2D Reconstruction of Small Intestine's Interior Wall

Examining and interpreting of a large number of wireless endoscopic images from the gastrointestinal tract is a tiresome task for physicians. A practical solution is to automatically construct a two dimensional representation of the gastrointestinal tract for easy inspection. However, little has been done on wireless endoscopic image stitching, let alone systematic investigation. The proposed new wireless endoscopic image stitching method consists of two main steps to improve the accuracy and efficiency of image registration. First, the keypoints are extracted by Principle Component Analysis and Scale Invariant Feature Transform (PCA-SIFT) algorithm and refined with Maximum Likelihood Estimation SAmple Consensus (MLESAC) outlier removal to find the most reliable keypoints. Second, the optimal transformation parameters obtained from first step are fed to the Normalised Mutual Information (NMI) algorithm as an initial solution. With modified Marquardt-Levenberg search strategy in a multiscale framework, the NMI can find the optimal transformation parameters in the shortest time. The proposed methodology has been tested on two different datasets - one with real wireless endoscopic images and another with images obtained from Micro-Ball (a new wireless cubic endoscopy system with six image sensors). The results have demonstrated the accuracy and robustness of the proposed methodology both visually and quantitatively.Comment: Journal draf

Do Subsampled Newton Methods Work for High-Dimensional Data?

Subsampled Newton methods approximate Hessian matrices through subsampling techniques, alleviating the cost of forming Hessian matrices but using sufficient curvature information. However, previous results require $\Omega (d)$ samples to approximate Hessians, where $d$ is the dimension of data points, making it less practically feasible for high-dimensional data. The situation is deteriorated when $d$ is comparably as large as the number of data points $n$, which requires to take the whole dataset into account, making subsampling useless. This paper theoretically justifies the effectiveness of subsampled Newton methods on high dimensional data. Specifically, we prove only $\widetilde{\Theta}(d^\gamma_{\rm eff})$ samples are needed in the approximation of Hessian matrices, where $d^\gamma_{\rm eff}$ is the $\gamma$-ridge leverage and can be much smaller than $d$ as long as $n\gamma \gg 1$. Additionally, we extend this result so that subsampled Newton methods can work for high-dimensional data on both distributed optimization problems and non-smooth regularized problems

Privacy-Preserving Distributed SVD via Federated Power

Singular value decomposition (SVD) is one of the most fundamental tools in machine learning and statistics.The modern machine learning community usually assumes that data come from and belong to small-scale device users. The low communication and computation power of such devices, and the possible privacy breaches of users' sensitive data make the computation of SVD challenging. Federated learning (FL) is a paradigm enabling a large number of devices to jointly learn a model in a communication-efficient way without data sharing. In the FL framework, we develop a class of algorithms called FedPower for the computation of partial SVD in the modern setting. Based on the well-known power method, the local devices alternate between multiple local power iterations and one global aggregation to improve communication efficiency. In the aggregation, we propose to weight each local eigenvector matrix with Orthogonal Procrustes Transformation (OPT). Considering the practical stragglers' effect, the aggregation can be fully participated or partially participated, where for the latter we propose two sampling and aggregation schemes. Further, to ensure strong privacy protection, we add Gaussian noise whenever the communication happens by adopting the notion of differential privacy (DP). We theoretically show the convergence bound for FedPower. The resulting bound is interpretable with each part corresponding to the effect of Gaussian noise, parallelization, and random sampling of devices, respectively. We also conduct experiments to demonstrate the merits of FedPower. In particular, the local iterations not only improve communication efficiency but also reduce the chance of privacy breaches

Maternal Thermal Effects on Female Reproduction and Hatchling Phenotype in the Chinese Skink (Plestiodon chinensis)</i>

We maintained gravid Chinese skinks (Plestiodon chinensis) at three constant temperatures (25, 28 and 31 °C) during gestation, and randomly assigned eggs from each female to one of the same three temperatures for incubation to determine maternal thermal effects on female reproduction and hatchling phenotype. Maternal temperature affected egg-laying date, hatching success and hatchling linear size (snout-vent length, SVL) but not clutch size, egg size, egg component, and embryonic stage at laying. More specifically, females at higher temperatures laid eggs earlier than did those at low temperatures, eggs laid at 31 °C were less likely to hatch than those laid at 25 °C or 28 °C, and hatchlings from eggs laid at 31 °C were smaller in SVL. Our finding that maternal temperature (pre-ovipositional thermal condition) rather than incubation temperature (post-ovipositional thermal condition) affected hatching success indicated that embryos at early stages were more vulnerable to temperature than those at late stages. Our data provide an inference that moderate maternal temperatures enhance reproductive fitness in P. chinensis