An Ant Colony-based Heuristic Algorithm for Joint Scheduling of Post-earthquake Road Repair and Relief Distribution

Emergency road repair and distribution of relief goods are crucial for post-earthquake response. However, interrelationships between those two tasks are not adequately considered in their work schedules, especially in cases with very limited repair resources, leading to unnecessary delay and expenditure. A time-space network model is constructed to better describe the constraints arising from the interrelationships in joint scheduling of road repair and relief distribution works. An ant colony-based heuristic algorithm is developed to solve the NP-hard model efficiently for practical use, followed by a case study of Wenchuan earthquake to validate the planning tool and to demonstrate its feasibility for resolving real world problem

Discriminative Nonparametric Latent Feature Relational Models with Data Augmentation

We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features. Under the generic RegBayes (regularized Bayesian inference) framework, we handily incorporate the prediction loss with probabilistic inference of a Bayesian model; set distinct regularization parameters for different types of links to handle the imbalance issue in real networks; and unify the analysis of both the smooth logistic log-loss and the piecewise linear hinge loss. For the nonconjugate posterior inference, we present a simple Gibbs sampler via data augmentation, without making restricting assumptions as done in variational methods. We further develop an approximate sampler using stochastic gradient Langevin dynamics to handle large networks with hundreds of thousands of entities and millions of links, orders of magnitude larger than what existing LFRM models can process. Extensive studies on various real networks show promising performance.Comment: Accepted by AAAI 201

A ZX-Calculus Approach for the Construction of Graph Codes

Quantum Error-Correcting Codes (QECCs) play a crucial role in enhancing the robustness of quantum computing and communication systems against errors. Within the realm of QECCs, stabilizer codes, and specifically graph codes, stand out for their distinct attributes and promising utility in quantum technologies. This study underscores the significance of devising expansive QECCs and adopts the ZX-calculus a graphical language adept at quantum computational reasoning-to depict the encoders of graph codes effectively. Through the integration of ZX-calculus with established encoder frameworks, we present a nuanced approach that leverages this graphical representation to facilitate the construction of large-scale QECCs. Our methodology is rigorously applied to examine the intricacies of concatenated graph codes and the development of holographic codes, thus demonstrating the practicality of our graphical approach in addressing complex quantum error correction challenges. This research contributes to the theoretical understanding of quantum error correction and offers practical tools for its application, providing objective advancements in the field of quantum computing

Resistance imparted by vitamin C, vitamin e and vitamin B12 to the acute hepatic glycogen change in rats caused by noise.

The effects of vitamin C, vitamin E and vitamin B12 on the noise-induced acute change in hepatic glycogen content in rats were investigated. The exposure of rats to 95 dB and 110 dB of noise acutely reduced their hepatic glycogens. Vitamin C (ascorbic acid) and vitamin E (alpha -tocopherol) attenuated the noise-inducedacute reduction in the hepatic glycogen contents. This result suggests that antioxidants could reduce the change via reactive oxygen species. Vitamin B12 (cobalamin) delayed the noiseinduced change, a finding that suggests that vitamin B12 could postpone the acute change via compensating for vitamin B12 deficiency