PAPER ID: TMRB-06-24-OA-0958 1 Model-based optimisation for the personalisation of robot-assisted gait training Andreas Christou, Daniel F. N. Gordon, Theodoros Stouraitis, Juan C. Moreno and Sethu Vijayakumar Abstract--Personalised rehabilitation can be key to promoting gait independence and quality of life. Robots can enhance therapy by systematically delivering support in gait training, but often use one-size-fits-all control methods, which can be suboptimal. Here, we describe a model-based optimisation method for designing and fine-tuning personalised robotic controllers. As a case study, we formulate the objective of providing assistance as needed as an optimisation problem, and we demonstrate how musculoskeletal modelling can be used to develop personalised interventions. Eighteen healthy participants (age = 26 4) were recruited and the personalised control parameters for each were obtained to provide assistance as needed during a unilateral tracking task. A comparison was carried out between the personalised controller and the non-personalised controller. In simulation, a significant improvement was predicted when the personalised parameters were used. Experimentally, responses varied: six subjects showed significant improvements with the personalised parameters, eight subjects showed no obvious change, while four subjects performed worse. High interpersonal and intra-personal variability was observed with both controllers. This study highlights the importance of personalised control in robot-assisted gait training, and the need for a better estimation of human-robot interaction and human behaviour to realise the benefits of model-based optimisation. I. Introduction Motor function deficits are often the result of neurological disorders and can significantly impact the quality of This research was supported in part by the Engineering and Physical Sciences Research Council (EPSRC, grant reference EP/L016834/1) as part of the Centre for Doctoral Training in Robotics and Autonomous Systems at Heriot-Watt University and The University of Edinburgh, in part by the Alan Turing Institute, U.K., in part by Project I+D+i RED2022-134319-T (Spain), and in part by the Japan Science and Technology Agency (JST) Moonshot R&D Program (Grant No. JPMJMS2239). This includes one multimedia MP4 format movie clip, which provides scenes of the experimental setup. This material is 24.1 MB in size. T. Stouraitis is with DeepSea Technologies, 105 64 Athens, Greece (email: stoutheo@gmail.com).

Accurate traffic forecasting plays a crucial role in modern Intelligent Transportation Systems (ITS), as it enables real-time traffic flow management, reduces congestion, and improves the overall efficiency of urban transportation networks. With the rise of Quantum Machine Learning (QML), it has emerged a new paradigm possessing the potential to enhance predictive capabilities beyond what classical machine learning models can achieve. In the present work we pursue a heuristic approach to explore the potential of QML, and focus on a specific transport issue. In particular, as a case study we investigate a traffic forecast task for a major urban area in Athens (Greece), for which we possess high-resolution data. In this endeavor we explore the application of Quantum Neural Networks (QNN), and, notably, we present the first application of quantum data re-uploading in the context of transport forecasting. This technique allows quantum models to better capture complex patterns, such as traffic dynamics, by repeatedly encoding classical data into a quantum state. Aside from providing a prediction model, we spend considerable effort in comparing the performance of our hybrid quantum-classical neural networks with classical deep learning approaches. Our results show that hybrid models achieve competitive accuracy with state-of-the-art classical methods, especially when the number of qubits and re-uploading blocks is increased. While the classical models demonstrate lower computational demands, we provide evidence that increasing the complexity of the quantum model improves predictive accuracy. These findings indicate that QML techniques, and specifically the data re-uploading approach, hold promise for advancing traffic forecasting models and could be instrumental in addressing challenges inherent in ITS environments.

On behalf of our client an innovative company providing AI services for the marine industry, we are currently looking for a knowledgeable Lead Data Scientist to join the data analytics team and become the Data Science Lead. As a Lead Data Scientist, you will lead a team of data scientists, data-engineers, machine-learning engineers and software engineers with the primary goal to maintain and utilize data assets. You will be a part of an innovative environment, where you will be able to gain more knowledge in new technologies and take the next step in your career. Your opinions and ideas will be heard, and your daily contribution will make a difference for thousands of customers globally. On top of that, a competitive package depending on your experience will be a bonus to this great experience, along with private insurance, extra benefits and the ability to work hybrid-remotely.

Optasia is a fully-integrated B2B2X financial technology platform covering scoring, financial decisioning, disbursement & collection. We provide a versatile AI Platform powering financial inclusion, delivering responsible financing decision-making and driving a superior business model & strong customer experience with presence in 30 Countries anchored by 7 Regional Offices. We are seeking for enthusiastic professionals, with energy, who are results driven and have can-do attitude, who want to be part of a team of likeminded individuals who are delivering solutions in an innovative and exciting environment. Data Scientists are significant contributors of Optasia's advanced risk management and revenue optimization. As member of the Data Science team in Optasia, you will have an opportunity to combine the disciplines of risk management, research, and technology to operate trading strategies across multiple projects.

Tesla is on a major hiring spree for the teams that will build its humanoid robot Optimus, which is set to be unveiled September 30 at the company's AI Day. As CEO Elon Musk has recently touted the robot's potential uses - in domestic and manufacturing scenarios - and said that people will be'blown away' by the artificial intelligence-powered machine, the company's hiring efforts seem to be ramping up. Konstantinos Laskaris, Tesla's principle motor designer, shared a post on LinkedIn that called for nine different roles related to building and fine-tuning the humanoid bot - most of them based in Palo Alto, California, and one based in Athens, Greece. Tesla is on a major hiring spree for the teams that will build its humanoid robot Optimus, which is set to be unveiled September 30 at the company's AI Day CEO Elon Musk has touted the robot's potential uses - in domestic and manufacturing scenarios - and said people will be'blown away' by the artificial intelligence machine Konstantinos Laskaris, Tesla's principle motor designer, shared a post on LinkedIn that called for nine different roles related to building and fine-tuning the humanoid bot - most of them based in Palo Alto, California, and one based in Athens, Greece'The ability to simultaneously optimize designs for performance, efficiency, cost, and manufacturability is what makes Tesla the leader in Electric Motor Technology,' Laskaris wrote on the careers website. 'Developing a HUMANOID ROBOT requires pushing the technology boundaries even further, bringing us new challenges.

Dimitris Drandakis: Back in the late '00s, I worked as a software engineer in Athens – Greece. After 10 years in the field, I felt I was coming to a stand-still career so I decided to move to an Ionian Sea island, switching to the tourism industry. That proved to be a wise decision since the economic turbulence hit my country harder than all the rest and tourism was one of the few untouched industries. A few years later when software engineering knocked on my door again with Mediastalker, my heart beat strongly. I put on the CTO cap and the rest is history in the making.

Greece is just one example of a population where the share of older people is expanding, and with it the incidences of neurodegenerative diseases. Among these, Alzheimer's disease is the most prevalent, accounting for 70% of neurodegenerative disease cases in Greece. According to estimates published by the Alzheimer Society of Greece, 197,000 people are suffering from the disease at present. This number is expected to rise to 354,000 by 2050. Dr. Andreas Papadopoulos1, a physician and scientific coordinator at Iatropolis Medical Group, a leading diagnostic provider near Athens, Greece, explains the key role of early diagnosis: "The likelihood of developing Alzheimer's may be only 1% to 2% at age 65. But then it doubles every five years. Existing drugs cannot reverse the course of the degeneration; they can only slow it down. This is why it's crucial to make the right diagnosis in the preliminary stages--when the first mild cognitive disorder appears--and to filter out Alzheimer's patients2."

The ACM constitution provides that our Association hold a general election in the even-numbered years for the positions of President, Vice President, Secretary/Treasurer, and Members-at-Large. Biographical information and statements of the candidates appear on the following pages (candidates' names appear in random order). In addition to the election of ACM's officers--President, Vice President, Secretary/Treasurer--two Members-at-Large will be elected to serve on ACM Council. The 2022 candidates for ACM President, Yannis Ioannidis and Joseph A. Konstan, are working together to solicit and answer questions from the computing community! Please refer to the instructions posted at https://vote.escvote.com/acm. Please note the election email will be addressed from acmhelp@mg.electionservicescorp.com. Please return your ballot in the enclosed envelope, which must be signed by you on the outside in the space provided. The signed ballot envelope may be inserted into a separate envelope for mailing if you prefer this method. All ballots must be received by no later than 16:00 UTC on 23 May 2022. Validation by the Elections Committee will take place at 14:00 UTC on 25 May 2022. Yannis Ioannidis is Professor of Informatics & Telecom at the U. of Athens, Greece (since 1997). Prior to that, he was a professor of Computer Sciences at the U. of Wisconsin-Madison (1986-1997).

Athens, Greece – "Let's go see something that looks really nice," says Anastasios Salis, head of information and communications technology at the Greek Migration and Asylum Ministry in Athens, before entering an airtight room sealed behind two interlocking doors, accessible only with an ID card and fingerprint scan. Beyond these doors is the ministry's newly-installed centralised surveillance room. The front wall is covered by a vast screen. More than a dozen rectangles and squares display footage from three refugee camps already connected to the system. Another screen shows the playground and another the inside of one of the containers where people socialise.

In this 2-hour webinar, we are going to discuss about Machine Learning and TensorFlow.JS, the popular Machine Learning framework by Google and the magic it brings to web applications in the browser and Node.JS. There will be a Q&A session and a live coding demonstration with TensorFlow.JS. Date: March 1st 2021 Time: 19.00 - 21.00 Eastern European Standard Time, Athens Greece (GMT 2) Google Meet Link: https://meet.google.com/bfi-xdwh-izx