Composition of proteins in wheat grain streams obtained by sieve classification

Sieve classification was used to prepare grain streams of distinct protein content and composition. Both reverse‐phase high‐performance liquid chromatography and two‐dimensional electrophoresis revealed differences in protein composition between large (F1), medium (F2–F3), and small (F4) kernel fractions of three Polish cultivars: Sukces and Tonacja (winter wheats) and Nawra (spring wheat). Statistical analysis indicated that grain protein composition is affected by the cultivar and kernel size, as well as by interaction of these traits. The albumin/globulin and glutenin fractions showed a tendency to decrease with diminishing kernel size. The gliadin content was affected to a greater extent by the kernel size than by the wheat genotype. The highest content of gliadins was observed in the smallest and the largest kernel fractions. Copyright © 2007 Society of Chemical Industry     

Effect of self-differentiation and anonymity in group on deindividuation.

Investigated the effects of deindividuating conditions (i.e., anonymity) on the behaviors and feelings of self-differentiated and less self-differentiated individuals. 40 male 17–18 yr olds were divided on the basis of their scores on the portable rod-and-frame test into self-differentiated and undifferentiated groups. Each S was exposed to transgressive and prosocial behavior of 2 group members who were confederates. Exposure to the models' behavior occurred under conditions of anonymity or identifiability. Additionally, measures of Ss' feelings were taken. Data indicate that relative to identifiability conditions, the transgressions and prosocial actions of the models had stronger effects on the behaviors of undifferentiated Ss under anonymity conditions. Also, undifferentiated Ss felt less self-conscious and less inhibited under anonymity than identifiability conditions. The behaviors and feelings of self-differentiated Ss were relatively unaffected by the anonymity–identifiability manipulation. A conceptual link between self-differentiation, self-awareness, and deindividuation is proposed. (22 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Computing parameter ranges in constructive geometric constraint solving: Implementation and correctness proof

In parametric design, changing values of parameters to get different solution instances to the problem at hand is a paramount operation. One of the main issues when generating the solution instance for the actual set of parameters is that the user does not know in general which is the set of parameter values for which the parametric solution is feasible. Similarly, in constraint-based dynamic geometry, knowing the set of critical points where construction feasibility changes would allow to avoid unexpected and unwanted behaviors.We consider parametric models in the Euclidean space with one internal degree of freedom. In this scenario, in general, the set of values of the variant parameter for which the parametric model is realizable and defines a valid shape is a set of intervals on the real line.In this work we report on our experiments implementing the van der Meiden Approach to compute the set of parameter values that bound intervals for which the parametric object is realizable. The implementation is developed on top of a constructive, ruler-and-compass geometric constraint solver. We formalize the underlying concepts and prove that our implementation is correct, that is, the approach exactly computes all the feasible interval bounds.     

3DFCNN: real-time action recognition using 3D deep neural networks with raw depth information

Multimedia Tools and Applications - This work describes an end-to-end approach for real-time human action recognition from raw depth image-sequences. The proposal is based on a 3D fully...     

Hybrid parallelization of molecular dynamics simulations to reduce load imbalance

The most widely used technique to allow for parallel simulations in molecular dynamics is spatial domain decomposition, where the physical geometry is divided into boxes, one per processor. This technique can inherently produce computational load imbalance when either the spatial distribution of particles or the computational cost per particle is not uniform. This paper shows the benefits of using a hybrid MPI+OpenMP model to deal with this load imbalance. We consider LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator), a prototypical molecular dynamics simulator that provides its own balancing mechanism and an OpenMP implementation for many of its modules, allowing for a hybrid setup. In this work, we extend the current OpenMP implementation of LAMMPS and optimize it and evaluate three different setups: MPI-only, MPI with the LAMMPS balance mechanism, and hybrid setup using our improved OpenMP version. This comparison is made using the five standard benchmarks included in the LAMMPS distribution plus two additional test cases. Results show that the hybrid approach can deal with load balancing problems better and more effectively (50% improvement versus MPI-only for a highly imbalanced test case) than the LAMMPS balance mechanism (only 43% improvement) and improve simulations with issues other than load imbalance.

     

A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition

With the advent of new cable and satellite services, and the next generation of digital TV systems, people are faced with an unprecedented level of program choice. This often means that viewers receive much more information than they can actually manage, which may lead them to believe that they are missing programs that could likely interest them. In this context, TV program recommendation systems allow us to cope with this problem by automatically matching user’s likes to TV programs and recommending the ones with higher user preference.This paper describes the design, development, and startup of queveo.tv: a Web 2.0 TV program recommendation system. The proposed hybrid approach (which combines content-filtering techniques with those based on collaborative filtering) also provides all typical advantages of any social network, such as supporting communication among users as well as allowing users to add and tag contents, rate and comment the items, etc. To eliminate the most serious limitations of collaborative filtering, we have resorted to a well-known matrix factorization technique in the implementation of the item-based collaborative filtering algorithm, which has shown a good behavior in the TV domain. Every step in the development of this application was taken keeping always in mind the main goal: to simplify as much as possible the user task of selecting what program to watch on TV.     

Recursive alignment block classification technique for word reordering in statistical machine translation

Statistical machine translation (SMT) is based on alignment models which learn from bilingual corpora the word correspondences between source and target language. These models are assumed to be capable of learning reorderings. However, the difference in word order between two languages is one of the most important sources of errors in SMT. In this paper, we show that SMT can take advantage of inductive learning in order to solve reordering problems. Given a word alignment, we identify those pairs of consecutive source blocks (sequences of words) whose translation is swapped, i.e. those blocks which, if swapped, generate a correct monotonic translation. Afterwards, we classify these pairs into groups, following recursively a co-occurrence block criterion, in order to infer reorderings. Inside the same group, we allow new internal combination in order to generalize the reorder to unseen pairs of blocks. Then, we identify the pairs of blocks in the source corpora (both training and test) which belong to the same group. We swap them and we use the modified source training corpora to realign and to build the final translation system. We have evaluated our reordering approach both in alignment and translation quality. In addition, we have used two state-of-the-art SMT systems: a Phrased-based and an Ngram-based. Experiments are reported on the EuroParl task, showing improvements almost over 1 point in the standard MT evaluation metrics (mWER and BLEU).     

Use of lidar data to simulate forest net primary production

A method has been recently presented to predict the net primary production (NPP) of Mediterranean forests by integrating conventional and remote-sensing data. This method was based on the use of two models, C-Fix and BIOME-BGC, whose outputs are combined with estimates of stem volume and tree age to predict the NPP of the examined ecosystems. This article investigates the possibility of deriving these two forest attributes from airborne high-resolution lidar data. The research was carried out in the San Rossore pine forest, a test site in Central Italy where several investigations have been conducted. First, estimates of stand stem volume and tree age were obtained from lidar data by application of a simplified method based on existing literature and a few ground measurements. The accuracy of these stand attributes was assessed by comparison with the independent ground data derived from a recent forest inventory. Next, the stem volume and tree age estimates were used to drive the NPP modelling strategy, whose outputs were evaluated against the inventory measurements of current annual increment (CAI). The simplified lidar data processing method produces stand stem volume and tree age estimates having moderate accuracy, which are useful to feed the modelling strategy and predict CAI at a stand level. This method's success raises the possibility of integrating ecosystem modelling techniques and lidar data for the simulation of net forest carbon fluxes.     

On software verification for sensor nodes

We consider software written for networked, wireless sensor nodes, and specialize software verification techniques for standard C programs in order to locate programming errors in sensor applications before the software's deployment on motes. Ensuring the reliability of sensor applications is challenging: low-level, interrupt-driven code runs without memory protection in dynamic environments. The difficulties lie with (i) being able to automatically extract standard C models out of the particular flavours of embedded C used in sensor programming solutions, and (ii) decreasing the resulting program's state space to a degree that allows practical verification times.We contribute a platform-dependent, OS-independent software verification tool for OS-wide programs written in MSP430 embedded C with asynchronous hardware interrupts. Our tool automatically translates the program into standard C by modelling the MCU's memory map and direct memory access. To emulate the existence of hardware interrupts, calls to hardware interrupt handlers are added, and their occurrence is minimized with a double strategy: a partial-order reduction technique, and a supplementary reachability check to reduce overapproximation. This decreases the program's state space, while preserving program semantics. Safety specifications are written as C assertions embedded in the code. The resulting sequential program is then passed to CBMC, a bounded software verifier for sequential ANSI C. Besides standard errors (e.g., out-of-bounds arrays, null-pointer dereferences), this tool chain is able to verify application-specific assertions, including low-level assertions upon the state of the registers and peripherals.Verification for wireless sensor network applications is an emerging field of research; thus, as a final note, we survey current research on the topic.