Control theory for principled heap sizing

We propose a new, principled approach to adaptive heap sizing based on control theory. We review current state-of-the-art heap sizing mechanisms, as deployed in Jikes RVM and HotSpot. We then formulate heap sizing as a control problem, apply and tune a standard controller algorithm, and evaluate its performance on a set of well-known benchmarks. We find our controller adapts the heap size more responsively than existing mechanisms. This responsiveness allows tighter virtual machine memory footprints while preserving target application throughput, which is ideal for both embedded and utility computing domains. In short, we argue that formal, systematic approaches to memory management should be replacing ad-hoc heuristics as the discipline matures. Control-theoretic heap sizing is one such systematic approach

A Data Set for Fault Detection Research on Component-Based Robotic Systems

Wienke J, Meyer zu Borgsen S, Wrede S. A Data Set for Fault Detection Research on Component-Based Robotic Systems. In: Alboul L, Damian D, Aitken JM, eds. Towards Autonomous Robotic Systems. Lecture Notes in Artificial Intelligence. Vol 9716. Springer International Publishing; 2016: 339-350.Fault detection and identification methods (FDI) are an important aspect for ensuring consistent behavior of technical systems. In robotics FDI promises to improve the autonomy and robustness. Existing FDI research in robotics mostly focused on faults in specific areas, like sensor faults. While there is FDI research also on the overarching software system, common data sets to benchmark such solutions do not exist. In this paper we present a data set for FDI research on robot software systems to bridge this gap. We have recorded an HRI scenario with our RoboCup@Home platform and induced diverse empirically grounded faults using a novel, structured method. The recordings include the complete event-based communication of the system as well as detailed performance counters for all system components and exact ground-truth information on the induced faults. The resulting data set is a challenging benchmark for FDI research in robotics which is publicly available

Dynamic graphical instructions result in improved attitudes and decreased task completion time in human−robot co-working: an experimental manufacturing study

Collaborative robots offer opportunities to increase the sustainability of work and workforces by increasing productivity, quality, and efficiency, whilst removing workers from hazardous, repetitive, and strenuous tasks. They also offer opportunities for increasing accessibility to work, supporting those who may otherwise be disadvantaged through age, ability, gender, or other characteristics. However, to maximise the benefits, employers must overcome negative attitudes toward, and a lack of confidence in, the technology, and must take steps to reduce errors arising from misuse. This study explores how dynamic graphical signage could be employed to address these issues in a manufacturing task. Forty employees from one UK manufacturing company participated in a field experiment to complete a precision pick-and-place task working in conjunction with a collaborative robotic arm. Twenty-one participants completed the task with the support of dynamic graphical signage that provided information about the robot and the activity, while the rest completed the same task with no signage. The presence of the signage improved the completion time of the task as well as reducing negative attitudes towards the robots. Furthermore, participants provided with no signage had worse outcome expectancies as a function of their response time. Our results indicate that the provision of instructional information conveyed through appropriate graphical signage can improve task efficiency and user wellbeing, contributing to greater workforce sustainability. The findings will be of interest for companies introducing collaborative robots as well as those wanting to improve their workforce wellbeing and technology acceptance

A Modular Digital Twinning Framework for Safety Assurance of Collaborative Robotics

Digital twins offer a unique opportunity to design, test, deploy, monitor, and control real-world robotic processes. In this paper we present a novel, modular digital twinning framework developed for the investigation of safety within collaborative robotic manufacturing processes. The modular architecture supports scalable representations of user-defined cyber-physical environments, and tools for safety analysis and control. This versatile research tool facilitates the creation of mixed environments of Digital Models, Digital Shadows, and Digital Twins, whilst standardising communication and physical system representation across different hardware platforms. The framework is demonstrated as applied to an industrial case-study focused on the safety assurance of a collaborative robotic manufacturing process. We describe the creation of a digital twin scenario, consisting of individual digital twins of entities in the manufacturing case study, and the application of a synthesised safety controller from our wider work. We show how the framework is able to provide adequate evidence to virtually assess safety claims made against the safety controller using a supporting validation module and testing strategy. The implementation, evidence and safety investigation is presented and discussed, raising exciting possibilities for the use of digital twins in robotic safety assurance

Systems developmental biology: the use of ontologies in annotating models and in identifying gene function within and across species

Systems developmental biology is an approach to the study of embryogenesis that attempts to analyze complex developmental processes through integrating the roles of their molecular, cellular, and tissue participants within a computational framework. This article discusses ways of annotating these participants using standard terms and IDs now available in public ontologies (these are areas of hierarchical knowledge formalized to be computationally accessible) for tissues, cells, and processes. Such annotations bring two types of benefit. The first comes from using standard terms: This allows linkage to other resources that use them (e.g., GXD, the gene-expression [G-E] database for mouse development). The second comes from the annotation procedure itself: This can lead to the identification of common processes that are used in very different and apparently unrelated events, even in other organisms. One implication of this is the potential for identifying the genes underpinning common developmental processes in different tissues through Boolean analysis of their G-E profiles. While it is easiest to do this for single organisms, the approach is extendable to analyzing similar processes in different organisms. Although the full computational infrastructure for such an analysis has yet to be put in place, two examples are briefly considered as illustration. First, the early development of the mouse urogenital system shows how a line of development can be graphically formalized using ontologies. Second, Boolean analysis of the G-E profiles of the mesenchyme-to-epithelium transitions that take place during mouse development suggest Lhx1, Foxc1, and Meox1 as candidate transcription factors for mediating this process

Protocol for the stimulating β3-Adrenergic receptors for peripheral artery disease (STAR-PAD) trial: a double-blinded, randomised, placebo-controlled study evaluating the effects of mirabegron on functional performance in patients with peripheral arterial disease

Introduction: There is currently only one approved medication effective at improving walking distance in people with intermittent claudication. Preclinical data suggest that the β3-adrenergic receptor agonist (mirabegron) could be repurposed to treat intermittent claudication associated with peripheral artery disease. The aim of the Stimulating β3-Adrenergic Receptors for Peripheral Artery Disease (STAR-PAD) trial is to test whether mirabegron improves walking distance in people with intermittent claudication. Methods and analysis: The STAR-PAD trial is a Phase II, multicentre, double-blind, randomised, placebo-controlled trial of mirabegron versus placebo on walking distance in patients with PAD. A total of 120 patients aged ≥40 years with stable PAD and intermittent claudication will be randomly assigned (1:1 ratio) to receive either mirabegron (50 mg orally once a day) or matched placebo, for 12 weeks. The primary endpoint is change in peak walking distance as assessed by a graded treadmill test. Secondary endpoints will include: (i) initial claudication distance; (ii) average daily step count and total step count and (iii) functional status and quality of life assessment. Mechanistic substudies will examine potential effects of mirabegron on vascular function, including brachial artery flow-mediate dilatation; MRI assessment of lower limb blood flow, tissue perfusion and arterial stiffness and numbers and angiogenesis potential of endothelial progenitor cells. Given that mirabegron is safe and clinically available for alternative purposes, a positive study is positioned to immediately impact patient care. Ethics and dissemination: The STAR-PAD trial is approved by the Northern Sydney Local Health District Human Research Ethics Committee (HREC/18/HAWKE/50). The study results will be published in peer-reviewed medical or scientific journals and presented at scientific meetings, regardless of the study outcomes

Floor determination in the operation of a lift by a mobile guide robot

Robotic assistants operating in multi-floor buildings are required to use lifts to transition between floors. To reduce the need for environments to be tailored to suit robots, and to make robot assistants more applicable, it is desirable that they should make use of existing navigational cues and interfaces designed for human users. In this paper, we examine the scenario whereby a guide robot uses a lift to transition between floors in a building. We describe an experiment into combining multiple data sources, available to a typical robot with simple sensors, to determine which floor of the building it is on. We show the robustness of this approach to realistic scenarios in a busy working environment

Ketamine effects on memory reconsolidation favor a learning model of delusions.

Delusions are the persistent and often bizarre beliefs that characterise psychosis. Previous studies have suggested that their emergence may be explained by disturbances in prediction error-dependent learning. Here we set up complementary studies in order to examine whether such a disturbance also modulates memory reconsolidation and hence explains their remarkable persistence. First, we quantified individual brain responses to prediction error in a causal learning task in 18 human subjects (8 female). Next, a placebo-controlled within-subjects study of the impact of ketamine was set up on the same individuals. We determined the influence of this NMDA receptor antagonist (previously shown to induce aberrant prediction error signal and lead to transient alterations in perception and belief) on the evolution of a fear memory over a 72 hour period: they initially underwent Pavlovian fear conditioning; 24 hours later, during ketamine or placebo administration, the conditioned stimulus (CS) was presented once, without reinforcement; memory strength was then tested again 24 hours later. Re-presentation of the CS under ketamine led to a stronger subsequent memory than under placebo. Moreover, the degree of strengthening correlated with individual vulnerability to ketamine's psychotogenic effects and with prediction error brain signal. This finding was partially replicated in an independent sample with an appetitive learning procedure (in 8 human subjects, 4 female). These results suggest a link between altered prediction error, memory strength and psychosis. They point to a core disruption that may explain not only the emergence of delusional beliefs but also their persistence