Yandex

About: Yandex is a company organization based out in Moscow, Russia. It is known for research contribution in the topics: Branching fraction & Web search query. The organization has 1285 authors who have published 1592 publications receiving 27537 citations. The organization is also known as: Yandex N.V. & yandex.ru.

CatBoost: unbiased boosting with categorical features

Abstract: This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boosting implementations in terms of quality on a variety of datasets. Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms. In this paper, we provide a detailed analysis of this problem and demonstrate that proposed algorithms solve it effectively, leading to excellent empirical results.

Neural Codes for Image Retrieval

Abstract: It has been shown that the activations invoked by an image within the top layers of a large convolutional neural network provide a high-level descriptor of the visual content of the image. In this paper, we investigate the use of such descriptors (neural codes) within the image retrieval application. In the experiments with several standard retrieval benchmarks, we establish that neural codes perform competitively even when the convolutional neural network has been trained for an unrelated classification task (e.g. Image-Net). We also evaluate the improvement in the retrieval performance of neural codes, when the network is retrained on a dataset of images that are similar to images encountered at test time.

The Circumpolar Arctic vegetation map

Abstract: Question: What are the major vegetation units in the Arctic, what is their composition, and how are they distributed among major bioclimate subzones and countries? Location: The Arctic tundra region, north of the tree line. Methods: A photo-interpretive approach was used to delineate the vegetation onto an Advanced Very High Resolution Radiometer (AVHRR) base image. Mapping experts within nine Arctic regions prepared draft maps using geographic information technology (ArcInfo) of their portion of the Arctic, and these were later synthesized to make the final map. Area analysis of the map was done according to bioclimate subzones, and country. The integrated mapping procedures resulted in other maps of vegetation, topography, soils, landscapes, lake cover, substrate pH, and above-ground biomass. Results: The final map was published at 1:7 500 000 scale map. Within the Arctic (total area = 7.11 · 106 km 2 ), about 5.05 · 10 6 km 2 is vegetated. The remainder is ice covered. The map legend generally portrays the zonal vegetation within each map polygon. About 26% of the vegetated area is erect shrublands, 18% peaty graminoid tundras, 13% mountain complexes, 12% barrens, 11% mineral graminoid tundras, 11% prostrate-shrub tundras, and 7% wetlands. Canada has by far the most terrain in the High Arctic mostly associated with abundant barren types and prostrate dwarf-shrub tundra, whereas Russia has the largest area in the Low Arctic, predominantly low-shrub tundra. Conclusions: The CAVM is the first vegetation map of an entire global biome at a comparable resolution. The consistent treatment of the vegetation across the circumpolar Arctic, abundant ancillary material, and digital database should promote the application to numerous land-use, and climate-change applications and will make updating the map relatively easy.

Texture networks: feed-forward synthesis of textures and stylized images

Abstract: Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods require a slow and memory-consuming optimization process. We propose here an alternative approach that moves the computational burden to a learning stage. Given a single example of a texture, our approach trains compact feed-forward convolutional networks to generate multiple samples of the same texture of arbitrary size and to transfer artistic style from a given image to any other image. The resulting networks are remarkably light-weight and can generate textures of quality comparable to Gatys et al., but hundreds of times faster. More generally, our approach highlights the power and flexibility of generative feed-forward models trained with complex and expressive loss functions.

Observation of $J/\psi p$ Resonances Consistent with Pentaquark States in $\Lambda_b^0 \to J/\psi K^- p$ Decays

Abstract: Observations of exotic structures in the J/ψp channel, that we refer to as pentaquark-charmonium states, in Λ0b→J/ψK−p decays are presented. The data sample corresponds to an integrated luminosity of 3/fb acquired with the LHCb detector from 7 and 8 TeV pp collisions. An amplitude analysis is performed on the three-body final-state that reproduces the two-body mass and angular distributions. To obtain a satisfactory fit of the structures seen in the J/ψp mass spectrum, it is necessary to include two Breit-Wigner amplitudes that each describe a resonant state. The significance of each of these resonances is more than 9 standard deviations. One has a mass of 4380±8±29 MeV and a width of 205±18±86 MeV, while the second is narrower, with a mass of 4449.8±1.7±2.5 MeV and a width of 39±5±19 MeV. The preferred JP assignments are of opposite parity, with one state having spin 3/2 and the other 5/2.