Université de Montréal

About: Université de Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 45641 authors who have published 100476 publications receiving 4004007 citations. The organization is also known as: University of Montreal & UdeM.

Strategizing in pluralistic contexts: Rethinking theoretical frames:

Abstract: Pluralistic organizations characterized by multiple objectives, diffuse power and knowledge-based work processes present a complex challenge both for strategy theorists and for strategy practitioners because the very nature of strategy as usually understood (an explicit and unified direction for the organization) appears to contradict the natural dynamics of these organizations. Yet pluralism is to some extent always present in organizations and perhaps increasingly so. This article explores the usefulness of three alternate and complementary theoretical frames for understanding and influencing strategy practice in pluralistic contexts: Actor-Network Theory, Conventionalist Theory and the social practice perspective. Each of these frameworks has a predominant focus on one of the fundamental attributes of pluralism: power, values and knowledge. Together, they offer a multi-faceted understanding of the complex practice of strategizing in pluralistic contexts.

Congenital amusia: a group study of adults afflicted with a music-specific disorder.

Abstract: The condition of congenital amusia, commonly known as tone-deafness, has been described for more than a century, but has received little empirical attention. In the present study, a research effort has been made to document in detail the behavioural manifestations of congenital amusia. A group of 11 adults, fitting stringent criteria of musical disabilities, were examined in a series of tests originally designed to assess the presence and specificity of musical disorders in brain-damaged patients. The results show that congenital amusia is related to severe deficiencies in processing pitch variations. The deficit extends to impairments in music memory and recognition as well as in singing and the ability to tap in time to music. Interestingly, the disorder appears specific to the musical domain. Congenital amusical individuals process and recognize speech, including speech prosody, common environmental sounds and human voices, as well as control subjects. Thus, the present study convincingly demonstrates the existence of congenital amusia as a new class of learning disabilities that affect musical abilities.

Microbiome-derived inosine modulates response to checkpoint inhibitor immunotherapy

Abstract: Several species of intestinal bacteria have been associated with enhanced efficacy of checkpoint blockade immunotherapy, but the underlying mechanisms by which the microbiome enhances antitumor immunity are unclear. In this study, we isolated three bacterial species-Bifidobacterium pseudolongum, Lactobacillus johnsonii, and Olsenella species-that significantly enhanced efficacy of immune checkpoint inhibitors in four mouse models of cancer. We found that intestinal B. pseudolongum modulated enhanced immunotherapy response through production of the metabolite inosine. Decreased gut barrier function induced by immunotherapy increased systemic translocation of inosine and activated antitumor T cells. The effect of inosine was dependent on T cell expression of the adenosine A2A receptor and required costimulation. Collectively, our study identifies a previously unknown microbial metabolite immune pathway activated by immunotherapy that may be exploited to develop microbial-based adjuvant therapies.

Outliers detection and treatment: a review.

Abstract: Outliers are observations or measures that are suspicious because they are much smaller or much larger than the vast majority of the observations. These observations are problematic because they may not be caused by the mental process under scrutiny or may not reflect the ability under examination. The problem is that a few outliers is sometimes enough to distort the group results (by altering the mean performance, by increasing variability, etc.). In this paper, various techniques aimed at detecting potential outliers are reviewed. These techniques are subdivided into two classes, the ones regarding univariate data and those addressing multivariate data. Within these two classes, we consider the cases where the population distribution is known to be normal, the population is not normal but known, or the population is unknown. Recommendations will be put forward in each case.

On the Number of Linear Regions of Deep Neural Networks

Abstract: We study the complexity of functions computable by deep feedforward neural networks with piecewise linear activations in terms of the symmetries and the number of linear regions that they have. Deep networks are able to sequentially map portions of each layer's input-space to the same output. In this way, deep models compute functions that react equally to complicated patterns of different inputs. The compositional structure of these functions enables them to re-use pieces of computation exponentially often in terms of the network's depth. This paper investigates the complexity of such compositional maps and contributes new theoretical results regarding the advantage of depth for neural networks with piecewise linear activation functions. In particular, our analysis is not specific to a single family of models, and as an example, we employ it for rectifier and maxout networks. We improve complexity bounds from pre-existing work and investigate the behavior of units in higher layers.