4,579 research outputs found

    Anticipating Daily Intention using On-Wrist Motion Triggered Sensing

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    Anticipating human intention by observing one's actions has many applications. For instance, picking up a cellphone, then a charger (actions) implies that one wants to charge the cellphone (intention). By anticipating the intention, an intelligent system can guide the user to the closest power outlet. We propose an on-wrist motion triggered sensing system for anticipating daily intentions, where the on-wrist sensors help us to persistently observe one's actions. The core of the system is a novel Recurrent Neural Network (RNN) and Policy Network (PN), where the RNN encodes visual and motion observation to anticipate intention, and the PN parsimoniously triggers the process of visual observation to reduce computation requirement. We jointly trained the whole network using policy gradient and cross-entropy loss. To evaluate, we collect the first daily "intention" dataset consisting of 2379 videos with 34 intentions and 164 unique action sequences. Our method achieves 92.68%, 90.85%, 97.56% accuracy on three users while processing only 29% of the visual observation on average

    Effects of Symmetry Energy in the Reaction 40Ca+124Sn at 140 MeV/nucleon

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    The density-dependent symmetry energy is a hot topic in nuclear physics. Many laboratories over the world are planning to perform related experiments to probe the symmetry energy. Based on the semiclassical Boltzmann-Uehling-Uhlenbeck (BUU) transport model, we study the effects of nuclear symmetry energy in the central reaction 40Ca+124Sn at 140MeV/nucleon in the laboratory system. It is found that the rapidity distribution of free nucleon's neutron-to-proton ratio is sensitive to the symmetry energy, especially at large rapidities. The free neutron-to-proton ratios at small or large rapidities may reflect high or low density behavior of nuclear symmetry energy. To probe the density dependence of nuclear symmetry energy, it is better to give the kinetic distribution and the rapidity distribution of emitted nucleons at the same time.Comment: 4 pages, 6 figures. arXiv admin note: text overlap with arXiv:1204.085

    Innovation Novelty and Firm Value: Deep Learning based Text Understanding

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    Innovation is widely acknowledged as a key driver of firm performance, with patents serving as unique indicators of a company’s technological advancements. This study aims to investigate the impact of textual novelty within patents on firm performance, focusing specifically on biotechnology startups listed on the Nasdaq. Utilizing deep learning-based approaches, we construct measures for semantic originality in patent texts. Through panel vector autoregressive (VAR) analysis, our empirical findings demonstrate a positive correlation between textual novelty and abnormal stock returns. Further, impulse response function analysis indicates that the impact of textual novelty peaks approximately one week after patent issuance and gradually diminishes within a month. These insights offer valuable contributions to both the theoretical understanding and practical application of innovation management and strategic planning

    Immigrated urn models - asymptotic properties and applications

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    Urn models have been widely studied and applied in both scientific and social science disciplines. In clinical studies, the adoption of urn models in treatment allocation schemes has been proved to be beneficial to both researchers, by providing more efficient clinical trials, and patients, by increasing the likelihood of receiving the better treatment. In this paper, we propose a new and general class of immigrated urn (IMU) models that incorporates the immigration mechanism into the urn process. Theoretical properties are developed and the advantages of the IMU models are discussed. In general, the IMU models have smaller variabilities than the classical urn models, yielding more powerful statistical inferences in applications. Illustrative examples are presented to demonstrate the wide applicability of the IMU models. The proposed IMU framework, including many popular classical urn models, not only offers a unify perspective for us to comprehend the urn process, but also enables us to generate several novel urn models with desirable properties

    Increased epithelial stem cell traits in advanced endometrial endometrioid carcinoma

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    <p>Abstract</p> <p>Background</p> <p>It has been recognized cancer cells acquire characters reminiscent of those of normal stem cells, and the degree of stem cell gene expression correlates with patient prognosis. Lgr5(+) or CD133(+) epithelial stem cells (EpiSCs) have recently been identified and these cells are susceptible to neoplastic transformation. It is unclear, however, whether genes enriched in EpiSCs also contribute in tumor malignancy. Endometrial endometrioid carcinoma (EEC) is a dominant type of the endometrial cancers and is still among the most common female cancers. Clinically endometrial carcinoma is classified into 4 FIGO stages by the degree of tumor invasion and metastasis, and the survival rate is low in patients with higher stages of tumors. Identifying genes shared between advanced tumors and stem cells will not only unmask the mechanisms of tumor malignancy but also provide novel therapeutic targets.</p> <p>Results</p> <p>To identify EpiSC genes in late (stages III-IV) EECs, a molecular signature distinguishing early (stages I-II) and late EECs was first identified to delineate late EECs at the genomics level. ERBB2 and CCR1 were genes activated in late EECs, while APBA2 (MINT2) and CDK inhibitor p16 tumor suppressors in early EECs. MAPK pathway was significantly up in late EECs, indicating drugs targeting this canonical pathway might be useful for treating advanced EECs. A six-gene mini-signature was further identified to differentiate early from advanced EECs in both the training and testing datasets. Advanced, invasive EECs possessed a clear EpiSC gene expression pattern, explaining partly why these tumors are more malignant.</p> <p>Conclusions</p> <p>Our work provides new insights into the pathogenesis of EECs and reveals a previously unknown link between adult stem cells and the histopathological traits of EECs. Shared EpiSC genes in late EECs may contribute to the stem cell-like phenotypes shown by advanced tumors and hold the potential of being candidate therapeutic targets and novel prognosis biomarkers.</p

    Accelerated induction of apoptosis in insect cells by baculovirus-expressed SARS-CoV membrane protein

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    AbstractIt has been shown that severe acute respiratory syndrome-associated coronavirus (SARS-CoV) 3a and 7a proteins, but not membrane (M) protein, induce apoptosis in mammalian cells. Upon expression of SARS-CoV M protein using the baculovirus/insect cell expression system, however, we found that the expressed M protein triggered accelerated apoptosis in insect cells, as characterized by rapid cell death, elevated cytotoxicity, cell shrinkage, nuclear condensation and DNA fragmentation. Conversely, the M protein expressed in mammalian cells did not induce apoptosis. This is the first report describing the induction of apoptosis by SARS-CoV M protein in animal cells and possible implications are discussed
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