bandit algorithms on a large cohort (n = 1,592) of patients with MCC. These advancements highlight the potential of the proposed approach to transform predictive healthcare analytics, enabling a more personalized and proactive approach to MCC management.

Considered in this work is the initial value problem (IVP) associated to a higher order water wave model \begin{equation*} \begin{cases} \eta_t+\eta_x-\gamma_1 \eta_{xxt}+\gamma_2\eta_{xxx}+\delta_1 \eta_{xxxxt}+\delta_2\eta_{xxxxx}+\frac{3}{2}\eta \eta_x+\gamma (\eta^2)_{xxx}-\frac{7}{48}(\eta_x^2)_x-\frac{1}{8}(\eta^3)_x=0,\\ \eta(x,0) = \eta_0(x). \end{cases} \end{equation*} The main interest is in addressing the well-posedness issues of the IVP when the given initial data are considered in the modulation space $M_s^{2,p}(\mathbb{R})$ or the $L^p$-based Sobolev spaces $H^{s,p}(\mathbb{R})$, $1\leq p<\infty$. We derive some multilinear estimates in these spaces and prove that the above IVP is locally well-posed for data in $M_s^{2,p}(\mathbb{R})$ whenever $s>1$ and $p\geq 1$, and in $H^{s,p}(\mathbb{R})$ whenever $p\in [1,\infty)$ and $s\geq \max\left\{ \frac1{p}+\frac12, 1 \right\}$. We also use a combination of high-low frequency technique and an {\em a priori estimate}, and prove that the local solution with data in the modulation spaces $M_s^{2,p}(\mathbb{R})$ can be extended globally to the time interval $[0, T]$ for any given $T\gg1$ if $1\leq \frac32-\frac1p

Recent research has seen notable progress in the development of linkage-based artificial hands. While previous designs have focused on adaptive grasping, dexterity and biomimetic artificial skin, only a few systems have proposed a lightweight, accessible solution integrating tactile sensing with a compliant linkage-based mechanism. This paper introduces OPENGRASP LITE, an open-source, highly integrated, tactile, and lightweight artificial hand. Leveraging compliant linkage systems and MEMS barometer-based tactile sensing, it offers versatile grasping capabilities with six degrees of actuation. By providing tactile sensors and enabling soft grasping, it serves as an accessible platform for further research in tactile artificial hands., Comment: Accepted at IEEE/RSJ International Conference on Intelligent Robots and Systems, 14-18 October 2024

We present multiwavelength observations of the Swift short $\gamma$-ray burst GRB 231117A, localized to an underlying galaxy at redshift $z = 0.257$ at a small projected offset ($\sim 2~$kpc). We uncover long-lived X-ray (Chandra) and radio/millimeter (VLA, MeerKAT, and ALMA) afterglow emission, detected to $\sim 37~$days and $\sim 20~$days (rest frame), respectively. We measure a wide jet ($\sim 10.4^\circ$) and relatively high circumburst density ($\sim 0.07~{\rm cm}^{-3}$) compared to the short GRB population. Our data cannot be easily fit with a standard forward shock model, but they are generally well fit with the incorporation of a refreshed forward shock and a reverse shock at $< 1~$day. We incorporate GRB 231117A into a larger sample of 132 X-ray detected events, 71 of which were radio-observed (17 cm-band detections), for a systematic study of the distributions of redshifts, jet and afterglow properties, galactocentric offsets, and local environments of events with and without detected radio afterglows. Compared to the entire short GRB population, the majority of radio-detected GRBs are at relatively low redshifts ($z < 0.6$) and have high circumburst densities ($> 10^{-2}~{\rm cm}^{-3}$), consistent with their smaller ($< 8~$kpc) projected galactocentric offsets. We additionally find that 70% of short GRBs with opening angle measurements were radio-detected, indicating the importance of radio afterglows in jet measurements, especially in the cases of wide ($> 10^\circ$) jets where observational evidence of collimation may only be detectable at radio wavelengths. Owing to improved observing strategies and the emergence of sensitive radio facilities, the number of radio-detected short GRBs has quadrupled in the past decade., Comment: 30 pages, 11 figures, submitted to ApJ

The reactivity and mechanistic intricacies of azatrienes in Diels-Alder reactions have been relatively unexplored despite their intriguing potential applications. In this study, we employ Molecular Electron Density Theory to theoretically investigate the hetero-Diels-Alder reaction involving azatrienes with ethyl vinyl ether and allenyl methyl ether. Analysis of Conceptual Density Functional Theory, energetic profiles, and the topological characteristics is conducted to elucidate the reactions. The revealed mechanism manifests as a polar one-step two-stages process under kinetic control. We establish a clear relationship of between the periselectivity, regioselectivity, and stereoselectivity on one hand and the characteristics of the reactions mechanism on the other hand. The influence of weak interactions on reaction activation barriers and bonding evolution are discussed in detail. We demonstrate that substituents enhancing the reverse electron density flux facilitate the feasibility of the reactions. The results lay ground for a meticulous control of the reaction of azatriene in similar synthetic scenarios., Comment: accepted in Journal of Molecular Graphics and Modelling

Context. Observational evidence has accumulated in the past years, showing that the Galactic bulge includes two populations, a metal poor and a metal rich one that, in addition to a different metallicity, show different alpha over iron abundances, spatial distribution, and kinematics. While the metal rich, barred component has been fairly well characterized, the metal poor, spheroidal component has been more elusive and harder to describe. RR Lyrae variables are clean tracers of the old bulge component, and they are, on average, more metal poor than red clump stars. Aims. In the present paper, we provide a new catalog of 16488 ab type RR Lyrae variables in the bulge region within -10

We aim to identify QSO2 candidates in the redshift desert using optical and infrared photometry. At this intermediate redshift range, most of the prominent optical emission lines in QSO2 sources (e.g. CIV1549; [OIII]4959,5008) fall either outside the wavelength range of the SDSS optical spectra or in particularly noisy wavelength ranges, making QSO2 identification challenging. Therefore, we adopted a semi-supervised machine learning approach to select candidates in the SDSS galaxy sample. Recent applications of machine learning in astronomy focus on problems involving large data sets, with small data sets often being overlooked. We developed a few-shot learning approach for the identification and classification of rare-object classes using limited training data (200 sources). The new AMELIA pipeline uses a transfer-learning based approach with decision trees, distance-based, and deep learning methods to build a classifier capable of identifying rare objects on the basis of an observational training data set. We validated the performance of AMELIA by addressing the problem of identifying QSO2s at 1 $\leq$ z $\leq$ 2 using SDSS and WISE photometry, obtaining an F1-score above 0.8 in a supervised approach. We then used AMELIA to select new QSO2 candidates in the redshift desert and examined the nature of the candidates using SDSS spectra, when available. In particular, we identified a sub-population of [NeV]3426 emitters at z $\sim$ 1.1, which are highly likely to contain obscured AGNs. We used X-ray and radio cross-matching to validate our classification and investigated the performance of photometric criteria from the literature showing that our candidates have an inherent dusty nature. Finally, we derived physical properties for our QSO2 sample using photoionisation models and verified the AGN classification using an SED fitting., Comment: 20 pages, 9 figures, Accepted for publication in A&A

The structure and kinematics of the old component of the Galactic bulge are still a matter of debate. The bulk of the bulge as traced by red clump stars includes two main components, which are usually identified as the metal-rich and metal-poor components. They have different shapes, kinematics, mean metallicities, and alpha-element abundances. It is our current understanding that they are associated with a bar and a spheroid, respectively. On the other hand, RR Lyrae variables trace the oldest population of the bulge. While it would be natural to think that they follow the structure and kinematics of the metal-poor component, the data analysed in the literature show conflicting results. We aim to derive a rotation curve for bulge RR Lyrae stars in order to determine that the old component traced by these stars is distinct from the two main components observed in the Galactic bulge. This paper combines APOGEE-2S spectra with OGLE-IV light curves, near-IR photometry, and proper motions from the VISTA Variables in the V\'ia L\'actea survey for 4197 RR Lyrae stars. Six-dimensional phase-space coordinates were used to calculate orbits within an updated Galactic potential and to isolate the stars. The stars that stay confined within the bulge represent 57% of our sample. Our results show that bulge RR Lyrae variables rotate more slowly than metal-rich red clump stars and have a lower velocity dispersion. Their kinematics is compatible with them being the low-metallicity tail of the metal-poor component. We confirm that a rather large fraction of halo RR Lyrae stars pass by the bulge within their orbits, increasing the velocity dispersion. A proper orbital analysis is therefore critical to isolate bona fide bulge variables. Finally, bulge RR Lyrae seem to trace a spheroidal component, although the current data do now allow us to reach a firm conclusion about the spatial distribution., Comment: 14 pages, 13 figures, accepted by A&A

Among the silicon bearing species discovered in the interstellar medium, SiS and SiO stand out as key tracers due to their distinct chemistry and abundances in interstellar and circumstellar environments. Our objective is to enhance the network of Si- and S-bearing chemical reactions for a gas-grain model in molecular clouds, encompassing both low and high metallicities. We have calculated the energies and rate coefficients for 6 neutral atom-diatom reactions involved in the SiCS triatomic system, with a special focus on the C+SiS and S+SiC collisions. We employ the coupled cluster method with single and double substitutions and a perturbative treatment of triple substitutions (CCSD(T)) refined at the explicitly correlated CCSD(T)-F12 level. With these computational results in conjunction with data from the literature, we construct an extended network of neutral-neutral chemical reactions. We performed time-dependent models employing the Nautilus gas-grain code, setting the gas temperature to 10 K and the density to 2x10$^4$ cm$^{-3}$. The temperature dependence for the reactions involving SiS were modelled using $k(T)=\alpha \left( T/300 \right)^{\beta} \exp{(-\gamma/T)}$. The high-metallicity models significantly boost the SiS production. Higher initial abundances of C, S, and Si, roughly $\sim$2, 190, and 210 times higher, respectively, contribute to this. Around 10$^3$ yr, destruction mechanisms become relevant. The proposed production reaction S + SiC $\rightarrow$ C + SiS, mitigates these effects. By expanding the gas reaction network using a high metallicity model, we derived estimates for the abundances of interstellar molecules. The inclusion of neutral-neutral mechanisms, particularly via Si+HS and S+SiC channels, played a pivotal role in determining SiS abundance. These mechanisms carry a significance on a par with the well-known and fast ion-neutral reactions., Comment: 11 pages, 5 figures, accepted for publication in Astronomy and Astrophysics

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

Patchick 99 is a candidate globular cluster located in the direction of the Galactic bulge, with a proper motion almost identical to the field and extreme field star contamination. A recent analysis suggests it is a low-luminosity globular cluster with a population of RR Lyrae stars. We present new spectra of stars in and around Patchick 99, targeting specifically the 3 RR Lyrae stars associated with the cluster as well as the other RR Lyrae stars in the field. A sample of 53 giant stars selected from proper motions and a position on CMD are also observed. The three RR Lyrae stars associated with the cluster have similar radial velocities and distances, and two of the targeted giants also have radial velocities in this velocity regime and [Fe/H] metallicities that are slightly more metal-poor than the field. Therefore, if Patchick 99 is a bonafide globular cluster, it would have a radial velocity of -92+/-10 km s-1, a distance of 6.7+/-0.4 kpc (as determined from the RR Lyrae stars), and an orbit that confines it to the inner bulge., Comment: Accepted to The Astrophysical Journal Letters. Replaced due to a typo in the title

The relationship between acoustic parameters and the microstructure of a Cu30Zn brass plate subjected to plastic deformation was evaluated. The plate, previously annealed at 550 {\deg}C for 30 minutes, was cold rolled to reductions in the 10-70\% range. Using the pulse-echo method, linear ultrasonic measurements were performed on each of the nine specimens, corresponding to the nine different reductions, recording the wave times of flight of longitudinal wave along the thickness axis. Subsequently, acoustic measurements were performed to determine the nonlinear parameter ($\beta$) through the second harmonic generation. X-ray diffraction analysis revealed a steady increase and subsequent saturation of deformation twins at 40\% thickness reduction. At higher deformations, the microstructure revealed the generation and proliferation of shear bands, which coincided with a decrease in the twinning structure and an increase in dislocation density rate. Longitudinal wave velocity exhibited a 0.9\% decrease at 20\% deformation, followed by a continuous increase of 2\% beyond this point. These results can be rationalized as a competition between a proliferation of dislocations, which tends to decrease the linear sound velocity, and a decrease in average grain size, which tends to increase it. These variations are in agreement with the values obtained with XRD, Vickers hardness and metallography measurements. The nonlinear parameter $\beta$ shows a significant maximum, at the factor of 8 level, at 40\% deformation. This maximum correlates well with a similar maximum, at a factor of ten level and also at 40\% deformation, of the twinning fault probability., Comment: 15 pages, 8 figures

The progressive prevalence of robots in human-suited environments has given rise to a myriad of object manipulation techniques, in which dexterity plays a paramount role. It is well-established that humans exhibit extraordinary dexterity when handling objects. Such dexterity seems to derive from a robust understanding of object properties (such as weight, size, and shape), as well as a remarkable capacity to interact with them. Hand postures commonly demonstrate the influence of specific regions on objects that need to be grasped, especially when objects are partially visible. In this work, we leverage human-like object understanding by reconstructing and completing their full geometry from partial observations, and manipulating them using a 7-DoF anthropomorphic robot hand. Our approach has significantly improved the grasping success rates of baselines with only partial reconstruction by nearly 30% and achieved over 150 successful grasps with three different object categories. This demonstrates our approach's consistent ability to predict and execute grasping postures based on the completed object shapes from various directions and positions in real-world scenarios. Our work opens up new possibilities for enhancing robotic applications that require precise grasping and manipulation skills of real-world reconstructed objects., Comment: Accepted to RA-L 2023

We consider the initial value problem (IVP) associated to a higher order nonlinear Schr\"odinger (h-NLS) equation $ \partial_{t}u+ia \partial^{2}_{x}u+ b\partial^{3}_{x}u+ic_1|u|^{2}u+c_2 |u|^{2}\partial_{x}u=0, \quad x,t \in \mathbb{R}, $ for given data in the modulation space $M_s^{2,p}(\mathbb{R})$. Using ideias of Killip, Visan, Zhang, Oh, Wang, we prove that the IVP associated to the h-NLS equation is globally well-posed in the modulation spaces $M^{s,p}$ for $s\geq\frac14$ and $p\geq2$.

We developed and trained a pipeline of three machine learning (ML) models than can predict which sources are more likely to be an AGN and to be detected in specific radio surveys. Also, it can estimate redshift values for predicted radio-detectable AGNs. These models, which combine predictions from tree-based and gradient-boosting algorithms, have been trained with multi-wavelength data from near-infrared-selected sources in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) Spring field. Training, testing, calibration, and validation were carried out in the HETDEX field. Further validation was performed on near-infrared-selected sources in the Stripe 82 field. In the HETDEX validation subset, our pipeline recovers 96% of the initially labelled AGNs and, from AGNs candidates, we recover 50% of previously detected radio sources. For Stripe 82, these numbers are 94% and 55%. Compared to random selection, these rates are two and four times better for HETDEX, and 1.2 and 12 times better for Stripe 82. The pipeline can also recover the redshift distribution of these sources with $\sigma_{\mathrm{NMAD}}$ = 0.07 for HETDEX ($\sigma_{\mathrm{NMAD}}$ = 0.09 for Stripe 82) and an outlier fraction of 19% (25% for Stripe 82), compatible with previous results based on broad-band photometry. Feature importance analysis stresses the relevance of near- and mid-infrared colours to select AGNs and identify their radio and redshift nature. Combining different algorithms in ML models shows an improvement in the prediction power of our pipeline over a random selection of sources. Tree-based ML models (in contrast to deep learning techniques) facilitate the analysis of the impact that features have on the predictions. This prediction can give insight into the potential physical interplay between the properties of radio AGNs (e.g. mass of black hole and accretion rate)., Comment: Accepted for publication in A&A, 24 pages and 21 figures. Updated to include information on retrieval of data and models, which can be obtained from https://zenodo.org/doi/10.5281/zenodo.10220008