Matrix Product State for Higher-Order Tensor Compression and Classification

© 2017 IEEE. This paper introduces matrix product state (MPS) decomposition as a new and systematic method to compress multidimensional data represented by higher order tensors. It solves two major bottlenecks in tensor compression: computation and compression quality. Regardless of tensor order, MPS compresses tensors to matrices of moderate dimension, which can be used for classification. Mainly based on a successive sequence of singular value decompositions, MPS is quite simple to implement and arrives at the global optimal matrix, bypassing local alternating optimization, which is not only computationally expensive but cannot yield the global solution. Benchmark results show that MPS can achieve better classification performance with favorable computation cost compared to other tensor compression methods

Concatenated image completion via tensor augmentation and completion

© 2016 IEEE. This paper proposes a novel framework called concatenated image completion via tensor augmentation and completion (ICTAC), which recovers missing entries of color images with high accuracy. Typical images are second-or third-order tensors (2D/3D) depending if they are grayscale or color, hence tensor completion algorithms are ideal for their recovery. The proposed framework performs image completion by concatenating copies of a single image that has missing entries into a third-order tensor, applying a dimensionality augmentation technique to the tensor, utilizing a tensor completion algorithm for recovering its missing entries, and finally extracting the recovered image from the tensor. The solution relies on two key components that have been recently proposed to take advantage of the tensor train (TT) rank: A tensor augmentation tool called ket augmentation (KA) that represents a low-order tensor by a higher-order tensor, and the algorithm tensor completion by parallel matrix factorization via tensor train (TMac-TT), which has been demonstrated to outperform state-of-the-art tensor completion algorithms. Simulation results for color image recovery show the clear advantage of our framework against current state-of-the-art tensor completion algorithms

Apport des données Landsat Thematic Mapper pour la cartographie des sols dans la région de Menzel Habib

Un essai de cartographie des sols a été réalisé au niveau de la zone de Menzel Habib, située en Tunisie présaharienne dans la région naturelle des basses plaines méridionales. Cette zone fait partie d’un Réseau d'Observatoires de Surveillance Écologique à Long Terme (ROSELT) mise en place par l’Observatoire du Sahel et du Sahara(OSS), dans le cadre d’un programme de suivi environnemental de la désertification. Ainsi, conformément aux objectifs généraux de ce programme insistant sur l’intérêt de la valorisation et l’exploitation des données anciennes pertinentes en relation avec le thème recherché par la présente étude. On a adopté une approche méthodologique reposant principalement sur la classification multispectrale d’une image satellite en ayant recours aux cartes des ressources en sols et pédologiques existantes. Il en ressort une carte pédologique couvrant toute la zone de Menzel Habib répartis en huit classes.Mots-clés : sols, cartographie, classification multispectrale, image satellite

Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train

© 1992-2012 IEEE. This paper proposes a novel approach to tensor completion, which recovers missing entries of data represented by tensors. The approach is based on the tensor train (TT) rank, which is able to capture hidden information from tensors thanks to its definition from a well-balanced matricization scheme. Accordingly, new optimization formulations for tensor completion are proposed as well as two new algorithms for their solution. The first one called simple low-rank tensor completion via TT (SiLRTC-TT) is intimately related to minimizing a nuclear norm based on TT rank. The second one is from a multilinear matrix factorization model to approximate the TT rank of a tensor, and is called tensor completion by parallel matrix factorization via TT (TMac-TT). A tensor augmentation scheme of transforming a low-order tensor to higher orders is also proposed to enhance the effectiveness of SiLRTC-TT and TMac-TT. Simulation results for color image and video recovery show the clear advantage of our method over all other methods

Matrix Product State for Feature Extraction of Higher-Order Tensors

This paper introduces matrix product state (MPS) decomposition as a computational tool for extracting features of multidimensional data represented by higher-order tensors. Regardless of tensor order, MPS extracts its relevant features to the so-called core tensor of maximum order three which can be used for classification. Mainly based on a successive sequence of singular value decompositions (SVD), MPS is quite simple to implement without any recursive procedure needed for optimizing local tensors. Thus, it leads to substantial computational savings compared to other tensor feature extraction methods such as higher-order orthogonal iteration (HOOI) underlying the Tucker decomposition (TD). Benchmark results show that MPS can reduce significantly the feature space of data while achieving better classification performance compared to HOOI

Efficient tensor completion: Low-rank tensor train

This paper proposes a novel formulation of the tensor completion problem to impute missing entries of data represented by tensors. The formulation is introduced in terms of tensor train (TT) rank which can effectively capture global information of tensors thanks to its construction by a well-balanced matricization scheme. Two algorithms are proposed to solve the corresponding tensor completion problem. The first one called simple low-rank tensor completion via tensor train (SiLRTC-TT) is intimately related to minimizing the TT nuclear norm. The second one is based on a multilinear matrix factorization model to approximate the TT rank of the tensor and called tensor completion by parallel matrix factorization via tensor train (TMac-TT). These algorithms are applied to complete both synthetic and real world data tensors. Simulation results of synthetic data show that the proposed algorithms are efficient in estimating missing entries for tensors with either low Tucker rank or TT rank while Tucker-based algorithms are only comparable in the case of low Tucker rank tensors. When applied to recover color images represented by ninth-order tensors augmented from third-order ones, the proposed algorithms outperforms the Tucker-based algorithms

Involvement of Noradrenergic Neurotransmission in the Stress- but not Cocaine-Induced Reinstatement of Extinguished Cocaine-Induced Conditioned Place Preference in Mice: Role for β-2 Adrenergic Receptors

The responsiveness of central noradrenergic systems to stressors and cocaine poses norepinephrine as a potential common mechanism through which drug re-exposure and stressful stimuli promote relapse. This study investigated the role of noradrenergic systems in the reinstatement of extinguished cocaine-induced conditioned place preference by cocaine and stress in male C57BL/6 mice. Cocaine- (15 mg/kg, i.p.) induced conditioned place preference was extinguished by repeated exposure to the apparatus in the absence of drug and reestablished by a cocaine challenge (15 mg/kg), exposure to a stressor (6-min forced swim (FS); 20–25°C water), or administration of the α-2 adrenergic receptor (AR) antagonists yohimbine (2 mg/kg, i.p.) or BRL44408 (5, 10 mg/kg, i.p.). To investigate the role of ARs, mice were administered the nonselective β-AR antagonist, propranolol (5, 10 mg/kg, i.p.), the α-1 AR antagonist, prazosin (1, 2 mg/kg, i.p.), or the α-2 AR agonist, clonidine (0.03, 0.3 mg/kg, i.p.) before reinstatement testing. Clonidine, prazosin, and propranolol failed to block cocaine-induced reinstatement. The low (0.03 mg/kg) but not high (0.3 mg/kg) clonidine dose fully blocked FS-induced reinstatement but not reinstatement by yohimbine. Propranolol, but not prazosin, blocked reinstatement by both yohimbine and FS, suggesting the involvement of β-ARs. The β-2 AR antagonist ICI-118551 (1 mg/kg, i.p.), but not the β-1 AR antagonist betaxolol (10 mg/kg, i.p.), also blocked FS-induced reinstatement. These findings suggest that stress-induced reinstatement requires noradrenergic signaling through β-2 ARs and that cocaine-induced reinstatement does not require AR activation, even though stimulation of central noradrenergic neurotransmission is sufficient to reinstate

The sudden change phenomenon of quantum discord

Even if the parameters determining a system's state are varied smoothly, the behavior of quantum correlations alike to quantum discord, and of its classical counterparts, can be very peculiar, with the appearance of non-analyticities in its rate of change. Here we review this sudden change phenomenon (SCP) discussing some important points related to it: Its uncovering, interpretations, and experimental verifications, its use in the context of the emergence of the pointer basis in a quantum measurement process, its appearance and universality under Markovian and non-Markovian dynamics, its theoretical and experimental investigation in some other physical scenarios, and the related phenomenon of double sudden change of trace distance discord. Several open questions are identified, and we envisage that in answering them we will gain significant further insight about the relation between the SCP and the symmetry-geometric aspects of the quantum state space.Comment: Lectures on General Quantum Correlations and their Applications, F. F. Fanchini, D. O. Soares Pinto, and G. Adesso (Eds.), Springer (2017), pp 309-33

The role of attachment in body weight gain and weight loss in bariatric patients

Purpose: To explore the role of attachment styles in obesity. Material and methods: The present study explored differences in insecure attachment styles between an obese sample waiting for bariatric surgery (n=195) and an age, sex and height matched normal weight control group (n=195). It then explored the role of attachment styles in predicting change in BMI one year post bariatric surgery (n=143). Results: The bariatric group reported significantly higher levels of anxious attachment and lower levels of avoidant attachment than the control non obese group. Baseline attachment styles did not, however, predict change in BMI post-surgery. Conclusion: Attachment style is different in those that are already obese from those who are not. Attachment was not related to weight loss post-surgery