On the evening of Oct. 31, 25-year-old Fukuoka native Kyota Hattori -- wearing makeup and a purple and green ensemble to emulate the villainous Joker of "Batman" franchise fame -- boarded a Keio Line train at Keio-Hachioji Station, heading for central Tokyo. After spending half an hour meandering around Shibuya, which was packed with costumed revelers feting Halloween, Hattori headed back toward Hachioji, but reversed direction again at Chofu, where he changed to a Shinjuku-bound limited express train. Soon after the doors closed, according to eye witness reports, he removed a survival knife and liquids from a backpack. When a 72-year-old male passenger tried to intervene, Hattori allegedly stabbed the man and proceeded to pursue fleeing passengers, splashing them with lighter fluid, which he then ignited. The stabbing victim was hospitalized in a critical condition and 16 other passengers suffered burns and smoke inhalation.
Soon there will be no need for a passenger of the Moscow subway to pause in front of the turnstiles and frantically search their pockets for a transit card or ticket. Starting from Oct. 15, a glance at the camera will open the pay gate. On Wednesday, Moscow Mayor Sergei Sobyanin announced that the Face Pay system will soon be available at all subway stations (about 300). To be able to use it, commuters register in the Moscow subway app, upload a photo of their face, and attach their bank card. Once the user approaches turnstiles, the camera recognizes the face (even if the person is wearing a mask), the fare is debited from their account, and the pay gate opens.
We assess the demand effects of discounts on train tickets issued by the Swiss Federal Railways, the so-called `supersaver tickets', based on machine learning, a subfield of artificial intelligence. Considering a survey-based sample of buyers of supersaver tickets, we investigate which customer- or trip-related characteristics (including the discount rate) predict buying behavior, namely: booking a trip otherwise not realized by train, buying a first- rather than second-class ticket, or rescheduling a trip (e.g.\ away from rush hours) when being offered a supersaver ticket. Predictive machine learning suggests that customer's age, demand-related information for a specific connection (like departure time and utilization), and the discount level permit forecasting buying behavior to a certain extent. Furthermore, we use causal machine learning to assess the impact of the discount rate on rescheduling a trip, which seems relevant in the light of capacity constraints at rush hours. Assuming that (i) the discount rate is quasi-random conditional on our rich set of characteristics and (ii) the buying decision increases weakly monotonically in the discount rate, we identify the discount rate's effect among `always buyers', who would have traveled even without a discount, based on our survey that asks about customer behavior in the absence of discounts. We find that on average, increasing the discount rate by one percentage point increases the share of rescheduled trips by 0.16 percentage points among always buyers. Investigating effect heterogeneity across observables suggests that the effects are higher for leisure travelers and during peak hours when controlling several other characteristics.
People are making groundbreaking innovation and tackling world issues that are making our lives easier. All the technology which we have mentioned in the article has the potential to change the world that we live in. It can revolutionaries how we travel, it can change our health care system and even how we work. So without any delay let's get started. The first technology that we have in our list is Quantum computer's.
Cost savings of up to £30M were identified on Network Rail's Great Western project through use of machine learning and wider construction industry benefits are set to be unlocked by a new investment from a Google venture business. Network Rail leveraged the savings by using construction risk forecasting platform nPlan, which has just been boosted by a £13.5M investment from GV (formerly Google Ventures). According to nPlan, wider use of machine learning that this investment will support could save the global construction sector almost £730bn each year through spotting delays and recommending improvements with an accuracy and scale previously not possible. The firm has said that such data-led insights would effectively reduce the volatility of and increase investor confidence in construction projects. By using some of the most powerful machine learning capabilities in the world to analyse what worked and what didn't in past projects, we can help our customers work out what's going to derail their own initiatives, and stop problems happening before they even appear." Speaking about the due diligence undertaken by GV ahead of the investment, GV general partner Tom Hulme said that he was "blown away" by the benefits organisations such as Network Rail saw in applying modern machine learning techniques to such a difficult analogue problem".
Delseny, Hervé, Gabreau, Christophe, Gauffriau, Adrien, Beaudouin, Bernard, Ponsolle, Ludovic, Alecu, Lucian, Bonnin, Hugues, Beltran, Brice, Duchel, Didier, Ginestet, Jean-Brice, Hervieu, Alexandre, Martinez, Ghilaine, Pasquet, Sylvain, Delmas, Kevin, Pagetti, Claire, Gabriel, Jean-Marc, Chapdelaine, Camille, Picard, Sylvaine, Damour, Mathieu, Cappi, Cyril, Gardès, Laurent, De Grancey, Florence, Jenn, Eric, Lefevre, Baptiste, Flandin, Gregory, Gerchinovitz, Sébastien, Mamalet, Franck, Albore, Alexandre
Machine Learning (ML) seems to be one of the most promising solution to automate partially or completely some of the complex tasks currently realized by humans, such as driving vehicles, recognizing voice, etc. It is also an opportunity to implement and embed new capabilities out of the reach of classical implementation techniques. However, ML techniques introduce new potential risks. Therefore, they have only been applied in systems where their benefits are considered worth the increase of risk. In practice, ML techniques raise multiple challenges that could prevent their use in systems submitted to certification constraints. But what are the actual challenges? Can they be overcome by selecting appropriate ML techniques, or by adopting new engineering or certification practices? These are some of the questions addressed by the ML Certification 3 Workgroup (WG) set-up by the Institut de Recherche Technologique Saint Exup\'ery de Toulouse (IRT), as part of the DEEL Project.
Traffic forecasting is important for the success of intelligent transportation systems. Deep learning models, including convolution neural networks and recurrent neural networks, have been extensively applied in traffic forecasting problems to model spatial and temporal dependencies. In recent years, to model the graph structures in transportation systems as well as contextual information, graph neural networks have been introduced and have achieved state-of-the-art performance in a series of traffic forecasting problems. In this survey, we review the rapidly growing body of research using different graph neural networks, e.g. graph convolutional and graph attention networks, in various traffic forecasting problems, e.g. road traffic flow and speed forecasting, passenger flow forecasting in urban rail transit systems, and demand forecasting in ride-hailing platforms. We also present a comprehensive list of open data and source resources for each problem and identify future research directions. To the best of our knowledge, this paper is the first comprehensive survey that explores the application of graph neural networks for traffic forecasting problems. We have also created a public GitHub repository where the latest papers, open data, and source resources will be updated.
The idea of hurtling down a vacuum tube in a levitating pod at speeds of over 670 miles an hour may sound like the plot of the latest science fiction blockbuster, but it could soon become a reality. Virgin Hyperloop is developing the futuristic technology, which it claims could transform the way we travel. At the end of last year, the company demonstrated the technology in action, transporting two brave participants for the first time. Now, the tech giant has shared a step-by-step video of the passenger experience on board its Hyperloop system, all the way from arriving at the portal, to taking off on board a hyperloop pod. 'Showing the passenger experience of Virgin Hyperloop is a glimpse of the future, following the success three months ago when people rode in a hyperloop pod for the first time,' said Sultan Bin Sulayem, Chairman of Virgin Hyperloop and Group Chairman and CEO of DP World.
Ontology, as a discipline of philosophy, explains the nature of existence and has its roots in Aristotle and Plato studies on "metaphysics" (Welty and Guarino, 2001). However, the word ontology originated from two Greek words: ontos (being) and logos (word), and conceived for the first time during the Sixteen century by German philosophers (Welty and Guarino, 2001). From then till the mid-twentieth, ontology evolved mainly as a branch of philosophy. However, with the advent of Artificial Intelligence since the 1950s, researchers perceived the necessity of ontology to describe a new world of intelligent systems (Welty and Guarino, 2001). Moreover, with the development of the World Wide Web in the 1990s, ontology development got to be common among different domain specialists to define and share the concepts and entities in their fields on the Internet (Noy et al., 2001). During the last three decades, ontology development studies have evolved and shifted from theoretical issues of ontology to practical implications of the use of ontology in real-world, large-scale applications (Noy et al., 2001). Nowadays, ontology development focuses mainly on defining machine interpretable concepts and their relationships in a domain. However, ontology development also pursues other goals, such as providing a common conceptualization of the domain on which different experts agree, (Métral and Cutting-Decelle, 2011) and enable them to reuse the domain knowledge (Noy et al., 2001). It also enables researchers to easily analyze the domain knowledge and eloquently express the domain assumptions.
In the coming years, you may be able to reach your destination by taking an ultra-high-speed pod that zips through a vacuum in a metal tube. Virgin Hyperloop took a step closer to making its high-speed transit technology a reality on Sunday with the first official test of the system with humans inside the pod. The company, part of billionaire business titan Richard Branson's Virgin Group, said the test took place on its 500-meter track outside of Las Vegas. Co-founder and chief technology officer Josh Giegel and Sara Luchian, director of passenger experience, rode in a two-person hyperloop pod for about 15 seconds, reaching a top speed of 107 miles per hour. The eventual goal is to reach speeds of about 1,000 kilometers per hour, or about 621 mph, in yet-to-be-designed 28-person pods.