Miracle-dz/UniFAR-FTUs huggingface dataset:
A thesaurus of genetic variation for interrogation of repetitive genomic regions

Detecting genetic variation is one of the main applications of high-throughput sequencing, but is still challenging wherever aligning short reads poses ambiguities. Current state-of-the-art variant calling approaches avoid such regions, arguing that it is necessary to sacrifice detection sensitivity to limit false discovery. We developed a method that links candidate variant positions within repetitive genomic regions into clusters. The technique relies on a resource, a thesaurus of genetic variation, that enumerates genomic regions with similar sequence. The resource is computationally intensive to generate, but once compiled can be applied efficiently to annotate and prioritize variants in repetitive regions. We show that thesaurus annotation can reduce the rate of false variant calls due to mappability by up to three orders of magnitude. We apply the technique to whole genome datasets and establish that called variants in low mappability regions annotated using the thesaurus can be experimentally validated. We then extend the analysis to a large panel of exomes to show that the annotation technique opens possibilities to study variation in hereto hidden and under-studied parts of the genome.
User Centric Access control policy management framework for Cloud applications

Cloud computing environment is a collection of various Cloud applications deployed by different Cloud service vendors for their customers. The online availability, variety and easy access of Cloud applications allow users to create, upload and store numerous resources across the Cloud. However, Protection of these resources from different security threats in Cloud environment is still a serious concern for the Cloud users. Cloud applications provide diverse and complex authorization and access control mechanisms to different Cloud users. Moreover, Access control is limited and tightly bound to the functionality of the applications and does not cater the access control requirements of individual users. Securing every resource with different, complex and limited access control solutions is a tedious task and results in poorly protected resources susceptible to unauthorized access and different other security threats. A new approach to access control in Cloud environment is presented in this paper. It externalizes access control from Cloud applications and enables users to create, and manage access control policies on their resources according to their own security and access control requirements. The framework also provides users with a central control point, standard policy definition language and easy to use interface to specify and manage access control on all their resources scattered across the Cloud. We presented the framework and described the protocol which defines the interaction between different components of the system to specify and enforce User-Centric policies using XACML standards.
Training multilayer perceptron by using optimal input normalization

In this paper, we propose a novel second order paradigm called optimal input normalization (OIN) to solve the problems of slow convergence and high complexity of MLP. By optimizing the non-orthogonal transformation matrix of input units in an equivalent network, OIN absorbs separate optimal learning factor for each synaptic weight as well as the threshold of hidden unit, leading to an improvement in the performance for MLP training. Moreover, by using a whitening transformation of negative Jacobian matrix of hidden weights, a modified version of OIN called optimal input normalization with hidden weights optimization (OIN-HWO) is also proposed. The Hessian matrices in both OIN and OIN-HWO are computed by using Gauss-Newton method. All the linear equations are solved via orthogonal least square (OLS). Regression simulations are performed on several real-life datasets and the results show that the proposed OIN has not only much better convergence rate and generalization ability than output weights optimization-back propagation (OWO-BP), optimal input gains (OIG) and even Levenberg-Marquardt (LM) method, but also takes less computational time than OWO-BP. Although OIN-HWO takes a little expensive computational burden than OIN, its convergence rate is faster than OIN and often close to or rivals LM. It is therefore suggested that OIN-based algorithms are potentially very good choices for practical applications.
A 1.9GHz 1W CMOS class E power amplifier for wireless communications

A CMOS implementation of a Class E power amplifier for wireless communications which operates in the GHz region is reported. The concept of mode-locking is introduced in which the amplifier acts as an oscillator whose output is forced to run at the input frequency. A prototype was built, which delivered over 1W of output power with 48% power added efficiency, centred at 1.9GHz using a 2V supply.
The Innovation Model of Teaching and Human Resource Cultivation in Major of Public Administration in the Process of State Governance Modernization

The modernization of state governance is the extension and development of China's "four modernizations" (of industry, agriculture, national defence and science & technology). With the progress of China's governance modernization, the shortage of highly qualified administrative professionals comes into sight. The cultivation of highly qualified administrative professionals and talent pool are important factors that determine the process and level of China's governance modernization. Therefore, the teaching and training model of administrative professionals is extremely important. By using the method of literature review, in this paper, the author studies the current situation of the teaching and human resource training mode of major of public administration in colleges and universities of China, learns about the current situation of the cultivation of administrative professionals in China, and proposes improvement measures for administrative professionals from the perspective of educational authorities, colleges and universities and individuals.
Improving Signal-to-Noise Ratio Estimation for Autonomous Receivers

A method by which a popular signal-to-noise ratio (SNR) estimator can be reconfigured to yield improved performance is proposed. The reconfiguration consists of partitioning the symbol interval into a larger (but even) number of subdivisions than the two that have been traditionally suggested for this estimator but still processing the observables in successive pairs. The optimum number of subdivisions is shown to depend on the SNR range in which the true SNR lies; however, we also show that these SNR regions can be significantly widened with very little loss in performance. Most important is the fact that, with this reconfiguration, the SNR estimator tracks the Cramer–Rao bound on the variance of the estimator over the entire range of SNR values.
The Bad, the Good, and the Ugly about Oxidative Stress

Alzheimer's disease (AD), Parkinson's disease (PD), and cancer (e.g., leukemia) are the most devastating disorders affecting millions of people worldwide. Except for some kind of cancers, no effective and/or definitive therapeutic treatment aimed to reduce or to retard the clinic and pathologic symptoms induced by AD and PD is presently available. Therefore, it is urgently needed to understand the molecular basis of these disorders. Since oxidative stress (OS) is an important etiologic factor of the pathologic process of AD, PD, and cancer, understanding how intracellular signaling pathways respond to OS will have a significant implication in the therapy of these diseases. Here, we propose a model of minimal completeness of cell death signaling induced by OS as a mechanistic explanation of neuronal and cancer cell demise. This mechanism might provide the basis for therapeutic design strategies. Finally, we will attempt to associate PD, cancer, and OS. This paper critically analyzes the evidence that support the “oxidative stress model” in neurodegeneration and cancer.
Model adjustable predictive control with stability guarantees

For stabilizing model predictive control adjusting the prediction model requires the adjustment of the terminal set and terminal cost. However, the conventional methods to design these are not practical, and often impossible, to implement in microcontrollers. In this paper, we pre-compute the terminal cost and terminal set in a form that allows to adjust them with minimal computational effort, following an adjustment of the prediction model. For unconstrained systems, a terminal cost and terminal controller are designed based on parameter-dependent Lyapunov functions. For constrained systems, the terminal function is also used to derive a robust polyhedral terminal set. We prove that the proposed method guarantees the existence of a terminal set with non-empty interior, and asymptotic stability of the closed-loop system.
PerformanceComparisonBetweenthe Paxos and Chandra-TouegConsensusAlgorithms

Protocolswhich solveagreementproblemsare essentialbuilding blocks for fault tolerant distributed applications. While manyprotocolshavebeenpublished,little hasbeendoneto analyze their performance. Thispaperrepresentsa startingpoint for such studies,byfocusingontheconsensusproblem,a problemrelatedto mostotheragreementproblems.Thepapercomparesthe latency of two consensusalgorithmsdesignedfor theasynchronousmodelwith failure detector s: thePaxos algorithm and the Chandra-Toueg algorithm. We varied the numberof processeswhich take part in the execution. Moreover, we evaluatedthe latencyin different classesof runs: (1) runs with no failuresnor failure suspicions,(2) runs with failuresbut no wrong suspicions.We determinedthe latencyby measur ementson a clusterof PCsinterconnectedwith a 100MbpsEthernetnetwork.We foundthat thePaxosalgorithmis moreefficientthantheChandra-Toueg algorithmwhentheprocess that coordinatesthe first roundof the protocol crashes.Thetwo algorithmshavealmostthe same performancein all othercases.
A Digital Control Method with Feedforward Reconstruction for Improved Sampling Interference Suppression in GaN HEMTs based Totem-pole PFC Converters

The duty-ratio feedforward is usually used in totem-pole power factor correction (PFC) converters with digital control to achieve high power factor. However, the sampling interference can affect the input current through the feedforward channel and reduce system performance. In this paper, an improved digital control method with feedforward reconstruction is presented. The proposed method reconstructs the input voltage feedforward in the control system using the input voltage root mean square (RMS) and phase-locked loop, and it can be applied in digitally controlled totem-pole PFC converters based on GaN high electron mobility transistors (HEMTs) to suppress sampling interference and achieve high power factor. Finally, the improved method is verified at a 3.6kW totem-pole PFC prototype with 99.26% peak efficiency. The measured power factor remains above 0.99 from half load to full load. The input current total harmonics distribution (THD) is 2.42% at full load and meet the IEC-61000-3-2 standard.
Internet mission control of the ROMEO unmanned underwater vehicle using the CORAL mission controller

This paper addresses the problem of mission control of unmanned underwater vehicles (UUVs) through the Internet. Its main focus is on the integration of a Petri Net based mission control system developed by the Istituto Superior Tecnico using the CORAL environment, with the control system of ROMEO, a prototype ROV developed by the Robotics Department of CNR-IAN. The system has been evaluated when controlling ROMEO's missions directly from Lisbon in the underwater virtual world (UVW) in the IAN lab and in a pool in Genoa. In particular, the reliability of the Internet connection has been verified and the constraints introduced by communication delays have been examined. This research and development effort aims at contributing to the development of reliable mission control systems for the operation of robotic ocean vehicles at a distance, over communication channels that may experience considerable delays. This is a subject of great relevance, in view of the widespread interest in the development of systems to allow a scientific end-user to program, execute, and follow the state of progress of robotic vehicle missions at sea from the comfort of his/her laboratory.
MicroRNA-203 inhibits malignant melanoma cell migration by targeting versican

MicroRNA (miR)-203 has been demonstrated to function as a suppressor in tumorigenesis. Recently, miR-203 was reported to play a role in malignant melanoma (MM); however, the detailed function of miR-203 in MM remains unclear. In the present study, the expression of miR-203 was shown to be significantly downregulated in MM tissues when compared with normal adjacent tissues. Based on a bioinformatic prediction, versican was further identified as a novel target of miR-203, and the expression of versican was markedly increased in MM tissues. Inhibition of miR-203 increased the protein expression of versican, while upregulation of miR-203 inhibited the protein expression of versican in MM A375 cells. In addition, the upregulation of versican significantly promoted A375 cell migration; however, upregulation of miR-203 suppressed A375 cell migration. The present study further investigated whether miR-203 was involved in versican-mediated A375 cell migration, and the results indicated that upregulation of miR-203 significantly inhibited A375 cell migration, which was impaired by overexpression of versican. These observations indicated that versican functions as a downstream effector in miR-203-mediated MM cell migration. Therefore, the results demonstrated that miR-203 exhibited an inhibitory effect on MM cell migration via directly targeting versican, thus, may become an effective inhibitor for MM metastasis.
A method to make accurate inventory of smart meters in multi-tags group-reading environment

RFID plays a very important role as auto-identification solution for many industrial applications. In electric power industry, UHF RFID tags are attached on smart meters, and a tag provides a unique electronic identification for a smart meter, so RFID is applied to make asset inventory for smart meters in a warehouse. However, there are just about 95% of tags that are readable for their reader because of the interference of PCB and electronic components in smart meter, thereby it has to take a lot of time to manually find out the non-responsive tags. This paper puts forward a feasible method of testing tag's performance according to multi-tags group-reading environment in the application of smart meter asset inventory, and verifies the feasibility of the method through inventory experiments, and finally gives out an optimized parameters to read 100% of tags with the sensitivity of -16.25dBm in a multi-tags environment. The method is suitable for the test of large quantities of smart meters that are densely packed in a plastic box. The proposed method can guide electric power companies to select tags and readers, and improve the efficiency of inventory for smart meters asset management.
Effect of square srr structure on the radiation pattern of the patch antenna in the x-band

Metamaterials are artificial structures that their constituent electromagnetic materials can be regulated by manipulating their structures. The unconventional properties of these materials such as permittivity and permeability coefficient and also the negative refractive index, have caused to Increase using the metamaterials in various fields such as microwave engineering. Metamaterial antennas are some kind of antennas that use metamaterials to increase the performance of small antenna systems (small electrical ones) as well as improve the radiation power of the antenna. In this paper, a microstrip antenna is designed using CST software at x-band applications and, then, improving the antenna performance like radiation pattern and directivity is demonstrated by utilization a specific metamaterial coverage.
Revisiting the role of the hippocampal mossy fiber synapse

The mossy fiber pathway has long been considered to provide the major source of excitatory input to pyramidal cells of hippocampal area CA3. In this review we describe anatomical and physiological properties of this pathway that challenge this view. We argue that the mossy fiber pathway does not provide the main input to CA3 pyramidal cells, and that the short‐term plasticity and amplitude variance of mossy fiber synapses may be more important features than their long‐term plasticity or absolute input strength. Hippocampus 2001;11:408–417. © 2001 Wiley‐Liss, Inc.
The Dynamics of Seller Reputation: Evidence from Ebay

We construct a panel of eBay seller histories and examine the importance of eBay's reputation mechanism. We find that, when a seller first receives negative feedback, his weekly sales rate drops from a positive 5% to a negative 8%; subsequent negative feedback ratings arrive 25% more rapidly than the first one and don't have nearly as much impact as the first one. We also find that a seller is more likely to exit the lower his reputation is; and that, just before exiting, sellers receive more negative feedback than their lifetime average.
Learning to adapt: Cognitive architecture based on biologically inspired memory

This paper describes a cognitive architecture that is predominantly substantiated by data processing principles of biologically inspired multiple memory systems. The current memory configuration involves episodic, semantic, procedural and working memory units. We introduce the reader to essentials of the mammalian memory system and explain our highly abstracted software realization of it as a distributed, concurrently communicating node structure. We also show our first results of its implementation for cognitive function as novelty detection and exploration of a maze in the case of simulated autonomous mobile robot.
Human Rescue Based on Autonomous Robot KUKA YouBot with Deep Learning Approach

We report the successful integration of deep learning approach for autonomous robot KUKA YouBot navigation. The incorporation of deep learning approach was achieved thanks the environments like Open Source Computer Vision Library (OpenCV), Python and Robot Operating System (ROS)working together. The ROS with Hydro medusa distribution provides the managing of odometry, kinematics and path planning nodes in robot KUKA YouBot. The combination of all nodes by using Gazebo software offers the enough support in order to perform the simulation of the autonomous robot navigation in a human rescue task and provides the application of Deep Learning with the intention of providing autonomy in the KUKA Youbot during simulation and practical tasks. Python software lets the communication tasks taking advantage of data processing tools with the deep learning algorithms. Then, OpenCV supports the integration of the deep learning approach by using the Single Shot Detector algorithm which provides robustness in velocity and precision which mainly allows the human detection by using a trained neuronal network. The combination of OpenCV, Python and ROS software is a capable architecture for providing smart autonomous navigation capability for different applications like: social robots, human rescue, precision farming, and especially it is possible to mention the applicability in the public administration in which the robot assists in the rescue or assistance of the employees.
Gestão do conhecimento como fonte de vantagem competitiva em uma paraestatal mineira

RESUMO O presente estudo foi desenvolvido com o objetivo identificar e analisar os principais fatores que exercem influência no processo de gestão do conhecimento em uma instituição paraestatal de ensino profissionalizante localizada no estado de Minas Gerais. Para tal foi desenvolvida uma pesquisa descritiva, à luz de uma abordagem quantitativa, baseada em um estudo de caso, cuja amostra foi composta por 1.413 funcionários da instituição objeto de estudo. Em um ambiente de análise foi desenvolvido um modelo fatorial composto pelos construtos ‘estratégia e comunicação’, ‘políticas e práticas de gestão de pessoas’ e ‘processos de trabalho’, cuja explicação total do fenômeno em questão foi de 70,344%. Dentre os principais resultados alcançados destaca-se a importância da gestão do conhecimento no processo de planejamento estratégico organizacional. Nesse mesmo sentido, faz-se importante compreender a relação que se estrutura entre estratégia organizacional, políticas e práticas de gestão de pessoas e processos de trabalho como uma variável relevante para uma gestão do conhecimento eficiente, eficaz e efetiva. Em termos agregados, a compreensão, aplicabilidade e gestão da tríade aqui discutida permitem que a empresa se torne mais competitiva e adaptável frente às constantes mudanças do mercado em que atua.
Brownian motion, hydrodynamics, and the osmotic pressure

It is shown that the osmotic pressure of a colloidal dispersion can be interpreted as the isotropic part of the macroscopic particle stress in the suspension. The particle stress is in turn expressible in terms of hydrodynamic interactions among the suspended particles. Thus, there is a completely mechanical definition of the osmotic pressure, just as there is for the pressure in a molecular fluid. For an equilibrium suspension of colloidal particles subjected to thermal Brownian forces, this mechanical definition is shown to be exactly equal to the usual ‘‘thermodynamic’’ one. The derivation given here places the equilibrium and nonequilibrium properties of macroparticle fluids on the same mechanical foundation that underlies the statistical mechanics of simple liquids. Furthermore, through this development the relationship between hydrodynamics and kinetic‐theory‐like descriptions of colloids is explained.
Optimal Control of Wake Up Mechanisms of Femtocells in Heterogeneous Networks

We study, in this work, optimal sleep/wake up schemes for the base stations of network-operated femto cells deployed within macro cells for the purpose of offloading part of its traffic. Our aim is to minimize the energy consumption of the overall heterogeneous network while preserving the Quality of Service (QoS) experienced by users. We model such a system at the flow level, considering a dynamic user configuration, and derive, using Markov Decision Processes (MDPs), optimal sleep/wake up schemes based on the information on traffic load and user localization in the cell, in the cases where this information is complete, partial or delayed. Our results quantify the energy consumption and QoS perceived by the users in each of these cases and identify the tradeoffs between those two quantities. We also illustrate numerically the optimal policies in different traffic scenarios.
Fresh-Variable Automata for Service Composition

To model Web services handling data from an infinite domain, or with multiple sessions, we introduce fresh-variable automata, a simple extension of finite-state automata in which some transitions are labeled with variables that can be refreshed in some specified states. We prove several closure properties for this class of automata and study their decision problems. We then introduce a notion of simulation that enables us to reduce the Web service composition problem to the construction of a simulation of a target service by the asynchronous product of existing services, and prove that this construction is computable.