Lautum Regularization for Semi-supervised Transfer Learning

Transfer learning is a very important tool in deep learning as it allows propagating information from one "source dataset" to another "target dataset", especially in the case of a small number of training examples in the latter. Yet, discrepancies between the underlying distributions of the source and target data are commonplace and are known to have a substantial impact on algorithm performance. In this work we suggest a novel information theoretic approach for the analysis of the performance of deep neural networks in the context of transfer learning. We focus on the task of semi-supervised transfer learning, in which unlabeled samples from the target dataset are available during the network training on the source dataset. Our theory suggests that one may improve the transferability of a deep neural network by imposing a Lautum information based regularization that relates the network weights to the target data. We demonstrate the effectiveness of the proposed approach in various transfer learning experiments

Implementation of a parallel unstructured Euler solver on shared and distributed memory architectures

An efficient three dimensional unstructured Euler solver is parallelized on a Cray Y-MP C90 shared memory computer and on an Intel Touchstone Delta distributed memory computer. This paper relates the experiences gained and describes the software tools and hardware used in this study. Performance comparisons between two differing architectures are made

Universal aging properties at a disordered critical point

We investigate, analytically near the dimension $d_{uc}=4$ and numerically in $d=3$, the non equilibrium relaxational dynamics of the randomly diluted Ising model at criticality. Using the Exact Renormalization Group Method to one loop, we compute the two times $t,t_w$ correlation function and Fluctuation Dissipation Ratio (FDR) for any Fourier mode of the order parameter, of finite wave vector $q$. In the large time separation limit, the FDR is found to reach a non trivial value $X^{\infty}$ independently of (small) $q$ and coincide with the FDR associated to the the {\it total} magnetization obtained previously. Explicit calculations in real space show that the FDR associated to the {\it local} magnetization converges, in the asymptotic limit, to this same value $X^{\infty}$. Through a Monte Carlo simulation, we compute the autocorrelation function in three dimensions, for different values of the dilution fraction $p$ at $T_c(p)$. Taking properly into account the corrections to scaling, we find, according to the Renormalization Group predictions, that the autocorrelation exponent $\lambda_c$ is independent on $p$. The analysis is complemented by a study of the non equilibrium critical dynamics following a quench from a completely ordered state.Comment: 8 pages, 5 figure

An approach design and analysis of resource management software for multihoming in-vehicle of IPv6 network

The resource management of Multihoming in nested mobile network raises new issues in the host mobility of ipv6 network. Multihoming facilitates the possibility of switching over to the network path offering better performance. However the key factor in attaining the benefit of multihoming is to ensure that the handoff switches over from one network interface to the other interface with minimal interruption. The aim of mobility support is to make mobile nodes keep connectivity without modification when roaming. The contribution of this paper is to simulate a policy based routing protocol framework that focuses on session preservation by redundancy and Load balancing by selecting the best available interface or enabling multiple interfaces simultaneously, since their management is critical for mobile communication. To manage these two critical resources, we identify the mobility characteristics and hardware configurations of in vehicle networks. The study then proposed a policy based routing protocol using two adaptive session preservation schemes in multihoming, which is refer to as best-connected MR selections based on location and path based type to support nested network of MIPV6 which was simulated using network simulator (NS2)