Degraded Broadcast Channel with Side Information, Confidential Messages and Noiseless Feedback

In this paper, first, we investigate the model of degraded broadcast channel with side information and confidential messages. This work is from Steinberg's work on the degraded broadcast channel with causal and noncausal side information, and Csisz$\acute{a}$r-K\"{o}rner's work on broadcast channel with confidential messages. Inner and outer bounds on the capacity-equivocation regions are provided for the noncausal and causal cases. Superposition coding and double-binning technique are used in the corresponding achievability proofs. Then, we investigate the degraded broadcast channel with side information, confidential messages and noiseless feedback. The noiseless feedback is from the non-degraded receiver to the channel encoder. Inner and outer bounds on the capacity-equivocation region are provided for the noncausal case, and the capacity-equivocation region is determined for the causal case. Compared with the model without feedback, we find that the noiseless feedback helps to enlarge the inner bounds for both causal and noncausal cases. In the achievability proof of the feedback model, the noiseless feedback is used as a secret key shared by the non-degraded receiver and the transmitter, and therefore, the code construction for the feedback model is a combination of superposition coding, Gel'fand-Pinsker's binning, block Markov coding and Ahlswede-Cai's secret key on the feedback system.Comment: Part of this paper has been accepted by ISIT2012, and this paper is submitted to IEEE Transactions on Information Theor

X\mathcal{X}-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing

This paper presents a generic probabilistic framework for estimating the statistical dependency and finding the anatomical correspondences among an arbitrary number of medical images. The method builds on a novel formulation of the $N$-dimensional joint intensity distribution by representing the common anatomy as latent variables and estimating the appearance model with nonparametric estimators. Through connection to maximum likelihood and the expectation-maximization algorithm, an information\hyp{}theoretic metric called $\mathcal{X}$-metric and a co-registration algorithm named $\mathcal{X}$-CoReg are induced, allowing groupwise registration of the $N$ observed images with computational complexity of $\mathcal{O}(N)$. Moreover, the method naturally extends for a weakly-supervised scenario where anatomical labels of certain images are provided. This leads to a combined\hyp{}computing framework implemented with deep learning, which performs registration and segmentation simultaneously and collaboratively in an end-to-end fashion. Extensive experiments were conducted to demonstrate the versatility and applicability of our model, including multimodal groupwise registration, motion correction for dynamic contrast enhanced magnetic resonance images, and deep combined computing for multimodal medical images. Results show the superiority of our method in various applications in terms of both accuracy and efficiency, highlighting the advantage of the proposed representation of the imaging process

A Locking Sweeping Method Based Path Planning for Unmanned Surface Vehicles in Dynamic Maritime Environments

Unmanned surface vehicles (USVs) are new marine intelligent platforms that can autonomously operate in various ocean environments with intelligent decision-making capability. As one of key technologies enabling such a capability, path planning algorithms underpin the navigation and motion control of USVs by providing optimized navigational trajectories. To accommodate complex maritime environments that include various static/moving obstacles, it is important to develop a computational efficient path planning algorithm for USVs so that real-time operation can be effectively carried out. This paper therefore proposes a new algorithm based on the fast sweeping method, named the locking sweeping method (LSM). Compared with other conventional path planning algorithms, the proposed LSM has an improved computational efficiency and can be well applied in dynamic environments that have multiple moving obstacles. When generating an optimal collision-free path, moving obstacles are modelled with ship domains that are calculated based upon ships’ velocities. To evaluate the effectiveness of the algorithm, particularly the capacity in dealing with practical environments, three different sets of simulations were undertaken in environments built using electronic nautical charts (ENCs). Results show that the proposed algorithm can effectively cope with complex maritime traffic scenarios by generating smooth and safe trajectories

BInGo: Bayesian Intrinsic Groupwise Registration via Explicit Hierarchical Disentanglement

Multimodal groupwise registration aligns internal structures in a group of medical images. Current approaches to this problem involve developing similarity measures over the joint intensity profile of all images, which may be computationally prohibitive for large image groups and unstable under various conditions. To tackle these issues, we propose BInGo, a general unsupervised hierarchical Bayesian framework based on deep learning, to learn intrinsic structural representations to measure the similarity of multimodal images. Particularly, a variational auto-encoder with a novel posterior is proposed, which facilitates the disentanglement learning of structural representations and spatial transformations, and characterizes the imaging process from the common structure with shape transition and appearance variation. Notably, BInGo is scalable to learn from small groups, whereas being tested for large-scale groupwise registration, thus significantly reducing computational costs. We compared BInGo with five iterative or deep learning methods on three public intrasubject and intersubject datasets, i.e. BraTS, MS-CMR of the heart, and Learn2Reg abdomen MR-CT, and demonstrated its superior accuracy and computational efficiency, even for very large group sizes (e.g., over 1300 2D images from MS-CMR in each group)

Genetic diversity of Pogonatherum paniceum (Lam.) Hack in Southwest China revealed by ISSR

Inter-simple sequence repeats markers were used to estimate the genetic diversity of Pogonatherum paniceum (Lam.) Hack. from Sichuan Province, Yunnan Province, Chongqing City and Guangxi Zhuang Autonomous Region in China. 100 primers were carried out on 22 wild populations, 14 could produce highly reproducible inter-simple sequence repeats markersbands. Out of the 239 discernable DNA fragments, 227 were polymorphic. The percentage of polymorphic bands was 94.98% at the species level. Nei’s gene diversity and Shannon information index were 0.312 and 0.471, respectively. This indicated that the genetic diversity of P. paniceum (Lam.) Hack. was low. The values of genetic identity ranged from 0.548 to 0.820 with a mean of 0.673. Nei’s genetic distance between 22 populations ranged from 0.198 to 0.601. Unweighted pair group method with arithmetic mean cluster analysis based on Nei’s genetic distance showed that most populations were positioned into the relevant areas. Significant correlation between genetic and geographic altitude distances among populations was found by Mantel test. The high score of percentage of polymorphic bands might be caused by low frequent polymorphism distributed in different populations

Investing during a Fintech Revolution:ambiguity and return risk in cryptocurrencies

Rationally justifying Bitcoin’s immense price fluctuations has remained a persistent challenge for both investors and researchers in this field. A primary reason is our potential weakness toward robustly quantifying unquantifiable risks or ambiguity in Bitcoin returns. This paper introduces a behavioral channel to argue that the degree of ambiguity aversion is a prominent source of abnormal returns from investment in Bitcoin markets. Using dataover a ten-year period, we show that Bitcoin investors exhibit, on average, an increasing aversion to ambiguity. Furthermore, investors are found to earn abnormal returns only when ambiguity is low. Robustness exercises reassure on the validity of our results