Inparticular,foreach task at testing time, theclasses effectivelypresent intheunlabeled query setareknown a priori, and correspond exactly to the set of classes represented in the labeled supportset.

Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-based competition comprising two challenges. First, the Transfer Challenge asks participants to build and test a model that can zero-shot decode new tasks and new subjects from their EEG data. Second, the Psychopathology factor prediction Challenge asks participants to infer subject measures of mental health from EEG data. For this, we use an unprecedented, multi-terabyte dataset of high-density EEG signals (128 channels) recorded from over 3,000 child to young adult subjects engaged in multiple active and passive tasks. We provide several tunable neural network baselines for each of these two challenges, including a simple network and demographic-based regression models. Developing models that generalise across tasks and individuals will pave the way for ML network architectures capable of adapting to EEG data collected from diverse tasks and individuals. Similarly, predicting mental health-relevant personality trait values from EEG might identify objective biomarkers useful for clinical diagnosis and design of personalised treatment for psychological conditions. Ultimately, the advances spurred by this challenge could contribute to the development of computational psychiatry and useful neurotechnology, and contribute to breakthroughs in both fundamental neuroscience and applied clinical research.

Recent advances in Diffusion Models have enabled the generation of images from text, with powerful closed-source models like DALL-E and Midjourney leading the way. However, open-source alternatives, such as StabilityAI's Stable Diffusion, offer comparable capabilities. These open-source models, hosted on Hugging Face, come equipped with ethical filter protections designed to prevent the generation of explicit images. This paper reveals first their limitations and then presents a novel text-based safety filter that outperforms existing solutions. Our research is driven by the critical need to address the misuse of AI-generated content, especially in the context of information warfare. DiffGuard enhances filtering efficacy, achieving a performance that surpasses the best existing filters by over 14%.

Challenges can be seen as a type of game that motivates participants to solve serious tasks. As a result, competition organizers must develop effective game rules. However, these rules have multiple objectives beyond making the game enjoyable for participants. These objectives may include solving real-world problems, advancing scientific or technical areas, making scientific discoveries, and educating the public. In many ways, creating a challenge is similar to launching a product. It requires the same level of excitement and rigorous testing, and the goal is to attract ''customers'' in the form of participants. The process begins with a solid plan, such as a competition proposal that will eventually be submitted to an international conference and subjected to peer review. Although peer review does not guarantee quality, it does force organizers to consider the impact of their challenge, identify potential oversights, and generally improve its quality. This chapter provides guidelines for creating a strong plan for a challenge. The material draws on the preparation guidelines from organizations such as Kaggle 1 , ChaLearn 2 and Tailor 3 , as well as the NeurIPS proposal template, which some of the authors contributed to.

Memristive devices that mimic neuron-connecting synapses could serve as the hardware for neural networks that copy the way the brain learns. Now two new studies may help solve key problems these components face not just with yields and reliability, but with finding applications beyond neural nets. Memristors, or memory resistors, are essentially switches that can remember which electric state they were toggled to after their power is turned off. Scientists worldwide aim to use memristors and similar components to build electronics that, like neurons, can both compute and store data. These memristive devices may greatly reduce the energy and time lost in conventional microchips shuttling data back and forth between processors and memory.

Emotion analysis in texts suffers from two major limitations: annotated gold-standard corpora are mostly small and homogeneous, and emotion identification is often simplified as a sentence-level classification problem. To address these issues, we introduce a new annotation scheme for exploring emotions and their causes, along with a new French dataset composed of autobiographical accounts of an emotional scene. The texts were collected by applying the Cognitive Analysis of Emotions developed by A. Finkel to help people improve on their emotion management. The method requires the manual analysis of an emotional event by a coach trained in Cognitive Analysis. We present a rule-based approach to automatically annotate emotions and their semantic roles (e.g. emotion causes) to facilitate the identification of relevant aspects by the coach. We investigate future directions for emotion analysis using graph structures.

Montreal-based centre unites strengths of McGill University, ÉTS, Mila, CNRS, Université Paris-Saclay, and CentraleSupélec A consortium of research organizations has gathered together to form a new International Research Laboratory (IRL) focused on artificial intelligence (AI) in Montreal. The new centre gathers together McGill University, École de technologie supérieure (ÉTS), Mila – Quebec AI Institute, France’s Centre Nationale de la Recherche Scientifique (CNRS), Université Paris-Saclay, and the École CentraleSupélec. The move confirms Montreal’s status as a leader in AI. While great strides have been made in AI recently, there is still a pressing need for new theoretical knowledge to better understand not only the capacities of this new technology, but how it achieves its results. The ILLS will focus on five main themes of research: fundamental aspects of artificial intelligence, sequential (real-time) machine learning, robust autonomous systems, natural language and speech processing, and applications to computer vision, signals, and information processing. In addition, the new centre will emphasize interdisciplinary collaborations with an aim to develop new methodologies and integrate these techniques into learning systems. “This new laboratory confirms Montreal’s global leadership in AI,” said Benoit Boulet, Associate Vice-Principal, Research & Innovation at McGill University. “This is a major hub with a talent pool that continues to deepen, and McGill researchers and students are embedded at every level of this activity. This new initiative will offer opportunities for our researchers to make even more breakthrough discoveries.” “The expertise of ÉTS in AI includes several laboratories and research chairs in artificial intelligence. This collaboration between France and Quebec makes it possible to innovate and deepen research in AI, a cross-cutting discipline from which we can benefit in many fields, including health, the built environment, robotics, and the Internet of Things. It is therefore with pride that ÉTS welcomes the new ILLS centre within its establishment,” said Christian Casanova, Director of Research and Partnerships at ÉTS. “Through its tools of international cooperation, CNRS supports the most promising cutting-edge joint research projects. The new international research laboratory brings together a powerful network of researchers from France and Québec to advance the knowledge and applications of AI. For the CNRS, this new lab is also an opportunity to strengthen more broadly its ties with the whole Canadian AI community,” said Antoine Petit, Chairman and CEO of CNRS. “AI at Paris-Saclay involves nearly 1,000 researchers, teacher-researchers, engineers and technicians and around forty laboratories, grouped together within our DataIA Institute. We will make our contribution to the ILLS in the form of the mobility of researchers, including the reception of Canadian colleagues at Paris-Saclay, the reception of Masters trainees, thesis funding in particular/among others. The University of Paris-Saclay is honored and proud to be associated with this signing ceremony for the creation of the IRL ILLS and to ensure its joint supervision" added Michel Guidal, Deputy Vice-President Research Sciences and Engineering at Université Paris-Saclay. “The ILLS, resulting from an unprecedented and international union, offers a unique potential for progress in the field of AI. It is an honor for CentraleSupélec to participate with our prestigious partners in this laboratory. Backed by this research, our teaching will thus be at the forefront of the world in terms of AI,” added Romain Soubeyran, Director of CentraleSupélec. The ILLS will join a burgeoning artificial intelligence (AI) sector in Montreal, which has attracted other major investments from government and business for the past several years. As a result, the city is one of the world’s leading hubs in this domain, with an estimated 27,000 workers in AI-related technologies and over 14,000 post-secondary students enrolled in AI-related study programs. The ILLS is the latest such laboratory to be launched in Canada, specifically in Quebec. In 2014, the CNRS and the Fonds de recherche du Québec – Nature et technologie (FRQNT) signed a letter of intent to support and promote the tradition of scientific cooperation that exists between France and Quebec. This collaboration has resulted in two International Research Laboratories in Quebec, as well as other shared research activities across the province. The CNRS has also established three other IRLs in Canada in partnership with other institutions. Present at the signing ceremony were: Frédéric Sanchez (Consul General of France), Remi Quirion (Quebec’s Chief Scientist), Antoine Petit (CNRS), Suzanne Fortier (McGill University), Francois Gagnon (ETS), Michel Guidal (Université Paris-Saclay), Franck Richecoeur (École CentraleSupélec), and Laurence Beaulieu (Mila). About McGill University Founded in Montreal, Quebec, in 1821, McGill University is Canada’s top ranked medical doctoral university. McGill is consistently ranked as one of the top universities, both nationally and internationally. It is a world-renowned institution of higher learning with research activities spanning three campuses, 11 faculties, 13 professional schools, 300 programs of study and over 39,000 students, including more than 10,400 graduate students. McGill attracts students from over 150 countries around the world, its 12,000 international students making up 30% of the student body. Over half of McGill students claim a first language other than English, including approximately 20% of our students who say French is their mother tongue.

In Proc. of the 2016 Conf. on Embedded Real Time Softw. and Systems.

We present a novel instance based approach to handle regression tasks in the context of supervised domain adaptation. The approach developed in this paper relies on the assumption that the task on the target domain can be efficiently learned by adequately reweighting the source instances during training phase. We introduce a novel formulation of the optimization objective for domain adaptation which relies on a discrepancy distance characterizing the difference between domains according to a specific task and a class of hypotheses. To solve this problem, we develop an adversarial network algorithm which learns both the source weighting scheme and the task in one feed-forward gradient descent. We provide numerical evidence of the relevance of the method on public datasets for domain adaptation through reproducible experiments accessible via an online demo interface.

A French university has set up a panel to explore the ethical implications of research into new technologies such as artificial intelligence. Research ethics boards are commonplace for scientists working in biomedicine and psychology. When a study involves humans or animals, a board scrutinises its aims, its proposed research methods and its progress as well as looking at the risks and benefits of the work. But academics at the University of Paris-Saclay want to take this practice a step further and incorporate ethical considerations into work being done in other parts of the university, such as the engineering and computer science departments. Its Research Ethics and Scientific Integrity Council will provide a forum for researchers working on emerging technologies, such as artificial intelligence and the internet of things, to discuss any ethical conundrums their work throws up.