Retinal vessel segmentation using Gabor Filter and Textons

This paper presents a retinal vessel segmentation method that is inspired by the human visual system and uses a Gabor filter bank. Machine learning is used to optimize the filter parameters for retinal vessel extraction. The filter responses are represented as textons and this allows the corresponding membership functions to be used as the framework for learning vessel and non-vessel classes. Then, vessel texton memberships are used to generate segmentation results. We evaluate our method using the publicly available DRIVE database. It achieves competitive performance (sensitivity=0.7673, specificity=0.9602, accuracy=0.9430) compared to other recently published work. These figures are particularly interesting as our filter bank is quite generic and only includes Gabor responses. Our experimental results also show that the performance, in terms of sensitivity, is superior to other methods

What Drives U.S. Banking Mergers: Misvaluation, Gambling or Envy?

The thesis consists of three essays that examine whether U.S. bank mergers are motivated by market inefficiency and managerial psychology biases. Essay I investigates equity misvaluation as a possible driver for United States banking mergers from the perspective of market inefficiency, and finds that bidders tend to use overvalued equity to buy undervalued targets. Essay II, motivated by the cumulative prospect theory of Tversky and Kahneman (1992), tests whether managerial gambling attitudes are linked with lottery characteristics of target banks (i.e., high skewness, high volatility, and low price). The evidence shows that banking acquisitions are influenced by gambling attitudes rooted into house money effects. Essay III examines whether managerial envy plays a key role in shaping merger waves. The empirical evidence shows that envy influences bank merger waves

On the periodic homogenization of elliptic equations in non-divergence form with large drifts

We study the quantitative homogenization of linear second order elliptic equations in non-divergence form with highly oscillating periodic diffusion coefficients and with large drifts, in the so-called ``centered'' setting where homogenization occurs and the large drifts contribute to the effective diffusivity. Using the centering condition and the invariant measures associated to the underlying diffusion process, we transform the equation into divergence form with modified diffusion coefficients but without drift. The latter is in the standard setting for which quantitative homogenization results have been developed systematically. An application of those results then yields quantitative estimates, such as the convergence rates and uniform Lipschitz regularity, for equations in non-divergence form with large drifts.Comment: 16 page

Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks

The identification of pulmonary lobes is of great importance in disease diagnosis and treatment. A few lung diseases have regional disorders at lobar level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this work, we propose an automated segmentation of pulmonary lobes using coordination-guided deep neural networks from chest CT images. We first employ an automated lung segmentation to extract the lung area from CT image, then exploit volumetric convolutional neural network (V-net) for segmenting the pulmonary lobes. To reduce the misclassification of different lobes, we therefore adopt coordination-guided convolutional layers (CoordConvs) that generate additional feature maps of the positional information of pulmonary lobes. The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0.947 $\pm$ 0.044.Comment: ISBI 2019 (Oral

NOVEL SECURED INTER-PERSONAL AREA NETWORK GROUP MANAGEMENT IN LOW-POWER AND LOSSY NETWORKS

Low-power and Lossy Network (LLN) environments may comprise, possibly among other things, different personal area networks (PANs) resulting in, for example, node communication challenges across or between PANs. To address these types of challenges, techniques are presented herein that support a novel secure group management method to self-solve the inter-PAN problem that is both low-cost and customer-friendly. Aspects of the techniques presented herein encompass establishing a secure node-to-node (N2N) communication link between involved inter-PAN nodes, automatically looking for the relay neighbors between different PANs (as the inter-PAN node can help with forwarding the local the Routing Protocol for LLN (RPL) messages), automatically propagating the group information and maintaining the local RPL tree between the inter-PAN nodes, etc. Aspects of the techniques presented herein employ, among other things, spreading PAN advertisement (PA) messages with group and hop information to identify a feasible routing path, using the Extensible Authentication Protocol-Tunneled Transport Layer Security (EAP-TTLS) protocol to establish a secure transport tunnel, automatically unicasting a destination-oriented directed acyclic graph (DODAG) Information Solicitation (DIS) message to join the group tree, etc. Under aspects of the techniques presented herein an application server need not know the topology of a network

BLOCK ACKNOWLEDGEMENT MECHANISM FOR SPEEDING UP NODE-TO-NODE TRAFFIC IN LOW-POWER AND LOSSY NETWORKS

The Wireless Smart Utility Network (Wi-SUN) Alliance promotes interoperability for large scale wireless mesh networks (WMNs). Such Low-Power and Lossy Networks (LLNs) are widely used in industrial Internet of Things (IoT) settings. In order to improve throughput in node-to-node (N2N) communications for Wi-SUN based wireless nodes, techniques are presented herein that support the addition of a Block Acknowledgement (BA) mechanism to the Wi-SUN protocol. Such a mechanism may, among other things, eliminate a significant acknowledgement (ACK) confirmation overhead when a pair of nodes are using an Extended Directional Frame Exchange (EDFE) mode or an Adaptive Modulation (AM) mode to overcome radio interference

General Spatial Photonic Ising Machine Based on Interaction Matrix Eigendecomposition Method

The spatial photonic Ising machine has achieved remarkable advancements in solving combinatorial optimization problems. However, it still remains a huge challenge to flexibly mapping an arbitrary problem to Ising model. In this paper, we propose a general spatial photonic Ising machine based on interaction matrix eigendecomposition method. Arbitrary interaction matrix can be configured in the two-dimensional Fourier transformation based spatial photonic Ising model by using values generated by matrix eigendecomposition. The error in the structural representation of the Hamiltonian decreases substantially with the growing number of eigenvalues utilized to form the Ising machine. In combination with the optimization algorithm, as low as 65% of the eigenvalues is required by intensity modulation to guarantee the best probability of optimal solution for a 20-vertex graph Max-cut problem, and this probability decreases to below 20% for zero best chance. Our work provides a viable approach for spatial photonic Ising machines to solve arbitrary combinatorial optimization problems with the help of multi-dimensional optical property

Soft BPR Loss for Dynamic Hard Negative Sampling in Recommender Systems

In recommender systems, leveraging Graph Neural Networks (GNNs) to formulate the bipartite relation between users and items is a promising way. However, powerful negative sampling methods that is adapted to GNN-based recommenders still requires a lot of efforts. One critical gap is that it is rather tough to distinguish real negatives from massive unobserved items during hard negative sampling. Towards this problem, this paper develops a novel hard negative sampling method for GNN-based recommendation systems by simply reformulating the loss function. We conduct various experiments on three datasets, demonstrating that the method proposed outperforms a set of state-of-the-art benchmarks.Comment: 9 pages, 16 figure

ARAI-MVSNet: A multi-view stereo depth estimation network with adaptive depth range and depth interval

Multi-View Stereo~(MVS) is a fundamental problem in geometric computer vision which aims to reconstruct a scene using multi-view images with known camera parameters. However, the mainstream approaches represent the scene with a fixed all-pixel depth range and equal depth interval partition, which will result in inadequate utilization of depth planes and imprecise depth estimation. In this paper, we present a novel multi-stage coarse-to-fine framework to achieve adaptive all-pixel depth range and depth interval. We predict a coarse depth map in the first stage, then an Adaptive Depth Range Prediction module is proposed in the second stage to zoom in the scene by leveraging the reference image and the obtained depth map in the first stage and predict a more accurate all-pixel depth range for the following stages. In the third and fourth stages, we propose an Adaptive Depth Interval Adjustment module to achieve adaptive variable interval partition for pixel-wise depth range. The depth interval distribution in this module is normalized by Z-score, which can allocate dense depth hypothesis planes around the potential ground truth depth value and vice versa to achieve more accurate depth estimation. Extensive experiments on four widely used benchmark datasets~(DTU, TnT, BlendedMVS, ETH 3D) demonstrate that our model achieves state-of-the-art performance and yields competitive generalization ability. Particularly, our method achieves the highest Acc and Overall on the DTU dataset, while attaining the highest Recall and $F_{1}$-score on the Tanks and Temples intermediate and advanced dataset. Moreover, our method also achieves the lowest $e_{1}$ and $e_{3}$ on the BlendedMVS dataset and the highest Acc and $F_{1}$-score on the ETH 3D dataset, surpassing all listed methods.Project website: https://github.com/zs670980918/ARAI-MVSNe