Abstract--Accurate taxi demand-supply forecasting is a challenging applicationof ITS (Intelligent Transportation Systems), due to the complex spatial and temporal patterns. We investigate the impact of different spatial partitioning techniques on the prediction performance of an LSTM (Long Short-Term Memory) network, in the context of taxi demand-supply forecasting. We consider two tessellation schemes: (i) the variable-sized Voronoi tessellation, and (ii) the fixed-sized Geohash tessellation. While the widely employed ConvLSTM (Convolutional LSTM) can model fixed-sized Geohash partitions, the standard convolutional filters cannot be applied on the variable-sized Voronoi partitions. To explore the Voronoi tessellation scheme, we propose the use of GraphLSTM (Graph-based LSTM), by representing the Voronoi spatial partitions as nodes on an arbitrarily structured graph. The GraphLSTM offers competitive performance against ConvLSTM, atlower computational complexity, across three realworld large-scale taxi demand-supply data sets, with different performance metrics. To ensure superior performance across diverse settings, a HEDGE based ensemble learning algorithm is applied over the ConvLSTM and the GraphLSTM networks. I. INTRODUCTION Spatiotemporal forecasting has a wide range of applications, rangingfrom epidemic detection [1], energy management [2], to cellular traffic [3], among others. Location-based taxi demand and supply forecasting, one of the key components ofITS (Intelligent Transportation Systems), also relies heavily on accurate spatiotemporal forecasting. Mobility-on- Demand services such as e-hailing taxis, which have gained tremendous popularity in the recent years, often face taxi demand-supply imbalances. During peak and off-peak hours, mismatches occur between the spatial distributions of the taxi demand and the available drivers, resulting in either scarcity or abundance of vacant taxis. For example, Figure 1 presents a case of demand-supply mismatch averaged over all Mondays near the city center in Bengaluru, India.

The Stellar Daisy, a massive South Korean tanker that sank in March 2017, was spotted on the floor of the South Atlantic Ocean nearly two years later, the CEO of an ocean exploration company revealed Sunday. This discovery could shed new light on exactly what caused the vessel to tilt and sink and provide some closure to the families of the 22 crew members who died. "We are pleased to report that we have located Stellar Daisy, in particular for our client, the South Korean Government, but also for the families of those who lost loved ones in this tragedy," Ocean Infinity CEO Oliver Plunkett said. "Through the deployment of multiple state of the art (autonomous underwater vehicles), we are covering the seabed with unprecedented speed and accuracy." The Stellar Daisy sank on March 31, 2017, nearly 2,500 miles east of Uruguay, while transporting iron ore from Brazil to China.

In 2015, all 193 member countries of the United Nations ratified the 2030 "Sustainable Development Goals" (SDG): a call to action to "end poverty, protect the planet and ensure that all people enjoy peace and prosperity." The 17 goals – shown in the chart below – are measured against 169 targets, set on a purposefully aggressive timeline. The first of these targets, for example, is: "by 2030, [to] eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day". The UN emphasizes that Science, Technology and Innovation (STI) will be critical in the pursuit of these ambitious targets. Rapid advances in technologies which have only really emerged in the past decade – such as the internet of things (IoT), blockchain, and advanced network connectivity – have exciting SDG applications.

The human rights job landscape is changing rapidly. Current and future challenges in combating human rights violations require new skills and tactics. We have compiled a list of 7 free online courses and specializations that will equip you with the knowledge and skills for the human rights jobs of the future. Machine learning and artificial intelligence create new opportunities and challenges for the protection of human rights. Artificial intelligence can help make education, health and economic systems more efficient but also bears the risk to amplify polarization, bias and discrimination against certain groups.

Due to globalization, geographic boundaries no longer serve as effective shields for the spread of infectious diseases. In order to aid bio-surveillance analysts in disease tracking, recent research has been devoted to developing information retrieval and analysis methods utilizing the vast corpora of publicly available documents on the internet. In this work, we present methods for the automated retrieval and classification of documents related to active public health events. We demonstrate classification performance on an auto-generated corpus, using recurrent neural network, TF-IDF, and Naive Bayes log count ratio document representations. By jointly modeling the title and description of a document, we achieve 97% recall and 93.3% accuracy with our best performing bio-surveillance event classification model: logistic regression on the combined output from a pair of bidirectional recurrent neural networks.

Shanghai-based artificial intelligence company Yitu Technology announced this month that is launching its first R&D center outside of China in Singapore. The move is part of a larger trend among Chinese tech companies hoping to achieve two goals: Access top foreign engineering and scientific talent by setting up R&D centers in key global knowledge hubs, and embed themselves deeper in local ecosystems to spur new long-term growth engines -- most notably in Southeast Asia. The ultimate goal for many of China's leading tech companies is to become true multinationals. Their strategy is to build a significant presence in their huge home market and then leverage that to branch out internationally. However, they face a steep learning curve: The free-for-all ethos and Darwinian natural selection that guide their modus operandi in China often prove to be counter-productive in smaller, more insulated markets.

I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anankumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.

Today I'm pleased to present to readers what's next up in our series featuring author Richard Abbott, whose space jaunts have so delighted me--and many others. Of course, I'd previously reviewed Abbott's debut sci-fi novel, Far from the Spaceports, followed up by another for its sequel, Timing. The audio excerpts below come from the second novel and, like our previous entry, utilize Amazon's Polly software, which is enabled for text-to-speech in multiple accents and intonations. This compares to Alexa, a single voice. Before moving forward, for those unfamiliar with the novels and their plots, I've linked the book covers to their respective Amazon blurbs.

The mention of the words Artificial Intelligence (AI) conjures up science fiction-like images in the minds of many people, but it is becoming a very real part of day to day life without us even realising it. AI is and has been making a lasting impression on a number of key industries, not only streamlining otherwise tedious processes but also changing the way business is conducted on a much larger scale. Elnur Amikishiyev via 123RF 1. Education AI will likely be used predominantly to take the labour out of admin during the early stages of implementation, taking over things like grading assignments, recording marks, and any other computational tasks where machines could surpass people. The human element, however, will remain a constant in the form of teachers who will have greater freedom to focus on students' individual needs and finding ways to fill gaps in learning. Most notably AI is used to mark multiple-choice tests, but advancements in machine learning could soon enable it to evaluate and efficiently mark written responses. The technology could also be utilised to make the enrollment and admissions processes at educational institutions more efficient.

At Callaway, the high-end golf-equipment stalwart, the process of making clubs has always been quite labor-intensive--from grinding and polishing clubheads to crafting wood-and-steel-shafted irons and wedges. The company has also long combined such artisanal handwork with technological innovation, even partnering with aerospace titan Boeing recently to codesign several aerodynamic clubs. So when the company set out about four years ago to make its latest club line, called Epic Flash, it took the next evolutionary technological step, turning to artificial intelligence and machine learning for help. A typical club-design process might involve five to seven physical prototypes; for Epic Flash, Callaway created 15,000 virtual ones. From those, an algorithm determined the best design, selecting for peak performance--i.e., ball speed--while also conforming to the rules set forth by the U.S. Golf Association.