Quantum Anomalous Hall Effect in Graphene from Rashba and Exchange Effects

We investigate the possibility of realizing quantum anomalous Hall effect in graphene. We show that a bulk energy gap can be opened in the presence of both Rashba spin-orbit coupling and an exchange field. We calculate the Berry curvature distribution and find a non-zero Chern number for the valence bands and demonstrate the existence of gapless edge states. Inspired by this finding, we also study, by first principles method, a concrete example of graphene with Fe atoms adsorbed on top, obtaining the same result.Comment: 4 papges, 5 figure

Using Fine-grained Emotion Computing Model to Analyze the Interactions between Netizens’ Sentiments and Stock Returns

From the perspective of behavioural finance, this paper combines the fine-grained sentiment calculation with the stock market econometric model to explore the interactions between netizens’ sentiments and stock returns, analyze the differences in the influences of various emotions expressed by netizens on the stock market. First, it constructs a sentiment dictionary for the financial field; then, it calculates the emotion values contained in the text corpus, and constructs a textual sentiment classifier based on the recurrent neural network, calculates the emotion value and establishes the daily netizen sentiment index; and finally, it builds an econometric model to study the interactions between the netizen sentiment index and the stock returns. The results show that this model improves the accuracy of sentiment classification, reduces the number of iterations and saves computing resources; and that the netizen sentiment index, especially, “disgust” and “like”, has significant effects on the stock price changes and transaction volumes, while on the other hand, the listed company’s stock returns data has no reverse effect on the netizen sentiment index

Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems

Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems, and learning-to-rank is an important way to optimize the models in cascade ranking. Previous works on learning-to-rank usually focus on letting the model learn the complete order or top-k order, and adopt the corresponding rank metrics (e.g. OPA and NDCG@k) as optimization targets. However, these targets can not adapt to various cascade ranking scenarios with varying data complexities and model capabilities; and the existing metric-driven methods such as the Lambda framework can only optimize a rough upper bound of limited metrics, potentially resulting in sub-optimal and performance misalignment. To address these issues, we propose a novel perspective on optimizing cascade ranking systems by highlighting the adaptability of optimization targets to data complexities and model capabilities. Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively. We also introduce permutation matrix to represent the rank metrics and employ differentiable sorting techniques to relax hard permutation matrix with controllable approximate error bound. This enables us to optimize both the relaxed and full targets directly and more appropriately. We named this method as Adaptive Neural Ranking Framework (abbreviated as ARF). Furthermore, we give a specific practice under ARF. We use the NeuralSort to obtain the relaxed permutation matrix and draw on the variant of the uncertainty weight method in multi-task learning to optimize the proposed losses jointly. Experiments on a total of 4 public and industrial benchmarks show the effectiveness and generalization of our method, and online experiment shows that our method has significant application value.Comment: 12 pages, Accepted by www202

Colossal switchable photocurrents in topological Janus transition metal dichalcogenides

Nonlinear optical properties, such as bulk photovoltaic effects, possess great potential in energy harvesting, photodetection, rectification, etc. To enable efficient light-current conversion, materials with strong photo-responsivity are highly desirable. In this work, we predict that monolayer Janus transition metal dichalcogenides (JTMDs) in the 1T' phase possess colossal nonlinear photoconductivity owing to their topological band mixing, strong inversion symmetry breaking, and small electronic bandgap. 1T' JTMDs have inverted bandgaps on the order of 10 meV and are exceptionally responsive to light in the terahertz (THz) range. By first-principles calculations, we reveal that 1T' JTMDs possess shift current (SC) conductivity as large as $2300 ~\rm nm \cdot \mu A / V^2$, equivalent to a photo-responsivity of $2800 ~\rm mA/W$. The circular current (CC) conductivity of 1T' JTMDs is as large as $10^4~ \rm nm \cdot \mu A / V^2$. These remarkable photo-responsivities indicate that the 1T' JTMDs can serve as efficient photodetectors in the THz range. We also find that external stimuli such as the in-plane strain and out-of-plane electric field can induce topological phase transitions in 1T' JTMDs and that the SC can abruptly flip their directions. The abrupt change of the nonlinear photocurrent can be used to characterize the topological transition and has potential applications in 2D optomechanics and nonlinear optoelectronics

Uncertainty Theory Based Reliability-Centric Cyber-Physical System Design

Cyber-physical systems (CPSs) are built from, and depend upon, the seamless integration of software and hardware components. The most important challenge in CPS design and verification is to design CPS to be reliable in a variety of uncertainties, i.e., unanticipated and rapidly evolving environments and disturbances. The costs, delays and reliability of the designed CPS are highly dependent on software-hardware partitioning in the design. The key challenges in partitioning CPSs is that it is difficult to formalize reliability characterization in the same way as the uncertain cost and time delay. In this paper, we propose a new CPS design paradigm for reliability assurance while coping with uncertainty. To be specific, we develop an uncertain programming model for partitioning based on the uncertainty theory, to support the assured reliability. The uncertainty effect of the cost and delay time of components to be implemented can be modeled by the uncertainty variables with uncertainty distributions, and the reliability characterization is recursively derived. We convert the uncertain programming model and customize an improved heuristic to solve the converted model. Experiment results on some benchmarks and random graphs show that the uncertain method produces the design with higher reliability. Besides, in order to demonstrate the effectiveness of our model for in coping with uncertainty in design stage, we apply this uncertain framework and existing deterministic models in the design process of a sub-system that is used in real world subway control. The system implemented based on the uncertain model works better than the result of deterministic models. The proposed design paradigm has the potential to be generalized to the design of CPSs for greater assurances of safety and security under a variety of uncertainties

4-Bromo-2-[1-(4-eth­oxy­phen­yl)-1-methyl­eth­yl]-1-methyl­benzene

In title compound, C18H21BrO, the dihedral angle between two rings is 85.72°. No classical hydrogen bonds are found and only van der Waals forces stabilize the crystal packing