Generating useful node representations in these networks allows for more powerful predictive models with decreased computational expense, enabling broader application of machine learning techniques. Because current models neglect the temporal dimensions of networks, this research presents a novel temporal network-embedding approach aimed at graph representation learning. By extracting low-dimensional features from massive, high-dimensional networks, this algorithm enables the prediction of temporal patterns in dynamic networks. The proposed algorithm introduces a novel dynamic node embedding algorithm which capitalizes on the shifting nature of networks. A basic three-layered graph neural network is applied at each time step to extract node orientation, employing Given's angle method. Our temporal network-embedding algorithm, TempNodeEmb, underwent validation by comparison with seven top-tier benchmark network-embedding models. In their application, these models are utilized on eight dynamic protein-protein interaction networks and three further real-world networks: dynamic email networks, online college text message networks, and human real contact datasets. To bolster our model, we've considered time encoding and proposed an additional enhancement, TempNodeEmb++. In most instances, our proposed models, judged by two evaluation metrics, exhibit superior performance to current leading models, as the results reveal.
Homogeneous models, a common feature in complex system representations, portray each element as possessing the same spatial, temporal, structural, and functional properties. Yet, the majority of natural systems are not homogeneous; only a few components manifest greater size, strength, or velocity. Homogeneous systems often exhibit a state of criticality—a delicate equilibrium between change and constancy, order and disorder—in a narrow region of the parameter space, proximate to a phase transition. Applying random Boolean networks, a general representation of discrete dynamical systems, we discover that heterogeneity in time, structure, and function can extend the parameter space for critical behavior in an additive fashion. Beyond this, parameter zones wherein antifragility is prominent are correspondingly broadened with the introduction of diverse elements. Despite the fact that maximum antifragility exists, this holds true only for specific parameters in consistent networks. Our observations demonstrate that finding the optimal balance between uniformity and diversity is a multifaceted, situational, and, at times, an evolving issue in our work.
Reinforced polymer composite material development has produced a substantial influence on the complicated matter of high-energy photon shielding, particularly with regards to X-rays and gamma rays, impacting both industrial and healthcare applications. The shielding effectiveness of heavy materials presents a promising avenue for enhancing the structural integrity of concrete conglomerates. The mass attenuation coefficient serves as the key physical parameter for assessing the attenuation of narrow gamma rays within composite materials comprising magnetite, mineral powders, and concrete. For assessing the gamma-ray shielding characteristics of composites, data-driven machine learning techniques offer a potential alternative to theoretical calculations, which can prove to be resource-intensive and time-consuming during workbench tests. We crafted a dataset utilizing magnetite and seventeen distinct mineral powder combinations, varying in density and water/cement ratios, which were subsequently exposed to photon energies ranging from 1 to 1006 kiloelectronvolts (KeV). The -ray shielding characteristics (LAC) of concrete were computed via the National Institute of Standards and Technology (NIST) photon cross-section database and software methodology (XCOM). A variety of machine learning (ML) regressors were employed to leverage the XCOM-derived LACs and seventeen mineral powders. Through a data-driven lens, machine learning techniques were used to investigate the possibility of replicating the available dataset and XCOM-simulated LAC. Using the minimum absolute error (MAE), root mean squared error (RMSE), and R-squared (R2) measures, we assessed the performance of our proposed machine learning models—specifically, support vector machines (SVM), 1D convolutional neural networks (CNNs), multi-layer perceptrons (MLPs), linear regressors, decision trees, hierarchical extreme learning machines (HELM), extreme learning machines (ELM), and random forest networks. A comparison of performance metrics indicated that our novel HELM architecture achieved better results than the leading SVM, decision tree, polynomial regressor, random forest, MLP, CNN, and conventional ELM models. GNE-987 cost The forecasting accuracy of machine learning approaches was further evaluated, relative to the XCOM benchmark, through stepwise regression and correlation analysis. The HELM model's statistical analysis showcased a strong alignment between predicted LAC values and the XCOM results. Compared to the other models in this study, the HELM model achieved a higher accuracy, marked by the best R-squared value and the lowest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).
Creating a lossy compression strategy for complex data sources using block codes poses a challenge, specifically in approximating the theoretical distortion-rate limit. GNE-987 cost A lossy compression technique for Gaussian and Laplacian data is presented in this paper. This scheme introduces a novel transformation-quantization route, superseding the traditional quantization-compression approach. Transformation using neural networks and quantization via lossy protograph low-density parity-check codes are integral components of the proposed scheme. Ensuring the system's workability involved resolving neural network issues, such as parameter updates and optimized propagation algorithms. GNE-987 cost Simulation data indicated a strong performance regarding distortion rate.
The study of signal occurrence location, a classic one-dimensional noisy measurement problem, is presented in this paper. In the absence of overlapping signal occurrences, we cast the detection task as a constrained likelihood optimization problem, devising a computationally efficient dynamic programming algorithm that yields the optimal solution. A simple implementation, combined with scalability and robustness to model uncertainties, defines our proposed framework. Our extensive numerical experiments demonstrate that our algorithm precisely determines locations in dense, noisy environments, surpassing alternative methods.
An informative measurement constitutes the most efficient strategy for understanding an unknown state. A general-purpose dynamic programming algorithm, based on first principles, is presented to find an optimal series of informative measurements by maximizing, step-by-step, the entropy of potential measurement outcomes. This algorithm provides autonomous agents and robots with the capability to ascertain the ideal sequence of measurements, subsequently allowing for the optimal path planning for future measurements. Given either continuous or discrete states and controls, along with stochastic or deterministic agent dynamics, the algorithm is applicable, including Markov decision processes and Gaussian processes. Recent advancements in approximate dynamic programming and reinforcement learning, encompassing online approximation methods like rollout and Monte Carlo tree search, facilitate real-time measurement task resolution. Incorporating non-myopic paths and measurement sequences, the generated solutions typically surpass, sometimes substantially, the performance of standard greedy approaches. Global searches benefit from on-line planning of a series of local searches, which empirically results in approximately half the measurement count. The algorithm, a variant for Gaussian processes, is derived for active sensing.
With the constant integration of spatially referenced data into different industries, there has been a notable rise in the adoption of spatial econometric models. In this study of the spatial Durbin model, a robust variable selection method is introduced, incorporating exponential squared loss and the adaptive lasso. Under benign circumstances, we demonstrate the asymptotic and oracle characteristics of the suggested estimator. Nevertheless, solving model problems using algorithms encounters challenges due to the nonconvex and nondifferentiable characteristics of the programming. This problem is tackled by designing a BCD algorithm and performing a DC decomposition of the squared exponential loss. In the presence of noise, numerical simulations show that this method is more robust and accurate compared to current variable selection techniques. The 1978 Baltimore housing market's price data was also incorporated into the model's evaluation.
A new trajectory control system is described in this paper for application on four-mecanum-wheel omnidirectional mobile robots (FM-OMR). In view of the uncertainty's effect on tracking accuracy, a self-organizing fuzzy neural network approximator (SOT1FNNA) is presented to evaluate the uncertainty. The predefined structure of traditional approximation networks frequently gives rise to input restrictions and redundant rules, which consequently compromise the controller's adaptability. Therefore, a self-organizing algorithm, including the elements of rule growth and local access, is designed to conform to the tracking control requirements of omnidirectional mobile robots. A preview strategy (PS) is proposed, utilizing a Bezier curve trajectory re-planning approach, to overcome the instability of tracking curves originating from delays in starting point tracking. At last, the simulation examines the efficiency of this methodology in enhancing tracking and optimizing initial trajectory points.
We delve into the generalized quantum Lyapunov exponents Lq, which are derived from the growth rate of the powers of the square commutator. The exponents Lq, used in a Legendre transform, could possibly relate to a thermodynamic limit appropriately defined for the spectrum of the commutator, which acts as a large deviation function.