Role requiring'No experience data provided' months of experience in None

Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic forecasting of up to 1 hour, long-term traffic forecasting that spans multiple hours remains a major challenge. Moreover, most of the existing deep learning traffic forecasting models are black box, presenting additional challenges related to explainability and interpretability. We develop Graph Pyramid Autoformer (X-GPA), an explainable attention-based spatial-temporal graph neural network that uses a novel pyramid autocorrelation attention mechanism. It enables learning from long temporal sequences on graphs and improves long-term traffic forecasting accuracy. Our model can achieve up to 35 % better long-term traffic forecast accuracy than that of several state-of-the-art methods. The attention-based scores from the X-GPA model provide spatial and temporal explanations based on the traffic dynamics, which change for normal vs. peak-hour traffic and weekday vs. weekend traffic.

Smartphones, like many consumer products, arrive in the US on giant container ships, vessels that are leading killers of endangered whales that play crucial roles in the climate and ocean health. Now a high-tech initiative called Whale Safe is detecting the huge marine mammals off the coast of San Francisco and alerting ship captains to slow down to avoid deadly collisions. Launched on Wednesday, Whale Safe aims to create "school zones" for imperiled blue whales, fin whales and humpback whales in busy shipping lanes, according to the project's managers at the Benioff Ocean Science Laboratory at the University of California at Santa Barbara and at the Bay Area's Marine Mammal Center. Speeders are caught by satellite surveillance and cited online. That gives consumers the opportunity to see, for instance, if that cruise they're contemplating is operated by a company with a history of ignoring sea speed limits.

Modal split prediction in transportation networks has the potential to support network operators in managing traffic congestion and improving transit service reliability. We focus on the problem of hourly prediction of the fraction of travelers choosing one mode of transportation over another using high-dimensional travel time data. We use logistic regression as base model and employ various regularization techniques for variable selection to prevent overfitting and resolve multicollinearity issues. Importantly, we interpret the prediction accuracy results with respect to the inherent variability of modal splits and travelers' aggregate responsiveness to changes in travel time. By visualizing model parameters, we conclude that the subset of segments found important for predictive accuracy changes from hour-to-hour and include segments that are topologically central and/or highly congested. We apply our approach to the San Francisco Bay Area freeway and rapid transit network and demonstrate superior prediction accuracy and interpretability of our method compared to pre-specified variable selection methods.

Whether it's playing tennis or writing an essay, most people have a preferred hand. Now, a study has shown that despite having eight arms to choose from, octopuses also have favourite appendages. Researchers from the University of Minnesota recorded octopuses attacking various prey, and found they preferred certain arms over others when hunting. The team hopes the findings could be used to develop next-generation, highly manipulative soft robots. 'If we can learn from octopuses, then we can apply that to making an underwater vehicle or soft robot application,' said Dr Trevor Wardill, an author of the study.

Smartphones, like many consumer products, arrive in the US on giant container ships, vessels that are leading killers of endangered whales that play crucial roles in the climate and ocean health. Now a high-tech initiative called Whale Safe is detecting the huge marine mammals off the coast of San Francisco and alerting ship captains to slow down to avoid deadly collisions. Launched on Wednesday, Whale Safe aims to create "school zones" for imperilled blue whales, fin whales and humpback whales in busy shipping lanes, according to the project's managers at the Benioff Ocean Science Laboratory at the University of California at Santa Barbara and at the Bay Area's Marine Mammal Center. Speeders are caught by satellite surveillance and cited online. That gives consumers the opportunity to see, for instance, if that cruise they're contemplating is operated by a company with a history of ignoring sea speed limits.

Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transport engineering to derive insights from past accidents. This paper proposes a new fusion framework for predicting the incident duration from limited information by using an integration of machine learning with traffic flow/speed and incident description as features, encoded via several Deep Learning methods (ANN autoencoder and character-level LSTM-ANN sentiment classifier). The paper constructs a cross-disciplinary modelling approach in transport and data science. The approach improves the incident duration prediction accuracy over the top-performing ML models applied to baseline incident reports. Results show that our proposed method can improve the accuracy by $60\%$ when compared to standard linear or support vector regression models, and a further $7\%$ improvement with respect to the hybrid deep learning auto-encoded GBDT model which seems to outperform all other models. The application area is the city of San Francisco, rich in both traffic incident logs (Countrywide Traffic Accident Data set) and past historical traffic congestion information (5-minute precision measurements from Caltrans Performance Measurement System).

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One key challenge in multi-document summarization is to capture the relations among input documents that distinguish between single document summarization (SDS) and multi-document summarization (MDS). Few existing MDS works address this issue. One effective way is to encode document positional information to assist models in capturing cross-document relations. However, existing MDS models, such as Transformer-based models, only consider token-level positional information. Moreover, these models fail to capture sentences' linguistic structure, which inevitably causes confusions in the generated summaries. Therefore, in this paper, we propose document-aware positional encoding and linguistic-guided encoding that can be fused with Transformer architecture for MDS. For document-aware positional encoding, we introduce a general protocol to guide the selection of document encoding functions. For linguistic-guided encoding, we propose to embed syntactic dependency relations into the dependency relation mask with a simple but effective non-linear encoding learner for feature learning. Extensive experiments show the proposed model can generate summaries with high quality.

Exygy seeks an enthusiastic, experienced, and creative Full Stack Engineer who is passionate about making a difference in the world with technology. Join our tight-knit and growing team to build a wide variety of high-impact projects with our civic and health sector clients. This is a full-time remote position. As a senior engineer, you'll spend most of your time on multiple client project teams, building new web applications, while supporting and expanding existing projects. You'll work with our cross-functional core team, and our network of remote contributors to define, design, and deliver high-quality web software.