Improving End-to-End Sequential Recommendations with Intent-aware Diversification

Sequential Recommendation (SRs) that capture users' dynamic intents by modeling user sequential behaviors can recommend closely accurate products to users. Previous work on SRs is mostly focused on optimizing the recommendation accuracy, often ignoring the recommendation diversity, even though it is an important criterion for evaluating the recommendation performance. Most existing methods for improving the diversity of recommendations are not ideally applicable for SRs because they assume that user intents are static and rely on post-processing the list of recommendations to promote diversity. We consider both recommendation accuracy and diversity for SRs by proposing an end-to-end neural model, called Intent-aware Diversified Sequential Recommendation (IDSR). Specifically, we introduce an Implicit Intent Mining module (IIM) into SRs to capture different user intents reflected in user behavior sequences. Then, we design an Intent-aware Diversity Promoting (IDP) loss to supervise the learning of the IIM module and force the model to take recommendation diversity into consideration during training. Extensive experiments on two benchmark datasets show that IDSR significantly outperforms state-of-the-art methods in terms of recommendation diversity while yielding comparable or superior recommendation accuracy

Answering Ambiguous Questions via Iterative Prompting

In open-domain question answering, due to the ambiguity of questions, multiple plausible answers may exist. To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity. An alternative is to gather candidate answers and aggregate them, but this method can be computationally costly and may neglect dependencies among answers. In this paper, we present AmbigPrompt to address the imperfections of existing approaches to answering ambiguous questions. Specifically, we integrate an answering model with a prompting model in an iterative manner. The prompting model adaptively tracks the reading process and progressively triggers the answering model to compose distinct and relevant answers. Additionally, we develop a task-specific post-pretraining approach for both the answering model and the prompting model, which greatly improves the performance of our framework. Empirical studies on two commonly-used open benchmarks show that AmbigPrompt achieves state-of-the-art or competitive results while using less memory and having a lower inference latency than competing approaches. Additionally, AmbigPrompt also performs well in low-resource settings. The code are available at: https://github.com/sunnweiwei/AmbigPrompt.Comment: To be published in ACL 202

Suppression of renal cell carcinoma growth in vivo by forced expression of vascular endothelial growth inhibitor

Vascular endothelial growth inhibitor (VEGI) has been associated with tumor-related vasculature in certain malignancies. However, its implication in renal cell carcinoma (RCC), an angiogenesis-dependent tumor, remains unknown. In the present study, we investigated the role played by VEGI in RCC. The expression of VEGI was examined in human renal tissue and RCC cell lines using immunohistochemical staining and RT-PCR, respectively. The biological impact of modifying the expression of VEGI in RCC cells was evaluated using in vitro and in vivo models. We show that VEGI mRNA is expressed in a wide variety of human RCC cell lines, all of normal renal and most of RCC tissue specimens. VEGI protein expression was observed in normal renal tubular epithelial cells, but was decreased or absent in RCC specimens, particularly in tumors with high grade. Moreover, forced expression of VEGI led to an inhibition of vascular endothelial tube formation, decrease in the motility and adhesion of RCC cells in vitro. Interestingly, forced expression of VEGI had no bearing on growth, apoptosis and invasive capacity of RCC cells. However, tumor growth was reduced in xenograft models. Immunohistochemical staining showed that microvessel density decreased in VEGI forced expression xenograft tumor samples. Taken together, our findings showed that the expression of VEGI is decreased in RCC, particularly in tumors with higher grade. Together with its inhibitory effect on cellular motility, adhesion, vascular endothelial tube formation and tumor growth in vivo, this suggests that VEGI functions mainly through inhibition of angiogenesis and is a negative regulator of aggressiveness during the development and progression of RCC

Knowledge Structure and Frontier Evolution of Research on Chromitite: A Scientometric Review

Big data analysis can reveal the relevance, hidden patterns, and bursts of activity in data. Therefore, big data analysis has recently aroused great interest and curiosity among scientists in various fields. The powerful data organization and visualization capabilities of CiteSpace software is an effective way to achieve this goal. Chromitite is a strategic mineral resource of global importance with several industrial applications, including steel manufacturing. Research on chromitite has not only had high economic significance, but also has important scientific value. An understanding of chromitite can be used to obtain insight into the processes operating deep within the crust and mantle. However, no big-data analysis has been performed on chromitite-related publications; hence, the evolution of various views over time is unclear. The purpose of this study was to rapidly assess and summarize the development of research in the field, and to identify and briefly describe current research developments. The CiteSpace software was used to reveal research hotspots and predict future trends. The results of the co-occurrence network analysis indicate an active collaboration among current chromitite researchers, and the countries and institutions in which they are based. Hot research topics include a focus on podiform chromitite, the origin of chromitites, and the co-occurrence of platinum group elements (PGE). The main subject of current research is podiform chromitite containing ultrahigh-pressure minerals, which will help to elucidate the relationship between chromitite and the deep processes within the earth

Knowledge Structure and Frontier Evolution of Research on Chromitite: A Scientometric Review

Big data analysis can reveal the relevance, hidden patterns, and bursts of activity in data. Therefore, big data analysis has recently aroused great interest and curiosity among scientists in various fields. The powerful data organization and visualization capabilities of CiteSpace software is an effective way to achieve this goal. Chromitite is a strategic mineral resource of global importance with several industrial applications, including steel manufacturing. Research on chromitite has not only had high economic significance, but also has important scientific value. An understanding of chromitite can be used to obtain insight into the processes operating deep within the crust and mantle. However, no big-data analysis has been performed on chromitite-related publications; hence, the evolution of various views over time is unclear. The purpose of this study was to rapidly assess and summarize the development of research in the field, and to identify and briefly describe current research developments. The CiteSpace software was used to reveal research hotspots and predict future trends. The results of the co-occurrence network analysis indicate an active collaboration among current chromitite researchers, and the countries and institutions in which they are based. Hot research topics include a focus on podiform chromitite, the origin of chromitites, and the co-occurrence of platinum group elements (PGE). The main subject of current research is podiform chromitite containing ultrahigh-pressure minerals, which will help to elucidate the relationship between chromitite and the deep processes within the earth

Incorporating surfactants within protein-polysaccharide hybrid particles for high internal phase emulsions (HIPEs) : toward plant-based mayonnaise

Plant-based diets are currently gaining more popularity among healthily-and environmentally-conscious con-sumers. Herein, we reported a facile and mild method to fabricate high internal phase emulsions (HIPEs) using plant-based ingredients that feature the physical and sensory of traditional egg-based mayonnaise through interfacial engineering. Protein-polysaccharide-surfactant ternary composite particles with appropriate dimen-sion and moderate wettability were constructed by regulating the ratios among zein, corn fiber gum (CFG) and lecithin (Lc). The resultant Zein-CFG-Lc particles (ZCLPs) could function as not only particulate emulsifiers, sterically hindering the close approach of droplets, but also surfactants that decreased the interfacial tension through molecular rearrangement at the oil-water interface. HIPEs with a droplet size less than 3.0 mu m (the threshold value of granular sensation by oral cavity) were successfully fabricated under a minimum ZCLPs concentration of 1.2%. The specific interfacial architecture was observed with the combination of zein particles, a CFG network and Lc-based membrane, which jointly contributed to conserve a high concentration of oil phase. HIPEs were determined with a lower tribological property compared to egg-based mayonnaise, endowing them with more creaminess and smoothness sensory attributes. In addition, HIPEs exhibited an extremely higher physical and thermal stability than the mayonnaise. In vitro digestion fate indicated that the digestible fat in HIPEs could arrive at a minimal value of one-twelfth of that in the mayonnaise as ZCLPs could effectively inhibit the lipid digestion of HIPEs in the small intestine, making it a low-calorie formulation. This study opens a promising pathway for producing edible HIPEs as replacers for egg-based mayonnaise appealing to calorie -sensitive consumers and plant-based diet lovers

Genetic Diversity of Tea Plant (Camellia sinensis (L.) Kuntze) Germplasm Resources in Wuyi Mountain of China Based on Single Nucleotide Polymorphism (SNP) Markers

Wuyi Mountain in Southeast China is the origin of black tea and oolong tea. It is also considered the ‘treasure trove of tea cultivars’ because of its rich tea germplasm resources. In the present study, the population structure and genetic diversity of 137 tea germplasms from Wuyi Mountain and its adjacent areas were analyzed by SNPs. The information index (I), observed heterozygosity (Ho), expected heterozygosity (He) and fixation index (F) polymorphisms of the selected SNPs were high, stable and reliable. Ho had an average of 0.389, while He had an average of 0.324, indicating that Wuyi Mountain tea germplasms had rich genetic diversity. The AMOVA results showed that genetic variation came mainly from intrapopulation variation, accounting for 66% of the total variation. The differences in the Fst and Nei values of tea germplasm between Wuyi Mountain and its adjacent areas are similar to the geographical differences. Multiple analyses based on high-quality SNPs found that the landraces of tea plants on Wuyi Mountain had different genetic backgrounds from the wild-type landraces and the landraces of Wuyi Mountain tea plants underwent population differentiation. This study provides a basis for the effective protection and utilization of tea germplasms on Wuyi Mountain and lays a foundation for identifying potential parents to optimize tea cultivation

GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Model

High-concurrency asynchronous training upon parameter server (PS) architecture and high-performance synchronous training upon all-reduce (AR) architecture are the most commonly deployed distributed training modes for recommender systems. Although the synchronous AR training is designed to have higher training efficiency, the asynchronous PS training would be a better choice on training speed when there are stragglers (slow workers) in the shared cluster, especially under limited computing resources. To take full advantages of these two training modes, an ideal way is to switch between them upon the cluster status. We find two obstacles to a tuning-free approach: the different distribution of the gradient values and the stale gradients from the stragglers. In this paper, we propose Global Batch gradients Aggregation (GBA) over PS, which aggregates and applies gradients with the same global batch size as the synchronous training. A token-control process is implemented to assemble the gradients and decay the gradients with severe staleness. We provide the convergence analysis to demonstrate the robustness of GBA over the recommendation models against the gradient staleness. Experiments on three industrial-scale recommendation tasks show that GBA is an effective tuning-free approach for switching. Compared to the state-of-the-art derived asynchronous training, GBA achieves up to 0.2% improvement on the AUC metric, which is significant for the recommendation models. Meanwhile, under the strained hardware resource, GBA speeds up at least 2.4x compared to the synchronous training

Suppression of renal cell carcinoma growth in vivo by forced expression of vascular endothelial growth inhibitor

Vascular endothelial growth inhibitor (VEGI) has been associated with tumor-related vasculature in certain malignancies. However, its implication in renal cell carcinoma (RCC), an angiogenesis-dependent tumor, remains unknown. In the present study, we investigated the role played by VEGI in RCC. The expression of VEGI was examined in human renal tissue and RCC cell lines using immunohistochemical staining and RT-PCR, respectively. The biological impact of modifying the expression of VEGI in RCC cells was evaluated using in vitro and in vivo models. We show that VEGI mRNA is expressed in a wide variety of human RCC cell lines, all of normal renal and most of RCC tissue specimens. VEGI protein expression was observed in normal renal tubular epithelial cells, but was decreased or absent in RCC specimens, particularly in tumors with high grade. Moreover, forced expression of VEGI led to an inhibition of vascular endothelial tube formation, decrease in the motility and adhesion of RCC cells in vitro. Interestingly, forced expression of VEGI had no bearing on growth, apoptosis and invasive capacity of RCC cells. However, tumor growth was reduced in xenograft models. Immunohistochemical staining showed that microvessel density decreased in VEGI forced expression xenograft tumor samples. Taken together, our findings showed that the expression of VEGI is decreased in RCC, particularly in tumors with higher grade. Together with its inhibitory effect on cellular motility, adhesion, vascular endothelial tube formation and tumor growth in vivo, this suggests that VEGI functions mainly through inhibition of angiogenesis and is a negative regulator of aggressiveness during the development and progression of RCC