A quantum-inspired tensor network method for constrained combinatorial optimization problems

Combinatorial optimization is of general interest for both theoretical study and real-world applications. Fast-developing quantum algorithms provide a different perspective on solving combinatorial optimization problems. In this paper, we propose a quantum inspired algorithm for general locally constrained combinatorial optimization problems by encoding the constraints directly into a tensor network state. The optimal solution can be efficiently solved by borrowing the imaginary time evolution from a quantum many-body system. We demonstrate our algorithm with the open-pit mining problem numerically. Our computational results show the effectiveness of this construction and potential applications in further studies for general combinatorial optimization problems

Evaluation Method for Probability of Blowout after the Failure of Offshore Well Killing

249-259With the development of offshore oil industry, the influx and blowout are inevitable. Well control methods have been well researched, but how to recognize the failure of well control earlier and how to evaluate the probability of blowout for taking steps to avoid are imperfect. Based on the two-phase gas-liquid flow, the characteristic of well killing curve before and after killing are analyzed. Then the method for recognizing the failure of well killing is established by the probabilistic and covariance processing method. Then the blowout due to the failure of well killing is studied and the build-up pressure template is established. According to this, three evaluation methods for blowout probability are established, the shut-off pressure, the standing and casing pressure, formation parameters and underbalanced level varying methods. Final, four hardware systems and one evaluation system are recommended for decreasing or avoiding the risk during the failure of well killing

EANT: Distant Supervision for Relation Extraction with Entity Attributes via Negative Training

Distant supervision for relation extraction (DSRE) automatically acquires large-scale annotated data by aligning the corpus with the knowledge base, which dramatically reduces the cost of manual annotation. However, this technique is plagued by noisy data, which seriously affects the model’s performance. In this paper, we introduce negative training to filter them out. Specifically, we train the model with the complementary label based on the idea that “the sentence does not express the target relation”. The trained model can discriminate the noisy data from the training set. In addition, we believe that additional entity attributes (such as description, alias, and types) can provide more information for sentence representation. On this basis, we propose a DSRE model with entity attributes via negative training called EANT. While filtering noisy sentences, EANT also relabels some false negative sentences and converts them into useful training data. Our experimental results on the widely used New York Times dataset show that EANT can significantly improve the relation extraction performance over the state-of-the-art baselines

Adaptive Synchronization for Two Different Stochastic Chaotic Systems with Unknown Parameters via a Sliding Mode Controller

This paper investigates the problem of synchronization for two different stochastic chaotic systems with unknown parameters and uncertain terms. The main work of this paper consists of the following aspects. Firstly, based on the Lyapunov theory in stochastic differential equations and the theory of sliding mode control, we propose a simple sliding surface and discuss the occurrence of the sliding motion. Secondly, we design an adaptive sliding mode controller to realize the asymptotical synchronization in mean squares. Thirdly, we design an adaptive sliding mode controller to realize the almost surely synchronization. Finally, the designed adaptive sliding mode controllers are used to achieve synchronization between two pairs of different stochastic chaos systems (Lorenz-Chen and Chen-Lu) in the presence of the uncertainties and unknown parameters. Numerical simulations are given to demonstrate the robustness and efficiency of the proposed robust adaptive sliding mode controller

Polydimethylsiloxane/Nanodiamond Composite Sponge for Enhanced Mechanical or Wettability Performance

Polydimethylsiloxane (PDMS) is widely utilized in material science, chemical engineering, and environmental science due to its excellent properties. By utilizing fillers, so-called composite materials can be obtained with enhanced mechanical, wettability, or thermal conductivity performance. Here, we present a simple, cost-effective approach to vary either the mechanical properties (Young’s modulus) or surface wettability of bulk PDMS and PDMS sponges simply by adding nanodiamond filler with different surface terminations, either oxidized (oND) or hydrogenated (reduced, rND) nanodiamond. Minuscule amounts of oxidized nanodiamond particles as filler showed to benefit the compressive Young’s modulus of composite sponges with up to a 52% increase in its value, while the wettability of composite sponges was unaffected. In contrast, adding reduced nanodiamond particles to PDMS yielded inclined water contact angles on the PDMS/nanodiamond composite sponges. Finally, we show that the PDMS/rND composites are readily utilized as an absorbent for oil/water separation problems. This signifies that the surface termination of the ND particle has a crucial effect on the performance of the composite

The effects of catalysts on the conversion of organic matter and bio-fuel production in the microwave pyrolysis of sludge at different temperatures

International audienceAdding catalyst could improve the yields and qualities of bio-gas and bio-oil, and realize the oriented production. Results showed that the catalytic gas-production capacities of CaO were higher than those of Fe2O3, and the bio-gas yield at 800°C reached a maximum of 35.1%. Because the polar cracking active sites of CaO reduced the activation energy of the pyrolysis reaction and resulted in high catalytic cracking efficiencies. In addition, the quality of bio-oil produced by CaO was superior to that by Fe2O3, although the bio-oil yield of CaO was relatively weak. The light bio-fuel oriented catalytic pyrolysis could be realized when adding different catalysts. At 800°C, CaO was 45% higher than Fe2O3 in aspect of H2 production while Fe2O3 was 103% higher than CaO in aspect of CH4 production. Therefore, CaO was more suitable for H2 production and Fe2O3 was more suitable for CH4 production

Programmable Structure Control in Cigarlike TiO₂ Nanofibers and UV-Light Photocatalysis Performance of Resultant Fabrics

Novel cigarlike nanofibers with an outer-shell and inner-continuous-pore structure and resultant fabrics have been fabricated by coupling the self-assembly of polystyrene-<i>block</i>-poly­(ethylene oxide) (PS-<i>b</i>-PEO) containing titanium precursors with the electrospinning technique in our previous work [You et al. ACS Appl. Mater. Interfaces 2013, 5, 2278]. In the current work, the structure control in these nanofibers has been investigated in detail using scanning electron microscopy, focused ion beam, and small angle X-ray scattering. Our results indicate that electrospinning conditions, the adopted solvent, the volume fraction of PS-<i>b</i>-PEO block copolymer, and the amount of titanium tetraisopropoxide in the mixture produce significant effects on both outer-shell and inner-continuous structures in the nanofibers. The parameters discussed above make it possible to achieve programmable structure control in the aspect of the diameter, thickness of the outer shell, and inner continuous pore. As a result, both micropores among fibers and nanopores in certain fibers are under their control. Furthermore, the photocatalytic activity of resultant TiO₂ fabrics was investigated by taking the photodegradation of Rhodamine B as an example. The results suggest that the degradation efficiency and rate constant exhibit sensitivity on the structure of nanofibers