Vertical Transport Timescale of Surface-Produced Particulate Material in the Chesapeake Bay

Accumulation and remineralization of surface-produced particulate organic matter (POM) in the water column and seabed link closely to hypoxia and the health of aquatic ecosystems. The POM retention time provides a key timescale to interpret biochemical reaction processes. In this study, we investigated the spatiotemporal variations in the vertical particulate age (VPA) of surface-produced POM, which is the mean time elapsed since the particulates last contact the surface, by incorporating major physical processes including sinking, resuspension, and deposition in the Chesapeake Bay. It was found that the vertical transport time for the particulates (i.e., VPA) is much longer than the dissolved counterparts as the former consists of new material from the surface and the resuspended aged material that has elongated resting on the seabed after deposition. The VPA is sensitive to settling velocity, especially in low-frequent resuspension environments, and varies over 2 orders of magnitude with settling velocity from 0 to 10 m/day. Slow-sinking material can remain in suspension and seldom settle to the seabed, thus mainly contribute to pelagic processes, while the fast-sinking material connects closely with benthic processes. The seasonality of VPA decreases as the settling velocity increases. No significant difference in VPA was found between wet and dry years, yet the episodic strong flood events entrain old materials from the depositional lateral shoals to increase VPA in the channel. The transport age bridges cross disciplinaries by providing the fourth-dimensional age information as a common currency to compare the physical transport timescale with the timescales for biochemical reactions

Word Embedding based Correlation Model for Question/Answer Matching

With the development of community based question answering (Q&A) services, a large scale of Q&A archives have been accumulated and are an important information and knowledge resource on the web. Question and answer matching has been attached much importance to for its ability to reuse knowledge stored in these systems: it can be useful in enhancing user experience with recurrent questions. In this paper, we try to improve the matching accuracy by overcoming the lexical gap between question and answer pairs. A Word Embedding based Correlation (WEC) model is proposed by integrating advantages of both the translation model and word embedding, given a random pair of words, WEC can score their co-occurrence probability in Q&A pairs and it can also leverage the continuity and smoothness of continuous space word representation to deal with new pairs of words that are rare in the training parallel text. An experimental study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new method's promising potential.Comment: 8 pages, 2 figure

Traffic Safety Evaluation and Accident Prediction of Freeway: Evidence from China

In recent years, freeway safety accidents occurred frequently, causing serious harm to people\u27s lives and property safety. Therefore, how to evaluate freeway traffic safety and predict the number of accidents scientifically is a practical problem to be solved. The influencing factors of freeway traffic safety could be summarized as human behaviour characteristics, vehicle factors, road factors, environmental factors and traffic safety factors after a systematic analysis. To evaluate traffic safety and predicate freeway accident, using the data of Zhejiang province, China from 2015 to 2019, a freeway safety evaluation system was constructed. The freeway safety level was measured by using hierarchical entropy method, and the future traffic accidents in the sample area were predicted by using the Autoregressive Integrated Moving Average (ARIMA) model. Results show that the traffic safety level of freeway in the sample areas presents a fluctuating upward trend, and has a relatively safe state with a safety level 2. The average error rate is only 0.47% in the predication of freeway accident, showing a high degree of fitting and accuracy. Based on the above conclusions, this study puts forward the corresponding improvement strategies to provide a scientific basis for the decision-making of the government and transportation departments

Injecting Image Details into CLIP's Feature Space

Although CLIP-like Visual Language Models provide a functional joint feature space for image and text, due to the limitation of the CILP-like model's image input size (e.g., 224), subtle details are lost in the feature representation if we input high-resolution images (e.g., 2240). In this work, we introduce an efficient framework that can produce a single feature representation for a high-resolution image that injects image details and shares the same semantic space as the original CLIP. In the framework, we train a feature fusing model based on CLIP features extracted from a carefully designed image patch method that can cover objects of any scale, weakly supervised by image-agnostic class prompted queries. We validate our framework by retrieving images from class prompted queries on the real world and synthetic datasets, showing significant performance improvement on these tasks. Furthermore, to fully demonstrate our framework's detail retrieval ability, we construct a CLEVR-like synthetic dataset called CLVER-DS, which is fully annotated and has a controllable object scale

A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations

A spatial quality control method, ARF, is proposed. The ARF method incorporates the optimization ability of the artificial fish swarm algorithm and the random forest regression function to provide quality control for multiple surface air temperature stations. Surface air temperature observations were recorded at stations in mountainous and plain regions and at neighboring stations to test the performance of the method. Observations from 2005 to 2013 were used as a training set, and observations from 2014 were used as a testing set. The results indicate that the ARF method is able to identify inaccurate observations; and it has a higher rate of detection, lower rate of change for the quality control parameters, and fewer type I errors than traditional methods. Notably, the ARF method yielded low performance indexes in areas with complex terrain, where traditional methods were considerably less effective. In addition, for stations near the ocean without sufficient neighboring stations, different neighboring stations were used to test the different methods. Whereas the traditional methods were affected by station distribution, the ARF method exhibited fewer errors and higher stability. Thus, the method is able to effectively reduce the effects of geographical factors on spatial quality control