Free radical OH, a molecule of astrophysical and aeronomic interest

The chemistry and physics of the gaseous OH free radical as it applies to interstellar space, planetary atmospheres, and the sun is presented. Topics considered are: (1) rotational-vibrational transitions; (2) dissociation and ionization processes; (3) spectral characteristics

Absolute Rayleigh scattering cross sections of gases and freons of stratospheric interest in the visible and ultraviolet regions

The laboratory measurements of absolute Rayleigh scattering cross sections as a function wavelength are reported for gas molecules He, Ne, Ar, N2, H2, O2, CO2, CH4 and for vapors of most commonly used freons CCl2F2, CBrF3, CF4, and CHClf2. These cross sections are determined from the measurements of photon scattering at an angle of 54 deg 44 min which yield the absolute values independent of the value of normal depolarization ratios. The present results show that in the spectral range 6943-3638A deg, the values of the Rayleigh scattering cross section can be extrapolated from one wavelength to the other using 1/lambda (4) law without knowing the values of the polarizabilities. However, such an extrapolation can not be done in the region of shorter wavelengths

Dead Angles of Personalization, Integrating Curation Algorithms in the Fabric of Design

International audienceThe amount of information available on the web is too vast for individuals to be able to process it all. To cope with this issue, digital platforms started relying on algorithms to curate, filter and recommend content to their users. This problem has generally been envisioned from a technical perspective, as an optimization issue and has been mostly untouched by design considerations. Through 16 interviews with daily users of platforms, we analyze how curation algorithms influence their daily experience and the strategies they use to try to adapt them to their own needs. Based on these empirical findings, we propose a set of four speculative design alternatives to explore how we can integrate curation algorithms as part of the larger fabric of design on the web. By exploring interactions to counter the binary nature of curation algorithms, their uniqueness, their anti-historicity and their implicit data collection, we provide tools to bridge the current divide between curation algorithms and people

Location-aware online learning for top-k recommendation

We address the problem of recommending highly volatile items for users, both with potentially ambiguous location that may change in time. The three main ingredients of our method include (1) using online machine learning for the highly volatile items; (2) learning the personalized importance of hierarchical geolocation (for example, town, region, country, continent); finally (3) modeling temporal relevance by counting recent items with an exponential decay in recency.For (1), we consider a time-aware setting, where evaluation is cumbersome by traditional measures since we have different top recommendations at different times. We describe a time-aware framework based on individual item discounted gain. For (2), we observe that trends and geolocation turns out to be more important than personalized user preferences: user-item and content-item matrix factorization improves in combination with our geo-trend learning methods, but in itself, they are greatly inferior to our location based models. In fact, since our best performing methods are based on spatiotemporal data, they are applicable in the user cold start setting as well and perform even better than content based cold start methods. Finally for (3), we estimate the probability that the item will be viewed by its previous views to obtain a powerful model that combines item popularity and recency.To generate realistic data for measuring our new methods, we rely on Twitter messages with known GPS location and consider hashtags as items that we recommend the users to be included in their next message. © 2016 Elsevier B.V

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports