Large language models(LLMs)have demonstrated impressive capabilities across a wide range of reasoning tasks.To address the lack of faithfulness in prompt-based reasoning methods such as Chain-of-Thought(CoT),researchers have explored neuro-symbol-ic approaches to bolster LLM logical reasoning capabilities.However,existing methods still face challenges with information loss dur-ing.To overcome these limitations,this paper introduce Iterative Feedback-Driven Neuro-Symbolic(IFDNS),a novel prompt-based method that employs a multi-round feedback mechanism to address LLM limitations in handling complex logical relationships.IFDNS utilizes iterative feedback during the logic extraction phase to accurately extract causal relationship statements and translate them into propositional and logical implication expressions,effectively mitigating information loss issues.Furthermore,IFDNS is orthogonal to existing prompt methods,allowing for seamless integration with various prompting approaches.Empirical evaluations across six data-sets demonstrate the effectiveness of IFDNS in significantly improving the performance of CoT and Chain-of-Thought with Self-Con-sistency(CoT-SC).
In the field of medical image analysis, the scarcity of Chinese chest X-ray report datasets has hindered the development of technology for generating Chinese chest X-ray reports. On one hand, the construction of a Chinese chest X-ray report dataset is limited by the time-consuming and costly process of accurate expert disease annotation. On the other hand, a single natural language generation metric is commonly used to evaluate the similarity between generated and ground-truth reports, while the clinical accuracy and effectiveness of the generated reports rely on an accurate disease labeler (classifier). To address the issues, this study proposes a disease labeler tailored for the generation of Chinese chest X-ray reports. This labeler leverages a dual BERT architecture to handle diagnostic reports and clinical information separately and constructs a hierarchical label learning algorithm based on the affiliation between diseases and body parts to enhance text classification performance. Utilizing this disease labeler, a Chinese chest X-ray report dataset comprising 51,262 report samples was established. Finally, experiments and analyses were conducted on a subset of expert-annotated Chinese chest X-ray reports, validating the effectiveness of the proposed disease labeler.
With the expansion of network scales, the B/S architecture of monolithic applications is gradually being replaced by microservices. The unbundling of services has led to exponential growth in the size of APIs. When handling massive microservice requests, the commercial NIC shows limitations in three aspects: deterministic, programmability, and data copy. To ensure that each microservice node handles requests efficiently, flexibly, and precisely, this paper proposes a programmable deterministic multi-queue FPGA Accelerator. The Accelerator relies on the instantiated 1000 queues and the queue management unit to extend the rule-based RSS algorithm for the serverless-friendly programmability of packet distribution. A PTP hardware clock is added to collaborate with the queue management unit to control the deterministic delivery. To improve the sending and receiving efficiency of network node data, a driver adapted to the FPGA accelerator is designed to realize zero-copy. Experiments conducted on a 100 Gbps FPGA show that the Accelerator can support the multi-queue transmission with various packet sizes, define the forwarding behavior, and almost approach the line rate on an 8-core FPGA device. In addition, it can forward packets with low latency close to that of the current state-of-the-art ovs-DPDK. This Accelerator overcomes, to some extent, the limitations of commercial NICs when oriented to microservice architectures.
In knowledge discovery and data mining, removing redundant and irrelevant data attributes is crucial. Traditional algorithms, however, struggle with efficiency in high-dimensional big data contexts. To solve this problem, this paper proposes a novel global search attribute reduction method namely RHC-IGWO (Rough Hypercuboid Structure via Improved Grey Wolf Optimizer) by integrating the rough hypercuboid method and Improved Grey Wolf Optimizer (IGWO) with the pheromone mechanism. The algorithm is embedded into the Apache Spark parallel computing framework (Parallel computing Rough Hypercuboid Structure via Improved Grey Wolf Optimizer, PcRHC-IGWO) to accelerate and simplify the attribute reduction process. The algorithm divides the decision table into several independent blocks, introduces the pheromone mechanism to simulate the wolf pack behavior, and uses the IGWO for global search, which is conducive to efficient local search and global information sharing between individuals. The position of the individual is initialized by calculating the relevance between the attributes, and the pheromone value is dynamically updated according to the reduction quality. This allows automatically giving more search focus to more promising attribute regions. Experiments with public and real datasets demonstrate the RHC-IGWO algorithm's significant speedup and its efficacy in maintaining or enhancing classification accuracy. Particularly noteworthy is its performance on schizophrenia datasets, where the proposed method achieves outstanding classification accuracies of 86.2%, 88.89%, and 92.86% across various classifiers. These results not only demonstrate its effectiveness but also underline its potential in advanced data analysis scenarios. Additionally, on some large-scale datasets, the time required for processing has been reduced by 85.71%, showcasing the algorithm's efficiency in handling complex and voluminous data.
Graph representation learning aims to learn embedding representations of nodes in graph-structured data through supervised or unsupervised methods. However, for unlabeled or sparsely labeled graph datasets, conventional supervised approaches fail to perform adequately, and existing unsupervised methods have difficulty achieving high accuracy on multiple downstream tasks such as node classification and link prediction at lower embedding dimensions. To address this issue, we propose a novel self-supervised graph representation learning approach called GAN-based Message Passing Graph Representation Learning. This approach significantly improves the accuracy of node embeddings without relying on label information. Specifically, GMP-GL has developed a generator based on neighbor-weighted random walk to obtain positive and negative node sample pairs, as well as a discriminator that distinguishes whether a real connection exists between node pairs. Through adversarial learning, GMP-GL automatically learns node embeddings. Furthermore, GMP-GL optimizes the training outcomes of the generative adversarial model through self-supervised embedding aggregation learning, thus introducing rich structural information from the graph for the subsequent rounds of generative adversarial training. In addition, GMP-GL employs multiple autoencoders to generate and filter original embeddings, ensuring that the pre-embeddings preserve a significant amount of original feature information for formal learning. The study selects six real-world datasets and conducts comparative experiments with ten SOTA baseline methods. The experimental results demonstrate that the proposed model not only surpasses previous unsupervised methods but also matches or even exceeds the performance of supervised methods. This indicates the great potential of the proposed approach for representation learning tasks on unlabeled graph datasets and its effective applicability to real-world problems.
