Gaurav Srivastava

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Recent functional imaging studies have revealed coactivation in a distributed network of cortical regions that characterizes the resting state, or default mode, of the human brain. Among the brain regions implicated in this network, several, including the posterior cingulate cortex and inferior parietal lobes, have also shown decreased metabolism early in(More)
We propose a human activity classification algorithm that has a distributed and lightweight implementation appropriate for wireless camera networks. With input from multiple cameras, our algorithm achieves invariance to the orientation of the actor and to the camera viewpoint. We conceptually describe how the algorithm can be implemented on a distributed(More)
The tumor suppressor gene, Von Hippel-Lindau (VHL), is frequently mutated in the most common form of kidney cancer, clear cell renal cell carcinoma (CCRCC). In hypoxic conditions, or when there is a VHL mutation, the hypoxia inducible factors, HIF1α and HIF2α, are stabilized and transcribe a panel of genes associated with cancer such as vascular endothelial(More)
Patients with germline fumarate hydratase (FH) mutation are predisposed to develop aggressive kidney cancer with few treatment options and poor therapeutic outcomes. Activity of the proto-oncogene ABL1 is upregulated in FH-deficient kidney tumors and drives a metabolic and survival signaling network necessary to cope with impaired mitochondrial function and(More)
This paper presents Locality-constrained Low Rank Coding (LLRC) as a novel approach for image classification. The widely used Sparse representation based algorithms reconstruct a test sample using a sparse linear combination of training samples. But they do not consider the underlying structure of the data in the feature space. On the other hand, Low Rank(More)
We propose a novel evidence accumulation framework that accurately estimates the positions of humans in a 3D environment. The framework consists of a network of distributed agents having different functionalities. The modular structure of the network allows scalability to large surveillance areas and robust operation. The framework does not assume reliable(More)
Topology control in an ad-hoc network can provide better spatial reuse of the wireless channel and conserve power. Topology construction and maintenance is a challenging issue. A number of distributed topology control algorithms have been proposed to remove the need of a centralised controller. Distributed algorithms such as location information no topology(More)
We address the problem of predicting category labels for unlabeled videos in a large video dataset by using a ground-truth set of objectively labeled videos that we have created. Large video databases like YouTube require that a user uploading a new video assign to it a category label from a prescribed set of labels. Such category labeling is likely to be(More)
Visual search over large image repositories in real time is one of the key challenges for applications such as mobile visual queryby-capture, augmented reality, and biometricsbased identification. Figure 1 illustrates examples of this new wave of applications enabled by large visual search capabilities. In these applications, search accuracy and response(More)