Machine learning for ultrafast X-ray diffraction patterns on large-scale GPU clusters

The classical method of determining the atomic structure of complex molecules by analyzing diffraction patterns is currently undergoing drastic developments. Modern techniques for producing extremely bright and coherent X-ray lasers allow a beam of streaming particles to be intercepted and hit by an ultrashort high energy X-ray beam. Through machine learning methods the data thus collected can be transformed into a three-dimensional volumetric intensity map of the particle itself. The computational complexity associated with this problem is very high such that clusters of data parallel accelerators are required. We have implemented a distributed and highly efficient algorithm for inversion of large collections of diffraction patterns targeting clusters of hundreds of GPUs. With the expected enormous amount of diffraction data to be produced in the foreseeable future, this is the required scale to approach real time processing of data at the beam site. Using both real and synthetic data we look at the scaling properties of the application and discuss the overall computational viability of this exciting and novel imaging technique

Manipulating Space, Changing Realities: space as primary carrier of meaning in sonic arts

Space is an essential element of human experience. In our daily lives we move about in a multi-dimensional sound field, constantly processing spatial cues in our encounters with our surroundings. Awareness of space as a fundamental compo-nent of sound is nevertheless limited among artists and listeners. This paper presents a framework for recognizing, analyzing and working with sonic space, based on identifying and categorizing spatial components from the level of the individual sound, via the combination of sounds in virtual spaces, to the experience of the fusion of composed space and the listening environment

Improved contact prediction in proteins: Using pseudolikelihoods to infer Potts models

Spatially proximate amino acids in a protein tend to coevolve. A protein's three-dimensional (3D) structure hence leaves an echo of correlations in the evolutionary record. Reverse engineering 3D structures from such correlations is an open problem in structural biology, pursued with increasing vigor as more and more protein sequences continue to fill the data banks. Within this task lies a statistical inference problem, rooted in the following: correlation between two sites in a protein sequence can arise from firsthand interaction but can also be network-propagated via intermediate sites; observed correlation is not enough to guarantee proximity. To separate direct from indirect interactions is an instance of the general problem of inverse statistical mechanics, where the task is to learn model parameters (fields, couplings) from observables (magnetizations, correlations, samples) in large systems. In the context of protein sequences, the approach has been referred to as direct-coupling analysis. Here we show that the pseudolikelihood method, applied to 21-state Potts models describing the statistical properties of families of evolutionarily related proteins, significantly outperforms existing approaches to the direct-coupling analysis, the latter being based on standard mean-field techniques. This improved performance also relies on a modified score for the coupling strength. The results are verified using known crystal structures of specific sequence instances of various protein families. Code implementing the new method can be found at http://plmdca.csc.kth.se/.Comment: 19 pages, 16 figures, published versio

Inverse Ising inference using all the data

We show that a method based on logistic regression, using all the data, solves the inverse Ising problem far better than mean-field calculations relying only on sample pairwise correlation functions, while still computationally feasible for hundreds of nodes. The largest improvement in reconstruction occurs for strong interactions. Using two examples, a diluted Sherrington-Kirkpatrick model and a two-dimensional lattice, we also show that interaction topologies can be recovered from few samples with good accuracy and that the use of $l_1$-regularization is beneficial in this process, pushing inference abilities further into low-temperature regimes.Comment: 5 pages, 2 figures. Accepted versio

A comparison of general and ambulance specific stressors: predictors of job satisfaction and health problems in a nationwide one-year follow-up study of Norwegian ambulance personnel

Objectives To address the relative importance of general job-related stressors, ambulance specific stressors and individual characteristics in relation to job satisfaction and health complaints (emotional exhaustion, psychological distress and musculoskeletal pain) among ambulance personnel. Materials and methods A nationwide prospective questionnaire survey of ambulance personnel in operational duty at two time points (n = 1180 at baseline, T1 and n = 298 at one-year follow up, T2). The questionnaires included the Maslach Burnout Inventory, The Job Satisfaction Scale, Hopkins Symptom Checklist (SCL-10), Job Stress Survey, the Norwegian Ambulance Stress Survey and the Basic Character Inventory. Results Overall, 42 out of the possible 56 correlations between job stressors at T1 and job satisfaction and health complaints at T2 were statistically significant. Lower job satisfaction at T2 was predicted by frequency of lack of leader support and severity of challenging job tasks. Emotional exhaustion at T2 was predicted by neuroticism, frequency of lack of support from leader, time pressure, and physical demands. Adjusted for T1 levels, emotional exhaustion was predicted by neuroticism (beta = 0.15, p < .05) and time pressure (beta = 0.14, p < 0.01). Psychological distress at T2 was predicted by neuroticism and lack of co-worker support. Adjusted for T1 levels, psychological distress was predicted by neuroticism (beta = 0.12, p < .05). Musculoskeletal pain at T2 was predicted by, higher age, neuroticism, lack of co-worker support and severity of physical demands. Adjusted for T1 levels, musculoskeletal pain was predicted neuroticism, and severity of physical demands (beta = 0.12, p < .05). Conclusions Low job satisfaction at T2 was predicted by general work-related stressors, whereas health complaints at T2 were predicted by both general work-related stressors and ambulance specific stressors. The personality variable neuroticism predicted increased complaints across all health outcomes