139 research outputs found
Toward a Brain-Inspired System: Deep Recurrent Reinforcement Learning for a Simulated Self-Driving Agent
An effective way to achieve intelligence is to simulate various intelligent behaviors in the human brain. In recent years, bio-inspired learning methods have emerged, and they are different from the classical mathematical programming principle. From the perspective of brain inspiration, reinforcement learning has gained additional interest in solving decision-making tasks as increasing neuroscientific research demonstrates that significant links exist between reinforcement learning and specific neural substrates. Because of the tremendous research that focuses on human brains and reinforcement learning, scientists have investigated how robots can autonomously tackle complex tasks in the form of making a self-driving agent control in a human-like way. In this study, we propose an end-to-end architecture using novel deep-Q-network architecture in conjunction with a recurrence to resolve the problem in the field of simulated self-driving. The main contribution of this study is that we trained the driving agent using a brain-inspired trial-and-error technique, which was in line with the real world situation. Besides, there are three innovations in the proposed learning network: raw screen outputs are the only information which the driving agent can rely on, a weighted layer that enhances the differences of the lengthy episode, and a modified replay mechanism that overcomes the problem of sparsity and accelerates learning. The proposed network was trained and tested under a third-party OpenAI Gym environment. After training for several episodes, the resulting driving agent performed advanced behaviors in the given scene. We hope that in the future, the proposed brain-inspired learning system would inspire practicable self-driving control solutions
Chinese Character Recognition with Radical-Structured Stroke Trees
The flourishing blossom of deep learning has witnessed the rapid development
of Chinese character recognition. However, it remains a great challenge that
the characters for testing may have different distributions from those of the
training dataset. Existing methods based on a single-level representation
(character-level, radical-level, or stroke-level) may be either too sensitive
to distribution changes (e.g., induced by blurring, occlusion, and zero-shot
problems) or too tolerant to one-to-many ambiguities. In this paper, we
represent each Chinese character as a stroke tree, which is organized according
to its radical structures, to fully exploit the merits of both radical and
stroke levels in a decent way. We propose a two-stage decomposition framework,
where a Feature-to-Radical Decoder perceives radical structures and radical
regions, and a Radical-to-Stroke Decoder further predicts the stroke sequences
according to the features of radical regions. The generated radical structures
and stroke sequences are encoded as a Radical-Structured Stroke Tree (RSST),
which is fed to a Tree-to-Character Translator based on the proposed Weighted
Edit Distance to match the closest candidate character in the RSST lexicon. Our
extensive experimental results demonstrate that the proposed method outperforms
the state-of-the-art single-level methods by increasing margins as the
distribution difference becomes more severe in the blurring, occlusion, and
zero-shot scenarios, which indeed validates the robustness of the proposed
method
Bilateral stenting methods for hilar biliary obstructions
OBJECTIVE: There is no consensus regarding the most appropriate methods (i.e., the side-by-side versus the stent-in-stent technique) for placing bilateral stents for malignant hilar biliary obstructions. We aimed to perform a quantitative review of the published data regarding the clinical efficacy of the side-by-side and stentinstent bilateral drainage techniques for hilar biliary obstructions. METHODS: A comprehensive search of several databases was conducted and a fixed-effects or random-effects model was used to pool the data from all of the study end-points. RESULTS: Four clinical trials were identified. A comparison of the side-by-side and stent-in-stent groups revealed no significant differences with respect to the rates of successful placement, successful drainage, early complications, late complications and stent occlusions. There were also no significant inter-group differences in stent patency and patient survival and no publication bias was observed. CONCLUSIONS: The performance of the side-by-side technique appears to be similar to that of the stent-instent technique for bilateral drainage in patients with malignant hilar biliary obstructions
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