Abstract

In the current digital era, about 80% of the digital data which is being generated is unstructured and unlabeled natural language text. In the development cycle of information retrieval and text mining applications, text representation is the most fundamental and critical step, as its effectiveness directly impacts the application’s performance. The existing traditional text representation frameworks are mostly frequency distribution-based. In this work, we explored the spatial distribution of word embeddings and proposed two text representational models. The experimental demonstrated that proposed models perform consistently better at text mining tasks compared to baseline methods.

Abstract

Purpose : Foveal Hypoplasia (FH) is a macular abnormality characterised by incomplete development of the foveal region. This abnormality is most commonly observed alongside rare inherited disorders such as albinism, but the precise mechanisms behind abnormal foveal development remain unclear. Thus, we aimed to identify genetic loci associated with FH, to provide insight into the genetic architecture of normal and abnormal foveal development. Methods : We developed a deep learning model to analyse retinal OCT scans from a subset of UK Biobank participants, classifying individuals of European descent into FH (n=4403) and control groups (n=30069). FH classification was then used to conduct a genome-wide association study (GWAS) using these 34,472 individuals, and associations were mapped to causative genes using single nuclei multi-omics data obtained from embryonic and foetal retinal tissue. Further prioritisation of candidate genes was sought by performing CRISPR-Cas9 mediated knockout of target genes in Zebrafish. Genes were prioritised based on the impact of gene knockouts on visual function in Zebrafish, assessed using the optokinetic response. Results : Our GWAS identified 44 genetic variants independently associated with FH (P<5×10-8), including 31 novel variants not previously linked to FH or related traits. These novel associations include variants in genes such as TYR and OCA2, known for their roles in FH in a Mendelian context, and 28 novel genes not previously associated with FH. The identified genes operate in various biological processes such as the melanin biosynthesis pathway, melanosome function, cell fate and temporal patterning. This aligns with the functional characteristics of causative FH genes. Conclusions : In the first GWAS study of FH, we uncovered novel genetic associations critical to understanding foveal development. The identified associations offer new avenues for research, providing mechanistic insight into the genetic factors underpinning the foveal region's development and the pathogenesis of FH.

Abstract

The present disclosure provides a system and a method for generating a secure digital certificate for a user using cryptographic hashing and storing the digital certificate on a smart contract by a decentralized blockchain. The method includes (i) generating a registration token for a first user using the cryptographic hashing,(ii) storing the registration token of the first user as a second hash in the smart contract,(iii) receiving digital request through the first user device,(iv) validating (a) the digital request of the first user by (m) authenticating whether the biometrics of the first user, and (n) checking whether the second hash exists in the decentralized blockchain, and (b) the second user associated with a second user device using a driver algorithm,(v) sending a message to the second user device to initiate the digital request and (vi) generating the digital certificate when the first user receives a verification.

Abstract

The present disclosure provides a system for detecting and neutralizing a plurality of security attacks on a blockchain network to improve security of the blockchain network. The system includes a sender node that is configured to send a transaction to a user device associated with at least one user. The blockchain network is configured to (i) receive the transaction from the sender node when the first block gets validated in the first blockchain,(ii) validate the transaction of the first block to neutralize the first attack,(iii) determine a second attack by initiating a split in the first blockchain to determine a second blockchain, and (iv) invalidate the second blockchain when the latest block of the first blockchain invalidates the second block of the second blockchain when a proof of invalidity (POI) is added to the second block to neutralize the second attack.

Abstract

(57) ABSTRACT A blockchain system for mining a transaction using parallel blocks to improve scalability is provided. The blockchain system includes a sender node, a blockchain network, and mining nodes. The sender node (i) creates a transaction, and (ii) broadcasts the transaction on the blockchain network. Each of the mining nodes is configured to (I) split the transaction into disjoint subsets based on a hash value of the public key;(II) assign the disjoint subsets to a plurality of sub-chains that are mined in parallel by a plurality of miners;(III) mine the parallel block followed by a mining block; and (IV) verify multiple sets of transactions associated with the parallel block using an umbrella-proof-of-work method, thereby improving scalability of the blockchain system.

Abstract

Knowledge Graph Completion (KGC) aims to predict missing entities or relations in knowledge graph but it becomes computationally expensive as KG scales. Existing research focuses on bilinear pooling-based factorization methods (LowFER, TuckER) to solve this problem. These approaches introduce too many trainable parameters which obstruct the deployment of these techniques in many real-world scenarios. In this paper, we introduce a novel parameter-efficient framework, KGRP which a) approximates bilinear pooling using Kernelized Random Projection matrix b) employs CNN for the better fusion of entities and relations to infer missing links. Our experimental results show that KGRP has 73% fewer parameters as compared to the state-of-theart approaches (LowFER, TuckER) for the knowledge graph completion task while retaining 88% performance for the best baseline. Furthermore, we also provide novel insights on the interpretability of relation embeddings. We also test the effectiveness of KGRP on a large-scale recruitment knowledge graph of 0.25 M entities.

Abstract

There are certain dynamics while being non-Markovian, do never exhibit information backflow. We show that if two such dynamical maps are considered in a scenario where the order of application of these two dynamical maps are not definite, the effective channel can manifest information backflow. In particular, we use quantum SWITCH to activate such a channel. In contrast, activation of those channels are not possible even if one uses many copies of such channels in series or in parallel action. We then investigate the dynamics behind the quantum SWITCH experiment and find out that after the action of quantum SWITCH both the CP (Complete Positive)- divisibility and P (Positive)- divisibility of the channel breaks down, along with the activation of information backflow. Our study elucidate the advantage of quantum SWITCH by investigating its dynamical behavior.

Abstract

The coronavirus disease (COVID-19) caused over 170 million illnesses and over 3 million deaths worldwide. Researchers from all over the world have been using a variety of machine-learning techniques to classify the DNA sequences of the SARS-CoV-2 virus. To classify SARS-CoV-2 (Covid-19) whole genome sequences with other viruses including Dengue, Ebola, Influenza, and also Coronaviruses that may infect humans and are members of the family of Coronaviridae like SARS-CoV, MERS-CoV, we have proposed a multi-dimensional frequency encoding scheme. In the proposed method each genome is converted into four frequency-encoded sequences for each nucleotide. Then each encoded sequence is partitioned into quartiles and fed to the CNN. The findings of the binary classification of SARS-CoV-2 with other viruses and multi-class classification of six different viruses using five well-known machine learning algorithms and the proposed approach using CNN were reported in this study. Furthermore, we applied machine learning methods for the multi-class classification of eight different SARS-CoV-2 variants. The proposed approach performed well among all machine-learning techniques for classifying genome sequences of viruses and SARS-CoV-2 variants with accuracy of 99% and 98% respectively. In addition, we have also compared the performance of our proposed approach with the existing methods from the literature.

Abstract

Multivariate drought indices including various hydrological processes into account can be more valuable under climatic and anthropogenic changes. Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized precipitation Actual Evapotranspiration Index (SPAEI) are the drought indices used to estimate drought index, considering precipitation, and evapotranspiration (ET) into account. Many studies used empirical, machine learning, and process-based model estimates of ET for the calculation of drought indices of SPEI and SPAEI. However, the sensitivity of ET estimates on drought characteristics at the catchment scale is highly complex. The present study aimed to include and analyze the sensitivity of various approaches of empirical (Budyko, Penman–Monteith, Hargreaves, and Turc), modeled (SWAT), and remote sensing (MODIS) in the drought characterization using SPEI and SPAEI. The present methodology was tested on a dry-sub-humid river catchment of India, the Tunga-Bhadra River catchment for the period of 2000–2012. The performance of statistical indicators (Nash–Sutcliffe Efficiency and R2) for SPEI values by various empirical methods of Potential Evapotranspiration (PET) (i) Penman–Monteith (ii) Hargreaves against remote sensing PET were 0.93 and 0.95, respectively, which are high in comparison with SWAT simulated PET-based SPEI values, which shows NSE values of 0.85 against remote sensing PET-based SPEI.

Abstract

Infinite terrain generation is an important use case for computer graphics, games and simulations. However, current techniques are often procedural which reduces their realism. We introduce a learning-based generative framework for infinite terrain generation along with a novel learningbased approach for level-of-detailing of terrains. Our framework seamlessly integrates with quad-tree-based terrain rendering algorithms. Our approach leverages image completion techniques for infinite generation and progressive super-resolution for terrain enhancement. Notably, we propose a novel quad-tree-based training method for terrain enhancement which enables seamless integration with quad-tree-based rendering algorithms while minimizing the errors along the edges of the enhanced terrain. Comparative evaluations against existing techniques demonstrate our framework’s ability to generate highly realistic terrain with effective level-of-detailing.