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

Discriminative Block-Diagonal Representation Learning for Image Recognition

Existing block-diagonal representation studies mainly focuses on casting block-diagonal regularization on training data, while only little attention is dedicated to concurrently learning both block-diagonal representations of training and test data. In this paper, we propose a discriminative block-diagonal low-rank representation (BDLRR) method for recognition. In particular, the elaborate BDLRR is formulated as a joint optimization problem of shrinking the unfavorable representation from off-block-diagonal elements and strengthening the compact block-diagonal representation under the semisupervised framework of LRR. To this end, we first impose penalty constraints on the negative representation to eliminate the correlation between different classes such that the incoherence criterion of the extra-class representation is boosted. Moreover, a constructed subspace model is developed to enhance the self-expressive power of training samples and further build the representation bridge between the training and test samples, such that the coherence of the learned intraclass representation is consistently heightened. Finally, the resulting optimization problem is solved elegantly by employing an alternative optimization strategy, and a simple recognition algorithm on the learned representation is utilized for final prediction. Extensive experimental results demonstrate that the proposed method achieves superb recognition results on four face image data sets, three character data sets, and the 15 scene multicategories data set. It not only shows superior potential on image recognition but also outperforms the state-of-the-art methods

2-Bromo-1-(4-methoxy­phen­yl)ethanone

The title compound, C9H9BrO2, prepared by the reaction of 4-methoxy­acetophenone and cupric bromide, , is approximately planar (r.m.s. deviation 0.0008 Å). In the crystal, weak inter­molecular aromatic C—H⋯Ocarbon­yl hydrogen-bonding inter­actions result in a one-dimensional chain structure

A bibliometric analysis of COVID-19 publications in neurology by using the visual mapping method

BackgroundThe characteristic symptom of coronavirus disease 2019 (COVID-19) is respiratory distress, but neurological symptoms are the most frequent extra-pulmonary symptoms. This study aims to explore the current status and hot topics of neurology-related research on COVID-19 using bibliometric analysis.MethodsPublications regarding neurology and COVID-19 were retrieved from the Web of Science Core Collection (WoSCC) on March 28 2022. The Advanced search was conducted using “TS = (‘COVID 19’ or ‘Novel Coronavirus 2019’ or ‘Coronavirus disease 2019’ or ‘2019-nCOV’ or ‘SARS-CoV-2’ or ‘coronavirus-2’) and TS = (‘neurology’or ‘neurological’ or ‘nervous system’ or ‘neurodegenerative disease’ or ‘brain’ or ‘cerebra’ or ‘nerve’)”. Microsoft Excel 2010 and VOSviewer were used to characterize the largest contributors, including the authors, journals, institutions, and countries. The hot topics and knowledge network were analyzed by CiteSpace and VOSviewer.ResultsA total of 5,329 publications between 2020 and 2022 were retrieved. The United States, Italy, and the United Kingdom were three key contributors to this field. Harvard Medical School, the Tehran University of Medical Sciences, and the UCL Queen Square Institute of Neurology were the major institutions with the largest publications. Josef Finsterer from the University of São Paulo (Austria) was the most prolific author. Tom Solomon from the University of Liverpool (UK) was the most cited author. Neurological Sciences and Frontiers in Neurology were the first two most productive journals, while Journal of Neurology held the first in terms of total citations and citations per publication. Cerebrovascular diseases, neurodegenerative diseases, encephalitis and encephalopathy, neuroimmune complications, neurological presentation in children, long COVID and mental health, and telemedicine were the central topics regarding the neurology-related research on COVID-19.ConclusionNeurology-related research on COVID-19 has attracted considerable attention worldwide. Research topics shifted from “morality, autopsy, and telemedicine” in 2020 to various COVID-19-related neurological symptoms in 2021, such as “stroke,” “Alzheimer's disease,” “Parkinson's disease,” “Guillain–Barre syndrome,” “multiple sclerosis,” “seizures in children,” and “long COVID.” “Applications of telemedicine in neurology during COVID-19 pandemic,” “COVID-19-related neurological complications and mechanism,” and “long COVID” require further study

Aqua­chlorido{1-[1-(4-hydroxy­phen­yl)-1H-tetra­zol-5-ylsulfan­yl]acetato}(methanol)(1,10-phenanthroline)manganese(II)

The title complex, [Mn(C9H7N4O3S)Cl(C12H8N2)(CH4O)(H2O)], contains an MnII ion six-coordinated by one O atom from the 2-[1-(4-hydroxy­phen­yl)-1H-tetra­zol-5-ylsulfan­yl]­acetate ligand, two N atoms from a chelating 1,10-phenanthroline ligand, one O atom from a methanol mol­ecule, one Cl atom and one water mol­ecule in a distorted octa­hedral coordination geometry. The existence of O—H⋯Cl, O—H⋯N and O—H⋯O hydrogen bonds further produces a two-dimensional structure

An Improved Fuzzy c

To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM) based on particle swarm optimization (PSO) and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect

(3,5-Dinitro-1,3,5-triazinan-1-yl)methanone

In the title compound, C5H9N5O5, prepared from hexa­mine by acetyl­ation and nitration, the triazine ring adopts a chair conformation with all three substituent groups lying on the same side of the ring