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Virtual reality technology has been applied in virtual labs and remote experiment teaching. This paper designs an electronic circuit virtual laboratory based on virtual reality technology, and relevant experimental teaching content. The system uses Unity 3D and 3ds max tools to build a three-dimensional model of general instrumentation, electronic components, and laboratory scenes in electronic circuit experiments. The C# is used to develop general virtual instrumentation functions and electronic circuit simulation, the electronic circuit virtual reality laboratory based on VR equipment was realized. We have developed VR electronic circuit virtual laboratory on PC and web. Students can use VR electronic circuit virtual laboratory to learn basic electronic circuit knowledge and carry out electronic circuit virtual experiment immersively.

Previous studies have paid little attention to the spatial heterogeneity of residents' marginal willingness to pay (MWTP) for clean air at a city level. To fill this gap, this study adopts a geographically weighted regression (GWR) model to quantify the spatial heterogeneity of residents' MWTP for clean air in Shanghai. First, Shanghai was divided into 218 census tracts and each tract was the smallest research unit. Then, the impacts of air pollutants and other built environment variables on housing prices were chosen to reflect residents' MWTP and a GWR model was used to analyze the spatial heterogeneity of the MWTP. Finally, the total losses caused by air pollutants in Shanghai were estimated from the perspective of housing market value. Empirical results show that air pollutants have a negative impact on housing prices. Using the marginal rate of transformation between housing prices and air pollutants, the results show Shanghai residents, on average, are willing to pay 50 and 99 Yuan/m2 to reduce the mean concentration of PM2.5 and NO2 by 1 μg/m3, respectively. Moreover, residents' MWTP for clean air is higher in the suburbs and lower in the city center. This study can help city policymakers formulate regional air management policies and provide support for the green and sustainable development of the real estate market in China.

Deterministic databases are able to run transactions efficiently in a distributed setting with minimal coordination. Research has shown many benefits from having determinism, from more lightweight database replication, to no/cheaper distributed commit, to high throughput during live migration. Besides those known applications, permissioned blockchain can also be viewed as an application of the deterministic database with security added. A permissioned blockchain maintains a fully-replicated database, where each replica is synchronized by getting blocks of updates. Early permissioned blockchains run a consensus protocol (e.g., PBFT) to agree on the input and rely on the replicas to execute the transactions serially to uphold determinism. Fabric allows concurrent transaction executions but rely on running consensus to agree on the read-write-sets and broadcasting them, which incurs an excessive volume of network traffic. Deterministic concurrency control combines the best of both worlds. It enables solving consensus on the small input, and each replica is able to execute transactions independently with high concurrency. Although powerful, contemporary deterministic databases are still primitive because they support only one isolation level -- Serializable. Furthermore, many deterministic concurrency control schemes are overly pessimistic and cause many unnecessary transaction aborts. These factors motivate us to design DCC, a suite of better deterministic concurrency control schemes, not only for Serializable but also for practical isolation levels beyond Serializable.

In applications such as network monitoring and sensor measurements, massive amounts of data arrive as a high-rate stream and real-time analytic over the stream data is required. Maintaining a succinct sketch over the data stream has been a dominant approach to support analysis in those applications. Recent applications, however, demand more sophisticated types of analytics such as range-counting. Unfortunately, state-of-the-art sketches perform poorly when facing range-counting as none of them was designed to support range-counting at the outset. In this paper, we aim to fill the gap and present RC-Sketch, a sketch that supports range-counting over rapid data stream. Empirical results show that RC-Sketch outperforms the state-of-the-art in range-count accuracy with comparable throughput.

The integrated circuit (IC) is a subject for which researchers need practical experience, but its experiment cost is high, the risk involved is high, and it is not easy to carry out experiments on a large scale. This paper designs a three-dimensional integrated circuit virtual experiment platform based on Unity3d. The platform uses the Unity3d and 3ds Max tools to build a three-dimensional model of instruments, equipment, electronic components, and ultra cleanroom laboratory scenes in an integrated circuit experiment. In addition, it uses C# script to develop the functions and three-dimensional simulation of general virtual instruments and equipment and deploys the experimental website using the frame of the jspxcms open source website. The experimental platform arranges the three-dimensional web file WebGL file in the cloud. Students can use video and text materials to acquire basic IC knowledge at any time and conduct IC virtual experiments safely, efficiently, and without constraints. At present, the platform has been tested and used in teaching and has received high praise and recognition from students.

The International Aerial Robotics Competition (IARC) aims to move the state-of-the-art in aerial robotics forward through mission challenges. In the IARC Mission 7, the aerial robot will navigate without external navigation aids, interact with autonomous ground robots, and avoid dynamic obstacles in the herding problem. In 2017 competition, our team firstly accomplished to interactively herd one iRobot to one end of the arena through accurate and rapid aerial touch and won the first place in system control. This paper presents the self-localization, control and tracking strategies for a micro unmanned aerial vehicle (UAV) to accurately and agilely touch ground moving robots. A real-time self-localization framework is proposed to estimate the ego motion of the UAV. A computationally efficient visual tracking scheme is designed to detect the ground robot and estimate its direction. Inspired by the dance of the dragonfly, tracking and control schemes are presented for the UAV to achieve 3-D agile interaction with ground autonomous robots. Simulation and flight experimental results validate the effectiveness of the proposed methods.

Ever since the success of Bitcoin, blockchain technology has enabled countless decentralized applications. However, one major issue of blockchain is scalability. Taking Bitcoin as an example, its maximum transaction throughput is estimated to be less than 10 transactions per second, which is restricted by the constant block size and the constant average confirmation time per block of the Bitcoin blockchain. Some other cryptocurrencies that use similar blockchain technology, such as Ethereum and Bitcoin Cash, increase their throughput by enlarging these two constants. Yet, none of them achieves similar throughput of mainstream payment networks like Visa (over 1500 transaction/s) or Paypal (~200 transactions/s). A more serious problem is that we cannot increase these two parameters arbitrarily. Doing so may damage the underlying decentralized consensus protocol, as well as increase the cost of running full nodes. The performance bottleneck hinders blockchain's applicability, especially in resource-constrained platforms such as IoT.

Recent years, many applications have been driven advances by the use of Machine Learning (ML). Nowadays, it is common to see industrial-strength machine learning jobs that involve millions of model parameters, terabytes of training data, and weeks of training. Good efficiency, i.e., fast completion time of running a specific ML job, therefore, is a key feature of a successful ML system. While the completion time of a long-running ML job is determined by the time required to reach model convergence, practically that is also largely influenced by the values of various system settings. In this paper, we contribute techniques towards building self-tuning parameter servers. Parameter Server (PS) is a popular system architecture for large-scale machine learning systems; and by self-tuning we mean while a long-running ML job is iteratively training the expert-suggested model, the system is also iteratively learning which system setting is more efficient for that job and applies it online. While our techniques are general enough to various PS-style ML systems, we have prototyped our techniques on top of TensorFlow. Experiments show that our techniques can reduce the completion times of a variety of long-running TensorFlow jobs from 1.4x to 18x., Comment: 13 pages