Impact of AQUA Satellite Data on Hurricane Forecast: Danielle 2010

This study focuses on the impact of AQUA satellite data from AIRS and AMSU on the forecast of hurricane Danielle by the Global Forecast System (GFS) model. The data assimilation method adopted to ingest the data is the Gridpoint Statistical method (GSI) which is based on the three dimensional variational (3DVAR) data assimilation technique. Two experiments were carried out to investigate the impact of AQUA satellite radiance observation on the forecast of hurricane Danielle. The first experiment (Control) assimilated all the available data while the second experiment (No AQUA) incorporated all the observations but the AQUA satellite data. Data assimilation cycling started one week prior to hurricane genesis, on 15 August 2010 06 UTC. The root mean square track forecast error shows slightly negative impact at the early lead time and slightly positive impact at later lead time. However, the root mean square intensity forecast errors by the Control are shown to be lower than No AQUA for all forecast hours, indicating positive impact of the AQUA data on the intensity forecast

Aspect-Aware Latent Factor Model: Rating Prediction with Ratings and Reviews

Although latent factor models (e.g., matrix factorization) achieve good accuracy in rating prediction, they suffer from several problems including cold-start, non-transparency, and suboptimal recommendation for local users or items. In this paper, we employ textual review information with ratings to tackle these limitations. Firstly, we apply a proposed aspect-aware topic model (ATM) on the review text to model user preferences and item features from different aspects, and estimate the aspect importance of a user towards an item. The aspect importance is then integrated into a novel aspect-aware latent factor model (ALFM), which learns user's and item's latent factors based on ratings. In particular, ALFM introduces a weighted matrix to associate those latent factors with the same set of aspects discovered by ATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, an aspect rating is weighted by an aspect importance, which is dependent on the targeted user's preferences and targeted item's features. Therefore, it is expected that the proposed method can model a user's preferences on an item more accurately for each user-item pair locally. Comprehensive experimental studies have been conducted on 19 datasets from Amazon and Yelp 2017 Challenge dataset. Results show that our method achieves significant improvement compared with strong baseline methods, especially for users with only few ratings. Moreover, our model could interpret the recommendation results in depth.Comment: This paper has been accepted by the WWW 2018 Conferenc

Impact of AQUA Satellite Data on Hurricane Forecast: Danielle 2010

This study focuses on the impact of AQUA satellite data from AIRS and AMSU on the forecast of hurricane Danielle by the Global Forecast System (GFS) model. The data assimilation method adopted to ingest the data is the Gridpoint Statistical method (GSI) which is based on the three dimensional variational (3DVAR) data assimilation technique. Two experiments were carried out to investigate the impact of AQUA satellite radiance observation on the forecast of hurricane Danielle. The first experiment (Control) assimilated all the available data while the second experiment (No AQUA) incorporated all the observations but the AQUA satellite data. Data assimilation cycling started one week prior to hurricane genesis, on 15 August 2010 06 UTC. The root mean square track forecast error shows slightly negative impact at the early lead time and slightly positive impact at later lead time. However, the root mean square intensity forecast errors by the Control are shown to be lower than No AQUA for all forecast hours, indicating positive impact of the AQUA data on the intensity forecast

Pixel and Feature Transfer Fusion for Unsupervised Cross-Dataset Person Re-Identification

Publisher Copyright: IEEERecently, unsupervised cross-dataset person reidentification (Re-ID) has attracted more and more attention, which aims to transfer knowledge of a labeled source domain to an unlabeled target domain. There are two common frameworks: one is pixel-alignment of transferring low-level knowledge, and the other is feature-alignment of transferring high-level knowledge. In this article, we propose a novel recurrent autoencoder (RAE) framework to unify these two kinds of methods and inherit their merits. Specifically, the proposed RAE includes three modules, i.e., a feature-transfer (FT) module, a pixel-transfer (PT) module, and a fusion module. The FT module utilizes an encoder to map source and target images to a shared feature space. In the space, not only features are identity-discriminative but also the gap between source and target features is reduced. The PT module takes a decoder to reconstruct original images with its features. Here, we hope that the images reconstructed from target features are in the sourcestyle. Thus, the low-level knowledge can be propagated to the target domain. After transferring both high- and low-level knowledge with the two proposed modules above, we design another bilinear pooling layer to fuse both kinds of knowledge. Extensive experiments on Market-1501, DukeMTMC-ReID, and MSMT17 datasets show that our method significantly outperforms either pixel-alignment or feature-alignment Re-ID methods and achieves new state-of-the-art results.Peer reviewe

Conflicts Between GHG Accounting Methodologies in the Steel Industry

Accurate, verifiable, and comparable greenhouse gas (GHG) emissions data throughout supply chains in the materials sector are necessary to drive decarbonization. This is particularly the case for the steel supply chain, a major source of GHG emissions with untapped potential for reduction. However, emissions accounting methods used by the steel industry suffer from gaps and misalignment, resulting in significant differences in reported GHG emissions. The result is a patchwork reporting landscape vulnerable to manipulation and miscommunication, generating little actionable data for policymakers, producers, customers, and investors. These shortcomings highlight the need for a harmonized carbon accounting framework for the steel industry that bridges these disparities. Such a framework is urgently needed to guide time- and capital-intensive investments towards decarbonizing steel supply chains. A leading partner of the Coalition on Materials Emissions Transparency (COMET), the Columbia Center on Sustainable Investment (CCSI) conducted a comprehensive comparison of GHG accounting frameworks pertinent to the steel industry. This comparison identified the critical discrepancies existing between the methods examined and resulted in proposed solutions to be detailed in further research

Stone-Wales Defects Preserve Hyperuniformity in Amorphous Two-Dimensional Materials

Crystalline two-dimensional (2D) materials such as graphene possess unique physical properties absent in their bulk form, enabling many novel device applications. Yet, little is known about their amorphous counterparts, which can be obtained by introducing the Stone-Wales (SW) topological defects via proton radiation. Here we provide strong numerical evidence that SW defects preserve hyperuniformity in hexagonal 2D materials, a recently discovered new state of matter characterized by vanishing normalized infinite-wavelength density fluctuations, which implies that all amorphous states of these materials are hyperuniform. Specifically, the static structure factor S(k) of these materials possesses the scaling S(k) ~ k^{\alpha} for small wave number k, where 1<=\alpha(p)<=2 is monotonically decreasing as the SW defect concentration p increases, indicating a transition from type-I to type-II hyperuniformity at p ~= 0.12 induced by the saturation of the SW defects. This hyperuniformity transition marks a structural transition from perturbed lattice structures to truly amorphous structures, and underlies the onset of strong correlation among the SW defects as well as a transition between distinct electronic transport mechanisms associated with different hyperuniformity classes

An improved extreme-point symmetric mode decomposition method and its application to rolling bearing fault diagnosis

HHT (Hilbert-Huang Transform) which consist of EMD (Empirical Mode Decomposition) and HT (Hilbert Transform) now is the most widely used time-frequency analysis technique for rolling element bearing fault diagnosis, however, its fault characteristic information extraction accuracy is usually limited due to the problem of mode mixing in EMD. ESMD (Extreme-point symmetric mode decomposition) is a novel development of HHT which is promising to alleviate this limitation and it has been applied to some fields successfully, but its application for rolling bearing fault diagnosis has rarely been seen in the literature. In this paper, ESMD is applied to extract the bearing fault characteristics for rolling bearing fault detection, and the results proved that ESMD can have a better fault diagnose effect than EMD and HT. What’s more, for further improving bearing fault characteristic extraction accuracy of rolling bearing vibration signals, the sifting scheme is proposed for selecting the sensitive fault-related IMFs (intrinsic mode functions) generated by ESMD, in which a weighted kurtosis index is introduced for automatic selection and reconstruction of the fault-related IMFs, and then the original and reconstructed bearing fault vibration signal after performing Hilbert transform as the results to diagnose the incipient rolling bearing fault. ESMD combined with the proposed sifting scheme are applied to diagnose the simulated and experimental signals, and the results confirmed that the sifting scheme based ESMD is superior to the other conventional method in rolling bearings fault diagnosis