Changes in goal selection induced by cue conflicts are in register with predictions from changes in place cell field locations.

In the cognitive mapping theory of hippocampal function, currently active place cells represent a rat's spatial location (J. O'Keefe & L. Nadel, 1978). A systematic shift of firing field locations should therefore produce a similar shift in a rat's judgment of its location. A. A. Fenton, G. Csizmadia, and R. U. Muller (2000a) recorded place cells in cylinders with 2 cue cards separated by 135°. When the separation was changed, firing fields moved systematically, as described by a vector-field equation (A. A. Fenton, G. Csizmadia, & R. U. Muller, 2000b). Given this cohesive movement of firing fields, the mapping theory predicts that a rat's decisions about the location of an unmarked goal should move after card separation changes, as described by the vector-field equation. The authors tested this reasoning with a task in which the rat earned a food reward by pausing in a small, unmarked goal zone. When cues were shifted in the absence of reward, goal choice shifts were accurately predicted by the vector-field equation, providing strong support for the notion that a rat's judgment of its spatial location is intimately related to the across-cell discharge pattern of simultaneously active place cells. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Silhouette-based human action recognition using sequences of key poses

In this paper, a human action recognition method is presented in which pose representation is based on the contour points of the human silhouette and actions are learned by making use of sequences of multi-view key poses. Our contribution is twofold. Firstly, our approach achieves state-of-the-art success rates without compromising the speed of the recognition process and therefore showing suitability for online recognition and real-time scenarios. Secondly, dissimilarities among different actors performing the same action are handled by taking into account variations in shape (shifting the test data to the known domain of key poses) and speed (considering inconsistent time scales in the classification). Experimental results on the publicly available Weizmann, MuHAVi and IXMAS datasets return high and stable success rates, achieving, to the best of our knowledge, the best rate so far on the MuHAVi Novel Actor test.     

Virtualized Web server cluster self-configuration to optimize resource and power use

This work proposes a reusable architecture that enables the self-configuration of a supporting infrastructure for Web server clusters using virtual machines. The goal of the architecture is to ensure service quality, evaluating how broadly it complies with the application's operating restrictions and proportionally acting on the configuration of physical servers (hosts) or virtual machines. In addition, through the rational use of resources, the proposal aims at saving energy. A prototype of the architecture was developed and a performance evaluation carried out with two different resource management approaches. This evaluation shows how fully functional and advantageous the proposal is in terms of using resources, avoiding waste, yet maintaining the application's quality of service within acceptable levels. The architecture also shows to be flexible enough to accept, with a reasonable amount of effort, different resource self-configuration policies.     

面向小功率驱动应用的新型SOI驱动器和SmartPACK／Smart PIM模块

本文基于SOI技术,开发了一款新型的600V/0.3A 6-pack IGBT/MOSFET驱动器芯片.此款名为6ED003L06-F的芯片集成了三相电平转换、门极驱动和电路保护等诸多特性，在缩小芯片尺寸的同时也提高了性能，例如抗反相瞬态电压的鲁棒性等。另外，本文还利用该SOI驱动芯片开发了SmartPACK/SmartPIM系列新型IPM模块，该模块在一块基于DCB层的底板上集成了SOI驱动器，600V IGBT3和自举功能，并且无须支承板。这种IPM可对应6A-12A的额定电流，同时具有结构紧凑、成本低廉、操作简单等优点，适用于各类小功率驱动特别是消费类驱动应用。     

Analysis of supercritical carbon dioxide extracts from cones and leaves of a Humulus lupulus L cultivar

Extracts obtained by supercritical carbon dioxide extraction of cones and leaves of hop (Humulus lupulus L) at different combinations of temperature and pressure were analysed for their α- and β-acids and volatiles, using HPLC and GC respectively. The yield and composition of the mixtures of bitter compounds from the cones were largely influenced by the temperature and pressure applied during the extraction. Bitter compounds could not be detected in the extracts from the leaves. The most important volatile components identified were β-myrcene, β-caryophyllene and α-humulene. The extraction parameters also influenced the composition of the mixture of volatiles from the cones and leaves, 40°C and 20.0 MPa being optimum for the extraction of both bitter compounds and volatiles, which are thought to be important for the aroma of beer.     

Performance monitoring in optical networks using asynchronously acquired samples with nonideal sampling systems and intersymbol interference

This paper presents a study on the influence of nonideal sampling in asynchronous Q-factor monitoring. An overestimation of the Q-factor of signals impaired with optical noise may occur if the averaging effects due to nonideal sampling affect only the noise component of the monitored signal. It is shown that this problem can be rectified using a correction factor, dependent only on the receiver parameters. This allows reasonable Q-factor estimates using sampling systems with aperture times close to the monitored signal bit period. Nonideal sampling may also affect the signal component of the monitored signal. For such cases, a novel and accurate technique is presented to estimate the Q factor using asynchronous histograms, which takes into account changes in the shape of the monitored signal due to averaging effects. Numerical simulations are used to evaluate the proposed technique achieving Q-factor estimates within a 14% error margin, independently of the sampling aperture for the considered range. Distortion affecting the signal due to the transmission system can also be handled by the new technique presented in this paper. It is verified that the impact of intersymbol interference due to chromatic dispersion or electrical filtering in the receiver on the performance of the system is well reflected by the estimated Q factor.     

Deep learning-based hybrid reconstruction algorithm for fibre instance segmentation from 3D x-ray tomographic images

3D x-ray tomography is a powerful scanning technique used for generating images of complex fibre structures. A novel machine-learning algorithm to identify and separate individual fibres using 3D images is proposed in this article. The developed four-step hybrid 3D fibre segmentation algorithm involves deep-learning aided semantic segmentation that slices 3D images to create 2D images for fibre extraction, elliptical contour estimation combined with the marker-controlled watershed algorithm for separating fibres from the background area, identifying individual fibres through 3D reconstruction, and, lastly, the 3D object refining approach based on outlier object detection and replacement. The proposed methodology is implemented on a real-time sample of nylon fibre bundle under compression and its 3D x-ray image volume to validate the performance. The results show its superior performance compared to off-the-shelf image processing algorithms in terms of precision, that is, with a validation accuracy greater than 90%, and efficiency, that is, preventing the need for a huge data set and reducing the complexity.