Mash extends the MinHash dimensionality-reduction technique to include a pairwise mutation distance and P value significance test, enabling the efficient clustering and search of massive sequence collections. Mash reduces large sequences and sequence sets to small, representative sketches, from which global mutation distances can be rapidly estimated. We demonstrate several use cases, including the clustering of all 54,118 NCBI RefSeq genomes in 33 CPU h; real-time database search using assembled or unassembled Illumina, Pacific Biosciences, and Oxford Nanopore data; and the scalable clustering of hundreds of metagenomic samples by composition. Mash is freely released under a BSD license (
 https://github.com/marbl/mash
 ).

/pdf/mash-fast-genome-and-metagenome-distance-estimation-using-3b84vfnedh.pdf

Mash: fast genome and metagenome distance estimation using MinHash.

In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query. The problem is of significant interest in a wide variety of areas.

/pdf/near-optimal-hashing-algorithms-for-approximate-nearest-2u17o6r986.pdf

Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions

This comprehensive textbook presents a clean and coherent account of most fundamental tools and techniques in Parameterized Algorithms and is a self-contained guide to the area. The book covers many of the recent developments of the field, including application of important separators, branching based on linear programming, Cut & Count to obtain faster algorithms on tree decompositions, algorithms based on representative families of matroids, and use of the Strong Exponential Time Hypothesis. A number of older results are revisited and explained in a modern and didactic way. The book provides a toolbox of algorithmic techniques. Part I is an overview of basic techniques, each chapter discussing a certain algorithmic paradigm. The material covered in this part can be used for an introductory course on fixed-parameter tractability. Part II discusses more advanced and specialized algorithmic ideas, bringing the reader to the cutting edge of current research. Part III presents complexity results and lower bounds, giving negative evidence by way of W[1]-hardness, the Exponential Time Hypothesis, and kernelization lower bounds. All the results and concepts are introduced at a level accessible to graduate students and advanced undergraduate students. Every chapter is accompanied by exercises, many with hints, while the bibliographic notes point to original publications and related work.

Parameterized Algorithms

We present an algorithm for the c-approximate nearest neighbor problem in a d-dimensional Euclidean space, achieving query time of O\left( {dn^{1/c^2 + o(1)} } \right) and space O\left( {dn + n^{1 + 1/c^2 + o(1)} } \right). This almost matches the lower bound for hashing-based algorithm recently obtained in [27]. We also obtain a space-efficient version of the algorithm, which uses dn+n log^{O(1)} n space, with a query time of dn^{O(1/c^2 )}. Finally, we discuss practical variants of the algorithms that utilize fast bounded-distance decoders for the Leech Lattice.

/pdf/near-optimal-hashing-algorithms-for-approximate-nearest-1nji4ps3op.pdf

Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions

We present two algorithms for the approximate nearest neighbor problem in high dimensional spaces. For data sets of size n living in IR, the algorithms require space that is only polynomial in n and d, while achieving query times that are sub-linear in n and polynomial in d. We also show applications to other high-dimensional geometric problems, such as the approximate minimum spanning tree.

/pdf/approximate-nearest-neighbor-towards-removing-the-curse-of-33ayyf74ky.pdf

Approximate Nearest Neighbor: Towards Removing the Curse of Dimensionality

Seveso II Directive imposes for high hazardous plants quantitative risk evaluation of the major accident. In a general context the risk is defined as product between frequency and consequences of accident state. There are five steps in quantitative risk assessment: identification of significant accident initiating events, development of accident sequences, frequency estimation for accident sequences, computation of post accident events parameters and consequences estimation. In the case of hazardous emissions, post accident events characterization means calculus of flow rates, quantities and duration. The paper presents mathematical models used to describe the process fluids release in emergency states, locally and by safety systems, as well as the results obtained with simulation programs, elaborated for the heavy water concentrations plants based on chemical exchange between water and hydrogen sulphide.

/pdf/accident-states-simulation-process-fluids-release-zen7yx97ju.pdf

Accident states simulation: process fluids release

We start by presenting a short explanation of the micro actuator working principle. The main contribution of this paper is the characterization of the coefficient of stiction between two silicon nitride parts of a MEMS actuator. Having one flat contact surface while the other is composed of many relatively sharp circular bumps, has the advantage that the effective contact area is drastically reduced. The stiction coefficient varies between 0-0.53 for the device configuration presented. The exact value depends closely on the electrostatically applied force which presses the textured part on to the smooth one. Individual measurements used are highly accurate (typically within 5 nm noise band) and identical measurements for the characterization of stiction show only a small variation, typically under 25nm on a range of 11.8µm.

http://core.ac.uk/download/pdf/11474545.pdf

Mihai Patrascu

Papers

Accident states simulation: process fluids release

Characterization of stiction effects of an electrostatic micro positioner for probe storage