In a granular binary mixture, the Boltzmann equation for d-dimensional inelastic Maxwell models is utilized to calculate second, third, and fourth-degree collisional moments. When diffusion is nonexistent, (resulting in a vanishing mass flux for each species), the velocity moments of each constituent's distribution function yield an exact account of collisional events. The coefficients of normal restitution, along with the mixture's parameters (masses, diameters, and composition), determine the associated eigenvalues and cross coefficients. Applying these results, the analysis of moments' time evolution, scaled by a thermal speed, is performed in two different non-equilibrium situations: the homogeneous cooling state (HCS) and the uniform shear flow (USF). In the HCS, a divergence in the third and fourth degree moments over time is observable, contrasting with the behavior of simple granular gases, which is dependent on system parameters. An in-depth analysis of the mixture's parameter space's influence on the time-dependent behavior of these moments is performed. see more The tracer limit's impact on the time evolution of the second- and third-degree velocity moments is investigated within the USF, where the concentration of one component is vanishingly small. Unsurprisingly, the second-degree moments, while always convergent, exhibit the possibility of divergent third-degree moments for the tracer species in the long run.

This paper investigates the optimal containment control of nonlinear multi-agent systems with partially known dynamics, employing an integral reinforcement learning approach. Drift dynamics are less critical when integral reinforcement learning is utilized. The proposed control algorithm, which relies on the integral reinforcement learning method, is shown to be equivalent to model-based policy iteration, thereby guaranteeing its convergence. The Hamilton-Jacobi-Bellman equation, for each follower, is solved by a single critic neural network, this network utilizing a modified updating law to guarantee the asymptotic stability of the weight error. The critic neural network, processing input-output data, yields an approximate optimal containment control protocol for each follower. The closed-loop containment error system's stability is implicitly assured by the proposed optimal containment control scheme. Through simulation, the effectiveness of the presented control approach is clearly demonstrated.
The vulnerability of natural language processing (NLP) models built on deep neural networks (DNNs) to backdoor attacks is well-documented. Despite existing defenses, backdoor vulnerabilities remain susceptible to attacks in a variety of contexts. A deep feature classification-based approach to textual backdoor defense is proposed. The method utilizes deep feature extraction techniques alongside classifier construction. Deep features in poisoned data and uncompromised data are distinct; this method capitalizes on this difference. In both offline and online contexts, backdoor defense is in place. Two datasets and two models underwent defense experiments in response to a multitude of backdoor attacks. The experimental findings reveal that this defense method performs better than the baseline, demonstrating its effectiveness.

Models used for forecasting financial time series often benefit from the addition of sentiment analysis data to their feature set, a practice aimed at boosting their capacity. Deep learning architectures and state-of-the-art approaches are seeing greater application owing to their proficiency. Financial time series forecasting, incorporating sentiment analysis, is the focus of this comparison of cutting-edge methods. An experimental investigation, using 67 feature setups, examined the impact of stock closing prices and sentiment scores across a selection of diverse datasets and metrics. Thirty state-of-the-art algorithmic schemes were applied in two separate case studies, one dedicated to evaluating method comparisons, and another to assessing variations in input feature setups. 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.

The probabilistic portrayal of quantum mechanics is briefly reviewed, including illustrations of probability distributions for quantum oscillators at temperature T and examples of the evolution of quantum states of a charged particle traversing the electric field of an electrical capacitor. 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. An analysis of the entropies linked to the probability distributions of starting coherent states for charged particles is undertaken. A clear association between the probabilistic representation of quantum mechanics and the Feynman path integral has been established.

Interest in vehicular ad hoc networks (VANETs) has significantly increased recently because of their extensive potential to enhance road safety, streamline traffic management, and improve support for infotainment services. As a standard for vehicular ad-hoc networks (VANETs), IEEE 802.11p has been a topic of discussion for more than a decade, particularly with regard to its application in the medium access control (MAC) and physical (PHY) layers. Analyses of the performance of the IEEE 802.11p MAC protocol, though existing, necessitate the development of more effective analytical methods. This paper presents a two-dimensional (2-D) Markov model that considers the capture effect under a Nakagami-m fading channel, in order to analyze the saturated throughput and average packet delay of the IEEE 802.11p MAC protocol within VANETs. Importantly, the mathematical representations for successful transmission, collisions during transmission, saturated throughput, and the average packet delay are carefully deduced. Through simulation, the proposed analytical model's accuracy is verified, showcasing its superior performance in saturated throughput and average packet delay compared to previously established models.

To create the probability representation of quantum system states, the quantizer-dequantizer formalism is employed. A review of the probability representation of classical system states is undertaken, discussing its comparisons to existing systems. Probability distributions describing parametric and inverted oscillators are exemplified.

A preliminary thermodynamic analysis of particles adhering to monotone statistical rules is presented in this paper. Realizing realistic physical applications requires a modified approach, block-monotone, built upon a partial order resulting from the natural ordering of the spectrum of a positive Hamiltonian with a compact resolvent. The block-monotone scheme's performance is not comparable to the weak monotone scheme's, and it degrades into the typical monotone scheme in cases where all eigenvalues of the associated Hamiltonian are non-degenerate. 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.

The importance of image-classification adversarial attacks in AI security cannot be overstated. Image-classification adversarial attack methods predominantly operate within white-box scenarios, requiring access to the target model's gradients and network architecture, which poses a significant practical limitation in real-world applications. Despite the limitations described above, black-box adversarial attacks, along with reinforcement learning (RL), appear to be a practical avenue for the development of an optimized evasion policy. To our dismay, existing reinforcement learning-based attack methods exhibit a success rate that is lower than anticipated. see more Recognizing the issues, we present an ensemble-learning-based adversarial attack strategy (ELAA), incorporating and optimizing multiple reinforcement learning (RL) base learners, thereby further exposing vulnerabilities in image classification systems. An experimental analysis of attack success rates shows the ensemble model outperforming a single model by roughly 35%. Baseline methods exhibit a success rate 15% lower than ELAA's attack success rate.

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. To be more precise, we employed the asymmetric multifractal detrended fluctuation analysis (A-MF-DFA) approach to examine the temporal development of the asymmetric multifractal spectrum's parameters. We also examined the evolution over time of Fuzzy entropy, non-extensive Tsallis entropy, Shannon entropy, and Fisher information. We undertook research to gain a deeper understanding of how the pandemic affected two crucial currencies, impacting the modern financial system in novel ways. see more Consistent BTC/USD returns were observed before and after the pandemic, while EUR/USD returns exhibited an anti-persistent pattern, as per our findings. Furthermore, the COVID-19 pandemic's onset coincided with a surge in multifractality, a rise in substantial price swings, and a notable drop in the complexity (meaning a rise in order and information content, and a decline in randomness) of both BTC/USD and EUR/USD returns. The WHO's announcement classifying COVID-19 as a global pandemic, in all likelihood, led to a profound escalation in the complexity.