Université de Sherbrooke

About: Université de Sherbrooke is a education organization based out in Sherbrooke, Quebec, Canada. It is known for research contribution in the topics: Population & Receptor. The organization has 14922 authors who have published 28783 publications receiving 792511 citations. The organization is also known as: Universite de Sherbrooke & Sherbrooke University.

Lifshitz critical point in the cuprate superconductor YBa2Cu3Oy from high-field Hall effect measurements

Abstract: The Hall coefficient RH of the cuprate superconductor YBa 2Cu3Oy was measured in magnetic fields up to 60 T for a hole concentration p from 0.078 to 0.152 in the underdoped regime. In fields large enough to suppress superconductivity, RH(T) is seen to go from positive at high temperature to negative at low temperature, for p0.08. This change of sign is attributed to the emergence of an electron pocket in the Fermi surface at low temperature. At p<0.08, the normal-state R H(T) remains positive at all temperatures, increasing monotonically as T→0. We attribute the change of behavior across p=0.08 to a Lifshitz transition, namely a change in Fermi-surface topology occurring at a critical concentration pL=0.08, where the electron pocket vanishes. The loss of the high-mobility electron pocket across pL coincides with a tenfold drop in the conductivity at low temperature, revealed in measurements of the electrical resistivity ρ at high fields, showing that the so-called metal-insulator crossover of cuprates is in fact driven by a Lifshitz transition. It also coincides with a jump in the in-plane anisotropy of ρ, showing that without its electron pocket, the Fermi surface must have strong twofold in-plane anisotropy. These findings are consistent with a Fermi-surface reconstruction caused by a unidirectional spin-density wave or stripe order. © 2011 American Physical Society.

Unsupervised State Representation Learning in Atari

Abstract: State representation learning, or the ability to capture latent generative factors of an environment is crucial for building intelligent agents that can perform a wide variety of tasks. Learning such representations in an unsupervised manner without supervision from rewards is an open problem. We introduce a method that tries to learn better state representations by maximizing mutual information across spatially and temporally distinct features of a neural encoder of the observations. We also introduce a new benchmark based on Atari 2600 games where we evaluate representations based on how well they capture the ground truth state. We believe this new framework for evaluating representation learning models will be crucial for future representation learning research. Finally, we compare our technique with other state-of-the-art generative and contrastive representation learning methods.

An Autoencoder Approach to Learning Bilingual Word Representations

Abstract: Cross-language learning allows one to use training data from one language to build models for a different language. Many approaches to bilingual learning require that we have word-level alignment of sentences from parallel corpora. In this work we explore the use of autoencoder-based methods for cross-language learning of vectorial word representations that are coherent between two languages, while not relying on word-level alignments. We show that by simply learning to reconstruct the bag-of-words representations of aligned sentences, within and between languages, we can in fact learn high-quality representations and do without word alignments. We empirically investigate the success of our approach on the problem of cross-language text classification, where a classifier trained on a given language (e.g., English) must learn to generalize to a different language (e.g., German). In experiments on 3 language pairs, we show that our approach achieves state-of-the-art performance, outperforming a method exploiting word alignments and a strong machine translation baseline.

Typology of students at risk of dropping out of school : Description by personal, family and school factors

Abstract: The purpose of this study was to identify the different subgroups of students at risk of dropping out of school. The typology was developed based on the three main contexts associated with school dropout risk, namely, the personal, family and school contexts. On the basis of these factors, the clustering results enabled us to categorize at-risk students into four subgroups: (1) the Anti-Social Covert behavior type, (2) the Uninterested in school type, (3) the School and Social Adjustment Difficulties type, and (4) the Depressive type. Moreover, considering all the contexts involved in school dropout, the clustering technique confirms the importance of behavior problems and learning difficulties, while emphasizing the significance of both depression and the family and classroom environments in the development of dropout risk. Students at risk of dropping out of school report many family organisational problems and that they obtain little emotional support from their parents. They also perceive little order or organisation in the classroom.

Spring Normalized Difference Vegetation Index (NDVI) predicts annual variation in timing of peak faecal crude protein in mountain ungulates

Abstract: Summary 1. In recent years, the Normalized Difference Vegetation Index (NDVI) has been used to assess the relationships between habitat quality and animal life-history traits. Since numerous ecological studies now use NDVI rather than perform direct vegetation assessments, field validations are essential to provide confidence in the biological significance of NDVI estimates. While some studies have compared NDVI with plant biomass, very few have examined the relationship between NDVI and changes in vegetation quality. 2. Using data from two long-term studies of alpine ungulates, we assessed the relationship between two NDVI indices and the date of peak in faecal crude protein (FCP), which represents temporal variability in the availability of high-quality vegetation. We also evaluated if NDVI data could predict annual variation in the timing of spring green-up. 3. In both populations, integrated NDVI in June was negatively correlated with the date of the peak in FCP, indicating that high integrated NDVI values corresponded to early springs in alpine habitats. Maximum NDVI increase during spring green-up was positively correlated with the timing of peak FCP, illustrating that rapid increases in NDVI represented delayed springs. 4. Predicted values of date of peak FCP estimated each year from NDVI data satisfactorily fitted observed values, and prediction intervals included all observed values. These results suggest that NDVI can reliably predict variation over years in the timing of spring. 5. Synthesis and applications. Our long-term studies demonstrate that a multi-year time series of Normalized Difference Vegetation Index (NDVI) can reliably measure yearly changes in the timing of the availability of high-quality vegetation for temperate herbivores. This finding therefore supports the use of NDVI as a proxy for vegetation attributes in population ecology and wildlife management studies.