Physical Sciences and Mathematics Commons^™

Lattice Quantum Algorithm For The Schrodinger Wave Equation In 2+1 Dimensions With A Demonstration By Modeling Soliton Instabilities, Jeffrey Yepez, George Vahala, Linda L. Vahala

Electrical & Computer Engineering Faculty Publications

A lattice-based quantum algorithm is presented to model the non-linear Schrödinger-like equations in 2 + 1 dimensions. In this lattice-based model, using only 2 qubits per node, a sequence of unitary collide (qubit-qubit interaction) and stream (qubit translation) operators locally evolve a discrete field of probability amplitudes that in the long-wavelength limit accurately approximates a non-relativistic scalar wave function. The collision operator locally entangles pairs of qubits followed by a streaming operator that spreads the entanglement throughout the two dimensional lattice. The quantum algorithmic scheme employs a non-linear potential that is proportional to the moduli square of the wave function. …

Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota

Dartmouth Scholarship

The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs). In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD) is a powerful method to derive primary components of a given matrix. Applying SVD …

Using Genetic Algorithms To Map First-Principles Results To Model Hamiltonians: Application To The Generalized Ising Model For Alloys, Gus L. W. Hart, Volker Blum, Michael J. Walorski, Alex Zunger

Faculty Publications

The cluster expansion method provides a standard framework to map first-principles generated energies for a few selected configurations of a binary alloy onto a finite set of pair and many-body interactions between the alloyed elements. These interactions describe the energetics of all possible configurations of the same alloy, which can hence be readily used to identify ground state structures and, through statistical mechanics solutions, find finite-temperature properties. In practice, the biggest challenge is to identify the types of interactions which are most important for a given alloy out of the many possibilities. We describe a genetic algorithm which automates this …

Boosted Decision Trees For Word Recognition In Handwritten Document Retrieval, Nicholas Howe, Toni M. Rath, R. Manmatha

Computer Science: Faculty Publications

Recognition and retrieval of historical handwritten material is an unsolved problem. We propose a novel approach to recognizing and retrieving handwritten manuscripts, based upon word image classification as a key step. Decision trees with normalized pixels as features form the basis of a highly accurate AdaBoost classifier, trained on a corpus of word images that have been resized and sampled at a pyramid of resolutions. To stem problems from the highly skewed distribution of class frequencies, word classes with very few training samples are augmented with stochastically altered versions of the originals. This increases recognition performance substantially. On a standard …

Hot Event Detection And Summarization By Graph Modeling And Matching, Yuxin Peng, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This paper proposes a new approach for hot event detection and summarization of news videos. The approach is mainly based on two graph algorithms: optimal matching (OM) and normalized cut (NC). Initially, OM is employed to measure the visual similarity between all pairs of events under the one-to-one mapping constraint among video shots. Then, news events are represented as a complete weighted graph and NC is carried out to globally and optimally partition the graph into event clusters. Finally, based on the cluster size and globality of events, hot events can be automatically detected and selected as the summaries of …

Live Data Views: Programming Pervasive Applications That Use “Timely” And “Dynamic” Data, Jay Black, Paul Castro, Archan Misra, Jerome White

Research Collection School Of Computing and Information Systems

In the absence of generic programming abstractions for dynamic data in most enterprise programming environments, individual applications treat data streams as a special case requiring custom programming. With the growing number of live data sources such as RSS feeds, messaging and presence servers, multimedia streams, and sensor data. a general-purpose client-server programming model is needed to easily incorporate live data into applications. In this paper, we present Live Data Views, a programming abstraction that represents live data as a time-windowed view over a set of data streams. Live Data Views allow applications to create and retrieve stateful abstractions of dynamic …

Some Nonparametric And Semiparametric Methods For Discriminant Analysis., Anil Kumar Ghosh Dr.

Doctoral Theses

Discriminant analysis (see e.g., Devijver and Kittler, 1982; Duda, Hart and Stork, 2000; Hastle, Tibahirani and Friedman, 2001) deals with the separation of different groups of obaervationa and allocation of a new oboervation to one of the previously delined grouga. In a J-class discriminant analysis problem, we usually hae a training sample of the form {(xk, ck) : k = 1,2,...,N}, where xk = (Ik1,Ik2,...J) is a d-dimensional measarement vector, and ca € {1,2,...,J} is its class label. On the basis of thia training sample, one aims to form a decision rule d(x) : Rd + (1,2,...,J} for clasifying the …

Quadratic Regression Analysis For Gene Discovery And Pattern Recognition For Non-Cyclic Short Time-Course Microarray Experiments, Hua Liu, Sergey Tarima, Aaron S. Borders, Thomas V. Getchell, Marilyn L. Getchell, Arnold J. Stromberg

Statistics Faculty Publications

BACKGROUND: Cluster analyses are used to analyze microarray time-course data for gene discovery and pattern recognition. However, in general, these methods do not take advantage of the fact that time is a continuous variable, and existing clustering methods often group biologically unrelated genes together.

RESULTS: We propose a quadratic regression method for identification of differentially expressed genes and classification of genes based on their temporal expression profiles for non-cyclic short time-course microarray data. This method treats time as a continuous variable, therefore preserves actual time information. We applied this method to a microarray time-course study of gene expression at short …

Hydrogeophysical Investigation At Luxor, Southern Egypt, Ahmed Ismail, Neil Lennart Anderson, J. David Rogers

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Over the past 35 years, the exposed stone foundations of the ancient Egyptian monuments at Luxor have deteriorated at an alarmingly accelerated rate. Accelerated deterioration is attributable to three principal factors: 1) excavation and exposure of foundation stone; 2) construction of the Aswan High Dam; and 3) changes in the regional groundwater regime. In an effort to better elucidate the hydrostratigraphy in the Luxor study area that extends from the River Nile to the boundaries of the Nile Valley and covers about 70 km², a geophysical/hydrological investigation was conducted. Forty Schlumberger vertical electrical soundings (VES), two approximately 6 …

Cryptographic And Combinatorial Properties Of Boolean Functions And S-Boxes., Kishan Chand Gupta Dr.

Doctoral Theses

In this thesis we study combinatorial aspects of Boolean functions and S-boxes with impor- tant cryptographic properties and construct new functions possesing such properties. These have possible applications in the design of private key (symmetric key) cryptosystems.Symmetric key cryptosystems are broadly divided into two classes.1. Stream Ciphers,2. Block Ciphers.Some recent proposals of stream ciphers are SNOW [37], SCREAM [52], TURING (98], MUGI (117), HBB (102], RABBIT (9), HELIX (38] and some proposals of block ciphers are DES, AES, RC6 [97), MARS (12], SERPENT (6], TWOFISH (104].In stream cipher cryptography a pseudorandom sequence of bits of length cqual to the message …

An Efficient Scheme For Authenticating Public Keys In Sensor Networks, Wenliang Du, Ronghua Wang, Peng Ning

Electrical Engineering and Computer Science - All Scholarship

With the advance of technology, Public Key Cryptography (PKC) will sooner or later be widely used in wireless sensor networks. Recently, it has been shown that the performance of some public key algorithms, such as Elliptic Curve Cryptography (ECC), is already close to being practical on sensor nodes. However, the energy consumption of PKC is still expensive, especially compared to symmetric-key algorithms. To maximize the lifetime of batteries, we should minimize the use of PKC whenever possible in sensor networks. This paper investigates how to replace one of the important PKC operations–the public key authentication–with symmetric key operations that are …

Incremental Test Collections, Ben Carterette, James Allan

Computer Science Department Faculty Publication Series

Corpora and topics are readily available for information retrieval research. Relevance judgments, which are necessary for system evaluation, are expensive; the cost of obtaining them prohibits in-house evaluation of retrieval systems on new corpora or new topics. We present an algorithm for cheaply constructing sets of relevance judgments. Our method intelligently selects documents to be judged and decides when to stop in such a way that with very little work there can be a high degree of confidence in the result of the evaluation. We demonstrate the algorithm's effectiveness by showing that it produces small sets of relevance judgments that …