Data-Driven Sparse Structure Selection for Deep Neural Networks

Deep convolutional neural networks have liberated its extraordinary power on various tasks. However, it is still very challenging to deploy state-of-the-art models into real-world applications due to their high computational complexity. How can we design a compact and effective network without massive experiments and expert knowledge? In this paper, we propose a simple and effective framework to learn and prune deep models in an end-to-end manner. In our framework, a new type of parameter -- scaling factor is first introduced to scale the outputs of specific structures, such as neurons, groups or residual blocks. Then we add sparsity regularizations on these factors, and solve this optimization problem by a modified stochastic Accelerated Proximal Gradient (APG) method. By forcing some of the factors to zero, we can safely remove the corresponding structures, thus prune the unimportant parts of a CNN. Comparing with other structure selection methods that may need thousands of trials or iterative fine-tuning, our method is trained fully end-to-end in one training pass without bells and whistles. We evaluate our method, Sparse Structure Selection with several state-of-the-art CNNs, and demonstrate very promising results with adaptive depth and width selection.Comment: ECCV Camera ready versio

Heavy-quark contribution to the proton's magnetic moment

We study the contribution to the proton's magnetic moment from a heavy quark sea in quantum chromodynamics. The heavy quark is integrated out perturbatively to obtain an effective dimension-6 magnetic moment operator composed of three gluon fields. The leading contribution to the matrix element in the proton comes from a quadratically divergent term associated with a light-quark tensor operator. With an approximate knowledge of the proton's tensor charge, we conclude that a heavy sea-quark contribution to the proton's magnetic moment is positive in the asymptotic limit. We comment on the implication of this result for the physical strange quark.Comment: 4 pages, 2 figure

Spatial heterogeneity of air pollution statistics in Europe

Air pollution is one of the leading causes of death globally, and continues to have a detrimental effect on our health. In light of these impacts, an extensive range of statistical modelling approaches has been devised in order to better understand air pollution statistics. However, the time-varying statistics of different types of air pollutants are far from being fully understood. The observed probability density functions (PDFs) of concentrations depend very much on the spatial location and on the pollutant substance. In this paper, we analyse a large variety of data from 3544 different European monitoring sites and show that the PDFs of nitric oxide (NO), nitrogen dioxide ([Formula: see text] ) and particulate matter ([Formula: see text] and [Formula: see text] ) concentrations generically exhibit heavy tails and are asymptotically well approximated by q-exponential distributions with a given width parameter [Formula: see text] . We observe that the power-law parameter q and the width parameter [Formula: see text] vary widely for the different spatial locations. For each substance, we find different patterns of parameter clouds in the [Formula: see text] plane. These depend on the type of pollutants and on the environmental characteristics (urban/suburban/rural/traffic/industrial/background). This means the effective statistical physics description of air pollution exhibits a strong degree of spatial heterogeneity

Robust paramagnetism in Bi2-xMxRu2O7 (M=Mn,Fe,Co,Ni,Cu) pyrochlore

We report physical property characterization of Bi2-xMxRu2O7 pyrochlores, including magnetic suseptibility, resistivity, and Seebeck coefficients. The solid solution exists up to x=0.5 for (M=Cu,Ni,Co) and up to x=0.1 for (M=Fe,Mn). None of the doped materials exhibit ferromagnetism or any localized ruthenium moment behavior. Instead we find the Ru-O and Bi-O sublattices to be essentially independent, with any magnetism resulting from the unpaired transition metal dopant spins. Cobalt substitution for bismuth results in localized Co{2+}, and low temperature spin-glass transitions in several cases. Nickel moments on the pyrochlore lattice display properties intermediate to localized and itinerant. Finally, copper doping results in only an enhancement of the Pauli metallic density of states.Comment: submitted, Phys. Rev.

Multifactor consumption based asset pricing models using the US stock market as a reference: Evidence from a panel of developed economies

This article was submitted and presented at the European Economics and Finance Society Conference, 2012, at Koç University, Istanbul, and the final version was published in a Special Section of Economic Modelling. The special section editor was John Hunter from Brunel University London.In this paper we extend the time series analysis to the panel framework to test the C-CAPM driven by wealth references for developed countries. Speci cally, we focus on a linearised form of the Consumption-based CAPM in a pooled cross section panel model with two-way error com- ponents. The empirical findings of this two-factor model with various specifications all indicate that there is significant unobserved heterogeneity captured by cross-country fixed e¤ects when consumption growth is treated as a common factor, of which the average risk aversion coefficient is 4.285. However, the cross-sectional impact of home consumption growth varies dramatically over the countries, where unobserved heterogeneity of risk aversion can also be addressed by random effects

On some entropy functionals derived from R\'enyi information divergence

We consider the maximum entropy problems associated with R\'enyi $Q$-entropy, subject to two kinds of constraints on expected values. The constraints considered are a constraint on the standard expectation, and a constraint on the generalized expectation as encountered in nonextensive statistics. The optimum maximum entropy probability distributions, which can exhibit a power-law behaviour, are derived and characterized. The R\'enyi entropy of the optimum distributions can be viewed as a function of the constraint. This defines two families of entropy functionals in the space of possible expected values. General properties of these functionals, including nonnegativity, minimum, convexity, are documented. Their relationships as well as numerical aspects are also discussed. Finally, we work out some specific cases for the reference measure $Q(x)$ and recover in a limit case some well-known entropies

Covid-19 impact on air quality in megacities

Air pollution is among the highest contributors to mortality worldwide, especially in urban areas. During spring 2020, many countries enacted social distancing measures in order to slow down the ongoing Covid-19 pandemic. A particularly drastic measure, the "lockdown", urged people to stay at home and thereby prevent new Covid-19 infections. In turn, it also reduced traffic and industrial activities. But how much did these lockdown measures improve air quality in large cities, and are there differences in how air quality was affected? Here, we analyse data from two megacities: London as an example for Europe and Delhi as an example for Asia. We consider data during and before the lockdown and compare these to a similar time period from 2019. Overall, we find a reduction in almost all air pollutants with intriguing differences between the two cities. In London, despite smaller average concentrations, we still observe high-pollutant states and an increased tendency towards extreme events (a higher kurtosis during lockdown). For Delhi, we observe a much stronger decrease of pollution concentrations, including high pollution states. These results could help to design rules to improve long-term air quality in megacities.Comment: 13 pages. Preliminary version of Supplementary Information and open code available here https://osf.io/jfw7n/?view_only=9b1d2320cf2c46a1ad890dff079a2f6