Frontiers of Computer Science最新文献

In this paper, we propose GSR-NTT and demonstrate that K-NTT, H-NTT, and G3-NTT are specific instances of GSR-NTT. We introduce a succinct methodology for complexity analysis, and utilize our GSR-NTT to accelerate polynomial multiplications in NTTRU and power-of-three cyclotomic rings.

In this paper, we propose the first fully dynamic parallel allpairs shortest path algorithm in the MPC model with a worstcase update rounds of (O({n^{{2 over 3} - {alpha over 6}}}log n/alpha )). We compare our algorithm with the existing static APSP algorithms in the MPC model, demonstrating the efficiency of our approach

Autonomous agents have long been a research focus in academic and industry communities. Previous research often focuses on training agents with limited knowledge within isolated environments, which diverges significantly from human learning processes, and makes the agents hard to achieve human-like decisions. Recently, through the acquisition of vast amounts of Web knowledge, large language models (LLMs) have shown potential in human-level intelligence, leading to a surge in research on LLM-based autonomous agents. In this paper, we present a comprehensive survey of these studies, delivering a systematic review of LLM-based autonomous agents from a holistic perspective. We first discuss the construction of LLM-based autonomous agents, proposing a unified framework that encompasses much of previous work. Then, we present a overview of the diverse applications of LLM-based autonomous agents in social science, natural science, and engineering. Finally, we delve into the evaluation strategies commonly used for LLM-based autonomous agents. Based on the previous studies, we also present several challenges and future directions in this field.

In this study, we propose SCREEN, a novel method for predicting perturbation responses of scRNA-seq data. Through extensive experiments on various datasets, we validated the effectiveness and advantages of SCREEN for the prediction of single-cell gene expression perturbation responses. Besides, we demonstrated the ability of SCREEN to facilitate biological implications in downstream analysis. Moreover, we showed the robustness of SCREEN to noise degree, number of cell types, and cell type imbalance, indicating its broader applicability. Source codes and detailed tutorials of SCREEN are freely accessible at Github (Califorya/SCREEN). We anticipate SCREEN will greatly assist with perturbational single-cell omics and precision medicine.

Most truss-based community search methods are usually confronted with the fragmentation issue. We propose a Biased edge Enhancement method for Truss-based Community Search (BETCS) to address the issue. This paper mainly solves the fragmentation problem in truss community query through data enhancement. In future work, we will consider applying the methods in the text to directed graphs or dynamic graphs.

This work introduces a GNN library, XGCN, which is designed to assist users in rapidly developing and running large-scale GNN recommendation models. We offer highly scalable GNN reproductions and include a recently proposed GNN model: xGCN. Experimental evaluations on datasets of varying scales demonstrate the superior scalability of our XGCN library.

Uncertain Knowledge Graphs (UKGs) are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs. The research on the embedding of UKG has only recently begun, Uncertain Knowledge Graph Embedding (UKGE) model has a certain effect on solving this problem. However, there are still unresolved issues. On the one hand, when reasoning the confidence of unseen relation facts, the introduced probabilistic soft logic cannot be used to combine multi-path and multi-step global information, leading to information loss. On the other hand, the existing UKG embedding model can only model symmetric relation facts, but the embedding problem of asymmetric relation facts has not be addressed. To address the above issues, a Multiplex Uncertain Knowledge Graph Embedding (MUKGE) model is proposed in this paper. First, to combine multiple information and achieve more accurate results in confidence reasoning, the Uncertain ResourceRank (URR) reasoning algorithm is introduced. Second, the asymmetry in the UKG is defined. To embed asymmetric relation facts of UKG, a multi-relation embedding model is proposed. Finally, experiments are carried out on different datasets via 4 tasks to verify the effectiveness of MUKGE. The results of experiments demonstrate that MUKGE can obtain better overall performance than the baselines, and it helps advance the research on UKG embedding.

Numerous studies have demonstrated that human microRNAs (miRNAs) and diseases are associated and studies on the microRNA-disease association (MDA) have been conducted. We developed a model using a low-rank approximation-based link propagation algorithm with Hilbert–Schmidt independence criterion-based multiple kernel learning (HSIC-MKL) to solve the problem of the large time commitment and cost of traditional biological experiments involving miRNAs and diseases, and improve the model effect. We constructed three kernels in miRNA and disease space and conducted kernel fusion using HSIC-MKL. Link propagation uses matrix factorization and matrix approximation to effectively reduce computation and time costs. The results of the experiment show that the approach we proposed has a good effect, and, in some respects, exceeds what existing models can do.

In this paper, we address the problem of unsuperised social network embedding, which aims to embed network nodes, including node attributes, into a latent low dimensional space. In recent methods, the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction performance. However, the non-linear property of node attributes and network structure is not efficiently fused in existing methods, which is potentially helpful in learning a better network embedding. To this end, in this paper, we propose a novel model called ASM (Adaptive Specific Mapping) based on encoder-decoder framework. In encoder, we use the kernel mapping to capture the non-linear property of both node attributes and network structure. In particular, we adopt two feature mapping functions, namely an untrainable function for node attributes and a trainable function for network structure. By the mapping functions, we obtain the low dimensional feature vectors for node attributes and network structure, respectively. Then, we design an attention layer to combine the learning of both feature vectors and adaptively learn the node embedding. In encoder, we adopt the component of reconstruction for the training process of learning node attributes and network structure. We conducted a set of experiments on seven real-world social network datasets. The experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.

In this work, we propose a stateless blockchain called CompactChain, which compacts the entire state of the UTXO (Unspent Transaction Output) based blockchain systems into two RSA accumulators. The first accumulator is called Transaction Output (TXO) commitment which represents the TXO set. The second one is called Spent Transaction Output (STXO) commitment which represents the STXO set. In this work, we discuss three algorithms: (i) To update the TXO and STXO commitments by the miner. The miner also provides the proofs for the correctness of the updated commitments; (ii) To prove the transaction’s validity by providing a membership witness in TXO commitment and non-membership witness against STXO commitment for a coin being spent by a user; (iii) To update the witness for the coin that is not yet spent; The experimental results evaluate the performance of the CompactChain in terms of time taken by a miner to update the commitments and time taken by a validator to verify the commitments and validate the transactions. We compare the performance of CompactChain with the existing state-of-the-art works on stateless blockchains. CompactChain shows a reduction in commitments update complexity and transaction witness size which inturn reduces the mempool size and propagation latency without compromising the system throughput (Transactions per second (TPS)).