Franck-Condon Factors as Spectral Probes of Polaron Structure

We apply the Merrifield variational method to the Holstein molecular crystal model in D dimensions to compute non-adiabatic polaron band energies and Franck-Condon factors at general crystal momenta. We analyze these observable properties to extract characteristic features related to polaron self-trapping and potential experimental signatures. These results are combined with others obtained by the Global-Local variational method in 1D to construct a polaron phase diagram encompassing all degrees of adiabaticity and all electron-phonon coupling strengths. The polaron phase diagram so constructed includes disjoint regimes occupied by "small" polarons, "large" polarons, and a newly-defined class of "compact" polarons, all mutually separated by an intermediate regime occupied by transitional structures

DNArch: Learning Convolutional Neural Architectures by Backpropagation

We present Differentiable Neural Architectures (DNArch), a method that jointly learns the weights and the architecture of Convolutional Neural Networks (CNNs) by backpropagation. In particular, DNArch allows learning (i) the size of convolutional kernels at each layer, (ii) the number of channels at each layer, (iii) the position and values of downsampling layers, and (iv) the depth of the network. To this end, DNArch views neural architectures as continuous multidimensional entities, and uses learnable differentiable masks along each dimension to control their size. Unlike existing methods, DNArch is not limited to a predefined set of possible neural components, but instead it is able to discover entire CNN architectures across all feasible combinations of kernel sizes, widths, depths and downsampling. Empirically, DNArch finds performant CNN architectures for several classification and dense prediction tasks on sequential and image data. When combined with a loss term that controls the network complexity, DNArch constrains its search to architectures that respect a predefined computational budget during training

Are All Successful Communities Alike? Characterizing and Predicting the Success of Online Communities

The proliferation of online communities has created exciting opportunities to study the mechanisms that explain group success. While a growing body of research investigates community success through a single measure -- typically, the number of members -- we argue that there are multiple ways of measuring success. Here, we present a systematic study to understand the relations between these success definitions and test how well they can be predicted based on community properties and behaviors from the earliest period of a community's lifetime. We identify four success measures that are desirable for most communities: (i) growth in the number of members; (ii) retention of members; (iii) long term survival of the community; and (iv) volume of activities within the community. Surprisingly, we find that our measures do not exhibit very high correlations, suggesting that they capture different types of success. Additionally, we find that different success measures are predicted by different attributes of online communities, suggesting that success can be achieved through different behaviors. Our work sheds light on the basic understanding of what success represents in online communities and what predicts it. Our results suggest that success is multi-faceted and cannot be measured nor predicted by a single measurement. This insight has practical implications for the creation of new online communities and the design of platforms that facilitate such communities.Comment: To appear at The Web Conference 201

Finding large stable matchings

When ties and incomplete preference lists are permitted in the stable marriage and hospitals/residents problems, stable matchings can have different sizes. The problem of finding a maximum cardinality stable matching in this context is known to be NP-hard, even under very severe restrictions on the number, size, and position of ties. In this article, we present two new heuristics for finding large stable matchings in variants of these problems in which ties are on one side only. We describe an empirical study involving these heuristics and the best existing approximation algorithm for this problem. Our results indicate that all three of these algorithms perform significantly better than naive tie-breaking algorithms when applied to real-world and randomly-generated data sets and that one of the new heuristics fares slightly better than the other algorithms, in most cases. This study, and these particular problem variants, are motivated by important applications in large-scale centralized matching schemes

On the spin modulated circular polarization from the intermediate polars NY Lup and IGRJ1509-6649

We report on high time resolution, high signal/noise, photo-polarimetry of the intermediate polars NY Lup and IGRJ1509-6649. Our observations confirm the detection and colour dependence of circular polarization from NY Lup and additionally show a clear white dwarf, spin modulated signal. From our new high signal/noise photometry we have unambiguously detected wavelength dependent spin and beat periods and harmonics thereof. IGRJ1509-6649 is discovered to also have a particularly strong spin modulated circularly polarized signal. It appears double peaked through the I filter and single peaked through the B filter, consistent with cyclotron emission from a white dwarf with a relatively strong magnetic field. We discuss the implied accretion geometries in these two systems and any bearing this may have on the possible relationship with the connection between polars and soft X-ray-emitting IPs. The relatively strong magnetic fields is also suggestive of them being polar progenitors.Comment: 8 pages, 6 figures and 1 table. Accepted for publication in MNRA