Shape from Shading: Recognizing the Mountains through a Global View

Resolving local ambiguities is an important issue for shape from shading (SFS). Pixel ambiguities of SFS can be eliminated by propagation approaches. However, patch ambiguities still exist. Therefore, we formulate the global disambiguation problem to resolve these ambiguities. Intuitively, it can be interpreted as flipping patches and adjusting heights such that the result surface has no kinks. The problem i s intractable because exponentially many possible configurations need to be checked. Alternatively, we solve the integrability testing problem closely related to the original one. It can be viewed as finding a surface which satisfies the global integrability constraint. To encode the constraints, we introduce a graph formulation called configuration graph. Searching the solution on this graph can be reduced to a Max-cut problem and its solution is computable using semidefinite programming (SDP) relaxation. Tests carried out on synthetic and real images show that the global disambiguation works well fro complex shapes

Self-powered microfluidic chips for multiplexed protein assays from whole blood

We report herein on a self-powered, self-contained microfluidic-based chip designed to separate plasma from whole blood, and then execute an assay of a multiplexed panel of plasma biomarker proteins. The power source is based upon a chemical reaction that is catalytically triggered by the push of a button on the chip. We demonstrate assays of a dozen blood-based protein biomarkers using this automated, self-contained device. This platform can potentially permit high throughput, accurate, multiplexed blood diagnostic measurements in remote locations and by minimally trained individuals

Untangling Cycles for Contour Grouping

We introduce a novel topological formulation for contour grouping. Our grouping criterion, called untangling cycles, exploits the inherent topological 1D structure of salient contours to extract them from the otherwise 2D image clutter. To define a measure for topological classification robust to clutter and broken edges, we use a graph formulation instead of the standard computational topology. The key insight is that a pronounced 1D contour should have a clear ordering of edgels, to which all graph edges adhere, and no long range entanglements persist. Finding the contour grouping by optimizing these topological criteria is challenging. We introduce a novel concept of circular embedding to encode this combinatorial task. Our solution leads to computing the dominant complex eigenvectors/ eigenvalues of the random walk matrix of the contour grouping graph. We demonstrate major improvements over state-of-the-art approaches on challenging real images

Single Cell Proteomics in Biomedicine: High-dimensional Data Acquisition, Visualization and Analysis

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions

Saliency Based Opportunitstic Search for Object Part Extraction and Labeling

We study the task of object part extraction and labeling, which seeks to understand objects beyond simply identifiying their bounding boxes. We start from bottom-up segmentation of images and search for correspondences between object parts in a few shape models and segments in images. Segments comprising different object parts in the image are usually not equally salient due to uneven contrast, illumination conditions, clutter, occlusion and pose changes. Moreover, object parts may have different scales and some parts are only distinctive and recognizable in a large scale. Therefore, we utilize a multi-scale shape representation of objects and their parts, figural contextual information of the whole object and semantic contextual information for parts. Instead of searching over a large segmentation space, we present a saliency based opportunistic search framework to explore bottom-up segmentation by gradually expanding and bounding the search domain.We tested our approach on a challenging statue face dataset and 3 human face datasets. Results show that our approach significantly outperforms Active Shape Models using far fewer exemplars. Our framework can be applied to other object categories

Single Cell Proteomics in Biomedicine: High-dimensional Data Acquisition, Visualization and Analysis

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions

Dimensionless Study on Efficiency and Speed Characteristics of a Compressed Air Engine

To eliminate the pollutants exhausting, this paper presents an idea of using compressed air as the power source for engines. Instead of an internal combustion (IC) engine, this automobile is equipped with a compressed air engines (CAEs), which transforms the energy of the compressed air into mechanical kinematic energy. Through analysis of the working process of a CAE, the mathematical model is setup. Experiments are carried out to verify the engine performance and the basic model's validity. By selecting the appropriate reference values, the mathematical model is transformed to a dimensionless expression. The dimensionless speed and efficiency characteristics of the CAE are obtained. Through analysis, it can be obtained that the dimensionless average rotating speed is mainly determined by the intake duration angle, the dimensionless inertia parameter, the dimensionless exhaust pressure, and the scale factor of exhaust valve. Moreover, the efficiency of the CAE is mainly determined by the dimensionless exhaust pressure, the intake duration angle and the dimensionless cylinder clearance. This research can be referred to in the design of CAE and the study on optimization of the CAE