Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters

Segmentation of an object from a video is a challenging task in multimedia applications. Depending on the application, automatic or interactive methods are desired; however, regardless of the application type, efficient computation of video object segmentation is crucial for time-critical applications; specifically, mobile and interactive applications require near real-time efficiencies. In this paper, we address the problem of video segmentation from the perspective of efficiency. We initially redefine the problem of video object segmentation as the propagation of MRF energies along the temporal domain. For this purpose, a novel and efficient method is proposed to propagate MRF energies throughout the frames via bilateral filters without using any global texture, color or shape model. Recently presented bi-exponential filter is utilized for efficiency, whereas a novel technique is also developed to dynamically solve graph-cuts for varying, non-lattice graphs in general linear filtering scenario. These improvements are experimented for both automatic and interactive video segmentation scenarios. Moreover, in addition to the efficiency, segmentation quality is also tested both quantitatively and qualitatively. Indeed, for some challenging examples, significant time efficiency is observed without loss of segmentation quality.Comment: Multimedia, IEEE Transactions on (Volume:16, Issue: 5, Aug. 2014

Predictive Missile Guidance with Online Trajectory Learning

This study presents a predictive guidance scheme for tactical missiles. The modern day targets, with improved manoeuverability, have revealed insufficient performance of the conventional guidance laws. The underlying cause of this poor performance is the reactive nature of the conventional guidance laws such as proportional navigation (PN) and pure pursuit (PP). Predictive guidance offers an alternative approach to the classical methods by taking proactive actions by estimating target’s future trajectory. However, most of the existing predictive guidance approaches assume that the interceptor have a model of the target dynamics. A guidance strategy is developed in this study, that can learn the target dynamics iteratively and adapt the interceptor actions accordingly. A recursive least squares (RLS) estimation algorithm is employed for learning and estimating the possible future target positions, and a fixed horizon nonlinear program is employed for selecting the optimal interception action. Monte-Carlo simulations show that the guidance algorithm introduced in this work demonstrates a significantly improved performance compared to the alternatives in terms of interception time and miss distance

The Endotoxin-Induced Neuroinflammation Model of Parkinson's Disease

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by the progressive loss of dopaminergic (DA) neurons in the substantia nigra. Although the exact cause of the dopaminergic neurodegeneration remains elusive, recent postmortem and experimental studies have revealed an essential role for neuroinflammation that is initiated and driven by activated microglial and infiltrated peripheral immune cells and their neurotoxic products (such as proinflammatory cytokines, reactive oxygen species, and nitric oxide) in the pathogenesis of PD. A bacterial endotoxin-based experimental model of PD has been established, representing a purely inflammation-driven animal model for the induction of nigrostriatal dopaminergic neurodegeneration. This model, by itself or together with genetic and toxin-based animal models, provides an important tool to delineate the precise mechanisms of neuroinflammation-mediated dopaminergic neuron loss. Here, we review the characteristics of this model and the contribution of neuroinflammatory processes, induced by the in vivo administration of bacterial endotoxin, to neurodegeneration. Furthermore, we summarize the recent experimental therapeutic strategies targeting endotoxin-induced neuroinflammation to elicit neuroprotection in the nigrostriatal dopaminergic system. The potential of the endotoxin-based PD model in the development of an early-stage specific diagnostic biomarker is also emphasized

MicroRNAs and Multiple Sclerosis

MicroRNAs (miRNAs) have recently emerged as a new class of modulators of gene expression. miRNAs control protein synthesis by targeting mRNAs for translational repression or degradation at the posttranscriptional level. These noncoding RNAs are endogenous, single-stranded molecules approximately 22 nucleotides in length and have roles in multiple facets of immunity, from regulation of development of key cellular players to activation and function in immune responses. Recent studies have shown that dysregulation of miRNAs involved in immune responses leads to autoimmunity. Multiple sclerosis (MS) serves as an example of a chronic and organ-specific autoimmune disease in which miRNAs modulate immune responses in the peripheral immune compartment and the neuroinflammatory process in the brain. For MS, miRNAs have the potential to serve as modifying drugs. In this review, we summarize current knowledge of miRNA biogenesis and mode of action and the diverse roles of miRNAs in modulating the immune and inflammatory responses. We also review the role of miRNAs in autoimmunity, focusing on emerging data regarding miRNA expression patterns in MS. Finally, we discuss the potential of miRNAs as a disease marker and a novel therapeutic target in MS. Better understanding of the role of miRNAs in MS will improve our knowledge of the pathogenesis of this disease

The Nrf2/ARE Pathway: A Promising Target to Counteract Mitochondrial Dysfunction in Parkinson's Disease

Mitochondrial dysfunction is a prominent feature of various neurodegenerative diseases as strict regulation of integrated mitochondrial functions is essential for neuronal signaling, plasticity, and transmitter release. Many lines of evidence suggest that mitochondrial dysfunction plays a central role in the pathogenesis of Parkinson's disease (PD). Several PD-associated genes interface with mitochondrial dynamics regulating the structure and function of the mitochondrial network. Mitochondrial dysfunction can induce neuron death through a plethora of mechanisms. Both mitochondrial dysfunction and neuroinflammation, a common denominator of PD, lead to an increased production of reactive oxygen species, which are detrimental to neurons. The transcription factor nuclear factor E2-related factor 2 (Nrf2, NFE2L2) is an emerging target to counteract mitochondrial dysfunction and its consequences in PD. Nrf2 activates the antioxidant response element (ARE) pathway, including a battery of cytoprotective genes such as antioxidants and anti-inflammatory genes and several transcription factors involved in mitochondrial biogenesis. Here, the current knowledge about the role of mitochondrial dysfunction in PD, Nrf2/ARE stress-response mechanisms, and the evidence for specific links between this pathway and PD are summarized. The neuroprotection of nigral dopaminergic neurons by the activation of Nrf2 through several inducers in PD is also emphasized as a promising therapeutic approach

Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment

Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility. In this work, we develop a novel simulation environment that emulates these challenges and train a deep reinforcement learning agent that yields consistent performance in a variety of dynamic and uncertain traffic scenarios. Results show that the proposed data-driven approach performs significantly better in noisy environments compared to methods that rely solely on heuristics.Comment: Accepted to IEEE Intelligent Transportation Systems Conference - ITSC 201

P2X7 receptor activates multiple selective dye-permeation pathways

ABSTRACT P2X7 receptor has gained an increasing importance as a drug target. One important response to P2X7 receptor stimulation is the uptake of large molecular weight tracers into cells. However, mechanism for this response is not understood clearly, but it is generally believed that a nonselective large pore protein forms this P2X7 receptor-activated permeability pathway. We examined human embryonic kidney (HEK) 293 cells transfected with rat P2X7 receptors (HEK-rP2X7) and a macrophage derived cell line, RAW 264.7, that expresses an endogenous P2X7 receptor. We used confocal microscopy to investigate uptake of different types of dyes into these cells after ATP application. Stimulation of P2X7 receptors in HEK-rP2X7 cells activated two different dye uptake pathways. The first was permeable to the cationic fluorescent dyes YO-PRO-1 and TO-TO-1 but not to the anionic dyes lucifer yellow and calcein and did not require intracellular Ca 2ϩ concentration ([Ca 2ϩ ] i ) increase to be activated. The second pathway permeated only lucifer yellow and was completely dependent on [Ca 2ϩ ] i for activation. In RAW 264.7 cells, P2X7 receptor stimulation activated uptake of ethidium, YO-PRO-1, TO-TO-1, lucifer yellow, and calcein. Again, two different permeation pathways were discerned in RAW 264.7 cells: one permeated only ethidium and the other one, only lucifer yellow. We did observed no clear [Ca 2ϩ ] i dependence for these permeation pathways. Our results demonstrate that instead of a single nonselective pore, P2X7 receptor seems to activate at least two permeation pathways, one for cationic and one for anionic dyes with different activation properties. The P2X7 receptor is a member of P2X receptor family, which is composed of ligand-gated ion channels. Activated P2X7 receptor causes not only a cationic membrane current, but also permeabilization of the cell membrane to large molecular weight molecules P2X7 receptors are known to be important in the pathophysiology of arthritis and mediation of pain (for review, see Article, publication date, and citation information can be found a

PURSUhInT: In Search of Informative Hint Points Based on Layer Clustering for Knowledge Distillation

We propose a novel knowledge distillation methodology for compressing deep neural networks. One of the most efficient methods for knowledge distillation is hint distillation, where the student model is injected with information (hints) from several different layers of the teacher model. Although the selection of hint points can drastically alter the compression performance, there is no systematic approach for selecting them, other than brute-force hyper-parameter search. We propose a clustering based hint selection methodology, where the layers of teacher model are clustered with respect to several metrics and the cluster centers are used as the hint points. The proposed approach is validated in CIFAR-100 dataset, where ResNet-110 network was used as the teacher model. Our results show that hint points selected by our algorithm results in superior compression performance with respect to state-of-the-art knowledge distillation algorithms on the same student models and datasets