Object Discovery From a Single Unlabeled Image by Mining Frequent Itemset With Multi-scale Features

TThe goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly

SPECT Molecular Imaging in Parkinson's Disease

Parkinson's disease (PD) is a common disorder, and the diagnosis of Parkinson's disease is clinical and relies on the presence of characteristic motor symptoms. The accuracy of the clinical diagnosis of PD is still limited. Functional neuroimaging using SPECT technique is helpful in patients with first signs of parkinsonism. The changes detected may reflect the disease process itself and/or compensatory responses to the disease, or they may arise in association with disease- and/or treatment-related complications. This paper addresses the value of SPECT in early differential diagnosis of PD and its potential as a sensitive tool to assess the pathophysiology and progression, as well as the therapeutic efficacy of PD

Intramolecular Torque, an Indicator of the Internal Rotation Direction of Rotor Molecules and Similar Systems

Torque is ubiquitous in many molecular systems, including collisions, chemical reactions, vibrations, electronic excitations and especially rotor molecules. We present a straightforward theoretical method based on forces acting on atoms and obtained from atomistic quantum mechanics calculations, to quickly and qualitatively determine whether a molecule or sub-unit thereof has a tendency to rotation and, if so, around which axis and in which sense: clockwise or counterclockwise. The method also indicates which atoms, if any, are predominant in causing the rotation. Our computational approach can in general efficiently provide insights into the rotational ability of many molecules and help to theoretically screen or modify them in advance of experiments or before analyzing their rotational behavior in more detail with more extensive computations guided by the results from the torque approach. As an example, we demonstrate the effectiveness of the approach using a specific light-driven molecular rotary motor which was successfully synthesized and analyzed in prior experiments and simulations.Comment: 11 pages, 4 figures, 1 SI fil

(E)-N′-(2,5-Dimethoxy­benzyl­idene)-3,4-dihydroxy­benzohydrazide monohydrate

In the title compound, C16H16N2O5·H2O, the dihedral angle between the two benzene rings is 25.9 (1)°. Intra­molecular O—H⋯O and N—H⋯O hydrogen bonds are observed. In the crystal, the components are linked into a three-dimensional network by O—H⋯O and O—H⋯(O,O) hydrogen bonds