The presence of Byzantine agents introduces a fundamental trade-off between the pursuit of optimality and the maintenance of resilience. Subsequently, we develop a robust algorithm, demonstrating the near-certain convergence of the value functions for all trustworthy agents to the vicinity of the optimal value function for all trustworthy agents, contingent upon specific network topology characteristics. We demonstrate that all reliable agents can learn the optimal policy under our algorithm, provided that the optimal Q-values for different actions are sufficiently separated.
The development of algorithms has been revolutionized by quantum computing. Regrettably, the current quantum landscape is dominated by noisy intermediate-scale quantum devices, thus restricting the development and implementation of quantum algorithms in circuits in several ways. Within this article, we propose a framework built on kernel machines, which constructs quantum neurons; these neurons are distinctive due to their individual feature space mappings. Our generalized framework, having contemplated earlier quantum neurons, has the capacity to generate supplementary feature mappings to enable a more effective approach to real-world issues. In the context of this framework, we introduce a neuron using a tensor-product feature mapping to access a space exponentially larger in dimension. Implementing the proposed neuron, a circuit of constant depth is constructed using a linear count of elementary single-qubit gates. The quantum neuron from before utilizes a phase-dependent feature mapping, requiring a circuit implementation that's exponentially costly, even when leveraging multi-qubit gates. Furthermore, the suggested neuron possesses parameters capable of altering the configuration of its activation function. In this demonstration, we explore and depict the shape of the activation function for each quantum neuron. Parametrization, it transpires, enables the proposed neuron to perfectly align with underlying patterns that the existing neuron struggles to capture, as evidenced in the nonlinear toy classification tasks presented here. Through executions on a quantum simulator, the demonstration also examines the feasibility of those quantum neuron solutions. Our final assessment involves the comparison of kernel-based quantum neurons within the context of handwritten digit recognition, further contrasting their performance with quantum neurons utilizing classical activation functions. Real-world problem instances repeatedly validating the parametrization potential of this approach strongly imply that this work crafts a quantum neuron featuring improved discriminatory aptitude. In consequence, a generalized quantum neural architecture holds potential for achieving tangible quantum superiority.
Deep neural networks (DNNs), lacking sufficient labeling, are particularly prone to overfitting, thereby producing suboptimal performance and impacting the training phase negatively. As a result, numerous semi-supervised methods are focused on capitalizing on unlabeled data to alleviate the shortage of labeled samples. Even so, the growing availability of pseudolabels clashes with the fixed structure of traditional models, impeding their application. In summary, a proposal for a deep-growing neural network with manifold constraints (DGNN-MC) is made. Semi-supervised learning benefits from a high-quality pseudolabel pool, enabling a deeper network structure while preserving the local relationship between the original and high-dimensional data. The framework, in its initial step, filters the results from the shallow network, selecting pseudo-labeled samples displaying high confidence. These high-confidence examples are then assimilated into the original training dataset to form a revised pseudo-labeled training dataset. SS-31 Second, the network's architecture's layer depth is determined by the size of the new training data, initiating the subsequent training. Eventually, the algorithm creates fresh pseudo-labeled examples and deepens the network architecture repeatedly until growth reaches its limit. This article's model, adaptable to transformations in depth, can be utilized in other multilayer networks. Taking the HSI classification paradigm, a quintessential semi-supervised learning scenario, as a benchmark, our experimental results clearly demonstrate the superiority and efficacy of our approach. This method extracts more trustworthy data, optimizing its utilization and expertly managing the ever-growing pool of labeled data against the network's learning prowess.
Automatic universal lesion segmentation (ULS) of computed tomography (CT) images can free up radiologists, enabling a more precise assessment than the current Response Evaluation Criteria in Solid Tumors (RECIST) approach. However, this endeavor is constrained by the scarcity of vast pixel-wise labeled datasets. Within this paper, a weakly supervised learning framework is presented to employ large-scale lesion databases housed within hospital Picture Archiving and Communication Systems (PACS) for ULS applications. Unlike preceding strategies for generating pseudo-surrogate masks in fully supervised training via shallow interactive segmentation, we introduce a novel framework, RECIST-induced reliable learning (RiRL), which leverages implicit information from RECIST annotations. Importantly, our approach incorporates a novel label generation process and an on-the-fly soft label propagation strategy to address training noise and generalization limitations. RECIST-induced geometric labeling, in its use of clinical RECIST characteristics, reliably and preliminarily propagates the label. A trimap's role in the labeling process is to divide lesion slices into three regions: foreground, background, and ambiguous sections. This enables a powerful and dependable supervision signal throughout a large region. A topological graph, informed by knowledge, is built for the purpose of real-time label propagation, in order to refine the segmentation boundary optimally. A comparison based on a public benchmark dataset showcases the proposed method's substantial performance increase over the existing leading RECIST-based ULS methods. Compared to existing leading methods, our approach demonstrably outperforms them by more than 20%, 15%, 14%, and 16% in terms of Dice score across ResNet101, ResNet50, HRNet, and ResNest50 backbones, respectively.
This paper introduces a chip designed for the wireless monitoring of the heart's interior. The design's key components are: a three-channel analog front-end; a pulse-width modulator, with output frequency offset and temperature calibration; and inductive data telemetry. Implementing a resistance-boosting technique within the instrumentation amplifier's feedback mechanism results in a pseudo-resistor exhibiting lower non-linearity, ultimately causing a total harmonic distortion under 0.1%. Moreover, the boosting method improves the system's resilience to feedback, resulting in a smaller feedback capacitor and, as a result, a diminished overall size. The modulator's output frequency is rendered impervious to temperature and process fluctuations through the integration of fine-tuning and coarse-tuning algorithms. The front-end channel's ability to extract intra-cardiac signals stands at an impressive 89 effective bits, with input-referred noise below 27 Vrms, and a power consumption of just 200 nW per channel. The on-chip transmitter, operating at 1356 MHz, is driven by an ASK-PWM modulator that encodes the front-end output. The proposed System-on-Chip (SoC), fabricated in 0.18 µm standard CMOS technology, has a power consumption of 45 watts and a footprint of 1125 mm².
The impressive performance of video-language pre-training on various downstream tasks has made it a topic of significant recent interest. Existing techniques in cross-modality pre-training commonly employ architectures focused on either individual modalities or the combination of modalities. Environment remediation This paper introduces the Memory-augmented Inter-Modality Bridge (MemBridge), a novel architecture distinct from preceding methods, which utilizes learned intermediate modality representations to bridge the gap between video and language representations. The cross-modality encoder, employing a transformer architecture, introduces learnable bridge tokens for interaction, restricting video and language tokens' information intake to these tokens and their own information. Moreover, a memory bank is designed to collect and store significant amounts of multimodal interaction data to dynamically generate bridge tokens in accordance with various cases, bolstering the capacity and robustness of the inter-modality bridge. Pre-training allows MemBridge to explicitly model representations for a more comprehensive inter-modality interaction. genetic adaptation Our approach, as demonstrated through thorough experiments, achieves performance on a par with previous techniques in different downstream tasks, encompassing video-text retrieval, video captioning, and video question answering, across diverse datasets, thereby highlighting the proposed method's effectiveness. One can obtain the MemBridge code from the repository at https://github.com/jahhaoyang/MemBridge.
The neurological process of filter pruning functions by a mechanism of forgetting and re-remembering. Typically used methodologies, in their initial phase, discard secondary information originating from an unstable baseline, expecting minimal performance deterioration. Undeniably, the model's ability to remember unsaturated bases sets a limit on the improved model, generating suboptimal performance figures. A failure to initially recall this point would result in permanent data loss. This design presents the Remembering Enhancement and Entropy-based Asymptotic Forgetting (REAF) approach for filter pruning, a novel technique. From the perspective of robustness theory, we initially augmented memory retention by over-parameterizing the baseline with fusible compensatory convolutions, thereby freeing the pruned model from the baseline's restrictions without affecting the inference process. Consequently, the original and compensatory filters' collateral implications demand a mutually agreed-upon pruning standard.
Improvement and Assessment involving Receptive Feeding Guidance Playing cards to Strengthen the UNICEF Child and also Youngster Giving Counselling Bundle.
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