A Novel Light Field Coding Scheme Based on Deep Belief Network and Weighted Binary Images for Additive Layered Displays

Light field display caters to the viewer's immersive experience by providing binocular depth sensation and motion parallax. Glasses-free tensor light field display is becoming a prominent area of research in auto-stereoscopic display technology. Stacking light attenuating layers is one of the approaches to implement a light field display with a good depth of field, wide viewing angles and high resolution. This paper presents a compact and efficient representation of light field data based on scalable compression of the binary represented image layers suitable for additive layered display using a Deep Belief Network (DBN). The proposed scheme learns and optimizes the additive layer patterns using a convolutional neural network (CNN). Weighted binary images represent the optimized patterns, reducing the file size and introducing scalable encoding. The DBN further compresses the weighted binary patterns into a latent space representation followed by encoding the latent data using an h.254 codec. The proposed scheme is compared with benchmark codecs such as h.264 and h.265 and achieved competitive performance on light field data

Fake News as a Democratic Anathema: A Comparative Study between India and Indonesia

The undeniably mind boggling media landscape has tossed fresh difficulties to an unsettled environment of media policy and that is why the market is denuded with fake news: scattered through social media intermediaries. Absence of effective laws for the same, have worsened the situation in recent past. Through this paper the researchers have tried to inspect how the propagation of fake news has upset the public sphere and potential arrangements that can be executed to check the plague of fake news in context of India and Indonesia, the prime democracies. There is boisterous discussion on fake news being utilized to create a rosy impression of the politicians in the minds of citizens. Therefore, the researcher shall also cover this aspect by analyzing how fake news has affected elections and how it was used as a tool of mass deception respectively. Finally, it attempts to analyze various strategic initiatives taken by both the nations, and the potential measures which could be adopted to limit the progression of fake news

On Factorization of a Special type of Vandermonde Rhotrix

Vandermonde matrices have important role in many branches of applied mathematics such as combinatorics, coding theory and cryptography. Some authors discuss Vandermonde rhotrices in the literature for its mathematical enrichment. Here, we introduce a special type of Vandermonde rhotrix and obtain its LR factorization, namely left and right triangular factorization which is further used to obtain the inverse of the rhotrix

A Novel Approach for Neuromorphic Vision Data Compression based on Deep Belief Network

A neuromorphic camera is an image sensor that emulates the human eyes capturing only changes in local brightness levels. They are widely known as event cameras, silicon retinas or dynamic vision sensors (DVS). DVS records asynchronous per-pixel brightness changes, resulting in a stream of events that encode the brightness change's time, location, and polarity. DVS consumes little power and can capture a wider dynamic range with no motion blur and higher temporal resolution than conventional frame-based cameras. Although this method of event capture results in a lower bit rate than traditional video capture, it is further compressible. This paper proposes a novel deep learning-based compression scheme for event data. Using a deep belief network (DBN), the high dimensional event data is reduced into a latent representation and later encoded using an entropy-based coding technique. The proposed scheme is among the first to incorporate deep learning for event compression. It achieves a high compression ratio while maintaining good reconstruction quality outperforming state-of-the-art event data coders and other lossless benchmark techniques

2T-UNET: A Two-Tower UNet with Depth Clues for Robust Stereo Depth Estimation

Stereo correspondence matching is an essential part of the multi-step stereo depth estimation process. This paper revisits the depth estimation problem, avoiding the explicit stereo matching step using a simple two-tower convolutional neural network. The proposed algorithm is entitled as 2T-UNet. The idea behind 2T-UNet is to replace cost volume construction with twin convolution towers. These towers have an allowance for different weights between them. Additionally, the input for twin encoders in 2T-UNet are different compared to the existing stereo methods. Generally, a stereo network takes a right and left image pair as input to determine the scene geometry. However, in the 2T-UNet model, the right stereo image is taken as one input and the left stereo image along with its monocular depth clue information, is taken as the other input. Depth clues provide complementary suggestions that help enhance the quality of predicted scene geometry. The 2T-UNet surpasses state-of-the-art monocular and stereo depth estimation methods on the challenging Scene flow dataset, both quantitatively and qualitatively. The architecture performs incredibly well on complex natural scenes, highlighting its usefulness for various real-time applications. Pretrained weights and code will be made readily available

Regulatory mechanisms of Leishmania Aquaglyceroporin AQP1

Pentavalent antimonials [Sb(V)] are the primary drug of choice against all forms of leishmaniasis. Emergence of antimony unresponsiveness is a major issue. There is a dire need of understanding antimony resistance mechanisms in Leishmania. One important mechanism is the down regulation of the trivalent antimony [Sb(III)] (the active form of Sb(V)) uptake system. To date, Leishmania aquaglyceroporin AQP1 is the only reported facilitator of Sb(III). Leishmania do not have promoters. They primarily regulate their genes at post-transcriptional and/or post-translational levels. We reported that mitogen activated protein kinase 2 (MPK2) positively regulated AQP1 stability through the phosphorylation of the threonine 197 (T197) residue of AQP1. The goal of this study was to elucidate the regulatory mechanism(s) of AQP1 in Leishmania in order to advance our understanding about the physiological role(s) of AQP1 in Leishmania biology. When Leishmania promastigotes were treated with the proteasome inhibitor MG132, SbIII accumulation was increased due to upregulation of AQP1. Alteration of lysine 12 of AQP1 to either alanine or arginine improved protein stability. Cells co-expressing a dominant-negative MPK2 mutant exhibited severely reduced AQP1 expression, which was reversed upon addition of MG132. Interestingly, the dominant-negative MPK2 mutant could not destabilize either AQP1K12A /AQP1K12R. Stabilization of AQP1 by MPK2 led to its relocalization from the flagellum to the entire surface of the parasite. Both altered AQP1K12A and AQP1K12R were restricted to the flagellum only. The data demonstrated that lysine12 was targeted for AQP1 proteasomal degradation playing an integral role in subcellular localization of AQP1 as well as its interaction with MPK2. This study also demonstrated that the stability of AQP1 mRNA in different Leishmania species was regulated by their respective 3’-untranslated regions. Cutaneous leishmaniasis causing species accumulated more antimonite and therefore, exhibited higher sensitivity to antimonials than species responsible for visceral leishmaniasis. This species-specific differential sensitivity to antimonite was found to be directly proportional to the expression levels of AQP1 mRNA. The differential regulation of AQP1 mRNA explained the distinct antimonial sensitivity of each species. This study will help us to identify new drugs for treatment in the future and also lead to a novel understanding of parasite biology aspects such as integral membrane protein trafficking and regulation