Grand Challenge Problems Research Articles

Effective use of color information for large scale face verification

This paper presents a method for effective use of color information and a new similarity measure with application to large scale face verification. Specifically, three effective color component images are first obtained from a new color model that takes advantage of the subtraction of the primary colors. A compact color image representation is then derived through discrete cosine transform and feature selection for redundancy reduction and computational efficiency. The effective color features in terms of class separability are further extracted by means of discriminant analysis. A new similarity measure is finally presented for improving pattern recognition performance. The effectiveness of the proposed method is evaluated using a large scale, grand challenge pattern recognition problem, namely, the Face Recognition Grand Challenge (FRGC) problem. Specifically, the experiments using 36,818 FRGC color images show that the new color model improves upon other image modalities, such as the RGB color image, and the grayscale image; and the new similarity measure consistently performs better than other popular similarity measures, such as the Euclidean distance measure, the cosine similarity measure, and the normalized correlation.

A Novel Probabilistic Matching Algorithm for Multi-Stage Attack Forecasts

Current intrusion detection systems (IDSs) can only discover single-step attacks but not complicated multi-stage attacks. Therefore, it is not only important, but also challenging for security managers to correlate security alerts with specific patterns to predict a multi-stage attack. In this paper, we propose Judge Evaluation of Attack intensioN (JEAN), which inspects the security alerts in the network and provides a probabilistic approach for the projection of the multi-stage attack by measuring the difference between the stored and the actual multi-stage attack session graphs (ASG). The experimental results show that JEAN is able to project possible attacks with more accuracy than Longest Common Subsequence (LCS) based approaches on DARPA 2000 and DARPA GCP (Grand Challenge Problem) specific attack scenario datasets.

Normal state of the copper oxide high-temperature superconductors

Superconductivity in the copper oxide ceramics stands as a grand challenge problem as its solution is fundamentally rooted in the physics of strong coupling. In such problems, traditional calculational schemes based on the properties of single particles fail. Rather, the physics of strong coupling

A water-swap reaction coordinate for the calculation of absolute protein–ligand binding free energies

The accurate prediction of absolute protein-ligand binding free energies is one of the grand challenge problems of computational science. Binding free energy measures the strength of binding between a ligand and a protein, and an algorithm that would allow its accurate prediction would be a powerful tool for rational drug design. Here we present the development of a new method that allows for the absolute binding free energy of a protein-ligand complex to be calculated from first principles, using a single simulation. Our method involves the use of a novel reaction coordinate that swaps a ligand bound to a protein with an equivalent volume of bulk water. This water-swap reaction coordinate is built using an identity constraint, which identifies a cluster of water molecules from bulk water that occupies the same volume as the ligand in the protein active site. A dual topology algorithm is then used to swap the ligand from the active site with the identified water cluster from bulk water. The free energy is then calculated using replica exchange thermodynamic integration. This returns the free energy change of simultaneously transferring the ligand to bulk water, as an equivalent volume of bulk water is transferred back to the protein active site. This, directly, is the absolute binding free energy. It should be noted that while this reaction coordinate models the binding process directly, an accurate force field and sufficient sampling are still required to allow for the binding free energy to be predicted correctly. In this paper we present the details and development of this method, and demonstrate how the potential of mean force along the water-swap coordinate can be improved by calibrating the soft-core Coulomb and Lennard-Jones parameters used for the dual topology calculation. The optimal parameters were applied to calculations of protein-ligand binding free energies of a neuraminidase inhibitor (oseltamivir), with these results compared to experiment. These results demonstrate that the water-swap coordinate provides a viable and potentially powerful new route for the prediction of protein-ligand binding free energies.

Diffusive Corrections to $P_N$ Approximations

In this paper, we investigate moment methods from a general point of view using an operator notation. This theoretical approach lets us explore the moment closure problem in more detail. This gives rise to a new idea, proposed in Levermore [Transition Regime Models for Radiative Transport, Presentation at the Institute for Pure & Applied Mathematics: Grand Challenge Problems in Computational Astrophysics, Workshop on Transfer Phenomena, 2005, http://www.ipam.ucla.edu/publications/pcaws4/pcaws4_5724.pdf, and Moment Closures for Radiative Transport, manuscript, 2009], of how to improve the well-known $P_N$ approximations. We systematically develop a diffusive correction to the $P_N$ equations from the operator formulation—the so-called $D_N$ approximation. We validate the new approach with numerical examples in one and two dimensions.

Parallel computing techniques applied to the simultaneous design of structure and material

In this work a computational procedure for two-scale topology optimization problem using parallel computing techniques is developed. The goal is to obtain simultaneously the best structure and material, minimizing structural compliance. An algorithmic strategy is presented in a suitable way for parallelization. In terms of parallel computing facilities, an IBM Cluster 1350 is used comprising 70 computing nodes each with two dual core processors, for a total of 280 cores. Scalability studies are performed with mechanical structures of low/moderate dimensions. Finally the applicability of the proposed methodology is demonstrated solving a grand challenge problem that is the simulation of trabecular bone adaptation.

Japan's Next-Generation Supercomputer R&D Project and Grand Challenges in Life Science

Event Abstract Back to Event Japan's Next-Generation Supercomputer R&D Project and Grand Challenges in Life Science Ryutaro Himeno1* 1 RIKEN, Integrated Simulation of Living Matter Group , Japan MEXT and RIKEN started the Next-Generation Supercomputer R&D Project in 2006, of which goal is to develop a supercomputer with 10 Peta FLOPS in 2011FY. The supercomputer will be installed in a new supercomputer center in Kobe, where the buildings and facilities will be completed by the end of May, 2010. In this R&D project, not only hardware development but also software development in Nano Science and Life Science as Grand Challenge problems are planed and included. The Institute of Molecular Science leads the Nono Science Grand Challenge and RIKEN leads the Life Science one. As the Life Science Grand Challenge, we have two approaches to achieve comprehensive understanding of life phenomena and to contribute to our society by creating medicine and developing medical/surgical treatment. Those approaches are 1) theoretical simulation approach and 2) data-driven approach. In addition to these approaches, we have a HPC team which is responsible to deliver maximum performance of the 10 Peta FLOPS supercomputer to tune the application software and to develop and provide tools for visualization and parallelization to other teams. These software tools are also available to anyone who will use the supercomputer. In 2007, we have added Brain and Neural Sytems Team lead by Prof. Ishii, Kyoto Universities. Current status of supercomputer development and the application software development in Life Scinece will be shown in the talk. Conference: Neuroinformatics 2010 , Kobe, Japan, 30 Aug - 1 Sep, 2010. Presentation Type: Oral Presentation Topic: INCF Japan-Node Special Symposium "How Neuroinformatics can revolutionize Neuroscience" Citation: Himeno R (2010). Japan's Next-Generation Supercomputer R&D Project and Grand Challenges in Life Science. Front. Neurosci. Conference Abstract: Neuroinformatics 2010 . doi: 10.3389/conf.fnins.2010.13.00149 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Jun 2010; Published Online: 21 Jun 2010. * Correspondence: Ryutaro Himeno, RIKEN, Integrated Simulation of Living Matter Group, Saitama, Japan, himeno@riken.jp Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Ryutaro Himeno Google Ryutaro Himeno Google Scholar Ryutaro Himeno PubMed Ryutaro Himeno Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Color space normalization: Enhancing the discriminating power of color spaces for face recognition

This paper presents the concept of color space normalization (CSN) and two CSN techniques, i.e., the within-color-component normalization technique (CSN-I) and the across-color-component normalization technique (CSN-II), for enhancing the discriminating power of color spaces for face recognition. Different color spaces usually display different discriminating power, and our experiments on a large scale face recognition grand challenge (FRGC) problem reveal that the RGB and XYZ color spaces are weaker than the I 1I 2I 3, YUV, YIQ, and LSLM color spaces for face recognition. We therefore apply our CSN techniques to normalize the weak color spaces, such as the RGB and the XYZ color spaces, the three hybrid color spaces XGB, YRB and ZRG, and 10 randomly generated color spaces. Experiments using the most challenging FRGC version 2 Experiment 4 with 12,776 training images, 16,028 controlled target images, and 8,014 uncontrolled query images, show that the proposed CSN techniques can significantly and consistently improve the discriminating power of the weak color spaces. Specifically, the normalized RGB, XYZ, XGB, and ZRG color spaces are more effective than or as effective as the I 1I 2I 3, YUV, YIQ and LSLM color spaces for face recognition. The additional experiments using the AR database validate the generalization of the proposed CSN techniques. We finally explain why the CSN techniques can improve the recognition performance of color spaces from the color component correlation point of view.

High performance I/O architectures and systems

I/O- and data-intensive workloads, as represented by the Grand Challenge problems, multimedia applications, cosmology simulations, climate modeling, and large collaborative visualizations, to name a few, entail innovative approaches to alleviate the I/O (both bandwidth and data access) performance bottlenecks. The advent of low-cost hardware platforms, such as the Beowulf clusters, has opened up numerous possibilities in mass data storage, scalable architectures, and large-scale simulations. The objective of this Special Issue is to discuss problems and solutions, to identify new issues, and to help shape future research and development directions in these areas. From these perspectives, the Special Issue addresses the problems encountered at the hardware, middleware, and application levels, providing conceptual as well as empirical treatments.

What are the grand challenges for data mining?

We discuss what makes exciting and motivating Grand Challenge problems for Data Mining, and propose criteria for a good Grand Challenge. We then consider possible GC problems from multimedia mining, link mining, large-scale modeling, text mining, and proteomics. This report is the result of a panel held at KDD-2006 conference.

Improving the quality of alerts and predicting intruder’s next goal with Hidden Colored Petri-Net

Intrusion detection systems (IDS) often provide poor quality alerts, which are insufficient to support rapid identification of ongoing attacks or predict an intruder’s next likely goal. In this paper, we propose a novel approach to alert postprocessing and correlation, the Hidden Colored Petri-Net (HCPN). Different from most other alert correlation methods, our approach treats the alert correlation problem as an inference problem rather than a filter problem. Our approach assumes that the intruder’s actions are unknown to the IDS and can be inferred only from the alerts generated by the IDS sensors. HCPN can describe the relationship between different steps carried out by intruders, model observations (alerts) and transitions (actions) separately, and associate each token element (system state) with a probability (or confidence). The model is an extension to Colored Petri-Net (CPN). It is so called “hidden” because the transitions (actions) are not directly observable but can be inferred by looking through the observations (alerts). These features make HCPN especially suitable for discovering intruders’ actions from their partial observations (alerts) and predicting intruders’ next goal. Our experiments on DARPA evaluation datasets and the attack scenarios from the Grand Challenge Problem (GCP) show that HCPN has promise as a way to reducing false positives and negatives, predicting intruder’s next possible action, uncovering intruders’ intrusion strategies after the attack scenario has happened, and providing confidence scores.

Simulating and visualizing the human arterial system on the TeraGrid

We present a Grid solution to a grand challenge problem, the simulation and visualization of the human arterial system. We implemented our simulation and visualization system on the National Science Foundation’s TeraGrid and demonstrated it at the iGrid 2005 conference in San Diego, California. We discuss our experience in running on a computational Grid and present observations and suggestions for improving similar experiences.

ADVANCES IN MODELLING AND SIMULATION OF LARGE PARALLEL/DISTRIBUTED SYSTEMS

Many systems being designed today are extremely large and are being used to solve huge computational problems. The new system architectures being proposed are huge distributed and parallel systems with thousands of processors distributed widely over space and executing large applications. These new systems are used to solve the Grand Challenge problems. Significant emphasis has been placed on the evaluation of the performance of these systems. This paper surveys recent performance evaluation tools that have been developed to evaluate the performance of such parallel and distributed systems. A new methodology of performance evaluation using hardware description languages is proposed and its advantages over the existing approaches are discussed.

Open source search

Commercial search engines provide a quality service at no cost to consumers thanks to embedded targeted marketing. Despite this, I argue there are still reasons why an open source effort should be encouraged in the community: as part of broader open publishing initiatives, to allow quality subject specific search engines to develop, and "because we can," because search is one of the great grand challenge problems and we, the research community, cannot join in without an accessible, non-proprietary system. This talk outlines arguments and discusses some of the new technology that could go into such a system, as well as the infrastructure that would be required to make it work.

An out-of-core high-resolution FFT algorithm for determining large-scale imperfections of surface potentials in crystals

We present a simple out-of-core algorithm for computing the Fast-Fourier Transform (FFT) needed to determine the two-dimensional potential of surface crystals with large-scale features, like faults, at ultra-high resolution, with around 10 9 grid points. This algorithm represents a proof of concept that a simple and easy-to-code, out-of-core algorithm can be easily implemented and used to solve large-scale problems on low-cost hardware. The main novelties of our algorithm are: (1) elapsed and I/O times decrease with the number of single records (lines) being read; (2) only basic reading and writing routines is necessary for making the out-of-core access. Our method can be easily extended to 3D and be applied to many grand-challenge problems in science and engineering, such as fluid dynamics.

Smart devices and soft controllers

Many of the enabling technologies developed by research communities that haven't always communicated with each other historically. But convergence is in the air - and smart objects and controllers require it. As the Web reaches out in the Internet of things, we increasingly add memory, processing, communications, and intelligence capabilities to the things around us. Targeting a world full of smart objects might provide us a clear grand challenge problem that can lead us to the world where everything truly is alive.

Computational biology and high-performance computing

Understanding evolution and the basic structure and function of proteins are two grand challenge problems in biology that can be solved only through the use of high-performance computing.

Parallel I/O scheduling using randomized, distributed edge coloring algorithms

A growing imbalance in CPU and I/O speeds has led to a communications bottleneck in distributed architectures, especially for data-intensive applications such as multimedia information systems, databases, and Grand Challenge problems. Our solution is to schedule parallel I/O operations explicitly. We present a class of decentralized scheduling algorithms that eliminate contention for I/O ports while maintaining an efficient use of bandwidth. These algorithms, based on edge-coloring and matching of bipartite graphs, rely upon simple heuristics to obtain shorter schedules. We use simulation to evaluate the ability of our algorithms to obtain near optimal solutions in a distributed context, and compare our work with that of other researchers. Our results show that our algorithms produce schedules within 5% of the optimal schedule, a substantial improvement over existing algorithms.

Grid-distributed visualizations using connectionless protocols

The NERSC and Lawrence Berkeley National Laboratory visualization group has developed the Visapult tool to attack grand challenge problems. Visapult is a distributed, parallel, volume rendering application that leverages parallel computation and high-performance networking resources that are on the same scale as the supercomputers generating the data. We've improved Visapult's effectiveness using aggressive network tuning and network protocol modifications. In particular, we used a new connectionless user datagram protocol (UDP) to improve network efficiency from a 25 to 88 percent line rate increase for multigigabit networks. This connectionless protocol also dramatically reduces the latency of network event delivery, improving the responsiveness of wide area distributed interactive graphics applications as compared to transmission control protocol (TCP) streams. We believe that this UDP protocol, as well as transport encodings and algorithms that can tolerate loss gracefully, will become a fundamental component of future grid visualization architectures.

Challenges in mathematical computing

Almost all interesting mathematical algorithmic questions relate to NP-hard questions. Such computation is prone to explode exponentially. The authors anticipate the greatest benefit will come from mathematical platforms that allow for computer-assisted insight generation, not from solutions of grand-challenge problems.