Lexically scoped distribution: what you see is what you get

We define a lexically scoped, asynchronous and distributed π-calculus, with local communication and process migration. This calculus adopts the network-awareness principle for distributed programming and follows a simple model of distribution for mobile calculi: a lexical scope discipline combines static scoping with dynamic linking, associating channels to a fixed site throughout computation. This discipline provides for both remote invocation and process migration. A simple type system is a straightforward extension of that of the π-calculus, adapted to take into account the lexical scope of channels. An equivalence law captures the essence of this model: a process behavior depends on the channels it uses, not on where it runs.     

From Pixels to Semantic Spaces: Advances in Content-Based Image Retrieval

The paradigm for image retrieval has evolved from low-level image representations to semantic concept models to higher-level semantic inferences. UCSD's Statistical Visual Computing Laboratory has developed effective techniques for each paradigm that equate retrieval with classification and strive for minimum-probability-of-error optimality.     

Experimental and modelling study of gas dispersion in a double turbine stirred tank

Gas dispersion in a double turbine stirred tank is experimentally characterised by measuring local gas holdups and local bubble size distributions throughout the tank, for three liquid media: tap water, aqueous sulphate solution and aqueous sulphate solution with PEG. For all these media, bubble coalescence generally prevails over breakage. Where average bubble size decreases, this can be attributed to the difference in slip velocity between different sized bubbles. Most of the coalescence takes place in the turbine discharge stream.A compartment model that takes into account the combined effect of bubble coalescence and breakage is used to simulate gas dispersion. The model predicts spatial distribution of gas holdup and of average bubble size, with average bubble size at the turbines as an input. Reasonable agreement between experiment and simulation is achieved with optimisation of two parameters, one affecting mainly the slip velocity, the other related mainly to the bubble coalescence/breakage balance. Different sets of parameters are required for each of the three liquid systems under study, but are independent of stirring/aeration conditions. The model only fails to simulate the smaller average bubble diameters at the bottom of the tank.     

A nonlinear position and attitude observer on SE(3) using landmark measurements

This paper addresses the problem of position and attitude estimation, based on landmark readings and velocity measurements. A derivation of a nonlinear observer on SE(3) is presented, using a Lyapunov function conveniently expressed as a function of the difference between the estimated and the measured landmark coordinates. The resulting feedback laws are explicit functions of the landmark measurements and velocity readings, exploiting the sensor information directly in the observer. The proposed observer yields almost global asymptotic stabilization of the position and attitude errors and exponential convergence in any closed ball inside the region of attraction. Also, it is shown that the asymptotic convergence of the estimation error trajectories is shaped by the landmark geometry and observer design parameters. The problem of non-ideal velocity readings is also considered, and the observer is augmented to compensate for bias in the angular and linear velocity measurements. The resulting position, attitude, and bias estimation errors are shown to converge exponentially fast to the desired equilibrium points, for bounded initial estimation errors. Simulation results are presented to illustrate the stability and convergence properties of the observer.     

Natural Image Statistics and Low-Complexity Feature Selection

Low-complexity feature selection is analyzed in the context of visual recognition. It is hypothesized that high-order dependences of bandpass features contain little information for discrimination of natural images. This hypothesis is characterized formally by the introduction of the concepts of conjunctive interference and decomposability order of a feature set. Necessary and sufficient conditions for the feasibility of low-complexity feature selection are then derived in terms of these concepts. It is shown that the intrinsic complexity of feature selection is determined by the decomposability order of the feature set and not its dimension. Feature selection algorithms are then derived for all levels of complexity and are shown to be approximated by existing information-theoretic methods, which they consistently outperform. The new algorithms are also used to objectively test the hypothesis of low decomposability order through comparison of classification performance. It is shown that, for image classification, the gain of modeling feature dependencies has strongly diminishing returns: best results are obtained under the assumption of decomposability order 1. This suggests a generic law for bandpass features extracted from natural images: that the effect, on the dependence of any two features, of observing any other feature is constant across image classes.     

Holistic context models for visual recognition

A novel framework to context modeling based on the probability of co-occurrence of objects and scenes is proposed. The modeling is quite simple, and builds upon the availability of robust appearance classifiers. Images are represented by their posterior probabilities with respect to a set of contextual models, built upon the bag-of-features image representation, through two layers of probabilistic modeling. The first layer represents the image in a semantic space, where each dimension encodes an appearance-based posterior probability with respect to a concept. Due to the inherent ambiguity of classifying image patches, this representation suffers from a certain amount of contextual noise. The second layer enables robust inference in the presence of this noise by modeling the distribution of each concept in the semantic space. A thorough and systematic experimental evaluation of the proposed context modeling is presented. It is shown that it captures the contextual “gist” of natural images. Scene classification experiments show that contextual classifiers outperform their appearance-based counterparts, irrespective of the precise choice and accuracy of the latter. The effectiveness of the proposed approach to context modeling is further demonstrated through a comparison to existing approaches on scene classification and image retrieval, on benchmark data sets. In all cases, the proposed approach achieves superior results.     

Building a bridge between performance management,radical innovation,and innovation networks: A systematic literature review

Research on performance management (PM) of networks and radical innovation has largely developed along two isolated paths. On the one hand, a growing body of knowledge addresses the use of performance management systems in the context of networks (notably for production and incremental innovation). On the other hand, radical innovation scholars have shown how innovation initiatives that are more uncertain demand the use of new approaches (e.g., new project management routines or different evaluative tools for portfolios). Some scholars even suggest that traditional PM may suppress radical innovation. Our study takes a different approach. It aims to bridge radical innovation and network performance management through a systematic review of the literature. Here, we adopt a hybrid approach that combines bibliometrics with content analysis. Whereas the literature often separates the issues of network performance management and radical innovation, our contribution expands the theory of network management and radical innovation by developing a conceptual framework based on these concepts, thus combining them. We also offer suggestions for further research on this approach. Our framework suggests that overall, the performance management of radical innovation networks presents different features (e.g., recursive rather than linear) and constructs (e.g., openness and unintended performance) from those of innovation network management.     

Reducing prepartum urine pH by supplementing anionic feed ingredients: Effects on physiological and productive responses of Holstein × Gir cows

This study compared physiological and productive parameters in 3/4 Holstein × 1/4 Gir dairy cows receiving a prepartum concentrate containing ammonium chloride to reduce urine pH near 7.0 (CON; n = 17), or a commercial anionic supplement to reduce urine pH near 6.0 (SUPP; n = 17). Nonlactating, multiparous, pregnant cows were assigned to receive SUPP or CON beginning 21 d before expected date of calving. Cows were maintained in a single drylot pen with ad libitum access to corn silage, and individually received their prepartum concentrate once daily (0800 h) before calving. Cows from both treatments completely consumed their concentrate allocation within 30 min after feeding. Cow body weight and body condition score were recorded once weekly, urine pH measured every 3 d, and blood samples collected on d ?21, ?14, ?9, ?6, and ?3 relative to expected calving date. After calving (d 0), cows were moved to an adjacent drylot pen with ad libitum access to water and a total mixed ration, and were milked twice daily (0600 and 1700 h). Cow body weight and body condition score were recorded once weekly and individual milk production was recorded daily until 30 d in milk (DIM). Blood samples were collected before each milking during the first 5 DIM, as well as at 6, 9, 16, 23, and 30 DIM before the morning milking. Based on actual calving dates, cows received SUPP or CON for (mean ± standard error) 19.2 ± 1.2 and 19.0 ± 0.9 d before calving, respectively. Urine pH was less in SUPP versus CON cows during the last 15 d of gestation (6.12 vs. 7.15, respectively). Milk yield during the first 5 DIM and throughout the experimental period was greater in SUPP versus CON cows (by 20 and 14%, respectively), whereas serum Ca concentrations did not differ between treatments during the first 5 DIM. Serum concentrations of fatty acids were greater in SUPP versus CON cows 3 d before and at calving (by 52 and 22%, respectively), whereas SUPP cows had lower serum glucose and cortisol concentration at calving (by 23 and 27%, respectively). Hence, the SUPP treatment decreased prepartum urine pH near 6.0 in Holstein × Gir dairy cows without depressing concentrate intake compared with CON, although total dry matter intake was not evaluated to fully investigate feed intake responses. Moreover, the SUPP treatment transiently affected serum glucose, fatty acids, and cortisol concentrations near the time of calving, and resulted in greater milk yield during the initial 30 DIM compared with CON.     

A new input indicator for the assessment of science &; technology research?

Traditional input indicators of research performance, such as research funding, number of active scientists, and international collaborations, have been widely used to assess countries’ publication output. However, while publication in today’s English-only research world requires sound research in readable English, English proficiency may be a problem for the productivity of non-native English-speaking (NNES) countries. Data provided by the Brazilian National Research Council (CNPq) containing the academic profile of 51,223 Brazilian researchers show a correlation between English proficiency and publication output. According to our results, traditional input indicators may fall short of providing an accurate representation of the research performance of NNES developing countries.