Multi-Source Multi-View Clustering via Discrepancy Penalty

With the advance of technology, entities can be observed in multiple views. Multiple views containing different types of features can be used for clustering. Although multi-view clustering has been successfully applied in many applications, the previous methods usually assume the complete instance mapping between different views. In many real-world applications, information can be gathered from multiple sources, while each source can contain multiple views, which are more cohesive for learning. The views under the same source are usually fully mapped, but they can be very heterogeneous. Moreover, the mappings between different sources are usually incomplete and partially observed, which makes it more difficult to integrate all the views across different sources. In this paper, we propose MMC (Multi-source Multi-view Clustering), which is a framework based on collective spectral clustering with a discrepancy penalty across sources, to tackle these challenges. MMC has several advantages compared with other existing methods. First, MMC can deal with incomplete mapping between sources. Second, it considers the disagreements between sources while treating views in the same source as a cohesive set. Third, MMC also tries to infer the instance similarities across sources to enhance the clustering performance. Extensive experiments conducted on real-world data demonstrate the effectiveness of the proposed approach

Online Unsupervised Multi-view Feature Selection

In the era of big data, it is becoming common to have data with multiple modalities or coming from multiple sources, known as "multi-view data". Multi-view data are usually unlabeled and come from high-dimensional spaces (such as language vocabularies), unsupervised multi-view feature selection is crucial to many applications. However, it is nontrivial due to the following challenges. First, there are too many instances or the feature dimensionality is too large. Thus, the data may not fit in memory. How to select useful features with limited memory space? Second, how to select features from streaming data and handles the concept drift? Third, how to leverage the consistent and complementary information from different views to improve the feature selection in the situation when the data are too big or come in as streams? To the best of our knowledge, none of the previous works can solve all the challenges simultaneously. In this paper, we propose an Online unsupervised Multi-View Feature Selection, OMVFS, which deals with large-scale/streaming multi-view data in an online fashion. OMVFS embeds unsupervised feature selection into a clustering algorithm via NMF with sparse learning. It further incorporates the graph regularization to preserve the local structure information and help select discriminative features. Instead of storing all the historical data, OMVFS processes the multi-view data chunk by chunk and aggregates all the necessary information into several small matrices. By using the buffering technique, the proposed OMVFS can reduce the computational and storage cost while taking advantage of the structure information. Furthermore, OMVFS can capture the concept drifts in the data streams. Extensive experiments on four real-world datasets show the effectiveness and efficiency of the proposed OMVFS method. More importantly, OMVFS is about 100 times faster than the off-line methods

A region-based image caption generator with refined descriptions

Describing the content of an image is a challenging task. To enable detailed description, it requires the detection and recognition of objects, people, relationships and associated attributes. Currently, the majority of the existing research relies on holistic techniques, which may lose details relating to important aspects in a scene. In order to deal with such a challenge, we propose a novel region-based deep learning architecture for image description generation. It employs a regional object detector, recurrent neural network (RNN)-based attribute prediction, and an encoder–decoder language generator embedded with two RNNs to produce refined and detailed descriptions of a given image. Most importantly, the proposed system focuses on a local based approach to further improve upon existing holistic methods, which relates specifically to image regions of people and objects in an image. Evaluated with the IAPR TC-12 dataset, the proposed system shows impressive performance and outperforms state-of-the-art methods using various evaluation metrics. In particular, the proposed system shows superiority over existing methods when dealing with cross-domain indoor scene images

Disturbances of apoptotic cell clearance in systemic lupus erythematosus

Systemic lupus erythematosus is a multifactorial autoimmune disease with an as yet unknown etiopathogenesis. It is widely thought that self-immunization in systemic lupus is driven by defective clearance of dead and dying cells. In lupus patients, large numbers of apoptotic cells accumulate in various tissues including germinal centers. In the present review, we discuss the danger signals released by apoptotic cells, their triggering of inflammatory responses, and the breakdown of B-cell tolerance. We also review the pathogenic role of apoptotic cell clearance in systemic lupus erythematosus

Calculating the lighting performance gap in higher education classrooms

The background of performance gap measurement is outlined and field measurements are gathered and applied retrospectively to lighting upgrades in classrooms. The lighting upgrade projects in three university buildings and their assumptions are explained in relation to the operational hours proposed using the industry ‘Energy assessment and reporting method’. We used relatively inexpensive environmental data loggers, which can be implemented prior to upgrade works or energy efficiency retrofits. Our results reveal different patterns of lights on use, occupancy and booking hours from those assumed by a priori estimates. In our study, the consequence of reporting energy savings using assumptions and estimates in calculations for classrooms resulted in limited overall differences in the savings achieved in practice. However despite the industry metrics of power consumption and carbon being reported significant wasted lighting hours were prevalent across all classrooms studied

Pseudodoping of Metallic Two-Dimensional Materials by The Supporting Substrates

We demonstrate how hybridization between a two-dimensional material and its substrate can lead to an apparent heavy doping, using the example of monolayer TaS$_2$ grown on Au(111). Combining $\textit{ab-initio}$ calculations, scanning tunneling spectroscopy experiments and a generic model, we show that strong changes in Fermi areas can arise with much smaller actual charge transfer. This mechanism, which we refer to as pseudodoping, is a generic effect for metallic two-dimensional materials which are either adsorbed to metallic substrates or embedded in vertical heterostructures. It explains the apparent heavy doping of TaS$_2$ on Au(111) observed in photoemission spectroscopy and spectroscopic signatures in scanning tunneling spectroscopy. Pseudodoping is associated with non-linear energy-dependent shifts of electronic spectra, which our scanning tunneling spectroscopy experiments reveal for clean and defective TaS$_2$ monolayer on Au(111). The influence of pseudodoping on the formation of charge ordered, magnetic, or superconducting states is analyzed.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0022

Multitasking by the OC lineage during bone infection: Bone resorption, immune modulation, and microbial niche

Bone infections, also known as infectious osteomyelitis, are accompanied by significant inflammation, osteolysis, and necrosis. Osteoclasts (OCs) are the bone-resorbing cells that work in concert with osteoblasts and osteocytes to properly maintain skeletal health and are well known to respond to inflammation by increasing their resorptive activity. OCs have typically been viewed merely as effectors of pathologic bone resorption, but recent evidence suggests they may play an active role in the progression of infections through direct effects on pathogens and via the immune system. This review discusses the host- and pathogen-derived factors involved in the in generation of OCs during infection, the crosstalk between OCs and immune cells, and the role of OC lineage cells in the growth and survival of pathogens, and highlights unanswered questions in the field