Miracle-dz/UniFAR-FTUs huggingface dataset:
Species Selection Regime and Phylogenetic Tree Shape.

Species selection, the effect of heritable traits in generating between-lineage diversification rate differences, provides a valuable conceptual framework for understanding the relationship between traits, diversification and phylogenetic tree shape. An important challenge, however, is that the nature of real diversification landscapes - curves or surfaces which describe the propensity of species-level lineages to diversify as a function of one or more traits - remains poorly understood. Here we present a novel, time-stratified extension of the QuaSSE model in which speciation/extinction rate is specified as a static or temporally-shifting Gaussian or skewed-Gaussian function of the diversification trait. We then use simulations to show that the generally imbalanced nature of real phylogenetic trees, as well as their generally greater-than-expected frequency of deep branching events, are typical outcomes when diversification is treated as a dynamic, trait-dependent process. Focusing on four basic models (Gaussian-speciation with and without background extinction; skewed-speciation; Gaussian-extinction), we also show that particular features of the species selection regime produce distinct tree shape signatures and that, consequently, a combination of tree shape metrics has the potential to reveal the species selection regime under which a particular lineage diversified. We evaluate this idea empirically by comparing the phylogenetic trees of plant lineages diversifying within climatically- and geologically-stable environments of the Greater Cape Floristic Region, with those of lineages diversifying in environments that have experienced major change through the Late Miocene-Pliocene. Consistent with our expectations, the trees of lineages diversifying in a dynamic context are less balanced, show a greater concentration of branching events close to the present, and display stronger diversification rate-trait correlations. We suggest that species selection plays an important role in shaping phylogenetic trees but recognize the need for an explicit probabilistic framework within which to assess the likelihoods of alternative diversification scenarios as explanations of a particular tree shape.
Multi-Temporal Speckle Reduction of Polarimetric SAR Images: a Ratio-Based Approach

The availability of multi-temporal stacks of SAR images opens the way to new speckle reduction methods. Beyond mere spatial filtering, the time series can be used to improve the signal-to-noise ratio of structures that persist for several dates. Among multi-temporal filtering strategies to reduce speckle fluctuations, a recent approach has proved to be very effective: ratio-based filtering (RABASAR). This method, developed to reduce the speckle in multi-temporal intensity images, first computes a "mean image" with a high signal-to-noise ratio (a so-called super-image), and then processes the ratio between the multi-temporal stack and the super-image. In this paper, we propose an extension of this approach to polarimetric SAR images. We illustrate its potential on a stack of fully-polarimetric images from RADARSAT-2 satellite.
Synchronization Tracking of a Class of Uncertain Nonidentical Networked Euler-Lagrange Systems on Directed Graphs

In this paper, the adaptive distributed synchronization tracking control is studied for uncertain nonidentical networked Euler-Lagrange systems. The communication status is described by a directed graph, and only the informed subsystems can access the time-varying reference trajectory. A fully distributed adaptive control law is developed by estimating the reference trajectory for uninformed subsystems, and the asymptotic synchronization tracking is ensured without prior information about the global directed topology. Simulation results on nonlinear manipulators with two revolute joints are provided to show the effectiveness of the proposed control algorithm.
Simulation-Based Team Leadership Training Improves Team Leadership During Actual Trauma Resuscitations: A Randomized Controlled Trial.

OBJECTIVES Trauma resuscitations are complex critical care events that present patient safety-related risk. Simulation-based leadership training is thought to improve trauma care; however, there is no robust evidence supporting the impact of leadership training on clinical performance. The objective of this study was to assess the clinical impact of simulation-based leadership training on team leadership and patient care during actual trauma resuscitations. DESIGN Randomized controlled trial. SETTING Harborview Medical Center (level 1 trauma center). SUBJECTS Seventy-nine second- and third-year residents were randomized and 360 resuscitations were analyzed. INTERVENTIONS Subjects were randomized to a 4-hour simulation-based leadership training (intervention) or standard orientation (control) condition. MEASUREMENTS AND MAIN RESULTS Participant-led actual trauma resuscitations were video recorded and coded for leadership behaviors and patient care. We used random coefficient modeling to account for the nesting effect of multiple observations within residents and to test for post-training group differences in leadership behaviors while controlling for pre-training behaviors, Injury Severity Score, postgraduate training year, and days since training occurred. Sixty participants completed the study. There was a significant difference in post-training leadership behaviors between the intervention and control conditions (b1 = 4.06, t (55) = 6.11, p < 0.001; intervention M = 11.29, SE = 0.66, 95% CI, 9.99-12.59 vs control M = 7.23, SE = 0.46, 95% CI, 6.33-8.13, d = 0.92). Although patient care was similar between conditions (b = 2.00, t (55) = 0.99, p = 0.325; predicted means intervention M = 62.38, SE = 2.01, 95% CI, 58.43-66.33 vs control M = 60.38, SE = 1.37, 95% CI, 57.69-63.07, d = 0.15), a test of the mediation effect between training and patient care suggests leadership behaviors mediate an effect of training on patient care with a significant indirect effect (b = 3.44, 95% CI, 1.43-5.80). Across all trauma resuscitations leadership was significantly related to patient care (b1 = 0.61, SE = 0.15, t (273) = 3.64, p < 0.001). CONCLUSIONS Leadership training resulted in the transfer of complex skills to the clinical environment and may have an indirect effect on patient care through better team leadership.
CZT Correction of Range-Dependent Residual-RCM For Airborne SAR Motion Compensation

For the airborne SAR imaging, the motion compensation (MOCO) is required because of the phase error caused by the atmospheric turbulence. In the case of ultrahigh-resolution and wide-swath, the phase error may be rangedependent (RD), which may induce a RD residual-RCM after the correction of range cell migration (RCM) by using the range migration algorithm (RMA). Currently, phase gradient autofocus (PGA) is widely used to estimate the phase error from the raw data. However, the RD residual-RCM may degrade the accuracy of the PGA. To overcome such a problem, we present a CZT correction algorithm to correct the RD residual-RCM. Processing of airborne real data validates the effectiveness of the proposed algorithm.
Highly sensitive piezoresistive graphene-based stretchable composites for sensing applications.

Nanocarbonaceous materials with specific geometries and physico-chemical properties allow the development of high-performance polymer-based smart composite materials. Among them, chemical treatments of graphene allow tailoring its electrical conductivity and, therefore, to tune functional response of materials for sensing applications. Polymer-based nanocomposites have been developed from styrene-ethylene/butylene-styrene, SEBS, a high deformation thermoplastic elastomer, and different graphene-based fillers, including graphene oxide (GO), reduced graphene oxide (rGO) and graphene nanoplatelets (G-NPL). Its shown that the electrical conductivity shows a percolation threshold around 2 wt. % for GO and rGO, remaining nearly independent of the filler content for G-NPL filler contents up to 6 wt. %. Further, GO/SEBS and rGO/SEBS composites show high piezoresistive sensibility with gauge factors ranging from 15 up to 120 for strains up to 10% of strain. Thus, GO/SEBS and rGO/SEBS composites can represent a new generation of materials for strain sensor applications, as demonstrated in their implementation in a hand-glove prototype with finger movement monitoring.
After Almost 10 Years in Orbit: First Glance at Synergisms and New Results

The Soil Moisture and Ocean Salinity mission has been collecting data for over 9 years. The whole data currently being reprocessed (Version 721 for levels 1 and 2 and version 4 for level 3 CATDS) an used to see trends and finalise potential applications. This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surfaces (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. After 9 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events. Also we now have access the Soil Moisture Active and Passive (SMAP) mission and there are obvious synergisms to infer.
UAV Air Combat Autonomous Maneuver Decision Based on DDPG Algorithm

Based on the reinforcement learning theory, this paper establishes the learning model of the autonomous air combat maneuver decision of the UAV. Aiming at the problem that traditional reinforcement learning and DQN algorithm cannot deal with continuous action space, a policy gradient based DDPG algorithm is adopted, which enables the model to output continuous and smooth control values, thus improving the accuracy of autonomous control of UAV. The DDPG algorithm explores the action space by adding noise to the action values. The randomly generated initial action value combination contains a large number of invalid or low-quality individuals, which leads to inefficient learning and localization. Aiming at this problem, this paper proposes to use the optimization algorithm to generate the air combat maneuver action value, and add the optimization action as the initial sample to the DDPG replay buffer. This method filters out a large number of invalid action values and guarantees the correctness of the action value. At the same time, the possibility of exploring diversity is preserved, and the learning efficiency of the DDPG algorithm is improved.
Data-Driven Distributionally Robust Shortest Path Problem Using the Wasserstein Ambiguity Set

This paper proposes a data-driven distributionally robust shortest path (DRSP) model where the distribution of the travel time is only observable through a finite training dataset. Our DRSP model adopts the Wasserstein metric to construct the ambiguity set of probability distributions based on the uniform distribution on training dataset, and seeks optimal paths that minimize the worst-case α-reliable mean-excess travel time (METT) within this Wasserstein ambiguity set. The DRSP model with the Wasserstein ambiguity set is able to hedge against the risk from the uncertainty both in the travel time and its distribution. We prove that under a mild assumption, our model can be reformulated to a mixed 0-1 second order cone program (SOCP) that can be solved efficiently by the optimization techniques. Finally, we test the performance of our data-driven DRSP model and the experimental results show that the proposed model can achieve a considerable out-of-sample performance.
Dominant Physical Scattering Mechanism Analysis for GF-3 Typical Ground Objects by Polarimetric Decomposition

The dominant scattering mechanism is of great significance for the application of ground objects classification and target detection. It can also verify the quality of the polarimetric data by check the dominant scattering mechanism of known ground objects. In order to improve the application performance, this paper studies the dominant scattering mechanism of GF-3 typical ground objects based on a large number of data slices. The GF-3 fully polarimetric data slices are classified based on the MODIS global classification map, and the GF-3 slice library of typical ground objects is constructed. Based on large amounts of GF-3 samples, we carry out the statistical analysis of dominant scattering mechanism separation results for typical GF-3 ground objects (building, woodland, cultivated land, grassland and waters) of by means of h/alpha/A decomposition. The quantitative results reveal the polarimetric scattering feature of different ground objects, and provide reference for fully polarimetric SAR application.
Prevalence and Mortality of Individuals With X-Linked Hypophosphatemia: A United Kingdom Real-World Data Analysis

Abstract Background X-linked hypophosphatemia (XLH) is a rare multisystemic disease with a prominent musculoskeletal phenotype. We aim here to improve understanding of the prevalence of XLH across the life course and of overall survival among people with XLH. Methods This was a population-based cohort study using a large primary care database in the United Kingdom (UK) from 1995 to 2016. XLH cases were matched by age, gender, and practice to up to 4 controls. Trends in prevalence over the study period were estimated (stratified by age) and survival among cases was compared with that of controls. Findings From 522 potential cases, 122 (23.4%) were scored as at least possible XLH, while 62 (11.9%) were classified as highly likely or likely (conservative definition). In main analyses, prevalence (95% CI) increased from 3.1 (1.5–6.7) per million in 1995–1999 to 14.0 (10.8–18.1) per million in 2012–2016. Corresponding estimates using the conservative definition were 3.0 (1.4–6.5) to 8.1 (5.8–11.4). Nine (7.4%) of the possible cases died during follow-up, at median age of 64 years. Fourteen (2.9%) of the controls died at median age of 72.5 years. Mortality was significantly increased in those with possible XLH compared with controls (hazard ratio [HR] 2.93; 95% CI, 1.24–6.91). Likewise, among those with likely or highly likely XLH (HR 6.65; 1.44–30.72). Conclusions We provide conservative estimates of the prevalence of XLH in children and adults within the UK. There was an unexpected increase in mortality in later life, which may have implications for other fibroblast growth factor 23–related disorders.
A novel mutation in USF1 gene is associated with familial combined hyperlipidemia

Familial combined hyperlipidemia or FCHL is one of the most common genetic causes of hyperlipidemia and is associated with elevation of cholesterol, triglycerides or both, and increased serum apolipoprotein B (apoB). Linkage analysis and next generation sequencing have been successfully used for identifying rare genetic variants that have moderate‐to‐large effects.
Using vertebrate environmental DNA from seawater in biomonitoring of marine habitats

Conservation and management of marine biodiversity depends on biomonitoring of marine habitats, but current approaches are resource‐intensive and require different approaches for different organisms. Environmental DNA (eDNA) extracted from water samples is an efficient and versatile approach to detecting aquatic animals. In the ocean, eDNA composition reflects local fauna at fine spatial scales, but little is known about the effectiveness of eDNA‐based monitoring of marine communities at larger scales. We investigated the potential of eDNA to characterize and distinguish marine communities at large spatial scales by comparing vertebrate species composition among marine habitats in Qatar, the Arabian Gulf (also known as the Persian Gulf), based on eDNA metabarcoding of seawater samples. We conducted species accumulation analyses to estimate how much of the vertebrate diversity we detected. We obtained eDNA sequences from a diverse assemblage of marine vertebrates, spanning 191 taxa in 73 families. These included rare and endangered species and covered 36% of the bony fish genera previously recorded in the Gulf. Sites of similar habitat type were also similar in eDNA composition. The species accumulation analyses showed that the number of sample replicates was insufficient for some sampling sites but suggested that a few hundred eDNA samples could potentially capture >90% of the marine vertebrate diversity in the study area. Our results confirm that seawater samples contain habitat‐characteristic molecular signatures and that eDNA monitoring can efficiently cover vertebrate diversity at scales relevant to national and regional conservation and management.
Explicit Data Correlations-Directed Metadata Prefetching Method in Distributed File Systems

Metadata performance in distributed file systems (DFS) is critical, due to the following trends: (a) the growing size of modern storage systems is expected to exceed billions of files and most files are small; (b) over half of the file accesses are metadata operations. In this work, we present SMeta, a metadata prefetching method that is seamlessly integrated into DFS for easy-of-use and significantly scales the metadata performance. Previous prefetching proposals primarily focus on mining groups of files that tend to be accessed together from the access history. Nevertheless, our study discovered that these solutions likely miss a huge number of correlated files whose co-occurrence frequency is not high enough. Unlike access correlations, we take a novel and completely different approach to explore explicit data correlations by understanding the reference relationships between files encoded in some forms of hyperlinks, which naturally exist in many applications. To embrace this new concept, SMeta explores correlations upon files are written via a light-weight pattern matching algorithm, stores correlations in the reserved extended attributes of file metadata to avoid changes in DFS APIs, and collapses multiple I/O rounds for accessing metadata of the target file and its data-correlated files into one round. A cost-efficient adaptive feedback mechanism is introduced to improve prefetching accuracy. We implemented SMeta atop of Ceph and evaluated it using synthetic and real system workloads. Compared to baselines, SMeta provides better metadata performance in terms of latency, throughput and scalability.
Vehicle Service Management and Live Monitoring With Predictive Maintenance System

In this paper an efficient vehicle service management system is presented which can automatically manage complete servicing process and at the same time monitor the changes and operations done on the vehicle. Regular vehicle service is required to keep a check on various parts of the vehicle to ensure proper functioning and efficient working of the vehicle. Vehicle service management and monitoring system constantly checks the state of the vehicle after frequent intervals. It warns through android application about any improper values received and suggests that servicing is now required. When the vehicle is at any of the service centers, previous details of vehicle service can be shared with garage on authentication by the customer using the same android application. IoT technology is used to update various sensors data in the application in real time. Sensor interfaced with raspberry pi constantly monitors different subsystems of the vehicle. It also provides customers with live streaming of garage when their vehicle is in service centers. This paper also talks about predictive vehicle maintenance. It predicts the time after which vehicle’s, components may need servicing.
Recurrence Analysis of Human Body Movements during Activities of Daily Living

Recurrence quantification analysis (RQA) is used to differentiate and analyze the regular and irregular parts of a time-series signal using recurrence plots and quantification measures. This work presents RQA for human body movements during routine activities of daily life (ADL) using parameters recorded using a wearable sensor attached to the test subjects waist. The current research uses data from 8 subjects performing 5 different daily life activities, lying and stand, pick and stand, sitting and stand, step up and down, and walking. Simulating the RQA plots for activity and non-activity phases for squared vector magnitude parameter for each of the record we quantify the level of signal stability and disruption in terms of RQA analysis measures recurrence rate (RR), determinism (DET) and line entropy (ENT). The RQA parameters reveal a chaotic behavior in case of activity (RR=0.249, DET=0.510, ENT=0.732), and a stable or least chaotic behavior in case of non-activity (RR=0.466, DET=0.726, ENT=1.205) regions of time. Distinguishing values for RQA-based measures for different human body movements taking place during daily life activities might be used for human activity monitoring, fall detection for elderly and body movement modelling and analysis alaorithms.
Iterative Random Training Sample Selection for Hyperspectral Image Classification

Hyperspectral image classification (HSIC) has received considerable interest in recent years. In particular, spectral-spatial classification methods are proposed to jointly consider spectral and spatial together. However, one of challenging issues in hyperspectral image classifications is the random training sample selection which produces inconsistent results. A general approach to resolving this problem is so-called k-fold method which implements randomly selected training samples k times and takes their average with respect to the standard deviation to be used describe a confidence interval. This paper develops an approach to mitigating such a random issue by introducing an iterative process to remove uncertainty caused by randomness. Its idea is to repeatedly feedback the classification results in an iterative manner that the randomness caused by the randomly selected samples can be largely reduced. The iterative process is terminated as long as the classification results obtained by two consecutive iterations agree with a prescribed tolerance. Experimental results demonstrate that our proposed method works very effectively not only to reduce result inconsistency but also to improve classification results.
A Hybrid SLAM Method for Indoor Micro Aerial Vehicles

In this paper, a new simultaneous localization and mapping (SLAM) method for micro aerial vehicles (MAVs) is put forward. Its main contributions are the hybrid iterative closest points and normal distribution transform (ICP-NDT) point cloud registration algorithm as well as the extended Kalman filter (EKF) algorithm for data fusion and estimation based on the dynamic model of the quadrotor. In this method, a 2-dimensional (2D) lidar is used to obtain surrounding obstacle information in the region. Its data can be turned into displacement by the hybrid ICP-NDT registration algorithm, and projected to a planar occupancy grid submap by the imaging algorithm. The displacement can be integrated into EKF for data fusion with the other sensors to get the optimal position for the MAV, and the submap can be inserted into this optimal position for updating the map. As the process repeats, the map can establish. The presented algorithm is tested in two pieces of the real scene, and the MAV is capable of getting its position and establishing the map for the region. In these tests, the maps can reflect the planar features of the environment with satisfactory accuracy.