Miracle-dz/UniFAR-FTUs huggingface dataset:
Solar filament impact on 21 January 2005: Geospace consequences

On 21 January 2005, a moderate magnetic storm produced a number of anomalous features, some seen more typically during superstorms. The aim of this study is to establish the differences in the space environment from what we expect (and normally observe) for a storm of this intensity, which make it behave in some ways like a superstorm. The storm was driven by one of the fastest interplanetary coronal mass ejections in solar cycle 23, containing a piece of the dense erupting solar filament material. The momentum of the massive solar filament caused it to push its way through the flux rope as the interplanetary coronal mass ejection decelerated moving toward 1 AU creating the appearance of an eroded flux rope (see companion paper by Manchester et al. (2014)) and, in this case, limiting the intensity of the resulting geomagnetic storm. On impact, the solar filament further disrupted the partial ring current shielding in existence at the time, creating a brief superfountain in the equatorial ionosphere—an unusual occurrence for a moderate storm. Within 1 h after impact, a cold dense plasma sheet (CDPS) formed out of the filament material. As the interplanetary magnetic field (IMF) rotated from obliquely to more purely northward, the magnetotail transformed from an open to a closed configuration and the CDPS evolved from warmer to cooler temperatures. Plasma sheet densities reached tens per cubic centimeter along the flanks—high enough to inflate the magnetotail in the simulation under northward IMF conditions despite the cool temperatures. Observational evidence for this stretching was provided by a corresponding expansion and intensification of both the auroral oval and ring current precipitation zones linked to magnetotail stretching by field line curvature scattering. Strong Joule heating in the cusps, a by‐product of the CDPS formation process, contributed to an equatorward neutral wind surge that reached low latitudes within 1–2 h and intensified the equatorial ionization anomaly. Understanding the geospace consequences of extremes in density and pressure is important because some of the largest and most damaging space weather events ever observed contained similar intervals of dense solar material.
The Accurate Modeling and Stability Analysis for Flyback Converter Based on Describing Function Method

Flyback converter is widely used in switching power supply and the stability analysis for it is critical. However, the result of the stability analysis method based on the traditional linear small-signal model is not accurate enough because the nonlinear characteristics of the switching process are ignored in the analysis process. In this paper, the accurate modeling and stability analysis for flyback converter are carried out by using describing function method. Firstly, the linear and nonlinear small-signal model are respectively established for flyback converter. Following that, the stability criterions are derived from the two above models accordingly. Finally, according to the two criterions, the stability analysis results are respectively presented. It has been proved that the mentioned stability analysis method is precise by comparing the simulation and experimental results.
NFFT Meets Krylov Methods: Fast Matrix-Vector Products for the Graph Laplacian of Fully Connected Networks

The graph Laplacian is a standard tool in data science, machine learning, and image processing. The corresponding matrix inherits the complex structure of the underlying network and is in certain applications densely populated. This makes computations, in particular matrix-vector products, with the graph Laplacian a hard task. A typical application is the computation of a number of its eigenvalues and eigenvectors. Standard methods become infeasible as the number of nodes in the graph is too large. We propose the use of the fast summation based on the nonequispaced fast Fourier transform (NFFT) to perform the dense matrix-vector product with the graph Laplacian fast without ever forming the whole matrix. The enormous flexibility of the NFFT algorithm allows us to embed the accelerated multiplication into Lanczos-based eigenvalues routines or iterative linear system solvers and even consider other than the standard Gaussian kernels. We illustrate the feasibility of our approach on a number of test problems from image segmentation to semi-supervised learning based on graph-based PDEs. In particular, we compare our approach with the Nystr\"om method. Moreover, we present and test an enhanced, hybrid version of the Nystr\"om method, which internally uses the NFFT.
Practical Collaborative Learning for Crowdsensing in the Internet of Things with Differential Privacy

Machine learning is increasingly used to produce predictive models for crowdsensing applications such as health monitoring and query suggestion. These models are more accurate when trained on large amount of data collected from different sources. However, such massive data collection presents serious privacy concerns. The personal crowdsensing data such as photos, voice records, and locations is often highly sensitive, and once being sent out to the collecting companies, falls out of the control of the crowdsensing users who own it. This may preclude the practice of transmitting all user data to a central location and training there using conventional machine learning approaches. In this paper, we advocate an alternative approach that leaves data stored on the user side and learns a shared model by coordinating local training of crowdsensing users in an iterative process. Specifically, we focus on regularized empirical risk minimization and propose an efficient scheme based on decomposition that enables multiple crowdsensing users to jointly learn an accurate learning model for a given learning objective without sharing their private crowdsensing data. We exploit the fact that the optimization problems used in many learning tasks are decomposable and can be solved in a parallel and distributed way by the alternating direction method of multipliers (ADMM). Considering the heterogeneity of different user devices in practice, we propose an asynchronous ADMM algorithm to speed up the training process. Our scheme lets users train independently on their own crowdsensing data and only share some updated model parameters instead of raw data. Moreover, secure computation and distributed noise generation are novelly integrated in our scheme to guarantee differential privacy of the shared parameters in the execution of the asynchronous ADMM algorithm. We analyze the privacy guarantee and demonstrate the privacy-utility trade-off of our privacy-preserving collaborative learning scheme empirically based on real-world data.
Mitochondria-targeted betulinic and ursolic acid derivatives: synthesis and anticancer activity

Conjugation of native triterpenoids, namely, betulinic and ursolic acids, with a lypophilic triphenylphosphonium cation led to the dramatic enhancement, as compared to betulinic acid, of their ability to trigger the mitochondrial apoptosis pathway in various types of cancer cells.
A Comparative Analysis of SVM, Naive Bayes and GBDT for Data Faults Detection in WSNs

Due to the fragility of sensor nodes, namely: vulnerability to damage, calibration, unstable communication links, limited carrying energy, and being vulnerable to environmental influences, sensor nodes in wireless sensor networks are prone to fault. In this paper, we mainly focus on noise fault, short-term fault, and fixed fault caused by low battery and calibration. In recent years, a large number of machine learning-based algorithms have been proposed for the anomaly detection of wireless sensor networks. In this paper, we compare and analyze the performance of Support Vector Machine (SVM), Naive Bayes, and Gradient Lifting Decision Tree (GBDT) in identifying and classifying fault. We introduce a comparative study of the above methods on experimental data sets. Experiments show that GBDT algorithm obtains a better fault detection rate.
A Novel Integration Method for a Bipolar Receiver Pad Using LCC Compensation Topology for Wireless Power Transfer

A bipolar pad (BPP) is the newcomer magnetic structure of inductive power transfer (IPT). This pad has a better performance in the lateral misalignment compared to the other single-sided pads such as double-D pad (DDP). Also, it has less amount of copper compared to the double-D quadrature pad. Double-sided LCC compensation topology is an efficient way to compensate the IPT contrasted with the other compensation types. However, due to the utilization of compensation inductances, the system volume is larger than the other formats. Thus, to have a compact compensation method, these inductances are integrated into the main coils, considering the effects of the coils on each other. In this paper, for the first time, compensation of a BPP with novel integrated inductances of the LCC compensation method is introduced. This integration is analyzed and its effects are optimized to an insignificant rate with three-dimensional finite element analysis tool ANSYS MAXWELL. Also, the primary compensation is changed to a series method to decrease the number of compensation components and assess the effects of this variation on the circuit. Finally, DDP transmitter and BPP receiver with integrated double-sided LCC compensation topology are prototyped to verify the analytical results.
Acute control of the sleep switch in Drosophila reveals a role for gap junctions in regulating behavioral responsiveness

Sleep is a dynamic process in most animals, involving distinct stages that probably perform multiple functions for the brain. Before sleep functions can be initiated, it is likely that behavioral responsiveness to the outside world needs to be reduced, even while the animal is still awake. Recent work in Drosophila has uncovered a sleep switch in the dorsal fan-shaped body (dFB) of the fly’s central brain, but it is not known whether these sleep-promoting neurons also govern the acute need to ignore salient stimuli in the environment during sleep transitions. We found that optogenetic activation of the sleep switch suppressed behavioral responsiveness to mechanical stimuli, even in awake flies, indicating a broader role for these neurons in regulating arousal. The dFB-mediated suppression mechanism and its associated neural correlates requires innexin6 expression, suggesting that the acute need to reduce sensory perception when flies fall asleep is mediated in part by electrical synapses.
Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective

With the recent advancements in analyzing high-volume, complex, and unstructured data, modern learning methods are playing an increasingly critical role in the field of personalized medicine. Personalized medicine (i.e., providing tailored medical treatment to individual patients through the identification of common features, including their genetics, inheritance, and lifestyle) has attracted the attention of many researchers in recent years. This paper provides an overview of the research progress in the application of learning methods, with a focus on deep learning in personalized medicine. In particular, three domains of applications are reviewed: drug development, disease characteristic identification, and therapeutic effect prediction. The main objective of this review is to consider the applied methods in detail and to offer insights into their pros and cons. Although having demonstrated advantages in coping with data complexity and nonlinearity and in recognizing features and associating structural data, the studied learning methods are not a panacea to all medical problems. Hence, we discuss the existing research challenges and clarify future study directions.
First-Line Trastuzumab Plus an Aromatase Inhibitor, With or Without Pertuzumab, in Human Epidermal Growth Factor Receptor 2-Positive and Hormone Receptor-Positive Metastatic or Locally Advanced Breast Cancer (PERTAIN): A Randomized, Open-Label Phase II Trial.

PURPOSE To assess pertuzumab plus trastuzumab and an aromatase inhibitor (AI) in patients with human epidermal growth factor receptor 2 (HER2)-positive and hormone receptor-positive metastatic/locally advanced breast cancer (MBC/LABC). PATIENTS AND METHODS The PERTAIN trial (NCT01491737) is an ongoing randomized, open-label, multicenter-80 sites and eight countries-phase II trial. Patients have HER2-positive, hormone receptor-positive MBC/LABC and no prior systemic therapy with the exception of endocrine. Random assignment was 1:1 to intravenous pertuzumab (840 mg loading dose followed by 420 mg every 3 weeks) plus trastuzumab (8 mg/kg followed by 6 mg/kg every 3 weeks), and oral anastrozole (1 mg every day) or letrozole (2.5 mg every day), or trastuzumab and an AI. Induction intravenous docetaxel every 3 weeks or paclitaxel every week could be administered for 18 to 24 weeks at the investigator's discretion (decided before but given after random assignment). Primary end point was progression-free survival (PFS). Patients were stratified by whether they received induction chemotherapy and their time since adjuvant hormone therapy. RESULTS One hundred twenty-nine patients were randomly assigned per arm (February 2012 to October 2014; intent-to-treat populations); 75 in one arm and 71 in the other were chosen to receive induction chemotherapy. Stratified median PFS was 18.89 months (95% CI, 14.09 to 27.66 months) in the pertuzumab plus trastuzumab arm and 15.80 months (95% CI, 11.04 to 18.56 months) in the trastuzumab arm (stratified hazard ratio, 0.65; 95% CI, 0.48 to 0.89; P = .0070). Serious adverse events (AEs) were reported for 42 (33.1%) of 127 and 24 (19.4%) of 124 patients in the safety populations of the pertuzumab plus trastuzumab and trastuzumab arms, respectively. Rates of grade ≥ 3 AEs were 64 (50.4%) of 127 and 48 (38.7%) of 124, respectively. There were no deaths as a result of AEs. CONCLUSION PERTAIN met its primary PFS end point. Pertuzumab plus trastuzumab and an AI is effective for the treatment of HER2-positive MBC/LABC. The safety profile was consistent with previous trials of pertuzumab plus trastuzumab.
Measuring Student's Utilization of Video Resources and Its Effect on Academic Performance

Massive video resources were produced to meet the needs of learning knowledge and skills anytime and anywhere through internet. Therefore, whether these video resources were fully utilized by students is an important issue for schools and teachers. This paper proposes three indicators based on student's log data and course's video information to measure the utilization of video resources. In addition, the proposed indicators are applied in a case study to analyze how different utilization patterns affect students' academic performance in a large-scale online distance education context.
Magneto-optical Faraday effect in dysprosium oxide (Dy2O3) based ceramics obtained by vacuum sintering.

The method of self-propagating high-temperature synthesis of submicron powders with subsequent vacuum sintering was used to produce a series of optical ceramics based on dysprosium oxide (DyxY0.95-xLa0.05)2O3, where x=0.7, 0.85, and 0.9. The influence of ceramics composition on optical transmission of the obtained samples was investigated. The studied materials had several transparency windows in the visible 500-730 nm, near-IR 1900-2300 nm, and mid-IR 3500-7500 nm ranges. The dependence of the Verdet constant on wavelength in the 400-1940 nm range was measured for each ceramics composition, and analytical approximations were obtained. When Y and La dopants were used, the Verdet constant depended linearly on the concentration of Dy3+ ions, and its value in the visible wavelength range was about twice that of the analogous value for a terbium gallium garnet crystal. The high transmission and relatively high Verdet constant make the produced materials highly promising for fabricating Faraday devices for Tm3+ and Ho3+ lasers and for lasers emitting in the visible spectrum.
Explicit semantic path mining via wikipedia knowledge tree

While classical bag-of-word (BoG) approaches represent text content in the word level, recent studies show that knowledge-based concept indexation is a promising approach to further enhance the text search and mining performance. In this study, we propose a new knowledge indexation/extraction method, Explicit Semantic Path Mining (ESPM), for knowledge-base text mining. It has roots in a concept-based vector constructing method, Explicit Semantic Analysis (ESA), which has shown success in text mining tasks. For this new method, given an input piece of text, ESPM can efficiently identify the independent and optimized semantic path(s) on a concept map, which is, in this study, the Wikipedia category tree. Unlike earlier studies focusing on BoG based vector space, ESPM is a semantic path mining algorithm, which generates the top down semantic categories of a given text by leveraging the rich link information between Wikipedia categories and articles. Preliminary experiment based on ODP data shows ESPM delivers high quality independent semantic paths from both precision and ranking viewpoints.
De novo Transcriptome Assembly of the Clown Anemonefish (Amphiprion percula): A New Resource to Study the Evolution of Fish Color

A fundamental question of evolutionary biology is, why are some animals conspicuously colored? This question may be addressed from both a proximate (genetic and ontogenetic) and ultimate (adaptive value and evolutionary origins) perspective, and integrating these perspectives can provide further insights. Over the last few decades we have made great advances in understanding the causes of conspicuous coloration in terrestrial systems, e.g., birds and butterflies, but we still know relatively little about the causes of conspicuous, ‘poster’ coloration in coral reef fishes. Of all coral reef fishes, the clownfish Amphiprion percula, is perhaps the most conspicuously colored, possessing a bright orange body with three iridescent white bars bordered with pitch black. Here, we review what is known about the proximate and ultimate causes of the conspicuous coloration of clownfishes Amphiprion sp. : coloration has a heritable genetic basis; coloration is influenced by development and environment; coloration has multiple plausible signaling functions; there is a phylogenetic component to coloration. Subsequently, to provide new insights into the genetic mechanisms and potential functions of A. percula coloration we i) generate the first de novo transcriptome for this species, ii) conduct differential gene expression analyses across different colored epidermal tissues, and iii) conduct gene ontology (GO) enrichment analyses to characterize function of these differentially expressed genes. BUSCO indicated that transcriptome assembly was successful and many genes were found to be differentially expressed between epidermal tissues of different colors. In orange tissue, relative to white and black, many GO terms associated with muscle were over-represented. In white tissue, relative to orange and black tissue, there were very few over- or under-represented GO terms. In black tissue, relative to orange and white, many GO terms related to immune function were over-represented, supporting the hypothesis that black (melanin) coloration may serve a protective function. Overall, this study presents the assembly of the A. percula transcriptome, and represents a first step in an integrative investigation of the proximate and ultimate causes of conspicuous coloration of this iconic coral reef fish.
Constructions of Maximally Recoverable Local Reconstruction Codes via Function Fields

Local Reconstruction Codes (LRCs) allow for recovery from a small number of erasures in a local manner based on just a few other codeword symbols. They have emerged as the codes of choice for large scale distributed storage systems due to the very efficient repair of failed storage nodes in the typical scenario of a single or few nodes failing, while also offering fault tolerance against worst-case scenarios with more erasures. A maximally recoverable (MR) LRC offers the best possible blend of such local and global fault tolerance, guaranteeing recovery from all erasure patterns which are information-theoretically correctable given the presence of local recovery groups. MR LRCs have received much attention recently, with many explicit constructions covering different regimes of parameters. Unfortunately, all known constructions require a large field size. In this work, we develop an approach based on function fields to construct MR LRCs. Our method recovers, and in most parameter regimes improves, the field size of previous approaches. The improvements are modest, but more importantly are obtained in a unified manner via a promising new idea.
Quo Vadis Industry 4.0: An Overview Based on Scientific Publications Analytics

The introduction of Industry 4.0 to modernize the existing industrial manufacturing systems has created a wave of disruption all over the world, that changed not only the industry paradigm, but also the research directions in the intelligent manufacturing systems field. After five years since its presentation, similar initiatives have fostered in several countries worldwide to promote the adoption of Industry 4.0 principles. This paper aims to analyze and discuss the current state of adoption of this initiative and to verify the way it changed the research directions, particularly at the level of applying artificial intelligence and ICT technologies in the cyber-physical systems context. For this purpose, an analytical study of the scientific publications related to Industry 4.0 domain has been performed, considering a dataset of scientific publications retrieved from the IEEE Xplore database. This dataset considers two distinct time periods, separated by the introduction of Industry 4.0 as the digitalization threshold: before digital era (B.D.) and after digital era (A.D.).
Bayesian inference of antigenic and non-antigenic variables from haemagglutination inhibition assays for influenza surveillance

Haemagglutination inhibition (HI) assays are typically used for comparing and characterizing influenza viruses. Data obtained from the assays (titres) are used quantitatively to determine antigenic differences between influenza strains. However, the use of these titres has been criticized as they sometimes fail to capture accurate antigenic differences between strains. Our previous analytical work revealed how antigenic and non-antigenic variables contribute to the titres. Building on this previous work, we have developed a Bayesian method for decoupling antigenic and non-antigenic contributions to the titres in this paper. We apply this method to a compendium of HI titres of influenza A (H3N2) viruses curated from 1968 to 2016. Remarkably, the results of this fit indicate that the non-antigenic variable, which is inversely correlated with viral avidity for the red blood cells used in HI assays, oscillates during the course of influenza virus evolution, with a period that corresponds roughly to the timescale on which antigenic variants replace each other. Together, the results suggest that the new Bayesian method is applicable to the analysis of long-term dynamics of both antigenic and non-antigenic properties of influenza virus.