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

3D object recognition accuracy can be improved by learning the multi-scale spatial features from 3D spatial geometric representations of objects such as point clouds, 3D models, surfaces, and RGB-D data. Current deep learning approaches learn such features either using structured data representations (voxel grids and octrees) or from unstructured representations (graphs and point clouds). Learning features from such structured representations is limited by the restriction on resolution and tree depth while unstructured representations creates a challenge due to non-uniformity among data samples. In this paper, we propose an end-to-end multi-level learning approach on a multi-level voxel grid to overcome these drawbacks. To demonstrate the utility of the proposed multi-level learning, we use a multi-level voxel representation of 3D objects to perform object recognition. The multi-level voxel representation consists of a coarse voxel grid that contains volumetric information of the 3D object. In addition, each voxel in the coarse grid that contains a portion of the object boundary is subdivided into multiple fine-level voxel grids. The performance of our multi-level learning algorithm for object recognition is comparable to dense voxel representations while using significantly lower memory.

/pdf/multi-level-3d-cnn-for-learning-multi-scale-spatial-features-hy04j80gch.pdf

Multi-Level 3D CNN for Learning Multi-Scale Spatial Features

Symbolic dynamic filtering (SDF) has been reported in recent literature for early detection of anomalies (i.e., deviations from the nominal behavior) in complex dynamical systems. In this context, instead of solely relying on physics- based modeling that may be difficult to formulate and validate, this paper proposes data-driven modeling and system identification based on the concept of symbolic dynamics, automata theory, and information theory. For anomaly detection in inter-connected complex dynamical systems, with or without closed loop control, the input excitation to an individual component is likely to deviate from the nominal condition as a result of deterioration of some other component(s) or to accommodate disturbance rejection by feedback control actions. This paper presents a formal-language-based syntactic method of anomaly detection to account for deviations in the pertinent input excitation. A training algorithm is formulated to generate an automaton model of the underlying subsystem or component from a set of input-output combinations for different classes of inputs, where the objective is to detect (possibly gradually evolving) anomalies under different input conditions. The proposed method has been validated on a test apparatus of nonlinear active electronics.

/pdf/symbolic-identification-and-anomaly-detection-in-complex-21cgdt0x2m.pdf

Symbolic identification and anomaly detection in complex dynamical systems

This paper presents a novel distributed optimization framework to achieve energy efficiency in large-scale buildings. The modular problem formulation presented in this paper decouples the supervisory optimization scheme from the data-driven micro-level modeling aspect leading to significant scalability and flexibility. Recently developed generalized gossip protocol is used as a robust distributed optimization technique. A supervisory control design problem for multi-zone temperature regulation and energy usage minimization is considered as a case study to describe the generic framework. Numerical simulation results, presented based on a physical testbed in the Iowa Energy Center, demonstrate the advantages of the distributed optimization methodology compared to a typical baseline strategy. The paper also outlines a software architecture based on the VOLTTRON platform, recently developed by the Pacific Northwest National Laboratory (PNNL), for real-life implementation of the proposed framework.

Scalable supervisory control of building energy systems using generalized gossip

Deep Reinforcement Learning for Adaptive Traffic Signal Control

Deep learning (DL) methods have transformed the way we extract plant traits – both under laboratory as well as field conditions. Evidence suggests that “well-trained” DL models can significantly si...

Soumik Sarkar

Papers

Multi-Level 3D CNN for Learning Multi-Scale Spatial Features

Symbolic identification and anomaly detection in complex dynamical systems

Scalable supervisory control of building energy systems using generalized gossip

Deep Reinforcement Learning for Adaptive Traffic Signal Control

Plant Phenotyping with Limited Annotation: Doing More with Less