Attentive Aspect Modeling for Review-aware Recommendation

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user's reviews and a product's reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users' vocabularies. Second, a user's interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this paper, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product and aspect information is constructed to capture a user's attention towards aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on top-N recommendation task.Comment: Camera-ready manuscript for TOI

Robust Visual Tracking Using the Bidirectional Scale Estimation

Object tracking with robust scale estimation is a challenging task in computer vision. This paper presents a novel tracking algorithm that learns the translation and scale filters with a complementary scheme. The translation filter is constructed using the ridge regression and multidimensional features. A robust scale filter is constructed by the bidirectional scale estimation, including the forward scale and backward scale. Firstly, we learn the scale filter using the forward tracking information. Then the forward scale and backward scale can be estimated using the respective scale filter. Secondly, a conservative strategy is adopted to compromise the forward and backward scales. Finally, the scale filter is updated based on the final scale estimation. It is effective to update scale filter since the stable scale estimation can improve the performance of scale filter. To reveal the effectiveness of our tracker, experiments are performed on 32 sequences with significant scale variation and on the benchmark dataset with 50 challenging videos. Our results show that the proposed tracker outperforms several state-of-the-art trackers in terms of robustness and accuracy

Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image Generation

The recently rising markup-to-image generation poses greater challenges as compared to natural image generation, due to its low tolerance for errors as well as the complex sequence and context correlations between markup and rendered image. This paper proposes a novel model named "Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment" (FSA-CDM), which introduces contrastive positive/negative samples into the diffusion model to boost performance for markup-to-image generation. Technically, we design a fine-grained cross-modal alignment module to well explore the sequence similarity between the two modalities for learning robust feature representations. To improve the generalization ability, we propose a contrast-augmented diffusion model to explicitly explore positive and negative samples by maximizing a novel contrastive variational objective, which is mathematically inferred to provide a tighter bound for the model's optimization. Moreover, the context-aware cross attention module is developed to capture the contextual information within markup language during the denoising process, yielding better noise prediction results. Extensive experiments are conducted on four benchmark datasets from different domains, and the experimental results demonstrate the effectiveness of the proposed components in FSA-CDM, significantly exceeding state-of-the-art performance by about 2%-12% DTW improvements. The code will be released at https://github.com/zgj77/FSACDM.Comment: Accepted to ACM MM 2023. The code will be released at https://github.com/zgj77/FSACD

Hyperspectral Band Selection for Lithologic Discrimination and Geological Mapping

Classification techniques applied to hyperspectral images are very useful for lithologic discrimination and geological mapping. Classifiers are often applied either to all spectral channels or only to absorption spectral channels. However, it is difficult to obtain different lithology information using specific absorption regions from the narrow bandwidth and contiguous spectral channels due to spectral variability among rocks. In this article, we propose a band selection (BS) method for hyperspectral lithologic discrimination, in which the lithological superpixels are first gathered. A spectral bands selection criterion is learned by measuring the homogeneity and the variation of the lithological superpixels, and lithologic discriminating bands are identified by an efficient clustering algorithm based on affinity propagation. In this article, two geologic test sites, i.e., the Airborne Visible/Infrared Imaging Spectrometer data of the Cuprite, Nevada, USA, including 11 lithologic units (9 types of rocks) and the Hyperion data of Junggar, China, with 5 lithologic units, are chosen for validation. The performance of the proposed BS method is compared with those of using all the bands, specific absorption spectral channels, and two literature BS techniques. Experimental results show that the proposed method improves mapping accuracy by selecting fewer bands with higher lithologic discrimination capability than the other considered methods

Integrative proteomic and metabonomic profiling elucidates amino acid and lipid metabolism disorder in CA-MRSA-infected breast abscesses

ObjectiveBacterial culture and drug sensitivity testing have been the gold standard for confirming community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) infection in breast abscess with a long history. However, these tests may delay treatment and increase the risk of nosocomial infections. To handle and improve this critical situation, this study aimed to explore biomarkers that could facilitate the rapid diagnosis of CA-MRSA infection.MethodsThis study for the first time applied label-free quantitative proteomics and non-targeted metabonomics to identify potential differentially expressed proteins (DEPs) and differentially expressed metabolites (DEMs) in breast abscess infected with CA-MRSA compared to methicillin-susceptible S. aureus (MSSA). The two omics data were integrated and analyzed using bioinformatics, and the results were validated using Parallel Reaction Monitoring (PRM). Receiver operating characteristic (ROC) curves were generated to evaluate the predictive efficiency of the identified biomarkers for diagnosing CA-MRSA infection.ResultsAfter using the above-mentioned strategies, 109 DEPs were identified, out of which 86 were upregulated and 23 were downregulated. Additionally, a total of 61 and 26 DEMs were initially screened in the positive and negative ion modes, respectively. A conjoint analysis indicated that the amino acid metabolism, glycosphingolipid biosynthesis, and glycerophospholipid metabolism pathways were co-enriched by the upstream DEPs and downstream DEMs, which may be involved in structuring the related network of CA-MRSA infection. Furthermore, three significant DEMs, namely, indole-3-acetic acid, L-(−)-methionine, and D-sedoheptulose 7-phosphate, displayed good discriminative abilities in early identification of CA-MRSA infection in ROC analysis.ConclusionAs there is limited high-quality evidence and multiple omics research in this field, the explored candidate biomarkers and pathways may provide new insights into the early diagnosis and drug resistance mechanisms of CA-MRSA infection in Chinese women

Image dataset of tea chrysanthemums in complex outdoor scenes

There is currently no publicly available tea chrysanthemum dataset to the authors’ knowledge. Consequently, we provide an image dataset for six varieties of tea chrysanthemums in three camera view angles obtained under complex outdoor scenes, and this open-source image dataset can greatly promote the development of tea chrysanthemums detection methodology

Laparoscopic metabolic surgery for the treatment of type 2 diabetes in Asia: a scoping review and evidence-based analysis

BACKGROUND: Laparoscopic metabolic surgery has been previously shown to be an effective treatment for obese patients with type 2 diabetes (T2DM). The objective of this scoping review is to determine the impact of metabolic surgery for the treatment of type 2 diabetes in Asia and perform an evidence-based analysis. METHODS: We performed a literature search in PubMed for research on laparoscopic metabolic surgery for the treatment of T2DM in Asia region. We classified the included studies based on the Oxford Center for Evidence Based Medicine guidelines. And performed and evidence analysis. RESULTS: In total, 205 articles were identified. 62.9% of the studies were from East Asia. The evidence of 26 studies are level I, 59 are level II. Laparoscopic sleeve gastrectomy (LSG) was the most commonly reported surgical procedure (63.1%) in Asia. The number of laparoscopic metabolic surgery for T2DM in Asian countries has increased rapidly over the last 8 years. We identified 16 studies which showed that laparoscopic metabolic surgery is an effective and safe treatment for T2DM in patients with a BMI of > 25 kg/m2 to < 35 kg/m2 in Asia. CONCLUSIONS: Our results suggest that laparoscopic metabolic surgery might be an effective and safe treatment for T2DM patients with BMI < 35 kg/m2, and that LSG is the most commonly performed surgical procedure for this in Asia

Hybrid weakness and continuous flowering caused by compound expression of FTLs in Chrysanthemum morifolium × Leucanthemum paludosum intergeneric hybridization

Hybridization is an important evolutionary mechanism ubiquitous to plants. Previous studies have shown that hybrid polyploidization of cultivated chrysanthemum, ‘Zhongshanzigui’, and Leucanthemum paludosum exhibit spring-flowering traits. This study explores the function of the LpFTLs gene via the phenotype of A. thaliana after heterologous transformation of the LpFTLs gene, and analyzes the mechanism ofthe continuous flowering phenotype and heterosis of hybrid offspring. The results suggest that the flowering phenotype of hybrid offspring in spring may be related to the expression of the LpFTLs gene. Ectopic expression of Leucanthemum paludosumLpFTLs in Arabidopsis thaliana resulted in earlier flowering, indicating that the LpFTLs gene also affects the flowering time in L. paludosum. Compound expression of FTLs in C. morifolium × L. paludosum intergeneric hybridization directly leads to serious heterosis in the hybrid offspring. Moreover, continuous flowering appears to be accompanied by hybrid weakness under the balance of vegetative and reproductive growth. Therefore, in future studies on chrysanthemum breeding, a suitable balance point must be established to ensure the target flowering time under normal growth