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Prediction of microbial communities for urban metagenomics using neural network approach.
BACKGROUND:Microbes are greatly associated with human health and disease, especially in densely populated cities. It is essential to understand the microbial ecosystem in an urban environment for cities to monitor the transmission of infectious diseases and detect potentially urgent threats. To achieve this goal, the DNA sample collection and analysis have been conducted at subway stations in major cities. However, city-scale sampling with the fine-grained geo-spatial resolution is expensive and laborious. In this paper, we introduce MetaMLAnn, a neural network based approach to infer microbial communities at unsampled locations given information reflecting different factors, including subway line networks, sampling material types, and microbial composition patterns. RESULTS:We evaluate the effectiveness of MetaMLAnn based on the public metagenomics dataset collected from multiple locations in the New York and Boston subway systems. The experimental results suggest that MetaMLAnn consistently performs better than other five conventional classifiers under different taxonomic ranks. At genus level, MetaMLAnn can achieve F1 scores of 0.63 and 0.72 on the New York and the Boston datasets, respectively. CONCLUSIONS:By exploiting heterogeneous features, MetaMLAnn captures the hidden interactions between microbial compositions and the urban environment, which enables precise predictions of microbial communities at unmeasured locations
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Exploring Hybrid Question Answering via Program-based Prompting
Question answering over heterogeneous data requires reasoning over diverse
sources of data, which is challenging due to the large scale of information and
organic coupling of heterogeneous data. Various approaches have been proposed
to address these challenges. One approach involves training specialized
retrievers to select relevant information, thereby reducing the input length.
Another approach is to transform diverse modalities of data into a single
modality, simplifying the task difficulty and enabling more straightforward
processing. In this paper, we propose HProPro, a novel program-based prompting
framework for the hybrid question answering task. HProPro follows the code
generation and execution paradigm. In addition, HProPro integrates various
functions to tackle the hybrid reasoning scenario. Specifically, HProPro
contains function declaration and function implementation to perform hybrid
information-seeking over data from various sources and modalities, which
enables reasoning over such data without training specialized retrievers or
performing modal transformations. Experimental results on two typical hybrid
question answering benchmarks HybridQA and MultiModalQA demonstrate the
effectiveness of HProPro: it surpasses all baseline systems and achieves the
best performances in the few-shot settings on both datasets
Make-or-buy configurational approaches in product-service ecosystems and performance
This research examines firm boundary configurations for manufacturers' product-service offerings. We argue that the building of a product-service ecosystem through collaboration with service providers in certain types of business services can increase performance as a result of the superior knowledge-based resources coming from specialized partners. By using fuzzy set qualitative analysis on a sample of 370 multinational manufacturing enterprises (MMNEs), the results reveal that effective servitization is heterogeneous across manufacturing industries and across business service offerings. The findings indicate that most industries achieve their highest performance through collaborations with value-added service providers in two out of three of the service continuum stages (Base and Intermediate services); while keeping the development of Advanced services in-house. The results help to contextualize the best practices for implementing service business models in MMNEs by detailing which service capabilities should be retained in-house and which should be outsourced to specialized partners in various industrial contexts.Peer ReviewedPreprin
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