The dynamic effects of fiscal policy : a FAVAR approach

We implement a recently developed econometric model, the Factor Augmented VAR (FAVAR), to investigate the dynamic effects of government spending on key macroeconomic variables. In line with existing literature, we find that a government spending shock has positive effects on consumption and output. By splitting the sample in a pre-and post- Volcker period, we find that the positive effects of government spending on consumption and output over the whole sample are largely due to the first part of the sample

Graph reconstruction from the observation of diffused signals

Signal processing on graphs has received a lot of attention in the recent years. A lot of techniques have arised, inspired by classical signal processing ones, to allow studying signals on any kind of graph. A common aspect of these technique is that they require a graph correctly modeling the studied support to explain the signals that are observed on it. However, in many cases, such a graph is unavailable or has no real physical existence. An example of this latter case is a set of sensors randomly thrown in a field which obviously observe related information. To study such signals, there is no intuitive choice for a support graph. In this document, we address the problem of inferring a graph structure from the observation of signals, under the assumption that they were issued of the diffusion of initially i.i.d. signals. To validate our approach, we design an experimental protocol, in which we diffuse signals on a known graph. Then, we forget the graph, and show that we are able to retrieve it very precisely from the only knowledge of the diffused signals.Comment: Allerton 2015 : 53th Annual Allerton Conference on Communication, Control and Computing, 30 september - 02 october 2015, Allerton, United States, 201

Characterization and Inference of Graph Diffusion Processes from Observations of Stationary Signals

Many tools from the field of graph signal processing exploit knowledge of the underlying graph's structure (e.g., as encoded in the Laplacian matrix) to process signals on the graph. Therefore, in the case when no graph is available, graph signal processing tools cannot be used anymore. Researchers have proposed approaches to infer a graph topology from observations of signals on its nodes. Since the problem is ill-posed, these approaches make assumptions, such as smoothness of the signals on the graph, or sparsity priors. In this paper, we propose a characterization of the space of valid graphs, in the sense that they can explain stationary signals. To simplify the exposition in this paper, we focus here on the case where signals were i.i.d. at some point back in time and were observed after diffusion on a graph. We show that the set of graphs verifying this assumption has a strong connection with the eigenvectors of the covariance matrix, and forms a convex set. Along with a theoretical study in which these eigenvectors are assumed to be known, we consider the practical case when the observations are noisy, and experimentally observe how fast the set of valid graphs converges to the set obtained when the exact eigenvectors are known, as the number of observations grows. To illustrate how this characterization can be used for graph recovery, we present two methods for selecting a particular point in this set under chosen criteria, namely graph simplicity and sparsity. Additionally, we introduce a measure to evaluate how much a graph is adapted to signals under a stationarity assumption. Finally, we evaluate how state-of-the-art methods relate to this framework through experiments on a dataset of temperatures.Comment: Submitted to IEEE Transactions on Signal and Information Processing over Network

The dynamic effects of fiscal policy : a FAVAR approach

We implement a recently developed econometric model, the Factor Augmented VAR (FAVAR), to investigate the dynamic effects of government spending on key macroeconomic variables. In line with existing literature, we find that a government spending shock has positive effects on consumption and output. By splitting the sample in a pre-and post- Volcker period, we find that the positive effects of government spending on consumption and output over the whole sample are largely due to the first part of the sample.

Toward An Uncertainty Principle For Weighted Graphs

International audienceThe uncertainty principle states that a signal cannot be localized both in time and frequency. With the aim of extending this result to signals on graphs, Agaskar & Lu introduce notions of graph and spectral spreads. They show that a graph uncertainty principle holds for some families of unweighted graphs. This principle states that a signal cannot be simultaneously localized both in graph and spectral domains. In this paper, we aim to extend their work to weighted graphs. We show that a naive extension of their definitions leads to inconsistent results such as discontinuity of the graph spread when regarded as a function of the graph structure. To circumvent this problem, we propose another definition of graph spread that relies on an inverse similarity matrix. We also discuss the choice of the distance function that appears in this definition. Finally, we compute and plot uncertainty curves for families of weighted graphs

A Strong and Simple Deep Learning Baseline for BCI MI Decoding

We propose EEG-SimpleConv, a straightforward 1D convolutional neural network for Motor Imagery decoding in BCI. Our main motivation is to propose a very simple baseline to compare to, using only very standard ingredients from the literature. We evaluate its performance on four EEG Motor Imagery datasets, including simulated online setups, and compare it to recent Deep Learning and Machine Learning approaches. EEG-SimpleConv is at least as good or far more efficient than other approaches, showing strong knowledge-transfer capabilities across subjects, at the cost of a low inference time. We advocate that using off-the-shelf ingredients rather than coming with ad-hoc solutions can significantly help the adoption of Deep Learning approaches for BCI. We make the code of the models and the experiments accessible

Nucleocytoplasmic shuttling of the rabies virus P protein requires a nuclear localization signal and a CRM1-dependent nuclear export signal

AbstractRabies virus P protein is a co-factor of the viral RNA polymerase. It has been shown previously that P mRNA directs the synthesis of four N-terminally truncated P products P2, P3, P4, and P5 due to translational initiation by a leaky scanning mechanism at internal Met codons. Whereas P and P2 are located in the cytoplasm, P3, P4, and P5 are found in the nucleus. Here, we have analyzed the molecular basis of the subcellular localization of these proteins. Using deletion mutants fused to GFP protein, we show the presence of a nuclear localization signal (NLS) in the C-terminal part of P (172–297). This domain contains a short lysine-rich stretch (211KKYK214) located in close proximity with arginine 260 as revealed by the crystal structure of P. We demonstrate the critical role of lysine 214 and arginine 260 in NLS activity. In the presence of Leptomycin B, P is retained in the nucleus indicating that it contains a CRM1-dependent nuclear export signal (NES). The subcellular distribution of P deletion mutants indicates that the domain responsible for export is the amino-terminal part of the protein. The use of fusion proteins that have amino terminal fragments of P fused to β-galactosidase containing the NLS of SV40 T antigen allows us to identify a NES between residues 49 and 58. The localization of NLS and NES determines the cellular distribution of the P gene products