sum of two squares

Although not commonly used, correlation filters can track complex objects through rotations, occlusions and other distractions at over 20 times the rate of current state-ofthe-art techniques. The oldest and simplest correlation filters use simple templates and generally fail when applied to tracking. More modern approaches such as ASEF and UMACE perform better, but their training needs are poorly suited to tracking. Visual tracking requires robust filters to be trained from a single frame and dynamically adapted as the appearance of the target object changes. This paper presents a new type of correlation filter, a Minimum Output Sum of Squared Error (MOSSE) filter, which produces stable correlation filters when initialized using a single frame. A tracker based upon MOSSE filters is robust to variations in lighting, scale, pose, and non-rigid deformations while operating at 669 frames per second. Occlusion is detected based upon the peak-tosidelobe ratio, which enables the tracker to pause and resume where it left off when the object reappears. Note: This paper contains additional figures and content that was excluded from CVPR 2010 to meet length requirements.

This study examines what kinds of events cause large shifts in the volatility of emerging stock markets. We first determine when large changes in the volatility of emerging stock market returns occur and then examine global and local events (social, political, and economic) during the periods of increased volatility. An iterated cumulative sums of squares (ICSS) algorithm is used to identify the points of shocks/sudden changes in the variance of returns in each market and how long the shift lasts. Both increases and decreases in the variance are identified. We then identify events around the time period when shifts in volatility occur. Most events tend to be local and include the Mexican Peso crisis, periods of hyperinflation in Latin America, the Marcos-Aquino conflict in the Philippines, and the stock market scandal in India. The October 1987 crash is the only "global event" during the period 1985-1995 that caused a significant jump in the volatility of several emerging stock markets.

Two-dimensional electrical imaging surveys are now widely used in engineering and environmental surveys to map moderately complex structures. In order to adequately resolve such structures with arbitrary resistivity distributions, the regularised least-squares optimisation method with a cell-based model is frequently used in the inversion of the electrical imaging data. The L 2 norm based least-squares optimisation method that attempts to minimise the sum of squares of the spatial changes in the model resistivity is often used. The resulting inversion model has a smooth variation in the resistivity values. In cases where the true subsurface resistivity consists of several regions that are approximately homogenous internally and separated by sharp boundaries, the result obtained by the smooth inversion method is not optimal. It tends to smear out the boundaries and give resistivity values that are too low or too high. The blocky or L 1 norm optimisation method can be used for such situations. This method attempts to minimise the sum of the absolute values of the spatial changes in the model resistivity. It tends to produce models with regions that are piecewise constant and separated by sharp boundaries. Results from tests of the smooth and blocky inversion methods with several synthetic and field data sets highlight the strengths and weaknesses of both methods. The smooth inversion method gives better results for areas where the subsurface resistivity changes in a gradual manner, while the blocky inversion method gives significantly better results where there are sharp boundaries. While fast computers and software have made the task of interpreting data from electrical imaging surveys much easier, it remains the responsibility of the interpreter to choose the appropriate tool for the task based on the available geological information.

We consider the problem of minimizing a polynomial over a semialgebraic set defined by polynomial equations and inequalities, which is NP-hard in general. Hierarchies of semidefinite relaxations have been proposed in the literature, involving positive semidefinite moment matrices and the dual theory of sums of squares of polynomials. We present these hierarchies of approximations and their main properties: asymptotic/finite convergence, optimality certificate, and extraction of global optimum solutions. We review the mathematical tools underlying these properties, in particular, some sums of squares representation results for positive polynomials, some results about moment matrices (in particular, of Curto and Fialkow), and the algebraic eigenvalue method for solving zero-dimensional systems of polynomial equations. We try whenever possible to provide detailed proofs and background.

Many cognitive processes occur on time scales that can significantly affect the shape of the blood oxygenation level-dependent (BOLD) response in eventrelated functional MRI. This shape can be estimated from event related designs, even if these processes occur in a fixed temporal sequence (J. M. Ollinger, G. L. Shulman, and M. Corbetta. 2001. NeuroImage 13: 210 -217). Several important considerations come into play when interpreting these time courses. First, in single subjects, correlations among neighboring time points give the noise a smooth appearance that can be confused with changes in the BOLD response. Second, the variance and degree of correlation among estimated time courses are strongly influenced by the timing of the experimental design. Simulations show that optimal results are obtained if the intertrial intervals are as short as possible, if they follow an exponential distribution with at least three distinct values, and if 40% of the trials are partial trials. These results are not particularly sensitive to the fraction of partial trials, so accurate estimation of time courses can be obtained with lower percentages of partial trials (20 -25%). Third, statistical maps can be formed from F statistics computed with the extra sum of square principle or by t statistics computed from the cross-correlation of the time courses with a model for the hemodynamic response. The latter method relies on an accurate model for the hemodynamic response. The most robust model among those tested was a single gamma function. Finally, the power spectrum of the measured BOLD signal in rapid event-related paradigms is similar to that of the noise. Nevertheless, highpass filtering is desirable if the appropriate model for the hemodynamic response is used.

We present a partitional cluster algorithm that minimizes the sum-of-squared-error criterion while imposing a hard constraint on the cluster variance. Conceptually, hypothesized clusters act in parallel and cooperate with their neighboring clusters in order to minimize the criterion and to satisfy the variance constraint. In order to enable the demarcation of the cluster neighborhood without crucial parameters, we introduce the notion of foreign cluster samples. Finally, we demonstrate a new method for cluster tendency assessment based on varying the variance constraint parameter.

We develop a new k-means clustering algorithm for data streams of points from a Euclidean space. We call this algorithm StreamKM++. Our algorithm computes a small weighted sample of the data stream and solves the problem on the sample using the k-means++ algorithm of Arthur and Vassilvitskii (SODA '07). To compute the small sample, we propose two new techniques. First,

We consider a polynomial programming problem P on a compact semi-algebraic set K ⊂ R n , described by m polynomial inequalities g j (X) ≥ 0, and with criterion f ∈ R[X]. We propose a hierarchy of semidefinite relaxations in the spirit those of Waki et al. [9]. In particular, the SDP-relaxation of order r has the following two features: (a) The number of variables is O(κ 2r) where κ = max[κ 1 , κ 2 ] witth κ 1 (resp. κ 2) being the maximum number of variables appearing the monomials of f (resp. appearing in a single constraint g j (X) ≥ 0). (b) The largest size of the LMI's (Linear Matrix Inequalities) is O(κ r). This is to compare with the respective number of variables O(n 2r) and LMI size O(n r) in the original SDP-relaxations defined in [11]. Therefore, great computational savings are expected in case of sparsity in the data {g j , f }, i.e. when κ is small, a frequent case in practical applications of interest. The novelty with respect to [9] is that we prove convergence to the global optimum of P when the sparsity pattern satisfies a condition often encountered in large size problems of practical applications, and known as the running intersection property in graph theory. In such cases, and as a by-product, we also obtain a new representation result for polynomials positive on a basic closed semialgebraic set, a sparse version of Putinar's Positivstellensatz [16].

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We consider the problem of dividing a set of m points in Euclidean n-space into k clusters (m, n are variable while k is fixed), so as to minimize the sum of distances squared of each point to its "cluster center". This formulation differs in two ways from the most frequently considered clustering problems in the literature, namely, here we have k fixed and m, n variable, and we use the sum of squared distances as our measure; we will argue that our problem is natural in many contexts.

Numerous tasks in control systems involve optimization problems over polynomials, and unfortunately these problems are in general nonconvex. In order to cope with this difficulty, linear matrix inequality (LMI) techniques have been introduced because they allow one to obtain bounds to the sought solution by solving convex optimization problems and because the conservatism of these bounds can be decreased in general by suitably increasing the size of the problems. This survey aims to provide the reader with a significant overview of the LMI techniques that are used in control systems for tackling optimization problems over polynomials, describing approaches such as decomposition in sum of squares, Positivstellensatz, theory of moments, Pólya's theorem, and matrix dilation. Moreover, it aims to provide a collection of the essential problems in control systems where these LMI techniques are used, such as stability and performance investigations in nonlinear systems, uncertain systems, time-delay systems, and genetic regulatory networks. It is expected that this survey may be a concise useful reference for all readers.

We classify integrable irreducible highest weight representations of non-twisted affine Lie superalgebras. We give a free field construction in the level 1 case. The analysis of this construction shows, in particular, that in the simplest case of the sℓ(2|1) level 1 affine superalgebra the characters are expressed in terms of the Appell elliptic function. Our results demonstrate that the representation theory of affine Lie superalgebras is quite different from that of affine Lie algebras.

We present closed-form expressions for the probability density function (PDF) and the cumulative distribution function (CDF) of the sum of non-identical squared Nakagami-m random variables (RVs) with integer-order fading parameters. As it is shown, they can be written as a weighted sum of Erlang PDFs and CDFs, respectively, while the analysis includes both independent and correlated sums of RVs. The proposed formulation significantly improves previously published results, which are either in the form of infinite sums or higher order derivatives of the fading parameter m. The obtained formulas can be applied to the performance analysis of diversity combining receivers operating over Nakagami-m fading channels.

The taxonomic status of Homo erectus sensu lato has been a source of debate since the early 1980s, when a series of publications suggested that the early African fossils may represent a separate species, H. ergaster. To gain further resolution regarding this debate, 3D geometric morphometric data were used to quantify overall shape variation in the cranial vault within H. erectus using a new metric, the sum of squared pairwise Procrustes distances (SSD). Bootstrapping methods were used to compare the H. erectus SSD to a broad range of human and nonhuman primate samples in order to ascertain whether variation in H. erectus most clearly resembles that seen in one or more species. The reference taxa included relevant phylogenetic, ecological, and temporal analogs including humans, apes, and both extant and extinct papionin monkeys. The mean cranial shapes of different temporogeographic subsets of H. erectus fossils were then tested for significance using exact randomization tests and compared to the distances between regional groups of modern humans and subspecies/species of the ape and papionin monkey taxa. To gauge the influence of sexual dimorphism on levels of variation, comparisons were also made between the mean cranial shapes of single-sex samples for the reference taxa. Results indicate that variation in H. erectus is most comparable to single species of papionin monkeys and the genus Pan, which included two species. However, H. erectus encompasses a limited range of variation given its extensive geographic and temporal range, leading to the conclusion that only one species should be recognized. In addition, there are significant differences between the African/Georgian and Asian H. erectus samples, but not between H. ergaster (Georgia þ Africa, excluding OH 9 and Daka) and H. erectus sensu stricto. This finding is in line with expectations for intraspecific variation in a long-lived species with a wide, but probably discontinuous, geographic distribution.

Several functions were used to model the fixed part of the lactation curve and genetic parameters of milk test-day records to estimate using French Holstein data. Parametric curves (Legendre polynomials, Ali-Schaeffer curve, Wilmink curve), fixed classes curves (5-d classes), and regression splines were tested. The latter were appealing because they adjusted the data well, were relatively insensitive to outliers, were flexible, and resulted in smooth curves without requiring the estimation of a large number of parameters. Genetic parameters were estimated with an Average Information REML algorithm where the average information matrix and the first derivatives of the likelihood functions were pooled over 10 samples. This approach made it possible to handle larger data sets. The residual variance was modeled as a quadratic function of days in milk. Quartic Legendre polynomials were used to estimate (co)variances of random effects. The estimates were within the range of most other stud...

In this paper a least squares method is developed for minimizing Y -XB over the matrix B T 2 F subject to the constraint that the columns of B are unimodal, i.e., each has only one peak, and M being the sum of squares of all elements of M. This method is directly applicable in many 2 F curve resolution problems, but also for stabilizing other problems where unimodality is known to be a valid assumption. Typical problems arise in certain types of time series analysis like chromatography or flow injection analysis. A fundamental and surprising result of this work is that unimodal least squares regression (including optimization of mode location) is not anymore difficult than two simple Kruskal monotone regressions. This has not earlier been realized leading to either the use of undesirable ad hoc methods or very time consuming exhaustive search algorithms. The new method is useful in and exemplified with two-and multi-way methods based on alternating least squares regression solving problems from fluorescence spectroscopy and flow injection analysis.

Financial processes as processes in nature, are subject to stochastic fluctuations. Stochastic differential equations turn out to be an advantageous representation of such noisy, real-world problems, and together with their identification, they play an important role in the sectors of finance, but also in physics and biotechnology. These equations, however, are often hard to represent and to resolve. Thus we express them in a simplified manner of approximation by discretization and additive models based on splines. This defines a trilevel problem consisting of an optimization and a representation problem (portfolio optimization), and a parameter estimation (Weber et al. Financial Regression and Organization. In: Special Issue on Optimization in Finance, DCDIS-B, 2010). Two types of parameters dependency, linear and nonlinear, are considered by constructing a penalized residual sum of squares and investigating the related Tikhonov regularization problem for the first one. In the nonlinear case Gauss–Newton’s method and Levenberg–Marquardt’s method are employed in determining the iteration steps. Both cases are treated using continuous optimization techniques by the elegant framework of conic quadratic programming. These convex problems are well-structured, hence, allowing the use of the efficient interior point methods. Furthermore, we present nonparametric and related methods, and introduce into research done at the moment in our research groups which ends with a conclusion.

The vertical driving-point mechanical impedance characteristics applicable to seated vehicle drivers are measured in the 0.625-10 Hz frequency range with excitation amplitudes ranging from 1.0 to 2.0 m s\ using a whole-body vehicular vibration simulator. The measurements are performed for seated subjects with feet supported and hands held in a driving position. Variations in the seated posture, backrest angle, and nature and amplitude of the vibration excitation are introduced within a prescribed range of likely conditions to illustrate their influence on the driving-point mechanical impedance of seated vehicle drivers. Within the 0.75-10 Hz frequency range and for excitation amplitudes maintained below 4 m s\, a four-degree-of-freedom linear driver model is proposed for which the parameters are estimated to satisfy both the measured driving-point mechanical impedance and the seat-to-head transmissibility characteristics defined from a synthesis of published data for subjects seated erect without backrest support. The parameter identification technique involves the solution of a multivariable optimization function comprising the sum of squared magnitude and phase errors associated with both the mechanical impedance and seat-to-head transmissibility target values, subject to limit constraints identified from the anthropometric and biomechanical data. The model response, however, is found to provide a closer agreement with the mechanical impedance target values than that with the seat-to-head transmissibility. From the model, the main body resonant frequencies computed on the basis of both biodynamic response functions are found to be within close bounds to that expected for the human body. Relevance to industry The development of an appropriate analytical seated vehicle driver model should provide means of estimating the forces and motions being transmitted within the body under specific vehicular vibration environments. Furthermore, its use in conjunction with a corresponding model for the vehicle seat should allow the prediction of the driver's vibration exposure levels and the seat's ability to attenuate the vibration in particular vehicles.

Incomplete knowledge of the pattern of magnetic lineations and fossil transform faults represented by sea floor spreading data on two plates generated by the same spreading center leads to uncertainties in a reconstruction of the past relative configuration of the plates. A reconstruction may be represented by the finite rotation that describes one configuration relative to another. In this paper a method of reconstruction is provided that reflects the uncertainties in the data. The method m'mimizes a weighted least squares measure of fit as a function of the rotation parameters. For a given rotation the measure of fit represents the' sum of squares of the weighted distances of fixed and rotated data points (representing corresponding former plate margin segments) from a common plate margin. The common margin is estimated from the locations of both fixed and rotated data points. The estimated margin consists of a set of great-circle arcs. It is shown how the method of reconstruction may be utilized to obtain an uncertainty region for the pole and angle of rotation that characterize the finite rotation. These techniques are then used to study the uncertainties of reconstructions in the South Pacific for the times of anomalies 13 and 18. The best fit reconstructions and the uncertainty region for the anomaly 18 pole are in good agreement with previous work. However, the uncertainties in the anomaly 13 pole were substantially underestimated by previous investigators.

The kinetics of mineralization of carbonaceous substrates has been explained by a deterministic model which is applicable to either growth or nongrowth conditions in soil. The mixed-order nature of the model does not require a priori decisions about reaction order, discontinuity period of lag or stationary phase, or correction for endogenous mineralization rates. The integrated equation is simpler than the integrated form of the Monod equation because of the following: (i) only two, rather than four, interdependent constants have to be determined by nonlinear regression analysis, (ii) substrate or product formation can be expressed explicitly as a function of time, (iii) biomass concentration does not have to be known, and (iv) the required initial estimate for the nonlinear regression analysis can be easily obtained from a linearized form rather than from an interval estimate of a differential equation. 14CO2 evolution data from soil have been fitted to the model equation. All data except those from irradiated soil gave better fits by residual sum of squares (RSS) by assuming growth in soil was linear (RSS = 0.71) as opposed to exponential (RSS = 2.87). The underlying reasons for growth (exponential versus linear), no growth, and relative degradation rates of substrates are consistent with the basic mechanisms from which the model is derived.

Determining the surface protonation behavior of natural adsorbents is essential to understand how they interact with their environments. ProtoFit is a tool for analysis of acid-base titration data and optimization of surface protonation models. The program offers a number of useful features including: (1) enables visualization of adsorbent buffering behavior; (2) uses an optimization approach independent of starting titration conditions or initial surface charge; (3) does not require an initial surface charge to be defined or to be treated as an optimizable parameter; (4) includes an error analysis intrinsically as part of the computational methods; and (5) generates simulated titration curves for comparison with observation. ProtoFit will typically be run through ProtoFit-GUI, a graphical user interface providing user-friendly control of model optimization, simulation, and data visualization. ProtoFit calculates an adsorbent proton buffering value as a function of pH from raw titration data (including pH and volume of acid or base added). The data is reduced to a form where the protons required to change the pH of the solution are subtracted out, leaving protons exchanged between solution and surface per unit mass of adsorbent as a function of pH. The buffering intensity function Q Ã ads is calculated as the instantaneous slope of this reduced titration curve. Parameters for a surface complexation model are obtained by minimizing the sum of squares between the modeled (i.e. simulated) buffering intensity curve and the experimental data. The variance in the slope estimate, intrinsically produced as part of the Q Ã ads calculation, can be used to weight the sum of squares calculation between the measured buffering intensity and a simulated curve. Effects of analytical error on data visualization and model optimization are discussed. Examples are provided of using ProtoFit for data visualization, model optimization, and model evaluation. r

This paper describes a newly developed speed sensorless drive based on neural networks. A backpropagation neural network is used to provide real-time adaptive identification of the motor speed. The estimation objective is the sum of squared errors between a target trajectory and the neural network model output. A backpropagation algorithm is used to adjust the motor speed, so that the neural model output follows the target trajectory. Backpropagation forces the estimated speed to follow precisely the actual motor speed. The zero-speed crossing phenomena is also described, and experimental results are presented and analyzed.

In most statistical process control applications, the quality of a process or product is characterized by univariate or multivariate quality characteristics and monitored by the corresponding univariate and multivariate control charts, respectively. However, sometimes, the quality of a process or a product is better characterized by a relationship between a response variable and one or more explanatory variables. This relationship, which can be linear, nonlinear, or even a complicated model, is referred to as a profile. So far, several methods have been proposed for monitoring simple linear profiles. In this paper, a new method based on cumulative sum statistics is proposed to enhance monitoring of linear profiles in phase II. The performance of the proposed method is evaluated by average run length criterion. A comprehensive comparison is also conducted between the performance of the proposed method and the existing methods for monitoring simple linear profiles. The results show that the proposed method performs satisfactorily. In addition, the effects of reference value, sample size, and corrected sum of squares of explanatory variables on the performance of the proposed method are investigated.

The nature of genotype by environment (G × E) interactions in rainfed lowland rice was examined using data for 37 genotypes across 36 environments in India, Bangladesh, Thailand, Indonesia and the Philippines from 1994 to 1997. G × E interaction accounted for 32% of the total sum of squares, with environment and genotype responsible for 63% and 5%, respectively. More than 47% of the G × E sum of squares was captured by a nine genotype group by nine environment group summary. Sites with similar characteristics were tightly grouped, as were related genotypes. Environment groups included some with favourable water supply, and others with early drought, late drought, rapid-onset late drought, and submergence. Groupings of genotypes could be explained by their performance in relation to these conditions. PSBRc14, IR36 and IR64 had high yield potential and performed well over most environments, while CT9897-55-2-M-3-M and the F1 hybrids IR64615H and IR68877H also had high yield potential but only performed well with adequate water supply and where standing water remained shallow. Groups including Mahsuri, and IR62266-42-6-1 and IR57514-PMI-5-B-1-2, were stable across environments. NSG19 was preferentially adapted to environments with rapid-onset late drought, and Sabita and KDML105 to environments favouring late maturity or recovery after drought. Implications of these results for choice and management of testing sites, and identification of suitable reference lines for the breeding program were discussed. A probe set of six lines was identified to include Sabita or KDML105, NSG19, Mahsuri, IR57514-PMI-5-B-1-2 or IR62266-42-6-1, PSBRc14 and CT9897-55-2-M-3-M, which represent broad and specific adaptations to the major target subecosystems in rainfed lowland rice systems.

Visual tracking has been a challenging problem in computer vision over the decades. The applications of visual tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. Mean-shift tracker, which gained attention recently, is known for tracking objects in a cluttered environment. In this work, we propose a new method to track objects by combining two well-known trackers, sum-of-squared differences (SSD) and color-based mean-shift (MS) tracker. In the proposed combination, the two trackers complement each other by overcoming their respective disadvantages. The rapid model change in SSD tracker is overcome by the MS tracker module, while the inability of MS tracker to handle large displacements is circumvented by the SSD module. The performance of the combined tracker is illustrated to be better than those of the individual trackers, for tracking fast-moving objects. Since the MS tracker relies on global object parameters such as color, the performance of the tracker degrades when the object undergoes partial occlusion. To avoid adverse effects of the global model, we use MS tracker to track local object properties instead of the global ones. Further, likelihood ratio weighting is used for the SSD tracker to avoid drift during partial occlusion and to update the MS tracking modules. The proposed tracker outperforms the traditional MS tracker as illustrated.

Relative orientation is the recovery of the position and orientation of one imaging system relative to another from correspondences among five or more ray pairs. It is one of four core problems in photogrammetry and is of central importance in binocular stereo as well as in long-range motion vision. While five ray correspondences are sufficient to yield a finite number of solutions, more than five correspondences are used in practice
to ensure an accurate solution with least-squares methods. Most iterative schemes for minimizmg the sum of the squares of weighted errors require a good guess as a starting value. The author has previously published a method that results in the best solution without requiring an initial guess [J. Opt. Soc. Am. A 4, 629 (1987)]. An even simpler method is presented here that utilizes the representation of rotations by unit quaternions.

The k-means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and are inefficient for solving clustering problems in large datasets. Recently, incremental approaches have been developed to resolve difficulties with the choice of starting points. The global k-means and the modified global k-means algorithms are based on such an approach. They iteratively add one cluster center at a time. Numerical experiments show that these algorithms considerably improve the k-means algorithm. However, they require storing the whole affinity matrix or computing this matrix at each iteration. This makes both algorithms time consuming and memory demanding for clustering even moderately large datasets. In this paper, a new version of the modified global k-means algorithm is proposed. We introduce an auxiliary cluster function to generate a set of starting points lying in different parts of the dataset. We exploit information gathered in previous iterations of the incremental algorithm to eliminate the need of computing or storing the whole affinity matrix and thereby to reduce computational effort and memory usage. Results of numerical experiments on six standard datasets demonstrate that the new algorithm is more efficient than the global and the modified global k-means algorithms.

This paper studies the so-called bi-quadratic optimization over unit spheres min x∈R n ,y∈R m 1≤i,k≤n, 1≤j,l≤m b ijkl xiyjx k y l subject to x = 1, y = 1. We show that this problem is NP-hard and there is no polynomial time algorithm returning a positive relative approximation bound. After that, we present various approximation methods based on semidefinite programming (SDP) relaxations. Our theoretical results are: For general bi-quadratic forms, we develop a 1 2max{m,n} 2-approximation algorithm; for bi-quadratic forms that are square-free, we get a relative approximation bound 1 nm ; when min{n, m} is a constant, we present two polynomial time approximation schemes (PTASs) which are based on sum of squares (SOS) relaxation hierarchy and grid sampling of the standard simplex. For practical computational purposes, we propose the first order SOS relaxation, a convex quadratic SDP relaxation and a simple minimum eigenvalue method, and give their quality analyses. Some illustrative numerical examples are also given.

To obtain optimal operating rules for storage reservoirs, large numbers of simulation and optimization models have been developed over the past several decades, which vary significantly in their mechanisms and applications. As every model has its own limitations, the selection of appropriate model for derivation of reservoir operating rule curves is difficult and most often there is a scope for further improvement as the model selection depends on data available. Hence, evaluation and modifications related to the reservoir operation remain classical. In the present study a Genetic Algorithm model has been developed and applied to Pechiparai reservoir in Tamil Nadu, India to derive the optimal operational strategies. The objective function is set to minimize the annual sum of squared deviation form desired irrigation release and desired storage volume. The decision variables are release for irrigation and other demands (industrial and municipal demands), from the reservoir. Since the rule curves are derived through random search it is found that the releases are same as that of demand requirements. Hence based on the present case study it is concluded that GA model could perform better if applied in real world operation of the reservoir.

The purpose of the present study is to model the regeneration success using quantifiable growth factors. The hypothesis is that the regeneration success can be predicted from a model that incorporates canopy and ground flora leaf area indexes (LAD, soil water content and soil carbon content. In April 1992, 10 plots (1 m*) were established in each of 22 Danish beech stands with natural regeneration originating from the 1989 seed fall. The regeneration success was investigated until autumn 1994. In each of the 220 plots the light conditions, soil water supply and soil carbon content were quantified. The survival was high during the study period. From spring 1992 to autumn 1993 the mean number of saplings decreased from 58 me2 to 54 rnm2. The sapling number was reduced by increased canopy density but it was neither influenced by water supply nor by soil carbon content. The height growth variation was evident. After the fifth growing season (autumn 19941 the height of the tallest sam3pling per plot ranged from 7 to 120 cm. Generally, an increased canopy opening increased the potential for and variance of height growth. Increased soil water content caused a significant increase in the growth of the saplings; whereas, a significant reduction in growth occurred with increased soil carbon content. The shading from the competing ground flora had no significant effect. However, the contribution to the total sum of squares from the quantitative variables of the relevant statistical models was only 3%-13%. Therefore, the models were unable to predict regeneration success satisfactorily and it was not possible to establish the causal effects underlying silvicultural treatments and site. It is concluded that the beech saplings probably were so well established at the beginning of the study that they had become tolerant to poor growing conditions, such as the drought in 1992. This indicates that the growing conditions during the early phases of seedling establishment may be crucial for the regeneration success. For silviculturists, these conclusions stress the importance of creating favourable growth conditions prior to the first growing season. Future research should examine the early phases of seedling establishment by quantifying growth factors in more detail than was done in the present study. 0 1997 Elsevier Science B.V.

Carbon mineralization kinetics of 17 organic materials were studied in a Mediterranean sandy soil. These added organic matters (AOM) used in the organic fertilizer industry differed in their origin and composition: plant residues from the agri-food industry, animal wastes, manures (plant and animal origin), composts at different composting times and organic fertilizers. The mixtures AOM-soils were incubated under aerobic conditions at 288C during 6 months. Soil moisture was maintained at 75% water holding capacity and respired-CO 2 was regularly trapped into alkali media in closed chambers, then checked by HCl titration. Analyses of CO 2 were performed in triplicate at 17 sampling occasions. The mineralized AOM fraction (MAOMF) varied according to the AOM origin: from 12±33% of added C for composts, to 65±90% for animal-originated AOM, with many intermediate patterns for plant-originated AOM.

We consider the problem of computing the minimum value p min taken by a polynomial p(x) of degree d over the standard simplex ∆. This is an NP-hard problem already for degree d = 2. For any integer k ≥ 1, by minimizing p(x) over the set of rational points in ∆ with denominator k, one obtains a hierarchy of upper bounds p ∆(k) converging to p min as k −→ ∞. These upper approximations are intimately linked to a hierarchy of lower bounds for p min constructed via Pólya's theorem about representations of positive forms on the simplex. Revisiting the proof of Pólya's theorem allows us to give estimates on the quality of these upper and lower approximations for p min. Moreover, we show that the bounds p ∆(k) yield a polynomial time approximation scheme for the minimization of polynomials of fixed degree d on the simplex, extending an earlier result of Bomze and De Klerk for degree d = 2.

An integrated methodology for determining the unique combination of complex permittivity based on measured transmission coefficient and time difference of arrival (TDOA) information in free-space measurements is proposed. The methodology consists of an estimation procedure of the real part of complex permittivity based on TDOA, and a root-searching procedure based on parametric system identification (SI) together with an error sum of squares (SSE) criterion. Generally, non-unique combinations of dielectric constant and loss factor are encountered when lossy or low-loss materials are measured and the proposed methodology is aimed at the determination of unique combinations of dielectric constant and loss factor for such materials. The proposed methodology is validated by measurements of several materials with known dielectric properties. The estimated complex permittivity values for Teflon, Lexan, Bakelite, and concrete are in good agreement with those reported in the literature. The method has potential for in-situ measurement of dielectric properties for construction materials. Applicability and limitations of the methodology are discussed.

The joint durum wheat (Triticum turgidum L var 'durum') breeding program of the International Maize and Wheat Improvement Center (CIMMYT) and the International Center for Agricultural Research in the Dry Areas (ICARDA) for the Mediterranean region employs extensive multilocation testing. Multilocation testing produces significant genotype-environment (GE) interaction that reduces the accuracy for estimating yield and selecting appropriate germ plasm. The sum of squares (SS) of GE interaction was partitioned by linear regression techniques into joint, genotypic, and environinental regressions, and by Additive Main effects and the .Multiplicative Interactions (AMMI) model into five significant Interaction Principal Component Axes (IPCA). The AMMI model was more effective in partitioning the interaction SS than the linear regression technique. The SS contained in the AMMI model was 6 times higher than the SS for all three regressions. Postdictive assessment recommended the use of the first five IPCA axes, while predictive assessment AMMI1 (main effects plus IPCA1). After elimination of random variation, AMMI1 estimates for genotypic yields within sites were more precise than unadjusted means. This increased precision was equivalent to increasing the number of replications by a factor of 3.7.

We examine moving average (MA) filters for estimating a financial asset price's integrated variance in a framework where high frequency price data are observed with measurement errors, due to market microstructure effects. We introduce an enhancement to the existing formula for the MA-based estimator, and then benchmark it in simulations to a non-parametric kernel-based estimator proposed in .

We propose several preprocessing steps to be used before biomarker clustering or classifying for high-throughput Mass Spectrometry (MS) data. These preprocessing steps for the mass spectra are multiple alignment of technical replicates, baseline correction and normalization along the mass/charge axis. While the benefits from baseline correction and alignment seem obvious we studied more carefully the benefit from normalizing using some human prostate cancer SELDI TOF MS data (obtained from the Virginia Prostate Center Tissue and body Fluid Bank and approved by the Eastern Virginia Medical School). We show on these data that our global normalization by scaling helps in distinguishing between different cancer groups as well as between cancer and non-cancer groups. We used the Between to Within sum of squares ratio introduced by Fisher as well as visual inspection to illustrate the improvement brought by the normalization.

Contemporary surveys provide a huge number of detections of small solar system bodies, mostly asteroids. Typically, the reported astrometry is not enough to compute an orbit and/or perform an identification with an already discovered object. The classical methods for preliminary orbit determination fail in such cases: a new approach is necessary. When the observations are not enough to compute an orbit we represent the data with an attributable (two angles and their time derivatives). The undetermined variables range and range rate span an admissible region of solar system orbits, which can be sampled by a set of Virtual Asteroids (VAs) selected by an optimal triangulation. The attributable results from a fit and has an uncertainty represented by a covariance matrix, thus the predictions of future observations can be described by a quasi-product structure (admissible region times confidence ellipsoid), which can be approximated by a triangulation with each node surrounded by a confidence ellipsoid. The problem of identifying two independent short arcs of observations has been solved. For each VA in the admissible region of the first arc we consider prediction at the time of the second arc and the corresponding covariance matrix, and we compare them with the attributable of the second arc with its own covariance. By using the penalty (increase in the sum of squares, as in the algorithms for identification) we select the VAs which can fit together both arcs and compute a preliminary orbit. Even two attributables may not be enough to compute an orbit with a convergent differential corrections algorithm. The preliminary orbits are used as first guess for constrained differential corrections, providing solutions along the Line Of Variations (LOV) which can be used as second generation VAs to further predict the observations at the time of a third arc. In general the identification with a third arc will ensure a least squares orbit, with uncertainty described by the covariance matrix.

In this paper we present a theoretical analysis of the on-line Sum-of-Squares algorithm (SS) for bin packing along with several new variants. SS is applicable to any instance of bin packing in which the bin capacity B and item sizes s(a) are integral (or can be scaled to be so), and runs in time O(nB). It performs remarkably well from an average case point of view: For any discrete distribution in which the optimal expected waste is sublinear, SS also has sublinear expected waste. For any discrete distribution where the optimal expected waste is bounded, SS has expected waste at most O(log n). We also discuss several interesting variants on SS, including a randomized O(nB log B)-time on-line algorithm SS * whose expected behavior is essentially optimal for all discrete distributions. Algorithm SS * depends on a new linear-programmingbased pseudopolynomial-time algorithm for solving the NP-hard problem of determining, given a discrete distribution F , just what is the growth rate for the optimal expected waste.