1,265 research outputs found

    Treatment of Sporadic Acute Puerperal Mastitis

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    Objective: The purposes of this study were to compare the efficacy of amoxicillin and cephradine for the treatment of sporadic acute puerperal mastitis (SAPM) and to evaluate the microbiology and clinical parameters of this infection

    A promising material for non-linear optics: Observation of second harmonic generation from 4-[N-(4-carboxypentyl)-N-methylamino]-4′-nitrostilbene- coated substrates

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    Glass coated with a nitro amino stilbene carboxylic acid using the Langmuir-Blodgett technique gave a non-centrosymmetric material exhibiting second harmonic generation, 1.06 to 0.53 μm

    Book Reviews

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    Is radical cystectomy indicated in patients with regional lymphatic metastases?

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    The records of 21 patients who had radical cystectomies for the treatment of locally advanced bladder cancer and were found to have regional lymphatic metastases have been reviewed. Ten of these patients had only one lymph node involved (NI), and 11 patients had metastases in more than one lymph node (N2-3). Four patients with N1 disease and 1 patient with N2-3 disease survived tumor free greater than forty months postoperatively. Radical cystectomy can produce long-term disease free survival in some patients with limited pelvic metastases.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/27385/1/0000416.pd

    Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing

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    Object pushing presents a key non-prehensile manipulation problem that is illustrative of more complex robotic manipulation tasks. While deep reinforcement learning (RL) methods have demonstrated impressive learning capabilities using visual input, a lack of tactile sensing limits their capability for fine and reliable control during manipulation. Here we propose a deep RL approach to object pushing using tactile sensing without visual input, namely tactile pushing. We present a goal-conditioned formulation that allows both model-free and model-based RL to obtain accurate policies for pushing an object to a goal. To achieve real-world performance, we adopt a sim-to-real approach. Our results demonstrate that it is possible to train on a single object and a limited sample of goals to produce precise and reliable policies that can generalize to a variety of unseen objects and pushing scenarios without domain randomization. We experiment with the trained agents in harsh pushing conditions, and show that with significantly more training samples, a model-free policy can outperform a model-based planner, generating shorter and more reliable pushing trajectories despite large disturbances. The simplicity of our training environment and effective real-world performance highlights the value of rich tactile information for fine manipulation. Code and videos are available at https://sites.google.com/view/tactile-rl-pushing/.Comment: Accepted by IEEE Robotics and Automation Letters (RA-L

    Bostonia. Volume 4

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    Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs
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