Reconstruction error based implicit regularization method and its engineering application to lung cancer diagnosis

International audienceThe automatic diagnosis of lung cancer via artificial intelligence faces two hotspot issues: (1) insufficient data and (2) excessive redundant information, which make it difficult for convolutional neural networks (CNNs) to learn discriminative information of lung cancer. In this paper, we present the reconstruction error based implicit regularization method (REbIRM) that regularizes CNNs at the loss layer. During each training iteration, the reconstruction errors introduced by the two-stage discriminative auto-encoder are used to sharpen the generalization ability of deep CNNs by improving the decision boundary. In the application process, the trained deep CNN is used for completing computed tomography (CT) diagnostics. The main clinical benefit of our approach is that it is domain independent, requiring no specialized knowledge, and can therefore be applied to different types of datasets. To the best of our knowledge, this is the first attempt to implicitly regularize CNNs based on the reconstruction errors. Finally, experimental results on three CT image classification datasets show that REbIRM can achieve impressive results and that, in conjunction with Dropout, it obtains the state-of-the-art performance. REbIRM is also robust to the selection of hyper-parameters and only has the sublinear influence on the convergence of deep CNNs. Besides, empirical and theoretical evidence are provided to indicate that REbIRM prefers to converges in a constrained parameter space with flatter minima, which explains why it can generalize to new data. Finally, the nature of REbIRM is further explored through visualization techniques to analyze how it works in training deep CNNs

Deep learning-based early detection of absence seizures in children

International audienceBackground: Childhood Absence epilepsy is a common generalized epileptic syndrome in children. The seizures involve momentary lapses in consciousness, aligning with generalized spike-wave discharges on EEG. This study proposes an AI-based algorithm for early detection of absence seizure onset, allowing for application of sensory stimulation, such as acoustic stimulation, prone to abort the seizure. Method: We propose a deep learning-based model designed for early detection of the onset of absence seizures in children. The use of deep learning algorithms offers a promising solution to this problem by leveraging their ability to analyze complex patterns. The model was evaluated under two configurations. Clinical configuration to assess feasibility of an accurate detector of the onset seizures, and a wearable device configuration intended to implement the model on a portable closed-loop stimulator. Results: The performance analysis, in term of accuracy and time delay, assessed on a clinical EEG database of 117 patients with confirmed childhood absence epilepsy, are promising: sensitivity of 0.859, precision of 0.819, F1-score of 0.837, and a mean time delay of 0.522 s. Furthermore, the algorithm performance evaluated using reduced number of electrodes, as required for a wearable device, is still stable with a sensitivity = 0.837, precision = 0.808, F1-score = 0.820 and detection delays around 0.5 s. Conclusion: The performance of the proposed method on clinical configuration demonstrates the feasibility of a robust and universal detector of the onset of absence seizures in children. In addition, the consistency of results when only two bipolar EEG channels are utilized makes the pipeline suitable to be embedded in a wearable stimulator

Ruthenium and palladium nanocatalysts in water for selective hydrogenation. Scaling up the processes in terpenic renewables chemistry

International audienceHydrogenation reactions of biomass terpene derivatives constitute great chemical transformations, leading to key platform molecules for the production of high added value fine chemicals, such as citronellal of Florsantol (R), (R), for fragrances industries. One of the main challenges remains the high selectivity control to avoid the formation of co-products that could alter the quality of the follow-up products. For that purpose, metal nanoparticles are considered as unavoidable catalytic tools owing to their surface reactivity properties and their potential recycling under adapted conditions. Herein, we report the use of aqueous suspensions of ruthenium and palladium nanoparticles as relevant catalysts for the industrial transformation of terpenic agro-resources into value-added renewable perfume ingredients. First, alpha-pinene could selectively be hydrogenated into cis- pinane using ruthenium nanoparticles, while palladium is relevant to produce citronellal through the selective reduction of the conjugated double bond of citral as well as Florsantol (R) (R) by the controlled ring opening of the corresponding epoxide. Interestingly, all these processes have been scaled-up, thus demonstrating the potential of reusable metallic nanocatalysts in water for industrial applications

Secrecy by typing in the computational model

International audienceIn this paper, we propose a way to automate proofs of cryptographic protocols in the computational setting. We focus on non-deducibility -- a weak notion of secrecy -- and we aim to use type systems. Techniques based on typing were mainly used in symbolic models, and we show how they can be adapted to the \ccsa framework to obtain computational guarantees.We consider for now a fixed set of primitives, namely symmetric and asymmetric encryption, as well as pairing (\ie concatenation). Our approach has the usual benefit of type systems: it is modular, allows the security analysis for an unbounded number of sessions, and could be extended to other primitives (e.g. hashing) without excessive difficulties. We successfully applied our framework on several protocols from the literature and the ISO/IEC 11770 standard

Well-being at school. A public problem

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Die öffentliche Finanzierung der Evakuierungen in Frankreich und in Deutschland

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Succès et échec de l'héroisation de l'Antiquité à l'actualité européenne

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Photo-modulation of the two-photon excited fluorescence of dithienylethene bis-(1-pyrenyl) compounds: An experimental and theoretical study

International audiencePhotomodulation of two-photon excited fluorescence has been effectively controlled in 1-pyrene-based dithienylethenes. The specific molecular design enables photocommutation of two-photon excited fluorescence during sequential one- and two-photon excitations without interference nor destructive optical readout. Theoretical studies have concluded that the internal functionalization of pyrene is responsible for the photoactivity in these systems. Photophysical studies indicate that the presence of a second laterally introduced pyrene is crucial for maintaining high photocommutation contrast and durability

HERO: Holistic Envisioned Reinforcement Learning Multi-Domain Orchestration with Latent ODE

International audience6G promises E2E cross domains continuous intelligence to optimize resource management and orchestration. However, state-of-the-art methods fall short in providing promised reliable and optimal resource management due to their inefficient proactive decision-making and planning capabilities. This paper proposes a novel Holistic Predictive Framework designed to enhance decision-making and achieve proactive multi-domain resource management. Our framework comprises of predictive, focus, and decision making elements, enabling exceptional proactive planning, and decisions-making based on a holistic vision of network's future. To select the best predictive and decisionmaking elements, Various combinations of predictive Machine Learning and Reinforcement Learning algorithms were examined in our testbed. To demonstrate the superiority of our framework, we have conducted another test where our framework was compared with state-of-the-art solutions. The test results indicate that coupling the predictive element and attention-augmented decision making unit significantly improves the orchestrator's performance. Based on the result of both tests, our multi-domain orchestration solution, which exploits Latent ODE, outperforms all Cutting-Edge frameworks and is the best combination of the algorithms for our framework

The German project for an economic Europe: the Ordnungsdenken in the EEC

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