Academic Articles & Research Papers

The rank of contextuality

Quantum contextuality is one of the most recognized resources in quantum communication and computing scenarios. We provide a new quantifier of this resource, the rank of contextuality (RC). We define RC as the minimum number of non-contextu...

FEATURES using reversible logic operations IN MODELING IRREVERSIBLE COMPUTING on the reversibly COMPUTER

Information can be recorded in the quantum state and are transmitted via the physical channel. The theory of quantum coding and information theory quantum computing related. In any information system ( classical or quantum) will always cont...

Quantum Interference of Electromechanically Stabilized Emitters in Nanophotonic Devices

Photon-mediated coupling between distant matter qubits may enable secure communication over long distances, the implementation of distributed quantum computing schemes, and the exploration of new regimes of many-body quantum dynamics. Solid...

QG-WRN: A Quantum-Enhanced Graph Convolutional Wide Residual Network for ASD Diagnosis via Neuroimaging Sensing Technology

The pathological mechanism of autism spectrum disorder (ASD) exhibits dual heterogeneity: abnormal local energy metabolism and brain-wide high-order topological failure. To synergistically characterize these complex signals captured by adva...

Scalable and High-Fidelity Quantum Random Access Memory in Spin-Photon Networks

A quantum random access memory (qRAM) is considered an essential computing unit to enable polynomial speedups in quantum information processing. Proposed implementations include the use of neutral atoms and superconducting circuits to const...

Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware

Abstract Quantum computing offers exciting opportunities for simulating complex quantum systems and optimizing large-scale combinatorial problems, but its practical use is limited by device noise and constrained connectivity. Designing quan...

Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum

This paper presents a novel approach to enhancing the security and reliability of drone communications through the integration of Quantum Random Number Generators (QRNG) in Frequency Hopping Spread Spectrum (FHSS) systems. We propose a mult...

Natural Language Processing and Chinese Computing

Quantum Algorithms for Compositional Natural Language Processing

We propose a new application of quantum computing to the field of natural language processing. Ongoing work in this field attempts to incorporate grammatical structure into algorithms that compute meaning. In (Coecke, Sadrzadeh and Clark, 2...

Transformer-Based Approach for Solving Mathematical Problems Using Automatic Speech Recognition

In this paper, we introduce Vox Calculi, a system designed to solve mathematical problems using voice transcriptions. By leveraging state-of-the-art pretrained Automatic Speech Recognition (ASR) models, we accurately transcribe users&#x2019...

Data augmentation techniques in natural language processing

Data Augmentation (DA) methods – a family of techniques designed for synthetic generation of training data – have shown remarkable results in various Deep Learning and Machine Learning tasks. Despite its widespread and successful adoption w...

Information Extraction From FDA Drug Labeling to Enhance Product-Specific Guidance Assessment Using Natural Language Processing

Towards the objectives of the UnitedStates Food and Drug Administration (FDA) generic drug science and research program, it is of vital importance in developing product-specific guidances (PSGs) with recommendations that can facilitate and ...

Few-shot learning for automated content analysis: Efficient coding of arguments and claims in the debate on arms deliveries to Ukraine

Pre-trained language models (PLM) based on transformer neural networks developed in the field of natural language processing (NLP) offer great opportunities to improve automatic content analysis in communication science, especially for the ...

Machine Reading Comprehension for the Tamil Language With Translated SQuAD

Machine Reading Comprehension (MRC) is a challenging task in Natural Language Processing (NLP), crucial for automated customer support, enabling chatbots and virtual assistants to accurately understand and respond to queries. It also enhanc...

Natural language processing for information assurance and security

This research paper explores a promising interface between natural language processing (NLP) and information assurance and security (IAS). More specificall~ it is devoted to possible applications to, and further dedicated development of, th...

Multi-Task Learning in Natural Language Processing: An Overview

Deep learning approaches have achieved great success in the field of Natural Language Processing (NLP). However, directly training deep neural models often suffer from overfitting and data scarcity problems that are pervasive in NLP tasks. ...

On the Explainability of Natural Language Processing Deep Models

Despite their success, deep networks are used as black-box models with outputs that are not easily explainable during the learning and the prediction phases. This lack of interpretability is significantly limiting the adoption of such model...

SAM-trace: a fuzzy logic approach in identifying interaction effects in the exacerbation of childhood malnutrition severity

Abstract Pediatric malnutrition remains a major global health challenge. Conventional WHO-based binary classification (Severe Acute Malnutrition (SAM) / Moderate Acute Malnutrition (MAM) relies solely on anthropometric thresholds. It fails ...

Different hybrid machine intelligence techniques for handling IoT‐based imbalanced data

Abstract In the era of automatic task processing or designing complex algorithms, to analyse data, it is always pertinent to find real‐life solutions using cutting‐edge tools and techniques to generate insights into the data. The data‐drive...

Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing

Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing a...