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For love dynamical models, a new idea combining piecewise concept for integer-order, stochastic, and fractional derivatives is presented in order to capture the chaos and several crossover emotional scenerios. Under the assumptions of linear growth and Lipschitz condition, the fixed-point theorem explain the uniqueness and existence to the models under the investigation. The piecewise derivatives were approximated utilising the Lagrange interpolation method, and the computer results were demonstrated numerically for several values of order $\alpha$. It was observed that the recently presented new idea in love dynamical models can represent disordered emotional patterns in passionate loving partnerships.

This study uses the Lotka Volterra Predator-Prey model to offer a notion of piecewise patterns for the various piecewise derivatives. Using the piecewise derivatives, we produced numerical solutions that are referred to as the Adams-Bashforth method. The computer results show piecewise patterns in the Lotka Volterra Predator-Prey model's real-world behaviours. The Lotka-Volterra model looks into the relationships between competition and abundance between two competing species. Changes in the abundance of one species are modelled as a function of the abundance of its competitors, but the competitive mechanism is given and evaluated. This notion led some scholars to label certain mathematical expressions as "phenomenological" and to propose a different theoretical framework that gives resources special consideration. The Lotka-Volterra model, often called the predator-prey model or the Lotka-Volterra model, is a nonlinear mathematical expression that is frequently used to analyse the dynamical behaviours of biological systems in which two species interact, one as a predator and the other as prey. The variation in the populations over time is illustrated by the mathematical statement., Comment: 20

The widespread use of Deep Neural Networks (DNNs) has recently resulted in their application to challenging scientific visualization tasks. While advanced DNNs demonstrate impressive generalization abilities, understanding factors like prediction quality, confidence, robustness, and uncertainty is crucial. These insights aid application scientists in making informed decisions. However, DNNs lack inherent mechanisms to measure prediction uncertainty, prompting the creation of distinct frameworks for constructing robust uncertainty-aware models tailored to various visualization tasks. In this work, we develop uncertainty-aware implicit neural representations to model steady-state vector fields effectively. We comprehensively evaluate the efficacy of two principled deep uncertainty estimation techniques: (1) Deep Ensemble and (2) Monte Carlo Dropout, aimed at enabling uncertainty-informed visual analysis of features within steady vector field data. Our detailed exploration using several vector data sets indicate that uncertainty-aware models generate informative visualization results of vector field features. Furthermore, incorporating prediction uncertainty improves the resilience and interpretability of our DNN model, rendering it applicable for the analysis of non-trivial vector field data sets., Comment: Accepted for publication at IEEE Visualization 2024

In the present study, we use cross-domain classification using quantum machine learning for quantum advantages to readdress the entanglement versus separability paradigm. The inherent structure of quantum states and its relation to a particular class of quantum states are used to intuitively classify testing states from domains different from training states, called \textit{cross-domain classification}. Using our quantum machine learning algorithm, we demonstrate efficient classifications of two-qubit mixed states into entangled and separable classes. For analyzing the quantumness of correlations, our model adequately classifies Bell diagonal states as zero and non-zero discord states. In addition, we also extend our analysis to evaluate the robustness of our model using random local unitary transformations. Our results demonstrate the potential of the quantum support vector machine for classifying quantum states across the multi-dimensional Hilbert space in comparison to classical support vector machines and neural networks.

This article comprehensively explores matrices and their prerequisites for achieving positive semidefiniteness. The study delves into a series of theorems concerning pure quantum states in the context of weighted graphs. The main objective of this study is to establish a graph-theoretic framework for the study of quantum discord and to identify the necessary and sufficient conditions for zero quantum discord states using unitary operators. This research aims to advance the understanding of quantum discord and its implications for quantum information theory with a graph-theoretic framework.

We investigate the domain of correlated non-Markovian channels, exploring the potential memory arising from the correlated action of channels and the inherent memory due to non-Markovian dynamics. The impact of channel correlations is studied using different non-Markovianity indicators and measures. In addition, the dynamical aspects of correlated non-Markovian channels, including entanglement dynamics as well as changes in the volume of accessible states, are explored. The analysis is presented for both unital and non-unital correlated channels. A new correlated channel constructed with modified Ornstein-Uhlenbeck noise is also presented and explored. Further, the geometrical effects of the non-Markovianity of the correlated non-Markovian channels are discussed with a study of change in the volume of the accessible states. The link between the correlation factor and error correction success probability is highlighted.

This article delves into an analysis of the intrinsic entanglement and separability feature in quantum states as depicted by graph Laplacian. We show that the presence or absence of edges in the graph plays a pivotal role in defining the entanglement or separability of these states. We propose a set of criteria for ascertaining the separability of quantum states comprising $n$-qubit within a composite Hilbert space, indicated as $H=H_1 \otimes H_2 \otimes \dots \otimes H_n$. This determination is achieved through a combination of unitary operators, neighbourhood sets, and equivalence relations.

A classical decision tree is completely based on splitting measures, which utilize the occurrence of random events in correspondence to its class labels in order to optimally segregate datasets. However, the splitting measures are based on greedy strategy, which leads to construction of an imbalanced tree and hence decreases the prediction accuracy of the classical decision tree algorithm. An intriguing approach is to utilize the foundational aspects of quantum computing for enhancing decision tree algorithm. Therefore, in this work, we propose to use fidelity as a quantum splitting criterion to construct an efficient and balanced quantum decision tree. For this, we construct a quantum state using the occurrence of random events in a feature and its corresponding class. The quantum state is further utilized to compute fidelity for determining the splitting attribute among all features. Using numerical analysis, our results clearly demonstrate that the proposed algorithm cooperatively ensures the construction of a balanced tree. We further compared the efficiency of our proposed quantum splitting criterion to different classical splitting criteria on balanced and imbalanced datasets. Our simulation results show that the proposed splitting criterion exceeds all classical splitting criteria for all possible evaluation metrics.

Narrowband power line communication (NB-PLC) systems are an attractive solution for supporting current and future smart grids. A technology proposed to enhance data rate in NB-PLC is multiple-input multiple-output (MIMO) transmission over multiple power line phases. To achieve reliable communication over MIMO NB-PLC, a key challenge is to take into account and mitigate the effects of temporally and spatially correlated cyclostationary noise. Noise samples in a cycle can be divided into three classes with different distributions, i.e. Gaussian, moderate impulsive, and strong impulsive. However, in this paper we first show that the impulsive classes in their turn can be divided into sub-classes with normal distributions and, after deriving the theoretical capacity, two noise sample sets with such characteristics are used to evaluate achievable information rates: one sample set is the measured noise in laboratory and the other is produced through MIMO frequency-shift (FRESH) filtering. The achievable information rates are attained by means of a spatio-temporal whitening of the portions of the cyclostationary correlated noise samples that belong to the Gaussian sub-classes. The proposed approach can be useful to design the optimal receiver in terms of bit allocation using waterfilling algorithm and to adapt modulation order., Comment: 13 pages, 10 figures, submitted to IEEE Transactions on Communications

In this manuscript, we analyze universal quantum teleportation in the presence of memory or memory-less dynamics with applications of partial collapse measurement operators. Our results show that the combined effects of memory or non-Markovianity and weak measurements can lead to universal quantum teleportation (UQT). Our study involves noise models of physical importance having characteristic Markovian and non-Markovian regions allowing one to observe a transition in quantum properties as one switches from non-Markovian to Markovian dynamics. For this, we characterize the effects of different types of non-Markovianity for efficient UQT both due to retention of correlations for a longer duration and due to information backflow. We further analyze memory effects arising from a correlated channel with or without weak measurements. Interestingly, our analysis for a correlated amplitude damping channel shows that memory effects are of significant advantage to minimize the fidelity deviation. The presence of weak measurements further enhances the realization of UQT in the presence of memory. The ability of memory effects in achieving zero fidelity deviation at non-zero time is interesting and of experimental importance.

Learning effective representations of source code is critical for any Machine Learning for Software Engineering (ML4SE) system. Inspired by natural language processing, large language models (LLMs) like Codex and CodeGen treat code as generic sequences of text and are trained on huge corpora of code data, achieving state of the art performance on several software engineering (SE) tasks. However, valid source code, unlike natural language, follows a strict structure and pattern governed by the underlying grammar of the programming language. Current LLMs do not exploit this property of the source code as they treat code like a sequence of tokens and overlook key structural and semantic properties of code that can be extracted from code-views like the Control Flow Graph (CFG), Data Flow Graph (DFG), Abstract Syntax Tree (AST), etc. Unfortunately, the process of generating and integrating code-views for every programming language is cumbersome and time consuming. To overcome this barrier, we propose our tool COMEX - a framework that allows researchers and developers to create and combine multiple code-views which can be used by machine learning (ML) models for various SE tasks. Some salient features of our tool are: (i) it works directly on source code (which need not be compilable), (ii) it currently supports Java and C#, (iii) it can analyze both method-level snippets and program-level snippets by using both intra-procedural and inter-procedural analysis, and (iv) it is easily extendable to other languages as it is built on tree-sitter - a widely used incremental parser that supports over 40 languages. We believe this easy-to-use code-view generation and customization tool will give impetus to research in source code representation learning methods and ML4SE. Tool: https://pypi.org/project/comex - GitHub: https://github.com/IBM/tree-sitter-codeviews - Demo: https://youtu.be/GER6U87FVbU, Comment: The paper has been accepted for publication at ASE 2023 (Tool Demonstrations Track)