Sibling Neural Estimators: Improving Iterative Image Decoding with Gradient Communication. (arXiv:1911.08478v1 [cs.CV])

For lossy image compression, we develop a neural-based system which learns a nonlinear estimator for decoding from quantized representations. The system links two recurrent networks that \help” each other reconstruct same target image patches using complementary portions of spatial context that communicate via gradient signals. This dual agent system builds upon prior work that proposed…

Modal-aware Features for Multimodal Hashing. (arXiv:1911.08479v1 [cs.CV])

Many retrieval applications can benefit from multiple modalities, e.g., text that contains images on Wikipedia, for which how to represent multimodal data is the critical component. Most deep multimodal learning methods typically involve two steps to construct the joint representations: 1) learning of multiple intermediate features, with each intermediate feature corresponding to a modality, using…

The Future Time Traveller Project: Career Guidance on Future Skills, Jobs and Career Prospects of Generation Z through a Game-Based Virtual World Environment. (arXiv:1911.08480v1 [cs.HC])

Future Time Traveller is a European project that aims at transforming career guidance of generation Z through an innovative, games-based scenario approach and to prepare the next generation for the jobs of the future. The pro-ject objective is to foster innovative thinking and future-oriented mindset of young people, through an innovative game-based virtual world environment.…

Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction. (arXiv:1911.08483v1 [eess.IV])

Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity. Accurate segmentation of gliomas and theirsub-regions on multi-parametric magnetic resonance images (mpMRI)is of great clinical importance, which defines tumour size, shape andappearance and provides abundant information for preoperative diag-nosis, treatment planning and survival prediction. Recent developmentson deep learning have significantly improved the performance of auto-mated…

Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks. (arXiv:1911.08508v1 [astro-ph.IM])

In this paper, we present the first study that compares different models of Bayesian Neural Networks (BNNs) to predict the posterior distribution of the cosmological parameters directly from the Cosmic Microwave Background (CMB) map. We focus our analysis on four different methods to sample the weights of the network during training: Dropout, DropConnect, Reparameterization Trick…

Optimal Complexity and Certification of Bregman First-Order Methods. (arXiv:1911.08510v1 [math.OC])

We provide a lower bound showing that the $O(1/k)$ convergence rate of the NoLips method (a.k.a. Bregman Gradient) is optimal for the class of functions satisfying the $h$-smoothness assumption. This assumption, also known as relative smoothness, appeared in the recent developments around the Bregman Gradient method, where acceleration remained an open issue. On the way,…

Action Recognition Using Volumetric Motion Representations. (arXiv:1911.08511v1 [cs.CV])

Traditional action recognition models are constructed around the paradigm of 2D perspective imagery. Though sophisticated time-series models have pushed the field forward, much of the information is still not exploited by confining the domain to 2D. In this work, we introduce a novel representation of motion as a voxelized 3D vector field and demonstrate how…

Topological properties of secure wireless sensor networks under the q-composite key predistribution scheme with unreliable links. (arXiv:1911.08513v1 [cs.NI])

Security is an important issue in wireless sensor networks (WSNs), which are often deployed in hostile environments. The q-composite key predistribution scheme has been recognized as a suitable approach to secure WSNs. Although the q-composite scheme has received much attention in the literature, there is still a lack of rigorous analysis for secure WSNs operating…

Audita: A Blockchain-based Auditing Framework for Off-chain Storage. (arXiv:1911.08515v1 [cs.CR])

The cloud changed the way we manage and store data. Today, cloud storage services offer clients an infrastructure that allows them a convenient source to store, replicate, and secure data online. However, with these new capabilities also come limitations, such as lack of transparency, limited decentralization, and challenges with privacy and security. And, as the…