Submodular memetic approximation for multiobjective parallel test paper generation

Parallel test paper generation is a biobjective distributed resource optimization problem, which aims to generate multiple similarly optimal test papers automatically according to multiple user-specified assessment criteria. Generating high-quality parallel test papers is challenging due to its NP-hardness in both of the collective objective functions. In this paper, we propose a submodular memetic approximation algorithm for solving this problem. The proposed algorithm is an adaptive memetic algorithm (MA), which exploits the submodular property of the collective objective functions to design greedy-based approximation algorithms for enhancing steps of the multiobjective MA. Synergizing the intensification of submodular local search mechanism with the diversification of the population-based submodular crossover operator, our algorithm can jointly optimize the total quality maximization objective and the fairness quality maximization objective. Our MA can achieve provable near-optimal solutions in a huge search space of large datasets in efficient polynomial runtime. Performance results on various datasets have shown that our algorithm has drastically outperformed the current techniques in terms of paper quality and runtime efficiency

Spatial and spatio-temporal analyses of neighborhood retail food environments: evidence for food planning and interventions

The food system has been increasingly recognized as an indispensable component in professional planning in Canada. As its retailing part, the Retail Food Environment (RFE) has recently gained considerable attention, since it plays an important role in shaping residents’ eating behaviors and diet-related health outcomes, especially obesity. Identifying the strengths and weaknesses of the RFE in a neighborhood is essential for successful food planning and interventions. Yet current neighborhood RFE assessment mainly uses secondary food outlet datasets to evaluate absolute food access, largely overlooks the dynamic nature of the RFE and the variations of in-store features between food outlets, and predominantly applies descriptive RFE measures. Comprised of three articles that focus on a common theme, neighborhood RFE assessment, this dissertation uses novel spatial and spatio-temporal statistical modeling approaches to explore neighborhood RFE in the Regional Municipality of Waterloo with food outlet datasets that include the information of both the community and consumer nutrition environments. Firstly, this research explores spatio-temporal variations of relative healthy food access (RHFA) with a multiple-year RFE dataset. The results suggest that food swamps are more prevalent than food deserts in the study region and that food swamps have become more prevalent during the study period. Spatio-temporal food swamps, neighborhoods where RHFA is decreasing faster than the regional trend, are highlighted for interventions. Secondly, this research investigates the association between marginalization and neighborhood RFE at various geographical scales. ‘Healthy’ and ‘less healthy’ food outlets are differentiated based on in-store features from a primary food outlet dataset. RFE ‘healthfulness’ is a relative measure of healthy food access, which is modeled via probability distributions. The results indicate that neighborhoods with higher residential instability, material deprivation, and population density are more likely to have access to healthy food outlets within a walkable distance from a binary ‘have’ or ‘not have’ access perspective. At the walkable distance scale however, materially deprived neighborhoods are found to have less healthy RFE (i.e., lower RHFA). Finally, this research applies a spatial factor analysis model to assess neighborhood restaurant environment (NRE) for the city of Kitchener with multiple restaurant assessment indicators. Neighborhoods with least healthy NRE (simultaneously suffer from lower relative availability of healthy eating options, higher prices of healthy eating, and lower/higher healthy eating facilitator/barrier) are identified. Facilitator/barrier is found to be most relevant with NRE healthfulness. This research significantly advances our understanding of neighborhood RFE. Conceptually, it extends the definition of food swamps by incorporating a temporal dimension and provides empirical evidence that the deprivation amplification hypothesis in the RFE context holds only at specific geographical scales when neighborhood RFE is assessed with specific strategies. It also challenges the uncertainties associated with descriptive RFE measures that purport to represent the underlying concept – the ‘healthfulness’ of neighborhood RFE. Methodologically, this research facilitates the application of spatial and spatio-temporal statistical approaches in RFE studies. Findings from this research could assist planners and policy makers in developing food intervention programs to improve neighborhood RFE and promote population-wide healthy eating in the Region of Waterloo

FAT: An In-Memory Accelerator with Fast Addition for Ternary Weight Neural Networks

Convolutional Neural Networks (CNNs) demonstrate excellent performance in various applications but have high computational complexity. Quantization is applied to reduce the latency and storage cost of CNNs. Among the quantization methods, Binary and Ternary Weight Networks (BWNs and TWNs) have a unique advantage over 8-bit and 4-bit quantization. They replace the multiplication operations in CNNs with additions, which are favoured on In-Memory-Computing (IMC) devices. IMC acceleration for BWNs has been widely studied. However, though TWNs have higher accuracy and better sparsity than BWNs, IMC acceleration for TWNs has limited research. TWNs on existing IMC devices are inefficient because the sparsity is not well utilized, and the addition operation is not efficient. In this paper, we propose FAT as a novel IMC accelerator for TWNs. First, we propose a Sparse Addition Control Unit, which utilizes the sparsity of TWNs to skip the null operations on zero weights. Second, we propose a fast addition scheme based on the memory Sense Amplifier to avoid the time overhead of both carry propagation and writing back the carry to memory cells. Third, we further propose a Combined-Stationary data mapping to reduce the data movement of activations and weights and increase the parallelism across memory columns. Simulation results show that for addition operations at the Sense Amplifier level, FAT achieves 2.00X speedup, 1.22X power efficiency, and 1.22X area efficiency compared with a State-Of-The-Art IMC accelerator ParaPIM. FAT achieves 10.02X speedup and 12.19X energy efficiency compared with ParaPIM on networks with 80% average sparsity.Comment: 14 page

A CRISPR/Cas12a-assisted rapid detection platform by biosensing the apxIVA of Actinobacillus pleuropneumoniae

Actinobacillus pleuropneumoniae is an important respiratory pig pathogen that causes substantial losses in the worldwide swine industry. Chronic or subclinical infection with no apparent clinical symptoms poses a challenge for preventing transmission between herds. Rapid diagnostics is important for the control of epidemic diseases. In this study, we formulated an A. pleuropneumoniae species-specific apxIVA-based CRISPR/Cas12a-assisted rapid detection platform (Card) that combines recombinase polymerase amplification (RPA) of target DNA and subsequent Cas12a ssDNase activation. Card has a detection limit of 10 CFUs of A. pleuropneumoniae, and there is no cross-reactivity with other common swine pathogens. The detection process can be completed in 1 h, and there was 100% agreement between the conventional apxIVA-based PCR and Card in detecting A. pleuropneumoniae in lung samples. Microplate fluorescence readout enables high-throughput use in diagnostic laboratories, and naked eye and lateral flow test readouts enable use at the point of care. We conclude that Card is a versatile, rapid, accurate molecular diagnostic platform suitable for use in both laboratory and low-resource settings

Diving into the consumer nutrition environment: A Bayesian spatial factor analysis of neighborhood restaurant environment

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.sste.2017.12.001 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/Neighborhood restaurant environment (NRE) plays a vital role in shaping residents' eating behaviors. While NRE 'healthfulness' is a multi-facet concept, most studies evaluate it based only on restaurant type, thus largely ignoring variations of in-restaurant features. In the few studies that do account for such features, healthfulness scores are simply averaged over accessible restaurants, thereby concealing any uncertainty that attributed to neighborhoods' size or spatial correlation. To address these limitations, this paper presents a Bayesian Spatial Factor Analysis for assessing NRE healthfulness in the city of Kitchener, Canada. Several in-restaurant characteristics are included. By treating NRE healthfulness as a spatially correlated latent variable, the adopted modeling approach can: (i) identify specific indicators most relevant to NRE healthfulness, (ii) provide healthfulness estimates for neighborhoods without accessible restaurants, and (iii) readily quantify uncertainties in the healthfulness index. Implications of the analysis for intervention program development and community food planning are discussed. (c) 2017 Elsevier Ltd. All rights reserved

A Lifting Relation from Macroscopic Variables to Mesoscopic Variables in Lattice Boltzmann Method: Derivation, Numerical Assessments and Coupling Computations Validation

In this paper, analytic relations between the macroscopic variables and the mesoscopic variables are derived for lattice Boltzmann methods (LBM). The analytic relations are achieved by two different methods for the exchange from velocity fields of finite-type methods to the single particle distribution functions of LBM. The numerical errors of reconstructing the single particle distribution functions and the non-equilibrium distribution function by macroscopic fields are investigated. Results show that their accuracy is better than the existing ones. The proposed reconstruction operator has been used to implement the coupling computations of LBM and macro-numerical methods of FVM. The lid-driven cavity flow is chosen to carry out the coupling computations based on the numerical strategies of domain decomposition methods (DDM). The numerical results show that the proposed lifting relations are accurate and robust

Osteoporosis guidelines on TCM drug therapies: a systematic quality evaluation and content analysis

ObjectiveThe aims of this study were to evaluate the quality of osteoporosis guidelines on traditional Chinese medicine (TCM) drug therapies and to analyze the specific recommendations of these guidelines.MethodsWe systematically collected guidelines, evaluated the quality of the guidelines using the Appraisal of Guidelines Research and Evaluation (AGREE) II tool, and summarized the recommendations of TCM drug therapies using the Patient-Intervention-Comparator-Outcome (PICO) model as the analysis framework.Results and conclusionsA total of 20 guidelines were included. Overall quality evaluation results revealed that four guidelines were at level A, four at level B, and 12 at level C, whose quality needed to be improved in the domains of “stakeholder involvement”, “rigor of development”, “applicability” and “editorial independence”. Stratified analysis suggested that the post-2020 guidelines were significantly better than those published before 2020 in the domains of “scope and purpose”, “stakeholder involvement” and “editorial independence”. Guidelines with evidence systems were significantly better than those without evidence systems in terms of “stakeholder involvement”, “rigor of development”, “clarity of presentation” and “applicability”. The guidelines recommended TCM drug therapies for patients with osteopenia, osteoporosis and osteoporotic fracture. Recommended TCM drugs were mainly Chinese patent medicine alone or combined with Western medicine, with the outcome mainly focused on improving bone mineral density (BMD)

Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach

Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs. Although conventional node-output graph neural networks (GNN) can extract features of edge nodes when the network scales, they fail to handle a new scalability issue whereas the dimension of the decision space may change as the network scales. To address the issue, in this paper, a novel link-output GNN (LOGNN)-based resource management approach is proposed to flexibly optimize the resource allocation in MEC for an arbitrary number of edge nodes with extremely low algorithm inference delay. Moreover, a label-free unsupervised method is applied to train the LOGNN efficiently, where the gradient of edge tasks processing delay with respect to the LOGNN parameters is derived explicitly. In addition, a theoretical analysis of the scalability of the node-output GNN and link-output GNN is performed. Simulation results show that the proposed LOGNN can efficiently optimize the MEC resource allocation problem in a scalable way, with an arbitrary number of servers and users. In addition, the proposed unsupervised training method has better convergence performance and speed than supervised learning and reinforcement learning-based training methods. The code is available at \url{https://github.com/UNIC-Lab/LOGNN}