Diderot's \textit{Encyclop\'edie} is a reference work from XVIIIth century in Europe that aimed at collecting the knowledge of its era. \textit{Wikipedia} has the same ambition with a much greater scope. However, the lack of digital connection between the two encyclopedias may hinder their comparison and the study of how knowledge has evolved. A key element of \textit{Wikipedia} is Wikidata that backs the articles with a graph of structured data. In this paper, we describe the annotation of more than 10,300 of the \textit{Encyclop\'edie} entries with Wikidata identifiers enabling us to connect these entries to the graph. We considered geographic and human entities. The \textit{Encyclop\'edie} does not contain biographic entries as they mostly appear as subentries of locations. We extracted all the geographic entries and we completely annotated all the entries containing a description of human entities. This represents more than 2,600 links referring to locations or human entities. In addition, we annotated more than 9,500 entries having a geographic content only. We describe the annotation process as well as application examples. This resource is available at https://github.com/pnugues/encyclopedie_1751

Retrieving the exact position of the connected objects has become an important feature of the Internet of Things (IoT). Such connected objects have indeed been widespread over the last few years thanks to the low cost of the radio integrated chips and sensors and their possibility of being embedded in plurality of the devices. By this they can help in fast development of large-scale physical monitoring and crowdsensing systems (like smart cities, factories, transportation, etc.). For the location-dependent application and services these abilities to associate accurate location with physical data gives huge opportunities [25]. For example, the fine-grain and dynamic update of air pollution and/or weather maps could benefit from geo-referenced mobile sensing [1] (e.g., aboard taxis, buses, bicycles...), thus continuously complementing the data from static stations. One of the localization techniques is Global Positioning System (GPS) which has been widely used over the past decades.

Increased capabilities such as recognition and self-adaptability are now required from IoT applications. While IoT node power consumption is a major concern for these applications, cloud-based processing is becoming unsustainable due to continuous sensor or image data transmission over the wireless network. Thus optimized ML capabilities and data transfers should be integrated in the IoT node. Moreover, IoT applications are torn between sporadic data-logging and energy-hungry data processing (e.g. image classification). Thus, the versatility of the node is key in addressing this wide diversity of energy and processing needs. This paper presents SamurAI, a versatile IoT node bridging this gap in processing and in energy by leveraging two on-chip sub-systems: a low power, clock-less, event-driven Always-Responsive (AR) part and an energy-efficient On-Demand (OD) part. AR contains a 1.7MOPS event-driven, asynchronous Wake-up Controller (WuC) with a 207ns wake-up time optimized for sporadic computing, while OD combines a deep-sleep RISC-V CPU and 1.3TOPS/W Machine Learning (ML) for more complex tasks up to 36GOPS. This architecture partitioning achieves best in class versatility metrics such as peak performance to idle power ratio. On an applicative classification scenario, it demonstrates system power gains, up to 3.5x compared to cloud-based processing, and thus extended battery lifetime.

Many large-scale applications can be elegantly represented using graph structures. Their scalability, however, is often limited by the domain knowledge required to apply them. To address this problem, we propose a novel Causal Temporal Graph Convolutional Neural Network (CTGCN). Our CTGCN architecture is based on a causal discovery mechanism, and is capable of discovering the underlying causal processes. The major advantages of our approach stem from its ability to overcome computational scalability problems with a divide and conquer technique, and from the greater explainability of predictions made using a causal model. We evaluate the scalability of our CTGCN on two datasets to demonstrate that our method is applicable to large scale problems, and show that the integration of causality into the TGCN architecture improves prediction performance up to 40% over typical TGCN approach. Our results are obtained without requiring additional domain knowledge, making our approach adaptable to various domains, specifically when little contextual knowledge is available.

The debate around the interpretability of attention mechanisms is centered on whether attention scores can be used as a proxy for the relative amounts of signal carried by sub-components of data. We propose to study the interpretability of attention in the context of set machine learning, where each data point is composed of an unordered collection of instances with a global label. For classical multiple-instance-learning problems and simple extensions, there is a well-defined "importance" ground truth that can be leveraged to cast interpretation as a binary classification problem, which we can quantitatively evaluate. By building synthetic datasets over several data modalities, we perform a systematic assessment of attention-based interpretations. We find that attention distributions are indeed often reflective of the relative importance of individual instances, but that silent failures happen where a model will have high classification performance but attention patterns that do not align with expectations. Based on these observations, we propose to use ensembling to minimize the risk of misleading attention-based explanations.

A man has been able to move all four of his paralysed limbs using a groundbreaking mind-controlled exoskeleton, scientists have said. The tetraplegic 30-year-old, known only as Thibault, said his first steps in the robotic suit felt like being "the first man on the Moon". The system, which works by recording and decoding brain signals, was trialled in a two-year study by French researchers at biomedical research centre Clinatec and the University of Grenoble. Scientists conceded the suit was an experimental treatment far from clinical application but said it had the potential to improve patients' quality of life and autonomy. Wearing the robotic limbs, Thibault was able to walk and move his arms using a ceiling-mounted harness for balance.

In Grenoble, France, one company is aiming to make an impact in the field which is so visibly dominated by multi-billion dollar corporations. We caught up with the company's Business Unit Director responsible for introducing their products to the Automotive market, Stéphane Cordova, to find out more, ahead of their attendance at AutoSens Detroit in May. The company's approach to "Supercomputing on a chip" has evolved from a the business origins providing components and software services to data centres, where high speed and reliability as well as low power consumption and significantly reduced heat generation were all key factors in processor component design. What helped you decide to commit to exhibiting at AutoSens again? Kalray's technology will be at the heart of autonomous driving.

On a recent trip to Grenoble, I met with the team behind a "digital nose" that was designed to provide a digital means of detecting smells (you can read about it here). Such digitally augmented sensing is clearly something the area specializes in, as a team of researchers from the GIPSA-Lab in Grenoble has also developed a virtual tongue. The work, which was documented in a recently published paper, uses an ultrasound probe positioned under the jaw with a machine learning algorithm, then takes this data and converts them into virtual replicas in an avatar. The avatar is capable of replicating the movements in the face, the lips, the tongue, and teeth. The researchers believe this visual biofeedback system could provide valuable information for things such as speech therapy.

HPE has just announced a bunch of new things to help organisations everywhere tap into AI. The new offerings include an integrated hardware-software solution, a set of guiding tools, a research collaboration platform, and a place to get access to the latest expertise. Deep Learning, as a subset of AI, is key for things like facial or voice recognition, image classification or other challenging tasks. It requires a high performance compute infrastructure to build and train learning models that can handle vasts amount of data, and that is something many organisations lack. This is also the core problem HPE is trying to solve with its new solution.

Artificial intelligence pioneer Alain Colmerauer passed away on May 12. Alain Colmerauer, a French computer scientist and a father of the logic programming language Prolog, passed away on May 15 at the age of 76. Alain Marie Albert Colmerauer was born in the French town of Carcassonne on Jan. 24, 1941. He earned a degree in computer science from the Institut polytechnique de Grenoble (Grenoble Institute of Technology) in 1963, and a doctorate in the discipline in 1967 from the École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble, which is part of the Institut. The newly minted doctor spent 1967–1970 as assistant professor at the University of Montreal, where he created Q-Systems, a method of directed graph transformations according to given grammar rules. Colmerauer moved to the University of Aix-Marseille at Luminy in 1970 as Professeur 2ème classe (associate professor).