However , likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significantly differ from training data . In this paper , we pose that this problem is due to the excessive influence that input complexity has in generative models ' likelihoods . We report a set of experiments supporting this hypothesis , and use an estimate of input complexity to derive an efficient and parameter-free OOD score , which can be seen as a likelihood-ratio , akin to Bayesian model comparison . Altogether , we hope that our results encourage researchers to pay more attention to temporal dynamics in eye movement data , as well as to the inevitable variation in the individual level . The spatio-temporal analysis of eye movements presented here is novel and covers a wide range of methods from functional summary statistics to the likelihood-based modelling . The methods and tools presented are applicable to other eye movement data collected in a freeviewing condition , but we believe that the developed models , being rather simple but flexible , could also be useful for the analysis of spatio-temporal sequences outside the field . First , the frequency distributions of ship dynamic attributes , such as the mean and variance of Speed over Ground , Course over Ground , are obtained , respectively , according to the verified AIS data samples . Subsequently , the correlations between the attributes and belief degrees of the categories are established based on likelihood modeling . In this case , the attributes were characterized into several pieces of evidence , and the evidence can be combined with the Evidential Reasoning rule . We propose a strategy for discriminative training of the ivector extractor in speaker recognition . The original i-vector extractor training was based on the maximum-likelihood generative modeling , where the EM algorithm was used . In our approach , the i-vector extractor parameters are numerically optimized to minimize the discriminative cross-entropy error function . These demonstrations suggest interesting applications can be developed using representations learned by our models . It has been previously demonstrated that conditional generative models can learn to convincingly model object attributes like scale , rotation , and position dosovitskiy2014learning . This is to our knowledge the first demonstration of this occurring in purely unsupervised models . section : Related work Inference Generation Substantial work on probabilistic generative models has focused on training models using maximum likelihood . One class of maximum likelihood models are those described by probabilistic undirected graphs , such as Restricted Boltzmann Machines smolensky1986information and Deep Boltzmann Machines salakhutdinov2009deep . In this paper , we describe the " PixelGAN autoencoder " , a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels ( PixelCNN ) that is conditioned on a latent code , and the recognition path uses a generative adversarial network ( GAN ) to impose a prior distribution on the latent code . We show that different priors result in different decompositions of information between the latent code and the autoregressive decoder . Hierarchical Bayesian methods can unify many related tasks ( e.g. k-shot classification , conditional and unconditional generation ) as inference within a single generative model . However , when this generative model is expressed as a powerful neural network such as a PixelCNN , we show that existing learning techniques typically fail to effectively use latent variables . To address this , we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class . When conditioned on an embedding produced by a convolutional network given a single image of an unseen face , it generates a variety of new portraits of the same person with different facial expressions , poses and lighting conditions . We also show that conditional PixelCNN can serve as a powerful decoder in an image autoencoder . Additionally , the gated convolutional layers in the proposed model improve the log-likelihood of PixelCNN to match the state-of-the-art performance of PixelRNN on ImageNet , with greatly reduced computational cost . Our method results from the observation that the task of automated colorization is relatively easy given a low-resolution version of the color image . We first train a conditional PixelCNN to generate a low resolution color for a given grayscale image . Then , given the generated low-resolution color image and the original grayscale image as inputs , we train a second CNN to generate a high-resolution colorization of an image . Generating Images from Patches . In this paper we propose Spatial PixelCNN , a conditional autoregressive model that generates images from small patches . By conditioning on a grid of pixel coordinates and global features extracted from a Variational Autoencoder ( VAE ) , we are able to train on patches of images , and reproduce the full-sized image . We show that it not only allows for generating high quality samples at the same resolution as the underlying dataset , but is also capable of upscaling images to arbitrary resolutions ( tested at resolutions up to $ 50\times$ ) on the MNIST dataset . Compared to a PixelCNN++ baseline , Spatial PixelCNN quantitatively and qualitatively achieves similar performance on the MNIST dataset . This paper proposed a novel visual tracking method based on the Gated PixelCNN and particle filtration . First , unlike the convolutional layers in traditional trackers , Gated PixelCNN is a generative model of images with a tractable likelihood . When trained , it is not limited to the existing classified datasets and video datasets . This can be sped up by caching activations , but still involves generating each pixel sequentially . In this work , we propose a parallelized PixelCNN that allows more efficient inference by modeling certain pixel groups as conditionally independent . Our new PixelCNN model achieves competitive density estimation and orders of magnitude speedup - O(log N ) sampling instead of O(N ) - enabling the practical generation of 512x512 images . Samples from and are provided to the adversarial network as the negative and positive examples respectively , and the generator of the adversarial network , which is also the encoder of the autoencoder , tries to match to by the gradient that comes through the discriminative adversarial network . The adversarial network , the PixelCNN decoder and the encoder are trained jointly in two phases – the reconstruction phase and the adversarial phase – executed on each mini - batch . In the reconstruction phase , the ground truth input along with the hidden code inferred by the encoder are provided to the PixelCNN decoder . For implementing the conditioning adaptive bias in the PixelCNN decoder , we explore two different architectures pixelcnn . In the location - invariant bias , for each PixelCNN layer , we use the latent code to construct a vector that is broadcasted within each feature map of the layer and then added as an adaptive bias to that layer . In the location - dependent bias , we use the latent code to construct a spatial feature map that is broadcasted across different feature maps and then added only to the first layer of the decoder as an adaptive bias . This approach is very similar to our example of the PixelGAN autoencoder in : mnist ] Figure [ reference ] . However , the question that has remained unanswered is whether it is possible to achieve a complete decomposition of content and style in an unsupervised fashion , where the class label or discrete structure information is encoded in the latent code , and the remaining continuous structure such as style is captured by a powerful and deep PixelCNN decoder . This kind of decomposition is particularly interesting as it can be directly used for clustering and semi - supervised classification . section : Conclusion In this paper , we proposed the PixelGAN autoencoder , which is a generative autoencoder that combines a generative PixelCNN with a GAN inference network that can impose arbitrary priors on the latent code . We showed that imposing different distributions as the prior enables us to learn a latent representation that captures the type of statistics that we care about , while the remaining structure of the image is captured by the PixelCNN decoder . b. The PixelCNN decoder uses both the vertical and horizontal stacks similar to pixelcnn and uses a discretized logistic mixture likelihood cost function with 10 logistic distribution as proposed in pixelcnn ++ . The PixelCNN uses the location - invariant bias as described in endix : conditioning_of_pixelcnn ] Appendix [ reference ] . Yan et al . suggest an attribute - conditioned deep variational auto - encoder which extracts the latent variables from a reference image and combines them with attributes to produce the generated image with a generative model . Oord et al . propose a conditional image generation model based on PixelCNN decoder for image generation conditioned on an arbitrary feature vector . However , the identity of the generated face is not emphasized in , making them not directly applicable to attribute transfer . To work with the 12.5 ms frame hop of the spectrogram frames , only 2 upsampling layers are used in the conditioning stack instead of 3 layers . Instead of predicting discretized buckets with a softmax layer , we follow PixelCNN ++ and Parallel WaveNet and use a 10 - component mixture of logistic distributions ( MoL ) to generate 16 - bit samples at 24 kHz . To compute the logistic mixture distribution , the WaveNet stack output is passed through a ReLU activation followed by a linear projection to predict parameters ( mean , log scale , mixture weight ) for each mixture component . The estimation of TRX chains ' complex responses is needed . Two estimation algorithms are used , referred as linear and non-linear least squares estimation . These estimation algorithms need to use the information regarding the antenna array mutual coupling matrix and over-the-air ( OTA ) self-measurements between pairs of elements in the antenna array . Methods to estimate signal or parameter values are frequently employed in multimedia signal analysis . In this chapter , a more general introduction into principles of linear and nonlinear estimation is given . This is partially done for examples from signal restoration , which can straightforwardly be extended to prediction and interpolation of incomplete signals ; most of the methods introduced are not restricted to the estimation of signals , but likewise applicable to the estimation of parameters . Double exponential algorithm of photon correlation spectroscopy ( PCS ) is realized by the nonlinear estimation , its inversion accuracy is affected by four initial parameters choice . According to linear relationship of the decay linewidth distribution coefficients and exponential terms of autocorrelation function ( ACF ) , a new inversion algorithm combining linear and nonlinear estimation is proposed . By changing four parameters optimization problem into two parameters optimization problem in turns , new algorithm improves the inversion accuracy and reduces two initial parameters . This paper presents the use of nonlinear distributed estimation in a wireless system transmitting over channels with random gains . Specifically , we discuss the development of estimators and analytically determine their attainable variance for two conditions : 1 ) when full channel state information ( CSI ) is available at the transmitter and receiver ; and 2 ) when only channel gain statistics and phase information are available . This is particularly important if clinical diagnoses are to be based on estimated metabolite levels , as in applications of MR Spectroscopic Imaging for human studies . In this report , a standard method of obtaining confidence intervals for nonlinear estimation is applied to simulated data and short-TE clinical proton spectroscopic imaging data sets of human brain . So-called " confidence images " are generated to serve as visual indicators of how much trust should be placed in interpretation of spatial variations seen in images derived from fitted metabolite parameter estimates . Linear estimation methods are implemented to select the initial lag orders of candidate models . Model parameters are found by nonlinear estimation and candidate models are compared using AIC , SBC criteria and other diagnostic checks . The modeling approach is shown to work well in practice by estimating optimum RBF-ARX models for real and simulated time series data and comparing the results with those of previous authors . Above ground biomass was estimated from stem density and size measurements using available allometric relationships . The study demonstrates potential for retrieval of regrowth structure and biomass through nonlinear estimation . These estimations are based on concentration changes . The conclusion is that it is possible to estimate the parameters in a general rate model using nonlinear least square estimation and numerical simulation of the model . Copyright ( C ) 2001 IFAC . With different classifications and different assumptions on salience , we obtain three different estimation models . With the relationship expressed by non-linear algebraic equations given , we then apply nonlinear least-square estimation ( LSE ) and regulated nonlinear LSE to conduct parameter estimation . Case studies are conducted to illustrate the estimation procedure . Based on the sparse dictionary , the sparse signal model is then established . A nonlinear least square estimation is first applied to roughly extract the precession frequency in the sparse dictionary . Based on the time segmented radar signal , a sparse component analysis method using the orthogonal matching pursuit algorithm is then proposed to jointly estimate the precession frequency and the scattering centers of the missile . In this paper we present an algorithm for multipoint joint processing of range measurements with trial results accumulation , evaluate its accuracy analytically in terms of range mean square error and finally validate it through an experiment . The methods used for analytical evaluation is matrix analysis and nonlinear least squares estimation theory . The approach for experimental validation is a software-defined radio ( SDR ) transceiver working on National Instruments ( NI ) Multimodal visual haptic user interfaces can be made more effective by accurately colocating the workspaces of their components . We have developed a coregistration technique for pose measurement devices based on nonlinear least squares parameter estimation . A reduced quaternion parameterization is used for representing the orientation component of coordinate transformations , which avoids the numerical instability of traditional approaches . While these models can provide better spectrum estimation than DFT , they may not be the best option because the parameter estimation is only suboptimal of least squares or the methods have very narrow convergence area . In this paper , the traditional nonlinear separable estimation has been modified and a new time-domain least-squares method was developed for the identification of multi-frequency signal parameters . Algorithm convergence was analyzed and improved through segmentation of data . The design of the ADPCM signal coding system can be formalised by posing the problem as a beinput one-output nonlinear control problem with a certain output objective . This problem can be solved using nonlinear state-variable feedback in conjunction with nonlinear state variable estimation ( nonlinear observer ) . It has been shown in previous work that the extended Kalman filter does not perform adequately in such a case . A message of the NICE-X is encrypted with the random mask of the encryption function of the NICE , instead of the encrypted key . Then the reduced security problem of the NICE-X is enhanced from the Gap-SKEP to the SKEP . This paper contends a novel role of the discriminator by reusing it for encoding the images of the target domain . The proposed architecture , termed as NICE-GAN , exhibits two advantageous patterns over previous approaches : First , it is more compact since no independent encoding component is required ; Second , this plug-in encoder is directly trained by the adversary loss , making it more informative and trained more effectively if a multi-scale discriminator is applied . The main issue in NICE-GAN is the coupling of translation with discrimination along the encoder , which could incur training inconsistency when we play the min-max game via GAN . There has been a lot of recent interest in the potential of inter-base station cooperation techniques to manage excessive out-of-cell interference in evolving cellular networks . In this paper , we propose and describe a signal processing approach based on a novel Network Interference Cancellation Engine ( NICE ) , which opportunistically cancels dominant out-of-cell interference by exchanging decoded dominant interferer data among cooperating cells and reconstructing interfering signals . Simulation results with NICE processing in cellular networks show that substantial improvements in signal-to-interference-plus-noise ratio may be achieved over current non-cooperative multi-antenna receiver processing techniques while allowing backhaul overhead to be reduced by approximately between one and two orders of magnitude relative to joint processing techniques employing the exchange of complex-valued received signal samples . This paper presents a methodology based on the NICE integration scheme [ 1 , 2 ] for simple and rapid numerical implementation of a class of plasticity constitutive models . In this regard , an algorithm is purposely developed for the implementation of newly developed advanced constitutive models into explicit finite element framework . Hence , conjugate gradient type methods , when applied to solving these preconditioned block-Toeplitz systems with small size blocks , converge very fast . Recursive computation of such block diagonal and Schur complement preconditioners is considered by using the nice matrix representation of the inverse of a block-Toeplitz matrix . Applications to block-Toeplitz systems arising from least squares filtering problems and queueing networks are presented . Number of programming languages are born , including OOP of course has advantages and disadvantages of each . Through this scientific writing , we will analyze the performance comparison of object-based programming language and C Nice . Analyzing the performance comparison of the two languages will be implemented through the creation of a simple program to find roots of nonlinear equations using Bisection and Rule Falsi , so will get a fact about the advantages and disadvantages of both languages . Finally , the Strategist controls the overall operation of the Generator and theAnalyzer . 1 INTRODUCTION In this paper , we introduce the architecture and organizational prin-ciples of NAG ( Nice Argument Generator ) , a system capable of an-alyzing and composing arguments that are persuasive for particularinterlocutors . Given a user model and a goal proposition , NAG willproduce an argument supporting the goal which , according to its usermodel , will be effective in bringing the user to a targeted degree ofbelief in the goal proposition . The comment at 0:25 says " why has nt this guy uplooaded in 3 weeks is he dead " . Nice grammar . This old computer In this article , we address this problem and propose a locality-aware P2P overlay construction method , called Nearcast , which builds an efficient overlay multicast tree by letting each peer node choose physically closer nodes as its logical children . We have conducted extensive simulations to evaluate the performance of Nearcast in comparison with the existing RTT and NICE protocols . Also , Nearcast has been deployed on a wide-area network testbed to delivery video coursed to about 7200 users distributed across 100 collages in 32 cities in China . And then , according to these factors , we propose a multicast tree construction algorithm concerning the nodes ' relative leaving probability based on NICE NRLP-NICE . NRLP-NICE compares the distance among nodes and selects the centre nodes as the leaders to build new multicast tree . Meanwhile , NRLP-NICE switches the rule of the distribution of nodes ' session time into nodes ' relative leaving probability .
However , likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significantly differ from training data . In this paper , we pose that this problem is due to the excessive influence that input complexity has in generative models ' likelihoods . We report a set of experiments supporting this hypothesis , and use an estimate of input complexity to derive an efficient and parameter-free OOD score , which can be seen as a likelihood-ratio , akin to Bayesian model comparison .
Altogether , we hope that our results encourage researchers to pay more attention to temporal dynamics in eye movement data , as well as to the inevitable variation in the individual level . The spatio-temporal analysis of eye movements presented here is novel and covers a wide range of methods from functional summary statistics to the likelihood-based modelling . The methods and tools presented are applicable to other eye movement data collected in a freeviewing condition , but we believe that the developed models , being rather simple but flexible , could also be useful for the analysis of spatio-temporal sequences outside the field .
First , the frequency distributions of ship dynamic attributes , such as the mean and variance of Speed over Ground , Course over Ground , are obtained , respectively , according to the verified AIS data samples . Subsequently , the correlations between the attributes and belief degrees of the categories are established based on likelihood modeling . In this case , the attributes were characterized into several pieces of evidence , and the evidence can be combined with the Evidential Reasoning rule .
We propose a strategy for discriminative training of the ivector extractor in speaker recognition . The original i-vector extractor training was based on the maximum-likelihood generative modeling , where the EM algorithm was used . In our approach , the i-vector extractor parameters are numerically optimized to minimize the discriminative cross-entropy error function .
These demonstrations suggest interesting applications can be developed using representations learned by our models . It has been previously demonstrated that conditional generative models can learn to convincingly model object attributes like scale , rotation , and position dosovitskiy2014learning . This is to our knowledge the first demonstration of this occurring in purely unsupervised models .
section : Related work Inference Generation Substantial work on probabilistic generative models has focused on training models using maximum likelihood . One class of maximum likelihood models are those described by probabilistic undirected graphs , such as Restricted Boltzmann Machines smolensky1986information and Deep Boltzmann Machines salakhutdinov2009deep .
In this paper , we describe the " PixelGAN autoencoder " , a generative autoencoder in which the generative path is a convolutional autoregressive neural network on pixels ( PixelCNN ) that is conditioned on a latent code , and the recognition path uses a generative adversarial network ( GAN ) to impose a prior distribution on the latent code . We show that different priors result in different decompositions of information between the latent code and the autoregressive decoder .
Hierarchical Bayesian methods can unify many related tasks ( e.g. k-shot classification , conditional and unconditional generation ) as inference within a single generative model . However , when this generative model is expressed as a powerful neural network such as a PixelCNN , we show that existing learning techniques typically fail to effectively use latent variables . To address this , we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class .
When conditioned on an embedding produced by a convolutional network given a single image of an unseen face , it generates a variety of new portraits of the same person with different facial expressions , poses and lighting conditions . We also show that conditional PixelCNN can serve as a powerful decoder in an image autoencoder . Additionally , the gated convolutional layers in the proposed model improve the log-likelihood of PixelCNN to match the state-of-the-art performance of PixelRNN on ImageNet , with greatly reduced computational cost .
Our method results from the observation that the task of automated colorization is relatively easy given a low-resolution version of the color image . We first train a conditional PixelCNN to generate a low resolution color for a given grayscale image . Then , given the generated low-resolution color image and the original grayscale image as inputs , we train a second CNN to generate a high-resolution colorization of an image .
Generating Images from Patches . In this paper we propose Spatial PixelCNN , a conditional autoregressive model that generates images from small patches . By conditioning on a grid of pixel coordinates and global features extracted from a Variational Autoencoder ( VAE ) , we are able to train on patches of images , and reproduce the full-sized image . We show that it not only allows for generating high quality samples at the same resolution as the underlying dataset , but is also capable of upscaling images to arbitrary resolutions ( tested at resolutions up to $ 50\times$ ) on the MNIST dataset . Compared to a PixelCNN++ baseline , Spatial PixelCNN quantitatively and qualitatively achieves similar performance on the MNIST dataset .
This paper proposed a novel visual tracking method based on the Gated PixelCNN and particle filtration . First , unlike the convolutional layers in traditional trackers , Gated PixelCNN is a generative model of images with a tractable likelihood . When trained , it is not limited to the existing classified datasets and video datasets .
This can be sped up by caching activations , but still involves generating each pixel sequentially . In this work , we propose a parallelized PixelCNN that allows more efficient inference by modeling certain pixel groups as conditionally independent . Our new PixelCNN model achieves competitive density estimation and orders of magnitude speedup - O(log N ) sampling instead of O(N ) - enabling the practical generation of 512x512 images .
Samples from and are provided to the adversarial network as the negative and positive examples respectively , and the generator of the adversarial network , which is also the encoder of the autoencoder , tries to match to by the gradient that comes through the discriminative adversarial network . The adversarial network , the PixelCNN decoder and the encoder are trained jointly in two phases – the reconstruction phase and the adversarial phase – executed on each mini - batch . In the reconstruction phase , the ground truth input along with the hidden code inferred by the encoder are provided to the PixelCNN decoder .
For implementing the conditioning adaptive bias in the PixelCNN decoder , we explore two different architectures pixelcnn . In the location - invariant bias , for each PixelCNN layer , we use the latent code to construct a vector that is broadcasted within each feature map of the layer and then added as an adaptive bias to that layer . In the location - dependent bias , we use the latent code to construct a spatial feature map that is broadcasted across different feature maps and then added only to the first layer of the decoder as an adaptive bias .
This approach is very similar to our example of the PixelGAN autoencoder in : mnist ] Figure [ reference ] . However , the question that has remained unanswered is whether it is possible to achieve a complete decomposition of content and style in an unsupervised fashion , where the class label or discrete structure information is encoded in the latent code , and the remaining continuous structure such as style is captured by a powerful and deep PixelCNN decoder . This kind of decomposition is particularly interesting as it can be directly used for clustering and semi - supervised classification .
section : Conclusion In this paper , we proposed the PixelGAN autoencoder , which is a generative autoencoder that combines a generative PixelCNN with a GAN inference network that can impose arbitrary priors on the latent code . We showed that imposing different distributions as the prior enables us to learn a latent representation that captures the type of statistics that we care about , while the remaining structure of the image is captured by the PixelCNN decoder .
b. The PixelCNN decoder uses both the vertical and horizontal stacks similar to pixelcnn and uses a discretized logistic mixture likelihood cost function with 10 logistic distribution as proposed in pixelcnn ++ . The PixelCNN uses the location - invariant bias as described in endix : conditioning_of_pixelcnn ] Appendix [ reference ] .
Yan et al . suggest an attribute - conditioned deep variational auto - encoder which extracts the latent variables from a reference image and combines them with attributes to produce the generated image with a generative model . Oord et al . propose a conditional image generation model based on PixelCNN decoder for image generation conditioned on an arbitrary feature vector . However , the identity of the generated face is not emphasized in , making them not directly applicable to attribute transfer .
To work with the 12.5 ms frame hop of the spectrogram frames , only 2 upsampling layers are used in the conditioning stack instead of 3 layers . Instead of predicting discretized buckets with a softmax layer , we follow PixelCNN ++ and Parallel WaveNet and use a 10 - component mixture of logistic distributions ( MoL ) to generate 16 - bit samples at 24 kHz . To compute the logistic mixture distribution , the WaveNet stack output is passed through a ReLU activation followed by a linear projection to predict parameters ( mean , log scale , mixture weight ) for each mixture component .
The estimation of TRX chains ' complex responses is needed . Two estimation algorithms are used , referred as linear and non-linear least squares estimation . These estimation algorithms need to use the information regarding the antenna array mutual coupling matrix and over-the-air ( OTA ) self-measurements between pairs of elements in the antenna array .
Methods to estimate signal or parameter values are frequently employed in multimedia signal analysis . In this chapter , a more general introduction into principles of linear and nonlinear estimation is given . This is partially done for examples from signal restoration , which can straightforwardly be extended to prediction and interpolation of incomplete signals ; most of the methods introduced are not restricted to the estimation of signals , but likewise applicable to the estimation of parameters .
Double exponential algorithm of photon correlation spectroscopy ( PCS ) is realized by the nonlinear estimation , its inversion accuracy is affected by four initial parameters choice . According to linear relationship of the decay linewidth distribution coefficients and exponential terms of autocorrelation function ( ACF ) , a new inversion algorithm combining linear and nonlinear estimation is proposed . By changing four parameters optimization problem into two parameters optimization problem in turns , new algorithm improves the inversion accuracy and reduces two initial parameters .
This paper presents the use of nonlinear distributed estimation in a wireless system transmitting over channels with random gains . Specifically , we discuss the development of estimators and analytically determine their attainable variance for two conditions : 1 ) when full channel state information ( CSI ) is available at the transmitter and receiver ; and 2 ) when only channel gain statistics and phase information are available .
This is particularly important if clinical diagnoses are to be based on estimated metabolite levels , as in applications of MR Spectroscopic Imaging for human studies . In this report , a standard method of obtaining confidence intervals for nonlinear estimation is applied to simulated data and short-TE clinical proton spectroscopic imaging data sets of human brain . So-called " confidence images " are generated to serve as visual indicators of how much trust should be placed in interpretation of spatial variations seen in images derived from fitted metabolite parameter estimates .
Linear estimation methods are implemented to select the initial lag orders of candidate models . Model parameters are found by nonlinear estimation and candidate models are compared using AIC , SBC criteria and other diagnostic checks . The modeling approach is shown to work well in practice by estimating optimum RBF-ARX models for real and simulated time series data and comparing the results with those of previous authors .
Above ground biomass was estimated from stem density and size measurements using available allometric relationships . The study demonstrates potential for retrieval of regrowth structure and biomass through nonlinear estimation .
These estimations are based on concentration changes . The conclusion is that it is possible to estimate the parameters in a general rate model using nonlinear least square estimation and numerical simulation of the model . Copyright ( C ) 2001 IFAC .
With different classifications and different assumptions on salience , we obtain three different estimation models . With the relationship expressed by non-linear algebraic equations given , we then apply nonlinear least-square estimation ( LSE ) and regulated nonlinear LSE to conduct parameter estimation . Case studies are conducted to illustrate the estimation procedure .
Based on the sparse dictionary , the sparse signal model is then established . A nonlinear least square estimation is first applied to roughly extract the precession frequency in the sparse dictionary . Based on the time segmented radar signal , a sparse component analysis method using the orthogonal matching pursuit algorithm is then proposed to jointly estimate the precession frequency and the scattering centers of the missile .
In this paper we present an algorithm for multipoint joint processing of range measurements with trial results accumulation , evaluate its accuracy analytically in terms of range mean square error and finally validate it through an experiment . The methods used for analytical evaluation is matrix analysis and nonlinear least squares estimation theory . The approach for experimental validation is a software-defined radio ( SDR ) transceiver working on National Instruments ( NI )
Multimodal visual haptic user interfaces can be made more effective by accurately colocating the workspaces of their components . We have developed a coregistration technique for pose measurement devices based on nonlinear least squares parameter estimation . A reduced quaternion parameterization is used for representing the orientation component of coordinate transformations , which avoids the numerical instability of traditional approaches .
While these models can provide better spectrum estimation than DFT , they may not be the best option because the parameter estimation is only suboptimal of least squares or the methods have very narrow convergence area . In this paper , the traditional nonlinear separable estimation has been modified and a new time-domain least-squares method was developed for the identification of multi-frequency signal parameters . Algorithm convergence was analyzed and improved through segmentation of data .
The design of the ADPCM signal coding system can be formalised by posing the problem as a beinput one-output nonlinear control problem with a certain output objective . This problem can be solved using nonlinear state-variable feedback in conjunction with nonlinear state variable estimation ( nonlinear observer ) . It has been shown in previous work that the extended Kalman filter does not perform adequately in such a case .
A message of the NICE-X is encrypted with the random mask of the encryption function of the NICE , instead of the encrypted key . Then the reduced security problem of the NICE-X is enhanced from the Gap-SKEP to the SKEP .
This paper contends a novel role of the discriminator by reusing it for encoding the images of the target domain . The proposed architecture , termed as NICE-GAN , exhibits two advantageous patterns over previous approaches : First , it is more compact since no independent encoding component is required ; Second , this plug-in encoder is directly trained by the adversary loss , making it more informative and trained more effectively if a multi-scale discriminator is applied . The main issue in NICE-GAN is the coupling of translation with discrimination along the encoder , which could incur training inconsistency when we play the min-max game via GAN .
There has been a lot of recent interest in the potential of inter-base station cooperation techniques to manage excessive out-of-cell interference in evolving cellular networks . In this paper , we propose and describe a signal processing approach based on a novel Network Interference Cancellation Engine ( NICE ) , which opportunistically cancels dominant out-of-cell interference by exchanging decoded dominant interferer data among cooperating cells and reconstructing interfering signals . Simulation results with NICE processing in cellular networks show that substantial improvements in signal-to-interference-plus-noise ratio may be achieved over current non-cooperative multi-antenna receiver processing techniques while allowing backhaul overhead to be reduced by approximately between one and two orders of magnitude relative to joint processing techniques employing the exchange of complex-valued received signal samples .
This paper presents a methodology based on the NICE integration scheme [ 1 , 2 ] for simple and rapid numerical implementation of a class of plasticity constitutive models . In this regard , an algorithm is purposely developed for the implementation of newly developed advanced constitutive models into explicit finite element framework .
Hence , conjugate gradient type methods , when applied to solving these preconditioned block-Toeplitz systems with small size blocks , converge very fast . Recursive computation of such block diagonal and Schur complement preconditioners is considered by using the nice matrix representation of the inverse of a block-Toeplitz matrix . Applications to block-Toeplitz systems arising from least squares filtering problems and queueing networks are presented .
Number of programming languages are born , including OOP of course has advantages and disadvantages of each . Through this scientific writing , we will analyze the performance comparison of object-based programming language and C Nice . Analyzing the performance comparison of the two languages will be implemented through the creation of a simple program to find roots of nonlinear equations using Bisection and Rule Falsi , so will get a fact about the advantages and disadvantages of both languages .
Finally , the Strategist controls the overall operation of the Generator and theAnalyzer . 1 INTRODUCTION In this paper , we introduce the architecture and organizational prin-ciples of NAG ( Nice Argument Generator ) , a system capable of an-alyzing and composing arguments that are persuasive for particularinterlocutors . Given a user model and a goal proposition , NAG willproduce an argument supporting the goal which , according to its usermodel , will be effective in bringing the user to a targeted degree ofbelief in the goal proposition .
The comment at 0:25 says " why has nt this guy uplooaded in 3 weeks is he dead " . Nice grammar . This old computer
In this article , we address this problem and propose a locality-aware P2P overlay construction method , called Nearcast , which builds an efficient overlay multicast tree by letting each peer node choose physically closer nodes as its logical children . We have conducted extensive simulations to evaluate the performance of Nearcast in comparison with the existing RTT and NICE protocols . Also , Nearcast has been deployed on a wide-area network testbed to delivery video coursed to about 7200 users distributed across 100 collages in 32 cities in China .
And then , according to these factors , we propose a multicast tree construction algorithm concerning the nodes ' relative leaving probability based on NICE NRLP-NICE . NRLP-NICE compares the distance among nodes and selects the centre nodes as the leaders to build new multicast tree . Meanwhile , NRLP-NICE switches the rule of the distribution of nodes ' session time into nodes ' relative leaving probability .
Input complexity and out-of-distribution detection with likelihood-based generative models Statistical inference for eye movement sequences using spatial and spatio-temporal point processes An Internet of Things Approach for Extracting Featured Data Using AIS Database: An Application Based on the Viewpoint of Connected Ships Discriminatively Trained i-vector Extractor for Speaker Verification Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Density estimation using Real NVP PixelGAN Autoencoders The Variational Homoencoder: Learning to learn high capacity generative models from few examples Conditional Image Generation with PixelCNN Decoders PixColor: Pixel Recursive Colorization Spatial PixelCNN: Generating Images from Patches Visual Tracking by Gated PixelCNN Model Parallel Multiscale Autoregressive Density Estimation PixelGAN Autoencoders Deep Identity-aware Transfer of Facial Attributes Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions Millimeter Wave Antenna Array Calibration and Validation for 5G New Radio Access Signal and Parameter Estimation Inversion algorithm of photon correlation spectroscopy by combining linear and nonlinear estimation Nonlinear Amplify-and-Forward Distributed Estimation Over Nonidentical Channels Confidence images for MR spectroscopic imaging. RBF-ARX MODELING FOR PREDICTION AND CONTROL Forest parameter retrieval from SAR data using an estimation algorithm applied to regrowing forest stands in Queensland, Australia Parameter estimation of protein chromatographic processes based on breakthrough curves Nonlinear least-square estimation (LSE)-based parameter identification of a synchronous generator Precession missile feature extraction using sparse component analysis of radar measurements Experimental validation of multipoint joint processing of range measurements via software-defined radio testbed Coregistration of Pose Measurement Devices Using Nonlinear Least Squares Parameter Estimation A time-domain model-based method for the identification of multi-frequency signal parameters Nonlinear Obseryer Design for Adpcm Signal Coding Systems An IND-CCA2 Public-Key Cryptosystem with Fast Decryption Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation Uplink cooperation in wireless networks with NICE processing Rapid implementation of advanced constitutive models Block Diagonal and Schur Complement Preconditioners for Block-Toeplitz Systems with Small Size Blocks COMPARATIVE PERFORMANCE ANALYSIS OF THE LANGUAGE PROGRAMMING AND C NICE ON THE IMPLEMENTATION OF THE SEARCH FOR THE ROOTS OF EQUATIONS NON LINEAR BISECTION METHOD AND RULE FALSI Perambulations on the Way to an Architecture for a Nice Argument Generator My Computer Broke! (ft. Not-A-Cat-Cat) Nearcast: A locality-aware P2P live streaming approach for distance education Multicast tree construction: a nodes' relative leaving probability algorithm based on NICE in ALM
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INTERSPEECH ICLR ICLR CSAI NIPS ArXiv 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) IGARSS ISSPA ICISC
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