Auxiliary information, such as knowledge graph (KG), has become increasingly crucial in recommender systems. However, the current KG-based recommendation still has some limitations: (1) low link rates between items and KG entities, (2) redundant knowledge in KG. In this paper, we introduce the aspect, which refers to keywords describing item attributes in reviews, to KG-based recommendation, and propose a new model, Collaborative Aspect Graph enhanced Knowledge-based Network (CAGK). Firstly, CAGK builds a Collaborative Aspect Graph (CAG) with user-item interactions, aspects and KG, where aspects can fill most of the sparsity. Secondly, we leverage interactive information and aspect features to generate aspect-aware guidance signals to customize knowledge extraction and eliminate redundant knowledge. Lastly, we utilize low ratings and negative aspect sentiment to capture features of that users dislike to prevent repetitive recommendations of disliked items. Experimental results on two widely used benchmark datasets, Amazon-book and Yelp2018, confirm the superiority of CAGK.

Due to the high heterogeneity and complexity of cancers, patients with different cancer subtypes often have distinct groups of genomic and clinical characteristics. Therefore, the discovery and identification of cancer subtypes are crucial to cancer diagnosis, prognosis and treatment. Recent technological advances have accelerated the increasing availability of multi-omics data for cancer subtyping. To take advantage of the complementary information from multi-omics data, it is necessary to develop computational models that can represent and integrate different layers of data into a single framework. Here, we propose a decoupled contrastive clustering method (Subtype-DCC) based on multi-omics data integration for clustering to identify cancer subtypes. The idea of contrastive learning is introduced into deep clustering based on deep neural networks to learn clustering-friendly representations. Experimental results demonstrate the superior performance of the proposed Subtype-DCC model in identifying cancer subtypes over the currently available state-of-the-art clustering methods. The strength of Subtype-DCC is also supported by the survival and clinical analysis.

Weakly-supervised fine-grained recognition aims to detect potential differences between subcategories at a more detailed scale without using any manual annotations. While most recent works focus on classical image-based fine-grained recognition that recognizes subcategories at image-level, video-based fine-grained recognition is much more challenging and specifically needed. In this paper, we propose a Multimodal Audio-Visual Transformer for Weakly-supervised Fine-Grained Recognition (MAVT-FG) model which incorporates audio-visual modalities. Specifically, MAVT-FG consists of Audio-Visual Dual-Encoder for feature extraction, Cross-Decoder for Audio-Visual Fusion (DAVF) to exploit inherent cues and correspondences between two modalities, and Search-and-Select Fine-grained Branch (SSFG) to capture the most discriminative regions. Furthermore, we construct a new benchmark: Fine-grained Birds of Audio-Visual (FGB-AV) for audio-visual weakly-supervised fine-grained recognition at video-level. Experimental results show that our method achieves superior performance and outperforms other state-of-the-art methods.

Prime compilation aims to generate all prime implicates/implicants of a Boolean formula. Recently, prime compilation of non-clausal formulae has received great attention. Since it is hard for$\Sigma_{2}^{P}$, existing methods have performance issues. We argue that the main performance bottleneck stems from enlarging the search space using dual rail (DR) encoding, and computing a minimal clausal formula as a by-product. To deal with the issue, we propose a two-phase approach, namely CoAPI, for prime compilation of non-clausal formulae. Thanks to the two-phase framework, we construct a clausal formula without using DR encoding. In addition, to improve performance, the key in our work is a novel bounded prime extraction (BPE) method that, interleaving extracting prime implicates with extracting small implicates, enables constructing a succinct clausal formula rather than a minimal one. Following the assessment way of the state-of-the-art (SOTA) work, we show that CoAPI achieves SOTA performance. Particularly, for generating all prime implicates, CoAPI is up to about one order of magnitude faster. Moreover, we evaluate CoAPI on a benchmark sourcing from real-world industries. The results also confirm the outperformance of CoAPI11Our code and benchmarks are publicly available at https://github.com/LuoWeiLinWillam/CoAPI.

The boundary conditions (BCs) have shown great potential in requirements engineering because a BC captures the particular combination of circumstances, i.e., divergence, in which the goals of the requirement cannot be satisfied as a whole. Existing researches have attempted to automatically identify lots of BCs. Unfortunately, a large number of identified BCs make assessing and resolving divergences expensive. Existing methods adopt a coarse-grained metric, generality, to filter out less general BCs. However, the results still retain a large number of redundant BCs since a general BC potentially captures redundant circumstances that do not lead to a divergence. Furthermore, the likelihood of BC can be misled by redundant BCs resulting in costly repeatedly assessing and resolving divergences. In this paper, we present a fine-grained metric to filter out the redundant BCs. We first introduce the concept of contrasty of BC. Intuitively, if two BCs are contrastive, they capture different divergences. We argue that a set of contrastive BCs should be recommended to engineers, rather than a set of general BCs that potentially only indicates the same divergence. Then we design a post-processing framework (PPFc) to produce a set of contrastive BCs after identifying BCs. Experimental results show that the contrasty metric dramatically reduces the number of BCs recommended to engineers. Results also demonstrate that lots of BCs identified by the state-of-the-art method are redundant in most cases. Besides, to improve efficiency, we propose a joint framework (JFc) to interleave assessing based on the contrasty metric with identifying BCs. The primary intuition behind JFc is that it considers the search bias toward contrastive BCs during identifying BCs, thereby pruning the BCs capturing the same divergence. Experiments confirm the improvements of JFc in identifying contrastive BCs.