A Petri-Net-Based Scheduling Strategy for Dual-Arm Cluster Tools With Wafer Revisiting

International audienceThere are wafer fabrication processes in cluster tools that require wafer revisiting. The adoption of a swap strategy for such tools forms a 3-wafer cyclic (3-WC) period with three wafers completed in each period. It has been shown that, by such a scheduling strategy, the minimal cycle time cannot be reached for some cases. This raises a question of whether there is a scheduling method such that the performance can be improved. To answer this question, a dual-arm cluster tool with wafer revisiting is modeled by a Petri net. Based on the model, the dynamical behavior of the process is analyzed. Then, a 2-wafer cyclic (2-WC) scheduling strategy is revealed for the first time. Cycle time analysis is conducted for the proposed strategy to evaluate its performance. It shows that, for some cases, the performance obtained by a 2-WC schedule is better than that obtained by any existing 3-WC ones. Thus, they can be used to complement each other in scheduling dual-arm cluster tools with wafer revisiting. Illustrative examples are given

Tweet-to-Act: Towards Tweet-Mining Framework for Extracting Terrorist Attack-related Information and Reporting

The widespread popularity of social networking is leading to the adoption of Twitter as an information dissemination tool. Existing research has shown that information dissemination over Twitter has a much broader reach than traditional media and can be used for effective post-incident measures. People use informal language on Twitter, including acronyms, misspelled words, synonyms, transliteration, and ambiguous terms. This makes incident-related information extraction a non-trivial task. However, this information can be valuable for public safety organizations that need to respond in an emergency. This paper proposes an early event-related information extraction and reporting framework that monitors Twitter streams, synthesizes event-specific information, e.g., a terrorist attack, and alerts law enforcement, emergency services, and media outlets. Specifically, the proposed framework, Tweet-to-Act (T2A), employs word embedding to transform tweets into a vector space model and then utilizes theWord Mover’s Distance (WMD) to cluster tweets for the identification of incidents. To extract reliable and valuable information from a large dataset of short and informal tweets, the proposed framework employs sequence labeling with bidirectional Long Short-Term Memory based Recurrent Neural Networks (bLSTM-RNN). Extensive experimental results suggest that our proposed framework, T2A, outperforms other state-of-the-art methods that use vector space modeling and distance calculation techniques, e.g., Euclidean and Cosine distance. T2A achieves an accuracy of 96% and an F1-score of 86.2% on real-life datasets

Contextual Bandit Modeling for Dynamic Runtime Control in Computer Systems

Modern operating systems and microarchitectures provide a myriad of mechanisms for monitoring and affecting system operation and resource utilization at runtime. Dynamic runtime control of these mechanisms can tailor system operation to the characteristics and behavior of the current workload, resulting in improved performance. However, developing effective models for system control can be challenging. Existing methods often require extensive manual effort, computation time, and domain knowledge to identify relevant low-level performance metrics, relate low-level performance metrics and high-level control decisions to workload performance, and to evaluate the resulting control models. This dissertation develops a general framework, based on the contextual bandit, for describing and learning effective models for runtime system control. Random profiling is used to characterize the relationship between workload behavior, system configuration, and performance. The framework is evaluated in the context of two applications of progressive complexity; first, the selection of paging modes (Shadow Paging, Hardware-Assisted Page) in the Xen virtual machine memory manager; second, the utilization of hardware memory prefetching for multi-core, multi-tenant workloads with cross-core contention for shared memory resources, such as the last-level cache and memory bandwidth. The resulting models for both applications are competitive in comparison to existing runtime control approaches. For paging mode selection, the resulting model provides equivalent performance to the state of the art while substantially reducing the computation requirements of profiling. For hardware memory prefetcher utilization, the resulting models are the first to provide dynamic control for hardware prefetchers using workload statistics. Finally, a correlation-based feature selection method is evaluated for identifying relevant low-level performance metrics related to hardware memory prefetching

Principles of Human Learning

What are the general principles that drive human learning in different situations? I argue that much of human learning can be understood with just three principles. These are generalization, adaptation, and simplicity. To verify this conjecture, I introduce a modeling framework based on the same principles. This framework combines the idea of meta-learning -- also known as learning-to-learn -- with the minimum description length principle. The models that result from this framework capture many aspects of human learning across different domains, including decision-making, associative learning, function learning, multi-task learning, and reinforcement learning. In the context of decision-making, they explain why different heuristic decision-making strategies emerge and how appropriate strategies are selected. The same models furthermore capture order effects found in associative learning, function learning and multi-task learning. In the reinforcement learning context, they resemble individual differences between human exploration strategies and explain empirical data better than any other strategy under consideration. The proposed modeling framework -- together with its accompanying empirical evidence -- may therefore be viewed as a first step towards the identification of a minimal set of principles from which all human behavior derives