Miracle-dz/UniFAR-FTUs huggingface dataset:
HashCount at SemEval-2018 Task 3: Concatenative Featurization of Tweet and Hashtags for Irony Detection

This paper proposes a novel feature extraction process for SemEval task 3: Irony detection in English tweets. The proposed system incorporates a concatenative featurization of tweet and hashtags, which helps distinguishing between the irony-related and the other components. The system embeds tweets into a vector sequence with widely used pretrained word vectors, partially using a character embedding for the words that are out of vocabulary. Identification was performed with BiLSTM and CNN classifiers, achieving F1 score of 0.5939 (23/42) and 0.3925 (10/28) each for the binary and the multi-class case, respectively. The reliability of the proposed scheme was verified by analyzing the Gold test data, which demonstrates how hashtags can be taken into account when identifying various types of irony.
Analysis of Genetic Variation Indicates DNA Shape Involvement in Purifying Selection

Abstract Noncoding DNA sequences, which play various roles in gene expression and regulation, are under evolutionary pressure. Gene regulation requires specific protein–DNA binding events, and our previous studies showed that both DNA sequence and shape readout are employed by transcription factors (TFs) to achieve DNA binding specificity. By investigating the shape-disrupting properties of single nucleotide polymorphisms (SNPs) in human regulatory regions, we established a link between disruptive local DNA shape changes and loss of specific TF binding. Furthermore, we described cases where disease-associated SNPs may alter TF binding through DNA shape changes. This link led us to hypothesize that local DNA shape within and around TF binding sites is under selection pressure. To verify this hypothesis, we analyzed SNP data derived from 216 natural strains of Drosophila melanogaster. Comparing SNPs located in functional and nonfunctional regions within experimentally validated cis-regulatory modules (CRMs) from D. melanogaster that are active in the blastoderm stage of development, we found that SNPs within functional regions tended to cause smaller DNA shape variations. Furthermore, SNPs with higher minor allele frequency were more likely to result in smaller DNA shape variations. The same analysis based on a large number of SNPs in putative CRMs of the D. melanogaster genome derived from DNase I accessibility data confirmed these observations. Taken together, our results indicate that common SNPs in functional regions tend to maintain DNA shape, whereas shape-disrupting SNPs are more likely to be eliminated through purifying selection.
What Challenges Are Boys Facing , and What Opportunities Exist To Address Those Challenges ?

A starter on his high school basketball team, he had practiced hard every evening that week for the big game against a crosstown rival and still had to make time to study for his first period geometry test. After dinner with his family, he headed over to a party at the house of a schoolmate whose parents were out of town. When he got there, the alcohol was already flowing. And then someone offered David some pot. He had to make a decision. Fact Sheet Substance Abuse
Consistencies and Inconsistencies between the Iranian EFL Teachers ’ Beliefs and Practices

This instrumental-comparative qualitative case study attempts to investigate the relations between teachers’ beliefs and classroom practices. For this purpose, five Iranian EFL teachers from three private language institutes were interviewed for their beliefs. Then, their classroom practices were observed and videotaped. The data were analyzed using the constant comparative method around common categories, which were identified as distinctive features of teachers’ beliefs; these same themes were then compared with their practices. The data for each case were also compared with the others so that possible causes of the inconsistencies could be traced. Based on the causes, some suggestions for teacher education and educational management are made.
Machine Learning from Observation to Detect Abnormal Driving Behavior in Humans

Detection of abnormal behavior is the catalyst for many applications that seek to react to deviations from behavioral expectations. However, this is often difficult to do when direct communication with the performer is impractical. Therefore, we propose to create models of normal human performance and then compare their performance to a human’s actual behavior. Any detected deviations can be then used to determine what condition(s) could possibly be influencing the deviant behavior. We build the models of human behavior through machine learning from observation; more specifically, we employ the Genetic Context Learning algo-rithm to create models of normal car driving behaviors of different humans with and without ADHD (Attention Deficit Hyperactivity Disorder). We use a car simulator for our studies to eliminate risk to our test subjects and to other drivers. Our results show that different driving situations have varying utility in abnormal behavior detection. Learning from Observation was successful in building models to be applied to abnormal behavior detection.
Determining the scale of image patches using a deep learning approach

Detecting the scale of histopathology images is important because it allows to exploit various sources of information to train deep learning (DL) models to recognise biological structures of interest. Large open access databases with images exist, such as The Cancer Genome Atlas (TCGA) and PubMed Central but very few models can use such datasets because of the variability of the data in color and scale and a lack of metadata. In this article, we present and compare two deep learning architectures, to detect the scale of histopathology image patches. The approach is evaluated on a patch dataset from whole slide images of the prostate, obtaining a Cohen's kappa coefficient of 0.9897 in the classification of patches with a scale of 5×, 10× and 20×. The good results represent a first step towards magnification detection in histopathology images that can help to solve the problem on more heterogeneous data sources.
OPRA Technique for M-QAM over Nakagami-m Fading Channel with Imperfect CSI

Analysis of an Optimum Power and Rate Adaptation (OPRA) technique has been carried out for Multilevel-Quadrature Amplitude Modulation (M-QAM) over Nakagami-m ? at fading channels considering an imperfect channel estimation at the receiver side. The optimal solution has been derived for a continuous adaptation, which is a specific bound function and not possible to express in close mathematical form. Therefore, a sub-optimal solution is derived for the continuous adaptation and it has been observed that it tends to the optimum solution as the correlation coefficient between the true channel gain and its estimation tends to one. It has been observed that the receiver performance degrades with an increase in estimation error.
The Role of Infectious Diseases in Natural Communities WHAT INTRODUCED SPECIES TELL US 5

on host populations, but these predictions have been challenging to test. Controlled parasite addition and removal experiments have provided some of the most valuable insights into theoretical predictions. Like these experiments, species invasions may involve the addition and removal of infectious disease agents and may therefore add to our understanding of the effects of infectious disease. For example, species that invade without their parasites give us an idea of how populations perform in the absence of parasites, whereas biological control programs and introduced diseases can provide before-and-after comparisons. Studies of introduced species indicate that parasites can reduce host performance and may dramatically reduce host abundance. These effects on host species can have indirect effects on ecosystems. Species introductions can result in novel host-parasite contacts, which provide insight into how hostparasite evolution drives host specificity and indicate that host-parasite evolution can occur in only a few generations. The Role of Infectious Diseases in Natural Communities
Causal language and strength of inference in academic and media articles shared in social media (CLAIMS): A systematic review

Background The pathway from evidence generation to consumption contains many steps which can lead to overstatement or misinformation. The proliferation of internet-based health news may encourage selection of media and academic research articles that overstate strength of causal inference. We investigated the state of causal inference in health research as it appears at the end of the pathway, at the point of social media consumption. Methods We screened the NewsWhip Insights database for the most shared media articles on Facebook and Twitter reporting about peer-reviewed academic studies associating an exposure with a health outcome in 2015, extracting the 50 most-shared academic articles and media articles covering them. We designed and utilized a review tool to systematically assess and summarize studies’ strength of causal inference, including generalizability, potential confounders, and methods used. These were then compared with the strength of causal language used to describe results in both academic and media articles. Two randomly assigned independent reviewers and one arbitrating reviewer from a pool of 21 reviewers assessed each article. Results We accepted the most shared 64 media articles pertaining to 50 academic articles for review, representing 68% of Facebook and 45% of Twitter shares in 2015. Thirty-four percent of academic studies and 48% of media articles used language that reviewers considered too strong for their strength of causal inference. Seventy percent of academic studies were considered low or very low strength of inference, with only 6% considered high or very high strength of causal inference. The most severe issues with academic studies’ causal inference were reported to be omitted confounding variables and generalizability. Fifty-eight percent of media articles were found to have inaccurately reported the question, results, intervention, or population of the academic study. Conclusions We find a large disparity between the strength of language as presented to the research consumer and the underlying strength of causal inference among the studies most widely shared on social media. However, because this sample was designed to be representative of the articles selected and shared on social media, it is unlikely to be representative of all academic and media work. More research is needed to determine how academic institutions, media organizations, and social network sharing patterns impact causal inference and language as received by the research consumer.
Workload-Aware CPU Performance Scaling for Transactional Database Systems

Natural short term fluctuations in the load of transactional data systems present an opportunity for power savings. For example, a system handling 1000 requests per second on average can expect more than 1000 requests in some seconds, fewer in others. By quickly adjusting processing capacity to match such fluctuations, power consumption can be reduced. Many systems do this already, using dynamic voltage and frequency scaling (DVFS) to reduce processor performance and power consumption when the load is low. DVFS is typically controlled by frequency governors in the operating system, or by the processor itself. In this paper, we show that transactional database systems can manage DVFS more effectively than the underlying operating system. This is because the database system has more information about the workload, and more control over that workload, than is available to the operating system. We present a technique called POLARIS for reducing the power consumption of transactional database systems. POLARIS directly manages processor DVFS and controls database transaction scheduling. Its goal is to minimize power consumption while ensuring the transactions are completed within a specified latency target. POLARIS is workload-aware, and can accommodate concurrent workloads with different characteristics and latency budgets. We show that POLARIS can simultaneously reduce power consumption and reduce missed latency targets, relative to operating-system-based DVFS governors.
Classification of Targets and Distractors Present in Visual Hemifields Using Time-Frequency Domain EEG Features

This paper presents a classification system to classify the cognitive load corresponding to targets and distractors present in opposite visual hemifields. The approach includes the study of EEG (electroencephalogram) signal features acquired in a spatial attention task. The process comprises of EEG feature selection based on the feature distribution, followed by the stepwise discriminant analysis- (SDA-) based channel selection. Repeated measure analysis of variance (rANOVA) is applied to test the statistical significance of the selected features. Classifiers are developed and compared using the selected features to classify the target and distractor present in visual hemifields. The results provide a maximum classification accuracy of 87.2% and 86.1% and an average classification accuracy of 76.5 ± 4% and 76.2 ± 5.3% over the thirteen subjects corresponding to the two task conditions. These correlates present a step towards building a feature-based neurofeedback system for visual attention.
Reliability and Validity of an Asian Indian Dietary Acculturation Measure (AIDAM)

Dietary acculturation (adopting the eating patterns/practices of the host environment) of individuals who relocate to another country could be a risk factor for diet-related chronic diseases. A reliable and validated measure for the assessment of dietary acculturation may facilitate understanding of the relationship between dietary acculturation and diet-related chronic diseases in various nonnative populations. We aimed to determine the reliability and validity of a culturally sensitive Asian Indian Dietary Acculturation Measure (AIDAM) for Asian Indians. A cross-sectional survey was administered via Qualtrics Research Suite to 191 Asian Indian adults in the United States. The web survey consisted of AIDAM, a food frequency questionnaire consisting of Asian Indian (AI-FFQ) and non-Indian (NI-FFQ) foods and participant sociodemographic questions. Reliability and validity were examined through a polytomous Rasch model. Except for one item that was excluded due to misfit, 50 items were stable. The root mean square error was . 08 for all the items, and the item and person reliabilities were . 98 and . 88, respectively. AIDAM was positively related to NI-FFQ (r = .265) and negatively related to AI-FFQ (r = −.432) based on correlations and linear regressions (p < .001). Our preliminary analysis showed the AIDAM to have good reliability and validity when tested with a sample of Asian Indians in the United States. Further large studies with Asian Indians using AIDAM are needed to support our findings. This tool can be used by health professionals and researchers to determine the level of dietary acculturation as well as diet quality implications when assessing risk for diet-related diseases.
Idiopathic Pancreatitis as a Rare Gastrointestinal Manifestation of Myotonic Muscular Dystrophy

Myotonic muscular dystrophy (DM) is a multi-system disorder affecting skeletal muscles as well as smooth and cardiac muscles. Patients with DM experience disturbances in gastrointestinal motility; however, pancreatobiliary manifestations have rarely been described. We report the case of a 58-year-old male with MD who presented with a sudden onset of vomiting and abdominal pain. Laboratory and radiological findings were consistent with acute pancreatitis. No identifiable cause of pancreatitis could be identified despite an extensive workup. Sphincter of Oddi dysfunction (SOD) was felt to be the most likely cause of our patient’s acute pancreatitis. SOD leading to acute pancreatitis results from spasm of both the distal common biliary duct and the duct of Wirsung. It is a very rarely reported gastrointestinal manifestation of MD, but one that should not be overlooked.
Polarimetry-Based Distributed Scatterer Processing Method for PSI Applications

Permanent scatterer interferometry is a multitemporal interferometric synthetic aperture radar technique that produces high-accuracy ground deformation measurement. A high density of permanent scatterer (PS) is required to provide accurate results. In natural environments with low PS density, distributed scatterers (DSs) could serve as additional coherent observations. This paper introduces a polarimetric scattering property-based adaptive filtering method that preserves PS candidates and filters DS candidates. To further increase the coherence estimate of DS candidates, the technique includes a complex coherence decomposition that adaptively selects the most stable scattering mechanisms, thus improving pixel coherence estimation. The proposed method was evaluated on 11 quad-polarized ALOS PALSAR images and 21 dual-polarized Sentinel-1 images acquired over San Fernando Valley, CA, USA, and Groningen, The Netherlands, respectively. The application of this method increased the number of coherent pixels by almost a factor of eight compared with a single-polarization channel. This paper concludes that a coherence estimate can be significantly improved by applying scattering property-based adaptive filtering and coherence matrix decomposition and accurate displacement measurements can be achieved.
Improved reconciliation with polar codes in quantum key distribution

Quantum key distribution (QKD) is a cryptographic system that generates an information-theoretically secure key shared by two legitimate parties. QKD consists of two parts: quantum and classical. The latter is referred to as classical post-processing (CPP). Information reconciliation is a part of CPP in which parties are given correlated variables and attempt to eliminate the discrepancies between them while disclosing a minimum amount of information. The elegant reconciliation protocol known as \emph{Cascade} was developed specifically for QKD in 1992 and has become the de-facto standard for all QKD implementations. However, the protocol is highly interactive. Thus, other protocols based on linear block codes such as Hamming codes, low-density parity-check (LDPC) codes, and polar codes have been researched. In particular, reconciliation using LDPC codes has been mainly studied because of its outstanding performance. Nevertheless, with small block size, the bit error rate performance of polar codes under successive-cancellation list (SCL) decoding with a cyclic redundancy check (CRC) is comparable to state-of-the-art turbo and LDPC codes. In this study, we demonstrate the use of polar codes to improve the performance of information reconciliation in a QKD system with small block size. The best decoder for polar codes, a CRC-aided SCL decoder, requires CRC-precoded messages. However, messages that are sifted keys in QKD are obtained arbitrarily as a result of a characteristic of the QKD protocol and cannot be CRC-precoded. We propose a method that allows arbitrarily obtained sifted keys to be CRC precoded by introducing a virtual string. Thus the best decoder can be used for reconciliation using polar codes and improves the efficiency of the protocol.
High power class F GaN HEMT power amplifier in L band for global positioning systems application

An L-band high power and high efficiency solid-state power amplifier (SSPA) design is presented. This compact, lightweight GaN HEMT SSPA is designed for nano-satellite GPS applications. This paper focuses on class F to achieve high efficiency by using waveform shaping methods. The optimal magnitude and phase of the fundamental, second and third harmonics (fo, 2fo and 3fo) are optimized by harmonic load-pull to produce a half-rectified sine wave for the drain current and a square wave for the drain voltage. To realize the design, a low impedance transformer with two harmonic traps are implemented by using second and third harmonic open circuit stubs. In CW, the SSPA can achieve 49W of saturated output power with gain of 17.2dB and PAE of 46.2% at 1.575GHz.
Optogenetic Activation of Colon Epithelium of the Mouse Produces High-Frequency Bursting in Extrinsic Colon Afferents and Engages Visceromotor Responses

Epithelial cells of the colon provide a vital interface between the internal environment (lumen of the colon) and colon parenchyma. To examine epithelial–neuronal signaling at this interface, we analyzed mice in which channelrhodopsin (ChR2) was targeted to either TRPV1-positive afferents or to villin-expressing colon epithelial cells. Expression of a ChR2-EYFP fusion protein was directed to either primary sensory neurons or to colon epithelial cells by crossing Ai32 mice with TRPV1-Cre or villin-Cre mice, respectively. An ex vivo preparation of the colon was used for single-fiber analysis of colon sensory afferents of the pelvic nerve. Afferents were characterized using previously described criteria as mucosal, muscular, muscular-mucosal, or serosal and then tested for blue light-induced activation. Light activation of colon epithelial cells produced robust firing of action potentials, similar to that elicited by physiologic stimulation (e.g., circumferential stretch), in 50.5% of colon afferents of mice homozygous for ChR2 expression. Light-induced activity could be reduced or abolished in most fibers using a cocktail of purinergic receptor blockers suggesting ATP release by the epithelium contributed to generation of sensory neuron action potentials. Using electromyographic recording of visceromotor responses we found that light stimulation of the colon epithelium evoked behavioral responses in Vil-ChR2 mice that was similar to that seen with balloon distension of the colon. These ex vivo and in vivo data indicate that light stimulation of colon epithelial cells alone, without added mechanical or chemical stimuli, can directly activate colon afferents and elicit behavioral responses. SIGNIFICANCE STATEMENT Abdominal pain that accompanies inflammatory diseases of the bowel is particularly vexing because it can occur without obvious changes in the structure or inflammatory condition of the colon. Pain reflects abnormal sensory neuron activity that may be controlled in part by release of substances from lining epithelial cells. In support of this mechanism we determined that blue-light stimulation of channelrhodopsin-expressing colon epithelial cells could evoke action potential firing in sensory neurons and produce changes in measures of behavioral sensitivity. Thus, activity of colon epithelial cells alone, without added mechanical or chemical stimuli, is sufficient to activate pain-sensing neurons.
Effector T Helper Cell Subsets in Inflammatory Bowel Diseases

The gastrointestinal tract is a site of high immune challenge, as it must maintain a delicate balance between tolerating luminal contents and generating an immune response toward pathogens. CD4+ T cells are key in mediating the host protective and homeostatic responses. Yet, CD4+ T cells are also known to be the main drivers of inflammatory bowel disease (IBD) when this balance is perturbed. Many subsets of CD4+ T cells have been identified as players in perpetuating chronic intestinal inflammation. Over the last few decades, understanding of how each subset of Th cells plays a role has dramatically increased. Simultaneously, this has allowed development of therapeutic innovation targeting specific molecules rather than broad immunosuppressive agents. Here, we review the emerging evidence of how each subset functions in promoting and sustaining the chronic inflammation that characterizes IBD.