Miracle-dz/UniFAR-FTUs huggingface dataset:
Sleep regularity in healthy adolescents: Associations with sleep duration, sleep quality, and mental health

Current evidence points to the importance of sleep for adolescent physical and mental health. To date, most studies have examined the association between sleep duration/quality and health in adolescence. An emerging line of research suggests that regularity in the timing of sleep may also play an important role in well‐being. To address this aspect of sleep, the present study investigated daily variability of sleep, quantified using the sleep regularity index (SRI), in 46 adolescents (M = 12.78 ± 1.07 years) and its association with depressive symptoms/mental health. Sleep was measured during a 6 month period (M = 133.11 ± 36.42 nights) using actigraphs to quantify SRI values calculated for school days, weekends and holidays. Depressive symptoms and general psychopathology were assessed at the beginning (baseline) and end (follow‐up) of the actigraphy measurements. Sleep was most regular during school days and associated with a longer total sleep time, shorter sleep onset latency, and higher sleep efficiency. Moreover, a higher SRI on school days was associated with fewer depressive symptoms at follow‐up, whereas higher SRI on weekends was associated with less overall psychopathology at follow‐up. Furthermore, the change in overall psychopathology, but not depressive symptoms across the two assessments was correlated with sleep regularity index. Our results suggest that regular timing of sleep is associated with sleep that is of longer duration and higher quality and may be protective of adolescent mental health. Therefore, adolescents should be encouraged not only to get enough sleep, but also to retain regular sleeping patterns to promote well‐being and mental health.
B-spline estimation for spatial data

Data collected on the surface of the earth often have spatial interaction. In this paper, a global smoothing procedure is developed using a tensor product of B-spline function approximations for estimating the spatial multi-dimensional conditional regression function. Under mild regularity assumptions, the global convergence rates of the B-spline estimators are established. Asymptotic results show that our B-spline estimators achieve the optimal convergence rate. The asymptotic normality of our estimator is also derived. Finite sample properties of our procedures are studied through Monte Carlo simulations.
Effects of configuration interaction on the radiative rates of Li- and Be-like ions

Relativistic configuration interaction results are presented for the x-ray rates from 2s{sup 2}3p and 1s2s{sup 2}3p configurations of Li- and Be-like ions, respectively. The x-ray energies and rates are calculated for 14 ions in the range Z=10-54 using multiconfiguration Dirac-Fock wave functions in the active space approximation. The convergence of the eigenvalues and the effect of different shells on the rates have been studied as the active space is being gradually increased. The influence of configuration mixing on the decay rates of x rays from Li- and Be-like ions are analyzed. Our calculations show that strong configuration mixing reduces substantially the single configuration decay rates from Li-like ions considered in this work whereas it enhances significantly the allowed dipole rates from Be-like ions with Z<32. For other ions, the dipole rate decreases. The configuration interaction reduces the spin-forbidden rates of all the ions. The results are compared with other available values.
Lifetime-configurable soft robots via photodegradable silicone elastomer composites

Developing soft robots that can control their own life cycle and degrade on-demand while maintaining hyperelasticity is a notable research challenge. On-demand degradable soft robots, which conserve their original functionality during operation and rapidly degrade under specific external stimulation, present the opportunity to self-direct the disappearance of temporary robots. This study proposes soft robots and materials that exhibit excellent mechanical stretchability and can degrade under ultraviolet light by mixing a fluoride-generating diphenyliodonium hexafluorophosphate with a silicone resin. Spectroscopic analysis revealed the mechanism of Si─O─Si backbone cleavage using fluoride ion (F−) and thermal analysis indicated accelerated decomposition at elevated temperatures. In addition, we demonstrated a robotics application by fabricating electronics integrated gaiting robot and a fully closed-loop trigger disintegration robot for autonomous, application-oriented functionalities. This study provides a simple yet novel strategy for designing life cycle mimicking soft robotics that can be applied to reduce soft robotics waste, explore hazardous areas, and ensure hardware security with on-demand destructive material platforms.
Emergent scale and anomalous dynamics in certain quasi-periodic systems

We study localisation transition in a class of quasi-periodic systems that has two competing periodic scales. We show that such class of systems show a re-entrant localisation transition where the energy scale of transition is set by the periodicities of these two scales. Furthermore we show dynamical properties in these systems, exhibits various kinds critical dynamics including sub-diffusive, super-diffusive and diffusive spread of an initially localised wave-packet. Finally we show that these characteristics of quasi-periodic systems with two periodic scales can be realised within the regime of current experiments.
X3: A High-mass Young Stellar Object Close to the Supermassive Black Hole Sgr A*

To date, the proposed observation of young stellar objects (YSOs) in the Galactic center still raises the question of where and how these objects could have formed due to the violent vicinity of Sgr A*. Here, we report the multiwavelength detection of a highly dynamic YSO close to Sgr A* that might be a member of the IRS 13 cluster. We observe the beforehand known coreless bow-shock source X3 in the near- and mid-infrared (NIR/MIR) with SINFONI (VLT), NACO (VLT), ISAAC (VLT), VISIR (VLT), SHARP (NTT), and NIRCAM2 (KECK). In the radio domain, we use CO continuum and H30α ALMA observations to identify system components at different temperatures and locations concerning the central stellar source. It is suggested that these radio/submillimeter observations in combination with the NIR Brγ line can be associated with a protoplanetary disk of the YSO, which is consistent with manifold VISIR observations that reveal complex molecules and elements such as PAH, S iv, Ne ii, and Ar iii in a dense and compact region. Based on the photometric multiwavelength analysis, we infer the mass of 15−5+10M⊙ for the YSO with a related age of a few 104 yr. Due to this age estimate and the required relaxation timescales for high-mass stars, this finding is an indication of ongoing star formation in the inner parsec. The proper motion and 3D distance imply a relation between X3 and IRS 13. We argue that IRS 13 may serve as a birthplace for young stars that are ejected due to the evaporation of the cluster.
Stochastic Gradient Descent under Markovian Sampling Schemes

We study a variation of vanilla stochastic gradient descent where the optimizer only has access to a Markovian sampling scheme. These schemes encompass applications that range from decentralized optimization with a random walker (token algorithms), to RL and online system identification problems. We focus on obtaining rates of convergence under the least restrictive assumptions possible on the underlying Markov chain and on the functions optimized. We first unveil the theoretical lower bound for methods that sample stochastic gradients along the path of a Markov chain, making appear a dependency in the hitting time of the underlying Markov chain. We then study Markov chain SGD (MC-SGD) under much milder regularity assumptions than prior works (e.g., no bounded gradients or domain, and infinite state spaces). We finally introduce MC-SAG, an alternative to MC-SGD with variance reduction, that only depends on the hitting time of the Markov chain, therefore obtaining a communication-efficient token algorithm.
Taylor TD-learning

Many reinforcement learning approaches rely on temporal-difference (TD) learning to learn a critic. However, TD-learning updates can be high variance. Here, we introduce a model-based RL framework, Taylor TD, which reduces this variance in continuous state-action settings. Taylor TD uses a first-order Taylor series expansion of TD updates. This expansion allows Taylor TD to analytically integrate over stochasticity in the action-choice, and some stochasticity in the state distribution for the initial state and action of each TD update. We include theoretical and empirical evidence that Taylor TD updates are indeed lower variance than standard TD updates. Additionally, we show Taylor TD has the same stable learning guarantees as standard TD-learning with linear function approximation under a reasonable assumption. Next, we combine Taylor TD with the TD3 algorithm, forming TaTD3. We show TaTD3 performs as well, if not better, than several state-of-the art model-free and model-based baseline algorithms on a set of standard benchmark tasks.
Parametrizing Product Shape Manifolds by Composite Networks

Parametrizations of data manifolds in shape spaces can be computed using the rich toolbox of Riemannian geometry. This, however, often comes with high computational costs, which raises the question if one can learn an efficient neural network approximation. We show that this is indeed possible for shape spaces with a special product structure, namely those smoothly approximable by a direct sum of low-dimensional manifolds. Our proposed architecture leverages this structure by separately learning approximations for the low-dimensional factors and a subsequent combination. After developing the approach as a general framework, we apply it to a shape space of triangular surfaces. Here, typical examples of data manifolds are given through datasets of articulated models and can be factorized, for example, by a Sparse Principal Geodesic Analysis (SPGA). We demonstrate the effectiveness of our proposed approach with experiments on synthetic data as well as manifolds extracted from data via SPGA.
Exposure-Based Multi-Agent Inspection of a Tumbling Target Using Deep Reinforcement Learning

As space becomes more congested, on orbit inspection is an increasingly relevant activity whether to observe a defunct satellite for planning repairs or to de-orbit it. However, the task of on orbit inspection itself is challenging, typically requiring the careful coordination of multiple observer satellites. This is complicated by a highly nonlinear environment where the target may be unknown or moving unpredictably without time for continuous command and control from the ground. There is a need for autonomous, robust, decentralized solutions to the inspection task. To achieve this, we consider a hierarchical, learned approach for the decentralized planning of multi-agent inspection of a tumbling target. Our solution consists of two components: a viewpoint or high-level planner trained using deep reinforcement learning and a navigation planner handling point-to-point navigation between pre-specified viewpoints. We present a novel problem formulation and methodology that is suitable not only to reinforcement learning-derived robust policies, but extendable to unknown target geometries and higher fidelity information theoretic objectives received directly from sensor inputs. Operating under limited information, our trained multi-agent high-level policies successfully contextualize information within the global hierarchical environment and are correspondingly able to inspect over 90% of non-convex tumbling targets, even in the absence of additional agent attitude control.
The Classification of Short and Long-term Driving Behavior for an Advanced Driver Assistance System by Analyzing Bidirectional Driving Features

Insight into individual driving behavior and habits is essential in traffic operation, safety, and energy management. With Connected Vehicle (CV) technology aiming to address all three of these, the identification of driving patterns is a necessary component in the design of personalized Advanced Driver Assistance Systems (ADAS) for CVs. Our study aims to address this need by taking a unique approach to analyzing bidirectional (i.e. longitudinal and lateral) control features of drivers, using a simple rule-based classification process to group their driving behaviors and habits. We have analyzed high resolution driving data from the real-world CV-testbed, Safety Pilot Model Deployment, in Ann Arbor, Michigan, to identify diverse driving behavior on freeway, arterial, and ramp road types. Using three vehicular features known as jerk, leading headway, and yaw rate, driving characteristics are classified into two groups (Safe Driving and Hostile Driving) on short-term classification, and drivers habits are categorized into three classes (Calm Driver, Rational Driver, and Aggressive Driver). Proposed classification models are tested on unclassified datasets to validate the model conviction regarding speeding and steep acceleration. Through the proposed method, behavior classification has been successfully identified about 90 percent of speeding and similar level of acute acceleration instances. In addition, our study advances an ADAS interface that interacts with drivers in real-time in order to transform information about driving behaviors and habits into feedback to individual drivers. We propose an adaptive and flexible classification approach to identify both short-term and long-term driving behavior from naturalistic driving data to identify and, eventually, communicate adverse driving behavioral patterns.
Distributed Fixed-Time Consensus Control for Multiple AUV Systems with Input Saturations

This study proposes a new distributed control method based on an adaptive fuzzy control for multiple collaborative autonomous underwater vehicles (AUVs) to track a desired formation shape within a fixed time. First, a formation control protocol based on a fixed-time backstepping sliding mode control is designed, in which the consensus cooperative tracking errors for each AUV will be formulated. Then, to compensate for the saturated control torques, an adaptive auxiliary variable is introduced. Finally, a fixed-time adaptive fuzzy logic control (FLC) is derived to approximate the unknown dynamics, in which the adaptive laws of the FLC is derived such that the adaptive signals and errors can be convergent within a fixed time. The fixed time convergence is desired in practice because it provides an exciting property that the global convergence of the whole system is independent with the initial states of the AUVs. The computer simulation results for a consensus formation control of four AUVs show that the proposed formation control can provide high tracking performance with lower and smoother control efforts
A Big Data Driven Framework for Duplicate Device Detection from Multi-sourced Mobile Device Location Data

Mobile Device Location Data (MDLD) has been popularly utilized in various fields. Yet its large-scale applications are limited because of either biased or insufficient spatial coverage of the data from individual data vendors. One approach to improve the data coverage is to leverage the data from multiple data vendors and integrate them to build a more representative dataset. For data integration, further treatments on the multi-sourced dataset are required due to several reasons. First, the possibility of carrying more than one device could result in duplicated observations from the same data subject. Additionally, when utilizing multiple data sources, the same device might be captured by more than one data provider. Our paper proposes a data integration methodology for multi-sourced data to investigate the feasibility of integrating data from several sources without introducing additional biases to the data. By leveraging the uniqueness of travel pattern of each device, duplicate devices are identified. The proposed methodology is shown to be cost-effective while it achieves the desired accuracy level. Our findings suggest that devices sharing the same imputed home location and the top five most-visited locations during a month can represent the same user in the MDLD. It is shown that more than 99.6% of the sample devices having the aforementioned attribute in common are observed at the same location simultaneously. Finally, the proposed algorithm has been successfully applied to the national-level MDLD of 2020 to produce the national passenger origin-destination data for the NextGeneration National Household Travel Survey (NextGen NHTS) program.
Nautilus: A Framework for Cross-Layer Cartography of Submarine Cables and IP Links

Submarine cables constitute the backbone of the Internet. However, these critical infrastructure components are vulnerable to several natural and man-made threats, and during failures, are difficult to repair in remote oceans. In spite of their crucial role, we have a limited understanding of the impact of submarine cable failures on global connectivity, particularly on the higher layers of the Internet. In this paper, we present Nautilus, a framework for cross-layer cartography of submarine cables and IP links. Using a corpus of public datasets and Internet cartographic techniques, Nautilus identifies IP links that are likely traversing submarine cables and maps them to one or more potential cables. Nautilus also gives each IP to cable assignment a prediction score that reflects the confidence in the mapping. Nautilus generates a mapping for 3.05 million and 1.43 million IPv4 and IPv6 links, respectively, spanning 91% of all active cables. In the absence of ground truth data, we validate Nautilus mapping using three techniques: analyzing past cable failures, using targeted traceroute measurements, and comparing with public network maps of two operators.
Learned Risk Metric Maps for Kinodynamic Systems

We present Learned Risk Metric Maps (LRMM) for real-time estimation of coherent risk metrics of high-dimensional dynamical systems operating in unstructured, partially observed environments. LRMM models are simple to design and train-requiring only procedural generation of obstacle sets, state and control sampling, and supervised training of a function approximator-which makes them broadly applicable to arbitrary system dynamics and obstacle sets. In a parallel autonomy setting, we demonstrate the model's ability to rapidly infer collision probabilities of a fast-moving car-like robot driving recklessly in an obstructed environment; allowing the LRMM agent to intervene, take control of the vehicle, and avoid collisions. In this time-critical scenario, we show that LRMMs can evaluate risk metrics 20-100x times faster than alternative safety algorithms based on control barrier functions (CBFs) and Hamilton-Jacobi reachability (HJ-reach), leading to 5–15 % fewer obstacle collisions by the LRMM agent than CBFs and HJ-reach. This performance improvement comes in spite of the fact that the LRMM model only has access to local/partial observation of obstacles, whereas the CBF and HJ-reach agents are granted privileged/global information. We also show that our model can be equally well trained on a 12-dimensional quadrotor system operating in an obstructed indoor environment. The LRMM codebase is provided at https://github.com/mit-drl/pyrmm.
Modern Bayesian Experimental Design

Bayesian experimental design (BED) provides a powerful and general framework for optimizing the design of experiments. However, its deployment often poses substantial computational challenges that can undermine its practical use. In this review, we outline how recent advances have transformed our ability to overcome these challenges and thus utilize BED effectively, before discussing some key areas for future development in the field.
Temporal evolution of the extreme excursions of multivariate k$$ k $$th order Markov processes with application to oceanographic data

We develop two models for the temporal evolution of extreme events of multivariate th order Markov processes. The foundation of our methodology lies in the conditional extremes model of Heffernan and Tawn (Journal of the Royal Statistical Society: Series B (Methodology), 2014, 66, 497–546), and it naturally extends the work of Winter and Tawn (Journal of the Royal Statistical Society: Series C (Applied Statistics), 2016, 65, 345–365; Extremes, 2017, 20, 393–415) and Tendijck et al. (Environmetrics 2019, 30, e2541) to include multivariate random variables. We use cross‐validation‐type techniques to develop a model order selection procedure, and we test our models on two‐dimensional meteorological‐oceanographic data with directional covariates for a location in the northern North Sea. We conclude that the newly‐developed models perform better than the widely used historical matching methodology for these data.
Learning to Estimate Two Dense Depths from LiDAR and Event Data

Event cameras do not produce images, but rather a continuous flow of events, which encode changes of illumination for each pixel independently and asynchronously. While they output temporally rich information, they lack any depth information which could facilitate their use with other sensors. LiDARs can provide this depth information, but are by nature very sparse, which makes the depth-to-event association more complex. Furthermore, as events represent changes of illumination, they might also represent changes of depth; associating them with a single depth is therefore inadequate. In this work, we propose to address these issues by fusing information from an event camera and a LiDAR using a learning-based approach to estimate accurate dense depth maps. To solve the"potential change of depth"problem, we propose here to estimate two depth maps at each step: one"before"the events happen, and one"after"the events happen. We further propose to use this pair of depths to compute a depth difference for each event, to give them more context. We train and evaluate our network, ALED, on both synthetic and real driving sequences, and show that it is able to predict dense depths with an error reduction of up to 61% compared to the current state of the art. We also demonstrate the quality of our 2-depths-to-event association, and the usefulness of the depth difference information. Finally, we release SLED, a novel synthetic dataset comprising events, LiDAR point clouds, RGB images, and dense depth maps.
Scalable End-to-End ML Platforms: from AutoML to Self-serve

ML platforms help enable intelligent data-driven applications and maintain them with limited engineering effort. Upon sufficiently broad adoption, such platforms reach economies of scale that bring greater component reuse while improving efficiency of system development and maintenance. For an end-to-end ML platform with broad adoption, scaling relies on pervasive ML automation and system integration to reach the quality we term self-serve that we define with ten requirements and six optional capabilities. With this in mind, we identify long-term goals for platform development, discuss related tradeoffs and future work. Our reasoning is illustrated on two commercially-deployed end-to-end ML platforms that host hundreds of real-time use cases -- one general-purpose and one specialized.