For the power delay profile ( PDP ) , we perform cluster identification and obtain the cumulative distribution function ( CDF ) of the inter-cluster interval . A statistical model of the intracluster decay time constant is also presented , and an exponential decay model is applied for modeling the inter-cluster decay of the PDP . For delay spread and Doppler spread , the root mean square ( RMS ) delay spreads in the LOS case are compared with those in the NLOS case . Both Schrodinger and Heisenberg picture of the system evolution are presented . We show that the resulting time evolution leads to exponential decay law . Moreover , we analyse mixing of particle flavours and show that it can lead to flavour oscillation phenomenon . The tomato was cut into halves , quarters and eighths and dried at temperatures of 55 and 65 ° C . During drying , the moisture content followed an exponential decay curve with R 2 > 0.98 . The time required to achieve the critical moisture content for storage ( 15 % ) for the tomato halves , quarters and eights were 36 , 26 and 20 h and 23 , 18 and 13 h , at the temperatures of 55 and 65 ° C , respectively . 4.2 and 4.3 . We focus on a Bayesian over-dispersed Poisson ( ODP ) model with an exponential decay curve component ( Verrall et al. 2012 ) . Reversible jump MCMC is used to simulate a sample from this model . In this paper , we derive the error characteristics incurred by the CD errors and , through computer simulations , we show that the CD errors are propagated and not negligible to the decoded qualities of the next frames within single GOP . Then , we propose an exponential-decaying model that describes well the characteristics of propagation/accumulation errors . Finally , it is shown that the proposed model can be effectively used for estimating the overall distortions incurred by the CD errors . In this article , based on the recent literature on nonparametric Bayesian models , we have developed two generative models : DPChain and HDP-HTM . DPChain is derived from the Dirichlet process mixture ( DPM ) model , with an exponential decaying component along with the time . HDP-HTM combines the hierarchical dirichlet process ( HDP ) with a hierarchical transition matrix ( HTM ) based on the proposed Infinite hierarchical Markov state model ( iHMS ) . The amplitude of the channel impulse responses in the wideband case is mostly Rayleigh distributed small-scale fading signal , with only few paths exhibiting Ricean distributions , whereas the ones in the UWB case tend to be log-normally distributed . For the path amplitude , we suggest an exponential decay profile , which has a constant slope in dB scale , with the corresponding parameters for the UWB case . For the wideband case , a two-fold exponential decay profile provides excellent fits to the measured data . The R-EWRLS algorithm is developed further to allow for a self-tuned forgetting factor in the formulation . The forgetting factor is an important tool to account for non-stationarity in the data through an exponential decay profile which assigns more weight to the more recent data . The new algorithm is assessed against the R-EWRLS algorithm using various performance measures . Under natural assumptions , it is shown that , as N → ∞ , the overflow probability has an exponential decay rate . We find a lower bound and an upper bound for the exponential decay rate . At that these bounds have different exponential rates , because , to construct these bounds , we consider different ( majorant and minorant ) classical buffered systems . Under rather weak regularity assumptions on the data , we prove a well-posedness result and give an explicit representation of solutions . Further , we prove an exponential decay rate for the energy in the dissipative case . We also show that lower order regularizations lead to ill-posedness , also for higher order equations . At 662 keV γ-rays RLGC : 1 % Ce3 + crystal showed energy resolution and light yield of 8.1 % ( FWHM ) and ~13,000 ph/MeV , respectively at room temperature . Under γ-ray excitation , RLGC : 1 % Ce3 + crystal showed three exponential decay time components . Further improvement in the scintillation properties is expected with higher values of Ce- concentrations . At 662 keV γ-rays RLGB : 5 % Ce3 + crystal shows an energy resolution and light yield of 5.5 % ( FWHM ) and ∼ 25,500±2,600 ph/MeV , respectively . Under γ-ray excitation , RLGB : Ce3 + crystals showed four exponential decay time components . Improvement in the scintillation properties is expected with higher the higher Ce-doping . In this paper , we provide a study of different chaotic maps with symmetric distributions , used as pseudorandom number generators ( PRNGs ) in FSS . In addition , we incorporate exponential step decay in order to improve the accuracy of the solutions produced by the algorithm . The obtained results of the conducted numerical experiments show that the use of chaotic maps instead of other commonly used high-quality PRNGs can speed up the algorithm , and the incorporated exponential step decay can improve the accuracy of the obtained solution . We provide a general framework that takes any offline-coreset and gives a time-decay coreset for polynomial time decay functions . We also consider the exponential time decay model for $ k$-median clustering , where we provide a constant factor approximation algorithm that utilizes the online facility location algorithm . Our algorithm stores $ \mathcal{O}(k\log(h\Delta)+h)$ points where $ h$ is the half-life of the decay function and $ \Delta$ is the aspect ratio of the dataset . The non-iterative Prony 's method is discussed for the deconvolution of multi-exponential fluorescence decays . The performance of these algorithms in the case of a two- exponential decay process is evaluated , using a Monte-Carlo simulation , in terms of the estimation errors caused by the signal noise . The results which are presented show that the performance of Prony 's method can be greatly improved with the selection of an optimized observation window width and a few algorithm-related parameters . Due to the huge size of data , we set the mini - batch size to be 5000 and use Adam as the optimizer . We apply exponential decay , in which learning rate starts at 0.001 and decay rate is set to 0.9 . The statistics of all the above datasets is shown in Table [ reference ] . For more details of architecture design , please refer to the supplementary material . For training , we use the Adam optimizer with a batch size of , a learning rate of , and exponential decay rates . In all experiments , we set the hyper - parameters as follows : , , , , , and . The training was repeated three times with different random seeds for model weight initialization while the random seeds were consistent between the different methods to allow for fair comparisons . All CNNs were trained for 200 epochs with stochastic gradient descent via adaptive moment estimation kingma2015adam using exponential decay rates and ( PyTorch default ) and learning rate . In addition , we computed the Cumulative Score ( CS ) as the proportion of images for which the absolute differences between the predicted rank labels and the ground truth are below a threshold : Dropout layers are applied before all linear layers and after word - embedding layer . We use an exponential decayed keep rate during training , where the initial keep rate is 1.0 and the decay rate is 0.977 for every 10 , 000 step . We initialize our word embeddings with pre - trained 300D GloVe 840B vectors while the out - of - vocabulary word are randomly initialized with uniform distribution . Parameters are optimised with stochastic gradient descent ( SGD ) with an initial learning rate of and momentum of . Exponential learning rate decay is applied every 5 epochs with a factor of . To reduce the impact of exploding gradients , we employ gradient clipping at . We use the Adam optimizer with an initial learning rate of , , and . An exponential learning rate decay schedule is applied with a decay rate of 0.99 every 1 , 000 iterations . section : Datasets We describe the structure of the gradient of a validation error w.r.t . the learning rate schedule - the hypergradient . Based on this , we introduce MARTHE , a novel online algorithm guided by cheap approximations of the hypergradient that uses past information from the optimization trajectory to simulate future behaviour . We are able to improve recognition performance of first-pass systems from a 12 % relative word error rate reduction reported previously to 21 % , compared to MFCC baselines on a Tagalog conversational telephone speech corpus . This is achieved by using different input features , training the network to predict context-dependent targets , employing an efficient learning rate schedule and varying several architectural details . Evaluations on two larger German and French speech transcription tasks show that the optimizations proposed are universally applicable and yield comparable gains on other corpora ( 19.9 % and 22.8 % , respectively ) . The so-called Rogers ' paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies , but is often eliminated when the other methods are used . Moreover , Rogers ' paradox is not observed for the mixed learning strategies and learning schedules that we review . We believe that further theoretical work is necessary on learning schedules and biased social learning , based on models that are logically consistent and empirically pertinent . Recently , speaker adaptation methods in deep neural networks ( DNNs ) have been widely studied for automatic speech recognition . However , almost all adaptation methods for DNNs have to consider various heuristic conditions such as mini-batch sizes , learning rate scheduling , stopping criteria , and initialization conditions because of the inherent property of a stochastic gradient descent (SGD)-based training process . Unfortunately , those heuristic conditions are hard to be properly tuned . We focus on hyperparameters that are related to the optimization algorithm , e.g. learning rates , which have a large impact on the training speed and the resulting accuracy . Typically , fixed learning rate schedules are employed during training . We propose Hyperdyn a dynamic hyperparameter optimization method that selects new learning rates on the fly at the end of each epoch . The challenges are to come up with appropriate optimistic predictions and correction terms in this framework . Some of our results also crucially rely on using a sophisticated increasing learning rate schedule . Current state-of-the-art NMT systems use large neural networks that are not only slow to train , but also often require many heuristics and optimization tricks , such as specialized learning rate schedules and large batch sizes . This is undesirable as it requires extensive hyperparameter tuning . Adjusting the learning rate schedule in stochastic gradient methods is an important unresolved problem which requires tuning in practice . If certain parameters of the loss function such as smoothness or strong convexity constants are known , theoretical learning rate schedules can be applied . However , in practice , such parameters are not known , and the loss function of interest is not convex in any case . Actor-critic ( AC ) methods have exhibited great empirical success compared with other reinforcement learning algorithms , where the actor uses the policy gradient to improve the learning policy and the critic uses temporal difference learning to estimate the policy gradient . Under the two time-scale learning rate schedule , the asymptotic convergence of AC has been well studied in the literature . However , the non-asymptotic convergence and finite sample complexity of actor-critic methods are largely open . So we use 0.01 to warm up the training until the training error is below 80 % ( about 400 iterations ) , and then go back to 0.1 and continue training . The rest of the learning schedule is as done previously . This 110 - layer network converges well The following learning rate schedule was applied ( ) ( iterations [ learning rate ] ) . For fair comparisons , we used this learning rate schedule for all networks . During previous experiments , this schedule was optimized for ReLU networks , however as ELUs converge faster they would benefit from an adjusted schedule . We follow settings in the re - implementation of ResNets by Gross and Wilber as possible . But , we use a linear learning rate schedule , which was reported as a better choice by Mishkin et al . . Take Model A in Table [ reference ] for example . We encoded labels as binary vectors using k-hot encoding . We study the ResNet34 architecture , pre-trained on ImageNet , where two key modifications were incorporated into the training framework : ( 1 ) Stochastic gradient descent with momentum and with restarts using cosine annealing , ( 2 ) Variable image sizes for fine-tuning to prevent overfitting . Additionally , we use a heuristic algorithm to select a good learning rate . We also evaluate their calibration using expected calibration error . We additionally propose a novel method of neural network parameter estimation called RECAST , which combines cosine annealing with warm restarts with Stochastic Gradient Langevin Dynamics , capturing more diverse parameter distributions . When benchmarked against mutilated data from MNIST , we show that RECAST is well-calibrated and when combined with predictive entropy and epistemic uncertainty it offers the best calibrated measure of uncertainty when compared to recent methods . A graph-based solution to the mapping problem using the simulated annealing optimization heuristic is developed . An automated two-phase mapping strategy is formulated : process annealing assigns parallel processes to processing nodes , and connection annealing schedules traffic connections on network data links so that interprocess communication conflicts are minimized . To evaluate the quality of generated mappings . This optimum position is determined by performing one-dimensional searches along the edges of the placed macrocells . The presented test results show that the quality of solutions obtained is better than that obtained by simulated annealing , analytic annealing , and genetic placement procedures . Also , for two other test problems , the results are presented and compared with the results obtained by two published programs , one based on a heuristic procedure and the other based on an analytical technique . The results clearly demonstrate the improved performance of the analytic annealing procedure for macrocell placement optimization . No training data symbols are necessary , but a priori information may be incorporated , if available . The basic structure consists of an iterative approximation of the joint maximum-likelihood ( ML ) solution , which is enhanced by adaptive filtering , by shift operations , by coefficient annealing , as well as by using a priori information . To our best knowledge , this is the first time that : ( i ) a priori information of the data has been exploited in the context of blind equalization ; and that ( ii ) the DPSK/ISI super trellis has been explicitly exploited for blind equalization rather than doing differential decoding for resolving the problem of phase ambiguity . Computer simulation of annealing provides a way to model annealing processes at the grain scale . The various methods available to simulate grain growth and recrystallization are reviewed . The results of experiment show that the temperature controller has several specific characteristics , which include : high control precision , small overshoot and better adaptive control etc . This controller can definitely satisfy the request of crystal annealing technology in high vacuum . Lower stretchability of IPL annealed Cu film is attributed to its inherent cracks and porous film morphologies . IPL annealing induces the local melting at the interface between the substrate and Cu film , which increases the adhesion strength of the Cu film . These results provide useful information regarding the mechanical flexibility and durability of the nanoparticle films for the development of flexible electronics . Training S3VM involves either a combinatorial or non-convex optimization problem and thus finding the global optimal solution is intractable in practice . It has been demonstrated that a key to successfully find a good ( local ) solution of S3VM is to gradually increase the effect of unlabeled data , a la annealing . However , existing algorithms suffer from the trade-off between the resolution of annealing steps and the computation cost . Computational results show that the proposed model can allocate jamming resource effectively and optimize the combat capability of electronic warfare systems . The research on simulant annealing algorithm provides some theoretical gist and new thoughts on jamming resource allocation and improves the decision-making ability of electronic warfare systems . Thanks to the capability of this algorithm to include heuristic information , the learning process is accelerated without model accuracy losses . Its behavior is successful compared with other processes based on genetic algorithms and sinlulated annealing when solving two modeling applications .
For the power delay profile ( PDP ) , we perform cluster identification and obtain the cumulative distribution function ( CDF ) of the inter-cluster interval . A statistical model of the intracluster decay time constant is also presented , and an exponential decay model is applied for modeling the inter-cluster decay of the PDP . For delay spread and Doppler spread , the root mean square ( RMS ) delay spreads in the LOS case are compared with those in the NLOS case .
Both Schrodinger and Heisenberg picture of the system evolution are presented . We show that the resulting time evolution leads to exponential decay law . Moreover , we analyse mixing of particle flavours and show that it can lead to flavour oscillation phenomenon .
The tomato was cut into halves , quarters and eighths and dried at temperatures of 55 and 65 ° C . During drying , the moisture content followed an exponential decay curve with R 2 > 0.98 . The time required to achieve the critical moisture content for storage ( 15 % ) for the tomato halves , quarters and eights were 36 , 26 and 20 h and 23 , 18 and 13 h , at the temperatures of 55 and 65 ° C , respectively .
4.2 and 4.3 . We focus on a Bayesian over-dispersed Poisson ( ODP ) model with an exponential decay curve component ( Verrall et al. 2012 ) . Reversible jump MCMC is used to simulate a sample from this model .
In this paper , we derive the error characteristics incurred by the CD errors and , through computer simulations , we show that the CD errors are propagated and not negligible to the decoded qualities of the next frames within single GOP . Then , we propose an exponential-decaying model that describes well the characteristics of propagation/accumulation errors . Finally , it is shown that the proposed model can be effectively used for estimating the overall distortions incurred by the CD errors .
In this article , based on the recent literature on nonparametric Bayesian models , we have developed two generative models : DPChain and HDP-HTM . DPChain is derived from the Dirichlet process mixture ( DPM ) model , with an exponential decaying component along with the time . HDP-HTM combines the hierarchical dirichlet process ( HDP ) with a hierarchical transition matrix ( HTM ) based on the proposed Infinite hierarchical Markov state model ( iHMS ) .
The amplitude of the channel impulse responses in the wideband case is mostly Rayleigh distributed small-scale fading signal , with only few paths exhibiting Ricean distributions , whereas the ones in the UWB case tend to be log-normally distributed . For the path amplitude , we suggest an exponential decay profile , which has a constant slope in dB scale , with the corresponding parameters for the UWB case . For the wideband case , a two-fold exponential decay profile provides excellent fits to the measured data .
The R-EWRLS algorithm is developed further to allow for a self-tuned forgetting factor in the formulation . The forgetting factor is an important tool to account for non-stationarity in the data through an exponential decay profile which assigns more weight to the more recent data . The new algorithm is assessed against the R-EWRLS algorithm using various performance measures .
Under natural assumptions , it is shown that , as N → ∞ , the overflow probability has an exponential decay rate . We find a lower bound and an upper bound for the exponential decay rate . At that these bounds have different exponential rates , because , to construct these bounds , we consider different ( majorant and minorant ) classical buffered systems .
Under rather weak regularity assumptions on the data , we prove a well-posedness result and give an explicit representation of solutions . Further , we prove an exponential decay rate for the energy in the dissipative case . We also show that lower order regularizations lead to ill-posedness , also for higher order equations .
At 662 keV γ-rays RLGC : 1 % Ce3 + crystal showed energy resolution and light yield of 8.1 % ( FWHM ) and ~13,000 ph/MeV , respectively at room temperature . Under γ-ray excitation , RLGC : 1 % Ce3 + crystal showed three exponential decay time components . Further improvement in the scintillation properties is expected with higher values of Ce- concentrations .
At 662 keV γ-rays RLGB : 5 % Ce3 + crystal shows an energy resolution and light yield of 5.5 % ( FWHM ) and ∼ 25,500±2,600 ph/MeV , respectively . Under γ-ray excitation , RLGB : Ce3 + crystals showed four exponential decay time components . Improvement in the scintillation properties is expected with higher the higher Ce-doping .
In this paper , we provide a study of different chaotic maps with symmetric distributions , used as pseudorandom number generators ( PRNGs ) in FSS . In addition , we incorporate exponential step decay in order to improve the accuracy of the solutions produced by the algorithm . The obtained results of the conducted numerical experiments show that the use of chaotic maps instead of other commonly used high-quality PRNGs can speed up the algorithm , and the incorporated exponential step decay can improve the accuracy of the obtained solution .
We provide a general framework that takes any offline-coreset and gives a time-decay coreset for polynomial time decay functions . We also consider the exponential time decay model for $ k$-median clustering , where we provide a constant factor approximation algorithm that utilizes the online facility location algorithm . Our algorithm stores $ \mathcal{O}(k\log(h\Delta)+h)$ points where $ h$ is the half-life of the decay function and $ \Delta$ is the aspect ratio of the dataset .
The non-iterative Prony 's method is discussed for the deconvolution of multi-exponential fluorescence decays . The performance of these algorithms in the case of a two- exponential decay process is evaluated , using a Monte-Carlo simulation , in terms of the estimation errors caused by the signal noise . The results which are presented show that the performance of Prony 's method can be greatly improved with the selection of an optimized observation window width and a few algorithm-related parameters .
Due to the huge size of data , we set the mini - batch size to be 5000 and use Adam as the optimizer . We apply exponential decay , in which learning rate starts at 0.001 and decay rate is set to 0.9 . The statistics of all the above datasets is shown in Table [ reference ] .
For more details of architecture design , please refer to the supplementary material . For training , we use the Adam optimizer with a batch size of , a learning rate of , and exponential decay rates . In all experiments , we set the hyper - parameters as follows : , , , , , and .
The training was repeated three times with different random seeds for model weight initialization while the random seeds were consistent between the different methods to allow for fair comparisons . All CNNs were trained for 200 epochs with stochastic gradient descent via adaptive moment estimation kingma2015adam using exponential decay rates and ( PyTorch default ) and learning rate . In addition , we computed the Cumulative Score ( CS ) as the proportion of images for which the absolute differences between the predicted rank labels and the ground truth are below a threshold :
Dropout layers are applied before all linear layers and after word - embedding layer . We use an exponential decayed keep rate during training , where the initial keep rate is 1.0 and the decay rate is 0.977 for every 10 , 000 step . We initialize our word embeddings with pre - trained 300D GloVe 840B vectors while the out - of - vocabulary word are randomly initialized with uniform distribution .
Parameters are optimised with stochastic gradient descent ( SGD ) with an initial learning rate of and momentum of . Exponential learning rate decay is applied every 5 epochs with a factor of . To reduce the impact of exploding gradients , we employ gradient clipping at .
We use the Adam optimizer with an initial learning rate of , , and . An exponential learning rate decay schedule is applied with a decay rate of 0.99 every 1 , 000 iterations . section : Datasets
We describe the structure of the gradient of a validation error w.r.t . the learning rate schedule - the hypergradient . Based on this , we introduce MARTHE , a novel online algorithm guided by cheap approximations of the hypergradient that uses past information from the optimization trajectory to simulate future behaviour .
We are able to improve recognition performance of first-pass systems from a 12 % relative word error rate reduction reported previously to 21 % , compared to MFCC baselines on a Tagalog conversational telephone speech corpus . This is achieved by using different input features , training the network to predict context-dependent targets , employing an efficient learning rate schedule and varying several architectural details . Evaluations on two larger German and French speech transcription tasks show that the optimizations proposed are universally applicable and yield comparable gains on other corpora ( 19.9 % and 22.8 % , respectively ) .
The so-called Rogers ' paradox is inherent in the two-timescale method as originally applied to the evolution of pure strategies , but is often eliminated when the other methods are used . Moreover , Rogers ' paradox is not observed for the mixed learning strategies and learning schedules that we review . We believe that further theoretical work is necessary on learning schedules and biased social learning , based on models that are logically consistent and empirically pertinent .
Recently , speaker adaptation methods in deep neural networks ( DNNs ) have been widely studied for automatic speech recognition . However , almost all adaptation methods for DNNs have to consider various heuristic conditions such as mini-batch sizes , learning rate scheduling , stopping criteria , and initialization conditions because of the inherent property of a stochastic gradient descent (SGD)-based training process . Unfortunately , those heuristic conditions are hard to be properly tuned .
We focus on hyperparameters that are related to the optimization algorithm , e.g. learning rates , which have a large impact on the training speed and the resulting accuracy . Typically , fixed learning rate schedules are employed during training . We propose Hyperdyn a dynamic hyperparameter optimization method that selects new learning rates on the fly at the end of each epoch .
The challenges are to come up with appropriate optimistic predictions and correction terms in this framework . Some of our results also crucially rely on using a sophisticated increasing learning rate schedule .
Current state-of-the-art NMT systems use large neural networks that are not only slow to train , but also often require many heuristics and optimization tricks , such as specialized learning rate schedules and large batch sizes . This is undesirable as it requires extensive hyperparameter tuning .
Adjusting the learning rate schedule in stochastic gradient methods is an important unresolved problem which requires tuning in practice . If certain parameters of the loss function such as smoothness or strong convexity constants are known , theoretical learning rate schedules can be applied . However , in practice , such parameters are not known , and the loss function of interest is not convex in any case .
Actor-critic ( AC ) methods have exhibited great empirical success compared with other reinforcement learning algorithms , where the actor uses the policy gradient to improve the learning policy and the critic uses temporal difference learning to estimate the policy gradient . Under the two time-scale learning rate schedule , the asymptotic convergence of AC has been well studied in the literature . However , the non-asymptotic convergence and finite sample complexity of actor-critic methods are largely open .
So we use 0.01 to warm up the training until the training error is below 80 % ( about 400 iterations ) , and then go back to 0.1 and continue training . The rest of the learning schedule is as done previously . This 110 - layer network converges well
The following learning rate schedule was applied ( ) ( iterations [ learning rate ] ) . For fair comparisons , we used this learning rate schedule for all networks . During previous experiments , this schedule was optimized for ReLU networks , however as ELUs converge faster they would benefit from an adjusted schedule .
We follow settings in the re - implementation of ResNets by Gross and Wilber as possible . But , we use a linear learning rate schedule , which was reported as a better choice by Mishkin et al . . Take Model A in Table [ reference ] for example .
We encoded labels as binary vectors using k-hot encoding . We study the ResNet34 architecture , pre-trained on ImageNet , where two key modifications were incorporated into the training framework : ( 1 ) Stochastic gradient descent with momentum and with restarts using cosine annealing , ( 2 ) Variable image sizes for fine-tuning to prevent overfitting . Additionally , we use a heuristic algorithm to select a good learning rate .
We also evaluate their calibration using expected calibration error . We additionally propose a novel method of neural network parameter estimation called RECAST , which combines cosine annealing with warm restarts with Stochastic Gradient Langevin Dynamics , capturing more diverse parameter distributions . When benchmarked against mutilated data from MNIST , we show that RECAST is well-calibrated and when combined with predictive entropy and epistemic uncertainty it offers the best calibrated measure of uncertainty when compared to recent methods .
A graph-based solution to the mapping problem using the simulated annealing optimization heuristic is developed . An automated two-phase mapping strategy is formulated : process annealing assigns parallel processes to processing nodes , and connection annealing schedules traffic connections on network data links so that interprocess communication conflicts are minimized . To evaluate the quality of generated mappings .
This optimum position is determined by performing one-dimensional searches along the edges of the placed macrocells . The presented test results show that the quality of solutions obtained is better than that obtained by simulated annealing , analytic annealing , and genetic placement procedures .
Also , for two other test problems , the results are presented and compared with the results obtained by two published programs , one based on a heuristic procedure and the other based on an analytical technique . The results clearly demonstrate the improved performance of the analytic annealing procedure for macrocell placement optimization .
No training data symbols are necessary , but a priori information may be incorporated , if available . The basic structure consists of an iterative approximation of the joint maximum-likelihood ( ML ) solution , which is enhanced by adaptive filtering , by shift operations , by coefficient annealing , as well as by using a priori information . To our best knowledge , this is the first time that : ( i ) a priori information of the data has been exploited in the context of blind equalization ; and that ( ii ) the DPSK/ISI super trellis has been explicitly exploited for blind equalization rather than doing differential decoding for resolving the problem of phase ambiguity .
Computer simulation of annealing provides a way to model annealing processes at the grain scale . The various methods available to simulate grain growth and recrystallization are reviewed .
The results of experiment show that the temperature controller has several specific characteristics , which include : high control precision , small overshoot and better adaptive control etc . This controller can definitely satisfy the request of crystal annealing technology in high vacuum .
Lower stretchability of IPL annealed Cu film is attributed to its inherent cracks and porous film morphologies . IPL annealing induces the local melting at the interface between the substrate and Cu film , which increases the adhesion strength of the Cu film . These results provide useful information regarding the mechanical flexibility and durability of the nanoparticle films for the development of flexible electronics .
Training S3VM involves either a combinatorial or non-convex optimization problem and thus finding the global optimal solution is intractable in practice . It has been demonstrated that a key to successfully find a good ( local ) solution of S3VM is to gradually increase the effect of unlabeled data , a la annealing . However , existing algorithms suffer from the trade-off between the resolution of annealing steps and the computation cost .
Computational results show that the proposed model can allocate jamming resource effectively and optimize the combat capability of electronic warfare systems . The research on simulant annealing algorithm provides some theoretical gist and new thoughts on jamming resource allocation and improves the decision-making ability of electronic warfare systems .
Thanks to the capability of this algorithm to include heuristic information , the learning process is accelerated without model accuracy losses . Its behavior is successful compared with other processes based on genetic algorithms and sinlulated annealing when solving two modeling applications .
Channel Measurement and Modeling of the Small-Scale Fading Characteristics for Urban Inland River Environment Many Unstable Particles from an Open Quantum System's Perspective Effects of Tomato Geometries and Air Temperature on the Drying Behavior of Plum Tomato Bayesian Chain Ladder Models An Analysis and Modeling of Propagation/Accumulation Errors Incurred by CD in the FD-CD Transcoding Generative Models for Evolutionary Clustering UWB and Wideband Channel Models for Working Machine Environment Adaptive Regression Methods with Application to Streaming Financial Data A large deviation analysis of retrial models with constant and classic retrial rates Strong and Mild Extrapolated L2-Solutions to the Heat Equation with Constant Delay Scintillation characterizations of Rb2LiGdCl6: Ce3+ single crystals Scintillation properties of Rb 2 LiGdBr 6 : Ce 3+ single crystals A Study of Chaotic Maps Producing Symmetric Distributions in the Fish School Search Optimization Algorithm with Exponential Step Decay Improved Algorithms for Time Decay Streams Deconvolution of fluorescence decays and estimation errors Deep Interest Network for Click-Through Rate Prediction Diverse Image-to-Image Translation via Disentangled Representations Consistent Rank Logits for Ordinal Regression with Convolutional Neural Networks Natural Language Inference over Interaction Space Evaluating the Utility of Hand-crafted Features in Sequence Labelling Towards Viewpoint Invariant 3D Human Pose Estimation MARTHE: Scheduling the Learning Rate Via Online Hypergradients. Optimizing deep bottleneck feature extraction Evolution of learning strategies in temporally and spatially variable environments: a review of theory. Deep Least Squares Regression for Speaker Adaptation Dynamically Learning the Learning Rates: Online Hyperparameter Optimization More Adaptive Algorithms for Adversarial Bandits Competence-based Curriculum Learning for Neural Machine Translation WNGrad: Learn the Learning Rate in Gradient Descent A Finite Time Analysis of Two Time-Scale Actor Critic Methods Deep Residual Learning for Image Recognition Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) Wider or Deeper: Revisiting the ResNet Model for Visual Recognition Automated identification of thoracic pathology from chest radiographs with enhanced training pipeline Towards calibrated and scalable uncertainty representations for neural networks Heuristic Technique for Processor and Link Assignment in Multicomputers A constructive procedure for optimizing the placement of macrocells Analytic annealing for macrocell placement optimization Blind equalization with iterative joint channel and data estimation for wireless DPSK systems Chapter 16 – Computer Modeling and Simulation of Annealing Research on temperature controller for high vacuum annealing furnace Electromechanical properties of printed copper ink film using a white flash light annealing process for flexible electronics Infinitesimal Annealing for Training Semi-Supervised Support Vector Machines Jamming resource allocation aimed to data link based on simulant annealing algorithm Improvement to the cooperative rules methodology by using the ant colony system algorithm
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VTC Spring KDD ECCV ArXiv ICLR EMNLP ECCV RIVF INTERSPEECH NAACL-HLT (1) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR Pattern Recognit. Medical Imaging: Computer-Aided Diagnosis ISCAS (5) GLOBECOM ICML (3)
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