Annotated record of the detailed examination of Mn deposits from COCOTOW Expedition stations in the intersection of the Siqueiros Transform Fault and the East Pacific Rise

A near-bottom geological and geophysical survey was conducted at the western intersection of the Siqueiros Transform Fault and the East Pacific Rise. Transform-fault shear appears to distort the east flank of the rise crest in an area north of the fracture zone. In ward-facing scarps trend 335° and do not parallel the regional axis of spreading. Small-scale scarps reveal a hummocky bathymetry. The center of spreading is not a central peak but rather a 20-40 m deep, 1 km wide valley superimposed upon an 8 km wide ridge-crest horst. Small-scale topography indicates widespread volcanic flows within the valley. Two 0.75 km wide blocks flank the central valley. Fault scarps are more dominant on the western flank. Their alignment shifts from directions intermediate to parallel to the regional axis of spreading (355°). A median ridge within the fracture zone has a fault-block topography similar to that of the East Pacific Rise to the north. Dominant eastward-facing scarps trending 335° are on the west flank. A central depression, 1 km wide and 30 m deep, separates the dominantly fault-block regime of the west from the smoother topography of the east flank. This ridge originated by uplift due to faulting as well as by volcanism. Detailed mapping was concentrated in a perched basin (Dante's Hole) at the intersection of the rise crest and the fracture zone. Structural features suggest that Dante's Hole is an area subject to extreme shear and tensional drag resulting from transition between non-rigid and rigid crustal behavior. Normal E-W crustal spreading is probably taking place well within the northern confines of the basin. Possible residual spreading of this isolated rise crest coupled with shear drag within the transform fault could explain the structural isolation of Dante's Hole from the remainder of the Siqueiros Transform Fault.

Large volume metrology process model:measurability analysis with integration of metrology classification model and feature-based selection model

Aircraft manufacturing industries are looking for solutions in order to increase their productivity. One of the solutions is to apply the metrology systems during the production and assembly processes. Metrology Process Model (MPM) (Maropoulos et al, 2007) has been introduced which emphasises metrology applications with assembly planning, manufacturing processes and product designing. Measurability analysis is part of the MPM and the aim of this analysis is to check the feasibility for measuring the designed large scale components. Measurability Analysis has been integrated in order to provide an efficient matching system. Metrology database is structured by developing the Metrology Classification Model. Furthermore, the feature-based selection model is also explained. By combining two classification models, a novel approach and selection processes for integrated measurability analysis system (MAS) are introduced and such integrated MAS could provide much more meaningful matching results for the operators. © Springer-Verlag Berlin Heidelberg 2010.

Detection and Classification of Whale Acoustic Signals

This dissertation focuses on two vital challenges in relation to whale acoustic signals: detection and classification.

In detection, we evaluated the influence of the uncertain ocean environment on the spectrogram-based detector, and derived the likelihood ratio of the proposed Short Time Fourier Transform detector. Experimental results showed that the proposed detector outperforms detectors based on the spectrogram. The proposed detector is more sensitive to environmental changes because it includes phase information.

In classification, our focus is on finding a robust and sparse representation of whale vocalizations. Because whale vocalizations can be modeled as polynomial phase signals, we can represent the whale calls by their polynomial phase coefficients. In this dissertation, we used the Weyl transform to capture chirp rate information, and used a two dimensional feature set to represent whale vocalizations globally. Experimental results showed that our Weyl feature set outperforms chirplet coefficients and MFCC (Mel Frequency Cepstral Coefficients) when applied to our collected data.

Since whale vocalizations can be represented by polynomial phase coefficients, it is plausible that the signals lie on a manifold parameterized by these coefficients. We also studied the intrinsic structure of high dimensional whale data by exploiting its geometry. Experimental results showed that nonlinear mappings such as Laplacian Eigenmap and ISOMAP outperform linear mappings such as PCA and MDS, suggesting that the whale acoustic data is nonlinear.

We also explored deep learning algorithms on whale acoustic data. We built each layer as convolutions with either a PCA filter bank (PCANet) or a DCT filter bank (DCTNet). With the DCT filter bank, each layer has different a time-frequency scale representation, and from this, one can extract different physical information. Experimental results showed that our PCANet and DCTNet achieve high classification rate on the whale vocalization data set. The word error rate of the DCTNet feature is similar to the MFSC in speech recognition tasks, suggesting that the convolutional network is able to reveal acoustic content of speech signals.

Decadal-to-centennial scale variations during the Holocene reconstructed from sediment core D13902

Sea surface temperature (SST), marine productivity, and fluvial input have been reconstructed for the last 11.5 calendar (cal) ka B.P. using a high-resolution study of C37 alkenones, coccolithophores, iron content, and higher plant n-alkanes and n-alkan-1-ols in sedimentary sequences from the inner shelf off the Tagus River Estuary in the Portuguese Margin. The SST record is marked by a continuous decrease from 19C, at 10.5 and 7 ka, to 15C at present. This trend is interrupted by a fall from 18C during the Roman and Medieval Warm Periods to 16C in the Little Ice Age. River input was very low in the early Holocene but increased in the last 3 cal ka B.P. in association with an intensification of agriculture and deforestation and possibly the onset of the North Atlantic Oscillation/Atlantic Multidecadal Oscillation modes of variability. River influence must have reinforced the marine cooling trend relative to the lower amplitude in similar latitude sites of the eastern Atlantic. The total concentration of alkenones reflects river-induced productivity, being low in the early Holocene but increasing as river input became more important. Rapid cooling, of 1-2C occurring in 250 years, is observed at 11.1, 10.6, 8.2, 6.9, and 5.4 cal ka B.P. The estimated age of these events matches the ages of equivalent episodes common in the NE Atlantic- Mediterranean region. This synchronicity reveals a common widespread climate feature, which considering the twentieth century analog between colder SSTs and negative North Atlantic Oscillation (NAO), is likely to reflect periods of strong negative NAO.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

Multi-scale colorectal tumour segmentation using a novel coarse to fine strategy

This paper addresses the problem of colorectal tumour segmentation in complex real world imagery. For efficient segmentation, a multi-scale strategy is developed for extracting the potentially cancerous region of interest (ROI) based on colour histograms while searching for the best texture resolution. To achieve better segmentation accuracy, we apply a novel bag-of-visual-words method based on rotation invariant raw statistical features and random projection based l2-norm sparse representation to classify tumour areas in histopathology images. Experimental results on 20 real world digital slides demonstrate that the proposed algorithm results in better recognition accuracy than several state of the art segmentation techniques.

A sub-kiloparsec scale view of star formation in M31

Thesis (Ph.D.)--University of Washington, 2016-08

Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain-expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel Genetic Programming based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation, and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation, and has significantly outperformed, or achieved a comparable performance to, the baseline methods.

Alteration of the bone tissue material properties in type 1 diabetes mellitus: A Fourier transform infrared microspectroscopy study

International audience

EFFECT OF INTERMITTENT FRYING ON FATTY ACIDS, VITAMIN E, LIPID OXIDATION AND ACRYLAMIDE IN OILS AND PLANTAIN CHIPS COLLECTED FROM SMALL- SCALE PRODUCERS IN CAMEROON

Deep-fat frying is susceptible to induce the formation of undesirable products as lipid oxidation products and acrylamide in fried foods. Plantain chips produced by small-scale producers are sold to consumers without any control. The objective of this study was to evaluate the quality of plantain chips from local producers in relation to production process parameters and oils, and to identify the limiting factors for the production of acrylamide in plantain chips. Samples of frying oils and plantain chips prepared with either palm olein or soybean oil were collected from 10 producers in Yaoundé. Quality parameters determined in this study were: fatty acid composition of the oils, determined by gas chromatography (GC) of free acid methyl ester; trans fatty acids, determined by Fourier transform infra-red spectroscopy; Tocopherols and tocotrienols as markers of nutritional quality were analyzed by High Performance Liquid Chromatography in isocratic mode. Free fatty acids and acylglycerols as markers of lipid hydrolysis were analyzed by GC of trimethylsilyl derivatives of glycerides. Conjugated dienes, Anisidine value and viscosity as markers of lipid oxidation and thermal decomposition of the oils; acrylamide which is formed through Maillard reaction and identified as a toxic compound in various fried products. Asparagine content of the raw fresh plantain powder was also determined. Fatty acid composition of palm oleins was stable within a day of intermittent frying. In soybean oils, about 57% and 62.5% of linoleic and linolenic acids were lost but trans fatty acids were not detected. Soybean oils were partly hydrolysed leading to the formation of free fatty acids, monoacylglycerols and diacylglycerols. In both oils, tocopherols and tocotrienols contents decreased significantly by about 50%. Anisidine value (AV) and polymers contents increased slightly in fried palm oleins while conjugated hydroperoxides, AV and polymers greatly increased in soybean oils. Acrylamide was not detected in the chips. This is explained by the absence of asparagine in the raw plantains, the other acrylamide precursors being present. This study shows that the plantain chips prepared at the small-scale level in Yaounde with palm olein are of good quality regarding oxidation and hydrolysis parameters and the absence of acrylamide. In contrast, oxidation developed with soybean oil whose usage for frying should be questioned. Considering that asparagine is the limiting factor for the formation of acrylamide in plantain chips, its content depending on several factors such as production parameters and maturity stage should be explored.

Highly localized accelerating beams using nano-scale metallic gratings

Spatially accelerating beams are non-diffracting beams whose intensity is localized along curvilinear trajectories, also incomplete circular trajectories, before diffraction broadening governs their propagation. In this paper we report on numerical simulations showing the conversion of a high-numerical-aperture focused beam into a nonparaxial shape-preserving accelerating beam having a beam-width near the diffraction limit. Beam shaping is induced near the focal region by a diffractive optical element that consists of a non-planar subwavelength grating enabling a Bessel signature.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

Consumer behaviour: conceptualisation, scale development and validation of agro-food chain fairness

Although there is broad agreement on the need to transition to a fairer agro-food system, consumer potential in shaping a fair food system has often been overlooked. There is no unique definition of the concept of fairness from the consumer’s perspective. In addition, there are no scales in the academic literature that address fairness in its broad sense, as the existing scales focus on specific and limited aspects that provide a partial picture of the concept. Lack of a true and trustworthy measurement of the notion has been a significant barrier to the knowledge of fairness in agro-food systems from the individual-differences perspective. The individual-differences perspective helps explain why some individuals are more likely than others to put emphasis on the extent to which agro-food chains are fair. Individual consumer perception of an ethical problem is followed by the perception of various alternatives that might lead to a solution. Therefore, the current research intends to make two significant contributions by resolving these constraints. First, advance the literature by providing a new viewpoint to understand fairness in the agro-food chain. Indeed, the research provides a comprehensive conceptualisation of fairness that embraces different aspects of fairness and describes the concept in all its facets and nuances. Second, the research provides a valid, reliable, and invariant measurement of the individual disposition toward fairness in agro-food chains by rooting the items in the theoretical underpinnings of the fairness literature. Overall, this research provides a comprehensive suite of approaches and tools to enhance the resilience, integrity and sustainability of agro-food chains.

Analysis Of The Dna Fourier Transform-infrared Microspectroscopic Signature Using An All-reflecting Objective.

The Fourier transform-infrared (FT-IR) signature of dry samples of DNA and DNA-polypeptide complexes, as studied by IR microspectroscopy using a diamond attenuated total reflection (ATR) objective, has revealed important discriminatory characteristics relative to the PO2(-) vibrational stretchings. However, DNA IR marks that provide information on the sample's richness in hydrogen bonds have not been resolved in the spectral profiles obtained with this objective. Here we investigated the performance of an all reflecting objective (ARO) for analysis of the FT-IR signal of hydrogen bonds in DNA samples differing in base richness types (salmon testis vs calf thymus). The results obtained using the ARO indicate prominent band peaks at the spectral region representative of the vibration of nitrogenous base hydrogen bonds and of NH and NH2 groups. The band areas at this spectral region differ in agreement with the DNA base richness type when using the ARO. A peak assigned to adenine was more evident in the AT-rich salmon DNA using either the ARO or the ATR objective. It is concluded that, for the discrimination of DNA IR hydrogen bond vibrations associated with varying base type proportions, the use of an ARO is recommended.