To assess the collisional moments of the second, third, and fourth degrees in a granular binary mixture, the analysis centers on the Boltzmann equation for d-dimensional inelastic Maxwell models. Precisely evaluating collisional instances necessitates the utilization of the velocity moments from the distribution function for each species, a condition that is fulfilled when diffusion is absent, meaning that the mass flux of every substance is void. As functions of the coefficients of normal restitution and the mixture parameters (masses, diameters, and composition), the associated eigenvalues and cross coefficients are obtained. To analyze the time evolution of moments, scaled by thermal speed, in the homogeneous cooling state (HCS) and uniform shear flow (USF) states, these results are applied. The HCS, in contrast to the behavior of simple granular gases, shows the possibility of time-dependent divergence in the third and fourth degree moments, contingent upon the values of the system's parameters. The time evolution of these moments, under the influence of the mixture's parameter space, is investigated in an exhaustive study. HC-030031 The time evolution of the second- and third-order velocity moments in the USF is investigated in the tracer regime, where the concentration of a specific substance is negligible. The convergence of second-degree moments, as foreseen, stands in contrast to the possible divergence of third-degree moments for the tracer species in the long term.
An integral reinforcement learning strategy is presented in this paper to address the optimal containment control problem for nonlinear multi-agent systems with partial dynamic knowledge. Integral reinforcement learning enables a more flexible approach to drift dynamics. Empirical evidence confirms the equivalence between the integral reinforcement learning method and model-based policy iteration, leading to the guaranteed convergence of the proposed control algorithm. By employing a single critic neural network with a modified updating law, the Hamilton-Jacobi-Bellman equation is solved for each follower, which ensures the asymptotic stability of the weight error. A critic neural network, fed with input-output data, generates the approximate optimal containment control protocol for each follower. The proposed optimal containment control scheme is responsible for ensuring the stability of the closed-loop containment error system. The simulated data underscores the viability of the presented control system.
Deep neural networks (DNNs) in natural language processing (NLP) systems are frequently targets of backdoor attacks. Existing defensive methods against backdoor exploits are limited in their ability to fully cover all attack possibilities. A deep feature classification-based approach to textual backdoor defense is proposed. The method involves deep feature extraction and the creation of a classifier. Deep features in poisoned data and uncompromised data are distinct; this method capitalizes on this difference. Backdoor defense is present within both online and offline environments. We performed defense experiments across two datasets and two models, targeting a diversity of backdoor attacks. The experimental results highlight the outperformance of this defense strategy compared to the baseline method's capabilities.
The capacity of financial time series models can be expanded by the inclusion of relevant sentiment analysis data as part of the features used for prediction. In addition, the sophisticated architectures of deep learning and advanced techniques are used more extensively because of their operational efficiency. Financial time series forecasting, incorporating sentiment analysis, is the focus of this comparison of cutting-edge methods. 67 feature configurations, blending stock closing prices with sentiment scores, were subjected to a wide-ranging experimental process, analyzed across diverse datasets and metrics. Across two case studies, encompassing a comparison of methods and a comparison of input feature configurations, a total of 30 cutting-edge algorithmic approaches were employed. The sum of the results indicates, concurrently, the high adoption rate of the suggested approach and a conditional rise in model effectiveness following the integration of sentiment analyses within particular predictive windows.
A concise examination of the probability representation in quantum mechanics is presented, along with illustrations of probability distributions for quantum oscillator states at temperature T and the time evolution of quantum states for a charged particle within an electrical capacitor's electric field. Employing explicit time-dependent integral forms of motion, linear in position and momentum, enables the derivation of shifting probability distributions that characterize the evolving states of the charged particle. A comprehensive exploration of the entropies associated with the probability distributions of initial coherent states of a charged particle are examined. A clear association between the probabilistic representation of quantum mechanics and the Feynman path integral has been established.
Vehicular ad hoc networks (VANETs) have seen a surge in interest recently, thanks to their substantial potential for improving road safety, assisting in traffic management, and providing support for infotainment services. IEEE 802.11p, a standard for vehicular ad hoc networks (VANETs), has been under consideration for more than ten years, focusing on the medium access control (MAC) and physical (PHY) layers. Performance analyses of the IEEE 802.11p MAC protocol, while conducted, reveal a need for improved analytical methods. Employing a two-dimensional (2-D) Markov model that accounts for the capture effect under a Nakagami-m fading channel, this paper assesses the saturated throughput and average packet delay experienced by the IEEE 802.11p MAC protocol in VANETs. Beyond that, detailed derivations provide the closed-form expressions for successful transmission, collided transmission, saturated throughput, and average packet latency. The proposed analytical model's accuracy is rigorously tested and validated using simulation results, which reveals a superior precision in saturated throughput and average packet delay compared to the existing models.
The probability representation of a quantum system's states is derived by utilizing the quantizer-dequantizer formalism. The probability representation of classical system states is compared, and the discussion is outlined. Illustrative examples of probability distributions for parametric and inverted oscillator systems are presented.
A preliminary exploration of the thermodynamics of particles following monotone statistics is undertaken in this paper. We present a revised approach, block-monotone, for achieving realistic physical outcomes, based on a partial order arising from the natural ordering in the spectrum of a positive Hamiltonian possessing a compact resolvent. Whenever all eigenvalues of the Hamiltonian are non-degenerate, the block-monotone scheme becomes equivalent to, and therefore, is not comparable to the weak monotone scheme, finally reducing to the standard monotone scheme. A deep dive into a model based on the quantum harmonic oscillator reveals that (a) the grand partition function's calculation doesn't use the Gibbs correction factor n! (associated with indistinguishable particles) in its series expansion based on activity; and (b) the elimination of terms from the grand partition function produces a kind of exclusion principle, analogous to the Pauli exclusion principle affecting Fermi particles, that stands out at high densities but fades at low densities, consistent with expectations.
Image-classification adversarial attacks are essential for enhancing AI security. Within the realm of image classification, most adversarial attack strategies are tailored for white-box scenarios, demanding access to the gradients and network architecture of the targeted model, which is a significant practical limitation when confronting real-world complexities. In contrast to the limitations mentioned previously, black-box adversarial attacks, augmented by reinforcement learning (RL), seem to be a viable approach for researching an optimal evasion policy. To our dismay, existing reinforcement learning-based attack methods exhibit a success rate that is lower than anticipated. HC-030031 Amidst these hurdles, we propose an ensemble-learning-based adversarial attack, ELAA, constructed from multiple reinforcement learning (RL) base learners, which are aggregated and refined to expose the vulnerabilities in image-classification models. Empirical findings demonstrate that the ensemble model's attack success rate surpasses that of a single model by approximately 35%. The attack success rate of ELAA is superior to that of the baseline methods by 15%.
The article investigates the modifications in fractal characteristics and dynamical complexity of Bitcoin/US dollar (BTC/USD) and Euro/US dollar (EUR/USD) returns throughout the period both before and after the commencement of the COVID-19 pandemic. Specifically, we applied the method of asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) to study the temporal variation of asymmetric multifractal spectrum parameters. Moreover, the temporal development of Fuzzy entropy, non-extensive Tsallis entropy, Shannon entropy, and Fisher information was scrutinized. Motivated by the desire to understand the pandemic's effect on two significant currencies, and the changes they underwent within the modern financial system, our research was conducted. HC-030031 Prior to and subsequent to the pandemic, our findings indicated a persistent behavior in BTC/USD returns, in contrast to the anti-persistent behavior shown by EUR/USD returns. After the COVID-19 outbreak, a greater degree of multifractality, more pronounced large fluctuations in prices, and a marked decrease in the complexity (i.e., a gain in order and information content and a loss of randomness) were observed for the return patterns in both BTC/USD and EUR/USD. The World Health Organization's (WHO) announcement that COVID-19 was a global pandemic appears to be a key contributing factor in the rapid increase of complexities.