Constrained low-tubal-rank tensor recovery for hyperspectral images mixed noise removal by bilateral random projections

In this paper, we propose a novel low-tubal-rank tensor recovery model, which directly constrains the tubal rank prior for effectively removing the mixed Gaussian and sparse noise in hyperspectral images. The constraints of tubal-rank and sparsity can govern the solution of the denoised tensor in the recovery procedure. To solve the constrained low-tubal-rank model, we develop an iterative algorithm based on bilateral random projections to efficiently solve the proposed model. The advantage of random projections is that the approximation of the low-tubal-rank tensor can be obtained quite accurately in an inexpensive manner. Experimental examples for hyperspectral image denoising are presented to demonstrate the effectiveness and efficiency of the proposed method.Comment: Accepted by IGARSS 201

Quantum tunneling of the magnetic moment

En aquest article presentem el treball realitzat en relaxació magnètica durant els darrers deu anys en partícules monodomini i en molècules magnètiques i la seva contribució al descobriment de l'efecte túnel del moment magnètic. En primer lloc, presentem les expressions teòriques i la seva connexió amb la relaxació quàntica, i, en segon lloc, mostrem i discutim els resultats experimentals.In this paper we review the work done on magnetic relaxation during the last ten years on both single domain particles and magnetic molecules and its contribution to the discovery of quantum tunneling of the magnetic moment. We present first the theoretical expressions and their connection to quantum relaxation and secondly we show and discuss the experimental results

Dual Co-Matching Network for Multi-choice Reading Comprehension

Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure. Given passage and question, a correct answer need to be selected from a set of candidate answers. In this paper, we propose \textbf{D}ual \textbf{C}o-\textbf{M}atching \textbf{N}etwork (\textbf{DCMN}) which model the relationship among passage, question and answer bidirectionally. Different from existing approaches which only calculate question-aware or option-aware passage representation, we calculate passage-aware question representation and passage-aware answer representation at the same time. To demonstrate the effectiveness of our model, we evaluate our model on a large-scale multiple choice machine reading comprehension dataset (i.e. RACE). Experimental result show that our proposed model achieves new state-of-the-art results.Comment: arXiv admin note: text overlap with arXiv:1806.04068 by other author