The effort for combating the COVID-19 pandemic around the world has resulted in a huge amount of data, e.g., from testing, contact tracing, modelling, treatment, vaccine trials, and more. In addition to numerous challenges in epidemiology, healthcare, biosciences, and social sciences, there has been an urgent need to develop and provide visualisation and visual analytics (VIS) capacities to support emergency responses under difficult operational conditions. In this paper, we report the experience of a group of VIS volunteers who have been working in a large research and development consortium and providing VIS support to various observational, analytical, model-developmental, and disseminative tasks. In particular, we describe our approaches to the challenges that we have encountered in requirements analysis, data acquisition, visual design, software design, system development, team organisation, and resource planning. By reflecting on our experience, we propose a set of recommendations as the first step towards a methodology for developing and providing rapid VIS capacities to support emergency responses.
We propose a general, flexible, and scalable framework dpart, an open source Python library for differentially private synthetic data generation. Central to the approach is autoregressive modelling -- breaking the joint data distribution to a sequence of lower-dimensional conditional distributions, captured by various methods such as machine learning models (logistic/linear regression, decision trees, etc.), simple histogram counts, or custom techniques. The library has been created with a view to serve as a quick and accessible baseline as well as to accommodate a wide audience of users, from those making their first steps in synthetic data generation, to more experienced ones with domain expertise who can configure different aspects of the modelling and contribute new methods/mechanisms. Specific instances of dpart include Independent, an optimized version of PrivBayes, and a newly proposed model, dp-synthpop. Code: https://github.com/hazy/dpart
Scientific research has moved from an isolated environment into a collaborated culture due to data explosion and the experiment complexity. There are strong demands to track and share the data sources and the analysis processes that generate particular results and conclusions. However, the existing systems do not support well the end-to-end data analysis and sharing environment which involves searching information, conducting experiments and sharing results. In this work, we present a process provenance service system which supports the scientific collaboration.
With the constant increase in the size of terrain data, it is becoming less feasible to have all the data in main memory when performing visualization. The data exchange between main memory and secondary storage becomes a bottle-neck in terrain visualization. The slow-down of the visualization performance is even more obvious when visualizing terrain in a client-server environment because of the long response time caused by transferring large amount of data over the network. This paper surveys recent secondary storage terrain visualization techniques in a client-server environment in recent years, with a particular focus on reducing data exchange between main memory and secondary storage, and between the client and server using spatial indexes with Multiresolution Terrain Model (MTM). Previous works are compared according to their ability to reduce data retrieval, reduce network transmission, data caching and support for different data structures. Research problems that require further investigation are identified and discussed.
Superradiant phase transitions (SPTs) are important for understanding light-matter interactions at the quantum level [1, 2], and play a central role in criticality-enhanced quantum sensing [3]. So far, SPTs have been observed in driven-dissipative systems [4-9], but the emergent light fields did not show any nonclassical characteristic due to the presence of strong dissipation. Here we report an experimental demonstration of the SPT featuring the emergence of a highly nonclassical photonic field, realized with a resonator coupled to a superconducting qubit, implementing the quantum Rabi model [10, 11]. We fully characterize the light-matter state by Wigner matrix tomography. The measured matrix elements exhibit quantum interference intrinsic of a photonic Schr\"{o}dinger cat state [12], and reveal light-matter entanglement. Besides their fundamental importance, these hitherto unobserved emergent quantum phenomena are useful for quantum metrology and fault-tolerant quantum computation.
With a fast paced development of bioinformatics in recent years, we have witnessed a rapid growth of the number of databases and tools available for aiding in scientific research and knowledge discovery for bioinformaticians. Web service is an enabling technique to facilitate bioinformaticians in this discovery process by integrating the databases and tools. In this paper, we will propose a novel system, called bio-sense, for supporting the sharing and exploration in bioinformatics using semantic Web services. The promising features of bio-sense will be discussed in this paper.
Aimed at the particularity of ammunition maintenance,a new safety evaluation method of ammunition maintenance was put forward under the consideration of the influence of basic condition construction,operation management level,environment,and the ammunition.The safety of ammunition maintenance was evaluated.The method was verified with the example of some typical ammunition maintenance based on definitude of the main method and steps of ammunition maintenance safety evaluation which includes establishment of the evaluation index systems,first-order evaluation,and second-order evaluation.The result indicated that the method can get rational evaluation result.
Intrusion detection systems based on recurrent neural network (RNN) have been considered as one of the effective methods to detect time-series data of in-vehicle networks. However, building a model for each arbitration bit is not only complex in structure but also has high computational overhead. Convolutional neural network (CNN) has always performed excellently in processing images, but they have recently shown great performance in learning features of normal and attack traffic by constructing message matrices in such a manner as to achieve real-time monitoring but suffer from the problem of temporal relationships in context and inadequate feature representation in key regions. Therefore, this paper proposes a temporal convolutional network with global attention to construct an in-vehicle network intrusion detection model, called TCAN-IDS. Specifically, the TCAN-IDS model continuously encodes 19-bit features consisting of an arbitration bit and data field of the original message into a message matrix, which is symmetric to messages recalling a historical moment. Thereafter, the feature extraction model extracts its spatial-temporal detail features. Notably, global attention enables global critical region attention based on channel and spatial feature coefficients, thus ignoring unimportant byte changes. Finally, anomalous traffic is monitored by a two-class classification component. Experiments show that TCAN-IDS demonstrates high detection performance on publicly known attack datasets and is able to accomplish real-time monitoring. In particular, it is anticipated to provide a high level of symmetry between information security and illegal intrusion.
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