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

Interstitial lung disease (ILD) has been considered as an unusual pulmonary manifestation of systemic lupus erythematosus. Patients of ILD usually present with progressive dry cough and exertional dyspnea. ILD is usually seen in long standing SLE patients and follows a chronic course. ILD as the initial manifestation of SLE is very rare. Our middle aged female patient presented with pulmonary symptoms, renal impairment and arthralgia. She was finally diagnosed as having SLE by relevant autoantibody profile and renal biopsy along with HRCT proven ILD.

/pdf/interstitial-lung-disease-as-presenting-feature-of-sle-v3pdmvy8n0.pdf

Interstitial Lung Disease as Presenting Feature of SLE

Autonomous Mobile Sensing Platform for Spatio-Temporal Plant Phenotyping

This paper presents an analytical tool for online fatigue damage detection in polycrystalline alloys that are commonly used in mechanical structures. The underlying theory is built upon symbolic dynamic filtering (SDF) that optimally partitions time series data for feature extraction and pattern classification. The proposed method has been experimentally validated on a fatigue test apparatus that is equipped with ultrasonics sensors and a traveling optical microscope for fatigue damage detection.

/pdf/optimal-partitioning-of-ultrasonic-data-for-fatigue-damage-3ntut42jk6.pdf

Optimal partitioning of ultrasonic data for fatigue damage detection

Labelled Two-photon Lithography Dataset (LLNL-MI-808019)

Collaborative decision making in multi-agent systems typically requires a predefined communication protocol among agents. Usually, agent-level observations are locally processed and information is exchanged using the predefined protocol, enabling the team to perform more efficiently than each agent operating in isolation. In this work, we consider the situation where agents, with complementary sensing modalities must co-operate to achieve a common goal/task by learning an efficient communication protocol. We frame the problem within an actor-critic scheme, where the agents learn optimal policies in a centralized fashion, while taking action in a distributed manner. We provide an interpretation of the emergent communication between the agents. We observe that the information exchanged is not just an encoding of the raw sensor data but is, rather, a specific set of directive actions that depend on the overall task. Simulation results demonstrate the interpretability of the learnt communication in a variety of tasks.

Soumik Sarkar

Papers

Interstitial Lung Disease as Presenting Feature of SLE

Autonomous Mobile Sensing Platform for Spatio-Temporal Plant Phenotyping

Optimal partitioning of ultrasonic data for fatigue damage detection

Labelled Two-photon Lithography Dataset (LLNL-MI-808019)

A perspective on multi-agent communication for information fusion