Histology analysis of the tumor micro-environment integrated with genomic assays is widely regarded as the cornerstone for cancer analysis and survival prediction. This paper jointly incorporates genomics and Whole Slide Images (WSIs), and focuses on addressing the primary challenges involved in multi-modality prognosis analysis: 1) the high-order relevance is difficult to be modeled from dimensional imbalanced gigapixel WSIs and tens of thousands of genetic sequences, and 2) the lack of medical expertise and clinical knowledge hampers the effectiveness of prognosis-oriented multi-modal fusion. Due to the nature of the prognosis task, statistical priors and clinical knowledge are essential factors to provide the likelihood of survival over time, which, however, has been under-studied. To this end, we propose a prognosis-oriented image-omics fusion framework, dubbed Clinical Stage Prompt induced Multimodal Prognosis (CiMP). Concretely, we leverage the capabilities of the advanced LLM to generate descriptions derived from structured clinical records and utilize the generated clinical staging prompts to inquire critical prognosis-related information from each modality intentionally. In addition, we propose a Group Multi-Head Self-Attention module to capture structured group-specific features within cohorts of genomic data. Experimental results on five TCGA datasets show the superiority of our proposed method, achieving state-of-the-art performance compared to previous multi-modal prognostic models. Furthermore, the clinical interpretability and discussion also highlight the immense potential for further medical applications. Our code will be released at https://github.com/DeepMed-Lab-ECNU/CiMP/.

Spatially resolved transcriptomics (SRT) enable the comprehensive characterization of transcriptomic profiles in the context of tissue microenvironments. Unveiling spatial transcriptional heterogeneity needs to effectively incorporate spatial information accounting for the substantial spatial correlation of expression measurements. Here, we develop a computational method, SpaSRL (spatially aware self-representation learning), which flexibly enhances and decodes spatial transcriptional signals to simultaneously achieve spatial domain detection and spatial functional genes identification. This novel tunable spatially aware strategy of SpaSRL not only balances spatial and transcriptional coherence for the two tasks, but also can transfer spatial correlation constraint between them based on a unified model. In addition, this joint analysis by SpaSRL deciphers accurate and fine-grained tissue structures and ensures the effective extraction of biologically informative genes underlying spatial architecture. We verified the superiority of SpaSRL on spatial domain detection, spatial functional genes identification and data denoising using multiple SRT datasets obtained by different platforms and tissue sections. Our results illustrate SpaSRL's utility in flexible integration of spatial information and novel discovery of biological insights from spatial transcriptomic datasets.

The H ∞ control method is an effective approach for attenuating the effect of disturbances on practical systems, but it is difficult to obtain the H ∞ controller due to the nonlinear Hamilton—Jacobi—Isaacs equation, even for linear systems. This study deals with the design of an H ∞ controller for linear discrete-time systems. To solve the related game algebraic Riccati equation (GARE), a novel model-free minimax Q -learning method is developed, on the basis of an offline policy iteration algorithm, which is shown to be Newton’s method for solving the GARE. The proposed minimax Q -learning method, which employs off-policy reinforcement learning, learns the optimal control policies for the controller and the disturbance online, using only the state samples generated by the implemented behavior policies. Different from existing Q -learning methods, a novel gradient-based policy improvement scheme is proposed. We prove that the minimax Q -learning method converges to the saddle solution under initially admissible control policies and an appropriate positive learning rate, provided that certain persistence of excitation (PE) conditions are satisfied. In addition, the PE conditions can be easily met by choosing appropriate behavior policies containing certain excitation noises, without causing any excitation noise bias. In the simulation study, we apply the proposed minimax Q -learning method to design an H ∞ load-frequency controller for an electrical power system generator that suffers from load disturbance, and the simulation results indicate that the obtained H ∞ load-frequency controller has good disturbance rejection performance.

In this paper, a novel synchronous Q-learning method is proposed for solving discrete-time linear quadratic regulator (LQR) problems. To begin with, the Bellman equation corresponding to the optimal Q-function is reformulated into a consistency equation on the parameters of the optimal Q-function and the optimal controller. Then an actor-critic structure is introduced to learn the optimal Q-function and the optimal controller online in real time by using the state samples generated by the behavior policy. Particularly, the proposed synchronous Q-learning scheme simultaneously updates the Q-function approximation and the optimal controller approximation, rather than iterating between policy evaluation and policy improvement. The proposed control scheme is proved to be uniformly ultimately bounded (UUB) under appropriate learning rates, provided that certain persistence of excitation (PE) conditions are satisfied. Besides, the PE conditions can be easily met by injecting appropriate exploration noise into the behavior policy without causing any excitation noise bias. Finally, one simulation example is provided to verify the effectiveness of the proposed synchronous Q-learning method.

Abstract In this study, a cotton disease diagnosis method that uses a combined algorithm of case-based reasoning (CBR) and fuzzy logic was designed and implemented. It focuses on the prevention, diagnosis and control of diseases affecting cotton production in China. Conventional methods of disease diagnosis are primarily based on CBR with reference to user-provided symptoms; however, in most cases, user-provided symptoms do not fully meet the requirements of CBR. To address this problem, fuzzy logic is incorporated into CBR to allow for more flexible and accurate models. With the help of CBR and fuzzy reasoning, three diagnostic results can be obtained by the cotton disease diagnosis system (CDDS) constructed in this study: success, success but not exact and failure. To verify the reliability of the CDDS and its ability to diagnose cotton diseases, its diagnostic accuracy and stability were analyzed and compared with the results obtained by the traditional expert scoring method. The analysis results reveal that the CDDS can achieve a high diagnostic success rate (above 90%) and better diagnostic stability than the traditional expert scoring method when at least four disease symptoms are input. The CDDS provides an independent and objective source of information to assist farmers in the diagnosis and prevention of cotton diseases.

Abstract There is rich knowledge in expert systems that can be used to solve practical problems, but its promotion and application must rely on information facilities. The application of both computers and the Internet for Chinese farmers are not common, which leads to restrictions on the promotion and application of expert systems in rural areas of China. On the other hand, the existing call centers lack a professional knowledge base and the method of automatically calling the knowledge base in real-time, which makes it difficult to meet the needs of users wanting to obtain knowledge in a timely manner. To address these problems, a call center embedded in an expert system inference algorithm and knowledge base for farmers to obtain agricultural knowledge through mobile phones or fixed-line telephones was established. By studying the event-condition-action-based (ECA-based) database triggering model, remote method invocation-based (RMI-based) communication and iterative dichotomiser 3 algorithm-based (ID3-based) parameter extraction, the cohesion between the call center and the expert system was realized. The agricultural knowledge audio acquisition model was then coupled with the call center and the expert system was constructed, allowing farmers to acquire agricultural knowledge through mobile phones or fixed phones with fast responses. When used for cotton disease diagnosis, it can achieve a high diagnostic success rate (above 75%) when at least three disease symptoms are input into the expert system via the voice call, which provides an effective channel for Chinese farmers to obtain agricultural knowledge. It presents good application prospects in China, where 5G technology is currently developing rapidly.

Dynamic music emotion prediction is to recognize the continuous emotion information in music, which is necessary for music retrieval and recommendation. In this paper, we adopt the dimensional valence-arousal (V-A) emotion model to represent the dynamic emotion in music. In our opinion, music and V-A emotion label do not have the one-to-one correspondence in the time domain, while the expression of music emotion at one moment is the accumulation of previous music content for a period of time, so we propose Long Short-Term Memory (LSTM) based sequence-to-one mapping for dynamic music emotion prediction. Based on this sequence-to-one music emotion mapping, it is proved that different time scales' preceding content has an influence on the LSTM model's performance, so we further propose the Multi-scale Context based Attention (MCA) for dynamic music emotion prediction. We evaluate our proposed method on the database of Emotion in Music task at MediaEval 2015, and the results show that our proposed method outperforms most of the models using the same features and achieves a competitive performance with the state-of-the-art methods.

Music Dynamic Emotion Prediction is a challenging and significant task. In this paper, We adopt the dimensional valence-arousal (V-A) emotion model to represent the dynamic emotion in music. Considering the high context correlation among the music feature sequence and the advantage of Bidirectional Long Short-Term Memory (BLSTM) in capturing sequence information, we propose a multi-scale approach, Deep BLSTM (DBLSTM) based multi-scale regression and fusion with Extreme Learning Machine (ELM), to predict the V-A values in music. We achieved the best performance on the database of Emotion in Music task in MediaEval 2015 compared with other submitted results. The experimental results demonstrated the effectiveness of our novel proposed multi-scale DBLSTM-ELM model.

Dynamic music emotion prediction is to recognize the continuous emotion contained in music, and has various applications. In recent years, dynamic music emotion recognition is widely studied, while the inside structure of the emotion in music remains unclear. We conduct a data observation based on the database provided by Free Music Archive (FMA), and find that emotion dynamic shows different properties under different scales. According to the data observation, we propose a new method, Double-scale Support Vector Regression (DS-SVR), to dynamically recognize the music emotion. The new method decouples two scales of emotion dynamics apart, and recognizes them separately. We apply the DS-SVR to MediaEval 2015, Emotion in Music database, and achieve an outstanding performance, significantly better than the baseline provided by organizer.

Dynamic Music Emotion Prediction is crucial to the emerging applications of music retrieval and recommendation. Considering the influence of temporal context and hierarchical structure on emotion in music, we propose a Deep Bidirectional Long Short-Term Memory (DBLSTM) based multi-scale regression method. In this method, a post-processing component is utilised for individual DBSLTM output to further enhance the ability of temporal context processing and a fusion component is to integrate the output of all DBLSTM models with different scales. In addition, we investigate how the difference of sequence length between the training and predicting phase affects the performance of DBLSTM. We conduct our experiments on a public database of Emotion in Music task at MediaEval 2015, and the result shows that our method achieves significant improvement when compared with the state-of-art methods.