Self-consistent, polycrystal rate-independent crystal plasticity modeling for yield surface determination

The evolution of the macroscopically observed yield surface has been the subject of many studies due to its significant effect on the numerical simulation of metal forming processes. Although macroscopic models exist that aim to define this evolution accurate data for calibration as well as validation of these models are difficult to obtain. One common approach is to use crystal plasticity simulations for analyzing the mesoscopic behavior followed by a homogenization scheme for gathering the aggregate behavior. In this study a similar approach is followed the difference being the choice of the crystal plasticity and homogenization methods. A rate-independent crystal plasticity framework where all slip system activities are solved implicitly using a backward Euler approach in combination with an interior point method for constrained optimization is used for single crystal behavior. The aggregate behavior is obtained using a self-consistent analytical homogenization scheme. The results of the homogenization scheme are compared against full-field crystal plasticity finite element simulations. The determination of the yield surface is done by considering the macroscopic behavior where the strain rate direction and magnitude changes over a threshold during stress-based loading.

Utilizing Web Trackers for Sybil Defense

User tracking has become ubiquitous practice on the Web, allowing services to recommend behaviorally targeted content to users. In this article, we design Alibi, a system that utilizes such readily available personalized content, generated by recommendation engines in real time, as a means to tame Sybil attacks. In particular, by using ads and other tracker-generated recommendations as implicit user “certificates,” Alibi is capable of creating meta-profiles that allow for rapid and inexpensive validation of users’ uniqueness, thereby enabling an Internet-wide Sybil defense service. We demonstrate the feasibility of such a system, exploring the aggregate behavior of recommendation engines on the Web and demonstrating the richness of the meta-profile space defined by such inputs. We further explore the fundamental properties of such meta-profiles, i.e., their construction, uniqueness, persistence, and resilience to attacks. By conducting a user study, we show that the user meta-profiles are robust and show important scaling effects. We demonstrate that utilizing even a moderate number of popular Web sites empowers Alibi to tame large-scale Sybil attacks.

Repeated Interaction and Its Impact on Cooperation and Surplus Allocation—An Experimental Analysis

This paper investigates how the possibility of affecting group composition combined with the possibility of repeated interaction impacts cooperation within groups and surplus distribution. We developed and tested experimentally a Surplus Allocation Game where cooperation of four agents is needed to produce surplus, but only two have the power to allocate it among the group members. Three matching procedures (corresponding to three separate experimental treatments) were used to test the impact of the variables of interest. A total of 400 subjects participated in our research, which was computer-based and conducted in a laboratory. Our results show that allowing for repeated interaction with the same partners leads to a self-selection of agents into groups with different life spans, whose duration is correlated with the behavior of both distributors and receivers. While behavior at the group level is diverse for surplus allocation and amount of cooperation, aggregate behavior is instead similar when repeated interaction is allowed or not allowed. We developed a behavioral model that captures the dynamics observed in the experimental data and sheds light into the rationales that drive the agents’ individual behavior, suggesting that the most generous distributors are those acting for fear of rejection, not for true generosity, while the groups lasting the longest are those composed by this type of distributors and “undemanding” receivers.

A Reusable Characterization of the Memory System Behavior of SPEC2017 and SPEC2006

The SPEC CPU Benchmarks are used extensively for evaluating and comparing improvements to computer systems. This ubiquity makes characterization critical for researchers to understand the bottlenecks the benchmarks do and do not expose and where new designs should and should not be expected to show impact. However, in characterization there is a tradeoff between accuracy and reusability: The more precisely we characterize a benchmark’s performance on a given system, the less usable it is across different micro-architectures and varying memory configurations. For SPEC, most existing characterizations include system-specific effects (e.g., via performance counters) and/or only look at aggregate behavior (e.g., averages over the full application execution). While such approaches simplify characterization, they make it difficult to separate the applications’ intrinsic behavior from the system-specific effects and/or lose the diverse phase-based behaviors. In this work we focus on characterizing the applications’ intrinsic memory behaviour by isolating them from micro-architectural configuration specifics. We do this by providing a simplified generic system model that evaluates the applications’ memory behavior across multiple cache sizes, with and without prefetching, and over time. The resulting characterization can be reused across a range of systems to understand application behavior and allow us to see how frequently different behaviors occur. We use this approach to compare the SPEC 2006 and 2017 suites, providing insight into their memory system behaviour beyond previous system-specific and/or aggregate results. We demonstrate the ability to use this characterization in different contexts by showing a portion of the SPEC 2017 benchmark suite that could benefit from giga-scale caches, despite aggregate results indicating otherwise.

Local Communication in Populations of Thermostatically Controlled Loads

Thermostatically controlled loads (TCLs) have shown great potential for demand response (DR) in electric grid operations. However, it has been commonly seen that DR events using TCLs may cause load synchronization and unwanted oscillatory effects, especially in homogeneous populations. In an attempt to mitigate the negative impacts of DR events, a decentralized method is proposed that modifies each thermostat behavior based on the activity of a small number of nearby TCLs. This feedback introduces the possibility of instability in the aggregate behavior. A stability analysis is performed on a linearized model of the aggregate system and the results of that analysis compared to simulation results. The proposed modification of thermostat behavior results in fourfold reduction in the post-DR peak while suppressing ensuing oscillations at the expense of a modest increase in compressor cycling. The linearized model also provides insight into the aggregate behavior of the population.

Image versus Information: Changing Societal Norms and Optimal Privacy

We analyze the costs and benefits of using social image to foster desirable behaviors. Each agent acts based on his intrinsic motivation, private assessment of the public good, and reputational concern for appearing prosocial. A Principal sets the general degree of privacy, observes the social outcome, and implements a policy: investment, subsidy, law, etc. Individual visibility reduces free riding but makes aggregate behavior (“descriptive norm”) less informative about societal preferences (“prescriptive norm”). We derive the level of privacy (and material incentives) that optimally trades off social enforcement and learning, and we characterize its variations with the economy’s stochastic and informational structure. (JEL D82, D83, D91, Z13)

Spatial behavior of corn leaf aphid and syrphid flies in corn crop in the northeast of Pará

The aim of this study was to verify the spatial and temporal behavior of corn leaf aphid (Rhopalosiphum maidis Fitch.) and hoverflies in corn crop in the municipality of Igarapé-Açu, northeast of Pará, Brazil, as well as the influence of adjacent areas on the occurrence of these insects. An experimental area of 1.0 ha (100 x 100 m) was used in the years 2015 and 2016, adjacent areas consisted of mango agroecosystem, pasture and secondary forest. The area was divided into grid with 100 plots of 100 m² (10 x 10 m). Ten plants were randomly selected, totaling 1,000 plants per sampling date. Corn leaf aphid colonies (= or> 15 aphid) and syrphid flies adult was visually analyzed throughout the aerial parts of the plants. The spatial behavior was analyzed by semivariogram modeling and kriging interpolation maps. The semivariograms and kriging maps were made by the R software for Windows. Gaussian, spherical and exponential models were the best fit for corn leaf aphid in both harvests, showing aggregate behavior. The strong and moderate spatial dependence index prevailed, with range ranging from 12.46 to 93.04 m for R. maidis. The syrphids flies showed spatial interaction with the corn leaf aphid and they also show aggregate behavior, confirmed by most adjustments in the spherical and exponential models. The spatial dependence index of the prevailing syrphids flies were moderate and weak, ranging from 14.00 to 101.33 m. Adjacent areas showed influence on occurrence and dispersal of both corn leaf aphid and syrphids flies.