The sum of squared errors between the reconstructed approximation and original adjacency matrix is then calculated . An online learning algorithm is then used to estimate a Gaussian distribution that models the error distribution . Outlier error values are deemed to represent anomalous traffic flows corresponding to possible attacks . This paper discusses a realistic turbo coding system with the signal phase which has not been perfectly estimated . We propose improved decoding algorithms for the situations when the residual phase error can be modelled by the Gaussian probability distribution and a Markov chain , a model which can be used in many actual phase estimators . It is shown that increasing the state space of the decoders can decrease the bit error probability . A stochastic channel model is then derived as a function of time , antenna array displacement and frequency , which falls in the category of tapped delay line model . Specifically , the taps can be represented as a combination of a possible line of sight or dominant reflected path and a Gaussian distributed component comprised of unresolvable scattered paths . After that , general analytical solutions are provided for the 3D temporal-spectral-spatial correlations of SSF via the exploitation of channel statistical properties . This paper describes a real-life behavior framework in simulation game based on Probabilistic State Machine ( PSM ) with Gaussian random distribution . According to the dynamic environment information , NPC can generate behavior planning autonomously associated with defined FSM . In case of multiple erroneous ( incorrect ) selected answers for random twenty questions submitted by any arbitrary virtual student member out of 500 virtual students . Interestingly , obtained results in case of testing two penalty values ( zero & one third ) shown to have bell shape close similar to Gaussian statistical distribution . Furthermore , these results have become in agreement with learning achievement results , after running of simulation program of adopted realistic ANN model considering different learning rate values . Computer routines to eliminate density wedging due to nonuniform exposure and spikes caused by scratches or foreign particles improved the reliability of the RMS granularity calculation . The probability density distribution of the density data for a flashed specimen with pelloids removed is very near a Gaussian density distribution after data imperfections are removed . ( Author ) In our work we use two clustering algorithms , DBSCAN and k-MXT . Moreover , we develop a modification of the k-MXT algorithm called the k-MXT-Gauss algorithm , where the calculation of the weights of the graph edges is transformed using the Gaussian distribution density . We compare the performance of k-MXT-Gauss algorithm with the performance of k-MXT and DBSCAN algorithms both on simulated data and real data . A work-flow analysis will be carried out demonstrating that the accuracy relevant for a clinical application comprises several error sources including imaging , target and entry point selection , image to frame coordinates registration , and the setting of mechanical parameters of the frame . These error sources will be postulated to obey a Gaussian distribution probability density . The linear , i.e. , Gaussian , error propagation , will be used to link all error contributions thus to calculate the cumulative accuracy of the frame used in the application . The model is solved and results obtained for several cases , including similar performers , one dominant performer , and performance in sections . For a large ensemble , a stochastic differential equation model describes the phase and tempo evolution , resulting in a Gaussian probability density distribution for tempo and phase . A model for phase correction is also derived . Here we assume that is a Gaussian distribution whose mean and variance is predicted by the encoder network : . In this case , the stochasticity in comes from both the data - distribution and the randomness of the Gaussian distribution at the output of the encoder . We can use the same re - parametrization trick of vae for back - propagation through the encoder network . Here we only discuss the scenario where the latent variables are continuous and the parameterised diagonal Gaussian is employed as the variational distribution . However the framework is also suitable for discrete units , and the only modification needed is to replace the Gaussian with a multinomial parameterised by the outputs of a softmax function . Though the reparameterisation trick for continuous variables is not applicable for this case , a policy gradient approach mnih2014neural can help to alleviate the high variance problem during stochastic estimation . Expected Improvement of trying other parameters Bayesian Optimization ( Mockus ' 78 ) - Method for the global optimization of multi - modal , computationally expensive black box functions - Assumes that the unknown function was sampled from a Gaussian Process ( prior ) and uses the observations ( likelihood ) to maintain a posterior - Observations are the measure of generalization performance under different settings of the hyperparameters we wish to optimize . - The next set of hyperparameters are selected using the maintained posterior – using a strategy determined by the acquisition function Gaussian Processes Specifies a distribution over functions such that any finite subset of N points follows a Multivariate Gaussian Distribution . We produced an empirical win - probability matrix by summing wins across all games and used this matrix to evaluate Elo scores . A ranking difference of 400 corresponds to the odds of winning of 10:1 under the Gaussian assumption . subsection : Contextual priority tree We initialized the recurrent weight matrices and as random orthogonal matrices . For and , we initialized them by sampling each element from the Gaussian distribution of mean and variance . All the elements of and all the bias vectors were initialized to zero . We introduce convolutional DRAW . It features convolutions , latent prior modeling , a Gaussian input distribution ( for natural images ) and , in some experiments , a multi - layer architecture . However , it does not use an explicit attentional mechanism . The kernel size is fixed to 15 15 . To further expand the degradation space , we also consider a more general kernel assumption , , anisotropic Gaussian , which is characterized by a Gaussian probability density function with zero mean and varying covariance matrix . The space of such Gaussian kernel is determined by rotation angle of the eigenvectors of and scaling of corresponding eigenvalues . We use pre - trained 300 - D Glove 840B vectors to initialize word embeddings . Out - of - vocabulary ( OOV ) words are initialized randomly with Gaussian samples . All vectors are updated during training . We train the full model end - to - end in a single step of optimization . We train the convolutional and recurrent network from scratch with all weights initialized from a Gaussian with . Gradients are computed using and flow through the recurrent and convolutional networks . The empirical evidence shows that attempts to forecast precise values , point estimates , for w(T ) are not very successful . A better approach consists in describing w(T ) by a probability distribution . This in turn requires the building of an adequate model which describes the dynamics of the risk factors , i.e. their evolution through time . The 2D target position estimates are obtained by a combination of blob detection , edge searching and gray-level matching , with the aid of model geometrical structure projection using current estimates of camera geometry . The information is represented in the form of a probability density distribution , and propagated in a Bayes Net . The Bayesian reasoning that is performed in the 2D image is coupled by the rigid model geometry constraint in 3D space . Thirdly , we further investigate on the impact of high-bit-rate coding from the more fundamental signal characteristics point of view . A probability distribution analysis on DCT coefficients from the H.264/AVC codec under different bit rates is conducted to reveal that the prediction residual in the form of DCT coefficients have a near uniform distribution for all scanning positions . To further understand this phenomenon , a correlation based analysis was conducted to show that the different types of correlations existed in the video frame and the distribution of these correlations highly impact the coding efficiency . Here , typical practices fail . This article introduces a probability distribution‐based approach to assess the significance of performance differences among alternatives that allows LCA practitioners to focus analyses on those aspects most influential to the decision , identify the areas that would benefit the most from data refinement given the level of uncertainty , and complement existing hotspot analyses . In a case study of a comparative LCA of five photovoltaic technologies , findings show that thin‐film cadmium telluride and amorphous silicon cell panels are most likely to perform better than other alternatives . This paper presents the optimal placement of solar-microturbine and wind-microturbine power plant with minimum network losses and a reliable power supply . The output power of these intermittent distributed generators has been computed on probability-based distribution . Beta probability distribution and Weibull probability distribution have been used to compute probable power generation of these Natural power Resources . The invention discloses a multi-time-scale intermittent energy generation predication method for an active power distribution network , and relates to the technical field of generation predication . The method is that the predication data of generation power under long time scale can be obtained according to the probability distribution characteristic that the intermittent energy generation changes as the environmental changes and the historical generation power data ; the predication data of the generation powder under the long time scale are decomposed by an empirical mode into a series of mode components with frequency from low to high and one residual component ; the generation power predication data are corrected according to the time scale corresponding to the frequency of each mode component . According to the method , the predication data are corrected under different short time scales during the long-time-scale predication intermittent energy generation process , thus the predication precision of the intermittent energy generation is increased , and the reliable data support is provided to optimal dispatching of the active power distribution network . Two approaches are contrasted . The first extends Montague Semantics with a probability distribution over models . It has nice theoretical properties , but does not account for the ubiquitous nature of ambiguity ; moreover inference is NP hard . The invention provides a mode selection method and a mode selection system based on video scalable coding compression . The method comprises the steps of A ) detecting whether a macro block corresponding to a base layer is intra-frame coded or not , if so , detecting all intra-frame modes , then selecting a current optimal mode as a macro block mode , and if not , executing a step B. The mode selection method and the mode selection system based on video scalable coding compression have the beneficial effects that aiming at solving the problems that the macro block modes in SVC ( Scalable Video Coding ) are multiple and the influence on coding efficiency is great , by performing probability distribution statistics on the basis of macro block modes at an enhanced layer and macro block modes at the base layer , analyzing the correlation of the macro block modes at the enhanced layer and the macro block modes at the base layer and using the information of the base layer , candidate modes are reduced and the complexity of an algorithm is reduced ; and aiming at the macro block mode distribution characteristic in video coding , by adopting a hierarchical selection strategy , the complexity of the algorithm is reduced , the SVC coding speed is improved and the coding quality is guaranteed at the same time . This paper points out some of the more common misconceptions and their implications upon the total project . The Beta distribution as the underlying probability distribution is evaluated as to first , the overall effects of the inherent errors that are imposed by the basic PERT assumptions , and second , the effects , from a probability distribution viewpoint , of some of the more common PERT misconceptions . Finally , several alternatives to the basic PERT methodology are explored , both from the theoretical and practical viewpoints . ( b ) is obtained using Property ( iii ) , while ( c ) follows from the Cauchy - Schwarz inequality and Property ( i ) . Under Assumptions ( A ) and ( B ) , k ( x , · ) is Bochner integrable with respect to any probability distribution P with finite first moment and the following relation holds : Proof . Liao et al . ( 2007 ) use the time of day to use in a probability model building assumptions about the participant ’s context . While this might be a useful technique to identify different activities , it might not be applicable to participants that have abnormal working hours for example . Since we use equal step size in quantizing , the normal p.d.f . ( probability density function ) is a natural choice to generate the ground - truth from and : where . Fig . section : Euclidean Distance as Confusion Since the conditional probability distribution over N classes is an element within R N on the unit simplex , we can consider the Euclidean distance to be a metric of " confusion " between two conditional probability distributions . Analogous to the previous setting , we define the Euclidean Confusion D EC ( · , · ) for a pair of inputs x 1 , x 2 with model parameters θ as : Generative image modeling is a central problem in unsupervised learning . Probabilistic density models can be used for a wide variety of tasks that range from image compression and forms of reconstruction such as image inpainting ( e.g. , see Figure [ reference ] ) and deblurring , to generation of new images . When the model is conditioned on external information , possible applications also include creating images based on text descriptions or simulating future frames in a planning task . Formally , we replace Equations [ reference ] and [ reference ] in the paper with the following equations respectively : Our final ensemble model , DR - BiLSTM ( Ensemble ) is the combination of the following 6 models : tanh - Projection , DR - BiLSTM ( with 1 round of dependent reading ) , DR - BiLSTM ( with 3 rounds of dependent reading ) , and 3 DR - BiLSTMs with different initialization seeds . We also experiment with majority voting and averaging the probability distribution strategies for ensemble models using the same set of models as our weighted averaging ensemble method ( as described above ) . Figure [ reference ] shows the behavior of the majority voting strategy with different number of models . * the cat ] In order to build a good probability estimate for the target word w k in a context W k−1 we need a way of combining an arbitrary number of skip - gram features f k−1 , which do not fall into a simple hierarchy like regular n - gram features . The following section describes a simple , yet novel approach for combining such predictors in a way that is computationally easy , scales up gracefully to large amounts of data and as it turns out is also very effective from a modeling point of view . In this paper , we present a new algorithm for the statistical static timing analysis of a CMOS combinatorial circuit , which can treat correlations of arrival times of input signals to a logic gate and correlations of switching delays in a logic gate . We model each switching delay by a normal distribution , and use a normal distribution of two stochastic variables with a coefficient of correlation for computing the distribution of output delay of a logic gate . Since the algorithm takes the correlation into account , the time complexity is O(n*m ) in the worst-case , where n and m are the numbers of vertices and edges of the acyclic graph representing a given combinatorial circuit . This study reviews three approaches that can be used to develop robust frequentist or Bayesian stock assessment methods . Using simulated ¢sheries as examples , we demonstrate how these approaches can be used to develop the frequentist and Bayesian stock assessment approaches that are robust to outliers in ¢sheries data and compare the robust approaches with the commonly used normal distribution-based approach . The proposed robust approaches provide alternative ways to developing frequentist or Bayesian stock assessment methods . Abstract Several approaches for the Bayesian design of experiments have been proposed in the literature ( e.g. , D-optimal , E-optimal , A-optimal designs ) . Most of these approaches assume that the available prior knowledge is represented by a normal probability distribution . In addition , most nonlinear design approaches involve assuming normality of the posterior distribution and approximate its variance using the expected Fisher information matrix . Generalized Estimating Equations and Generalized Mixed Models : Treating Toenail Infection Introduction Methods for Analyzing Longitudinal Data Where the Response Variable Can not Be Assumed to Have a Normal Distribution Analysis Using SAS Survival Analysis : Gastric Cancer , the Treatment of Heroin Addicts , and Heart Transplants Introduction Describing Survival Data Cox 's Regression Analysis Using SAS Principal Components Analysis and Factor Analysis : Olympic Decathlon and Statements about Pain Introduction Principal Components Analysis and Factor Analysis Analysis Using SAS Cluster Analysis : Air Pollution in the United States Introduction Cluster Analysis Analysis Using SAS Discriminant Function Analysis : Classifying Tibetan Skulls Description of Data Discriminant Function Analysis Analysis Using SAS Correspondence Analysis : Smoking and Motherhood , Sex and the Single Girl , and European Stereotypes Description of Data Displaying Contingency Table Data Graphically Using Correspondence Analysis Analysis Using SAS Appendix References Index Exercises appear at the end of each chapter . The summation of the squared range error between the node and the anchors is used as the objective function to be minimized in this work . Different simulation tests with different topologies are conducted based on normal random distribution for time of arrival ( TOA ) measurements and log-normal distribution for received signal strength ( RSS ) measurements . The simulation test results showed effectiveness of the proposed approach in case of TOA measurements when compared with the lower variance achievable by any unbiased location estimator . Aiming at resolving the issue of designing appropriate proposal distribution in Markov Chain Monte Carlo ( MCMC ) algorithm , an improved MCMC scheme is developed in this paper . The presented scheme employs normal density distribution as proposal distribution to sample in objective function , and together with the historical sampling information , the proposal distribution runs to proper distribution by adaptive self-regulation . The improved scheme is applied to parameter estimation of Pearson-III distribution to figure out the problems of runoff frequency forecast . However , the underlying calculation procedure has not been elaborated and consequently there is no practical and unambiguous guidance for the consulting engineer on how to perform such a statistical analysis . In this paper , the theory for the statistical analysis is presented based on the assumption that the potential distribution for both actively corroding and passive steel can be adequately described by a normal probability density distribution . This theory is applied to a number of situations to exemplify the procedure by clear graphs . y l ) , whose entries are all zero except for the index corresponding to the true label ( output ) y l . Likewise , for regression tasks , we can use the normal distribution centered at y l with constant variance , or the delta function with the atom at y = y l . Stacking Gaussian Mixture layers Deep GMMs are best introduced by looking at some special cases : the multivariate normal distribution and the Gaussian Mixture model . One way to define a multivariate normal variable x is as a standard normal variable z ⇠ N ( 0 , In ) that has been transformed with a certain linear transformation : x Where kth_excluding ( v , k , i ) means the kth highest component of v , excluding component i. Simplifying , we get : Where Φ is the CDF of the standard normal distribution . We can now define the load loss to be the square of the coefficient of variation of the load vector , multiplied by a hand - tuned scaling factor w load . Using cross validation we found that gave best results . Weights are initialzed to small random values drawn from a zero - mean normal distribution with standard deviation 0.01 . Momentum is used to speed up learning . Since the output of the segmentation decoder has 3 channels ( predictions for each tumor subregion ) , we simply add the three dice loss functions together . is an L2 loss on the VAE branch output to match the input image : is standard VAE penalty term , a KL divergence between the estimated normal distribution and a prior distribution , which has a closed form representation : where N is total number of image voxels . We empirically found a hyper - parameter weight of to provide a good balance between dice and VAE loss terms in Equation [ reference ] . Recall that concavity corresponds to risk - averse and convexity to risk - seeking policies . Second , we consider the distortion risk measure proposed by wang2000class , where and are taken to be the standard Normal cumulative distribution function and its inverse : For , this produces risk - averse policies and we include it due to its simple interpretation and ability to switch between risk - averse and risk - seeking distortions . Third , we consider a simple power formula for risk - averse ( ) or risk - seeking ( ) policies : Finally , we consider conditional value - at - risk ( CVaR ) : CVaR has been widely studied in and out of reinforcement learning . propose a two side attention mechanism to compute the matching between the passage and query . match the passage and query from several perspectives and predict the answer by globally normalizing probability distributions . propose a bi - directional attention flow to achieve a query - aware context representation . Specifically , we propose the following forỹ : In words , we first find the target word vector which corresponds to the target word token ( resulting in u t ) , and then take the inner product of the target word vector with all the other word vectors to get an unnormalized probability distribution . We adjust this with the same temperature parameter τ used for obtainingŷ t and apply softmax . No pre - processing was applied to training images besides scaling to the range of the tanh activation function [ - 1 , 1 ] . All models were trained with mini - batch stochastic gradient descent ( SGD ) with a mini - batch size of 128 . All weights were initialized from a zero - centered Normal distribution with standard deviation 0.02 . In the LeakyReLU , the slope of the leak was set to 0.2 in all models . This not only contributes to understanding a particular coal mining area 's situation in regards to CMIES vulnerability , but also helps to discover a meaningful benchmark to learn the experiences in terms of action programmes formulation . In this study , based on the analysis of the vulnerability response mechanism of CMIES to economic fluctuations , an initial indicator system for vulnerability assessment of CMIES was constructed . Ultimately , 14 vulnerability-evaluating indicators and their weights were obtained using rough set attribute reduction . It concludes based on the analyzed literature that the identification of RFID systems ’ industrial indicators lies in accessibility and reliability data used for their validation . Finally , the indicator-based framework for sustainability assessment is verified for its usefulness on the case of dynamic spaghetti diagrams supported by Real Time Locating System . The paper concludes with a brief discussion on opportunities and limits of RFID-dedicated sustainability indicators . The proposed MOBFA aims to handle the convergence and diversity separately via evolving two cooperative search engines with different evolution rules . Specifically , in the colony-level interaction , the primary concept is to first assign two different performance evaluation principles ( i.e. , Pareto-based measure and indicator-based measure ) to the two engines for evolving each archive respectively , and then use the comprehensive learning mechanism over the two archives to boost the population diversity . In the individual-level foraging , the neighbor-discount-information ( NDI ) learning based on reinforcement learning ( RL ) is integrated into the single-objective searching to adjust the flight trajectories of foraging bee . subsection : Data Adaptive Activation Function PReLU is a commonly used activation function where is one dimension of the input of activation function and is an indicator function which controls to switch between two channels of and . in the second channel is a learning parameter . Here we refer to as the control function . If our model successfully labels the right knee as occluded , we wish to prevent the error feedback loss from backpropagating through our network . To achieve this , we include the indicator term which only backpropagates pose error feedback if a particular joint is visible in the original image . A secondary benefit is that we do not force the regressor to output dummy real values ( if a joint is occluded ) which may skew the model The effect of self-interference ( interference from another simultaneously transmitted symbol in the STTD scheme for the same user ) due to imperfect channel estimation is investigated . Based on the characteristic function method , a closed-form expression of the bit error rate ( BER ) is derived . Numerical results for 16/64-QAM show that , with the Alamouti STTD technique , the BER performance of the QAM system can be improved significantly . The logarithm is typically taken to base 2 so that the mutual information is given in units of bits . For a discrete distribution where the two random variables are defined over alphabets , X ∈ Χ and Y ∈ Ψ , this becomes ( , ) ( ; ) ( , ) log ( ) ( ) x y p x y I X Y p x y p x p y ∈Χ ∈Ψ = ∑ ∑ ( 2 ) which is equivalent to ( 1 ) in the limit of Χ and Ψ approaching the support sets of X and Y respectively . This provides a measure of the amount of information that a measurement of X contains regarding the measurement of Y , and vice-versa . In this paper , by modeling the exponential backoff process as a Markov chain , we can use the signal transfer function of the generalized state transition diagram to derive an approximate probability distribution of the MAC layer service time . We then present the discrete probability distribution for MAC layer packet service time , which is shown to accurately match the simulation data from network simulations . Based on the probability model for the MAC layer service time , we can analyze a few performance metrics of the wireless LAN and give better explanation to the performance degradation in delay and throughput at various traffic loads . Service times of messages of each priority class are i.i.d . according to a general discrete distribution function that may differ between two classes . The completion time and the stability condition for our system are investigated . The constantly changing background has been modeled at the pixel level . The feature vector for each pixel is represented with a discrete probability distribution function . The histogram colors and co-occurrence vectors have been calculated . Real-time systems used in media processing and transmission produce bursty workloads with highly variable execution and transmission times . To avoid the drawbacks of using the worst-case approach with these workloads , this paper uses a variation of the usual real-time task model where the WCET is replaced by a discrete statistical distribution . Using this approach , tasks are characterized by their processing time over a sampling period . In order to solve the discrete model , the z-transform method is used , and for solution of the continuous model - the Laplace transform is used . The stochastic matrix of Markov chains completely identifies the discrete model of distribution of the budget of an investment project between enterprises , and the differential matrix - the continuous model of this distribution . The use of z-transform and Laplace transform allows finding a solution of tasks in the analytical form . In this paper , our goal aims to develop an estimated matrix for determining whether the secret share is a secure share or not . The method of KL Divergence used discrete random distribution is taken consideration into our proposed scheme . We apply the some examples to demonstrate how to operate our proposed idea . Each channel variable simply takes one of 256 distinct values . The discrete distribution is representationally simple and has the advantage of being arbitrarily multimodal without prior on the shape ( see Fig . [ reference ] ) . However , there are many applications where the counts tend to be small and include many zeros . In this case , the observations can not be adequately modeled with a continuous distribution . During the last three decades , there has been significant progress in the area of count time-series analysis [ 1 ] , [ 2 ] . The filtering process reduces the sensitivity of the recognition system to input fluctuations caused by noise . The system developed uses continuous probability density function , single Gaussian mixture HMMs trained on filterback output vectors . Using autocorrelation statistics collected for speech and noise during training , noise canceling Wiener filters are designed for each hidden Markov model state . Achievable secrecy rates over a multiple-input multiple-output multiple-eavesdropper ( MIMOME ) wiretap channel are considered , when the legitimate users have perfect knowledge only of the legitimate channel state and the eavesdropper channel is drawn from a ( possibly unknown ) continuous probability density . Legitimate users are assumed to deploy more antennas than the eavesdropper . However , in order to solve the difficulties of the existing Importance Sampling techniques , we adopted the discrete probability mass function in the new method for estimating the statistical characteristics of received signals from the measured central moments up to Nth orders . From the discrete probability mass function constructed with the measured central moments with less number of the received signal samples than the one required in the MC technique , continuous cumulative probability distribution function can be exactly estimated by using interpolation and extrapolation technique . By assuming that the in-phase and quadrature phase received signals are independent and by estimating the channel characteristics of the in-phase and quadrature phase received signals separately , the implementation of the proposed method could be simplified . With the optimization routine performed off-line , the resulting multi-controller realization has a low computational load . The proposed technique allows the designer to trade off performance versus memory , even when the process to be controlled is described via a continuous distribution of models . 2 ≈ i e base vector in dimension i ( Chapter 5 ) ε small positive error ) ( x f transfer function ) ( ' x f , ) ( " x f reward function ) ( x g reward function g auxiliary machining-delay vector ( Chapter 5 ) ) ( ' x h , ) ( " x h reward function 8 j I Palmer index for job j ij η visibility of a trail from node i to node j j j the jth job under processing k the number of actions sharing the same , maximal action-state value κ minimum of the difference between each action-state value pair ) ( A L link state description of node A λ decay rate for eligibility traces M matrix of machining times ) ( i m number of actions in the ith state ( Chapter 2 ) n number of states ν small positive redundancy variable 1 o , 2 o abstract machining times a ss P ' probability of getting into state ' s from state s via action a i p probability of selecting action i a p ˆ scaled probability value ) ( x p continuous probability distribution function p , r permutation vectors ( Chapter 5 ) i pred predecessor node ij pred predecessor node ( matrix representation ) ) , ( j i i a s π …
The sum of squared errors between the reconstructed approximation and original adjacency matrix is then calculated . An online learning algorithm is then used to estimate a Gaussian distribution that models the error distribution . Outlier error values are deemed to represent anomalous traffic flows corresponding to possible attacks .
This paper discusses a realistic turbo coding system with the signal phase which has not been perfectly estimated . We propose improved decoding algorithms for the situations when the residual phase error can be modelled by the Gaussian probability distribution and a Markov chain , a model which can be used in many actual phase estimators . It is shown that increasing the state space of the decoders can decrease the bit error probability .
A stochastic channel model is then derived as a function of time , antenna array displacement and frequency , which falls in the category of tapped delay line model . Specifically , the taps can be represented as a combination of a possible line of sight or dominant reflected path and a Gaussian distributed component comprised of unresolvable scattered paths . After that , general analytical solutions are provided for the 3D temporal-spectral-spatial correlations of SSF via the exploitation of channel statistical properties .
This paper describes a real-life behavior framework in simulation game based on Probabilistic State Machine ( PSM ) with Gaussian random distribution . According to the dynamic environment information , NPC can generate behavior planning autonomously associated with defined FSM .
In case of multiple erroneous ( incorrect ) selected answers for random twenty questions submitted by any arbitrary virtual student member out of 500 virtual students . Interestingly , obtained results in case of testing two penalty values ( zero & one third ) shown to have bell shape close similar to Gaussian statistical distribution . Furthermore , these results have become in agreement with learning achievement results , after running of simulation program of adopted realistic ANN model considering different learning rate values .
Computer routines to eliminate density wedging due to nonuniform exposure and spikes caused by scratches or foreign particles improved the reliability of the RMS granularity calculation . The probability density distribution of the density data for a flashed specimen with pelloids removed is very near a Gaussian density distribution after data imperfections are removed . ( Author )
In our work we use two clustering algorithms , DBSCAN and k-MXT . Moreover , we develop a modification of the k-MXT algorithm called the k-MXT-Gauss algorithm , where the calculation of the weights of the graph edges is transformed using the Gaussian distribution density . We compare the performance of k-MXT-Gauss algorithm with the performance of k-MXT and DBSCAN algorithms both on simulated data and real data .
A work-flow analysis will be carried out demonstrating that the accuracy relevant for a clinical application comprises several error sources including imaging , target and entry point selection , image to frame coordinates registration , and the setting of mechanical parameters of the frame . These error sources will be postulated to obey a Gaussian distribution probability density . The linear , i.e. , Gaussian , error propagation , will be used to link all error contributions thus to calculate the cumulative accuracy of the frame used in the application .
The model is solved and results obtained for several cases , including similar performers , one dominant performer , and performance in sections . For a large ensemble , a stochastic differential equation model describes the phase and tempo evolution , resulting in a Gaussian probability density distribution for tempo and phase . A model for phase correction is also derived .
Here we assume that is a Gaussian distribution whose mean and variance is predicted by the encoder network : . In this case , the stochasticity in comes from both the data - distribution and the randomness of the Gaussian distribution at the output of the encoder . We can use the same re - parametrization trick of vae for back - propagation through the encoder network .
Here we only discuss the scenario where the latent variables are continuous and the parameterised diagonal Gaussian is employed as the variational distribution . However the framework is also suitable for discrete units , and the only modification needed is to replace the Gaussian with a multinomial parameterised by the outputs of a softmax function . Though the reparameterisation trick for continuous variables is not applicable for this case , a policy gradient approach mnih2014neural can help to alleviate the high variance problem during stochastic estimation .
Expected Improvement of trying other parameters Bayesian Optimization ( Mockus ' 78 ) - Method for the global optimization of multi - modal , computationally expensive black box functions - Assumes that the unknown function was sampled from a Gaussian Process ( prior ) and uses the observations ( likelihood ) to maintain a posterior - Observations are the measure of generalization performance under different settings of the hyperparameters we wish to optimize . - The next set of hyperparameters are selected using the maintained posterior – using a strategy determined by the acquisition function Gaussian Processes Specifies a distribution over functions such that any finite subset of N points follows a Multivariate Gaussian Distribution .
We produced an empirical win - probability matrix by summing wins across all games and used this matrix to evaluate Elo scores . A ranking difference of 400 corresponds to the odds of winning of 10:1 under the Gaussian assumption . subsection : Contextual priority tree
We initialized the recurrent weight matrices and as random orthogonal matrices . For and , we initialized them by sampling each element from the Gaussian distribution of mean and variance . All the elements of and all the bias vectors were initialized to zero .
We introduce convolutional DRAW . It features convolutions , latent prior modeling , a Gaussian input distribution ( for natural images ) and , in some experiments , a multi - layer architecture . However , it does not use an explicit attentional mechanism .
The kernel size is fixed to 15 15 . To further expand the degradation space , we also consider a more general kernel assumption , , anisotropic Gaussian , which is characterized by a Gaussian probability density function with zero mean and varying covariance matrix . The space of such Gaussian kernel is determined by rotation angle of the eigenvectors of and scaling of corresponding eigenvalues .
We use pre - trained 300 - D Glove 840B vectors to initialize word embeddings . Out - of - vocabulary ( OOV ) words are initialized randomly with Gaussian samples . All vectors are updated during training .
We train the full model end - to - end in a single step of optimization . We train the convolutional and recurrent network from scratch with all weights initialized from a Gaussian with . Gradients are computed using and flow through the recurrent and convolutional networks .
The empirical evidence shows that attempts to forecast precise values , point estimates , for w(T ) are not very successful . A better approach consists in describing w(T ) by a probability distribution . This in turn requires the building of an adequate model which describes the dynamics of the risk factors , i.e. their evolution through time .
The 2D target position estimates are obtained by a combination of blob detection , edge searching and gray-level matching , with the aid of model geometrical structure projection using current estimates of camera geometry . The information is represented in the form of a probability density distribution , and propagated in a Bayes Net . The Bayesian reasoning that is performed in the 2D image is coupled by the rigid model geometry constraint in 3D space .
Thirdly , we further investigate on the impact of high-bit-rate coding from the more fundamental signal characteristics point of view . A probability distribution analysis on DCT coefficients from the H.264/AVC codec under different bit rates is conducted to reveal that the prediction residual in the form of DCT coefficients have a near uniform distribution for all scanning positions . To further understand this phenomenon , a correlation based analysis was conducted to show that the different types of correlations existed in the video frame and the distribution of these correlations highly impact the coding efficiency .
Here , typical practices fail . This article introduces a probability distribution‐based approach to assess the significance of performance differences among alternatives that allows LCA practitioners to focus analyses on those aspects most influential to the decision , identify the areas that would benefit the most from data refinement given the level of uncertainty , and complement existing hotspot analyses . In a case study of a comparative LCA of five photovoltaic technologies , findings show that thin‐film cadmium telluride and amorphous silicon cell panels are most likely to perform better than other alternatives .
This paper presents the optimal placement of solar-microturbine and wind-microturbine power plant with minimum network losses and a reliable power supply . The output power of these intermittent distributed generators has been computed on probability-based distribution . Beta probability distribution and Weibull probability distribution have been used to compute probable power generation of these Natural power Resources .
The invention discloses a multi-time-scale intermittent energy generation predication method for an active power distribution network , and relates to the technical field of generation predication . The method is that the predication data of generation power under long time scale can be obtained according to the probability distribution characteristic that the intermittent energy generation changes as the environmental changes and the historical generation power data ; the predication data of the generation powder under the long time scale are decomposed by an empirical mode into a series of mode components with frequency from low to high and one residual component ; the generation power predication data are corrected according to the time scale corresponding to the frequency of each mode component . According to the method , the predication data are corrected under different short time scales during the long-time-scale predication intermittent energy generation process , thus the predication precision of the intermittent energy generation is increased , and the reliable data support is provided to optimal dispatching of the active power distribution network .
Two approaches are contrasted . The first extends Montague Semantics with a probability distribution over models . It has nice theoretical properties , but does not account for the ubiquitous nature of ambiguity ; moreover inference is NP hard .
The invention provides a mode selection method and a mode selection system based on video scalable coding compression . The method comprises the steps of A ) detecting whether a macro block corresponding to a base layer is intra-frame coded or not , if so , detecting all intra-frame modes , then selecting a current optimal mode as a macro block mode , and if not , executing a step B. The mode selection method and the mode selection system based on video scalable coding compression have the beneficial effects that aiming at solving the problems that the macro block modes in SVC ( Scalable Video Coding ) are multiple and the influence on coding efficiency is great , by performing probability distribution statistics on the basis of macro block modes at an enhanced layer and macro block modes at the base layer , analyzing the correlation of the macro block modes at the enhanced layer and the macro block modes at the base layer and using the information of the base layer , candidate modes are reduced and the complexity of an algorithm is reduced ; and aiming at the macro block mode distribution characteristic in video coding , by adopting a hierarchical selection strategy , the complexity of the algorithm is reduced , the SVC coding speed is improved and the coding quality is guaranteed at the same time .
This paper points out some of the more common misconceptions and their implications upon the total project . The Beta distribution as the underlying probability distribution is evaluated as to first , the overall effects of the inherent errors that are imposed by the basic PERT assumptions , and second , the effects , from a probability distribution viewpoint , of some of the more common PERT misconceptions . Finally , several alternatives to the basic PERT methodology are explored , both from the theoretical and practical viewpoints .
( b ) is obtained using Property ( iii ) , while ( c ) follows from the Cauchy - Schwarz inequality and Property ( i ) . Under Assumptions ( A ) and ( B ) , k ( x , · ) is Bochner integrable with respect to any probability distribution P with finite first moment and the following relation holds : Proof .
Liao et al . ( 2007 ) use the time of day to use in a probability model building assumptions about the participant ’s context . While this might be a useful technique to identify different activities , it might not be applicable to participants that have abnormal working hours for example .
Since we use equal step size in quantizing , the normal p.d.f . ( probability density function ) is a natural choice to generate the ground - truth from and : where . Fig .
section : Euclidean Distance as Confusion Since the conditional probability distribution over N classes is an element within R N on the unit simplex , we can consider the Euclidean distance to be a metric of " confusion " between two conditional probability distributions . Analogous to the previous setting , we define the Euclidean Confusion D EC ( · , · ) for a pair of inputs x 1 , x 2 with model parameters θ as :
Generative image modeling is a central problem in unsupervised learning . Probabilistic density models can be used for a wide variety of tasks that range from image compression and forms of reconstruction such as image inpainting ( e.g. , see Figure [ reference ] ) and deblurring , to generation of new images . When the model is conditioned on external information , possible applications also include creating images based on text descriptions or simulating future frames in a planning task .
Formally , we replace Equations [ reference ] and [ reference ] in the paper with the following equations respectively : Our final ensemble model , DR - BiLSTM ( Ensemble ) is the combination of the following 6 models : tanh - Projection , DR - BiLSTM ( with 1 round of dependent reading ) , DR - BiLSTM ( with 3 rounds of dependent reading ) , and 3 DR - BiLSTMs with different initialization seeds . We also experiment with majority voting and averaging the probability distribution strategies for ensemble models using the same set of models as our weighted averaging ensemble method ( as described above ) . Figure [ reference ] shows the behavior of the majority voting strategy with different number of models .
* the cat ] In order to build a good probability estimate for the target word w k in a context W k−1 we need a way of combining an arbitrary number of skip - gram features f k−1 , which do not fall into a simple hierarchy like regular n - gram features . The following section describes a simple , yet novel approach for combining such predictors in a way that is computationally easy , scales up gracefully to large amounts of data and as it turns out is also very effective from a modeling point of view .
In this paper , we present a new algorithm for the statistical static timing analysis of a CMOS combinatorial circuit , which can treat correlations of arrival times of input signals to a logic gate and correlations of switching delays in a logic gate . We model each switching delay by a normal distribution , and use a normal distribution of two stochastic variables with a coefficient of correlation for computing the distribution of output delay of a logic gate . Since the algorithm takes the correlation into account , the time complexity is O(n*m ) in the worst-case , where n and m are the numbers of vertices and edges of the acyclic graph representing a given combinatorial circuit .
This study reviews three approaches that can be used to develop robust frequentist or Bayesian stock assessment methods . Using simulated ¢sheries as examples , we demonstrate how these approaches can be used to develop the frequentist and Bayesian stock assessment approaches that are robust to outliers in ¢sheries data and compare the robust approaches with the commonly used normal distribution-based approach . The proposed robust approaches provide alternative ways to developing frequentist or Bayesian stock assessment methods .
Abstract Several approaches for the Bayesian design of experiments have been proposed in the literature ( e.g. , D-optimal , E-optimal , A-optimal designs ) . Most of these approaches assume that the available prior knowledge is represented by a normal probability distribution . In addition , most nonlinear design approaches involve assuming normality of the posterior distribution and approximate its variance using the expected Fisher information matrix .
Generalized Estimating Equations and Generalized Mixed Models : Treating Toenail Infection Introduction Methods for Analyzing Longitudinal Data Where the Response Variable Can not Be Assumed to Have a Normal Distribution Analysis Using SAS Survival Analysis : Gastric Cancer , the Treatment of Heroin Addicts , and Heart Transplants Introduction Describing Survival Data Cox 's Regression Analysis Using SAS Principal Components Analysis and Factor Analysis : Olympic Decathlon and Statements about Pain Introduction Principal Components Analysis and Factor Analysis Analysis Using SAS Cluster Analysis : Air Pollution in the United States Introduction Cluster Analysis Analysis Using SAS Discriminant Function Analysis : Classifying Tibetan Skulls Description of Data Discriminant Function Analysis Analysis Using SAS Correspondence Analysis : Smoking and Motherhood , Sex and the Single Girl , and European Stereotypes Description of Data Displaying Contingency Table Data Graphically Using Correspondence Analysis Analysis Using SAS Appendix References Index Exercises appear at the end of each chapter .
The summation of the squared range error between the node and the anchors is used as the objective function to be minimized in this work . Different simulation tests with different topologies are conducted based on normal random distribution for time of arrival ( TOA ) measurements and log-normal distribution for received signal strength ( RSS ) measurements . The simulation test results showed effectiveness of the proposed approach in case of TOA measurements when compared with the lower variance achievable by any unbiased location estimator .
Aiming at resolving the issue of designing appropriate proposal distribution in Markov Chain Monte Carlo ( MCMC ) algorithm , an improved MCMC scheme is developed in this paper . The presented scheme employs normal density distribution as proposal distribution to sample in objective function , and together with the historical sampling information , the proposal distribution runs to proper distribution by adaptive self-regulation . The improved scheme is applied to parameter estimation of Pearson-III distribution to figure out the problems of runoff frequency forecast .
However , the underlying calculation procedure has not been elaborated and consequently there is no practical and unambiguous guidance for the consulting engineer on how to perform such a statistical analysis . In this paper , the theory for the statistical analysis is presented based on the assumption that the potential distribution for both actively corroding and passive steel can be adequately described by a normal probability density distribution . This theory is applied to a number of situations to exemplify the procedure by clear graphs .
y l ) , whose entries are all zero except for the index corresponding to the true label ( output ) y l . Likewise , for regression tasks , we can use the normal distribution centered at y l with constant variance , or the delta function with the atom at y = y l .
Stacking Gaussian Mixture layers Deep GMMs are best introduced by looking at some special cases : the multivariate normal distribution and the Gaussian Mixture model . One way to define a multivariate normal variable x is as a standard normal variable z ⇠ N ( 0 , In ) that has been transformed with a certain linear transformation : x
Where kth_excluding ( v , k , i ) means the kth highest component of v , excluding component i. Simplifying , we get : Where Φ is the CDF of the standard normal distribution . We can now define the load loss to be the square of the coefficient of variation of the load vector , multiplied by a hand - tuned scaling factor w load .
Using cross validation we found that gave best results . Weights are initialzed to small random values drawn from a zero - mean normal distribution with standard deviation 0.01 . Momentum is used to speed up learning .
Since the output of the segmentation decoder has 3 channels ( predictions for each tumor subregion ) , we simply add the three dice loss functions together . is an L2 loss on the VAE branch output to match the input image : is standard VAE penalty term , a KL divergence between the estimated normal distribution and a prior distribution , which has a closed form representation : where N is total number of image voxels . We empirically found a hyper - parameter weight of to provide a good balance between dice and VAE loss terms in Equation [ reference ] .
Recall that concavity corresponds to risk - averse and convexity to risk - seeking policies . Second , we consider the distortion risk measure proposed by wang2000class , where and are taken to be the standard Normal cumulative distribution function and its inverse : For , this produces risk - averse policies and we include it due to its simple interpretation and ability to switch between risk - averse and risk - seeking distortions . Third , we consider a simple power formula for risk - averse ( ) or risk - seeking ( ) policies : Finally , we consider conditional value - at - risk ( CVaR ) : CVaR has been widely studied in and out of reinforcement learning .
propose a two side attention mechanism to compute the matching between the passage and query . match the passage and query from several perspectives and predict the answer by globally normalizing probability distributions . propose a bi - directional attention flow to achieve a query - aware context representation .
Specifically , we propose the following forỹ : In words , we first find the target word vector which corresponds to the target word token ( resulting in u t ) , and then take the inner product of the target word vector with all the other word vectors to get an unnormalized probability distribution . We adjust this with the same temperature parameter τ used for obtainingŷ t and apply softmax .
No pre - processing was applied to training images besides scaling to the range of the tanh activation function [ - 1 , 1 ] . All models were trained with mini - batch stochastic gradient descent ( SGD ) with a mini - batch size of 128 . All weights were initialized from a zero - centered Normal distribution with standard deviation 0.02 . In the LeakyReLU , the slope of the leak was set to 0.2 in all models .
This not only contributes to understanding a particular coal mining area 's situation in regards to CMIES vulnerability , but also helps to discover a meaningful benchmark to learn the experiences in terms of action programmes formulation . In this study , based on the analysis of the vulnerability response mechanism of CMIES to economic fluctuations , an initial indicator system for vulnerability assessment of CMIES was constructed . Ultimately , 14 vulnerability-evaluating indicators and their weights were obtained using rough set attribute reduction .
It concludes based on the analyzed literature that the identification of RFID systems ’ industrial indicators lies in accessibility and reliability data used for their validation . Finally , the indicator-based framework for sustainability assessment is verified for its usefulness on the case of dynamic spaghetti diagrams supported by Real Time Locating System . The paper concludes with a brief discussion on opportunities and limits of RFID-dedicated sustainability indicators .
The proposed MOBFA aims to handle the convergence and diversity separately via evolving two cooperative search engines with different evolution rules . Specifically , in the colony-level interaction , the primary concept is to first assign two different performance evaluation principles ( i.e. , Pareto-based measure and indicator-based measure ) to the two engines for evolving each archive respectively , and then use the comprehensive learning mechanism over the two archives to boost the population diversity . In the individual-level foraging , the neighbor-discount-information ( NDI ) learning based on reinforcement learning ( RL ) is integrated into the single-objective searching to adjust the flight trajectories of foraging bee .
subsection : Data Adaptive Activation Function PReLU is a commonly used activation function where is one dimension of the input of activation function and is an indicator function which controls to switch between two channels of and . in the second channel is a learning parameter . Here we refer to as the control function .
If our model successfully labels the right knee as occluded , we wish to prevent the error feedback loss from backpropagating through our network . To achieve this , we include the indicator term which only backpropagates pose error feedback if a particular joint is visible in the original image . A secondary benefit is that we do not force the regressor to output dummy real values ( if a joint is occluded ) which may skew the model
The effect of self-interference ( interference from another simultaneously transmitted symbol in the STTD scheme for the same user ) due to imperfect channel estimation is investigated . Based on the characteristic function method , a closed-form expression of the bit error rate ( BER ) is derived . Numerical results for 16/64-QAM show that , with the Alamouti STTD technique , the BER performance of the QAM system can be improved significantly .
The logarithm is typically taken to base 2 so that the mutual information is given in units of bits . For a discrete distribution where the two random variables are defined over alphabets , X ∈ Χ and Y ∈ Ψ , this becomes ( , ) ( ; ) ( , ) log ( ) ( ) x y p x y I X Y p x y p x p y ∈Χ ∈Ψ = ∑ ∑ ( 2 ) which is equivalent to ( 1 ) in the limit of Χ and Ψ approaching the support sets of X and Y respectively . This provides a measure of the amount of information that a measurement of X contains regarding the measurement of Y , and vice-versa .
In this paper , by modeling the exponential backoff process as a Markov chain , we can use the signal transfer function of the generalized state transition diagram to derive an approximate probability distribution of the MAC layer service time . We then present the discrete probability distribution for MAC layer packet service time , which is shown to accurately match the simulation data from network simulations . Based on the probability model for the MAC layer service time , we can analyze a few performance metrics of the wireless LAN and give better explanation to the performance degradation in delay and throughput at various traffic loads .
Service times of messages of each priority class are i.i.d . according to a general discrete distribution function that may differ between two classes . The completion time and the stability condition for our system are investigated .
The constantly changing background has been modeled at the pixel level . The feature vector for each pixel is represented with a discrete probability distribution function . The histogram colors and co-occurrence vectors have been calculated .
Real-time systems used in media processing and transmission produce bursty workloads with highly variable execution and transmission times . To avoid the drawbacks of using the worst-case approach with these workloads , this paper uses a variation of the usual real-time task model where the WCET is replaced by a discrete statistical distribution . Using this approach , tasks are characterized by their processing time over a sampling period .
In order to solve the discrete model , the z-transform method is used , and for solution of the continuous model - the Laplace transform is used . The stochastic matrix of Markov chains completely identifies the discrete model of distribution of the budget of an investment project between enterprises , and the differential matrix - the continuous model of this distribution . The use of z-transform and Laplace transform allows finding a solution of tasks in the analytical form .
In this paper , our goal aims to develop an estimated matrix for determining whether the secret share is a secure share or not . The method of KL Divergence used discrete random distribution is taken consideration into our proposed scheme . We apply the some examples to demonstrate how to operate our proposed idea .
Each channel variable simply takes one of 256 distinct values . The discrete distribution is representationally simple and has the advantage of being arbitrarily multimodal without prior on the shape ( see Fig . [ reference ] ) .
However , there are many applications where the counts tend to be small and include many zeros . In this case , the observations can not be adequately modeled with a continuous distribution . During the last three decades , there has been significant progress in the area of count time-series analysis [ 1 ] , [ 2 ] .
The filtering process reduces the sensitivity of the recognition system to input fluctuations caused by noise . The system developed uses continuous probability density function , single Gaussian mixture HMMs trained on filterback output vectors . Using autocorrelation statistics collected for speech and noise during training , noise canceling Wiener filters are designed for each hidden Markov model state .
Achievable secrecy rates over a multiple-input multiple-output multiple-eavesdropper ( MIMOME ) wiretap channel are considered , when the legitimate users have perfect knowledge only of the legitimate channel state and the eavesdropper channel is drawn from a ( possibly unknown ) continuous probability density . Legitimate users are assumed to deploy more antennas than the eavesdropper .
However , in order to solve the difficulties of the existing Importance Sampling techniques , we adopted the discrete probability mass function in the new method for estimating the statistical characteristics of received signals from the measured central moments up to Nth orders . From the discrete probability mass function constructed with the measured central moments with less number of the received signal samples than the one required in the MC technique , continuous cumulative probability distribution function can be exactly estimated by using interpolation and extrapolation technique . By assuming that the in-phase and quadrature phase received signals are independent and by estimating the channel characteristics of the in-phase and quadrature phase received signals separately , the implementation of the proposed method could be simplified .
With the optimization routine performed off-line , the resulting multi-controller realization has a low computational load . The proposed technique allows the designer to trade off performance versus memory , even when the process to be controlled is described via a continuous distribution of models .
2 ≈ i e base vector in dimension i ( Chapter 5 ) ε small positive error ) ( x f transfer function ) ( ' x f , ) ( " x f reward function ) ( x g reward function g auxiliary machining-delay vector ( Chapter 5 ) ) ( ' x h , ) ( " x h reward function 8 j I Palmer index for job j ij η visibility of a trail from node i to node j j j the jth job under processing k the number of actions sharing the same , maximal action-state value κ minimum of the difference between each action-state value pair ) ( A L link state description of node A λ decay rate for eligibility traces M matrix of machining times ) ( i m number of actions in the ith state ( Chapter 2 ) n number of states ν small positive redundancy variable 1 o , 2 o abstract machining times a ss P ' probability of getting into state ' s from state s via action a i p probability of selecting action i a p ˆ scaled probability value ) ( x p continuous probability distribution function p , r permutation vectors ( Chapter 5 ) i pred predecessor node ij pred predecessor node ( matrix representation ) ) , ( j i i a s π …
Anomaly Detection in Network Traffic Using Dynamic Graph Mining with a Sparse Autoencoder Phase offset estimation using enhanced turbo decoders Study on Channel Correlation Using Temporal-Spectral-Spatial Information Gaussian Distribution for NPC Character in Real-Life Simulation Optimal Estimation of Penalty Value for On Line Multiple Choice Questions Using Simulation of Neural Networks and Virtual Students' Testing Practical Considerations in Granularity Measurements. The Clusterization of Geo-Tagged Data for Finding City Sights with Use of a Modification of k-MXT Algorithm A Gaussian approach for the calculation of the accuracy of stereotactic frame systems A Model for Tempo Synchronization in Music Performance Adversarial Autoencoders Neural Variational Inference for Text Processing Practical Bayesian Optimization of Machine Learning Algorithms The Reactor: A Sample-Efficient Actor-Critic Architecture Neural Machine Translation by Jointly Learning to Align and Translate Towards Conceptual Compression Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Attention Boosted Sequential Inference Model Towards Viewpoint Invariant 3D Human Pose Estimation Modelling the Dynamics of the Risk Factors Model-driven active visual tracking Advanced techniques for high fidelity video coding Tradeoff Evaluation Improves Comparative Life Cycle Assessment: A Photovoltaic Case Study Optimal Summation of Natural Power Distributed Resources in Grid Connected MicroGrid Multi-time-scale intermittent energy generation predication method for active power distribution network Efficiency in Ambiguity: Two Models of Probabilistic Semantics for Natural Language Mode selection method and system based on video scalable coding compression IMPLICATIONS OF THE UNDERLYING ASSUMPTIONS OF PERT On gradient regularizers for MMD GANs Inferring hybrid transportation modes from sparse GPS data using a moving window SVM classification Deep Label Distribution Learning With Label Ambiguity Pairwise Confusion for Fine-Grained Visual Classification Pixel Recurrent Neural Networks DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference Skip-gram Language Modeling Using Sparse Non-negative Matrix Probability Estimation A statistical static timing analysis considering correlations between delays Developing robust frequentist and Bayesian fish stock assessment methods A stochastic programming approach for the Bayesian experimental design of nonlinear systems A Handbook of Statistical Analyses Using SAS Localization of wireless sensor network using Bees Optimization Algorithm An Improved Markov Chain Monte Carlo Scheme for Parameter Estimation Analysis Development of a calculation procedure for the statistical interpretation of the results of potential mapping performed on reinforced concrete structures Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Factoring Variations in Natural Images with Deep Gaussian Mixture Models Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer Improving neural networks by preventing co-adaptation of feature detectors 3D MRI brain tumor segmentation using autoencoder regularization Implicit Quantile Networks for Distributional Reinforcement Learning MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Assessing industrial ecosystem vulnerability in the coal mining area under economic fluctuations An Indicators Framework for Sustainability Assessment of RFID Systems in Manufacturing Cooperative two-engine multi-objective bee foraging algorithm with reinforcement learning Deep Interest Network for Click-Through Rate Prediction Towards Viewpoint Invariant 3D Human Pose Estimation Performance Analysis of Alamouti Transmit Diversity with QAM in Imperfect Channel Estimation Computation of Generalized Mutual Information from Multichannel Audio Data Performance analysis of IEEE 802.11 MAC protocols in wireless LANs Discrete-Time GeoX/G/1 Queue with Preemptive Repeat Different Priority Video Image Segmentation Based on Bayesian Learning Analysis of Self-Similar Workload on Real-Time Systems Continuous Model of Financial Interaction of Enterprises when Carrying out an Investment Project A KL Divergence Function for Randomized Secret Shares Pixel Recurrent Neural Networks Count Time-Series Analysis: A Signal Processing Perspective Noisy speech recognition using hidden Markov model state-based filtering MIMOME Gaussian Channels with GMM Signals in High-SNR Regime: Fundamental Limits and Tradeoffs Performance Evaluation Method for Digital Satellite Communication Channels Performance-oriented transfer for switching control Combined Use of Reinforcement Learning and Simulated Annealing: Algorithms and Applications
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