Confidence in prediction: an approach for dynamic weighted ensemble.

Combining classifiers in an ensemble is beneficial in achieving better prediction than using a single classifier. Furthermore, each classifier can be associated with a weight in the aggregation to boost the performance of the ensemble system. In this work, we propose a novel dynamic weighted ensemble method. Based on the observation that each classifier provides a different level of confidence in its prediction, we propose to encode the level of confidence of a classifier by associating with each classifier a credibility threshold, computed from the entire training set by minimizing the entropy loss function with the mini-batch gradient descent method. On each test sample, we measure the confidence of each classifier’s output and then compare it to the credibility threshold to determine whether a classifier should be attended in the aggregation. If the condition is satisfied, the confidence level and credibility threshold are used to compute the weight of contribution of the classifier in the aggregation. By this way, we are not only considering the presence but also the contribution of each classifier based on the confidence in its prediction on each test sample. The experiments conducted on a number of datasets show that the proposed method is better than some benchmark algorithms including a non-weighted ensemble method, two dynamic ensemble selection methods, and two Boosting methods

An experimental study of combining evolutionary algorithms with KD-tree to solving dynamic optimisation problems

This paper studies the idea of separating the explored and unexplored regions in the search space to improve change detection and optima tracking. When an optimum is found, a simple sampling technique is used to estimate the basin of attraction of that optimum. This estimated basin is marked as an area already explored. Using a special tree-based data structure named KD-Tree to divide the search space, all explored areas can be separated from unexplored areas. Given such a division, the algorithm can focus more on searching for unexplored areas, spending only minimal resource on monitoring explored areas to detect changes in explored regions. The experiments show that the proposed algorithm has competitive performance, especially when change detection is taken into account in the optimisation process. The new algorithm was proved to have less computational complexity in term of identifying the appropriate sub-population/region for each individual. We also carry out investigations to find out why the algorithm performs well. These investigations reveal a positive impact of using the KD-Tree

A Novel Online Dynamic Temporal Context Neural Network Framework for the Prediction of Road Traffic Flow

Traffic flow exhibits different magnitudes of temporal patterns, such as short-term (daily and weekly) and long-term (monthly and yearly). Existing research into road traffic flow prediction has focused on short-term patterns; little research has been done to determine the effect of different long-term patterns on road traffic flow prediction. Providing more temporal contextual information through the use of different temporal data segments, could improve prediction results. In this paper, we have investigated different magnitudes of temporal patterns, such as short-term and long-term, through the use of different temporal data segments to understand how contextual temporal data can improve prediction. Furthermore, to learn temporal patterns dynamically, we have proposed a novel online dynamic temporal context neural network framework. The framework uses different temporal data segments as input features, and during online learning, the updating scheme dynamically determines how useful a temporal data segment (short and long-term temporal patterns) is for prediction, and weights it accordingly for use in the regression model. Therefore, the framework can include short-term and relevant long-term patterns in the regression model leading to improved prediction results. We have conducted a thorough experimental evaluation with a real dataset containing daily, monthly and yearly data segments. The experiment results show that both short and long-term temporal patterns improved prediction accuracy. In addition, the proposed online dynamical framework improved predication results by 10.8% when compared with a deep gated recurrent model

Observation of Fourier transform limited lines in hexagonal boron nitride

© 2018 American Physical Society. Single defect centers in layered hexagonal boron nitride are promising candidates as single-photon sources for quantum optics and nanophotonics applications. However, spectral instability hinders many applications. Here, we perform resonant excitation measurements and observe Fourier transform limited linewidths down to ≈50 MHz. We investigated the optical properties of more than 600 single-photon emitters (SPEs) in hBN. The SPEs exhibit narrow zero-phonon lines distributed over a spectral range from 580 to 800 nm and with dipolelike emission with a high polarization contrast. Finally, the emitters withstand transfer to a foreign photonic platform, namely, a silver mirror, which makes them compatible with photonic devices such as optical resonators and paves the way to quantum photonics applications

Birth data accessibility via primary care health records to classify health status in a multi-ethnic population of children: an observational study

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Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates

Computational drug design based on artificial intelligence is an emerging research area. At the time of writing this paper, the world suffers from an outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus replication is via protease inhibition. We propose an evolutionary multi-objective algorithm (EMOA) to design potential protease inhibitors for SARS-CoV-2's main protease. Based on the SELFIES representation the EMOA maximizes the binding of candidate ligands to the protein using the docking tool QuickVina 2, while at the same time taking into account further objectives like drug-likeliness or the fulfillment of filter constraints. The experimental part analyzes the evolutionary process and discusses the inhibitor candidates.Comment: 15 pages, 7 figures, submitted to PPSN 202

Single photon emission from plasma treated 2D hexagonal boron nitride

© 2018 The Royal Society of Chemistry. Artificial atomic systems in solids are becoming increasingly important building blocks in quantum information processing and scalable quantum nanophotonic networks. Amongst numerous candidates, 2D hexagonal boron nitride has recently emerged as a promising platform hosting single photon emitters. Here, we report a number of robust plasma and thermal annealing methods for fabrication of emitters in tape-exfoliated hexagonal boron nitride (hBN) crystals. A two-step process comprising Ar plasma etching and subsequent annealing in Ar is highly robust, and yields an eight-fold increase in the concentration of emitters in hBN. The initial plasma-etching step generates emitters that suffer from blinking and bleaching, whereas the two-step process yields emitters that are photostable at room temperature with emission wavelengths greater than ∼700 nm. Density functional theory modeling suggests that the emitters might be associated with defect complexes that contain oxygen. This is further confirmed by generating the emitters via annealing hBN in air. Our findings advance the present understanding of the structure of quantum emitters in hBN and enhance the nanofabrication toolkit needed to realize integrated quantum nanophotonic circuits

The impact of albendazole treatment on the incidence of viral- and bacterial-induced diarrhea in school children in southern Vietnam: study protocol for a randomized controlled trial

Anthelmintics are one of the more commonly available classes of drugs to treat infections by parasitic helminths (especially nematodes) in the human intestinal tract. As a result of their cost-effectiveness, mass school-based deworming programs are becoming routine practice in developing countries. However, experimental and clinical evidence suggests that anthelmintic treatments may increase susceptibility to other gastrointestinal infections caused by bacteria, viruses, or protozoa. Hypothesizing that anthelmintics may increase diarrheal infections in treated children, we aim to evaluate the impact of anthelmintics on the incidence of diarrheal disease caused by viral and bacterial pathogens in school children in southern Vietnam.This is a randomized, double-blinded, placebo-controlled trial to investigate the effects of albendazole treatment versus placebo on the incidence of viral- and bacterial-induced diarrhea in 350 helminth-infected and 350 helminth-uninfected Vietnamese school children aged 6-15 years. Four hundred milligrams of albendazole, or placebo treatment will be administered once every 3 months for 12 months. At the end of 12 months, all participants will receive albendazole treatment. The primary endpoint of this study is the incidence of diarrheal disease assessed by 12 months of weekly active and passive case surveillance. Secondary endpoints include the prevalence and intensities of helminth, viral, and bacterial infections, alterations in host immunity and the gut microbiota with helminth and pathogen clearance, changes in mean z scores of body weight indices over time, and the number and severity of adverse events.In order to reduce helminth burdens, anthelmintics are being routinely administered to children in developing countries. However, the effects of anthelmintic treatment on susceptibility to other diseases, including diarrheal pathogens, remain unknown. It is important to monitor for unintended consequences of drug treatments in co-infected populations. In this trial, we will examine how anthelmintic treatment impacts host susceptibility to diarrheal infections, with the aim of informing deworming programs of any indirect effects of mass anthelmintic administrations on co-infecting enteric pathogens.ClinicalTrials.gov: NCT02597556 . Registered on 3 November 2015

Inhibition of Y1 receptor signaling improves islet transplant outcome

Failure to secrete sufficient quantities of insulin is a pathological feature of type-1 and type-2 diabetes, and also reduces the success of islet cell transplantation. Here we demonstrate that Y1 receptor signaling inhibits insulin release in β-cells, and show that this can be pharmacologically exploited to boost insulin secretion. Transplanting islets with Y1 receptor deficiency accelerates the normalization of hyperglycemia in chemically induced diabetic recipient mice, which can also be achieved by short-term pharmacological blockade of Y1 receptors in transplanted mouse and human islets. Furthermore, treatment of non-obese diabetic mice with a Y1 receptor antagonist delays the onset of diabetes. Mechanistically, Y1 receptor signaling inhibits the production of cAMP in islets, which via CREB mediated pathways results in the down-regulation of several key enzymes in glycolysis and ATP production. Thus, manipulating Y1 receptor signaling in β-cells offers a unique therapeutic opportunity for correcting insulin deficiency as it occurs in the pathological state of type-1 diabetes as well as during islet transplantation.Islet transplantation is considered one of the potential treatments for T1DM but limited islet survival and their impaired function pose limitations to this approach. Here Loh et al. show that the Y1 receptor is expressed in β- cells and inhibition of its signalling, both genetic and pharmacological, improves mouse and human islet function.info:eu-repo/semantics/publishe