2 We thankfully acknowledge the contribution of the following friends , colleagues and students in making the problem set complete : A comprehensive account of some real world optimization problems have been presented in details . These problems can be used to evaluate the performance of different stochastic optimization algorithm . After all , every optimization algorithm has to be applied to some real world problems . This paper presents four algorithms to generate random forecast error time series , including a truncated-normal distribution model , a state-space based Markov model , a seasonal autoregressive moving average ( ARMA ) model , and a stochastic-optimization based model . The error time series are used to create real-time ( RT ) , hour-ahead ( HA ) , and day-ahead ( DA ) wind and load forecast time series that statistically match historically observed forecasting data sets , used for variable generation integration studies . With increasing numbers of wind power plants ( WPPs ) integrated into power systems , their variability and uncertain power outputs become a challenge to maintaining system reliability . Traditional stochastic optimization-based models face the curse of dimensionality when the number of WPPs increases . This paper proposes a novel decentralized wind power uncertainty management model . However , evidence surrounding the causes of negative outcomes remains inconclusive . Continuous glucose monitoring ( CGM ) devices allow researchers to investigate glucose complexity , using detrended fluctuation analysis ( DFA ) , to determine whether it is associated with negative outcomes . AIM : The aim of this study was to investigate the effects of CGM device type/calibration and CGM sensor location on results from DFA . However , it is sometimes multiplexed , due to asynchronous scheduling . Modeling the network traffic patterns of multiplexed ICS streams using Deterministic Finite Automata ( DFA ) for anomaly detection typically produces a very large DFA and a high false-alarm rate . In this article , we introduce a new modeling approach that addresses this gap . Regular expression ( RegEx ) matching has become an important engine of modern network device such as DPI , protocol analysis , and so on . Deterministic Finite Automata ( DFA ) is efficient at regular expression matching while it takes once memory access to match each input symbol . Ternary Content Addressable Memories ( TCAMs ) have been widely deployed in modern network devices with the feature of parallel searching and “ Do n’t Care ” matching capability . It is known that deterministic finite automata ( DFAs ) can be algorithmically minimized , i.e. , a DFA M can be converted to an equivalent DFA M ' which has a minimal number of states . The minimization can be done efficiently ( in : Z. Kohavi ( Ed . ) , Theory of Machines and Computations , Academic Press , New York , 1971 , pp . It is based on a set of graph grammar rules for combining many graphs ( DFA ) to obtain another desired graph ( DFA ) . The graph grammar rules are presented in the form of a parsing algorithm that converts a regular expression R into a minimal deterministic finite automaton M such that the language accepted by DFA M is same as the language described by regular expression R. The proposed algorithm removes the dependency over the necessity of lengthy chain of conversion , that is , regular expression → NFA with e-transitions → NFA without e- transitions → DFA → minimal DFA . Therefore the main advantage of our minimal DFA construction algorithm is its minimal intermediate memory requirements and hence , the reduced time complexity . A deterministic finite automaton(DFA ) based complex agent was proposed to assist cooperative object detection and tracking of robots team . This agent was improved with the behavior model which calls different modules depending on the state in DFA.To evaluate the available evidence on the efficacy and feasibility of the agent , the path data generated by the original algorithm was compared with the DFA based agent . Furthermore , it was applied to the region based multi-object tracking experiment . For the evaluation of proposed strategy , we use a multi-objective fitness function with two main goals in the nuclear core pattern design i.e. maximizing the core multiplication factor ( K eff ) in order to extract the maximum cycle energy and minimizing the power peaking factor due to safety constraints . For this purpose , Discrete Firefly Algorithm Nodal Expansion Code ( DFANEC ) has been developed with two main modules including a neutronic solver which models reactor core with coarse meshes using second order nodal expansion method and also DFA module to core pattern optimization . These codes have been coupled in the DFANEC for our fuel management operation . However , in regular expression matching , the traditional DFA method has serious performance degradation with a large-scale rule set . And building a hybrid DFA will lead to a substantial increase in space requirements . To address these problems , according to the rule set ’s characteristics and combining with multi-pattern idea , a matching mechanism based on multi-pattern finite automation is proposed . This paper represents a further attempt to provide definite results about the asymptotic performance of protocols of the dynamic frame Aloha ( DFA ) family for radio frequency identification ( RFID ) systems . Here we deal with a simple and popular backlog estimate known as Schoute 's estimate , apt to the DFA version that do not make use of the Frame Restart capability . We also generalize the result to reductions computed byfinite-crossingmachines . As a corollary , we show that all sets complete for C under two-way DFA reductions are polynomial-time isomorphic to each other . The patterns in packet payloads , especially complex patterns , can be efficiently represented by regular expressions , which can be translated by the use of Deterministic Finite Automata ( DFA ) . DFA algorithms are fast but consume very large amounts of memory with certain kinds of regular expressions . In this thesis , memory efficient algorithms are proposed based on the transition compressions of the DFAs . Despite the pattern partition , FREME does not sacrifice any matching correctness with the aid of RMT . Evaluation based on real-world pattern sets ( open source and commercial ) shows that FREME scales linearly with the size of pattern set , meanwhile keeps fast matching based on nonexplosive DFA . In contrast , FREME outperforms state-of-the-art matching engines up to two orders of magnitude . It is likewise closely related to the “ linearized ” parse trees proposed by and to the top - down , left - to - right decompositions of trees used in previous generative parsing and language modeling work . A further connection is to parsing which uses an unbounded lookahead ( compactly represented by a DFA ) to distinguish between parse alternatives in a top - down parser ; however , our parser uses an RNN encoding of the lookahead rather than a DFA . Input : One of the ways in which the BMJ 's 150 year anniversary is being celebrated is by publication of Apollo , an outstanding anthology of poems by medical writers . Some might ask if the existence of numerous doctor poets is not just a pure coexistence of interests in medicine and writing poetry . Abstract The development of a framework for the parallel structural optimization on a network of engineering workstations , using general purpose finite element and optimization programs , is reported . A flexible library of FORTRAN callable routines for execution on a network of Apollo workstations was developed . The optimization of a helicopter tail boom was performed to test this computational framework . The performance of parallel computing in a network of Apollo workstations where the processes use the remote procedure call ( RPC ) mechanism for communication is addressed . The speedup in such systems can not be accurately estimated without taking into account the relatively large communication overheads . Over the last four years , post-genomic data from proteome and transcriptome studies has been incorporated along with predicted orthologs in 33 species , annotations from the community and publications from the scientific literature . Available tools include BioMart for complex queries , GBrowse2 for genome browsing , the Apollo genome editor for expert curation of gene models , a Blast server , a motif finder , and a wiki for protocols , nomenclature guidelines and other documentation . In-house tools have been developed for ontology browsing and evaluation of off-target RNAi matches . All non-serial dependent analyses are executed in parallel on a compute cluster for efficiency of data generation . The uniform data model and backend relational database management system of Pegasys allow for results of heterogeneous programs included in the workflow to be integrated and exported into General Feature Format for further analyses in GFF-dependent tools , or GAME XML for import into the Apollo genome editor . The modularity of the design allows for new tools to be added to the system with little programmer overhead . During the flight of Apollo 13 , an oxygen tank blew up threatening the lives of the astronauts . When it happened , James Lovell Jr. told the Houston control center , " Hey Houston , we ' ve had a problem here . " This article describes a system that allows a reduced-function workstation , an Apple Macintosh , to perform as a low-end node in an Apollo Domain workstation network . Programs that run in the Apollo environment do not have to be modified to be accessed from the Macintosh ; the appropriate system subroutines are intercepted and recast into a suitable form . This paper describes the difficulties in evaluating large-scale scientific programs . These difficulties are illustrated through a single case study of the Apollo moon program . The paper describes some of the results of a three and a half year investigation into the beliefs of 42 of the most eminent scientists who studied the moon rocks . Secondary requirements included a return to flight time of 15 minutes , translation capability in the X and Y axes , and wireless safe-mode shutdown . LEAPFROG was developed to follow Apollo-like landing approaches using a kerosenepowered JetCat P200 jet engine for hover and descent flight . Lateral and rotational control was provided through 12 cold-gas thrusters . Since each Apollo manned space flight makes new demands on the computer configuration and operating system , data processing efficiency is tested before each flight by simulation described in this paper . Discussed is the dynamic gathering of operating system performance data during real-time simulation , achieved by incorporating appropriate routines in the Apollo control program . The data thus collected is used as input to improved system models . Specifications for a network design to connect the Apollo DOMAIN network with the VAX system are discussed in this paper . Three networking approaches were considered which represent three distinct chronological stages in the evolution of computer communications . This technique uses special flip-flop designs for re-configuring all sytem variables ( i.e. flip-flops ) as a contiguous shift register . The APOLLO DNlOOOO is an example of a complex digital system which uses scan technology to provide improved access to the sysytem state inorder to allow improved test and prototype sysytem debug . Architectural and technological aspects of the scan sub-system of the DNlOOOO has been described elsewhere[2 ] . Commonly referred to as the weight transport problem , each neuron 's dependence on the weights and errors located deeper in the network require exhaustive data movement which presents a key problem in enhancing the performance and energy-efficiency of machine-learning hardware . In this work , we propose a bio-plausible alternative to backpropagation drawing from advances in feedback alignment algorithms in which the error computation at a single synapse reduces to the product of three scalar values . Using a sparse feedback matrix , we show that a neuron needs only a fraction of the information previously used by the feedback alignment algorithms . In our approach , we first calculate coarse inter-frame pose estimation by matching the feature points . Subsequently , we use both direct image alignment and a multiscale pyramid method , for refining the previous estimation to attain better precision . Furthermore , we perform online photometric calibration along with pose estimation , to reduce un-modelled errors . Given a template image with known 3D structure , we show how to refine the rough reconstruction of nonrigid surfaces from existing feature-based methods through efficient direct image alignment . Under the mild assumption that the barycentric coordinates of each 3D point on the surface keep constant , we prove that the template and the input image are correlated by piecewise homography , based on which a direct Lucas-Kanade image alignment method is proposed to iteratively recover an inextensible surface even with poor texture and sharp creases . In this study , we present the first optical co-processor able to accelerate the training phase of digitally-implemented neural networks . We rely on direct feedback alignment as an alternative to backpropagation , and perform the error projection step optically . Leveraging the optical random projections delivered by our co-processor , we demonstrate its use to train a neural network for handwritten digits recognition . By training a network to fool the corresponding discriminator , it can learn the other network 's feature map distribution . We show that our method performs better than the conventional direct alignment method such as L1 and is more suitable for online distillation . Also , we propose a novel cyclic learning scheme for training more than two networks together . However , they could not become popular because their training accuracy and the computational efficiency were worse than BP . One of them was direct feedback alignment ( DFA ) , but it showed low training performance especially for the convolutional neural network ( CNN ) . In this paper , we overcome the limitation of the DFA algorithm by combining with the conventional BP during the CNN training . Furthermore , this model provides us qualitative understandings about dropconnect probability commonly used in the dropconnect algorithm and its relationship with the redundancy phenomenon . In addition , we combine the dropconnect and the random feedback alignment for feedforward and backward pass in a deep network training respectively , and observe fast learning and improved test performance in classifying a benchmark handwritten digits dataset . The thesis work deals with the implementation of three different algorithms to be used during the training of a network to replace the classic algorithm of backpropagation using one of them [ 15 ] . They are based on the Random Feedback Alignment , which reduces the computational work required to obtain the transposed of the forwarding weights necessary during the classical backpropagation using random matrices [ 13 ] . The error information is propagated from the last layer to the others using the random matrices . Many types of diagrams use spacing and alignment to communicate implicit relationships between objects within the diagram . We describe and evaluate a direct manipulation alignment tool based on a push-broom metaphor . This presents a key challenge in performing efficient on chip training for non von-Neumann architectures . In this work we demonstrate an alternative to backpropagation called sparse direct feedback alignment which bypasses the weight transport problem . We simulate crossbars of HfOx RRAM based on experimental data to explore the performance , area , and energy trade-offs of using bio-plausible algorithms on the MNIST and EMNIST datasets . While it is commonly accepted that batch size is an important parameter for offline tuning , the benefits of online selection of batches remain poorly understood . We investigate online batch selection strategies for two state-of-the-art methods of stochastic gradient-based optimization , AdaDelta and Adam . As the loss function to be minimized for the whole dataset is an aggregation of loss functions of individual datapoints , intuitively , datapoints with the greatest loss should be considered ( selected in a batch ) more frequently . Adaptive stochastic gradient descent methods , such as AdaGrad , Adam , AdaDelta , Nadam , AMSGrad , etc . , have been demonstrated efficacious in solving non-convex stochastic optimization , such as training deep neural networks . However , their convergence rates have not been touched under the non-convex stochastic circumstance except recent breakthrough results on AdaGrad [ 34 ] and perturbed AdaGrad [ 22 ] . The results showed that BCI used Wavelet , and RNN can drive external devices of non-training data with an accuracy of 79.6 % . The experiment gave AdaDelta model is better than the Adam model in terms of accuracy and value losses . Whereas in computational learning time , Adam 's model is faster than AdaDelta 's model . Fast Fourier Transform ( FFT ) extraction can increase accuracy from 60 % to 80.3 % from the use of Adam 's optimization model . Meanwhile , the AdaDelta model gave 20 % accuracy without FFT . And its condition increased to 79.9 % with FFT extraction . In this paper , we perform a comparative evaluation of seven most commonly used first-order stochastic gradient-based optimization techniques in a simple Convolutional Neural Network ( ConvNet ) architectural setup . The investigated techniques are the Stochastic Gradient Descent ( SGD ) , with vanilla ( vSGD ) , with momentum ( SGDm ) , with momentum and nesterov ( SGDm+n ) ) , Root Mean Square Propagation ( RMSProp ) , Adaptive Moment Estimation ( Adam ) , Adaptive Gradient ( AdaGrad ) , Adaptive Delta ( AdaDelta ) , Adaptive moment estimation Extension based on infinity norm ( Adamax ) and Nesterov-accelerated Adaptive Moment Estimation ( Nadam ) . We trained the model and evaluated the optimization techniques in terms of convergence speed , accuracy and loss function using three randomly selected publicly available image classification datasets . A new approach is developed for synthetic aperture radar ( SAR ) automatic target recognition based on convolutional neural network ( CNN ) . In order to improve the low efficiency caused by fixed learning rate in CNN , the AdaDelta method is introduced to adjust the learning rate adaptively . The experimental results on Moving and Stationary Target Acquisition and Recognition ( MSTAR ) data sets show that convergence is significantly faster in proposed method than CNN with fixed learning rate . Experiments were conducted using a 5-fold cross-validation based on insect image dataset . As a result , the ResNet-50 and AdaDelta combination showed the best performance ( 89.98 ± 1.40)% in the insect dataset . The loss is calculated as a regularized sum : where is the one - hot represented ground truth , is the estimated probability for each class by softmax , is the number of target classes , and is an L2 regularization hyper - parameter . Training is done through stochastic gradient descent over shuffled mini - batches with the AdaDelta update rule . Training details are further introduced in Section [ reference ] . We use 100 convolutional filters each for window sizes of ( 3 , 3 ) , 2D pooling size of ( 2 , 2 ) . We set the mini - batch size as 10 and the learning rate of AdaDelta as the default value 1.0 . For regularization , we employ Dropout operation with dropout rate of 0.5 for the word embeddings , 0.2 for the BLSTM layer and 0.4 for the penultimate layer , we also use l2 penalty with coefficient over the parameters . The model is optimised by minimising categorical cross - entropy with respect to the correct labels . We use AdaDelta ( Zeiler , 2012 ) for calculating an adaptive learning rate during training , which accounts for a higher baseline performance compared to previous results . section : Evaluation The maximum reasoning step T max is set to 10 in SQuAD experiments . We use AdaDelta optimizer [ reference ] for parameter optimization with an initial learning rate of 0.5 and a batch size of 32 . Models are trained on GTX In addition , all of the models use an additional neural network layer to map these 300d embeddings into the lower - dimensional phrase and sentence embedding space . All of the models are randomly initialized using standard techniques and trained using AdaDelta minibatch SGD until performance on the development set stops improving . We applied L2 regularization to all models , manually tuning the strength coefficient for each , and additionally applied dropout to the inputs and outputs of the sentence embedding models ( though not to its internal connections ) with a fixed dropout rate . The model has about 2.6 million parameters . We use the AdaDelta adadelta optimizer , with a minibatch size of 60 and an initial learning rate of , for 12 epochs . A dropout dropout rate of is used for the CNN , all LSTM layers , and the linear transformation before the softmax for the answers .
2 We thankfully acknowledge the contribution of the following friends , colleagues and students in making the problem set complete : A comprehensive account of some real world optimization problems have been presented in details . These problems can be used to evaluate the performance of different stochastic optimization algorithm . After all , every optimization algorithm has to be applied to some real world problems .
This paper presents four algorithms to generate random forecast error time series , including a truncated-normal distribution model , a state-space based Markov model , a seasonal autoregressive moving average ( ARMA ) model , and a stochastic-optimization based model . The error time series are used to create real-time ( RT ) , hour-ahead ( HA ) , and day-ahead ( DA ) wind and load forecast time series that statistically match historically observed forecasting data sets , used for variable generation integration studies .
With increasing numbers of wind power plants ( WPPs ) integrated into power systems , their variability and uncertain power outputs become a challenge to maintaining system reliability . Traditional stochastic optimization-based models face the curse of dimensionality when the number of WPPs increases . This paper proposes a novel decentralized wind power uncertainty management model .
However , evidence surrounding the causes of negative outcomes remains inconclusive . Continuous glucose monitoring ( CGM ) devices allow researchers to investigate glucose complexity , using detrended fluctuation analysis ( DFA ) , to determine whether it is associated with negative outcomes . AIM : The aim of this study was to investigate the effects of CGM device type/calibration and CGM sensor location on results from DFA .
However , it is sometimes multiplexed , due to asynchronous scheduling . Modeling the network traffic patterns of multiplexed ICS streams using Deterministic Finite Automata ( DFA ) for anomaly detection typically produces a very large DFA and a high false-alarm rate . In this article , we introduce a new modeling approach that addresses this gap .
Regular expression ( RegEx ) matching has become an important engine of modern network device such as DPI , protocol analysis , and so on . Deterministic Finite Automata ( DFA ) is efficient at regular expression matching while it takes once memory access to match each input symbol . Ternary Content Addressable Memories ( TCAMs ) have been widely deployed in modern network devices with the feature of parallel searching and “ Do n’t Care ” matching capability .
It is known that deterministic finite automata ( DFAs ) can be algorithmically minimized , i.e. , a DFA M can be converted to an equivalent DFA M ' which has a minimal number of states . The minimization can be done efficiently ( in : Z. Kohavi ( Ed . ) , Theory of Machines and Computations , Academic Press , New York , 1971 , pp .
It is based on a set of graph grammar rules for combining many graphs ( DFA ) to obtain another desired graph ( DFA ) . The graph grammar rules are presented in the form of a parsing algorithm that converts a regular expression R into a minimal deterministic finite automaton M such that the language accepted by DFA M is same as the language described by regular expression R. The proposed algorithm removes the dependency over the necessity of lengthy chain of conversion , that is , regular expression → NFA with e-transitions → NFA without e- transitions → DFA → minimal DFA . Therefore the main advantage of our minimal DFA construction algorithm is its minimal intermediate memory requirements and hence , the reduced time complexity .
A deterministic finite automaton(DFA ) based complex agent was proposed to assist cooperative object detection and tracking of robots team . This agent was improved with the behavior model which calls different modules depending on the state in DFA.To evaluate the available evidence on the efficacy and feasibility of the agent , the path data generated by the original algorithm was compared with the DFA based agent . Furthermore , it was applied to the region based multi-object tracking experiment .
For the evaluation of proposed strategy , we use a multi-objective fitness function with two main goals in the nuclear core pattern design i.e. maximizing the core multiplication factor ( K eff ) in order to extract the maximum cycle energy and minimizing the power peaking factor due to safety constraints . For this purpose , Discrete Firefly Algorithm Nodal Expansion Code ( DFANEC ) has been developed with two main modules including a neutronic solver which models reactor core with coarse meshes using second order nodal expansion method and also DFA module to core pattern optimization . These codes have been coupled in the DFANEC for our fuel management operation .
However , in regular expression matching , the traditional DFA method has serious performance degradation with a large-scale rule set . And building a hybrid DFA will lead to a substantial increase in space requirements . To address these problems , according to the rule set ’s characteristics and combining with multi-pattern idea , a matching mechanism based on multi-pattern finite automation is proposed .
This paper represents a further attempt to provide definite results about the asymptotic performance of protocols of the dynamic frame Aloha ( DFA ) family for radio frequency identification ( RFID ) systems . Here we deal with a simple and popular backlog estimate known as Schoute 's estimate , apt to the DFA version that do not make use of the Frame Restart capability .
We also generalize the result to reductions computed byfinite-crossingmachines . As a corollary , we show that all sets complete for C under two-way DFA reductions are polynomial-time isomorphic to each other .
The patterns in packet payloads , especially complex patterns , can be efficiently represented by regular expressions , which can be translated by the use of Deterministic Finite Automata ( DFA ) . DFA algorithms are fast but consume very large amounts of memory with certain kinds of regular expressions . In this thesis , memory efficient algorithms are proposed based on the transition compressions of the DFAs .
Despite the pattern partition , FREME does not sacrifice any matching correctness with the aid of RMT . Evaluation based on real-world pattern sets ( open source and commercial ) shows that FREME scales linearly with the size of pattern set , meanwhile keeps fast matching based on nonexplosive DFA . In contrast , FREME outperforms state-of-the-art matching engines up to two orders of magnitude .
It is likewise closely related to the “ linearized ” parse trees proposed by and to the top - down , left - to - right decompositions of trees used in previous generative parsing and language modeling work . A further connection is to parsing which uses an unbounded lookahead ( compactly represented by a DFA ) to distinguish between parse alternatives in a top - down parser ; however , our parser uses an RNN encoding of the lookahead rather than a DFA . Input :
One of the ways in which the BMJ 's 150 year anniversary is being celebrated is by publication of Apollo , an outstanding anthology of poems by medical writers . Some might ask if the existence of numerous doctor poets is not just a pure coexistence of interests in medicine and writing poetry .
Abstract The development of a framework for the parallel structural optimization on a network of engineering workstations , using general purpose finite element and optimization programs , is reported . A flexible library of FORTRAN callable routines for execution on a network of Apollo workstations was developed . The optimization of a helicopter tail boom was performed to test this computational framework .
The performance of parallel computing in a network of Apollo workstations where the processes use the remote procedure call ( RPC ) mechanism for communication is addressed . The speedup in such systems can not be accurately estimated without taking into account the relatively large communication overheads .
Over the last four years , post-genomic data from proteome and transcriptome studies has been incorporated along with predicted orthologs in 33 species , annotations from the community and publications from the scientific literature . Available tools include BioMart for complex queries , GBrowse2 for genome browsing , the Apollo genome editor for expert curation of gene models , a Blast server , a motif finder , and a wiki for protocols , nomenclature guidelines and other documentation . In-house tools have been developed for ontology browsing and evaluation of off-target RNAi matches .
All non-serial dependent analyses are executed in parallel on a compute cluster for efficiency of data generation . The uniform data model and backend relational database management system of Pegasys allow for results of heterogeneous programs included in the workflow to be integrated and exported into General Feature Format for further analyses in GFF-dependent tools , or GAME XML for import into the Apollo genome editor . The modularity of the design allows for new tools to be added to the system with little programmer overhead .
During the flight of Apollo 13 , an oxygen tank blew up threatening the lives of the astronauts . When it happened , James Lovell Jr. told the Houston control center , " Hey Houston , we ' ve had a problem here . "
This article describes a system that allows a reduced-function workstation , an Apple Macintosh , to perform as a low-end node in an Apollo Domain workstation network . Programs that run in the Apollo environment do not have to be modified to be accessed from the Macintosh ; the appropriate system subroutines are intercepted and recast into a suitable form .
This paper describes the difficulties in evaluating large-scale scientific programs . These difficulties are illustrated through a single case study of the Apollo moon program . The paper describes some of the results of a three and a half year investigation into the beliefs of 42 of the most eminent scientists who studied the moon rocks .
Secondary requirements included a return to flight time of 15 minutes , translation capability in the X and Y axes , and wireless safe-mode shutdown . LEAPFROG was developed to follow Apollo-like landing approaches using a kerosenepowered JetCat P200 jet engine for hover and descent flight . Lateral and rotational control was provided through 12 cold-gas thrusters .
Since each Apollo manned space flight makes new demands on the computer configuration and operating system , data processing efficiency is tested before each flight by simulation described in this paper . Discussed is the dynamic gathering of operating system performance data during real-time simulation , achieved by incorporating appropriate routines in the Apollo control program . The data thus collected is used as input to improved system models .
Specifications for a network design to connect the Apollo DOMAIN network with the VAX system are discussed in this paper . Three networking approaches were considered which represent three distinct chronological stages in the evolution of computer communications .
This technique uses special flip-flop designs for re-configuring all sytem variables ( i.e. flip-flops ) as a contiguous shift register . The APOLLO DNlOOOO is an example of a complex digital system which uses scan technology to provide improved access to the sysytem state inorder to allow improved test and prototype sysytem debug . Architectural and technological aspects of the scan sub-system of the DNlOOOO has been described elsewhere[2 ] .
Commonly referred to as the weight transport problem , each neuron 's dependence on the weights and errors located deeper in the network require exhaustive data movement which presents a key problem in enhancing the performance and energy-efficiency of machine-learning hardware . In this work , we propose a bio-plausible alternative to backpropagation drawing from advances in feedback alignment algorithms in which the error computation at a single synapse reduces to the product of three scalar values . Using a sparse feedback matrix , we show that a neuron needs only a fraction of the information previously used by the feedback alignment algorithms .
In our approach , we first calculate coarse inter-frame pose estimation by matching the feature points . Subsequently , we use both direct image alignment and a multiscale pyramid method , for refining the previous estimation to attain better precision . Furthermore , we perform online photometric calibration along with pose estimation , to reduce un-modelled errors .
Given a template image with known 3D structure , we show how to refine the rough reconstruction of nonrigid surfaces from existing feature-based methods through efficient direct image alignment . Under the mild assumption that the barycentric coordinates of each 3D point on the surface keep constant , we prove that the template and the input image are correlated by piecewise homography , based on which a direct Lucas-Kanade image alignment method is proposed to iteratively recover an inextensible surface even with poor texture and sharp creases .
In this study , we present the first optical co-processor able to accelerate the training phase of digitally-implemented neural networks . We rely on direct feedback alignment as an alternative to backpropagation , and perform the error projection step optically . Leveraging the optical random projections delivered by our co-processor , we demonstrate its use to train a neural network for handwritten digits recognition .
By training a network to fool the corresponding discriminator , it can learn the other network 's feature map distribution . We show that our method performs better than the conventional direct alignment method such as L1 and is more suitable for online distillation . Also , we propose a novel cyclic learning scheme for training more than two networks together .
However , they could not become popular because their training accuracy and the computational efficiency were worse than BP . One of them was direct feedback alignment ( DFA ) , but it showed low training performance especially for the convolutional neural network ( CNN ) . In this paper , we overcome the limitation of the DFA algorithm by combining with the conventional BP during the CNN training .
Furthermore , this model provides us qualitative understandings about dropconnect probability commonly used in the dropconnect algorithm and its relationship with the redundancy phenomenon . In addition , we combine the dropconnect and the random feedback alignment for feedforward and backward pass in a deep network training respectively , and observe fast learning and improved test performance in classifying a benchmark handwritten digits dataset .
The thesis work deals with the implementation of three different algorithms to be used during the training of a network to replace the classic algorithm of backpropagation using one of them [ 15 ] . They are based on the Random Feedback Alignment , which reduces the computational work required to obtain the transposed of the forwarding weights necessary during the classical backpropagation using random matrices [ 13 ] . The error information is propagated from the last layer to the others using the random matrices .
Many types of diagrams use spacing and alignment to communicate implicit relationships between objects within the diagram . We describe and evaluate a direct manipulation alignment tool based on a push-broom metaphor .
This presents a key challenge in performing efficient on chip training for non von-Neumann architectures . In this work we demonstrate an alternative to backpropagation called sparse direct feedback alignment which bypasses the weight transport problem . We simulate crossbars of HfOx RRAM based on experimental data to explore the performance , area , and energy trade-offs of using bio-plausible algorithms on the MNIST and EMNIST datasets .
While it is commonly accepted that batch size is an important parameter for offline tuning , the benefits of online selection of batches remain poorly understood . We investigate online batch selection strategies for two state-of-the-art methods of stochastic gradient-based optimization , AdaDelta and Adam . As the loss function to be minimized for the whole dataset is an aggregation of loss functions of individual datapoints , intuitively , datapoints with the greatest loss should be considered ( selected in a batch ) more frequently .
Adaptive stochastic gradient descent methods , such as AdaGrad , Adam , AdaDelta , Nadam , AMSGrad , etc . , have been demonstrated efficacious in solving non-convex stochastic optimization , such as training deep neural networks . However , their convergence rates have not been touched under the non-convex stochastic circumstance except recent breakthrough results on AdaGrad [ 34 ] and perturbed AdaGrad [ 22 ] .
The results showed that BCI used Wavelet , and RNN can drive external devices of non-training data with an accuracy of 79.6 % . The experiment gave AdaDelta model is better than the Adam model in terms of accuracy and value losses . Whereas in computational learning time , Adam 's model is faster than AdaDelta 's model .
Fast Fourier Transform ( FFT ) extraction can increase accuracy from 60 % to 80.3 % from the use of Adam 's optimization model . Meanwhile , the AdaDelta model gave 20 % accuracy without FFT . And its condition increased to 79.9 % with FFT extraction .
In this paper , we perform a comparative evaluation of seven most commonly used first-order stochastic gradient-based optimization techniques in a simple Convolutional Neural Network ( ConvNet ) architectural setup . The investigated techniques are the Stochastic Gradient Descent ( SGD ) , with vanilla ( vSGD ) , with momentum ( SGDm ) , with momentum and nesterov ( SGDm+n ) ) , Root Mean Square Propagation ( RMSProp ) , Adaptive Moment Estimation ( Adam ) , Adaptive Gradient ( AdaGrad ) , Adaptive Delta ( AdaDelta ) , Adaptive moment estimation Extension based on infinity norm ( Adamax ) and Nesterov-accelerated Adaptive Moment Estimation ( Nadam ) . We trained the model and evaluated the optimization techniques in terms of convergence speed , accuracy and loss function using three randomly selected publicly available image classification datasets .
A new approach is developed for synthetic aperture radar ( SAR ) automatic target recognition based on convolutional neural network ( CNN ) . In order to improve the low efficiency caused by fixed learning rate in CNN , the AdaDelta method is introduced to adjust the learning rate adaptively . The experimental results on Moving and Stationary Target Acquisition and Recognition ( MSTAR ) data sets show that convergence is significantly faster in proposed method than CNN with fixed learning rate .
Experiments were conducted using a 5-fold cross-validation based on insect image dataset . As a result , the ResNet-50 and AdaDelta combination showed the best performance ( 89.98 ± 1.40)% in the insect dataset .
The loss is calculated as a regularized sum : where is the one - hot represented ground truth , is the estimated probability for each class by softmax , is the number of target classes , and is an L2 regularization hyper - parameter . Training is done through stochastic gradient descent over shuffled mini - batches with the AdaDelta update rule . Training details are further introduced in Section [ reference ] .
We use 100 convolutional filters each for window sizes of ( 3 , 3 ) , 2D pooling size of ( 2 , 2 ) . We set the mini - batch size as 10 and the learning rate of AdaDelta as the default value 1.0 . For regularization , we employ Dropout operation with dropout rate of 0.5 for the word embeddings , 0.2 for the BLSTM layer and 0.4 for the penultimate layer , we also use l2 penalty with coefficient over the parameters .
The model is optimised by minimising categorical cross - entropy with respect to the correct labels . We use AdaDelta ( Zeiler , 2012 ) for calculating an adaptive learning rate during training , which accounts for a higher baseline performance compared to previous results . section : Evaluation
The maximum reasoning step T max is set to 10 in SQuAD experiments . We use AdaDelta optimizer [ reference ] for parameter optimization with an initial learning rate of 0.5 and a batch size of 32 . Models are trained on GTX
In addition , all of the models use an additional neural network layer to map these 300d embeddings into the lower - dimensional phrase and sentence embedding space . All of the models are randomly initialized using standard techniques and trained using AdaDelta minibatch SGD until performance on the development set stops improving . We applied L2 regularization to all models , manually tuning the strength coefficient for each , and additionally applied dropout to the inputs and outputs of the sentence embedding models ( though not to its internal connections ) with a fixed dropout rate .
The model has about 2.6 million parameters . We use the AdaDelta adadelta optimizer , with a minibatch size of 60 and an initial learning rate of , for 12 epochs . A dropout dropout rate of is used for the CNN , all LSTM layers , and the linear transformation before the softmax for the answers .
Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems A comparison of forecast error generators for modeling wind and load uncertainty Decentralized wind uncertainty management: Alternating direction method of multipliers based distributionally-robust chance constrained optimal power flow Complexity of Continuous Glucose Monitoring Data in Critically Ill Patients: CGM Devices, Sensor Locations, and DFA Methods Automatic Construction of Statechart-Based Anomaly Detection Models for Multi-Threaded Industrial Control Systems Cool DFACAM: 以DFA為基礎的深度封包檢測之TCAM節能架構 Minimizing finite automata is computationally hard Conversion of Regular Expression in To Finite Automata Design and implementation of complex Agent model using DFA Multi-objective loading pattern enhancement of PWR based on the Discrete Firefly Algorithm Multi-pattern Finite Automation Based Regular Expression Matching Asymptotic analysis of Schoute's estimate for dynamic frame Aloha Polynomial-Time Isomorphism of 1-L-Complete Sets HARDWARE ACCELERATION FOR POWER EFFICIENT DEEP PACKET INSPECTION FREME: A pattern partition based engine for fast and scalable regular expression matching in practice Recurrent Neural Network Grammars Rhyme and reason Parallel structural optimization on a network of computer workstations Parallel Computing Optimization in the Apollo Domain Network ParameciumDB in 2011: new tools and new data for functional and comparative genomics of the model ciliate Paramecium tetraurelia Pegasys: software for executing and integrating analyses of biological sequences Houston: We are in Overload A Bridge from Full-Function to Reduced-Function Workstations On Evaluating the Scientific Contribution of the Apollo Moon Missions Via Information Theory: A Study of the Scientist-Scientist Relationship LEAPFROG: Lunar Entry and Approach Platform For Research On Ground Statistics Gathering and Simulation for the Apollo Real-Time Operating System Internetting between VAX and Apollo ring network ATLAS/ELA: Scan-Based Software Tools for Reducing System Debug Time in a State-of-the-Art Workstation Direct Feedback Alignment With Sparse Connections for Local Learning A Robust Stereo Semi-direct SLAM System Based on Hybrid Pyramid 3D Structure Refinement of Nonrigid Surfaces through Efficient Image Alignment Light-in-the-loop: using a photonics co-processor for scalable training of neural networks Feature-map-level Online Adversarial Knowledge Distillation Efficient Convolutional Neural Network Training with Direct Feedback Alignment Redundancy in active paths of deep networks: a random active path model Analysis of Deep Neural Networks based on Feedback Alignment algorithms Sweeping away disorder with the broom alignment tool Local Learning in RRAM Neural Networks with Sparse Direct Feedback Alignment Online Batch Selection for Faster Training of Neural Networks On the Convergence of AdaGrad with Momentum for Training Deep Neural Networks Brain-Computer Interface of Focus and Motor Imagery Using Wavelet and Recurrent Neural Networks Detection of EEG Signal Post-Stroke Using FFT and Convolutional Neural Network A Comparative Analysis of Gradient Descent-Based Optimization Algorithms on Convolutional Neural Networks Adaptive learning rate CNN for SAR ATR Classification Performance Analysis of Weight Update Method Applied to Various ConvNet Models Text Classification Improved by Integrating Bidirectional LSTM with Two-dimensional Max Pooling Artificial Error Generation with Machine Translation and Syntactic Patterns ReasoNet: Learning to Stop Reading in Machine Comprehension A large annotated corpus for learning natural language inference Bidirectional Attention Flow for Machine Comprehension
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SoftCOM HLT-NAACL ICSM SIGCOMM DAC IROS ACCV (4) CHI Extended Abstracts ISLPED COLING BEA@EMNLP CoCo@NIPS EMNLP ICLR
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