In physical design, human designers typically place macros via trial and error, which is a Markov decision process. Reinforcement learning (RL) methods have demonstrated superhuman performance on the macro placement. In this paper, we propose an extension to this prior work [1]. We first describe the details of the policy and value network architecture. We replace the force-directed method with DREAMPlace for placing standard cells in the RL environment. We also compare our improved method with other academic placers on public benchmarks.

Query optimizers depend on selectivity estimates of query predicates to produce a good execution plan. When a query contains multiple predicates, today's optimizers use a variety of assumptions, such as independence between predicates, to estimate selectivity. While such techniques have the benefit of fast estimation and small memory footprint, they often incur large selectivity estimation errors. In this work, we reconsider selectivity estimation as a regression problem. We explore application of neural networks and tree-based ensembles to the important problem of selectivity estimation of multi-dimensional range predicates. While their straightforward application does not outperform even simple baselines, we propose two simple yet effective design choices, i.e., regression label transformation and feature engineering, motivated by the selectivity estimation context. Through extensive empirical evaluation across a variety of datasets, we show that the proposed models deliver both highly accurate estimates as well as fast estimation.

No abstract

Nowadays mobile phones are not only communication devices, but also a source of rich sensory data that can be collected and exploited by distributed people-centric sensing applications. Among them, environmental monitoring and emergency response systems can particularly benefit from people-based sensing. Due to the limited resources of mobile devices, sensed data are usually offloaded to the cloud. However, state-of-the art solutions lack a unified approach suitable to support diverse applications, while reducing the energy consumption of the mobile device. In this paper, we specifically address mobile devices as rich sources of multimodal data collected by users. In this context, we propose an integrated framework for storing, processing and delivering sensed data to people-centric applications deployed in the cloud. Our integrated platform is the foundation of a new delivery model, namely, Mobile Application as a Service (MAaaS), which allows the creation of people-centric applications across different domains, including participatory sensing and mobile social networks. We specifically address a case study represented by an emergency response system for fire detection and alerting. Through a prototype testbed implementation, we show that the proposed framework can reduce the energy consumption of mobile devices, while satisfying the application requirements.

Abstract. Sensor nodes in a Wireless Sensor Network (WSN) are responsible for sensing the environment and propagating the collected data in the network. The communication between sensor nodes may fail due to different factors, such as hardware failures, energy depletion, temporal variations of the wireless channel and interference. To maximize efficiency, the sensor network deployment must be robust and resilient to such failures. One effective solution to this problem has been inspired by Gene Regulatory Networks (GRNs). Owing to millions of years of evolution, GRNs display intrinsic properties of adaptation and robustness, thus making them suitable for dynamic network environments. In this paper, we exploit real biological gene structures to deploy wireless sensor networks, called bio-inspired WSNs. Exhaustive structural analysis of the network and experimental results demonstrate that the topology of bio-inspired WSNs is robust, energy-efficient, and resilient to node and link failures.