Unifying and Merging Well-trained Deep Neural Networks for Inference Stage

We propose a novel method to merge convolutional neural-nets for the inference stage. Given two well-trained networks that may have different architectures that handle different tasks, our method aligns the layers of the original networks and merges them into a unified model by sharing the representative codes of weights. The shared weights are further re-trained to fine-tune the performance of the merged model. The proposed method effectively produces a compact model that may run original tasks simultaneously on resource-limited devices. As it preserves the general architectures and leverages the co-used weights of well-trained networks, a substantial training overhead can be reduced to shorten the system development time. Experimental results demonstrate a satisfactory performance and validate the effectiveness of the method.Comment: To appear in the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, 2018. (IJCAI-ECAI 2018

Flow-based Intrinsic Curiosity Module

In this paper, we focus on a prediction-based novelty estimation strategy upon the deep reinforcement learning (DRL) framework, and present a flow-based intrinsic curiosity module (FICM) to exploit the prediction errors from optical flow estimation as exploration bonuses. We propose the concept of leveraging motion features captured between consecutive observations to evaluate the novelty of observations in an environment. FICM encourages a DRL agent to explore observations with unfamiliar motion features, and requires only two consecutive frames to obtain sufficient information when estimating the novelty. We evaluate our method and compare it with a number of existing methods on multiple benchmark environments, including Atari games, Super Mario Bros., and ViZDoom. We demonstrate that FICM is favorable to tasks or environments featuring moving objects, which allow FICM to utilize the motion features between consecutive observations. We further ablatively analyze the encoding efficiency of FICM, and discuss its applicable domains comprehensively.Comment: The SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserved. The link is provided as follows: https://www.ijcai.org/Proceedings/2020/28

Knowledge of Cardiovascular Medications in a Culturally Diverse Elderly Community: Health Assessment Outcomes by Nursing Students

Lack of knowledge in medication use appears a major hindrance in managing cardiovascular diseases. The cross-sectional study examined the determinants of knowledge of cardiovascular medications in elderly community, using the survey questionnaire and structured interviews to collect data from 99 culturally-diverse elderly people at independent-living facilities in California. Results indicate that the majority of participants was women (82.8%), living alone with an educational level of high-school or higher. Sixty-six participants took at least one cardiovascular medication, and the average number of cardiovascular medications taken was 2.02 (±1.10). The most frequently used cardiovascular medications were lipid-lowering agents and aspirin. Thirtyeight participants demonstrated a lack of knowledge of cardiovascular medication use. After adjusting for age, gender, education, and living status, it was found that having a BMI higher than 25 (OR: 5.46; 95% CI; 1.12, 26.52), drinking alcohol beverages (OR: 0.075; 95% CI: 0.01, 0.83), and having a history of ever-smoking (OR: 54.90; 95% CI: 4.39, 686.29) were statistically significant, independent predictors of a lack of knowledge about cardiovascular medications

Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution

Implicit neural representation has recently shown a promising ability in representing images with arbitrary resolutions. In this paper, we present a Local Implicit Transformer (LIT), which integrates the attention mechanism and frequency encoding technique into a local implicit image function. We design a cross-scale local attention block to effectively aggregate local features. To further improve representative power, we propose a Cascaded LIT (CLIT) that exploits multi-scale features, along with a cumulative training strategy that gradually increases the upsampling scales during training. We have conducted extensive experiments to validate the effectiveness of these components and analyze various training strategies. The qualitative and quantitative results demonstrate that LIT and CLIT achieve favorable results and outperform the prior works in arbitrary super-resolution tasks

A Parametric Study of Piled Raft Foundation in Clay Subjected to Concentrated Loading

The use of piled raft foundation in building and infrastructure constructions is increasingly popular because of its effectiveness in reducing overall and differential settlements. Parameters influencing the performance of the piled raft foundation need to be comprehended in order to optimize the design of the piled raft system. Most of the current available literature focused on the piled raft foundation subjected to a uniform distributed load in sandy material.  This parametric study aims to provide insights into the performance of the piled raft foundations subjected to concentrated loading in clay. A series of 2D finite element analyses were performed to investigate the influencing parameters affecting the load distribution and settlement behaviour of the piled raft. The results suggested that increases in both pile length and raft thickness, as well as a decrease in pile spacing would reduce the differential settlement of the piled raft. Comparatively, raft thickness was the most significant controlling parameter affecting the differential settlement. The study also revealed the importance of placing the pile nearer to the location of concentrated load as it would yield a more uniform load distribution, and hence a lower differential settlement

The Decline of Physical Activity with Age in School-Aged Children with Cerebral Palsy: A Single-Center Cross-Sectional Observational Study

Maintaining physical activity is important for children with cerebral palsy (CP). This study examined whether age predicted habitual physical activity (HPA) or cardiorespiratory fitness (CRF) in school-aged children with CP and clarified the relationship between HPA and CRF. We utilized cross-sectional data from 39 children with CP (18 girls and 21 boys; mean age 7.44 years; mean body weight 24.76 kg; mean body mass index 15.97 kg/m2; hemiplegic or diplegic CP). The participants wore an accelerometer (ActiGraph) for five days to measure HPA, physical activity energy expenditure (kcal/kg/d), sedentary physical activity (%SPA), light physical activity, moderate-to-vigorous physical activity (%MVPA), and activity counts (counts/min). Participants underwent cardiopulmonary exercise tests on a treadmill using a modified Naughton protocol. Linear regression and correlation analyses were performed. p-value (two-tailed) \u3c 0.05 was considered statistically significant. Age was positively associated with SPA. MVPA negatively correlated with resting heart rate (HR), and activity counts were negatively correlated with resting HR. In conclusion, our study found strong evidence of a negative association between HPA and age in school-aged children with CP. It highlights the importance of creating and improving recreational opportunities that promote physical activity in all children with CP, regardless of whether they are considered therapeutic

The Effectiveness of Moxibustion: An Overview During 10 Years

Moxibustion has been used to treat various types of disease. However, there is still insufficient evidence regarding its effectiveness. This study was performed to summarize and evaluate the effectiveness of moxibustion. A search was performed for all randomized controlled trials in PubMed between January 1998 and July 2008 with no language restriction. The results yielded 47 trials in which six moxibustion types were applied to 36 diseases ranging from breech presentation to digestive disorders. Moxibustion was compared to three types of control group: general care, Oriental medical therapies or waiting list. Moxibustion was superior to the control in 14 out of 54 control groups in 46 studies. There were no significant differences among groups in 7 studies, and the outcome direction was not determined in 33 studies. Seven studies were included in a meta-analysis. Moxibustion was more effective than medication in two ulcerative colitis studies (relative risk (95% CI), 2.20 (1.37, 3.52), P = .001, I2 = 0%). Overall, our results did not support the effectiveness of moxibustion in specific diseases due to the limited number and low quality of the studies and inadequate use of controls. In order to provide appropriate evidence regarding the effectiveness of moxibustion, more rigorous clinical trials using appropriate controls are warranted