Miracle-dz/UniFAR-FTUs huggingface dataset:
Hard Sample Mining and Learning for Skeleton-Based Human Action Recognition and Identification

The analysis of human actions, such as recognition and person identification, is important but challenging. Previous studies usually separate person identification from action recognition. In this paper, we propose a skeleton-based end-to-end model that can simultaneously implement both person identification and action recognition and strengthen the learning of hard samples. Inspired by the recent application of recurrent neural networks and convolution neural networks to action analysis, the models of the temporal and spatial domains are created by modeling the global, local, and detailed feature-related information of the human skeleton. Moreover, during training, we add a mining and learning mechanism for hard samples that mines and focuses on them to avoid overfitting. This mining and learning mechanism enables the model to avoid a bottleneck and improve performance. The proposed model is experimentally verified on three datasets, where the results show that it can yield the state-of-the-art performance.
Optimal model of urban bus frequency

Bus dispatching is the core of entire public traffic enterprise management.Improve the level of urban bus dispatching is an important way to make urban bus have a better quality and more attractive.In this paper,the problem of bus dispatching in the angle of passengers' satisfactory degree of waiting is analyzed and a model which based on the passengers' need and satisfies the passengers' satisfactory degree is established.The algorithm of this model is presented and a case study shows that the model is feasible.
AAP Textbook of Pediatric Care, 2nd Ed

The landmark guide to pediatric medicine - updated and streamlined for today's clinicians and students Highlighting the numerous advances and developements in pediatrics, the new second edition provides a complete update of this premier clinical reference including signs and symptoms, behavioral health, care of healthy and high-risk infants, adolescent health, critical situations, practice management, ethical and legal concerns, and much more.
Gastric Ulcer Etiology

A gastric ulcer, also called stomach ulcer, is a break in the normal gastric mucosa integrity that extends through the muscularis mucosa into the submucosa or deeper. The incidence varies with the age, gender, geographical location and is associated with severe complications including hemorrhages, perforations, gastrointestinal obstruction, and malignancy. Thus, this clinical condition represents a worldwide health problem because of its high morbidity, mortality and economic loss (Brown & Wilson, 1999; Dimaline & Varro, 2007). The normal stomach mucosa maintains a balance between protective and aggressive factors. Some of the main aggressive factors are gastric acid, abnormal motility, pepsin, bile salts, use of alcohol and nonsteroidal anti-inflammatory drugs (NSAID), as well as infection with microorganisms (Helicobacter pylori and others). On the other hand, mucus secretion, bicarbonate production, gastroprotective prostaglandin synthesis and normal tissue microcirculation protect against ulcer formation. Although in most cases the etiology of ulcer is unknown yet, it is generally accepted that gastric ulcers are multifactorial and develop when aggressive factors (endogenous, exogenous and/or infectious agents) overcome mucosal defense mechanisms (Allen & Garner, 1980; Wallace, 1992; Peskar & Marici, 1998; Tulassay & Herszenyi, 2010). In this context, the present chapter aims to address etiologies multiples of gastric ulcer development, clarifying how the imbalance between aggressive and defensive factors leads to this clinical condition. For this purpose, we review basic components of gastric mucosal defense and discuss conditions in which mucosal injury is directly related to impairment in mucosal defense.
Promoting positive adolescent sexual health & preventing teenage pregnancy - a review of recent effectiveness research

This review was commissioned by the Crisis Pregnancy Agency to inform their work in the area of prevention of crisis pregnancy. The review commences with a brief description of the current epidemiological data on teenage pregnancy and adolescent sexual behaviour in Ireland. This is followed by an analysis of current understanding of the antecedents for early sexual initiation, teenage pregnancy and/or unprotected sexual intercourse. A consideration of the evidence of the effectiveness of prevention programmes will then be presented. Two main approaches to the prevention of teenage pregnancy and the promotion of positive sexual health are examined: educational interventions (primarily home- and school-based), and the provision and delivery of contraceptive and sexual health services. The review concludes with recommendations for health, education, health promotion and social services, and suggests areas for further research
A multiple channel assignment algorithm and routing protocol base on wireless mesh networks

In this paper, a hybrid routing protocol TOIRP and a distributed channel assignment algorithm CLBCA of WMN in the multi-channel environment are proposed. The tree base protocol and the traditional AODV routing protocol are integrated together to reduce delay in the routing protocol, which is proposed by HWMP. A new routing metric CETTI is used in this routing protocol, which uses a new method to quantify the interference of communication. It divided interference into two parts: inter-flow interference and intra- flow interference, and it integrate the considerations of hop- count and delay. This improves the throughput of WMN. The channel assignment algorithm is divided into two parts: radio- neighbor binding and channel-radio binding. This algorithm is based on the channel load and avoids the channel-dependency problem which is common in the multi-channel environment. In the same time, it increases the utilization of each channel.
The Pharmacological Mydriatic Pupil-to-Limbal Diameter Ratio as an Intuitive Predictor for the Risk of Intraoperative Floppy Iris Syndrome

Purpose To predict development of intraoperative floppy iris syndrome (IFIS) using the preoperative pharmacologically dilated pupil-to-limbal diameter (PL) ratio. Methods The subjects were male patients treated by phacoemulsification who were or were not taking α1-adrenoceptor antagonists (ARAs). The PL ratio was calculated from the horizontal dilated pupil diameter and the horizontal corneal white-to-white distance measured by two observers in surgical videos. IFIS severity was graded using the criteria of Chang et al. We predicted the intuitive PL ratio to describe how precisely the experimenter can estimate the PL ratio without any tools. Results There were 36 eyes in the α1-ARA group and 48 eyes in the control group. The pupil diameter and PL ratio were both significantly smaller in the α1-ARA group compared to the control group (p < 0.001). All of pupil diameter, PL ratio, and intuitive PL ratio were negatively correlated with IFIS severity. The cutoff value for prediction of IFIS from the ROC curve was 7.20 mm for the pupil diameter, 58.7% for the PL ratio, and 62.5% for the intuitive PL ratio. The AUC for the ROC curve using the PL ratio (0.913) and intuitive PL ratio (0.892) did not perform substantially worse than that for the ROC curve based on the pupil diameter (0.875). Conclusions The PL ratio is a simple and useful parameter for compensated prediction of IFIS development. Patients in whom this ratio is <60% are particularly likely to develop IFIS, and measures against onset of IFIS should be considered. This study is registered with UMIN000033012.
Unsupervised Bayesian Lexicalized Dependency Grammar Induction

This dissertation investigates learning dependency grammars for statistical natural language parsing from corpora without parse tree annotations. Most successful work in unsupervised dependency grammar induction has assumed that the input consists of sequences of parts-of-speech, ignoring words and using extremely simple probabilistic models. However, supervised parsing has long shown the value of more sophisticated models which use lexical features. These more sophisticated models however require probability distributions with complex conditioning information, which must be smoothed to avoid sparsity issues. In this work we explore several dependency grammars that use smoothing, and lexical features. We explore a variety of different smoothing regimens, and find that smoothing is helpful for even unlexicalized models such as the Dependency Model with Valence. Furthermore , adding lexical features yields the highest accuracy dependency induction on the Penn Treebank WJS10 corpus to date. In sum, this dissertation extends unsupervised grammar induction by incorporating lexical conditional information, by investigating smoothing in an unsupervised framework. Dean of the Graduate School iii Acknowledgements In memory of my mother. I would like to express my appreciation to everyone who contributed their support during my time at Brown which culmulated in this dissertation. Let begin with my advisors: Mark Johnson, who kept me focused and whose enthusiasm and insight are much appreciated; and Eugene Charniak, whose door was always open for valuable discussion and advice. Their patience and expertise were invaluable, and I learned a great deal from each of them. Thanks also to the rest of my committee, Jason Eisner and Stuart Geman, for their helpful comments and insights. I am indebted also to many other professors, especially Tom Griffiths for introducing me to the virtues of Bayesian Statistics, as well as Mark Maloof and Sharon Caraballo, for sparking my interest in machine learning and natural language processing while I was an undergraduate at Georgetown. for making me glad my research did not require zookeeping duties; as well as many others. Finally I would like to thank my family: my parents Jeff and Ginny, and my brother Andrew, whose love, support, and occasional badgering were essential to completing this dissertation.
HEIF: Highly Efficient Stochastic Computing-Based Inference Framework for Deep Neural Networks

Deep convolutional neural networks (DCNNs) are one of the most promising deep learning techniques and have been recognized as the dominant approach for almost all recognition and detection tasks. The computation of DCNNs is memory intensive due to large feature maps and neuron connections, and the performance highly depends on the capability of hardware resources. With the recent trend of wearable devices and Internet of Things, it becomes desirable to integrate the DCNNs onto embedded and portable devices that require low power and energy consumptions and small hardware footprints. Recently stochastic computing (SC)-DCNN demonstrated that SC as a low-cost substitute to binary-based computing radically simplifies the hardware implementation of arithmetic units and has the potential to satisfy the stringent power requirements in embedded devices. In SC, many arithmetic operations that are resource-consuming in binary designs can be implemented with very simple hardware logic, alleviating the extensive computational complexity. It offers a colossal design space for integration and optimization due to its reduced area and soft error resiliency. In this paper, we present HEIF, a highly efficient SC-based inference framework of the large-scale DCNNs, with broad applications including (but not limited to) LeNet-5 and AlexNet, that achieves high energy efficiency and low area/hardware cost. Compared to SC-DCNN, HEIF features: 1) the first (to the best of our knowledge) SC-based rectified linear unit activation function to catch up with the recent advances in software models and mitigate degradation in application-level accuracy; 2) the redesigned approximate parallel counter and optimized stochastic multiplication using transmission gates and inverse mirror adders; and 3) the new optimization of weight storage using clustering. Most importantly, to achieve maximum energy efficiency while maintaining acceptable accuracy, HEIF considers holistic optimizations on cascade connection of function blocks in DCNN, pipelining technique, and bit-stream length reduction. Experimental results show that in large-scale applications HEIF outperforms previous SC-DCNN by the throughput of $4.1\times $ , by area efficiency of up to $6.5\times $ , and achieves up to ${5.6\times }$ energy improvement.
The Morpheme Interference Effect in Hebrew: A Generalization across the Verbal and Nominal Domains

An extensive body of psycholinguistic research suggests that word reading involves morphological decomposition: Individual morphemes are extracted and lexically accessed when skilled readers are presented with multi-morphemic orthographic stimuli. This view is supported by the Morpheme Interference Effect (MIE): Responses to pseudowords that contain real morphemes are slower and less accurate than responses to pseudowords that contain invented morphemes. The MIE was previously demonstrated in several languages with linear morphologies. Here, we examined whether the MIE applies to Hebrew, a language with an interleaved morphology, and whether it generalizes across the nominal and verbal domains. Participants performed a lexical decision task on visually presented Hebrew words and pseudowords derived from real or invented roots. The results showed robust MIEs in both the verbal and nominal domains. Specifically, pseudowords derived from real roots induced significantly lower accuracy and longer response times compared to pseudowords derived from invented roots. Participants’ verbal and nominal MIEs were significantly correlated, suggesting that the MIE captures a general sensitivity to morphological structure.
Fuzzy object models for newborn brain MR image segmentation

Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.
Video Compression Based on DCT-DWT Technique

The increasing demand to incorporate video data into telecommunications services, the corporate environment, the entertainment industry, and even at home has made digital video technology a necessity. To reduce bandwidth requirement in transferring files is a major challenge today. This review paper introduces advancement in video compression technology with perceiving quality and content of video. Block matching algorithms used for motion estimation in video compression
Atlas of Gastrointestinal Cytodiagnosis

Exfoliative Cytopathology , by Zuher M. Naib, 398 pp, 155 illus, $15, Boston: Little, Brown & Co., 1970. Two texts on cytodiagnosis have recently been published— Atlas of Gastrointestinal Cytodiagnosis , by Henning and Witte, translated by Nieburgs, and Exfoliative Cytopathology , by Naib. The volume on gastroenterology has limited appeal. Even though much of the photography is unremarkable, there is considerable interest in the comparison of microscopic techniques used to examine the entire gastrointestinal tract; included are photographs of the Papanicolaou stain, Pappenheim stain, intravital staining with atabrine, a fluorescent dye, and phase-contrast microscopy. Cytodiagnosis, endoscopy, and roentgenologic findings are also compared, as well as various collection techniques. Of much wider appeal is Exfoliative Cytopathology . In the preface, Dr. Naib states that he has "made special effort to present, in the simplest and most concise manner, the basic systemic knowledge needed for interpretation" of cytology. He has completely fulfilled his aim. This
Pointers to conceptual understanding

Concept inventories are tests used to elicit student misunderstandings and misconceptions. Traditionally, they exist as a set of multiple-choice questions (MCQs), including the correct option, as well as some distractors (Libarkin, 2008). This multiple-choice format allows for faster marking and feedback; however, it does not identify conceptual misunderstandings, or if a student has guessed the correct answer. By adding a space for students to add a textual justification (Goncher, Jayalath, & Boles, 2016), their answers can be checked to ensure that the concepts are correctly understood. Automated textual analysis will allow insights to be uncovered, and to help speed up the process of grading to give feedback to students and informing educators. As part of that process, we endeavour to address the following questions: 1. What pointers can be identified that indicate a student’s conceptual understanding? 2. What conclusions can we make from these identified pointers to conceptual understanding? Over the past four years, two concept inventories have been deployed, both with multiple choice questions, as well as a free text field for students to give reasoning and explanation. We will combine several machine learning techniques to analyse the textual response data, including: • Word2vec – which allows words to be modelled as vectors, for easier computation (Mikolov, Chen, Corrado, & Dean, 2013) • LDA (Latent Dirichlet Allocation) – Allows classification and grouping of topics and areas (Blei, et al., 2003) • SVMs (Support vector machines) – which allow classification to be performed and similar areas grouped Four pointers were identified to help to automatically determine if conceptual understanding is present. The first three pointers can be determined with certainty, the fourth “validity of the response” is one that is traditionally determined by a human marker. Comparing with an expert marked dataset, the algorithm to determine this pointer achieved a 75% accuracy. Using the four identified pointers we are able to detect if a student has correctly identified the concept which they were being tested for in a particular question. The four pointers, allow some leniency if one of these is not achieved, and can also allow us to draw conclusions as to where issues lie in a student’s understanding. This presents several opportunities for benefits such as individualised feedback for students and entire class feedback for educators.
Online Self-Tuning Precompensation for a PID Heading Control of a Flying Robot

In this paper, an online self-tuning precompensation for a Proportional-Integral-Derivative (PID) controller is proposed to control heading direction of a flying robot. The flying robot is a highly nonlinear plant, it is a modified X-Cell 60 radio-controlled helicopter. Heading direction is controlled to evaluate efficiency of the proposed precompensation algorithm. The heading control is based on the conventional PID control combined with an online self-tuning precompensation so that both the desired transient and steady state responses can be achieved. The precompensation is applied to compensate unsatisfied performances of the conventional PID controller by adjusting reference command. The precompensator is based on Takagi-Sugeno's type fuzzy model, which learns to tune itself online. The main contribution of the proposed controller is to enhance the controlled performance of the conventional PID controller by adding a self-tuning precompensator on the existing conventional PID controller. The results show that the conventional PID controller with an online self-tuning precompensation has a superior performance than the conventional PID controller. In addition, the online self-tuning precompensation algorithm is implemented simply by adding the precompensator to the existing conventional PID controller and letting the self-tuning mechanism tune itself online.
Commentary by Quinn

Perceived health status or self-rated health is a global measure that is thought to capture overall health status. The simple question of “How do you rate your health? ” has been found to be useful in predicting clinical outcomes and mortality (Fayers & Sprangers, 2002). Using perceived health as a health indicator may be particularly useful in populations that are plagued with lack of health care access such as homeless individuals. Little is known about perceived health in homeless individuals. As the authors of this study point out, homeless individuals are at significant risk for tuberculosis (TB), and issues of treatment adherence remain a challenge in this population. An understanding of perceived health in homeless individuals with latent TB provides important insight into the goal of reducing TB in this at-risk population. The focus of my commentary will revolve around issues related to characteristics of the participants. Measurement issues related to participant characteristics warrant further discussion. Piloting the instruments in minority populations is a strength of this study. Reliability and validity of the Center for Epidemiological Studies depression scale (Jones-Webb & Snowden, 1993) and the Short-Form Health Survey from the Medical Outcomes Study (Sedgwick, Pearce, & Gulliford, 2003) has been reported in African American populations. Questions still remain regarding the Short-Form Health Survey perceived health item in the homeless population. For example, Fayers and Sprangers (2002) suggest that asking the question “How would you rate your health? ” provokes the question, “Compared with what? ” Considering the reference group when asking this question may be important, particularly in a marginalized population such as the homeless. Ethnicity also requires further consideration in this study. African Americans comprise 37% of homeless populations (Health Care for the Homeless Information Resource Center [HCH], 2003). The sample in this study is primarily comprised of African American individuals (81%). This raises concerns about sampling issues, unless the authors’ intent was to target African American homeless individuals. Gender is another participant characteristic of interest in this study. Although males comprise the largest proportion of homeless individuals, the