Towards learning free naive bayes nearest neighbor-based domain adaptation

As of today, object categorization algorithms are not able to achieve the level of robustness and generality necessary to work reliably in the real world. Even the most powerful convolutional neural network we can train fails to perform satisfactorily when trained and tested on data from different databases. This issue, known as domain adaptation and/or dataset bias in the literature, is due to a distribution mismatch between data collections. Methods addressing it go from max-margin classifiers to learning how to modify the features and obtain a more robust representation. Recent work showed that by casting the problem into the image-to-class recognition framework, the domain adaptation problem is significantly alleviated [23]. Here we follow this approach, and show how a very simple, learning free Naive Bayes Nearest Neighbor (NBNN)-based domain adaptation algorithm can significantly alleviate the distribution mismatch among source and target data, especially when the number of classes and the number of sources grow. Experiments on standard benchmarks used in the literature show that our approach (a) is competitive with the current state of the art on small scale problems, and (b) achieves the current state of the art as the number of classes and sources grows, with minimal computational requirements. © Springer International Publishing Switzerland 2015

The orbifold cohomology of moduli of genus 3 curves

In this work we study the additive orbifold cohomology of the moduli stack of smooth genus g curves. We show that this problem reduces to investigating the rational cohomology of moduli spaces of cyclic covers of curves where the genus of the covering curve is g. Then we work out the case of genus g=3. Furthermore, we determine the part of the orbifold cohomology of the Deligne-Mumford compactification of the moduli space of genus 3 curves that comes from the Zariski closure of the inertia stack of M_3.Comment: 29 pages, 2 figures. Minor changes, to appear in Manuscripta Mat

Surrogate modeling of RF circuit blocks

Surrogate models are a cost-effective replacement for expensive computer simulations in design space exploration. Literature has already demonstrated the feasibility of accurate surrogate models for single radio frequency (RF) and microwave devices. Within the European Marie Curie project O-MOORE-NICE! (Operational Model Order Reduction for Nanoscale IC Electronics) we aim to investigate the feasibility of the surrogate modeling approach for entire RF circuit blocks. This paper presents an overview about the surrogate model type selection problem for low noise amplifier modeling

Are You Tampering With My Data?

We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.Comment: 18 page

Why do continents break-up parallel to ancient orogenic belts?

International audienceThe frequently observed parallelism between rifts and the pre­ existing orogenic fabric of continents suggests that the inherited tectonic fabric of the lithosphere influences the rupture of continents. We propose that the existence of a pervasive fabric in the lithospheric mantle induces an anisotropie strength in the lithosphere, that guides the propagation of continental rifts. Subcrustal mantle mechanical anisotropy is supported by (i) the anisotropie strength of olivine, (ii) an ubiquitous tectonic fabric in exposed mantle rocks, and (iii) measurements of seismic and electrical anisotropy. During major episodes of continent Rifting parallel to orogenic belts Ocean-opening through rifting and continent break-up is frequently related to the occurrence ofhotspots. There is, howevcr, a discrepancy between hot­ spots acting as pin point sources of heat and the linear extent of rifts over thou­ sands ofkilometres. Moreover rifts tend to parallel pre-existing orogenic fab

Joint supervised and self-supervised learning for 3D real world challenges

Point cloud processing and 3D shape understanding are challenging tasks for which deep learning techniques have demonstrated great potentials. Still further progresses are essential to allow artificial intelligent agents to interact with the real world. In many practical conditions the amount of annotated data may be limited and integrating new sources of knowledge becomes crucial to support autonomous learning. Here we consider several scenarios involving synthetic and real world point clouds where supervised learning fails due to data scarcity and large domain gaps. We propose to enrich standard feature representations by leveraging self-supervision through a multi-task model that can solve a 3D puzzle while learning the main task of shape classification or part segmentation. An extensive analysis investigating few-shot, transfer learning and cross-domain settings shows the effectiveness of our approach with state-of-the-art results

The Arbuscular Mycorrhizal Fungus Glomus viscosum Improves the Tolerance to Verticillium Wilt in Artichoke by Modulating the Antioxidant Defense Systems

Verticillium wilt, caused by the fungal pathogen Verticillium dahliae, is the most severe disease that threatens artichoke (Cynara scolymus L.) plants. Arbuscular mycorrhizal fungi (AMF) may represent a useful biological control strategy against this pathogen attack, replacing chemical compounds that, up to now, have been not very effective. In this study, we evaluated the effect of the AMF Glomus viscosum Nicolson in enhancing the plant tolerance towards the pathogen V. dahliae. The role of the ascorbate-glutathione (ASC-GSH) cycle and other antioxidant systems involved in the complex network of the pathogen-fungi-plant interaction have been investigated. The results obtained showed that the AMF G. viscosum is able to enhance the defense antioxidant systems in artichoke plants affected by V. dahliae, alleviating the oxidative stress symptoms. AMF-inoculated plants exhibited significant increases in ascorbate peroxidase (APX), monodehydroascorbate reductase (MDHAR), and superoxide dismutase (SOD) activities, a higher content of ascorbate (ASC) and glutathione (GSH), and a decrease in the levels of lipid peroxidation and hydrogen peroxide (H2O2). Hence, G. viscosum may represent an effective strategy for mitigating V. dahliae pathogenicity in artichokes, enhancing the plant defense systems, and improving the nutritional values and benefit to human health

Inverse Raman Scattering in Femtosecond Broadband Transient Absorption Experiments

This chapter reports on one of the nonlinear spectral features, the inverse Raman scattering (IRS), observed upon the interaction of ultrafast‐pulsed lasers in a Raman‐active medium. Hereby, a comprehensive theoretical description of the IRS is exposed. Furthermore, the investigation carried out on synthetic eumelanin dispersions is addressed to show how the transient absorption measurements can be influenced by the IRS, if probing at energies close to Stokes and anti‐Stokes vibrational modes of the medium. A thorough analysis demonstrates that the IRS affects the sign of dynamics but not relaxation times. A specific kind of spectroscopy based on the IRS effect (ultrafast Raman loss spectroscopy) is eventually illustrated as valuable tool to characterize the structure of molecules and to investigate their dynamics during chemical reactions, even occurring at ultrafast timescales