Miracle-dz/UniFAR-FTUs huggingface dataset:
Average Wage Gaps and Oaxaca-Blinder Decompositions

In this paper I develop a new version of the Oaxaca–Blinder decomposition whose unexplained component recovers a parameter which I refer to as the average wage gap. Under a particular conditional independence assumption, this estimand is equivalent to the average treatment effect (ATE). I also provide treatment-effects reinterpretations of the Reimers, Cotton, and Fortin decompositions as well as estimate average wage gaps, average wage gains for men, and average wage losses for women in the United Kingdom. Conditional wage gaps increase across the wage distribution and therefore, on average, male gains are larger than female losses.
Information gain using single tracers under steady state and transient flow conditions : the Gårdsjön G1 multiple tracer experiments

Tracer experiments are a common method of obtaining spatially integrated information about aquifer properties. However, the uncertainty of unique experiments can rarely be assessed. The Gardsjon G1 roofed catchment was used to perform a series of steady state experiments with almost identical boundary conditions. Br, Cl, and 2 H tracers were applied in parallel. Tracer breakthrough curves are modelled by a two-region convection-dispersion model. Parameter values determined by inverse modelling critically depend on the underlying assumptions, e.g. whether a tracer is conservative or not cannot be assessed from the model alone. Furthermore, in the case of the Br tracer our experiments exhibit a clear dependency on the temporal scale of the measurements. In spite of very similar boundary conditions, the shapes of breakthrough curves, and the corresponding model parameter values obtained in different experiments, differ considerably. In addition, the transit time probabilities for steady and transient flow conditions differ substantially.
Comparisons of berg balance scale following core stabilization training in women elderly

Since imbalance considers as main cause of falling among the elderly, balance is part of the scientific challenge for last decades. The aim of this study was to comparisons of Berg Balance Scale (BBS) following six weeks core stabilization training in female elderly individuals. Sixty adult female subjects (age: 66.58±5.98 years, weight: 57.76±5.78 Kg, height: 159.76±7.8 cm) voluntarily participated in study. A week prior to starting training program the balance measured with the BBT and subjects divided in two groups on obtained score in BBT, low risk falling (group1), high risk falling (group2). Then subjects divided to two control and experimental groups. The experimental groups performed a core stabilization program included 6- week and three times per week. The post-test of BBT did for two groups afterward. Paired sample t-test, and t-test for independent groups used to analyze the data (P≤0.05). Significant differences were seen between pre and post BBT after the applying core stabilization training program for experimental groups. However the results not showed any difference between pre and post BBT for control group. Core stabilization specifically considering the methods of this study could be implemented for balance in older adults.
Study of a Pendulum in Vivo Electromechanical Generator to be Used in a Knee Prosthesis

This paper presents the principle and the energy potential of an original electromechanical generator that uses human body natural motions during walking, in order to create an autonomous generator. This in vivo and noninvasive system is intended to be used in intelligent knee prosthesis. As the combined human, mechanical, and electrical phenomena are very significant, a mechanical and an electrical study are then carried to evaluate the recoverable power.
Classification of Acute Leukemias Perspective 2

A useful classification of acute leukemia must be reproducible, must impart understanding of leukemogenesis and clinical behavior, and (most importantly) be clinically relevant, which makes such a classification indispensable for designing and comparing clinical trials. Classification systems by their very nature may influence the design of treatment regimens and may even bias investigations of leukemogenesis. The discovery over 30 years ago that the acute lymphoid and acute myeloid leukemias differed in their responses to chemotherapeutic agents set the stage for the development of clinically useful classifications. The first of these was based on the morphologic features of leukemic blasts, with different groups of hematologists establishing their own terminology and diagnostic criteria. This lack of uniform nomenclature and hence comparable classifications posed a major obstacle to rapid progress in the treatment and understanding of leukemia pathobiology. The first internationally accepted classification of acute leukemia was proposed in 1976 by the French–American–British (FAB) Cooperative Group (1). The initial FAB classification was based solely on morphologic criteria that were subsequently refined in 1981 and 1985 (2–4). Unfortunately, the revisions largely ignored exciting immunologic and cytogenetic discoveries that were contributing to an improved understanding and better treatment designs in acute leukemia. The morphologic, immunologic, and cytogenetic (MIC) classification, introduced in 1988, was the first to recognize the usefulness of cytogenetics for identifying subgroups of acute leukemia (5,6). The MIC system recognized additional subgroups of acute leukemia not discernible in the FAB classification. Modifications of the FAB classification were also recommended by a National Cancer Institute-sponsored workshop in 1990 (7). Treatment of pediatric leukemia is one of the great successes of modern cancer therapy. Much of the success can be attributed to the recognition of important patient risk factors and the design of effective therapy for patients at high risk of treatment failure (8). Although they are still important, older risk factors based on a patient’s physical manifestations or hematologic and biochemical testing have been largely replaced by biologic features of the leukemic cell. Present risk assignments depend heavily on combined morphologic, immunologic, cytogenetic, and (more recently) molecular genetic studies. The recently introduced World Health Organization (WHO) classification takes into account morphologic and immunologic features plus well-studied, common nonrandom chromosomal abnormalities that clearly influence the laboratory and clinical features of 2
Maximizing Effectiveness of Educational Games through Gaming Experience

Educational games face a dual challenge in both trying to meaningfully educate players while holding their attention long and well enough for the lessons to be engrained. This paper surveys research pertaining to the effectiveness of educational games and how to provide the best Gaming Experience to engage students while providing challenge and opportunities for “flow”. Author
Super-R´enyi entropy & Wilson loops for N = 4 SYM and their gravity duals

: We compute the supersymmetric R´enyi entropies across a spherical entanglement surface in N = 4 SU( N ) SYM theory using localization on the four-dimensional ellipsoid. We extract the leading result at large N and λ and match its universal part to a gravity calculation involving a hyperbolically sliced supersymmetric black hole solution of N = 4 + SU(2) × U(1) gauged supergravity in ﬁve dimensions. We repeat the analysis in the presence of a Wilson loop insertion and ﬁnd again a perfect match with the dual string theory. Understanding the Wilson loop operator requires knowledge of the full ten-dimensional IIB supergravity solution which we elaborate upon
Lateral I - Mos (Impact - Ionization) Transistor

One of the "fundamental" problems in the continued scaling of MOSFETs is the 60 mV/decade room temperature limit in sub-threshold slope. Therefore initial studies on a new kind of transistor, the I-MOS. is done. The I - MOS uses modulation of the breakdown voltage of a gated p - i - n structure in order to switch from the OFF to the ON state and vice versa. Since impact-ionization is an abrupt function of the electric field (or the carrier energy), simulations show that the device has a sub-threshold slope much lower than the above mentioned. This TCAD Sentaurus project simulates the electrical characteristics of an n-channel impact ionization metal-oxide-semiconductor (n-IMOS) transistor. The Id-Vg characteristics of a two dimensional (2D) n-IMOS structure are simulated. The Id-Vg characteristics are simulated for drain biases of 0 V and 1.1 V. In addition, various electrical parameters such as the breakdown voltage, the threshold voltage, the sub- threshold slope, and the on-current and off-current are extracted. Keywords - I-MOS,p-i-n structure,sub-threshold,threshold voltage,on current,off current
Investigating context influence in character Level LSTM methods for Japanese Auto Punctuation

Automatic speech recognition (ASR) systems for Latin languages generate a stream of words. For Asia language, ASR systems usually just produce a sequence of characters, like Chinese and Japanese. Both kinds of the above mentioned systems don’t get punctuated directly. However, in many situations, people do need punctuation marks to help them understand better and faster. For instance, during the process of meeting minutes transcription and simultaneous interpretation, the accurate punctuation for ASR output can save lot efforts and help people concentrate on linguistic proofread. So far, plenty of works have been done in auto punctuation. Before deep learning comes to trend, the most popular methods can be addressed as: 1. Introduce punctuations during N-gram language model training and predict it in the same ways as words. 2. Combine lexical features and prosodic features (pause duration, etc.) [12], and do classification with machine learning algorithms. 3. Treat the task as a sequence-labeling problem and solve it with conditional ransom fields (CRFs)[2]. Considering deep learning’s high performance in other tasks, many researchers starts to apply it on auto punctuation. CNN based method that proposed by Che. X et al. [6] made use of context formatted pre-trained Word Vectors as input. They treated the punctuation task as a 4-class classification problem. Tilk et al. [3] proposed a two-stage RNN model using long short-term memory (LSTM) [10] units, their first stage use only lexical features in large corpus, the second stage use both lexical feature and pause duration information. To include both the past and future observation, Xu K. et al. [4] adopted a multilayer Bidirectional LSTM (Bi-LSTM) framework in use of word Embedding features, and punctuate Chinese sentences with 3-class labels. Ballesteros M et al. [7] used character-based embedding and LSTM model to do punctuation prediction on full range of punctuation marks across languages. Tilk O et al. [5] also improved their model by introducing an Attention Mechanism [9] to their Bi-GRU models. Almost all above models are trained on languages that do not need word segmentation. [4] predicts Chinese punctuations but with segmented corpus processed by Mecab[1]. Inspired by [7,13], we base our models on character embedding for Japanese punctuation. Since Japanese does not have word boundary and have introduced many foreign words, using word level embedding can cause huge vocabulary and sparse model. In addition, we investigated different ways of utilizing context information. Our results shows using local attention information in Bi-LSTM can elevate the performance. The model has been deployed in our IME product (Simeji voice input), and serves our users.
Daytime Rainy Cloud Detection and Convective Precipitation Delineation Based on a Deep Neural Network Method Using GOES-16 ABI Images

Precipitation, especially convective precipitation, is highly associated with hydrological disasters (e.g., floods and drought) that have negative impacts on agricultural productivity, society, and the environment. To mitigate these negative impacts, it is crucial to monitor the precipitation status in real time. The new Advanced Baseline Imager (ABI) onboard the GOES-16 satellite provides such a precipitation product in higher spatiotemporal and spectral resolutions, especially during the daytime. This research proposes a deep neural network (DNN) method to classify rainy and non-rainy clouds based on the brightness temperature differences (BTDs) and reflectances (Ref) derived from ABI. Convective and stratiform rain clouds are also separated using similar spectral parameters expressing the characteristics of cloud properties. The precipitation events used for training and validation are obtained from the IMERG V05B data, covering the southeastern coast of the U.S. during the 2018 rainy season. The performance of the proposed method is compared with traditional machine learning methods, including support vector machines (SVMs) and random forest (RF). For rainy area detection, the DNN method outperformed the other methods, with a critical success index (CSI) of 0.71 and a probability of detection (POD) of 0.86. For convective precipitation delineation, the DNN models also show a better performance, with a CSI of 0.58 and POD of 0.72. This automatic cloud classification system could be deployed for extreme rainfall event detection, real-time forecasting, and decision-making support in rainfall-related disasters.
DEVELOPMENT OF A COMPREHENSIVE ANALYSIS MODEL FOR HUMAN ERROR PREVENTION IN INDUSTRIAL FIELDS

Abstract: A lot of models have been developed for prevention of human errors. But most of them failed to attract attention of industry which has been looking for an integrative model that can show practical countermeasures as well as causal factors of human errors. This research aimed to develop a comprehensive model that can mainly be applied to industrial fields. Therefore, in the model, it was tried to explain sequences of an operator's information process that might cause human errors on one hand, and life cycle stages of facilities involved when human errors occur on the other hand. This model was validated using a typical accident case. With this comprehensive model presented in this research, one can follow up the sequence of human errors caused by operators. Further, errors made at the design stage which could be a main cause of accidents can be tracked. It is expected that much attention would be paid to this model in preventing human errors in industrial fields since safety personnel can easily find out errors through the life cycle stages of man-machine facilities.
Short Communication: Fishing inside Qeshm Island’s MPA, A tortuous approach

Abstract. Chamanara V, Kamrani E, Zahirinia M, Manez KS. 2016. Short Communication: Fishing inside Qeshm Island’s MPA, A tortuous approach. Nusantara Bioscience 8: 169-173. Incremental pressure of population growth in coastal areas has led to loss in many mangrove areas. Although these areas are fish nursery grounds and are very important in stock assessments, there is no accurate and up-to-date available data about them. In this research, the catch data in the Qeshm Island Marine Protected Area (MPA) of Iran was investigated using 11 stake nets. Totally, 81 specimens belonging to 69 genera and 63 families were caught, which majority of them were discarded. The commercial fish were young which were mostly smaller than LM50. In case of White Pomfret as well, only 15.3% of the caught individuals were in the legal catch size. Hence, this catch composition would be a potential threat to fish stocks. The major numerical and weight based specimens were Dussumieria acuta (10.05%) and Arius maculatus (5.9%), respectively. Containing 7 species, the Clupeidae was the most abundant family in the catch composition. Eventually, total annual catch by abovementioned stake nets within the mangrove forests in the Qeshm Island marine protected area was estimated to be 186.77-188.75 tonnes, approximately.
Models of optimal foraging and resource partitioning: deep corollas for long tongues

We model the optimal foraging strategies for 2 nectarivore species, differing in the length of their proboscis, that exploit the nectar provided by 2 types of flowers, differing in the depths of their corollas. When like flowers appear in clumps, nectarivores must decide whether to forage at a patch of deep or shallow flowers. If nectarivores forage optimally, at least one flower type will be used by a single nectarivore species. Long-tongued foragers will normally visit deep flowers and short-tongued foragers shallow flowers, although extreme asymmetries in metabolic costs may lead to the opposite arrangement. When deep and shallow flowers are randomly interspersed, nectarivores must decide, on encounter with a flower, whether to collect its nectar or continue searching. At low nectarivore densities, the optimal strategy involves exploiting every encountered flower; however, as nectarivore densities increase and resources become scarce, long-tongued individuals should start concentrating on deep flowers and short-tongued individuals on shallow flowers. Therefore, regardless of the spatial distribution of flowers, corolla depth can determine which nectarivore species exploit the nectar from each flower type in a given community. It follows that corolla elongation can evolve as a means to keep nectar thieves at bay if short-tongued visitors are less efficient pollinators than long-tongued visitors. Copyright 2006.
Seeded self-play for language learning

How can we teach artificial agents to use human language flexibly to solve problems in real-world environments? We have an example of this in nature: human babies eventually learn to use human language to solve problems, and they are taught with an adult human-in-the-loop. Unfortunately, current machine learning methods (e.g. from deep reinforcement learning) are too data inefficient to learn language in this way. An outstanding goal is finding an algorithm with a suitable ‘language learning prior’ that allows it to learn human language, while minimizing the number of on-policy human interactions. In this paper, we propose to learn such a prior in simulation using an approach we call, Learning to Learn to Communicate (L2C). Specifically, in L2C we train a meta-learning agent in simulation to interact with populations of pre-trained agents, each with their own distinct communication protocol. Once the meta-learning agent is able to quickly adapt to each population of agents, it can be deployed in new populations, including populations speaking human language. Our key insight is that such populations can be obtained via self-play, after pre-training agents with imitation learning on a small amount of off-policy human language data. We call this latter technique Seeded Self-Play (S2P). Our preliminary experiments show that agents trained with L2C and S2P need fewer on-policy samples to learn a compositional language in a Lewis signaling game.
The Empirical Interpolation Method

In this thesis we look at the Empirical Interpolation Method (EIM) and how it can be used in different applications. We propose a new formulation of EIM to make it easier to perform analytical operations like differentiation and integration of the basis functions as well as to apply EIM to a variety of problems. The new formulation is used to develop quadrature rules for the circle and semicircle, as well as for arbitrary simple polygons. The new formulation is also used to solve partial differential equations using a collocation approach on various domains including the circle, semicircle and triangle. The framework is briefly applied to compression of 3D animation in addition to recognition of images and sound. Several of the methods show great potential, with exponential convergence for quadrature and collocation for regular problems. However, there are also serious issues that must be addressed if the methods are to be developed further. These issues are related to making the methods more robust and stable.
Analysis of serotypes, spaA gene phylogeny and PFGE genetic patterns of Erysipelothrix rhusiopathiae isolated between 2012 and 2015 in Jianghuai areas

[Objective] To study the serotypes, phylogeny of surface protective antigen A gene (spaA), 魏文涛等: 2012–2015年江淮地区猪丹毒杆菌分离株血清型、spaA基因遗传进化及 PFGE基因型分析 665 Tel: 010-64807511; E-mail: tongbao@im.ac.cn; http://journals.im.ac.cn/wswxtbcn and molecular subtyping of Erysipelothrix rhusiopathiae isolated between 2012 and 2015 in Jianghuai area. [Methods] We collected 42 clinically isolated Erysipelothrix rhusiopathiae strains. Agar gel precipitation tests, PCR and sequence analysis methods, and pulsed-field gel electrophoresis were used to investigate the serotypes, phylogeny of spaA gene, and PFGE typing, respectively. [Results] All 42 strains belonged to serotype 1a. spaA gene was 98.5% to 100% homologous to the reference strains. The sequences showed that the mutations occurred from T to G and C to A at base 609th and 769th respectively, and the corresponding amino acid showed mutations from Ile to Met as Met-203-type, and Leu to Ile as Ile-257-type strains; PFGE revealed 8 different genotypes, with 88.8% to 100% similarity. ER2 (54.8%) was the dominant PFGE genotype, and erysipelas attenuated vaccine strains (G4T10 and GC42) were the same genotype strains. [Conclusion] Erysipelothrix rhusiopathiae serotype 1a was the prevalent serotypes in Jianghuai area. The homology of the spaA was high and had small variations. All the isolates came from one clone strain. Met-203-type and Ile-257-type strains had high pathogenicity and were the most prevalent pathogens type in Jianghuai area.
Public Debt, the Unit Root Hypothesis and Structural Breaks: A Multi-Country Analysis

We assess fiscal performances in G7 and selected Latin American and Asian countries. We consider two questions: (i) Have public finances been sustainable? (ii) Do countries follow more restrictive fiscal policies when debt starts to rise? We find that: (i) the traditional unit root tests often overlook the corrective actions taken by many governments; controlling for structural breaks changes the non-stationarity results dramatically among the three groups; (ii) estimation of a reaction function for governments, expanded by incorporating structural breaks, provides further evidence for significant active anti-debt policies among G7 countries, and to a lesser extent in the other regions.
ASSOCIATION FOR CONSUMER RESEARCH

About two years ago I was diagnosed with celiac disease for which the only treatment is a strict gluten-free diet which eliminate all foods with wheat, rye, or barley. Then, nine months ago I was found to have developed diabetes which added further dietary restrictions, blood sugar monitoring, and medications. This paper describes, in ethnographic fashion, the multitude of changes in my consumer behavior and offers insights into the behavior of many other consumers who face dramatic restrictions in their diets.
MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering

This paper studies the problem of non-factoid question answering, where the answer may span over multiple sentences. Existing solutions can be categorized into representation- and interaction-focused approaches. We combine their complementary strength, by a hybrid approach allowing multi-granular interactions, but represented at word level, enabling an easy integration with strong word-level signals. Specifically, we propose MICRON: Multigranular Interaction for Contextualizing RepresentatiON, a novel approach which derives contextualized uni-gram representation from n-grams. Our contributions are as follows: First, we enable multi-granular matches between question and answer n-grams. Second, by contextualizing word representation with surrounding n-grams, MICRON can naturally utilize word-based signals for query term weighting, known to be effective in information retrieval. We validate MICRON in two public non-factoid question answering datasets: WikiPassageQA and InsuranceQA, showing our model achieves the state of the art among baselines with reported performances on both datasets.