Industry diversity, competition and firm relatedness: The impact on employment before and after the 2008 global financial crisis

Industry diversity, competition and firm relatedness: the impact on employment before and after the 2008 global financial crisis. Regional Studies. This study investigates the extent to which indicators of external-scale economies impacted employment growth in Canada over the period 2004–11. It focuses on knowledge spillovers between firms while accounting for Marshallian specialization, Jacobs’ diversity and competition by industry, as well as related and unrelated firm varieties in terms of employment and sales. It is found that the employment growth effects of local competition and diversity are positive, while the effect of Marshallian specialization is negative. Diversification is found to be particularly important for employment growth during the global financial crisis and immediately thereafter

A robust and efficient video representation for action recognition

This paper introduces a state-of-the-art video representation and applies it to efficient action recognition and detection. We first propose to improve the popular dense trajectory features by explicit camera motion estimation. More specifically, we extract feature point matches between frames using SURF descriptors and dense optical flow. The matches are used to estimate a homography with RANSAC. To improve the robustness of homography estimation, a human detector is employed to remove outlier matches from the human body as human motion is not constrained by the camera. Trajectories consistent with the homography are considered as due to camera motion, and thus removed. We also use the homography to cancel out camera motion from the optical flow. This results in significant improvement on motion-based HOF and MBH descriptors. We further explore the recent Fisher vector as an alternative feature encoding approach to the standard bag-of-words histogram, and consider different ways to include spatial layout information in these encodings. We present a large and varied set of evaluations, considering (i) classification of short basic actions on six datasets, (ii) localization of such actions in feature-length movies, and (iii) large-scale recognition of complex events. We find that our improved trajectory features significantly outperform previous dense trajectories, and that Fisher vectors are superior to bag-of-words encodings for video recognition tasks. In all three tasks, we show substantial improvements over the state-of-the-art results

Transcription and the Pitch Angle of DNA

The question of the value of the pitch angle of DNA is visited from the perspective of a geometrical analysis of transcription. It is suggested that for transcription to be possible, the pitch angle of B-DNA must be smaller than the angle of zero-twist. At the zero-twist angle the double helix is maximally rotated and its strain-twist coupling vanishes. A numerical estimate of the pitch angle for B-DNA based on differential geometry is compared with numbers obtained from existing empirical data. The crystallographic studies shows that the pitch angle is approximately 38 deg., less than the corresponding zero-twist angle of 41.8 deg., which is consistent with the suggested principle for transcription.Comment: 7 pages, 3 figures; v2: minor modifications; v3: major modifications compared to v2. Added discussion about transcription, and reference

Optimally sparse approximations of 3D functions by compactly supported shearlet frames

We study efficient and reliable methods of capturing and sparsely representing anisotropic structures in 3D data. As a model class for multidimensional data with anisotropic features, we introduce generalized three-dimensional cartoon-like images. This function class will have two smoothness parameters: one parameter \beta controlling classical smoothness and one parameter \alpha controlling anisotropic smoothness. The class then consists of piecewise C^\beta-smooth functions with discontinuities on a piecewise C^\alpha-smooth surface. We introduce a pyramid-adapted, hybrid shearlet system for the three-dimensional setting and construct frames for L^2(R^3) with this particular shearlet structure. For the smoothness range 1<\alpha =< \beta =< 2 we show that pyramid-adapted shearlet systems provide a nearly optimally sparse approximation rate within the generalized cartoon-like image model class measured by means of non-linear N-term approximations.Comment: 56 pages, 6 figure

Tentative detection of the gravitational magnification of type Ia supernovae

The flux from distant type Ia supernovae (SN) is likely to be amplified or de-amplified by gravitational lensing due to matter distributions along the line-of-sight. A gravitationally lensed SN would appear brighter or fainter than the average SN at a particular redshift. We estimate the magnification of 26 SNe in the GOODS fields and search for a correlation with the residual magnitudes of the SNe. The residual magnitude, i.e. the difference between observed and average magnitude predicted by the "concordance model" of the Universe, indicates the deviation in flux from the average SN. The linear correlation coefficient for this sample is r=0.29. For a similar, but uncorrelated sample, the probability of obtaining a correlation coefficient equal to or higher than this value is ~10%, i.e. a tentative detection of lensing at ~90% confidence level. Although the evidence for a correlation is weak, our result is in accordance with what could be expected given the small size of the sample.Comment: 7 pages, 2 figure

Distribution learning via neural differential equations: a nonparametric statistical perspective

Ordinary differential equations (ODEs), via their induced flow maps, provide a powerful framework to parameterize invertible transformations for the purpose of representing complex probability distributions. While such models have achieved enormous success in machine learning, particularly for generative modeling and density estimation, little is known about their statistical properties. This work establishes the first general nonparametric statistical convergence analysis for distribution learning via ODE models trained through likelihood maximization. We first prove a convergence theorem applicable to arbitrary velocity field classes $\mathcal{F}$ satisfying certain simple boundary constraints. This general result captures the trade-off between approximation error (`bias') and the complexity of the ODE model (`variance'). We show that the latter can be quantified via the $C^1$-metric entropy of the class $\mathcal F$. We then apply this general framework to the setting of $C^k$-smooth target densities, and establish nearly minimax-optimal convergence rates for two relevant velocity field classes $\mathcal F$: $C^k$ functions and neural networks. The latter is the practically important case of neural ODEs. Our proof techniques require a careful synthesis of (i) analytical stability results for ODEs, (ii) classical theory for sieved M-estimators, and (iii) recent results on approximation rates and metric entropies of neural network classes. The results also provide theoretical insight on how the choice of velocity field class, and the dependence of this choice on sample size $n$ (e.g., the scaling of width, depth, and sparsity of neural network classes), impacts statistical performance

The creation of effective states in the OECD since 1870 : The role of inequality

Research shows that state capacity is crucial for economic development, yet the impact of inequality on state capacity is not well understood. This paper examines the impact of income inequality on three key dimensions of state capacity, namely legal, fiscal and collective capacity using annual data for a core of 21 OECD countries over the period 1870–2013. We find that the marked reduction in inequality over most of the last century starting from 1916 was pivotal to the significant improvements in legal, fiscal and collective capacity in the OECD countries over the same period.Peer reviewe