Friendly Training: Neural Networks Can Adapt Data To Make Learning Easier

In the last decade, motivated by the success of Deep Learning, the scientific community proposed several approaches to make the learning procedure of Neural Networks more effective. When focussing on the way in which the training data are provided to the learning machine, we can distinguish between the classic random selection of stochastic gradient-based optimization and more involved techniques that devise curricula to organize data, and progressively increase the complexity of the training set. In this paper, we propose a novel training procedure named Friendly Training that, differently from the aforementioned approaches, involves altering the training examples in order to help the model to better fulfil its learning criterion. The model is allowed to simplify those examples that are too hard to be classified at a certain stage of the training procedure. The data transformation is controlled by a developmental plan that progressively reduces its impact during training, until it completely vanishes. In a sense, this is the opposite of what is commonly done in order to increase robustness against adversarial examples, i.e., Adversarial Training. Experiments on multiple datasets are provided, showing that Friendly Training yields improvements with respect to informed data sub-selection routines and random selection, especially in deep convolutional architectures. Results suggest that adapting the input data is a feasible way to stabilize learning and improve the generalization skills of the network.Comment: 9 pages, 5 figure

Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks

Amongst a variety of approaches aimed at making the learning procedure of neural networks more effective, the scientifc community developed strategies to order the examples according to their estimated complexity, to distil knowledge from larger networks, or to exploit the principles behind adversarial machine learning. A different idea has been recently proposed, named Friendly Training, which consists in altering the input data by adding an automatically estimated perturbation, with the goal of facilitating the learning process of a neural classifer. The transformation progressively fadesout as long as training proceeds, until it completely vanishes. In this work we revisit and extend this idea, introducing a radically different and novel approach inspired by the effectiveness of neural generators in the context of Adversarial Machine Learning. We propose an auxiliary multi-layer network that is responsible of altering the input data to make them easier to be handled by the classifer at the current stage of the training procedure. The auxiliary network is trained jointly with the neural classifer, thus intrinsically increasing the “depth” of the classifer, and it is expected to spot general regularities in the data alteration process. The effect of the auxiliary network is progressively reduced up to the end of training, when it is fully dropped and the classifer is deployed for applications. We refer to this approach as Neural Friendly Training. An extended experimental procedure involving several datasets and different neural architectures shows that Neural Friendly Training overcomes the originally proposed Friendly Training technique, improving the generalization of the classifer, especially in the case of noisy data

Continual Learning with Pretrained Backbones by Tuning in the Input Space

The intrinsic difficulty in adapting deep learning models to non-stationary environments limits the applicability of neural networks to real-world tasks. This issue is critical in practical supervised learning settings, such as the ones in which a pre-trained model computes projections toward a latent space where different task predictors are sequentially learned over time. As a matter of fact, incrementally fine-tuning the whole model to better adapt to new tasks usually results in catastrophic forgetting, with decreasing performance over the past experiences and losing valuable knowledge from the pre-training stage. In this paper, we propose a novel strategy to make the fine-tuning procedure more effective, by avoiding to update the pre-trained part of the network and learning not only the usual classification head, but also a set of newly-introduced learnable parameters that are responsible for transforming the input data. This process allows the network to effectively leverage the pre-training knowledge and find a good trade-off between plasticity and stability with modest computational efforts, thus especially suitable for on-the-edge settings. Our experiments on four image classification problems in a continual learning setting confirm the quality of the proposed approach when compared to several fine-tuning procedures and to popular continual learning methods

Expressed Alu repeats as a novel, reliable tool for normalization of real-time quantitative RT-PCR data

Expressed Alu repeats are a reliable, accurate and universal reference for use in RT-qPCR normalization of human gene

Meningococcus Hijacks a β2-Adrenoceptor/β-Arrestin Pathway to Cross Brain Microvasculature Endothelium

SummaryFollowing pilus-mediated adhesion to human brain endothelial cells, meningococcus (N. meningitidis), the bacterium causing cerebrospinal meningitis, initiates signaling cascades, which eventually result in the opening of intercellular junctions, allowing meningeal colonization. The signaling receptor activated by the pathogen remained unknown. We report that N. meningitidis specifically stimulates a biased β2-adrenoceptor/β-arrestin signaling pathway in endothelial cells, which ultimately traps β-arrestin-interacting partners, such as the Src tyrosine kinase and junctional proteins, under bacterial colonies. Cytoskeletal reorganization mediated by β-arrestin-activated Src stabilizes bacterial adhesion to endothelial cells, whereas β-arrestin-dependent delocalization of junctional proteins results in anatomical gaps used by bacteria to penetrate into tissues. Activation of β-adrenoceptor endocytosis with specific agonists prevents signaling events downstream of N. meningitidis adhesion and inhibits bacterial crossing of the endothelial barrier. The identification of the mechanism used for hijacking host cell signaling machineries opens perspectives for treatment and prevention of meningococcal infection.PaperFlic

Targeting of β-Arrestin2 to the Centrosome and Primary Cilium: Role in Cell Proliferation Control

International audienceBackground: The primary cilium is a sensory organelle generated from the centrosome in quiescent cells and found at the surface of most cell types, from where it controls important physiological processes. Specific sets of membrane proteins involved in sensing the extracellular milieu are concentrated within cilia, including G protein coupled receptors (GPCRs). Most GPCRs are regulated by b-arrestins, barr1 and barr2, which control both their signalling and endocytosis, suggesting that barrs may also function at primary cilium.Methodology/Principal Findings: In cycling cells, βarr2 was observed at the centrosome, at the proximal region of the centrioles, in a microtubule independent manner. However, βarr2 did not appear to be involved in classical centrosome-associated functions. In quiescent cells, both in vitro and in vivo, βarr2 was found at the basal body and axoneme of primary cilia. Interestingly, βarr2 was found to interact and colocalize with 14-3-3 proteins and Kif3A, two proteins known to be involved in ciliogenesis and intraciliary transport. In addition, as suggested for other centrosome or cilia-associated proteins, βarrs appear to control cell cycle progression. Indeed, cells lacking βarr2 were unable to properly respond to serum starvation and formed less primary cilia in these conditions.Conclusions/Significance: Our results show that βarr2 is localized to the centrosome in cycling cells and to the primary cilium in quiescent cells, a feature shared with other proteins known to be involved in ciliogenesis or primary cilium function. Within cilia, βarr2 may participate in the signaling of cilia-associated GPCRs and, therefore, in the sensory functions of this cell “antenna”

Polychlorinated Biphenyls and Semen Quality in Healthy Young Men Living in a Contaminated Area

Polychlorinated biphenyls (PCBs) are persistent organic pollutants and endocrine disruptors that have been implicated in potential damage to human semen. However, the studies conducted so far provide contrasting results. Our study aimed to investigate the associations between PCB serum and semen levels and semen quality in high school and university students living in a highly PCB-polluted area of Italy. Subjects with a normal body mass index who did not make daily use of tobacco, alcohol, drugs, or medication were selected. All participants provided a fasting blood and a semen sample. Gas chromatography-mass spectrometry was used to determine the concentrations of 26 PCB congeners. The concentrations of PCB functional groups and total PCBs were also computed. A total of 143 subjects (median age 20, range 18–22 years) were enrolled. The median total PCB concentrations were 3.85 ng/mL (range 3.43–4.56 ng/mL) and 0.29 ng/mL (range 0.26–0.32 ng/mL) in serum and semen, respectively. The analysis of the associations between sperm PCB concentration and semen parameters showed (a) negative associations between some PCB congeners, functional groups and total PCBs and sperm total motility; (b) negative associations of total PCBs with sperm normal morphology; and (c) no association of PCBs with sperm concentration. Subjects at the highest quartile of semen total PCB concentration had 19% and 23% mean reductions in total motility and normal morphology, respectively, compared to those at the lowest quartile. The analysis of the associations of serum PCB levels with sperm parameters yielded null or mixed (some positive, other negative) results. In conclusion, the present study provides evidence of a negative effect of some PCB congeners and total PCBs in semen on sperm motility and normal morphology. However, the associations between the concentration of serum and semen PCB congeners and functional groups and sperm quality parameters were inconsistent

Is chlorophyll-athe best surrogate for organic matter enrichment in submicron primary marine aerosol?

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Lamin A/C sustains PcG protein architecture, maintaining transcriptional repression at target genes

Beyond its role in providing structure to the nuclear envelope, lamin A/C is involved in transcriptional regulation. However, its cross talk with epigenetic factors--and how this cross talk influences physiological processes--is still unexplored. Key epigenetic regulators of development and differentiation are the Polycomb group (PcG) of proteins, organized in the nucleus as microscopically visible foci. Here, we show that lamin A/C is evolutionarily required for correct PcG protein nuclear compartmentalization. Confocal microscopy supported by new algorithms for image analysis reveals that lamin A/C knock-down leads to PcG protein foci disassembly and PcG protein dispersion. This causes detachment from chromatin and defects in PcG protein-mediated higher-order structures, thereby leading to impaired PcG protein repressive functions. Using myogenic differentiation as a model, we found that reduced levels of lamin A/C at the onset of differentiation led to an anticipation of the myogenic program because of an alteration of PcG protein-mediated transcriptional repression. Collectively, our results indicate that lamin A/C can modulate transcription through the regulation of PcG protein epigenetic factors