The feedback load is an important parameter with a large impact in Orthogonal Frequency Division Multiple Access ( OFDMA ) techniques , as a feedback over each one of the subcarriers will induce a non-acceptable signaling load in the system , so that some optimization and feedback reduction strategies are required to meet practical systems demands . The decision over the best strategy for feedback optimization highly depends on the number of available users in the system . The efficient and robust realization of such large , highly-dynamic and complex networking environments is a challenging algorithmic and technological task . In this work we present and discuss two protocols for efficient and robust data propagation in wireless sensor networks : LTP ( a “ local target ” optimization protocol ) and PFR ( a multi-path probabilistic forwarding protocol ) . Furthermore , we present the NanoPeers architecture paradigm , a peer-to-peer network of lightweight devices , lacking all or most of the capabilities of their computer-world counterparts . Clustering is effective as a technique for discerning the structure of and unraveling the complex relationship between massive amounts of data . An improved technique which combines chaotic map particle swarm optimization with an acceleration strategy is proposed , since results of one of the most used clustering algorithm , K-means can be jeopardized by improper choices made in the initializing stage . Accelerated chaotic particle swarm optimization ( ACPSO ) searches through arbitrary data sets for appropriate cluster centers and can effectively and efficiently find better solutions . However , it faces the same challenges of noise and complexity of images . In this paper , inspired by our earlier research on optimizing parameter settings for CBIR using an evolutionary algorithm [2][3 ] , we introduce a Genetic Algorithm-based optimization step in our spotting algorithm to improve each spotting result . Experiments using an image dataset of journal pages reveal promising performance , in that the precision is significantly improved but without compromising the recall of the overall spotting result . We propose a parallel sequential Monte Carlo optimization method to minimize cost functions which are computed as the sum of many component functions . The proposed scheme is a stochastic zeroth order optimization algorithm which uses only evaluations of small subsets of component functions to collect information from the problem . This thesis considers di erent methods of utilising the contextual information on webpages and ads in order to improve the tting of a Bayesian Poisson model to historic data using L-BFGS . The data and optimization algorithm is provided by Admeta , an advertising optimization company that uses the model for click-rate predictions . The di erent methods tried to get added contextual information include categorization and developing di erent similarity measures between web-pages and ads using keywords . RTL to GDSII design flow is briefly described from environment setting , constraint checking , clock plan , synthesis , floorplan and power plan , placement and optimization with DFT , clock tree synthesis , post-CTS optimization , routing and optimization , physical verification , RC extraction , static timing analysis , functional verification , formal check , automatic test pattern generation , etc . Traditionally , standard chemoinformatics methods ignored this problem and focused on the sequential optimization of each single biological or chemical property ( ie , a single objective ) . This approach , known as single-objective optimization ( SOOP ) , strives to discover a single optimal solution to the optimization problem . Implicitly , SOOP-based methods assume that the optimal solution for an objective will also be the optimum for any other objectives involved in the profiling of a molecule . Circulating cortisol is contained in a single body compartment with an apparent volume of distribution ( 5.3 plus or minus 0.55 liters/m2 ) from which elimination occurs by first-order metabolic clearance . The biological half-life averaged 0.96 plus or minus 0.18 h. Upon least-square optimization of selected kinetic parameters , the circadian-episodic model increases the accountable variation ( r2 x 100 ) to 89 % in comparison with the 35 % obtained by use of only a periodic function . Although such MDPs arise naturally in many practical applications , they are often difficult to solve exactly due to the enormous size of the state space of the complete system , which grows exponentially with the number of components . In this paper , we propose an approximate solution approach to decomposable MDPs that is based on re-solving a fluid linear optimization formulation of the problem at each decision epoch . This formulation tractably approximates the problem by modeling transition behavior at the level of the individual components rather than the complete system . Furthermore , this is a much less noisy update since the target distribution is exact and deterministic . Therefore , unless all the examples exclusively belong to a specific class with probability 1 , the optimization will act much differently and train with greatly improved supervision . The idea proposed in the recent work by [ reference ] might be considered as an application of this framework , where they try to obtain a good set ofỹ 's by training very large models and using the model prediction distributions of those . In this work , we study feature learning techniques for graph - structured inputs . Our starting point is previous work on Graph Neural Networks scarselli2009graph , which we modify to use gated recurrent units and modern optimization techniques and then extend to output sequences . The result is a flexible and broadly useful class of neural network models that has favorable inductive biases relative to purely sequence - based models ( e.g. , LSTMs ) when the problem is graph - structured . In this paper , we survey a range of classical objective functions that have been widely used to train linear models for structured prediction and apply them to neural sequence to sequence models . Our experiments show that these losses can perform surprisingly well by slightly outperforming beam search optimization in a like for like setup . We also report new state of the art results on both IWSLT’14 German - English translation as well as Gigaword abstractive summarization . subsection : Comparison to Beam - Search Optimization Next , we compare classical sequence - level training to the recently proposed Beam Search Optimization wiseman2016acl . To enable a fair comparison , we re - implement their baseline , a single layer LSTM encoder / decoder model with 256 - dimensional hidden layers and word embeddings as well as attention and input feeding luong2015effective . 4 - Step Alternating Training . In this paper , we adopt a pragmatic 4 - step training algorithm to learn shared features via alternating optimization . In the first step , we train the RPN as described in Section [ reference ] . Given that our optimization objective combines two translation systems , this would require generating an n - best list for first and , for each entry on it , generating a new n - best list with , yielding a combined n - best list with entries . So as to make it more efficient , we propose an alternating optimization approach where we fix the parameters of one model and optimize the other with standard MERT . Thanks to this , we do not need to expand the search space of the fixed model , so we can do with an n - best list of entries alone . The predicted residuals at each level are used to efficiently reconstruct the HR image through upsampling and addition operations . While the proposed LapSRN consists of a set of cascaded sub - networks , we train the network with a robust Charbonnier loss function in an end - to - end fashion ( i.e. , without stage - wise optimization ) . As depicted in Figure [ reference ] ( e ) , our network architecture naturally accommodates deep supervision ( i.e. , supervisory signals can be applied simultaneously at each level of the pyramid ) . They found that features lower in the unrolled hierarchy cluster low - level concepts , while features later in the hierarchy capture high - level concepts . Our method differs in that we use convolutional neural networks rather than unrolling a sparse coding optimization . In the context of unsupervised clustering [ 35 ] exploited agglomerative clustering as a regularizer ; this approach was formulated as a recurrent network . Gucluturk et al . train a feed - forward CNN with perceptual loss for sketch inversion . Yeh et al . apply DCGAN to semantic face inpainting in an optimization manner . section : Deep CNNs for Identity - aware Attribute Transfer However , obtaining these 2D - 3D correspondences from local patches is difficult and the output is typically very noisy for these methods . A robust optimization is then needed to estimate the pose . In this paper , we argue for a " holistic " approach , in the sense that we predict the pose of an object directly from its appearance , instead of identifying its individual surface points . We do not employ style losses in our method . While initially style transfer was obtained by a slow optimization process styletransfer , recently , the emphasis was put on feed - forward methods ulyanov16texture , Johnson2016Perceptual . There are many links between style transfer and our work : both are unsupervised and generate a sample under constancy given an input sample . incorporate long range matching into the traditional optical flow objective function . This approach of combining image matching to capture large motions , with a variational or discrete optimization for fine motions , can produce accurate results . Of course spatial pyramids are widely used in other areas of computer vision and have recently been used in deep neural networks to learn generative image models . GPN also introduces a local greedy inference algorithm by assuming independence among person detections for producing optimal multi - person joint configurations . This local optimization strategy reduces the search space of the graph partition problem for finding optimal poses , avoiding the high joint partition complexity challenging the bottom - up strategy . Moreover , the local greedy inference algorithm exploits reliable global affinity cues from the embedding space for inferring joint configurations within robust person detections , leading to performance improvement . Intervention " has been widely used for recognizing a causal relation ontologically . In this paper , we propose a causal inference framework for visual reasoning via docalculus . To study the intervention effects on pixel-level features for causal reasoning , we introduce pixel-wise masking and adversarial perturbation . We developed an efficient and robust computational framework to perform Bayesian model comparison of causal inference strategies , which incorporates a number of alternative assumptions about the observers . With this framework , we investigated whether human observers ’ performance in an explicit cause attribution and an implicit heading discrimination task can be modeled as a causal inference process . In the explicit causal inference task , all subjects accounted for cue disparity when reporting judgments of common cause , although not necessarily all in a Bayesian fashion . Here , we conduct a large-scale data study of exercise and sleep effects through an analysis of 8 months of exercise and sleep data for 20 k users , combined with search query logs , location information and aggregated social media data . We analyze factors correlated with better sleep and more effective exercise , and confirm these relationships through causal inference analysis . Further , we build linear models to predict individuals ’ sleep and exercise quality . Its purpose is to evaluate the extent to which any given reserve has achieved its stated objectives . MAREA provides specific guidance on data collection and formatting , and then conducts rigorous causal inference analysis based on data input by the user , providing real-time outputs about the effectiveness of the reserve . MAREA ’s ease of use , standardization of state-of-the-art inference methods , and ability to analyze marine reserve effectiveness across ecological , socioeconomic , and governance objectives could dramatically further our understanding and support of effective marine reserve management . Causal Techniques for conducting these tests , which include the elicited constraint information are described . Overviews of Morphological Analysis , Causal Inference Theory and Statistical Testing Approaches are provided in the context of a Bayesian simulation of an example problem . Existing theories that justify these techniques are well developed but either require exact matching , which is usually infeasible in practice , or sacrifice some simplicity via asymptotic theory , specialized bias corrections , and novel variance estimators ; and extensions to approximate matching with multicategory treatments have not yet appeared . As an alternative , we show how conceptualizing continuous variables as having logical breakpoints ( such as phase transitions when measuring temperature or high school or college degrees in years of education ) is both natural substantively and can be used to simplify causal inference theory . The result is a finite sample theory that is widely applicable , simple to understand , and easy to implement by using matching to preprocess the data , after which one can use whatever method would have been applied without matching . Methods ESC-DAGs operates on empirical studies identified by a literature search , ideally a novel systematic review or review of systematic reviews . It involves three key stages : ( i ) the conclusions of each study are ‘ mapped ’ into a DAG ; ( ii ) the causal structures in these DAGs are systematically assessed using several causal inference principles and are corrected accordingly ; ( iii ) the resulting DAGs are then synthesised into one or more ‘ integrated DAGs ’ . This demonstration article didactically applies ESC-DAGs to the literature on parental influences on offspring alcohol use during adolescence . The spurious relationships and non-interpretable or wrong evidence hinder many data mining applications , especially in medical and social science areas . We have been using some well-known causal inference principles to discover causal relationships and building causally interpretable data mining models , such as causal rules [ 2 ] and causal decision trees [ 3 ] , and applying causal discovery methods to real world biological problems [ 4 , 5 ] . In this talk , I will discuss some of our exploratory work in this promising direction . This result has direct consequences for the design and analysis of large experiments — for instance , in online social platforms — where the belief is that large sample sizes automatically guarantee small variance . More broadly , our results suggest that although strategies for causal inference in the presence of interference borrow their formalism and main concepts from the traditional causal inference literature , much of the intuition from the no-interference case do not easily transfer to the interference setting . In particular , we use parametric link functions from statistics to better model the adoption and abandonment of bicycling . From machine learning , we use decision trees to represent the non-compensatory decision protocols that individuals appear to follow when deciding whether to commute by bicycle , and we use diagrams from the causal inference literature to gain insight into how we can bet- ter model the effects of bike lane investments on bicycle commute mode shares . All together , we not only make methodological contributions to the fields of discrete choice , statistics , machine learning , and causal inference , but we contribute to the efforts of transportation planners and modelers who are trying to make our cities and regions more sustainable and environmentally friendly . A causal inference perspective on compositional data and collider ‘ bias ’ . Background Compositional data ( CD ) comprise the parts of some whole , for which all parts sum to that whole ; the whole may vary across individual units of analysis or remain fixed . In the present work , we discuss the conceptual framework underlying both approaches and study the relationship between them using theoretical elements and the analysis of a real case study . Furthermore , we show that the meta-analytic approach can be embedded within a causal-inference framework on the one hand and that it can be heuristically justified why surrogate endpoints successfully evaluated using this approach will often be appealing from a causal-inference perspective as well , on the other . A newly developed and user friendly R package Surrogate is provided to carry out the evaluation exercise . In particular , we assume access to a collection { ( Si , li)}i=1 , where each Si is a sample drawn from the probability distribution ofXi × Yi , and li is a binary label indicating whether “ Xi → Yi ” or “ Xi ← Yi ” . Given these data , we build a causal inference rule in two steps . First , we featurize each Si using the kernel mean embedding associated with some characteristic kernel . In particular , we assume access to a collection $ \{(S_i , l_i)\}_{i=1}^n$ , where each $ S_i$ is a sample drawn from the probability distribution of $ X_i \times Y_i$ , and $ l_i$ is a binary label indicating whether " $ X_i \to Y_i$ " or " $ X_i \leftarrow Y_i$ " . Given these data , we build a causal inference rule in two steps . First , we featurize each $ S_i$ using the kernel mean embedding associated with some characteristic kernel . While joiners and converts have a shorter lifespan in the community in comparison to the veterans , joiners are more active during their shorter tenure , becoming increasingly engaged over time . Finally , to investigate how these events affect users ’ conspiratorial discussions , we opted a causal inference based approach to analyze user comments around the time of the events . We find that discussions happening after the event exhibit signs of emotional shock , increased language complexity , and simultaneous expressions of certainty and doubtfulness . We propose a neurally-inspired , causal , sparse inference scheme based on the Locally Competitive Algorithm ( LCA ) over a temporal-spectral neighborhood . We demonstrate that this causal inference scheme can achieve lower sparsity levels and better signal fidelity than current filter and threshold approaches . Additionally , for some regimes , the sparsity level approaches those of Matching Pursuit while still maintaining signal integrity . Abstract The basic Harris Hawks optimization algorithm can not take full advantage of the information sharing capability of the Harris Hawks while cooperatively searching for prey , and it is difficult to balance the exploration and development capacities of this algorithm . These factors limit the Harris Hawks optimization algorithm , such as in terms of premature convergence and ease of falling into a local optimum . To this end , an improved Harris Hawks optimization algorithm based on information exchange is proposed to optimize the continuous function and its application to engineering problems . The utilized time-series data included water demand , reservoir storage . In this article , a model based on the Harris Hawks Optimization Algorithm ( HHO ) was developed for the optimization of the water distribution network . The analysis showed that the best solutions achieved by the Harris Hawks Optimization Algorithm ( HHO ) were 35,508 $ . The results revealed that the HHO algorithm was well in the optimal design of water supply networks problem . Abstract The first powerful variant of the Harris hawks optimization ( HHO ) is proposed in this work . HHO is a recently developed swarm-based stochastic algorithm that has previously shown excellent performance . However , it easily get trapped into local search space for constrained engineering optimization problems . In order to accelerate the global search phase of existing Harris Hawks optimizer and to stuck it out of local search space , the proposed research aims to explore the exploration phase of the existing optimizer , the hybrid variant of Harris Hawks optimizer has been developed using sine cosine algorithm and named as Hybrid Harris Hawks-Sine Cosine Algorithm ( hHHO-SCA ) The effectiveness of the proposed optimizer has been tested for various nonlinear , non-convex and highly constrained engineering design problem . In order to validate the results of the proposed algorithm , 65 standard benchmark problems including CEC2017 , CEC2018 and eleven multidisciplinary engineering design optimization problems has been taken into consideration . RDR works on finding the particles that could n’t find better solutions within a consecutive number of iterations , and then moving those particles towards the best solutions so far . The performance of IMPA has been validated on nine chest X-Ray images with threshold levels between 10 and 100 and compared with five state-of-art algorithms : equilibrium optimizer ( EO ) , whale optimization algorithm ( WOA ) , sine cosine algorithm ( SCA ) , Harris-hawks algorithm ( HHA ) , and salp swarm algorithms ( SSA ) . The experimental results demonstrate that the proposed hybrid model outperforms all other algorithms for a range of metrics . Abstract This paper presents an enhanced Harris Hawks Optimizer ( HHO ) to tackle global optimization and determine the optimal threshold values for multi-level image segmentation problems . HHO is a new swarm-based metaheuristic technique that simulates the behaviors of Harris hawks during the process of catching the rabbits . Abstract In this paper , a novel hybrid optimization algorithm is introduced by hybridizing a Harris hawks optimization algorithm(HHO ) and simulated annealing for the purpose of accelerating its glo ... In this paper , a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism ( DHHO/M ) is proposed . Compared with the original Harris hawks optimization ( HHO ) , the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability . For intensive justification , the modified harris hawks optimizer reconfiguration approach is validated based on an experimental work for 9 × 9 photovoltaic array with twelve shade patterns during a day from 8 ante meridiem to 7 post meridiem with one hour step . Comparing the produced power based on the modified harris hawks optimizer arrangements to those of total-cross-tied and harris hawks optimizer schemes over the daily hours proves the superiority of the modified harris hawks optimizer interconnected system in generating the highest values of power with a uniform dispersion for the shadow and smooth characteristics with saving the energy by 12.569 % compared with that produced by total-cross-tied across the day hours . The TTUR convergence carries over to the Adam stochastic optimiza-tion , which can be described by a second order differential equation . Experiments show that TTUR improves learning for original GANs , Wasserstein GANs , deep convolutional GANs , and boundary equilibrium GANs . TTUR is compared to conventional GAN training on MNIST , CelebA , Billion Word Benchmark , and LSUN bedrooms . However , the convergence of GAN training has still not been proved . We propose a two time-scale update rule ( TTUR ) for training GANs with stochastic gradient descent on arbitrary GAN loss functions . TTUR has an individual learning rate for both the discriminator and the generator . Using the theory of stochastic approximation , we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium . The convergence carries over to the popular Adam optimization , for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape . However , the convergence of GAN training has still not been proved . We propose a two time-scale update rule ( TTUR ) for training GANs with stochastic gradient descent on arbitrary GAN loss functions . TTUR has an individual learning rate for both the discriminator and the generator . For the evaluation of the performance of GANs at image generation , we introduce the " Frechet Inception Distance " ( FID ) which captures the similarity of generated images to real ones better than the Inception Score . In experiments , TTUR improves learning for DCGANs and Improved Wasserstein GANs ( WGAN-GP ) outperforming conventional GAN training on CelebA , CIFAR-10 , SVHN , LSUN Bedrooms , and the One Billion Word Benchmark . To solve the problems above , we use the dense residual network and the residual networks to construct a generator and a discriminator of the networks Combing the Generative Adversarial Networks and Residual Dense Networks ( RDGAN ) , respectively , and use the spectrum normalization model to constrain the GAN networks which can prevent the parameter size . To avoid gradient anomaly , combining with the TTUR optimization strategy , we design and implement several simulation experiments on the 102 Category Flower Dataset . Experimental results show that our method is superior to most existing methods in most cases . In Appendix [ reference ] , we investigate the impact of discriminator output dimension on the performance of repulsive loss . Hyper - parameters : We used Adam optimizer ( ) with momentum parameters , ; two - timescale update rule ( TTUR ) ( ) with two learning rates chosen from ( 16 combinations in total ) ; and batch size . Fine - tuning on learning rates may improve the model performance , but constant learning rates were used for simplicity . Abstract Harris hawks optimizer ( HHO ) is a recently developed , efficient meta-heuristic optimization approach , which is inspired by the chasing style and collaborative behavior of Harris hawks in nature . However , for some optimization cases , the algorithm suffers from an immature balance between exploitation and exploration . Abstract This paper proposes a reliable approach-based Harris Hawks Optimizer ( HHO ) to evaluate the optimal parameters of the Proportional-Integral ( PI ) controller simulating load frequency control ( LFC ) incorporated in a multi-interconnected system with renewable energy sources ( RESs ) . During the optimization process , the integral time absolute error ( ITAE ) of the frequency and tie-line power is handled as the objective function . This paper presents an application of Harris Hawks Algorithm ( HHA ) as an intelligent method for optimal tuning of integral classical controllers to design the appropriate load frequency controllers . A mathematically model is presented for investigating the optimized controller parameters and enhancing the system performance . We use a mathematical framework using discrete-time dynamical system theory to analyze the stability and convergence of the proposed method . We provide frequency domain analysis for classifier parameters to investigate the achieved improvement of the XCS algorithm , employing a two-step update rule in the transient and steady-state stages of learning . An experimental analysis is performed to learn to solve a multiplexer benchmark problem to compare the results of the proposed update rules with the original XCS . In addition to multimodal similarity learning for text and image features of AttnGAN , cosine and feature matching losses of real and generated images are included while employing a classification loss for " significant attributes " . In order to improve the stability of the training and solve the issue of model collapse , spectral normalization and two-time scale update for the discriminator are used together with instance noise . Our experiments show that e-AttnGAN outperforms state-of-the-art methods on the FashionGen and DeepFashion-Synthesis datasets . In addition to multimodal similarity learning for text and image features of AttnGAN [ 1 ] , similarity and feature matching losses between real and generated images are included while employing classification losses for “ significant attributes ” . In order to improve the stability of the training and solve the mode collapse issue , spectral normalization and two-time scale update rule are used for the discriminator together with instance noise . Our experiments show that e-AttnGAN outperforms state-of-the-art methods using the FashionGen and DeepFashion-Synthesis datasets in terms of inception score , R-precision and classification accuracy . document : GANs Trained by a Two Time - Scale Update Rule Converge to a Local Nash Equilibrium Generative Adversarial Networks ( GANs ) excel at creating realistic images with complex models for which maximum likelihood is infeasible .
The feedback load is an important parameter with a large impact in Orthogonal Frequency Division Multiple Access ( OFDMA ) techniques , as a feedback over each one of the subcarriers will induce a non-acceptable signaling load in the system , so that some optimization and feedback reduction strategies are required to meet practical systems demands . The decision over the best strategy for feedback optimization highly depends on the number of available users in the system .
The efficient and robust realization of such large , highly-dynamic and complex networking environments is a challenging algorithmic and technological task . In this work we present and discuss two protocols for efficient and robust data propagation in wireless sensor networks : LTP ( a “ local target ” optimization protocol ) and PFR ( a multi-path probabilistic forwarding protocol ) . Furthermore , we present the NanoPeers architecture paradigm , a peer-to-peer network of lightweight devices , lacking all or most of the capabilities of their computer-world counterparts .
Clustering is effective as a technique for discerning the structure of and unraveling the complex relationship between massive amounts of data . An improved technique which combines chaotic map particle swarm optimization with an acceleration strategy is proposed , since results of one of the most used clustering algorithm , K-means can be jeopardized by improper choices made in the initializing stage . Accelerated chaotic particle swarm optimization ( ACPSO ) searches through arbitrary data sets for appropriate cluster centers and can effectively and efficiently find better solutions .
However , it faces the same challenges of noise and complexity of images . In this paper , inspired by our earlier research on optimizing parameter settings for CBIR using an evolutionary algorithm [2][3 ] , we introduce a Genetic Algorithm-based optimization step in our spotting algorithm to improve each spotting result . Experiments using an image dataset of journal pages reveal promising performance , in that the precision is significantly improved but without compromising the recall of the overall spotting result .
We propose a parallel sequential Monte Carlo optimization method to minimize cost functions which are computed as the sum of many component functions . The proposed scheme is a stochastic zeroth order optimization algorithm which uses only evaluations of small subsets of component functions to collect information from the problem .
This thesis considers di erent methods of utilising the contextual information on webpages and ads in order to improve the tting of a Bayesian Poisson model to historic data using L-BFGS . The data and optimization algorithm is provided by Admeta , an advertising optimization company that uses the model for click-rate predictions . The di erent methods tried to get added contextual information include categorization and developing di erent similarity measures between web-pages and ads using keywords .
RTL to GDSII design flow is briefly described from environment setting , constraint checking , clock plan , synthesis , floorplan and power plan , placement and optimization with DFT , clock tree synthesis , post-CTS optimization , routing and optimization , physical verification , RC extraction , static timing analysis , functional verification , formal check , automatic test pattern generation , etc .
Traditionally , standard chemoinformatics methods ignored this problem and focused on the sequential optimization of each single biological or chemical property ( ie , a single objective ) . This approach , known as single-objective optimization ( SOOP ) , strives to discover a single optimal solution to the optimization problem . Implicitly , SOOP-based methods assume that the optimal solution for an objective will also be the optimum for any other objectives involved in the profiling of a molecule .
Circulating cortisol is contained in a single body compartment with an apparent volume of distribution ( 5.3 plus or minus 0.55 liters/m2 ) from which elimination occurs by first-order metabolic clearance . The biological half-life averaged 0.96 plus or minus 0.18 h. Upon least-square optimization of selected kinetic parameters , the circadian-episodic model increases the accountable variation ( r2 x 100 ) to 89 % in comparison with the 35 % obtained by use of only a periodic function .
Although such MDPs arise naturally in many practical applications , they are often difficult to solve exactly due to the enormous size of the state space of the complete system , which grows exponentially with the number of components . In this paper , we propose an approximate solution approach to decomposable MDPs that is based on re-solving a fluid linear optimization formulation of the problem at each decision epoch . This formulation tractably approximates the problem by modeling transition behavior at the level of the individual components rather than the complete system .
Furthermore , this is a much less noisy update since the target distribution is exact and deterministic . Therefore , unless all the examples exclusively belong to a specific class with probability 1 , the optimization will act much differently and train with greatly improved supervision . The idea proposed in the recent work by [ reference ] might be considered as an application of this framework , where they try to obtain a good set ofỹ 's by training very large models and using the model prediction distributions of those .
In this work , we study feature learning techniques for graph - structured inputs . Our starting point is previous work on Graph Neural Networks scarselli2009graph , which we modify to use gated recurrent units and modern optimization techniques and then extend to output sequences . The result is a flexible and broadly useful class of neural network models that has favorable inductive biases relative to purely sequence - based models ( e.g. , LSTMs ) when the problem is graph - structured .
In this paper , we survey a range of classical objective functions that have been widely used to train linear models for structured prediction and apply them to neural sequence to sequence models . Our experiments show that these losses can perform surprisingly well by slightly outperforming beam search optimization in a like for like setup . We also report new state of the art results on both IWSLT’14 German - English translation as well as Gigaword abstractive summarization .
subsection : Comparison to Beam - Search Optimization Next , we compare classical sequence - level training to the recently proposed Beam Search Optimization wiseman2016acl . To enable a fair comparison , we re - implement their baseline , a single layer LSTM encoder / decoder model with 256 - dimensional hidden layers and word embeddings as well as attention and input feeding luong2015effective .
4 - Step Alternating Training . In this paper , we adopt a pragmatic 4 - step training algorithm to learn shared features via alternating optimization . In the first step , we train the RPN as described in Section [ reference ] .
Given that our optimization objective combines two translation systems , this would require generating an n - best list for first and , for each entry on it , generating a new n - best list with , yielding a combined n - best list with entries . So as to make it more efficient , we propose an alternating optimization approach where we fix the parameters of one model and optimize the other with standard MERT . Thanks to this , we do not need to expand the search space of the fixed model , so we can do with an n - best list of entries alone .
The predicted residuals at each level are used to efficiently reconstruct the HR image through upsampling and addition operations . While the proposed LapSRN consists of a set of cascaded sub - networks , we train the network with a robust Charbonnier loss function in an end - to - end fashion ( i.e. , without stage - wise optimization ) . As depicted in Figure [ reference ] ( e ) , our network architecture naturally accommodates deep supervision ( i.e. , supervisory signals can be applied simultaneously at each level of the pyramid ) .
They found that features lower in the unrolled hierarchy cluster low - level concepts , while features later in the hierarchy capture high - level concepts . Our method differs in that we use convolutional neural networks rather than unrolling a sparse coding optimization . In the context of unsupervised clustering [ 35 ] exploited agglomerative clustering as a regularizer ; this approach was formulated as a recurrent network .
Gucluturk et al . train a feed - forward CNN with perceptual loss for sketch inversion . Yeh et al . apply DCGAN to semantic face inpainting in an optimization manner . section : Deep CNNs for Identity - aware Attribute Transfer
However , obtaining these 2D - 3D correspondences from local patches is difficult and the output is typically very noisy for these methods . A robust optimization is then needed to estimate the pose . In this paper , we argue for a " holistic " approach , in the sense that we predict the pose of an object directly from its appearance , instead of identifying its individual surface points .
We do not employ style losses in our method . While initially style transfer was obtained by a slow optimization process styletransfer , recently , the emphasis was put on feed - forward methods ulyanov16texture , Johnson2016Perceptual . There are many links between style transfer and our work : both are unsupervised and generate a sample under constancy given an input sample .
incorporate long range matching into the traditional optical flow objective function . This approach of combining image matching to capture large motions , with a variational or discrete optimization for fine motions , can produce accurate results . Of course spatial pyramids are widely used in other areas of computer vision and have recently been used in deep neural networks to learn generative image models .
GPN also introduces a local greedy inference algorithm by assuming independence among person detections for producing optimal multi - person joint configurations . This local optimization strategy reduces the search space of the graph partition problem for finding optimal poses , avoiding the high joint partition complexity challenging the bottom - up strategy . Moreover , the local greedy inference algorithm exploits reliable global affinity cues from the embedding space for inferring joint configurations within robust person detections , leading to performance improvement .
Intervention " has been widely used for recognizing a causal relation ontologically . In this paper , we propose a causal inference framework for visual reasoning via docalculus . To study the intervention effects on pixel-level features for causal reasoning , we introduce pixel-wise masking and adversarial perturbation .
We developed an efficient and robust computational framework to perform Bayesian model comparison of causal inference strategies , which incorporates a number of alternative assumptions about the observers . With this framework , we investigated whether human observers ’ performance in an explicit cause attribution and an implicit heading discrimination task can be modeled as a causal inference process . In the explicit causal inference task , all subjects accounted for cue disparity when reporting judgments of common cause , although not necessarily all in a Bayesian fashion .
Here , we conduct a large-scale data study of exercise and sleep effects through an analysis of 8 months of exercise and sleep data for 20 k users , combined with search query logs , location information and aggregated social media data . We analyze factors correlated with better sleep and more effective exercise , and confirm these relationships through causal inference analysis . Further , we build linear models to predict individuals ’ sleep and exercise quality .
Its purpose is to evaluate the extent to which any given reserve has achieved its stated objectives . MAREA provides specific guidance on data collection and formatting , and then conducts rigorous causal inference analysis based on data input by the user , providing real-time outputs about the effectiveness of the reserve . MAREA ’s ease of use , standardization of state-of-the-art inference methods , and ability to analyze marine reserve effectiveness across ecological , socioeconomic , and governance objectives could dramatically further our understanding and support of effective marine reserve management .
Causal Techniques for conducting these tests , which include the elicited constraint information are described . Overviews of Morphological Analysis , Causal Inference Theory and Statistical Testing Approaches are provided in the context of a Bayesian simulation of an example problem .
Existing theories that justify these techniques are well developed but either require exact matching , which is usually infeasible in practice , or sacrifice some simplicity via asymptotic theory , specialized bias corrections , and novel variance estimators ; and extensions to approximate matching with multicategory treatments have not yet appeared . As an alternative , we show how conceptualizing continuous variables as having logical breakpoints ( such as phase transitions when measuring temperature or high school or college degrees in years of education ) is both natural substantively and can be used to simplify causal inference theory . The result is a finite sample theory that is widely applicable , simple to understand , and easy to implement by using matching to preprocess the data , after which one can use whatever method would have been applied without matching .
Methods ESC-DAGs operates on empirical studies identified by a literature search , ideally a novel systematic review or review of systematic reviews . It involves three key stages : ( i ) the conclusions of each study are ‘ mapped ’ into a DAG ; ( ii ) the causal structures in these DAGs are systematically assessed using several causal inference principles and are corrected accordingly ; ( iii ) the resulting DAGs are then synthesised into one or more ‘ integrated DAGs ’ . This demonstration article didactically applies ESC-DAGs to the literature on parental influences on offspring alcohol use during adolescence .
The spurious relationships and non-interpretable or wrong evidence hinder many data mining applications , especially in medical and social science areas . We have been using some well-known causal inference principles to discover causal relationships and building causally interpretable data mining models , such as causal rules [ 2 ] and causal decision trees [ 3 ] , and applying causal discovery methods to real world biological problems [ 4 , 5 ] . In this talk , I will discuss some of our exploratory work in this promising direction .
This result has direct consequences for the design and analysis of large experiments — for instance , in online social platforms — where the belief is that large sample sizes automatically guarantee small variance . More broadly , our results suggest that although strategies for causal inference in the presence of interference borrow their formalism and main concepts from the traditional causal inference literature , much of the intuition from the no-interference case do not easily transfer to the interference setting .
In particular , we use parametric link functions from statistics to better model the adoption and abandonment of bicycling . From machine learning , we use decision trees to represent the non-compensatory decision protocols that individuals appear to follow when deciding whether to commute by bicycle , and we use diagrams from the causal inference literature to gain insight into how we can bet- ter model the effects of bike lane investments on bicycle commute mode shares . All together , we not only make methodological contributions to the fields of discrete choice , statistics , machine learning , and causal inference , but we contribute to the efforts of transportation planners and modelers who are trying to make our cities and regions more sustainable and environmentally friendly .
A causal inference perspective on compositional data and collider ‘ bias ’ . Background Compositional data ( CD ) comprise the parts of some whole , for which all parts sum to that whole ; the whole may vary across individual units of analysis or remain fixed .
In the present work , we discuss the conceptual framework underlying both approaches and study the relationship between them using theoretical elements and the analysis of a real case study . Furthermore , we show that the meta-analytic approach can be embedded within a causal-inference framework on the one hand and that it can be heuristically justified why surrogate endpoints successfully evaluated using this approach will often be appealing from a causal-inference perspective as well , on the other . A newly developed and user friendly R package Surrogate is provided to carry out the evaluation exercise .
In particular , we assume access to a collection { ( Si , li)}i=1 , where each Si is a sample drawn from the probability distribution ofXi × Yi , and li is a binary label indicating whether “ Xi → Yi ” or “ Xi ← Yi ” . Given these data , we build a causal inference rule in two steps . First , we featurize each Si using the kernel mean embedding associated with some characteristic kernel .
In particular , we assume access to a collection $ \{(S_i , l_i)\}_{i=1}^n$ , where each $ S_i$ is a sample drawn from the probability distribution of $ X_i \times Y_i$ , and $ l_i$ is a binary label indicating whether " $ X_i \to Y_i$ " or " $ X_i \leftarrow Y_i$ " . Given these data , we build a causal inference rule in two steps . First , we featurize each $ S_i$ using the kernel mean embedding associated with some characteristic kernel .
While joiners and converts have a shorter lifespan in the community in comparison to the veterans , joiners are more active during their shorter tenure , becoming increasingly engaged over time . Finally , to investigate how these events affect users ’ conspiratorial discussions , we opted a causal inference based approach to analyze user comments around the time of the events . We find that discussions happening after the event exhibit signs of emotional shock , increased language complexity , and simultaneous expressions of certainty and doubtfulness .
We propose a neurally-inspired , causal , sparse inference scheme based on the Locally Competitive Algorithm ( LCA ) over a temporal-spectral neighborhood . We demonstrate that this causal inference scheme can achieve lower sparsity levels and better signal fidelity than current filter and threshold approaches . Additionally , for some regimes , the sparsity level approaches those of Matching Pursuit while still maintaining signal integrity .
Abstract The basic Harris Hawks optimization algorithm can not take full advantage of the information sharing capability of the Harris Hawks while cooperatively searching for prey , and it is difficult to balance the exploration and development capacities of this algorithm . These factors limit the Harris Hawks optimization algorithm , such as in terms of premature convergence and ease of falling into a local optimum . To this end , an improved Harris Hawks optimization algorithm based on information exchange is proposed to optimize the continuous function and its application to engineering problems .
The utilized time-series data included water demand , reservoir storage . In this article , a model based on the Harris Hawks Optimization Algorithm ( HHO ) was developed for the optimization of the water distribution network . The analysis showed that the best solutions achieved by the Harris Hawks Optimization Algorithm ( HHO ) were 35,508 $ . The results revealed that the HHO algorithm was well in the optimal design of water supply networks problem .
Abstract The first powerful variant of the Harris hawks optimization ( HHO ) is proposed in this work . HHO is a recently developed swarm-based stochastic algorithm that has previously shown excellent performance .
However , it easily get trapped into local search space for constrained engineering optimization problems . In order to accelerate the global search phase of existing Harris Hawks optimizer and to stuck it out of local search space , the proposed research aims to explore the exploration phase of the existing optimizer , the hybrid variant of Harris Hawks optimizer has been developed using sine cosine algorithm and named as Hybrid Harris Hawks-Sine Cosine Algorithm ( hHHO-SCA ) The effectiveness of the proposed optimizer has been tested for various nonlinear , non-convex and highly constrained engineering design problem . In order to validate the results of the proposed algorithm , 65 standard benchmark problems including CEC2017 , CEC2018 and eleven multidisciplinary engineering design optimization problems has been taken into consideration .
RDR works on finding the particles that could n’t find better solutions within a consecutive number of iterations , and then moving those particles towards the best solutions so far . The performance of IMPA has been validated on nine chest X-Ray images with threshold levels between 10 and 100 and compared with five state-of-art algorithms : equilibrium optimizer ( EO ) , whale optimization algorithm ( WOA ) , sine cosine algorithm ( SCA ) , Harris-hawks algorithm ( HHA ) , and salp swarm algorithms ( SSA ) . The experimental results demonstrate that the proposed hybrid model outperforms all other algorithms for a range of metrics .
Abstract This paper presents an enhanced Harris Hawks Optimizer ( HHO ) to tackle global optimization and determine the optimal threshold values for multi-level image segmentation problems . HHO is a new swarm-based metaheuristic technique that simulates the behaviors of Harris hawks during the process of catching the rabbits .
Abstract In this paper , a novel hybrid optimization algorithm is introduced by hybridizing a Harris hawks optimization algorithm(HHO ) and simulated annealing for the purpose of accelerating its glo ...
In this paper , a novel satellite image segmentation technique based on dynamic Harris hawks optimization with a mutation mechanism ( DHHO/M ) is proposed . Compared with the original Harris hawks optimization ( HHO ) , the dynamic control parameter strategy and mutation operator used in DHHO/M can avoid falling into the local optimum and efficiently enhance the search capability .
For intensive justification , the modified harris hawks optimizer reconfiguration approach is validated based on an experimental work for 9 × 9 photovoltaic array with twelve shade patterns during a day from 8 ante meridiem to 7 post meridiem with one hour step . Comparing the produced power based on the modified harris hawks optimizer arrangements to those of total-cross-tied and harris hawks optimizer schemes over the daily hours proves the superiority of the modified harris hawks optimizer interconnected system in generating the highest values of power with a uniform dispersion for the shadow and smooth characteristics with saving the energy by 12.569 % compared with that produced by total-cross-tied across the day hours .
The TTUR convergence carries over to the Adam stochastic optimiza-tion , which can be described by a second order differential equation . Experiments show that TTUR improves learning for original GANs , Wasserstein GANs , deep convolutional GANs , and boundary equilibrium GANs . TTUR is compared to conventional GAN training on MNIST , CelebA , Billion Word Benchmark , and LSUN bedrooms .
However , the convergence of GAN training has still not been proved . We propose a two time-scale update rule ( TTUR ) for training GANs with stochastic gradient descent on arbitrary GAN loss functions . TTUR has an individual learning rate for both the discriminator and the generator . Using the theory of stochastic approximation , we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium . The convergence carries over to the popular Adam optimization , for which we prove that it follows the dynamics of a heavy ball with friction and thus prefers flat minima in the objective landscape .
However , the convergence of GAN training has still not been proved . We propose a two time-scale update rule ( TTUR ) for training GANs with stochastic gradient descent on arbitrary GAN loss functions . TTUR has an individual learning rate for both the discriminator and the generator .
For the evaluation of the performance of GANs at image generation , we introduce the " Frechet Inception Distance " ( FID ) which captures the similarity of generated images to real ones better than the Inception Score . In experiments , TTUR improves learning for DCGANs and Improved Wasserstein GANs ( WGAN-GP ) outperforming conventional GAN training on CelebA , CIFAR-10 , SVHN , LSUN Bedrooms , and the One Billion Word Benchmark .
To solve the problems above , we use the dense residual network and the residual networks to construct a generator and a discriminator of the networks Combing the Generative Adversarial Networks and Residual Dense Networks ( RDGAN ) , respectively , and use the spectrum normalization model to constrain the GAN networks which can prevent the parameter size . To avoid gradient anomaly , combining with the TTUR optimization strategy , we design and implement several simulation experiments on the 102 Category Flower Dataset . Experimental results show that our method is superior to most existing methods in most cases .
In Appendix [ reference ] , we investigate the impact of discriminator output dimension on the performance of repulsive loss . Hyper - parameters : We used Adam optimizer ( ) with momentum parameters , ; two - timescale update rule ( TTUR ) ( ) with two learning rates chosen from ( 16 combinations in total ) ; and batch size . Fine - tuning on learning rates may improve the model performance , but constant learning rates were used for simplicity .
Abstract Harris hawks optimizer ( HHO ) is a recently developed , efficient meta-heuristic optimization approach , which is inspired by the chasing style and collaborative behavior of Harris hawks in nature . However , for some optimization cases , the algorithm suffers from an immature balance between exploitation and exploration .
Abstract This paper proposes a reliable approach-based Harris Hawks Optimizer ( HHO ) to evaluate the optimal parameters of the Proportional-Integral ( PI ) controller simulating load frequency control ( LFC ) incorporated in a multi-interconnected system with renewable energy sources ( RESs ) . During the optimization process , the integral time absolute error ( ITAE ) of the frequency and tie-line power is handled as the objective function .
This paper presents an application of Harris Hawks Algorithm ( HHA ) as an intelligent method for optimal tuning of integral classical controllers to design the appropriate load frequency controllers . A mathematically model is presented for investigating the optimized controller parameters and enhancing the system performance .
We use a mathematical framework using discrete-time dynamical system theory to analyze the stability and convergence of the proposed method . We provide frequency domain analysis for classifier parameters to investigate the achieved improvement of the XCS algorithm , employing a two-step update rule in the transient and steady-state stages of learning . An experimental analysis is performed to learn to solve a multiplexer benchmark problem to compare the results of the proposed update rules with the original XCS .
In addition to multimodal similarity learning for text and image features of AttnGAN , cosine and feature matching losses of real and generated images are included while employing a classification loss for " significant attributes " . In order to improve the stability of the training and solve the issue of model collapse , spectral normalization and two-time scale update for the discriminator are used together with instance noise . Our experiments show that e-AttnGAN outperforms state-of-the-art methods on the FashionGen and DeepFashion-Synthesis datasets .
In addition to multimodal similarity learning for text and image features of AttnGAN [ 1 ] , similarity and feature matching losses between real and generated images are included while employing classification losses for “ significant attributes ” . In order to improve the stability of the training and solve the mode collapse issue , spectral normalization and two-time scale update rule are used for the discriminator together with instance noise . Our experiments show that e-AttnGAN outperforms state-of-the-art methods using the FashionGen and DeepFashion-Synthesis datasets in terms of inception score , R-precision and classification accuracy .
document : GANs Trained by a Two Time - Scale Update Rule Converge to a Local Nash Equilibrium Generative Adversarial Networks ( GANs ) excel at creating realistic images with complex models for which maximum likelihood is infeasible .
Best-M Feedback Technique in 4G-LTE Systems Efficient Information Propagation Algorithms in Smart Dust and NanoPeer Networks Chaotic particle swarm optimization for data clustering A modular approach for query spotting in document images and its optimization using genetic algorithms Parallel sequential Monte Carlo for stochastic optimization Machine Learning for On-line Advertising Using Contextual Information The Summary of RTL to GDSII Design Flow Molecular optimization using computational multi-objective methods. Partial pharmacodynamic model for the circadian-episodic secretion of cortisol in man. Decomposable Markov Decision Processes: A Fluid Optimization Approach Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling Gated Graph Sequence Neural Networks Classical Structured Prediction Losses for Sequence to Sequence Learning Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks An Effective Approach to Unsupervised Machine Translation Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution Learning Deep Parsimonious Representations Deep Identity-aware Transfer of Facial Attributes BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth Unsupervised Cross-Domain Image Generation Optical Flow Estimation Using a Spatial Pyramid Network Generative Partition Networks for Multi-Person Pose Estimation When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception Modeling behaviors and lifestyle with online and social data for predicting and analyzing sleep and exercise quality A user-friendly tool to evaluate the effectiveness of no-take marine reserves Simulation validation using Causal Inference Theory with morphological constraints How Coarsening Simplifies Matching-Based Causal Inference Theory ∗ Evidence synthesis for constructing directed acyclic graphs (ESC-DAGs): a novel and systematic method for building directed acyclic graphs Beyond Understanding and Prediction: Data Mining for Action Limitations of Design-based Causal Inference and A/B Testing under Arbitrary and Network Interference The Holy Trinity: Blending Statistics, Machine Learning and Discrete Choice, with Applications to Strategic Bicycle Planning OP78 A causal inference perspective on compositional data and collider ‘bias’ On the relationship between the causal-inference and meta-analytic paradigms for the validation of surrogate endpoints. Towards a Learning Theory of Causation Towards a Learning Theory of Cause-Effect Inference Conspiracies Online: User Discussions in a Conspiracy Community Following Dramatic Events A causal Locally Competitive Algorithm for the sparse decomposition of audio signals Harris Hawks optimization with information exchange Optimization of water distribution of network systems using the Harris Hawks optimization algorithm (Case study: Homashahr city) Multi-population differential evolution-assisted Harris hawks optimization: Framework and case studies An intensify Harris Hawks optimizer for numerical and engineering optimization problems A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based Diversity Reduction Strategy A competitive chain-based Harris Hawks Optimizer for global optimization and multi-level image thresholding problems A novel hybrid Harris hawks-simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails Dynamic Harris Hawks Optimization with Mutation Mechanism for Satellite Image Segmentation Optimal photovoltaic array reconfiguration for alleviating the partial shading influence based on a modified harris hawks optimizer Two Time-Scale Update Rule for Generative Adversarial Nets GANs Trained by a Two Time-Scale Update Rule Converge to a Nash Equilibrium GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium Sample Generation Combining Generative Adversarial Networks and Residual Dense Networks Improving MMD-GAN Training with Repulsive Loss Function Opposition-based learning Harris hawks optimization with advanced transition rules: principles and analysis Recent methodology based Harris Hawks optimizer for designing load frequency control incorporated in multi-interconnected renewable energy plants Application of Harris Hawks Algorithm for Frequency Response Enhancement of Two-Area Interconnected Power System with DFIG Based Wind Turbine Two-Step Markov Update Algorithm for Accuracy-Based Learning Classifier Systems Semantically Consistent Hierarchical Text to Fashion Image Synthesis with an Enhanced-Attentional Generative Adversarial Network Semantically consistent text to fashion image synthesis with an enhanced attentional generative adversarial network GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
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