On Qi Gong’s Philosophy of Calligraphy Education

Chinese calligrapher Qi Gong has always excelled in calligraphy with great appreciation of aesthetic values and a high level of artistic education. He made important contributions to several fields, especially in the philosophy of calligraphy education. His influence has been unique and profound, which shapes today’s calligraphy education and gives important guidance to the cultivation of calligraphy talents and the construction of the calligraphy discipline.This paper summarized Qi Gong’s expositions on calligraphy and induced his calligraphy educational philosophy, which involves subject construction, learning targets, learning methods, etc. These philosophies will become our latest contemporary calligraphy heritage

Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction

Click-Through Rate prediction is an important task in recommender systems, which aims to estimate the probability of a user to click on a given item. Recently, many deep models have been proposed to learn low-order and high-order feature interactions from original features. However, since useful interactions are always sparse, it is difficult for DNN to learn them effectively under a large number of parameters. In real scenarios, artificial features are able to improve the performance of deep models (such as Wide & Deep Learning), but feature engineering is expensive and requires domain knowledge, making it impractical in different scenarios. Therefore, it is necessary to augment feature space automatically. In this paper, We propose a novel Feature Generation by Convolutional Neural Network (FGCNN) model with two components: Feature Generation and Deep Classifier. Feature Generation leverages the strength of CNN to generate local patterns and recombine them to generate new features. Deep Classifier adopts the structure of IPNN to learn interactions from the augmented feature space. Experimental results on three large-scale datasets show that FGCNN significantly outperforms nine state-of-the-art models. Moreover, when applying some state-of-the-art models as Deep Classifier, better performance is always achieved, showing the great compatibility of our FGCNN model. This work explores a novel direction for CTR predictions: it is quite useful to reduce the learning difficulties of DNN by automatically identifying important features

Pricing finite maturity American style stock loans

Master'sMASTER OF SCIENC

Exploration and Setup of Power Delivery System Attacks

Especially with the rise of AI/ML, graphics processing units (GPUs) are becoming increasingly important in personal and enterprise computing. As a result, GPU hardware and software security has never been more important. This paper will focus on the hardware security of the NVIDIA Jetson Nano’s GPU. Because high clock frequency has been known to induce faults in computer systems, this paper will serve as a guide supplement explaining how to overclock the NVIDIA Jetson Nano’s CPU and GPU. This will provide an attack environment for future security researchers. The viability of future NVIDIA Jetson Nano hardware security research will be addressed. Finally, this report will include a recommendation for a discrete graphics card to purchase, providing another avenue for GPU hardware security research

Associations between diet, cognitive function and dementia risk in UK adults

Cognitive decline and dementia are of increasing concern in aging societies worldwide. Diet, as a modifiable lifestyle factor, represents a target for prevention or limiting progression. However, evidence on associations of cognitive function and dementia with diet remains limited and inconsistent, especially on meat consumption summarized in the systematic review of this project. Cross-sectional associations of dietary factors with one cognitive test (reaction time) and dementia (ascertained via death registers) were conducted in UK Women’s Cohort Study (UKWCS). The results showed that consumption of specific food groups, energy-adjusted nutrient intakes, and adherence to dietary patterns were not statistically associated with reaction time and dementia in the UKWCS. Cross-sectional and longitudinal associations of food consumption, especially meat intakes, with five cognitive tests (visual memory, numeric memory, prospective memory, fluid intelligence, and reaction time) and dementia (ascertained via self-report and linkages to hospital admission data and death registers) were conducted in UK Biobank (UKB). Incident dementia cases occurring within 1-year or 3-year follow-up were excluded due to potential reverse causation, and similar results were observed between the two types of exclusion. The results showed that high consumption of processed meat was associated with increased risks of prevalent and incident dementia; with a non-linear pattern of this association indicated in the UKB. Associations between consumption of other meat types and cognitive performance and dementia risk were not consistent in the UKB. A diet-gene interaction of APOE ε4 allele on dementia risk was explored, and all P values for interaction were not significant. In addition, high consumption of vegetables, fruits, and fish were observed to be associated with poor cognitive performance and increased risk of incident dementia in the UKB although effect sizes were small. This project highlights potentially non-linear associations between meat consumption and dementia risk, which may be independent of APOE ε4 allele carriage. Findings on consumption of vegetables, fruits, and fish were not consistent with the hypotheses proposed of a protective effect in this thesis. However, the effect sizes were relatively small and therefore need to be interpreted with caution and to be confirmed in other studies

Hand gesture recognition in complex background based on convolutional pose machine and fuzzy gaussian mixture models

© 2020, The Author(s). Hand gesture is one of the most intuitive and natural ways for human to communicate with computers, and it has been widely adopted in many human–computer interaction applications. However, it is still a challenging problem when confronted with complex background, illumination variation and occlusion in real-world scenarios. In this paper, a two-stage hand gesture recognition method is proposed to tackle these problems. At the first stage, hand pose estimation is developed to locate the hand keypoints using the convolutional pose machine, which can effectively localize hand keypoints even in a complex background. At the second stage, the Fuzzy Gaussian mixture models (FGMMs) are tailored to reject the nongesture patterns and classify the gestures based on the estimated hand keypoints. Extensive experiments are conducted to evaluate the performance of the proposed method, and the result demonstrates that the proposed algorithm is effective, robust, and satisfactory in real-time scenarios