File size models for shared content over the BitTorrent Peer-to-Peer network

Peer-to-Peer (P2P) traffic has increased rapidly over the past few years, with file sharing providing the main drive behind such traffic. In this work we perform a measurement study of the content shared over the popular BitTorrent P2P file sharing network. We mathematically model the file size distributions of shared files after categorizing them into Audio, Video, Archive and CD image classes. For each of these categories we look into the most popular shared file formats and investigate their file size statistics. This provides an important milestone to building a realistic simulation framework for P2P systems, and for future analytical modeling of P2P networks.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.     

Visual tracking using convolutional features with sparse coding

Artificial Intelligence Review - Visual object tracking has become one of the most active research topics in computer vision, and it has been applied in several commercial...     

Priority-based joint EDF–RM scheduling algorithm for individual real-time task on distributed systems

The Journal of Supercomputing - Multiple tasks arrive in the distributed systems that can be executed in either parallel or sequential manner. Before the execution, tasks are scheduled prioritywise...     

Structural health monitoring of railway tracks using IoT-based multi-robot system

Neural Computing and Applications - A multi-robot-based fault detection system for railway tracks is proposed to eliminate manual human visual inspection. A hardware prototype is designed to...     

Deep neural network architectures for dysarthric speech analysis and recognition

Neural Computing and Applications - This paper investigates the ability of deep neural networks (DNNs) to improve the automatic recognition of dysarthric speech through the use of convolutional...     

Robust H ∞ static output feedback stabilization of T-S fuzzy systems subject to actuator saturation

This paper proposes a method for designing robust H_?? static output feedback stabilization of Takagi-Sugeno (T-S) fuzzy systems under actuator saturation. In this paper, the input saturation is represented by a polytopic model and the modeling error is assumed a norm-bounded uncertainty. A set invariance condition for robust H_?? static output feedback system under actuator saturation is first established. Then, the estimation of the largest domain of attraction for the system is formulated and solved as a Linear Matrix Inequality (LMI) optimization problem. Two examples are used to demonstrate the effectiveness of the proposed design method.     

Influence of annealing on properties and structure of alumina

Alumina bodies were prepared from pure alumina powder (98.9% Al₂O₃ consisting of 82% > 53m). The powder was compacted by hot-pressing at 1200° C, Compacted bodies were annealed at 1300, 1400 and 1500° C. Annealing continued at each maximum temperature for 25, 50 and 100 h. Strong bodies were obtained with maximum bulk density of 2.32 g cm^–3 and minimum apparent porosity of 30.21%. The change in sintering parameters with annealing was correlated with developed structure.     

Characterization of Urine Stem Cell-Derived Extracellular Vesicles Reveals B Cell Stimulating Cargo

Elucidation of the biological functions of extracellular vesicles (EVs) and their potential roles in physiological and pathological processes is an expanding field of research. In this study, we characterized USC–derived EVs and studied their capacity to modulate the human immune response in vitro. We found that the USC–derived EVs are a heterogeneous population, ranging in size from that of micro–vesicles (150 nm–1 μm) down to that of exosomes (60–150 nm). Regarding their immunomodulatory functions, we found that upon isolation, the EVs (60–150 nm) induced B cell proliferation and IgM antibody secretion. Analysis of the EV contents unexpectedly revealed the presence of BAFF, APRIL, IL–6, and CD40L, all known to play a central role in B cell stimulation, differentiation, and humoral immunity. In regard to their effect on T cell functions, they resembled the function of mesenchymal stem cell (MSC)–derived EVs previously described, suppressing T cell response to activation. The finding that USC–derived EVs transport a potent bioactive cargo opens the door to a novel therapeutic avenue for boosting B cell responses in immunodeficiency or cancer.     

Differential Effects of STCH and Stress-Inducible Hsp70 on the Stability and Maturation of NKCC2

Mutations in the Na-K-2Cl co-transporter NKCC2 lead to type I Bartter syndrome, a life-threatening kidney disease. We previously showed that export from the ER constitutes the limiting step in NKCC2 maturation and cell surface expression. Yet, the molecular mechanisms involved in this process remain obscure. Here, we report the identification of chaperone stress 70 protein (STCH) and the stress-inducible heat shock protein 70 (Hsp70), as two novel binding partners of the ER-resident form of NKCC2. STCH knock-down increased total NKCC2 expression whereas Hsp70 knock-down or its inhibition by YM-01 had the opposite effect. Accordingly, overexpressing of STCH and Hsp70 exerted opposite actions on total protein abundance of NKCC2 and its folding mutants. Cycloheximide chase assay showed that in cells over-expressing STCH, NKCC2 stability and maturation are heavily impaired. In contrast to STCH, Hsp70 co-expression increased NKCC2 maturation. Interestingly, treatment by protein degradation inhibitors revealed that in addition to the proteasome, the ER associated degradation (ERAD) of NKCC2 mediated by STCH, involves also the ER-to-lysosome-associated degradation pathway. In summary, our data are consistent with STCH and Hsp70 having differential and antagonistic effects with regard to NKCC2 biogenesis. These findings may have an impact on our understanding and potential treatment of diseases related to aberrant NKCC2 trafficking and expression.