The presence of Byzantine agents introduces a fundamental trade-off between the pursuit of optimality and the maintenance of resilience. We subsequently develop a resilient algorithm, proving the almost-certain convergence of value functions for all trustworthy agents to the neighborhood of the optimal value function for all trustworthy agents, dependent upon constraints in the network's layout. 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 transformed by the revolutionary nature of quantum computing. Despite the limitations, only noisy intermediate-scale quantum devices are currently viable, leading to several restrictions on the application of quantum algorithms within circuit implementations. This article describes a framework that utilizes kernel machines to create quantum neurons. Each neuron's distinctiveness is defined by the mapping of its feature space. Our generalized framework, extending beyond the analysis of prior quantum neurons, is able to generate alternative feature mappings, leading to superior problem-solving abilities for real-world applications. Within the framework outlined, we introduce a neuron that utilizes a tensor product feature mapping to an exponentially expanded dimensional space. A constant-depth circuit comprising a linear number of elementary single-qubit gates is responsible for the implementation of the proposed neuron. The previous quantum neuron, utilizing a phase-dependent feature mapping, has an exponentially expensive circuit implementation, even with the aid of multi-qubit gates. Besides this, the neuron proposed has parameters that are capable of transforming the configuration of its activation function. In this demonstration, we explore and depict the shape of the activation function for each quantum neuron. The parametrization of the proposed neuron is shown to provide an optimal fit for underlying patterns that the existing neuron struggles to represent in the nonlinear toy classification problems tackled here. Executions on a quantum simulator are also utilized within the demonstration to evaluate the viability 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. The parameterization potential of this method, corroborated through practical problem instances, suggests that the resulting quantum neuron exhibits improved discriminatory effectiveness. Thus, the generalizable quantum neuron framework has the potential to enable practical quantum superiority.
Due to a scarcity of proper labels, deep neural networks (DNNs) are prone to overfitting, compromising performance and increasing difficulties in training effectively. Thus, numerous semi-supervised techniques focus on utilizing unlabeled samples to address the shortage of labeled data. In spite of that, the escalating number of pseudolabels presents a hurdle for the rigid structure of traditional models, thereby restricting their effectiveness. In light of the foregoing, a deep-growing neural network with manifold constraints (DGNN-MC) is formulated. The expansion of a high-quality pseudolabel pool in semi-supervised learning allows for a deeper network structure, maintaining the local structure between the original and higher dimensional data. Using the shallow network's output, the framework distinguishes pseudo-labeled examples with high confidence and appends them to the original training set, creating a revised pseudo-labeled training set. Co-infection risk assessment 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. The model, developed in this article, is applicable to any multilayer network, given that the depth parameter can be changed. As illustrated by the HSI classification example, a natural semi-supervised learning problem, our experimental findings attest to the method's superiority and efficiency. The method extracts more reliable information for enhanced utility and carefully balances the growing amount of labeled data with the network's learning power.
Automatic universal lesion segmentation from computed tomography (CT) images can alleviate the workload for radiologists and offer more precise assessments compared to the current Response Evaluation Criteria In Solid Tumors (RECIST) guideline measurements. Nonetheless, the inadequacy of this assignment stems from a lack of extensively labeled pixel data. For ULS, this paper introduces a weakly supervised learning framework that leverages the extensive lesion databases present in hospital Picture Archiving and Communication Systems (PACS). While previous methods relied on shallow interactive segmentation for constructing pseudo-surrogate masks in fully supervised training, our proposed RECIST-induced reliable learning (RiRL) framework extracts implicit knowledge from RECIST annotations for a unified solution. We introduce, in particular, a novel label generation procedure and an on-the-fly soft label propagation strategy aimed at avoiding noisy training and poor generalization. Clinically characterized by RECIST, the method of RECIST-induced geometric labeling, reliably and preliminarily propagates the label. Through the labeling process, a trimap separates lesion slices into three zones: specific foreground regions, background regions, and ambiguous areas. This differentiation facilitates a powerful and dependable supervisory signal over a wide area. For the purpose of enhancing segmentation boundary optimization, a knowledge-based topological graph is created for dynamic label propagation. Results obtained from a public benchmark dataset reveal that the proposed method demonstrates a substantial improvement over existing state-of-the-art RECIST-based ULS methods. In comparison to the best existing approaches, our methodology achieves a notable 20%, 15%, 14%, and 16% Dice score improvement when using ResNet101, ResNet50, HRNet, and ResNest50 as backbones, respectively.
This paper presents a chip for wirelessly monitoring the interior of the heart. 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. Through the application of resistance-boosting techniques to the instrumentation amplifier's feedback, the pseudo-resistor shows lower non-linearity, which translates to a total harmonic distortion of less than 0.1%. In addition, the boosting procedure strengthens the system's resistance to feedback, leading to a decrease in the feedback capacitor's dimensions and, subsequently, a reduction in the 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. With an impressive 89 effective bits, the front-end channel excels at extracting intra-cardiac signals, exhibiting input-referred noise less than 27 Vrms and consuming only 200 nW per channel. An ASK-PWM modulator encodes the front-end output, driving a 1356 MHz on-chip transmitter. Fabricated using 0.18 µm standard CMOS technology, the System-on-Chip (SoC) proposed consumes 45 watts and occupies 1125 square millimeters of space.
Pre-training video and language models has become a topic of substantial recent interest, given their impressive performance in diverse downstream tasks. For cross-modality pre-training, the majority of existing methods utilize architectural designs that are either modality-specific or encompass multiple modalities. intracellular biophysics This paper presents a new architecture, the Memory-augmented Inter-Modality Bridge (MemBridge), which distinguishes itself from previous methods by using learnable intermediate modality representations to connect video and language. Our transformer-based cross-modality encoder implements a novel interaction mechanism by introducing learnable bridge tokens, through which video and language tokens gain knowledge solely from these bridge tokens and their inherent data. Beyond that, a memory bank is being suggested to retain extensive modality interaction data to allow for the adaptive generation of bridge tokens in diverse contexts, thus fortifying the inter-modality bridge's capacity and resilience. MemBridge's pre-training process is designed to explicitly model representations for more effective inter-modality interaction. Scriptaid cost Through extensive trials, our method has displayed performance comparable to previous methods on various downstream tasks, which include video-text retrieval, video captioning, and video question answering, on a multitude of datasets, demonstrating the strength of the proposed method. The code for MemBridge is situated on GitHub, specifically at https://github.com/jahhaoyang/MemBridge.
Neurologically, the act of filter pruning manifests as a process of both forgetting and recalling previously stored information. Existing methodologies, in their initial stages, promptly overlook less significant details arising from a weak baseline, hoping for minimal consequences on performance. However, the model's storage capacity for unsaturated bases imposes a limit on the streamlined model's potential, causing it to underperform. To initially forget this crucial detail would trigger an irreversible loss of data. We propose a new filter pruning paradigm, called Remembering Enhancement and Entropy-based Asymptotic Forgetting (REAF), in this work. Inspired by robustness theory, our initial improvement to remembering involved over-parameterizing the baseline with fusible compensatory convolutions, thereby emancipating the pruned model from the baseline's limitations, all without any computational cost at inference time. The collateral link between the original and compensatory filters dictates a two-way pruning approach.
Advancement and Tests involving Reactive Giving Guidance Credit cards to Strengthen the UNICEF Toddler as well as Child Giving Counseling Package.
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