SYSTEMS AND METHODS FOR DEEP LEARNING TELEVISION (TV) ADVERTISEMENT DETECTION

The method detailed herein relates to a classifier for classifying a video as an advertisement or program without using a reference database of frames. The method can include classifying videos with a single-frame classifier that analyzes a single frame to determine whether the frame is an advertisement or a program. Furthermore, the method includes classifying videos with a sequence classifier that analyzes sequences of frames to determine whether the frame is an advertisement or a program. In some embodiments, the method relates to classifying predefined amounts of frames as advertisements or programs to determine whether a video is an advertisement or a program. In some embodiments, the method relates to classifying frames of a reference database of an advertisement matching system in order to adjust various analysis weights so that the performance of the advertisement matching system does not fall due to locally inserted advertisements that may not be part of the reference database

Artificial Intelligence Powered Brand Identification and Attribution for On Screen Content

The method detailed herein relates to second screen experiences for a smart television (TV). The method includes capturing one or more frames from a video (e.g., movie, television show, advertisement) playing on the smart TV and sending the captured frames to a cloud platform for image processing. The method further includes using machine learning, via the cloud platform, to identify objects (e.g., people, products, brands, and places) in the received frames. The method includes pushing, from the cloud platform, a notification to a mobile device including information relating to the identified objects

Neutrophil gelatinase-associated lipocalin (NGAL) as a prognostic marker in chronic myeloid Leukemia: an observational study

Background: Neutrophil gelatinase-associated lipocalin (NGAL) is a protein which is associated with various inflammatory conditions affecting human tissues, such as those in the respiratory, gastro-enteric and urinary tracts, with a marked increase in the local and systemic expression. Different experimental evidences reveal that NGAL is required for the induction and pathogenesis of chronic myeloid leukemia (CML).Methods: The present study was conducted in department of Biochemistry in a tertiary care institute of Haryana. 30 cases of CML were included in the study. It was a hospital based observational study which was conducted for one-year duration. Apart from routine biochemical investigations, serum NGAL estimation was done before the initiation of therapy and after 3 months of therapy.Results: The median age at presentation was 39 years. Male to female ratio was 1.3:1. Weight loss was the most common presentation of patients (53.3%). More than half of the cases occurred in age group of 21-40 years. Serum NGAL was significantly higher in CML patients (358.47±125.65) before treatment as compared to serum NGAL value after treatment (85.03±62.77). In patients who achieved hematological remission, mean serum NGAL levels (62.46 ng/ml±23.72) were statistically lower than mean serum NGAL values in patients who did not achieve remission (231.75 ng/ml±16.7).Conclusions: The present study concluded that serum NGAL levels can be used as diagnostic and prognostic marker in CML

Numerical Solution of Burgers\u27 equation arising in Longitudinal Dispersion Phenomena in Fluid Flow through Porous Media by Crank-Nicolson Scheme

The present paper discusses the numerical solution of the Burgers’ equation arising in longitudinal dispersion phenomenon in fluid flow through porous media. In the porous medium pure water, salt water or contaminated water disperse in longitudinal direction gives rise to a non-linear partial differential equation in the form of Burgers’ equation. The equation is solved by using Crank-Nicolson finite difference scheme with appropriate initial and boundary conditions. The longitudinal dispersion phenomenon may be miscible or immiscible fluid flow through porous media. The problem of miscible displacement can be seen in the coastal areas, where fresh water beds are gradually displaced by sea water. Longitudinal dispersion phenomenon plays an important role to control salinity of the soil in western seashore region of the Gujarat state in India. To control salinity, the government of Gujarat has developed many check dams from where contaminated water diverted and poured to the soil of the farms, where the crops of cumin seed (jeera), fennel (saunf) and other grains are grown. In this region due to the infiltration of this infiltered water, free surface of sweet water table rises, consequently, saline seawater cannot cross the threshold in the nearby area of the seashore. In such a way, the dispersion of contaminated water plays key role to solve salinity problem. The immiscible dispersion also plays an important role in petroleum engineering during secondary oil recovery process, in which water or gas is injected in oil formatted area to drive the oil towards production well. An unconditionally stable Crank-Nicolson finite difference scheme has been employed to find the concentration C(X, T) of salty or contaminated water dispersion in uni-direction. The outcome is consistent with physical phenomenon of longitudinal dispersion in miscible fluid flow through porous media. It is concluded, that the concentration C(X, T) decreases as distance X as well as time T increases. The tables and graphs are developed by using MATLAB coding

Factored axis-aligned filtering for rendering multiple distribution effects

Monte Carlo (MC) ray-tracing for photo-realistic rendering often requires hours to render a single image due to the large sampling rates needed for convergence. Previous methods have attempted to filter sparsely sampled MC renders but these methods have high reconstruction overheads. Recent work has shown fast performance for individual effects, like soft shadows and indirect illumination, using axis-aligned filtering. While some components of light transport such as indirect or area illumination are smooth, they are often multiplied by high-frequency components such as texture, which prevents their sparse sampling and reconstruction. We propose an approach to adaptively sample and filter for simultaneously rendering primary (defocus blur) and secondary (soft shadows and indirect illumination) distribution effects, based on a multi-dimensional frequency analysis of the direct and indirect illumination light fields. We describe a novel approach of factoring texture and irradiance in the presence of defocus blur, which allows for pre-filtering noisy irradiance when the texture is not noisy. Our approach naturally allows for different sampling rates for primary and secondary effects, further reducing the overall ray count. While the theory considers only Lambertian surfaces, we obtain promising results for moderately glossy surfaces. We demonstrate 30x sampling rate reduction compared to equal quality noise-free MC. Combined with a GPU implementation and low filtering over-head, we can render scenes with complex geometry and diffuse and glossy BRDFs in a few seconds.National Science Foundation (U.S.) (Grant CGV 1115242)National Science Foundation (U.S.) (Grant CGV 1116303)Intel Corporation (Science and Technology Center for Visual Computing

Plaque Contact Surface Area and Flow Lumen Volume Predict Stroke Risk in Extracranial Carotid Artery Stenosis

The standard indication for intervention in asymptomatic disease is currently percent stenosis in the internal carotid artery as measured by the NASCET method, which remains limited in discriminating power. CT angiography (CTA) is widely used to calculate NASCET stenosis but also offers the opportunity to analyze carotid artery plaques from a morphological perspective that has not been widely utilized. We aim to improve stroke risk stratification of patients with carotid artery stenosis using plaque 3D modeling and image analysis. Patients with CTAs appropriate for 3D reconstruction were identified from an NIH designated stroke center database, and carotid arteries were segmented and analyzed using software algorithms to calculate contact surface area between the plaque and blood flow (CSA), and volume of the flow lumen within the region of the plaque (FLV). These novel parameters factor in the 3D morphometry inherent to each carotid plaque. A total of 134 carotid arteries were analyzed, 33 of which were associated with an ipsilateral stroke. Plaques associated with stroke demonstrated statistically significant increases in average CSA and FLV when compared to those not associated with stroke. When compared to NASCET percent stenosis, CSA and FLV both demonstrated a larger area under the receiver operating characteristics curve (AUC) in predicting stroke risk in patients with carotid stenosis. The data presented here demonstrate morphological features of carotid plaques that are independent of NASCET criteria stratification and may present an improved method in assessing stroke risk in patients with carotid artery stenosis

Generation of Hidden Optical-Polarization: Squeezing and Non-Classicality

A monochromatic double-mode coherent light endowed with orthogonally polarized photons propagating collinearly is studied in Degenerate Parametric Amplification. Generation of Hidden Optical- Polarized States is shown by non-zero values of Index of Hidden Optical-Polarization. Squeezing in HOPS is demonstrated by recognizing a Squeezing function. The Non-Classical feature of HOPS is observed by 'degree of Hidden Optical-Polarization' which attains non-classical value 'greater than unity'. The dynamical nature of Generation, Squeezing and Non-Classicality are numerically presented.Comment: 14 pages and 02 figure