Transfer Learning for Content-Based Recommender Systems using Tree Matching

In this paper we present a new approach to content-based transfer learning for solving the data sparsity problem in cases when the users' preferences in the target domain are either scarce or unavailable, but the necessary information on the preferences exists in another domain. We show that training a system to use such information across domains can produce better performance. Specifically, we represent users' behavior patterns based on topological graph structures. Each behavior pattern represents the behavior of a set of users, when the users' behavior is defined as the items they rated and the items' rating values. In the next step we find a correlation between behavior patterns in the source domain and behavior patterns in the target domain. This mapping is considered a bridge between the two domains. Based on the correlation and content-attributes of the items, we train a machine learning model to predict users' ratings in the target domain. When we compare our approach to the popularity approach and KNN-cross-domain on a real world dataset, the results show that on an average of 83$$ of the cases our approach outperforms both methods

High-resolution spatial mapping of a superconducting NbN wire using single-electron detection

Superconducting NbN wires have recently received attention as detectors for visible and infrared photons. We present experiments in which we use a NbN wire for high-efficiency (40 %) detection of single electrons with keV energy. We use the beam of a scanning electron microscope as a focussed, stable, and calibrated electron source. Scanning the beam over the surface of the wire provides a map of the detection efficiency. This map shows features as small as 150 nm, revealing wire inhomogeneities. The intrinsic resolution of this mapping method, superior to optical methods, provides the basis of a characterization tool relevant for photon detectors.Comment: 2009 IEEE Toronto International Conference, Science and Technology for Humanity (TIC-STH

Dynamics of Protein Hydration Water

We present the frequency- and temperature-dependent dielectric properties of lysozyme solutions in a broad concentration regime, measured at subzero temperatures and compare the results with measurements above the freezing point of water and on hydrated lysozyme powder. Our experiments allow examining the dynamics of unfreezable hydration water in a broad temperature range including the so-called No Man's Land (160 - 235 K). The obtained results prove the bimodality of the hydration shell dynamics and are discussed in the context of the highly-debated fragile-to-strong transition of water.Comment: 5 pages, 3 figure

A Heterosynaptic Learning Rule for Neural Networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre- and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean learning time increases with the number of patterns to be learned polynomially, indicating efficient learning.Comment: 19 page

DWM07 global empirical model of upper thermospheric storm-induced disturbance winds

We present a global empirical disturbance wind model (DWM07) that represents average geospace-storm-induced perturbations of upper thermospheric (200-600 km altitude) neutral winds. DWM07 depends on the following three parameters: magnetic latitude, magnetic local time, and the 3-h Kp geomagnetic activity index. The latitude and local time dependences are represented by vector spherical harmonic functions ( up to degree 10 in latitude and order 3 in local time), and the Kp dependence is represented by quadratic B-splines. DWM07 is the storm time thermospheric component of the new Horizontal Wind Model (HWM07), which is described in a companion paper. DWM07 is based on data from the Wind Imaging Interferometer on board the Upper Atmosphere Research Satellite, the Wind and Temperature Spectrometer on board Dynamics Explorer 2, and seven ground-based Fabry-Perot interferometers. The perturbation winds derived from the three data sets are in good mutual agreement under most conditions, and the model captures most of the climatological variations evident in the data