Observations of atmospheric conditions and processes in cities are fundamental to understanding the interactions between the urban surface and weather/climate, improving the performance of urban weather, air quality, and climate models, and providing key information for city end users (e.g., decision makers, stakeholders, public). In this paper, Shanghai’s Urban Integrated Meteorological Observation Network (SUIMON) and some examples of intended applications are introduced. Its characteristics include being multipurpose (e.g., forecast, research, service), multifunction (e.g., high-impact weather, city climate, special end users), multiscale (e.g., macro/meso, urban, neighborhood, street canyon), multivariable (e.g., thermal, dynamic, chemical, biometeorological, ecological), and multiplatform (e.g., radar, wind profiler, ground based, satellite based, in situ observation/sampling). Underlying SUIMON is a data management system to facilitate exchange of data and information. The overall aim of the network is to improve coordination strategies and instruments, to identify data gaps based on science- and user-driven requirements, and to intelligently combine observations from a variety of platforms by using a data assimilation system that is tuned to produce the best estimate of the current state of the urban atmosphere.

No abstract

Implicit waveform-specific (IWS) learning during a typical continuous tracking task paradigm has been reported for decades, as evidenced by better tracking improvement on the repeated segment of a specific target waveform than random segments. However, the occurrence of the IWS learning in such a task paradigm has been challenged by several unsuccessful results in recent literature. This research concerns reliable detection of the induced IWS learning and to this end, proposes to use the similarity between the cursor and the target along the direction corresponding to the waveform pattern as the performance measure. A 3-day experiment designed with full examination on IWS learning including a practice phase, an immediate test phase and a consolidation test phase after 24 hours was conducted to validate the feasibility and sensitivity of the Pearson’s correlation coefficient on the vertical movement r v in this study. Experiment results indicate that r v is more sensitive in detecting the IWS learning in all phases compared to the conventional root mean square error (RMSE) performance measure. The findings confirm the importance of the performance measure in implicit learning research and the similarity measure in accordance with the waveform could be promising for waveform-specific learning detection in this paradigm.

No abstract

The popularity of smart-home assistant systems such as Amazon Alexa and Google Home leads to a booming thirdparty application market (over 70,000 applications across the two stores). While existing works have revealed security issues in these systems, it is not well understood how to help application developers to enforce security requirements. In this paper, we perform a preliminary case study to examine the security vetting mechanisms adopted by Amazon Alexa and Google Home app stores. With a focus on the authentication mechanisms between Alexa/Google cloud and third-party application servers (i.e. endpoints), we show the current security vetting is insufficient as developers' mistakes cannot be effectively detected and notified. A weak authentication would allow attackers to spoof the cloud to insert/retrieve data into/from the application endpoints. We validate the attack through ethical proof-of-concept experiments. To confirm vulnerable applications have indeed passed the security vetting and entered the markets, we develop a heuristicbased searching method. We find 219 real-world Alexa endpoints that carry the vulnerability, many of which are related to critical applications that control smart home devices and electronic cars. We have notified Amazon and Google about our findings and offered our suggestions to mitigate the issue.

No abstract

No abstract

Objective. Implicit motor learning, which is a non-conscious form of learning characterized by motor performance improvement with practice, plays an essential role in various daily activities. Earlier study using neurofeedback training (NFT), a type of brain-computer interaction that enables the user to learn self-regulating his/her own brain activity, demonstrated that down-regulating alpha over primary motor cortex by NFT could immediately facilitate the implicit motor learning in a relatively simple motor task. However, detailed effects on EEG and implicit motor learning due to NFT especially in a more complex motor task are still unclear. Approach. We designed a single-blind sham-controlled between-subject study to examine whether alpha down-regulation NFT could facilitate implicit motor learning and also its consolidation in a more difficult and motor predominant task. At left primary motor cortex (C3) in two days, the alpha NFT group received alpha down-regulation training through auditory feedback while the sham-control group received random beta NFT. At the end of NFT, all participants performed the continuous tracking task with their dominant (right) hand to evaluate the implicit motor learning immediately. Finally, the continuous tracking task was performed again on the next day to assess consolidation effects. Main results. The alpha NFT group successfully decreased alpha amplitude during NFT, whereas the sham-control group maintained alpha at a relatively stable level. There was unfortunately no statistical evidence proving that the alpha NFT group significantly enhanced the implicit motor learning at the end of NFT and the consolidation on the next day compared to the sham-control group. Nevertheless, a significant correlation was found between the alpha change trend during NFT and the implicit motor learning for all participants, suggesting that faster alpha down-regulation was associated with better implicit motor learning. Significance. The findings suggested a close link between implicit motor learning and alpha change induced by NFT.