This note investigates the output feedback stabilization of networked control systems (NCSs). The sensor-to-controller (S-C) and controller-to-actuator (C-A) random network-induced delays are modeled ...as Markov chains. The focus is on the design of a two-mode-dependent controller that depends on not only the current S-C delay but also the most recent available C-A delay at the controller node. The resulting closed-loop system is transformed to a special discrete-time jump linear system. Then, the sufficient and necessary conditions for the stochastic stability are established. Further, the output feedback controller is designed via the iterative linear matrix inequality (LMI) approach. Simulation examples illustrate the effectiveness of the proposed method.
Distributed secure quantum machine learning （DSQML） enables a classical client with little quantum technology to delegate a remote quantum machine learning to the quantum server with the privacy data ...preserved. Moreover, DSQML can be extended to a more general case that the client does not have enough data, and resorts both the remote quantum server and remote databases to perform the secure machi~ learning. Here we propose a DSQML protocol that the client can classify two-dimensional vectors to dif- ferent clusters, resorting to a remote small-scale photon quantum computation processor. The protocol is secure without leaking any relevant information to the Eve. Any eavesdropper who attempts to intercept and disturb the learning process can be noticed. In principle, this protocol can be used to classify high dimensional vectors and may provide a new viewpoint and application for future ＂big data＂.
With the further development of Internet technology, online learning has become an important way for learners in the digital age. As an important learning strategy, self-regulated learning plays an ...important role in e-learning. Whether learners can succeed in the network learning environment largely depends on their online self-regulating learning ability. This research reviews the theories and models of self-regulated learning, and analyzes the influences of individual factors and external factors on second language self-regulated learning in the online learning environment. And some implications in learning and teaching are given, as well as suggestions for future research.
Catalytic difunctionalization of alkenes has been an ideal strategy to generate structurally complex molecules with diverse substitution patterns. Although both phosphonyl and carboxyl groups are ...valuable functional groups, the simultaneous incorporation of them via catalytic difunctionalization of alkenes, ideally from abundant, inexpensive and easy-to-handle raw materials, has not been realized. Herein, we report the phosphonocarboxylation of alkenes with CO
via visible-light photoredox catalysis. This strategy is sustainable, general and practical, providing facile access to important β-phosphono carboxylic acids, including structurally complex unnatural α-amino acids. Diverse alkenes, including enamides, styrenes, enolsilanes and acrylates, undergo such reactions efficiently under mild reaction conditions. Moreover, this method represents a rare example of redox-neutral difunctionalization of alkenes with H-P(O) compounds, including diaryl- and dialkyl- phosphine oxides and phosphites. Importantly, these transition-metal-free reactions also feature low catalyst loading, high regio- and chemo-selectivities, good functional group tolerance, easy scalability and potential for product derivatization.
Quantum networks play an extremely important role in quantum information science, with application to quantum communication, computation, metrology, and fundamental tests. One of the key challenges ...for implementing a quantum network is to distribute entangled flying qubits to spatially separated nodes, at which quantum interfaces or transducers map the entanglement onto stationary qubits. The stationary qubits at the separated nodes constitute quantum memories realized in matter while the flying qubits constitute quantum channels realized in photons. Dedicated efforts around the world for more than 20 years have resulted in both major theoretical and experimental progress toward entangling quantum nodes and ultimately building a global quantum network. Here, the development of quantum networks and the experimental progress over the past two decades leading to the current state of the art for generating entanglement of quantum nodes based on various physical systems such as single atoms, cold atomic ensembles, trapped ions, diamonds with nitrogen‐vacancy centers, and solid‐state host doped with rare‐earth ions are reviewed. Along the way, the merits are discussed and the potential of each of these systems toward realizing a quantum network is compared.
Quantum networks linking multiple remote quantum nodes consist of quantum memories served as stationary quantum nodes and flying photonic qubits served as quantum channels. This review summarizes and discusses the state of the art and future challenges for constructing quantum networks in various physical systems like single neutral atoms, cold atomic ensembles, trapped ions, NV centers, and rare‐earth‐ion doped solids.
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Quantum secure direct communication (QSDC) attracts much attention for it can transmit secret messages directly without sharing a key. In this article, we propose a one-step QSDC ...protocol, which only requires to distribute polarization-spatial-mode hyperentanglement for one round. In this QSDC protocol, the eavesdropper cannot obtain any message, so that this protocol is unconditionally secure in principle. This protocol is a two-way quantum communication and has high capacity for it can transmit two bits of secret messages with one pair of hyperentanglement. With entanglement fidelities of both polarization and spatial-mode degrees of freedom being 0.98, the maximal communication distance of this one-step QSDC can reach about 216 km. QSDC can also be used to generate the key. In this regard, the key generation rate is estimated about 2.5 times of that in the entanglement-based QKD with the communication distance of 150 km. With the help of future quantum repeaters, this QSDC protocol can provide unconditionally secure communication over arbitrarily long distance.
Too big to fail in light of Gaia Kaplinghat, Manoj; Valli, Mauro; Yu, Hai-Bo
Monthly notices of the Royal Astronomical Society,
11/2019, Letnik:
490, Številka:
1
Journal Article
Recenzirano
Odprti dostop
ABSTRACT
We point out an anticorrelation between the central dark matter (DM) densities of the bright Milky Way dwarf spheroidal galaxies (dSphs) and their orbital pericenter distances inferred from ...Gaia data. The dSphs that have not come close to the Milky Way centre (like Fornax, Carina and Sextans) are less dense in DM than those that have come closer (like Draco and Ursa Minor). The same anticorrelation cannot be inferred for the ultrafaint dSphs due to large scatter, while a trend that dSphs with more extended stellar distributions tend to have lower DM densities emerges with ultrafaints. We discuss how these inferences constrain proposed solutions to the Milky Way’s too-big-to-fail problem and provide new clues to decipher the nature of DM.
The deadly threat that landslide has brought about is drawing more and more attention to analyze the mechanisms of landslides and the relationship between landslides and climate change. Due to the ...limited record of historical landslides in developing countries, relevant research is mostly conducted in developed countries. Owing to the publicly available global long time-series Landsat images, such unbalance can be avoided by proposing a practical landslide detection model, especially in terms of national scale. This paper takes the advantage of google earth engine platform to synthesize the annual Landsat images covering the national scale of Nepal into one image and builds an end-to-end contour-based landslide detection deep learning framework. The framework consists of two parts, one is potential landslide detection using vegetation index and degradation of DEM, the other is exact landslide detection using semantic segmentation deep learning model based on the contour regions extracted from the detected potential landslide. The proposed method is applied to detect landslides of Nepal in the year of 2015 and achieves a satisfactory performance with 65% recall and 55.35% precision. The performance is 44% higher accurate than similarly published works, validating its promising applicability in practical landslide detection for national cases.
•Resist the imbalanced distribution of landslides using contour-based method.•Avoid feature engineering by applying deep learning framework.•Enlarge the practical applicability by evaluation in Nepal.
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