A renormalization approach for the 2D Anderson model at the band edge: Scaling of the localization volume

We study the localization volumes $V$ (participation ratio) of electronic wave functions in the 2d-Anderson model with diagonal disorder. Using a renormalization procedure, we show that at the band edges, i.e. for energies $E\approx \pm 4$, $V$ is inversely proportional to the variance \var of the site potentials. Using scaling arguments, we show that in the neighborhood of $E=\pm 4$, $V$ scales as V=\var^{-1}g((4-\ve E\ve)/\var) with the scaling function $g(x)$. Numerical simulations confirm this scaling ansatz

Scaling of the localization length in linear electronic and vibrational systems with long-range correlated disorder

The localization lengths of long-range correlated disordered chains are studied for electronic wavefunctions in the Anderson model and for vibrational states. A scaling theory close to the band edge is developed in the Anderson model and supported by numerical simulations. This scaling theory is mapped onto the vibrational case at small frequencies. It is shown that for small frequencies, unexpectateley the localization length is smaller for correlated than for uncorrelated chains.Comment: to be published in PRB, 4 pages, 2 Figure

Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

Real-life control tasks involve matters of various substances---rigid or soft bodies, liquid, gas---each with distinct physical behaviors. This poses challenges to traditional rigid-body physics engines. Particle-based simulators have been developed to model the dynamics of these complex scenes; however, relying on approximation techniques, their simulation often deviates from real-world physics, especially in the long term. In this paper, we propose to learn a particle-based simulator for complex control tasks. Combining learning with particle-based systems brings in two major benefits: first, the learned simulator, just like other particle-based systems, acts widely on objects of different materials; second, the particle-based representation poses strong inductive bias for learning: particles of the same type have the same dynamics within. This enables the model to quickly adapt to new environments of unknown dynamics within a few observations. We demonstrate robots achieving complex manipulation tasks using the learned simulator, such as manipulating fluids and deformable foam, with experiments both in simulation and in the real world. Our study helps lay the foundation for robot learning of dynamic scenes with particle-based representations.Comment: Accepted to ICLR 2019. Project Page: http://dpi.csail.mit.edu Video: https://www.youtube.com/watch?v=FrPpP7aW3L

Localized low-frequency Neumann modes in 2d-systems with rough boundaries

We compute the relative localization volumes of the vibrational eigenmodes in two-dimensional systems with a regular body but irregular boundaries under Dirichlet and under Neumann boundary conditions. We find that localized states are rare under Dirichlet boundary conditions but very common in the Neumann case. In order to explain this difference, we utilize the fact that under Neumann conditions the integral of the amplitudes, carried out over the whole system area is zero. We discuss, how this condition leads to many localized states in the low-frequency regime and show by numerical simulations, how the number of the localized states and their localization volumes vary with the boundary roughness.Comment: 7 pages, 4 figure

Propagation Networks for Model-Based Control Under Partial Observation

There has been an increasing interest in learning dynamics simulators for model-based control. Compared with off-the-shelf physics engines, a learnable simulator can quickly adapt to unseen objects, scenes, and tasks. However, existing models like interaction networks only work for fully observable systems; they also only consider pairwise interactions within a single time step, both restricting their use in practical systems. We introduce Propagation Networks (PropNet), a differentiable, learnable dynamics model that handles partially observable scenarios and enables instantaneous propagation of signals beyond pairwise interactions. Experiments show that our propagation networks not only outperform current learnable physics engines in forward simulation, but also achieve superior performance on various control tasks. Compared with existing model-free deep reinforcement learning algorithms, model-based control with propagation networks is more accurate, efficient, and generalizable to new, partially observable scenes and tasks.Comment: Accepted to ICRA 2019. Project Page: http://propnet.csail.mit.edu Video: https://youtu.be/ZAxHXegkz4

Scaling behavior of a one-dimensional correlated disordered electronic System

A one-dimensional diagonal tight binding electronic system with correlated disorder is investigated. The correlation of the random potential is exponentially decaying with distance and its correlation length diverges as the concentration of "wrong sign" approaches to 1 or 0. The correlated random number sequence can be generated easily with a binary sequence similar to that of a one-dimensional spin glass system. The localization length (LL) and the integrated density of states (IDOS) for long chains are computed. A comparison with numerical results is made with the recently developed scaling technique results. The Coherent Potential Approximation (CPA) is also adopted to obtain scaling functions for both the LL and the IDOS. We confirmed that the scaling functions show a crossover near the band edge and establish their relation to the concentration. For concentrations near to 0 or 1 (longer correlation length case), the scaling behavior is followed only for a very limited range of the potential strengths.Comment: will appear in PR

M-BISON: Microarray-based integration of data sources using networks

BACKGROUND: The accurate detection of differentially expressed (DE) genes has become a central task in microarray analysis. Unfortunately, the noise level and experimental variability of microarrays can be limiting. While a number of existing methods partially overcome these limitations by incorporating biological knowledge in the form of gene groups, these methods sacrifice gene-level resolution. This loss of precision can be inappropriate, especially if the desired output is a ranked list of individual genes. To address this shortcoming, we developed M-BISON (Microarray-Based Integration of data SOurces using Networks), a formal probabilistic model that integrates background biological knowledge with microarray data to predict individual DE genes. RESULTS: M-BISON improves signal detection on a range of simulated data, particularly when using very noisy microarray data. We also applied the method to the task of predicting heat shock-related differentially expressed genes in S. cerevisiae, using an hsf1 mutant microarray dataset and conserved yeast DNA sequence motifs. Our results demonstrate that M-BISON improves the analysis quality and makes predictions that are easy to interpret in concert with incorporated knowledge. Specifically, M-BISON increases the AUC of DE gene prediction from .541 to .623 when compared to a method using only microarray data, and M-BISON outperforms a related method, GeneRank. Furthermore, by analyzing M-BISON predictions in the context of the background knowledge, we identified YHR124W as a potentially novel player in the yeast heat shock response. CONCLUSION: This work provides a solid foundation for the principled integration of imperfect biological knowledge with gene expression data and other high-throughput data sources