Procedure planning in instructional videos, producing a structured and plannable action sequence facilitating the transition from the start to the goal states, has achieved significant progress. The dominant single-branch non-autoregressive planning paradigm guides action sequence generation through action labels, overlooking the limitation of the absence of intermediate visual information. Hence, we introduce the procedure knowledge decoupled distillation strategy to address the above issue. This innovative strategy deliberately lets the teacher model see the real visual information among the start and goal states to enhance its action semantic understanding and relationship modeling ability, producing the potential probability distribution containing the real action class and other action classes that may occur. Accordingly, we introduce a decoupled intermediate information knowledge distillation loss, which comprises single action knowledge distillation and sequence distribution knowledge distillation for the student model. The former improves the student model's precise inference ability for individual actions by transferring knowledge of a single action target category using binary classification loss. Conversely, the latter uses MSE loss to constrain the student model to learn the action sequence probability distribution from the teacher model, thereby enhancing the student model's global planning capability. Extensive experiments on three datasets demonstrate that our strategy can improve the performance of multiple weakly supervised models, achieving promising procedure knowledge modeling ability and plug-and-play flexibility.

Optimization problems are ubiquitous across various domains, such as resource scheduling, production planning, and sales management. Traditionally, they are modeled manually, leading to inefficiencies due to difficulties in communication and collaboration between modeling and domain experts. The emergence of Large Language Models (LLMs) has made automated modeling possible. However, real-world applications are often large-scale and have numerous variables and constraints, limiting the applicability of existing methods. To address this, we propose PaMOP, a novel modeling framework based on LLMs, to model optimization problems automatically, given only natural language descriptions. Specifically, we extract and partition the problems using a tree structure, guiding the LLMs to model each set of constraints with self-augmented prompts, thus reducing the demands on the LLM's capabilities of large contents. The mathematical model is then iteratively corrected and validated through our correction procedures. The experiments demonstrate that our method improves performance on the common benchmark dataset NLP4LP, achieving an accuracy of 62.3% and a code executability rate of 86.8% when tested on GPT-4. Additionally, we demonstrate the effectiveness of our PaMOP in handling large real-world problems.

The Fata morgana algorithm (FATA), with its combination of virtual and real search mechanisms and outstanding global optimization ability, has demonstrated strong performance in various optimization problems. This paper attempts to introduce it into the field of feature selection and proposes an enhanced version, named EFATA, which effectively improves the ability to escape local optima and global search by incorporating a collaborative search strategy inspired by swarm foraging. In the experiments, the Support Vector Machine (SVM) is used to guide the feature selection process, enabling the effective identification of key feature across multiple UCI datasets, and experimental results show that it is superior to FATA and other comparison algorithms in terms of classification accuracy and F-score, fully validating the superiority and robustness of the EFATA in feature selection tasks.

Multi-source physiological signals are valuable for quantitative pain assessment, but their high dimensionality, nonlinearity, and strong inter-variable correlations present challenges for clinical decision-making. To improve diagnostic accuracy and efficiency, we propose a novel feature selection framework based on an enhanced Rime optimization algorithm (b-CDRIME), which incorporates a co-adaptive hunting strategy and a dispersed foraging strategy. The framework discretizes the original continuous CDRIME algorithm by introducing a V-shaped binary coding strategy, and constructs a multi-objective weighted fitness function that integrates classification accuracy, feature dimension and AUC, which is combined with the dynamic feedback mechanism of the classifier to achieve efficient feature screening and classification performance optimization. On a multi-source physiological signal public pain dataset, this paper utilizes 10 classical classifiers to test our methods on feature selection and performance improvement. The comparative experiments results show that the pain assessment model constructed based on CDRIME with XGBoost has an average classification accuracy of 81.6% while significantly reducing feature dimensionality. These findings validate the good performance and application potential of this study in the task of processing high-dimensional multi-source pain physiological signals.

The Central Nervous System (CNS) acts as the main element of the biological system, regulating and commanding numerous organs in the human body. Neurons play a crucial role in the central nervous system, and it is necessary to thoroughly examine, replicate, simulate, and integrate various aspects of the CNS to develop a comprehensive neuronal system that can mimic the actual nervous system. In this research, a neuron model called the Quartic Neuron Model is employed to imitate the fundamental nervous functions of the human brain. The proposed method, known as Digital-QNM (D-QNM) is accomplished by employing power-2 based approximation and linear approaches to modify the fourth-degree function. These power-2 based functions are digital-friendly terms (high-accurate, low-cost and leads to high-frequency implementation). By eliminating the high-cost function, the presented model offers advantages such as low error, high speed, and efficient resource utilization compared to the basic main state. In order to validate the final hardware design, a digital FPGA board (specifically, the Xilinx Virtex-5 FPGA board) is employed. The process of digitally synthesizing the hardware demonstrates that our proposed approach can replicate the QNM with improved frequency, performance, and reduced hardware costs. The implementation outcomes show a significant reduction of 98% in FPGA resources cost and a higher operating frequency of the suggested model, reaching 190 MHz. This frequency is considerably higher than the original model’s 105 MHz.

Convolution Neural Networks (CNN) have evolved to be the state-of-art technique for machine learning tasks. However, CNNs bring a significant increase in the computation and parameter storage costs, which makes it difficult to deploy on embedded devices with limited hardware resources and a tight power budget. In recent years, people focus on reducing these overheads by compressing the CNN models, such as pruning weights and pruning filters. Compared with the method of pruning weights, the method of pruning filters does not result in sparse connectivity patterns. And it is conducive to the parallel acceleration on hardware platforms. In this paper, we proposed a new method to judge the importance of filters. In order to make the judgement more accurate, we use the standard deviation to represent the amount of information extracted by the filter. In the process of pruning, the unimportant filters can be removed directly without loss in the test accuracy. We also proposed a multilayer pruning method to avoid setting the pruning rate layer by layer. This holistic pruning method can improve the pruning efficiency. In order to verify the effectiveness of our algorithm, we do experiments with simple network VGG16 and complex networks ResNet18/34. We re-trained the pruned CNNs to compensate the accuracy loss caused by the pruning process. The results showed that our pruning method can reduce inference cost by up to 50% for VGG16 and 35% for ResNet18/34 on CIFAR10 with little accuracy loss.