Statistical Analysis of fMRI Time-Series: A Critical Review of the GLM Approach.

Functional magnetic resonance imaging (fMRI) is one of the most widely used tools to study the neural underpinnings of human cognition. Standard analysis of fMRI data relies on a general linear model (GLM) approach to separate stimulus induced signals from noise. Crucially, this approach relies on a number of assumptions about the data which, for inferences to be valid, must be met. The current paper reviews the GLM approach to analysis of fMRI time-series, focusing in particular on the degree to which such data abides by the assumptions of the GLM framework, and on the methods that have been developed to correct for any violation of those assumptions. Rather than biasing estimates of effect size, the major consequence of non-conformity to the assumptions is to introduce bias into estimates of the variance, thus affecting test statistics, power, and false positive rates. Furthermore, this bias can have pervasive effects on both individual subject and group-level statistics, potentially yielding qualitatively different results across replications, especially after the thresholding procedures commonly used for inference-making

Openness and Income Dispaities: Does Trade Explain the 'Mezzogiorno' Effect?

Many theoretical models show that trade openness has positive welfare implications. Yet, openness might affect different social groups and regions asymmetrically, even within a given country. We use Italian regional data to answer the question whether trade openness affects within-country income differentials. In Italy, the more affluent regions are internationally more open than poorer ones not only with respect to trade in goods, but also with respect to FDI and international migration. Prima facie, there is a positive correlation between openness and per capita income. Studying this relationship empirically requires taking into account the endogenous component of openness. We apply panel cointegration and instrumental variables techniques to account for the endogeneity of trade. Our results show a positive link between trade openness and the level of income per capita.Openness, growth, regional income disparities, Italian regions

Generative Convolutional Networks for Latent Fingerprint Reconstruction

Performance of fingerprint recognition depends heavily on the extraction of minutiae points. Enhancement of the fingerprint ridge pattern is thus an essential pre-processing step that noticeably reduces false positive and negative detection rates. A particularly challenging setting is when the fingerprint images are corrupted or partially missing. In this work, we apply generative convolutional networks to denoise visible minutiae and predict the missing parts of the ridge pattern. The proposed enhancement approach is tested as a pre-processing step in combination with several standard feature extraction methods such as MINDTCT, followed by biometric comparison using MCC and BOZORTH3. We evaluate our method on several publicly available latent fingerprint datasets captured using different sensors

Trade's Impact on the Labor Share: Evidence from German and Italian Regions

Has the labor share declined? And what is the impact of international trade? These questions are not only relevant in an international context they also matter for understanding the regional distribution of incomes in a given country. In this paper, we study two regions with trade exposures that differ from the rest of the country, and which display distinct changes in the labor share. East German and Southern Italian regions have a degree of international openness which is below the countries’ averages. At the same time, there has been a more pronounced decline in the labor share in East Germany than in West Germany. In Southern Italy, the labor share has increased in recent years. We show that increased trade openness is not the main culprit behind changing labor shares.labor share, trade, regions

Making simple proofs simpler

An open partition \pi{} [Cod09a, Cod09b] of a tree T is a partition of the vertices of T with the property that, for each block B of \pi, the upset of B is a union of blocks of \pi. This paper deals with the number, NP(n), of open partitions of the tree, V_n, made of two chains with n points each, that share the root

Texas Business Review, May 1975

The Business Situation in Texas; Nuclear Power in Texas 1954-1975; Texas ConstructionBureau of Business Researc

Effects of Marangoni numbers on thermocapillary drop migration: constant for quasi-steady state?

The overall {\it steady}-state energy balance with two phases in a flow domain requires that the change in energy of the domain is equal to the difference between the total energy entering the domain and that leaving the domain. From the condition, the integral thermal flux across the surface is studied for a {\it steady} thermocapillary drop migration in a flow field with uniform temperature gradient at small and large Marangoni (Reynolds) numbers. The drop is assumed to have only a slight axisymmetric deformation from a sphere. It is identified that a conservative/nonconservative integral thermal flux across the surface in the {\it steady} thermocapillary drop migration at small/large Marangoni (Reynolds) numbers. The conservative flux confirms the assumption of {\it quasi-steady} state in the thermocapillary drop migration at small Marangoni (Reynolds) numbers. The nonconservative flux may well result from the invalid assumption of {\it quasi-steady} state, which indicates that the thermocapillary drop migration at large Marangoni (Reynolds) numbers cannot reach {\it steady} state and is thus a {\it unsteady} process.Comment: 21 pages. arXiv admin note: text overlap with arXiv:1112.276

Ambiguity, monetary policy and trend inflation

Allowing for ambiguity, or Knightian uncertainty, about the behavior of the policymaker helps explain the evolution of trend inflation in the US in a simple new-Keynesian model, without resorting to exogenous changes in the inflation target. Using Blue Chip survey data to gauge the degree of private sector confidence, our model helps reconcile the difference between target inflation and the inflation trend measured in the data. We also show how, in the presence of ambiguity, it is optimal for policymakers to lean against the private sectors pessimistic expectations

CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters

The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. Our model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely-connected graphs, and can handle different constructions of Laplacian operators. Extensive experimental results show the superior performance of our approach, in comparison to other spectral domain convolutional architectures, on spectral image classification, community detection, vertex classification and matrix completion tasks