The operations on the aircraft carrier flight deck are carried out in a time‐critical and resource‐constrained environment with uncertainty, and it is of great significance to optimize the makespan and obtain a robust schedule and resource allocation plan for a greater sortie generation capacity and better operational management of an aircraft carrier. In this paper, a proactive robust optimization method for flight deck scheduling with stochastic operation durations is proposed. Firstly, an operation on node‐flow (OONF) network is adopted to model the precedence relationships of multi‐aircraft operations, and resource constraints categorized into personnel, support equipment, workstation space, and supply resource are taken into consideration. On this basis, a mathematical model of the robust scheduling problem for flight deck operation (RSPFDO) is established, and the goal is to maximize the probability of completing within the limitative makespan (PCLM) and minimize the weighted sum of expected makespan and variance of makespan (IRM). Then, in terms of proactive planning, both serial and parallel schedule generation schemes for baseline schedule and robust personnel allocation scheme and equipment allocation adjustment scheme for resource allocation are designed. In terms of executing schedules, an RSPFDO‐oriented preconstraint scheduling policy (C^PC) is proposed. To optimize the baseline schedule and resource allocation, a hybrid teaching‐learning‐based optimization (HTLBO) algorithm is designed which integrates differential evolution operators, peak crossover operator, and learning‐automata‐based adaptive variable neighborhood search strategy. Simulation results shows that the HTLBO algorithm outperforms both some other state‐of‐the‐art algorithms for deterministic cases and some existing algorithms for stochastic project scheduling, and the robustness of the flight deck operations can be improved with the proposed resource allocation schemes and C^PC policy.

Multimodal sentiment analysis (MSA) seeks to predict subjective human sentiments by utilizing information from multiple modalities. It has been applied in diverse scenarios. Recent studies suggest that MSA benefits from integrating diverse modalities, emphasizing the fusion of multimodal information at different levels and the joint learning of modality-consistent and modality-inconsistent representations. In this work, we put forward a cross-modality dual-attention fusion network (CMDAF) that incorporates a cross-modality dual-attention fusion module. This module facilitates the integration of representations across modalities, enabling effective fusion from shallow to deep levels. A Transformer-based projection module transforms the fused representations into modality-consistent and modality-inconsistent forms, further enhanced through multi-scale contrast learning. Experiments on three MSA datasets (MOSI, MOSEI, and CH-SIMS) demonstrate the superiority of CMDAF, achieving state-of-the-art performance on most metrics.

With the rapid growth in astronomical spectra produced by large sky survey telescopes, traditional manual classification processes can no longer fulfill the requirements of precision and efficiency of spectral classification. There is an urgent need to employ machine learning approaches to conduct automated spectral classification tasks. Feature extraction is a critical step which has a great impact on any classification result. In this paper, a novel gradient-based method together with principal component analysis is proposed for the extraction of partial features of stellar spectra, that is, a feature vector indicating obvious local changes in data, which corresponds to the element line positions in the spectra. Furthermore, a general feature vector is utilized as an additional characteristic centering on the overall tendency of spectra, which can indicate stellar effective temperature. The two feature vectors and raw data are input into three neural networks, respectively, for training and each network votes for a predicted category of spectra. By selecting the class having the maximum votes, different types of spectra can be classified with high accuracy. The experimental results prove that a better performance can be achieved using the partial and general methods in this paper. The method could also be applied to other similar one-dimensional spectra, and the concepts proposed could ultimately expand the scope of machine learning application in astronomical spectral processing.

Jiao-Liao Mandarin, a distinguished dialect in China, reflects the linguistic features and cultural heritage of the Jiao-Liao region. However, the labor-intensive and costly nature of manual transcription limits the scale of transcribed corpora, posing challenges for speech recognition. We present JLMS25, a transcribed corpus for Jiao-Liao Mandarin, alongside a novel multi-dialect knowledge transfer (MDKT) framework for low-resource speech recognition. By leveraging phonetic and linguistic knowledge from neighboring dialects, the MDKT framework improves recognition in resource-constrained settings. It comprises an acoustic feature extractor, a dialect feature extractor, and two modules—WFAdapter (weight decomposition adapter) and AttAdapter (attention-based adapter)—to enhance adaptability and mitigate overfitting. The training involves a three-phase strategy: multi-dialect AID-ASR multi-task learning in phase one, freezing the dialect feature extractor in phase two, and fine-tuning only the adapters in phase three. Experiments on the Jiao-Liao Mandarin subset of the KeSpeech dataset and JLMS25 dataset show that MDKT outperforms full-parameter fine-tuning, reducing Character Error Rate (CER) by 5.4% and 7.7% and Word Error Rate (WER) by 6.1% and 10.8%, respectively.