An optical processor for matrix-by-vector multiplication: an application to the distance geometry problem in 1D

We present the architecture of a new optical processor specialized in matrix-by-vector multiplication via the manipulation of the light wavefront. This processor can reach up to 1.2 Giga MAC (multiply-accumulate) operations per second using commercially available devices. Moreover, this architecture is compatible with hardware upgrade with potential to achieve processing speed above Tera MAC per second. We initially present the optical processor, and then discuss the use of such a processor for tackling a special class of the one-dimensional Distance Geometry Problem (DGP), which is a well-known NP-hard problem.

Relative earthquake location procedure for clustered seismicity with a single station

Summary Earthquake location is one of the oldest problems in seismology, yet remains an active research topic. With dense seismic monitoring networks it is possible to obtain reliable locations for microearthquakes; however, in many cases dense networks are lacking, limiting the location accuracy, or preventing location when there are too few observations. For small events in all settings, recording may be sparse and location may be difficult due to low signal-to-noise ratio. In this work we introduce a new, distance-geometry-based method to locate seismicity clusters using only one or two seismic stations. A Distance Geometry Problem determines the location of sets of points based only on the distances between some member pairs. Applied to seismology, our approach allows earthquake location using the inter-event distance between earthquakes pairs, which can be estimated using only one or two seismic stations. We first validate the method with synthetic data that resemble common cluster shapes, and then test the method with two seismic sequences in California: the August 2014 Mw 6.0 Napa earthquake band the July 2019 Mw 6.4 Ridgecrest earthquake sequence. We demonstrate that our approach provides robust and reliable results even for a single station. When using two seismic stations, the results capture the same structures recovered with high resolution Double Difference locations based on multiple stations. The proposed method is particularly useful for poorly monitored areas, where only a limited number of stations are available.

Homology Initialization for Protein Structure Determination via Distance Geometry

The problem of finding the configuration of points given partial information on pairwise inter-point distances, the Euclidean distance geometry problem, appears in multiple applications. In this paper, we propose an approach that integrates homology modeling and a nonconvex distance geometry algorithm for the protein structure determination problem. Preliminary numerical experiments show promising results.

diSTruct v1.0: generating biomolecular structures from distance constraints

Summary The distance geometry problem is often encountered in molecular biology and the life sciences at large, as a host of experimental methods produce ambiguous and noisy distance data. In this note, we present diSTruct; an adaptation of the generic MaxEnt-Stress graph drawing algorithm to the domain of biological macromolecules. diSTruct is fast, provides reliable structural models even from incomplete or noisy distance data and integrates access to graph analysis tools. Availability and implementation diSTruct is written in C++, Cython and Python 3. It is available from https://github.com/KIT-MBS/distruct.git or in the Python package index under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.