Optimal Control of Brownian Inventory Models with Convex Inventory Cost: Discounted Cost Case

We consider an inventory system in which inventory level fluctuates as a Brownian motion in the absence of control. The inventory continuously accumulates cost at a rate that is a general convex function of the inventory level, which can be negative when there is a backlog. At any time, the inventory level can be adjusted by a positive or negative amount, which incurs a fixed positive cost and a proportional cost. The challenge is to find an adjustment policy that balances the inventory cost and adjustment cost to minimize the expected total discounted cost. We provide a tutorial on using a three-step lower-bound approach to solving the optimal control problem under a discounted cost criterion. In addition, we prove that a four-parameter control band policy is optimal among all feasible policies. A key step is the constructive proof of the existence of a unique solution to the free boundary problem. The proof leads naturally to an algorithm to compute the four parameters of the optimal control band policy

Exposing market mechanism design trade-offs via multi-objective evolutionary search

Market mechanisms are a means by which resources in contention can be allocated between contending parties, both in human economies and those populated by software agents. Designing such mechanisms has traditionally been carried out by hand, and more recently by automation. Assessing these mechanisms typically involves them being evaluated with respect to multiple conflicting objectives, which can often be nonlinear, noisy, and expensive to compute. For typical performance objectives, it is known that designed mechanisms often fall short on being optimal across all objectives simultaneously. However, in all previous automated approaches, either only a single objective is considered, or else the multiple performance objectives are combined into a single objective. In this paper we do not aggregate objectives, instead considering a direct, novel application of multi-objective evolutionary algorithms (MOEAs) to the problem of automated mechanism design. This allows the automatic discovery of trade-offs that such objectives impose on mechanisms. We pose the problem of mechanism design, specifically for the class of linear redistribution mechanisms, as a naturally existing multi-objective optimisation problem. We apply a modified version of NSGA-II in order to design mechanisms within this class, given economically relevant objectives such as welfare and fairness. This application of NSGA-II exposes tradeoffs between objectives, revealing relationships between them that were otherwise unknown for this mechanism class. The understanding of the trade-off gained from the application of MOEAs can thus help practitioners with an insightful application of discovered mechanisms in their respective real/artificial markets

Static, dynamic, and adaptive heterogeneity in distributed smart camera networks

We study heterogeneity among nodes in self-organizing smart camera networks, which use strategies based on social and economic knowledge to target communication activity efficiently. We compare homogeneous configurations, when cameras use the same strategy, with heterogeneous configurations, when cameras use different strategies. Our first contribution is to establish that static heterogeneity leads to new outcomes that are more efficient than those possible with homogeneity. Next, two forms of dynamic heterogeneity are investigated: nonadaptive mixed strategies and adaptive strategies, which learn online. Our second contribution is to show that mixed strategies offer Pareto efficiency consistently comparable with the most efficient static heterogeneous configurations. Since the particular configuration required for high Pareto efficiency in a scenario will not be known in advance, our third contribution is to show how decentralized online learning can lead to more efficient outcomes than the homogeneous case. In some cases, outcomes from online learning were more efficient than all other evaluated configuration types. Our fourth contribution is to show that online learning typically leads to outcomes more evenly spread over the objective space. Our results provide insight into the relationship between static, dynamic, and adaptive heterogeneity, suggesting that all have a key role in achieving efficient self-organization

A survey of self-awareness and its application in computing systems

Novel computing systems are increasingly being composed of large numbers of heterogeneous components, each with potentially different goals or local perspectives, and connected in networks which change over time. Management of such systems quickly becomes infeasible for humans. As such, future computing systems should be able to achieve advanced levels of autonomous behaviour. In this context, the system's ability to be self-aware and be able to self-express becomes important. This paper surveys definitions and current understanding of self-awareness and self-expression in biology and cognitive science. Subsequently, previous efforts to apply these concepts to computing systems are described. This has enabled the development of novel working definitions for self-awareness and self-expression within the context of computing systems

Discovering transcriptional modules by Bayesian data integration

Motivation: We present a method for directly inferring transcriptional modules (TMs) by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both datasets. In particular, it allows us to identify the subset of genes that share the same structure of transcriptional modules in both datasets. Results: We find that by working on a gene-by-gene basis, our model is able to extract clusters with greater functional coherence than existing methods. By combining gene expression and transcription factor binding (ChIP-chip) data in this way, we are better able to determine the groups of genes that are most likely to represent underlying TMs

Bulk ultrafine grained/nanocrystalline metals via slow cooling.

Cooling, nucleation, and phase growth are ubiquitous processes in nature. Effective control of nucleation and phase growth is of significance to yield refined microstructures with enhanced performance for materials. Recent studies reveal that ultrafine grained (UFG)/nanocrystalline metals exhibit extraordinary properties. However, conventional microstructure refinement methods, such as fast cooling and inoculation, have reached certain fundamental limits. It has been considered impossible to fabricate bulk UFG/nanocrystalline metals via slow cooling. Here, we report a new discovery that nanoparticles can refine metal grains to ultrafine/nanoscale by instilling a continuous nucleation and growth control mechanism during slow cooling. The bulk UFG/nanocrystalline metal with nanoparticles also reveals an unprecedented thermal stability. This method overcomes the grain refinement limits and may be extended to any other processes that involve cooling, nucleation, and phase growth for widespread applications

The divergent DSL ligand Dll3 does not activate Notch signaling but cell autonomously attenuates signaling induced by other DSL ligands

Mutations in the DSL (Delta, Serrate, Lag2) Notch (N) ligand Delta-like (Dll) 3 cause skeletal abnormalities in spondylocostal dysostosis, which is consistent with a critical role for N signaling during somitogenesis. Understanding how Dll3 functions is complicated by reports that DSL ligands both activate and inhibit N signaling. In contrast to other DSL ligands, we show that Dll3 does not activate N signaling in multiple assays. Consistent with these findings, Dll3 does not bind to cells expressing any of the four N receptors, and N1 does not bind Dll3-expressing cells. However, in a cell-autonomous manner, Dll3 suppressed N signaling, as was found for other DSL ligands. Therefore, Dll3 functions not as an activator as previously reported but rather as a dedicated inhibitor of N signaling. As an N antagonist, Dll3 promoted Xenopus laevis neurogenesis and inhibited glial differentiation of mouse neural progenitors. Finally, together with the modulator lunatic fringe, Dll3 altered N signaling levels that were induced by other DSL ligands

Traffic, Susceptibility, and Childhood Asthma

Results from studies of traffic and childhood asthma have been inconsistent, but there has been little systematic evaluation of susceptible subgroups. In this study, we examined the relationship of local traffic-related exposure and asthma and wheeze in southern California school children (5–7 years of age). Lifetime history of doctor-diagnosed asthma and prevalent asthma and wheeze were evaluated by questionnaire. Parental history of asthma and child’s history of allergic symptoms, sex, and early-life exposure (residence at the same home since 2 years of age) were examined as susceptibility factors. Residential exposure was assessed by proximity to a major road and by modeling exposure to local traffic-related pollutants. Residence within 75 m of a major road was associated with an increased risk of lifetime asthma [odds ratio (OR) = 1.29; 95% confidence interval (CI), 1.01–1.86], prevalent asthma (OR = 1.50; 95% CI, 1.16–1.95), and wheeze (OR = 1.40; 95% CI, 1.09–1.78). Susceptibility increased in long-term residents with no parental history of asthma for lifetime asthma (OR = 1.85; 95% CI, 1.11–3.09), prevalent asthma (OR = 2.46; 95% CI, 0.48–4.09), and recent wheeze (OR = 2.74; 95% CI, 1.71–4.39). The higher risk of asthma near a major road decreased to background rates at 150–200 m from the road. In children with a parental history of asthma and in children moving to the residence after 2 years of age, there was no increased risk associated with exposure. Effect of residential proximity to roadways was also larger in girls. A similar pattern of effects was observed with traffic-modeled exposure. These results indicate that residence near a major road is associated with asthma. The reason for larger effects in those with no parental history of asthma merits further investigation

MTA3 Represses Cancer Stemness by Targeting the SOX2OT/SOX2 Axis

Cancer cell stemness (CCS) plays critical roles in both malignancy maintenance and metastasis, yet the underlying molecular mechanisms are far from complete. Although the importance of SOX2 in cancer development and CCS are well recognized, the role of MTA3 in these processes is unknown. In this study, we used esophageal squamous cell carcinoma (ESCC) as a model system to demonstrate that MTA3 can repress both CCS and metastasis in vitro and in vivo. Mechanistically, by forming a repressive complex with GATA3, MTA3 downregulates SOX2OT, subsequently suppresses the SOX2OT/SOX2 axis, and ultimately represses CCS and metastasis. More importantly, MTA