Testing new tribo-systems for sheet metal forming of advanced high strength steels and stainless steels

Testing of new tribo-systems in sheet metal forming has become an important issue due to new legislation, which forces industry to replace current, hazardous lubricants. The present paper summarizes the work done in a recent PhD project at the Technical University of Denmark on the development of a methodology for off-line testing of new tribo-systems for advanced high strength steels and stainless steels. The methodology is presented and applied to an industrial case, where different tribo-systems are tested. A universal sheet tribotester has been developed, which can run automatically repetitive Bending Under Tension tests. The overall results show that the methodology ensures satisfactory agreement between laboratory tests and production tests, although disagreement can occur, if tribological conditions are not the same in the two cases

Product recognition in store shelves as a sub-graph isomorphism problem

The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy. However, verifying compliance of real shelves to the ideal layout is a costly task routinely performed by the store personnel. In this paper, we propose a computer vision pipeline to recognize products on shelves and verify compliance to the planned layout. We deploy local invariant features together with a novel formulation of the product recognition problem as a sub-graph isomorphism between the items appearing in the given image and the ideal layout. This allows for auto-localizing the given image within the aisle or store and improving recognition dramatically.Comment: Slightly extended version of the paper accepted at ICIAP 2017. More information @project_page --> http://vision.disi.unibo.it/index.php?option=com_content&view=article&id=111&catid=7

ClassCut for Unsupervised Class Segmentation

Abstract. We propose a novel method for unsupervised class segmentation on a set of images. It alternates between segmenting object instances and learning a class model. The method is based on a segmentation energy defined over all images at the same time, which can be optimized efficiently by techniques used before in interactive segmentation. Over iterations, our method progressively learns a class model by integrating observations over all images. In addition to appearance, this model captures the location and shape of the class with respect to an automatically determined coordinate frame common across images. This frame allows us to build stronger shape and location models, similar to those used in object class detection. Our method is inspired by interactive segmentation methods [1], but it is fully automatic and learns models characteristic for the object class rather than specific to one particular object/image. We experimentally demonstrate on the Caltech4, Caltech101, and Weizmann horses datasets that our method (a) transfers class knowledge across images and this improves results compared to segmenting every image independently; (b) outperforms Grabcut [1] for the task of unsupervised segmentation; (c) offers competitive performance compared to the state-of-the-art in unsupervised segmentation and in particular it outperforms the topic model [2].

Ellipsometric measurements of the refractive indices of linear alkylbenzene and EJ-301 scintillators from 210 to 1000 nm

We report on ellipsometric measurements of the refractive indices of LAB-PPO, Nd-doped LAB-PPO and EJ-301 scintillators to the nearest +/-0.005, in the wavelength range 210-1000 nm.Comment: 7 pages, 4 figure

A Review of Rare Pion and Muon Decays

After a decade of no measurements of pion and muon rare decays, PIBETA, a new experimental program is producing its first results. We report on a new experimental study of the pion beta decay, Pi(+) -> Pi(0) e(+) Nu, the Pi(e2 gamma) radiative decay, Pi(+) -> e(+) Nu Gamma, and muon radiative decay, Mu -> e Nu Gamma. The new results represent four- to six-fold improvements in precision over the previous measurements. Excellent agreement with Standard Model predictions is observed in all channels except for one kinematic region of the Pi(e2 gamma) radiative decay involving energetic photons and lower-energy positrons.Comment: 10 pages, 6 figures, 2 tables, invited talk presented at MESON 2004, 8th Int'l. Workshop on Meson Production, Properties and Interaction, Krakow, Poland 4-8 June 200

Tabulation, bibliography, and structure of binary intermetallic compounds. V. Compounds of aluminum and indium

This report is the fifth and last in a series. The previous reports listed the compounds of elements

Deep Discrete Hashing with Self-supervised Pairwise Labels

Hashing methods have been widely used for applications of large-scale image retrieval and classification. Non-deep hashing methods using handcrafted features have been significantly outperformed by deep hashing methods due to their better feature representation and end-to-end learning framework. However, the most striking successes in deep hashing have mostly involved discriminative models, which require labels. In this paper, we propose a novel unsupervised deep hashing method, named Deep Discrete Hashing (DDH), for large-scale image retrieval and classification. In the proposed framework, we address two main problems: 1) how to directly learn discrete binary codes? 2) how to equip the binary representation with the ability of accurate image retrieval and classification in an unsupervised way? We resolve these problems by introducing an intermediate variable and a loss function steering the learning process, which is based on the neighborhood structure in the original space. Experimental results on standard datasets (CIFAR-10, NUS-WIDE, and Oxford-17) demonstrate that our DDH significantly outperforms existing hashing methods by large margin in terms of~mAP for image retrieval and object recognition. Code is available at \url{https://github.com/htconquer/ddh}