The power gating technique is an effective way to reduce the high static power consumption in a network-on-Chip (NoC). However, with notable wakeup delay, the power gating technique incurs significant packet latency i...
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Many new physics models, including versions of supersymmetry characterized by R-parity violation (RPV), compressed mass spectra, long decay chains, or additional hidden sectors, predict the production of events with t...
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Many new physics models, including versions of supersymmetry characterized by R-parity violation (RPV), compressed mass spectra, long decay chains, or additional hidden sectors, predict the production of events with top quarks, low missing transverse momentum, and many additional quarks or gluons. The results of a search for new physics in events with two top quarks and additional jets are reported. The search is performed using events with at least seven jets and exactly one electron or muon. No requirement on missing transverse momentum is imposed. The study is based on a sample of proton-proton collisions at s=13TeV corresponding to 137 fb−1 of integrated luminosity collected with the CMS detector at the LHC in 2016–2018. The data are used to determine best fit values and upper limits on the cross section for pair production of top squarks in scenarios of RPV and stealth supersymmetry. Top squark masses up to 670 (870) GeV are excluded at 95% confidence level for the RPV (stealth) scenario, and the maximum observed local signal significance is 2.8 standard deviations for the RPV scenario with top squark mass of 400 GeV.
In order to avoid the data loss in cloud storage, some users prefer to store multiple replicas on the cloud server. Multiple-Replica Provable data Possession (MR-PDP) schemes are proposed to check the integrity of rem...
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A search is performed for narrow resonances decaying to final states of two jets, with at least one jet originating from a b quark, in proton-proton collisions at √s = 13 TeV. The data set corresponds to an integrate...
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The online video streaming service is of huge market values with billions of worldwide users. For online video providers, e.g., Net-flix, Youku, the crucial question is how to understand users' view behaviors and ...
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Production cross sections of Υ(1S), Υ(2S), and Υ(3S) states decaying into µ+µ− in proton-lead (pPb) collisions are reported using data collected by the CMS experiment at √sNN = 5.02 TeV. A comparison is ...
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Demographic attributes are important resources for market analysis, which are widely used to characterize different types of users. However, such signals are only available for a small fraction of users due to the dif...
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PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive w...
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PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multi-center setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 hours was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n=9 teams), for instrument presence detection between 38.5% and 63.8% (n=8 teams), but for action recognition only between 21.8% and 23.3% (n=5 teams). The average absolute error for skill assessment was 0.78 (n=1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but are not solved yet, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost impo
Large High Altitude Air Shower Observatory (LHAASO) is located at Mt. Haizi (4410 m a.s.l., 600 g/cm2, 29° 21' 27.56" N, 100° 08' 19.66" E) in Daocheng, Sichuan province, P.R. China. LHAASO...
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With stable development of container transportation in shipping industry, the transit shipment from road to railway of inland ports plays a significant role in multimodal transport. This paper presents the method of i...
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