The price of bank mergers in the 1990s

This article examines the primary motivations for the massive wave of bank mergers in the U.S. during the 1990s by analyzing the prices paid for target banks. The authors find that these prices reflect both general market and firm-specific characteristics. For example, the lifting of regulatory restrictions on geographic markets for bank mergers has a significant impact on the average price paid. Additionally, more profitable target banks tend to command a significantly higher market price.Bank mergers

Apprentissage de représentations pour la classification d’images biomédicales

Résumé La disponibilité croissante d'images médicales ouvre la porte à de nombreuses applications cliniques qui ont une incidence sur la prise en charge du patient. De nouveaux traits caractéristiques cliniquement pertinents peuvent alors être découverts pour expliquer, décrire et représenter une maladie. Les algorithmes traditionnels qui se basent sur des règles d'association manuellement construits font souvent défaut dans le domaine biomédical à cause de leur incapacité à capturer la forte variabilité au sein des données. L'apprentissage de représentations apprend plusieurs niveaux de représentations pour mieux capturer les facteurs de variation des données. L'hypothèse du projet de recherche du mémoire est que la classification par apprentissage de représentations apportera une information supplémentaire au médecin afin de l'aider dans son processus de décision. L'objectif principal, qui en découle, vise à étudier la faisabilité de l'apprentissage de représentations pour le milieu médical en vue de découvrir des structures cliniquement pertinentes au sein des données.Dans un premier temps, un algorithme d'apprentissage non-supervisé extrait des traits caractéristiques discriminants des déformations de la colonne vertébrale de patients atteints de la scoliose idiopathique de l'adolescent qui nécessitent une intervention chirurgicale. Le sous-objectif consiste à proposer une alternative aux systèmes de classification existants qui décrivent les déformations seulement selon deux plans alors que la scoliose déforme le rachis dans les trois dimensions de l'espace. Une large base de données a été rassemblée, composée de 915 reconstructions de la colonne vertébrale issues de 663 patients. Des auto-encodeurs empilés apprennent une représentation latente de ces reconstructions. Cette représentation de plus faible dimension démêle les facteurs de variation. Des sous-groupes sont par la suite formés par un algorithme de k-moyennes++. Onze sous-groupes statistiquement significatifs sont alors proposés pour expliquer la répartition de la déformation de la colonne vertébrale. Dans un second temps, un algorithme d'apprentissage supervisé extrait des traits caractéristiques discriminants au sein d'images médicales. Le sous-objectif consiste à classifier chaque voxel de l'image afin de produire une segmentation des reins. Une large base de données a été rassemblée, composée de 79 images tomographiques avec agent de contraste issues de 63 patients avec de nombreuses complications rénales. Un réseau à convolution est entrainé sur des patches de ces images pour apprendre des représentations discriminantes. Par la suite, des modifications sont appliquées à l'architecture, sans modifier les paramètres appris, pour produire les segmentations des reins. Les résultats obtenus permettent d'atteindre des scores élevés selon les métriques utilisées pour évaluer les segmentations en un court délai de calcul. Des coefficients de Dice de 94,35% pour le rein gauche et 93,07% pour le rein droit ont été atteints.Les résultats du mémoire offrent de nouvelles perspectives pour les pathologies abordées. L'application de l'apprentissage de représentations dans le domaine biomédical montre de nombreuses opportunités pour d'autres tâches à condition de rassembler une base de données d'une taille suffisante.----------Abstract The growing accessibility of medical imaging provides new clinical applications for patient care. New clinically relevant features can now be discovered to understand, describe and represent a disease. Traditional algorithms based on hand-engineered features usually fail in biomedical applications because of their lack of ability to capture the high variability in the data. Representation learning, often called deep learning, tackles this challenge by learning multiple levels of representation. The hypothesis of this master's thesis is that representation learning for biomedical image classification will yield additional information for the physician in his decision-making process. Therefore, the main objective is to assess the feasibility of representation learning for two different biomedical applications in order to learn clinically relevant structures within the data. First, a non-supervised learning algorithm extracts discriminant features to describe spine deformities that require a surgical intervention in patients with adolescent idiopathic scoliosis. The sub-objective is to propose an alternative to existing scoliosis classifications that only characterize spine deformities in 2D whereas a scoliotic is often deformed in 3D. 915 spine reconstructions from 663 patients were collected. Stacked auto-encoders learn a hidden representation of these reconstructions. This low-dimensional representation disentangles the main factors of variation in the geometrical appearance of spinal deformities. Sub-groups are clustered with the k-means++ algorithm. Eleven statistically significant sub-groups are extracted to explain how the different deformations of a scoliotic spine are distributed. Secondly, a supervised learning algorithm extracts discriminant features in medical images. The sub-objective is to classify every voxel in the image in order to produce kidney segmentations. 79 contrast-enhanced CT scans from 63 patients with renal complications were collected. A convolutional network is trained on a patch-based training scheme. Simple modifications to the architecture of the network, without modifying the parameters, compute the kidney segmentations on the whole image in a small amount of time. Results show high scores on the metrics used to assess the segmentations. Dice scores are 94.35% for the left kidney and 93.07% for the right kidney. The results show new perspectives for the diseases addressed in this master's thesis. Representation learning algorithms exhibit new opportunities for an application in other biomedical tasks as long as enough observations are available

Open Cross-Domain Visual Search

This paper addresses cross-domain visual search, where visual queries retrieve category samples from a different domain. For example, we may want to sketch an airplane and retrieve photographs of airplanes. Despite considerable progress, the search occurs in a closed setting between two pre-defined domains. In this paper, we make the step towards an open setting where multiple visual domains are available. This notably translates into a search between any pair of domains, from a combination of domains or within multiple domains. We introduce a simple -- yet effective -- approach. We formulate the search as a mapping from every visual domain to a common semantic space, where categories are represented by hyperspherical prototypes. Open cross-domain visual search is then performed by searching in the common semantic space, regardless of which domains are used as source or target. Domains are combined in the common space to search from or within multiple domains simultaneously. A separate training of every domain-specific mapping function enables an efficient scaling to any number of domains without affecting the search performance. We empirically illustrate our capability to perform open cross-domain visual search in three different scenarios. Our approach is competitive with respect to existing closed settings, where we obtain state-of-the-art results on several benchmarks for three sketch-based search tasks.Comment: Accepted at Computer Vision and Image Understanding (CVIU

A Layer-Based Sequential Framework for Scene Generation with GANs

The visual world we sense, interpret and interact everyday is a complex composition of interleaved physical entities. Therefore, it is a very challenging task to generate vivid scenes of similar complexity using computers. In this work, we present a scene generation framework based on Generative Adversarial Networks (GANs) to sequentially compose a scene, breaking down the underlying problem into smaller ones. Different than the existing approaches, our framework offers an explicit control over the elements of a scene through separate background and foreground generators. Starting with an initially generated background, foreground objects then populate the scene one-by-one in a sequential manner. Via quantitative and qualitative experiments on a subset of the MS-COCO dataset, we show that our proposed framework produces not only more diverse images but also copes better with affine transformations and occlusion artifacts of foreground objects than its counterparts.Comment: This paper was accepted at AAAI 201

Automatic Labeling of Vertebral Levels Using a Robust Template-Based Approach

Context. MRI of the spinal cord provides a variety of biomarkers sensitive to white matter integrity and neuronal function. Current processing methods are based on manual labeling of vertebral levels, which is time consuming and prone to user bias. Although several methods for automatic labeling have been published; they are not robust towards image contrast or towards susceptibility-related artifacts. Methods. Intervertebral disks are detected from the 3D analysis of the intensity profile along the spine. The robustness of the disk detection is improved by using a template of vertebral distance, which was generated from a training dataset. The developed method has been validated using T1- and T2-weighted contrasts in ten healthy subjects and one patient with spinal cord injury. Results. Accuracy of vertebral labeling was 100%. Mean absolute error was 2.1 ± 1.7 mm for T2-weighted images and 2.3 ± 1.6 mm for T1-weighted images. The vertebrae of the spinal cord injured patient were correctly labeled, despite the presence of artifacts caused by metallic implants. Discussion. We proposed a template-based method for robust labeling of vertebral levels along the whole spinal cord for T1- and T2-weighted contrasts. The method is freely available as part of the spinal cord toolbox

In-vivo optical detection of cancer using chlorin e6 – polyvinylpyrrolidone induced fluorescence imaging and spectroscopy

<p>Abstract</p> <p>Background</p> <p>Photosensitizer based fluorescence imaging and spectroscopy is fast becoming a promising approach for cancer detection. The purpose of this study was to examine the use of the photosensitizer chlorin e6 (Ce6) formulated in polyvinylpyrrolidone (PVP) as a potential exogenous fluorophore for fluorescence imaging and spectroscopic detection of human cancer tissue xenografted in preclinical models as well as in a patient.</p> <p>Methods</p> <p>Fluorescence imaging was performed on MGH human bladder tumor xenografted on both the chick chorioallantoic membrane (CAM) and the murine model using a fluorescence endoscopy imaging system. In addition, fiber optic based fluorescence spectroscopy was performed on tumors and various normal organs in the same mice to validate the macroscopic images. In one patient, fluorescence imaging was performed on angiosarcoma lesions and normal skin in conjunction with fluorescence spectroscopy to validate Ce6-PVP induced fluorescence visual assessment of the lesions.</p> <p>Results</p> <p>Margins of tumor xenografts in the CAM model were clearly outlined under fluorescence imaging. Ce6-PVP-induced fluorescence imaging yielded a specificity of 83% on the CAM model. In mice, fluorescence intensity of Ce6-PVP was higher in bladder tumor compared to adjacent muscle and normal bladder. Clinical results confirmed that fluorescence imaging clearly captured the fluorescence of Ce6-PVP in angiosarcoma lesions and good correlation was found between fluorescence imaging and spectral measurement in the patient.</p> <p>Conclusion</p> <p>Combination of Ce6-PVP induced fluorescence imaging and spectroscopy could allow for optical detection and discrimination between cancer and the surrounding normal tissues. Ce6-PVP seems to be a promising fluorophore for fluorescence diagnosis of cancer.</p

Solving parabolic equations on the unit sphere via Laplace transforms and radial basis functions

We propose a method to construct numerical solutions of parabolic equations on the unit sphere. The time discretization uses Laplace transforms and quadrature. The spatial approximation of the solution employs radial basis functions restricted to the sphere. The method allows us to construct high accuracy numerical solutions in parallel. We establish $L_2$ error estimates for smooth and nonsmooth initial data, and describe some numerical experiments.Comment: 26 pages, 1 figur

Skin Impedance Measurements for Acupuncture Research: Development of a Continuous Recording System

Skin impedance at acupuncture points (APs) has been used as a diagnostic/therapeutic aid for more than 50 years. Currently, researchers are evaluating the electrophysiologic properties of APs as a possible means of understanding acupuncture's mechanism. To comprehensively assess the diagnostic, therapeutic and mechanistic implications of acupuncture point skin impedance, a device capable of reliably recording impedances from 100 kΩ to 50 MΩ at multiple APs over extended time periods is needed. This article describes design considerations, development and testing of a single channel skin impedance system (hardware, control software and customized electrodes). The system was tested for accuracy against known resistors and capacitors. Two electrodes (the AMI and the ORI) were compared for reliability of recording over 30 min. Two APs (LU 9 and PC 6) and a nearby non-AP site were measured simultaneously in four individuals for 60 min. Our measurement system performed accurately (within 5%) against known resistors (580 kΩ–10 MΩ) and capacitors (10 nF–150 nF). Both the AMI electrode and the modified ORI electrode recorded skin impedance reliably on the volar surface of the forearm (r = 0.87 and r = 0.79, respectively). In four of four volunteers tested, skin impedance at LU 9 was less than at the nearby non-AP site. In three of four volunteers skin impedance was less at PC 6 than at the nearby non-AP site. We conclude that our system is a suitable device upon which we can develop a fully automated multi-channel device capable of recording skin impedance at multiple APs simultaneously over 24 h