York University

About: York University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Politics. The organization has 18899 authors who have published 43357 publications receiving 1568560 citations.

Ecological statistics of Gestalt laws for the perceptual organization of contours.

Abstract: Although numerous studies have measured the strength of visual grouping cues for controlled psychophysical stimuli, little is known about the statistical utility of these various cues for natural images. In this study, we conducted experiments in which human participants trace perceived contours in natural images. These contours are automatically mapped to sequences of discrete tangent elements detected in the image. By examining relational properties between pairs of successive tangents on these traced curves, and between randomly selected pairs of tangents, we are able to estimate the likelihood distributions required to construct an optimal Bayesian model for contour grouping. We employed this novel methodology to investigate the inferential power of three classical Gestalt cues for contour grouping: proximity, good continuation, and luminance similarity. The study yielded a number of important results: (1) these cues, when appropriately defined, are approximately uncorrelated, suggesting a simple factorial model for statistical inference; (2) moderate image-to-image variation of the statistics indicates the utility of general probabilistic models for perceptual organization; (3) these cues differ greatly in their inferential power, proximity being by far the most powerful; and (4) statistical modeling of the proximity cue indicates a scale-invariant power law in close agreement with prior psychophysics.

International variation in rates of uptake of preventive options in BRCA1 and BRCA2 mutation carriers.

Abstract: Several options for cancer prevention are available for women with a BRCA1 or BRCA2 mutation, including prophylactic surgery, chemoprevention and screening. The authors report on preventive practices in women with mutations from 9 countries and examine differences in uptake according to country. Women with a BRCA1 or BRCA2 mutation were contacted after receiving their genetic test result and were questioned regarding their preventive practices. Information was recorded on prophylactic mastectomy, prophylactic oophorectomy, use of tamoxifen and screening (MRI and mammography). Two thousand six hundred seventy-seven women with a BRCA1 or BRCA2 mutation from 9 countries were included. The follow-up questionnaire was completed a mean of 3.9 years (range 1.5-10.3 years) after genetic testing. One thousand five hundred thirty-one women (57.2%) had a bilateral prophylactic oophorectomy. Of the 1,383 women without breast cancer, 248 (18.0%) had had a prophylactic bilateral mastectomy. Among those who did not have a prophylactic mastectomy, only 76 women (5.5%) took tamoxifen and 40 women (2.9%) took raloxifene for breast cancer prevention. Approximately one-half of the women at risk for breast cancer had taken no preventive option, relying solely on screening. There were large differences in the uptake of the different preventive options by country of residence. Prophylactic oophorectomy is now generally accepted by women and their physicians as a cancer preventive measure. However, only the minority of women with a BRCA1 or BRCA2 mutation opt for prophylactic mastectomy or take tamoxifen for the prevention of hereditary breast cancer. Approximately one-half of women at risk for breast cancer rely on screening alone.

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Abstract: This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.