Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission

By deploying machine-learning algorithms at the network edge, edge learning can leverage the enormous real-time data generated by billions of mobile devices to train AI models, which enable intelligent mobile applications. In this emerging research area, one key direction is to efficiently utilize radio resources for wireless data acquisition to minimize the latency of executing a learning task at an edge server. Along this direction, we consider the specific problem of retransmission decision in each communication round to ensure both reliability and quantity of those training data for accelerating model convergence. To solve the problem, a new retransmission protocol called data-importance aware automatic-repeat-request (importance ARQ) is proposed. Unlike the classic ARQ focusing merely on reliability, importance ARQ selectively retransmits a data sample based on its uncertainty which helps learning and can be measured using the model under training. Underpinning the proposed protocol is a derived elegant communication-learning relation between two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data uncertainty. This relation facilitates the design of a simple threshold based policy for importance ARQ. The policy is first derived based on the classic classifier model of support vector machine (SVM), where the uncertainty of a data sample is measured by its distance to the decision boundary. The policy is then extended to the more complex model of convolutional neural networks (CNN) where data uncertainty is measured by entropy. Extensive experiments have been conducted for both the SVM and CNN using real datasets with balanced and imbalanced distributions. Experimental results demonstrate that importance ARQ effectively copes with channel fading and noise in wireless data acquisition to achieve faster model convergence than the conventional channel-aware ARQ.Comment: This is an updated version: 1) extension to general classifiers; 2) consideration of imbalanced classification in the experiments. Submitted to IEEE Journal for possible publicatio

Optimal control of hepatitis C antiviral treatment programme delivery for prevention amongst a population of injecting drug users.

In most developed countries, HCV is primarily transmitted by injecting drug users (IDUs). HCV antiviral treatment is effective, and deemed cost-effective for those with no re-infection risk. However, few active IDUs are currently treated. Previous modelling studies have shown antiviral treatment for active IDUs could reduce HCV prevalence, and there is emerging interest in developing targeted IDU treatment programmes. However, the optimal timing and scale-up of treatment is unknown, given the real-world constraints commonly existing for health programmes. We explore how the optimal programme is affected by a variety of policy objectives, budget constraints, and prevalence settings. We develop a model of HCV transmission and treatment amongst active IDUs, determine the optimal treatment programme strategy over 10 years for two baseline chronic HCV prevalence scenarios (30% and 45%), a range of maximum annual budgets (£50,000-300,000 per 1,000 IDUs), and a variety of objectives: minimising health service costs and health utility losses; minimising prevalence at 10 years; minimising health service costs and health utility losses with a final time prevalence target; minimising health service costs with a final time prevalence target but neglecting health utility losses. The largest programme allowed for a given budget is the programme which minimises both prevalence at 10 years, and HCV health utility loss and heath service costs, with higher budgets resulting in greater cost-effectiveness (measured by cost per QALY gained compared to no treatment). However, if the objective is to achieve a 20% relative prevalence reduction at 10 years, while minimising both health service costs and losses in health utility, the optimal treatment strategy is an immediate expansion of coverage over 5-8 years, and is less cost-effective. By contrast, if the objective is only to minimise costs to the health service while attaining the 20% prevalence reduction, the programme is deferred until the final years of the decade, and is the least cost-effective of the scenarios

Stability and resource allocation in project planning.

The majority of resource-constrained project scheduling efforts assumes perfect information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule is executed. In reality, project activities are subject to considerable uncertainty, which generally leads to numerous schedule disruptions. In this paper, we present a resource allocation model that protects a given baseline schedule against activity duration variability. A branch-and-bound algorithm is developed that solves the proposed resource allocation problem. We report on computational results obtained on a set of benchmark problems.Constraint satisfaction; Information; Model; Planning; Problems; Project management; Project planning; Project scheduling; Resource allocati; Scheduling; Stability; Uncertainty; Variability;

Prioritizing Opportunities to Reduce the Risk of Foodborne Illness: A Conceptual Framework

Determining the best use of food safety resources is a difficult task faced by public policymakers, regulatory agencies, state and local food safety and health agencies, as well as private firms. The Food Safety Research Consortium (FSRC) has developed a conceptual framework for priority setting and resource allocation for food safety that takes full account of the food system’s complexity and available data but is simple enough to be workable and of practical value to decisionmakers. The conceptual framework addresses the question of how societal resources, both public and private, can be used most effectively to reduce the public health burden of foodborne illness by quantitatively ranking risks and considering the availability, effectiveness, and cost of interventions to address these risks. We identify two types of priority-setting decisions: Purpose 1 priority setting that guides risk-based allocation of food safety resources, primarily by government food safety agencies, across a wide range of opportunities to reduce the public health impact of foodborne illness; and Purpose 2 priority setting that guides the choice of risk management actions and strategies with respect to particular hazards and commodities. It is essential that such a framework be grounded in a systems approach, multi-disciplinary in approach and integration of data, practical, flexible, and dynamic by including ongoing evaluation and continuous updating of risk rankings and other elements. The conceptual framework is a synthesis of ideas and information generated in connection with and during the three FSRC workshops convened under a project funded by the Cooperative State Research, Education, and Extension Service of USDA. Workshop materials are available on the project website: http://www.card.iastate.edu/food_safety/.

Distributional Impacts of a U.S. Greenhouse Gas Policy: A General Equilibrium Analysis of Carbon Pricing

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).We develop a new model of the U.S., the U.S. Regional Energy Policy (USREP) model that is resolved for large states and regions of the U.S. and by income class and apply the model to investigate a $15 per ton CO2 equivalent price on greenhouse gas emissions. Previous estimates of distributional impacts of carbon pricing have been done outside of the model simulation and have been based on energy expenditure patterns of households in different regions and of different income levels. By estimating distributional effects within the economic model, we include the effects of changes in capital returns and wages on distribution and find that the effects are significant and work against the expenditure effects. We find the following: First, while results based only on energy expenditure have shown carbon pricing to be regressive we find the full distributional effect to be neutral or slightly progressive. This demonstrates the importance of tracing through all economic impacts and not just focusing on spending side impacts. Second, the ultimate impact of such a policy on households depends on how allowances, or the revenue raised from auctioning them, is used. Free distribution to firms would be highly regressive, benefiting higher income households and forcing lower income households to bear the full cost of the policy and what amounts to a transfer of wealth to higher income households. Lump sum distribution through equal-sized household rebates would make lower income households absolutely better off while shifting the costs to higher income households. Schemes that would cut taxes are generally slightly regressive but improve somewhat the overall efficiency of the program. Third, proposed legislation would distribute allowances to local distribution companies (electricity and natural gas distributors) and public utility commissions would then determine how the value of those allowances was used. A significant risk in such a plan is that distribution to households might be perceived as lowering utility rates That reduced the efficiency of the policy we examined by 40 percent. Finally, the states on the coasts bear little cost or can benefit because of the distribution of allowance revenue while mid-America and southern states bear the highest costs. This regional pattern reflects energy consumption and energy production difference among states. Use of allowance revenue to cut taxes generally exacerbates these regional differences because coastal states are also generally higher income states, and those with higher incomes benefit more from tax cuts.MIT Joint Program on the Science and Policy of Global Change through a combination of government, industry, and foundation funding, the MIT Energy Initiative, and additional support for this work from a coalition of industrial sponsors