Johnson: Computers and Intractability-A Guide to the Theory of NP-Completeness

From the Publisher:
A valuable reference for the novice as well as for the expert who needs a wider scope of coverage within the area of cryptography, this book provides easy and rapid access of information and includes more than 200 algorithms and protocols; more than 200 tables and figures; more than 1,000 numbered definitions, facts, examples, notes, and remarks; and over 1,250 significant references, including brief comments on each paper.

Handbook of Applied Cryptography

ROUGE stands for Recall-Oriented Understudy for Gisting Evaluation. It includes measures to automatically determine the quality of a summary by comparing it to other (ideal) summaries created by humans. The measures count the number of overlapping units such as n-gram, word sequences, and word pairs between the computer-generated summary to be evaluated and the ideal summaries created by humans. This paper introduces four different ROUGE measures: ROUGE-N, ROUGE-L, ROUGE-W, and ROUGE-S included in the ROUGE summarization evaluation package and their evaluations. Three of them have been used in the Document Understanding Conference (DUC) 2004, a large-scale summarization evaluation sponsored by NIST.

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ROUGE: A Package for Automatic Evaluation of Summaries

The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.

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RADAR: an in-building RF-based user location and tracking system

We describe a new computer program, SnpEff, for rapidly categorizing the effects of variants in genome sequences. Once a genome is sequenced, SnpEff annotates variants based on their genomic locations and predicts coding effects. Annotated genomic locations include intronic, untranslated region, upstream, downstream, splice site, or intergenic regions. Coding effects such as synonymous or non-synonymous amino acid replacement, start codon gains or losses, stop codon gains or losses, or frame shifts can be predicted. Here the use of SnpEff is illustrated by annotating ~356,660 candidate SNPs in ~117 Mb unique sequences, representing a substitution rate of ~1/305 nucleotides, between the Drosophila melanogaster w1118; iso-2; iso-3 strain and the reference y1; cn1 bw1 sp1 strain. We show that ~15,842 SNPs are synonymous and ~4,467 SNPs are non-synonymous (N/S ~0.28). The remaining SNPs are in other categories, such as stop codon gains (38 SNPs), stop codon losses (8 SNPs), and start codon gains (297 SNPs) in...

A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3

We provide new competitive upper bounds on the performance of the memoryless, randomized caching algorithm RAND. Our bounds are expressed in terms of the inherent hit rate α of the sequence of memory references, which is the highest possible hit rate that any algorithm can achieve on the sequence for a cache of a given size. Our results show that RAND is (1 - αe-1/α)/(1 - α)-competitive on any reference sequence with inherent hit rate α. Since our new competitive bound does not scale up with the size k of the cache, it beats the putative Ω(lg k) lower bound on the competitiveness of randomized caching algorithms.

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A New Competitive Analysis of Randomized Caching

The Dagstuhl Seminar 04301 ``Cache-Oblivious and Cache-Aware Algorithms'' was held
in the International Conference and Research Center (IBFI), Schloss Dagstuhl, from 18.07.2004 to 23.07.2004.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available.

04301 Abstracts Collection -- Cache-Oblivious and Cache-Aware Algorithms

: The complexity of integrated-circuit chips produced today makes it feasible to build inexpensive, special-purpose subsystems that rapidly solve sophisticated problems on behalf of a general-purpose host computer This paper contributes to the design methodology of efficient VLSI algorithms We present a transformation that converts synchronous systems into more time-efficient, systolic implementations by removing combinational rippling The problem of determining the optimized system can be reduced to the graph-theoretic single-destination-shortest-paths problem More importantly from an engineering standpoint, however, the kinds of rippling that can be removed from a circuit at essentially no cost can be easily characterized For example, if the only global communication in a system is broadcasting from the host computer, the broadcast can always be replaced by local communication (Author)

Optimixing Synchronous Systems.

Humanity's understanding of the Earth's weather and climate depends critically on accurate forecasting and state-estimation technology. It is not clear how to build an effective dynamic data-driven application system (DDDAS) in which computer models of the planet and observations of the actual conditions interact, however. We are designing and building a laboratory-scale dynamic data-driven application system (DDDAS), called Planet-in-a-Bottle , as a practical and inexpensive step toward a planet-scale DDDAS for weather forecasting and climate model. The Planet-in-a-Bottle DDDAS consists of two interacting parts: a fluid lab experiment and a numerical simulator. The system employs data assimilation in which actual observations are fed into the simulator to keep the models on track with reality, and employs sensitivity-driven observations in which the simulator targets the real-time deployment of sensors to particular geographical regions and times for maximal effect, and refines the mesh to better predict the future course of the fluid experiment. In addition, the feedback loop between targeting of both the observational system and mesh refinement will be mediated, if desired, by human control.

/pdf/planet-in-a-bottle-a-numerical-fluid-laboratory-system-42fbcjtp0z.pdf

Planet-in-a-Bottle: A Numerical Fluid-Laboratory System

Improving the training and inference performance of graph neural networks (GNNs) is faced with a challenge uncommon in general neural networks: creating mini-batches requires a lot of computation and data movement due to the exponential growth of multi-hop graph neighborhoods along network layers. Such a unique challenge gives rise to a diverse set of system design choices. We argue in favor of performing mini-batch training with neighborhood sampling in a distributed multi-GPU environment, under which we identify major performance bottlenecks hitherto under-explored by developers: mini-batch preparation and transfer. We present a sequence of improvements to mitigate these bottlenecks, including a performance-engineered neighborhood sampler, a shared-memory parallelization strategy, and the pipelining of batch transfer with GPU computation. We also conduct an empirical analysis that supports the use of sampling for inference, showing that test accuracies are not materially compromised. Such an observation unifies training and inference, simplifying model implementation. We report comprehensive experimental results with several benchmark data sets and GNN architectures, including a demonstration that, for the ogbn-papers100M data set, our system SALIENT achieves a speedup of 3x over a standard PyTorch-Geometric implementation with a single GPU and a further 8x parallel speedup with 16 GPUs. Therein, training a 3-layer GraphSAGE model with sampling fanout (15, 10, 5) takes 2.0 seconds per epoch and inference with fanout (20, 20, 20) takes 2.4 seconds, attaining test accuracy 64.58%.

Charles E. Leiserson

Papers

A New Competitive Analysis of Randomized Caching

04301 Abstracts Collection -- Cache-Oblivious and Cache-Aware Algorithms

Optimixing Synchronous Systems.

Planet-in-a-Bottle: A Numerical Fluid-Laboratory System

Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining.