Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing

Within the context of autonomous driving a model-based reinforcement learning algorithm is proposed for the design of neural network-parameterized controllers. Classical model-based control methods, which include sampling- and lattice-based algorithms and model predictive control, suffer from the trade-off between model complexity and computational burden required for the online solution of expensive optimization or search problems at every short sampling time. To circumvent this trade-off, a 2-step procedure is motivated: first learning of a controller during offline training based on an arbitrarily complicated mathematical system model, before online fast feedforward evaluation of the trained controller. The contribution of this paper is the proposition of a simple gradient-free and model-based algorithm for deep reinforcement learning using task separation with hill climbing (TSHC). In particular, (i) simultaneous training on separate deterministic tasks with the purpose of encoding many motion primitives in a neural network, and (ii) the employment of maximally sparse rewards in combination with virtual velocity constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl

A review of urban computing for mobile phone traces

In this work, we present three classes of methods to extract information from triangulated mobile phone signals, and describe applications with different goals in spatiotemporal analysis and urban modeling. Our first challenge is to relate extracted information from phone records (i.e., a set of time-stamped coordinates estimated from signal strengths) with destinations by each of the million anonymous users. By demonstrating a method that converts phone signals into small grid cell destinations, we present a framework that bridges triangulated mobile phone data with previously established findings obtained from data at more coarse-grained resolutions (such as at the cell tower or census tract levels). In particular, this method allows us to relate daily mobility networks, called motifs here, with trip chains extracted from travel diary surveys. Compared with existing travel demand models mainly relying on expensive and less-frequent travel survey data, this method represents an advantage for applying ubiquitous mobile phone data to urban and transportation modeling applications. Second, we present a method that takes advantage of the high spatial resolution of the triangulated phone data to infer trip purposes by examining semantic-enriched land uses surrounding destinations in individual's motifs. In the final section, we discuss a portable computational architecture that allows us to manage and analyze mobile phone data in geospatial databases, and to map mobile phone trips onto spatial networks such that further analysis about flows and network performances can be done. The combination of these three methods demonstrate the state-of-the-art algorithms that can be adapted to triangulated mobile phone data for the context of urban computing and modeling applications.BMW GroupAustrian Institute of TechnologySingapore. National Research FoundationMassachusetts Institute of Technology. School of EngineeringMassachusetts Institute of Technology. Dept. of Urban Studies and PlanningSingapore-MIT Alliance for Research and Technology (Center for Future Mobility

Asymptotically-optimal path planning for manipulation using incremental sampling-based algorithms

A desirable property of path planning for robotic manipulation is the ability to identify solutions in a sufficiently short amount of time to be usable. This is particularly challenging for the manipulation problem due to the need to plan over high-dimensional configuration spaces and to perform computationally expensive collision checking procedures. Consequently, existing planners take steps to achieve desired solution times at the cost of low quality solutions. This paper presents a planning algorithm that overcomes these difficulties by augmenting the asymptotically-optimal RRT* with a sparse sampling procedure. With the addition of a collision checking procedure that leverages memoization, this approach has the benefit that it quickly identifies low-cost feasible trajectories and takes advantage of subsequent computation time to refine the solution towards an optimal one. We evaluate the algorithm through a series of Monte Carlo simulations of seven, twelve, and fourteen degree of freedom manipulation planning problems in a realistic simulation environment. The results indicate that the proposed approach provides significant improvements in the quality of both the initial solution and the final path, while incurring almost no computational overhead compared to the RRT algorithm. We conclude with a demonstration of our algorithm for single-arm and dual-arm planning on Willow Garage's PR2 robot

Phytowaste as nutraceuticals in boosting public health

AbstractThe utilization of bioactive constituent of peels and seeds provide an effective, environment friendly and inexpensive therapy for different forms of human disease, and the production, improvement and documentation of novel nutraceuticals. This review systematically presents findings and further understanding of the reported benefits and therapeutic applications of peel and seed extracts on innovative cell culture and animal studies, as well as phased clinical human trial research. The extracts of seed and peels were reported to possess high quantities of bioactive substances with antioxidative, antidiabetic, hepatorenal protective, antithyroidal, anti-inflammatory, antibacterial, cardiovascular protective, neuro-protective effects, anticancer and wound healing activities. Therapeutic activities of the bioactive substances of peel and seed extracts include elevation of Superoxide dismutase (SOD), GSH-Px, t-GPx, Catalase and GST activities, with the suppression of MDA levels, hydroperoxide generation and lipid peroxidized products, the extracts also regulate inflammatory mediators and cytokines as they are reported to suppress the secretion of inflammatory cytokines, which include; IL-1β, PGE2, TGF-β and TNF-α and induces apoptosis and cell differentiation. This review revealed the therapeutic importance and best utilization of peels and seed extracts of fruits and vegetables

Sampling-based Algorithms for Optimal Motion Planning

During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i.e., such that the cost of the returned solution converges almost surely to the optimum. Moreover, it is shown that the computational complexity of the new algorithms is within a constant factor of that of their probabilistically complete (but not asymptotically optimal) counterparts. The analysis in this paper hinges on novel connections between stochastic sampling-based path planning algorithms and the theory of random geometric graphs.Comment: 76 pages, 26 figures, to appear in International Journal of Robotics Researc

Analisi degli aspetti della contabilità del materiale fissile connessi con le prestazioni strumentali

Studio di fattibilità per la determinazione della MDA ottimale per misure di Salvaguardia Nuclear

A new respirometric endpoint-based biosensor to assess the relative toxicity of chemicals on immobilized human cells

Several functional and biochemical parameters have been proposed as biomarkers of effect of environmental pollutants. A rapid biosensor working with immobilized human U-937 cells was developed and applied to environmentally relevant chemicals with different structures and toxicological pathways, i.e. benzalkonium chloride, clofibric acid, diclofenac, mercury nitrate, ofloxacin, and sodium dodecyl sulphate. Respiration of cells was relied upon as a comprehensive biochemical effect for screening purposes. Analytical parameter (?ppmO2) and toxicological index (respiratory inhibition, ?%) measured after 1 h of exposure were utilized for dose–response relationship study. Results (toxicity rating scales based on ?50% and steepness) were compared with those obtained by the same approach previously optimized on Saccharomyces cerevisiae. The toxicity rating scale obtained by the biomarker based on human mitochondrial and cell metabolic activities compared well with previous scale obtained on yeast cells and with available in-vivo acute toxicity indexes; respiration was confirmed as toxicological endpoint reliably measurable by the biosensor

COMPUTERISED METHODS FOR TIME CORRELATION IN PASSIVE NEUTRON COUNTING FOR FISSILE MATERIAL DETERMINATION

Application of time correlation methods for plutonium determinatio