In the presence of Byzantine agents, a fundamental trade-off between optimality and resilience is inherent. We then engineer a resilient algorithm, demonstrating near-certain convergence of the value functions for all dependable agents to the surrounding area of the ideal value function for all dependable agents, subject to particular stipulations concerning the network's architecture. Our algorithm proves that all reliable agents can learn the optimal policy when the optimal Q-values for different actions are adequately separated.
Quantum computing is revolutionizing the field of algorithm development. Currently, only noisy intermediate-scale quantum devices are accessible, which unfortunately places several limitations on the practical application of quantum algorithms to circuit designs. A framework for constructing quantum neurons based on kernel machines is presented in this article, the individual neurons differentiated via their distinctive feature space mappings. Not only does our generalized framework consider prior quantum neurons, but it also has the potential to create other feature mappings, thereby improving the solution to real-world problems. This framework underpins the presentation of a neuron, which implements a tensor-product feature mapping into a far more extensive space that expands exponentially. The proposed neuron finds implementation within a circuit of constant depth, which contains a linearly scalable number of elementary single-qubit gates. Using a phase-dependent feature map, the previous quantum neuron has an exponentially expensive circuit, even when employing multi-qubit gates. Furthermore, the suggested neuron possesses parameters capable of altering the configuration of its activation function. The activation function shapes of all the quantum neurons are shown in this illustration. The existing neuron's limitations in fitting underlying patterns are overcome by the parametrization of the proposed neuron, as exemplified in the nonlinear toy classification problems discussed in this work. Executions on a quantum simulator within the demonstration contemplate the practicality of those quantum neuron solutions. Finally, we analyze the performance of kernel-based quantum neurons applied to the task of handwritten digit recognition, where a direct comparison is made with quantum neurons employing classical activation functions. Repeated observations of the parametrization potential, realized within actual problems, support the conclusion that this work produces a quantum neuron with improved discriminatory abilities. Following this, the comprehensive quantum neuron model can contribute to demonstrable quantum advantages in real-world applications.
Deep neural networks (DNNs) frequently overfit when the quantity of labels is inadequate, resulting in diminished performance and complicating the training process. Consequently, many semi-supervised strategies attempt to use unlabeled examples to compensate for the limited amount of labeled data. Yet, as the number of pseudolabels grows, the static framework of traditional models encounters difficulty in adapting to them, consequently reducing their effectiveness. Finally, a deep-growing neural network with manifold constraints, abbreviated DGNN-MC, is devised. In semi-supervised learning, a high-quality pseudolabel pool's expansion deepens the network structure, simultaneously preserving the local structure connecting the original data with the high-dimensional representation. The framework's initial step involves sifting through the shallow network's output to select pseudo-labeled samples displaying high confidence. These are then integrated into the original training data to produce a new pseudo-labeled training set. Vastus medialis obliquus To commence training, the second step involves adjusting the network's layer depth based on the size of the new training dataset. At last, new pseudo-labeled examples are obtained and the network's layers are further developed until growth is completed. The model, developed in this article, is applicable to any multilayer network, given that the depth parameter can be changed. Using HSI classification as a model semi-supervised learning task, the results of our experiments prove the method's superiority and efficiency. The method effectively extracts more reliable information, optimizing its utilization and perfectly balancing the expanding volume of labeled data against the network's learning capacity.
Automatic universal lesion segmentation (ULS) of CT images is capable of easing the workload of radiologists and yielding more precise evaluations when contrasted with the current Response Evaluation Criteria In Solid Tumors (RECIST) measurement approach. Despite its merit, this task is underdeveloped because of the lack of a substantial dataset containing pixel-level labeling. 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. In contrast to prior methods of constructing pseudo-surrogate masks for fully supervised training using shallow interactive segmentation, our approach extracts implicit information from RECIST annotations to create a unified RECIST-induced reliable learning (RiRL) framework. Our novel contribution involves a label generation procedure and a dynamic soft label propagation technique, designed to circumvent the problems of noisy training and poor generalization. RECIST-induced geometric labeling, predicated on clinical RECIST features, reliably and preliminarily propagates the label. Employing a trimap during the labeling process, lesion slices are partitioned into three segments: foreground, background, and ambiguous zones. This establishes a strong and reliable supervisory signal encompassing a broad area. A topological graph, informed by knowledge, is built for the purpose of real-time label propagation, in order to refine the segmentation boundary optimally. On a publicly accessible benchmark dataset, the proposed method exhibits a considerable performance advantage compared to current 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 is composed of a three-channel analog front-end, a pulse-width modulator including adjustable output-frequency offset and temperature calibration, and inductive data telemetry. Utilizing a resistance-boosting strategy in the feedback circuit of the instrumentation amplifier, the pseudo-resistor demonstrates reduced non-linearity, producing a total harmonic distortion less than 0.1%. Furthermore, the boosting approach reinforces the system's resistance to feedback, which in turn leads to a smaller feedback capacitor and, ultimately, a decrease in the overall size. To ensure the modulator's output frequency remains stable despite temperature fluctuations and process variations, fine-tuning and coarse-tuning algorithms are employed. The front-end channel boasts an effective number of bits of 89 for intra-cardiac signal extraction, showcasing input-referred noise below 27 Vrms and a minimal power consumption of 200 nW per channel. The front-end's output, encoded by an ASK-PWM modulator, powers the 1356 MHz on-chip transmitter. The proposed System-on-Chip (SoC) is created by utilizing a 0.18-micron standard CMOS process, resulting in a power consumption of 45 watts and a die size of 1125 mm².
Pre-training video and language models has become a topic of substantial recent interest, given their impressive performance in diverse downstream tasks. In the realm of existing cross-modality pre-training methods, architectural strategies often involve either modality-specific representations or representations that combine multiple modalities. Median survival time This paper introduces a novel architecture, the Memory-augmented Inter-Modality Bridge (MemBridge), differing from previous approaches by using learnable intermediate modality representations to act as a bridge between videos 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. In addition, a memory bank is suggested to archive a substantial amount of modality interaction data, which facilitates adaptive bridge token generation in different circumstances, boosting the capability and reliability of the inter-modality bridge. By means of pre-training, MemBridge explicitly models representations enabling a more sufficient level of inter-modality interaction. selleck products Comprehensive tests show that our approach's performance is competitive with previous methods on several downstream tasks, including video-text retrieval, video captioning, and video question answering, over multiple datasets, signifying the efficacy of the proposed methodology. The source code is accessible at https://github.com/jahhaoyang/MemBridge.
Neurologically, the act of filter pruning manifests as a process of both forgetting and recalling previously stored information. Usual methods, at the initial stage, cast aside less critical information arising from an unreliable baseline, expecting only a minor performance reduction. Nevertheless, remembering unsaturated bases within the framework of the model places a ceiling on the minimized model's effectiveness, thereby resulting in sub-optimal performance. A failure to initially recall this point would result in permanent data loss. We introduce a novel filter pruning paradigm, Remembering Enhancement and Entropy-based Asymptotic Forgetting (REAF), in this design. Drawing inspiration from robustness theory, we initially bolster memory capacity by over-parameterizing the baseline model with fusible compensatory convolutions, thereby freeing the pruned model from the baseline's constraints without incurring any inference overhead. The interplay between original and compensatory filters consequently necessitates a collaborative pruning method, requiring mutual agreement.