Good Features to Correlate for Visual Tracking

During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.Comment: Accepted version of IEEE Transactions on Image Processin

Quadruplet Selection Methods for Deep Embedding Learning

Recognition of objects with subtle differences has been used in many practical applications, such as car model recognition and maritime vessel identification. For discrimination of the objects in fine-grained detail, we focus on deep embedding learning by using a multi-task learning framework, in which the hierarchical labels (coarse and fine labels) of the samples are utilized both for classification and a quadruplet-based loss function. In order to improve the recognition strength of the learned features, we present a novel feature selection method specifically designed for four training samples of a quadruplet. By experiments, it is observed that the selection of very hard negative samples with relatively easy positive ones from the same coarse and fine classes significantly increases some performance metrics in a fine-grained dataset when compared to selecting the quadruplet samples randomly. The feature embedding learned by the proposed method achieves favorable performance against its state-of-the-art counterparts.Comment: 6 pages, 2 figures, accepted by IEEE ICIP 201

Evaluating adult cor triatriatum with total anomalous pulmonary venous connections by multidetector computed tomography angiography

A 19-year-old female patient was admitted to our hospital with dyspnea, chest pain, and shortness of breath. A chest radiograph showed mild cardiomegaly. Echocardiography revealed an extra chamber in the heart. To evaluate this abnormality, ECG-gated 16-detector-row computed tomography angiography was performed. Multidetector computed tomography (MDCT), showing cor triatriatum with total anomalous pulmonary venous connections (TAPVC), clearly revealed cardiac and vascular anatomy. ECG-gated cardiac MDCT is a useful tool for detection and characterisation of cor triatriatum and related anomalies. (Folia Morphol 2011; 70, 4: 312–314

Ge nanocrystals embedded in ultrathin Si3N4 multilayers with SiO2 barriers

Multilayers of germanium nanocrystals (NCs) embedded in thin films of silicon nitride matrix separated with SiO2 barriers have been fabricated using plasma enhanced chemical vapor deposition (PECVD). SiGeN/SiO2 alternating bilayers have been grown on quartz and Si substrates followed by post annealing in Ar ambient from 600 to 900 °C. High resolution transmission electron microscopy (HRTEM) as well as Raman spectroscopy show good crystallinity of Ge confined to SiGeN layers in samples annealed at 900 °C. Strong compressive stress for SiGeN/SiO2 structures were observed through Raman spectroscopy. Size, as well as NC-NC distance were controlled along the growth direction for multilayer samples by varying the thickness of bilayers. Visible photoluminescence (PL) at 2.3 and 3.1 eV with NC size dependent intensity is observed and possible origin of PL is discussed. © 2017 Elsevier Lt

Cell-level pathway scoring comparison with a biologically constrained variational autoencoder

This preprint has not undergone peer review or any post-submission improvements or corrections. The Version of Record of this contribution is published in: Pang, J., Niehren, J. (eds) Computational Methods in Systems Biology. CMSB 2023. Lecture Notes in Computer Science, vol 14137. Springer, Cham. Available online at https://doi.org/10.1007/978-3-031-42697-1_

hMOB2 deficiency impairs homologous recombination-mediated DNA repair and sensitises cancer cells to PARP inhibitors

Monopolar spindle-one binder (MOBs) proteins are evolutionarily conserved and contribute to various cellular signalling pathways. Recently, we reported that hMOB2 functions in preventing the accumulation of endogenous DNA damage and a subsequent p53/p21-dependent G1/S cell cycle arrest in untransformed cells. However, the question of how hMOB2 protects cells from endogenous DNA damage accumulation remained enigmatic. Here, we uncover hMOB2 as a regulator of double-strand break (DSB) repair by homologous recombination (HR). hMOB2 supports the phosphorylation and accumulation of the RAD51 recombinase on resected single-strand DNA (ssDNA) overhangs. Physiologically, hMOB2 expression supports cancer cell survival in response to DSB-inducing anti-cancer compounds. Specifically, loss of hMOB2 renders ovarian and other cancer cells more vulnerable to FDA-approved PARP inhibitors. Reduced MOB2 expression correlates with increased overall survival in patients suffering from ovarian carcinoma. Taken together, our findings suggest that hMOB2 expression may serve as a candidate stratification biomarker of patients for HR-deficiency targeted cancer therapies, such as PARP inhibitor treatments

A Novel Parallel Triangle Counting Algorithm with Reduced Communication

Counting and finding triangles in graphs is often used in real-world analytics for characterizing the cohesiveness and identifying communities in graphs. In this paper, we present novel sequential and parallel triangle counting algorithms based on identifying horizontal-edges in a breadth-first search (BFS) traversal of the graph. The BFS allows our algorithm to drastically reduce the number of edges examined for set intersections. Our new approach is the first communication-optimal parallel algorithm that asymptotically reduces the communication on massive graphs such as from real social networks and synthetic graphs from the Graph500 Benchmark. In our estimate from massive-scale Graph500 graphs, our new algorithms reduces the communication by 21.8x on a scale 36 and by 180x on a scale 42. Because communication is known to be the dominant cost of parallel triangle counting, our new parallel algorithm, to our knowledge, is now the fastest method for counting triangles in large graphs.Comment: 10 page

Sparse representation of two- and three-dimensional images with fractional Fourier, Hartley, linear canonical, and Haar wavelet transforms

Sparse recovery aims to reconstruct signals that are sparse in a linear transform domain from a heavily underdetermined set of measurements. The success of sparse recovery relies critically on the knowledge of transform domains that give compressible representations of the signal of interest. Here we consider two- and three-dimensional images, and investigate various multi-dimensional transforms in terms of the compressibility of the resultant coefficients. Specifically, we compare the fractional Fourier (FRT) and linear canonical transforms (LCT), which are generalized versions of the Fourier transform (FT), as well as Hartley and simplified fractional Hartley transforms, which differ from corresponding Fourier transforms in that they produce real outputs for real inputs. We also examine a cascade approach to improve transform-domain sparsity, where the Haar wavelet transform is applied following an initial Hartley transform. To compare the various methods, images are recovered from a subset of coefficients in the respective transform domains. The number of coefficients that are retained in the subset are varied systematically to examine the level of signal sparsity in each transform domain. Recovery performance is assessed via the structural similarity index (SSIM) and mean squared error (MSE) in reference to original images. Our analyses show that FRT and LCT transform yield the most sparse representations among the tested transforms as dictated by the improved quality of the recovered images. Furthermore, the cascade approach improves transform-domain sparsity among techniques applied on small image patches. © 2017 Elsevier Lt