the geometry of enhancement in multiple regression in linear multiple regression enhancement is said to occur when r2 b r r r where b is a p 1 vector of standardized regression coefficients and r is a p 1 vector of correlations between a criterion y and a set of standardized regressors x when p 1 then b r and enhancement cannot occur when p 2 for all full rank rxx i rxx e xx v v where v v denotes the eigen decomposition of rxx 1 2 the set boldsymbol b 1 boldsymbol b i r 2 boldsymbol b i boldsymbol r i boldsymbol r i boldsymbol r i 0 boldsymbol r i boldsymbol r i 0 2 p both sets contain an uncountably infinite number of vectors geometrical arguments demonstrate that b1 occurs at the intersection of two hyper ellipsoids in p equations are provided for populating the sets b1 and b2 and for demonstrating that maximum enhancement occurs when b is collinear with the eigenvector that is associated with p the smallest eigenvalue of the predictor correlation matrix these equations are used to illustrate the logic and the underlying geometry of enhancement in population multiple regression models r code for simulating population regression models that exhibit enhancement of any degree and any number of predictors is included in appendices a and b

design of on line algorithms using hitting times random walks are well known for playing a crucial role in the design of randomized off line as well as on line algorithms in this work we prove some basic identities for ergodic markov chains e g an interesting characterization of reversibility in markov chains is obtained in terms of first passage times besides providing new insight into random walks on weighted graphs we show how these identities give us a way of designing competitive randomized on line algorithms for certain well known problems

lower bounds for randomized k serverand motion planning algorithms in this paper the authors prove lower bounds on the competitive ratio of randomized algorithms for two on line problems the k server problem suggested by manasse mcgeoch and sleator competitive algorithms for on line problems j algorithms 11 1990 pp 208 230 and an on line motion planning problem due to papadimitriou and yannakakis shortest paths without a map lecture notes in comput sci 372 springer verlag new york 1989 pp 610 620 the authors prove against an oblivious adversary an omega log k lower bound on the competitive ratio of any randomized on line k server algorithm in any sufficiently large metric space an omega log log k lower bound on the competitive ratio of any randomized on line k server algorithm in any metric space with at least k 1 points and an omega log log n lower bound on the competitive ratio of any on line motion planning algorithm for a scene with n obstacles previously no superconstant lower bound on the competitive ratio of randomized on line algorithms was known for any of these problems

high capacity image hiding scheme based on vector quantization a novel grayscale image hiding scheme that is capable of hiding multiple secret images into a host image of the same size is proposed in this paper the secret images to be hidden are first compressed by vector quantization with additional index compression process then the compressed secret images are encrypted and embedded into the least significant bits of the host pixels to provide good image quality of the stego image the modulus function and the image property are employed to hide the secret bits into the host pixels and determine the number of hidden bits in each host pixel respectively according to the results the proposed scheme provides a higher hiding capacity and a higher degree of security than that of the virtual image cryptosystem

a randomized algorithm for two servers in cross polytope spaces it has been a long standing open problem to determine the exact randomized competitiveness of the 2 server problem that is the minimum competitiveness of any randomized online algorithm for the 2 server problem for deterministic algorithms the best competitive ratio that can be obtained is 2 and no randomized algorithm is known that improves this ratio for general spaces for the line bartal et al 1998 2 give a 15578 competitive algorithm but their algorithm is specific to the geometry of the line we consider here the 2 server problem over cross polytope spaces m 2 4 we obtain an algorithm with competitive ratio of 1912 and show that this ratio is best possible this algorithm gives the second non trivial example of metric spaces with better than2 competitive ratio the algorithm uses a design technique called the knowledge state technique a method not specific to m 2 4

a general decomposition theorem for the k server problem the first general decomposition theorem for the k server problem is presented whereas all previous theorems are for the case of a finite metric with k 1 points the theorem given here allows an arbitrary number of points in the underlying metric space this theorem implies o polylog k competitive randomized algorithms for certain metric spaces consisting of a polylogarithmic number of widely separated subspaces and takes a first step toward a general o polylog k competitive algorithm the only other cases for which polylogarithmic competitive randomized algorithms are known are the uniform metric space and the weighted cache metric space with two weights

more on randomized on line algorithms for caching we address the tradeoff between the competitive ratio and the resources used by randomized on line algorithms for caching two algorithms reported in the literature that achieve the optimal ratio hk require a lot of memory and perform extensive computation at each step on the other hand a very simple algorithm called rmark has competitive ratio 2hk 1 within a factor of 2 of the optimum a natural question that arises here is whether there is a tradeoff between simplicity and the competitive ratio in particular is it possible to achieve a competitive ratio better than 2hk 1 with a simple algorithm like rmark we first consider marking algorithms that are natural generalizations of rmark and we prove that for any 0 there is no randomized marking algorithm for caching with competitive ratio 2 hk thus rmark is essentially optimal among marking algorithms another model of simple caching algorithms is that of trackless algorithms these are algorithms that do not store any information about items that are not in the cache it is known that for k 2 there is no randomized trackless algorithm for caching with ratio better than 3724 1 5416 the trivial upper bound is 2 achieved even by deterministic algorithms lru and fifo we reduce this gap by giving a trackless randomized algorithm with competitive ratio 14 3 13 1 6514

a polylogarithmic competitive algorithm for the k server problem we give the first polylogarithmic competitive randomized online algorithm for the k server problem on an arbitrary finite metric space in particular our algorithm achieves a competitive ratio of o log3 n log2 k for any metric space on n points our algorithm improves upon the deterministic 2k 1 competitive algorithm of koutsoupias and papadimitriou koutsoupias and papadimitriou 1995 for a wide range of n

on list update and work function algorithms the list update problem a well studied problem in dynamic data structures can be described abstractly as a metrical task system in this paper we prove that a generic metrical task system algorithm called the work function algorithm has constant competitive ratio for list update in the process we present a new formulation of the well known list factoring technique in terms of a partial order on the elements of the list this approach leads to a new simple proof that a large class of online algorithms including move to front is 2 1 k competitive for k the list length

simulating multivariate nonnormal distributions a method for generating multivariate nonnormal distributions with specified intercorrelations and marginal means variances skews and kurtoses is proposed as an example the method is applied to the generation of simulated scores on three psychological tests administered to a single group of individuals

the asymptotic covariance matrix of sample correlation coefficients under general conditions abstract a general matrix expression for the asymptotic covariance matrix of correlation coefficients is derived it is applicable when the data are drawn from any distribution with finite fourth order moments the result is specialized to the cases where the data have a distribution from the elliptical class and where the sample covariance matrix has a noncentral wishart distribution

a randomized algorithm for two servers on the line abstract in the k server problem we wish to minimize in an online fashion the movement cost of k servers in response to a sequence of requests for two servers it is known that the optimal deterministic algorithm has competitive ratio 2 and it has been a long standing open problem whether it is possible to improve this ratio using randomization we give a positive answer to this problem when the underlying metric space is a real line by providing a randomized online algorithm for this case with competitive ratio at most 155 78 1 987 this is the first algorithm for two servers that achieves a competitive ratio smaller than 2 in a nonuniform metric space with more than three points we consider a more general problem called the k l server problem in which a request is served using l out of k available servers we show that the randomized 2 server problem can be reduced to the deterministic 2l l server problem we prove a lower bound of 2 on the competitive ratio of the 4 2 server problem this implies that one unbiased random bit is not sufficient to improve the ratio of 2 for the two server problem then we give a 155 78 competitive algorithm for the 6 3 server problem on the real line our algorithm is simple and memoryless the solution has been obtained using linear programming techniques that may have applications for other online problems

better bootstrap confidence intervals abstract we consider the problem of setting approximate confidence intervals for a single parameter in a multiparameter family the standard approximate intervals based on maximum likelihood theory can be quite misleading in practice tricks based on transformations bias corrections and so forth are often used to improve their accuracy the bootstrap confidence intervals discussed in this article automatically incorporate such tricks without requiring the statistician to think them through for each new application at the price of a considerable increase in computational effort the new intervals incorporate an improvement over previously suggested methods which results in second order correctness in a wide variety of problems in addition to parametric families bootstrap intervals are also developed for nonparametric situations

the comparison of interdependent correlations between optimal linear composites a general procedure is provided for comparing correlation coefficients between optimal linear composites the procedure allows computationally efficient significance tests on independent or dependent multiple correlations partial correlations and canonical correlations with or without the assumption of multivariate normality evidence from some monte carlo studies on the effectiveness of the methods is also provided

on the k server conjecture we prove that the work function algorithm for the k server problem has a competitive ratio at most 2 k 1 manasse et al 1988 conjectured that the competitive ratio for the k server problem is exactly k it is trivially at least k previously the best known upper bound was exponential in k our proof involves three crucial ingredients a quasiconvexity property of work functions a duality lemma that uses quasiconvexity to characterize the configuration that achieve maximum increase of the work function and a potential function that exploits the duality lemma

a lower bound on the competitivity of memoryless algorithms for a generalization of the cnn problem we consider the cnn problem in arbitrary dimension and over any metric space containing the integers we prove that in every dimension at least 2 no memoryless online algorithm can achieve a constant competitive ratio under a weak symmetry constraint on the algorithm this generalizes in several aspects the lower bounds obtained by koutsoupias and taylor the cnn problem and other k server variants theoret comput sci 324 2004 347 359 for the original problem the proof consists in the analysis of carefully selected random walks which appear naturally in the framework of memoryless algorithms

a virtual image cryptosystem based upon vector quantization we propose a new image cryptosystem to protect image data it encrypts the original image into another virtual image since both original and virtual images are significant our new cryptosystem can confuse illegal users besides the camouflage this new cryptosystem has three other benefits first our cryptosystem is secure even if the illegal users know that our virtual image is a camouflage second this cryptosystem can compress image data finally our method is more efficient than a method that encrypts the entire image directly

investigating the performance of alternate regression weights by studying all possible criteria in regression models with a fixed set of predictors we describe methods for assessing all possible criteria i e dependent variables and subsets of criteria for regression models with a fixed set of predictors x where x is an n 1 vector of independent variables our methods build upon the geometry of regression coefficients hereafter called regression weights in n dimensional space for a full rank predictor correlation matrix rxx of order n and for regression models with constant r2 coefficient of determination the ols weight vectors for all possible criteria terminate on the surface of an n dimensional ellipsoid the population performance of alternate regression weights such as equal weights correlation weights or rounded weights can be modeled as a function of the cartesian coordinates of the ellipsoid these geometrical notions can be easily extended to assess the sampling performance of alternate regression weights in models with either fixed or random predictors and for models with any value of r2 to illustrate these ideas we describe algorithms and r r development core team 2009 code for 1 generating points that are uniformly distributed on the surface of an n dimensional ellipsoid 2 populating the set of regression weight vectors that define an elliptical arc in n and 3 populating the set of regression vectors that have constant cosine with a target vector in n each algorithm is illustrated with real data the examples demonstrate the usefulness of studying all possible criteria when evaluating alternate regression weights in regression models with a fixed set of predictors

a decomposition theorem for task systems and bounds for randomized server problems a lower bound of omega sqrt log k log log k is proved for the competitive ratio of randomized algorithms for the k server problem against an oblivious adversary the bound holds for arbitrary metric spaces having at least k 1 points and provides a new lower bound for the metrical task system problem as well this improves the previous best lower bound of omega log log k for arbitrary metric spaces h j karloff y rabani and y ravid siam j comput 23 1994 pp 293 312 and more closely approaches the conjectured lower bound of omega log k for the server problem on k 1 equally spaced points on a line which corresponds to a natural motion planning problem a lower bound of omega frac log k log log k is obtained the results are deduced from a general decomposition theorem for a simpler version of both the k server and the metrical task system problems called the pursuit evasion game it is shown that if a metric space cal m can be decomposed into two spaces cal m l and cal m r such that the distance between them is sufficiently large compared to their diameter then the competitive ratio for this game on cal m can be expressed nearly exactly in terms of the ratios on each of the two subspaces this yields a divide and conquer approach to bounding the competitive ratio of a space

the weighted 2 server problem we consider a generalization of the 2 server problem in which servers have different costs we prove that in uniform spaces a version of the work function algorithm is 5 competitive and that no better ratio is possible we also give a 5 competitive randomized memoryless algorithm for uniform spaces and a matching lower bound for arbitrary metric spaces in contrast with the non weighted case we prove that there is no memoryless randomized algorithm with finite competitive ratio we also propose a version of the problem in which a request specifies two points to be covered by the servers and the algorithm must decide which server to move to which point for this version we show a 9 competitive algorithm and we prove that no better ratio is possible

the normal theory and asymptotic distribution free adf covariance matrix of standardized regression coefficients theoretical extensions and finite sample behavior yuan and chan psychometrika 76 670 690 2011 recently showed how to compute the covariance matrix of standardized regression coefficients from covariances in this paper we describe a method for computing this covariance matrix from correlations next we describe an asymptotic distribution free adf browne in british journal of mathematical and statistical psychology 37 62 83 1984 method for computing the covariance matrix of standardized regression coefficients we show that the adf method works well with nonnormal data in moderate to large samples using both simulated and real data examples r code r development core team 2012 is available from the authors or through the psychometrika online repository for supplementary materials

a novel joint data hiding and compression scheme based on smvq and image inpainting in this paper we propose a novel joint data hiding and compression scheme for digital images using side match vector quantization smvq and image inpainting the two functions of data hiding and image compression can be integrated into one single module seamlessly on the sender side except for the blocks in the leftmost and topmost of the image each of the other residual blocks in raster scanning order can be embedded with secret data and compressed simultaneously by smvq or image inpainting adaptively according to the current embedding bit vector quantization is also utilized for some complex blocks to control the visual distortion and error diffusion caused by the progressive compression after segmenting the image compressed codes into a series of sections by the indicator bits the receiver can achieve the extraction of secret bits and image decompression successfully according to the index values in the segmented sections experimental results demonstrate the effectiveness of the proposed scheme

a simple and high hiding capacity method for hiding digit by digit data in images based on modulus function this study presents a simple method for high hiding capacity the basic concept uses a modulus operation the proposed method has the following advantages 1 the proposed method outperforms the simple lsb substitution method given the same range 0 m 1 of data digits in the embedded data 2 the proposed method achieves good image vision quality without the need for post processing 3 the proposed method is almost as simple as the lsb method in both coding and decoding 4 the smaller error advantage over the simple lsb substitution method can also be mathematically verified 5 the error is smaller than m 1 2 for almost every pixel 6 the proposed method has high hiding capacity for example the proposed method can hide a 256 256 or 256 512 image in a 512 512 host image

the generalized work function algorithm is competitive for the generalized 2 server problem the generalized 2 server problem is an online optimization problem where a sequence of requests has to be served at minimal cost requests arrive one by one and need to be served instantly by at least one of two servers we consider the general model where the cost function of the two servers may be different formally each server moves in its own metric space and a request consists of one point in each metric space it is served by moving one of the two servers to its request point requests have to be served without knowledge of future requests the objective is to minimize the total traveled distance the special case where both servers move on the real line is known as the cnn problem we show that the generalized work function algorithm mathrm wfa lambda is constant competitive for the generalized 2 server problem further we give an outline for a possible extension to k geqslant2 servers and discuss the applicability of our techniques and of the work function algorithm in general we co

the generalized two server problem we consider the generalized on line two server problem in which each server moves in its own metric space requests for service arrive one by one and every request is represented by two points one in each metric space the problem is to move at every request one of the two servers to its request point such that the total distance travelled by the two servers is minimized the special case in which both metric spaces are the real line is known as the cnn problem it has been a well known open question in on line optimization if an algorithm with a constant competitive ratio exists for this problem we answer this question in the affirmative by providing a constant competitive algorithm for the generalized two server problem on any metric space the basic result in this article is a characterization of competitiveness for metrical service systems that seems much easier to use when looking for a competitive algorithm the existence of a competitive algorithm for the generalized two server problem follows rather easily from this result

statistical inference based on pseudo maximum likelihood estimators in elliptical populations in this article we develop statistical inference based on the maximum likelihood method in elliptical populations with an unknown density function the method assuming the multivariate normal distribution using the sample mean and the sample covariance matrix is basically correct even for elliptical populations under a certain kurtosis adjustment but is not statistically efficient especially when the kurtosis of the population distribution has higher than moderate values on the other hand several methods of statistical inference assuming a particular family e g multivariate t distribution of elliptical distributions have been recommended as a robust procedure against outliers or distributions with heavy tails such inference also will be important to maintain a high efficiency of statistical inference in elliptical populations in practice however it is very difficult to choose an appropriate family of elliptical distributions and one may misspecify the family furthermore extra parameters

the minimum backlog problem we study the minimum backlog problem mbp this online problem arises e g in the context of sensor networks we focus on two main variants of mbp the discrete mbp is a 2 person game played on a graph g v e the player is initially located at a vertex of the graph in each time step the adversary pours a total of one unit of water into cups that are located on the vertices of the graph arbitrarily distributing the water among the cups the player then moves from her current vertex to an adjacent vertex and empties the cup at that vertex the player s objective is to minimize the backlog i e the maximum amount of water in any cup at any time the geometric mbp is a continuous time version of the mbp the cups are points in the two dimensional plane the adversary pours water continuously at a constant rate and the player moves in the plane with unit speed again the player s objective is to minimize the backlog we show that the competitive ratio of any algorithm for the mbp has a lower bound of d where d is the diameter of the graph for the discrete mbp or the diameter of the point set for the geometric mbp therefore we focus on determining a strategy for the player that guarantees a uniform upper bound on the absolute value of the backlog for the absolute value of the backlog there is a trivial lower bound of d and the deamortization analysis of dietz and sleator gives an upper bound of o d log n for n cups our main result is a tight upper bound for the geometric mbp we show that there is a strategy for the player that guarantees a backlog of o d independently of the number of cups we also study a localized version of the discrete mbp the adversary has a location within the graph and must act locally filling cups with respect to his position just as the player acts locally emptying cups with respect to her position we prove that deciding the value of this game is pspace hard

finding optimal least significant bit substitution in image hiding by dynamic programming strategy abstract the processing of simple least significant bit lsb substitution embeds the secret image in the least significant bits of the pixels in the host image this processing may degrade the host image quality so significantly that grabbers can detect that there is something going on in the image that interests them to overcome this drawback an exhaustive least significant bit substitution scheme was proposed by wang et al but it takes huge computation time wang et al then proposed another method that uses a genetic algorithm to search approximate optimal solutions and computation time is no longer so huge in this paper we shall use the dynamic programming strategy to get the optimal solution the experimental results will show that our method consumes less computation time and also gets the optimal solution

biases and standard errors of standardized regression coefficients the paper obtains consistent standard errors se and biases of order o 1 n for the sample standardized regression coefficients with both random and given predictors analytical results indicate that the formulas for ses given in popular text books are consistent only when the population value of the regression coefficient is zero the sample standardized regression coefficients are also biased in general although it should not be a concern in practice when the sample size is not too small monte carlo results imply that for both standardized and unstandardized sample regression coefficients se estimates based on asymptotics tend to under predict the empirical ones at smaller sample sizes

an optimal on line algorithm for k servers on trees the k server problem is investigated when the metric space is a tree for this case an on line k competitive algorithm for k servers is presented the competitiveness ratio k is optimal the algorithm is memoryless in the sense that it does not use any information from the past

online computation with advice we consider a model for online computation in which the online algorithm receives together with each request some information regarding the future referred to as advice the advice is a function defined by the online algorithm of the whole request sequence the advice provided to the online algorithm may allow an improvement in its performance compared to the classical model of complete lack of information regarding the future we are interested in the impact of such advice on the competitive ratio and in particular in the relation between the size b of the advice measured in terms of bits of information per request and the improved competitive ratio since b 0 corresponds to the classical online model and b log a where a is the algorithm s action space corresponds to the optimal offline one our model spans a spectrum of settings ranging from classical online algorithms to offline ones in this paper we propose the above model and illustrate its applicability by considering two of the most extensively studied online problems namely metrical task systems mts and the k server problem for mts we establish tight up to constant factors upper and lower bounds on the competitive ratio of deterministic and randomized online algorithms with advice for any choice of 1 b q logn where n is the number of states in the system we prove that any randomized online algorithm for mts has competitive ratio w log n b and we present a deterministic online algorithm for mts with competitive ratio o log n b for the k server problem we construct a deterministic online algorithm for general metric spaces with competitive ratio k o 1 b for any choice of q 1 b logk

a two codebook combination and three phase block matching based image hiding scheme with high embedding capacity image hiding is a technique that embeds the important images into a cover image such that the important images are imperceptible and can be securely transmitted to the receiver in such research the common goals are to enlarge the embedding capacity as much as possible since the visual quality of the cover image is degraded slightly and to keep high visual quality of the important images when they are extracted from the stego image in this paper we propose an image hiding method based on the two codebook combination the three phase block matching procedure and the modulus substitution the proposed method can achieve these benefits 1 multiple relatively large important images can be embedded into a relatively small cover image 2 the quality of the stego image after embedding the secret data is not distorted significantly 3 the important images have an acceptable visual quality after they are extracted the experimental results also show that the proposed method is more flexible than previous methods

a matrix derivation of the asymptotic covariance matrix of sample correlation coefficients abstract the asymptotic covariance matrix of the sample correlation matrix is derived in matrix form as an application of some new matrix theory in multivariate statistics

the 3 server problem in the plane in the k server problem we wish to minimize in an online fashion the movement cost of k servers in response to a sequence of requests we assume that k 2 the request issued at each step is specified by a point r in a given metric space m to serve this request one of the k servers must move to r it is known that if m has at least k 1 points then no online algorithm for the k server problem in m has competitive ratio smaller than k the best known upper bound on the competitive ratio in arbitrary metric spaces by koutsoupias and papadimitriou j acm 42 1995 971 is 2k 1 there are only a few special cases for which k competitive algorithms are known for k 2 when m is a tree or when m has at most k 2 points we prove that the work function algorithm is 3 competitive for the 3 server problem in the manhattan plane as a corollary we obtain a 4 243 competitive algorithm for 3 servers in the euclidean plane the best previously known competitive ratio for 3 servers in these metric spaces was 5

adaptive data hiding for vector quantization images based on overlapping codeword clustering in this paper an overlapping codeword clustering based data hiding scheme is presented in this scheme a mapping table is designed to determine the overlapping codeword clustering and to indicate the index modification in the secret embedding the mapping table explores the relationship among the sub codebook s size the codeword s order and the embedding secret message to which the codeword overlapping in sub codebooks with different sizes is permitted in addition the secret embedding is also determined according to the mapping table the experimental results showed that the number of partitioned sub codebooks was increased significantly the average hiding capacity was about 30k bits while the average embedding distortion was about 1 2db in comparison to similar methods the proposed scheme provided a larger hiding capacity than others while preserving a similar stego image quality furthermore the proposed scheme offered a better proportion of hiding compared to image distortion

some improvements in confidence intervals for standardized regression coefficients yuan and chan psychometrika 76 670 690 2011 doi 10 1007 s11336 011 9224 6 derived consistent confidence intervals for standardized regression coefficients under fixed and random score assumptions jones and waller psychometrika 80 365 378 2015 doi 10 1007 s11336 013 9380 y extended these developments to circumstances where data are non normal by examining confidence intervals based on browne s br j math stat psychol 37 62 83 1984 doi 10 1111 j 2044 8317 1984 tb00789 x asymptotic distribution free adf theory seven different heteroscedastic consistent hc estimators were investigated in the current study as potentially better solutions for constructing confidence intervals on standardized regression coefficients under non normality normal theory adf and hc estimators were evaluated in a monte carlo simulation findings confirmed the superiority of the hc3 mackinnon and white j econ 35 305 325 1985 doi 10 1016 0304 4076 85 90158 7 and hc5 cribari neto and da silva adv stat anal 95 129 146 2011 doi 10 1007 s10182 010 0141 2 interval estimators over jones and waller s adf estimator under all conditions investigated as well as over the normal theory method the hc5 estimator was more robust in a restricted set of conditions over the hc3 estimator some possible extensions of hc estimators to other effect size measures are considered for future developments

correlation weights in multiple regression a general theory on the use of correlation weights in linear prediction has yet to be proposed in this paper we take initial steps in developing such a theory by describing the conditions under which correlation weights perform well in population regression models using ols weights as a comparison we define cases in which the two weighting systems yield maximally correlated composites and when they yield minimally similar weights we then derive the least squares weights for any set of predictors that yield the largest drop in r 2 the coefficient of determination when switching to correlation weights our findings suggest that two characteristics of a model data combination are especially important in determining the effectiveness of correlation weights 1 the condition number of the predictor correlation matrix r xx and 2 the orientation of the correlation weights to the latent vectors of r xx

the k resource problem in uniform metric spaces we define a natural generalization of the prominent k server problem the k resource problem it occurs in a metric space with some integer demands given at its points the demands may vary with time but the total demand may never exceed k an online algorithm has k servers at its disposal and its goal is to satisfy demands by moving servers while minimizing the cost of their transport we show asymptotically tight bounds on the competitive ratio of the k resource problem in the uniform metric space of n points we prove that the optimal competitive ratios are between min k n 1 and min k 2 n 1 for deterministic algorithms and between min h k h n 1 and min h k email protected h n 1 for randomized ones this extends known results for k server in such spaces to the more general setting of k resource

compression unimpaired batch image encryption combining vector quantization and index compression as the network bandwidth has grown rapidly it has become common to share a large number of still images via the internet by means of batch transmission unfortunately most existing methods encrypt only a single image so there is room for improvement in our ability to send batches of images at one time a complete reconstruction of the methods is sometimes necessary especially when considering performance criteria this paper presents a novel batch image encryption algorithm that combines vector quantization vq and additional index compression process to benefit from their computational efficiency and low transmission bandwidth without affecting the original compression rate the experimental results show the performance of this new scheme in terms of compression rate and computational cost

on the competitive ratio of the work function algorithm for the k server problem the k server problem is one of the most fundamental online problems the problem is to schedule k mobile servers to visit a sequence of points in a metric space with minimum total mileage the k server conjecture of manasse mcgeogh and sleator states that there exists a k competitive online algorithm the conjecture has been open for over 15 years the top candidate online algorithm for settling this conjecture is the work function algorithm wfa which was shown to have competitive ratio at most 2k 1 in this paper we lend support to the conjecture that wfa is in fact k competitive by proving that it achieves this ratio in several special metric spaces the line the star and all metric spaces with k 2 points

adaptive embedding techniques for vq compressed images an embedding algorithm which can adaptively embed a binary message into a vq compressed image is proposed in this paper the proposed algorithm is divided into three phases in the codeword grouping phase a new group of codewords is initiated by the two most similar codewords which do not belong to any group for each codeword which does not belong to any group if the codeword is similar to all of the codewords in the group it will be added to the group in the embedding phase each codeword in a group will be assigned to embed a certain sub message whose length is determined by the number of codewords in the group the more codewords a group has the higher the embedding capacity of a codeword in the group will be in the extracting phase given a codeword and the number of codewords in the group to which the codeword belongs the embedded message can be extracted from the codeword by simply determining the order of the codeword in the group experimental results show that the proposed algorithm performs better than previous algorithms with regard to embedding capacity and image quality for the test images when the embedding capacity is less than 5bits per codeword index the difference of the psnr values between the stego image and its vq compressed cover image will be no more than 5db on average

image hiding by optimal lsb substitution and genetic algorithm in this paper we develop a novel method to embed important data in the host image so that the interceptors will not notice about the existence of the data the basic concept of the proposed method is by simple lsb substitution to prevent illicit access of data and increase the system performance the method of randomized process and the optimal lsb substitution are proposed the e ectiveness of the optimal lsb substitution in the worst case is proved we also develop a genetic algorithm to solve the problem of hiding important data in the rightmost k lsbs of the host image which may involve a huge computation time to nd the optimal result when k is large finally based on the concept of perceptual modeling an improved hiding technique is developed to obtain a high quality embedding result experimental results will reveal that the quality of the embedding result will not be seriously degraded and is near optimal by utilizing the proposed method 2001 pattern recognition society published by elsevier science ltd all rights reserved