The Gene Ontology (GO) project (http://www. geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.

This paper introduces dynamic self-invalidation (DSI), a new

Abstract-We introduce a curvature sensor composed of a thin, transparent elastomer film (polydimethylsiloxane, PDMS) embedded with a microchannel of conductive liquid (eutectic Gallium Indium, eGaIn) and a sensing element. Bending the sensor exerts pressure on the embedded microchannel via the sensing element. Deformation of the cross-section of the microchannel leads to a change in electrical resistance. We demonstrate the functionality of the sensor through testing on a finger joint. The film is wrapped around a finger with the sensing element positioned on top of the knuckle. Finger bending both stretches the elastomer and exerts pressure on the sensing element, leading to an enhanced change in the electrical resistance. Because the sensor is soft (elastic modulus E ∼ 1 MPa) and stretchable (>350%), it conforms to the host bending without interfering with the natural mechanics of motion. This sensor represents the first use of liquid-embedded elastomer electronics to monitor human or robotic motion.

Data-race freedom is a valuable safety property for multithreaded programs that helps with catching bugs, simplifying memory consistency model semantics, and verifying and enforcing both atomicity and determinism. Unfortunately, existing software-only dynamic race detectors are precise but slow; proposals with hardware support offer higher performance but are imprecise. Both precision and performance are necessary to achieve the many advantages always-on dynamic race detection could provide.To resolve this trade-off, we propose RADISH, a hybrid hardware-software dynamic race detector that is always-on and fully precise. In RADISH, hardware caches a principled subset of the metadata necessary for race detection; this subset allows the vast majority of race checks to occur completely in hardware. A flexible software layer handles persistence of race detection metadata on cache evictions and occasional queries to this expanded set of metadata. We show that RADISH is correct by proving equivalence to a conventional happens-before race detector.Our design has modest hardware complexity: caches are completely unmodified and we piggy-back on existing coherence messages but do not otherwise modify the protocol. Furthermore,RADISH can leverage type-safe languages to reduce overheads substantially. Our evaluation of a simulated 8-core RADISH processor using PARSEC benchmarks shows runtime overheads from negligible to 2x, outperforming the leading software-only race detector by 2x-37x.

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We present a novel method to extract iso-surfaces from distance volumes. It generates high quality semi-regular multiresolution meshes of arbitrary topology. Our technique proceeds in two stages. First, a very coarse mesh with guaranteed topology is extracted. Subsequently an iterative multi-scale force-based solver refines the initial mesh into a semi-regular mesh with geometrically adaptive sampling rate and good aspect ratio triangles. The coarse mesh extraction is performed using a new approach we call surface wavefront propagation. A set of discrete iso-distance ribbons are rapidly built and connected while respecting the topology of the iso-surface implied by the data. Subsequent multi-scale refinement is driven by a simple force-based solver designed to combine good iso-surface fit and high quality sampling through reparameterization. In contrast to the Marching Cubes technique our output meshes adapt gracefully to the iso-surface geometry, have a natural multiresolution structure and good aspect ratio triangles, as demonstrated with a number of examples.

Current hardware transactional memory systems seek to simplify parallel programming, but assume that large transactions are rare, so it is acceptable to penalize their performance or concurrency. However, future programmers may wish to use large transactions more often in order to integrate with higher-level programming models (e.g., database transactions) or perform selected I/O operations.To prevent the "small transactions are common" assumption from becoming self-fulfilling, this paper contributes TokenTM-an unbounded HTM that uses the abstraction of tokens to precisely track conflicts on an unbounded number of memory blocks. TokenTM implements tokens with new mechanisms, including metastate fission/fusion and fast token release. TokenTM executes small transactions fast, executes concurrent large transactions with no penalty to nonconflicting transactions, and gracefully handles paging, context switching, and System-V-style shared memory.

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