Part/attribute transfer networks are formulated to learn and extract representative features for novel attributes, leveraging supplementary prior knowledge as an auxiliary input. In conclusion, a prototype completion network is constructed to master the completion of prototypes based on these pre-existing concepts. Selleckchem Cisplatin To address the prototype completion error, a novel Gaussian-based prototype fusion strategy was developed. This fusion strategy incorporates both mean-based and completed prototypes with the aid of unlabeled samples. For a fair comparison against existing FSL methods, lacking external knowledge, we ultimately developed a comprehensive economic prototype version of FSL, one that does not necessitate gathering foundational knowledge. Extensive experiments support the claim that our methodology creates more accurate prototypes, leading to superior performance across inductive and transductive few-shot learning. You can find the open-source code for Prototype Completion for FSL at the GitHub repository https://github.com/zhangbq-research/Prototype Completion for FSL.

We detail in this paper the Generalized Parametric Contrastive Learning (GPaCo/PaCo) approach, which effectively handles both imbalanced and balanced data. Theoretical analysis shows that supervised contrastive loss is prone to bias toward high-frequency classes, thereby presenting an obstacle to effective imbalanced learning. From an optimization perspective, we introduce a set of parametric, class-wise, learnable centers for rebalancing. Furthermore, we examine our GPaCo/PaCo loss within a balanced framework. The analysis demonstrates GPaCo/PaCo's ability to dynamically heighten the pushing force of like samples as they draw closer to their centroid with sample accumulation, aiding in hard example learning. Long-tailed benchmarks, when subjected to experimentation, reveal the state-of-the-art methodology for long-tailed recognition. Compared to MAE models, CNNs and vision transformers trained with the GPaCo loss function manifest better generalization performance and robustness on the complete ImageNet dataset. Furthermore, GPaCo's applicability extends to semantic segmentation, showcasing demonstrably enhanced performance on four widely recognized benchmark datasets. The Parametric Contrastive Learning code resides on the GitHub platform, specifically at the location https://github.com/dvlab-research/Parametric-Contrastive-Learning.

White balancing in many imaging devices, a key function of Image Signal Processors (ISP), necessitates the application of computational color constancy. In recent times, deep convolutional neural networks (CNNs) have been implemented for the purpose of color constancy. Their performance significantly outperforms both shallow learning methodologies and statistical data points. While essential, the prerequisite for extensive training data, costly computations, and a large model size limits the applicability of CNN-based methods on ISPs with restricted resources in real-time. To overcome these bottlenecks and reach the performance level of CNN-based methods, a method for selecting the ideal simple statistics-based approach (SM) is developed for each image. Accordingly, we introduce a novel ranking-based color constancy method (RCC), which conceptualizes the choice of the best SM method as a label ranking issue. RCC's ranking loss function, specifically designed, utilizes a low-rank constraint for controlling model complexity, in conjunction with a grouped sparse constraint for effective feature selection. Finally, the RCC model is applied to anticipate the succession of the suggested SM approaches for a specimen image, and then calculating its illumination by adopting the projected ideal SM technique (or by combining the outcomes generated by the most effective k SM methods). Results from a thorough experimentation process illustrate that the proposed RCC technique outperforms practically all shallow learning-based methods, attaining comparable performance to (and occasionally exceeding) deep CNN-based methods, while utilizing only 1/2000th the model size and training duration. RCC's excellent generalization across various cameras is complemented by its strong robustness with constrained training data. Moreover, to eliminate reliance on ground truth illumination, we extend RCC to develop a novel ranking-based approach, RCC NO, that eschews ground truth illumination. This approach learns the ranking model using basic partial binary preference markings from untrained annotators instead of relying on experts. RCC NO consistently surpasses SM approaches and nearly all shallow learning methods, all with the advantage of reduced expenses in acquiring samples and measuring illumination.

The process of events-to-video reconstruction and video-to-events simulation forms two essential pillars of event-based vision research. Complex and hard-to-interpret deep neural networks are prevalent in the E2V reconstruction field. Subsequently, extant event simulators are fashioned to produce credible events, but research endeavors to enhance the process of generating events have been limited. We present a streamlined, model-driven deep learning network for E2V reconstruction in this paper, alongside an examination of the diversity of adjacent pixel values in the V2E generation process. This is followed by the development of a V2E2V architecture to evaluate the effects of varying event generation strategies on video reconstruction accuracy. In the E2V reconstruction, the relationship between events and intensity is modeled through the use of sparse representation models. Subsequently, a CISTA (convolutional ISTA network) is developed using the algorithm unfolding strategy. In Situ Hybridization In order to advance temporal coherence, long short-term temporal consistency (LSTC) constraints are implemented. Our V2E generation technique involves the interleaving of pixels, each having distinct contrast thresholds and low-pass bandwidths, with the expectation of extracting more relevant insights from the intensity data. hepatic arterial buffer response Finally, the V2E2V architectural design is used to assess the efficacy of this strategy. In comparison to state-of-the-art methods, the CISTA-LSTC network's results exhibit a significant improvement in temporal consistency. Recognizing the variety within generated events uncovers finer details, resulting in a substantially improved reconstruction.

Simultaneous optimization across multiple tasks represents a novel area of evolutionary research. A universal concern when tackling multitask optimization problems (MTOPs) is the effective transmission of shared knowledge between or among various tasks. Despite the presence of knowledge transfer mechanisms, current algorithms are restricted by two limitations. Knowledge moves across the aligned dimensions of various tasks, eschewing any connection with dimensions having similar or related characteristics. The dissemination of knowledge among the related facets contained within a single task is overlooked. This article proposes a novel and efficient solution to surmount these two limitations by partitioning individuals into multiple blocks and enabling knowledge transfer at that granular level, the block-level knowledge transfer (BLKT) framework. BLKT segments individuals across all tasks, forming a block-based population; each block encompasses a series of successive dimensions. Tasks, both identical and diverse, contribute similar blocks that are consolidated within the same evolving cluster. Through BLKT, knowledge is transferred between like dimensions, which may initially be either aligned or unaligned, and which may either relate to the same or distinct tasks, thereby revealing a more rational process. Comparative analysis of BLKT-based differential evolution (BLKT-DE) against state-of-the-art algorithms, assessed across diverse scenarios including the CEC17 and CEC22 MTOP benchmarks, a new, challenging composite MTOP test suite, and real-world MTOP problems, reveal BLKT-DE's superior performance. Beyond this, another significant observation is that the BLKT-DE system also displays promising capabilities in addressing single-task global optimization problems, achieving performance comparable to that of some of the leading algorithms.

A wireless networked cyber-physical system (CPS), comprised of distributed sensors, controllers, and actuators, is the focus of this article, which investigates the model-free remote control challenge. Data gathered from the controlled system's state by sensors is used to generate control instructions for the remote controller; actuators then execute these commands to maintain the system's stability. In a model-free control system, the deep deterministic policy gradient (DDPG) algorithm is implemented in the controller to achieve control without a model. This work proposes an alternative to the DDPG algorithm, which traditionally uses only the current system state. Instead, historical action data is included as part of the input. This enhancement allows for a more comprehensive data analysis and enables precise control, especially when communication latency is a factor. The experience replay mechanism within the DDPG algorithm also incorporates reward data through the prioritized experience replay (PER) method. Based on the simulation outcomes, the suggested sampling policy boosts convergence speed by leveraging the joint effect of temporal difference (TD) error and reward to determine transition probabilities.

Data journalism's growing presence in online news correlates with a concurrent rise in the use of visualizations within article thumbnail images. Nonetheless, scant investigation has been undertaken regarding the design principles behind visualization thumbnails, including the procedures of resizing, cropping, simplification, and ornamentation of charts embedded within the corresponding article. Accordingly, this research aims to comprehend these design choices and identify the characteristics that make a visualization thumbnail appealing and readily interpretable. For this undertaking, our initial approach entailed an overview of online-assembled visualization thumbnails, followed by an exchange of insights on visualization thumbnail practices with data journalists and news graphics designers.