Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Ni2+ Content on Broadband Ferromagnetic Resonance of Superparamagnetic Ni Ferrite (Having Fe2+)-Epoxy Composite

Superparamagnetic particles of Ni ferrite prepared by co-precipitation technique were mixed with epoxy and studied by broadband ferromagnetic resonance (BFMR) in the range 2–26 GHz. The XRD and TEM showed that particles had a spinel structure and a small size around 27 nm. The particles exhibited a hysteresis loop with superparamagnetic behavior and the moment-temperature analyses showed a blocking temperature higher than room temperature. SC-BFMR analysis was based on measuring the diode voltage ratio when the sample is over the SC to the diode voltage when the sample is absent. The BFMR spectrums of Ni ferrite-epoxy composites were more refined at low fields than the higher one, when different phenomena were interfered. Below 5000 G, the resonant frequency-field relation was linear for all samples. The effective g-factor and effective magnetization calculated from resonant frequency field were generally decreased as Ni2+ was increased. The losses were represented as the inverse of the quality factor which was increased with Ni2+ content and was dropped as frequency was growing.
Texture Mapping on Doo-Sabin Subdivision Surfaces Using Multiple Images

__________ Abstract We propose a texture mapping method on subdivision surfaces using multiple images and have implemented it on Doo-Sabin scheme. At the beginning, one texture map is specified for each control mesh face respectively. In a subdivision process, a new face of control meshes may fall into a region of multiple texture maps. To correctly texture map on such faces, we need to further split the new faces into multiple parts so that each of them falls into a region of only one texture map. A splitting algorithm is devised. The novelty of our work is on a generalization of the method of DeRose et al. for the treatment of the use of multiple images
Dynamic balance of pro‐ and anti‐inflammatory signals controls disease and limits pathology

Immune responses to pathogens are complex and not well understood in many diseases, and this is especially true for infections by persistent pathogens. One mechanism that allows for long‐term control of infection while also preventing an over‐zealous inflammatory response from causing extensive tissue damage is for the immune system to balance pro‐ and anti‐inflammatory cells and signals. This balance is dynamic and the immune system responds to cues from both host and pathogen, maintaining a steady state across multiple scales through continuous feedback. Identifying the signals, cells, cytokines, and other immune response factors that mediate this balance over time has been difficult using traditional research strategies. Computational modeling studies based on data from traditional systems can identify how this balance contributes to immunity. Here we provide evidence from both experimental and mathematical/computational studies to support the concept of a dynamic balance operating during persistent and other infection scenarios. We focus mainly on tuberculosis, currently the leading cause of death due to infectious disease in the world, and also provide evidence for other infections. A better understanding of the dynamically balanced immune response can help shape treatment strategies that utilize both drugs and host‐directed therapies.
Plane detection in 3D point cloud using octree-balanced density down-sampling and iterative adaptive plane extraction

In this paper, a new technique for plane detection from 3D point clouds is proposed. The algorithm depends on two concepts to balance between high-accuracy and fast performance. The first is the use of a new fast octree-based balanced density down-sampling technique to reduce the number of points. The second is the fact that the number of planes in any dataset is much less than the number of the points. Random points are examined to find the 3D planes. To increase the accuracy, the system utilizes an adaptive plane extraction technique to overcome data noise. Initially, the point cloud is subdivided using octree into small cubes with a limited number of points. Then the cubes are down-sampled based on the local density of each cube. The points are explored randomly for finding a planar surface by applying principal component analysis (PCA) on the points' spherical neighborhood obtained by the down-sampled octree structure. The adaptive plane extraction is used to adjust the plane orientation to find the best position that includes the maximum number of points. Experimental results demonstrate that the proposed algorithm is capable of processing large amounts of data efficiently to produce accurate results that are robust to noise.
Wide-Band Single-Ended-to-Balanced Power Divider with Broad-Band Common-Mode Suppression

A novel wide-band five-port single-ended-to- balanced power divider that features broad-band common-mode suppression is proposed in this paper. Ideal 180° phase inverters are used to replace the conventional half-wavelength transmission lines for the basic power divider, which allows to obtain wide-band common-mode suppression and circuit-size reduction. The reported five-port power divider exhibits broad-band operation with equal power division, high input/output power matching, high power isolation between the two balanced outputs, and wide-band common-mode suppression.
Reliability Evaluation for Microgrids Using Cross-Entropy Monte Carlo Simulation

This paper proposes a new method to evaluate reliability indices for load served within a grid-connected microgrid. It is based on sequential Monte Carlo simulation (MCS) and the cross-entropy method; considers time-dependent generating resources, load, and energy storage; and calculates the probability, frequency, and cost of loss of load events. Grid-connected microgrids are typically very reliable systems, and thus sequential MCS methods can face slow convergence times. The cross-entropy method, which is used here to distort transition rates for the distribution system and generators, greatly speeds convergence without sacrificing the primary advantages of sequential simulation. Validation is run against sequential MCS for several microgrid configurations. The new algorithm reproduces indices to within 6% and reduces run-times by factors of 5–19.
Serum levels of human epididymis protein 4 are more stable than cancer antigen 125 in early and mid‐term pregnancy

Previous studies have shown serum human epididymis protein 4 (HE4) and cancer antigen 125 (CA125) levels could serve as predictive markers in the diagnosis of ovarian cancer. However, HE4 levels have been less well studied during pregnancy, especially in threatened abortion, while CA125 levels were fluctuated. Our study aimed to assess the stability of HE4 and CA125 levels through trimesters and in cases of threatened abortion.
Adaptive Document Retrieval for Deep Question Answering

State-of-the-art systems in deep question answering proceed as follows: (1)an initial document retrieval selects relevant documents, which (2) are then processed by a neural network in order to extract the final answer. Yet the exact interplay between both components is poorly understood, especially concerning the number of candidate documents that should be retrieved. We show that choosing a static number of documents - as used in prior research - suffers from a noise-information trade-off and yields suboptimal results. As a remedy, we propose an adaptive document retrieval model. This learns the optimal candidate number for document retrieval, conditional on the size of the corpus and the query. We report extensive experimental results showing that our adaptive approach outperforms state-of-the-art methods on multiple benchmark datasets, as well as in the context of corpora with variable sizes.
The implementation of an automated coding system for cause-of-death statistics

ABSTRACT Background: The production of cause-of-death statistics requires the coding and selection of an underlying cause of death from death certificates. Nowadays, this is done manually in many countries around the world. However, automated coding systems have been available since the 1970s and more and more countries are switching from manual to automated coding. The introduction of an automated coding system is expected to change the coding process and its outcomes in a fundamental way. Therefore, we studied the implementation of such a system, called IRIS, in the Netherlands. Methods: We adapted the system to our situation step-by-step and measured the number of death certificates processed without any manual intervention. Medical coders analyzed and qualified death certificates that could not be processed by the system. We also performed a bridge (double) coding study on a set of death certificates so that we could compare the underlying cause of death assigned by IRIS with the underlying cause of death assigned by a medical coder to the same death certificate. Results: IRIS could handle 68.5% of the death certificates without any manual intervention. Of the (31.5%) rejected death certificates, the main reasons for rejection were the following: editing and/or coding problems (76%), not able to select an underlying cause of death (12%), ambiguous causal connection between codes (5%), a combination of the reasons mentioned (7%). In the bridge coding study, 78% of the death certificates coded by IRIS without any manual intervention showed exactly the same underlying cause of death (ICD-10, four digits) as death certificates coded manually. Conclusions: An automated coding system for causes of death reduces the workload for medical coders considerably. When editing or coding problems can be solved the system is expected to handle up to 85% of the death certificates without manual intervention. The performance of the system is strongly dependent on the quality of death certificates. A change from manual to automated coding brings about changes in the frequency of occurrence of major causes of death. Users of death statistics should be aware of these changes when studying trends in time or regional variations of causes of death.
Development and Characterization of a Safety System for Robotic Cells Based on Multiple Time of Flight (TOF) Cameras and Point Cloud Analysis

In this paper, a vision system for safety applications in human-robot collaboration is presented. The system is based on two Time-Of-Flight (TOF) cameras for 3D acquisition. The point clouds are registered in a common reference system, and human and robot recognition are then implemented. Human recognition is performed using a customized version of the Histogram of Oriented Gradient (HOG) algorithm. Robot recognition is achieved using a procedure based on the Kanade-Lucas-Tomasi (KLT) algorithm. Two safety strategies have been developed. The first one is based on the definition of suitable comfort zones of both the operator and the robot; the second implements virtual barriers between the operator and the robot. The vision system has been characterized in terms of (i) human and robot recognition performance, (ii) correctness of the detection of safety situations and (iii) evaluation of the time delays in the detection. The results show that the human operator is robustly recognized provided that he moves frontally with respect to the TOF cameras and the robot is always recognized. The safety situations are always identified correctly with an average time delay of 0.86 0.63 s (k=1).
Virtual Reality and Tracking the Mating Behavior of Fruit Flies: a Machine Learning Approach

Study of social behaviors of Drosophila melanogaster, i.e., fruit flies, is used to understand certain behaviors of human. Automation of capturing and real-time analysis of fruit fly social movements helps quantitative study of those behavior which in turns can be applied for studying human behavior. To achieve this, we present Virtual Reality environment setting to stimulate fruit flies behavior and tracking of their motions automatically in real-time. As an experiment in a real application, we selected study of mating behavior of fruit flies. Male fruit flies tend to extend their mating duration when exposed to rivals as published in previous biology studies. We designed a Virtual Reality environment where synthetic male fruit flies are virtually simulated to stimulate a male fruit fly to study the effect of rivals. Bezier curve fitting and Gaussian random distribution are utilized for movement simulation. A machine learning approaches (logistic regression) employed to track, detect, and classify fruit flies mating behavior. We performed connected component labeling as an operator for tracking and classification of mating and non-mating status for comparison purposes. The machine learning based approach shows superior result in terms of speed and accuracy.
Scalable Edge Partitioning

Edge-centric distributed computations have appeared as a recent technique to improve the shortcomings of think-like-a-vertex algorithms on large scale-free networks. In order to increase parallelism on this model, edge partitioning - partitioning edges into roughly equally sized blocks - has emerged as an alternative to traditional (node-based) graph partitioning. In this work, we give a distributed memory parallel algorithm to compute high-quality edge partitions in a scalable way. Our algorithm scales to networks with billions of edges, and runs efficiently on thousands of PEs. Our technique is based on a fast parallelization of split graph construction and a use of advanced node partitioning algorithms. Our extensive experiments show that our algorithm has high quality on large real-world networks and large hyperbolic random graphs, which have a power law degree distribution and are therefore specifically targeted by edge partitioning
A Sub-6 GHz Compact GaN MMIC Doherty PA with a 49.5% 6 dB back-off PAE for 5G Communications

This paper presents a sub-6 GHz MMIC Doherty power amplifier (DPA) for 5G communication applications based on a $\pmb{0.25-\mu }\mathbf{mGaN}$ HEMT process. The impedance inverter of the DPA is realized by combining a compact transmission line network with two shunt capacitors. Also, the ratio of power cells in the main and auxiliary amplifiers is optimized to achieve a high efficiency in output power back-off. The measured peak output power and $\mathbf{P}_{\pmb{1}\mathbf{d}\mathbf{B}}$ are 38.7 and 32.1 dBm, respectively at 5.9 GHz. The power-added-efficiency (PAE) at a 6-dB output power backoff is up to 49.5%.
Anaerobic Process

A review of the literatures published in 2017 on topics relating to anaerobic process issues in the improvement of biogas production and fermentation efficiency of various kinds of organic waste. New process methodology and technology of digestion is also presented. This review is divided into the following sections: pretreatment, organic waste and co‐digestion, multiple‐stage process, process methodology and technology.
Genome-Wide Screening of Aberrant Methylation Loci for Nonsyndromic Cleft Lip

Background: The pathogenicity of cleft lip (CL) is pretty complicated since it is influenced by the interaction of environment and genetic factors. The purpose of this study was to conduct a genome-wide screening of aberrant methylation loci in partial lesion tissues of patients with nonsyndromic CL (NSCL) and preliminarily validate candidate dysmethylated genes associated with NSCL. Methods: Fifteen healthy and sixteen NSCL fetal lip tissue samples were collected. The Infinium HumanMethylation450 BeadChip was used to screen aberrant methylation loci in three NSCL and three healthy lip tissues. The differential methylation sites and functions of the annotated genes between NSCL and healthy lip tissues were analyzed using minfi package of R software, cluster analysis, Gene Ontology (GO) annotation, and metabolic pathway annotation. Gene expression was assessed in nine differentially methylated genes by real-time polymerase chain reaction (PCR). The transcriptions mRNA levels of three out of nine candidate genes were downregulated remarkably in NSCL lip tissues, and these three genes’ abnormal methylation loci were validated by pyrosequencing in 16 NSCL cases and 15 healthy cases. Results: In total, 4879 sites in the genes of NSCL odinopoeia fetuses showed aberrant methylation when compared with normal lip tissue genome. Among these, 3661 sites were hypermethylated and 1218 sites were hypomethylated as compared to methylation levels in healthy specimens. These aberrant methylation sites involved 2849 genes and were widely distributed among the chromosomes. Most differentially methylated sites were located in cytosine-phosphoric acid-guanine islands. Based on GO analysis, aberrantly methylated genes were involved in 11 cellular components, 13 molecular functions, and a variety of biological processes. Notably, the transcription of DAB1, REELIN, and FYN was significantly downregulated in lesion tissues of NSCL fetus (P < 0.05). Pyrosequencing results validated that there were two loci in DAB1 with high methylation status in patient tissues (P < 0.05). Conclusions: We detected numerous aberrantly methylated loci in lesion tissues of NSCL fetus. Aberrant gene expression in the REELIN signaling pathway might be related with NSCL. Decreased transcription of DAB1, a member of REELIN signal pathway, resulted from its abnormal high methylation, which might be one of the factors underlying the occurrence of NSCL.
GILZ-dependent modulation of mTORC1 regulates spermatogonial maintenance

ABSTRACT Male fertility is dependent on spermatogonial stem cells (SSCs) that self-renew and produce differentiating germ cells. Growth factors produced within the testis are essential for SSC maintenance but intrinsic factors that dictate the SSC response to these stimuli are poorly characterised. Here, we have studied the role of GILZ, a TSC22D family protein and spermatogenesis regulator, in spermatogonial function and signalling. Although broadly expressed in the germline, GILZ was prominent in undifferentiated spermatogonia and Gilz deletion in adults resulted in exhaustion of the GFRα1+ SSC-containing population and germline degeneration. GILZ loss was associated with mTORC1 activation, suggesting enhanced growth factor signalling. Expression of deubiquitylase USP9X, an mTORC1 modulator required for spermatogenesis, was disrupted in Gilz mutants. Treatment with an mTOR inhibitor rescued GFRα1+ spermatogonial failure, indicating that GILZ-dependent mTORC1 inhibition is crucial for SSC maintenance. Analysis of cultured undifferentiated spermatogonia lacking GILZ confirmed aberrant activation of ERK MAPK upstream mTORC1 plus USP9X downregulation and interaction of GILZ with TSC22D proteins. Our data indicate an essential role for GILZ-TSC22D complexes in ensuring the appropriate response of undifferentiated spermatogonia to growth factors via distinct inputs to mTORC1. Summary: The cell regulator GILZ operates in a complex with TSC22D family proteins in undifferentiated spermatogonia, maintaining self-renewal capacity through mTORC1 suppression and promoting expression of essential spermatogenic factors.
MLCut: Exploring Multi-Level Cuts in Dendrograms for Biological Data

Choosing a single similarity threshold for cutting dendrograms is not sufficient for performing hierarchical clustering analysis of heterogeneous data sets. In addition, alternative automated or semi-automated methods that cut dendrograms in multiple levels make assumptions about the data in hand. In an attempt to help the user to find patterns in the data and resolve ambiguities in cluster assignments, we developed MLCut: a tool that provides visual support for exploring dendrograms of heterogeneous data sets in different levels of detail. The interactive exploration of the dendrogram is coordinated with a representation of the original data, shown as parallel coordinates. The tool supports three analysis steps. Firstly, a single-height similarity threshold can be applied using a dynamic slider to identify the main clusters. Secondly, a distinctiveness threshold can be applied using a second dynamic slider to identify "weak-edges" that indicate heterogeneity within clusters. Thirdly, the user can drill-down to further explore the dendrogram structure - always in relation to the original data - and cut the branches of the tree at multiple levels. Interactive drill-down is supported using mouse events such as hovering, pointing and clicking on elements of the dendrogram. Two prototypes of this tool have been developed in collaboration with a group of biologists for analysing their own data sets. We found that enabling the users to cut the tree at multiple levels, while viewing the effect in the original data, is a promising method for clustering which could lead to scientific discoveries.
Intelligent Middle-Level Game Control

We propose the concept of intelligent middle-level game control, which lies on a continuum of control abstraction levels between the following two dual opposites: 1) high-level control that translates player’s simple commands into complex actions (such as pressing Space key for jumping), and 2) low-level control which simulates real-life complexities by directly manipulating, e.g., joint rotations of the character as it is done in the runner game QWOP. We posit that various novel control abstractions can be explored using recent advances in movement intelligence of game characters. We demonstrate this through design and evaluation of a novel 2-player martial arts game prototype. In this game, each player guides a simulated humanoid character by clicking and dragging body parts. This defines the cost function for an online continuous control algorithm that executes the requested movement. Our control algorithm uses Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in a rolling horizon manner with custom population seeding techniques. Our playtesting data indicates that intelligent middle-level control results in producing novel and innovative gameplay without frustrating interface complexities.
Question Generation from SQL Queries Improves Neural Semantic Parsing

In this paper, we study how to learn a semantic parser of state-of-the-art accuracy with less supervised training data. We conduct our study on WikiSQL, the largest hand-annotated semantic parsing dataset to date. First, we demonstrate that question generation is an effective method that empowers us to learn a state-of-the-art neural network based semantic parser with thirty percent of the supervised training data. Second, we show that applying question generation to the full supervised training data further improves the state-of-the-art model. In addition, we observe that there is a logarithmic relationship between the accuracy of a semantic parser and the amount of training data.