Extensive real-world multi-view data trials confirm our method's superior performance when compared to currently leading state-of-the-art approaches.

Thanks to its ability to learn useful representations without any manual labeling, contrastive learning, built upon augmentation invariance and instance discrimination, has seen remarkable successes recently. Nevertheless, the inherent resemblance between examples clashes with the practice of differentiating each example as a distinct entity. This paper introduces Relationship Alignment (RA), a novel approach for leveraging the inherent relationships among instances in contrastive learning. RA compels different augmented representations of current batch instances to maintain consistent relationships with other instances in the batch. To effectively apply RA within existing contrastive learning structures, we created an alternating optimization algorithm, focusing on optimizing the relationship exploration and alignment phases separately. A further equilibrium constraint is applied to RA, precluding degenerate outcomes, and an expansion handler is implemented to guarantee its approximate fulfillment in practice. To more thoroughly grasp the intricate connections between instances, we further introduce Multi-Dimensional Relationship Alignment (MDRA), which seeks to analyze relationships from multiple perspectives. The process of decomposing the high-dimensional feature space into a Cartesian product of various low-dimensional subspaces, and performing RA in each one, is carried out in practice. We meticulously evaluated the effectiveness of our methodology across multiple self-supervised learning benchmarks, consistently surpassing leading contrastive learning techniques. Using the standard ImageNet linear evaluation protocol, our RA model yields substantial improvements over competing approaches. Our MDRA model, augmented from RA, ultimately delivers the best overall performance. Our approach's source code will be made publicly available shortly.

Presentation attack instruments (PAIs) are used to perform presentation attacks (PAs) against biometric systems. While deep learning and handcrafted feature-based PA detection (PAD) techniques abound, the difficulty of generalizing PAD to unknown PAIs persists. The empirical findings of this work highlight the critical influence of PAD model initialization on generalization performance, a topic rarely addressed in the field. Due to the insights gained, we formulated a self-supervised learning method, named DF-DM. DF-DM's task-specific representation for PAD is derived from a combined global-local view, further enhanced by de-folding and de-mixing. During the de-folding process, the proposed technique will explicitly minimize the generative loss, learning region-specific features for samples, represented by local patterns. To achieve a more encompassing representation of instance-specific characteristics, detectors are driven by de-mixing, incorporating global information while minimizing interpolation-based consistency. Comparative analysis of experimental results across intricate and hybrid datasets showcases the considerable advancement of the proposed method in face and fingerprint PAD, far outperforming existing state-of-the-art techniques. During CASIA-FASD and Idiap Replay-Attack training, the proposed method demonstrated an 1860% equal error rate (EER) on the OULU-NPU and MSU-MFSD datasets, surpassing the baseline's performance by 954%. Model-informed drug dosing To download the source code of the proposed technique, please navigate to https://github.com/kongzhecn/dfdm.

We are aiming to construct a transfer reinforcement learning system. This framework will enable the creation of learning controllers. These controllers can utilize pre-existing knowledge from prior tasks, along with the corresponding data, to enhance the learning process when tackling novel tasks. In pursuit of this objective, we formalize knowledge transfer by expressing knowledge in the value function of our problem setup; this approach is called reinforcement learning with knowledge shaping (RL-KS). Unlike most empirically-oriented transfer learning studies, our results present not just simulation verifications, but also a detailed analysis of algorithm convergence and solution optimality. Our RL-KS approach, in contrast to established potential-based reward shaping methods, which rely on demonstrations of policy invariance, paves the way for a fresh theoretical finding concerning positive knowledge transfer. Our research findings include two established strategies that address a broad spectrum of approaches for implementing prior knowledge within reinforcement learning knowledge systems. Our evaluations of the RL-KS method are comprehensive and methodical. The evaluation environments encompass not only standard reinforcement learning benchmark problems but also a demanding real-time robotic lower limb control scenario with a human user in the loop.

Using a data-driven technique, this article investigates the optimal control of large-scale systems. The existing control techniques applied to large-scale systems in this situation treat disturbances, actuator faults, and uncertainties individually. Employing a novel architectural design, this article extends prior methods to encompass a simultaneous assessment of all influencing elements, while also introducing a tailored optimization metric for the control system. Optimal control's reach is extended to encompass a more diverse class of large-scale systems by this diversification. Medicare Health Outcomes Survey A min-max optimization index is first established, predicated on the theoretical framework of zero-sum differential game theory. The decentralized zero-sum differential game strategy for stabilizing the large-scale system is found by merging the Nash equilibrium solutions of its constituent subsystems. Simultaneously, the system's performance is shielded from actuator failure repercussions by the implementation of adaptive parameters. check details Employing an adaptive dynamic programming (ADP) technique, the Hamilton-Jacobi-Isaac (HJI) equation's solution is obtained, eliminating the need for any pre-existing comprehension of the system's dynamics. A rigorous analysis of stability confirms that the proposed controller accomplishes asymptotic stabilization of the large-scale system. In conclusion, an illustration using a multipower system example validates the effectiveness of the proposed protocols.

Presented here is a collaborative neurodynamic optimization technique for distributing chiller loads in the context of non-convex power consumption functions and cardinality-constrained binary variables. Based on an augmented Lagrangian framework, we address a distributed optimization problem characterized by cardinality constraints, non-convex objectives, and discrete feasible sets. The nonconvexity of the formulated distributed optimization problem necessitates a novel collaborative neurodynamic optimization method. This method employs multiple coupled recurrent neural networks, whose initial states are repeatedly reset using a metaheuristic rule. We scrutinize experimental results obtained from two multi-chiller systems, utilizing data provided by the chiller manufacturers, to illustrate the efficacy of the suggested approach in contrast to various baseline solutions.

This article introduces the generalized N-step value gradient learning (GNSVGL) algorithm, which considers long-term prediction, for discounted near-optimal control of infinite-horizon discrete-time nonlinear systems. The GNSVGL algorithm, in its proposed form, accelerates the learning of adaptive dynamic programming (ADP) by benefiting from insights gleaned from multiple future reward signals, resulting in a superior performance. The GNSVGL algorithm differs from the NSVGL algorithm with its zero initial functions by employing positive definite functions in its initialization phase. Value-iteration-based algorithm convergence analysis is presented, taking into account different initial cost functions. Determining the stability of the iterative control policy relies on finding the iteration index that results in asymptotic stability of the system under the control law. In the event of such a condition, if the system exhibits asymptotic stability during the current iteration, then the subsequent iterative control laws are guaranteed to be stabilizing. The control law, along with the one-return costate function and the negative-return costate function, are approximated by distinct neural networks, specifically one action network and two critic networks respectively. For the purpose of action neural network training, the synergistic use of one-return and multiple-return critic networks is crucial. In conclusion, the developed algorithm's superiority is verified through simulation studies and comparative assessments.

Utilizing a model predictive control (MPC) method, this article explores the optimal switching time sequences within uncertain networked switched systems. Employing precisely discretized predicted trajectories, a substantial Model Predictive Control (MPC) problem is first formulated. Subsequently, a two-level hierarchical optimization scheme, reinforced by a localized compensation technique, is designed to tackle the formulated MPC problem. This hierarchical framework embodies a recurrent neural network structure, composed of a central coordination unit (CU) at a superior level and various local optimization units (LOUs), directly interacting with individual subsystems at a lower level. In conclusion, a real-time switching time optimization algorithm is developed for calculating the optimal series of switching times.

Real-world applications have made 3-D object recognition a captivating research focus. Still, most existing recognition models improbably presume that the classifications of three-dimensional objects stay constant in real-world temporal dimensions. Catastrophic forgetting of previously learned 3-D object classes could significantly impede their ability to learn new classes consecutively, stemming from this unrealistic assumption. Particularly, they cannot delineate which three-dimensional geometric characteristics are vital for reducing the impact of catastrophic forgetting on the recall of earlier classes of three-dimensional objects.