低炭素鋼におけるナノ合金炭化物の相界面析出組織

要約のみTohoku University古原忠課

ResumeNet: A Learning-based Framework for Automatic Resume Quality Assessment

Recruitment of appropriate people for certain positions is critical for any companies or organizations. Manually screening to select appropriate candidates from large amounts of resumes can be exhausted and time-consuming. However, there is no public tool that can be directly used for automatic resume quality assessment (RQA). This motivates us to develop a method for automatic RQA. Since there is also no public dataset for model training and evaluation, we build a dataset for RQA by collecting around 10K resumes, which are provided by a private resume management company. By investigating the dataset, we identify some factors or features that could be useful to discriminate good resumes from bad ones, e.g., the consistency between different parts of a resume. Then a neural-network model is designed to predict the quality of each resume, where some text processing techniques are incorporated. To deal with the label deficiency issue in the dataset, we propose several variants of the model by either utilizing the pair/triplet-based loss, or introducing some semi-supervised learning technique to make use of the abundant unlabeled data. Both the presented baseline model and its variants are general and easy to implement. Various popular criteria including the receiver operating characteristic (ROC) curve, F-measure and ranking-based average precision (AP) are adopted for model evaluation. We compare the different variants with our baseline model. Since there is no public algorithm for RQA, we further compare our results with those obtained from a website that can score a resume. Experimental results in terms of different criteria demonstrate the effectiveness of the proposed method. We foresee that our approach would transform the way of future human resources management.Comment: ICD

Projection-based reduced order modeling and data-driven artificial viscosity closures for incompressible fluid flows

Projection-based reduced order models rely on offline-online model decomposition, where the data-based energetic spatial basis is used in the expensive offline stage to obtain equations of reduced states that evolve in time during the inexpensive online stage. The online stage requires a solution method for the dynamic evolution of the coupled system of pressure and velocity states for incompressible fluid flows. The first contribution of this article is to demonstrate the applicability of the incremental pressure correction scheme for the dynamic evolution of pressure and velocity states. The evolution of a large number of these reduced states in the online stage can be expensive. In contrast, the accuracy significantly decreases if only a few reduced states are considered while not accounting for the interactions between unresolved and resolved states. The second contribution of this article is to compare three closure model forms based on global, modal and tensor artificial viscosity approximation to account for these interactions. The unknown model parameters are determined using two calibration techniques: least squares minimization of error in energy approximation and closure term approximation. This article demonstrates that an appropriate selection of solution methods and data-driven artificial viscosity closure models is essential for consistently accurate dynamics forecasting of incompressible fluid flows

New Survey Targeting Underrepresented Iowans Reveals Limited Usage of Lake Resources

A large region of low-to-no-oxygen zone—a hypoxic zone—forms every summer over the past three decades in the Gulf of Mexico, largely due to excessive nutrient runoff from states located within the upper Mississippi River Basin, including Iowa (Scavia et al. 2017; Rabalais and Turner 2019; Bianchi et al. 2010; Jones et al. 2018). Moreover, the excessive nutrients in local rivers and lakes results in the deterioration of water quality conditions within Iowa's water bodies. One of the emerging concerns pertains to the occurrence of harmful algal blooms (HABs), which pose health risks to individuals engaged in recreational activities within water bodies. Over the past two decades, the Center for Agricultural and Rural Development has undertaken numerous household surveys, funded by the US Environmental Protection Agency (EPA) and the Iowa Department of Natural Resources (DNR), with the aim of understanding the utilization of in-state lake recreational resources by Iowans, as well as the impact of water quality conditions on respondents’ choices of recreational destinations. Building upon the support provided by the Iowa Nutrient Reduction Center and the United States Geological Survey (USGS), we conducted a survey specifically designed to enhance our comprehension of lake recreation usage among households from underrepresented communities that were previously overlooked in earlier surveys

Iowa Lakes Drive over $1 Billion in Recreational Spending Each Year

Outdoor recreation in natural resource venues, such as state parks, lakes, and trails that accommodate a variety of recreational and wildlife-related pursuits, is one of the most popular forms of entertainment in the United States. As such, it makes large contributions to the nation’s economy. As a result, residents’ recreational usage and how they value water quality improvements are of interest to policymakers and researchers. In 2002, Iowa State’s Center for Agricultural and Rural Development (CARD), Department of Economics, Department of Ecology, Evolution, and Organismal Biology, and Limnology Laboratory worked together to create the Iowa Lakes Valuation Project to determine use and valuation information for more than 100 in-state lakes over a comparable time period (figure 1)