Simulating and Detecting the Quantum Spin Hall Effect in Kagom\'{e} Optical Lattice

We propose a model which includes a nearest-neighbor intrinsic spin-orbit coupling and a dimer Hamiltonian in the Kagom\'{e} lattice and promises to host the transition from the quantum spin Hall insulator to the normal insulator. In addition, we design an experimental scheme to simulate and detect this transition in the ultracold atom system. The lattice intrinsic spin-orbit coupling is generated via the laser-induced-gauge-field method. Furthermore, we establish the connection between the spin Chern number and the spin-atomic density which enables us to detect the topological quantum spin Hall insulator directly by the standard density-profile technique used in the atomic systems.Comment: 8 pages, 6 figure

Photocatalytic degradation of AZO dyes by supported TiO2+UV in aqueous solution

The photocatalytic degradation performance of photocatalysts TiO2 supported on 13-X, Na-Y, 4A zeolites with different loading content was evaluated using the photocatalytic oxidation of dyes direct fast scarlet 4BS and acid red 3B in aqueous medium. The results showed that the best reaction dosage of TiO2-zeolite catalysts is about 2 g/l and the photocatalytic kinetics follows first order for all supported catalysts. The photocatalytic activity order of the three series catalysts is 13X type >Y type >4A type. The physical state of titanium dioxide on the supports is evaluated by X-ray photoelectron spectra (XPS), powder X-ray diffraction (XRD), BET, and FTIR. (C) 2000 Elsevier Science Ltd. All rights reserved

Eightfold Fermionic Excitation in a Charge Density Wave Compound

Unconventional quasiparticle excitations in condensed matter systems have become one of the most important research frontiers. Beyond two- and fourfold degenerate Weyl and Dirac fermions, three-, six- and eightfold symmetry protected degeneracies have been predicted however remain challenging to realize in solid state materials. Here, charge density wave compound TaTe4 is proposed to hold eightfold fermionic excitation and Dirac point in energy bands. High quality TaTe4 single crystals are prepared, where the charge density wave is revealed by directly imaging the atomic structure and a pseudogap of about 45 meV on the surface. Shubnikov de-Haas oscillations of TaTe4 are consistent with band structure calculation. Scanning tunneling microscopy reveals atomic step edge states on the surface of TaTe4. This work uncovers that charge density wave is able to induce new topological phases and sheds new light on the novel excitations in condensed matter materials.Comment: Accepted by PRB: https://journals.aps.org/prb/accepted/7907cK4eW0b1ee0b93fd67c1b42942bbb08eafc3

A Quantitative Quality Control Model for Parallel and Distributed Crowdsourcing Tasks

Crowdsourcing is an emerging research area that has experienced rapid growth in the past few years. Although crowdsourcing has demonstrated its potential in numerous domains, several key challenges continue to hinder its application. One of the major challenges is quality control. How can crowdsourcing requesters effectively control the quality from the crowdsourcing workers? To address that challenge, a data-driven empirical model of quality control for crowdsourcing was designed to automatically assess the quality of an individual's contribution to a task, without much manual intervention or external data support. This model is designed to categorize the data from each crowdsourcing worker into one of several quality groups. The model was initiated by estimating thresholds for different quality groups based on analyzing the two categories of quantitative training data from tasks (i.e., user effort measures and task natures). Then the model integrated the expected variance within individual workers to adjust the initial estimates. These computed thresholds are then used to judge the quality of each user contribution. Two studies under different task domains were conducted to evaluate the model. The results from both studies support the effectiveness of the model. A comparison study was conducted between our model and the iterative voting approach, a commonly used quality judging method in crowdsourcing. The comparison study results confirmed the advantages of our model over iterative voting. A blacklist-based enhancement was added to the original model inspired by the Gold Standard method, to defend against gaming under the assumption that gamers will always cheat and never provide valid data inputs. A Java implementation of the quality judging model was shared as an open source package to allow an easy adoption of the designed model. Regarding theoretical contributions, this dissertation proposed a three-stage (i.e., training, refinement, and classification) quality judging model to automatically determine data quality based on two categories of quantitative measures for crowdsourcing tasks. Practically, the crowdsourcing community can directly use or build upon this work to control crowdsourcing data quality more effectively

Distributionally Robust Optimization Model for a Minimum Cost Consensus with Asymmetric Adjustment Costs Based on the Wasserstein Metric

When solving the problem of the minimum cost consensus with asymmetric adjustment costs, decision makers need to face various uncertain situations (such as individual opinions and unit adjustment costs for opinion modifications in the up and down directions). However, in the existing methods for dealing with this problem, robust optimization will lead to overly conservative results, and stochastic programming needs to know the exact probability distribution. In order to overcome these shortcomings, it is essential to develop a novelty consensus model. Thus, we propose three new minimum-cost consensus models with a distributionally robust method. Uncertain parameters (individual opinions, unit adjustment costs for opinion modifications in the up and down directions, the degree of tolerance, and the range of thresholds) were investigated by modeling the three new models, respectively. In the distributionally robust method, the construction of an ambiguous set is very important. Based on the historical data information, we chose the Wasserstein ambiguous set with the Wasserstein distance in this study. Then, three new models were transformed into a second-order cone programming problem to simplify the calculations. Further, a case from the EU Trade and Animal Welfare (TAW) program policy consultation was used to verify the practicability of the proposed models. Through comparison and sensitivity analysis, the numerical results showed that the three new models fit the complex decision environment better

Distributionally Robust Optimization Model for a Minimum Cost Consensus with Asymmetric Adjustment Costs Based on the Wasserstein Metric

When solving the problem of the minimum cost consensus with asymmetric adjustment costs, decision makers need to face various uncertain situations (such as individual opinions and unit adjustment costs for opinion modifications in the up and down directions). However, in the existing methods for dealing with this problem, robust optimization will lead to overly conservative results, and stochastic programming needs to know the exact probability distribution. In order to overcome these shortcomings, it is essential to develop a novelty consensus model. Thus, we propose three new minimum-cost consensus models with a distributionally robust method. Uncertain parameters (individual opinions, unit adjustment costs for opinion modifications in the up and down directions, the degree of tolerance, and the range of thresholds) were investigated by modeling the three new models, respectively. In the distributionally robust method, the construction of an ambiguous set is very important. Based on the historical data information, we chose the Wasserstein ambiguous set with the Wasserstein distance in this study. Then, three new models were transformed into a second-order cone programming problem to simplify the calculations. Further, a case from the EU Trade and Animal Welfare (TAW) program policy consultation was used to verify the practicability of the proposed models. Through comparison and sensitivity analysis, the numerical results showed that the three new models fit the complex decision environment better

Study on Grinding Additives in Cassiterite–Polymetallic Sulfide Ore Grinding

To attempt a new approach to improve the grinding of cassiterite–polymetallic sulfide ores while simultaneously reducing cassiterite overgrinding and sulfide undergrinding, this article looked into the effects of grinding chemical additives on the distribution of grinding product size. Six chemicals, namely sodium hexametaphosphate, triethanolamine, ferric sulphate, aluminum chloride, polyaluminum chloride and polyacrylamide, were compared in terms of their influence on the grinding product size distribution. The results showed that the six chemicals changed the distribution results with varying orientations and degrees and that the addition of polyacrylamide achieved the most satisfactory effect by decreasing the production of both coarse and fine size fractions and increasing the production of qualified particles. The effect of the molecular weight of polyacrylamide on the grinding was also discussed. The polyacrylamides with molecular weights of about 3 × 106, 5 × 106, 8 × 106 and 12 × 106 could help to produce less of the coarse size fraction and more of the qualified size fraction, but only the polyacrylamides with molecular weights of 3 × 106 and 5 × 106 produced pronounced changes. Moreover, the polyacrylamides could slightly reduce the production of the fine size fraction. Polyacrylamide with a 5 × 106 molecular weight was better than that with a 3 × 106 molecular weight in aiding the grinding of the discussed ore. It was also found that the aid action of the polyacrylamide with a 5 × 106 molecular weight was related to grinding concentration and that a low grinding concentration of less than 70% solid mass was helpful in exerting its aid action. Using polyacrylamide could shorten the grinding time that is needed to achieve the same, or even improved, product size distribution