Web applications employ key-value stores to cache the data that is most commonly accessed. The cache improves an web application's performance by serving its requests from memory, avoiding fetching them from the backend database. Since the memory space is limited, maximizing the memory utilization is a key to delivering the best performance possible. This has lead to the use of multi-tenant systems, allowing applications to share cache space. In addition, application data access patterns change over time, so the system should be adaptive in its memory allocation.

Contemporary micro-architecture research inherently relies on cycle-accurate simulators to test new ideas. Typical simulator implementations involve tens of thousands of lines of high-level code. Although general software engineering verification and validation techniques can be applied, the mere complexity of simulators makes using formal techniques difficult and calls for domain-specific knowledge to be a part of the verification process. This domain-specific information includes modeling the pipeline stages and the timing behavior of instructions with respect to these stages.

Modern science relies on simulation techniques for understanding phenomenon, exploring design options, or evaluating models. Assuring the correctness of simulators is a key problem where a multitude of solutions ranging from manual inspection to formal verification are applicable. Formal verification incorporates the rigor necessary but not all simulators are generated from formal specifications. Manual inspection is readily available but lacks the rigor and is prone to errors. In this paper, we describe an automated verification system (AVS) where the constraints that the system must adhere to are specified by the user in general purpose first-order logic. AVS translates these constraints into a verification program that scans the simulator traceand verifies that no constraints are violated. Computer microarchitecture simulations were successfully used to demonstrate the proposed approach. This paper describes the preliminary results and discusses how artificial intelligence techniques can be used to facilitate effective run-time verification of simulators.

In the 2012-2013 academic year, a survey to investigate why engineering and computer science students persist in their major was conducted at Michigan Technological University. This paper discusses the results of the survey and ties the findings to the literature. It focuses on: (1) who influenced students' decisions on picking a major or on changing a major (for example, friends, family, academic advisors, faculty, upper-division or graduate students, co-workers, and supervisors), and how did they affect students' persistence and (2) what is the impact of role models on student persistence. The analysis compares students who reported not having considered changing majors to students who considered switching to another major. The findings show that the students who did not consider changing majors reported having a stronger support system including faculty, academic advisors, and engineers who serve as role models. The data suggest that university faculty and staff need to reach out to the students who are deliberating about their initial choice of major and support the decision making process.

We present a partial-order, conformant, probabilistic planner, Probapop which competed in the blind track of the Probabilistic Planning Competition in IPC-4. We explain how we adapt distance based heuristics for use with probabilistic domains. Probapop also incorporates heuristics based on probability of success. We explain the successes and difficulties encountered during the design and implementation of Probapop.