The tendency to perceive the identity of the left half of a centrally viewed face more strongly than that of the right half is associated with visual processing of faces in the right hemisphere (RH). Here we investigate conditions under which this wellknown left visual field (LVF) half-face advantage fails to occur. Our findings challenge the sufficiency of its explanation as a function of RH specialization for face processing coupled with LVF-RH correspondence. In two experiments we show that the LVF half-face advantage occurs for normal faces and chimeric faces composed of different half-face identities. In a third experiment, we show that face inversion disrupts the LVF half-face advantage. In two additional experiments we show that half-faces viewed in isolation or paired with inverted half-faces fail to show the LVF advantage. Consistent with previous explanations of the LVF half-face advantage, our findings suggest that the LVF half-face advantage reflects RH superiority for processing faces and direct transfer of LVF face information to visual cortex in the RH. Critically, however, our findings also suggest the operation of a third factor, which involves the prioritization of face-processing resources to the LVF, but only when two upright face-halves compete for these resources. We therefore conclude that RH superiority alone does not suffice to explain the LVF advantage in face recognition. We also discuss the implications of our findings for specialized visual processing of faces by the right hemisphere, and we distinguish LVF advantages for faces viewed centrally and peripherally in divided field studies.
Standard Form 298 (Rev. 8-98)Prescribed by ANSI Std. Z39.18Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing this collection of information. Approved for public release; distribution is unlimited. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) SPONSOR / MONITOR'S ACRONYM(S) 9. SPONSORING / MONITORING AGENCY NAME(S) AND ADDRESS(ES) SPONSOR / MONITOR'S REPORT NUMBER(S)Computer processing and image analysis technologies have improved significantly to allow the recent development of effective video-based fire detection systems. Currently, smoke detection algorithms are the most mature. Typically, these systems are being designed and used in large facilities, outdoor locations, and tunnels. However, the technologies are also expected, with some modifications, to be effective in smaller, cluttered compartments found on ships. With the move to use onboard video surveillance, there are advantages in using the video images for other functions, such as fire detection. The video-based recognition technology also has future potential for personnel tracking, flooding detection, and physical damage assessment onboard ship as more event recognition algorithms are developed. This work represents the initial evaluation of video-based detection technologies for improved situations awareness and damage control assessment onboard Navy ships. The test results indicate that the video-based detection systems using smoke alarm algorithms can provide comparable to better fire detection than point-type smoke detectors. Damage control; Fire detections; Machine vision UnclassifiedUnclassified Unclassified UL 54
Crop management has a significant impact on crop yield, economic profit, and the environment. Although management guidelines exist, finding the optimal management practices is challenging. Previous work used reinforcement learning (RL) and crop simulators to solve the problem, but the trained policies either have limited performance or are not deployable in the real world. In this paper, we present an intelligent crop management system that optimizes nitrogen fertilization and irrigation simultaneously via RL, imitation learning (IL), and crop simulations using the Decision Support System for Agrotechnology Transfer (DSSAT). We first use deep RL, in particular, deep Q-network, to train management policies that require a large number of state variables from the simulator as observations (denoted as full observation). We then invoke IL to train management policies that only need a few state variables that can be easily obtained or measured in the real world (denoted as partial observation) by mimicking the actions of the RL policies trained under full observation. Simulation experiments using the maize crop in Florida (US) and Zaragoza (Spain) demonstrate that the trained policies from both RL and IL techniques achieved more than 45\% improvement in economic profit while causing less environmental impact compared with a baseline method. Most importantly, the IL-trained management policies are directly deployable in the real world as they use readily available information.
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