There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Soil Chemical Analysis

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Among various topics explored in the transfer learning community, domain adaptation (DA) has been of primary interest and successfully applied in diverse fields. However, theoretical understanding of learning convergence in DA has not been sufficiently explored. To address such an issue, this paper presents the Stochastic Augmented Lagrangian method (SALM) to solve the optimization problem associated with domain adaptation. In contrast to previous works, the SALM is able to find the optimal Lagrangian multipliers, as opposed to manually selecting the multipliers which could result in significantly suboptimal solutions. Additionally, the SALM is the first algorithm which can find a feasible point with arbitrary precision for domain adaptation problems with bounded penalty parameters. We also observe that with unbounded penalty parameters, the proposed algorithm is able to find an approximate stationary point of infeasibility. We validate our theoretical analysis with several experimental results using benchmark data sets including MNIST, SYNTH, SVHN, and USPS.

The Stochastic Augmented Lagrangian method for domain adaptation

While analytical solutions of critical (phase) transitions in physical systems are abundant for simple nonlinear systems, such analysis remains intractable for real-life dynamical systems. A key example of such a physical system is thermoacoustic instability in combustion, where prediction or early detection of an onset of instability is a hard technical challenge, which needs to be addressed to build safer and more energy-efficient gas turbine engines powering aerospace and energy industries. The instabilities arising in combustion chambers of engines are mathematically too complex to model. To address this issue in a data-driven manner instead, we propose a novel deep learning architecture called 3D convolutional selective autoencoder (3D-CSAE) to detect the evolution of self-excited oscillations using spatiotemporal data, i.e., hi-speed videos taken from a swirl-stabilized combustor (laboratory surrogate of gas turbine engine combustor). 3D-CSAE consists of filters to learn, in a hierarchical fashion, the complex visual and dynamic features related to combustion instability. We train the 3D-CSAE on frames of videos obtained from a limited set of operating conditions. We select the 3D-CSAE hyper-parameters that are effective for characterizing hierarchical and multiscale instability structure evolution by utilizing the dynamic information available in the video. The proposed model clearly shows performance improvement in detecting the precursors of instability. The machine learning-driven results are verified with physics-based off-line measures. Advanced active control mechanisms can directly leverage the proposed online detection capability of 3D-CSAE to mitigate the adverse effects of combustion instabilities on the engine operating under various stringent requirements and conditions.

3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems

Regret has been widely adopted as the metric of choice for evaluating the performance of online optimization algorithms for distributed, multi-agent systems. However, variations in data or model associated with each agent can significantly impact decisions, and requires consensus among agents. Moreover, most existing works have focused on developing approaches for (strongly) convex losses, and very few results have been obtained in terms of regret bounds in distributed online optimization for general non-convex losses. To address these two issues, we propose a novel composite regret with a new network-based metric to evaluate distributed online optimization algorithms. We concretely define static and dynamic forms of the composite regret. By leveraging the dynamic form of our composite regret, we develop a consensus-based online normalized gradient (CONGD) approach for pseudo-convex losses and then show a sublinear behavior for CONGD relating to a regularity term for the path variation of the optimizer. For general non-convex losses, we first explore the regrets defined based on the recent advances such that no deterministic algorithm can achieve the sublinear regret. We then develop the distributed online non-convex optimization with composite regret (DINOCO) without access to the gradients, depending on an offline optimization oracle. We show that DINOCO can achieve sublinear regret; to our knowledge, this is the first regret bound for general distributed online non-convex learning.

Distributed Online Non-convex Optimization with Composite Regret

Signals transmitted by satellite ORBIS at 10.004 Mc/s were received at Haringhata Field Station (geomagnetic lat. 12°15’ N, geomagnetic long. 32° W). Long-distance propagation paths could be established between the station and the satellite in some passes, while a few overhead passes could not be received. Absence of signals from a few overhead passes is explained in terms of blanketing-type sporadic E. It is concluded that long-distance propagation paths may be sustained either by hop modes in the presence of irregularities in the ionosphere or by ionospheric dueling.

Soumik Sarkar

Papers

The Stochastic Augmented Lagrangian method for domain adaptation

3D Convolutional Selective Autoencoder For Instability Detection in Combustion Systems

Distributed Online Non-convex Optimization with Composite Regret

Satellite ORBIS Experiment

Generative Semantic Domain Adaptation for Perception in Autonomous Driving