Motion Mapping Cognition: A Nondecomposable Primary Process in Human Vision

Human intelligence seems so mysterious that we have not successfully understood its foundation until now. Here, I want to present a basic cognitive process, motion mapping cognition (MMC), which should be a nondecomposable primary function in human vision. Wherein, I point out that, MMC process can be used to explain most of human visual functions in fundamental, but can not be effectively modelled by traditional visual processing ways including image segmentation, object recognition, object tracking etc. Furthermore, I state that MMC may be looked as an extension of Chen's theory of topological perception on human vision, and seems to be unsolvable using existing intelligent algorithm skills. Finally, along with the requirements of MMC problem, an interesting computational model, quantized topological matching principle can be derived by developing the idea of optimal transport theory. Above results may give us huge inspiration to develop more robust and interpretable machine vision models.Comment: 7 pages, 3 figure

Constructing Word-Context-Coupled Space Aligned with Associative Knowledge Relations for Interpretable Language Modeling

As the foundation of current natural language processing methods, pre-trained language model has achieved excellent performance. However, the black-box structure of the deep neural network in pre-trained language models seriously limits the interpretability of the language modeling process. After revisiting the coupled requirement of deep neural representation and semantics logic of language modeling, a Word-Context-Coupled Space (W2CSpace) is proposed by introducing the alignment processing between uninterpretable neural representation and interpretable statistical logic. Moreover, a clustering process is also designed to connect the word- and context-level semantics. Specifically, an associative knowledge network (AKN), considered interpretable statistical logic, is introduced in the alignment process for word-level semantics. Furthermore, the context-relative distance is employed as the semantic feature for the downstream classifier, which is greatly different from the current uninterpretable semantic representations of pre-trained models. Our experiments for performance evaluation and interpretable analysis are executed on several types of datasets, including SIGHAN, Weibo, and ChnSenti. Wherein a novel evaluation strategy for the interpretability of machine learning models is first proposed. According to the experimental results, our language model can achieve better performance and highly credible interpretable ability compared to related state-of-the-art methods.Comment: Accepted at ACL 2023, Finding

An Adversarial Multi-Task Learning Method for Chinese Text Correction with Semantic Detection

Text correction, especially the semantic correction of more widely used scenes, is strongly required to improve, for the fluency and writing efficiency of the text. An adversarial multi-task learning method is proposed to enhance the modeling and detection ability of character polysemy in Chinese sentence context. Wherein, two models, the masked language model and scoring language model, are introduced as a pair of not only coupled but also adversarial learning tasks. Moreover, the Monte Carlo tree search strategy and a policy network are introduced to accomplish the efficient Chinese text correction task with semantic detection. The experiments are executed on three datasets and five comparable methods, and the experimental results show that our method can obtain good performance in Chinese text correction task for better semantic rationality.Comment: Published on 31st International Conference on Artificial Neural Networ

Federated Two Stage Decoupling With Adaptive Personalization Layers

Federated learning has gained significant attention due to its groundbreaking ability to enable distributed learning while maintaining privacy constraints. However, as a consequence of data heterogeneity among decentralized devices, it inherently experiences significant learning degradation and slow convergence speed. Therefore, it is natural to employ the concept of clustering homogeneous clients into the same group, allowing only the model weights within each group to be aggregated. While most existing clustered federated learning methods employ either model gradients or inference outputs as metrics for client partitioning, with the goal of grouping similar devices together, may still have heterogeneity within each cluster. Moreover, there is a scarcity of research exploring the underlying reasons for determining the appropriate timing for clustering, resulting in the common practice of assigning each client to its own individual cluster, particularly in the context of highly non independent and identically distributed (Non-IID) data. In this paper, we introduce a two-stage decoupling federated learning algorithm with adaptive personalization layers named FedTSDP, where client clustering is performed twice according to inference outputs and model weights, respectively. Hopkins amended sampling is adopted to determine the appropriate timing for clustering and the sampling weight of public unlabeled data. In addition, a simple yet effective approach is developed to adaptively adjust the personalization layers based on varying degrees of data skew. Experimental results show that our proposed method has reliable performance on both IID and non-IID scenarios

Graph Fuzzy System: Concepts, Models and Algorithms

Fuzzy systems (FSs) have enjoyed wide applications in various fields, including pattern recognition, intelligent control, data mining and bioinformatics, which is attributed to the strong interpretation and learning ability. In traditional application scenarios, FSs are mainly applied to model Euclidean space data and cannot be used to handle graph data of non-Euclidean structure in nature, such as social networks and traffic route maps. Therefore, development of FS modeling method that is suitable for graph data and can retain the advantages of traditional FSs is an important research. To meet this challenge, a new type of FS for graph data modeling called Graph Fuzzy System (GFS) is proposed in this paper, where the concepts, modeling framework and construction algorithms are systematically developed. First, GFS related concepts, including graph fuzzy rule base, graph fuzzy sets and graph consequent processing unit (GCPU), are defined. A GFS modeling framework is then constructed and the antecedents and consequents of the GFS are presented and analyzed. Finally, a learning framework of GFS is proposed, in which a kernel K-prototype graph clustering (K2PGC) is proposed to develop the construction algorithm for the GFS antecedent generation, and then based on graph neural network (GNNs), consequent parameters learning algorithm is proposed for GFS. Specifically, three different versions of the GFS implementation algorithm are developed for comprehensive evaluations with experiments on various benchmark graph classification datasets. The results demonstrate that the proposed GFS inherits the advantages of both existing mainstream GNNs methods and conventional FSs methods while achieving better performance than the counterparts.Comment: This paper has been submitted to a journa

Low-Dose Immune Tolerance Induction in Children With Severe Hemophilia A With High-Titer Inhibitors: Type of Factor 8 Mutation and Outcomes

BACKGROUND: No studies evaluated the role of OBJECTIVES: To explore the association between METHODS: Children SHA with high-titer inhibitors who received low-dose ITI therapy at least for 1 year were included in this study. Based on the risk of inhibitor development, RESULTS: Of 104 children included, 101 had CONCLUSIONS: Types o

Case report: Effectiveness of sirolimus in treating partial DiGeorge Syndrome with Autoimmune Lymphoproliferative Syndrome (ALPS)-like features

BackgroundDiGeorge Syndrome (DGS) is a rare disease associated with 22q11.2 chromosomal microdeletion, also known as a velocardiofacial syndrome, based on the frequent involvements of the palate, facial, and heart problems. Hematologic autoimmunity is rare in DGS but presents with a refractory course and poor prognosis. Herein, we report a case of partial DGS in a patient with refractory immune cytopenia and autoimmune lymphoproliferative syndrome (ALPS)-like manifestations.Case descriptionA 10-year-old boy with growth retardation presented initially with a ventricular septal defect at 7 months old, which had been repaired soon after. The patient suffered from thrombocytopenia and progressed into chronic refractory immune thrombocytopenia (ITP) at 30 months old. One year later, the patient developed multilineage cytopenias including thrombocytopenia, neutropenia, and anemia. First-line treatment of ITP, like high-dose dexamethasone and intravenous immunoglobulin, had little or short-term effect on controlling symptoms. Whole-exome sequencing revealed the presence of a de novo heterozygous 2.520 Mb deletion on chromosome 22q11.21. Moreover, decreased proportion of naive T cells and elevated double-negative T cells were found. The patient was given sirolimus therapy (1.5 mg/m2, actual blood concentration range: 4.0–5.2 ng/ml) without adding other immunosuppressive agents. The whole blood cell count was gradually restored after a month, and the disease severity was soothed with less frequency of infections and bleeding events. Decreased spleen size and restrained lymph node expansion were achieved after 3-month sirolimus monotherapy.ConclusionsThis case is the first description on the efficacy of sirolimus monotherapy to treat refractory multilineage cytopenias of DGS presented with ALPS-like features

An Extended Reinforcement Learning Framework to Model Cognitive Development With Enactive Pattern Representation

In order to make machines more intelligent, it is inevitable to understand human-like cognitive development, in which adaptive, autonomous and progressive evolution of cognitive decision-making in interacting with the environment plays a key role. Inspired by enactive artificial intelligence and evolutionary sampling learning, anew cognitive development learning model termed evolutionary enactive learning is proposed in this work. The proposed model is constructed by extending the reinforcement learning framework and introducing the utility-selection theory to guide the coevolution of pattern representation and decision-making policies. Theoretical analysis on the model’s validity of evolutionary enactive learning is given. To further demonstrate the effectiveness of the proposed method,two simulated cognitive decision-making tasks are designed, in which pattern representation and decision-making must be jointly developed to achieve good cognitive performance. Our experimental results clearly demonstrate that the resulting learning process is rational and effective. Finally, we indicate that the proposed evolutionary enactive learning could be readily further extended by introducing existing machine learning techniques to solve more practical applications

An Extended Reinforcement Learning Framework to Model Cognitive Development with Enactive Pattern Representation

In order to make machines more intelligent, it is inevitable to understand human-like cognitive development, in which adaptive, autonomous and progressive evolution of cognitive decision-making in interacting with the environment plays a key role. Inspired by enactive artificial intelligence and evolutionary sampling learning, anew cognitive development learning model termed evolutionary enactive learning is proposed in this work. The proposed model is constructed by extending the reinforcement learning framework and introducing the utility-selection theory to guide the coevolution of pattern representation and decision-making policies. Theoretical analysis on the model’s validity of evolutionary enactive learning is given. To further demonstrate the effectiveness of the proposed method,two simulated cognitive decision-making tasks are designed, in which pattern representation and decision-making must be jointly developed to achieve good cognitive performance. Our experimental results clearly demonstrate that the resulting learning process is rational and effective. Finally, we indicate that the proposed evolutionary enactive learning could be readily further extended by introducing existing machine learning techniques to solve more practical applications