A significant problem is the lack of European HPC facilities in the global top 10. European scientists and engineers rely on expensive U.S. supercomputing facilities more than those in the EU by a factor of 10. At Solidus AITECH, we have completed the main build of our data centre and are ready to commence the installation of our HPC infrastructure which is based in Europe (Bucharest), which will significantly help to bridge this gap and will enable European companies to obtain their supercomputing power within the EU. We expect this facility to be more efficient and of lower cost than in most of the U.S. By operating in Bucharest, we have the benefit of low-cost electricity and fast internet speeds as well as our Intellectual Property (IP) and expect to run at around 40% less power consumption than the industry average. We have the capacity to scale up our operations and, as we do so, we expect to quickly become the top EU HPC centre and in the global top 10 HPC centres.

Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1,275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a participant's ability to recall having seen the video previously when shown a collection of videos. The recognition tasks were performed on videos seen within the previous few minutes for short-term memorability and within the previous 24 to 72 hours for long-term memorability. Data includes the reaction times for each recognition of each video. Associated with each video are text descriptions (captions) as well as a collection of image-level features applied to 3 frames extracted from each video (start, middle and end). Video-level features are also provided. The dataset was used in the Video Memorability task as part of the MediaEval benchmark in 2020.

Medical Visual Question Answering (VQA) is a combination of medical artificial intelligence and popular VQA challenges. Given a medical image and a clinically relevant question in natural language, the medical VQA system is expected to predict a plausible and convincing answer. Although the general-domain VQA has been extensively studied, the medical VQA still needs specific investigation and exploration due to its task features. In the first part of this survey, we cover and discuss the publicly available medical VQA datasets up to date about the data source, data quantity, and task feature. In the second part, we review the approaches used in medical VQA tasks. In the last part, we analyze some medical-specific challenges for the field and discuss future research directions.

Planning the layout of bicycle-sharing stations is a complex process, especially in cities where bicycle sharing systems are just being implemented. Urban planners often have to make a lot of estimates based on both publicly available data and privately provided data from the administration and then use the Location-Allocation model popular in the field. Many municipalities in smaller cities may have difficulty hiring specialists to carry out such planning. This thesis proposes a new solution to streamline and facilitate the process of such planning by using spatial embedding methods. Based only on publicly available data from OpenStreetMap, and station layouts from 34 cities in Europe, a method has been developed to divide cities into micro-regions using the Uber H3 discrete global grid system and to indicate regions where it is worth placing a station based on existing systems in different cities using transfer learning. The result of the work is a mechanism to support planners in their decision making when planning a station layout with a choice of reference cities.

Expertise in AI requires integrating computational, conceptual, and mathematical knowledge and representations. We propose this trifecta as an "AI triplet," similar in spirit to the "chemistry triplet" that has influenced the past four decades of chemistry education. We describe a rationale for this triplet and how it maps onto topics commonly taught in AI courses, such as tree search and gradient descent. Also, similar to impacts of the chemistry triplet on chemistry education, we suggest an initial example of how considering the AI triplet may help pinpoint obstacles in AI education, i.e., how student learning might be scaffolded to approach expert-level flexibility in moving between the points of the triplet.

PENRYN, ENGLAND - MAY 09: Engineered Arts prosthetic expert Mike Humphrey checks on Fred a recently ... [ ] completed Mesmer robot that was built at the company's headquarters in Penryn on May 9, 2018 in Cornwall, England. Founded in 2004, the Cornish company operating from an industrial unit near Falmouth, is a world leader in life sized commercial available humanoid robots for entertainment, information, education and research. The company has successfully sold its the fully interactive and multilingual RoboThespian robot around the world to science centres, theme parks and visitor attractions, and also to academic and commercial research groups where they are used as research and development platforms. However, more recently the company has been building a range of lifelike bio-mechanical Mesmer robots. Built on the sensors and the extensive software framework already developed for RoboThespian, the Mesmer robots can offer some of the smartest animatronics on the market, giving extensive interaction but can also move very smoothly, quietly and naturally too.

This paper presents a unified user profiling framework to identify hate speech spreaders by processing their tweets regardless of the language. The framework encodes the tweets with sentence transformers and applies an attention mechanism to select important tweets for learning user profiles. Furthermore, the attention layer helps to explain why a user is a hate speech spreader by producing attention weights at both token and post level. Our proposed model outperformed the state-of-the-art multilingual transformer models.

Not so long ago it may have seemed more like a futuristic vision of the workplace – or a hospital. But the hands-free door handles, self-cleaning surfaces, antimicrobial paint, air-monitoring display tools, UV light disinfection robots, and 135 other measures at an office block in Bucharest are here to stay, say the creators behind what they are touting as one of the world's most virus-resilient workplaces, which they hope will become the new normal in office design. Entering H3, a five-storey building in a western neighbourhood of the Romanian capital, is like learning the steps to a new dance. A flick of the wrist opens the door, and a red line marks the spot at which to stand from where a thermal body camera 2 metres away scans arrivals for signs of fever. Those who are "green-lighted" can follow the tracks to the self-clean lift, step on one of two foot pads and be transported through the building, safe in the knowledge that a UV lighting disinfection system installed in the ventilation shafts is keeping them infection-free between floors. Anyone whose head flashes red on the screen, however, is whisked away by a plastic-gloved "immune steward" into a nearby quarantine room: a glass box with a panic button and its own internal ventilation system shut off from the rest of the building.

EuroVoc is a multilingual thesaurus that was built for organizing the legislative documentary of the European Union institutions. It contains thousands of categories at different levels of specificity and its descriptors are targeted by legal texts in almost thirty languages. In this work we propose a unified framework for EuroVoc classification on 22 languages by fine-tuning modern Transformer-based pretrained language models. We study extensively the performance of our trained models and show that they significantly improve the results obtained by a similar tool - JEX - on the same dataset. The code and the fine-tuned models were open sourced, together with a programmatic interface that eases the process of loading the weights of a trained model and of classifying a new document.

The power system is undergoing rapid evolution with the roll-out of advanced metering infrastructure and local energy applications (e.g. electric vehicles) as well as the increasing penetration of intermittent renewable energy at both transmission and distribution level, which characterizes the peak load demand with stronger randomness and less predictability and therefore poses a threat to the power grid security. Since storing large quantities of electricity to satisfy load demand is neither economically nor environmentally friendly, effective peak demand management strategies and reliable peak load forecast methods become essential for optimizing the power system operations. To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature. To our best knowledge, this is the first comprehensive review on such topic. In this paper we first give a precise and unified problem definition of peak load demand forecast. Second, 139 papers on peak load forecast methods were systematically reviewed where methods were classified into different stages based on the timeline. Thirdly, a comparative analysis of peak load forecast methods are summarized and different optimizing methods to improve the forecast performance are discussed. The paper ends with a comprehensive summary of the reviewed papers and a discussion of potential future research directions.