Unintended Consequences of the Dodd–Frank Act on Credit Rating Risk and Corporate Finance

Prior research finds that Dodd–Frank Act’s regulations on credit rating agencies (CRAs) increase rated firms’ risk of rating downgrades, regardless of their credit quality. Our difference-in-difference estimates suggest that after Dodd–Frank, low-rated firms, which face steep costs from a further downgrade, significantly reduce their debt issuance and investments compared to similar unrated firms. Our results are not driven by credit supply or the financial crisis. They reveal an unintended consequence of Dodd–Frank: Greater regulatory pressure on CRAs leads to negative spillover effects on firms concerned about credit ratings, regardless of their credit quality

Deep Learning Criminal Networks

Recent advances in deep learning methods have enabled researchers to develop and apply algorithms for the analysis and modeling of complex networks. These advances have sparked a surge of interest at the interface between network science and machine learning. Despite this, the use of machine learning methods to investigate criminal networks remains surprisingly scarce. Here, we explore the potential of graph convolutional networks to learn patterns among networked criminals and to predict various properties of criminal networks. Using empirical data from political corruption, criminal police intelligence, and criminal financial networks, we develop a series of deep learning models based on the GraphSAGE framework that are capable to recover missing criminal partnerships, distinguish among types of associations, predict the amount of money exchanged among criminal agents, and even anticipate partnerships and recidivism of criminals during the growth dynamics of corruption networks, all with impressive accuracy. Our deep learning models significantly outperform previous shallow learning approaches and produce high-quality embeddings for node and edge properties. Moreover, these models inherit all the advantages of the GraphSAGE framework, including the generalization to unseen nodes and scaling up to large graph structures.Comment: 14 two-column pages, 5 figure

miR-132/212 knockout mice reveal roles for these miRNAs in regulating cortical synaptic transmission and plasticity

miR-132 and miR-212 are two closely related miRNAs encoded in the same intron of a small non-coding gene, which have been suggested to play roles in both immune and neuronal function. We describe here the generation and initial characterisation of a miR-132/212 double knockout mouse. These mice were viable and fertile with no overt adverse phenotype. Analysis of innate immune responses, including TLR-induced cytokine production and IFNβ induction in response to viral infection of primary fibroblasts did not reveal any phenotype in the knockouts. In contrast, the loss of miR-132 and miR-212, while not overtly affecting neuronal morphology, did affect synaptic function. In both hippocampal and neocortical slices miR-132/212 knockout reduced basal synaptic transmission, without affecting paired-pulse facilitation. Hippocampal long-term potentiation (LTP) induced by tetanic stimulation was not affected by miR-132/212 deletion, whilst theta burst LTP was enhanced. In contrast, neocortical theta burst-induced LTP was inhibited by loss of miR-132/212. Together these results indicate that miR-132 and/or miR-212 play a significant role in synaptic function, possibly by regulating the number of postsynaptic AMPA receptors under basal conditions and during activity-dependent synaptic plasticity

Um modelo para seleção de avaliações adaptativas em ambientes computacionais de aprendizagem

No sistema tradicional de ensino e também na grande maioria dos Ambientes Computacionais de Aprendizagem todos os estudantes são avaliados de maneira uniforme independente do seu nível de aquisição de conhecimentos e dos conteúdos abordados. O insucesso recorrente nessas avaliações pode ser desestimulante para o aprendiz e torna o processo de avaliação formativa ineficaz uma vez que os resultados não são utilizados para realimentar o próprio processo de avaliação. Este artigo tem por objetivo apresentar um modelo para a seleção de avaliações adaptativas num ambiente computacional de aprendizagem utilizando técnicas de mineração de dados com base no nível de aquisição de conhecimentos do estudante em cada item do domínio em questão e também nos conteúdos abordados nas unidades de Avaliação. A seleção de unidades de avaliação adequadas ao perfil atual do estudante criará condições para avaliações personalizadas de modo a proteger ou desafiar o aprendiz nos seus sucessos ou insucessos.In the traditional system of education and also in the great majority of Computational Environments of Learning all the students are evaluated in an independent uniform way it its level of acquisition of knowledge and them boarded contents. The recurrent failure in these evaluations can be discouraged for the apprentice and becomes the process of inefficacious formative evaluation once that the results are not used to feedback the proper process of evaluation. This article has for objective to present a model for the selection of adaptive evaluations in a computational environment of learning using data mining techniques based on the level of acquisition of knowledge of the student in each item of the domain in question and also in the boarded contents in the units of the Evaluation. The selection of adequate units of evaluation to the current profile of the student will create conditions for personalized evaluations in order to protect or to defy the apprentice in its successes or failures.VI Workshop de Tecnología Informática Aplicada en EducaciónRed de Universidades con Carreras en Informática (RedUNCI

Um modelo para seleção de avaliações adaptativas em ambientes computacionais de aprendizagem

No sistema tradicional de ensino e também na grande maioria dos Ambientes Computacionais de Aprendizagem todos os estudantes são avaliados de maneira uniforme independente do seu nível de aquisição de conhecimentos e dos conteúdos abordados. O insucesso recorrente nessas avaliações pode ser desestimulante para o aprendiz e torna o processo de avaliação formativa ineficaz uma vez que os resultados não são utilizados para realimentar o próprio processo de avaliação. Este artigo tem por objetivo apresentar um modelo para a seleção de avaliações adaptativas num ambiente computacional de aprendizagem utilizando técnicas de mineração de dados com base no nível de aquisição de conhecimentos do estudante em cada item do domínio em questão e também nos conteúdos abordados nas unidades de Avaliação. A seleção de unidades de avaliação adequadas ao perfil atual do estudante criará condições para avaliações personalizadas de modo a proteger ou desafiar o aprendiz nos seus sucessos ou insucessos.In the traditional system of education and also in the great majority of Computational Environments of Learning all the students are evaluated in an independent uniform way it its level of acquisition of knowledge and them boarded contents. The recurrent failure in these evaluations can be discouraged for the apprentice and becomes the process of inefficacious formative evaluation once that the results are not used to feedback the proper process of evaluation. This article has for objective to present a model for the selection of adaptive evaluations in a computational environment of learning using data mining techniques based on the level of acquisition of knowledge of the student in each item of the domain in question and also in the boarded contents in the units of the Evaluation. The selection of adequate units of evaluation to the current profile of the student will create conditions for personalized evaluations in order to protect or to defy the apprentice in its successes or failures.VI Workshop de Tecnología Informática Aplicada en EducaciónRed de Universidades con Carreras en Informática (RedUNCI

The On-Orbit Performance of the Galaxy Evolution Explorer

We report the first year on-orbit performance results for the Galaxy Evolution Explorer (GALEX), a NASA Small Explorer that is performing a survey of the sky in two ultraviolet bands. The instrument comprises a 50 cm diameter modified Ritchey-Chretien telescope with a 1.25 degree field of view, selectable imaging and objective grism spectroscopic modes, and an innovative optical system with a thin-film multilayer dichroic beam splitter that enables simultaneous imaging by a pair of photon counting, microchannel plate, delay line readout detectors. Initial measurements demonstrate that GALEX is performing well, meeting its requirements for resolution, efficiency, astrometry, bandpass definition and survey sensitivity.Comment: This paper will be published as part of the Galaxy Evolution Explorer (GALEX) Astrophysical Journal Letters Special Issu

HyperProbe consortium: innovate tumour neurosurgery with innovative photonic solutions

Recent advancements in imaging technologies (MRI, PET, CT, among others) have significantly improved clinical localisation of lesions of the central nervous system (CNS) before surgery, making possible for neurosurgeons to plan and navigate away from functional brain locations when removing tumours, such as gliomas. However, neuronavigation in the surgical management of brain tumours remains a significant challenge, due to the inability to maintain accurate spatial information of pathological and healthy locations intraoperatively. To answer this challenge, the HyperProbe consortium have been put together, consisting of a team of engineers, physicists, data scientists and neurosurgeons, to develop an innovative, all-optical, intraoperative imaging system based on (i) hyperspectral imaging (HSI) for rapid, multiwavelength spectral acquisition, and (ii) artificial intelligence (AI) for image reconstruction, morpho-chemical characterisation and molecular fingerprint recognition. Our HyperProbe system will (1) map, monitor and quantify biomolecules of interest in cerebral physiology; (2) be handheld, cost-effective and user-friendly; (3) apply AI-based methods for the reconstruction of the hyperspectral images, the analysis of the spatio-spectral data and the development and quantification of novel biomarkers for identification of glioma and differentiation from functional brain tissue. HyperProbe will be validated and optimised with studies in optical phantoms, in vivo against gold standard modalities in neuronavigational imaging, and finally we will provide proof of principle of its performances during routine brain tumour surgery on patients. HyperProbe aims at providing functional and structural information on biomarkers of interest that is currently missing during neuro-oncological interventions

Three cases of oral hemangioma sclerosis

Hemangiomas or hamartomas are systemic proliferative vascular lesions that often occur in the oral cavity. The lesion usually presents a progressive growth, causing injuries and incontrollable bleeding. Its etiology is multifactorial, and it may occur at any age and there is no gender predilection. Differential diagnosis can involve many different pathologies, including neoplasms. Patients complaints are often related to esthetics. The size, type, and degree of tissue involvement of the hemangioma dictates the need of a specific treatment. The aim of this clinical case series is to present multiple oral hemangioma scenarios managed with sclerotherapy through monoethanolamine oleate at 0.05 g/ml. The diagnosis, treatment, clinical procedures and risks of hemangiomas should be relevant to dental practitioners due to the high prevalence of this type of oral lesion.peer-reviewe

Instances and connectors : issues for a second generation process language

This work is supported by UK EPSRC grants GR/L34433 and GR/L32699Over the past decade a variety of process languages have been defined, used and evaluated. It is now possible to consider second generation languages based on this experience. Rather than develop a second generation wish list this position paper explores two issues: instances and connectors. Instances relate to the relationship between a process model as a description and the, possibly multiple, enacting instances which are created from it. Connectors refers to the issue of concurrency control and achieving a higher level of abstraction in how parts of a model interact. We believe that these issues are key to developing systems which can effectively support business processes, and that they have not received sufficient attention within the process modelling community. Through exploring these issues we also illustrate our approach to designing a second generation process language.Postprin