Tag2Pix: Line Art Colorization Using Text Tag With SECat and Changing Loss

Line art colorization is expensive and challenging to automate. A GAN approach is proposed, called Tag2Pix, of line art colorization which takes as input a grayscale line art and color tag information and produces a quality colored image. First, we present the Tag2Pix line art colorization dataset. A generator network is proposed which consists of convolutional layers to transform the input line art, a pre-trained semantic extraction network, and an encoder for input color information. The discriminator is based on an auxiliary classifier GAN to classify the tag information as well as genuineness. In addition, we propose a novel network structure called SECat, which makes the generator properly colorize even small features such as eyes, and also suggest a novel two-step training method where the generator and discriminator first learn the notion of object and shape and then, based on the learned notion, learn colorization, such as where and how to place which color. We present both quantitative and qualitative evaluations which prove the effectiveness of the proposed method.Comment: Accepted to ICCV 201

A Static Analyzer for Detecting Tensor Shape Errors in Deep Neural Network Training Code

We present an automatic static analyzer PyTea that detects tensor-shape errors in PyTorch code. The tensor-shape error is critical in the deep neural net code; much of the training cost and intermediate results are to be lost once a tensor shape mismatch occurs in the midst of the training phase. Given the input PyTorch source, PyTea statically traces every possible execution path, collects tensor shape constraints required by the tensor operation sequence of the path, and decides if the constraints are unsatisfiable (hence a shape error can occur). PyTea's scalability and precision hinges on the characteristics of real-world PyTorch applications: the number of execution paths after PyTea's conservative pruning rarely explodes and loops are simple enough to be circumscribed by our symbolic abstraction. We tested PyTea against the projects in the official PyTorch repository and some tensor-error code questioned in the StackOverflow. PyTea successfully detects tensor shape errors in these codes, each within a few seconds.N

Performance on the Benton Visual Retention Test in an educationally diverse elderly population

In this study, we investigated the effects of demographic variables on the performances of Administrations A and C of the Benton Visual Retention Test (BVRT) in a geriatric population with a wide range of educational achievement. We administered the test to 554 nondemented elders aged 60-90 years with an educational history of from zero to 25 years. Age and education significantly influenced Administrations A and C, although gender had no main effect. We observed significant Education x Gender interactions for Administrations A and C, Age x Gender interactions for Administration A, and Age x Education interactions for Administration C. Our results suggest that both nonverbal memory and constructional ability are influenced by age and education. Although there is no overall gender effect, men seem to outperform women in a poorly educated (for Administrations A and C) or relatively older (for Administration A) elderly population

Development of a Korean version of the behavior rating scale for dementia (BRSD-K)

OBJECTIVE: The purpose of this study was to develop a Korean version of the behavior rating scale for dementia (BRSD-K) for evaluating behavioral and psychological symptoms of dementia. METHODS: The BRSD-K was administered to the informants of 268 subjects with dementia. Internal, inter-rater and test-retest reliabilities were tested. To evaluate construct validity, exploratory factor analysis was performed. To evaluate concurrent validity, Pearson correlation coefficients between BRSD-K scores and the corresponding scores of the Korean version of the neuropsychiatric inventory (NPI-K) were calculated. RESULTS: BRSD-K demonstrated substantially high levels of reliabilities. Factor analysis identified seven factors, i.e. depressive symptoms, irritability/aggression, psychotic symptoms, behavioral dysregulations, sleep disturbance, inertia, and appetite. Correlations between BRSD-K and corresponding NPI-K scores were statistically significant (p < 0.05). CONCLUSIONS: BRSD-K was found to be a reliable and valid instrument for evaluating BPSD

Development of a screening algorithm for Alzheimer's disease using categorical verbal fluency.

We developed a weighted composite score of the categorical verbal fluency test (CVFT) that can more easily and widely screen Alzheimer's disease (AD) than the mini-mental status examination (MMSE). We administered the CVFT using animal category and MMSE to 423 community-dwelling mild probable AD patients and their age- and gender-matched cognitively normal controls. To enhance the diagnostic accuracy for AD of the CVFT, we obtained a weighted composite score from subindex scores of the CVFT using a logistic regression model: logit (case)  = 1.160+0.474× gender +0.003× age +0.226× education level - 0.089× first-half score - 0.516× switching score -0.303× clustering score +0.534× perseveration score. The area under the receiver operating curve (AUC) for AD of this composite score AD was 0.903 (95% CI = 0.883 - 0.923), and was larger than that of the age-, gender- and education-adjusted total score of the CVFT (p<0.001). In 100 bootstrapped re-samples, the composite score consistently showed better diagnostic accuracy, sensitivity and specificity for AD than the total score. Although AUC for AD of the CVFT composite score was slightly smaller than that of the MMSE (0.930, p = 0.006), the CVFT composite score may be a good alternative to the MMSE for screening AD since it is much briefer, cheaper, and more easily applicable over phone or internet than the MMSE

Bootstrap validation^a of the diagnostic accuracy for Alzheimer' disease of the Mini Mental Status Examination (MMSE), Categorical Fluency Test total score (CVFT-T) and composite score (CVFT-C).

<p>^a 100 runs; in each run, 60% samples were randomly selected for validations using bootstrap sampling estimation.</p><p>AUC, area under the receiver operator curver; SE, standard error; 95% CI, 95% confidence interval.</p