Approximation Algorithms for Correlated Knapsacks and Non-Martingale Bandits

In the stochastic knapsack problem, we are given a knapsack of size B, and a set of jobs whose sizes and rewards are drawn from a known probability distribution. However, we know the actual size and reward only when the job completes. How should we schedule jobs to maximize the expected total reward? We know O(1)-approximations when we assume that (i) rewards and sizes are independent random variables, and (ii) we cannot prematurely cancel jobs. What can we say when either or both of these assumptions are changed? The stochastic knapsack problem is of interest in its own right, but techniques developed for it are applicable to other stochastic packing problems. Indeed, ideas for this problem have been useful for budgeted learning problems, where one is given several arms which evolve in a specified stochastic fashion with each pull, and the goal is to pull the arms a total of B times to maximize the reward obtained. Much recent work on this problem focus on the case when the evolution of the arms follows a martingale, i.e., when the expected reward from the future is the same as the reward at the current state. What can we say when the rewards do not form a martingale? In this paper, we give constant-factor approximation algorithms for the stochastic knapsack problem with correlations and/or cancellations, and also for budgeted learning problems where the martingale condition is not satisfied. Indeed, we can show that previously proposed LP relaxations have large integrality gaps. We propose new time-indexed LP relaxations, and convert the fractional solutions into distributions over strategies, and then use the LP values and the time ordering information from these strategies to devise a randomized adaptive scheduling algorithm. We hope our LP formulation and decomposition methods may provide a new way to address other correlated bandit problems with more general contexts

Multi-scale reliability analysis of composite structures – Application to the Laroin footbridge

This work aims at developing a new methodology for the reliability assessment of composite structures and their design optimization. It relies on the coupling of well established methods: homogenization scheme for the mechanical modelling of composite materials and reliability methods to account for their inherent variability. Moreover, such approach is based on an accurate treatment of inherent uncertainties of these mechanical systems at various scales, including microscopic and macroscopic levels, that provides newperspectives for structural design. As an illustration, we propose to apply the multi-scale reliability analysis on the case of the Laroin footbridge (France) with carbon–epoxy stay cables. Since the reliability assessment of such structure is evaluated through the fibre failure, numerical simulations require the coupling of reliability methods, finite element modelling to derive macroscopic loading within cables and micromechanics to estimate the effective elastic properties of composite and local responses within constituents. Results demonstrate the feasibility of the coupled approach at a structure scale and its main interests for the optimization phase of materials and engineering structures

Skin-Tone and Academic Achievement Among 5-year-old Mexican Children

Skin-tone based social stratification has been characterized as an enduring part of the U.S. racial landscape (Hunter, 2002). Despite the plethora of research that examines the racial disparities in education (e.g., Reardon & Portilla, 2015), and an emerging literature finding that lighter skin-tones are associated with higher educational attainment among adults (Hunter, 2002) few studies have examined whether similar processes emerge during early childhood. Thus, grounded in Garcia Coll and colleagues’ (1996) integrative model, we tested whether skin-tone predicted children’s academic achievement, and whether these relations were modified by children’s ethnic-racial identification (i.e., positive ethnic-racial attitudes and centrality). Consistent with expectations, darker skin-tones were associated with lower math scores. Positive attitudes did not significantly moderate the relation between skin-tone and academic achievement. However, contrary to our hypothesis, high levels of ethnic racial centrality strengthened the association between skin-tone and academic achievement. Conclusions: These findings contribute to the literature by providing evidence for the early development of within race skin-tone based disparities in academic achievement and underscoring the need for further exploration of ethnic racial identification as protective or risk factors in the positive development of minority children

Fabricating Economic Development

Much of the literature, regardless of academic discipline, presents the publication of Economic Development in 1958 as analogous to a “big bang” event in the creation of modern Ireland. However, such a “big bang” perspective misrepresents the sophistication of economic debates prior to Whitaker’s report as well as distorting the interpretation of subsequent developments. This paper reappraises Irish economic thinking before and after the publication of Economic Development. It is argued that an economically “liberal” approach to Keynesianism, such as that favoured by T. K. Whitaker and George O’Brien, lost out in the 1960s to a more interventionist approach: only later did a more liberal approach to macroeconomic policy triumph. The rival approaches to academic economics were in turn linked to wider debates on the influence of religious authorities on Irish higher education. Academic economists were particularly concerned with preserving their intellectual independence and how a shift to planning would keep decisions on resource allocation out of the reach of conservative political and religious leaders.

International Capital Flows

The sharp increase in both gross and net capital flows over the past two decades has led to a renewed interest in their determinants. Most existing theories of international capital flows are in the context of models with only one asset, which only have implications for net capital flows, not gross flows. Moreover, there is no role for capital flows as a result of changing expected returns and risk-characteristics of assets as there is no portfolio choice. In this paper we develop a method for solving dynamic stochastic general equilibrium open-economy models with portfolio choice. We show why standard first and second-order solution methods no longer work in the presence of portfolio choice, and extend them giving special treatment to the optimality conditions for portfolio choice. We apply the solution method to a particular two-country, two-good, two-asset model and show that it leads to a much richer understanding of both gross and net capital flows. The approach highlights time-varying portfolio shares, resulting from time-varying expected returns and risk characteristics of the assets, as a potential key source of international capital flows.

Measuring the impact of market coupling on the Italian electricity market using ELFO++

This paper evaluates the impact on the Italian electricity market of replacing the current explicit auction mechanism with market coupling. Maximizing the use of the cross-border interconnection capacity, market coupling increases the level of market integration and facilitates the access to low-cost generation by consumers located in high-cost generation countries. Thus, it is expected that a high-priced area such as Italy could greatly benefit from the introduction of this mechanism. In this paper, the welfare benefits are estimated under alternative market scenarios for 2012, employing the optimal dispatch model ELFO++. The results of the simulations suggest that the improvement in social surplus is likely to be significant, especially when market fundamentals are tight.Market coupling; market integration; Italian day-ahead electricity market.

The moderating role of overcommitment in the relationship between psychological contract breach and employee mental health

Reimann M. The moderating role of overcommitment in the relationship between psychological contract breach and employee mental health. JOURNAL OF OCCUPATIONAL HEALTH. 2016;58(4-5):425-433.Objectives: This study investigated whether the association between perceived psychological contract breach (PCB) and employee mental health is moderated by the cognitive-motivational pattern of overcommitment (OC). Linking the psychological contract approach to the effort-reward imbalance model, this study examines PCB as an imbalance in employment relationships that acts as a psychosocial stressor in the work environment and is associated with stress reactions that in turn negatively affect mental health. Methods: The analyses were based on a sample of 3,667 employees who participated in a longitudinal linked employer employee survey representative of large organizations (with at least 500 employees who are subject so social security contributions) in Germany. Fixed-effects regression models, including PCB and OC, were estimated for employee mental health, and interaction effects between PCB and OC were assessed. Results: The multivariate fixed-effects regression analyses showed a significant negative association between PCB and employee mental health. The results also confirmed that OC does indeed significantly increase the negative effect of PCB on mental health and that OC itself has a significant and negative effect on mental health. Conclusions: The results suggest that employees characterized by the cognitive-motivational pattern of OC are at an increased risk of developing poor mental health if they experience PCB compared with employees who are not overly committed to their work. The results of this study support the assumption that psychosocial work stressors play an important role in employee mental health

Recurrent Neural Networks with Top-k Gains for Session-based Recommendations

RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner. The use of RNNs provides impressive performance benefits over classical methods in session-based recommendations. In this work we introduce novel ranking loss functions tailored to RNNs in the recommendation setting. The improved performance of these losses over alternatives, along with further tricks and refinements described in this work, allow for an overall improvement of up to 35% in terms of MRR and Recall@20 over previous session-based RNN solutions and up to 53% over classical collaborative filtering approaches. Unlike data augmentation-based improvements, our method does not increase training times significantly. We further demonstrate the performance gain of the RNN over baselines in an online A/B test.Comment: CIKM'18, authors' versio