Prelude to Compressed Baryonic Matter

This is intended to appear as the introduction to "The CBM Physics Book: compressed baryonic matter in laboratory experiments" (ed. B. Friman, C. H\"ohne, S. Leupold, J. Knoll, J. Randrup, R. Rapp, P. Senger), to be published by Springer. At the end there is a new proposal for numerically tractable models of interacting many-body systems.Comment: 12 pages, to appear in "The CBM Book: compressed baryonic matter in laboratory experiments

Communication channel analysis and real time compressed sensing for high density neural recording devices

Next generation neural recording and Brain- Machine Interface (BMI) devices call for high density or distributed systems with more than 1000 recording sites. As the recording site density grows, the device generates data on the scale of several hundred megabits per second (Mbps). Transmitting such large amounts of data induces significant power consumption and heat dissipation for the implanted electronics. Facing these constraints, efficient on-chip compression techniques become essential to the reduction of implanted systems power consumption. This paper analyzes the communication channel constraints for high density neural recording devices. This paper then quantifies the improvement on communication channel using efficient on-chip compression methods. Finally, This paper describes a Compressed Sensing (CS) based system that can reduce the data rate by > 10x times while using power on the order of a few hundred nW per recording channel

Video shot boundary detection: seven years of TRECVid activity

Shot boundary detection (SBD) is the process of automatically detecting the boundaries between shots in video. It is a problem which has attracted much attention since video became available in digital form as it is an essential pre-processing step to almost all video analysis, indexing, summarisation, search, and other content-based operations. Automatic SBD was one of the tracks of activity within the annual TRECVid benchmarking exercise, each year from 2001 to 2007 inclusive. Over those seven years we have seen 57 different research groups from across the world work to determine the best approaches to SBD while using a common dataset and common scoring metrics. In this paper we present an overview of the TRECVid shot boundary detection task, a high-level overview of the most significant of the approaches taken, and a comparison of performances, focussing on one year (2005) as an example

On the Scalability of Data Reduction Techniques in Current and Upcoming HPC Systems from an Application Perspective

We implement and benchmark parallel I/O methods for the fully-manycore driven particle-in-cell code PIConGPU. Identifying throughput and overall I/O size as a major challenge for applications on today's and future HPC systems, we present a scaling law characterizing performance bottlenecks in state-of-the-art approaches for data reduction. Consequently, we propose, implement and verify multi-threaded data-transformations for the I/O library ADIOS as a feasible way to trade underutilized host-side compute potential on heterogeneous systems for reduced I/O latency.Comment: 15 pages, 5 figures, accepted for DRBSD-1 in conjunction with ISC'1

Entropy-scaling search of massive biological data

Many datasets exhibit a well-defined structure that can be exploited to design faster search tools, but it is not always clear when such acceleration is possible. Here, we introduce a framework for similarity search based on characterizing a dataset's entropy and fractal dimension. We prove that searching scales in time with metric entropy (number of covering hyperspheres), if the fractal dimension of the dataset is low, and scales in space with the sum of metric entropy and information-theoretic entropy (randomness of the data). Using these ideas, we present accelerated versions of standard tools, with no loss in specificity and little loss in sensitivity, for use in three domains---high-throughput drug screening (Ammolite, 150x speedup), metagenomics (MICA, 3.5x speedup of DIAMOND [3,700x BLASTX]), and protein structure search (esFragBag, 10x speedup of FragBag). Our framework can be used to achieve "compressive omics," and the general theory can be readily applied to data science problems outside of biology.Comment: Including supplement: 41 pages, 6 figures, 4 tables, 1 bo

CNN-based fast source device identification

Source identification is an important topic in image forensics, since it allows to trace back the origin of an image. This represents a precious information to claim intellectual property but also to reveal the authors of illicit materials. In this paper we address the problem of device identification based on sensor noise and propose a fast and accurate solution using convolutional neural networks (CNNs). Specifically, we propose a 2-channel-based CNN that learns a way of comparing camera fingerprint and image noise at patch level. The proposed solution turns out to be much faster than the conventional approach and to ensure an increased accuracy. This makes the approach particularly suitable in scenarios where large databases of images are analyzed, like over social networks. In this vein, since images uploaded on social media usually undergo at least two compression stages, we include investigations on double JPEG compressed images, always reporting higher accuracy than standard approaches

Opportunities for large-scale energy storage in geological formations in mainland Portugal

This article presents the methodology and results of the first screening conducted in Portugal to identify geological formations suitable for large-scale storage of energy from renewable sources. The screening focused on the identification of adequate porous media rocks, salt formations and igneous host rocks that could act as reservoirs for gas (hydrogen or methane) storage, Compressed Air Energy Storage, Underground Pumped Hydro and Underground Thermal Energy Storage. Public access geological information was collected, compiled in a database and spatially referenced in a GIS environment. The GIS and database were cross-checked with criteria for selecting geological reservoirs and with existing or anticipated spatial, environmental and social constraints. In a third step the feasibility of deploying each large-scale energy storage technology in each prospective reservoir was assessed and classified according to confidence, ranging from unlikely to proven, and to proximity to areas with wind or solar energy potential, accessibility to power transmission lines and natural gas networks. The outcome is a first screening of priority sites to be studied at the local scale in future projects. Early target for detailed studies are the existing salt caverns and an abandoned salt mine in the Lusitanian Basin. Natural gas storage in salt formations is being carried in the region for decades, proving the adequacy of the salt formations and demonstrating the social acceptance. Porous media aquifers in the same Lusitanian basin may also hold an interesting potential, although there is considerable uncertainty due to the scarcity of geological data about deep aquifers. The Sines industrial cluster, in SW Portugal, is another interesting target area, due to the existence of a host rock with proven containment capacity. The technologies with the best potential for application in the Portuguese geologic context seem to be CAES and Underground Gas Storage linked to Power-to-gas projects