Miracle-dz/UniFAR-FTUs huggingface dataset:
Face analysis using CNN-UM

Novel on-chip algorithms are proposed for face analysis on pictures obtained from a multi-camera surveillance system. According to the objective of the face detection sub-system, spatial positional relations to be presented for which the methodology we used is detailed in the paper. The uniqueness of the approach is the methodology we applied provides superior performance (50 fps) on standard database compared to most of the known face detection systems. In the general model based face part identification schema we relied on, the heavy use of morphologic operations gave robust results, even in analog VLSI implementation.
Design of a Global Medical Database which is Searchable by Human Diagnostic Patterns

We describe a global medical database which is designed for efficient evaluation. It allows language independent search for human diagnostic parameters. Core of the database is a fully automated electronic archive and distribution server for medical histories of real but anonymous patients which contain patterns of diagnosis, chosen treatment, and outcome. Every pattern is represented by a feature vector which is usually a sequence of numbers, and labeled by an unambiguous "pattern name" which identifies its meaning. Similarity search is always done only over patterns with the same pattern name, because these are directly comparable. Similarities of patterns are mapped to spatial similarities (small distances) of their feature vectors using an appropriate metric. This makes them searchable. Pattern names can be "owned" like today domain names. This facilitates unbureaucratic definition of patterns e.g. by manufacturers of diagnostic devices. Application: If there is a new patient with certain diagnostic patterns, it is possible to combine a part or all of them and to search in the database for completed histories of patients with similar patterns to find the best treatment. Confinement of the result by conventional language based search terms is possible, and immediate individual statistics or regression analyses can quantify probabilities of success in case of different treatment choices. Conclusions: Efficient searching with diagnostic patterns is technically feasible. Labeled feature vectors induce a systematic and expandable approach. The database also allows immediate calculation of individual up to date prediction models.
Macromolecular confinement of therapeutic protein in polymeric particles for controlled release: insulin as a case study

Sustained release systems for therapeutic proteins have been widely studied targeting to improve the action of these drugs. Molecular entrapping of proteins is particularly challenging due to their conformational instability. We have developed a micro-structured poly-epsilon-caprolactone (PCL) particle system loaded with human insulin using a simple double-emulsion w/o/w method followed by solvent evaporation method. This formulation is comprised by spheric-shaped microparticles with average size of 10 micrometers. In vitro release showed a biphasic behavior such as a rapid release with about 50% of drug delivered within 2 hours and a sustained phase for up to 48 h. The subcutaneous administration of microencapsulated insulin showed a biphasic effect on glycemia in streptozotocin-induced diabetic mice, compatible with short and intermediate-acting behaviors, with first transition peak at about 2 h and the second phase exerting effect for up to 48h after s.c. administration. This study reveals that a simplified double-emulsion system results in biocompatible human-insulin-loaded PCL microparticles that might be used for further development of optimized sustained release formulations of insulin to be used in the restoration of hormonal levels.
Haptic display for object grasping and manipulating in virtual environment

A haptic display for grasping and manipulating virtual objects in a CAD environment is investigated for the development of rapid prototyping technology. The operator receives the sensation of contacting and tracing of the surface of the virtual object, and of grasping and manipulating the object from the haptic display. After the control of the haptic display is formulated, it is implemented on the Sarcos Dexterous Arm Master. The proposed haptic display is experimentally confirmed to provide realistic sensation that enables the operator to grasp and manipulate the virtual object easily as intended.
Classification of obsessive-compulsive disorder from resting-state fMRI

Obssesive-compulsive disorder (OCD) is a serious mental illness that affects the overall quality of the patients' daily lives. Accurate diagnosis of this disorder is a primary step towards effective treatment. Diagnosing OCD is a lengthy procedure that involves interviews, symptom rating scales and behavioral observation as well as the experience of a clinician. Discovering signal processing and network based biomarkers from functional magnetic resonance scans of patients may greatly assist the clinicians in their diagnostic assessments. In this paper, we explore the use of Pearson's correlation scores and network based features to predict if a subject has OCD. We extracted mean time series from 112 brain regions and decomposed them to 5-frequency bands. The mean time courses were used to calculate the Pearson's correlation matrix and network based features for each band. Minimum redundancy maximum relevance feature selection method is applied to the Pearson's correlation matrix and network based features from each frequency band to select the best features for the final predictor. A leave-one-out cross validation method is used for the final predictor performance. Our proposed methodology achieves 80% accuracy (23 out of 29 subjects classified correctly) with 81% sensitivity(13 out of 16 OCD subjects identified correctly) and 77% specificity (10 out of 13 controls identified correctly) using leave-one-out with in-fold feature ranking and selection. The most discriminating feature bands are 0.06-0.11 Hz for Pearson's correlation and 0.03-0.06 Hz for network based features. The high classification accuracy indicates the predictive power of the network features as well as carefully chosen Pearson's correlation values.
Real-time Manga-like depiction based on interpretation of bodily movements by using Kinect

In order to enrich visual communications, we propose a real-time system that interprets bodily movements by using Kinect [Kinect 2012]. Spatial and temporal positions of joints of body movements are captured by using Kinect. Based on captured data analysis, intensions of movements are interpreted. Finally, our system can automatically attach various Manga-like effects, such as speed lines, focus lines, or motion blur, to live action movies in real time. In addition, voice recognition function extends universality of our system's communication capabilities.
A Color Invariant Based Binary Coded Structured Light Range Scanner for Shiny Objects

Object range data provide valuable information in recognition and modeling applications. Therefore, it is extremely important to reliably extract the range data from a given object. There are various range scanners based on different principles. Among these, structured light based range scanners deserve spacial attention. In these systems, coded light stripes are projected onto the object. Using the bending of these light stripes on the object and the triangulation principle, range information can be obtained. Since this method is simple and fast, it is used in most industrial range scanners. Unfortunately, these range scanners can not scan shiny objects reliably. The main reason is either highlights on the shiny object or the ambient light in the environment. These disturb the coding by illumination. As the code is changed, the range data extracted from it will also be disturbed. In this study, we propose a color invariant based binary coded structured light range scanner to solve this problem. The color invariant used can eliminate the effects of highlights on the object and the ambient light from the environment. This way, we can extract the range data of shiny objects in a robust manner. To test our method, we developed a prototype range scanner. We provide the obtained range data of various test objects with our range scanner.
Prevalence, Demographics, and Health Outcomes of Comorbid Asthma and Overweight in Urban Children

Background. Asthma and overweight are epidemic in urban children but the relationship between these conditions is not fully understood. This study presents demographic and risk profiles of comorbidity for overweight asthmatic children, characterizes morbidity by comparing health outcomes among overweight asthmatics and healthy weight asthmatics, and examines socioeconomic factors associated with comorbidity. Objective. To construct a demographic profile of overweight asthmatic children in an urban setting and identify factors that contribute to prevalence. Method. Cross-sectional study of 5250 children in New York City public elementary schools using a parent-report questionnaire on body mass index, socioeconomic status, asthma, and asthma-related outcomes. Results. Prevalence of overweight (body mass index ≥ 85th percentile for age and gender) was 50.9%. The prevalence of overweight and ever being diagnosed with asthma was 10.9%. The prevalence of overweight with current asthma was 6.2%. Overweight current asthmatics had more night symptoms, missed school days, and asthma medication use than healthy weight asthmatics. Almost 50% of overweight current asthmatic children reported emergency department visits for asthma compared with 30% of healthy weight asthmatics. Comorbidity was most prevalent among males, Latinos, and children in low-income households, with the highest prevalence among Puerto Ricans. In multivariate analysis stratified by gender, the most significant factors associated with comorbidity among girls were low income and minority race/ethnicity, while among boys significant factors were parental education and parental history of asthma. Interestingly, there were few underweight children (7.8%) but they had high prevalence of asthma (13.8%). Conclusions. The comorbidity of overweight and asthma has a large impact on urban populations, causing greater disease burden than asthma alone. Overweight asthmatics show more uncontrolled asthma, evidenced by emergency department visits, quick-relief medication use, and days with asthma symptoms. The relationship between socioeconomic factors and the asthma–obesity comorbidity may vary by gender and requires further study to identify successful interventions to reduce disease in children.
Engineered mesenchymal stem cells and method of using same to treat tumors

The invention oil entirely using a mesenchymal stem cell (mesenchymal stem cell) strain provides a method for treating the afflicted individual to the tumor, and wherein each genetically modified mesenchymal stem cells to (i) ( ii), and the operation includes the exogenous nucleic acid including a possibly associated cytotoxic protein, an encryption region (region) (ii) a promoter (promoter) or a promoter / enhancer (enhancer) combination, a cytotoxic protein is genetically modified where when the mesenchymal stem cell is introduced into the blood stream of the number of valid objects in a therapeutically effective of (mesenchymal stem cell) to be selectively expressed when the stem cells are hayeoteul action in close proximity to the substrate tissue of the tumor. Additionally, the present invention provides a genetically modified mesenchymal stem cells for use in the method of the invention.
Data classification with a relaxed model of variable kernel density estimation

In recent years, kernel density estimation has been exploited by computer scientists to model several important problems in machine learning, bioinformatics, and computer vision. However, in case the dimension of the data set is high, then the conventional kernel density estimators suffer poor convergence rates of the pointwise mean square error (MSE) and the integrated mean square error (IMSE). Therefore, design of a novel kernel density estimator that overcomes this problem has been a great challenge for many years. This paper proposes a relaxed model of the variable kernel density estimation and analyzes its performance in data classification applications. It is proved in this paper that, in terms of pointwise MSE, the convergence rate of the relaxed variable kernel density estimator can approach O(n/sup -1/) regardless of the dimension of the data set, where n is the number of sampling instances. Experiments with the data classification applications have shown that the improved convergence rate of the pointwise MSE leads to higher prediction accuracy. In fact, the experimental results have also shown that the data classifier constructed based on the relaxed variable kernel density estimator is capable of delivering the same level of prediction accuracy as the SVM with the Gaussian kernel.
Isofuranodiene is the main volatile constituent of Smyrnium perfoliatum L. subsp. perfoliatum growing in central Italy

The essential oil hydrodistilled from the aerial parts of Smyrnium perfoliatum subsp. perfoliatum growing in central Italy was analysed by GC-MS. The main peak in the gas chromatogram was given by the furanosesquiterpene curzerene which is the Cope rearrangement product of isofuranodiene formed into injector and column during the gas chromatographic run. A truthful quantification of these compounds was achieved by HPLC-DAD analysis which showed that isofuranodiene is the main volatile component (180.0 mg/g eo) of S. perfoliatum subsp. perfoliatum, while curzerene occurs in small amounts (18.1 mg/g eo).
Color correction for faded image using classification in LCybCrg color space

Digital scanners transfer the image data from color photo prints or documents into digital images that are similar to the input data. However, when scanning faded images, there is sometimes a need to restore the original image. Most correction methods for faded images use illuminant estimation algorithms, such as the gray world assumption and white patch Retinex methods, as the phenomenon of color fading is regarded as an illuminant effect. However, this induces inaccurate faded color correction, as images fade at different rates according to the ink property, temperature, humidity, and illuminant. Therefore, this paper presents a color correction method for faded images using classification in LCybCrg color space. The input faded image is first separated according to the chromaticity based on LCybCrg opponent color space. The faded color correction is then performed based on the gray world assumption in RGB color space. Thereafter, weights calculated from CybCrg values are applied to reduce contour artifacts. As a result, the proposed method provides better color correction for faded images than previous methods.
Modeling Narrative Conflict to Generate Interesting Stories

From subtle political intrigue to outright physical combat, conflict is essential to interesting stories. Narratology research emphasizes that conflict provides structure and engagement, so narrative systems stand to benefit greatly from a computational model of this phenomenon. We present such a model based on AI planing, along with formulas for measuring seven essential properties: participants, subject, duration, directness, intensity, balance, and resolution. We also sketch an algorithm which uses this model to create stories structured around a central struggle.
Tree-based machine learning methods for survey research

Predictive modeling methods from the field of machine learning have become a popular tool across various disciplines for exploring and analyzing diverse data. These methods often do not require specific prior knowledge about the functional form of the relationship under study and are able to adapt to complex non-linear and non-additive interrelations between the outcome and its predictors while focusing specifically on prediction performance. This modeling perspective is beginning to be adopted by survey researchers in order to adjust or improve various aspects of data collection and/or survey management. To facilitate this strand of research, this paper (1) provides an introduction to prominent tree-based machine learning methods, (2) reviews and discusses previous and (potential) prospective applications of tree-based supervised learning in survey research, and (3) exemplifies the usage of these techniques in the context of modeling and predicting nonresponse in panel surveys.
Minor Oral Lesions Facilitate Transmission of Chronic Wasting Disease

ABSTRACT While chronic wasting disease (CWD) prion transmission, entry, and trafficking remain incompletely elucidated, natural exposure of the oral and/or nasal mucous membranes seems certain. Cervids commonly sustain minor lesions on oral mucous membranes that could have an impact on susceptibility to prion infection. To explore this potential cofactor, we studied cohorts of cervid PrP transgenic mice with or without superficial abrasions on the lingual mucosa to determine whether minor oral mucosa lesions may enhance susceptibility to CWD infections. Results demonstrated that minor lingual abrasions substantially facilitate CWD transmission, revealing a cofactor that may be significant in cervids and perhaps other species.
Combination of Document Image Binarization Techniques

Document image binarization has been studied for decades, and many practical binarization techniques have been proposed for different kinds of document images. However, many state-of-the-art methods are particularly suitable for the document images that suffer from certain specific type of image degradation or have certain specific type of image characteristics. In this paper, we propose a classification framework to combine different thresholding methods and produce better performance for document image binarization. Given the binarization results of some reported methods, the proposed framework divides the document image pixels into three sets, namely, foreground pixels, background pixels and uncertain pixels. A classifier is then applied to iteratively classify those uncertain pixels into foreground and background, based on the pre-selected froeground and background sets. Extensive experiments over different datasets including the Document Image Binarization Contest(DIBCO)2009 and Handwritten Document Image Binarization Competition(H-DIBCO)2010 show that our proposed framework outperforms most state-of-the-art methods significantly.
Does Osteopathic Manipulative Treatment (OMT) have an Effect in the Treatment of Patients Suffering from Gastro Esophageal Reflux Disease (GERD)

Background: During the last 50 years, the efficacy of medication in the treatment of gastro esophageal reflux disease (GERD) has increased with introduction of histamine 2 antagonists and proton pump inhibitors. Surgical treatment is effective, but irreversibleand complications may occur. Is it possible to develop an effective treatment procedure beside pharmaceuticals and surgery? Objective: To describe Osteopathic Manual Therapy (OMT) techniques and investigate the effect in patients suffering from GERD. Design: The study performed as an open, single-centre, one-armed interventional trial. Material: Eighteen female and four male GERD patients, mean age and disease duration of 47.4 years and 20.4 months, recruited from one single OMT practice. Method: The patients received three to 12OMT interventions consisting of thoracic spine and diaphragm mobilisation, traction of the cardia and posture correction. The interval between interventions was one week and the effect recorded as “none,” “slight,” “moderate” or “good. ” The presence of heartburn, retrosternal pain, acid in the mouth, epigastric pain and tension or pain in the lower thoracic spine recorded before and three month after the last treatment. Results: The prevalence and total number of GERD symptoms were significantly reduced (p
Multiwall Carbon Nanotube-Induced Apoptosis and Antioxidant Gene Expression in the Gills, Liver, and Intestine of Oryzias latipes

Multiwall carbon nanotubes (MWCNTs) have many attractive properties with potential applications in various fields. Despite their usefulness, however, the associated waste can be hazardous to the environment. To examine adverse effects in aquatic environments, Oryzias latipes were exposed to MWCNTs dispersed in water for 14 days and apoptosis and antioxidant gene expression were observed. This work showed that in gills exposed to 100 mg/L MWCNTs for 4 days, there was significant p53, caspase-3 (Cas3), caspase-8 (Cas8), and caspase-9 (Cas9) gene expression relative to the controls, while catalase (CAT) and glutathione-S-transferase (GST) expression were reduced. At 14 days, CAT, GST, and metallothionein (MT) were induced significantly in the gills and Cas3, Cas8, and Cas9 were induced in the liver. No significant gene induction was seen in intestine. Intracellular reactive oxygen species (ROS) were increased significantly only at 14 days. Histologically, no apoptosis was observed with exposure to 100 mg/L MWCNTs for 21 days. The gills were more sensitive to MWCNT toxicity than the other organs. Males had higher apoptosis gene induction than females. These results demonstrated that MWCNTs could cause apoptosis in a manner influenced by tissue and gender in aqueous environments.
A robust composite control for basic DC-DC converters

The aim of this work is to build a unified model for basic DC-DC converters that are subject to disturbances and parametric uncertainties on which a robust composite control is designed. Toward this end, three basic DC-DC converters, buck, boost, and buck-boost, operating in the continuous conduction mode are formulated by a unified model that control strategy is devised accordingly. A robust composite control strategy comprising an integral sliding-mode control and a robust linear control is implemented to the system. The advantage of integral sliding-mode control not only eliminates the so-called matched disturbances but maintains the system, namely equivalent system, on the designed sliding surface. While, in the mean time, the equivalent system asymptotically approaching the equilibrium by robust linear control with consideration of parametric uncertainties, such as inductance, capacitance, and load resistance; in this work, however, we treat also the input voltage as one of the parameters that subject to large variations. It is noted that although the control design are divided into two parts, the control, however, acts simultaneously to the system such that the design goals are reached.