Miracle-dz/UniFAR-FTUs huggingface dataset:
Applying Adaptive Control in Modeling Human Motion Behaviors in Reinforcement Robotic Learning from Demonstrations

In this paper, we propose to use an adaptive control method as the basis of a reinforcement learning algorithm for robotic imitation learning. In the learning stage, robots use adaptive control method-based reinforcement learning algorithm to learn the parameters of dynamical systems. In the generation stage, robots use the learned dynamic system parameters and the pre-defined controller to drive the configuration states of the robot to move along desired state trajectories. One simulation experiment and one practical experiment on a robot are carried out to validate the effectiveness of our algorithm. The experimental results validate that the learning of the system parameters converges very fast and the learning results can improve the system performance of generating similar motion trajectories.
Workload scheduling toward worst-case delay and optimal utility for single-hop Fog-IoT architecture

Fog computing is a distributed computing model that can utilise the storage, analysis and processing capabilities of fog nodes near edge devices. Although fog computing can support task processing for various Internet of Things (IoT) systems, Fog-IoT architecture faces several new challenges with the rapid development of IoT systems, especially delay-sensitive IoT systems, such as stochastic and dynamic data arrival, optimal utility and deadline of tasks. To address these challenges, workload scheduling toward worst-case delay and optimal utility for single-hop Fog-IoT architecture are studied and the workload dynamic scheduling algorithm (WDSA) is proposed. The proposed WDSA algorithm can maximise the average throughput utility while guarantees the worst-case delay of task processing. In addition, it is online and needs no prior information about future. The algorithm performance is analysed from the perspective of optimality and worst-case delay, demonstrating that the proposed WDSA algorithm can get an approximate optima and worst-case delay guarantees. Finally, simulation results demonstrate that the efficiency and efficacy of this kind of the algorithm can meet the requirement.
The Greedy Dirichlet Process Filter -An Online Clustering Multi-Target Tracker

Reliable collision avoidance is one of the main requirements for autonomous driving. Hence, it is important to correctly estimate the states of an unknown number of static and dynamic objects in real-time. Here, data association is a major challenge for every multi-target tracker. We propose a novel multi-target tracker called Greedy Dirichlet Process Filter (GDPF) based on the non-parametric Bayesian model called Dirichlet Processes and the fast posterior computation algorithm Sequential Updating and Greedy Search (SUGS). By adding a temporal dependence we get a real-time capable tracking framework without the need of a previous clustering or data association step. Real-world tests show that GDPF outperforms other multi-target tracker in terms of accuracy and stability.
Production of a Novel Functional Fruit Beverage Consisting of Cornelian Cherry Juice and Probiotic Bacteria

The present study describes the development of a novel functional beverage through the application of probiotic Lactobacillus plantarum ATCC (American Type Culture Collection) 14917 in Cornelian cherry juice fermentation. The probiotic was employed in free and immobilized in a delignified wheat bran carrier (DWB) form. Cornelian cherry juice was fermented for 24 h and then it was stored at 4 °C for 4 weeks. Several parameters were evaluated such as residual sugar, organic acid and alcohol levels, total phenolics content, and cell viability as well as consumers acceptance. Regarding sugar and organic acids analyses, it was proved that the probiotic free or immobilized biocatalyst was effective. The concentration of ethanol was maintained at low levels (0.3–0.9% v/v). The total phenolic content of fermented Cornelian cherry juice with immobilized cells was recorded in higher levels (214–264 mg GAE/100 mL) for all the cold storage time compared to fermented juice with free cells (165–199 mg GAE/100 mL) and non-fermented juice (135–169 mg GAE/100 mL). Immobilized cells retained their viability in higher levels (9.95 log cfu/mL at the 4th week) compared to free cells (7.36 log cfu/mL at the 4th week). No significant sensory differences were observed among the fermented and the non-fermented samples.
Mining spatio-temporal data for computing driver stress and observing its effects on driving behavior

With the increase in road fatalities due to various factors like aggressive driving and road rage, quantifying and monitoring the stress level of a driver is an important task for the preparation of driving rosters for the cab companies. Stress monitoring using physiological sensors is a costly and obstructive task, while stress factors impact differently for different individuals based on their personality traits. In this paper, we develop a learning-based model to predict the stress level of a driver and its effect on his driving behavior, solely based on spatio-temporal driving data collected through GPS and inertial sensors. We further establish a correlation between the stress level of a driver and his driving behavior; thus, we develop a complete system to infer stress profiling and its impact on driving behavior based on spatio-temporal driving data. The model has been tested over a publicly available dataset with 6 drivers for 500 minutes of driving data. We observe that the proposed model gives an average prediction accuracy of 79% with low false-positive rates.
Evaluation of the analytical performances of the Cobas c513 analyser for HbA1c assay

Introduction Haemoglobin A1c (HbA1c) is considered to be the gold standard for the follow-up of glycaemic control in patients with diabetes mellitus and is also a diagnostic tool. Accordingly, reliable and efficient methods must be used for its quantification. Roche Diagnostics have recently adapted the Tina-quant® HbA1c Third Generation immunoassay on a fully dedicated analyser, the Cobas c513, which allows a high throughput of up to 400 samples per hour. The present article deals with the evaluation of the analytical performances of this system which has been recently introduced to the market. Materials and methods Precision, comparison with two ion-exchange high-performance liquid chromatography (HPLC) methods (Variant II and D-100 systems, BioRad Laboratories) using Passing Bablok and Bland-Altman analyses, accuracy and interference of the most frequent haemoglobin (Hb) variants on HbA1c measurement were evaluated. Results Precision was high, with coefficients of variation lower than 1.1% (HbA1c values expressed in National Glycohemoglobin Standardization Program units, 1.7% for values expressed in International Federation of Clinical Chemistry and Laboratory Medicine [IFCC] units). The comparison study showed similar results with the two HPLC systems. The analysis of samples with IFCC-assigned values showed high methodological accuracy. Finally, no interference of bilirubin, triglycerides and common Hb variants (Hb AC, AD, AE, AS) was observed. Conclusions This evaluation showed that the analytical performance of the Cobas c513 analyser for HbA1c assay makes it suitable for a routine use in clinical chemistry laboratories.
Biomechanical Comparisons of One-Legged and Two-Legged Running Vertical Jumps

Abstract The purpose of this study was to determine the differences in biomechanical characteristics between one- and two-legged running vertical jumps (1-LRVJ and 2-LRVJ). Ten male college volleyball players voluntarily participated in this study. Two running vertical jumps used in volleyball were randomly performed. Three trials for each type of the running vertical jump were recorded for each participant. Data were collected using six infra-red Qualisys motion-capture cameras at a 180-Hz sampling rate and two AMTI force platforms at an 1800-Hz sampling rate. Jump height in the 2-LRVJ was significantly higher than that in the 1-LRVJ (p < 0.05). In the take-off phase, knee and hip extension impulses for the 1-LRVJ were significantly greater than those for the 2-LRVJ (p < 0.05). These results suggest that the 1-LRVJ produced greater leg stiffness than the 2-LRVJ did. We found that the 1-LRVJ caused greater lower-extremity stiffness and impulse compared to the 2-LRVJ, which is beneficial in the stretch-shortening cycle, and thus the more focus on practicing 1-LRVJs is recommended for coaches and athletes.
What's between KISS and KIDS: A Keep It Knowledgeable (KIKS) Principle for Cognitive Agent Design

The two common design principles for agent-based models, KISS (Keep It Simple, Stupid) and KIDS (Keep It Descriptive, Stupid) offer limited traction for developing cognitive agents, who typically have strong ties to research findings and established theories of cognition. A KIKS principle (Keep It Knowledgeable, Stupid) is proposed to capture the fact that cognitive agents are grounded in published research findings and theory, rather than simply selecting parameters in an adhoc way. In short, KIKS suggests that modelers should not focus on how many parameters, but should instead focus on choosing the right research papers and implement each of their key parameters and mechanisms. Based on this principle, a design process for creating cognitive agents based on cognitive models is proposed. This process is centered around steps that cognitive agent designers are already consider (e.g., literature search, validation, implementing a computational model). However, the KIKS process suggests two differences. First, KIKS calls for reporting explicit metadata on the empirical and theoretical relationships that an agent’s cognitive model is intended to capture. Each such relationship should be associated with a published paper that supports it. This metadata would serve a valuable purpose for comprehending, validating, and comparing the cognitive models used by different agents. Second, KIKS calls for validation tests to be specified before creating an agent’s cognitive model computationally. This process, known as test-driven design, can be used to monitor the adherence of a cognitive agent to its underlying knowledge base as it evolves through different versions. Implications, advantages, and limitations of the proposed process for KIKS are discussed.
Bacterial Community Composition in the Sea Surface Microlayer Off the Peruvian Coast

The sea surface microlayer (SML) is located at the air-sea interface, with microorganisms and organic matter in the SML influencing air-sea exchange processes. Yet understanding of the SML bacterial (bacterioneuston) community composition and assembly remains limited. Availability of organic matter, UV radiation and wind speed have previously been suggested to influence the community composition of bacterioneuston. Another mechanism potentially controlling bacterioneuston dynamics is bacterioplankton attached to gel-like particles that ascend through the water column into the SML. We analyzed the bacterial community composition, Transparent Exopolymer Particles (TEP) abundance and nutrient concentrations in the surface waters of the Peruvian upwelling region. The bacterioneuston and bacterioplankton communities were similar, suggesting a close spatial coupling. Four Bacteroidetes families were significantly enriched in the SML, two of them, the Flavobacteriaceae and Cryomorphaceae, were found to comprise the majority of SML-enriched operational taxonomic units (OTUs). The enrichment of these families was controlled by a variety of environmental factors. The SML-enriched bacterial families were negatively correlated with water temperature and wind speed in the SML and positively correlated with nutrient concentrations, salinity and TEP in the underlying water (ULW). The correlations with nutrient concentrations and salinity suggest that the enriched bacterial families were more abundant at the upwelling stations.
Discovering Conditional Functional Dependencies

This paper investigates the discovery of conditional functional dependencies (CFDs). CFDs are a recent extension of functional dependencies (FDs) by supporting patterns of semantically related constants, and can be used as rules for cleaning relational data. However, finding quality CFDs is an expensive process that involves intensive manual effort. To effectively identify data cleaning rules, we develop techniques for discovering CFDs from relations. Already hard for traditional FDs, the discovery problem is more difficult for CFDs. Indeed, mining patterns in CFDs introduces new challenges. We provide three methods for CFD discovery. The first, referred to as CFDMiner, is based on techniques for mining closed item sets, and is used to discover constant CFDs, namely, CFDs with constant patterns only. Constant CFDs are particularly important for object identification, which is essential to data cleaning and data integration. The other two algorithms are developed for discovering general CFDs. One algorithm, referred to as CTANE, is a levelwise algorithm that extends TANE, a well-known algorithm for mining FDs. The other, referred to as FastCFD, is based on the depth-first approach used in FastFD, a method for discovering FDs. It leverages closed-item-set mining to reduce the search space. As verified by our experimental study, CFDMiner can be multiple orders of magnitude faster than CTANE and FastCFD for constant CFD discovery. CTANE works well when a given relation is large, but it does not scale well with the arity of the relation. FastCFD is far more efficient than CTANE when the arity of the relation is large; better still, leveraging optimization based on closed-item-set mining, FastCFD also scales well with the size of the relation. These algorithms provide a set of cleaning-rule discovery tools for users to choose for different applications.
Prescriptive analytics system for long-range aircraft conflict detection and resolution

At the present time, there is no mechanism for Air Navigation Service Providers (ANSPs) to probe new flight plans filed by the Airlines Operation Centers (AOCs) against the existing approved flight plans to see if they are likely to cause conflicts or bring sector traffic densities beyond control. In the current Air Traffic Control (ATC) operations, aircraft conflicts and sector traffic densities are resolved tactically, increasing workload and leading to potential safety risks and loss of capacity and efficiency. We propose a novel Prescriptive Analytics System to address a long-range aircraft conflict detection and resolution (CDR) problem. Given a set of predicted trajectories, the system declares a conflict when a protected zone of an aircraft on its trajectory is infringed upon by another aircraft. The system resolves the conflict by prescribing an alternative solution that is optimized by perturbing at least one of the trajectories involved in the conflict. To achieve this, the system learns from descriptive patterns of historical trajectories and pertinent weather observations and builds a Hidden Markov Model (HMM). Using a variant of the Viterbi algorithm, the system avoids the airspace volume in which the conflict is detected and generates a new optimal trajectory that is conflict-free. The key concept upon which the system is built is the assumption that airspace is nothing more than horizontally and vertically concatenated set of spatio-temporal data cubes where each cube is considered as an atomic unit. We evaluate our system using real trajectory datasets with pertinent weather observations from two continents and demonstrate its effectiveness for strategic CDR.
Host membrane glycosphingolipids and lipid microdomains facilitate Histoplasma capsulatum internalisation by macrophages

Recognition and internalisation of intracellular pathogens by host cells is a multifactorial process, involving both stable and transient interactions. The plasticity of the host cell plasma membrane is fundamental in this infectious process. Here, the participation of macrophage lipid microdomains during adhesion and internalisation of the fungal pathogen Histoplasma capsulatum (Hc) was investigated. An increase in membrane lateral organisation, which is a characteristic of lipid microdomains, was observed during the first steps of Hc–macrophage interaction. Cholesterol enrichment in macrophage membranes around Hc contact regions and reduced levels of Hc–macrophage association after cholesterol removal also suggested the participation of lipid microdomains during Hc–macrophage interaction. Using optical tweezers to study cell‐to‐cell interactions, we showed that cholesterol depletion increased the time required for Hc adhesion. Additionally, fungal internalisation was significantly reduced under these conditions. Moreover, macrophages treated with the ceramide‐glucosyltransferase inhibitor (P4r) and macrophages with altered ganglioside synthesis (from B4galnt1−/− mice) showed a deficient ability to interact with Hc. Coincubation of oligo‐GM1 and treatment with Cholera toxin Subunit B, which recognises the ganglioside GM1, also reduced Hc association. Although purified GM1 did not alter Hc binding, treatment with P4 significantly increased the time required for Hc binding to macrophages. The content of CD18 was displaced from lipid microdomains in B4galnt1−/− macrophages. In addition, macrophages with reduced CD18 expression (CD18low) were associated with Hc at levels similar to wild‐type cells. Finally, CD11b and CD18 colocalised with GM1 during Hc–macrophage interaction. Our results indicate that lipid rafts and particularly complex gangliosides that reside in lipid rafts stabilise Hc–macrophage adhesion and mediate efficient internalisation during histoplasmosis.
Aula invertida mediada por el uso de plataformas virtuales: un estudio de caso en la formación de profesores de física

This article shows the main results of the implementation of a flipped classroom model by using a free access virtual platform in a didactic course for future physics teachers at a state university in Chile. Through a case study with a qualitative-descriptive research approach, at the end of the semester the 31 participants identified several advantages and disadvantages regarding the flipped classroom as an instructional strategy. Among them, the development of habits and self-regulation for learning are highlighted, which contrasts with the additionaldifficulty of constantly revising the pre-class materials available on the virtual platform selected as support for teaching. Implications for teacher training and suggestions for future implementations are discussed.
An update on LNCipedia: a database for annotated human lncRNA sequences

The human genome is pervasively transcribed, producing thousands of non-coding RNA transcripts. The majority of these transcripts are long non-coding RNAs (lncRNAs) and novel lncRNA genes are being identified at rapid pace. To streamline these efforts, we created LNCipedia, an online repository of lncRNA transcripts and annotation. Here, we present LNCipedia 3.0 (http://www.lncipedia.org), the latest version of the publicly available human lncRNA database. Compared to the previous version of LNCipedia, the database grew over five times in size, gaining over 90 000 new lncRNA transcripts. Assessment of the protein-coding potential of LNCipedia entries is improved with state-of-the art methods that include large-scale reprocessing of publicly available proteomics data. As a result, a high-confidence set of lncRNA transcripts with low coding potential is defined and made available for download. In addition, a tool to assess lncRNA gene conservation between human, mouse and zebrafish has been implemented.
Layout Design for Intelligent Warehouse by Evolution With Fitness Approximation

With the rapid growth of the express industry, intelligent warehouses that employ autonomous robots for carrying parcels have been widely used to handle the vast express volume. For such warehouses, the warehouse layout design plays a key role in improving transportation efficiency. However, this work is still done by human experts, which is expensive and leads to suboptimal results. In this paper, we aim to automate the warehouse layout designing process. We propose a two-layer evolutionary algorithm to efficiently explore the warehouse layout space, where an auxiliary objective fitness approximation model is introduced to predict the outcome of the designed warehouse layout and a two-layer population structure is proposed to incorporate the approximation model into the ordinary evolution framework. Empirical experiments show that our method can efficiently design effective warehouse layouts that outperform both heuristic-designed and vanilla evolution-designed warehouse layouts.
Team NimbRo at MBZIRC 2017: Fast landing on a moving target and treasure hunting with a team of micro aerial vehicles

The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2017 has defined ambitious new benchmarks to advance the state‐of‐the‐art in autonomous operation of ground‐based and flying robots. This study covers our approaches to solve the two challenges that involved micro aerial vehicles (MAV). Challenge 1 required reliable target perception, fast trajectory planning, and stable control of an MAV to land on a moving vehicle. Challenge 3 demanded a team of MAVs to perform a search and transportation task, coined “Treasure Hunt,” which required mission planning and multirobot coordination as well as adaptive control to account for the additional object weight. We describe our base MAV setup and the challenge‐specific extensions, cover the camera‐based perception, explain control and trajectory‐planning in detail, and elaborate on mission planning and team coordination. We evaluated our systems in simulation as well as with real‐robot experiments during the competition in Abu Dhabi. With our system, we—as part of the larger team NimbRo—won the MBZIRC Grand Challenge and achieved a third place in both subchallenges involving flying robots.
Fast analysis of upstream features on spatial networks (GIS cup)

We present a fast linear time algorithm that uses a block-cut tree for identifying upstream features from a set of starting points in a network. Our implementation has been parallelized and it can process a dataset with 32 million features in less than 8 seconds on a 8-core workstation. This problem is the 2018 ACM SIGSPATIAL CUP challenge and presents several applications mainly on the field of utility networks. Our code is freely available for nonprofit research and education at https://github.com/sallesviana/FastUpstream
Size Matters: Improving the Performance of Small Files in Hadoop

The Hadoop Distributed File System (HDFS) is designed to handle massive amounts of data, preferably stored in very large files. The poor performance of HDFS in managing small files has long been a bane of the Hadoop community. In many production deployments of HDFS, almost 25% of the files are less than 16 KB in size and as much as 42% of all the file system operations are performed on these small files. We have designed an adaptive tiered storage using in-memory and on-disk tables stored in a high-performance distributed database to efficiently store and improve the performance of the small files in HDFS. Our solution is completely transparent, and it does not require any changes in the HDFS clients or the applications using the Hadoop platform. In experiments, we observed up to 61 times higher throughput in writing files, and for real-world workloads from Spotify our solution reduces the latency of reading and writing small files by a factor of 3.15 and 7.39 respectively.