studies of spectral properties of short genes using the wavelet subspace hilbert huang transform wshht abstract this paper presents a new algorithm for the analysis of spectral properties of short genes using the wavelet transform and the hilbert huang transform hht a wavelet subspace algorithm combined with the empirical mode decomposition emd is introduced to create subdivided intrinsic mode functions imfs and a cross correlation analysis is applied to remove pseudo spectral components experiments are carried out on dna sequences with the double base db curve representation and the results show that the signal to noise ratio of buried signals can be enhanced using the proposed method yielding significant patterns that are rarely observed with conventional methods the wavelet subspace hilbert huang transform wshht algorithm is able to correctly identify spectral patterns of very short genes below 70 bp in dna sequences

the value of a kurtosis metric in estimating the hazard to hearing of complex industrial noise exposures a series of gaussian and non gaussian equal energy noise exposures were designed with the objective of establishing the extent to which the kurtosis statistic could be used to grade the severity of noise trauma produced by the exposures here 225 chinchillas distributed in 29 groups with 6 to 8 animals per group were exposed at 97 db spl the equal energy exposures were presented either continuously for 5 d or on an interrupted schedule for 19 d the non gaussian noises all differed in the level of the kurtosis statistic or in the temporal structure of the noise where the latter was defined by different peak interval and duration histograms of the impact noise transients embedded in the noise signal noise induced trauma was estimated from auditory evoked potential hearing thresholds and surface preparation histology that quantified sensory cell loss results indicated that the equal energy hypothesis is a valid unifying principle for estimating the consequences of an exposure if and only if the equivalent energy exposures had the same kurtosis furthermore for the same level of kurtosis the detailed temporal structure of an exposure does not have a strong effect on trauma

noise levels and hearing thresholds in the drop forging industry a weighted equivalent continuous noise levels for hammer and press operations in a drop forging industry were determined using both tape recordings of the noise and personal noise dosimeters the results indicated average a weighted leq values of 108 db for hammer operators and 99 db for press operators comparison of hearing level statistics for 716 hammer and press operators and 293 control subjects indicated the severe hazard to hearing of impact noise exposures for mean exposure times of less than 10 years hearing levels for the press 99 db and hammer 108 db operator age groups are nearly identical and in the latter case are less than those predicted for exposure to equivalent continuous noise for long term exposures of 10 years or more the results of this study indicate that hearing losses resulting from impact noise in the drop forging industry are as great or greater than those resulting from continuous noise

development of a noise metric for assessment of exposure risk to complex noises many noise guidelines currently use a weighted equivalent sound pressure level laeq as the noise metric and the equal energy hypothesis to assess the risk of occupational noises because of the time averaging effect involved with the procedure the current guidelines may significantly underestimate the risk associated with complex noises this study develops and evaluates several new noise metrics for more accurate assessment of exposure risks to complex and impulsive noises the analytic wavelet transform was used to obtain time frequency characteristics of the noise 6 basic unique metric forms that reflect the time frequency characteristics were developed from which 14 noise metrics were derived the noise metrics were evaluated utilizing existing animal test data that were obtained by exposing 23 groups of chinchillas to respectively different types of noise correlations of the metrics with the hearing losses observed in chinchillas were compared and the most promising noise metric was identified

surrogate testing of linear feedback processes with non gaussian innovations abstract surrogate testing is used widely to determine the nature of the process generating the given empirical sample in the present study the usefulness of phase randomized surrogates amplitude adjusted fourier transform and iterated amplitude adjusted fourier transform surrogates on statistical inference of linearly correlated noise with non gaussian innovations and their static invertible nonlinear transforms from their empirical samples are discussed existing surrogate testing procedures which retain the auto correlation function in the surrogates may not be appropriate in the presence of non gaussian innovations

hearing loss from interrupted intermittent and time varying gaussian noise exposures the applicability of the equal energy hypothesis eight groups of chinchillas n 74 were exposed to various equivalent energy 100 or 106 db a sound pressure level spl noise exposure paradigms six groups received an interrupted intermittent time varying iitv gaussian noise exposure that lasted 8 h d 5 d week for 3 weeks the exposures modeled an idealized workweek at each level three different temporal patterns of gaussian iitv noise were used the 100 db a iitv exposure had a db range of 90 108 db spl while the range of the 106 db a iitv exposure was 80 115 db spl two reference groups were exposed to a uniform 100 or 106 db a spl noise 24 h d for 5 days each reference group and the three corresponding iitv groups comprised a set of equivalent energy exposures evoked potentials were used to estimate hearing thresholds and surface preparation histology quantified sensory cell populations all six groups exposed to the iitv noise showed threshold toughening effects of up to 40 db all iitv exposures produced hearing and sensory cell loss that was similar to their respective equivalent energy reference group these results indicate that for gaussian noise the equal energy hypothesis for noise induced hearing loss is an acceptable unifying principle

probing the linearity and nonlinearity in dna sequences abstract in this paper we apply the principles of information theory that relate to the definition of nonlinear predictability which is a measure that describes both the linear and nonlinear components of a system by comparing this measure to a measure of linear predictability one can assess whether a given system has a strong linear or a strong nonlinear component this provides insights as to whether the system should be modeled by a nonlinear or a linear model we apply these ideas to dna sequences our results which extend previous results on this issue indicate that all dna sequences coding and noncoding exhibit strong nonlinear structure at the same time the results provide insights to understand dna structure and possible clues about evolutionary mechanisms

object recognition by discriminative combinations of line segments ellipses and appearance features we present a novel contour based approach that recognizes object classes in real world scenes using simple and generic shape primitives of line segments and ellipses compared to commonly used contour fragment features these primitives support more efficient representation since their storage requirements are independent of object size additionally these primitives are readily described by their geometrical properties and hence afford very efficient feature comparison we pair these primitives as shape tokens and learn discriminative combinations of shape tokens here we allow each combination to have a variable number of shape tokens this coupled with the generic nature of primitives enables a variety of class specific shape structures to be learned building on the contour based method we propose a new hybrid recognition method that combines shape and appearance features each discriminative combination can vary in the number and the types of features where these two degrees of variability empower the hybrid method with even more flexibility and discriminative potential we evaluate our methods across a large number of challenging classes and obtain very competitive results against other methods these results show the proposed shape primitives are indeed sufficiently powerful to recognize object classes in complex real world scenes

a discrete autoregressive process as a model for short range correlations in dna sequences we present a direct way to model short and medium range correlations in dna sequences and to separate them from long range correlations to do so we discuss symbol sequences generated by a discrete autoregressive process of order p dar p these sequences display higher order markov properties but are based on very few parameters the aim of our investigation is 1 to introduce with such dar p processes a parameter efficient tool for generating higher order markov processes on a discrete alphabet 2 to study how the parameters of the process determine the statistical properties of the sequence and 3 to provide the mathematical tools for estimating the parameters from a given experimental sequence the statistical properties of the generated sequences expressed in terms of parameters in the dar p process are monitored with methods from information theory the implications of our findings for dna sequences are discussed and some application is given in particular it is shown how short range correlations in dna sequences can be parameterised by such a process

estimation of the information by an adaptive partitioning of the observation space we demonstrate that it is possible to approximate the mutual information arbitrarily closely in probability by calculating the relative frequencies on appropriate partitions and achieving conditional independence on the rectangles of which the partitions are made empirical results including a comparison with maximum likelihood estimators are presented

applicability of the leq as a damage risk criterion an animal experiment within the european communities the leq8h is used as a damage risk criterion drc for hearing protection the use of such a drc supposes that sound exposures with equal energy imply equal risk for noise induced hearing loss nihl the aim of the present study carried out with guinea pigs was to test the applicability of the leq8h for estimating the hazard of different noises having the same spectrum and acoustic energy therefore only the temporal structure of noises was a variable continuous intermittent or impulsive and each noise had a leq8h of 92 db the results indicate the leq8h is not an accurate drc it overestimates the nihl for intermittent and short continuous noises whereas it seems to be useful for moderate intensity noises 95 db and for the impulse noise 101 db furthermore the data point out the notion of critical intensity which seems to play a determining role in the transition between metabolic and mechanical mechanisms involved in the acoustic trauma since the criti

a generalized approach to random noise synthesis theory and computer simulation a generalized approach to the synthesis of gaussian and non gaussian random noises as well as purely impulsive waveforms having a preselected amplitude spectrum has been developed the basic idea behind the synthesis is to construct the amplitude time waveform from the frequency domain i e from the amplitude and phase spectra by maintaining a predetermined reference amplitude spectrum and performing certain specific manipulations of the phase spectrum within any selected band of frequencies and then applying the inverse discrete fourier transform idft peaks in the non gaussian random waveform can be constructed from the selected band of frequencies that have been phase manipulated entire families of signals can thus be produced having the same energy spectrum but statistical characteristics that vary along the continuum from gaussian skewness 0 and kurtosis 3 through non gaussian variable skewness kurtosis and crest factor to purely impulsive shock transient signals the theoretical background and the results of a series of numerical simulations will be presented which demonstrate the functional relation between various phase spectrum manipulations and the descriptors of the synthesized random noise the results show that the approach is viable and that the synthesized random waveforms can be easily tailored to simulate a variety of real world acoustic vibration signals e g high kurtosis impulsive industrial noises helicopter noises missile vibrational signals etc

efficient learning of relational object class models we present an efficient method for learning part based object class models from unsegmented images represented as sets of salient features a model includes parts appearance as well as location and scale relations between parts the object class is generatively modeled using a simple bayesian network with a central hidden node containing location and scale information and nodes describing object parts the model s parameters however are optimized to reduce a loss function of the training error as in discriminative methods we show how boosting techniques can be extended to optimize the relational model proposed with complexity linear in the number of parts and the number of features per image this efficiency allows our method to learn relational models with many parts and features the method has an advantage over purely generative and purely discriminative approaches for learning from sets of salient features since generative method often use a small number of parts and features while discriminative methods tend to ignore geometrical relations between parts experimental results are described using some bench mark data sets and three sets of newly collected data showing the relative merits of our method in recognition and localization tasks

on the detection of determinism in a time series the nonlinearly scaled distributions of the strengths of the orthogonal modes in the data of a time series are compared with that derived from its surrogate counterpart to assess its chaoticity or the stochastic nature chaoticity manifests in the relatively decreasing strengths of the weaker modes with increasing dimension of the orthogonal spaces mapping the process the distributions of the strengths of the modes for the data and the surrogate are compared statistically singular value decomposition is used for the implementation the ability of the proposed method to distinguish between chaotic and stochastic dynamics is demonstrated through a number of simulated series including linear and nonlinear stochastic noise and processes described by lorenz rossler and mackey glass equations henon map and ecological map and some real life examples

constrained realization monte carlo method for hypothesis testing abstract we compare two theoretically distinct approaches to generate artificial or surrogate data for testing hypotheses about a given data set the first and more straightforward approach is to fit a single best model to the original data and then to generate surrogate data sets that are typical realizations of that model the second approach concentrates not on the model but directly on the original data it attempts to constrain the surrogate data sets so that they exactly agree with the original data for a specified set of sample statistics examples of these two approaches are provided for two simple cases a test for deviations from a gaussian distribution and a test for serial dependence in a time series additionally we consider test for nonlinearity in time series based on a fourier transform ft method and on more conventional autoregressive moving average arma fits to the data the comparative performance of hypothesis testing schemes based on these two approaches is found to depend on whether or not the discriminating statistic is pivotal a statistic is pivotal if its distribution is the same for all processes consistent with the null hypothesis the typical realization method requires that the discriminating statistic satisfy this property the constrained realization approach on the other hand does not share this requirement and can provide an accurate and powerful test without having to sacrifice flexibility in the choice of discriminating statistic

species independence of mutual information in coding and noncoding dna we explore if there exist universal statistical patterns that are different in coding and noncoding dna and can be found in all living organisms regardless of their phylogenetic origin we find that i the mutual information function symbol see text has a significantly different functional form in coding and noncoding dna we further find that ii the probability distributions of the average mutual information symbol see text are significantly different in coding and noncoding dna while iii they are almost the same for organisms of all taxonomic classes surprisingly we find that symbol see text is capable of predicting coding regions as accurately as organism specific coding measures

statistical analysis of gene and intergenic dna sequences abstract much of the on going statistical analysis of dna sequences is focused on the estimation of characteristics of coding and non coding regions that would possibly allow discrimination of these regions in the current approach we concentrate specifically on genes and intergenic regions to estimate the level and type of correlation in these regions we apply various statistical methods inspired from nonlinear time series analysis namely the probability distribution of tuplets the mutual information and the identical neighbour fit the methods are suitably modified to work on symbolic sequences and they are first tested for validity on sequences obtained from well known simple deterministic and stochastic models then they are applied to the dna sequence of chromosome 1 of arabidopsis thaliana the results suggest that correlations do exist in the dna sequence but they are weak and that intergenic sequences tend to be more correlated than gene sequences the use of statistical tests with surrogate data establish these findings in a rigorous statistical manner

energy independent factors influencing noise induced hearing loss in the chinchilla model the effects on hearing and the sensory cell population of four continuous non gaussian noise exposures each having an a weighted l eq 100 db spl were compared to the effects of an energy equivalent gaussian noise the non gaussian noise conditions were characterized by the statistical metric kurtosis beta computed on the unfiltered beta t and the filtered beta f time domain signals the chinchilla n 58 was used as the animal model hearing thresholds were estimated using auditory evoked potentials aep recorded from the inferior colliculus and sensory cell populations were obtained from surface preparation histology despite equivalent exposure energies the four non gaussian conditions produced considerably greater hearing and sensory cell loss than did the gaussian condition the magnitude of this excess trauma produced by the non gaussian noise was dependent on the frequency content but not on the average energy content of the impacts which gave the noise its non gaussian character these results indicate that beta t is an appropriate index of the increased hazard of exposure to non gaussian noises and that beta f may be useful in the prediction of the place specific additional outer hair cell loss produced by non gaussian exposures the results also suggest that energy based metrics while necessary for the prediction of noise induced hearing loss are not sufficient

additive logistic regression a statistical view of boosting boosting is one of the most important recent developments in classification methodology boosting works by sequentially applying a classification algorithm to reweighted versions of the training data and then taking a weighted majority vote of the sequence of classifiers thus produced for many classification algorithms this simple strategy results in dramatic improvements in performance we show that this seemingly mysterious phenomenon can be understood in terms of well known statistical principles namely additive modeling and maximum likelihood for the two class problem boosting can be viewed as an approximation to additive modeling on the logistic scale using maximum bernoulli likelihood as a criterion we develop more direct approximations and show that they exhibit nearly identical results to boosting direct multiclass generalizations based on multinomial likelihood are derived that exhibit performance comparable to other recently proposed multiclass generalizations of boosting in most situations and far superior in some we suggest a minor modification to boosting that can reduce computation often by factors of 10 to 50 finally we apply these insights to produce an alternative formulation of boosting decision trees this approach based on best first truncated tree induction often leads to better performance and can provide interpretable descriptions of the aggregate decision rule it is also much faster computationally making it more suitable to large scale data mining applications

chaos induced true randomness abstract we investigate functions of type x n p z n where p t is a periodic function and z are real parameters we show that these functions produce truly random sequences we prove that a class of autonomous dynamical systems containing nonlinear terms described by periodic functions of the variables can generate random dynamics we generalize these results to dynamical systems with nonlinearities in the form of noninvertible functions several examples are studied in detail we discuss how the complexity of the dynamics depends on the kind of nonlinearity we present real physical systems that can produce random time series we report the results of real experiments using nonlinear circuits with noninvertible i v characteristics in particular we show that a josephson junction coupled to a chaotic circuit can generate unpredictable dynamics

joint depth and semantic inference from a single image via elastic conditional random field the estimations of depth and regional semantics from a single image have traditionally been considered as two separated problems in this paper we argue that these two tasks provide complementary information which therefore can be performed jointly to reinforce individual tasks in terms of both accuracy and speed in particular we propose an elastic conditional random field e crf deployed upon superpixel segmentations which models the interdependency between depth and semantics to refine each other in an iterative manner differing from the traditional crfs e crf makes edges elastically hidden emergent during inference to conduct fast loopy belief propagation while explicitly modeling the depth label interdependency to achieve high inference accuracy moreover the structured support vector machine ssvm is further introduced to drastically speed up the inference we have conducted extensive evaluations on both make3d and nyu benchmark datasets which demonstrated that our e crf method significantly outperforms state of the art techniques in terms of precision while significantly accelerating the inference speed 2 3 orders of magnitude highlightsefficient joint inference of depth estimation and region labeling from a single image our first contribution is an efficient generative model called elastic conditional random field e crf to capture the interdependency between depth and labeling along with the spatial dependency among neighborhood superpixels our second contribution is to further accelerate the above lbp based generative inference from a large margin perspective by using a structured support vector machine ssvm

the application of frequency and time domain kurtosis to the assessment of hazardous noise exposures five computer synthesized broadband noises each having the same average spectrum and the same unweighted leq of 100 db spl but very different temporal structures were used to produce hearing loss in chinchillas despite the same exposure energies and spectra each noise exposure produced a different magnitude and frequency distribution of hearing loss and sensory cell loss the results indicate that the statistical properties of a signal are important in the determination of hearing loss when the audiometric and histological results are compared to a metric based upon kurtosis measured in the time and the frequency domain for each exposure there is a clear indication that these statistical metrics are good predictors of the relative magnitude and frequency distribution of the acoustic trauma

modulation of heart disease information to the 12 lead ecg multifractal distribution abstract in this paper a new viewpoint is proposed the synchronous 12 lead ecg multifractal singularity spectrum distribution is modulated by the heart disease information there is a well regulated multifractal singularity spectrum distribution among different leads we consider that the mean value of the area of human 12 lead ecg multifractal singularity spectrum is controlled mainly by his neurosystem and which shows the strength of the body s neuroautonomic control on the heart but not the extent of the heart disease the human heart s intrinsic physiological and pathological activity modulates the human ecg multifractal singularity spectrum distribution after modulation the abnormal lead multifractal singularity spectrum can denote the heart disease region

mass exponent spectrum analysis of human ecg signals and its application to complexity detection abstract the complexity of electrocardiogram ecg signal may reflect the physiological function and healthy status of the heart in this paper we introduced two novel intermediate parameters of multifractality the mass exponent spectrum curvature and area to characterize the nonlinear complexity of ecg signal these indicators express the nonlinear superposition of the discrepancies of singularity strengths from all the adjacent points of the spectrum curve and thus overall subsets of original fractal structure the evaluation of binomial multifractal sets validated these two variables were entirely effective in exploring the complexity of this time series we then studied the ecg mass exponent spectra taken from the cohorts of healthy ischemia and myocardial infarction mi sufferer based on a large sets of 12 leads recordings and took the statistical averages among each crowd experimental results suggest the two values from healthy ecg are apparently larger than those from the heart diseased while the values from ecg of mi sufferer are much smaller than those from the other two groups as for the ischemia sufferer they are almost of moderate magnitude afterward we compared these new indicators with the nonlinear parameters of singularity spectrum the classification indexes and results of total separating ratios tsr defined in the paper both indicated that our method could achieve a better effect these conclusions may be of some values in early diagnoses and clinical applications

hearing loss in the chinchilla from impact and continuous noise exposure the relative hazard posed to the peripheral auditory system by impact impulse and continuous noise of the same power spectrum was determined impact noise was generated by striking a nail with a hammer and was digitally recorded the acoustical power spectrum of the impact was determined and pink noise was filtered to produce a continuous noise stimulus with the same acoustic power spectrum pre exposure auditory evoked response aer thresholds were obtained at 1 2 4 and 8 khz on 16 adult chinchillas the pool of animals was divided into two equal groups based upon pre exposure aer thresholds one group was exposed to impact noise and the other group to the filtered pink noise exposures were 4 h day for 5 days thirty days following the exposure auditory evoked response thresholds were remeasured changes in auditory sensitivity were determined by subtracting the pre exposure thresholds from the post exposure thresholds hearing threshold shifts of the impact noise group were significantly greater

application of semantic features in face recognition we propose a new face recognition strategy which integrates the extraction of semantic features from faces with tensor subspace analysis the semantic features consist of the eyes and mouth plus the region outlined by the centers of the three components a new objective function is generated to fuse the semantic and tensor models for finding similarity between a face and its counterpart in the database furthermore singular value decomposition is used to solve the eigenvector problem in the tensor subspace analysis and to project the geometrical properties to the face manifold experimental results demonstrate that the proposed semantic feature based face recognition algorithm has favorable performance with more accurate convergence and less computational efforts

the kurtosis metric as an adjunct to energy in the prediction of trauma from continuous nongaussian noise exposures data from an earlier study hamernik et al 2003 j acoust soc am 114 386 395 were consistent in showing that for equivalent energy leq 100 db a and spectra exposure to a continuous nongaussian nong noise could produce substantially greater hearing and sensory cell loss in the chinchilla model than a gaussian g noise exposure and that the statistical metric kurtosis computed on the amplitude distribution of the noise could order the extent of the trauma this paper extends these results to leq 90 and 110 db a and to nong noises that are generated using broadband noise bursts and band limited impacts within a continuous g background noise data from nine new experimental groups with 11 or 12 chinchillas group is presented evoked response audiometry established hearing thresholds and surface preparation histology quantified sensory cell loss at the lowest level leq 90 db a there were no differences in the trauma produced by g and nong exposures for leq 90 db a nong exposures produced increased trauma relative to equivalent g exposures removing energy from the impacts by limiting their bandwidth reduced trauma the use of noise bursts to produce the nong noise instead of impacts also reduced the amount of trauma

testing for nonlinearity in time series the method of surrogate data we describe a statistical approach for identifying nonlinearity in time series the method first specifies some linear process as a null hypothesis then generates surrogate data sets which are consistent with this null hypothesis and finally computes a discriminating statistic for the original and for each of the surrogate data sets if the value computed for the original data is significantly different than the ensemble of values computed for the surrogate data then the null hypothesis is rejected and nonlinearity is detected we discuss various null hypotheses and discriminating statistics the method is demonstrated for numerical data generated by known chaotic systems and applied to a number of experimental time series which arise in the measurement of superfluids brain waves and sunspots we evaluate the statistical significance of the evidence for nonlinear structure in each case and illustrate aspects of the data which this approach identifies

improved surrogate data for nonlinearity tests current tests for nonlinearity compare a time series to the null hypothesis of a gaussian linear stochastic process for this restricted null assumption random surrogates can be constructed which are constrained by the linear properties of the data we propose a more general null hypothesis allowing for nonlinear rescalings of a gaussian linear process we show that such rescalings cannot be accounted for by a simple amplitude adjustment of the surrogates which leads to spurious detection of nonlinearity an iterative algorithm is proposed to make appropriate surrogates which have the same autocorrelations as the data and the same probability distribution