Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Planning of Fully Actuated Closed Kinematic Chains With Revolute Joints: A Comparative Analysis

This letter provides a comparative analysis of common strategies for sampling-based motion planning of fully actuated closed kinematic chains with revolute joints. Three strategies are reviewed: singularity sampling, swapping between the possible passive chains while using the active–passive chains kinematic approach, and using Newton–Raphson projections. The strategies are compared using analytical and empirical results. Various measures, such as sampling time and visibility, are used to analyze the strategies in several dual-arm planning problems using the three different planning algorithms. This comparative analysis provides insight into the strengths and weaknesses of each sampling strategy. Results indicate that the kinematic-based passive chains swapping strategy is more preferable in environments with obstacles but harder to implement for various robotic arms.
Stuttgart Fachbereich Mathematik The Strong-Coupling Polaron in Electromagnetic Fields

This paper is concerned with Fröhlich polarons subject to external electromagnetic fields in the limit of large electron-phonon coupling. To leading order in the coupling constant, √ α, the ground state energy is shown to be correctly given by the minimum of the Pekar functional including the electromagnetic fields, provided these fields in the Fröhlich model are scaled properly with α. As a corollary, the binding of two polarons in strong magnetic fields is obtained.
Music recommendation based on content and collaborative approach & reducing cold start problem

Nowadays, Recommendation becomes the most popular area for many researchers. The main aim of recommendation is to provide meaningful suggestions to users for particular item based on users past interest and behaviors towards items. There are two most popular recommendation algorithm is 1) Content-Based Filtering 2) Collaborative Filtering. Content-Based method recommends music based on user data. Collaborative method uses rating and content sharing between different users to recommend music. Here, to provide music recommendation by content-based method music subjective features Speechiness, loudness, Acoustiness etc. are analyzed. The extracted features are stores into database by using Kmean clustering algorithm. For Content-based method, whenever user fires query to database music feature attribute value compares with clusters centroid. Once attribute value match, music can be recommended to user as Content-based method. For collaborative method, rating given by user to particular music is considered and adjusted cosine similarity is used to find similarity between user-user. Once similarity found, prediction rating algorithm is used to provide recommendation to user. Cold-start is most common problem for new user. Here, most popular tracks are recommending to user to solve it.
Autonomous navigation & mapping of small bodies

Autonomous mapping and navigation around unknown small bodies is a challenging problem. In todays missions, small body mapping and navigation (SBMN) require significant human intervention on the ground for map refinement and supervision of the navigation and orbit selection process. Although current methodologies adequately performed in past missions (e.g., Rosetta, Hayabusa, Deep Space), they are not suitable for applications requiring a high level of autonomy. This work proposes a method for autonomous orbit selection and adaptation around a small body while mapping its surface. In particular, in this work, we will develop cost functions that quantify the orbit goodness in the sense of map improvement. In other words, we develop quantitative measures that characterize the accuracy of the small body map and use these measures in an optimization process to compute the next best orbit that maximally contributes to the map enhancement. The proposed framework reduces the human involvement in this process and takes a step toward the fully autonomous mapping and navigation around small bodies.
Influence of harmonics on the electromechanical stresses in a power transformer

Transformers are essential equipment to the power electric systems continuity and, therefore, they are object of several studies in order to determine the main factors that can exercise negative impact on the equipment performance. One of these factors are the mechanical stresses on the transformer windings. In this paper, the influence of the current harmonic profile on the transformer electromechanical stresses was analyzed and quantified using case studies based on a real harmonic profile. A significant increase in the mechanical stresses amplitudes and displacements was verified. The conclusion is that the harmonic distortion influence should not be overlooked in the process and analysis of electric transformers, especially in situations where the transformer is installed at a point in the system characterized by expressive harmonic pollution.
A Spatio-Temporal Entropy-based Framework for the Detection of Trajectories Similarity

The rapid proliferation of sensors and big data repositories offer many new opportunities for data science. Among many application domains, the analysis of large trajectory datasets generated from people’s movements at the city scale is one of the most promising research avenues still to explore. Extracting trajectory patterns and outliers in urban environments is a direction still requiring exploration for many management and planning tasks. The research developed in this paper introduces a spatio-temporal framework, so-called STE-SD (Spatio-Temporal Entropy for Similarity Detection), based on the initial concept of entropy as introduced by Shannon in his seminal theory of information and as recently extended to the spatial and temporal dimensions. Our approach considers several complementary trajectory descriptors whose distribution in space and time are quantitatively evaluated. The trajectory primitives considered include curvatures, stop-points, self-intersections and velocities. These primitives are identified and then qualified using the notion of entropy as applied to the spatial and temporal dimensions. The whole approach is experimented and applied to urban trajectories derived from the Geolife dataset, a reference data benchmark available in the city of Beijing.
Physics-inspired models for agile code and data in federated edges

We study the problem of flexibly, dynamically, and adaptively moving, positioning, and instantiating computing tasks and data in federated, distributed edge systems. We call this process “agile code and agile data” (ACAD). We explore the adaptation of physics-inspired models, used for atomistic simulations, to the ACAD problem, treating the code and data as particles on a graph, interacting through different potential energy models. We discuss the mapping between the different elements of ACAD problem and our particles-on-a-graph model, considering different frameworks for data analytics. We explore gravitational, elastic and Coulombic models, both with global and local energy minimization, finding that the Coulombic model obtains the most efficient solution.
SecMed: A Secure Approach for Proving Rightful Ownership of Medical Images in Encrypted Domain over Cloud

The wide attacking surface of the public cloud poses threats to the security of sensitive information such as medical information stored at these distributed cloud data centers. Obscuring this information using traditional encryption schemes would limit the processing capabilities in encrypted domain that are provided via various cloud services. Moreover, it is vital to address the issue of rightful ownership so that the person to which the medical information belongs to can be determined. To address these issues, Chinese Remainder Theorem (CRT) based secret sharing scheme has been employed to divide the medical images into multiple random looking shares which are information theoretically secure and reveal no information about the images. Based on a secret key, some of these encrypted shares are embedded with the secret owner specific information in the encrypted domain itself prior to outsourcing. To prove rightful ownership at the receiver end, this secret information can be extracted either directly from the shares stored at the cloud data centers or obtained after recovery of the medical information at the authentic entity end which possesses the secret keys. The robustness of the scheme against different attack scenarios while stored at the cloud,,,, data centers in encrypted domain has been tested to validate the efficacy of the proposed scheme.
Generating mutually recursive definitions

Many functional programs — state machines [Krishnamurthi 2006], top-down and bottom-up parsers [Hinze and Paterson 2003; Hutton and Meijer 1996], evaluators [Abelson et al. 1984], GUI initialization graphs [Syme 2006], &c. — are conveniently expressed as groups of mutually recursive bindings. One therefore expects program generators, such as those written in MetaOCaml, to be able to build programs with mutual recursion. Unfortunately, currently MetaOCaml can only build recursive groups whose size is hard-coded in the generating program. The general case requires something other than quotation, and seemingly weakens static guarantees on the resulting code. We describe the challenges and propose a new language construct for assuredly generating binding groups of arbitrary size – illustrating with a collection of examples for mutual, n-ary, heterogeneous, value and polymorphic recursion.
Crater-Exposed Intact Stratigraphy Blocks and Volcanogenic Origin

Introduction: Typical terrestrial uplift and exposure of layered and stratified rocks are usually not oriented at a high enough tilt to easily infer their geometries from aerial or orbital remote sensing techniques. However, when layers are brought to the surface in crater central stratigraphic uplifts, they are tilted at such high orientations (>45) as to be useful for such determinations as thickness, strike, dip, etc. Here we use a variety of Martian datasets to identify (THEMIS IR, MOLA) and classify (CTX and HiRISE) these unique bedrock exposures and textures in Martian craters [1]. A global survey of 868 Martin craters reveals three distinct classifications of bedrock textures, which includes uplifted and overturned strata that are exposed from depth. Crater-exposed bedrock provides a unique opportunity to not only understand the underlying mineral compositions within the Martian crust, but also gain insight into past geologic processes of Mars. Of the 868 craters surveyed, 210 have bedrock outcroppings that are sufficiently exposed and extensive enough to determine their overall textures. Of these, we classified 39 as Intact layered Stratigraphy (IS) (i.e., uplifted and inverted bedrock consisting of strongly layered and intact blocks of stratigraphy with layers ranging from meter to decameter scales in HiRISE images as seen in Figure 1). As seen in Figure 2, a strong correlation exists between the location of IS craters observed and the major volcanic provinces and mapped Hesperian flood lavas and ridged plains (purple units), generally interpreted as flood lavas [2]. Here we couple the geologic context and textures observed from depth with the measurable thickness and cyclicity of the layers, which alternate between competent and less competent layers within a given uplifted megablock, to better understand and relate past geologic events and processes across vast regions of Mars.
Research on personalized referral service and big data mining for e-commerce with machine learning

Machine learning (ML) is one of the main methods to address the problem of big data mining. ML can enable the e-commerce system upon self-innovate and improvement by accumulating prior knowledge. The big data produced by transaction, interaction and observation from e-commerce enterprises can greatly provide decision-making service for marketing strategy. In this paper we take the e-commerce data of tea-device enterprise as an example, use the FP-grow algorithm to get the frequent item sets, so as to mining and analyze association rule of user behavior to get feature vector as the basis of user classification, then use Naive Bayesian algorithm on the feature vector to implement clustering learning for precision marketing and personalized online referral services. Finally, we evaluate the feasibility of big data mining with ML through the profit produced by the sale of goods. Experimental results show that ML can not only greatly improve the performance in big data mining, can also achieve precise marketing, and can further increasing about 20% marginal profit for each type of goods.
Study case of the stochastic tarot® model: Comparison of the economic performance of Brazilian Electric Power Distribution Companies considering the inherent risks of the sector

This document proposes the application of a stochastic economic model using real data from national regulated distribution companies to evaluate economic performance considering the inherent risks of the sector. Furthermore, through the use of economic theories such as: risk-return binomial in the Markowitz plan and utility function of John Von Neumann and Oskar Morgenstern, the case study results in a comparison of the economic performances of the companies in a stochastic context where the risks are considered.
Substation security mechanism based on SDN and MMS

New solutions based on SDN has been proposed for substations. SDN has represented a significant gain in network configuration and dynamic management. Despite the advantages of using SDN technology, security mechanisms to prevent substation attacks are needed. In order to avoid Denial-of- service attacks, this work proposes an IED authentication mechanism. The proposal was implemented with Mininet and RYU controller. Results indicate the effectiveness of the mechanism and also it shows the need for security implementation in substations.
Enhanced Photoresponse in Interfacial Gated Graphene Phototransistor With Ultrathin Al2O3 Dielectric

An interfacial-gated graphene phototransistor sensitized by silicon substrate with an ultrathin 3-nm Al<sub>2</sub>O<sub>3</sub> dielectric layer is demonstrated. When applying gate voltages larger than a certain threshold, the phototransistor exhibits a photo-induced switching-on behavior, giving rise to its capacity of detecting light with the ultralow power of 470 pW, with a high responsivity of <inline-formula> <tex-math notation="LaTeX">$\textsf {1.4} \times \textsf {10}^{\textsf {4}}$ </tex-math> </inline-formula> A/W. The temporal measurements unveil a fast photoresponse speed with a rising time of <inline-formula> <tex-math notation="LaTeX">$2~\mu \text{s}$ </tex-math> </inline-formula>. The novel channel on-state trigged by light was not observed in a control device with 16-nm Al<sub>2</sub>O<sub>3</sub>. The difference of transfer characteristics in the dark and under illumination is assigned mainly to the photo-assisted modification of the silicon depletion region.
An optimization approach for dynamic allocation on reconfigurable resources at runtime

Reconfigurable computing can be divided into two types including static reconfiguration and dynamic reconfiguration according to the reconfiguration timing. Static reconfiguration can provide low runtime overhead for the precomputing configuration. Due to the failure to know the run-time information of the system, once the static prediction is different to the real profiling, static scheduling cannot obtain high performance without flexible. While dynamic scheduling can be used to schedule decisions according to the runtime information of the system. It will dynamically allocate the reconfigurable resources to the tasks at runtime, which can provide high performance and considerable flexibility. However, dynamic allocation at runtime will also have some overhead for the instant resource management. This paper presents an optimization approach for dynamic allocation on reconfigurable resources at runtime. Hardware tasks can obtain available reconfigurable resources according to its resource request and the system profiling at runtime. The experimental results show that this approach can provide better performance than traditional ones.
Adaptive Sliding Mode Fault-Tolerant Coordination Control for Four-Wheel Independently Driven Electric Vehicles

Four wheel independently driven (4WID) electric vehicles are promising vehicle architectures. However, the complex coordination control of the four driving motors and the operation reliability of the vehicle are challenges. In this paper, to address the multimotor coordinate operation against the actuator faults in the 4WID system, combining the adaptive sliding mode (ASM) control and the fault-tolerant (FT) control allocation, an adaptive sliding mode fault-tolerant coordination (ASM-FTC) control is proposed. First, a new vehicle dynamic model with a driving motor fault is set up. In the control layer, an adaptive variable exponential reaching law is used in the ASM to alleviate the chattering and improve the reaching speed, precision, and robustness. Then, the FTC allocation based on the quadratic programming is designed to properly coordinate the four in-wheel motors at the presentation of the motor fault. To verify the proposed control method, a 4WID electric vehicle platform is set up. Simulation and experimental results demonstrate the effectiveness of the ASM-FTC control.
DLL 4 regulates NOTCH signaling and growth of T acute lymphoblastic leukemia cells in NOD / SCID mice

5Present addresses: Klinische Chemie und Laboratoriumsmedizin, Universitätsklinikum des Saarlandes, Homburg/Saar, Germany, 6Tumor Angiogenesis Group, Catalan Institute of Oncology–IDIBELL, L’Hospitalet de Llobregat, Barcelona, Spain, 7Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, John Radcliffe Hospital, Headington, Oxford, UK and 8Gastroenterology Section, Department of Surgery Oncology and Gastroenterology, University of Padova, Padova, Italy.
The Prevalence of Trichomoniasis in Women Referred to Clinical Centers in South of Tehran, Iran during 2015–2016

Background: This study aimed to estimate the prevalence of trichomoniasis infection among females in Tehran, Iran. Methods: This study was conducted on 482 women referred to the 6 obstetrics and gynecology centers of Tehran during 2015–2016. Some information including education, occupation, and number of sexual partners was obtained and clinical signs and symptoms of the genital tract were diagnosed by clinical examination. Two swabs were collected from the posterior fornix of patients. Two laboratory techniques, wet mount, and culture were carried out. Finally, statistical analysis test was performed using SPSS software version 16.0. Results: Age distribution of patients was 15–60 yr. Trichomonas vaginalis was detected in 2 out of 482 participants (0.41%). All of the infected individuals were married (0.43%) and they had unique sexual partner and all of them had clinical symptoms. Significant association was observed between incidence of T. vaginalis infection and educational levels (P= 0.03), occupation (P=0.006), clinical symptoms (P=0.001), marriage (P=0.006) and bacterial infection (P=0.018). Conclusion: The prevalence of trichomoniasis was low and its incidence was associated with several risk factors.
Asymmetric Body Bias Control With Low-Power FD-SOI Technologies: Modeling and Power Optimization

Body bias control is a fundamental technique widely used to provide an efficient tradeoff between leakage power and performance in ultralow-power systems. Therefore, a lot of research about power optimization which provides optimal power supply and body bias voltages has been carried out. However, considering the actual voltage sources, the conventional approaches suffer from limited performance/power control granularity and may lead to degradation in terms of the energy efficiency. Therefore, in this paper, a power optimization method that improves the performance/power control granularity is proposed and evaluated with real processor chips. In the proposed optimization, the body biases for nMOSFET and pMOSFET are controlled independently, while the conventional methods control them uniformly. This increases the number of possible voltage combinations and allows finer target frequency selection leading to lower power consumption than the conventional methods at the cost of the optimization complexity. In order to ease this complexity, the proposed optimization is based on simple power and delay models. The model-based optimization does not require brute force search in the phase of real chip testing; thus, the testing time and cost can be significantly reduced. Since the coefficients of the models are extracted with real chip measurements, the error of the model can be suppressed to a few percent in average. The proposed approach is validated by real chips implemented with a 65-nm fully depleted silicon on insulator technology. The evaluation results show that the proposed optimization is an efficient mean of power reduction for a leakage current dominant chip. In fact, when compared with the conventional method, the proposed approach achieves 9.617% of average power reduction reaching up to 22.77% in the case of the V850 microcontroller.
DNN-based approach for identification of the level of attention of the TV-viewers using IoT network

In this paper, we define the concept of levels of attention towards a TV program. Then, we propose an approach to identify and classify them using a network of connected objects: a smartphone, a smart watch and a remote control. Seven participants were observed watching their favorite TV programs. Then each participant was asked to rate some of their own activities according to two situations: attentive to the TV program (Level 1) or not (Level 2). Attention levels were classified using a Deep Neural Network algorithm (DNN) after extracting a set of descriptors from the sensors' data of the three devices and fused them. The results show that the levels of attention were classified with an accuracy of 88.44% and 87.89% for the level 1 and level 2 respectively. This study shows that we can detect a level of attention towards a TV program with usual connected objects while remaining as intrusive as possible. This corresponds to real situations of sensing attention to a TV program.
Mitigation of voltage variations during cloud transients by using energy storage systems

The clouds passing over the photovoltaic generators may cause quick variations on active power generation and grid voltage. In this context, the present paper aims to analyse the effectiveness of battery systems applied to solve the mentioned impacts. In order to do that, the IEEE 13 bus was the simulated distribution system. Real measurement data referring to irradiation on a tipical clear and cloudy day was used, jointly with an artificial neural network based control method. The control quick response along with the battery system were able to substantially reduce the explained problem.