bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding Time-Series Networks: A Case Study in Rule Extraction
Authors: Craven Mark W. , Shavlik Jude W.
Published: October 2003

A significant limitation of neural networks is that the representation they learn are usually incomprehensible to humans. We have developed an algorithm, called TREPAN, for extracting comprehensible, symbolic representations from trained neural networks. Given a trained network, TREPAN produces a decision tree that approximates the concept represented by the network. In this article, we discuss the application of TREPAN to a neural network trained on a noisy time series task: predicting the Dollar–Mark exchange rate. We present experiments that show that TREPAN is able to extract a decision tree from this network that equals the network in terms of predictive accuracy, yet provides a comprehensible concept representation. Moreover, our experiments indicate that decision trees induced directly from the training data using conventional algorithms do not match the accuracy nor the comprehensibility of the tree extracted by TREPAN.
Modeling Volatility Using State Space Models
Authors: Timmer Jens , Weigend Andreas S.
Published: October 2003

In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of observational noise. To model and predict their time evolution adequately, we estimate state space models that explicitly include observational noise. We obtain relaxation times for shocks in the logarithm of volatility ranging from three weeks (for foreign exchange) to three to five months (for stock indices). In most cases, a two-dimensinal hidden state is required to yield residuals that are consistent with white noise. We compare these results with ordinary autoregressive models (without a hidden state) and find that autoregressive models underestimate the relaxation times by about two orders of magnitude since they do not distinguish between observational and dynamic noise. This new interpretation of the dynamics of volatility in terms of relaxators in a state space model carries over to stochastic volatility models and to GARCH models, and is useful for several problems in finance, including risk management and the pricing of derivative securities.

Data sets used. Olsen & Associates high frequency DEM/USD foreign exchange rates (8 years). Nikkei 225 index (40 years). Dow Jones Industrial Average (25 years).
A Constrained Neural Network Kalman Filter for Price Estimation in High Frequency Financial Data
Authors: Bolland Peter J. , Connor Jerome T.
Published: October 2003

In this paper we present a neural network extended Kalman filter for modeling noisy financial time series. The neural network is employed to estimate the nonlinear dynamics of the extended Kalman filter. Conditions for the neural network weight matrix are provided to guarantee the stability of the filter. The extended Kalman filter presented is designed to filter three types of noise commonly observed in financial data: process noise, measurement noise, and arrival noise. The erratic arrival of data (arrival noise) results in the neural network predictions being iterated into the future. Constraining the neural network to have a fixed point at the origin produces better iterated predictions and more stable results. The performance of constrained and unconstrained neural networks within the extended Kalman filter is demonstrated on "Quote" tick data from the $/DM exchange rate (1993–1995).
Nonlinear Trading Models Through Sharpe Ratio Maximization
Authors: Choey Mark , Weigend Andreas S.
Published: October 2003

While many trading strategies are based on price prediction, traders in financial markets are typically interested in optimizing risk-adjusted performance such as the Sharpe Ratio, rather than the price predictions themselves. This paper introduces an approach which generates a nonlinear strategy that explicitly maximizes the Sharpe Ratio. It is expressed as a neural network model whose output is the position size between a risky and a risk-free asset. The iterative parameter update rules are derived and compared to alternative approaches. The resulting trading strategy is evaluated and analyzed on both computer-generated data and real world data (DAX, the daily German equity index). Trading based on Sharpe Ratio maximization compares favorably to both profit optimization and probability matching (through cross-entropy optimization). The results show that the goal of optimizing out-of-sample risk-adjusted profit can indeed be achieved with this nonlinear approach.
Using a Financial Training Criterion Rather than a Prediction Criterion
Authors: Bengio Yoshua
Published: October 2003

The application of this work is to decision making with financial time series, using learning algorithms. The traditional approach is to train a model using a prediction criterion, such as minimizing the squared error between predictions and actual values of a dependent variable, or maximizing the likelihood of a conditional model of the dependent variable. We find here with noisy time series that better results can be obtained when the model is directly trained in order to maximize the financial criterion of interest, here gains and losses (including those due to transactions) incurred during trading. Experiments were performed on portfolio selection with 35 Canadian stocks.
Switching Portfolios
Authors: Singer Yoram
Published: October 2003

A constant rebalanced portfolio is an asset allocation algorithm which keeps the same distribution of wealth among a set of assets along a period of time. Recently, there has been work on on-line portfolio selection algorithms which are competitive with the best constant rebalanced portfolio determined in hindsight (Cover, 1991; Helmbold et al., 1996; Cover and Ordentlich, 1996). By their nature, these algorithms employ the assumption that high returns can be achieved using a fixed asset allocation strategy. However, stock markets are far from being stationary and in many cases the wealth achieved by a constant rebalanced portfolio is much smaller than the wealth achieved by an ad hoc investment strategy that adapts to changes in the market. In this paper we present an efficient portfolio selection algorithm that is able to track a changing market. We also describe a simple extension of the algorithm for the case of a general transaction cost, including the transactions cost models recently investigated in (Blum and Kalai, 1997). We provide a simple analysis of the competitiveness of the algorithm and check its performance on real stock data from the New York Stock Exchange accumulated during a 22-year period. On this data, our algorithm outperforms all the algorithms referenced above, with and without transaction costs.
Improved Option Pricing Using Artificial Neural Networks and Bootstrap Methods
Authors: Lajbcygier Paul R. , Connor Jerome T.
Published: October 2003

A hybrid neural network is used to predict the difference between the conventional option-pricing model and observed intraday option prices for stock index option futures. Confidence intervals derived with bootstrap methods are used in a trading strategy that only allows trades outside the estimated range of spurious model fits to be executed. Whilst hybrid neural network option pricing models can improve predictions they have bias. The hybrid option-pricing bias can be reduced with bootstrap methods. A modified bootstrap predictor is indexed by a parameter that allows the predictor to range from a pure bootstrap predictor, to a hybrid predictor, and finally the bagging predictor. The modified bootstrap predictor outperforms the hybrid and bagging predictors. Greatly improved performance was observed on the boundary of the training set and where only sparse training data exists. Finally, bootstrap bias estimates were studied.
A First Application of Independent Component Analysis to Extracting Structure from Stock Returns
Authors: Back Andrew D. , Weigend Andreas S.
Published: October 2003

This paper explores the appliation of a signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. ICA is shown to be a potentially powerful method of analyzing and understanding driving mechanisms in financial time series. The application to portfolio optimization is described in Chin and Weigend (1998).
Letter to the Editor: Neural Solution to the Correction of Miss Distance in Gun Fire Control System
Authors: Kim J. G. , Jeong H. , Lee Y. W.
Published: October 2003

Multilayer perceptrons trained with the backpropagation algorithm are tested in gun fire control system for error correction and are compared to optimal algorithms based on minimum mean square error.1,2 The structure of the proposed neural controller is described and performance results are shown.
Universal Distribution of Saliencies for Pruning in Layered Neural Networks
Authors: Gorodkin J. , Hansen L. K. , Lautrup B. , Solla S. A.
Published: October 2003

A better understanding of pruning methods based on a ranking of weights according to their saliency in a trained network requires further information on the statistical properties of such saliencies. We focus on two-layer networks with either a linear or nonlinear output unit, and obtain analytic expressions for the distribution of saliencies and their logarithms. Our results reveal unexpected universal properties of the log-saliency distribution and suggest a novel algorithm for saliency-based weight ranking that avoids the numerical cost of second derivative evaluations.
On a Recurrent Neural Network Producing Oscillations
Authors: Fredman T. P. , Saxén H.
Published: October 2003

A recurrent two-node neural network producing oscillations is analyzed. The network has no true inputs and the outputs from the network exhibit a circular phase portrait. The weight configuration of the network is investigated, resulting in analytical weight expressions, which are compared with numerical weight estimates obtained by training the network on the desired trajectories. The values predicted by the analytical expressions agree well with the findings from the numerical study, and can also explain the asymptotic properties of the networks studied.
A Cascading Structure and Training Method for Multilayer Neural Networks
Authors: Li Yan , Rad A. B.
Published: October 2003

A new structure and training method for multilayer neural networks is presented. The proposed method is based on cascade training of subnetworks and optimizing weights layer by layer. The training procedure is completed in two steps. First, a subnetwork, m inputs and n outputs as the style of training samples, is trained using the training samples. Secondly the outputs of the subnetwork is taken as the inputs and the outputs of the training sample as the desired outputs, another subnetwork with n inputs and n outputs is trained. Finally the two trained subnetworks are connected and a trained multilayer neural networks is created. The numerical simulation results based on both linear least squares back-propagation (LSB) and traditional back-propagation (BP) algorithm have demonstrated the efficiency of the proposed method.
Adaptive Rival Penalized Competitive Learning and Combined Linear Predictor Model for Financial Forecast and Investment
Authors: Cheung Yiu-Ming , Leung Wai-Man , Xu Lei
Published: October 2003

We propose a prediction model called Rival Penalized Competitive Learning (RPCL) and Combined Linear Predictor method (CLP), which involves a set of local linear predictors such that a prediction is made by the combination of some activated predictors through a gating network (Xu et al., 1994). Furthermore, we present its improved variant named Adaptive RPCL-CLP that includes an adaptive learning mechanism as well as a data pre-and-post processing scheme. We compare them with some existing models by demonstrating their performance on two real-world financial time series — a China stock price and an exchange-rate series of US Dollar (USD) versus Deutschmark (DEM). Experiments have shown that Adaptive RPCL-CLP not only outperforms the other approaches with the smallest prediction error and training costs, but also brings in considerable high profits in the trading simulation of foreign exchange market.
A Survey of Partially Connected Neural Networks
Authors: Elizondo D. , Fiesler E.
Published: October 2003

Almost all artificial neural networks are by default fully connected, which often implies a high redundancy and complexity. Little research has been devoted to the study of partially connected neural networks, despite its potential advantages like reduced training and recall time, improved generalization capabilities, reduced hardware requirements, as well as being a step closer to biological reality. This publication presents an extensive survey of the various kinds of partially connected neural networks, clustered into a clear framework, followed by a detailed comparative discussion.
The Prospects for Analogue Neural VLSI
Authors: Murray Alan F. , Woodburn Robin
Published: October 2003

In recent years, the efforts of analogue, neural-hardware designers have shifted from generic analogue neurocomputers to "niche" markets in sensor fusion and robotics, and we explain why this is so. We describe the main differences between digital and analogue computation, and consider the advantages of pure analogue and pulsed methods of design. We then investigate some important issues in analogue design of neural machines, namely weight storage (volatile and non-volatile), on-chip learning, and arithmetic accuracy and its relationship to noise. Finally, we outline those areas in which analogue techniques are likely to prove most useful, and speculate as to their likely long-term utility.
A Neural Network Method for Identification of Prokaryotic and Eukaryotic Signal Peptides and Prediction of their Cleavage Sites
Authors: Nielsen Henrik , Engelbrecht Jacob , Brunak Søren , Heijne Gunnar Von
Published: October 2003

We have developed a new method for the identification of signal peptides and their cleavage sites based on neural networks trained on separate sets of prokaryotic and eukaryotic sequences. The method performs significantly better than previous prediction schemes, and can easily be applied to genome-wide data sets. Discrimination between cleaved signal peptides and uncleaved N-terminal signal-anchor sequences is also possible, though with lower precision. Predictions can be made on a publicly available WWW server: .
Least-Squares Methods for Blind Source Separation Based on Nonlinear PCA
Authors: Pajunen Petteri , Karhunen Juha
Published: October 2003

In standard blind source separation, one tries to extract unknown source signals from their instantaneous linear mixtures by using a minimum of a priori information. We have recently shown that certain nonlinear extensions of principal component type neural algorithms can be successfully applied to this problem. In this paper, we show that a nonlinear PCA criterion can be minimized using least-squares approaches, leading to computationally efficient and fast converging algorithms. Several versions of this approach are developed and studied, some of which can be regarded as neural learning algorithms. A connection to the nonlinear PCA subspace rule is also shown. Experimental results are given, showing that the least-squares methods usually converge clearly faster than stochastic gradient algorithms in blind separation problems.
Motion Planning with Complete Knowledge Using a Colored Som
Authors: Vleugels Jules , Kok Joost N. , Overmars Mark
Published: October 2003

The motion planning problem requires that a collision-free path be determined for a robot moving amidst a fixed set of obstacles. Most neural network approaches to this problem are for the situation in which only local knowledge about the configuration space is available. The main goal of the paper is to show that neural networks are also suitable tools in situations with complete knowledge of the configuration space.

In this paper we present an approach that combines a neural network and deterministic techniques. We define a colored version of Kohonen's self-organizing map that consists of two different classes of nodes. The network is presented with random configurations of the robot and, from this information, it constructs a road map of possible motions in the work space. The map is a growing network, and different nodes are used to approximate boundaries of obstacles and the Voronoi diagram of the obstacles, respectively.

In a second phase, the positions of the two kinds of nodes are combined to obtain the road map. In this way a number of typical problems with small obstacles and passages are avoided, and the required number of nodes for a given accuracy is within reasonable limits. This road map is searched to find a motion connecting the given source and goal configurations of the robot.

The algorithm is simple and general; the only specific computation that is required is a check for intersection of two polygons. We implemented the algorithm for planar robots allowing both translation and rotation and experiments show that compared to conventional techniques it performs well, even for difficult motion planning scenes.
Two Constructive Methods for Designing Compact Feedforward Networks of Threshold Units
Authors: Amaldi Edoardo , Guenin Bertrand
Published: October 2003

We propose two algorithms for constructing and training compact feedforward networks of linear threshold units. The SHIFT procedure constructs networks with a single hidden layer while the PTI constructs multilayered networks. The resulting networks are guaranteed to perform any given task with binary or real-valued inputs. The various experimental results reported for tasks with binary and real-valued inputs indicate that our methods compare favorably with alternative procedures deriving from similar strategies, both in terms of size of the resulting networks and of their generalization properties.
Entropic Analysis and Incremental Synthesis of Multilayered Feedforward Neural Networks
Authors: Pelagotti Andrea , Piuri Vincenzo
Published: October 2003

Neural network architecture optimization is often a critical issue, particularly when VLSI implementation is considered. This paper proposes a new minimization method for multilayered feedforward ANNs and an original approach to their synthesis, both based on the analysis of the information quantity (entropy) flowing through the network. A layer is described as an information filter which selects the relevant characteristics until the complete classification is performed. The basic incremental synthesis method, including the supervised training procedure, is derived to design application-tailored neural paradigms with good generalization capability.
Stochastic ICA Contrast Maximisation Using Oja's Nonlinear PCA Algorithm
Authors: Girolami Mark , Fyfe Colin
Published: October 2003

Independent Component Analysis (ICA) is an important extension of linear Principal Component Analysis (PCA). PCA performs a data transformation to provide independence to second order, that is, decorrelation. ICA transforms data to provide approximate independence up to and beyond second order yielding transformed data with fully factorable probability densities. The linear ICA transformation has been applied to the classical statistical signal-processing problem of Blind Separation of Sources (BSS), that is, separating unknown original source signals from a mixture whose mode of mixing is undetermined. In this paper it is shown that Oja's Nonlinear PCA algorithm performs a general stochastic online adaptive ICA. This analysis is corroborated with three simulations. The first separates unknown mixtures of original natural images, which have sub-Gaussian densities, the second separates linear mixtures of natural speech whose densities are super-Gaussian. Finally unknown mixtures of original images, which have both sub- and super-Gaussian densities are separated.
ISO/MPEG Audio Coding
Authors: Noll Peter , Pan Davis
Published: October 2003

The Moving Pictures Expert Group within the International Organization of Standardization (ISO/MPEG) has developed, and is presently developing, a series of audiovisual standards. Its audio coding standard MPEG Phase 1 is the first international standard in the field of high quality digital audio compression and has been applied in many areas, both for consumer and professional audio. Typical application areas for digital audio are in the fields of audio production, program distribution and exchange, digital sound broadcasting, digital storage, and various multimedia applications. This paper will describe in some detail the main features of MPEG Phase 1 coders.

As a logical further step in digital audio a multichannel audio standard MPEG Phase 2 is being standardized to provide an improved stereophonic image for audio-only applications including teleconferencing and for improved television systems. The status of this standardization process will be covered briefly.
Lossless Compression for Text and Images
Authors: Moffat Alistair , Bell Timothy C. , Witten Ian H.
Published: October 2003

Most data that is inherently discrete needs to be compressed in such a way that it can be recovered exactly, without any loss. Examples include text of all kinds, experimental results, and statistical databases. Other forms of data may need to be stored exactly, such as images—particularly bilevel ones, or ones arising in medical and remote-sensing applications, or ones that may be required to be certified true for legal reasons. Moreover, during the process of lossy compression, many occasions for lossless compression of coefficients or other information arise.

This paper surveys techniques for lossless compression. The process of compression can be broken down into modeling and coding. We provide an extensive discussion of coding techniques, and then introduce methods of modeling that are appropriate for text and images. Standard methods used in popular utilities (in the case of text) and international standards (in the case of images) are described.
Organic Electroluminescence Displays
Authors: So Franky , Shi Song , Lee H. C.
Published: October 2003

Recently, organic light emitting diodes have received a lot of attention in different research laboratories world-wide. Red, green and blue emitting devices are readily available. Devices with luminous efficiencies greater than 15 lm/W and lifetimes longer than 10,000 hours have been demonstrated. In this article, we will discuss the basic devices used in physics, materials used in organic light emitting diodes, device degradation mechanisms, and the opportunities of using this technology for commercial display applications.
Gallium Nitride Based Semiconductors for Short Wavelength Optoelectronics
Authors: Denbaars S. P.
Published: October 2003

In this article we review the key technologies for GaN based materials and devices. Developments in the methods for thin film deposition by metalorganic chemical vapor deposition (MOCVD) and molecular beam epitaxy (MBE) and resulting film properties are highlighted. Breakthroughs in materials growth has enabled extremely high efficiency blue and green GaN LEDs to be achieved for the first time. GaN LEDs complete the primary color spectrum and have enabled bright and reliable full-color solid state displays to be realized. Recently, room temperature operation of pulsed current injection blue-violet lasers emitting at 417 nm has further increased possible applications for GaN based optoelectronic devices.
Free-Space Integrated Optics Realized by Surface-Micromachining
Authors: Wu M. C. , Lin L. Y. , Lee S. S. , King C. R.
Published: October 2003

A surface-micromachined free-space micro-optical bench (FS-MOB) technology has been proposed to monolithically integrate micro-optical elements, optomechanical structures, micropositioners, and microactuators on the same substrate. Novel three-dimensional micro-optical elements have been fabricated by surface-micromachining techniques. The optical axes of these optical elements are parallel to the substrate, which enables the entire free-space optical system to be integrated on a single substrate. Mocro-scale Fresnel lenses, refractive microlenses, mirrors, beam-splitters, gratings, and precision optical mounts have been successfully fabricated and characterized. Integration of micro-optical elements with translation or rotation stages provides on chip optical alignment or optomechanical switching. This new free-space micro-optical bench technology could significantly reduce the size, weight, an cost of most optical systems, and could have a significant impact on optical switching, optical sensing and optical data storage systems as well as packaging of optoelectronic components.
Wafer Fusion for Surface-Normal Optoelectronic Device Applications
Authors: Babić Dubravko I. , Bowers John. E. , Hu Evelyn L. , Yang Long , Carey Kent
Published: October 2003

This paper discusses the issues connected with the application of fusion bonding technology for surface-normal optoelectronic devices (III-V semiconductors). The InP/GaAs fusion bonding technology employed in the development of long-wavelength vertical-cavity lasers is described.
High Speed 1.55 μm Lasers for Fiber Optic Transmission
Authors: Morton Paul A.
Published: October 2003

This paper describes the essential elements for creating a practical wide bandwidth directly modulated laser source. This includes considerations of the intrinsic limitations of the laser structure, due to the resonant frequency and damping of the laser output, together with carrier transport issues to allow carriers in the device active region to be efficiently modulated at high speeds. The use of a P-doped compressively strained multiple-quantum well active region to provide high intrinsic speed and remove transport limitations is described, together with record setting results of 25 GHz modulation bandwidth for a 1.55 μm Fabry–Perot laser and 26 GHz bandwidth for a 1.55 μm DFB laser. The challenges of providing high bandwidth electrical connections to the laser on a suitable submount, together with fiber attachment and microwave packaging are discussed. Results of fully packaged 1.55 μm DFB laser with 25 GHz modulation bandwidth are shown. Digital modulation of the packaged 1.55 μm DFB including impedance matching is described, and the transient wavelength chirp is presented. This low chirp is reduced further using an optical filter, to provide a 10 GBit/s source that can transmit error free over 38.5 km of standard optical fiber.
Mode-Locked Diode Lasers for Microwave Optoelectronic Applications
Authors: Avrutin E. A. , Marsh J. H. , Portnoi E. L.
Published: October 2003

The paper reviews the recent progress in technology, theory and applications of diode lasers mode locked at microwave frequencies.
The Performance of Hg0.8Cd0.2Te Photodetectors by Using Photo Surface Treatment
Authors: Su Y. K. , Lin C. T.
Published: October 2003

The principal aim of this paper is to propose an easy, vapor phase, and reproducible photo surface treatment method to improve the device performance of the Hg0.8Cd0.2Te photoconductive detector. Experimental results, including Auger electron spectroscopy (AES), MIS leakage current, 1/f noise voltage spectrum, 1/fknee frequency, responsivity Rλ, and specific detectivity D* for stacked photo surface treatment and ZnS or CdTe passivation layers are presented. By using this method, we found that there is no accumulation of Hg in the oxide/HgCdTe interface regions. Since the photo chemical vapor native oxidation is a dry oxidation method deposited at a low temperature, it can effectively suppress the Hg enhancement and the Cd depletion effects and thus obtain a high quality interface. We also found that the photo surface treatment in combination with thermally eveporated ZnS or CdTe layer would shift the 1/fknee under 100Hz in an electrical field under 50 V/cm, reduce the noise power spectrum, and achieve a lower surface recombination velocity S of 300 cm/sec as well as a high D* of 3 × 1010 cm [Formula: see text] for blackbody radiation. It was also found that HgCdTe photoconductor passivated with stacked layers shows improved interface properties when compared to the photoconductor passivated with a single passivation layer.
Real-Time Shift-Invariant Optical Pattern Recognition
Authors: Cheng Yih-Shyang
Published: October 2003

Shift invariance is an asset of the VanderLugt correlator, from which the location of the identified object is automatically specified. The development of filters which possess two or three types of invariance (shift, rotation, size, and distortion) simultaneously is reviewed. Various real-time implementation of VanderLugt as well as joint-transform correlators by utilizing spatial light modulators are also reviewed.
Investigation of Multiwavelength Optical Power Limiting Amplifier and its Applications in High-Speed Sonet Wavelength-Division Multiplexing Self-Healing Ring Network
Authors: Chen Yung-Kuang , Liaw Shien-Kuei , Chi Sien
Published: October 2003

A multiwavelength optical power limiting amplifier (OLA) for high-speed SONET self-healing ring (SHR) networks is reported. Four possible OLA configurations are investigated. We find that the configuration consisting of a high-gain common erbium-doped fiber amplifier (EDFA) followed by a grating-multiplexed multiple-power-EDFA module is the best scheme for multiwavelength power-limiting operation. A constant channel output of > 11 dBm, small inter-channel power variation of ≤ 0.5 dB, and fairly low noise figure are obtained within a large dynamic range of 45 dB. Network application in a SHR network is also demonstrated and the ring size of 150 km served by a single three-WDM-channel OLA at channel rates of 2.488 Gb/s is obtained.
Lattice QCD Results and Prospects
Authors: Kenway Richard
Published: October 2003

In the Standard Model, quarks and gluons are permanently confined by the strong interaction into hadronic bound states. The values of the quark masses and the strengths of the decays of one quark flavour into another cannot be measured directly, but must be deduced from experiments on hadrons. This requires calculations of the strong-interaction effects within the bound states, which are only possible using numerical simulations of lattice QCD. These are computationally intensive and, for the past twenty years, have exploited leading-edge computing technology. In conjunction with experimental data from B Factories, over the next few years, lattice QCD may provide clues to physics beyond the Standard Model. These lectures provide a non-technical introduction to lattice QCD, some of the recent results, QCD computers, and the future prospects.
DGLAP and BFKL Equations now
Authors: Lipatov L. N.
Published: October 2003

The s and t-channel approaches to the high energy hadron scattering are reviewed. The parton model is applied to describe the high energy and deep-inelastic processes. In QCD the parton picture leads to the DGLAP evolution equations for the parton distributions. The gluon and quark are shown to lie on their Regge trajectories. The integrability properties of the effective theory for the high energy QCD in the multi-colour limit are outlined. The Baxter - Sklyanin representation for the wave function of the composite colourless states is formulated. Next-to-leading corrections to the BFKL equation in QCD and supersymmetric gauge theories are discussed. The effective action for the gluon and quark integrations local in the parton rapidities is presented.
The Puzzle of the Ultra-High Energy Cosmic Rays
Authors: Tkachev I. I.
Published: October 2003

In early years the cosmic ray studies were ahead of the accelerator research, starting from the discovery of positrons, through muons, to that of pions and strange particles. Today we are facing the situation that the puzzling saga of cosmic rays of the highest energies may again unfold in the discovery of new physics, now beyond the Standard Model; or it may bring to life an "extreme" astrophysics. After a short review of the Greisen-Zatsepin-Kuzmin puzzle, I discuss different models which were suggested for its resolution. Are there any hints pointing to the correct model? I argue that the small-scale clustering of arrival directions of cosmic rays gives a clue, and BL Lacs are probable sources of the observed events.
Highlights in Subnuclear Physics
Authors: Wolf Günter
Published: October 2003

An overview on new developments in experimental particle and astroparticle physics is presented. This includes observation of CP violation in the B-sector, detection of direct CP violation in the K0-system, new measurements on solar neutrinos, cosmic microwave background radiation and properties of the universe, electroweak results from the large colliders and tests of the Standard Model, measurement of the anomalous magnetic moment of muons, structure of the proton at large energy transfers and virtualities, and heavy ion collisions at very high energies. Finally, the physics reach of TEVATRONII, LHC and TESLA, a linear e+e-collider proposed for the 0.5 - 1 TeV regime, is briefly reviewed.
Experimental Highlights from Gran Sasso Laboratory
Authors: Bettini A.
Published: October 2003

The Gran Sasso National Laboratory of the INFN has given important contributions to the discovery of the phenomenon of neutrino oscillations with experiments both on electron neutrinos from the Sun and muon neutrinos indirectly produced by cosmic rays interactions in the atmosphere. Other experiments in the laboratory give the best limits on electron neutrino effective Majorana mass with two different isotopes. We appear to have entered a new physics domain in which the study of neutrinos may lead us to discover new phenomena, corresponding to energy scales by much higher than those of the present accelerators. Underground laboratories are showing their relevance complementary to the colliders for the advance of basic physics.

The physics program at the Gran Sasso Laboratory that we are defining will be focussed on neutrino physics with a complementary set of experiments on the muon neutrino beam from CERN (CNGS project), on solar neutrinos, on atmospheric neutrinos and on neutrinos from Supernova explosion. The relevant thermonuclear cross-sections will be measured. The Majorana vs. Dirac nature of electron neutrinos will be explored with the search for neutrino-less double beta decays in different isotopes.
The Anomalous Magnetic Moment of the Muon
Authors: Hughes Vernon W. , Sichtermann Ernst P.
Published: October 2003

A precise measurement of the anomalous g value, a = (g - 2)/2, for the positive muon has been made at the Brookhaven Alternating Gradient Synchrotron. The result aμ+ = 11 659 202(14)(6) × 10-10 (1.3 ppm) is in good agreement with previous measurements and has an error one third that of the combined previous data. The measurement tests standard theory and has the potential to discover new physics. The analysis of data collected in 2000 and 2001 is well underway and, when combined with data from a requested and final run in the fall of 2002 and winter of 2003, are expected to reduce the experimental uncertainty on aμ to 0.4 ppm.
Experimental Highlights from Super-Kamiokande
Authors: Totsuka Y.
Published: October 2003

Recent results from Super-Kamiokande are presented. Solar and atmospheric neutrinos have been studied in detail since 1996. The data of atmospheric neutrinos show strong up-down asymmetry and provide evidence for oscillation along their way through Earth. The selected data support oscillations of νμ → ντ and rule out the two-flavor νμ → νs oscillation. The underlying parameters for the νμ → ντ oscillations are determined to be [Formula: see text] and sin 22θ > 0.89ata90%C.L. Proton decays into various final states have been searched for but no evidence was found. Lower limits on the partial lifetimes are presented for major decay modes, especially those for p → e+π0 and [Formula: see text] are 5.0 × 1033 yr and 1.9 × 1033 yr , respectively.
Helicity of the W in Single-Lepton $t\bar{t}$ Events
Authors: Canelli Florencia
Published: October 2003

We have developed a procedure to study the helicity of the W boson in [Formula: see text] events. Using events generated with the PYTHIA program at a top Mt = 175 GeV/c2, we employ a maximum likelihood method to extract the fraction of right-handed, longitudinal and left-handed W bosons in top decays. Assuming V–A coupling in the decay, we obtain a longitudinal fraction of 0.696±0.097 for 50 simulated experiments of 100 events in lepton+jets [Formula: see text] final states, a result that is consistent with the input fraction of 0.703.
Baryogenesis with Four-Fermion Operators in Low-Scale Models
Authors: Dent Thomas
Published: October 2003

We describe a nonstandard proposal for baryogenesis in models with a low ([Formula: see text](10 – 100)TeV) fundamental scale. The scenario has Standard Model field content and enhanced baryon number violating operators deriving from time-dependent fermion localization in an extra dimension. The CKM matrix provides sufficient CP violation. The major constraints are the low reheating temperature and rate of perturbative B-violating reactions compared to the total entropy created. A sufficient baryon fraction may arise, but the cosmological evolution required is likely to be somewhat contrived. Based on work in collaboration with D.J.H. Chung.
Is the Massive Graviton a Viable Possibility?
Authors: Papazoglou Antonios
Published: October 2003

In this talk we discuss the scenario of multigravity according to which the gravity we observe in intermediate scales (1 mm < r < 1026 cm ) is mediated by both a massless graviton and one or more of ultralight spin-2 state. We present how this can be realized in a five dimensional brane-world theory with flat branes and the complications associated with the extra polarizations of the massive gravitons (van Dam-Veltman-Zakharov discontinuity) and the ghost radions corresponding to the fluctuations of the negative tension branes present in these models. We then show how we can construct models of AdS4 branes instead with exclusively positive tension and demonstrate how the van Dam-Veltman-Zakharov no-go theorem can be circumvented in curved space. These models, although they are consistent, face phenomenological problems related to the presence of a remnant negative cosmological constant on the branes. We finally present how we can obtain the same constructions in six dimensions with flat positive tensions branes only, in a manner that they are both theoretically consistent and phenomenologically acceptable. The latter come in two copies each and offer the first problem-free realization of the multigravity scenario.
Energy Estimate of Neutrino Induced Upgoing Muons
Authors: Scapparone E.
Published: October 2003

An estimate of the energy of neutrino-induced muons in MACRO is provided by a multiple Coulomb scattering measurement. The MACRO original upward-muon data sample has been subdivided according to the reconstructed muon energy. Data in each subset are then compared with expected fluxes from atmospheric neutrinos. The results are interpreted in terms of neutrino oscillations.
Relativistic Stars in Randall-Sundrum Gravity
Authors: Wiseman Toby
Published: October 2003

We review the non-linear behavior of compactifications with localized brane matter discussed in [1]. The example chosen to test is Randall-Sundrum gravity, with one brane, having no moduli problem and reproducing astrophysical gravity in the long wavelength linear regime. The full 5-dimensional Einstein equations are solved numerically for static spherically symmetric matter localized on the brane, yielding regular geometries in the bulk with axial symmetry. The algorithm performs best for small stars (radius less than the AdS length) yielding highly non-linear solutions, photons emitted from the stellar core having redshifts up to [Formula: see text]. An upper mass limit is observed in these small stars. We are able to show that the intrinsic geometry of large stars, with radius several times the AdS length, is described by 4-dimensional General Relativity past the perturbative regime and up to the largest mass stars studied with core photon redsbifts of [Formula: see text].
The Ten Challenges of Subnuclear Physics
Authors: Zichichi Antonino
Published: October 2003

The vitality of Subnuclear Physics is proved by the existence of its challenges, which are presented and discussed with reference to their implementation in the near future.
Scanning Probe Microscopy of Surface Plasmons
Authors: Smolyaninov Igor I.
Published: October 2003

Recent development of novel scanning probe techniques such as Scanning Tunneling Microscopy (STM), Atomic Force Microscopy (AFM), and Near-Field Optical Microscopy (NFOM) has opened new ways to study local field distribution of surface electromagnetic waves. A lot of experimental efforts have been concentrated on the study of surface plasmons (SP). Different techniques allow to excite and probe SPs with wavelengths from 1 nm down to the optical range along its entire dispersion curve. Large number of phenomena have been studied directly, such as SP scattering by individual defects, strong and weak localization of SP, SP induced local field enhancement, light emission from the tunneling junction, etc. Scanning probe techniques allow not only topography and field mapping but also surface modification and lithography on the nanometer scale. Combination of these features in the same experimental setup proved to be extremely useful in SP studies. For example, some prototype two dimensional optical elements able to control SP propagation have been demonstrated.
Application of the Global SO(4) Symmetry in the Diagonalization of Translationally Invariant Correlated Electron Models
Authors: Cuoco Mario , Romano Alfonso , Noce Canio
Published: October 2003

We present a method for the simultaneous implementation of the spin symmetry, the pseudospin symmetry and the translational invariance in models for correlated electron systems on finite-size clusters. The application of this method leads to a significative reduction of the size of the Hamiltonian matrix blocks, thus considerably simplifying the diagonalization procedure. We illustrate the method solving exactly the Hubbard model on a 8-site chain in the Hilbert subspaces labelled by the eigenvalues of the spin, the pseudospin and the momentum operators. Some relevant thermodynamical quantities, such as the specific heat and the static magnetic susceptibility, are then evaluated and a brief discussion on the tendency of the model to exhibit ferromagnetism is also presented.
Universal Algebraic Varieties and Ideals in Physics: Field Theory on Algebraic Varieties
Authors: Iguchi Kazumoto
Published: October 2003

A class of universal algebraic varieties in physics is discussed herein using the concepts of determinant ideals in algebraic geometry. It is shown that these algebraic varieties arise with very different physical contexts in many branches of physics and mathematics from high energy physics theory to chaos theory. In these physical systems the models are constructed by using the fields on usual manifolds such as vector fields in a Euclidean space and a Minkowskian space. But there is a universal mathematical aspect of linear algebra for linear vector spaces, where the linear independency and dependency are described using the Gramians of the vectors. These Gramians form a class of hypersurfaces in a higher-dimensional mathematical space: If there exist g vectors vi in an n-dimensional Euclidean space, the Gramian Gg is given as a g × g determinant Gg= Det [xij] with the inner products xij=(vi,vj), and exists in a g(g-1)/2-[g(g+1)/2-] dimensional space if the vectors are (not) normalized, xii=1(xii ≠ 1). It is also shown that the Gramians are invariant under automorphisms of the vectors. The mathematical structure of the Gramians is revealed to be equivalent to the concepts of determinant ideals Ig(v), each element of which is a g × g determinant constructed from components of an arbitrary N×N matrix with N>n and which have inclusion relation: R=I0(v)⊃ I1(v) ⊃···⊃ Ig(v) ⊃···, and Ig(v)=0 if g>n. In the various physical systems the ideals naturally emerge to give us dynamical flows on the hypersurfaces, and therefore, it is called the field theory on algebraic varieties. This viewpoint provides us a grand viewpoint in physics and mathematics.
Magnetocapacitance Investigation of Quantum Hall Effect and Edge States
Authors: Takaoka Sadao , Oto Kenichi , Murase Kazuo
Published: October 2003

The quantum Hall effect for the GaAs/AlGaAs heterostrcture is investigated by an ac capacitance measurement between the two-dimensional electron system (2DES) and the gate on GaAs/AlGaAs. The capacitance minima at the quantum Hall plateaus are mainly determined not by the 2DES area under the gate but by the edge length of 2DES. There exists the high conductive region due to the edge states along the 2DES boundary, when the bulk conductivity σxx is small enough at low temperatures and high magnetic fields. From the temperature and frequency dependence of the capacitance minima, it is found that the measured capacitance consists of the contribution from the edge states and that of the bulk state, which is treated as a distributed circuit of a resistive plate with the conductivity σxx. The evaluated width of edge states from the capacitance is much larger than the magnetic length and the cyclotron radius expected from the one-electron picture. This wide width of edge states can be explained by the compressible-incompressible strip model, in which the screening effect is taken into account. Further the bulk conductivity of less than 10-12 S (S=1/Ω) is measured by the capacitance of the Corbino geometry sample, where the edge states are absent and the capacitance is determined by only σxx in this geometry. The localization of the bulk state is investigated by the obtained σxx.
Composite Fermions in the Hilbert Space of the Lowest Electronic Landau Level
Authors: Jain J. K. , Kamilla R. K.
Published: October 2003

Single particle basis functions for composite fermions are obtained from which many-composite fermion states confined to the lowest electronic Landau level can be constructed in the standard manner, i.e. by building Slater determinants. This representation enables a Monte Carlo study of systems containing a large number of composite fermions, yielding new quantitative and qualitative information. The ground state energy and the gaps to charged and neutral excitations are computed for a number of fractional quantum Hall effect (FQHE) states, earlier off-limits to a quantitative investigation. The ground state energies are estimated to be accurate to ~0.1% and the gaps at the level of a few percent. It is also shown that at Landau level fillings smaller than or equal to 1/9 the FQHE is unstable to a spontaneous creation of excitons of composite fermions. In addition, this approach provides new conceptual insight into the structure of the composite fermion wave functions, resolving in the affirmative the question of whether it is possible to motivate the composite fermion theory entirely within the lowest Landau level, without appealing to higher Landau levels.