Enhanced molecular dynamics performance with a programmable graphics processor

Design considerations for molecular dynamics algorithms capable of taking advantage of the computational power of a graphics processing unit (GPU) are described. Accommodating the constraints of scalable streaming-multiprocessor hardware necessitates a reformulation of the underlying algorithm. Performance measurements demonstrate the considerable benefit and cost-effectiveness of such an approach, which produces a factor of 2.5 speed improvement over previous work for the case of the soft-sphere potential.Comment: 20 pages (v2: minor additions and changes; v3: corrected typos

Online contrastive divergence with generative replay: experience replay without storing data

Conceived in the early 1990s, Experience Replay (ER) has been shown to be a successful mechanism to allow online learning algorithms to reuse past experiences. Traditionally, ER can be applied to all machine learning paradigms (i.e., unsupervised, supervised, and reinforcement learning). Recently, ER has contributed to improving the performance of deep reinforcement learning. Yet, its application to many practical settings is still limited by the memory requirements of ER, necessary to explicitly store previous observations. To remedy this issue, we explore a novel approach, Online Contrastive Divergence with Generative Replay (OCD_GR), which uses the generative capability of Restricted Boltzmann Machines (RBMs) instead of recorded past experiences. The RBM is trained online, and does not require the system to store any of the observed data points. We compare OCD_GR to ER on 9 real-world datasets, considering a worst-case scenario (data points arriving in sorted order) as well as a more realistic one (sequential random-order data points). Our results show that in 64.28% of the cases OCD_GR outperforms ER and in the remaining 35.72% it has an almost equal performance, while having a considerably reduced space complexity (i.e., memory usage) at a comparable time complexity

Equivalent circuit parameter extraction of low-capacitance high-damping PTs

Existing equivalent circuit extraction techniques are inaccurate for piezoelectric transformers (PTs) with low-input capacitance or high damping. A new method is presented, offering improved accuracy in both damping resistance and resonant frequency extraction compared with state-of-the-art methods. Effectiveness is evaluated on two sample PTs, with the proposed method achieving up to 84% decrease in error compared with previous methods

Particle Acceleration in Cosmic Sites - Astrophysics Issues in our Understanding of Cosmic Rays

Laboratory experiments to explore plasma conditions and stimulated particle acceleration can illuminate aspects of the cosmic particle acceleration process. Here we discuss the cosmic-ray candidate source object variety, and what has been learned about their particle-acceleration characteristics. We identify open issues as discussed among astrophysicists. -- The cosmic ray differential intensity spectrum is a rather smooth power-law spectrum, with two kinks at the "knee" (~10^15 eV) and at the "ankle" (~3 10^18 eV). It is unclear if these kinks are related to boundaries between different dominating sources, or rather related to characteristics of cosmic-ray propagation. We believe that Galactic sources dominate up to 10^17 eV or even above, and the extragalactic origin of cosmic rays at highest energies merges rather smoothly with Galactic contributions throughout the 10^15--10^18 eV range. Pulsars and supernova remnants are among the prime candidates for Galactic cosmic-ray production, while nuclei of active galaxies are considered best candidates to produce ultrahigh-energy cosmic rays of extragalactic origin. Acceleration processes are related to shocks from violent ejections of matter from energetic sources such as supernova explosions or matter accretion onto black holes. Details of such acceleration are difficult, as relativistic particles modify the structure of the shock, and simple approximations or perturbation calculations are unsatisfactory. This is where laboratory plasma experiments are expected to contribute, to enlighten the non-linear processes which occur under such conditions.Comment: accepted for publication in EPJD, topical issue on Fundamental physics and ultra-high laser fields. From review talk at "Extreme Light Infrastructure" workshop, Sep 2008. Version-2 May 2009: adjust some wordings and references at EPJD proofs stag

Formation of an Edge Striped Phase in Fractional Quantum Hall Systems

We have performed an exact diagonalization study of up to N=12 interacting electrons on a disk at filling $\nu={1/3}$ for both Coulomb and $V_1$ short-range interaction for which Laughlin wave function is the exact solution. For Coulomb interaction and $N\geq 10$ we find persistent radial oscillations in electron density, which are not captured by the Laughlin wave function. Our results srongly suggest formation of a chiral edge striped phase in quantum Hall systems. The amplitude of the charge density oscillations decays slowly, perhaps as a square root of the distance from the edge; thus the spectrum of edge excitations is likely to be affected.Comment: 4 pages, 3 Figs. include

Computer simulations of hard pear-shaped particles

We report results obtained from Monte Carlo simulations investi- gating mesophase formation in two model systems of hard pear-shaped particles. The first model considered is a hard variant of the trun- cated Stone-Expansion model previously shown to form nematic and smectic mesophases when embedded within a 12-6 Gay-Berne-like po- tential [1]. When stripped of its attractive interactions, however, this system is found to lose its liquid crystalline phases. For particles of length to breadth ratio k = 3, glassy behaviour is seen at high pressures, whereas for k = 5 several bi-layer-like domains are seen, with high intradomain order but little interdomain orientational correlation. For the second model, which uses a parametric shape parameter based on the generalised Gay-Berne formalism, results are presented for particles with elongation k = 3; 4 and 5. Here, the systems with k = 3 and 4 fail to display orientationally ordered phases, but that with k = 5 shows isotropic, nematic and, unusually for a hard-particle model, interdigitated smectic A2 phases.</p

High-field magnetization study of the S = 1/2 antiferromagnetic Heisenberg chain [PM Cu(NO3_3)2_2(H2_2O)2_2]n_n with a field-induced gap

We present a high-field magnetization study of the $S$ = 1/2 antiferromagnetic Heisenberg chain [PM Cu(NO$_3$)$_2$(H$_2$O)$_2$]$_n$. For this material, as result of the Dzyaloshinskii-Moriya interaction and a staggered $g$ tensor, the ground state is characterized by an anisotropic field-induced spin excitation gap and a staggered magnetization. Our data reveal the qualitatively different behavior in the directions of maximum and zero spin excitation gap. The data are analyzed via exact diagonalization of a linear spin chain with up to 20 sites and on basis of the Bethe ansatz equations, respectively. For both directions we find very good agreement between experimental data and theoretical calculations. We extract the magnetic coupling strength $J/k_B$ along the chain direction to 36.3(5) K and determine the field dependence of the staggered magnetization component $m_s$.Comment: 5 pages, 2 figures (minor changes to manuscript and figures

Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science

Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from the network properties of biological neural networks (e.g. sparsity, scale-freeness), we argue that (contrary to general practice) artificial neural networks, too, should not have fully-connected layers. Here we propose sparse evolutionary training of artificial neural networks, an algorithm which evolves an initial sparse topology (Erdős–Rényi random graph) of two consecutive layers of neurons into a scale-free topology, during learning. Our method replaces artificial neural networks fully-connected layers with sparse ones before training, reducing quadratically the number of parameters, with no decrease in accuracy. We demonstrate our claims on restricted Boltzmann machines, multi-layer perceptrons, and convolutional neural networks for unsupervised and supervised learning on 15 datasets. Our approach has the potential to enable artificial neural networks to scale up beyond what is currently possible