Temporal video transcoding from H.264/AVC-to-SVC for digital TV broadcasting

Mobile digital TV environments demand flexible video compression like scalable video coding (SVC) because of varying bandwidths and devices. Since existing infrastructures highly rely on H.264/AVC video compression, network providers could adapt the current H.264/AVC encoded video to SVC. This adaptation needs to be done efficiently to reduce processing power and operational cost. This paper proposes two techniques to convert an H.264/AVC bitstream in Baseline (P-pictures based) and Main Profile (B-pictures based) without scalability to a scalable bitstream with temporal scalability as part of a framework for low-complexity video adaptation for digital TV broadcasting. Our approaches are based on accelerating the interprediction, focusing on reducing the coding complexity of mode decision and motion estimation tasks of the encoder stage by using information available after the H. 264/AVC decoding stage. The results show that when our techniques are applied, the complexity is reduced by 98 % while maintaining coding efficiency

Detectable HIV Viral Load in Kenya: Data from a Population-Based Survey.

IntroductionAt the individual level, there is clear evidence that Human Immunodeficiency Virus (HIV) transmission can be substantially reduced by lowering viral load. However there are few data describing population-level HIV viremia especially in high-burden settings with substantial under-diagnosis of HIV infection. The 2nd Kenya AIDS Indicator Survey (KAIS 2012) provided a unique opportunity to evaluate the impact of antiretroviral therapy (ART) coverage on viremia and to examine the risks for failure to suppress viral replication. We report population-level HIV viral load suppression using data from KAIS 2012.MethodsBetween October 2012 to February 2013, KAIS 2012 surveyed household members, administered questionnaires and drew serum samples to test for HIV and, for those found to be infected with HIV, plasma viral load (PVL) was measured. Our principal outcome was unsuppressed HIV viremia, defined as a PVL ≥ 550 copies/mL. The exposure variables included current treatment with ART, prior history of an HIV diagnosis, and engagement in HIV care. All point estimates were adjusted to account for the KAIS 2012 cluster sampling design and survey non-response.ResultsOverall, 61·2% (95% CI: 56·4-66·1) of HIV-infected Kenyans aged 15-64 years had not achieved virological suppression. The base10 median (interquartile range [IQR]) and mean (95% CI) VL was 4,633 copies/mL (0-51,596) and 81,750 copies/mL (59,366-104,134), respectively. Among 266 persons taking ART, 26.1% (95% CI: 20.0-32.1) had detectable viremia. Non-ART use, younger age, and lack of awareness of HIV status were independently associated with significantly higher odds of detectable viral load. In multivariate analysis for the sub-sample of patients on ART, detectable viremia was independently associated with younger age and sub-optimal adherence to ART.DiscussionThis report adds to the limited data of nationally-representative surveys to report population- level virological suppression. We established heterogeneity across the ten administrative and HIV programmatic regions on levels of detectable viral load. Timely initiation of ART and retention in care are crucial for the elimination of transmission of HIV through sex, needle and syringe use or from mother to child. Further refinement of geospatial mapping of populations with highest risk of transmission is necessary

Subspecialty preferences among Neurologists of the future.

INTRODUCTION: In the era of neurological subspecialization, most neurologists will have a field of specialist interest. The aim of this cross-sectional multi-national study was to identify the key areas of interest among trainees or junior specialists, assess the potential influence of an interest in research, and consider the results in light of population needs. METHODS: A total of 300 residents and junior neurologists who received a bursary to attend the European Academy of Neurology conference were invited to participate in this study. Demographic and work-related characteristics, as well as main subspecialty of choice were examined via an anonymous electronic questionnaire. Participants holding a higher degree (PhD/MD) or working in research posts were considered research oriented. RESULTS: In total, 191 Neurologists in training or junior specialists responded (response rate 63.7%). Full data were available for 187 participants (59.4% females). The study sample had a mean age of 30.5±3.4 years (range 25 - 45). The most popular subspecialty was movement disorders (18.2%), followed by multiple sclerosis (11.2%) and epilepsy (10.2%). This did not differ significantly between the participants who were or were not research-oriented. CONCLUSIONS: There is a potential mismatch between the interests of trainees, and the future needs of the populations they serve, which it is important to identify for workforce planning

Weakly supervised deep learning for the detection of domain generation algorithms

Domain generation algorithms (DGAs) have become commonplace in malware that seeks to establish command and control communication between an infected machine and the botmaster. DGAs dynamically and consistently generate large volumes of malicious domain names, only a few of which are registered by the botmaster, within a short time window around their generation time, and subsequently resolved when the malware on the infected machine tries to access them. Deep neural networks that can classify domain names as benign or malicious are of great interest in the real-time defense against DGAs. In contrast with traditional machine learning models, deep networks do not rely on human engineered features. Instead, they can learn features automatically from data, provided that they are supplied with sufficiently large amounts of suitable training data. Obtaining cleanly labeled ground truth data is difficult and time consuming. Heuristically labeled data could potentially provide a source of training data for weakly supervised training of DGA detectors. We propose a set of heuristics for automatically labeling domain names monitored in real traffic, and then train and evaluate classifiers with the proposed heuristically labeled dataset. We show through experiments on a dataset with 50 million domain names that such heuristically labeled data is very useful in practice to improve the predictive accuracy of deep learning-based DGA classifiers, and that these deep neural networks significantly outperform a random forest classifier with human engineered features

Biopython: freely available Python tools for computational molecular biology and bioinformatics

Summary: The Biopython project is a mature open source international collaboration of volunteer developers, providing Python libraries for a wide range of bioinformatics problems. Biopython includes modules for reading and writing different sequence file formats and multiple sequence alignments, dealing with 3D macro molecular structures, interacting with common tools such as BLAST, ClustalW and EMBOSS, accessing key online databases, as well as providing numerical methods for statistical learning. Availability: Biopython is freely available, with documentation and source code at www.biopython.org under the Biopython license. Contact: All queries should be directed to the Biopython mailing lists, see www.biopython.org/wiki/[email protected]

Financial phantasmagoria: corporate image-work in times of crisis

Our purpose in this article is to relate the real movements in the economy during 2008 to the ?image-work? of financial institutions. Over the period January?December 2008 we collected 241 separate advertisements from 61 financial institutions published in the Financial Times. Reading across the ensemble of advertisements for themes and evocative images provides an impression of the financial imaginaries created by these organizations as the global financial crisis unfolded. In using the term ?phantasmagoria? we move beyond its colloquial sense of a set of strange images designed to dazzle towards the more technical connotation used by Ranci�re (2004) who suggested that words and images can offer a trace of an overall determining set-up if they are torn from their obviousness so they become phantasmagoric figures. The key phantasmagoric figure we identify here is that of the financial institution as timeless, immortal and unchanging; a coherent and autonomous entity amongst other actors. This notion of uniqueness belies the commonality of thinking which precipitated the global financial crisis as well as the limited capacity for control of financial institutions in relation to market events. It also functions as a powerful naturalizing force, making it hard to question certain aspects of the recent period of ?capitalism in crisis?