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Clustering cancer patients into subgroups and identifying cancer subtypes is an important task in cancer genomics. Clustering based on comprehensive multi-omic molecular profiling can often achieve better results than those using a single data type, since each omic data type may contain complementary information. However, it is challenging to integrate heterogeneous omic data directly. Based on one...
Mid-Infrared (MIR) spectroscopy has emerged as the most economically viable technology to determine milk values as well as to identify a set of animal phenotypes related to health, feeding, well-being and environment. However, Fourier transform-MIR spectra incurs a significant amount of redundant data. This creates critical issues such as increased learning complexity while performing Fog and Cloud...
Driven by the dramatic growth of data both in terms of the size and sources, learning from heterogeneous data is emerging as an important research direction for many real applications. One of the biggest challenges of this type of problem is how to meaningfully integrate heterogeneous data to considerably improve the generality and quality of the learning model. In this paper, we first present a unified...
Visual tracking is a very challenging problem in computer vision as the performance of a tracking algorithm may be degraded due to many challenging issues in the scenes, such as illumination change, deformation, and background clutter. So far no algorithms can handle all these challenging issues. Recently, it has been shown that correlation filters can be implemented efficiently and, with suitable...
A Brain-Computer Interface (BCI) speller system based on the Steady-State Visually Evoked Potentials (SSVEP) paradigm is presented. The potentials are elicited through the gaze fixation at one out of the four checkerboards shown on screen, which are flickering at 5, 12, 15 and 20 Hz. After the feature extraction, two dimensionality reduction algorithms, Principal Components Analysis (PCA) and Linear...
In almost every mental disorder, there are deficiencies in both structure and function of the brain. So the need for analyzing complementary modalities that project all aspects of the brain is rising. The most severe kind of these disorders is schizophrenia. The main cause of schizophrenia is still unknown. Therefore, analyzing resting-state fMRI (rs-fMRI) and structural MRI (sMRI) to investigate...
Matrix factorization is a popular low dimensional representation approach that plays an important role in many pattern recognition and computer vision domains. Among them, convex and semi-nonnegative matrix factorizations have attracted considerable interest, owing to its clustering interpretation. On the other hand, the generalized correlation function (correntropy) as the error measure does not...
In the paper, we propose an effective long-term real-time tracking method to address the problem of robustness and tracking failure in visual tracking with UAVs. Most existing trackers only consider short-term tracking, therefore are unable to cope with partial and complete occlusion, which finally leads to object drifting or loss. Our method still follows the tracking-by-detection framework. However,...
Statistical analysis of rainfall-triggered landslides inventory patterns is a key for landslide hazard and risk prediction analysis of susceptible areas. The main objective of the study is to test if the landslides locations are spatially auto correlated, that could either be clustered (spatial attraction), dispersed or randomly distributed (spatially independent). Two categories of spatial distance...
We study large-scale multi-label classification (MLC) on two recently released datasets: Youtube-8M and Open Images that contain millions of data instances and thousands of classes. The unprecedented problem scale poses great challenges for MLC. First, finding out the correct label subset out of exponentially many choices incurs substantial ambiguity and uncertainty. Second, the large data-size and...
Although the existing correlation filter based on trackers has appeared to be more excellent in the visual tracking problem, there is still tremendous space for the improvement of the tracking performance, especially in the occlusion situation which is often ignored due to the difficulty in detection and processing. In this paper, a scale-adaptive tracker is proposed to handle the case of occlusion...
In the distillation process, many important process variables are often difficult to be measured online. For example, the aviation kerosene is an important index of operation quality, but current methods cannot obtain the real-time value of the aviation kerosene efficiently. To solve this problem, a method of selecting the input variable based on partial least squares regression (PLS) is proposed...
A major challenge in matching between vision and language is that they typically have completely different features and representations. In this work, we introduce a novel bridge between the modality-specific representations by creating a co-embedding space based on a recurrent residual fusion (RRF) block. Specifically, RRF adapts the recurrent mechanism to residual learning, so that it can recursively...
In this work, we address multimodal learning problem with Gaussian process latent variable models (GPLVMs) and their application to cross-modal retrieval. Existing GPLVM based studies generally impose individual priors over the model parameters and ignore the intrinsic relations among these parameters. Considering the strong complementarity between modalities, we propose a novel joint prior over the...
A heterogeneous memory system (HMS) consists of multiple memory components with different properties. GPU is a representative architecture with HMS. It is challenging to decide optimal placement of data objects on HMS because of the large exploration space and complicated memory hierarchy on HMS. In this paper, we introduce performance modeling techniques to predict performance of various data placements...
Heterogeneous defect prediction (HDP) aims to predict defect-prone software modules in one project using heterogeneous data collected from other projects. Recently, several HDP methods have been proposed. However, these methods do not sufficiently incorporate the two characteristics of the defect prediction data: (1) data could be linearly inseparable, and (2) data could be highly imbalanced. These...
Existing systematic constructions of optimal odd-length binary Z-complementary pairs (OBZCPs) only generate pairs with lengths 2α + 1 (where α is a non-negative integer). In this paper, we propose a new construction of optimal OBZCPs having generic lengths of 2α10β 26γ + 1, α ≥ 1 (where α, β and γ are non-negative integers). This is achieved with the aid of several interesting intrinsic structure...
AbstractłIn order to enhance the robustness of kernel correlation filters(KCF) in complex background environment, this paper proposes a mean shift method with adaptive local object tracking algorithm. KCF algorithm has speed advantage by using the single template, we introduce the confidence map in the process of the tracking to determine the result of the current frame. If the result of confidence...
Multipath is known to be one of the dominant error sources in high accuracy positioning systems, and multipath estimation is crucial for multipath mitigation. Most existing multipath estimation algorithms usually consider the cases of single mutlipath with Gaussian noise. However, non-Gaussian noises and two-multipath are often encountered in many practical environments. In this paper, a new algorithm...
Analyzing the spatial distribution of residential land and its spatial distribution relations with residential facilities and transportation facilities is of great reference value for the regional optimal allocation of human settlements and the balanced development of human settlements. This paper took Wuhan City Development Zone as an example, and used the sixth census data and survey data of urban...
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