Miracle-dz/UniFAR-FTUs huggingface dataset:
Smoking and subclinical interstitial lung disease.

The relationships between cigarette smoking and heart disease, lung cancer, and chronic obstructive pulmonary disease (COPD) are well known; in contrast, the topic of the relationship between smoking and interstitial lung disease is almost obscure. Despite its lack of a public persona, this relationship is important, since smoking plays a key role in the pathobiology of several interstitial lung diseases — respiratory bronchiolitis, desquamative interstitial pneumonia, pulmonary Langerhans'-cell histiocytosis, and perhaps idiopathic pulmonary fibrosis.1 It is widely known that in asymptomatic smokers with normal lung function, chest radiography reveals the presence of interstitial opacities (also known as dirty lung). 2 The .
Discretization of Continuous-Valued Attributes and Instance-Based Learning

Recent work on discretization of continuous-valued attributes in learning decision trees has produced some positive results. This paper adopts the idea of discretization of continuous-valued attributes and applies it to instance-based learning (Aha, 1990; Aha, Kibler & Albert, 1991). Our experiments have shown that instance-based learning (IBL) usually performs well in continuous-valued attribute domains and poorly in nominal attribute domains. Cost and Salzberg (1993) have devised the modiied value-diierence metric (MVDM) that raises the performance of IBL in nominal attribute domains. This paper explores a way in which continuous-valued attributes and nominal attributes can be treated cohesively in IBL. An algorithm which combines the discretization of continuous-valued attributes and IB1 (Aha, Kibler & Albert, 1991) using the modiied value-diierence metric is introduced. The empirical results show that the proposed algorithm, IB1-MVDM* achieves a substantial improvement over C4.5 (Quinlan, 1993), IB1 and IB1-MVDM in most of the domains tested. A performance comparison is also made with a naive Bayesian learner (Cestnik, 1990).
ZM-Net: Real-time Zero-shot Image Manipulation Network

Many problems in image processing and computer vision (e.g. colorization, style transfer) can be posed as 'manipulating' an input image into a corresponding output image given a user-specified guiding signal. A holy-grail solution towards generic image manipulation should be able to efficiently alter an input image with any personalized signals (even signals unseen during training), such as diverse paintings and arbitrary descriptive attributes. However, existing methods are either inefficient to simultaneously process multiple signals (let alone generalize to unseen signals), or unable to handle signals from other modalities. In this paper, we make the first attempt to address the zero-shot image manipulation task. We cast this problem as manipulating an input image according to a parametric model whose key parameters can be conditionally generated from any guiding signal (even unseen ones). To this end, we propose the Zero-shot Manipulation Net (ZM-Net), a fully-differentiable architecture that jointly optimizes an image-transformation network (TNet) and a parameter network (PNet). The PNet learns to generate key transformation parameters for the TNet given any guiding signal while the TNet performs fast zero-shot image manipulation according to both signal-dependent parameters from the PNet and signal-invariant parameters from the TNet itself. Extensive experiments show that our ZM-Net can perform high-quality image manipulation conditioned on different forms of guiding signals (e.g. style images and attributes) in real-time (tens of milliseconds per image) even for unseen signals. Moreover, a large-scale style dataset with over 20,000 style images is also constructed to promote further research.
Hessian-Regularized Co-Training for Social Activity Recognition

Co-training is a major multi-view learning paradigm that alternately trains two classifiers on two distinct views and maximizes the mutual agreement on the two-view unlabeled data. Traditional co-training algorithms usually train a learner on each view separately and then force the learners to be consistent across views. Although many co-trainings have been developed, it is quite possible that a learner will receive erroneous labels for unlabeled data when the other learner has only mediocre accuracy. This usually happens in the first rounds of co-training, when there are only a few labeled examples. As a result, co-training algorithms often have unstable performance. In this paper, Hessian-regularized co-training is proposed to overcome these limitations. Specifically, each Hessian is obtained from a particular view of examples; Hessian regularization is then integrated into the learner training process of each view by penalizing the regression function along the potential manifold. Hessian can properly exploit the local structure of the underlying data manifold. Hessian regularization significantly boosts the generalizability of a classifier, especially when there are a small number of labeled examples and a large number of unlabeled examples. To evaluate the proposed method, extensive experiments were conducted on the unstructured social activity attribute (USAA) dataset for social activity recognition. Our results demonstrate that the proposed method outperforms baseline methods, including the traditional co-training and LapCo algorithms.
Difference of neointimal growth patterns in bifurcation lesions among four kinds of drug‐eluting stents

Aim: Neointimal proliferation of bifurcation lesions after implantation of drug‐eluting stents (DES) has not been well evaluated. Thus, we compared neointimal proliferation of bifurcation lesions among four DES using optical coherence tomography (OCT). Methods: 8‐month follow‐up OCT was performed in 68 bifurcation lesions treated by 15 sirolimus‐eluting stents (SES) and 17 paclitaxel‐eluting stents (PES) as first‐generation DES, and by 17 zotarolimus‐eluting stents (ZES) and 19 everolimus‐eluting stents (EES) as second‐generation DES. Cross‐sectional images of the bifurcation lesion using OCT were analyzed every 450 µm. All images were divided into three areas: inner wall of the bifurcation (IB), outer wall of the bifurcation (OB), and ostium of the side branch (SB). We compared the incidence of uncovered struts (IUS) among three areas and the averaged neointimal thickness (NIH) between IB and OB in each stent and also compared these OCT parameters among all DES. Results: There were no significant differences of IUS between IB and OB in second‐generation DES, while in first‐generation DES, IUS of IB and OB showed significant differences. The IUS of SES in both areas was significantly higher than in the other DES (all P < 0.001). PES had a significantly higher IUS in SB than the others (all P < 0.001). NIH of OB was significantly higher than that of IB in PES, ZES, and EES, but in SES the NIH was similar in the two areas. Conclusions: OCT revealed different neointimal growth patterns among SES, PES, ZES, and EES in bifurcation lesions. © 2014 Wiley Periodicals, Inc.
Combined Use of Ultrasonic Pulse Velocity and Rebound Hammer for Structural Health Monitoring of Reinforced Concrete Structures

This work investigates the use of Non-destructive tests as a tool for monitoring the structural performance of concrete structures. The investigation encompassed four phases; the first of which involved the use of destructive and non-destructive mechanisms to assess concrete strength on cube specimens. The second phase research focused on site assessment for a twin engineering theatre located at the Faculty of Engineering, University of Lagos using rebound hammer and ultrasonic pulse velocity tester. The third phase was the use of linear regression analysis model with MATLAB to establish a relationship between calibrated strength as well as ultrasonic pulse velocities with their corresponding compressive strength values on cubes and values obtained from existing structures. Results show that the root-mean squared-R2 values for rebound hammer ranged between 0.275 and 0.742 while ultrasonic pulse velocity R2 values were in the range of 0.649 and 0.952 for air curing and water curing systems respectively. It initially appeared that the Ultrasonic pulse velocity was more suitable for predicting concrete strength than rebound hammer but further investigations showed that the latter was adequate for early age concrete while the former was more suited for aging concrete. Hence, a combined use is recommended in this work.
Vision in and out of Vehicles

At the Australian National University's Intelligent Vehicle Project, we are developing subsystems for: driver fatigue or inattention detection; pedestrian spotting; blind-spot checking and merging assistance to validate whether sufficient clearance exists between cars; driver feedback for lane keeping; computer-augmented vision (that is, lane boundary or vehicle highlighting on a head-up display); traffic sign detection and recognition; and human factors research aids Systems that perform such supporting tasks are generally called driver assistance systems (DAS). We believe that implementing DAS could prevent similar accidents or at least reduce their severity.
ASYMMETRIES IN BUSINESS CYCLES AND THE ROLE OF OIL PRICES

We estimate asymmetries in innovations to Solow residuals for 11 Organization for Economic Co-operation and Development (OECD) countries using stochastic frontier analysis. Likelihood ratio statistics and variance ratios imply that all countries with net energy imports have significant negative asymmetries, whereas other countries do not. We construct a simple theoretical model in which the measured Solow residual combines effects from technology, factor utilization, and the terms of trade. For oil importers, the model implies an asymmetric response of measured total factor productivity to oil price increases and decreases. When we condition Solow residuals separately on positive and negative oil price changes to allow asymmetric responses, evidence for remaining negative asymmetric innovations to the Solow residuals vanishes for all countries except Switzerland. Switzerland's relatively dominant financial sector suggests that their asymmetries could be due to a financial crisis, a hypothesis that we test and fail to reject.
Continuation and Maintenance Electroconvulsive Therapy for Mood Disorders: Review of the Literature

Background: Electroconvulsive therapy (ECT) is a highly effective treatment for mood disorders. Continuation ECT (C-ECT) and maintenance ECT (M-ECT) are required for many patients suffering from severe and recurrent forms of mood disorders. This is a review of the literature regarding C- and M-ECT. Methods: We conducted a computerized search using the words continuation ECT, maintenance ECT, depression, mania, bipolar disorder and mood disorders. We report on all articles published in the English language from 1998 to 2009. Results: We identified 32 reports. There were 24 case reports and retrospective reviews on 284 patients. Two of these reports included comparison groups, and 1 had a prospective follow-up in a subset of subjects. There were 6 prospective naturalistic studies and 2 randomized controlled trials. Conclusions: C-ECT and M-ECT are valuable treatment modalities to prevent relapse and recurrence of mood disorders in patients who have responded to an index course of ECT. C-ECT and M-ECT are underused and insufficiently studied despite positive clinical experience of more than 70 years. Studies which are currently under way should allow more definitive recommendations regarding the choice, frequency and duration of C-ECT and M-ECT following acute ECT.
Acoplamiento de micro multitudes para el desarrollo de videojuegos controlados por movimiento

Resumen. La simulacion de multitudes en tiempo real y los juegos controlados por movimiento se han vuelto muy populares en los ultimos anos. En conjunto estas dos tecnoloǵias proporcionan una mejor experiencia de juego en entornos virtuales logrando escenas mas realistas y vibrantes. Sin embargo, hasta ahora no se ha explotado la interaccion de multiples jugadores con una gran multitud bajo un entorno virtual. En este trabajo presentamos un sistema no intrusivo capaz de simular multitudes virtuales acopladas en tiempo real con varios usuarios, sentando con ello las bases para la creacion de juegos donde interactuen muchos jugadores con muchas personajes, para ello se realiza una deteccion de personas en una secuencia de video, nuestra contribucion consiste en utilizar patrones binarios semanticos locales (S-LBP) como descriptores, creando con ello un sistema inteligente capaz de segmentar detectar y seguir personas en vistas aereas, aśi mismo para aumentar la velocidad del dibujado y acoplamiento se aprovecha la unidad de procesamiento grafico (GPU), almacenando la informacion en texture shaders delineando una arquitectura de procesamiento concurrente. Los resultados experimentales muestran un sistema viable para la interaccion de multitudes virtuales contra mas de una docena de personas reales simulados en hardware de consumo y en tiempo real (30 FPS), aśi mismo el uso de los patrones binarios semanticos locales como descriptores permitieron reducir los falsos positivos que normalmente se producen al emplear descriptores HOG (Histograma de gradientes orientados) usados frecuentemente en el reconocimiento de personas.
Efficacy of Agomelatine in Generalized Anxiety Disorder: A Randomized, Double-Blind, Placebo-Controlled Study

Background: Agomelatine is a novel agent that acts on melatonergic (MT1, MT2) receptors and serotonergic (5-HT2C) receptors. Preclinical data and data from clinical trials in major depression suggest that agomelatine may have anxiolytic properties. A randomized, double-blind, placebo-controlled trial was designed to assess the efficacy of agomelatine in generalized anxiety disorder (GAD). Methods: One hundred twenty-one patients with Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition GAD and no comorbid disorders were randomized to agomelatine (25-50 mg/d) or placebo for 12 weeks. The primary outcome measure was the Hamilton Anxiety Rating Scale, whereas secondary outcome measures included the Clinical Global Impression scales, the Leeds Sleep Evaluation Questionnaire, and the Sheehan Disability Scale. Safety measures included assessment of spontaneously reported adverse events, laboratory monitoring, and the Discontinuation Emergent Signs and Symptoms Scale to evaluate discontinuation symptoms. Results: Analysis of covariance of change in the last Hamilton Anxiety Rating Scale total score from baseline demonstrated significant superiority of agomelatine 25 to 50 mg as compared with placebo (E [SE] = −3.28 [1.58]; 95% confidence interval = −6.41 to −0.15; P = 0.040). Data on secondary outcome measures, including clinical response, symptoms of insomnia, and improvement in associated disability, were consistent with the efficacy of agomelatine. Safety analysis indicated that agomelatine was tolerated as well as placebo and was devoid of discontinuation emergent symptoms. Conclusions: This study suggests that agomelatine is effective in the treatment of GAD and is well tolerated. Additional trials, using an active comparator and extending over a longer period, are needed to delineate the place of agomelatine in the contemporary pharmacotherapy for anxiety disorders.
A Temporal Community Contexts Based Funny Joke Generation

It is still a long way to communicate humans and machines emotionally. There are some tries to provide sentimental conversations among humans and machines. Computational humor is one of research topics in computational linguistics and artificial intelligence. We introduce a new method to generate jokes in a sentence related temporal and spatial contexts for continuous conversations with images. We propose a novel model based on a recurrent neural network with natural language processing (NLP) and understanding (NLU) methods. The method generates jokes in a sentence considering temporal and spatial context. The method can joke to trend sensitive users according to different points of humor that vary from region to region. Through this, the user can feel the interest of the conversational service with humorous responses or contents. We apply the method to some applications such as psychiatric counseling and stress management to enhance the applicability of conversational service.
Analysis of rat hemopoietic cells on the fluorescence-activated cell sorter. II. Isolation of terminal deoxynucleotidyl transferase-positive cells

A method is described by which highly enriched populations of viable terminal deoxynucleotidyl transferase-positive (TdT+) cells can be isolated from rat bone marrow by use of the fluorescence-activated cell sorter. Such cells have been postulated to be progenitors of thymocytes and, possibly, of B lymphocytes, and may serve as the targets of neoplastic transformation in acute lymphoblastic leukemia. The separation procedure is based on differences in relative low-angle light scatter and relative fluorescence intensity for Thy-1 antigen between TdT+ cells and other lymphohemopoietic cell populations in bone marrow. Simultaneous sorting of bone marrow cells according to these two parameters resulted in a mean 87% purification of TdT+ cells. The morphological characteristics of the isolated TdT+ cells are described at the light and electron miscroscopic levels.
Development of neural stem cells at different sites of fetus brain of different gestational age.

OBJECTIVE This study aimed to investigate the development of neural stem cells (NSCs) in fetal brain, which may provide experimental evidence for the clinical treatment of brain injury in children. METHODS A total of 60 fetuses were collected after labor induction and divided into 6 groups according to the gestational age (16 w, 20 w, 24 w, 28 w, 32 w and 36 w; n=10 per group). The hippocampus, striatum, subventricular zone, frontal lobe, temporal lobe, occipital lobe and parietal lobe were harvested. In situ hybridization, immunohistochemistry and light microscopy were done to determine the morphology and quantity of NSCs. RESULTS NSCs were identified in the brain of fetuses with different gestational age. NSCs were round, oval, spindle-shaped, starlike, triangular or polygonal. NSC colony was also observed with symmetrical or asymmetrical division. Single NSC, group-like NSCs and cluster-like NSCs were found in the different sites of fetal brain, and NCSs interacted with each other via synapses. However, the distribution, morphology, growth and quantity of NSCs were different in the brain of fetuses with different gestational age. The number of NSCs reduced with the increase in gestational age, but they were always observed. CONCLUSION The morphology of NSCs in fetal brain is variable and they are widely distributed in the hippocampus, subventricular zone, striatum and cortex. The number of NSCs reduced with the increase of gestational age.
Feature extraction and classifier evaluation of EEG for imaginary hand movements

Accurate classifying EEG signals for imaginary left-right hand movements is a crucial issue in brain-computer interface (BCI) technology. Aiming to the nonstationarity and nonlinearity of electroencephalogram (EEG) signal, in this work, based on the analysis of EEG time-frequency characteristics by wavelet packet transform and EEG uncertainty analyzing by information entropy, and EEG features for motor imagery from single trial are extracted. Then, the feature data is classified by the support vector machines (SVM), and an optimal search method is proposed to determine the kernel parameter V and penalty parameter C. Finally, some evaluation criteria including mutual information (MI) and misclassification rate (MR) are utilized to evaluate the performance of classifier. The classification accuracy could reach 90%, the MI was 0.65 bit. The test results have shown that the proposed method could accurately extract EEG substantial feature and provide an effective means to classify the motor mental tasks. It can be applied in BCI system of imaging movements.
Checklists to Reduce Diagnostic Errors

Diagnostic errors are common and can often be traced to physicians' cognitive biases and failed heuristics (mental shortcuts). A great deal is known about how these faulty thinking processes lead to error, but little is known about how to prevent them. Faulty thinking plagues other high-risk, high-reliability professions, such as airline pilots and nuclear plant operators, but these professions have reduced errors by using checklists. Recently, checklists have gained acceptance in medical settings, such as operating rooms and intensive care units. This article extends the checklist concept to diagnosis and describes three types of checklists: (1) a general checklist that prompts physicians to optimize their cognitive approach, (2) a differential diagnosis checklist to help physicians avoid the most common cause of diagnostic error—failure to consider the correct diagnosis as a possibility, and (3) a checklist of common pitfalls and cognitive forcing functions to improve evaluation of selected diseases. These checklists were developed informally and have not been subjected to rigorous evaluation. The purpose of this article is to argue for the further investigation and revision of these initial attempts to apply checklists to the diagnostic process. The basic idea behind checklists is to provide an alternative to reliance on intuition and memory in clinical problem solving. This kind of solution is demanded by the complexity of diagnostic reasoning, which often involves sense-making under conditions of great uncertainty and limited time.
CS 229-Final Report Speech Recording based Language Recognition ( Natural Language )

We construct a real time language classifier for communication purposes. Feature vectors are based on Shifted Delta Cepstral Coefficients (SDC). The classifier is constructed by a maximum likelihood estimator based on Gaussian Mixture Model (GMM) density estimations of the feature vectors. As expected, classification error decreases with model complexity to a certain limit. We observe that the optimal number of gaussians varies significantly across languages, and there is clustering pattern in the confusion matrix.
Magnetic Materials and Devices for the 21st Century: Stronger, Lighter, and More Energy Efficient

A new energy paradigm, consisting of greater reliance on renewable energy sources and increased concern for energy efficiency in the total energy lifecycle, has accelerated research into energy‐related technologies. Due to their ubiquity, magnetic materials play an important role in improving the efficiency and performance of devices in electric power generation, conditioning, conversion, transportation, and other energy‐use sectors of the economy. This review focuses on the state‐of‐the‐art hard and soft magnets and magnetocaloric materials, with an emphasis on their optimization for energy applications. Specifically, the impact of hard magnets on electric motor and transportation technologies, of soft magnetic materials on electricity generation and conversion technologies, and of magnetocaloric materials for refrigeration technologies, are discussed. The synthesis, characterization, and property evaluation of the materials, with an emphasis on structure–property relationships, are discussed in the context of their respective markets, as well as their potential impact on energy efficiency. Finally, considering future bottlenecks in raw materials, options for the recycling of rare‐earth intermetallics for hard magnets will be discussed.