The Random Forest Kernel and other kernels for big data from random partitions

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing $O(N)$ inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.

An important pitfall of pseudo-static finite element analysis

Finite Element (FE) pseudo-static analysis can provide a good compromise between simplified methods of dynamic analysis and time domain analysis. The pseudo-static FE approach can accurately model the in situ, stresses prior to seismic loading (when it follows a static analysis simulating the construction sequence) is relatively simple and not as computationally expensive as the time domain approach. However this method should be used with caution as the results can be sensitive to the choice of the mesh dimensions. In this paper two simple examples of pseudo-static finite element analysis are examined parametrically, a homogeneous slope and a cantilever retaining wall, exploring the sensitivity of the pseudo-static analysis results on the adopted mesh size. The mesh dependence was found to be more pronounced for problems with high critical seismic coefficients values (e.g. gentle slopes or small walls), as in these cases a generalised layer failure mechanism is developed simultaneously with the slope or wall mechanism. In general the mesh width was found not to affect notably the predicted value of critical seismic coefficient but to have a major impact on the predicted movements. © 2012 Elsevier Ltd.

A reversible infinite HMM using normalised random measures

Copyright 2014 by the author(s). We present a nonparametric prior over reversible Markov chains. We use completely random measures, specifically gamma processes, to construct a countably infinite graph with weighted edges. By enforcing symmetry to make the edges undirected we define a prior over random walks on graphs that results in a reversible Markov chain. The resulting prior over infinite transition matrices is closely related to the hierarchical Dirichlet process but enforces reversibility. A reinforcement scheme has recently been proposed with similar properties, but the de Finetti measure is not well characterised. We take the alternative approach of explicitly constructing the mixing measure, which allows more straightforward and efficient inference at the cost of no longer having a closed form predictive distribution. We use our process to construct a reversible infinite HMM which we apply to two real datasets, one from epigenomics and one ion channel recording.

A derivation of the asymptotic random-coding prefactor

This paper studies the subexponential prefactor to the random-coding bound for a given rate. Using a refinement of Gallager's bounding techniques, an alternative proof of a recent result by Altuǧ and Wagner is given, and the result is extended to the setting of mismatched decoding. © 2013 IEEE.

A fully three-dimensional atomistic quantum mechanical study on random dopant-induced effects in 25-nm MOSFETs

A fully 3-D atomistic quantum mechanical simulation is presented to study the random dopant-induced effects in nanometer metal-oxide-semiconductor field-effect transistors. The empirical pseudopotential is used to represent the single particle Hamiltonian, and the linear combination of bulk band method is used to solve the million atom Schrodinger equation. The gate threshold fluctuation and lowering due to the discrete dopant configurations are studied. It is found that quantum mechanical effects increase the threshold fluctuation while decreasing the threshold lowering. The increase of threshold fluctuation is in agreement with the researchers' early study based on an approximated density gradient approach. However, the decrease in threshold lowering is in contrast with the density gradient calculations.

An ultra-low power CMOS random number generator

This paper proposes an ultra-low power CMOS random number generator (RING), which is based on an oscillator-sampling architecture. The noisy oscillator consists of a dual-drain MOS transistor, a noise generator and a voltage control oscillator. The dual-drain MOS transistor can bring extra-noise to the drain current or the output voltage so that the jitter of the oscillator is much larger than the normal oscillator. The frequency division ratio of the high-frequency sampling oscillator and the noisy oscillator is small. The RNG has been fabricated in a 0.35 mu m CMOS process. It can produce good quality bit streams without any post-processing. The bit rate of this RNG could be as high as 100 kbps. It has a typical ultra-low power dissipation of 0.91 mu W. This novel circuit is a promising unit for low power system and communication applications. (c) 2007 Elsevier Ltd. All rights reserved.

Random telegraph noise in the photoluminescence of individual GaxIn1-xAs quantum dots in GaAs

We have investigated random telegraph noise in the photoluminescence from InGaAs quantum dots in GaAs. Dots switching among two and three levels have been measured. The experiments show that the switching InGaAs dots behave very similarly to switching InP dots in GaInP. but differently from the more commonly investigated colloidal dots. The switching is attributed to defects, and we show that the switching can be used as a monitor of the defect.

Fully lithography independent fabrication of nanogap electrodes for lateral phase-change random access memory application

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Efficient Simulation of Freak Waves in Random Oceanic Sea States

Numerical simulations of freak wave generation are studied in random oceanic sea states described by JONSWAP spectrum. The evolution of initial random wave trains is numerically carried out within the framework of the modified four-order nonlinear Schroedinger equation (mNLSE), and some involved influence factors are also discussed. Results show that if the sideband instability is satisfied, a random wave train may evolve into a freak wave train, and simultaneously the setting of the Phillips parameter and enhancement coefficient of JONSWAP spectrum and initial random phases is very important for the formation of freak waves. The way to increase the generation efficiency of freak waves though changing the involved parameters is also presented.

Synthesis and random laser application of ZnO nano-walls: a review

This review paper summarises briefly some important achievements of our recent research on the synthesis and novel applications of nanostructure ZnO such as honeycomb shaped 3-D (dimension) nano random-walls. A chemical reaction/vapour transportation deposition technique was employed to fabricate this structure on ZnO/SiO2/Si substrate without any catalyst and additive in a simple tube furnace to aim the low-cost and high qualified samples. Random laser action with strong coherent feedback at the wavelength between 375 nm and 395 nm has been firstly observed under 355 nm optical excitation with threshold pumping intensity of 0.38 MW/cm(2).

A Hybrid Random Number Generator Using Single Electron Tunneling Junctions and MOS Transistors

This paper proposes a novel single electron random number generator (RNG). The generator consists of multiple tunneling junctions (MTJ) and a hybrid single electron transistor (SET)/MOS output circuit. It is an oscillator-based RNG. MTJ is used to implement a high-frequency oscillator,which uses the inherent physical randomness in tunneling events of the MTJ to achieve large frequency drift. The hybrid SET and MOS output circuit is used to amplify and buffer the output signal of the MTJ oscillator. The RNG circuit generates high-quality random digital sequences with a simple structure. The operation speed of this circuit is as high as 1GHz. The circuit also has good driven capability and low power dissipation. This novel random number generator is a promising device for future cryptographic systems and communication applications.