Activation process driven by strongly non-Gaussian noises

The constructive role of non-Gaussian random fluctuations is studied in the context of the passage over the dichotomously switching potential barrier. Our attention focuses on the interplay of the effects of independent sources of fluctuations: an additive stable noise representing non-equilibrium external random force acting on the system and a fluctuating barrier. In particular, the influence of the structure of stable noises on the mean escape time and on the phenomenon of resonant activation (RA) is investigated. By use of the numerical Monte Carlo method it is documented that the suitable choice of the barrier switching rate and random external fields may produce resonant phenomenon leading to the enhancement of the kinetics and the shortest, most efficient reaction time.Comment: 11 pages, 8 figure

Resonant activation driven by strongly non-Gaussian noises

The constructive role of non-Gaussian random fluctuations is studied in the context of the passage over the dichotomously switching potential barrier. Our attention focuses on the interplay of the effects of independent sources of fluctuations: an additive stable noise representing non-equilibrium external random force acting on the system and a fluctuating barrier. In particular, the influence of the structure of stable noises on the mean escape time and on the phenomenon of resonant activation (RA) is investigated. By use of the numerical Monte Carlo method it is documented that the suitable choice of the barrier switching rate and random external fields may produce resonant phenomenon leading to the enhancement of the kinetics and the shortest, most efficient reaction time.Comment: 9 pages, 7 figures, RevTeX

Efficient IoT Inference via Context-Awareness

While existing strategies to execute deep learning-based classification on low-power platforms assume the models are trained on all classes of interest, this paper posits that adopting context-awareness i.e. narrowing down a classification task to the current deployment context consisting of only recent inference queries can substantially enhance performance in resource-constrained environments. We propose a new paradigm, CACTUS, for scalable and efficient context-aware classification where a micro-classifier recognizes a small set of classes relevant to the current context and, when context change happens (e.g., a new class comes into the scene), rapidly switches to another suitable micro-classifier. CACTUS features several innovations, including optimizing the training cost of context-aware classifiers, enabling on-the-fly context-aware switching between classifiers, and balancing context switching costs and performance gains via simple yet effective switching policies. We show that CACTUS achieves significant benefits in accuracy, latency, and compute budget across a range of datasets and IoT platforms.Comment: 12 pages, 8 figure

Calculating the minimum bounds of energy consumption for cloud networks

This paper is aiming at facilitating the energy-efficient operation of an integrated optical network and IT infrastructure. In this context we propose an energy-efficient routing algorithm for provisioning of IT services that originate from specific source sites and which need to be executed by suitable IT resources (e. g. data centers). The routing approach followed is anycast, since the requirement for the IT services is the delivery of results, while the exact location of the execution of the job can be freely chosen. In this scenario, energy efficiency is achieved by identifying the least energy consuming IT and network resources required to support the services, enabling the switching off of any unused network and IT resources. Our results show significant energy savings that can reach up to 55% compared to energy-unaware schemes, depending on the granularity with which a data center is able to switch on/off servers

Multi-core job submission and grid resource scheduling for ATLAS AthenaMP

AthenaMP is the multi-core implementation of the ATLAS software framework and allows the efficient sharing of memory pages between multiple threads of execution. This has now been validated for production and delivers a significant reduction on the overall application memory footprint with negligible CPU overhead. Before AthenaMP can be routinely run on the LHC Computing Grid it must be determined how the computing resources available to ATLAS can best exploit the notable improvements delivered by switching to this multi-process model. A study into the effectiveness and scalability of AthenaMP in a production environment will be presented. Best practices for configuring the main LRMS implementations currently used by grid sites will be identified in the context of multi-core scheduling optimisation

Information processes of task-switching and modality-shifting across development

Developmental research on flexible attentional control in young children has often focused on the role of attention in task-switching in a unimodal context. In real life, children must master the art of switching attention not only between task demands, but also between sensory modalities. Previous study has shown that young children can be efficient at switching between unimodal tasks when the situation allows, incurring no greater task-switching costs than adults. However, young children may still experience a greater demand to shift attention between modalities than older participants. To address this, we tested 4-year-olds, 6-year-olds and adults on a novel cross-modal task-switching paradigm involving multisensory detection tasks. While we found age differences in absolute reaction time and accuracy, young children and adults both exhibited strikingly similar effects in task-switching, modality-shifting, and the interaction between them. Young children did not exhibit a greater attentional bottleneck on either the task level, or on the modality level; thus, the evidence suggests that young children engaged in similar cognitive operations in the current cross-modal tasks to adult participants. It appears that cognitive operations in multisensory task configuration are relatively mature between 4 and 6 years old

GaN-based matrix resonant power converter for domestic induction heating

Flexible-surface induction cooktops must operate with a variety of induction heating loads with different behavior and power setpoints to be heated simultaneously. In this context, multi-output inverter topologies aim at achieving independent power management while featuring low power-device count and high power density. However, they suffer from limitations when applying classical modulation strategies to ensure soft switching, which is required to reduce transistor losses and achieve efficient operation. In this scenario, wide band-gap devices reduce switching losses, opening a new paradigm in power conversion where soft switching is not mandatory in order to achieve high efficiency. This paper proposes an implementation of a multi-output resonant inverter based on GaN HEMTs and evaluates various modulation strategies in terms of efficiency under different switching modes. The proposed approach is designed and experimentally validated by means of a 2-coil 2000 W prototype implementation