Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Demographic Factors, Early Family Relationships and Depressive Symptomatology in Teenage Pregnancy

Aim: Teenage pregnancy has been well studied from a demographic risk perspective, but less data examining the early interpersonal family experiences of teenage mothers are available. We aimed to explore the relative impact of demographic, early interpersonal family relationships and depressive symptomatology as associations for teenage, as compared to non-teenage, childbearing. Method: A prospective cross-sectional cohort study was undertaken. Institutional ethics committee approval and informed consent were obtained. Data from consecutive teenage (teenage) and non-teenage (control) subgroups of antenatal women were compared. Subjects were interviewed and completed the following questionnaires: demographic, drug use and lifestyle; early life experiences; Hospital Anxiety and Depression Scale (HADS); and General Health Questionnaire-28. Results: In multivariate analysis, the following factors had a significant independent association with younger age of motherhood in order of magnitude: a history of parental separation/divorce in early childhood; exposure to family violence in early childhood; illicit drug use (ever or in pregnancy); idealization of the pregnancy; low family income; a positive HADS-A or HADS-D subscale score; and a low level of education. Conclusion: Interventions to reduce the rate of teenage births need to be multifocal and should include strategies to address early childhood exposure to parental separation and violence, reduce idealization of pregnancy, diagnose psychological symptomatology and offer alternative career choices to children defaulting in the education system.
ACT Reporting Category Interpretation Guide: Version 1.0. ACT Working Paper 2016 (05).

ACT reporting categories and ACT Readiness Ranges are new features added to the ACT score reports starting in fall 2016. For each reporting category, the number correct score, the maximum points possible, the percent correct, and the ACT Readiness Range, along with an indicator of whether the reporting category score falls within the Readiness Range are provided. In the format of questions and answers, this document first gives an introduction of the reporting categories in terms of what they are, how they are reported, and how they are related to the previous subscores. Then, based on results from 12 ACT forms administered to randomly equivalent groups of about 3,000 examinees in each, information is provided on the technical characteristics of the reporting category scores, including score reliability, standard error of measurement, and classification consistency of the ACT Readiness Ranges. This document is intended to facilitate the interpretation of reporting category scores and the ACT Readiness Ranges.
HIERARCHICAL PATH PLANNING FOR WALKING (ALMOST) ANYWHERE

Abstract. Computerized path planning, not constrained to transportation networks, may be useful in a range of settings, from search and rescue to archaeology. This paper develops a method for general path planning intended to work across arbitrary distances and at the level of terrain detail afforded by aerial LiDAR scanning. Relevant information about terrain, trails, roads, and other infrastructure is encoded in a large directed graph. This basal graph is partitioned into strongly connected subgraphs such that the generalized diameter of each subgraphs is constrained by a set value, and with nominally as few subgraphs as possible. This is accomplished using the k-center algorithm adapted with heuristics suitable for large spatial graphs. A simplified graph results, with reduced (but known) position accuracy and complexity. Using a hierarchy of simplified graphs adapted to different length scales, and with careful selection of levels in the hierarchy based on geodesic distance, a shortest path search can be restricted to a small subset of the basal graph. The method is formulated using matrix-graph duality, suitable for linear algebra-oriented software. Extensive use is also made of public data, including LiDAR, as well as free and open software for geospatial data processing.
Methodology for developing reusable scheduler classes applicable for long term building energy simulation

The aim of this study is to accelerate developments of building simulation programs by using ObjectOriented programming. A reusable generalised scheduler classes and interfaces 1 ) for defining schedules in simulation programs were developed. ITermStructure the “interface” for a term structure was developed to make complex term structure general. By using a “Composite-Pattern”, all the concrete term classes that implements ITermStructure could be integrated into a complex tree structure. By using the “Type parameter”, specifications of a schedule class should not be specified until a scheduler classes is declared and instantiated 3 ) by a client code. Since the program codes developed in this research and a code made by a client were separated clearly, developed scheduler classes were generally applicable for long-term simulation programs. A concrete example of program, which uses the developed scheduler classes to control cooling tower’s operating schedules, was given. It demonstrates that the developed scheduler classes worked fine without any modifications by clients.
Multirater Assessment to Teacher Professionalism based on Pedagogical Content Knowledge

The Indonesian Government has performed teacher certification program since the establishment of the Teacher Law No. 14 year 2005 and PCK has been acknowledged as the indicator of science teachers’ professionalism in some countries. Therefore, this research aims to explore teachers’ PCK as a case study to 20 middle school science teachers with different teaching experiences in the special Region of Yogyakarta, Indonesia. This research was conducted in PCK planning and enacted PCK by collecting lesson plans, recorded the learning activities in the classroom, and structured interviews. The assessment to all research material was employed by 6 raters using PCK assessment rubric consisting of 12 items that was later analyzed with Many Facet Rasch Model. The analysis result informed that all items had good validity and reliability. This study found that only 15% of participants were classified as good teachers’PCK. In-depth analysis on the difficulty level item concluded that the teacher participant’s PCK ability on the representation and conceptual teaching strategies aspects still needed to be improved.
Sedimentation of gas-fluidized particles with random shape and size

This work deals with the fluidization and sedimentation of fine solid particles, of random shape and size, similar to those commonly involved in geophysical mass flows, such as pyroclastic flows. While heated to avoid the effect of moisture and the formation of clusters, particles were first uniformly fluidized by a hot gas flow, up to a high expansion rate, and then sedimented after stopping the gas supply. Three different materials are explored, involving contrasted geometries, each characterized by a specific particle volume fraction at packing pack. Within the range of values of the solid volume fraction s/ pack studied here, the dense suspension forms a fully fluidized homogeneous mixture, with no segregation, for which the fluidization and sedimentation velocities are equal. Despite a significant discrepancy between the intrinsic properties of the different materials used, all measured velocities are observed to collapse into a single master curve f ( s/ pack ) provided that they are normalized by the relevant scaling. Regarding the sedimentation velocity, pack turns out to be sufficient to characterize the material made with a random distribution in particle shape and size.
Optimization of complex high-dimensional layout configurations for IC physical designs using graph search, data analytics, and machine learning

A typical new IC design has millions of layout configurations, not seen on previous product or test chip designs. Knowing the disposition of each and every configuration, problematic or not, is the key to optimizing design for yield. In this paper, we present a method to systematically characterize the configuration coverage of any layout. Coverage can be compared between designs, and configurations for which there is a lack of coverage can also be computed. When combined with simulation, metrology, and defect data for some configurations, graph search and machine learning algorithms can be applied to optimize designs for manufacturing yield.
Distributional Models of Word Meaning

Distributional semantics is a usage-based model of meaning, based on the assumption that the statistical distribution of linguistic items in context plays a key role in characterizing their semantic behavior. Distributional models build semantic representations by extracting co-occurrences from corpora and have become a mainstream research paradigm in computational linguistics. In this review, I present the state of the art in distributional semantics, focusing on its assets and limits as a model of meaning and as a method for semantic analysis.
基于机器学习的墙纸纹理风格学习与场景推荐 Wallpaper Texture Style Learning and Scene Recommendation Based on Machine Learning

室内装饰性的墙纸纹理具有色彩多样、图案丰富的特点，包含现代风格、巴洛克风格、奢华风格等的多种不同风格，不同风格的墙纸适用于卧室、客厅、浴室等多种不同的现实场景。 墙纸纹理的风格变化趋势引起了许多墙纸设计公司、墙纸售卖者的广泛关注。 设计师需要识别不同风格墙纸纹理，并针对不同的房间场景，为客户推荐合适的纹理。 此过程耗费较多人力和时间，我们希望可以使用计算机实现自动墙纸纹理风格标注与推荐。 本文构建了包含多种墙纸纹理风格的图像数据集，在计算机视觉和机器学习方法的基础上，使用特征学习与SVM分类器构建了墙纸纹理的风格学习和应用场景推荐系统，实现了针对输入墙纸纹理图像进行风格分析预测，并对该墙纸纹理所适用的场景进行了推荐。 Indoor decorative wallpaper textures have the characters of full color printing and abundant pattern, including modern style, baroque style, luxury style and some different styles; the wallpapers with different styles are suitable for different scenes such as bedroom, living room and bathroom. The variation of wallpaper texture styles are drawing attention from wallpaper design companies and wallpaper sellers. The designers are supposed to recognize the style of different wallpaper textures, and recommend suitable textures to costumes according to the scenes, which required more people and time; we hope to realize wallpaper texture style labeling and recommendation automatically. In this paper, a database of wallpaper textures with different styles is collected. Based on computer vision and machine learning methods, a wallpaper texture style learning and scene recommendation system is established with feature learning and support vector machine classifier, which realized the process of style prediction and scene recommendation with input wallpaper textures.
Postmodern Discourse of Post-Soviet Large Housing Districts: Modelling the Possibilities

This article gives an overview of some aspects of postmodern reality, directly touching upon the large-scale housing estates, and searches for the evidence to prove such an assumption that the post-Soviet society has changed; thus, the high-quality existence in the large-scale housing estates becomes impossible without changing them essentially. The research is based on the analysis of literature, comparison of analogies: the experiences of cases of Western Europe. The result of this research is the conceptual framework for the regeneration of large-scale post-Soviet housing districts.
Capacity enhancement and flexible operation of unified power quality conditioner in smart and microgrid network

This paper presents a new approach to design Unified Power Quality Conditioner (UPQC), termed as distributed UPQC (D-UPQC), for smart or microgrid network where capacity enhancement and flexible operation of UPQC are the important issues. This paper shows the possibility of capacity enhancement and operational flexibility of UPQC through a coordinated control of existing resources. This UPQC consists of a single unit series active power filter (APFse) and multiple shunt APF (APFsh) units in a distributed (parallel) mode. These units can be connected with a common/separate dc linked capacitor(s). The requirement of capacity enhancement arises from the flexibility to cope up with the increased harmonic load demand at low voltage (LV) distribution network. This can be accomplished by a coordinated control where multiple APFsh units are operated by utilizing the capacity of APFse while it is in idle/low mode using. Operational flexibility can be accomplished by compensating (i) the reactive and harmonic current individually or (ii) splitting the combined reactive and harmonic current/power among the APFsh units. Design and control issues have been discussed to identify the capacity enhancement limit with the possibility of operational flexibility. A system then has been simulated in MATLAB to show the effectiveness of D-UPQC in capacity enhancement and flexible operation by applying its existing resource utilization capability.
On the Use of Formal Grammars to Predict HPC I/O Behaviors

The increasing gap between the computation performance of post-petascale machines and the performance of their I/O subsystem has motivated many I/O optimizations including prefetching, caching, and scheduling. In order to further improve these techniques, modeling and predicting spatial and temporal I/O patterns of HPC applications as they run has become crucial. In this paper we present Omnisc'IO, an approach that builds a grammar-based model of the I/O behavior of HPC applications and uses it to predict when future I/O operations will occur, and where and how much data will be accessed. To infer grammars, Omnisc'IO is based on StarSequitur, a novel algorithm extending Nevill-Manning's Sequitur algorithm. Omnisc'IO is transparently integrated into the POSIX and MPI I/O stacks and does not require any modification in applications or higher-level I/O libraries. It works without any prior knowledge of the application and converges to accurate predictions of any \emph{N} future I/O operations within a couple of iterations. Its implementation is efficient in both computation time and memory footprint.
An Investigation into Pollution of Selected Heavy Metals of Surface Soils in Hamadan Province Using Pollution Index

Soil pollution and accumulation of heavy metals in soils and crops are the most important bioenvironmental problems that threaten the life of plants, animals and humans. This study was conducted to explore contamination of heavy metals in soils of Hamadan province. A total of 286 composite surface soil samples (0-20 cm) were collected thoroughout the province. After preparation of the samples, the total contents of Zn, Pb, Cu, and Ni in soil samples were extracted using HNO3. Total contents of heavy metals were measured by ICP. Contamination factor results showed that most samples were moderately polluted and contamination factor for lead was highly polluted. Interpolated distribution map of contamination factors (CF) and pollution load index (PLI) of the heavy metals were prepared using GIS. The overlap of CF and PLI maps with geology and land use maps indicated that the concentrations of Ni, Pb, Zn, and Cu have been controlled by natural factors such as parent material, but agricultural activities according to excessive consumption of animal manure and chemical fertilizers can increase most of these elements in soil. Keyword: Soil pollution, Heavy Metal, Contamination Factor, Hamadan Province. 1. Dept. of Soil Sci., Isfa. Univ. of Technol. , Isfahan, Iran. 2. Dept. of Soil Sci., Shahid Bahonar Univ. of Kerman, Kerman, Iran. *: Corresponding Author, Email: kheirabadi@ymail.com
Annotating Satire in Italian Political Commentaries with Appraisal Theory

We present work carried out on journalistic political commentaries in two Italian newspapers, by two wellknown Italian journalists, Maria Novella Oppo, a woman, and Michele Serra, a man1. Political commentaries published on a daily basis consists of short texts not exceeding 400 words each. Sixty-four texts come from Michele Serra’s series titled “L'Amaca”, published daily on the newspaper “La Repubblica” between 2013 and 2014; usually the targeted subjects are politicians, bad social habits and in general every trendy current event. Forty-nine texts come from Maria Novella Oppo’s series titled “Fronte del video”, published daily on the newspaper “L’Unita” in a previous span of time, say from 2011 to 2012; the targeted subjects are usually politicians and televised political talk shows. The two journalists have been chosen for specific reasons: Oppo is a master in highly cutting and caustic writing, Serra is less so. Both are humorous, however, Oppo is more witty in building the overall logical structure of the underlying satiric network of connections. Oppo borders sarcasm, Serra never does so. Oppo's texts are slightly longer than Serra's. Italy is not included in the upper part of the list of countries where the freedom of information is very strong. Because of her trenchant and stinging style, Oppo has been publicly attacked by some of her favourite targets, politicians including Berlusconi and Beppe Grillo, who reacted bitterly to her commentaries by including her in a black list of journalists criticizing his movement called Movimento5Stelle. In particular, she has been attacked – and heavily offended by Grillo who claimed that since she has always been working for the same newspaper for all her career, she will be out of her job in case the Parliament approves the law that precludes newspapers from receiving public financial support. And she will be obliged to find a new job.
Target tracking of structure algorithm based on skeleton and corner for extended objects

Due to extended objects are influenced by occluded and blurred edge, the stability of target tracking is not good by the figure algorithms or the corner algorithms. In order to solute this problem, an improved multi-resolution(MR) fuzzy clustering algorithm based on Markov random field(MRF) is firstly used to segment the candidate targets of the extended objects from the observed images, then a new proposed target tracking structure algorithm, based on the stabilization of the extended objects’ skeletons and the partially un-occluded and un-blurred edge feature of the extended objects, is applied to extract the skeletons, corners, intersection points and their spatial location relationship of the candidate extended targets to determine the true tracking target or not. The experimental results show that the established algorithm can effectively complete the segmentation and extraction of the partially occluded and blurred extended objects with a very satisfied reliability and robustness.
Synthesis of a Novel Ladder Poly(azomethine-ester) Based on PET Waste Bottles

Abstract In the present investigation, a novel ladder polymer, poly(azomethine ester), was prepared via solution polycondensation polymerization between terephthalic acid and the novel monomer. Terephthalic acid was regenerated from PET waste bottles by saponification process, whereas p-phenylenediamine was obtained via Hoffmann rearrangement method. A novel monomer, namely N,N′-bis(2,5-dihydroxy benzylidene)-1,4-diaminobenzene was prepared from the reaction of 2,5-di-hydroxybenzadehyde with p-phenylenediamine in the ratio of 2:1, respectively. For the first time a solution polycondensation method has been employed for the synthesis of a ladder polymer which is otherwise prepared commonly via Diels-Alder cycloaddition reaction. The synthesized ladder polymer was characterized by Fourier transform infrared spectroscopy (FT-IR), proton nuclear magnetic resonance spectroscopy (1H NMR), carbon nuclear magnetic resonance spectroscopy (13C NMR), elemental analysis (CHN), X-ray diffraction (XRD), differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The results revealed that the ladder polymer possess highly regular ladder like framework, and that most of the ester groups have taken part in the side-by-side polymerization reaction.
Detecting Low-Complexity Confounders from Data

Statistical dependencies between two variables X and Y indicate that either X causes Y , or Y causes X, or there exists a latent variable Z which influences X and Y. In biology and medicine, an important problem is to find genetic or environmental unobserved causes of phenotypic difference between individuals. In this contribution, we introduce a novel approach to identify unobserved confounders in data. The proposed method is based on the state-of-the-art 3off2 causal network reconstruction algorithm, and on an evidence for a direct causal relation represented by purity of con-ditionals. The proposed method is implemented in Python, and it will be publicly available shortly. We discuss the results obtained on a real biomedi-cal dataset.
Establishing a novel fMRI approach to investigate visual cognitive properties in pigeons

Funktionelle Magnetresonanztomographie (fMRI) ist eine leistungsstarke und nicht-invasive Technik, die in der Vergangenheit zur Untersuchung der Diskrimination von Stimuli im Gehirn von Primaten verwendet wurde. Bisher wurde fMRI jedoch niemals zur Untersuchung kognitiver Prozesse in wachen Tieren eingesetzt, da Einschrankungen, wie unzureichende Immobilisierung der Tiere, fehlende Mittel zur Messung der Verhaltensreaktionen und Schwierigkeiten bei der Belohnung der Tiere im Scanner die Experimente massiv erschwerten. Um diese Lucke zu schliesen, habe ich in dieser Arbeit eine kombinierte Verhaltens- und fMRI-Plattform entwickelt, um den Gehirnmechanismus, der der Diskrimination von Farbstimuli in Tauben zu Grunde liegt, im Wachzustand zu untersuchen. Es steht damit nun ein System zur Verfugung, mit dem Diskriminations- und Entscheidungsprozesse im Gehirn von Tauben nichtinvasiv und auf einer Systemebene untersucht werden konnen.
Image quality assessment for video stream recognition systems

Recognition and machine vision systems have long been widely used in many disciplines to automate various processes of life and industry. Input images of optical recognition systems can be subjected to a large number of different distortions, especially in uncontrolled or natural shooting conditions, which leads to unpredictable results of recognition systems, making it impossible to assess their reliability. For this reason, it is necessary to perform quality control of the input data of recognition systems, which is facilitated by modern progress in the field of image quality evaluation. In this paper, we investigate the approach to designing optical recognition systems with built-in input image quality estimation modules and feedback, for which the necessary definitions are introduced and a model for describing such systems is constructed. The efficiency of this approach is illustrated by the example of solving the problem of selecting the best frames for recognition in a video stream for a system with limited resources. Experimental results are presented for the system for identity documents recognition, showing a significant increase in the accuracy and speed of the system under simulated conditions of automatic camera focusing, leading to blurring of frames.