Clinical observations indicate that during critical care at the hospitals, patients sleep positioning and motion affect recovery. Unfortunately, there is no formal medical protocol to record, quantify, and analyze patient motion. There is a small number of clinical studies, which use manual analysis of sleep poses and motion recordings to support medical benefits of patient positioning and motion monitoring. Manual processes are not scalable, are prone to human errors, and strain an already taxed healthcare workforce. This study introduces DECU (Deep Eye-CU): an autonomous mulitmodal multiview system, which addresses these issues by autonomously monitoring healthcare environments and enabling the recording and analysis of patient sleep poses and motion. DECU uses three RGB-D cameras to monitor patient motion in a medical Intensive Care Unit (ICU). The algorithms in DECU estimate pose direction at different temporal resolutions and use keyframes to efficiently represent pose transition dynamics. DECU combines deep features computed from the data with a modified version of Hidden Markov Model to more flexibly model sleep pose duration, analyze pose patterns, and summarize patient motion. Extensive experimental results are presented. The performance of DECU is evaluated in ideal (BC: Bright and Clear occlusion-free) and natural (DO: Dark and Occluded) scenarios at two motion resolutions in a mock-up and a real ICU. The results indicate that deep features allow DECU to match the classification performance of engineered features in BC scenes and increase the accuracy by up to 8 in DO scenes. In addition, the overall pose history summarization tracing accuracy shows an average detection rate of 85 in BC and of 76 in DO scenes. The proposed keyframe estimation algorithm allows DECU to reach an average 78 transition classification accuracy.
There is a large body of research that focuses on recognizing and tracking human motion. The latest developments in deep features and convolutional neural network architectures achieve impressive performance; however, these require large amounts of data @cite_5 , @cite_14 , @cite_13 , and @cite_7 . These methods tackle the recognition of actions performed at the center of the camera plane, except for @cite_3 , which uses static cameras to analyze actions. Method @cite_3 allows actions to not be centered on the plane; however, it requires scenes with good illumination and no occlusions. At its current stage of development the DECU framework cannot collect the large number of samples necessary to train a deep network without disrupting the hospital.
@cite_14 @cite_7 @cite_3 @cite_5 @cite_13
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Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . We investigate the minimization of Newton's functional for the problem of the body of minimal resistance with maximal height @math cite butt in the class of convex developable functions defined in a disc. This class is a natural candidate to find a (non-radial) minimizer in accordance with the results of cite lrp2. We prove that the minimizer in this class has a minimal set in the form of a regular polygon with @math sides centered in the disc, and numerical experiments indicate that the natural number @math is a non-decreasing function of @math . The corresponding functions all achieve a lower value of the functional than the optimal radially symmetric function with the same height @math . A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results. Scholars are increasingly citing electronic web references which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To webcite a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its instructions for authors accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) prospectively before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted citing articles (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues Abstract The periodic Lorentz gas is the dynamical system corresponding to the free motion of a point particle in a periodic system of fixed spherical obstacles of radius r centered at the integer points of the Euclidian plane, assuming all collisions of the particle with the obstacles to be elastic. In this Note, we study this motion on time intervals of order 1 r as r  0 + . To cite this article: E. Caglioti, F. Golse, C. R. Acad. Sci. Paris, Ser. I 346 (2008).