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We report the first instance of an M dwarf/brown dwarf obliquity measurement for the TOI-2119 system using the Rossiter-McLaughlin effect. TOI-2119 b is a transiting brown dwarf orbiting a young, active early M dwarf ($T_{\rm{eff}}$ = 3553 K). It has a mass of 64.4 M$_{\rm{J}}$ and radius of 1.08 R$_{\rm{J}}$, with an eccentric orbit ($e$ = 0.3) at a period of 7.2 days. For this analysis, we utilise NEID spectroscopic transit observations and ground based simultaneous transit photometry from the Astrophysical Research Consortium (ARC) and the Las Campanas Remote Observatory (LCRO). We fit all available data of TOI-2119 b to refine the brown dwarf parameters and update the ephemeris. The classical Rossiter-McLaughlin technique yields a projected star-planet obliquity of $\lambda=-0.8\pm1.1^\circ$ and a three-dimensional obliquity of $\psi=15.7\pm5.5^\circ$. Additionally, we spatially resolve the stellar surface of TOI-2119 utilising the Reloaded Rossiter-McLaughlin technique to determine the projected star-planet obliquity as $\lambda=1.26 \pm 1.3^{\circ}$. Both of these results agree within $2\sigma$ and confirm the system is aligned, where TOI-2119 b joins an emerging group of aligned brown dwarf obliquities. We also probe stellar surface activity on the surface of TOI-2119 in the form of centre-to-limb variations as well as the potential for differential rotation. Overall, we find tentative evidence for centre-to-limb variations on the star but do not detect evidence of differential rotation., Comment: Accepted and published in MNRAS. 12 pages, 9 figures, 4 tables. arXiv admin note: text overlap with arXiv:2304.12163

A method for analyzing liquid ligaments of a textural atomization process is presented in this article for the case of a rocket engine type assisted atomization process under combustion. The operating point positions the atomization process in the fiber type regime carrying an intense textural atomization process. Multiscale in nature, the method based on image analysis associates a scale distribution with a family of ligaments, this distribution being sensitive to the number, size and shape of these ligaments. The quality of scale distributions measured by image analysis depends on the spatial resolution and the precision of area measurements of surfaces with curved boundaries but described by square pixels. Part of the work consisted of improving the method for measuring scale distributions by using a subpixel image analysis technique and refining the surface area measurement method. Another part directed the multiscale analysis towards the estimation of the diameter distributions of the blobs that characterize the large-scale deformation of the ligaments. The analysis describes the atomization process at a level of detail never reached. For instance, assuming that the blobs are drops in formation, the estimated diameter distribution (bimodal in the case examined here) as well as the number of these drops are evaluated as a function of the distance from the injector. This information indicates where the process is most intense and where it stops. Furthermore, these diameter distributions receive a mathematical expression whose parameters report clear evolutions with the distance from the injector. This shows the possibility of elaborating mathematical models appropriate for textural atomization mechanisms.

The increasing deployment of autonomous systems in complex environments necessitates efficient communication and task completion among multiple agents. This paper presents SERN (Simulation-Enhanced Realistic Navigation), a novel framework integrating virtual and physical environments for real-time collaborative decision-making in multi-robot systems. SERN addresses key challenges in asset deployment and coordination through a bi-directional communication framework using the AuroraXR ROS Bridge. Our approach advances the SOTA through accurate real-world representation in virtual environments using Unity high-fidelity simulator; synchronization of physical and virtual robot movements; efficient ROS data distribution between remote locations; and integration of SOTA semantic segmentation for enhanced environmental perception. Our evaluations show a 15% to 24% improvement in latency and up to a 15% increase in processing efficiency compared to traditional ROS setups. Real-world and virtual simulation experiments with multiple robots demonstrate synchronization accuracy, achieving less than 5 cm positional error and under 2-degree rotational error. These results highlight SERN's potential to enhance situational awareness and multi-agent coordination in diverse, contested environments., Comment: Under Review for ICRA 2025

Autonomous navigation in unstructured outdoor environments is inherently challenging due to the presence of asymmetric traversal costs, such as varying energy expenditures for uphill versus downhill movement. Traditional reinforcement learning methods often assume symmetric costs, which can lead to suboptimal navigation paths and increased safety risks in real-world scenarios. In this paper, we introduce QuasiNav, a novel reinforcement learning framework that integrates quasimetric embeddings to explicitly model asymmetric costs and guide efficient, safe navigation. QuasiNav formulates the navigation problem as a constrained Markov decision process (CMDP) and employs quasimetric embeddings to capture directionally dependent costs, allowing for a more accurate representation of the terrain. This approach is combined with adaptive constraint tightening within a constrained policy optimization framework to dynamically enforce safety constraints during learning. We validate QuasiNav across three challenging navigation scenarios-undulating terrains, asymmetric hill traversal, and directionally dependent terrain traversal-demonstrating its effectiveness in both simulated and real-world environments. Experimental results show that QuasiNav significantly outperforms conventional methods, achieving higher success rates, improved energy efficiency, and better adherence to safety constraints., Comment: Under Review for ICRA 2025

In this paper, a definition of the gain and added noise of impedance matching networks for scanning microwave microscopy is given. This definition can be used to compare different impedance matching techniques independently of the instrument used to measure the S-parameter. As a demonstration, impedance matching devices consisting of a Beatty line, a tuner, and interferometric setups with and without amplifiers have been investigated. Measurement frequencies up to 28 GHz are used, and the maximal resulting gain found was 9504.7 per Siemens., Comment: IEEE Transactions on Instrumentation and Measurement (2024)

We introduce the OATMEAL survey, an effort to measure the obliquities of stars with transiting brown dwarf companions. We observed a transit of the close-in ($P_{\rm orb} = 1.74 \,$ days) brown dwarf GPX-1 b using the Keck Planet Finder (KPF) spectrograph to measure the sky-projected angle between its orbital axis and the spin axis of its early F-type host star ($\lambda$). We measured $\lambda = 6.88 \pm 1.72 ^\circ$ (with additional unquantified systematic uncertainty), suggesting an orbit that is prograde and well aligned with the stellar equator. Hot Jupiters around early F stars are frequently found to have highly misaligned orbits, with polar and retrograde orbits being commonplace. It has been theorized that these misalignments stem from dynamical interactions, such as von Zeipel-Kozai-Lidov cycles, and are retained over long timescales due to weak tidal dissipation in stars with radiative envelopes. By comparing GPX-1 to similar systems under the frameworks of different tidal evolution theories, we argued that the rate of tidal dissipation is too slow to have re-aligned the system. This suggests that GPX-1 may have arrived at its close-in orbit via coplanar high-eccentricity migration or migration through an aligned protoplanetary disk. Our result for GPX-1 is one of few measurements of the obliquity of a star with a transiting brown dwarf. By enlarging the number of such measurements and comparing them with hot Jupiter systems, we will more clearly discern the differences between the mechanisms that dictate the formation and evolution of both classes of objects., Comment: 8 pages, 4 figures

In this study, we present a novel platform based on scanning microwave microscopy for manipulating and detecting tiny vibrations of nanoelectromechanical resonators using a single metallic tip. The tip is placed on the top of a grounded silicon nitride membrane, acting as a movable top gate of the coupled resonator. We demonstrate its ability to map mechanical modes and investigate mechanical damping effects in a capacitive coupling scheme, based on its spatial resolution. We also manipulate the energy transfer coherently between the mode of the scanning tip and the underlying silicon nitride membrane, via parametric coupling. Typical features of optomechanics, such as anti-damping and electromechanically induced transparency, have been observed. Since the microwave optomechanical technology is fully compatible with quantum electronics and very low temperature conditions, it should provide a powerful tool for studying phonon tunnelling between two spatially separated vibrating elements, which could potentially be applied to quantum sensing.

Autonomous robots operating in complex environments face the critical challenge of identifying and utilizing environmental cover for covert navigation to minimize exposure to potential threats. We propose EnCoMP, an enhanced navigation framework that integrates offline reinforcement learning and our novel Adaptive Threat-Aware Visibility Estimation (ATAVE) algorithm to enable robots to navigate covertly and efficiently in diverse outdoor settings. ATAVE is a dynamic probabilistic threat modeling technique that we designed to continuously assess and mitigate potential threats in real-time, enhancing the robot's ability to navigate covertly by adapting to evolving environmental and threat conditions. Moreover, our approach generates high-fidelity multi-map representations, including cover maps, potential threat maps, height maps, and goal maps from LiDAR point clouds, providing a comprehensive understanding of the environment. These multi-maps offer detailed environmental insights, helping in strategic navigation decisions. The goal map encodes the relative distance and direction to the target location, guiding the robot's navigation. We train a Conservative Q-Learning (CQL) model on a large-scale dataset collected from real-world environments, learning a robust policy that maximizes cover utilization, minimizes threat exposure, and maintains efficient navigation. We demonstrate our method's capabilities on a physical Jackal robot, showing extensive experiments across diverse terrains. These experiments demonstrate EnCoMP's superior performance compared to state-of-the-art methods, achieving a 95% success rate, 85% cover utilization, and reducing threat exposure to 10.5%, while significantly outperforming baselines in navigation efficiency and robustness., Comment: Paper under review

Autonomous robots exploring unknown environments face a significant challenge: navigating effectively without prior maps and with limited external feedback. This challenge intensifies in sparse reward environments, where traditional exploration techniques often fail. In this paper, we present TopoNav, a novel topological navigation framework that integrates active mapping, hierarchical reinforcement learning, and intrinsic motivation to enable efficient goal-oriented exploration and navigation in sparse-reward settings. TopoNav dynamically constructs a topological map of the environment, capturing key locations and pathways. A two-level hierarchical policy architecture, comprising a high-level graph traversal policy and low-level motion control policies, enables effective navigation and obstacle avoidance while maintaining focus on the overall goal. Additionally, TopoNav incorporates intrinsic motivation to guide exploration toward relevant regions and frontier nodes in the topological map, addressing the challenges of sparse extrinsic rewards. We evaluate TopoNav both in the simulated and real-world off-road environments using a Clearpath Jackal robot, across three challenging navigation scenarios: goal-reaching, feature-based navigation, and navigation in complex terrains. We observe an increase in exploration coverage by 7- 20%, in success rates by 9-19%, and reductions in navigation times by 15-36% across various scenarios, compared to state-of-the-art methods, Comment: Accepted at the 37th IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024

This paper demonstrates how the limitations of pre-trained models and open evaluation datasets factor into assessing the performance of binary semantic similarity classification tasks. As (1) end-user-facing documentation around the curation of these datasets and pre-trained model training regimes is often not easily accessible and (2) given the lower friction and higher demand to quickly deploy such systems in real-world contexts, our study reinforces prior work showing performance disparities across datasets, embedding techniques and distance metrics, while highlighting the importance of understanding how data is collected, curated and analyzed in semantic similarity classification., Comment: To be published in EMNLP 2023 GEM Workshop

Crystal plasticity finite element method (CPFEM) has been an integrated computational materials engineering (ICME) workhorse to study materials behaviors and structure-property relationships for the last few decades. These relations are mappings from the microstructure space to the materials properties space. Due to the stochastic and random nature of microstructures, there is always some uncertainty associated with materials properties, for example, in homogenized stress-strain curves. For critical applications with strong reliability needs, it is often desirable to quantify the microstructure-induced uncertainty in the context of structure-property relationships. However, this uncertainty quantification (UQ) problem often incurs a large computational cost because many statistically equivalent representative volume elements (SERVEs) are needed. In this paper, we apply a multi-level Monte Carlo (MLMC) method to CPFEM to study the uncertainty in stress-strain curves, given an ensemble of SERVEs at multiple mesh resolutions. By using the information at coarse meshes, we show that it is possible to approximate the response at fine meshes with a much reduced computational cost. We focus on problems where the model output is multi-dimensional, which requires us to track multiple quantities of interest (QoIs) at the same time. Our numerical results show that MLMC can accelerate UQ tasks around 2.23x, compared to the classical Monte Carlo (MC) method, which is widely known as the ensemble average in the CPFEM literature.

While secondary mass inferences based on single-lined spectroscopic binary (SB1) solutions are subject to $\sin{i}$ degeneracies, this degeneracy can be lifted through the observations of eclipses. We combine the subset of Gaia Data Release (DR) 3 SB1 solutions consistent with brown dwarf-mass secondaries with the Transiting Exoplanet Survey Satellite (TESS) Object of Interest (TOI) list to identify three candidate transiting brown dwarf systems. Ground-based precision radial velocity follow-up observations confirm that TOI-2533.01 is a transiting brown dwarf with $M=72^{+3}_{-3}~M_{\text{Jup}}= 0.069^{+0.003}_{-0.003}~M_\odot$ orbiting TYC 2010-124-1 and that TOI-5427.01 is a transiting very low-mass star with $M=93^{+2}_{-2}~M_{\text{Jup}}=0.088^{+0.002}_{-0.002}~M_\odot$ orbiting UCAC4 515-012898. We validate TOI-1712.01 as a very low-mass star with $M=82^{+7}_{-7}~M_{\text{Jup}}=0.079^{+0.007}_{-0.007}~M_\odot$ transiting the primary in the hierarchical triple system BD+45 1593. Even after accounting for third light, TOI-1712.01 has radius nearly a factor of two larger than predicted for isolated stars with similar properties. We propose that the intense instellation experienced by TOI-1712.01 diminishes the temperature gradient near its surface, suppresses convection, and leads to its inflated radius. Our analyses verify Gaia DR3 SB1 solutions in the low Doppler semiamplitude limit, thereby providing the foundation for future joint analyses of Gaia radial velocities and Kepler, K2, TESS, and PLAnetary Transits and Oscillations (PLATO) light curves for the characterization of transiting massive brown dwarfs and very low-mass stars., Comment: 25 pages, 9 figures, 4 tables; Accepted to AJ

The Atacama desert stands as the most arid, non-polar, region on Earth and has accommodated a considerable portion of the world's ground-based astronomical observatories for an extended period. The comprehension of factors important for observational conditions in this region, and the potential alterations induced by the escalating impact of climate change, are, therefore, of the utmost significance. In this study, we conduct an analysis of the surface-level air temperature, water vapour density, and astronomical seeing at the European Southern Observatory (commonly known by its acronym, ESO) telescope sites in northern Chile. Our findings reveal a discernible rise in temperature across all sites during the last decade. Moreover, we establish a correlation between the air temperature and water vapour density with the El Ni\~no Southern Oscillation (ENSO) phases, wherein, the warm anomaly known as El Ni\~no (EN) corresponds to drier observing conditions, coupled with higher maximum daily temperatures favouring more challenging near-infrared observations. The outcomes of this investigation have potential implications for the enhancement of the long-term scheduling of observations at telescope sites in northern Chile, thereby aiding in better planning and allocation of resources for the astronomy community., Comment: Accepted for publication in Atmospheres (ISSN: 2073-4433) on Sept 17, 2023, 19 pages, 2 appendices, 7 Figures, 3 Tables

In recent years, there has been a growing interest in understanding complex microstructures and their effect on macroscopic properties. In general, it is difficult to derive an effective constitutive law for such microstructures with reasonable accuracy and meaningful parameters. One numerical approach to bridge the scales is computational homogenization, in which a microscopic problem is solved at every macroscopic point, essentially replacing the effective constitutive model. Such approaches are, however, computationally expensive and typically infeasible in multi-query contexts such as optimization and material design. To render these analyses tractable, surrogate models that can accurately approximate and accelerate the microscopic problem over a large design space of shapes, material and loading parameters are required. In this work, we develop a reduced order model based on Proper Orthogonal Decomposition (POD), Empirical Cubature Method (ECM) and a geometrical transformation method with the following key features: (i) large shape variations of the microstructure are captured, (ii) only relatively small amounts of training data are necessary, and (iii) highly non-linear history-dependent behaviors are treated. The proposed framework is tested and examined in two numerical examples, involving two scales and large geometrical variations. In both cases, high speed-ups and accuracies are achieved while observing good extrapolation behavior.

Large technology firms face the problem of moderating content on their online platforms for compliance with laws and policies. To accomplish this at the scale of billions of pieces of content per day, a combination of human and machine review are necessary to label content. Subjective judgement and human bias are of concern to both human annotated content as well as to auditors who may be employed to evaluate the quality of such annotations in conformance with law and/or policy. To address this concern, this paper presents a novel application of statistical analysis methods to identify human error and these sources of audit risk.

We present the discovery and characterization of HIP 33609 b, a transiting warm brown dwarf orbiting a late B star, discovered by NASA's Transiting Exoplanet Survey Satellite TESS as TOI-588 b. HIP 33609 b is a large (R$_{b}$ = 1.580$_{-0.070}^{+0.074}$ R$_{J}$) brown dwarf on a highly eccentric (e = 0.560$_{-0.031}^{+0.029}$) orbit with a 39-day period. The host star is a bright (V = 7.3 mag), T$_{eff}$ = 10,400$_{-660}^{+800}$ K star with a mass of M$_{*}$ = 2.383$_{-0.095}^{+0.10}$ M$_{\odot}$ and radius of R$_{*}$ = 1.863$_{-0.082}^{+0.087}$ R$_{\odot}$, making it the hottest transiting brown dwarf host star discovered to date. We obtained radial velocity measurements from the CHIRON spectrograph confirming the companion's mass of M$_{b}$ = 68.0$_{-7.1}^{+7.4}$ M$_{J}$ as well as the host star's rotation rate ($vsini_{*} = 55.6 \pm 1.8$ km/s). We also present the discovery of a new comoving group of stars, designated as MELANGE-6, and determine that HIP 33609 is a member. We use a combination of rotation periods and isochrone models fit to the cluster members to estimate an age of 150 $\pm$ 25 Myr. With a measured mass, radius, and age, HIP 33609 b becomes a benchmark for substellar evolutionary models., Comment: 16 pages, 5 figures, 4 tables, Submitted to AAS Journals

I report updates to the substellar mass-radius diagram for 11 transiting brown dwarfs (BDs) and low-mass stars published before the third data release from the Gaia mission (Gaia DR3). I reanalyse these transiting BD systems whose physical parameters were published between 2008 and 2019 and find that when using the parallax measurements from Gaia DR3, 7 BDs show significant differences in their radius estimate or an improvement in the radius uncertainty. This has important implications for how these BDs are used to test substellar evolutionary models in the mass-radius diagram. The remaining 4 BDs show mass-radius estimates that are consistent with their previous pre-Gaia DR3 measurements. The 7 BDs that show significant deviation from the original mass-radius measurements are AD 3116b, CoRoT-3b, CoRoT-15b, EPIC 201702477b, Kepler-39b, KOI-205b, and KOI-415b. Of these, AD 3116b is a known member of the Praesepe cluster at an age of 600 Myr. Additionally, some of the previously smallest known transiting BDs, KOI-205b and KOI-415b, are not as small as once thought, leaving the mass-radius region for the very oldest BDs relatively sparse as a result of this work., Comment: 25 pages, 17 tables, 6 figures. Accepted in MNRAS. This version contains easy-access data tables in Appendix B

In this work, we explore recurrent neural network architectures for tuberculosis (TB) cough classification. In contrast to previous unsuccessful attempts to implement deep architectures in this domain, we show that a basic bidirectional long short-term memory network (BiLSTM) can achieve improved performance. In addition, we show that by performing greedy feature selection in conjunction with a newly-proposed attention-based architecture that learns patient invariant features, substantially better generalisation can be achieved compared to a baseline and other considered architectures. Furthermore, this attention mechanism allows an inspection of the temporal regions of the audio signal considered to be important for classification to be performed. Finally, we develop a neural style transfer technique to infer idealised inputs which can subsequently be analysed. We find distinct differences between the idealised power spectra of TB and non-TB coughs, which provide clues about the origin of the features in the audio signal., Comment: Accepted for publication at Interspeech 2022

Physics-constrained machine learning is emerging as an important topic in the field of machine learning for physics. One of the most significant advantages of incorporating physics constraints into machine learning methods is that the resulting model requires significantly less data to train. By incorporating physical rules into the machine learning formulation itself, the predictions are expected to be physically plausible. Gaussian process (GP) is perhaps one of the most common methods in machine learning for small datasets. In this paper, we investigate the possibility of constraining a GP formulation with monotonicity on three different material datasets, where one experimental and two computational datasets are used. The monotonic GP is compared against the regular GP, where a significant reduction in the posterior variance is observed. The monotonic GP is strictly monotonic in the interpolation regime, but in the extrapolation regime, the monotonic effect starts fading away as one goes beyond the training dataset. Imposing monotonicity on the GP comes at a small accuracy cost, compared to the regular GP. The monotonic GP is perhaps most useful in applications where data is scarce and noisy, and monotonicity is supported by strong physical evidence.

Understanding structure-property relations is essential to optimally design materials for specific applications. Two-scale simulations are often employed to analyze the effect of the microstructure on a component's macroscopic properties. However, they are typically computationally expensive and infeasible in multi-query contexts such as optimization and material design. To make such analyses amenable, the microscopic simulations can be replaced by surrogate models that must be able to handle a wide range of microstructural parameters. This work focuses on extending the methodology of a previous work, where an accurate surrogate model was constructed for microstructures under varying loading and material parameters using proper orthogonal decomposition and Gaussian process regression, to treat geometrical parameters. To this end, a method that transforms different geometries onto a parent domain is presented. We propose to solve an auxiliary problem based on linear elasticity to obtain the geometrical transformations. Using these transformations, combined with the nonlinear microscopic problem, we derive a fast-to-evaluate surrogate model with the following key features: (1) the predictions of the effective quantities are independent of the auxiliary problem, (2) the predicted stress fields fulfill the microscopic balance laws and are periodic, (3) the method is non-intrusive, (4) the stress field for all geometries can be recovered, and (5) the sensitivities are available and can be readily used for optimization and material design. The proposed methodology is tested on several composite microstructures, where rotations and large variations in the shape of inclusions are considered. Finally, a two-scale example is shown, where the surrogate model achieves a high accuracy and significant speed up, demonstrating its potential in two-scale shape optimization and material design problems.

Let $\mathbb{H}$ be a field with $\mathbb{Q}\subset\mathbb{H}\subset\mathbb{C}$, and let $p(\lambda)$ be a polynomial in $\mathbb{H}[\lambda]$, and let $A\in\mathbb{H}^{n\times n}$ be nonderogatory. In this paper we consider the problem of finding a solution $X\in\mathbb{H}^{n\times n}$ to $p(X)=A$. A necessary condition for this to be possible is already known from a paper by M.P. Drazin. Under an additional condition we provide an explicit construction of such solutions. The similarities and differences with the derogatory case will be discussed as well. One of the tools needed in the paper is a new canonical form, which may be of independent interest. It combines elements of the rational canonical form with elements of the Jordan canonical form., Comment: 39 pages

We present a deep learning based automatic cough classifier which can discriminate tuberculosis (TB) coughs from COVID-19 coughs and healthy coughs. Both TB and COVID-19 are respiratory diseases, contagious, have cough as a predominant symptom and claim thousands of lives each year. The cough audio recordings were collected at both indoor and outdoor settings and also uploaded using smartphones from subjects around the globe, thus containing various levels of noise. This cough data include 1.68 hours of TB coughs, 18.54 minutes of COVID-19 coughs and 1.69 hours of healthy coughs from 47 TB patients, 229 COVID-19 patients and 1498 healthy patients and were used to train and evaluate a CNN, LSTM and Resnet50. These three deep architectures were also pre-trained on 2.14 hours of sneeze, 2.91 hours of speech and 2.79 hours of noise for improved performance. The class-imbalance in our dataset was addressed by using SMOTE data balancing technique and using performance metrics such as F1-score and AUC. Our study shows that the highest F1-scores of 0.9259 and 0.8631 have been achieved from a pre-trained Resnet50 for two-class (TB vs COVID-19) and three-class (TB vs COVID-19 vs healthy) cough classification tasks, respectively. The application of deep transfer learning has improved the classifiers' performance and makes them more robust as they generalise better over the cross-validation folds. Their performances exceed the TB triage test requirements set by the world health organisation (WHO). The features producing the best performance contain higher order of MFCCs suggesting that the differences between TB and COVID-19 coughs are not perceivable by the human ear. This type of cough audio classification is non-contact, cost-effective and can easily be deployed on a smartphone, thus it can be an excellent tool for both TB and COVID-19 screening., Comment: This paper has been published in 2022 International Conference on Electrical, Computer and Energy Technologies (ICECET)

MyShake is a free citizen science smartphone app that provides a range of features related to earthquakes. Features available globally include rapid post-earthquake notifications, live maps of earthquake damage as reported by MyShake users, safety tips and various educational features. The app also uses the accelerometer to detect earthquake shaking and to record and submit waveforms to a central archive. In addition, MyShake delivers earthquake early warning alerts in California, Oregon and Washington. In this study we compare the felt shaking reports provided by MyShake users in California with the US Geological Survey's "Did You Feel It?" intensity reports. The MyShake app simply asks "What strength of shaking did you feel" and users report on a five-level scale. When the reports are averaged in spatial bins, we find strong correlations with the Modified Mercalli Intensity scale values reported by the USGS based on the much more complex DYFI surveys. The MyShake felt reports can therefore also be used to generate shaking intensity maps.

We report the discovery of TOI-2119b, a transiting brown dwarf (BD) that orbits and is completely eclipsed by an active M-dwarf star. Using light curve data from the Transiting Exoplanet Survey Satellite mission and follow-up high-resolution Doppler spectroscopic observations, we find the BD has a radius of $R_b = 1.08 \pm 0.03{\rm R_J}$, a mass of $M_b = 64.4 \pm 2.3{\rm M_J}$, an orbital period of $P = 7.200865 \pm 0.00002$ days, and an eccentricity of $e=0.337\pm 0.002$. The host star has a mass of $M_\star = 0.53 \pm 0.02{\rm M_\odot}$, a radius of $R_\star= 0.50 \pm 0.01{\rm R_\odot}$, an effective temperature of $T_{\rm eff} = 3621 \pm 48$K, and a metallicity of $\rm [Fe/H]=+0.06\pm 0.08$. TOI-2119b joins an emerging population of transiting BDs around M-dwarf host stars, with TOI-2119 being the ninth such system. These M-dwarf--brown dwarf systems typically occupy mass ratios near $q = M_b/M_\star \approx 0.1-0.2$, which separates them from the typical mass ratios for systems with transiting substellar objects and giant exoplanets that orbit more massive stars. The nature of the secondary eclipse of the BD by the star enables us to estimate the effective temperature of the substellar object to be $2030\pm 84$K, which is consistent with predictions by substellar evolutionary models., Comment: 14 pages, 13 figures, 4 tables, accepted in MNRAS

Today's cyber defense tools are mostly watchers. They are not active doers. To be sure, watching too is a demanding affair. These tools monitor the traffic and events; they detect malicious signatures, patterns and anomalies; they might classify and characterize what they observe; they issue alerts, and they might even learn while doing all this. But they don't act. They do little to plan and execute responses to attacks, and they don't plan and execute recovery activities. Response and recovery - core elements of cyber resilience are left to the human cyber analysts, incident responders and system administrators. We believe things should change. Cyber defense tools should not be merely watchers. They need to become doers - active fighters in maintaining a system's resilience against cyber threats. This means that their capabilities should include a significant degree of autonomy and intelligence for the purposes of rapid response to a compromise - either incipient or already successful - and rapid recovery that aids the resilience of the overall system. Often, the response and recovery efforts need to be undertaken in absence of any human involvement, and with an intelligent consideration of risks and ramifications of such efforts. Recently an international team published a report that proposes a vision of an autonomous intelligent cyber defense agent (AICA) and offers a high-level reference architecture of such an agent. In this paper we explore this vision.