The leaky semicolon: compositional semantic dependencies for relaxed-memory concurrency

Program logics and semantics tell a pleasant story about sequential composition: when executing (S1;S2), we first execute S1 then S2. To improve performance, however, processors execute instructions out of order, and compilers reorder programs even more dramatically. By design, single-threaded systems cannot observe these reorderings; however, multiple-threaded systems can, making the story considerably less pleasant. A formal attempt to understand the resulting mess is known as a “relaxed memory model.” Prior models either fail to address sequential composition directly, or overly restrict processors and compilers, or permit nonsense thin-air behaviors which are unobservable in practice. To support sequential composition while targeting modern hardware, we enrich the standard event-based approach with preconditions and families of predicate transformers. When calculating the meaning of (S1; S2), the predicate transformer applied to the precondition of an event e from S2 is chosen based on the set of events in S1 upon which e depends. We apply this approach to two existing memory models.

Correlations of Rock-Physics Model Parameters From Bayesian Analysis: Pressure- and Porosity-Dependent Models Applied to Unconsolidated Sands

Correlations of rock-physics model inputs are important to know to help design informative prior models within integrated reservoir-characterization workflows. A Bayesian framework is optimal to determine such correlations. Within that framework, we use velocity and porosity measurements on unconsolidated, dry, and clean sands. Three pressure- and three porosity-dependent rock-physics models are applied to the data to examine relationships among the inputs. As with any Bayesian formulation, we define a prior model and calculate the likelihood in order to evaluate the posterior. With relatively few inputs to consider for each rock-physics model, we found that sampling the posterior exhaustively to be convenient. The results of the Bayesian analyses are multivariate histograms that indicate most likely values of the input parameters given the data to which the rock-physics model was fit. When the Bayesian procedure is repeated many times for the same data, but with different prior models, correlations emerged among the input parameters in a rock-physics model. These correlations were not known previously. Implications, for the pressure- and porosity-dependent models examined here, are that these correlations should be utilized when applying these models to other relevant data sets. Furthermore, additional rock-physics models should be examined similarly to determine any potential correlations in their inputs. These correlations can then be taken advantage of in forward and inverse problems posed in reservoir characterization.

Skin Cancer Diseases Classification using Deep Convolutional Neural Network with Transfer Learning Model

Skin cancer is becoming increasingly common. Fortunately, early discovery can greatly improve the odds of a patient being healed. Many Artificial Intelligence based approaches to classify skin lesions have recently been proposed. but these approaches suffer from limited classification accuracy. Deep convolutional neural networks show potential for better classification of cancer lesions. This paper presents a fine-tuning on Xception pretrained model for classification of skin lesions by adding a group of layers after the basic ones of the Xception model and all model weights are set to be trained. The model is fine-tuned over HAM10,000 dataset seven classes by augmentation approach to mitigate the data imbalance effect and conducted a comparative study with the most up to date approaches. In comparison to prior models, the results indicate that the proposed model is both efficient and reliable.

Technological rejection in regions of early gold innovation revealed by geospatial analysis

In research on early invention and innovation, technological “firsts” receive enormous attention, but technological “lasts”—instances of abandonment and rejection—are arguably more informative about human technological behavior. Yet, cases of technological discontinuance are largely ignored in studies of early innovation, as the lack of robust datasets makes identification and analysis difficult. A large-scale geospatial analysis of more than 4500 gold objects from the Caucasus, an early center of gold innovation, shows a precipitous decline at 1500 BC in precisely the places with the earliest global evidence of gold mining (c. 3000 BC). Testing various causal models reveals that social factors, rather than resource limitations or demographic disruption, were the primary causes of this rejection. These results indicate that prior models of technological rejection and loss have underestimated the range of conditions in which they can occur, and provide empirical support for theories of innovation that reject notions about the linearity of technological progress.

Flexible Path Planning Through Vicarious Trial and Error

Flexible planning is necessary for reaching goals and adapting when conditions change. We introduce a biologically plausible path planning model that learns its environment, rapidly adapts to change, and plans efficient routes to goals. Unlike prior models of hippocampal replay, our model addresses the decision-making process when faced with uncertainty. We tested the model in simulations of human and rodent navigation in mazes. Like the human and rat, the model was able to generate novel shortcuts, and take detours when familiar routes were blocked. Similar to rodent hippocampus recordings, the neural activity of the model resembles neural correlates of Vicarious Trial and Error (VTE) during early learning or during uncertain conditions. Similar to rodent studies, after learning, the neural activity resembles forward replay or preplay predicting a future route, and VTE activity decreases. We suggest that VTE, in addition to weighing possible outcomes, is a way in which an organism may gather information for future use.

Rapid Development Studio: An Intensive, Iterative Approach to Designing Online Learning

Given the increasing demand for online learning at the tertiary level, there currently exists a need to modify or develop instructional design (ID) models/approaches that can effectively facilitate the collaboration between learning designers and teachers, as well as to research the effectiveness of these models/approaches. Against this backdrop, adopting a design-based research approach, we tested a practical ID approach that is developed on two prior models: rapid prototyping and collaborative course development. Accordingly, a 2-week rapid development studio—an agile, intensive, iterative ID process—was arranged. Data from multiple sources were gleaned during the study to generate a comprehensive and in-depth understanding of the proposed approach. Overall, results suggest that the approach is effective for developing online courses in case of a limited time frame and was positively perceived by both course instructors and learning designers. Moreover, practical tips for replicating the process in other contexts are also shared. It is our hope that the study will stimulate further exploration of alternative ID models/approaches to improve online course design efficacy in other higher education institutions.

Reinforcement Learning Based Hierarchical Multi-Agent Robotic Search Team in Uncertain Environment

Field of robotics has been under the limelight because of recent advances in Artificial Intelligence (AI). Due to increased diversity in multi-agent systems, new models are being developed to handle complexity of such systems. However, most of these models do not address problems such as; uncertainty handling, efficient learning, agent coordination and fault detection. This paper presents a novel approach of implementing Reinforcement Learning (RL) on hierarchical robotic search teams. The proposed algorithm handles uncertainties in the system by implementing Q-learning and depicts enhanced efficiency as well as better time consumption compared to prior models. The reason for that is each agent can take action on its own thus there is less dependency on leader agent for RL policy. The performance of this algorithm is measured by introducing agents in an unknown environment with both Markov Decision Process (MDP) and RL policies at their disposal. Simulation-based comparison of the agent motion is presented using the results from of MDP and RL policies. Furthermore, qualitative comparison of the proposed model with prior models is also presented.

Impairment in acquisition of conditioned fear in schizophrenia: a pooled analysis of four studies

Background: Individuals with schizophrenia show impairments in associative learning. One well-studied, quantifiable form of associative learning is Pavlovian fear conditioning. However, to date, studies of fear conditioning in schizophrenia have been inconclusive, possibly because they lacked sufficient power. Methods: To address this issue, data were pooled from 4 independent fear conditioning studies that included a total of 77 individuals with schizophrenia and 74 control subjects. Skin conductance responses (SCRs) during fear conditioning to stimuli that were paired (the CS+) and not paired (CS-) with an aversive, unconditioned stimulus were measured, and the success of acquisition of differential conditioning (the magnitude of CS+ vs CS- SCRs) and responses to CS+ and CS- separately were assessed. Results: Acquisition of differential conditioned fear responses was significantly lower in individuals with schizophreania than in healthy controls (Cohen's d = 0.53). This effect was primarily related to a significantly higher response to the CS- stimulus in the schizophrenia compared to the control group. The magnitude of this response to the CS- in the schizophrenia group was correlated with the severity of delusional ideation. Other symptoms or antipsychotic dose were not associated with fear conditioning measures. Conclusions: Individuals with schizophrenia who endorse delusional beliefs are over-responsive to neutral stimuli during fear conditioning. This finding is consistent with prior models of aberrant learning in psychosis.

Optimal band structure for thermoelectrics with realistic scattering and bands

Understanding how to optimize electronic band structures for thermoelectrics is a topic of long-standing interest in the community. Prior models have been limited to simplified bands and/or scattering models. In this study, we apply more rigorous scattering treatments to more realistic model band structures—upward-parabolic bands that inflect to an inverted-parabolic behavior—including cases of multiple bands. In contrast to common descriptors (e.g., quality factor and complexity factor), the degree to which multiple pockets improve thermoelectric performance is bounded by interband scattering and the relative shapes of the bands. We establish that extremely anisotropic “flat-and-dispersive” bands, although best-performing in theory, may not represent a promising design strategy in practice. Critically, we determine optimum bandwidth, dependent on temperature and lattice thermal conductivity, from perfect transport cutoffs that can in theory significantly boost zT beyond the values attainable through intrinsic band structures alone. Our analysis should be widely useful as the thermoelectric research community eyes zT > 3.