Cardinality estimation over big network data consisting of numerous flows is a fundamental problem with many practical applications. Traditionally the research on this problem focused on using a small amount of memory to estimate each flow's cardinality from a large range (up to $10^9$). However, although the memory needed for each flow has been greatly compressed, when there is an extremely large number of flows, the overall memory demand can still be very high, exceeding the availability under some important scenarios, such as implementing online measurement modules in network processors using only on-chip cache memory. In this paper, instead of allocating a separated data structure (called estimator ) for each flow, we take a different path by viewing all the flows together as a whole: Each flow is allocated with a virtual estimator, and these virtual estimators share a common memory space. We discover that sharing at the register (multi-bit) level is superior than sharing at the bit level. We propose a framework of virtual estimators that allows us to apply the idea of sharing to an array of cardinality estimation solutions, achieving far better memory efficiency than the best existing work. Our experiment shows that the new solution can work in a tight memory space of less than 1 bit per flow or even one tenth of a bit per flow --- a quest that has never been realized before.

In many applications, while machine learning (ML) can be used to derive algorithmic models to aid decision processes, it is often difficult to learn a precise model when the number of similar data points is limited. One example of such applications is data reconstruction from historical visualizations, many of which encode precious data, but their numerical records are lost. On the one hand, there is not enough similar data for training an ML model. On the other hand, manual reconstruction of the data is both tedious and arduous. Hence, a desirable approach is to train an ML model dynamically using interactive classification, and hopefully, after some training, the model can complete the data reconstruction tasks with less human interference. For this approach to be effective, the number of annotated data objects used for training the ML model should be as small as possible, while the number of data objects to be reconstructed automatically should be as large as possible. In this article, we present a novel technique for the machine to initiate intelligent interactions to reduce the user’s interaction cost in interactive classification tasks. The technique of machine-initiated intelligent interaction (MI3) builds on a generic framework featuring active sampling and default labeling. To demonstrate the MI3 approach, we use the well-known cholera map visualization by John Snow as an example, as it features three instances of MI3 pipelines. The experiment has confirmed the merits of the MI3 approach.

Background: Malnutrition and sepsis remain the leading causes of death in gastrointestinal fistulas. Establishment of appropriate access of enteral nutrition is still challenging for patients with multiple fistulas. Case Presentation: We presented in this case a patient with multiple post-operative fistulas and anastomotic leakages. Because of the lack of intestinal integrity for enteral feeding, we performed a step-by-step assessment and monitoring to achieve maximum benefit. Conclusion: The establishment of nutritional feeding access to patients with multiple fistulas requires accurate assessment of sequencing of each fistula limb, percutaneous endoscopic enterostomy, and multiple times fistuloclysis to restore intestinal integrity. Conventional Treatment Conventional treatment including provision of fluid/electrolyte balance to replete fluid and electrolytes, adequate drainage, use of somatostatin, bowel rest via total parenteral