A stable graph-based representation for object recognition through high-order matching

Many Object recognition techniques perform some flavour of point pattern matching between a model and a scene. Such points are usually selected through a feature detection algorithm that is robust to a class of image transformations and a suitable descriptor is computed over them in order to get a reliable matching. Moreover, some approaches take an additional step by casting the correspondence problem into a matching between graphs defined over feature points. The motivation is that the relational model would add more discriminative power, however the overall effectiveness strongly depends on the ability to build a graph that is stable with respect to both changes in the object appearance and spatial distribution of interest points. In fact, widely used graph-based representations, have shown to suffer some limitations, especially with respect to changes in the Euclidean organization of the feature points. In this paper we introduce a technique to build relational structures over corner points that does not depend on the spatial distribution of the features

Evidence for moving breathers in a layered crystal insulator at 300K

We report the ejection of atoms at a crystal surface caused by energetic breathers which have travelled more than 10^7 unit cells in atomic chain directions. The breathers were created by bombardment of a crystal face with heavy ions. This effect was observed at 300K in the layered crystal muscovite, which has linear chains of atoms for which the surrounding lattice has C_2 symmetry. The experimental techniques described could be used to study breathers in other materials and configurations.Comment: 7 pages, 3 figure

Transductive Label Augmentation for Improved Deep Network Learning

A major impediment to the application of deep learning to real-world problems is the scarcity of labeled data. Small training sets are in fact of no use to deep networks as, due to the large number of trainable parameters, they will very likely be subject to overfitting phenomena. On the other hand, the increment of the training set size through further manual or semi-automatic labellings can be costly, if not possible at times. Thus, the standard techniques to address this issue are transfer learning and data augmentation, which consists of applying some sort of "transformation" to existing labeled instances to let the training set grow in size. Although this approach works well in applications such as image classification, where it is relatively simple to design suitable transformation operators, it is not obvious how to apply it in more structured scenarios. Motivated by the observation that in virtually all application domains it is easy to obtain unlabeled data, in this paper we take a different perspective and propose a \emph{label augmentation} approach. We start from a small, curated labeled dataset and let the labels propagate through a larger set of unlabeled data using graph transduction techniques. This allows us to naturally use (second-order) similarity information which resides in the data, a source of information which is typically neglected by standard augmentation techniques. In particular, we show that by using known game theoretic transductive processes we can create larger and accurate enough labeled datasets which use results in better trained neural networks. Preliminary experiments are reported which demonstrate a consistent improvement over standard image classification datasets.Comment: Accepted on IEEE International Conference on Pattern Recognitio

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update existing ones, assuming that it is sufficiently representative of the data that will be encountered at test time. This assumption is challenged by the threat of poisoning, an attack that manipulates the training data to compromise the model's performance at test time. Although poisoning has been acknowledged as a relevant threat in industry applications, and a variety of different attacks and defenses have been proposed so far, a complete systematization and critical review of the field is still missing. In this survey, we provide a comprehensive systematization of poisoning attacks and defenses in machine learning, reviewing more than 100 papers published in the field in the last 15 years. We start by categorizing the current threat models and attacks, and then organize existing defenses accordingly. While we focus mostly on computer-vision applications, we argue that our systematization also encompasses state-of-the-art attacks and defenses for other data modalities. Finally, we discuss existing resources for research in poisoning, and shed light on the current limitations and open research questions in this research field

Smooth crack-free targets for nuclear applications produced by molecular plating

Vascon A, Santi S, Isse AA, et al. Smooth crack-free targets for nuclear applications produced by molecular plating. Nuclear Instruments and Methods in Physics Research A. 2013;714:163-175.The production process of smooth and crack-free targets by means of constant current electrolysis in organic media, commonly known as molecular plating, was optimized. Using a Nd salt, i.e., [Nd(NO3)(3)center dot 6H(2)O], as model electrolyte several constant current density electrolysis experiments were carried out to investigate the effects of different parameters, namely the plating solvent (isopropanol and isobutanol mixed together, pyridine, and N,N-dimethylformamide), the electrolyte concentration (0.11, 0.22, 0.44 mM), the applied current density (0.17, 0.3, 0.7, and 1.3 mA/cm(2)), and the surface roughness of the deposition substrates (12 and 24 nm). Different environments (air and Ar) were used to dry the samples and the effects on the produced layers were investigated. The obtained deposits were characterized using gamma-ray spectroscopy for determining Nd deposition yields, X-ray photoelectron spectroscopy for chemical analysis of the produced surfaces, radiographic imaging for surface homogeneity inspection, atomic force microscopy for surface roughness evaluation, and scanning electron microscopy for surface morphology investigation. The results allowed identifying the optimum parameters for the production of smooth and crack-free targets by means of molecular plating. The smoothest layers, which had an average RMS roughness of ca. 20 nm and showed no cracks, were obtained using 0.22 mM [Nd(NO3)(3)center dot 6H(2)O] plated from N,N-dimethylformamide at current densities in the range of 0.3-0.7 mA/cm(2) on the smoothest deposition substrate available. (c) 2013 Elsevier B.V. All rights reserved

Detection of ice core particles via deep neural networks

Insoluble particles in ice cores record signatures of past climate parameters like vegetation, volcanic activity or aridity. Their analytical detection depends on intensive bench microscopy investigation and requires dedicated sample preparation steps. Both are laborious, require in-depth knowledge and often restrict sampling strategies. To help overcome these limitations, we present a framework based on Flow Imaging Microscopy coupled to a deep neural network for autonomous image classification of ice core particles. We train the network to classify 7 commonly found classes: mineral dust, felsic and basaltic volcanic ash (tephra), three species of pollen (Corylus avellana, Quercus robur, Quercus suber) and contamination particles that may be introduced onto the ice core surface during core handling operations. The trained network achieves 96.8 % classification accuracy at test time. We present the system’s potentials and limitations with respect to the detection of mineral dust, pollen grains and tephra shards, using both controlled materials and real ice core samples. The methodology requires little sample material, is non destructive, fully reproducible and does not require any sample preparation step. The presented framework can bolster research in the field, by cutting down processing time, supporting human-operated microscopy and further unlocking the paleoclimate potential of ice core records by providing the opportunity to identify an array of ice core particles. Suggestions for an improved system to be deployed within a continuous flow analysis workflow are also presented

Relaxation Labeling Meets GANs: Solving Jigsaw Puzzles with Missing Borders

This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with eroded or missing borders. Missing borders is a common real-world situation, for example, when dealing with the reconstruction of broken artifacts or ruined frescoes. In this particular condition, the puzzle’s pieces do not align perfectly due to the borders’ gaps; in this situation, the patches’ direct match is unfeasible due to the lack of color and line continuations. JiGAN, is a two-steps procedure that tackles this issue: first, we repair the eroded borders with a GAN-based image extension model and measure the alignment affinity between pieces; then, we solve the puzzle with the relaxation labeling algorithm to enforce consistency in pieces positioning, hence, reconstructing the puzzle. We test the method on a large dataset of small puzzles and on three commonly used benchmark datasets to demonstrate the feasibility of the proposed approach