The positions and views of other agents dictate the actions of agents, and reciprocally, the evolution of opinions is shaped by the physical closeness and the convergence of beliefs among agents. We utilize numerical simulations and formal analyses to study the feedback loop connecting opinion dynamics and the mobility of individuals in a social space. An analysis of this ABM's functioning across different operational conditions and diverse elements serves to explore the effect on the emergence of characteristics such as collective behavior and agreement. We scrutinize the empirical distribution, and in the hypothetical limit of an infinite number of agents, a simplified model, in the form of a partial differential equation (PDE), is developed. Numerical analyses provide compelling evidence that the generated PDE model offers a satisfactory approximation to the original agent-based model.
To understand the structure of protein signaling networks, Bayesian network techniques are key tools in the field of bioinformatics. Unfortunately, Bayesian network algorithms for learning primitive structures don't recognize the causal relationships between variables; this is important for the application of such models to protein signaling networks. The structure learning algorithms, facing a large search space in combinatorial optimization problems, unsurprisingly exhibit high computational complexities. In this paper, the causal flow between any two variables is initially calculated and stored in a graph matrix as one of the restrictions for structural learning. The subsequent formulation of a continuous optimization problem is based on the fitting losses from the associated structural equations as the target and the directed acyclic prior as an additional constraint. In the final stage, a pruning procedure is formulated to keep the solution from the continuous optimization problem sparse. Through experiments on both simulated and real-world datasets, the proposed technique demonstrates enhanced Bayesian network structures compared to existing methodologies, resulting in substantial computational savings.
Stochastic particle transport in a disordered two-dimensional layered medium, driven by correlated random velocity fields that vary with the y-coordinate, is commonly referred to as the random shear model. This model's superdiffusive behavior in the x-axis is attributable to the statistical nature of the disorder advection field. Leveraging layered random amplitude with a power-law discrete spectrum, the derivation of analytical expressions for the space and time velocity correlation functions and the position moments proceeds by employing two distinct averaging strategies. Averaging over a set of evenly spaced starting points is employed in the investigation of quenched disorder, despite the pronounced discrepancies between individual samples, leading to a universal scaling of time for even moments. Universality is evident in the scaling of moments computed from the average of disorder configurations. Antidepressant medication Derivation of the non-universal scaling form for advection fields, whether symmetrically or asymmetrically patterned, without disorder, is also presented.
Finding the central points for a Radial Basis Function Network is currently unresolved. This research employs a proposed gradient algorithm to establish cluster centers, where the forces applied to each data point are integral to the process. Data classification is facilitated by these centers, which are an integral part of a Radial Basis Function Network. The information potential underpins a threshold that distinguishes outliers. An analysis of the suggested algorithms is performed using databases, considering the factors of cluster quantity, cluster overlap, noise interference, and the uneven distribution of cluster sizes. The combined effect of the threshold, centers, and information forces yields favorable results when benchmarked against a comparable network employing a k-means clustering algorithm.
It was Thang and Binh who presented DBTRU to the community in 2015. To create a variant of NTRU, the integer polynomial ring is replaced by two binary truncated polynomial rings, each within the finite field GF(2)[x] and defined modulo (x^n + 1). DBTRU exhibits superior security and performance characteristics compared to NTRU. We demonstrate a polynomial-time linear algebraic attack on the DBTRU cryptosystem, successfully targeting all the recommended parameter sets presented. A single personal computer, leveraging a linear algebra attack, facilitates the extraction of plaintext in less than one second, according to the research presented in the paper.
Psychogenic non-epileptic seizures, while mimicking epileptic seizures, originate from non-epileptic sources. Electroencephalogram (EEG) signal analysis, utilizing entropy algorithms, could potentially show distinctive patterns to differentiate PNES from epilepsy. Beyond that, the use of machine learning could lower current diagnostic costs through automation of the classification stage. This study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and ECGs of 48 patients with PNES and 29 epilepsy subjects across the broad frequency bands, including delta, theta, alpha, beta, and gamma. Each feature-band pair's classification relied on the use of support vector machines (SVM), k-nearest neighbors (kNN), random forests (RF), and gradient boosting machines (GBM). Broad band data frequently produced more accurate classifications, contrasting with the relatively low accuracy of the gamma band, while combining all six bands collectively resulted in improved classifier outcomes. High accuracy across all bands was achieved with Renyi entropy as the superior feature. IWR-1-endo order Employing Renyi entropy and a combination of all bands excluding the broad band, the kNN method produced a balanced accuracy of 95.03%, the highest achieved. Entropy-based analysis successfully distinguished interictal PNES from epilepsy with high accuracy, and the performance gains emphasize the efficacy of combining frequency bands in the diagnosis of PNES from electroencephalographic and electrocardiographic recordings.
For a full decade, chaotic map-based image encryption techniques have been a subject of significant academic investigation. While various methods have been presented, a substantial proportion suffer from extended encryption times or, conversely, a weakening of the security measures employed to accelerate the process of encryption. This paper introduces an image encryption algorithm that is lightweight, secure, and efficient, built upon the principles of the logistic map, permutations, and the AES S-box. Using SHA-2, the plaintext image, the pre-shared key, and the initialization vector (IV) are combined in the proposed algorithm to produce the initial parameters for the logistic map. The chaotic logistic map generates random numbers, which are then utilized in the process of permutations and substitutions. Through the application of diverse metrics, including correlation coefficient, chi-square, entropy, mean square error, mean absolute error, peak signal-to-noise ratio, maximum deviation, irregular deviation, deviation from uniform histogram, number of pixel change rate, unified average changing intensity, resistance to noise and data loss attacks, homogeneity, contrast, energy, and key space and key sensitivity analysis, the security, quality, and efficiency of the proposed algorithm are tested and assessed rigorously. The experimental assessment of the proposed algorithm highlights its substantial speed advantage, up to 1533 times greater than that of contemporary encryption methods.
In recent years, object detection algorithms based on convolutional neural networks (CNNs) have achieved significant advancements, and a substantial portion of this research focuses on hardware accelerator designs. Prior research has demonstrated efficient FPGA implementations for single-stage detectors, such as YOLO. Yet, dedicated accelerator architectures that can swiftly process CNN features for faster region proposals, as in the Faster R-CNN algorithm, are still comparatively uncommon. Additionally, CNN architectures, with their inherently high computational and memory requirements, create difficulties in designing efficient acceleration hardware. Using OpenCL as the foundation, this paper proposes a novel software-hardware co-design strategy to implement the Faster R-CNN object detection algorithm on a field-programmable gate array. We initially craft a deep pipelined FPGA hardware accelerator, efficient and capable of executing Faster R-CNN algorithms on diverse backbone networks. An optimized software algorithm, taking into account hardware limitations, was subsequently proposed; it incorporated fixed-point quantization, layer fusion, and a multi-batch Regions of Interest (RoIs) detector. Finally, we propose a complete design exploration strategy to assess the resource utilization and performance of the proposed accelerator. Results from the conducted experiments show that the proposed design attained a peak throughput of 8469 GOP/s during operation at a frequency of 172 MHz. Pediatric medical device Our methodology demonstrates a 10 times improvement in inference throughput over the current state-of-the-art Faster R-CNN accelerator and a 21 times improvement over the one-stage YOLO accelerator.
A direct method, rooted in global radial basis function (RBF) interpolation at arbitrary collocation points, is introduced in this paper for variational problems involving functionals reliant on functions of several independent variables. Using an arbitrary radial basis function (RBF), this technique parameterizes solutions and converts the two-dimensional variational problem (2DVP) into a constrained optimization problem, achieved via arbitrary collocation points. This method's strength stems from its adaptability in choosing various RBFs for interpolation and defining a wide array of arbitrary nodal points. A constrained optimization problem, derived from the original constrained variation problem concerning RBFs, is formed by incorporating arbitrary collocation points for their centers. To translate an optimization problem into an algebraic equation system, the Lagrange multiplier method is used.