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Institution

Purdue University

EducationWest Lafayette, Indiana, United States
About: Purdue University is a education organization based out in West Lafayette, Indiana, United States. It is known for research contribution in the topics: Population & Heat transfer. The organization has 73219 authors who have published 163563 publications receiving 5775236 citations. The organization is also known as: Purdue & Purdue-West Lafayette.


Papers
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Journal ArticleDOI
TL;DR: The development of an efficient solution strategy for obtaining global optima of continuous, integer, and mixed-integer nonlinear programs is addressed and novel relaxation schemes, range reduction tests, and branching strategies are developed which are incorporated into the prototypical branch-and-bound algorithm.
Abstract: This work addresses the development of an efficient solution strategy for obtaining global optima of continuous, integer, and mixed-integer nonlinear programs. Towards this end, we develop novel relaxation schemes, range reduction tests, and branching strategies which we incorporate into the prototypical branch-and-bound algorithm. In the theoretical/algorithmic part of the paper, we begin by developing novel strategies for constructing linear relaxations of mixed-integer nonlinear programs and prove that these relaxations enjoy quadratic convergence properties. We then use Lagrangian/linear programming duality to develop a unifying theory of domain reduction strategies as a consequence of which we derive many range reduction strategies currently used in nonlinear programming and integer linear programming. This theory leads to new range reduction schemes, including a learning heuristic that improves initial branching decisions by relaying data across siblings in a branch-and-bound tree. Finally, we incorporate these relaxation and reduction strategies in a branch-and-bound algorithm that incorporates branching strategies that guarantee finiteness for certain classes of continuous global optimization problems. In the computational part of the paper, we describe our implementation discussing, wherever appropriate, the use of suitable data structures and associated algorithms. We present computational experience with benchmark separable concave quadratic programs, fractional 0–1 programs, and mixed-integer nonlinear programs from applications in synthesis of chemical processes, engineering design, just-in-time manufacturing, and molecular design.

579 citations

Journal ArticleDOI
TL;DR: The STAR Time Projection Chamber (TPC) as discussed by the authors is used to record the collisions at the Relativistic Heavy Ion Collider (RHIC) and provides complete coverage around the beam-line, and complete tracking for charged particles within ± 1.8 units of pseudo-rapidity of the center of mass frame.
Abstract: The STAR Time Projection Chamber (TPC) is used to record the collisions at the Relativistic Heavy Ion Collider (RHIC). The TPC is the central element in a suite of detectors that surrounds the interaction vertex. The TPC provides complete coverage around the beam-line, and provides complete tracking for charged particles within ± 1.8 units of pseudo-rapidity of the center-of-mass frame. Charged particles with momenta greater than

579 citations

Journal ArticleDOI
08 Apr 2009-Memory
TL;DR: It is proposed that many students experience illusions of competence while studying and that these illusions have significant consequences for the strategies students select when they monitor and regulate their own learning.
Abstract: Basic research on human learning and memory has shown that practising retrieval of information (by testing the information) has powerful effects on learning and long-term retention. Repeated testing enhances learning more than repeated reading, which often confers limited benefit beyond that gained from the initial reading of the material. Laboratory research also suggests that students lack metacognitive awareness of the mnemonic benefits of testing. The implication is that in real-world educational settings students may not engage in retrieval practise to enhance learning. To investigate students’ real-world study behaviours, we surveyed 177 college students and asked them (1) to list strategies they used when studying (an open-ended, free report question) and (2) to choose whether they would reread or practise recall after studying a textbook chapter (a forced report question). The results of both questions point to the same conclusion: A majority of students repeatedly read their notes or textbook (despite the limited benefits of this strategy), but relatively few engage in self-testing or retrieval practise while studying. We propose that many students experience illusions of competence while studying and that these illusions have significant consequences for the strategies students select when they monitor and regulate their own learning.

579 citations

Proceedings ArticleDOI
28 Oct 2007
TL;DR: VMwatcher is presented - an "out-of-the-box" approach that overcomes the semantic gap challenge and identifies two unique malware detection capabilities: view comparison-based malware detection and its demonstration in rootkit detection and "out of the box" deployment of host-based anti-malware software with improved detection accuracy and tamper-resistance.
Abstract: An alarming trend in malware attacks is that they are armed with stealthy techniques to detect, evade, and subvert malware detection facilities of the victim. On the defensive side, a fundamental limitation of traditional host-based anti-malware systems is that they run inside the very hosts they are protecting ("in the box"), making them vulnerable to counter-detection and subversion by malware. To address this limitation, recent solutions based on virtual machine (VM) technologies advocate placing the malware detection facilities outside of the protected VM ("out of the box"). However, they gain tamper resistance at the cost of losing the native, semantic view of the host which is enjoyed by the "in the box" approach, thus leading to a technical challenge known as the semantic gap.In this paper, we present the design, implementation, and evaluation of VMwatcher - an "out-of-the-box" approach that overcomes the semantic gap challenge. A new technique called guest view casting is developed to systematically reconstruct internal semantic views (e.g., files, processes, and kernel modules) of a VM from the outside in a non-intrusive manner. Specifically, the new technique casts semantic definitions of guest OS data structures and functions on virtual machine monitor (VMM)-level VM states, so that the semantic view can be reconstructed. With the semantic gap bridged, we identify two unique malware detection capabilities: (1) view comparison-based malware detection and its demonstration in rootkit detection and (2) "out-of-the-box" deployment of host-based anti-malware software with improved detection accuracy and tamper-resistance. We have implemented a proof-of-concept prototype on both Linux and Windows platforms and our experimental results with real-world malware, including elusive kernel-level rootkits, demonstrate its practicality and effectiveness.

578 citations

Journal ArticleDOI
TL;DR: A large scale comparison study for the major machine learning models for time series forecasting, applying the models on the monthly M3 time series competition data to reveal significant differences between the different methods.
Abstract: In this work we present a large scale comparison study for the major machine learning models for time series forecasting. Specifically, we apply the models on the monthly M3 time series competition data (around a thousand time series). There have been very few, if any, large scale comparison studies for machine learning models for the regression or the time series forecasting problems, so we hope this study would fill this gap. The models considered are multilayer perceptron, Bayesian neural networks, radial basis functions, generalized regression neural networks (also called kernel regression), K-nearest neighbor regression, CART regression trees, support vector regression, and Gaussian processes. The study reveals significant differences between the different methods. The best two methods turned out to be the multilayer perceptron and the Gaussian process regression. In addition to model comparisons, we have tested different preprocessing methods and have shown that they have different impacts on the pe...

578 citations


Authors

Showing all 73693 results

NameH-indexPapersCitations
Yi Cui2201015199725
Yi Chen2174342293080
David Miller2032573204840
Hongjie Dai197570182579
Chris Sander178713233287
Richard A. Gibbs172889249708
Richard H. Friend1691182140032
Charles M. Lieber165521132811
Jian-Kang Zhu161550105551
David W. Johnson1602714140778
Robert Stone1601756167901
Tobin J. Marks1591621111604
Joseph Wang158128298799
Ed Diener153401186491
Wei Zheng1511929120209
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023194
2022834
20217,499
20207,699
20197,294
20186,840