In online bilateral trade, a platform posts prices to incoming pairs of buyers and sellers that have private valuations for a certain good. If the price is lower than the buyers' valuation and higher than the sellers' valuation, then a trade takes place. Previous work focused on the platform perspective, with the goal of setting prices maximizing the gain from trade (the sum of sellers' and buyers' utilities). Gain from trade is, however, potentially unfair to traders, as they may receive highly uneven shares of the total utility. In this work we enforce fairness by rewarding the platform with the fair gain from trade, defined as the minimum between sellers' and buyers' utilities. After showing that any no-regret learning algorithm designed to maximize the sum of the utilities may fail badly with fair gain from trade, we present our main contribution: a complete characterization of the regret regimes for fair gain from trade when, after each interaction, the platform only learns whether each trader accepted the current price.

Yet, in the real world, 32 bits default precision combined with the size of deep learning problems makes it a hyperparameter of training methods.

Yet, in the real world, 32 bits default precision combined with the size of deep learning problems makes it a hyperparameter of training methods.

Mobility is a central issue in the transition to a more sustainable lifestyle. The average daily distance traveled by the French population has increased considerably, from 5 km on average in the 1950s to 45 km on average in 2011 [58], as has the number of personal cars (11,860 million cars in 1970 [7] compared to 38,3 million in 2021 [15, 28]). For example in Toulouse, cars concentrate 74% of the distances traveled by the inhabitants and contribute up to 88% to GHG emissions [25]. The evolution of mobility is therefore an essential question, both for the global climate crisis and for public health: negative impact of a sedentary lifestyle [9], road accidents, air and sound pollution [44]. Indeed, 40000 deaths per year are attributable to exposure to fine particles (PM2.5) and 7000 deaths per year attributable to exposure to nitrogen dioxide (NO2), i.e. 7% and 1% of the total annual mortality [38]; the 2-month lockdown of spring 2020 in France saved 2300 deaths by reducing exposure to particles, and 1200 more deaths by reducing exposure to nitrogen dioxide [38].

Acquiring factual knowledge for language models (LMs) in low-resource languages poses a serious challenge, thus resorting to cross-lingual transfer in multilingual LMs (ML-LMs). In this study, we ask how ML-LMs acquire and represent factual knowledge. Using the multilingual factual knowledge probing dataset, mLAMA, we first conducted a neuron investigation of ML-LMs (specifically, multilingual BERT). We then traced the roots of facts back to the knowledge source (Wikipedia) to identify the ways in which ML-LMs acquire specific facts. We finally identified three patterns of acquiring and representing facts in ML-LMs: language-independent, cross-lingual shared and transferred, and devised methods for differentiating them. Our findings highlight the challenge of maintaining consistent factual knowledge across languages, underscoring the need for better fact representation learning in ML-LMs.

An expert in computing responds to Tara Isabella Burton's "I Know Thy Works." In 2018 researchers from the Massachusetts Institute of Technology Media Lab, Harvard University, the University of British Columbia, and Université Toulouse Capitole shared the results of one of the largest moral experiments conducted to date. They recorded 40 million ethical decisions from millions of people across 233 countries. The experiment's "Moral Machine" posed to users variations of the classic trolley problem, imagining instead the trolley as a self-driving car. Should the car swerve and collide with jaywalking pedestrians or maintain its current trajectory, which would yield inevitable doom for the passengers inside?

This picture taken on Jan. 23 in Toulouse, southwestern France, shows screens displaying the logos of OpenAI and ChatGPT. ChatGPT is a conversational artificial intelligence software application developed by OpenAI. My close friend, who teaches at a primary school in Ho Chi Minh City, feels terribly worried about how adversely education and especially students will be affected by ChatGPT, which stands for Chat Generative Pre-training Transformer, an artificial intelligence-based chatbot which was provided by US startup, OpenAI, last November. He told me that "My students naturally converse with ChatGPT, asking tough questions about all subjects, and ChatGPT solves all exercises I give them! I will be unemployed soon!"

LATMOS-IPSL, Université Paris-Saclay, UVSQ, CNRS, 78280, Guyancourt, France *Météo-France, Avenue Coriolis, Toulouse Abstract-- Retrieval of rain from Passive Microwave from about 52,000 images to about 103,000 allowing us radiometers data has been a challenge ever since the to build a training database of 70,000 images for training launch of the first Defense Meteorological Satellite and 33,000 images for validation. Enormous progress has been years 2014 to 2018 and a few months from 2020 and made since the launch of the Tropical Rainfall 2021 are used but the whole year 2019 was kept separate Measuring Mission (TRMM) in 1997 but until for the performance assessment (test) and most results recently the data were processed pixel-by-pixel or presented hereafter are computed for that year. Deep large database is meant to dampen the effects of learning has obtained remarkable improvement in seasonal and interannual variability of rain. the computer vision field, and offers a whole new Second, DRAIN retrieves now a set of 99 quantiles way to tackle the rain retrieval problem. The Global instead of a simple averaged rain rate as in [1]. These Precipitation Measurement (GPM) Core satellite quantiles represent the probability that the rain rate is carries similarly to TRMM, a passive microwave below a certain threshold.

In recent decades, the average daily distance traveled by the French population has increased considerably (from 5 km on average in the 1950s to 45 km on average in 2011 [33]), as has the number of personal cars (11,860 million cars in 1970 [5] compared to 38,3 million in 2021 [9, 19]). For example in Toulouse, cars concentrate 74% of the distances traveled by the inhabitants and contribute up to 88% to GHG emissions [30]. The evolution of mobility is therefore an essential question, in the context of the climate crisis but also in terms of public health: the negative impact of a sedentary lifestyle [6], road accidents, air pollution and sound pollution [28]. Indeed, 40000 deaths per year are attributable to exposure to fine particles (PM2.5) and 7000 deaths per year attributable to exposure to nitrogen dioxide (NO2), i.e. 7% and 1% of the total annual mortality [16]; this report also concludes that the 2-month lockdown of spring 2020 in France made it possible to avoid 2300 deaths by reducing exposure to particles, and 1200 more deaths by reducing exposure to nitrogen dioxide. This shows that public policies and individual behaviour changes (modal shift towards cycling, more extensive teleworking) can have an impact on public health.

Laguna Hills, Calif. – DATE, 2022 – BrainChip Holdings Ltd (ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world's first commercial producer of ultra-low power neuromorphic AI IP, has extended the breadth and depth of its neuromorphic IP with two new patents granted by the US Patents and Trademarks Office (USPTO), and the acquisition of previously licensed technology from Toulouse Tech Transfer (TTT). These latest additions of technical assets reinforce BrainChip's event-based processor differentiation for high performance, ultra-low power AI inference and on-chip learning. BrainChip also acquired full ownership of the IP rights related to JAST learning rule and algorithms from French technology transfer-based company TTT, including issued patent EP3324344 and pending patents US2019/0286944 and EP3324343. The invention related to the acquired IP rights include pattern detection algorithms that provide BrainChip with significant competitive advantages. The company held an exclusive license for the IP prior to their acquisition.