Film and moving image studies: re-born digital? Some participant observations

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Clinical Features and Genetic Analysis of 20 Chinese Patients with X-Linked Hyper-IgM Syndrome

X-linked hyper-IgM syndrome (XHIGM) is one type of primary immunodeficiency diseases, resulting from defects in the CD40 ligand/CD40 signaling pathways. We retrospectively analyzed the clinical and molecular features of 20 Chinese patients diagnosed and followed up in hospitals affiliated to Shanghai Jiao Tong University School of Medicine from 1999 to 2013. The median onset age of these patients was 8.5 months (range: 20 days–21 months). Half of them had positive family histories, with a shorter diagnosis lag. The most common symptoms were recurrent sinopulmonary infections (18 patients, 90%), neutropenia (14 patients, 70%), oral ulcer (13 patients, 65%), and protracted diarrhea (13 patients, 65%). Six patients had BCGitis. Six patients received hematopoietic stem cell transplantations and four of them had immune reconstructions and clinical remissions. Eighteen unique mutations in CD40L gene were identified in these 20 patients from 19 unrelated families, with 12 novel mutations. We compared with reported mutation results and used bioinformatics software to predict the effects of mutations on the target protein. These mutations reflected the heterogeneity of CD40L gene and expanded our understanding of XHIGM

An Open Framework for Integrating Widely Distributed Hypermedia Resources

The success of the WWW has served as an illustration of how hypermedia functionality can enhance access to large amounts of distributed information. However, the WWW and many other distributed hypermedia systems offer very simple forms of hypermedia functionality which are not easily applied to existing applications and data formats, and cannot easily incorporate alternative functions which would aid hypermedia navigation to and from existing documents that have not been developed with hypermedia access in mind. This paper describes the extension to a distributed environment of the open hypermedia functionality of the Microcosm system, which is designed to support the provision of hypermedia access to a wide range of source material and application, and to offer straightforward extension of the system to incorporate new forms of information access

Vectors of Locally Aggregated Centers for Compact Video Representation

We propose a novel vector aggregation technique for compact video representation, with application in accurate similarity detection within large video datasets. The current state-of-the-art in visual search is formed by the vector of locally aggregated descriptors (VLAD) of Jegou et. al. VLAD generates compact video representations based on scale-invariant feature transform (SIFT) vectors (extracted per frame) and local feature centers computed over a training set. With the aim to increase robustness to visual distortions, we propose a new approach that operates at a coarser level in the feature representation. We create vectors of locally aggregated centers (VLAC) by first clustering SIFT features to obtain local feature centers (LFCs) and then encoding the latter with respect to given centers of local feature centers (CLFCs), extracted from a training set. The sum-of-differences between the LFCs and the CLFCs are aggregated to generate an extremely-compact video description used for accurate video segment similarity detection. Experimentation using a video dataset, comprising more than 1000 minutes of content from the Open Video Project, shows that VLAC obtains substantial gains in terms of mean Average Precision (mAP) against VLAD and the hyper-pooling method of Douze et. al., under the same compaction factor and the same set of distortions.Comment: Proc. IEEE International Conference on Multimedia and Expo, ICME 2015, Torino, Ital

Sensitivity of seismically cued antineutrino detectors to nuclear explosions

We evaluate the sensitivity of large, gadolinium-doped water detectors to antineutrinos released by nuclear fission explosions, using updated signal and background models and taking advantage of the capacity for seismic observations to provide an analysis trigger. Under certain realistic conditions, the antineutrino signature of a 250-kiloton pure fission explosion could be identified several hundred kilometers away in a detector about the size of the largest module currently proposed for a basic physics experiment. In principle, such an observation could provide rapid confirmation that the seismic signal coincided with a fission event, possibly useful for international monitoring of nuclear weapon tests. We discuss the limited potential for seismically cued antineutrino observations to constrain fission yield, differentiate pure fission from fusion-enhanced weapon tests, indicate that the seismic evidence of an explosion had been intentionally masked, or verify the absence of explosive testing in a targeted area. We conclude that advances in seismic monitoring and neutrino physics have made the detection of explosion-derived antineutrinos more conceivable than previously asserted, but the size and cost of sufficiently sensitive detectors continue to limit applications

RRR: Rank-Regret Representative

Selecting the best items in a dataset is a common task in data exploration. However, the concept of "best" lies in the eyes of the beholder: different users may consider different attributes more important, and hence arrive at different rankings. Nevertheless, one can remove "dominated" items and create a "representative" subset of the data set, comprising the "best items" in it. A Pareto-optimal representative is guaranteed to contain the best item of each possible ranking, but it can be almost as big as the full data. Representative can be found if we relax the requirement to include the best item for every possible user, and instead just limit the users' "regret". Existing work defines regret as the loss in score by limiting consideration to the representative instead of the full data set, for any chosen ranking function. However, the score is often not a meaningful number and users may not understand its absolute value. Sometimes small ranges in score can include large fractions of the data set. In contrast, users do understand the notion of rank ordering. Therefore, alternatively, we consider the position of the items in the ranked list for defining the regret and propose the {\em rank-regret representative} as the minimal subset of the data containing at least one of the top-$k$ of any possible ranking function. This problem is NP-complete. We use the geometric interpretation of items to bound their ranks on ranges of functions and to utilize combinatorial geometry notions for developing effective and efficient approximation algorithms for the problem. Experiments on real datasets demonstrate that we can efficiently find small subsets with small rank-regrets