Multi-decadal atmospheric and marine climate variability in southern Iberia during the mid- to late-Holocene

To assess the regional multi-decadal to multicentennial climate variability along the southern Iberian Peninsula during the mid- to late-Holocene record of paleoenvironmental indicators from marine sediments were established for two sites in the Alboran Sea (ODP-161-976A) and the Gulf of Cadiz (GeoB5901-2). High-resolution records of organic geochemical properties and planktic foraminiferal assemblages are used to decipher precipitation and vegetation changes as well as hydrological conditions with respect to sea surface temperature (SST) and marine primary productivity (MPP). As a proxy for precipitation change, records of plant-derived n-alkane composition suggest a series of five distinct dry episodes in southern Iberia at 5.4 +/- 0.3 ka cal BP, from 5.1 to 4.9 +/- 0.1 ka cal BP, from 4.8 to 4.7 +/- 0.1 ka cal BP, from 4.4 to 4.3 +/- 0.1 ka cal BP, and at 3.7 +/- 0.1 ka cal BP. During each dry episode the vegetation suffered from reduced water availability. Interestingly, the dry phase from 4.4 to 4.3 +/- 0.1 ka cal BP is followed by a rapid shift towards wetter conditions revealing a more complex pattern in terms of its timing and duration than was described for the 4.2 ka event in other regions. The series of dry episodes as well as closely connected hydrological variability in the Alboran Sea were probably driven by NAO-like (North Atlantic Oscillation) variability. In contrast, surface waters in the Gulf of Cadiz appear to have responded more directly to North Atlantic cooling associated with Bond events. In particular, during Bond events 3 and 4, a pronounced increase in seasonality with summer warming and winter cooling is found.DFG (German Research Foundation) CRC 1266 2901391021 Fundacao para a Ciencia e Tecnologia SFRH/BPD/111433/2015info:eu-repo/semantics/publishedVersio

Towards Pose-Invariant 2D Face Classification for Surveillance

A key problem for "face in the crowd" recognition from existing surveillance cameras in public spaces (such as mass transit centres) is the issue of pose mismatches between probe and gallery faces. In addition to accuracy, scalability is also important, necessarily limiting the complexity of face classification algorithms. In this paper we evaluate recent approaches to the recognition of faces at relatively large pose angles from a gallery of frontal images and propose novel adaptations as well as modifications. Specifically, we compare and contrast the accuracy, robustness and speed of an Active Appearance Model (AAM) based method (where realistic frontal faces are synthesized from non-frontal probe faces) against bag-of-features methods (which are local feature approaches based on block Discrete Cosine Transforms and Gaussian Mixture Models). We show a novel approach where the AAM based technique is sped up by directly obtaining pose-robust features, allowing the omission of the computationally expensive and artefact producing image synthesis step. Additionally, we adapt a histogram-based bag-of-features technique to face classification and contrast its properties to a previously proposed direct bag-of-features method. We also show that the two bag-of-features approaches can be considerably sped up, without a loss in classification accuracy, via an approximation of the exponential function. Experiments on the FERET and PIE databases suggest that the bag-of-features techniques generally attain better performance, with significantly lower computational loads. The histogram-based bag-of-features technique is capable of achieving an average recognition accuracy of 89% for pose angles of around 25 degrees

Fast Face Detector Training Using Tailored Views

Face detection is an important task in computer vision and often serves as the first step for a variety of applications. State-of-the-art approaches use efficient learning algorithms and train on large amounts of manually labeled imagery. Acquiring appropriate training images, however, is very time-consuming and does not guarantee that the collected training data is representative in terms of data variability. Moreover, available data sets are often acquired under con-trolled settings, restricting, for example, scene illumination or 3D head pose to a narrow range. This paper takes a look into the automated generation of adaptive training samples from a 3D morphable face model. Using statistical insights, the tailored training data guarantees full data variability and is enriched by arbitrary facial attributes such as age or body weight. Moreover, it can automatically adapt to environmental constraints, such as illumination or viewing angle of recorded video footage from surveillance cameras. We use the tailored imagery to train a new many-core imple-mentation of Viola Jones ’ AdaBoost object detection frame-work. The new implementation is not only faster but also enables the use of multiple feature channels such as color features at training time. In our experiments we trained seven view-dependent face detectors and evaluate these on the Face Detection Data Set and Benchmark (FDDB). Our experiments show that the use of tailored training imagery outperforms state-of-the-art approaches on this challenging dataset. 1

Higher sea surface temperature in the Indian Ocean during the Last Interglacial weakened the South Asian monsoon

Addressing and anticipating future South Asian monsoon changes under continuing global warming is of critical importance for the food security and socioeconomic well-being of one-quarter of the world’s population. However, climate model projections show discrepancies in future monsoon variability in South Asian monsoon domains, largely due to our still limited understanding of the monsoon response to warm climate change scenarios. Particularly, climate models are largely based on the assumption that higher solar insolation causes higher rainfall during similar warm climatic regimes, but this has not been verified by proxy data for different interglacial periods. Here, we compare Indian summer monsoon (ISM) variability during the Last Interglacial and Holocene using a sedimentary leaf wax δD and δ13C record from the northern Bay of Bengal, representing the Ganges–Brahmaputra–Meghna (G-B-M) river catchment. In combination with a seawater salinity record, our results show that ISM intensity broadly follows summer insolation on orbital scales, but ISM intensity during the Last Interglacial was lower than during the Holocene despite higher summer insolation and greenhouse gas concentrations. We argue that sustained warmer sea surface temperature in the equatorial and tropical Indian Ocean during the Last Interglacial increased convective rainfall above the ocean but dampened ISM intensity on land. Our study demonstrates that besides solar insolation, internal climatic feedbacks also play an important role for South Asian monsoon variability during warm climate states. This work can help to improve future climate model projections and highlights the importance of understanding controls of monsoonal rainfall under interglacial boundary conditions.Geological Setting and Proxy Records Results - Variations of n-Alkane δD and δ13C Values. - δDivc Values in Sediment Core 17286-1 Reflect ISM Intensity and Rainfall Amount. - ISM Rainfall Shifts in South Asia. - Vegetation Changes in South Asia. Discussion - Climatic Controls on ISM Intensity at Millennial to Orbital Time Scales. - Internal Climatic Feedback of South Asian Monsoon Variability during the Last Interglacial and the Holocene - Mechanisms Controlling Vegetation Variability in South Asia. - Perspectives. Method

Environmental Evaluation of Water Resources Development

Methodology for the utilization of LANDSAT-1 imagery and aerial photography on the environmental evaluation of water resources development is presented. Environmental impact statements for water resource projects were collected and reviewed for the various regions of Texas. The environmental effects of channelization and surface impoundments are discussed for twelve physiographic regions of the state as delineated on black and white satellite (LANDSAT-1) mosaic of band 7. With the aid of LANDSAT-1 imagery, representative or typical transects were chosen within each region. Profiles of each site were constructed from topographic maps and environmental data were accumulated for each site and related to low altitude aerial photography and enlarged LANDSAT-1 false color composites. Each diagrammatic transect, with accompanying data and photographs, provides significant information for input of environmental amenities on a local and regional scale into preliminary water resources development studies. The utilization of the transects provides a visual display of available information, aids in the identification and inventory of resources, assists in the identification of data gaps and provides a planning tool for additional data acquisition. Remote sensing techniques are readily adapted to water resources planning. LANDSAT-1 imagery as well as conventional low altitude aerial photography provides the planner with a synoptic overview of the resource area. The delineation of physiographic regions by LANDSAT-1 imagery will be helpful in defining delicate border areas and delineating broad environmental areas. Satellite imagery is applicable for transect siting in aerial river basin studies or regional analysis. The diagrammatic transects along with satellite imagery can be used to grossly quantify habitat types and amounts. The transects and accompanying data can be used in displays for public hearings and project monitoring. They lend themselves to constant update and can be included in resulting environmental impact statements

Precision of the current methods to measure the alkenone proxy UK'37 and absolute alkenone abundance in sediments : results of an interlaboratory comparison study

Measurements of the UK'37 index and the absolute abundance of alkenones in marine sediments are increasingly used in paleoceanographic research as proxies of past sea surface temperature and haptophyte (mainly coccolith-bearing species) primary productivity, respectively. An important aspect of these studies is to be able to compare reliably data obtained by different laboratories from a wide variety of locations. Hence the intercomparability of data produced by the research community is essential. Here we report results from an anonymous interlaboratory comparison study involving 24 of the leading laboratories that carry out alkenone measurements worldwide. The majority of laboratories produce data that are intercomparable within the considered confidence limits. For the measurement of alkenone concentrations, however, there are systematic biases between laboratories, which might be related to the techniques employed to quantify the components. The maximum difference between any two laboratories for any two single measurements of UK'37 in sediments is estimated, with a probability of 95%, to be <2.18C. In addition, the overall within-laboratory precision for the UK'37 temperature estimates is estimated to be <1.68C (95% probability). Similarly, from the analyses of alkenone concentrations the interlaboratory reproducibility is estimated at 32%, and the repeatability is estimated at 24%. The former is compared to a theoretical estimate of reproducibility and found to be excessively high. Hence there is certainly scope and a demonstrable need to improve reproducibility and repeatability of UK'37 and especially alkenone quantification data across the community of scientists involved in alkenone research

Iodido{4-phenyl-1-[1-(1,3-thia­zol-2-yl-κN)ethyl­idene]thio­semicarbazidato-κ2 N′,S}{4-phenyl-1-[1-(1,3-thia­zol-2-yl)ethyl­idene]thio­semicarbazide-κS}mercury(II)

In the title compound, [Hg(C12H11N4S2)I(C12H12N4S2)], the Hg atom is in a distorted square-pyramidal coordination, defined by the iodide ligand, by the S atom of the neutral ligand in the apical position, and by the N atom of the thia­zole ring, the thio­ureido N and the S atom of the deprotonated ligand. The deprotonated ligand intra­molecularly hydrogen bonds to the thia­zole ring N atom, while the deprotonated ligand forms an inter­molecular hydrogen bond to the thiol­ate S atom. The deprotonation of the tridentate ligand and its coordination to Hg via the S atom strikingly affects the C—S bond lengths. In the free ligand, the C—S bond distance is 1.685 (7) Å, whereas it is 1.749 (7) Å in the deprotonated ligand. Similarly, the Hg—S bond distance is slightly longer to the neutral ligand [2.6682 (18) Å] than to the deprotonated ligand [2.5202 (19) Å]. The Hg—I distance is 2.7505 (8) Å

Unsupervised Multi-View CNN for Salient View Selection of 3D Objects and Scenes

We present an unsupervised 3D deep learning framework based on a ubiquitously true proposition named view-object consistency as it states that a 3D object and its projected 2D views always belong to the same object class. To validate its effectiveness, we design a multi-view CNN for the salient view selection of 3D objects, which quintessentially cannot be handled by supervised learning due to the difficulty of data collection. Our unsupervised multi-view CNN branches off two channels which encode the knowledge within each 2D view and the 3D object respectively and also exploits both intra-view and inter-view knowledge of the object. It ends with a new loss layer which formulates the view-object consistency by impelling the two channels to generate consistent classification outcomes. We experimentally demonstrate the superiority of our method over state-of-the-art methods and showcase that it can be used to select salient views of 3D scenes containing multiple objects.</p

Enabling Viewpoint Learning through Dynamic Label Generation

Optimal viewpoint prediction is an essential task in many computer graphics applications. Unfortunately, common viewpoint qualities suffer from two major drawbacks: dependency on clean surface meshes, which are not always available, and the lack of closed-form expressions, which requires a costly search involving rendering. To overcome these limitations we propose to separate viewpoint selection from rendering through an end-to-end learning approach, whereby we reduce the influence of the mesh quality by predicting viewpoints from unstructured point clouds instead of polygonal meshes. While this makes our approach insensitive to the mesh discretization during evaluation, it only becomes possible when resolving label ambiguities that arise in this context. Therefore, we additionally propose to incorporate the label generation into the training procedure, making the label decision adaptive to the current network predictions. We show how our proposed approach allows for learning viewpoint predictions for models from different object categories and for different viewpoint qualities. Additionally, we show that prediction times are reduced from several minutes to a fraction of a second, as compared to state-of-the-art (SOTA) viewpoint quality evaluation. We will further release the code and training data, which will to our knowledge be the biggest viewpoint quality dataset available

Novel SCARB2 mutation in action myoclonus-renal failure syndrome and evaluation of SCARB2 mutations in isolated AMRF features

Background: Action myoclonus-renal failure syndrome is a hereditary form of progressive myoclonus epilepsy associated with renal failure. It is considered to be an autosomal-recessive disease related to loss-of-function mutations in SCARB2. We studied a German AMRF family, additionally showing signs of demyelinating polyneuropathy and dilated cardiomyopathy. To test the hypothesis whether isolated appearance of individual AMRF syndrome features could be related to heterozygote SCARB2 mutations, we screened for SCARB2 mutations in unrelated patients showing isolated AMRF features. Methods: In the AMRF family all exons of SCARB2 were analyzed by Sanger sequencing. The mutation screening of unrelated patients with isolated AMRF features affected by either epilepsy (n = 103, progressive myoclonus epilepsy or generalized epilepsy), demyelinating polyneuropathy (n = 103), renal failure (n = 192) or dilated cardiomyopathy (n = 85) was performed as high resolution melting curve analysis of the SCARB2 exons. Results: A novel homozygous 1 bp deletion (c.111delC) in SCARB2 was found by sequencing three affected homozygous siblings of the affected family. A heterozygous sister showed generalized seizures and reduction of nerve conduction velocity in her legs. No mutations were found in the epilepsy, renal failure or dilated cardiomyopathy samples. In the polyneuropathy sample two individuals with demyelinating disease were found to be carriers of a SCARB2 frameshift mutation (c.666delCCTTA). Conclusions: Our findings indicate that demyelinating polyneuropathy and dilated cardiomyopathy are part of the action myoclonus-renal failure syndrome. Moreover, they raise the possibility that in rare cases heterozygous SCARB2 mutations may be associated with PNP features