Semi-device-independent characterization of quantum measurements under a minimum overlap assumption

Recently, a novel framework for semi-device-independent quantum prepare-and-measure protocols has been proposed, based on the assumption of a limited distinguishability between the prepared quantum states. Here, we discuss the problem of characterizing an unknown quantum measurement device in this setting. We present several methods to attack this problem. Considering the simplest scenario of two preparations with lower bounded overlap, we show that genuine 3-outcome POVMs can be certified, even in the presence of noise. Moreover, we show that the optimal POVM for performing unambiguous state discrimination can be self-tested.Comment: 9 pages,7 figure

Carbon monoxide in an extremely metal-poor galaxy

Extremely metal-poor galaxies with metallicity below 10% of the solar value in the local universe are the best analogues to investigating the interstellar medium at a quasi-primitive environment in the early universe. In spite of the ongoing formation of stars in these galaxies, the presence of molecular gas (which is known to provide the material reservoir for star formation in galaxies, such as our Milky Way) remains unclear. Here, we report the detection of carbon monoxide (CO), the primary tracer of molecular gas, in a galaxy with 7% solar metallicity, with additional detections in two galaxies at higher metallicities. Such detections offer direct evidence for the existence of molecular gas in these galaxies that contain few metals. Using archived infrared data, it is shown that the molecular gas mass per CO luminosity at extremely low metallicity is approximately one-thousand times the Milky Way value.Comment: 12 pages, 3 figures, 1 table. Supplementary data at http://www.nature.com/article-assets/npg/ncomms/2016/161209/ncomms13789/extref/ncomms13789-s1.pd

Angiogenic inhibitors delivered by the type III secretion system of tumor-targeting Salmonella typhimurium safely shrink tumors in mice

published_or_final_versio

A method for measuring rotation of a thermal carbon nanomotor using centrifugal effect

A thermal nanomotor is relatively easy to fabricate and regulate as it contains just a few or even no accessory devices. Since the double-wall carbon nanotube (CNT)-based rotary nanomotor was established in a thermostat, assessment of the rotation of the rotor (inner tube) in the stator (outer tube) of the nanomotor has been critical, but remains challenging due to two factors: the small size of the rotor (only a few nanometers) and the high rotational frequency (»1 GHz). To measure the rotation of the nanomotor, in the present study, a probe test method is proposed. Briefly, the rotor is connected to an end-tube (CNT) through a graphene (GN) nanoribbon. As the CNT-probe is on the trajectory of the end-tube which rotates with the rotor, it will collide with the end-tube. The sharp fluctuation indicating the probe tip deflection can be observed and recorded. As a curly GN by hydrogenation is adopted for connecting the rotor and the end-tube, collision between the end-tube and the probe tip occurs only when the centrifugal force is higher than a threshold which can be considered as the rotational frequency of the rotor being measured by the present method.The authors are grateful for financial support from the National Natural-Science-Foundation of China (Grant No. 11372100) and the Australian Research Council (Grant No. DP140103137)

A UPnP-based Decentralized Service Discovery Improved Algorithm

The current UPnP service discovery algorithm in the presence of the service can cause severe drops in the digital home network. The reason is that the root devices instantly send delay sending response messages and randomly selected independent response message congestion through simulation analysis. To solve these problems, an improved UPnP service discovery algorithm was given. Considering the length of the message and the bandwidth of the router, derived by testing the router the packet loss rate can be reduce

Cerebral hemodynamic characteristics of acute mountain sickness upon acute high-altitude exposure at 3,700 m in young Chinese men.

PURPOSE: We aimed at identifying the cerebral hemodynamic characteristics of acute mountain sickness (AMS). METHODS: Transcranial Doppler (TCD) sonography examinations were performed between 18 and 24 h after arrival at 3,700 m via plane from 500 m (n = 454). A subgroup of 151 subjects received TCD examinations at both altitudes. RESULTS: The velocities of the middle cerebral artery, vertebral artery (VA) and basilar artery (BA) increased while the pulsatility indexes (PIs) and resistance indexes (RIs) decreased significantly (all p < 0.05). Velocities of BA were higher in AMS (AMS+) individuals when compared with non-AMS (AMS-) subjects (systolic velocity: 66 ± 12 vs. 69 ± 15 cm/s, diastolic velocity: 29 ± 7 vs. 31 ± 8 cm/s and mean velocity, 42 ± 9 vs. 44 ± 10 cm/s). AMS was characterized by higher diastolic velocity [V d_VA (26 ± 4 vs. 25 ± 4, p = 0.013)] with lower PI and RI (both p = 0.004) in VA. Furthermore, the asymmetry index (AI) of VAs was significantly lower in the AMS + group [-5.7 % (21.0 %) vs. -2.5 % (17.8 %), p = 0.016]. The AMS score was closely correlated with the hemodynamic parameters of BA and the V d_VA, PI, RI and AI of VA. CONCLUSION: AMS is associated with alterations in cerebral hemodynamics in the posterior circulation rather than the anterior one, and is characterized by higher blood velocity with lower resistance. In addition, the asymmetry of VAs may be involved in AMS

Refining Short-Term Power Load Forecasting: An Optimized Model with Long Short-Term Memory Network

Short-term power load forecasting involves the stable operation and optimal scheduling of the power system. Accurate load forecasting can improve the safety and economy of the power grid. Therefore, how to predict power load quickly and accurately has become one of the urgent problems to be solved. Based on the optimization parameter selection and data preprocessing of the improved Long Short-Term Memory Network, the study first integrated particle swarm optimization algorithm to achieve parameter optimization. Then, combined with convolutional neural network, the power load data were processed to optimize the data and reduce noise, thereby enhancing model performance. Finally, simulation experiments were conducted. The PSO-CNN-LSTM model was tested on the GEFC dataset and demonstrated stability of up to 90%. This was 22% higher than the competing CNN-LSTM model and at least 30% higher than the LSTM model. The PSO-CNN-LSTM model was trained with a step size of 1.9×10^4, the relative mean square error was 0.2345×10^-4. However, when the CNN-LSTM and LSTM models were trained for more than 2.0×10^4 steps, they still did not achieve the target effect. In addition, the fitting error of the PSOCNN-LSTM model in the GEFC dataset was less than 1.0×10^-7. In power load forecasting, the PSOCNN- LSTM model\u27s predicted results had an average absolute error of less than 1.0% when compared to actual data. This was an improvement of at least 0.8% compared to the average absolute error of the CNNLSTM prediction model. These experiments confirmed that the prediction model that combined two methods had further improved the speed and accuracy of power load prediction compared to traditional prediction models, providing more guarantees for safe and stable operation of the power system