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Radio telescopes are susceptible to interference arriving through its sidelobes. If a reflector antenna could be retrofitted with an adaptive null steering system, it could potentially mitigate this interference. The design of a reflectarray which can be used to reconfigure a radio telescopes radiation pattern by driving a null to the angle of incoming interference is presented. The reflectarray occupies only a portion of the rim of the original reflector and lays conformal to the paraboloid within this region. The conformal reflectarray contains unit cells with 1-bit reconfigurability stemming from two symmetrically placed PIN diodes. It is found that the dielectric and switch losses introduced by the reflectarray do not significantly affect the radio telescopes efficiency since the reflectarray is placed only along the outer rim of the reflector which is weakly illuminated. Simulation results of an L-band reconfigurable reflectarray for an 18m prime focus fed parabola are presented.

Terzan 5 is a heavily obscured stellar system located in the inner Galaxy. It has been postulated to be a stellar relic, a Bulge Fossil Fragment witnessing the complex history of the assembly of the Milky Way bulge. In this paper, we follow the chemical enrichment of a set of putative progenitors of Terzan 5 to assess whether the chemical properties of this cluster fit within a formation scenario in which it is the remnant of a primordial building block of the bulge. We can explain the metallicity distribution function and the runs of different element-to-iron abundance ratios as functions of [Fe/H] derived from optical-infrared spectroscopy of giant stars in Terzan 5, by assuming that the cluster experienced two major star formation bursts separated by a long quiescent phase. We further predict that the most metal-rich stars in Terzan 5 are moderately He-enhanced and a large spread of He abundances in the cluster, Y = 0.26-0.335. We conclude that current observations fit within a formation scenario in which Terzan 5 originated from a pristine, or slightly metal-enriched, gas clump about one order of magnitude more massive than its present-day mass. Losses of gas and stars played a major role in shaping Terzan 5 the way we see it now. The iron content of the youngest stellar population is better explained if the white dwarfs that give rise to type Ia supernovae (the main Fe factories) sink towards the cluster center, rather than being stripped by the strong tidal forces exerted by the Milky Way in the outer regions., Comment: 14 pages, 2 tables, 8 figures, accepted for publication in ApJ

Deep-learning techniques have been used widely to alleviate the labour-intensive and time-consuming manual annotation required for pixel-level tissue characterization. Our previous study introduced an efficient single dynamic network - Omni-Seg - that achieved multi-class multi-scale pathological segmentation with less computational complexity. However, the patch-wise segmentation paradigm still applies to Omni-Seg, and the pipeline is time-consuming when providing segmentation for Whole Slide Images (WSIs). In this paper, we propose an enhanced version of the Omni-Seg pipeline in order to reduce the repetitive computing processes and utilize a GPU to accelerate the model's prediction for both better model performance and faster speed. Our proposed method's innovative contribution is two-fold: (1) a Docker is released for an end-to-end slide-wise multi-tissue segmentation for WSIs; and (2) the pipeline is deployed on a GPU to accelerate the prediction, achieving better segmentation quality in less time. The proposed accelerated implementation reduced the average processing time (at the testing stage) on a standard needle biopsy WSI from 2.3 hours to 22 minutes, using 35 WSIs from the Kidney Tissue Atlas (KPMP) Datasets. The source code and the Docker have been made publicly available at https://github.com/ddrrnn123/Omni-Seg.

Since the introduction of 5G Release 18, non-terrestrial networks (NTNs) based positioning has garnered significant interest due to its numerous applications, including emergency services, lawful intercept, and charging and tariff services. This release considers single low-earth-orbit (LEO) positioning explicitly for $\textit{location verification}$ purposes, which requires a fairly coarse location estimate. To understand the future trajectory of NTN-based localization in 6G, we first provide a comprehensive overview of the evolution of 3rd Generation Partnership Project (3GPP) localization techniques, with specific emphasis on the current activities in 5G related to NTN location verification. We then delineate the suitability of LEOs for location-based services and emphasize increased interest in LEO-based positioning. In order to provide support for more accurate positioning in 6G using LEOs, we identify two NTN positioning systems that are likely study items for 6G: (i) multi-LEO positioning, and (ii) augmenting single-LEO and multi-LEO setups with Global Navigation Satellite System (GNSS), especially when an insufficient number of GNSS satellites (such as 2) are visible. We evaluate the accuracy of both systems through a 3GPP-compliant simulation study using a Cram\'{e}r-Rao lower bound (CRLB) analysis. Our findings suggest that NTN technology has significant potential to provide accurate positioning of UEs in scenarios where GNSS signals may be weak or unavailable, but there are technical challenges in accommodating these solutions in 3GPP. We conclude with a discussion on the research landscape and key open problems related to NTN-based positioning., Comment: 7 pages, 6 figures, submitted to IEEE Wireless Communications Magazine

In the context of a project aimed at characterizing the properties of the so-called Bulge Fossil Fragments (the fossil remnants of the bulge formation epoch), here we present the first determination of the metallicity distribution of Liller 1. For a sample of 64 individual member stars we used ESO- MUSE spectra to measure the equivalent width of the CaII triplet and then derive the iron abundance. To test the validity of the adopted calibration in the metal-rich regime, the procedure was first applied to three reference bulge globular clusters (NGC 6569, NGC 6440, and NGC 6528). In all the three cases, we found single-component iron distributions, with abundance values fully in agreement with those reported in the literature. The application of the same methodology to Liller 1 yielded, instead, a clear bimodal iron distribution, with a sub-solar component at $\text{[Fe/H]}= -0.48\,$dex ($\sigma = 0.22$) and a super-solar component at $\text{[Fe/H]}= +0.26\,$dex ($\sigma = 0.17$). The latter is found to be significantly more centrally concentrated than the metal-poor population, as expected in a self-enrichment scenario and in agreement with what found in another bulge system, Terzan 5. The obtained metallicity distribution is astonishingly similar to that predicted by the reconstructed star formation history of Liller 1, which is characterized by three main bursts and a low, but constant, activity of star formation over the entire lifetime. These findings provide further support to the possibility that, similar to Terzan 5, also Liller 1 is a Bulge Fossil Fragment., Comment: Accepted for publication in the ApJ, 15 pages, 9 figures

The study of dynamically cold stellar streams reveals information about the gravitational potential where they reside and provides important constraints on dark matter properties. However, their intrinsic faintness makes detection beyond Local environments highly challenging. Here we report the detection of an extremely faint stellar stream ($\mu_{g,max}=$ 29.5 mag arcsec$^{-2}$) with an extraordinarily coherent and thin morphology in the Coma Galaxy Cluster. This Giant Coma Stream spans ~510 kpc in length and appears as a free-floating structure located at a projected distance of 0.8 Mpc from the centre of Coma. We do not identify any potential galaxy remnant or core, and the stream structure appears featureless in our data. We interpret the Giant Coma Stream as being a recently accreted, tidally disrupting passive dwarf. Using the Illustris-TNG50 simulation, we identify a case with similar characteristics, showing that, although rare, these types of streams are predicted to exist in $\Lambda$-CDM. Our work unveils the presence of free-floating, extremely faint and thin stellar streams in galaxy clusters, widening the environmental context for their promising future applications in the study of dark matter properties., Comment: Accepted for publication in A&A

We present a search for extremely red, dust-obscured, $z>7$ galaxies with $\textit{JWST}$/NIRCam+MIRI imaging over the first 20 arcmin$^2$ of publicly-available Cycle 1 data from the COSMOS-Web, CEERS, and PRIMER surveys. Based on their red color in F277W$-$F444W ($\sim 2.5$ mag) and detection in MIRI/F770W ($\sim 25$ mag), we identify two galaxies$\unicode{x2014}$COS-z8M1 and CEERS-z7M1$\unicode{x2014}$which have best-fit photometric redshifts of $z=8.5^{+0.3}_{-0.4}$ and $z=7.6^{+0.1}_{-0.1}$, respectively. We perform SED fitting with a variety of codes (including BAGPIPES, PROSPECTOR, BEAGLE, and CIGALE), and find a $>95\%$ probability that these indeed lie at $z>7$. Both sources are compact ($R_{\rm eff} \lesssim 200$ pc), highly obscured ($A_V \sim 1.5$$\unicode{x2013}$$2.5$), and, at our best-fit redshift estimates, likely have strong [OIII]+H$\beta$ emission contributing to their $4.4\,\mu$m photometry. We estimate stellar masses of $\sim 10^{10}~M_\odot$ for both sources; by virtue of detection in MIRI at $7.7\,\mu$m, these measurements are robust to the inclusion of bright emission lines, for example, from an AGN. We identify a marginal (2.9$\sigma$) ALMA detection at 2 mm within $0.5''$ of COS-z8M1, which if real, would suggest a remarkably high IR luminosity of $\sim 10^{12} L_\odot$. These two galaxies, if confirmed at $z\sim 8$, would be extreme in their stellar and dust masses, and may be representative of a substantial population of modestly dust-obscured galaxies at cosmic dawn., Comment: 23 pages, 8 figures, submitted to ApJL, comments welcome

This paper attempts to characterize the kinds of physical scenarios in which an online learning-based cognitive radar is expected to reliably outperform a fixed rule-based waveform selection strategy, as well as the converse. We seek general insights through an examination of two decision-making scenarios, namely dynamic spectrum access and multiple-target tracking. The radar scene is characterized by inducing a state-space model and examining the structure of its underlying Markov state transition matrix, in terms of entropy rate and diagonality. It is found that entropy rate is a strong predictor of online learning-based waveform selection, while diagonality is a better predictor of fixed rule-based waveform selection. We show that these measures can be used to predict first and second-order stochastic dominance relationships, which can allow system designers to make use of simple decision rules instead of more cumbersome learning approaches under certain conditions. We validate our findings through numerical results for each application and provide guidelines for future implementations., Comment: 15 pages, 15 figures. Final version to appear in IEEE Transaction on Radar Systems. arXiv admin note: substantial text overlap with arXiv:2212.00597

A growing number of far-infrared bright sources completely invisible in deep extragalactic optical surveys hint at an elusive population of z>4 dusty, star-forming galaxies. Cycle 1 JWST surveys are now detecting their rest-frame optical light, which provides key insight into their stellar properties and statistical constraints on the population as a whole. This work presents the JWST/NIRCam counterpart from the COSMOS-Web survey to a far-infrared SCUBA-2 and ALMA source, AzTECC71, which was previously undetected at wavelengths shorter than 850 microns. AzTECC71, amongst the reddest galaxies in COSMOS-Web with F277W - F444W~0.9, is undetected in NIRCam/F150W and F115W and fainter in F444W than other sub-millimeter galaxies identified in COSMOS-Web by 2-4 magnitudes. This is consistent with the system having both a lower stellar mass and higher redshift than the median dusty, star-forming galaxy. With deep ground- and space-based upper limits combined with detections in F277W, F444W and the far-IR including ALMA Band 6, we find a high probability (99%) that AzTECC71 is at z>4 with z_phot=5.7(+0.8,-0.7). This galaxy is massive (logM*/Msun~10.7) and IR-luminous (logLIR/Lsun~12.7), comparable to other optically-undetected but far-IR bright dusty, star-forming galaxies at z>4. This population of luminous, infrared galaxies at z>4 is largely unconstrained but comprises an important bridge between the most extreme dust-obscured galaxies and more typical high-redshift star-forming galaxies. If further far-IR-selected galaxies that drop out of the F150W filter in COSMOS-Web have redshifts z>4 like AzTECC71, then the volume density of such sources may be ~3-10x greater than previously estimated., Comment: 13 pages, 7 figures, submitted to ApJ

Analyzing high resolution whole slide images (WSIs) with regard to information across multiple scales poses a significant challenge in digital pathology. Multi-instance learning (MIL) is a common solution for working with high resolution images by classifying bags of objects (i.e. sets of smaller image patches). However, such processing is typically performed at a single scale (e.g., 20x magnification) of WSIs, disregarding the vital inter-scale information that is key to diagnoses by human pathologists. In this study, we propose a novel cross-scale MIL algorithm to explicitly aggregate inter-scale relationships into a single MIL network for pathological image diagnosis. The contribution of this paper is three-fold: (1) A novel cross-scale MIL (CS-MIL) algorithm that integrates the multi-scale information and the inter-scale relationships is proposed; (2) A toy dataset with scale-specific morphological features is created and released to examine and visualize differential cross-scale attention; (3) Superior performance on both in-house and public datasets is demonstrated by our simple cross-scale MIL strategy. The official implementation is publicly available at https://github.com/hrlblab/CS-MIL.

Due to the complexity of many decision making problems, tree search algorithms often have inadequate information to produce accurate transition models. This results in ambiguities (uncertainties for which there are multiple plausible models). Faced with ambiguities, robust methods have been used to produce safe solutions--often by maximizing the lower bound over the set of plausible transition models. However, they often overlook how much the representation of uncertainty can impact how a decision is made. This work introduces the Ambiguity Attitude Graph Search (AAGS), advocating for more comprehensive representations of ambiguities in decision making. Additionally, AAGS allows users to adjust their ambiguity attitude (or preference), promoting exploration and improving users' ability to control how an agent should respond when faced with a set of plausible alternatives. Simulation in a dynamic sailing environment shows how environments with high entropy transition models can lead robust methods to fail. Results further demonstrate how adjusting ambiguity attitudes better fulfills objectives while mitigating this failure mode of robust approaches. Because this approach is a generalization of the robust framework, these results further demonstrate how algorithms focused on ambiguity have applicability beyond safety-critical systems., Comment: 6 pages, 5 figures, 2 algorithms. Accepted to the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems in Abu Dhabi, UAE (Oct 14-18, 2024) \c{opyright} 2024 IEEE

For emergency response scenarios like firefighting in urban environments, there is a need to both localize emergency responders inside the building and also support a high bandwidth communication link between the responders and a command-and-control center. The emergency networks for such scenarios can be established with the quick deployment of Unmanned Aerial Vehicles (UAVs). Further, the 3D mobility of UAVs can be leveraged to improve the quality of the wireless link by maneuvering them into advantageous locations. This has motivated recent propagation measurement campaigns to study low-altitude air-to-ground channels in both 5G-sub6 GHz and 5G-mmWave bands. In this paper, we develop a model for the link in a UAV-assisted emergency location and/or communication system. Specifically, given the importance of Line-of-Sight (LoS) links in localization as well as mmWave communication, we derive a closed-form expression for the LoS probability. This probability is parameterized by the UAV base station location, the size of the building, and the size of the window that offers the best propagation path. An expression for coverage probability is also derived. The LoS probability and coverage probabilities derived in this paper can be used to analyze the outdoor UAV-to-indoor propagation environment to determine optimal UAV positioning and the number of UAVs needed to achieve the desired performance of the emergency network., Comment: Accepted to be published in IEEE ICC 2023, Rome, Italy

Pretrained Language Models (PLMs) harbor inherent social biases that can result in harmful real-world implications. Such social biases are measured through the probability values that PLMs output for different social groups and attributes appearing in a set of test sentences. However, bias testing is currently cumbersome since the test sentences are generated either from a limited set of manual templates or need expensive crowd-sourcing. We instead propose using ChatGPT for the controllable generation of test sentences, given any arbitrary user-specified combination of social groups and attributes appearing in the test sentences. When compared to template-based methods, our approach using ChatGPT for test sentence generation is superior in detecting social bias, especially in challenging settings such as intersectional biases. We present an open-source comprehensive bias testing framework (BiasTestGPT), hosted on HuggingFace, that can be plugged into any open-source PLM for bias testing. User testing with domain experts from various fields has shown their interest in being able to test modern AI for social biases. Our tool has significantly improved their awareness of such biases in PLMs, proving to be learnable and user-friendly. We thus enable seamless open-ended social bias testing of PLMs by domain experts through an automatic large-scale generation of diverse test sentences for any combination of social categories and attributes.

In shared spectrum with multiple radio access technologies, wireless standard classification is vital for applications such as dynamic spectrum access (DSA) and wideband spectrum monitoring. However, interfering signals and the presence of unknown classes of signals can diminish classification accuracy. To reduce interference, signals can be isolated in time, frequency, and space, but the isolation process adds distortion that reduces the accuracy of deep learning classifiers. We find that the distortion can be partially mitigated by augmenting the classifier training data with the signal isolation steps. To address unknown signals, we propose an open set hybrid classifier, which combines deep learning and expert feature classifiers to leverage the reliability and explainability of expert feature classifiers and the lower computational complexity of deep learning classifiers. The hybrid classifier reduces the computational complexity by 2 to 7 times on average compared to the expert feature classifiers, while achieving an accuracy of 95% at 15 dB SNR for known signal classes. The hybrid classifier manages to detect unknown classes at nearly 100% accuracy, due to the robustness of the expert feature classifiers.

This work investigates online learning techniques for a cognitive radar network utilizing feedback from a central coordinator. The available spectrum is divided into channels, and each radar node must transmit in one channel per time step. The network attempts to optimize radar tracking accuracy by learning the optimal channel selection for spectrum sharing and radar performance. We define optimal selection for such a network in relation to the radar observation quality obtainable in a given channel. This is a difficult problem since the network must seek the optimal assignment from nodes to channels, rather than just seek the best overall channel. Since the presence of primary users appears as interference, the approach also improves spectrum sharing performance. In other words, maximizing radar performance also minimizes interference to primary users. Each node is able to learn the quality of several available channels through repeated sensing. We define hybrid cognition as the condition where both the independent radar nodes as well as the central coordinator are modeled as cognitive agents, with restrictions on the amount of information that can be exchanged between the radars and the coordinator. Importantly, each part of the network acts as an online learner, observing the environment to inform future actions. We show that in interference-limited spectrum, where the signal-to-interference-plus-noise ratio varies by channel and over time for a target with fixed radar cross section, a cognitive radar network is able to use information from the central coordinator in order to reduce the amount of time necessary to learn the optimal channel selection. We also show that even limited use of a central coordinator can eliminate collisions, which occur when two nodes select the same channel., Comment: 34 pages, single-column, 10 figures

There has been an increase in interest in missions that drive significantly longer distances per day than what has currently been performed. Further, some of these proposed missions require autonomous driving and absolute localization in darkness. For example, the Endurance A mission proposes to drive 1200km of its total traverse at night. The lack of natural light available during such missions limits what can be used as visual landmarks and the range at which landmarks can be observed. In order for planetary rovers to traverse long ranges, onboard absolute localization is critical to the ability of the rover to maintain its planned trajectory and avoid known hazardous regions. Currently, to accomplish absolute localization, a ground in the loop (GITL) operation is performed wherein a human operator matches local maps or images from onboard with orbital images and maps. This GITL operation limits the distance that can be driven in a day to a few hundred meters, which is the distance that the rover can maintain acceptable localization error via relative methods. Previous work has shown that using craters as landmarks is a promising approach for performing absolute localization on the moon during the day. In this work we present a method of absolute localization that utilizes craters as landmarks and matches detected crater edges on the surface with known craters in orbital maps. We focus on a localization method based on a perception system which has an external illuminator and a stereo camera. We evaluate (1) both monocular and stereo based surface crater edge detection techniques, (2) methods of scoring the crater edge matches for optimal localization, and (3) localization performance on simulated Lunar surface imagery at night. We demonstrate that this technique shows promise for maintaining absolute localization error of less than 10m required for most planetary rover missions., Comment: IEEE Aerospace Conference 2023

When should an online reinforcement learning-based frequency agile cognitive radar be expected to outperform a rule-based adaptive waveform selection strategy? We seek insight regarding this question by examining a dynamic spectrum access scenario, in which the radar wishes to transmit in the widest unoccupied bandwidth during each pulse repetition interval. Online learning is compared to a fixed rule-based sense-and-avoid strategy. We show that given a simple Markov channel model, the problem can be examined analytically for simple cases via stochastic dominance. Additionally, we show that for more realistic channel assumptions, learning-based approaches demonstrate greater ability to generalize. However, for short time-horizon problems that are well-specified, we find that machine learning approaches may perform poorly due to the inherent limitation of convergence time. We draw conclusions as to when learning-based approaches are expected to be beneficial and provide guidelines for future study., Comment: 6 pages, 5 figures

This paper describes important considerations and challenges associated with online reinforcement-learning based waveform selection for target identification in frequency modulated continuous wave (FMCW) automotive radar systems. We present a novel learning approach based on satisficing Thompson sampling, which quickly identifies a waveform expected to yield satisfactory classification performance. We demonstrate through measurement-level simulations that effective waveform selection strategies can be quickly learned, even in cases where the radar must select from a large catalog of candidate waveforms. The radar learns to adaptively select a bandwidth for appropriate resolution and a slow-time unimodular code for interference mitigation in the scene of interest by optimizing an expected classification metric., Comment: 5 pages, 3 figures

Labeling social-media data for custom dimensions of toxicity and social bias is challenging and labor-intensive. Existing transfer and active learning approaches meant to reduce annotation effort require fine-tuning, which suffers from over-fitting to noise and can cause domain shift with small sample sizes. In this work, we propose a novel Active Transfer Few-shot Instructions (ATF) approach which requires no fine-tuning. ATF leverages the internal linguistic knowledge of pre-trained language models (PLMs) to facilitate the transfer of information from existing pre-labeled datasets (source-domain task) with minimum labeling effort on unlabeled target data (target-domain task). Our strategy can yield positive transfer achieving a mean AUC gain of 10.5% compared to no transfer with a large 22b parameter PLM. We further show that annotation of just a few target-domain samples via active learning can be beneficial for transfer, but the impact diminishes with more annotation effort (26% drop in gain between 100 and 2000 annotated examples). Finally, we find that not all transfer scenarios yield a positive gain, which seems related to the PLMs initial performance on the target-domain task., Comment: Accepted to NeurIPS Workshop on Transfer Learning for Natural Language Processing, 2022, New Orleans

We consider a scenario where a fusion center must decide which updates to receive during each update period in a communication-limited cognitive radar network. When each radar node in the network only is able to obtain noisy state measurements for a subset of the targets, the fusion center may not receive updates on every target during each update period. The solution for the selection problem at the fusion center is not well suited for sequential learning frameworks. We derive an Age of Information-inspired track sensitive metric to inform node selection in such a network and compare it against less-informed techniques., Comment: 6 pages, 6 figures. Presented at IEEE RadarConf 2023

We present the survey design, implementation, and outlook for COSMOS-Web, a 255 hour treasury program conducted by the James Webb Space Telescope in its first cycle of observations. COSMOS-Web is a contiguous 0.54 deg$^2$ NIRCam imaging survey in four filters (F115W, F150W, F277W, and F444W) that will reach 5$\sigma$ point source depths ranging $\sim$27.5-28.2 magnitudes. In parallel, we will obtain 0.19 deg$^2$ of MIRI imaging in one filter (F770W) reaching 5$\sigma$ point source depths of $\sim$25.3-26.0 magnitudes. COSMOS-Web will build on the rich heritage of multiwavelength observations and data products available in the COSMOS field. The design of COSMOS-Web is motivated by three primary science goals: (1) to discover thousands of galaxies in the Epoch of Reionization ($64$ and place constraints on the formation of the Universe's most massive galaxies ($M_\star>10^{10}$\,M$_\odot$), and (3) directly measure the evolution of the stellar mass to halo mass relation using weak gravitational lensing out to $z\sim2.5$ and measure its variance with galaxies' star formation histories and morphologies. In addition, we anticipate COSMOS-Web's legacy value to reach far beyond these scientific goals, touching many other areas of astrophysics, such as the identification of the first direct collapse black hole candidates, ultracool sub-dwarf stars in the Galactic halo, and possibly the identification of $z>10$ pair-instability supernovae. In this paper we provide an overview of the survey's key measurements, specifications, goals, and prospects for new discovery., Comment: 46 pages, 16 figures, ApJ accepted

This paper presents a rigorous Bayesian analysis of the information in the signal (consisting of both the line-of-sight (LOS) path and reflections from multiple reconfigurable intelligent surfaces (RISs)) that originate from a single base station (BS) and is received by a user equipment (UE). For a comprehensive Bayesian analysis, both near and far field regimes are considered. The Bayesian analysis views both the location of the RISs and previous information about the UE as {\em a priori} information for UE localization. With outdated {\em a priori} information, the position and orientation offsets of the RISs become parameters that need to be estimated and fed back to the BS for correction. We first show that when the RIS elements have a half wavelength spacing, this RIS orientation offset is a factor in the pathloss of the RIS paths. Subsequently, we show through the Bayesian equivalent Fisher information matrix (EFIM) for the channel parameters that the RIS orientation offset cannot be corrected when there is an unknown phase offset in the received signal in the far-field regime. However, the corresponding EFIM for the channel parameters in the received signal observed in the near-field shows that this unknown phase offset does not hinder the estimation of the RIS orientation offset when the UE has more than one receive antenna. Furthermore, we use the EFIM for the UE location parameters to present bounds for UE localization in the presence of RIS uncertainty.

Andromeda (And) XXV has previously been reported as a dwarf spheroidal galaxy (dSph) with little-to-no dark matter. However, the uncertainties on this result were significant. In this study, we double the number of member stars and re-derive the kinematics and mass of And XXV. We find that And XXV has a systemic velocity of $\nu_\mathrm{r}=-107.7\pm1.0 \mathrm{~km s}^{-1}$ and a velocity dispersion of $\sigma_\nu=4.5\pm1.0\mathrm{~km s}^{-1}$. With this better constrained velocity dispersion, we derive a mass contained within the half-light radius of $M(r< r_\mathrm{h})=6.9^{+3.2}_{-2.8}\times10^6\mathrm{~M}_\odot$. This mass corresponds to a mass-to-light ratio of $\mathrm{[M/L]}_\mathrm{r_\mathrm{h}}=37^{+17}_{-15}\mathrm{~M}_\odot/\mathrm{L}_\odot$, demonstrating, for the first time, that And XXV has an unambiguous dark matter component. We also measure the metallicity of And XXV to be $\mathrm{[Fe/H]}=-1.9\pm0.1$$\mathrm{~}$dex, which is in agreement with previous results. Finally, we extend the analysis of And XXV to include mass modelling using GravSphere. We find that And XXV has a low central dark matter density, $\rho_\mathrm{DM}(150\mathrm{pc})= 2.7^{+1.8}_{-1.6}\times10^7\mathrm{~M}_\odot\mathrm{kpc}^{-3}$, making And XXV a clear outlier when compared to other Local Group (LG) dSphs of the similar stellar mass. In a companion paper, we will explore whether some combination of dark matter cusp-core transformations and/or tides can explain And XXV's low density., Comment: 13 pages, 8 figures (7 main, 1 appendix). Submitted to MNRAS

Model order estimation (MOE) is often a pre-requisite for Direction of Arrival (DoA) estimation. Due to limits imposed by array geometry, it is typically not possible to estimate spatial parameters for an arbitrary number of sources; an estimate of the signal model is usually required. MOE is the process of selecting the most likely signal model from several candidates. While classic methods fail at MOE in the presence of coherent multipath interference, data-driven supervised learning models can solve this problem. Instead of the classic MLP (Multiple Layer Perceptions) or CNN (Convolutional Neural Networks) architectures, we propose the application of Residual Convolutional Neural Networks (RCNN), with grouped symmetric kernel filters to deliver state-of-art estimation accuracy of up to 95.2\% in the presence of coherent multipath, and a weighted loss function to eliminate underestimation error of the model order. We show the benefit of the approach by demonstrating its impact on an overall signal processing flow that determines the number of total signals received by the array, the number of independent sources, and the association of each of the paths with those sources . Moreover, we show that the proposed estimator provides accurate performance over a variety of array types, can identify the overloaded scenario, and ultimately provides strong DoA estimation and signal association performance.