Several new infinite classes of 0-APN power functions over F2n\mathbb{F}_{2^n}

The investigation of partially APN functions has attracted a lot of research interest recently. In this paper, we present several new infinite classes of 0-APN power functions over $\mathbb{F}_{2^n}$ by using the multivariate method and resultant elimination, and show that these 0-APN power functions are CCZ-inequivalent to the known ones.Comment: arXiv admin note: text overlap with arXiv:2210.02207, arXiv:2210.15103 by other author

Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders

Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization. However, the traditional two-step paradigm of pre-training and sentence-ranking, creates a gap due to differing optimization objectives. To address this issue, we argue that utilizing pre-trained embeddings derived from a process specifically designed to optimize cohensive and distinctive sentence representations helps rank significant sentences. To do so, we propose a novel graph pre-training auto-encoder to obtain sentence embeddings by explicitly modelling intra-sentential distinctive features and inter-sentential cohesive features through sentence-word bipartite graphs. These pre-trained sentence representations are then utilized in a graph-based ranking algorithm for unsupervised summarization. Our method produces predominant performance for unsupervised summarization frameworks by providing summary-worthy sentence representations. It surpasses heavy BERT- or RoBERTa-based sentence representations in downstream tasks.Comment: Accepted by the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023

Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks

Language models' (LMs) proficiency in handling deterministic symbolic reasoning and rule-based tasks remains limited due to their dependency implicit learning on textual data. To endow LMs with genuine rule comprehension abilities, we propose "Neural Comprehension" - a framework that synergistically integrates compiled neural networks (CoNNs) into the standard transformer architecture. CoNNs are neural modules designed to explicitly encode rules through artificially generated attention weights. By incorporating CoNN modules, the Neural Comprehension framework enables LMs to accurately and robustly execute rule-intensive symbolic tasks. Extensive experiments demonstrate the superiority of our approach over existing techniques in terms of length generalization, efficiency, and interpretability for symbolic operations. Furthermore, it can be applied to LMs across different model scales, outperforming tool-calling methods in arithmetic reasoning tasks while maintaining superior inference efficiency. Our work highlights the potential of seamlessly unifying explicit rule learning via CoNNs and implicit pattern learning in LMs, paving the way for true symbolic comprehension capabilities.Comment: Accepted in ICLR 202

S3^3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering

Answering multi-hop questions over hybrid factual knowledge from the given text and table (TextTableQA) is a challenging task. Existing models mainly adopt a retriever-reader framework, which have several deficiencies, such as noisy labeling in training retriever, insufficient utilization of heterogeneous information over text and table, and deficient ability for different reasoning operations. In this paper, we propose a three-stage TextTableQA framework S3HQA, which comprises of retriever, selector, and reasoner. We use a retriever with refinement training to solve the noisy labeling problem. Then, a hybrid selector considers the linked relationships between heterogeneous data to select the most relevant factual knowledge. For the final stage, instead of adapting a reading comprehension module like in previous methods, we employ a generation-based reasoner to obtain answers. This includes two approaches: a row-wise generator and an LLM prompting generator~(first time used in this task). The experimental results demonstrate that our method achieves competitive results in the few-shot setting. When trained on the full dataset, our approach outperforms all baseline methods, ranking first on the HybridQA leaderboard.Comment: ACL 202

Multilingual Knowledge Graph Completion from Pretrained Language Models with Knowledge Constraints

Multilingual Knowledge Graph Completion (mKGC) aim at solving queries like (h, r, ?) in different languages by reasoning a tail entity t thus improving multilingual knowledge graphs. Previous studies leverage multilingual pretrained language models (PLMs) and the generative paradigm to achieve mKGC. Although multilingual pretrained language models contain extensive knowledge of different languages, its pretraining tasks cannot be directly aligned with the mKGC tasks. Moreover, the majority of KGs and PLMs currently available exhibit a pronounced English-centric bias. This makes it difficult for mKGC to achieve good results, particularly in the context of low-resource languages. To overcome previous problems, this paper introduces global and local knowledge constraints for mKGC. The former is used to constrain the reasoning of answer entities, while the latter is used to enhance the representation of query contexts. The proposed method makes the pretrained model better adapt to the mKGC task. Experimental results on public datasets demonstrate that our method outperforms the previous SOTA on Hits@1 and Hits@10 by an average of 12.32% and 16.03%, which indicates that our proposed method has significant enhancement on mKGC.Comment: 11 pages, ACL 202

Large Language Models are reasoners with Self-Verification

When a large language model (LLM) performs complex reasoning by chain of thought (CoT), it can be highly sensitive to individual mistakes. We have had to train verifiers to address this issue. As we all know, after human inferring a conclusion, they often check it by re-verifying it, which can avoid some mistakes. We propose a new method called self-verification that uses the conclusion of the CoT as a condition to build a new sample and asks the LLM to re-predict the original conditions which be masked. We calculate an explainable verification score based on the accuracy. This method can improve the accuracy of multiple arithmetics and logical reasoning datasets when using few-shot learning. we have demonstrated that LLMs can conduct explainable self-verification of their own conclusions and achieve competitive reasoning performance. Extensive experimentals have demonstrated that our method can help multiple large language models with self-verification can avoid interference from incorrect CoT. Code is available at \url{https://github.com/WENGSYX/Self-Verification

Dominant inflation of the Arctic Ocean’s Beaufort Gyre in a warming climate

Abstract The Arctic Ocean’s Beaufort Gyre, the largest Arctic freshwater reservoir, plays a crucial role for climate and marine ecosystems. Understanding how it changes in a warming climate is therefore essential. Here, using high-resolution simulations and Coupled Model Intercomparison Project phase 6 data, we find that the Beaufort Gyre will increasingly accumulate freshwater, elevate sea level, and spin up its circulation as the climate warms. These changes, collectively referred to as inflation, are more pronounced in the Beaufort Gyre region than in other Arctic areas, amplifying the spatial asymmetry of the Arctic Ocean. The inflation is driven by increased surface freshwater fluxes and intensified surface stress from wind strengthening and sea ice decline. Current climate models tend to underestimate this inflation, which could be alleviated by high-resolution ocean models and improved atmospheric circulation simulations. The inflation of the Beaufort Gyre underscores its growing importance in a warming climate.</jats:p

Perceptions of clinicians and research ethics boards regarding ethical issues in investigator-initiated trials: a multicenter qualitative study in China

Purpose: This multicenter qualitative study aimed to explore the perceptions of clinicians and research ethics boards (REBs) regarding ethical issues in Investigator Initiated Trials (IITs). Methods: Between February and April 2024, semi-structured interviews were conducted with 27 participants from 15 tertiary hospitals, including clinical doctors and members of REBs. Responses were grouped and analyzed using a descriptive phenomenological approach. Results: Clinicians expressed challenges in navigating the formal review process due to limited access to information and unclear guidelines. Academic review highlighted a deficiency in research literacy among clinical investigators, leading to flawed study design. Ethical review revealed concerns about inadequate ethical awareness among clinicians, resulting in failed ethical approvals. Moreover, delays in review processes and resource shortages were noted, hindering the efficient conduct of IITs. Conclusion: The findings underscore the need for comprehensive training programs to enhance clinicians’ research literacy and ethical awareness. Establishing a comprehensive system to support IITs, including enhanced guidance and support from REBs, is essential to ensure the quality and integrity of IITs in China

Mind the Propagation of States New Automatic Search Tool for Impossible Differentials and Impossible Polytopic Transitions (Full Version)

Impossible differentials cryptanalysis and impossible polytopic cryptanalysis are the most effective approaches to estimate the security of block ciphers. However, the previous automatic search methods of their distinguishers, impossible differentials and impossible polytopic transitions, neither consider the impact of key schedule in the single-key setting and the differential property of large S-boxes, nor apply to the block ciphers with variable rotations. Thus, unlike previous methods which focus on the propagation of the difference or $s$-difference, we redefine the impossible differentials and impossible $(s+1)$-polytopic transitions according to the propagation of state, which allow us to break through those limitations of the previous methods. Theoretically, we prove that traditional impossible differentials and impossible $(s+1)$-polytopic transitions are equivalent to part of our redefinitions, which have advantages from broader view. Technically, we renew the automatic search model and design an SAT-based tool to evaluate our redefined impossible differentials and impossible $(s+1)$-polytopic transitions efficiently. As a result, for GIFT64, we get the $6$-round impossible differentials which cannot be detected by all previous tools. For PRINTcipher, we propose the first modeling method for the key-dependent permutation and key-dependent S-box. For MISTY1, we derive 902 4-round impossible differentials by exploiting the differential property of S-boxes. For RC5, we present the first modeling method for the variable rotation and get 2.5-round impossible differentials for each version of it. More remarkable, our tool can be used to evaluate the security of given cipher against the impossible differentials, and we prove that there exists no 5-round 1 input active word and 1 output active word impossible differentials for AES-128 even consider the relations of 3-round keys. Besides, we also get the impossible $(s+1)$-polytopic transitions for PRINTcipher, GIFT64, PRESENT, and RC5, all of which can cover more rounds than their corresponding impossible differentials as far as we know