ESO observations of the Nucleus of Rosetta Target 67P/C-G

Rosetta is ESA's new comet orbiter mission, launched in March 2004 and currently en route to Jupiter-family comet 67P/Churyumov-Gerasimenko. The probe will rendezvous with the comet in 2014 and remain in orbit around the nucleus for on-going detailed physical and compositional analysis. Pre-encounter observations of the target are important for characterization of the heliocentric light-curve behaviour and the physical properties of the nucleus, information that is critical for mission planning. The nucleus was first characterized using HST observations in 2003 (Lamy et al. 2006) and observed directly in May 2005 by ground based telescopes (Lowry et al. 2006) when it was at 5.6 AU from the Sun. An extensive database of nucleus observations have since been acquired, not only from large ground-based telescopes like the ESO VLT (Tubiana et al. 2008 & 2011), but also from Spitzer (Kelley et al. 2006 & 2009; Lamy et al. 2008).

Turbulent Fluctuations in G-band and K-line Intensities Observed with the Rapid Oscillations in the Solar Atmosphere (ROSA) Instrument

Using the Rapid Oscillation in the Solar Atmosphere (ROSA) instrument at the Dunn Solar Telescope we have found that the spectra of fluctuations of the G-band (cadence 1.05 s) and Ca II K-line (cadence 4.2 s) intensities show correlated fluctuations above white noise out to frequencies beyond 300 mHz and up to 70 mHz, respectively. The noise-corrected G-band spectrum presents a scaling range (Ultra High Frequency “UHF”) for f = 25-100 mHz, with an exponent consistent with the presence of turbulent motions. The UHF power, is concentrated at the locations of magnetic bright points in the intergranular lanes, it is highly intermittent in time and characterized by a positive kurtosis κ. Combining values of G-band and K-line intensities, the UHF power, and κ, reveals two distinct “states” of the internetwork solar atmosphere. State 1, with κ ≍ 6, which includes almost all the data, is characterized by low intensities and low UHF power. State 2, with κ ≍ 3, including a very small fraction of the data, is characterized by high intensities and high UHF power. Superposed epoch analysis shows that for State 1, the K-line intensity presents 3.5 min chromospheric oscillations with maxima occurring 21 s after G-band intensity maxima implying a 150-210 km effective height difference. For State 2, the G-band and K-line intensity maxima are simultaneous, suggesting that in the highly magnetized environment sites of G-band and K-line emission may be spatially close together. Analysis of observations obtained with Hinode/SOT confirm a scaling range in the G-band spectrum up to 53 mHz also consistent with turbulent motions as well as the identification of two distinct states in terms of the H-line intensity and G-band power as functions of G-band intensity.

Vertex Clustering of Augmented Graph Streams

In this paper we propose a graph stream clustering algorithm with a unied similarity measure on both structural and attribute properties of vertices, with each attribute being treated as a vertex. Unlike others, our approach does not require an input parameter for the number of clusters, instead, it dynamically creates new sketch-based clusters and periodically merges existing similar clusters. Experiments on two publicly available datasets reveal the advantages of our approach in detecting vertex clusters in the graph stream. We provide a detailed investigation into how parameters affect the algorithm performance. We also provide a quantitative evaluation and comparison with a well-known offline community detection algorithm which shows that our streaming algorithm can achieve comparable or better average cluster purity.

Quantum Chromatic Numbers via Operator Systems

We define several new types of quantum chromatic numbers of a graph and characterize them in terms of operator system tensor products. We establish inequalities between these chromatic numbers and other parameters of graphs studied in the literature and exhibit a link between them and non-signalling correlation boxes.

Interspecific comparison of estrogen and testosterone concentrations in three species of amphipods (Gammarus duebeni celticus, G. pseudolimnaeus, and G. pulex)

The presence and biological significance of vertebrate-related steroid sex hormones in aquatic invertebrates are poorly understood. We compared the concentrations of estrogen (17β-estradiol) and testosterone between amplexing male and female freshwater amphipods of three species from two continents: Gammarus duebeni celticusLiljeborg, 1852 and G. pulex(L., 1758) from Europe, and G. pseudolimnaeusBousfield, 1958 from North America. All three species were found to have measureable concentrations of both hormones in whole body lysate samples but the concentrations differed between species, with testosterone differing significantly between species only for male amphipods and estradiol differing significantly between species only for female amphipods. Concentrations of both testosterone and estrogen differed between males and females in two of the three species ( G. duebeni celticusand G. pseudolimnaeus). Females had the highest concentration of both hormones in G. duebeni celticusand the lowest concentration of both hormones in G. pseudolimnaeus. These results contribute to a growing body of evidence that these hormones are endogenously produced and biologically relevant in amphipods. Such evidence is particularly important in light of increasing prevalence of endocrine-disrupting compounds in the environment and the central role played by amphipods in aquatic ecosystems.

Enabling Robust and Efficient Distributed Computation in Dynamic Peer-to-Peer Networks

Motivated by the need for designing efficient and robust fully-distributed computation in highly dynamic networks such as Peer-to-Peer (P2P) networks, we study distributed protocols for constructing and maintaining dynamic network topologies with good expansion properties. Our goal is to maintain a sparse (bounded degree) expander topology despite heavy {\em churn} (i.e., nodes joining and leaving the network continuously over time). We assume that the churn is controlled by an adversary that has complete knowledge and control of what nodes join and leave and at what time and has unlimited computational power, but is oblivious to the random choices made by the algorithm. Our main contribution is a randomized distributed protocol that guarantees with high probability the maintenance of a {\em constant} degree graph with {\em high expansion} even under {\em continuous high adversarial} churn. Our protocol can tolerate a churn rate of up to $O(n/\poly\log(n))$ per round (where $n$ is the stable network size). Our protocol is efficient, lightweight, and scalable, and it incurs only $O(\poly\log(n))$ overhead for topology maintenance: only polylogarithmic (in $n$) bits needs to be processed and sent by each node per round and any node's computation cost per round is also polylogarithmic. The given protocol is a fundamental ingredient that is needed for the design of efficient fully-distributed algorithms for solving fundamental distributed computing problems such as agreement, leader election, search, and storage in highly dynamic P2P networks and enables fast and scalable algorithms for these problems that can tolerate a large amount of churn.

The cyclin D1 (CCND1) rs9344 G>A polymorphism predicts clinical outcome in colon cancer patients treated with adjuvant 5-FU-based chemotherapy

Recent evidence indicates a potential prognostic and predictive value for germline polymorphisms in genes involved in cell cycle control. We investigated the effect of cyclin D1 (CCND1) rs9344 G>A in stage II/III colon cancer patients and validated the findings in an independent study cohort. For evaluation and validation set, a total of 264 and 234 patients were included. Patients treated with 5-fluorouracil-based chemotherapy, carrying the CCND1 rs9344 A/A genotype had significantly decreased time-to-tumor recurrence (TTR) in univariate analysis and multivariate analysis (hazard ratio (HR) 2.47; 95% confidence interval (CI) 1.16-5.29; P=0.019). There was no significant association between CCND1 rs9344 G>A and TTR in patients with curative surgery alone. In the validation set, the A allele of CCND1 rs9344 G>A remained significantly associated with decreased TTR in univariate and multivariate analyses (HR 1.94; 95% CI 1.05-3.58; P=0.035). CCND1 rs9344 G>A may be a predictive and/or prognostic biomarker in stage II/III colon cancer patients, however, prospective trials are warranted to confirm our findings.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

Memory-centric VDF Graph Transformations for Practical FPGA Implementation

Realising memory intensive applications such as image and video processing on FPGA requires creation of complex, multi-level memory hierarchies to achieve real-time performance; however commerical High Level Synthesis tools are unable to automatically derive such structures and hence are unable to meet the demanding bandwidth and capacity constraints of these applications. Current approaches to solving this problem can only derive either single-level memory structures or very deep, highly inefficient hierarchies, leading in either case to one or more of high implementation cost and low performance. This paper presents an enhancement to an existing MC-HLS synthesis approach which solves this problem; it exploits and eliminates data duplication at multiple levels levels of the generated hierarchy, leading to a reduction in the number of levels and ultimately higher performance, lower cost implementations. When applied to synthesis of C-based Motion Estimation, Matrix Multiplication and Sobel Edge Detection applications, this enables reductions in Block RAM and Look Up Table (LUT) cost of up to 25%, whilst simultaneously increasing throughput.

Entity Linking for Web Search Queries

We consider the problem of linking web search queries to entities from a knowledge base such as Wikipedia. Such linking enables converting a user’s web search session to a footprint in the knowledge base that could be used to enrich the user profile. Traditional methods for entity linking have been directed towards finding entity mentions in text documents such as news reports, each of which are possibly linked to multiple entities enabling the usage of measures like entity set coherence. Since web search queries are very small text fragments, such criteria that rely on existence of a multitude of mentions do not work too well on them. We propose a three-phase method for linking web search queries to wikipedia entities. The first phase does IR-style scoring of entities against the search query to narrow down to a subset of entities that are expanded using hyperlink information in the second phase to a larger set. Lastly, we use a graph traversal approach to identify the top entities to link the query to. Through an empirical evaluation on real-world web search queries, we illustrate that our methods significantly enhance the linking accuracy over state-of-the-art methods.

A Molecular Mechanism for Sequential Activation of a G Protein-Coupled Receptor

Ligands targeting G protein-coupled receptors (GPCRs) are currently classified as either orthosteric, allosteric, or dualsteric/bitopic. Here, we introduce a new pharmacological concept for GPCR functional modulation: sequential receptor activation. A hallmark feature of this is a stepwise ligand binding mode with transient activation of a first receptor site followed by sustained activation of a second topographically distinct site. We identify 4-CMTB (2-(4-chlorophenyl)-3-methyl-N-(thiazol-2-yl)butanamide), previously classified as a pure allosteric agonist of the free fatty acid receptor 2, as the first sequential activator and corroborate its two-step activation in living cells by tracking integrated responses with innovative label-free biosensors that visualize multiple signaling inputs in real time. We validate this unique pharmacology with traditional cellular readouts, including mutational and pharmacological perturbations along with computational methods, and propose a kinetic model applicable to the analysis of sequential receptor activation. We envision this form of dynamic agonism as a common principle of nature to spatiotemporally encode cellular information.