Truth-Valued-Flow Inference (TVFI) and its applications in approximate reasoning

The framework of the theory of Truth-valued-flow Inference (TVFI) is introduced. Even though there are dozens of papers presented on fuzzy reasoning, we think it is still needed to explore a rather unified fuzzy reasoning theory which has the following two features: (1) it is simplified enough to be executed feasibly and easily; and (2) it is well structural and well consistent enough that it can be built into a strict mathematical theory and is consistent with the theory proposed by L.A. Zadeh. TVFI is one of the fuzzy reasoning theories that satisfies the above two features. It presents inference by the form of networks, and naturally views inference as a process of truth values flowing among propositions

Universal Dependencies Parsing for Colloquial Singaporean English

Singlish can be interesting to the ACL community both linguistically as a major creole based on English, and computationally for information extraction and sentiment analysis of regional social media. We investigate dependency parsing of Singlish by constructing a dependency treebank under the Universal Dependencies scheme, and then training a neural network model by integrating English syntactic knowledge into a state-of-the-art parser trained on the Singlish treebank. Results show that English knowledge can lead to 25% relative error reduction, resulting in a parser of 84.47% accuracies. To the best of our knowledge, we are the first to use neural stacking to improve cross-lingual dependency parsing on low-resource languages. We make both our annotation and parser available for further research.Comment: Accepted by ACL 201

The effects of smart city construction on urban green total factor productivity: evidence from China

As a new urbanization mode, smart cities provide a way to achieve the mutually beneficial situation of both economic development and environmental protection. As such, the question of whether and how smart-city construction can promote the highquality development of urban economies is worth considering. By measuring the green total factor productivity (GTFP) of 233 cities in China from 2004–2019, this paper adopts the national pilot smart cities of China as a quasi-experiment and constructs a timevarying DID model to explore the direct, dynamic, and heterogeneity influence of smart-city construction on GTFP. The results indicate the following: (1) Smart-city construction can significantly promote the urban GTFP, which is mainly caused by the progress of green technology, and the effect is robust after a series of robustness tests; (2) smart-city construction can contribute to GTFP improvement primarily through accelerating technological innovation, promoting industrial upgrading, and realizing resource allocation optimization; and (3) smart cities that are larger in scale, have a higher administrative rank, and lie in eastern regions have stronger positive effects on GTFP. This study aims to contribute to the promotion of urban sustainable development

Dynamic Business Share Allocation in a Supply Chain with Competing Suppliers

This paper studies a repeated game between a manufacturer and two competing suppliers with imperfect monitoring. We present a principal-agent model for managing long-term supplier relationships using a unique form of measurement and incentive scheme. We measure a supplier's overall performance with a rating equivalent to its continuation utility (the expected total discounted utility of its future payoffs), and incentivize supplier effort with larger allocations of future business. We obtain the vector of the two suppliers' ratings as the state of a Markov decision process, and we solve an infinite horizon contracting problem in which the manufacturer allocates business volume between the two suppliers and updates their ratings dynamically based on their current ratings and the current performance outcome. Our contributions are both theoretical and managerial: we propose a repeated principal-agent model with a novel incentive scheme to tackle a common, but challenging, incentive problem in a multiperiod supply chain setting. Assuming binary effort choices and performance outcomes by the suppliers, we characterize the structure of the optimal contract through a novel fixed-point analysis. Our results provide a theoretical foundation for the emergence of “business-as-usual” (low effort) trapping states and tournament competition (high effort) recurrent states as the long-run incentive drivers for motivating critical suppliers

Bilinear Graph Neural Network with Neighbor Interactions

Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the representation of the target node. Nevertheless, the operation of weighted sum assumes the neighbor nodes are independent of each other, and ignores the possible interactions between them. When such interactions exist, such as the co-occurrence of two neighbor nodes is a strong signal of the target node's characteristics, existing GNN models may fail to capture the signal. In this work, we argue the importance of modeling the interactions between neighbor nodes in GNN. We propose a new graph convolution operator, which augments the weighted sum with pairwise interactions of the representations of neighbor nodes. We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes. In particular, we specify two BGNN models named BGCN and BGAT, based on the well-known GCN and GAT, respectively. Empirical results on three public benchmarks of semi-supervised node classification verify the effectiveness of BGNN -- BGCN (BGAT) outperforms GCN (GAT) by 1.6% (1.5%) in classification accuracy.Codes are available at: https://github.com/zhuhm1996/bgnn.Comment: Accepted by IJCAI 2020. SOLE copyright holder is IJCAI (International Joint Conferences on Artificial Intelligence), all rights reserve

MIMO Antenna Polynomial Weighted Average Design Method of Downward-Looking Array SAR

MIMO antenna polynomial weighted average design method of downward-looking array SAR was proposed from the angle of surveying and mapping in this paper, in order to solve the ill-posed problem that an equivalent virtual array can be implemented by a variety of physical transmitter-receiver arrays for bistatic MIMO linear array. For wave band, resolution, elevation precision, and working height concerned by the applications of surveying and mapping, the length of equivalent virtual array and actual physical array meeting the needs of large scale topographical mapping was solved. Then array numbers and position vectors of MIMO downward-looking array SAR of real aerial mapping platform were optimized. According to this design, some simulation experiments and comparisons were processed. The results proved the rationality and effectiveness of this array configuration by comparing the differences of 3D imaging results and the original simulation scene, counting mean and standard deviation of elevation reconstruction error eliminating the influence of shadow areas, and counting the probability of elevation reconstruction error within half a resolution of the whole scene and individual building area

A Reliable Multipath Routing Protocol Based on Link Stability

Wireless NanoSensor Network (WNSN) is a new type of sensor network with broad application prospects. In view of the limited energy of nanonodes and unstable links in WNSNs, we propose a reliable multi-path routing based on link stability (RMRLS). RMRLS selects the optimal path which perfects best in the link stability evaluation model, and then selects an alternative route by the routing similarity judgment model. RMRLS uses tew paths to cope with changes in the network topology. The simulation shows that the RMRLS protocol has advantages in data packet transmission success rate and average throughput, which can improve the stability and reliability of the network

Daily Variation of Thyroid Hormones in Broiler Under High-Temperature Conditions

Market-size (61-68 day-old) AA broiler chickens were exposed to simulated high-cyclic summer temperatures of North, Central and South China for 5 continuous days. Blood samples were collected at 0AM, 4AM, 8AM, 0PM, 4PM and 8PM each day, and concentrations of triiodothyronine (T3) and thyroxine (T4) were determined by double-antibody radioimmunoassay (RIA). T3, T4 concentration and T3/T4 ratio had two peaks, but the daily variation patterns of thyroid hormones were different between each other. T3 peaked at 12 AM and 12 PM, while T4 peaked at 8 AM and 12 PM, with the two peaks of T3/T4 ratio showing at 4 AM and 12 AM. The lowest concentrations of both T3 and T4 occurred at 4 PM. According to above results, the blood samples should be collected around the time corresponding to the peak of temperature sinusoid, when thyroid hormones (both T3 and T4 concentrations) are used to evaluate the heat stress status of broilers

3,5-Di-O-benzoyl-1,2-O-isopropyl­idene-α-d-ribo-hexos-3-ulo-1,4:3,6-difuran­ose

The title compound, C23H22O8, is a binary benzoyl ester whose nucleus consists of a fused system made up of a methyl­enedi­oxy ring and two tetra­hydro­furan rings. One of the benzoyl ester groups is attached at the junction of the two tetra­hydro­furan rings. The other is attached to the outer tetra­furan ring. Both the benzoyl ester groups are in an axial conformation with respect to the outer tetrhydro­furan ring. In the crystal, mol­ecules are linked by two weak C—H⋯O hydrogen bonds, forming a chain running parallel to the a axis