A Proof of Stake Sharding Protocol for Scalable Blockchains

Cryptocurrencies such as Bitcoin has drawn great attention recently. The public ledger blockchain serves as a secure database for cryptocurrencies. However, only 3 to 7 transactions can be processed per second, which means the blockchain does not scale. To address this problem, we propose a new consensus protocol based on sharding and proof of stake. The scalability of our proposed method is expected to increase linearly with the network size. We discuss proposed method from the scalability evaluation, complexity and security view

A Unified Algebraic Framework for Fuzzy Image Compression and Mathematical Morphology

In this paper we show how certain techniques of image processing, having different scopes, can be joined together under a common "algebraic roof"

DeepShaRM: Multi-View Shape and Reflectance Map Recovery Under Unknown Lighting

Geometry reconstruction of textureless, non-Lambertian objects under unknown natural illumination (i.e., in the wild) remains challenging as correspondences cannot be established and the reflectance cannot be expressed in simple analytical forms. We derive a novel multi-view method, DeepShaRM, that achieves state-of-the-art accuracy on this challenging task. Unlike past methods that formulate this as inverse-rendering, i.e., estimation of reflectance, illumination, and geometry from images, our key idea is to realize that reflectance and illumination need not be disentangled and instead estimated as a compound reflectance map. We introduce a novel deep reflectance map estimation network that recovers the camera-view reflectance maps from the surface normals of the current geometry estimate and the input multi-view images. The network also explicitly estimates per-pixel confidence scores to handle global light transport effects. A deep shape-from-shading network then updates the geometry estimate expressed with a signed distance function using the recovered reflectance maps. By alternating between these two, and, most important, by bypassing the ill-posed problem of reflectance and illumination decomposition, the method accurately recovers object geometry in these challenging settings. Extensive experiments on both synthetic and real-world data clearly demonstrate its state-of-the-art accuracy.Comment: 3DV 202

Finding Kinematic Structure in Time Series Volume Data

This paper presents a new scheme for acquiring 3D kinematic structure and motion from time series volume data. Our basic strategy is to first represent the shape structure of the target in each frame by Reeb graph which we compute by using geodesic distance of target's surface, and then estimate the kinematic structure of the target which is consistent with these shape structures. Although the shape structures can be very different from frame to frame, we propose to derive a unique kinematic structure by way of clustering some nodes of graph, based on the fact that they are partly coherent to a certain extent of time series. Once we acquire a unique kinematic structure, we fit it to other Reeb graphs in the remaining frames, and describe the motion throughout the entire time series. The only assumption we make is that human body can be approximated by an articulated body with certain numbers of end-points and branches. We demonstrate the efficacy of the proposed scheme through some experiments