Local Affine Research Articles

Patterns of drilling predation in relation to stratigraphy, locality and sieve size: Insights from the Eocene molluscan fauna of the Paris Basin

Predatory drilling traces are routinely used to evaluate temporal patterns in animal behaviour. However, for testing such trends it is important to consider natural and methodological variability that may play a role in shaping drilling patterns. The present study attempts to test this notion by evaluating the drilling patterns of the molluscan fauna in relation to stratigraphy, locality, sieve size (4mm, 2mm, 1mm) and taxonomic affinity at class level. We studied drilling predation using molluscan specimens from three localities (Grignon, La Ferme de l'Orme, Fleury-la-Rivière), representing a variety of elementary depositional sequences (EDS) from the Middle Lutetian strata of the Paris Basin.The overall drilling frequency (DF) averaged over the three localities is 17%; bivalves show a significantly higher overall DF (18%) compared to gastropods (14%) and scaphopods (<0.01%). For all higher taxa and at all localities DF is highest in the medium size class (2–4mm), which we relate to the dominance of preferred prey species in this category. Despite having similar diversity profiles of molluscan fauna, the three localities show distinct drilling patterns with highest frequencies in Grignon (overall 25%, bivalves 29%, gastropods 20%), followed by Fleury-la-Rivière (overall 18%, bivalves 17%, gastropods 18%) and almost a complete lack of drill holes in La Ferme de l'Orme. This pattern is true for each sieve size of molluscs, bivalves and gastropods. The results are similar for common species found in all three localities. EDS2-3 representing a mesotrophic environment during a regressive phase shows a slightly higher DF compared to oligotrophic EDS4, from the following transgression. Locality emerges as the strongest predictor of DF when evaluated for the relative role of stratigraphy, locality, sieve size and higher taxa. The occurrence of incomplete drill holes is relatively rare and increases with sieve size, implying a possible existence of handling limit of the predatory gastropods. There are very few occurrences of edge drilling. The combined variation documented in this study is comparable in magnitude with many temporal shifts documented previously. Therefore, in order to establish any temporal pattern in drilling behaviour it is vital to evaluate and control for the potential gradient of other natural variabilities in predatory behaviour.Species composition and diversity has been used previously to resolve the debate on climate conditions of the Eocene Paris basin through a comparison to modern analogues. We have used a similar approach by comparing predation patterns with Recent localities of subtropical and temperate regions. The available data from the Eocene Paris basin, from the warm-temperate northern Adriatic Sea and from the sub-tropical Red Sea show very heterogeneous patterns, depending on the drilling metric considered. Considering the strong patchiness of drilling predation within each of these basins and reported from many other studies, we conclude that a climatic affiliation of the Eocene Paris basin based on drilling metrics is not yet possible.

Block-Centric Visualization Of Histological Whole Slide Images With Application To Revealing Growth-Patterns Of Early Colorectal Adenomas And Aberrant Crypt Foci

Introduction/ Background Comfortable navigation through diagnostic images is a prospective challenge for the acceptance of virtual microscopy applications in routine pathology [1],[2]. Tracing different regions of interest through multiple sections on one or several slides is a typical task in diagnostic slide examination. This laborious and time-consuming co-localization is currently executed by pathologists. Retaining the relative positions of tissue structures while alternating between multiple slides is still not feasible in a satisfactory manner in conventional nor virtual microscopy. Aims To address this issue we present a more comfortable and intuitive method to read slides using computer-assisted navigation. Furthermore, we demonstrate the strengths of our method by applying it to large series of serial colorectal tissue sections, creating new kinds of visualizations of different adenomatous mucosal architectures in human tissue, while looking for human correlates of lesions recently described in mice [3]. Methods Histological images contain multiple distortions from different sources in the laboratory and digitalization process. An interconnection model was created to describe distortions by several layers, providing a normalized tissue representation. Layers were associated with specific distortions with each layer serving as a new level of abstraction. The first layers enabled a coarse alignment of tissue sections. Further alignment is achieved by piecewise, multi-resolution, SIFT-based [4] correspondence extraction and refinement. Inside the convex hull of all fiducial points local affine transformations were applied whereas a global affine transformation was used on the outside. Animated stacks were generated for regions of interest using local rigid transformations to preserve exact morphological coherences. For subsequent creation of 3D models, the relevant histological objects within these images were annotated by pathologists, partly using computer assisted segmentation based on active contours [5]. These annotations were used subsequently to create simplified 3D models by applying VTK [6]. Results The presented methods provide an efficient means to retrieve correspondences and additional spatial information from serial sections of histological slides. They also show good applicability for specimen from different origin. Alignment methods can be applied to generate block-centric visualizations such as parallel and transparent viewing of multiple stains. Moreover, the generated stack videos and 3D models demonstrate the very good accuracy of section alignment even in large series. The visualizations enable pathologists and researchers to grasp the 3D structural relationships in the tissue at a glance, providing an excellent tool to communicate more complex histomorphological findings. Interestingly, we see two kinds of tubular adenomas, which could imply multiple ways to tubular adenoma formation in FAP-patients, possibly akin to the recent observations in mice [3].

Matching of images with projective distortion using transform invariant low-rank textures

A matching method is presented for images with projective distortion based on transform invariant low-rank textures (TILT). In the method, the problem of matching images with projective distortion is first reduced to a problem of matching two rectified images just with scaling and translation distortions via TILT. Then a point-feature based matching method is employed to establish the corresponding points between the two rectified images. This is different from some traditional methods that try to directly seek local affine or projective invariants from input images. Moreover, no prior knowledge on the epipolar geometry is required in the proposed method. An automatic low-rank texture region detection method is presented to make the method more applicable in practice. Additionally, a new descriptor is constructed by combining a proposed geometric shape descriptor and the traditional SIFT descriptor to further improve the correct matching rate. Experimental results demonstrate the validity of the proposed method.

Unsupervised Spectral Mesh Segmentation Driven by Heterogeneous Graphs.

A fully automatic mesh segmentation scheme using heterogeneous graphs is presented. We introduce a spectral framework where local geometry affinities are coupled with surface patch affinities. A heterogeneous graph is constructed combining two distinct graphs: a weighted graph based on adjacency of patches of an initial over-segmentation, and the weighted dual mesh graph. The partitioning relies on processing each eigenvector of the heterogeneous graph Laplacian individually, taking into account the nodal set and nodal domain theory. Experiments on standard datasets show that the proposed unsupervised approach outperforms the state-of-the-art unsupervised methodologies and is comparable to the best supervised approaches.

Analysis of Vessel Connectivities in Retinal Images by Cortically Inspired Spectral Clustering

Retinal images provide early signs of diabetic retinopathy, glaucoma, and hypertension. These signs can be investigated based on microaneurysms or smaller vessels. The diagnostic biomarkers are the change of vessel widths and angles especially at junctions, which are investigated using the vessel segmentation or tracking. Vessel paths may also be interrupted; crossings and bifurcations may be disconnected. This paper addresses a novel contextual method based on the geometry of the primary visual cortex (V1) to study these difficulties. We have analyzed the specific problems at junctions with a connectivity kernel obtained as the fundamental solution of the Fokker-Planck equation, which is usually used to represent the geometrical structure of multi-orientation cortical connectivity. Using the spectral clustering on a large local affinity matrix constructed by both the connectivity kernel and the feature of intensity, the vessels are identified successfully in a hierarchical topology each representing an individual perceptual unit.

Reviewing a Friend - The Role of Social Ties in Review Work in Auditing

Hierarchically structured review work is a pervasive element in many professional work settings, including auditing. However, little is known about social factors underlying the pairing of reviewers and reviewees and their impact on quality. The German auditing setting provides a unique opportunity to empirically observe review work at the highest hierarchical level. We investigate the role of social ties in the pairing of concurring auditor (reviewer) and lead auditor (reviewee). We find that the presence of social ties between the concurring auditor and the lead auditor resulting from local affinity, i.e., having the same dialect and working in the same office, makes it more likely for the auditors to form pairs. In contrast, the presence of educational affinity, i.e., having a similar level of audit experience and the same field of study, makes it less likely for auditors to form pairs. Using an instrumental variable approach to control for selection effects, we find that pairs sharing a local background provide lower audit quality, whereas pairs sharing an educational background provide higher audit quality. Our findings should be of interest to regulators, audit firms, audit clients and investors.

Linear Time Illumination Invariant Stereo Matching

This paper proposes a new similarity measure that is invariant to global and local affine illumination changes. Unlike existing methods, its computational complexity is very low. When used for ster...

Depositor Discipline in Russian Regions: Flight to Familiarity or Trust in Local Authorities?

We analyse whether depositor familiarity with a bank affects depositor behaviour during a financial crisis. We measure familiarity by looking for regional or local cues in the bank’s name. We measure depositor behaviour by the their sensitivity to observable bank risk (market discipline). Using 2001-2010 bank-level and region-level data for Russia, we find that depositors of familiar banks become less sensitive to bank risk after a financial crisis relative to depositors of unfamiliar banks. To check that the results are not driven by any implicit support of banks with regional cues in their names by regional governments, but indeed by familiarity bias, we interact the variables of interest with measures of trust in local governments and regional affinity. We find that the flight to familiarity effect is strongly present in regions with strong regional affinity, while the effect is rejected in regions with more trust in regional and local governments. This indicates our results are driven by familiarity and not by any implicit protection from a trusted regional or local government

Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction

As a linear dimensionality reduction method, nonnegative matrix factorization (NMF) has been widely used in many fields, such as machine learning and data mining. However, there are still two major drawbacks for NMF: (a) NMF can only perform semantic factorization in Euclidean space, and it fails to discover the intrinsic geometrical structure of high-dimensional data distribution. (b) NMF suffers from noisy data, which are commonly encountered in real-world applications. To address these issues, in this paper, we present a new robust structure preserving nonnegative matrix factorization (RSPNMF) framework. In RSPNMF, a local affinity graph and a distant repulsion graph are constructed to encode the geometrical information, and noisy data influence is alleviated by characterizing the data reconstruction term of NMF withl2,1-norm instead ofl2-norm. With incorporation of the local and distant structure preservation regularization term into the robust NMF framework, our algorithm can discover a low-dimensional embedding subspace with the nature of structure preservation. RSPNMF is formulated as an optimization problem and solved by an effective iterative multiplicative update algorithm. Experimental results on some facial image datasets clustering show significant performance improvement of RSPNMF in comparison with the state-of-the-art algorithms.

Digital humor and the articulation of locality in an age of global flows

AbstractThis paper uses the lens of internet-based humor to examine how, amidst massive global flows of content, young Israelis articulate a sense of local-national affinity. We analyzed verbal and visual comic email forwards to trace: (a) the extent to which Israelis share local versus global content and (b) the means through which national affinity is conveyed. Results show that while Israelis’ humorous diet is mainly non-local, a pervasive use of the Hebrew vernacular plays an important role in creating local affinity. Our analysis yielded five discursive frames that mark locality in humor:

Protein–water dynamics in antifreeze protein III activity

We combine Terahertz absorption spectroscopy (THz) and molecular dynamics (MD) simulations to investigate the underlying molecular mechanism for the antifreeze activity of one class of antifreeze protein, antifreeze protein type III (AFP-III) with a focus on the collective water hydrogen bond dynamics near the protein. After summarizing our previous work on AFPs, we present a new investigation of the effects of cosolutes on protein antifreeze activity by adding sodium citrate to the protein solution of AFP-III. Our results reveal that for AFP-III, unlike some other AFPs, the addition of the osmolyte sodium citrate does not affect the hydrogen bond dynamics at the protein surface significantly, as indicated by concentration dependent THz measurements. The present data, in combination with our previous THz measurements and molecular simulations, confirm that while long-range solvent perturbation is a necessary condition for the antifreeze activity of AFP-III, the local binding affinity determines the size of the hysteresis.

Design of a PKCδ-specific small peptide as a theragnostic agent for glioblastoma

Glioblastoma is an aggressive malignant brain tumor that starts in the brain or spine and frequently recurs after anticancer treatment. The development of an accurate diagnostic system combined with effective cancer therapy is essential to improve prognosis of glioma patients. Peptides, produced from phage display, are attractive biomolecules for glioma treatment because of their biostability, nontoxicity, and small size. In this study, we employed phage display methodology to screen for peptides that specifically recognize the target PKCδ as a novel biomarker for glioma. The phage library screening yielded four different peptides displayed on phages with a 20- to 200-pM Kd value for the recombinant PKCδ catalytic domain. Among these four phage peptides, we selected one to synthesize and tagged it with fluorescein isothiocyanate (FITC) based on the sequence of the PKCδ-binding phage clone. The synthetic peptide showed a relative binding affinity for antibody and localization in the U373 glioma cell. The kinase activity of PKCδ was inhibited by FITC-labeled peptide with an IC50 of 1.4 μM in vitro. Consequently, the peptide found in this study might be a promising therapeutic agent against malignant brain tumor.

A novel local derivative quantized binary pattern for object recognition

Designing efficient and effective keypoint descriptors for an image plays a vital role in many computer vision tasks. The traditional binary descriptors such as local binary pattern and its variants directly perform a binarization operation on the intensity differences of the local affine covariant regions, thus their performance usually drops a lot because of the limited distinctiveness. In this paper, we propose a novel image keypoint descriptor, namely local derivative quantized binary pattern for object recognition. To incorporate the spatial information, we first divide the local affine covariant region into several subregions according to the intensity orders. For each sub region, we quantize the intensity differences between the central pixels and their neighbors in an adaptive way, and then we order the differences and use a hash function to map the differences into binary codes. The binary codes are histogramed to form the feature of each subregion. Furthermore, we utilize multi-scale support regions and pool the histograms together to represent the features of the image. Our approach does not need prior codebook training and hence it is more flexible and easy to be implemented. Moreover, our descriptor can preserve more local brightness and edge information than the traditional binary descriptors. Also, our descriptor is robust to rotation, illumination variations and other geometric transformations. Finally we conduct extensive experiments on three challenging datasets (i.e., 53 Objects, ZuBuD, and Kentucky) for object recognition and the experimental results show that our descriptor outperforms the existing state-of-the-art descriptors.

Color-weak compensation using local affine isometry based on discrimination threshold matching.

We develop algorithms for color-weak compensation and color-weak simulation based on Riemannian geometry models of color spaces. The objective function introduced measures the match of color discrimination thresholds of average normal observers and a color-weak observer. The developed matching process makes use of local affine maps between color spaces of color-normal and color-weak observers. The method can be used to generate displays of images that provide color-normal and color-weak observers with a similar color difference experience. It can also be used to simulate the perception of a color-weak observer for color-normal observers. We also introduce a new database of measurements of color discrimination threshold data for color-normal and color-weak observers obtained at different lightness levels in CIELUV space. The compensation methods include compensations of chromaticity using local affine maps between chromaticity planes of color-normal and color-weak observers, and one-dimensional (1D) compensation on lightness. We describe how to determine correspondences between the origins of local coordinates in color spaces of color-normal and color-weak observers using a neighborhood expansion method. After matching the origins of the two coordinate systems, a local affine map is estimated by solving a nonlinear equation, or singular-value-decomposition (SVD). We apply the methods to natural images and evaluate their performance using the semantic differential (SD) method.

An Efficient Approach for Local Affinity Pattern Detection in Remotely Sensed Big Data

Mining information in Big Data requires to design a new class of algorithms and methods so that the computational complexity load is acceptable and the informativity loss is avoided. Information theory-based methodologies can represent a valid option in this sense. In this paper, we analyze a recently introduced method, called PROMODE, to efficiently detect local affinity patterns (LAPs) within Big Data sets. This processing framework operates with a computational load lower than what is required by other algorithms in literature, and is flexible enough to be applied to very heterogeneous remotely sensed datasets. Examples for spaceborne SAR and hyperspectral datasets, as well as a dataset involving Earth observations and clinical records are provided to prove this point.

Creation of guiding patterns for directed self-assembly of block copolymers by resistless direct e-beam exposure

We present an approach for the creation of guiding patterns to direct the self-assembly of block copolymers. A neutral layer of a brush polymer is directly exposed by electrons, causing the cross-linking of the brush molecules, and thus changing its local affinity. The advantage relies on the achievable resolution and the reduction of the process steps in comparison with deep UV and conventional electron beam lithography, since it avoids the use of a resist. We envision that this method will be highly valuable for the investigation of high-chi directed self-assembly materials and complex guiding pattern designs, where pattern placement and resolution are becoming critical.

Multi-Graph Matching via Affinity Optimization with Graduated Consistency Regularization.

This paper addresses the problem of matching common node correspondences among multiple graphs referring to an identical or related structure. This multi-graph matching problem involves two correlated components: i) the local pairwise matching affinity across pairs of graphs; ii) the global matching consistency that measures the uniqueness of the pairwise matchings by different composition orders. Previous studies typically either enforce the matching consistency constraints in the beginning of an iterative optimization, which may propagate matching error both over iterations and across graph pairs; or separate affinity optimization and consistency enforcement into two steps. This paper is motivated by the observation that matching consistency can serve as a regularizer in the affinity objective function especially when the function is biased due to noises or inappropriate modeling. We propose composition-based multi-graph matching methods to incorporate the two aspects by optimizing the affinity score, meanwhile gradually infusing the consistency. We also propose two mechanisms to elicit the common inliers against outliers. Compelling results on synthetic and real images show the competency of our algorithms.

Robust Image Chroma-Keying: A Quadmap Approach Based on Global Sampling and Local Affinity

Chroma-keying is a technique used to replace solid-colored background of images or video frames. This technique is widely used in TV broadcasting, film production, augmented reality, and virtual environment. This paper proposes a new chroma-keying method, which can automatically remove the background color in an image and accurately segment the foreground objects along with their transparency property. Compared to conventional chroma-keying methods based on color clustering, color difference, or thresholding, the proposed method analyzes the color statistics and color confidence of the image in global range. By analyzing image color statistics, local lightness variation, and experience from human visual perception in HSV color space, a segmentation map called quadmap is automatically generated to segment the image into four types of regions: 1) foreground; 2) background; 3) transparent; and 4) reflective regions. By using quadmap, the proposed chroma-keying system can differentiate between transparent and reflective regions. This has always been a challenging problem in conventional chroma-keying or $\alpha $ matting systems. This improvement generates more reliable foreground colors in reflective regions. As a result, there can be less constrains for foreground scene used in TV-broadcasting or film making. The procedures of the proposed method consist of the following five steps: 1) background region detection based on color statistics and local lightness variation; 2) absolute foreground region detection based on the knowledge of background color and predefined thresholds in Hue channel of HSV color space; 3) reflective region detection based on experience from human visual system; 4) background color propagation based on Laplacian equation; and 5) transparency value and foreground color estimation based on global sampling and color confidence.

Accurate Affine Invariant Image Matching Using Oriented Least Square

AbstractImage matching is a vital process for many photogrammetric and remote sensing applications such as image registration and aerial triangulation. In this paper, an accurate affine invariant image matching approach is presented. The proposed approach consists of three main steps. In the first step, two affine invariant feature detectors, including MSER and Harris-Affine features are applied for feature extraction. In the second step, initial corresponding features are selected using Euclidean distance between feature descriptors, followed by a consistency check process. Finally to overcome low positional accuracy of the local affine feature, an advanced version of the least square matching (LSM) namely, Oriented Least Square Matching (OLSM) is developed. Wellknown LSM method has been widely accepted as one of the most accurate methods to obtain high reliable corresponding points from a stereo image pair. However, it is sensitive to significant geometric distortion and requires very good initial approximation. In the proposed OLSM method, shape and size of the matching window are appropriately approximated using obtained affine shape information of the initial elliptical feature pairs. The proposed method was successfully applied for matching various synthetic and real close range and satellite images. Results demonstrate its accuracy and capability compared to standard LSM method.

A multi-agent quantum Monte Carlo model for charge transport: Application to organic field-effect transistors

We have developed a multi-agent quantum Monte Carlo model to describe the spatial dynamics of multiple majority charge carriers during conduction of electric current in the channel of organic field-effect transistors. The charge carriers are treated by a neglect of diatomic differential overlap Hamiltonian using a lattice of hydrogen-like basis functions. The local ionization energy and local electron affinity defined previously map the bulk structure of the transistor channel to external potentials for the simulations of electron- and hole-conduction, respectively. The model is designed without a specific charge-transport mechanism like hopping- or band-transport in mind and does not arbitrarily localize charge. An electrode model allows dynamic injection and depletion of charge carriers according to source-drain voltage. The field-effect is modeled by using the source-gate voltage in a Metropolis-like acceptance criterion. Although the current cannot be calculated because the simulations have no time axis, using the number of Monte Carlo moves as pseudo-time gives results that resemble experimental I/V curves.