Miracle-dz/UniFAR-FTUs huggingface dataset:
On-line Decentralized Charging of Plug-In Electric Vehicles in Power Systems

The concept of plug-in electric vehicles (PEV) are gaining increasing popularity in recent years, due to the growing societal awareness of reducing greenhouse gas (GHG) emissions, and gaining independence on foreign oil or petroleum. Large-scale deployment of PEVs currently faces many challenges. One particular concern is that the PEV charging can potentially cause significant impacts on the existing power distribution system, due to the increase in peak load. As such, this work tries to mitigate the impacts of PEV charging by proposing a decentralized smart PEV charging algorithm to minimize the distribution system load variance, so that a `flat' total load profile can be obtained. The charging algorithm is myopic, in that it controls the PEV charging processes in each time slot based entirely on the current power system states, without knowledge about future system dynamics. We provide theoretical guarantees on the asymptotic optimality of the proposed charging algorithm. Thus, compared to other forecast based smart charging approaches in the literature, the charging algorithm not only achieves optimality asymptotically in an on-line, and decentralized manner, but also is robust against various uncertainties in the power system, such as random PEV driving patterns and distributed generation (DG) with highly intermittent renewable energy sources.
Right on Target, or Is it? The Role of Distributional Shape in Variance Targeting

Estimation of GARCH models can be simplified by augmenting quasi-maximum likelihood (QML) estimation with variance targeting, which reduces the degree of parameterization and facilitates estimation. We compare the two approaches and investigate, via simulations, how non-normality features of the return distribution affect the quality of estimation of the volatility equation and corresponding value-at-risk predictions. We find that most GARCH coefficients and associated predictions are more precisely estimated when no variance targeting is employed. Bias properties are exacerbated for a heavier-tailed distribution of standardized returns, while the distributional asymmetry has little or moderate impact, these phenomena tending to be more pronounced under variance targeting. Some effects further intensify if one uses ML based on a leptokurtic distribution in place of normal QML. The sample size has also a more favorable effect on estimation precision when no variance targeting is used. Thus, if computational costs are not prohibitive, variance targeting should probably be avoided.
Gender Differences in Remission and Recovery of Schizophrenic and Schizoaffective Patients: Preliminary Results of a Prospective Cohort Study

The aim of the paper was to evaluate rates of clinical remission and recovery according to gender in a cohort of chronic outpatients attending a university community mental health center who had been diagnosed with schizophrenia and schizoaffective disorder according to DSM-IV-TR. A sample of 100 consecutive outpatients (70 males and 30 females) underwent comprehensive psychiatric evaluation using the Structured Clinical Interview for Diagnosis of Axis I and II DSM-IV (SCID-I and SCID-II, Version R) and an assessment of psychopathology, social functioning, clinical severity, subjective wellbeing, and quality of life, respectively by means of PANSS (Positive and Negative Syndrome Scale), PSP (Personal and Social Performance), CGI-SCH (Clinical Global Impression—Schizophrenia scale), SWN-S (Subjective Well-being under Neuroleptics—scale), and WHOQOL (WHO Quality of Life). Rates of clinical remission and recovery according to different criteria were calculated by gender. Higher rates of clinical remission and recovery were generally observed in females than males, a result consistent with literature data. Overall findings from the paper support the hypothesis of a better outcome of the disorders in women, even in the very long term.
Subgroup Discovery in Smart Electricity Meter Data

This work presents data mining methods for discovering unusual consumption patterns and their associated descriptive models from smart electricity meter data. At present, data mining and knowledge discovery in electricity meter data suffer from three notable weaknesses: 1) insufficient focus on intelligent data analysis of subgroups (subsets) whose patterns vary significantly from aggregate patterns embodied in an entire dataset; 2) a lack of effort towards generating intuitively understandable and practically applicable knowledge for industrial practitioners to identify such subgroups; and 3) limited knowledge regarding the link between unusual consumption patterns and household consumers' socio-demographic characteristics. This paper addresses these practically important but technically challenging issues by applying subgroup discovery algorithms to a real smart electricity meter dataset. Subgroups whose patterns are unusual and whose sizes are large enough are discovered, and their descriptive and predictive models are generated. Furthermore, to enrich subgroup discovery algorithms, three new-quality measures for real-valued targets are proposed. The comparative studies empirically evaluate the effectiveness and usefulness of subgroup discovery on classification accuracy, predictive power, and computational resources. The methodologies and algorithms presented are generic, and therefore applicable to a wider range of data mining problems.
Qubit Neural Network: Its Performance and Applications

Since Shor (1994) proposed a way of factorizing large integers in polynomial time by using a quantum computing algorithm, the study of quantum information science, including quantum communication, quantum cryptography, quantum computer and so on, has been intensified (Nielsen & Chuang,2000). Shor’s proposal has not only proved itself to be a milestone in quantum computing, but also created a novel research paradigm of neural computing, i.e., quantum neural computing (Kak, 1995). Since then, various quantum neural computing models have been proposed for the improvement of the computational ability of neural networks so as to expand their applications ABSTrACT
Low energy adaptive clustering hierarchy with deterministic cluster-head selection

This paper focuses on reducing the power consumption of wireless microsensor networks. Therefore, a communication protocol named LEACH (low-energy adaptive clustering hierarchy) is modified. We extend LEACH's stochastic cluster-head selection algorithm by a deterministic component. Depending on the network configuration an increase of network lifetime by about 30% can be accomplished. Furthermore, we present a new approach to define lifetime of microsensor networks using three new metrics FND (First Node Dies), HNA (Half of the Nodes Alive), and LND (Last Node Dies).
Resistant learning on the envelope bulk for identifying anomalous patterns

Anomalous patterns are observations that lie far away from the fitting function deduced from the bulk of the given observations. This work addresses the research issue to effectively identify anomalous patterns in both contexts of resistant learning, where there is no assumption about the fitting function form, and of changing environments. The resistant learning means that the learning procedure is not impacted significantly by the outlying observations. In literature, there is the resistant learning with searching a near-perfect fitting function for identifying the bulk of the majority of observations. However, the learning algorithm with searching a near-perfect fitting function suffers from time inefficiency. To effectively identify anomalous patterns in both contexts of resistant learning and changing environments, this study proposes a new resistant learning algorithm with envelope module that learns to evolve a nonlinear fitting function wrapped with a constant-width envelope for containing the majority of observations and thus identifying anomalous patterns. An illustrative experiment is set up to justify the effectiveness of the envelope module and the experimental result shows the positive promise.
Spectropolarimetry of Supernovae

Overwhelming evidence has accumulated in recent years that supernova explosions are intrinsically three-dimensional phenomena with significant departures from spherical symmetry. We review the evidence derived from spectropolarimetry that has established several key results: Virtually all supernovae are significantly aspherical near maximum light; core-collapse supernovae behave differently than thermonuclear (Type Ia) supernovae; the asphericity of core-collapse supernovae is more pronounced in the inner layers, showing that the explosion process is strongly aspherical; core-collapse supernovae tend to establish a preferred direction of asymmetry; and the asphericity is stronger in the outer layers of thermonuclear supernovae, providing constraints on the burning process. We emphasize the utility of the Q/U plane as a diagnostic tool and revisit SN 1987A and SN 1993J in a contemporary context. An axially symmetric geometry can explain many basic features of core-collapse supernovae, but significant depar.
Hardware performance of eStream phase-III stream cipher candidates

This paper presents hardware implementation and performance metrics for the candidate stream ciphers remaining in the phase-III hardware profile. The results are presented in tabular and graphical format together with summarising the utility of the candidates against two notional applications: one for 10Mbps wireless network and a second for 100kHz RFID. An attempt has been made to quantify the flexibility and scalability dimensions of performance.
Retarded adults in the community : an investigation of neighborhood attitudes and concerns

The purpose of the study was to explore the nature of public attitudes toward community based residential f a c i l i t i e s for retarded adults, and thus obtain data which would aid administrators in the planning of group homes and the development of community awareness programs. The study was directed to determining the factors that affect attitudes toward integration, knowledge about mental retardation, and the specific concerns that people have regarding the presence of retarded adults in their community. Four areas of concern were determined: personal safety concerns, economic concerns, concerns about the retarded adult being a nuisance in the community, and concerns about the actual operation of the group home. In order to determine these areas of concern an open-ended questionnaire was administered to twenty people from middle and middleupper class areas of a large urban centre. Their responses were analyzed to develop a Likert-type testwhich could measure the extent of four separate areas of concern. A test of knowledge about mental retardation was also developed and validated using four known groups. These two tests, together with a test of attitudes toward integration were administered to a random sample of seventy-five adults
Words with attitude

The traditional notion of word meaning used in natural language processing is literal or lexical meaning as used in dictionaries and lexicons. This relatively objective notion of lexical meaning is different from more subjective notions of emotive or affective meaning. Our aim is to come to grips with subjective aspects of meaning expressed in written texts, such as the attitude or value expressed in them. This paper explores how the structure of the WordNet lexical database might be used to assess affective or emotive meaning. In particular, we construct measures based on Osgood’s semantic differential technique.
The Arabidopsis PLAT Domain Protein1 Is Critically Involved in Abiotic Stress Tolerance

Despite the completion of the Arabidopsis genome sequence, for only a relatively low percentage of the encoded proteins experimental evidence concerning their function is available. Plant proteins that harbour a single PLAT (Polycystin, Lipoxygenase, Alpha-toxin and Triacylglycerol lipase) domain and belong to the PLAT-plant-stress protein family are ubiquitously present in monocot and dicots. However, the function of PLAT-plant-stress proteins is still poorly understood. Therefore, we have assessed the function of the uncharacterised Arabidopsis PLAT-plant-stress family members through a combination of functional genetic and physiological approaches. PLAT1 overexpression conferred increased abiotic stress tolerance, including cold, drought and salt stress, while loss-of-function resulted in opposite effects on abiotic stress tolerance. Strikingly, PLAT1 promoted growth under non-stressed conditions. Abiotic stress treatments induced PLAT1 expression and caused expansion of its expression domain. The ABF/ABRE transcription factors, which are positive mediators of abscisic acid signalling, activate PLAT1 promoter activity in transactivation assays and directly bind to the ABRE elements located in this promoter in electrophoretic mobility shift assays. This suggests that PLAT1 represents a novel downstream target of the abscisic acid signalling pathway. Thus, we showed that PLAT1 critically functions as positive regulator of abiotic stress tolerance, but also is involved in regulating plant growth, and thereby assigned a function to this previously uncharacterised PLAT domain protein. The functional data obtained for PLAT1 support that PLAT-plant-stress proteins in general could be promising targets for improving abiotic stress tolerance without yield penalty.
Temporal Control in Real-time Systems: Languages and Systems

Temporal control is an important concept for real-time systems, and several systems and languages provide means for setting temporal constrains on the program. However, the way temporal constraints are placed on the program, the granularity, and the supported kinds of temporal constraints differ from system to system. This chapter provides an introduction to temporal control in real-time systems on the basis of temporal constraints. First, it introduces the notion and nature of a temporal constraint and presents a generic model of the task life cycle. Second, it discusses six different systems and languages with temporal control by adapting the generic model to the individual system and by presenting a short example of a stopwatch task. Finally and third, a summary and discussion of the differences among these systems conclude the chapter.
Credit-based low latency packet scheduling algorithm for real-time applications

The number of real-time applications are growing dramatically at an unprecedented rate. Efficient bandwidth allocations and strict delay requirements are indispensable for real-time flows such as audio streaming. Unfortunately, most of the well-known packet scheduling algorithms like Weighted Fair Queueing (WFQ) and Start-time Fair Queueing (SFQ) can not guarantee low and stable latencies for real-time flows due to the `unsteady queueing delay problem' that we will explain later. Only a few scheduling algorithms address this problem in recent years like Low Latency Queueing (LLQ) and Low Latency and Efficient Packet Scheduling algorithm (LLEPS). However, LLQ has unfair and starvation problems. The queueing delay of LLEPS is not steady enough in existing networks. Thus, in this paper, we propose a novel credit-based low latency packet scheduling algorithm (CBLLPS) that is well-suited for real-time applications. The performance of CBLLPS is validated by ns-2 simulations. The simulation results show that CBLLPS guarantees low and steady queueing delay for real-time flows and outperforms any of the existing scheduling algorithms in terms of queueing delay. Moreover, the functionalities of bandwidth sharing and bandwidth reservation of this new scheduling mechanism are still preserved.
Performance Analysis of Component-Based Systems

Dependability assessment of component-based systems must include verification of temporal and performance requirements as they can be of primary importance for many real-time and embedded systems. This paper uses labeled timed Petri nets as models of the behavior of components at their interfaces. These component models are systematically composed into an integrated model of the system which is used for verification of temporal characteristics and performance analysis.
2-W Broadband GaN Power-Amplifier RFIC Using the $f_{T}$ Doubling Technique and Digitally Assisted Distortion Cancellation

The method of derivative superposition is enhanced with digital techniques to cancel the intermodulation distortion generated by a 2-W power amplifier (PA) RF integrated circuit over a broad band of 6 GHz. Two amplifiers were fabricated and tested: a baseline PA without distortion cancellation and a PA with digitally assisted distortion cancellation to demonstrate the effectiveness of the new technique. The PAs are biased in class-A mode and have an OP1dB of 31 dBm and a PSAT of 33 dBm. Measurements reveal that the output third-order intercept point (OIP3) of the PA with digitally assisted distortion cancellation can be increased to 50.25±3.75 dBm between 1-6 GHz relative to the OIP3 of the baseline PA, which is 40.25±2.75 dBm over the same frequency span. The level of distortion cancellation is not only dependent on the frequency of the incident signal, but also on its power level. Data is presented that shows how the proposed digitally assisted distortion cancellation method also improves the OIP3 of the PA when the RF input power level is taken into account.
Hierarchical classification of Gene Ontology-based protein functions with neural networks

Hierarchical Multi-label Classification (HMC) is a classification task where classes are organized in a hierarchical taxonomy, and instances can be simultaneously classified in more than one class. This paper investigates the HMC problem of classifying proteins in functions organized according to the Gene Ontology hierarchical taxonomy. This is a complex task, since the Gene Ontology hierarchy is organized as a Directed Acyclic Graph with thousands of classes hierarchically represented. We propose a neural network-based method to incorporate label-dependency during learning. The experimental results show that the proposed method achieves competitive results when compared to the state-of-the-art methods from the literature.
No-Arbitrage Macroeconomic Determinants of the Yield Curve

No-arbitrage macro-finance models use variance decompositions to gauge the extent of association between the macro variables and yields. We show that results generated by this approach are sensitive to the order of variables in the recursive identification scheme. In a four-factor model, one may obtain 18 different sets of answers out of 24 possible. We propose an alternative measure that is based on levels of macro variables as opposed to shocks. We account for the correlation between the macro and latent factors via projection of the latter onto the former. As a result, the association between macro variables and yields can be computed uniquely via an R2. Macro variables explain 80% of the variation in the short rate and 50% of the slope, and 54% to 68% of the term premia.
A systematic methodology to identify delay marginalities in a design during first-silicon validation 1

In this paper, we present a systematic framework to identify delay marginalities in a design during first silicon validation. Our method guarantees the excitation of the worst-case delay of the chips in the first silicon batch without introducing any pessimism. It embodies several innovations, including a resilient gate delay model to capture process variations, new conditions that vectors must satisfy to invoke the maximum delay of a target path, and a new approach to generate multiple vectors (vector-spaces) guaranteed to invoke the worst-case delay of the target path. We also present extensive experimental results for benchmark circuits to demonstrate the effectiveness of our method.
An Approach for Text Summarization using Deep Learning Algorithm

Now days many research is going on for text summarization. Because of increasing information in the internet, these kind of research are gaining more a nd more attention among the researchers. Extractive text summarization generates a brief summary by extracting proper set of sentences from a document or multi ple documents by deep learning. The whole concept is to reduce or minimize the important information prese nt in the documents. The procedure is manipulated by Restricted Boltzmann Machine (RBM) algorithm for better efficiency by removing redundant sentences. The restricted Boltzmann machine is a graphical model for binary random variables. It consist of three layers input, hidden and output layer. The input data uni formly distributed in the hidden layer for operation. The experimentation is carried out and the summary is g enerated for three different document set from different kno wledge domain. The f-measure value is the identifie r to the performance of the proposed text summarization method. The top responses of the three different knowle dge domain in accordance with the f-measure are 0.85, 1 . 42 and 1.97 respectively for the three document se t.
Activation of Rac GTPase by p75 Is Necessary for c-jun N-Terminal Kinase-Mediated Apoptosis

The neurotrophin receptor p75 can induce apoptosis both in vitro and in vivo. The mechanisms by which p75 induces apoptosis have remained mostly unknown. Here, we report that p75 activates Rac GTPase, which in turn activates c-junN-terminal kinase (JNK), including an injury-specific JNK3, in an NGF-dependent manner. N17Rac blocks this JNK activation and subsequent NGF-dependent apoptosis, indicating that activation of Rac GTPase is required for JNK activation and apoptosis induced by p75. In addition, p75-mediated Rac activation is modulated by coactivation of Trk, identifying Rac GTPase as one of the key molecules whose activity is critical for cell survival and death in neurotrophin signaling. The crucial role of the JNK pathway in p75 signaling is further confirmed by the results that blocking p75 from signaling via the JNK pathway or suppressing the JNK activity itself led to inhibition of NGF-dependent death. Together, these results indicate that the apoptotic machinery of p75 comprises Rac GTPase and JNK.
A - Posteriori Data Integration for Life Sciences

Multiple datasets that add high value to biomedical research have been exposed on the web as part of the Life Sciences Linked Open Data (LS-LOD) Cloud. The ability to easily navigate through these datasets is crucial in order to draw meaningful biological co relations. However, navigating these multiple datasets is not trivial as most of these are only available as isolated SPARQL endpoints with very little vocabulary reuse. We propose an approach for Autonomous Resource Discovery and Indexing (ARDI), a set of configurable rules which can be used to discover links between biological entities in the LS-LOD cloud. We have catalogued and linked concepts and properties from 137 public SPARQL endpoints. The ARDI is used to dynamically assemble queries retrieving data from multiple SPARQL endpoints simultaneously.
Experiences with the application of the ADIC automatic differentiation tool for to the CSCMDO 3-D volume grid generation code

Automatic differentiation (AD) is a methodology for developing reliable sensitivity-enhanced versions of arbitrary computer programs with little human effort. It can vastly accelerate the use of advanced simulation codes in multidisciplinary design optimization, since the time for generating and verifying derivative codes is greatly reduced. In this paper, we report on the application of the recently developed ADIC automatic differentiation tool for ANSI C programs to the CSCMDO multiblock three-dimensional volume grid generator. The ADIC-generated code can easily be interfaced with Fortran derivative codes generated with the ADIFOR AD tool FORTRAN 77 programs, thus providing efficient sensitivity-enhancement techniques for multilanguage, multidiscipline problems.