Test sizes of planktic foraminifera from the South Atlantic

Geographical size distribution within entire Holocene foraminiferal assemblages is related to global environmental gradients such as temperature, primary productivity, and environmental variability. This study demonstrates that these correlations are also recognizable in late Quaternary assemblages from three locations in the South Atlantic on temporal and latitudinal scales. The size response to temporal paleoenvironmental changes during glacial-interglacial cycles mimics the geographic Holocene size variability. The amplitude of size variability is directly related to the amplitude of the climatic fluctuations as shown by the stable size-temperature relationship over time. The documented changes in the assemblage size are caused by species replacement and intraspecific size variability. The relative importance of these processes depends on the environmental setting. Species have been shown to reach their maximum size and abundance under certain optimum conditions and decrease in size if environmental conditions differ from these optima. We confirm that late Quaternary species sizes were largest at paleotemperatures identical to Holocene ones.

(Table 1) Age comparison between the GPTS and ODP Site 181-1123 age model for each chron base within the Middle Miocene section

Today the deep western boundary current (DWBC) east of New Zealand is the most important route for deep water entering the Pacific Ocean. Large-scale changes in deep water circulation patterns are thought to have been associated with the development of the East Antarctic Ice Sheet (EAIS) close to the main source of bottom water for the DWBC. Here we reconstruct the changing speed of the southwest Pacific DWBC during the middle Miocene from ~15.5-12.5 Ma, a period of significant global ice accumulation associated with EAIS growth. Sortable silt mean grain sizes from Ocean Drilling Program Site 1123 reveal variability in the speed of the Pacific inflow on the timescale of the 41 kyr orbital obliquity cycle. Similar orbital period flow changes have recently been demonstrated for the Pleistocene epoch. Collectively, these observations suggest that a strong coupling between changes in the speed of the deep Pacific inflow and high-latitude climate forcing may have been a persistent feature of the global thermohaline circulation system for at least the past 15 Myr. Furthermore, long-term changes in flow speed suggest an intensification of the DWBC under an inferred increase in Southern Component Water production. This occurred at the same time as decreasing Tethyan outflow and major EAIS growth between ~15.5 and 13.5 Ma. These results provide evidence that a major component of the deep thermohaline circulation was associated with the middle Miocene growth of the EAIS and support the view that this time interval represents an important step in the development of the Neogene icehouse climate.

(Table 1) Bulk Fe isotope values and pyrite framboids sizes of Cenomanian-Turonian samples

Organic carbon-rich shales from localities in England, Italy, and Morocco, which formed during the Cenomanian-Turonian oceanic anoxic event (OAE), have been examined for their total organic carbon (TOC) values together with their carbon, nitrogen, and iron isotope ratios. Carbon isotope stratigraphy (d13Corg and d13Ccarb) allows accurate recognition of the strata that record the oceanic anoxic event, in some cases allowing characterization of isotopic species before, during, and after the OAE. Within the black shales formed during the OAE, relatively heavy nitrogen isotope ratios, which correlate positively with TOC, suggest nitrate reduction (leading ultimately to denitrification and/or anaerobic ammonium oxidation). Black shales deposited before the onset of the OAE in Italy have unusually low bulk d57Fe values, unlike those found in the black shale (Livello Bonarelli) deposited during the oceanic anoxic event itself: These latter conform to the Phanerozoic norm for organic-rich sediments. Pyrite formation in the pre-OAE black shales has apparently taken place via dissimilatory iron reduction (DIR), within the sediment, a suboxic process that causes an approximately -2 per mil fractionation between a lithogenic Fe(III)oxide source and Fe(II)aq. In contrast, bacterial sulfate reduction (BSR), at least partly in the water column, characterized the OAE itself and was accompanied by only minor iron isotope fractionation. This change in the manner of pyrite formation is reflected in a decrease in the average pyrite framboid diameter from ~10 to ~7 µm. The gradual, albeit irregular increase in Fe isotope values during the OAE, as recorded in the Italian section, is taken to demonstrate limited isotopic evolution of the dissolved iron pool, consequent upon ongoing water column precipitation of pyrite under euxinic conditions. Given that evidence exists for both nitrate and sulfate reduction during the OAE, it is evident that redox conditions in the water column were highly variable, in both time and space.

City dweller aspirations for cities of the future: How do environmental and personal wellbeing feature?

This paper explores city dweller aspirations for cities of the future in the context of global commitments to radically reduce carbon emissions by 2050; cities contribute the vast majority of these emissions and a growing bulk of theworld's population lives in cities. The particular challenge of creating a carbon reduced future in democratic countries is that the measures proposed must be acceptable to the electorate. Such acceptability is fostered if carbon reduced ways of living are also felt to bewellbeing maximising. Thus the objective of the paper is to explore what kinds of cities people aspire to live in, to ascertain whether these aspirations align with or undermine carbon reduced ways of living, as well as personal wellbeing. Using a novel free associative technique, city aspirations are found to cluster around seven themes, encompassing physical and social aspects. Physically, people aspire to a city with a range of services and facilities, green and blue spaces, efficient transport, beauty and good design. Socially, people aspire to a sense of community and a safe environment. An exploration of these themes reveals that only a minority of the participants' aspirations for cities relate to lowering carbon or environmental wellbeing. Far more consensual is emphasis on, and a particular vision of, aspirations that will bring personal wellbeing. Furthermore, city dweller aspirations align with evidence concerning factors that maximise personal wellbeing but, far less, with those that produce lowcarbonways of living. In order to shape a lower carbon future that city dwellers accept the potential convergence between environmental and personal wellbeing will need to be capitalised on: primarily aversion to pollution and enjoyment of communal green space.

Feature recognition & tool path generation for 5 axis step-nc machining of free form / irregular contoured surfaces

This research paper presents a five step algorithm to generate tool paths for machining Free form / Irregular Contoured Surface(s) (FICS) by adopting STEP-NC (AP-238) format. In the first step, a parametrized CAD model with FICS is created or imported in UG-NX6.0 CAD package. The second step recognizes the features and calculates a Closeness Index (CI) by comparing them with the B-Splines / Bezier surfaces. The third step utilizes the CI and extracts the necessary data to formulate the blending functions for identified features. In the fourth step Z-level 5 axis tool paths are generated by adopting flat and ball end mill cutters. Finally, in the fifth step, tool paths are integrated with STEP-NC format and validated. All these steps are discussed and explained through a validated industrial component.

Feature Study on a Programmable Network Traffic Classifier

Monitoring and tracking of IP traffic flows are essential for network services (i.e. packet forwarding). Packet header lookup is the main part of flow identification by determining the predefined matching action for each incoming flow. In this paper, an improved header lookup and flow rule update solution is investigated. A detailed study of several well-known lookup algorithms reveals that searching individual packet header field and combining the results achieve high lookup speed and flexibility. The proposed hybrid lookup architecture is comprised of various lookup algorithms, which are selected based on the user applications and system requirements.

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.

Feature Selection for Anomaly Detection Using Optical Emission Spectroscopy

To maintain the pace of development set by Moore's law, production processes in semiconductor manufacturing are becoming more and more complex. The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low. As the dimension of process monitoring data can become extremely high anomaly detection systems are impacted by the curse of dimensionality, hence dimensionality reduction plays an important role. Classical dimensionality reduction approaches, such as Principal Component Analysis, generally involve transformations that seek to maximize the explained variance. In datasets with several clusters of correlated variables the contributions of isolated variables to explained variance may be insignificant, with the result that they may not be included in the reduced data representation. It is then not possible to detect an anomaly if it is only reflected in such isolated variables. In this paper we present a new dimensionality reduction technique that takes account of such isolated variables and demonstrate how it can be used to build an interpretable and robust anomaly detection system for Optical Emission Spectroscopy data.