Identification of a Widespread Monomolecular Odor Differentially Attractive to Several <Emphasis Type="Italic">Delia Radicum</Emphasis> Ground-dwelling Predators in the Field

Dimethyl disulfide (DMDS) was identified as a major volatile constituent of Brassica napus roots heavily infested by Delia radicum, the cabbage root fly. Attractiveness of this widespread compound was tested in the field in a naturally complex odorous environment. By using an original setup especially designed for ground dwelling beetles, different concentrations of the pure molecule as well as attractiveness of the natural blend emitted by the rotten part of infested roots were tested simultaneously. The use of general linear model (GLM) statistics permitted us to finely discriminate the responses among the different treatments. The main predators of D. radicum (i.e., two staphylinids Aleochara bilineata and Aleochara bipustulata and carabid beetles of the genus Bembidion) were significantly attracted by DMDS, but responded in different ways to the natural blend and to the different concentrations tested. The dose–response curves were similar for the two staphylinids. However, whereas A. bilineata was more attracted by the natural volatile blend than by its preferred DMDS concentration, A. bipustulata was attracted as much by the natural blend as by its preferred DMDS concentration. Carabid beetles exhibited a different response. They were not attracted by the natural blend, but responded to a wider range of DMDS concentrations that included low concentrations that did not attract the staphylinid beetles. These results are discussed according to the potential resources searched by each taxon studied and their specificity for the resources. The possible use of DMDS for enhancing biological control of D. radicum is mentioned.     

An enhanced Lemaitre model formulation for materials processing damage computation

The Lemaitre damage model is now widely used to deal with coupled damage analyses for various mechanical applications. In this article, different extensions of the model are presented and discussed to deal with complex multiaxial configurations—such as multi-stages bulk forming processes. A specific treatment is done to account for compressive damage growth, and a stress triaxiality cut-off value is considered to avoid any damage evolution below a critical negative triaxiality. The damage potential is also modified to deal with highly ductile materials, and the plastic strain is split into a negative part and a positive part to differentiate damage growth for compressive states of stress and for tensile states of stress. Finally, an anisotropic damage approach based on the comparison between grain flow orientation and principal loading directions is defined. A combination of these extensions is achieved within a single Lemaitre formulation. Application on different examples show the robustness and accuracy of the model defined in this paper.     

Multitrack Digital Data Storage: A Reduced Complexity Architecture

We describe a low-complexity noniterative detector for magnetic and optical multitrack high-density data storage. The detector is based on the M-algorithm architecture. It performs limited breadth-first detection on the equivalent one-dimensional (1-D) channel obtained by column-by-column helical unwinding of the two-dimensional (2-D) channel. The detection performance is optimized by the use of a specific 2-D minimum-phase factorization of the channel impulse response by the equalizer. An optimized path selection scheme maintains the complexity close to practical 1-D Viterbi. This scheme is based on an approximate path metric parallel sort network, taking advantage of the metrics' residual ordering from previous M-algorithm iterations. Such an architecture approaches maximum-likelihood performance on a high areal density uncoded channel for a practical number of retained paths M and bit error rate (BER) below 10^-4. The performance of the system is evaluated when the channel is encoded with multi-parity check (MPC) block inner code and an outer interleaved Reed-Solomon code. The inner code enhances the minimum error distance of the equalized channel and reduces the correct path losses of the M-algorithm path buffer. The decoding is performed noniteratively. Here, we compare the performance of the system to the soft iterative joint decoding of the read channels for data pages encoded with low-density parity check (LDPC) codes with comparable rates and block length. We provide an approximation of the 2-D channel capacity to further assess the performance of the system     

Probing a Device's Active Atoms

Materials science and device studies have, when implemented jointly as “operando” studies, better revealed the causal link between the properties of the device's materials and its operation, with applications ranging from gas sensing to information and energy technologies. Here, as a further step that maximizes this causal link, the paper focuses on the electronic properties of those atoms that drive a device's operation by using it to read out the materials property. It is demonstrated how this method can reveal insight into the operation of a macroscale, industrial‐grade microelectronic device on the atomic level. A magnetic tunnel junction's (MTJ's) current, which involves charge transport across different atomic species and interfaces, is measured while these atoms absorb soft X‐rays with synchrotron‐grade brilliance. X‐ray absorption is found to affect magnetotransport when the photon energy and linear polarization are tuned to excite Fe? O bonds parallel to the MTJ's interfaces. This explicit link between the device's spintronic performance and these Fe? O bonds, although predicted, challenges conventional wisdom on their detrimental spintronic impact. The technique opens interdisciplinary possibilities to directly probe the role of different atomic species on device operation, and shall considerably simplify the materials science iterations within device research.     

An Above-Elbow Prosthesis Employing Programmed Linkages

The use of EMG signals from residual muscles to control an above-elbow prosthesis has been tried, but presents many problems, not the least being that the prosthesis is under open-loop control. A more satisfactory control technique is extended physiological proprioception where the inherent proprioceptive feedback present within an intact joint is used to provide closed-loop control. We have developed a prototype of a self-contained above-elbow prosthesis which has electric-powered wrist and elbow joints controlled by an Intel 8751 microcomputer. Our technique is to control the positioning of this above-elbow prosthesis using the motion of the intact shoulder. Grasp, which does not involve positioning in space, is separately controlled using EMG signals from biceps and triceps muscles. The relationship between output (wrist and elbow positions) and input (shoulder position) can be set according to a program or linkage stored in the microcomputer memory. A choice from a range of linkages can enable the user to perform different tasks in different situations. We have verified experimentally that after an amputee has learned to associate the position of the wrist and elbow joints of the prosthesis with his shoulder position, the prosthesis will become a very useful aid. Also, because of the repeatability of operation and the inherent closed-loop nature of the prosthesis, the amputee will be motivated to wear it every day.     

Multimodal Machine Learning for Natural Language Processing: Disambiguating Prepositional Phrase Attachments with Images

Neural Processing Letters - Although documents are increasingly multimodal, their automatic processing is often monomodal. In particular, natural language processing tasks are typically performed...     

Handling bipolar knowledge with imprecise probabilities

Information is said to be bipolar when it has a positive and a negative part. The problem of representing and processing such bipolar information has recently received a lot of attention in uncertainty theories. In this paper, we are concerned with the representation of asymmetric bipolarity, i.e., with situations where positive and negative information are unrelated and processed in parallel. In this latter case, positive information consists of observations of experiment results, showing what values are possible, whereas negative information consists of constraints (e.g., provided by an expert), restricting the range of possible variable values. Up to now, there are no proposition as to how such bipolar information can be treated in the framework of imprecise probability theory, i.e., when information is represented by convex sets of probabilities. In this paper, we propose the basis of such a framework and provide some illustrative examples. © 2011 Wiley Periodicals, Inc.     

A novel target detection algorithm combining foreground and background manifold-based models

This paper focuses on the detection of small objects—more precisely on vehicles in aerial images—on complex backgrounds such as natural backgrounds. A key contribution of the paper is to show that, in such situations, learning a target model and a background model separately is better than training a unique discriminative model. This contrasts with standard object detection approaches for which objects vs. background classifiers use the same model as well as the same types of visual features for both. The second contribution lies in the manifold learning approach introduced to build these models. The proposed detection algorithm is validated on the publicly available OIRDS dataset, on which we obtain state-of-the-art results.     

An automatic model-to-model mapping and transformation methodology to serve model-based systems engineering

With enterprise collaboration becoming increasingly frequent, the ability of an enterprise to cooperate with others has become one of the core factors in gaining competitive advantage. This trend has led to an urgent requirement to improve cooperation ability. To this end, model-based systems engineering is being adapted so that it can be used to represent and simulate the working processes of enterprises. Model-to-model mappings and transformations, as important aspects in model-based systems engineering, have become two of the key factors in improving the cooperation capabilities of enterprises. However, the foundations for achieving automatic model-to-model transformation have not yet been built. Normally, model transformation rules are built on the basis of model mappings, and model mappings concern semantic or syntactic representations. One of the difficulties in achieving model-to-model mappings and transformations lies in detecting the semantics and semantic relations that are conveyed in different models. This paper presents an automatic model-to-model mapping and transformation methodology, which applies semantic and syntactic checking measurements to detect the meanings and relations between different models automatically. Both of the semantic and syntactic checking measurements are combined into a refined meta-model based model transformation process. To evaluate the performance of this methodology, we demonstrate its applicability with a realistic example.     

Filtering with clouds

Selecting a particular kernel to filter a given digital signal can be a difficult task. One solution to solve this difficulty is to filter with multiple kernels. However, this solution can be computationally costly. Using the fact that most kernels used for low-pass signal filtering can be assimilated to probability distributions (or linear combinations of probability distributions), we propose to model sets of kernels by convex sets of probabilities. In particular, we use specific representations that allow us to perform a robustness analysis without added computational costs. The result of this analysis is an interval-valued filtered signal. Among such representations are possibility distributions, from which have been defined maxitive kernels. However, one drawback of maxitive kernels is their limited expressiveness. In this paper, we extend this approach by considering another representation of convex sets of probabilities, namely clouds, from which we define cloudy kernels. We show that cloudy kernels are able to represent sets of kernels whose bandwidth is upper and lower bounded, and can therefore be used as a good trade-off between the classical and the maxitive approach, avoiding some of their respective shortcomings without making computations prohibitive. Finally, the benefits of using cloudy filters is demonstrated through some experiments.