The team leading NASA's first mission to take a rock sample from the asteroid Bennu has selected four sites for the OSIRIS-REx spacecraft to'tag'. The spacecraft has already mapped the entire Bennu meteor - dubbed the'apocalypse asteroid' - in order to identify the safest and most accessible spots to retrieve a chunk of its surface. Now, the four locations will be studied before the final two sites – a primary and backup – are selected in December, this year. The OSIRIS-REx sample collection is scheduled for the latter half of 2020, and the spacecraft will return the asteroid samples to Earth on September 24, 2023. Osprey is set in a small crater, 66 feet (20 m) in diameter, which is also located in Bennu's equatorial region at 11 degrees north latitude, while Sandpiper is located in the meteor's southern hemisphere, at 47 degrees south latitude Sites: Nightingale is the northern-most site, situated at 56 degrees north latitude on Bennu, while Kingfisher is located in a small crater near Bennu's equator at 11 degrees north latitude The four candidate sample sites on Bennu are designated Nightingale, Kingfisher, Osprey, and Sandpiper – all birds native to Egypt.
Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save fresh water. To finance this project, Conceptual cost models are important to accurately predict preliminary costs at the early stages of the project. The first step is to develop a conceptual cost model to identify key cost drivers affecting the project. Therefore, input variables selection remains an important part of model development, as the poor variables selection can decrease model precision. The study discovered the most important drivers of FCIPs based on a qualitative approach and a quantitative approach. Subsequently, the study has developed a parametric cost model based on machine learning methods such as regression methods, artificial neural networks, fuzzy model and case-based reasoning.
In this paper, we address the question answering challenge with the SQuAD 2.0 dataset. We design a model architecture which leverages BERT's capability of context-aware word embeddings and BiDAF's context interactive exploration mechanism. By integrating these two state-of-the-art architectures, our system tries to extract the contextual word representation at word and character levels, for better comprehension of both question and context and their correlations. We also propose our original joint posterior probability predictor module and its associated loss functions. Our best model so far obtains F1 score of 75.842% and EM score of 72.24% on the test PCE leaderboad.
Every year the technology industry gathers in Las Vegas for the Consumer Electronics Show (CES), an event that often sets the agenda for the coming 12 months. This is what CES 2019 taught us. The first 5G networks are expected to begin rolling out this year, and so the next-generation connectivity technology was being mentioned everywhere at CES. Intel, Qualcomm and Samsung all spoke about harnessing the technology to not just offer faster mobile internet speeds, but also to connect more devices and appliances to each other and be able to handle more data in the process. Experts at the show also commented on the higher capacity of 5G networks being able to support the software needed to power networks of driverless cars and robots. The halls of this year's CES hinted at a world where homes, cars and even entire cities are connected to one another, with people able to use these connections to complete tasks every day.
Between 750 and 1000 hieroglyphs were in use in Egypt thousands of years ago, and to this day we still don't know how to interpret all of them. Could artificial intelligence help us crack the code? How dangerous is AI's exponential growth? Is any job immune to automation? DW spoke to technologists and historians to better understand some of the technological and societal upheavals humanity is facing.
INRIX chose its criteria based on a future business model where an autonomous truck powered by electric batteries or diesel-hybrid motors would cross long highway miles and then be taken over by people who would pilot the rigs through crowded cities to the final loading dock or port, said Avery Ash, INRIX's autonomous vehicle director.
CAIRO – Al-Qaida's chief bomb maker, Ibrahim al-Asiri, who was behind the 2009 Christmas Day plot to down an airliner over Detroit and other foiled aviation-related terror attacks, was killed in a U.S. drone strike, Yemeni officials and a tribal leader said Friday. The killing of al-Asiri deals a heavy blow to the group's capabilities in striking western targets and piles pressure on the group that already lost some of its top cadres over the past years in similar drone strikes. A Yemeni security official said that al-Asiri is dead; a tribal leader and an al-Qaida-linked source also said that he was killed in a U.S. drone strike in the eastern Yemeni governorate of Marib. The tribal leader said that al-Asiri was struck, along with two or four of his associates, as he stood beside his car. He added that al-Asiri's wife, who hails from the well-known al-Awaleq tribe in the southern governorate of Shabwa, was briefly held months ago by the UAE-backed forces and later released.
Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and predicting informal settlements using only street intersections data, regardless of the variation of urban form, number of floors, materials used for construction or street width. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and a machine learning approach, using Multinomial Logistic Regression (MNL) and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics that these regions tend to be filled in with smaller subdivided lots of housing relative to the formal areas within the local context, and the paucity of services and infrastructure within the boundary of these settlements that require relatively bigger lots. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context.
For years, scientists have suggested women are worse at reading maps than men. A best-selling popular science book a few years ago even had the title'Why men don't listen and women can't read maps.' Research conducted across over half a million people in 57 countries shows overall this idea is still true: women are still worse overall than men at navigation. But the fairer sex are closing the gap, according to the research. Research conducted across over half a million people in 57 countries shows overall this idea is still true: women are still worse overall than men at navigation. In countries where women have made greater strides in the workplace and society - such as the United Kingdom, Australia and United States - women are better at navigating than in countries where women's role in society is less advanced - such as Egypt.
Bio: Ahmed Gad received his B.Sc. degree with excellent with honors in information technology from the Faculty of Computers and Information (FCI), Menoufia University, Egypt, in July 2015. For being ranked first in his faculty, he was recommended to work as a teaching assistant in one of the Egyptian institutes in 2015 and then in 2016 to work as a teaching assistant and a researcher in his faculty. His current research interests include deep learning, machine learning, artificial intelligence, digital signal processing, and computer vision.