CommonsenseVIS: Visualizing and Understanding Commonsense Reasoning Capabilities of Natural Language Models

Recently, large pretrained language models have achieved compelling performance on commonsense benchmarks. Nevertheless, it is unclear what commonsense knowledge the models learn and whether they solely exploit spurious patterns. Feature attributions are popular explainability techniques that identify important input concepts for model outputs. However, commonsense knowledge tends to be implicit and rarely explicitly presented in inputs. These methods cannot infer models' implicit reasoning over mentioned concepts. We present CommonsenseVIS, a visual explanatory system that utilizes external commonsense knowledge bases to contextualize model behavior for commonsense question-answering. Specifically, we extract relevant commonsense knowledge in inputs as references to align model behavior with human knowledge. Our system features multi-level visualization and interactive model probing and editing for different concepts and their underlying relations. Through a user study, we show that CommonsenseVIS helps NLP experts conduct a systematic and scalable visual analysis of models' relational reasoning over concepts in different situations.Comment: This paper is accepted by IEEE VIS, 2023. To appear in IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG). 14 pages, 11 figure

Plasma-induced PAA-ZnO coated PVDF membrane for oily wastewater treatment: Preparation, optimization, and characterization through Taguchi OA design and synchrotron-based X-ray analysis

A novel membrane surface modification approach was proposed to successfully obtain a poly(vinylidene fluoride)-poly(acrylic acid)-ZnO (PVDF-PAA-ZnO) membrane with super-high water permeability and great oil rejection through cold plasma-induced PAA graft-polymerization followed by simple nano-ZnO self-assembly. The experimental parameters of modification were optimized and their optimal combination was identified using Taguchi orthogonal array (OA) design method. The PVDF-PAA-ZnO membrane was comprehensively characterized and the mechanism of nano-ZnO self-assembly was explored by contact angle measurement, scanning electron microscope (SEM) images, elemental analysis, tension test, Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) and synchrotron-based X-ray analyses. It was revealed that ZnO nanoparticles were immobilized onto membrane surface through the adsorption of PAA layer to form a PAA-ZnO coating without valence change. The carboxyl groups of PAA layer provided complexing ligands to coordinate with Zn2+ and form bidentate species on the nano-ZnO surface. The firm PAA-ZnO coating on PVDF membrane surface converted its hydrophobic nature to hydrophilic, bringing about the dramatically improvement of membrane performance both in water permeation flux and oil rejection rate. The permeation flux of the PVDF-PAA-ZnO membrane was more than 10 times as great as that of the pristine PVDF membrane

PlanningVis: A visual analytics approach to production planning in smart factories

Production planning in the manufacturing industry is crucial for fully utilizing factory resources (e.g., machines, raw materials and workers) and reducing costs. With the advent of industry 4.0, plenty of data recording the status of factory resources have been collected and further involved in production planning, which brings an unprecedented opportunity to understand, evaluate and adjust complex production plans through a data-driven approach. However, developing a systematic analytics approach for production planning is challenging due to the large volume of production data, the complex dependency between products, and unexpected changes in the market and the plant. Previous studies only provide summarized results and fail to show details for comparative analysis of production plans. Besides, the rapid adjustment to the plan in the case of an unanticipated incident is also not supported. In this paper, we propose PlanningVis, a visual analytics system to support the exploration and comparison of production plans with three levels of details: a plan overview presenting the overall difference between plans, a product view visualizing various properties of individual products, and a production detail view displaying the product dependency and the daily production details in related factories. By integrating an automatic planning algorithm with interactive visual explorations, PlanningVis can facilitate the efficient optimization of daily production planning as well as support a quick response to unanticipated incidents in manufacturing. Two case studies with real-world data and carefully designed interviews with domain experts demonstrate the effectiveness and usability of PlanningVis

Aridity-driven shift in biodiversity–soil multifunctionality relationships

From Springer Nature via Jisc Publications RouterHistory: received 2021-01-07, accepted 2021-08-12, registration 2021-08-25, pub-electronic 2021-09-09, online 2021-09-09, collection 2021-12Publication status: PublishedFunder: National Natural Science Foundation of China (National Science Foundation of China); doi: https://doi.org/10.13039/501100001809; Grant(s): 31770430Abstract: Relationships between biodiversity and multiple ecosystem functions (that is, ecosystem multifunctionality) are context-dependent. Both plant and soil microbial diversity have been reported to regulate ecosystem multifunctionality, but how their relative importance varies along environmental gradients remains poorly understood. Here, we relate plant and microbial diversity to soil multifunctionality across 130 dryland sites along a 4,000 km aridity gradient in northern China. Our results show a strong positive association between plant species richness and soil multifunctionality in less arid regions, whereas microbial diversity, in particular of fungi, is positively associated with multifunctionality in more arid regions. This shift in the relationships between plant or microbial diversity and soil multifunctionality occur at an aridity level of ∼0.8, the boundary between semiarid and arid climates, which is predicted to advance geographically ∼28% by the end of the current century. Our study highlights that biodiversity loss of plants and soil microorganisms may have especially strong consequences under low and high aridity conditions, respectively, which calls for climate-specific biodiversity conservation strategies to mitigate the effects of aridification

Specific measures to response pandemic of COVID-19 in China: a systematic review

Contemporarily, the novel Coronavirus (SARS-CoV-2, abbreviation for COVID-19) has raged around the world in a short time, which attracts the attention of countries around the world. This virus is spreading fast with a considerable impact, posing a huge threat to global public health. The challenges COVID-19 presented require a robust response. As the world's best country in epidemic control, China has done a lot of control measures. These measures include laboratory confirmation, social distancing and vaccine. Evidences have proved that these measures taken by China have effectively reduced the incidence and mortality of COVID-19 in China. This article will provide a systematic review of these control measures in China, in the hope of providing information for global infectious disease control

Hypoxia Promotes Vascular Smooth Muscle Cell (VSMC) Differentiation of Adipose-Derived Stem Cell (ADSC) by Regulating Mettl3 and Paracrine Factors

Adipose-derived stem cell (ADSC) is an alternative and less invasive source of mesenchymal stem cells which can be used to develop biological treatment strategies for tissue regeneration, and their therapeutic applications hinge on an understanding of their physiological characteristics. N6-Methyladenosine (m6A) is the most common chemical modification of mRNAs and has recently been revealed to play important roles in cell lineage differentiation and development. However, the role of m6A modification in the vascular smooth muscle cell (VSMC) differentiation of ADSCs remains unclear. Herein, we investigated the expression of N6-adenosine methyltransferases (Mettl3) and demethylases (Fto and Alkbh5) and found that Mettl3 was upregulated in ADSCs undergoing vascular smooth muscle differentiation induction. Moreover, silence of Mettle3 reduced the expression level of VSMC-specific markers, including α-SMA, SM22α, calponin, and SM-MHC. Meanwhile, Mettl3 knockdown also decreased the expression of paracrine factors, including VEGF, HGF, TGF-β, GM-CSF, bFGF, and SDF-1. In addition, our results suggested that hypoxia stress promotes the ADSC differentiate into VMSCs and regulates the secretion of VEGF, HGF, TGF-β, GM-CSF, bFGF, and SDF-1 by mediating Mettl3 gene expression. These observations might contribute to novel progress in understanding the role of epitranscriptomic regulation in the VSMC differentiation of ADSCs and provide a promising perspective for new therapeutic strategies for tissue regeneration

KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

The purpose of this work is to enhance KinasePhos, a machine learning-based kinase-specific phosphorylation site prediction tool. Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus, UniProtKB, the GPS 5.0, and Phospho.ELM. In total, 41,421 experimentally verified kinase-specific phosphorylation sites were identified. A total of 1380 unique kinases were identified, including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree. Based on this kinase classification, a total of 771 predictive models were built at the individual, family, and group levels, using at least 15 experimentally verified substrate sites in positive training datasets. The improved models demonstrated their effectiveness compared with other prediction tools. For example, the prediction of sites phosphorylated by the protein kinase B, casein kinase 2, and protein kinase A families had accuracies of 94.5%, 92.5%, and 90.0%, respectively. The average prediction accuracy for all 771 models was 87.2%. For enhancing interpretability, the SHapley Additive exPlanations (SHAP) method was employed to assess feature importance. The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins. Additionally, considering the large scale of phosphoproteomic data, a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/download.html or https://github.com/tom-209/KinasePhos-3.0-executable-file

Identifying Urban Wetlands through Remote Sensing Scene Classification Using Deep Learning: A Case Study of Shenzhen, China

Urban wetlands provide cities with unique and valuable ecosystem services but are under great degradation pressure. Correctly identifying urban wetlands from remote sensing images is fundamental for developing appropriate management and protection plans. To overcome the semantic limitations of traditional pixel-level urban wetland classification techniques, we proposed an urban wetland identification framework based on an advanced scene-level classification scheme. First, the Sentinel-2 high-resolution multispectral image of Shenzhen was segmented into 320 m × 320 m square patches to generate sample datasets for classification. Next, twelve typical convolutional neural network (CNN) models were transformed for the comparison experiments. Finally, the model with the best performance was used to classify the wetland scenes in Shenzhen, and pattern and composition analyses were also implemented in the classification results. We found that the DenseNet121 model performed best in classifying urban wetland scenes, with overall accuracy (OA) and kappa values reaching 0.89 and 0.86, respectively. The analysis results revealed that the wetland scene in Shenzhen is generally balanced in the east–west direction. Among the wetland scenes, coastal open waters accounted for a relatively high proportion and showed an obvious southward pattern. The remaining swamp, marsh, tidal flat, and pond areas were scattered, accounting for only 4.64% of the total area of Shenzhen. For scattered and dynamic urban wetlands, we are the first to achieve scene-level classification with satisfactory results, thus providing a clearer and easier-to-understand reference for management and protection, which is of great significance for promoting harmony between humanity and ecosystems in cities

An integrated multi-level watershed-reservoir modeling system for examining hydrological and biogeochemical processes in small prairie watersheds

Eutrophication of small prairie reservoirs presents a major challenge in water quality management and has led to a need for predictive water quality modeling. Studies are lacking in effectively integrating watershed models and reservoir models to explore nutrient dynamics and eutrophication pattern. A water quality model specific to small prairie water bodies is also desired in order to highlight key biogeochemical processes with an acceptable degree of parameterization. This study presents a Multi-level Watershed-Reservoir Modeling System (MWRMS) to simulate hydrological and biogeochemical processes in small prairie watersheds. It integrated a watershed model, a hydrodynamic model and an eutrophication model into a flexible modeling framework. It can comprehensively describe hydrological and biogeochemical processes across different spatial scales and effectively deal with the special drainage structure of small prairie watersheds. As a key component of MWRMS, a three-dimensional Willows Reservoir Eutrophication Model (WREM) is developed to addresses essential biogeochemical processes in prairie reservoirs and to generate 3D distributions of various water quality constituents; with a modest degree of parameterization, WREM is able to meet the limit of data availability that often confronts the modeling practices in small watersheds. MWRMS was applied to the Assiniboia Watershed in southern Saskatchewan, Canada. Extensive efforts of field work and lab analysis were undertaken to support model calibration and validation. MWRMS demonstrated its ability to reproduce the observed watershed water yield, reservoir water levels and temperatures, and concentrations of several water constituents. Results showed that the aquatic systems in the Assiniboia Watershed were nitrogen-limited and sediment flux played a crucial role in reservoir nutrient budget and dynamics. MWRMS can provide a broad context of decision support for water resources management and water quality protection in the prairie region.Peer reviewed: YesNRC publication: Ye