Miracle-dz/UniFAR-FTUs huggingface dataset:
Filter-Based Active Damping of Voltage Source Converters With $LCL$ Filter

Pulsewidth modulation (PWM) voltage source converters are becoming a popular interface to the power grid for many applications. Hence, issues related to the reduction of PWM harmonics injection in the power grid are becoming more relevant. The use of high-order filters like LCL filters is a standard solution to provide the proper attenuation of PWM carrier and sideband voltage harmonics. However, those grid filters introduce potentially unstable dynamics that should be properly damped either passively or actively. The second solution suffers from control and system complexity (a high number of sensors and a high-order controller), even if it is more attractive due to the absence of losses in the damping resistors and due to its flexibility. An interesting and straightforward active damping solution consists in plugging in, in cascade to the main controller, a filter that should damp the unstable dynamics. No more sensors are needed, but there are open issues such as preserving the bandwidth, robustness, and limited complexity. This paper provides a systematic approach to the design of filter-based active damping methods. The tuning procedures, performance, robustness, and limitations of the different solutions are discussed with theoretical analysis, selected simulation, and experimental results.
Postprocessing decision trees to extract actionable knowledge

Most data mining algorithms and tools stop at discovered customer models, producing distribution information on customer profiles. Such techniques, when applied to industrial problems such as customer relationship management (CRM), are useful in pointing out customers who are likely attritors and customers who are loyal, but they require human experts to postprocess the mined information manually. Most of the postprocessing techniques have been limited to producing visualization results and interestingness ranking, but they do not directly suggest actions that would lead to an increase the objective function such as profit. Here, we present a novel algorithm that suggest actions to change customers from an undesired status (such as attritors) to a desired one (such as loyal) while maximizing objective function: the expected net profit. We develop these algorithms under resource constraints that are abound in reality. The contribution of the work is in taking the output from an existing mature technique (decision trees, for example), and producing novel, actionable knowledge through automatic postprocessing.
Psychological morbidity among suicide-bereaved and non-bereaved parents: a nationwide population survey

Objective To determine how psychological premorbidity affects the risk of depression in parents who lost a child through suicide. Design Population-based survey. Setting Sweden, between 2009 and 2010. Participants All parents who lost a child, age 15–30, through suicide between 2004 and 2007 according to National population registries. Non-bereaved parents matched for age, sex, living area, marital status, number of children. Exclusion criteria: born outside a Nordic country, not Swedish speaking, contact details missing. Participants: 666 of 915 (73%) suicide-bereaved and 377 of 508 (74%) non-bereaved parents. Main outcome measures Depression measured by the nine-item depression scale of the Patient Health Questionnaire (PHQ-9) and study-specific questions to assess psychological premorbidity and experience of the child's presuicidal morbidity. Results In all, 94 (14%) suicide-bereaved and 51 (14%) non-bereaved parents (relative risk 1.0; 95% CI 0.8 to 1.4) had received their first treatment for psychological problems or had been given a psychiatric diagnosis more than 10 years earlier. The prevalence of moderate-to-severe depression was 115 (18%) in suicide-bereaved versus 28 (7%) in non-bereaved parents (RR 2.3; 95% CI 1.6 to 3.5). For those without psychological premorbidity, the relative risk was 2.3 (95% CI 1.4 to 3.6). 339 (51%) suicide-bereaved parents expressed worry over the child's psychological health during the month preceding the suicide and 259 (39%) had anticipated the suicide. Conclusions In parents who lost a child through suicide in Sweden we did not find a higher prevalence of long-term psychological premorbidity than among parents who had not lost a child; the more than twofold risk of depression among the bereaved can probably be explained by the suicide and the stressful time preceding the suicide.
Inferring Past Effective Population Size from Distributions of Coalescent Times

Inferring and understanding changes in effective population size over time is a major challenge for population genetics. Here we investigate some theoretical properties of random-mating populations with varying size over time. In particular, we present an exact solution to compute the population size as a function of time, Ne(t), based on distributions of coalescent times of samples of any size. This result reduces the problem of population size inference to a problem of estimating coalescent time distributions. To illustrate the analytic results, we design a heuristic method using a tree-inference algorithm and investigate simulated and empirical population-genetic data. We investigate the effects of a range of conditions associated with empirical data, for instance number of loci, sample size, mutation rate, and cryptic recombination. We show that our approach performs well with genomic data (≥ 10,000 loci) and that increasing the sample size from 2 to 10 greatly improves the inference of Ne(t) whereas further increase in sample size results in modest improvements, even under a scenario of exponential growth. We also investigate the impact of recombination and characterize the potential biases in inference of Ne(t). The approach can handle large sample sizes and the computations are fast. We apply our method to human genomes from four populations and reconstruct population size profiles that are coherent with previous finds, including the Out-of-Africa bottleneck. Additionally, we uncover a potential difference in population size between African and non-African populations as early as 400 KYA. In summary, we provide an analytic relationship between distributions of coalescent times and Ne(t), which can be incorporated into powerful approaches for inferring past population sizes from population-genomic data.
Prostaglandin E2 Is a Novel Inducer of Oncostatin-M Expression in Macrophages and Microglia

Oncostatin-M (OSM), a pluripotent cytokine of the interleukin-6 (IL-6) family, is produced in a number of inflammatory conditions. Known sources of OSM include monocytes–macrophages and T-cells. Here we present microglia, the resident macrophages of the brain, as a source of OSM in the CNS. In this context, we describe a novel inducer of OSM, prostaglandin E2 (PGE2). PGE2 induces OSM expression in microglia, monocytes, and macrophages of human and murine origin. PGE2 induction of OSM is mimicked by cholera toxin, an activator of stimulatory G (Gs)-proteins; by forskolin, an activator of adenylate cyclase; and by the cAMP analog, dibutyryl-cAMP. PGE2 induction of OSM gene expression is inhibited by the adenylate cyclase inhibitor 2′,5′-dideoxyadenosine, by the protein kinase A (PKA) inhibitor H-89, and by a dominant-negative PKA construct. These data indicate that PGE2 signals via Gs-protein-coupled receptor(s), adenylate cyclase, and PKA to induce OSM expression. Accordingly, other activators of cAMP signaling such as norepinephrine and PGE1 induce OSM. The ability of PGE2 to induce OSM expression was tested under more physiological conditions, using cocultures of astrocytes and monocytes. Treatment of the cocultures with IL-1β or tumor necrosis factor-α (TNF-α) results in production of PGE2 and OSM. PGE2 produced in the cocultures is responsible for OSM induction, because pretreatment with indomethacin, an inhibitor of prostaglandin synthesis, as well as depletion of PGE2, abrogate OSM expression induced by IL-1β or TNF-α. These data suggest that in the CNS, OSM may be produced through collaboration of astrocytes and macrophages–microglia.
A docking analysis of the statistical physics of protein–protein recognition

We describe protein–protein recognition within the frame of the random energy model of statistical physics. We simulate, by docking the component proteins, the process of association of two proteins that form a complex. We obtain the energy spectrum of a set of protein–protein complexes of known three-dimensional structure by performing docking in random orientations and scoring the models thus generated. We use a coarse protein representation where each amino acid residue is replaced by its Voronoï cell, and derive a scoring function by applying the evolutionary learning program ROGER to a set of parameters measured on that representation. Taking the scores of the docking models to be interaction energies, we obtain energy spectra for the complexes and fit them to a Gaussian distribution, from which we derive physical parameters such as a glass transition temperature and a specificity transition temperature.
GA-Based Speaking Mouth Correlative Speech Feature Abstraction

The image-based lip animation synthesis approach is one kind of promising method that synthesizes the believable talking head. This paper seeks to show an improvement in the accuracy of mouth prediction with the speech stimulus, as well as showing the method used to extract the speaking mouth correlative speech feature. Our lip animation synthesis system is based on the construction of a frame level audiovisual mapping model between the acoustic speech class and speaking mouth image class. Taking the mapping model as a basis, genetic algorithm is used to extract the speaking mouth correlative speech feature. The key step used in this study is: fitness and coding scheme designing. Experimental results show that the extracted speech feature has a better correlation with the corresponding speaking mouth, compared to the single or mixed LPCC and MFCC. More research will be done in this specialist field of study the multi-layer speaking mouth correlative speech feature abstraction structure, and will attempt to show that the speaking mouth correlative speech feature should have better results
ECG data compression using truncated singular value decomposition

The method of truncated singular value decomposition (SVD) is proposed for electrocardiogram (ECG) data compression. The signal decomposition capability of SVD is exploited to extract the significant feature components of the ECG by decomposing the ECG into a set of basic patterns with associated scaling factors. The signal informations are mostly concentrated within a certain number of singular values with related singular vectors due to the strong interbeat correlation among ECG cycles. Therefore, only the relevant parts of the singular triplets need to be retained as the compressed data for retrieving the original signals. The insignificant overhead can be truncated to eliminate the redundancy of ECG data compression. The Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database was applied to evaluate the compression performance and recoverability in the retrieved ECG signals. The approximate achievement was presented with an average data rate of 143.2 b/s with a relatively low reconstructed error. These results showed that truncated SVD method can provide an efficient coding with high-compression ratios. The computational efficiency of the SVD method in comparing with other techniques demonstrated the method as an effective technique for ECG data storage or signals transmission. Index Terms-Data compression, electrocardiogram, feature extraction, quasi-periodic signal, singular value decomposition.
Isolation and Molecular Characterization of Novel Chlorpyrifos and 3,5,6-trichloro-2-pyridinol-degrading Bacteria from Sugarcane Farm Soils

Chlorpyrifos (CP) is one of the most widely used organophosphate pesticides in agriculture worldwide, but its extensive use has led to the contamination of various soil and water systems. Microbial bioremediation is considered to be one of the most viable options for the removal of CP from the environment; however, little is known about the soil bacterial diversity that degrade CP. Sequential soil and liquid culture enrichments enabled the isolation of bacterial CP degraders with sequence homologies to Xanthomonas sp. , Pseudomonas sp. , and Rhizobium sp. The efficacy of the three isolated strains: Xanthomonas sp. 4R3-M1, Pseudomonas sp. 4H1-M3, and Rhizobium sp. 4H1-M1 was further investigated for biodegradation of CP and its primary metabolic product, 3,5,6-trichloro-2-pyridinol (TCP). The results indicate that all three bacterial strains almost completely metabolized CP (10 mg/L) and TCP, occurring as a metabolic degradation product, in mineral salt media as a sole source of carbon and nitrogen. The isolated bacterial strains Xanthomonas sp. 4R3-M1 and Pseudomonas sp. 4H1-M3 could also degrade TCP (10 mg/L) as a sole carbon and nitrogen source, when provided externally. Thus, these bacterial strains may be effective in practical application of bioremediation of both CP and TCP.
Viral Sanctuaries during Highly Active Antiretroviral Therapy in a Nonhuman Primate Model for AIDS

ABSTRACT Highly active antiretroviral therapy (HAART) enables long-term suppression of plasma HIV-1 loads in infected persons, but low-level virus persists and rebounds following cessation of therapy. During HAART, this virus resides in latently infected cells, such as resting CD4+ T cells, and in other cell types that may support residual virus replication. Therapeutic eradication will require elimination of virus from all reservoirs. We report here a comprehensive analysis of these reservoirs in fluids, cells, and tissues in a rhesus macaque model that mimics HAART in HIV-infected humans. This nonhuman primate model uses RT-SHIV, a chimera of simian immunodeficiency virus containing the HIV-1 reverse transcriptase (RT). Methods were developed for extraction, preamplification, and real-time PCR analyses of viral DNA (vDNA) and viral RNA (vRNA) in tissues from RT-SHIV-infected macaques. These methods were used to identify viral reservoirs in RT-SHIV-infected macaques treated with a potent HAART regimen consisting of efavirenz, emtricitabine, and tenofovir. Plasma virus loads at necropsy ranged from 11 to 28 copies of vRNA per ml. Viral RNA and DNA were detected during HAART, in tissues from numerous anatomical locations. Additional analysis provided evidence for full-length viral RNA in tissues of animals with virus suppressed by HAART. The highest levels of vDNA and vRNA in HAART-treated macaques were in lymphoid tissues, particularly the spleen, lymph nodes, and gastrointestinal tract tissues. This study is the first comprehensive analysis of the tissue and organ distribution of a primate AIDS virus during HAART. These data demonstrate widespread persistence of residual virus in tissues during HAART.
Helix: online enterprise data analytics

The size, heterogeneity and dynamicity of data within an enterprise makes indexing, integration and analysis of the data increasingly difficult tasks. On the other hand, there has been a massive increase in the amount of high-quality open data available on the Web that could provide invaluable insights to data analysts and business intelligence specialists within the enterprise. The goal of Helix project is to provide users within the enterprise with a platform that allows them to perform online analysis of almost any type and amount of internal data using the power of external knowledge bases available on the Web. Such a platform requires a novel, data-format agnostic indexing mechanism, and light-weight data linking techniques that could link semantically related records across internal and external data sources of various characteristics. We present the initial architecture of our system and discuss several research challenges involved in building such a system.
Overexpression of the Notch3 receptor and its ligand Jagged1 in human clinically non-functioning pituitary adenomas

Human clinically non-functioning pituitary adenomas (NFPAs) primarily cause headaches, visual impairment and hypopituitarism due to the effect of the mass of the tumor. Surgery is the first-line treatment for these tumors. To date, no efficacious medical therapy exists for non-functioning adenomas. Previous studies have demonstrated that the Notch3 receptor is involved in the pathogenesis of various types of malignancies, including human NFPAs. The current study focused on the expression of the Notch3 receptor and its ligand Jagged1 in three types of pituitary adenomas and in the normal pituitary gland. Using quantitative real-time RT-PCR assays and western blot analyses, upregulated Notch3 and Jagged1 were observed in human NFPAs, but not in normal human pituitary glands or in hormone-secreting adenomas. Furthermore, Notch3 was positively correlated with Jagged1 at the mRNA and protein levels. These data indicate that Notch3 and Jagged1 may play an important role in the initiation and proliferation of human non-functioning adenomas, and there may be an interaction between Notch3 and Jagged1 in this process. Our study further elucidates the role of the Notch3 signaling pathway in the tumorigenesis of human NFPAs and provides a potential therapeutic target for the medical treatment of these tumors.
The Expanding Search Ratio of a Graph

We study the problem of searching for a hidden target in an environment that is modeled by an edge-weighted graph. A sequence of edges is chosen starting from a given root vertex such that each edge is adjacent to a previously chosen edge. This search paradigm, known as expanding search was recently introduced by Apern and Lidbetter [2013] for modeling problems such as searching for coal or minesweeping in which the cost of re-exploration is negligible. It can also be used to model a team of searchers successively splitting up in the search for a hidden adversary or explosive device, for example. We define the search ratio of an expanding search as the maximum over all vertices of the ratio of the time taken to reach the vertex and the shortest-path cost to it from the root. This can be interpreted as a measure of the multiplicative regret incurred in searching, and similar objectives have previously been studied in the context of conventional (pathwise) search. In this paper we address algorithmic and computational issues of minimizing the search ratio over all expanding searches, for a variety of search environments, including general graphs, trees and star-like graphs. Our main results focus on the problem of finding the randomized expanding search with minimum expected search ratio, which is equivalent to solving a zero-sum game between a Searcher and a Hider. We solve these problems for certain classes of graphs, and obtain constant-factor approximations for others.
Tailored Bregman Ball Trees for Effective Nearest Neighbors

Nearest Neighbor (NN) search is a crucial tool that remains critical in many challenging applications of computational geometry (e.g., surface reconstruction, clustering) and computer vision (e.g., image and information retrieval, classification, data mining). We present an effective Bregman ball tree [5] (Bb-tree) construction algorithm that adapts locally its internal node degrees to the inner geometric characteristics of the data-sets. Since symmetric measures are usually preferred for applications in content-based information retrieval, we furthermore extend the Bb-tree to the case of symmetrized Bregman divergences. Exact and approximate NN search experiments using high-dimensional real-world data-sets illustrate that our method improves significantly over the state of the art [5], sometimes by an order of magnitude.
Reduced Metabolism in Brain “Control Networks” following Cocaine-Cues Exposure in Female Cocaine Abusers

Objective Gender differences in vulnerability for cocaine addiction have been reported. Though the mechanisms are not understood, here we hypothesize that gender differences in reactivity to conditioned-cues, which contributes to relapse, are involved. Method To test this we compared brain metabolism (using PET and 18FDG) between female (n = 10) and male (n = 16) active cocaine abusers when they watched a neutral video (nature scenes) versus a cocaine-cues video. Results Self-reports of craving increased with the cocaine-cue video but responses did not differ between genders. In contrast, changes in whole brain metabolism with cocaine-cues differed by gender (p<0.05); females significantly decreased metabolism (−8.6%±10) whereas males tended to increase it (+5.5%±18). SPM analysis (Cocaine-cues vs Neutral) in females revealed decreases in frontal, cingulate and parietal cortices, thalamus and midbrain (p<0.001) whereas males showed increases in right inferior frontal gyrus (BA 44/45) (only at p<0.005). The gender-cue interaction showed greater decrements with Cocaine-cues in females than males (p<0.001) in frontal (BA 8, 9, 10), anterior cingulate (BA 24, 32), posterior cingulate (BA 23, 31), inferior parietal (BA 40) and thalamus (dorsomedial nucleus). Conclusions Females showed greater brain reactivity to cocaine-cues than males but no differences in craving, suggesting that there may be gender differences in response to cues that are not linked with craving but could affect subsequent drug use. Specifically deactivation of brain regions from “control networks” (prefrontal, cingulate, inferior parietal, thalamus) in females could increase their vulnerability to relapse since it would interfere with executive function (cognitive inhibition). This highlights the importance of gender tailored interventions for cocaine addiction.
Individual and Aggregate Electrical Load Forecasting: One for All and All for One

Electrical load forecasting is an important task for utility companies, in order to plan future production and to increase the efficiency of the distribution network. Although load forecasting at the aggregate level has been extensively studied in existing literature, forecasts for individual consumers have been shown to be prone to errors. This paper deals with the problem of electrical load forecasting at multiple scales, from individual consumers to the network as a whole. We use smart meter data from carefully selected sets of consumers for this purpose. First, we consider the problem of forecasting the load for individual consumers at the outermost nodes of the distribution network. We propose an algorithm which considers external available information like calendar or weather contexts along with the energy consumption profiles of different consumers for accurate mid-term and short-term load forecasting. Multiple aggregation approaches are considered for utility level forecasting, in order to characterize their error properties. We show that careful clustering of consumers for aggregation can result in smaller errors. We experiment with two public data sets for demonstrating the advantages of the proposed method over the state-of-the-art approaches.
A collocation method for real-time walking pattern generation

This paper presents a new real-time walking pattern generator that calculates center of mass trajectories from footstep locations. Key features are the calculation of reference torque patterns by quadratic programming and the solution of the equations of motion by spline collocation. Fast real-time planning is combined with offline optimisation of free parameters based on a comprehensive simulation of the closed loop system to automatically generate optimally tuned walking controllers. The effectiveness of the proposed method is shown using a dynamics simulation of the robot JOHNNIE.