Its better to have imprecise answers to the right questions than precise answers to the wrong questions.-Donald CampbellProcedures for identifying gifted and talented students are probably the most discussed and written about topic in our field. For the better part of the previous century, test scores dominated the identification process. Even with the advent of new theories of intelligence (e.g., Gardner, 1983;Sternberg, 1985) and broadened conceptions of giftedness (e.g., Gagné, 1999;Renzulli, 1978Renzulli, , 1988Simonton, 1997), actual practices specified in state and district guidelines continue to be dominated by cognitive ability test scores. Recognition of the need for a broader base of identification criteria has progressed from theoretical and research-based advances to generally accepted recommendations included in standard textbooks in the field (

Phosphor conversion of light‐emitting diode (LED) radiation has been used in many configurations and combinations to generate white light. The concept of down conversion of, e.g., blue LED emission, offers also the possibility to provide efficient generation of monochromatic, high‐color‐purity light, especially in wavelength ranges in which direct radiation from non‐converted LEDs is relatively inefficient, i.e., in the “yellow gap”. In the case described here, a blue‐emitting LED is ‘fully’ converted to amber emission with a peak wavelength of 595 nm and a color purity of >96%, while demonstrating an external quantum efficiency exceeding that of direct AlGaInP LEDs of the same wavelength by a factor of almost two at room temperature, and the lumen output under equal drive conditions at 85 °C by more than a factor of four. An essential component in this high performance is, besides the choice of the right – nitride – phosphor, the use of this phosphor in a densely sintered ceramic form which has considerable optical advantages over powders. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems. This has been made possible due to the availability of large annotated datasets, a better understanding of the non-linear mapping between input images and class labels as well as the affordability of GPUs. In this paper, we present the design details of a deep learning system for unconstrained face recognition, including modules for face detection, association, alignment and face verification. The quantitative performance evaluation is conducted using the IARPA Janus Benchmark A (IJB-A), the JANUS Challenge Set 2 (JANUS CS2), and the LFW dataset. The IJB-A dataset includes real-world unconstrained faces of 500 subjects with significant pose and illumination variations which are much harder than the Labeled Faces in the Wild (LFW) and Youtube Face (YTF) datasets. JANUS CS2 is the extended version of IJB-A which contains not only all the images/frames of IJB-A but also includes the original videos. Some open issues regarding DCNNs for face verification problems are then discussed.

The aim of this study is to study the clinical features and diagnostic performance of IgG4 in Chinese populations with IgG4-related diseases (IgG4-RDs).The medical records of 2901 adult subjects who underwent serum IgG4 level tests conducted between December 2007 and May 2014 were reviewed.Serum concentrations of IgG4 were measured in 2901 cases, including 161 (5.6%) patients with IgG4-RD and 2740 (94.4%) patients without IgG4-RD (non-IgG4-RD group). The mean age of the IgG4-RD patients was 58.4 ± 16.1 years (range: 21–87), and 48 (29.8%) were women. The mean serum IgG4 level was significantly much higher in IgG4-RD patients than in non-IgG4-RD (1062.6 vs 104.3 mg/dL, P < 0.001) participants. For IgG4 >135 mg/dL, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), likelihood ratio (LR)+, and LR− were 86%, 77%, 18%, 99%, 3.70, and 0.19, respectively. When the upper limit of normal was doubled for an IgG4 >270 mg/dL, the corresponding data were 75%, 94%, 43%, 98%, 12.79, and 0.26, respectively. For IgG4 >405 mg/dL (tripling the upper limit of normal), the corresponding data were 62%, 98%, 68%, 98%, 37.00, and 0.39, respectively. When calculated according to the manufacturer's package insert cutoff (>201 mg/dL) for the diagnosis of IgG4-RD, the corresponding sensitivity, specificity, PPV, NPV, LR+, and LR− were 80%, 89%, 29%, 99%, 7.00, and 0.23, respectively. For IgG4 >402 mg/dL (>2× the upper limit of the normal range), the corresponding data were 62%, 98%, 68%, 98%, 36.21, and 0.39, respectively. For IgG4 >603 mg/dL (>3× the upper limit of the normal range), the corresponding data were 50%, 99%, 84%, 97%, 90.77 and 0.51, respectively. The optimal cutoff value of serum IgG4 (measured by nephelometry using a Siemens BN ProSpec instrument and Siemens reagent) for the diagnosis of IgG4-RD was 248 mg/dL, the sensitivity and specificity were 77.6% and 92.8%, respectively.The present study demonstrated that 2 or 3 times the upper limit of the manufacturer's reference range of the IgG4 level was a useful marker for the diagnosis of various types of IgG4-RD and the optimal cutoff level was 248 mg/dL.

Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems. This has been made possible due to the availability of large annotated datasets, a better understanding of the non-linear mapping between input images and class labels as well as the affordability of GPUs. In this paper, we present the design details of a deep learning system for unconstrained face recognition, including modules for face detection, association, alignment and face verification. The quantitative performance evaluation is conducted using the IARPA Janus Benchmark A (IJB-A), the JANUS Challenge Set 2 (JANUS CS2), and the LFW dataset. The IJB-A dataset includes real-world unconstrained faces of 500 subjects with significant pose and illumination variations which are much harder than the Labeled Faces in the Wild (LFW) and Youtube Face (YTF) datasets. JANUS CS2 is the extended version of IJB-A which contains not only all the images/frames of IJB-A but also includes the original videos. Some open issues regarding DCNNs for face verification problems are then discussed.

The m-BWAP is a good predictor for weaning and extubation outcome in patients requiring LTMV for longer than 21 days.

Endometriosis, defined by the presence of viable extrauterine endometrial glands and stroma, can grow or bleed cyclically, and possesses characteristics including a destructive, invasive, and metastatic nature. Since endometriosis may result in pelvic inflammation, adhesion, chronic pain, and infertility, and can progress to biologically malignant tumors, it is a long-term major health issue in women of reproductive age. In this review, we analyze the Taiwan domestic research addressing associations between endometriosis and other diseases. Concerning malignant tumors, we identified four studies on the links between endometriosis and ovarian cancer, one on breast cancer, two on endometrial cancer, one on colorectal cancer, and one on other malignancies, as well as one on associations between endometriosis and irritable bowel syndrome, one on links with migraine headache, three on links with pelvic inflammatory diseases, four on links with infertility, four on links with obesity, four on links with chronic liver disease, four on links with rheumatoid arthritis, four on links with chronic renal disease, five on links with diabetes mellitus, and five on links with cardiovascular diseases (hypertension, hyperlipidemia, etc.). The data available to date support that women with endometriosis might be at risk of some chronic illnesses and certain malignancies, although we consider the evidence for some comorbidities to be of low quality, for example, the association between colon cancer and adenomyosis/endometriosis. We still believe that the risk of comorbidity might be higher in women with endometriosis than that we supposed before. More research is needed to determine whether women with endometriosis are really at risk of these comorbidities.

Abstract-Learning a classifier from ambiguously labeled face images is challenging since training images are not always explicitly-labeled. For instance, face images of two persons in a news photo are not explicitly labeled by their names in the caption. We propose a Matrix Completion for Ambiguity Resolution (MCar) method for predicting the actual labels from ambiguously labeled images. This step is followed by learning a standard supervised classifier from the disambiguated labels to classify new images. To prevent the majority labels from dominating the result of MCar, we generalize MCar to a weighted MCar (WMCar) that handles label imbalance. Since WMCar outputs a soft labeling vector of reduced ambiguity for each instance, we can iteratively refine it by feeding it as the input to WMCar. Nevertheless, such an iterative implementation can be affected by the noisy soft labeling vectors, and thus the performance may degrade. Our proposed Iterative Candidate Elimination (ICE) procedure makes the iterative ambiguity resolution possible by gradually eliminating a portion of least likely candidates in ambiguously labeled face. We further extend MCar to incorporate the labeling constraints between instances when such prior knowledge is available. Compared to existing methods, our approach demonstrates improvement on several ambiguously labeled datasets.