Similarity Learning for Provably Accurate Sparse Linear Classification

In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest for optimizing distance and similarity functions. Most of the state of the art focus on learning Mahalanobis distances (requiring to fulfill a constraint of positive semi-definiteness) for use in a local k-NN algorithm. However, no theoretical link is established between the learned metrics and their performance in classification. In this paper, we make use of the formal framework of good similarities introduced by Balcan et al. to design an algorithm for learning a non PSD linear similarity optimized in a nonlinear feature space, which is then used to build a global linear classifier. We show that our approach has uniform stability and derive a generalization bound on the classification error. Experiments performed on various datasets confirm the effectiveness of our approach compared to state-of-the-art methods and provide evidence that (i) it is fast, (ii) robust to overfitting and (iii) produces very sparse classifiers.Comment: Appears in Proceedings of the 29th International Conference on Machine Learning (ICML 2012

A Survey on Metric Learning for Feature Vectors and Structured Data

The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning a metric from data and has attracted a lot of interest in machine learning and related fields for the past ten years. This survey paper proposes a systematic review of the metric learning literature, highlighting the pros and cons of each approach. We pay particular attention to Mahalanobis distance metric learning, a well-studied and successful framework, but additionally present a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning. Recent trends and extensions, such as semi-supervised metric learning, metric learning for histogram data and the derivation of generalization guarantees, are also covered. Finally, this survey addresses metric learning for structured data, in particular edit distance learning, and attempts to give an overview of the remaining challenges in metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new method

Activation energy of the rate constant of P+Q−A absorption decay in reaction centers fromRhodobacter sphaeroides reconstituted with different anthraquinones

AbstractThe free energy differences between the first stable charge-separated state, P+Q−A and the thermally excited state, M, via which P+Q−A recombines, were measured in the reaction centers fromRhodobacter sphaeroides reconstituted with anthraquinones. We find that the free energy level of M is independent of the in situ redox potential of QA and consequently the free energy level of P+Q−A. This suggests that M is a state independent of P+Q−A. This is in support of the identification of M with a relaxed form of P+I−

Hip-hop Israël

Le rap a pris racine en Israël, d’où il s’exporte à son tour. En remontant à sa source, il y a plus de dix ans, dans un studio miteux de Tel- Aviv, nous avons éclairci un point d’histoire, retrouvé un personnage inattendu et recueilli une évidence : « La patrie biblique est le meilleur terreau pour développer le hip-hop »

Clinical Implementation of Hypofractionated Radiation therapy for Lung Malignancies

For patients with oligometastases, metastases limited in number and site, the use of radiation therapy treatment with a hypofractionated dose scheme has been proposed as a potential ablative approach. There are a limited number of prospective studies looking at hypofractionated radiation therapy (HRT) for lung oligometastasis. Normal lung tissue complication and radiation planning technique are significant limiting factors for the implementation of hypofractionated lung metastasis. The problem statement of this study is how to improve the clinical implementation of HRT for lung metastasis exploring lung toxicity predictors, and developing an efficient radiation planning method. In the first study, we analyzed the dose distribution for 28 patients with lung oligometastasis and treated with HRT to multiple metastases in the lungs. We identified several significant predictors for lung radiation pneumonitis (RP) including the mean lung dose (MLD), V13 and V20. In addition a dose-effect relation between the lung normalized total dose (NTD) and RP may exist up to 48 Gy in three fractions. The dose-response parameters derived in our study appear to agree with other hypofractionated results published in the literature. In the second study, we used an inverse planning algorithm to develop a new radiation planning method by limiting the number of segments per beam angle down to 1 segment. Single segment plans were able to significantly improve tumor coverage and conformality, reduce the risk of lung RP, while simplifying the planning process and delivery. Target conformality and normal lung tissue sparing did not gain much improvement from an increase of plan complexity to five segments over the simplified one segment technique. The automation of our method is a good alternative to more traditional methods and offers significant dosimetric benefits. In the third study, we verified the single segment planning technique via patient specific quality assurance (QA) in a motion phantom. We found good agreement between calculated and measured doses via thermoluminescent detectors (TLD) inside the target. A dose to distance agreement of 3%/3 mm and 2%/2 mm between calculation and film measurements for representative plans in a motion phantom was verified at 98.99% and 97.15%, respectively

Using Learned Conditional Distributions as Edit Distance

International audienc