Learning with Biased Complementary Labels

In this paper, we study the classification problem in which we have access to easily obtainable surrogate for true labels, namely complementary labels, which specify classes that observations do \textbf{not} belong to. Let $Y$ and $\bar{Y}$ be the true and complementary labels, respectively. We first model the annotation of complementary labels via transition probabilities $P(\bar{Y}=i|Y=j), i\neq j\in\{1,\cdots,c\}$, where $c$ is the number of classes. Previous methods implicitly assume that $P(\bar{Y}=i|Y=j), \forall i\neq j$, are identical, which is not true in practice because humans are biased toward their own experience. For example, as shown in Figure 1, if an annotator is more familiar with monkeys than prairie dogs when providing complementary labels for meerkats, she is more likely to employ "monkey" as a complementary label. We therefore reason that the transition probabilities will be different. In this paper, we propose a framework that contributes three main innovations to learning with \textbf{biased} complementary labels: (1) It estimates transition probabilities with no bias. (2) It provides a general method to modify traditional loss functions and extends standard deep neural network classifiers to learn with biased complementary labels. (3) It theoretically ensures that the classifier learned with complementary labels converges to the optimal one learned with true labels. Comprehensive experiments on several benchmark datasets validate the superiority of our method to current state-of-the-art methods.Comment: ECCV 2018 Ora

Distances from the Correlation between Galaxy Luminosities and Rotation Rates

A large luminosity--linewidth template sample is now available, improved absorption corrections have been derived, and there are a statistically significant number of galaxies with well determined distances to supply the zero point. A revised estimate of the Hubble Constant is H_0=77 +-4 km/s/Mpc where the error is the 95% probability statistical error. Systematic uncertainties are potentially twice as large.Comment: 21 pages, 9 figures. Invited chapter for the book `Post-Hipparcos Cosmic Candles', Eds. F. Caputo and A. Heck (Kluwer Academic Publishers, Dordrecht

Review: ‘Gimme five’: future challenges in multiple sclerosis. ECTRIMS Lecture 2009

This article is based on the ECTRIMS lecture given at the 25th ECTRIMS meeting which was held in Düsseldorf, Germany, from 9 to 12 September 2009. Five challenges have been identified: (1) safeguarding the principles of medical ethics; (2) optimizing the risk/benefit ratio; (3) bridging the gap between multiple sclerosis and experimental autoimmune encephalitis; (4) promoting neuroprotection and repair; and (5) tailoring multiple sclerosis therapy to the individual patient. Each of these challenges will be discussed and placed in the context of current research into the pathogenesis and treatment of multiple sclerosis

Estimating genomic breeding values and detecting QTL using univariate and bivariate models

Background Genomic selection is particularly beneficial for difficult or expensive to measure traits. Since multi-trait selection is an important tool to deal with such cases, an important question is what the added value is of multi-trait genomic selection. Methods The simulated dataset, including a quantitative and binary trait, was analyzed with four univariate and bivariate linear models to predict breeding values for juvenile animals. Two models estimated variance components with REML using a numerator (A), or SNP based relationship matrix (G). Two SNP based Bayesian models included one (BayesA) or two distributions (BayesC) for estimated SNP effects. The bivariate BayesC model sampled QTL probabilities for each SNP conditional on both traits. Genotypes were permuted 2,000 times against phenotypes and pedigree, to obtain significance thresholds for posterior QTL probabilities. Genotypes were permuted rather than phenotypes, to retain relationships between pedigree and phenotypes, such that polygenic effects could still be estimated. Results Correlations between estimated breeding values (EBV) of different SNP based models, for juvenile animals, were greater than 0.93 (0.87) for the quantitative (binary) trait. Estimated genetic correlation was 0.71 (0.66) for model G (A). Accuracies of breeding values of SNP based models were for both traits highest for BayesC and lowest for G. Accuracies of breeding values of bivariate models were up to 0.08 higher than for univariate models. The bivariate BayesC model detected 14 out of 32 QTL for the quantitative trait, and 8 out of 22 for the binary trait. Conclusions Accuracy of EBV clearly improved for both traits using bivariate compared to univariate models. BayesC achieved highest accuracies of EBV and was also one of the methods that found most QTL. Permuting genotypes against phenotypes and pedigree in BayesC provided an effective way to derive significance thresholds for posterior QTL probabilitie

Regulation of caspase-3 processing by cIAP2 controls the switch between pro-inflammatory activation and cell death in microglia.

Cell Death and Disease is an open-access journal published by Nature Publishing Group. This work is licensed under a Creative Commons Attribution 4.0 International Licence. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons licence, users will need to obtain permission from the licence holder to reproduce the material.The activation of microglia, resident immune cells of the central nervous system, and inflammation-mediated neurotoxicity are typical features of neurodegenerative diseases, for example, Alzheimer's and Parkinson's diseases. An unexpected role of caspase-3, commonly known to have executioner role for apoptosis, was uncovered in the microglia activation process. A central question emerging from this finding is what prevents caspase-3 during the microglia activation from killing those cells? Caspase-3 activation occurs as a two-step process, where the zymogen is first cleaved by upstream caspases, such as caspase-8, to form intermediate, yet still active, p19/p12 complex; thereafter, autocatalytic processing generates the fully mature p17/p12 form of the enzyme. Here, we show that the induction of cellular inhibitor of apoptosis protein 2 (cIAP2) expression upon microglia activation prevents the conversion of caspase-3 p19 subunit to p17 subunit and is responsible for restraining caspase-3 in terms of activity and subcellular localization. We demonstrate that counteracting the repressive effect of cIAP2 on caspase-3 activation, using small interfering RNA targeting cIAP2 or a SMAC mimetic such as the BV6 compound, reduced the pro-inflammatory activation of microglia cells and promoted their death. We propose that the different caspase-3 functions in microglia, and potentially other cell types, reside in the active caspase-3 complexes formed. These results also could indicate cIAP2 as a possible therapeutic target to modulate microglia pro-inflammatory activation and associated neurotoxicity observed in neurodegenerative disorders