Assuming a Chinese restaurant process (CRP) beforehand, this method precisely categorizes the present task as a previously encountered context or establishes a fresh context as required, independently of any external signal predicting environmental shifts. Subsequently, an expandable multi-headed neural network is applied, where the output layer expands in step with newly incorporated context, and a knowledge distillation regularization term is applied to maintain learned task performance. As a general deep reinforcement learning framework, DaCoRL consistently outperforms existing methods in terms of stability, performance, and generalization on robot navigation and MuJoCo locomotion tasks, this superiority being verified through extensive experimental data.
Chest X-ray (CXR) image analysis for pneumonia detection, especially in cases of coronavirus disease 2019 (COVID-19), stands as a crucial method for both diagnosing the condition and prioritizing patient care. The application of deep neural networks (DNNs) for the classification of CXR images suffers from the constraint of a limited and carefully selected dataset sample size. In order to achieve accurate CXR image classification, this article proposes a hybrid-feature fusion deep forest framework, specifically a distance transformation-based one (DTDF-HFF), to address this issue. In our proposed method, CXR image hybrid features are extracted through the dual methodology of hand-crafted feature extraction and multi-grained scanning. Different classifiers within the same layer of a deep forest (DF) system are fed with various features, and the resultant prediction vector at each layer is transformed into a distance vector according to a self-adaptive mechanism. Original features are augmented with distance vectors obtained from various classifiers, which are then concatenated and fed into the subsequent layer's classifier. The cascade's progression culminates when the DTDF-HFF is unable to reap any further benefits from the new layer. Our proposed approach is measured against other methods using public chest X-ray datasets, and the experimental outcomes highlight its achievement of peak performance. The code's public release location is specified as https://github.com/hongqq/DTDF-HFF.
Conjugate gradient (CG), a powerful acceleration technique for gradient descent algorithms, has demonstrated substantial promise and widespread application in tackling large-scale machine learning challenges. Conversely, CG and its variations have not been constructed for stochastic environments, resulting in a substantial degree of instability, and potentially causing divergence with the use of noisy gradients. The mini-batch approach facilitates the development of a novel, stable stochastic conjugate gradient (SCG) algorithm class, which accelerates convergence using variance reduction and an adaptive step size. By adopting the random stabilized Barzilai-Borwein (RSBB) method for online step-size computation, this article avoids the potentially problematic and time-consuming line search often found in CG-type optimization strategies, particularly when applied to SCG. authentication of biologics A rigorous analysis of the convergence properties of the proposed algorithms reveals a linear convergence rate for both strongly convex and non-convex scenarios. Our algorithms, as we exhibit, exhibit a total complexity that mirrors that of current stochastic optimization algorithms in varied situations. Numerical experiments conducted on diverse machine learning problems strongly support the conclusion that the proposed algorithms outperform the existing stochastic optimization algorithms.
To ensure high performance and economic implementation in industrial control, we propose iterative sparse Bayesian policy optimization (ISBPO), a multitask reinforcement learning (RL) scheme. In the context of continual learning, where multiple control tasks are learned consecutively, the ISBPO method safeguards previously acquired knowledge without any performance degradation, facilitates effective resource utilization, and improves the efficiency of learning new tasks. The iterative pruning method within the ISBPO scheme ensures that adding new tasks to a single policy network doesn't compromise the control performance of previously learned tasks. Heparin In a free-weight space for integrating new tasks, each task's learning relies on the pruning-aware sparse Bayesian policy optimization (SBPO) method, ensuring the effective distribution of limited policy network resources across multiple tasks. Furthermore, the weights from previous tasks are shared and reused during the learning of new tasks, resulting in improved sample efficiency and performance for learning new tasks. Experimental validation and simulations collectively demonstrate that the ISBPO scheme excels in sequentially learning multiple tasks, achieving superior performance conservation, resource utilization, and effective sample deployment.
In the realm of medical imaging, multimodal medical image fusion is profoundly impactful in facilitating effective disease diagnosis and treatment. Traditional MMIF methods struggle to achieve satisfactory fusion accuracy and robustness, hampered by the presence of human-created elements like image transformations and fusion strategies. Deep learning-based fusion methods often struggle to achieve optimal image fusion due to their reliance on pre-defined network architectures, simplistic loss functions, and a lack of consideration for human visual perception during the weight optimization process. To resolve these concerns, we've developed F-DARTS, an unsupervised MMIF method built on foveated differentiable architecture search. This method's weight learning process incorporates the foveation operator to fully exploit human visual characteristics, resulting in effective image fusion. For network training, a tailored unsupervised loss function is formulated, integrating mutual information, the summation of difference correlations, structural similarity, and edge preservation. medical malpractice The F-DARTS algorithm, in conjunction with the provided foveation operator and loss function, will be used to find an end-to-end encoder-decoder network architecture for the purpose of generating the fused image. Visual assessment and objective evaluation metrics confirm that F-DARTS, on three multimodal medical image datasets, outperforms traditional and deep learning-based fusion methods in achieving superior fused images.
Conditional generative adversarial networks, while effective in image-to-image translation for general computer vision tasks, encounter significant difficulties in medical imaging due to the pervasive presence of imaging artifacts and a scarcity of data, thereby affecting their efficacy. Our development of the spatial-intensity transform (SIT) is driven by the desire to improve output image quality, while precisely mirroring the target domain. The generator's spatial transformation, smooth and diffeomorphic, is confined by SIT, alongside sparse intensity adjustments. The modular and lightweight SIT network component excels in its effectiveness on diverse architectures and training approaches. This approach surpasses unconstrained baselines by noticeably increasing image fidelity, and our models show robust performance on multiple scanner types. Furthermore, SIT provides a detailed and segregated look at anatomical and textural alterations in each translation, making it easier to decipher the model's predictions in terms of physiological implications. We showcase the capability of SIT across two use cases, including the prediction of longitudinal brain MRI data for patients with diverse stages of neurodegeneration, and visual representation of age-related and stroke severity impacts on clinical brain scans of stroke patients. Concerning the first objective, our model accurately forecasted brain aging patterns without the requirement of supervised training on paired scans. The second task analyzes the correlation between ventricular dilatation and aging, along with the relationship between white matter hyperintensities and the degree of stroke severity. The growing versatility of conditional generative models for visualization and forecasting is complemented by our approach, which introduces a simple yet powerful technique to boost robustness, essential for their transition to clinical use. The public repository, github.com, contains the source code. The project clintonjwang/spatial-intensity-transforms investigates spatial intensity transforms within image processing.
In the context of gene expression data, biclustering algorithms are critical for proper processing. For the dataset to be processed by biclustering algorithms, the majority of these methods need the data matrix first converted into binary format. Unfortunately, this form of preprocessing might unfortunately introduce noise or cause a loss of information within the binary matrix, thereby diminishing the biclustering algorithm's capacity to identify the most ideal biclusters. A novel preprocessing approach, Mean-Standard Deviation (MSD), is proposed in this paper to tackle the identified problem. We present a new biclustering algorithm, Weight Adjacency Difference Matrix Biclustering (W-AMBB), aimed at the effective processing of datasets that contain overlapping biclusters. A weighted adjacency difference matrix is constructed by applying weights to a binary matrix, which, in turn, is derived from the data matrix; this is the fundamental concept. The identification of genes strongly linked in sample data results from the efficient location of similar genes exhibiting responses to specific conditions. In addition, the W-AMBB algorithm's performance was tested on synthetic and real datasets, and its results were compared with those of other classical biclustering methods. When tested on the synthetic dataset, the experiment results unequivocally show that the W-AMBB algorithm outperforms the compared biclustering methods in terms of robustness. In addition, the GO enrichment analysis results demonstrate that the W-AMBB method holds biological meaning in actual data.