Towards automatic Markov reliability modeling of computer architectures

The analysis and evaluation of reliability measures using time-varying Markov models is required for Processor-Memory-Switch (PMS) structures that have competing processes such as standby redundancy and repair, or renewal processes such as transient or intermittent faults. The task of generating these models is tedious and prone to human error due to the large number of states and transitions involved in any reasonable system. Therefore model formulation is a major analysis bottleneck, and model verification is a major validation problem. The general unfamiliarity of computer architects with Markov modeling techniques further increases the necessity of automating the model formulation. This paper presents an overview of the Automated Reliability Modeling (ARM) program, under development at NASA Langley Research Center. ARM will accept as input a description of the PMS interconnection graph, the behavior of the PMS components, the fault-tolerant strategies, and the operational requirements. The output of ARM will be the reliability of availability Markov model formulated for direct use by evaluation programs. The advantages of such an approach are (a) utility to a large class of users, not necessarily expert in reliability analysis, and (b) a lower probability of human error in the computation

Validation of multiprocessor systems

Experiments that can be used to validate fault free performance of multiprocessor systems in aerospace systems integrating flight controls and avionics are discussed. Engineering prototypes for two fault tolerant multiprocessors are tested

Optimal static and dynamic recycling of defective binary devices

The binary Defect Combination Problem consists in finding a fully working subset from a given ensemble of imperfect binary components. We determine the typical properties of the model using methods of statistical mechanics, in particular, the region in the parameter space where there is almost surely at least one fully-working subset. Dynamic recycling of a flux of imperfect binary components leads to zero wastage.Comment: 14 pages, 15 figure

Optimal combinations of imperfect objects

We address the question of how to make best use of imperfect objects, such as defective analog and digital components. We show that perfect, or near-perfect, devices can be constructed by taking combinations of such defects. Any remaining objects can be recycled efficiently. In addition to its practical applications, our `defect combination problem' provides a novel generalization of classical optimization problems.Comment: 4 pages, 3 figures, minor change

Wearable Haptic Devices for Gait Re-education by Rhythmic Haptic Cueing

This research explores the development and evaluation of wearable haptic devices for gait sensing and rhythmic haptic cueing in the context of gait re-education for people with neurological and neurodegenerative conditions. Many people with long-term neurological and neurodegenerative conditions such as Stroke, Brain Injury, Multiple Sclerosis or Parkinson’s disease suffer from impaired walking gait pattern. Gait improvement can lead to better fluidity in walking, improved health outcomes, greater independence, and enhanced quality of life. Existing lab-based studies with wearable devices have shown that rhythmic haptic cueing can cause immediate improvements to gait features such as temporal symmetry, stride length, and walking speed. However, current wearable systems are unsuitable for self-managed use for in-the-wild applications with people having such conditions. This work aims to investigate the research question of how wearable haptic devices can help in long-term gait re-education using rhythmic haptic cueing. A longitudinal pilot study has been conducted with a brain trauma survivor, providing rhythmic haptic cueing using a wearable haptic device as a therapeutic intervention for a two-week period. Preliminary results comparing pre and post-intervention gait measurements have shown improvements in walking speed, temporal asymmetry, and stride length. The pilot study has raised an array of issues that require further study. This work aims to develop and evaluate prototype systems through an iterative design process to make possible the self-managed use of such devices in-the-wild. These systems will directly provide therapeutic intervention for gait re-education, offer enhanced information for therapists, remotely monitor dosage adherence and inform treatment and prognoses over the long-term. This research will evaluate the use of technology from the perspective of multiple stakeholders, including clinicians, carers and patients. This work has the potential to impact clinical practice nationwide and worldwide in neuro-physiotherapy

Validation of Abstract Side-Channel Models for Computer Architectures

Observational models make tractable the analysis of information flow properties by providing an abstraction of side channels. We introduce a methodology and a tool, Scam-V, to validate observational models for modern computer architectures. We combine symbolic execution, relational analysis, and different program generation techniques to generate experiments and validate the models. An experiment consists of a randomly generated program together with two inputs that are observationally equivalent according to the model under the test. Validation is done by checking indistinguishability of the two inputs on real hardware by executing the program and analyzing the side channel. We have evaluated our framework by validating models that abstract the data-cache side channel of a Raspberry Pi 3 board with a processor implementing the ARMv8-A architecture. Our results show that Scam-V can identify bugs in the implementation of the models and generate test programs which invalidate the models due to hidden microarchitectural behavior

The Joint Influence of Intra- and Inter-Team Learning Processes on Team Performance: A Constructive or Destructive Combination?

In order for teams to build a shared conception of their task, team learning is crucial. Benefits of intra-team learning have been demonstrated in numerous studies. However, teams do not operate in a vacuum, and interact with their environment to execute their tasks. Our knowledge of the added value of inter-team learning (team learning with external parties) is limited. Do both types of team learning compete over limited resources, or do they form a synergistic combination? We aim to shed light on the interplay between intra- and inter-team learning in relation to team performance, by including adaptive and transformative sub-processes of intra-team learning. A quantitative field study was conducted among 108 university teacher teams. The joint influence of intra- and inter-team learning as well as structural (task interdependence) and cultural (team efficacy) team characteristics on self-perceived and externally rated team performance were explored in a path model. The results showed that adaptive intra-team learning positively influenced self-perceived team performance, while transformative intra-team learning positively influenced externally rated team performance. Moreover, intra-team and inter-team learning were found to be both a constructive and a destructive combination. Adaptive intra-team learning combined with inter-team learning led to increased team performance, while transformative intra-team learning combined with inter-team learning hurt team performance. The findings demonstrate the importance of distinguishing between both the scope (intra- vs. inter-team) and the level (adaptive vs. transformative) of team learning in understanding team performance