Probabilistic Metric Embedding via Metric Labeling

We consider probabilistic embedding of metric spaces into ultra-metrics (or equivalently to a constant factor, into hierarchically separated trees) to minimize the expected distortion of any pairwise distance. Such embeddings have been widely used in network design and online algorithms. Our main result is a polynomial time algorithm that approximates the optimal distortion on any instance to within a constant factor. We achieve this via a novel LP formulation that reduces this problem to a probabilistic version of uniform metric labeling

Response Spectrum Analysis of Printed Circuit Boards Subjected to Shock Loads

AbstractA spacecraft consists of a number of electronic packages to meet the functional requirements. An electronic package is generally an assembly of printed circuit boards placed in a mechanical housing. A number of electronic components are mounted on the printed circuit board (PCB). A spacecraft experiences various types of loads during its launch such as vibration, acoustic and shock loads. Prediction of response for printed circuit boards due to shock loads is important for mechanical design and reliability of electronic packages. The modeling and analysis of printed circuit boards is required for accurate prediction of response due to shock loads. The validated finite element model of the PCB can be adopted to perform response spectrum analysis. Shock response spectrum analysis of printed circuit boards subjected to a half-sine pulse excitation is carried out using finite element method. The objective of this paper is to predict the shock response spectrum of a printed circuit board due to launch environment. The analysis results are validated by conducting experimental tests of PCB

Jet: Multilevel Graph Partitioning on GPUs

The multilevel heuristic is the dominant strategy for high-quality sequential and parallel graph partitioning. Partition refinement is a key step of multilevel graph partitioning. In this work, we present Jet, a new parallel algorithm for partition refinement specifically designed for Graphics Processing Units (GPUs). We combine Jet with GPU-aware coarsening to develop a $k$-way graph partitioner. The new partitioner achieves superior quality when compared to state-of-the-art shared memory graph partitioners on a large collection of test graphs.Comment: Submitted as a non-archival track paper for SIAM ACDA 202

Approximation Algorithms for School Assignment: Group Fairness and Multi-criteria Optimization

We consider the problem of assigning students to schools, when students have different utilities for schools and schools have capacity. There are additional group fairness considerations over students that can be captured either by concave objectives, or additional constraints on the groups. We present approximation algorithms for this problem via convex program rounding that achieve various trade-offs between utility violation, capacity violation, and running time. We also show that our techniques easily extend to the setting where there are arbitrary covering constraints on the feasible assignment, capturing multi-criteria and ranking optimization

New Boron Analogues of Pyrophosphates and Deoxynucleoside Boranophosphates

Tetraethyldicyanoborane pyrophosphate (2) and 3'-(diethylphosphite-cyanoborano)-5'-dimethoxytrityl.N4-benzoyl-deoxycytidine (3) have been synthesized in 70% and 76% yields, respectively. The compatibility of the substituted boranophosphates with common protecting groups is hereby demonstrated

Molecular Docking Studies for the Assessment of Wound Healing Activity of Phytoconstituents in Heliotropium Indicum

One of the most crucial and complex processes is the skin's multi-stage process of healing after an injury. Heliotropium indicum is a potent antibiotic, anti- inflammatory, anti-neoplastic, anti-oxidant, and wound- healing agent. Heliotropium indicum Linn is the source of the chemical compound in question, which is abundant in sterols, ammines, volatile oils, and the pyrrolizidine alkaloids. Molecular docking studies were conducted on Heliotropium indicum using Argus lab 4.0.1 and Autodock 1.5.7. The proteins PDB ID:1YXO, 3V18, and 4G8R were selected because of their role in wound healing. The pieces work together with the protein responsible for mending wounds. The binding affinities of mupirocin and nitrofurazone are higher than those of the components stigmasterol, eugenol, borneol, and campesterol. In order to better customize Heliotropium indicum to our requirements, we now have a better knowledge of the components of the molecule that interact with their receptors in the wound healing process

Data Exchange Markets via Utility Balancing

This paper explores the design of a balanced data-sharing marketplace for entities with heterogeneous datasets and machine learning models that they seek to refine using data from other agents. The goal of the marketplace is to encourage participation for data sharing in the presence of such heterogeneity. Our market design approach for data sharing focuses on interim utility balance, where participants contribute and receive equitable utility from refinement of their models. We present such a market model for which we study computational complexity, solution existence, and approximation algorithms for welfare maximization and core stability. We finally support our theoretical insights with simulations on a mean estimation task inspired by road traffic delay estimation.Comment: To appear in WWW 202

Detailed Bathymetric Surveys in the Central Indian Basin

Over 420,000 line kilometers of echo-sounding data was collected in the Central Indian Basin. This data was digitized, merged with navigation data and a detailed bathymetric map of the Basin was prepared. The Basin can be broadly classified into three regions as high relief area, medium relief area and plain area represented by western, eastern and central portions of the Basin, respectively. The bathymetric map prepared from this survey is the first of its kind for this region and will in the future be used as a base by navigators and researchers

Mapping and analysis of Caenorhabditis elegans transcription factor sequence specificities

Caenorhabditis elegans is a powerful model for studying gene regulation, as it has a compact genome and a wealth of genomic tools. However, identification of regulatory elements has been limited, as DNA-binding motifs are known for only 71 of the estimated 763 sequence-specific transcription factors (TFs). To address this problem, we performed protein binding microarray experiments on representatives of canonical TF families in C. elegans, obtaining motifs for 129 TFs. Additionally, we predict motifs for many TFs that have DNA-binding domains similar to those already characterized, increasing coverage of binding specificities to 292 C. elegans TFs (~40%). These data highlight the diversification of binding motifs for the nuclear hormone receptor and C2H2 zinc finger families, and reveal unexpected diversity of motifs for T-box and DM families. Motif enrichment in promoters of functionally related genes is consistent with known biology, and also identifies putative regulatory roles for unstudied TFs

Exploring the Design Space of Static and Incremental Graph Connectivity Algorithms on GPUs

Connected components and spanning forest are fundamental graph algorithms due to their use in many important applications, such as graph clustering and image segmentation. GPUs are an ideal platform for graph algorithms due to their high peak performance and memory bandwidth. While there exist several GPU connectivity algorithms in the literature, many design choices have not yet been explored. In this paper, we explore various design choices in GPU connectivity algorithms, including sampling, linking, and tree compression, for both the static as well as the incremental setting. Our various design choices lead to over 300 new GPU implementations of connectivity, many of which outperform state-of-the-art. We present an experimental evaluation, and show that we achieve an average speedup of 2.47x speedup over existing static algorithms. In the incremental setting, we achieve a throughput of up to 48.23 billion edges per second. Compared to state-of-the-art CPU implementations on a 72-core machine, we achieve a speedup of 8.26--14.51x for static connectivity and 1.85--13.36x for incremental connectivity using a Tesla V100 GPU