Approximating Integer Solution Counting via Space Quantification for Linear Constraints

Solution counting or solution space quantification (means volume computation and volume estimation) for linear constraints (LCs) has found interesting applications in various fields. Experimental data shows that integer solution counting is usually more expensive than quantifying volume of solution space while their output values are close. So it is helpful to approximate the number of integer solutions by the volume if the error is acceptable. In this paper, we present and prove a bound of such error for LCs. It is the first bound that can be used to approximate the integer solution counts. Based on this result, an approximate integer solution counting method for LCs is proposed. Experiments show that our approach is over 20x faster than the state-of-the-art integer solution counters. Moreover, such advantage increases with the problem scale.

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Network Cleansing

Biological Data Mining in Protein Interaction Networks ◽

10.4018/978-1-60566-398-2.ch006 ◽

2009 ◽

pp. 80-97 ◽

Cited By ~ 2

Author(s):

Paolo Marcatili ◽

Anna Tramontano

Keyword(s):

Experimental Data ◽

Computational Methods ◽

Gold Standard ◽

State Of The Art ◽

Experimental Studies ◽

The State ◽

Biological Information ◽

Ppi Network

This chapter provides an overview of the current computational methods for PPI network cleansing. The authors first present the issue of identifying reliable PPIs from noisy and incomplete experimental data. Next, they address the questions of which are the expected results of the different experimental studies, of what can be defined as true interactions, of which kind of data are to be integrated in assigning reliability levels to PPIs and which gold standard should the authors use in training and testing PPI filtering methods. Finally, Marcatili and Tramontano describe the state of the art in the field, presenting the different classes of algorithms and comparing their results. The aim of the chapter is to guide the reader in the choice of the most convenient methods, experiments and integrative data and to underline the most common biases and errors to obtain a portrait of PINs which is not only reliable but as well able to correctly retrieve the biological information contained in such data.

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Learning deep optimizer for blind image deconvolution

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691319500449 ◽

2019 ◽

Vol 17 (06) ◽

pp. 1950044

Author(s):

Zhijian Luo ◽

Siyu Chen ◽

Yuntao Qian

Keyword(s):

Simple Form ◽

State Of The Art ◽

Solution Space ◽

The State ◽

Image Deconvolution ◽

Competitive Performance ◽

Propagation Behavior ◽

Art Works ◽

Blind Image Deconvolution ◽

Blind Image

In blind image deconvolution, priors are often leveraged to constrain the solution space, so as to alleviate the under-determinacy. Priors which are trained separately from the task of deconvolution tend to be unstable. We propose the Golf Optimizer, a novel but simple form of network that learns deep priors from data with better propagation behavior. Like playing golf, our method first estimates an aggressive propagation towards optimum using one network, and recurrently applies a residual CNN to learn the gradient of prior for delicate correction on restoration. Experiments show that our network achieves competitive performance on GoPro dataset, and our model is extremely lightweight compared with the state-of-the-art works.

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Sequence biases in CLIP experimental data are incorporated in protein RNA-binding models

10.1101/075259 ◽

2016 ◽

Cited By ~ 2

Author(s):

Yaron Orenstein ◽

Raghavendra Hosur ◽

Sean Simmons ◽

Jadwiga Bienkoswka ◽

Bonnie Berger

Keyword(s):

Experimental Data ◽

Binding Sites ◽

Rna Binding ◽

State Of The Art ◽

The State ◽

Enzyme Specificity ◽

Peak Calling ◽

Binding Models ◽

Downstream Analysis

We report a newly-identified bias in CLIP data that results from cleaving enzyme specificity. This bias is inadvertently incorporated into standard peak calling methods [1], which identify the most likely locations where proteins bind RNA. We further show how, in downstream analysis, this bias is incorporated into models inferred by the state-of-the-art GraphProt method to predict protein RNA-binding. We call for both experimental controls to measure enzyme specificities and algorithms to identify unbiased CLIP binding sites.

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Investigation of Design Power Margin and Correlation Allowance for Surface Ships

Marine Technology and SNAME News ◽

10.5957/mt1.1986.23.1.35 ◽

1986 ◽

Vol 23 (01) ◽

pp. 35-54

Author(s):

Grant R. Hagen ◽

Edward N. Comstock ◽

John J. Slager

Keyword(s):

Experimental Data ◽

State Of The Art ◽

The State ◽

Model Experiments ◽

Current Correlation ◽

Growing Body ◽

Current Design ◽

The Impact ◽

Design Power ◽

The U.S

This paper follows two earlier papers, published by the Society in 1962 and 1979, dealing with correlation allowance and design power margin. For some time it has been perceived that a need exists for changes in the numerical quantities which have been specified by the U.S. Navy for correlation allowance coefficients and design power margins. This perception results from the recognition of a growing body of experimental data, both from model experiments and from ship standardization trials, that provide the basis for both correlation and margin policies. In response to this need, an exhaustive investigation was undertaken to establish a sound basis for a revised correlation allowance policy and to evaluate its impact on design power margin policy. The investigation, which led to proposed revisions in both policies, provided the material for this paper. Presented herein are:a review of the state of the art in the areas of correlation allowance and speed-power margin;an updated database derived primarily from model experiments and standardization trials of U.S. Navy ships;an assessment and interpretation of the database;a proposed alternative to the current correlation allowance policy;an evaluation of the impact of applying the proposed policy in determining required speed-power margins for U.S. Navy ships; anda proposed alternative to the current design power margin policy for new U.S. Navy ships.

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Fast Strong Planning for FOND Problems with Multi-Root Directed Acyclic Graphs

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213014600288 ◽

2014 ◽

Vol 23 (06) ◽

pp. 1460028 ◽

Cited By ~ 1

Author(s):

Andres Calderon Jaramillo ◽

Jicheng Fu ◽

Vincent Ng ◽

Farokh B. Bastani ◽

I-Ling Yen

Keyword(s):

State Of The Art ◽

Solution Space ◽

Strong Solutions ◽

Search Space ◽

Directed Acyclic Graphs ◽

The State ◽

Benchmark Problems ◽

Acyclic Graphs ◽

Planning Problems ◽

Improve Planning

Recently, the state-of-the-art AI planners have significantly improved planning efficiency on Fully Observable Nondeterministic planning (FOND) problems with strong cyclic solutions. These strong cyclic solutions are guaranteed to achieve the goal if they terminate, implying that there is a possibility that they may run into indefinite loops. In contrast, strong solutions are guaranteed to achieve the goal, but few planners can effectively handle FOND problems with strong solutions. In this study, we aim to address this difficult, yet under-investigated class of planning problems: FOND planning problems with strong solutions. We present a planner that employs a new data structure, MRDAG (multi-root directed acyclic graph), to define how the solution space should be expanded. Based on the characteristics of MRDAG, we develop heuristics to ensure planning towards the relevant search direction and design optimizations to prune the search space to further improve planning efficiency. We perform extensive experiments to evaluate MRDAG, the heuristics, and the optimizations for pruning the search space. Experimental results show that our strong algorithm achieves impressive performance on a variety of benchmark problems: on average it runs more than three orders of magnitude faster than the state-of-the-art planners, MBP and Gamer, while demonstrating significantly better scalability.

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Decomposition Strategies to Count Integer Solutions over Linear Constraints

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/192 ◽

2021 ◽

Author(s):

Cunjing Ge ◽

Armin Biere

Keyword(s):

Structural Properties ◽

State Of The Art ◽

Linear Constraints ◽

Decomposition Techniques ◽

Counting Problems ◽

Integer Points ◽

Decomposition Strategies ◽

Counting Algorithms ◽

Integer Solutions ◽

Elimination Of Variables

Counting integer solutions of linear constraints has found interesting applications in various fields. It is equivalent to the problem of counting integer points inside a polytope. However, state-of-the-art algorithms for this problem become too slow for even a modest number of variables. In this paper, we propose new decomposition techniques which target both the elimination of variables as well as inequalities using structural properties of counting problems. Experiments on extensive benchmarks show that our algorithm improves the performance of state-of-the-art counting algorithms, while the overhead is usually negligible compared to the running time of integer counting.

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Practical Picture Processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051700 ◽

1974 ◽

Vol 32 ◽

pp. 338-339

Author(s):

T. A. Welton

Keyword(s):

Radiation Damage ◽

Coherence Length ◽

Spatial Information ◽

State Of The Art ◽

Coherent Radiation ◽

The State ◽

Energy Spread ◽

Electron Micrograph ◽

Picture Processing ◽

Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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