Semantic and Visual Similarities for Efficient Knowledge Transfer in CNN Training

International audienceIn recent years, representation learning approaches have disrupted many multimedia computing tasks. Among those approaches, deep convolutional neural networks (CNNs) have notably reached human level expertise on some constrained image classification tasks. Nonetheless, training CNNs from scratch for new task or simply new data turns out to be complex and time-consuming. Recently, transfer learning has emerged as an effective methodology for adapting pre-trained CNNs to new data and classes, by only retraining the last classification layer. This paper focuses on improving this process, in order to better transfer knowledge between CNN architectures for faster trainings in the case of fine tuning for image classification. This is achieved by combining and transfering supplementary weights, based on similarity considerations between source and target classes. The study includes a comparison between semantic and content-based similarities, and highlights increased initial performances and training speed, along with superior long term performances when limited training samples are available

Improved optimization strategies for deep Multi-Task Networks

In Multi-Task Learning (MTL), it is a common practice to train multi-task networks by optimizing an objective function, which is a weighted average of the task-specific objective functions. Although the computational advantages of this strategy are clear, the complexity of the resulting loss landscape has not been studied in the literature. Arguably, its optimization may be more difficult than a separate optimization of the constituting task-specific objectives. In this work, we investigate the benefits of such an alternative, by alternating independent gradient descent steps on the different task-specific objective functions and we formulate a novel way to combine this approach with state-of-the-art optimizers. As the separation of task-specific objectives comes at the cost of increased computational time, we propose a random task grouping as a trade-off between better optimization and computational efficiency. Experimental results over three well-known visual MTL datasets show better overall absolute performance on losses and standard metrics compared to an averaged objective function and other state-of-the-art MTL methods. In particular, our method shows the most benefits when dealing with tasks of different nature and it enables a wider exploration of the shared parameter space. We also show that our random grouping strategy allows to trade-off between these benefits and computational efficiency

Maximum Roaming Multi-Task Learning

Multi-task learning has gained popularity due to the advantages it provides with respect to resource usage and performance. Nonetheless, the joint optimization of parameters with respect to multiple tasks remains an active research topic. Sub-partitioning the parameters between different tasks has proven to be an efficient way to relax the optimization constraints over the shared weights, may the partitions be disjoint or overlapping. However, one drawback of this approach is that it can weaken the inductive bias generally set up by the joint task optimization. In this work, we present a novel way to partition the parameter space without weakening the inductive bias. Specifically, we propose Maximum Roaming, a method inspired by dropout that randomly varies the parameter partitioning, while forcing them to visit as many tasks as possible at a regulated frequency, so that the network fully adapts to each update. We study the properties of our method through experiments on a variety of visual multi-task data sets. Experimental results suggest that the regularization brought by roaming has more impact on performance than usual partitioning optimization strategies. The overall method is flexible, easily applicable, provides superior regularization and consistently achieves improved performances compared to recent multi-task learning formulations.Comment: Accepted at the 35th AAAI Conference on Artificial Intelligence (AAAI 2021

The cyclic ground state structure of the HF trimer revealed by far infrared jet-cooled Fourier transform spectroscopy.

International audienceThe rovibrationally resolved Fourier transform (FT) far infrared (FIR) spectra of two intermolecular librations of (HF)3, namely the in-plane ν6 and out-of-plane ν4 bending fundamentals centered, respectively, at about 494 cm(-1) and 602 cm(-1), have been recorded for the first time under jet-cooled conditions using the supersonic jet of the Jet-AILES apparatus. The simultaneous rotational analysis of 245 infrared transitions belonging to both bands enabled us to determine the ground state (GS), ν6 and ν4 rotational and centrifugal distortion constants. These results provided definite experimental answers to the structure of such a weakly bound trimer: firstly the vibrationally averaged planarity of cyclic (HF)3, also supported by the very small value of the inertia defect obtained in the GS, secondly the slight weakening of the hydrogen bond in the intermolecular excited states evidenced from the center of mass separations of the HF constituents determined in the ground, ν6 = 1 and ν4 = 1 states of (HF)3 as well as the decrease of the fitted rotational constants upon excitation. Finally, lower bounds of about 2 ns on ν6 and ν4 state lifetimes could be derived from the deconvolution of experimental linewidths. Such long lifetimes highlight the interest in probing low frequency intermolecular motions of molecular complexes to get rid of constraints related to the vibrational dynamics of coupled anharmonic vibrations at higher energy, resulting in loss of rotational information

Symptomatic postoperative compressive pneumocephalus after cholecystectomy

A 75-year-old woman with a history of chronic hydrocephalus due to stenosis of the aqueduct of Sylvius was examined at the emergency department for altered mental status. There was placement of a ventriculoperitoneal shunt in 1970 complicated by meningitis, leading to removal of the material and ventriculociternostomy as definitive treatment in 2004. About one month previously, she had undergone a laparoscopic cholecystectomy complicated by an intra-abdominal collection. Clinical examination at the emergency department revealed a Glasgow score of 8 (E3 V1 M4). In the emergency department the patient presented a tonic-clonic seizure before a cerebral CT scan was performed showing a massive compressive pneumocephalus, then a second seizure. The patient was finally admitted to the neurosurgery department and underwent surgery

Kidd Blood Group and Urea Transport Function of Human Erythrocytes Are Carried by the Same Protein

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The Coq Proof Assistant, Reference Manual, Version 5.10

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Ensconsin/Map7 promotes microtubule growth and centrosome separation in Drosophila neural stem cells.

International audienceThe mitotic spindle is crucial to achieve segregation of sister chromatids. To identify new mitotic spindle assembly regulators, we isolated 855 microtubule-associated proteins (MAPs) from Drosophila melanogaster mitotic or interphasic embryos. Using RNAi, we screened 96 poorly characterized genes in the Drosophila central nervous system to establish their possible role during spindle assembly. We found that Ensconsin/MAP7 mutant neuroblasts display shorter metaphase spindles, a defect caused by a reduced microtubule polymerization rate and enhanced by centrosome ablation. In agreement with a direct effect in regulating spindle length, Ensconsin overexpression triggered an increase in spindle length in S2 cells, whereas purified Ensconsin stimulated microtubule polymerization in vitro. Interestingly, ensc-null mutant flies also display defective centrosome separation and positioning during interphase, a phenotype also detected in kinesin-1 mutants. Collectively, our results suggest that Ensconsin cooperates with its binding partner Kinesin-1 during interphase to trigger centrosome separation. In addition, Ensconsin promotes microtubule polymerization during mitosis to control spindle length independent of Kinesin-1