These blocks are solved and discretized separately , while their connection is realized in the discrete system during run-time . In this paper a 2D wave field simulation program based on block based modeling is demonstrated . Elementary block models are solved with the FTM and can be connected together to create complex 2D geometries . It serves as a starting point for the more general idea of block-based physical modeling , where arbitrary discrete-time state space representations can communicate via wave variables . An example in musical acoustics shows the application of block-based modeling to multidimensional physical systems . In this study , the sorting options considered were by location and Fe grade . The effectiveness of each method was evaluated using the block model sub-sets to select the maximum tonnage of ore at a specified target grade . Block model data from an anonymous Pilbara iron ore project with about one million blocks averaging 1.132 kt were used . There have been rapid developments in model-based clustering of graphs , also known as block modelling , over the last ten years or so . We review different approaches and extensions proposed for different aspects in this area , such as the type of the graph , the clustering approach , the inference approach , and whether the number of groups is selected or estimated . We also review models that combine block modelling with topic modelling and/or longitudinal modelling , regarding how these models deal with multiple types of data . How different approaches cope with various issues will be summarised and compared , to facilitate the demand of practitioners for a concise overview of the current status of these areas of literature . Special attention is paid to geometric relationships to provide for easy upgrading and restructuring the blocks . To illustrate the research results there are given the main stages of the block-modelling with the help of Solid Works . The technique is multi-purpose and can be realized by means of other CAD applications , as well as for researching guiding devices of various design concepts To this end we mainly built upon the idea of blockmodels . We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically . We show that in the degree-corrected blockmodel , node degrees display an interesting scaling property , which is reminiscent of what is observed in real-world fractal networks . The cadastral spatial feature models are first defined based on geography markup language . An application schema is then defined , in which the cadastral features are described as CityModel , StreetModel , BlockModel , LandModel and BuildingModel , and the relationships between them are defined as CityMember , StreetMember , BlockMember and Landmember . The creation of a GML document is discussed in details . We propose a dynamic edge exchangeable network model that can capture sparse connections observed in real temporal networks , in contrast to existing models which are dense . The model achieved superior link prediction accuracy on multiple data sets when compared to a dynamic variant of the blockmodel , and is able to extract interpretable time-varying community structures from the data . In addition to sparsity , the model accounts for the effect of social influence on vertices ' future behaviours . Structure of 4-core embedded parallel processor is presented and FPGA validation platform that is called FPEP is built . To evaluate performance of multi-core processor platform , three feasible parallel algorithms with OpenMP on SUSAN that is a classic image processing algorithm : direct parallel SUSAN , image block processing and parallel processing of multiple images are proposed , and performance testing of these three parallel algorithms execute on Intel 4-core platform and FPGA validation platform of FPEP.Experimental results demonstrate that speedup ratio of three parallel algorithms on both 4-Core platforms is approximate 3.0,and speedup ratio of parallel processing of multiple images on FPGA validation platform of FPEP is approximate 4.0 . In order to overcome this problem , it shall be optimized to meet the real-time requirement . A modified real-time imaging tracking algorithm based on the available position prediction relevant tracking algorithm is presented by utilizing the image block processing to decrease the calculation load of traditional correlation tracking algorithm and improve the real-time performance . The modified algorithm was simulated and verified in the experiment . In order to improve the reconstruction speed , some improvement in the selection of the measurement matrix and the design of the reconstruction algorithm is made . The wavelet transform is used to sparse decompose the image , and the very sparse random projection matrix is used as the measurement matrix , after the image block processing we use the OMP algorithm to reconstruct the image . The experimental result shows that this method could reduce the algorithm time and improved the reconstruction speed greatly . Abstract This paper describes a binary image representation scheme , called Image Block Representation and presents new algorithms for a number of basic binary image processing and analysis operations , which are rapidly implemented on block-represented binary images . The main purpose of the Image Block Representation is to provide an efficient binary image representation that permits the execution of operations on image areas instead of image points . This correspondence firstly gives comprehensive study to fast algorithms for geometric moments , including image transform , Delta method , computation via corner points , method based on Green′s theorem and image block representation . Then an efficient fast algorithm for the computation of regular moment is proposed . Some of these binary images are substituted by an ideal image , which is called “ half-intensity ” image . The remaining binary images are represented using the image block representation concept and their moments are computed fast using block techniques . The proposed method computes approximated moment values with an error of 2–3 % from the exact values and operates in real time ( i.e. , video rate ) . We consider the problem associated to recovering the block structure of an Ising model given independent observations on the binary hypercube . This new model , called the Ising blockmodel , is a perturbation of the mean field approximation of the Ising model known as the Curie-Weiss model : the sites are partitioned into two blocks of equal size and the interaction between those of the same block is stronger than across blocks , to account for more order within each block . We study probabilistic , statistical and computational aspects of this model in the high-dimensional case when the number of sites may be much larger than the sample size . spacefromasequence oftwo-dimensional camera images . Thegoals aretodetermine theobjects ' movement towardorawayfromthecamera aswellasleft/right andup/down movementintheimage plane andtobuild models oftheblocks . Themovementinformation isusedaspartofahierarchical matching process that determines thecorrespondence ofblocks between scenes . Based on our previous work on an assembly planning framework using Function Blocks ( FBs ) , a novel approach to assembly planning and control is developed , which enables adaptive decision making besides effective plan execution . Following our previous work , this paper reports the latest development of design and simulation of an FB communication network in Matlab-Simulink environment , and validates the methodology through an example . The final predicted results are inferred by jointly taking both the forward and backward consistencies into account . Moreover , we embed the motion dynamics and the visual content into a single framework via the FB-Net architecture , which significantly differs from learning each component throughout the videos separately . We evaluate our FB-Net on the large-scale KTH and UCF101 datasets . The experiments show that it can introduce considerable margin improvements with respect to most recent leading studies . This paper deals with the development of reconfigurable control systems following the component-based International Industrial Standard IEC61499 . In this standard , a function block is an event-triggered component owning data and an application is a FB network that has to satisfy temporal properties . This network is totally changed or modified if a reconfiguration scenario is applied at run-time . The proposed solution greatly reduces signal swing at the 1st integrator , by not affecting the STF . Thus , it takes the advantages of the FF-topology , by keeping the advantages of the FB-topology . To overcome this constraint , a fuzzy Bayesian belief network ( FBBN ) has been increasingly used for risk assessment . Further project-specific studies based on FBBN are recommended to justify its broader application . Beyond fuzzy methods , the Credal network – an extended form of Bayesian network- is found to have potential for risk assessment under uncertainty . SwiftNet , which is a set of models discovered by GRAM , outperforms MobileNet-V2 by 2.15x higher accuracy density and 2.42x faster with similar accuracy . Compared with FBNet , SwiftNet reduces the search cost by 26x and achieves 2.35x higher accuracy density and 1.47x speedup while preserving similar accuracy . SwiftNetcan obtain 63.28 % top-1 accuracy on ImageNet-1 K with only 53 M MACs and 2.07 M parameters . FBNets ( Facebook-Berkeley-Nets ) , a family of models discovered by DNAS surpass state-of-the-art models both designed manually and generated automatically . FBNet-B achieves 74.1 % top-1 accuracy on ImageNet with 295 M FLOPs and 23.1 ms latency on a Samsung S8 phone , 2.4x smaller and 1.5x faster than MobileNetV2 - 1.3 with similar accuracy . Despite higher accuracy and lower latency than MnasNet , we estimate FBNet-B 's search cost is 420x smaller than MnasNet 's , at only 216 GPU-hours . This is an important property for causality inference since several network unfoldings , as in the case of Dynamic BNs , are not needed . This makes FBNs robust to the underlying model . The proposed method is tested using multivariate autoregressive ( MVAR ) and non-linear ( NL ) systems with the variable model order d. Networks are estimated from the MVAR(d ) and NL(d ) systems directly and also from a magnetoencephalographic (MEG)-simulated environment where beamforming is implemented for source inference . CPU times are used for a variety of purposes : to compare the speed of two different processors , to gauge how CPU-intensive a program is , and to measure the amount of processing time being allocated to different programs in a multitasking environment [ 2 ] . Overview of Fractal Block Coding : In this section we describe a generic fractal block coding scheme based on those in [ 12 ] [ 13 ] , and we provide some heuristic motivation for the scheme . A more complete overview of fractal coding techniques can be found in [ 14 ] [ 15 ] . A video coding method is proposed which is based upon fractal block coding . The method utilizes a novel three-dimensional partitioning of input frames for which a number of efficient block-matching search methods can be used , and permits spatio-temporal splitting of the input blocks to improve overall-encoding quality . Practical realization of the model-based emergency forecasting in the pulse system for the case of multiparameter variation in the admissible wide range associates with the problem of a priori one to one information ( about system dynamics ) forming . The fractal diagram is proposed to use for the purpose of the system dynamics presentation . This diagram allows establishing one to one correspondence between the parameter and phase subspaces by line up stages Practical realization of model-based emergency forecasting in pulse systems for the case of multi-parameter variation in a possible wide range associates with the problem of a-priori forming of single-statement information on system dynamics . The fractal diagram is proposed to use for the purpose of system dynamics presentation . This diagram allows one-to-one correspondence between the both , parameter and phase , spaces establishing by line up stages . Quantization simply causes shifts in estimates . The results suggest that one should not place much reliance in the absolute value of a fractal estimate , but that the estimates do vary monotonically with D and might be useful descriptors in tasks such as image segmentation and description . A basis is provided for an image-analysis algorithm for automatic recognition , considered in this article from the viewpoint of formalizing the process of distinguishing clusters either of constant or similar signal intensity , their number and size , and two-level decision-making . Such an approach is based on the fractal hypothesis , which consists of assuming that the number of constant-intensity pixels in a cluster has a power dependence on its rank and that the pixels in the cluster itself have a fractal spatial distribution . The rationale of the hypothesis is associated with harmony of the variability and stability of the image as a system . This paper introduces a fractal partition class defined over a unit wrapped space . The cells of a fractal partition have self-similarity property and can be used as a set of tiles that can tile Rn space periodically or quasi-periodically with non-uniform tiling density . An algorithm for generating set of fractal cells using Voronoi diagram computation is proposed with several applications in computer graphics and computational chemistry . Self similarity concepts of image compression are used in fractal video compression . However selfsimilarity means that fractal picture is consists of duplicates of itself that are interpreted and indicated by a change . More computational complexity is present in fractal video compression for reducing this complexity different technique has been implemented . This paper proposes the fractal features classification for liver biopsy images using probabilistic neural network ( PNN ) . Fractal set has the properties of self-similarity and self-affinity . It can be used to estimate the fractal dimension ( FD ) from two-dimensional ( 2D ) images , including the normal and cancerous liver tissue images . We study a map of the Internet ( at the autonomous systems level ) , by introducing and using the method of k-shell decomposition and the methods of percolation theory and fractal geometry , to find a model for the structure of the Internet . In particular , our analysis uses information on the connectivity of the network shells to separate , in a unique ( no parameters ) way , the Internet into three subcomponents : ( i ) a nucleus that is a small ( ≈100 nodes ) , very well connected globally distributed subgraph ; ( ii ) a fractal subcomponent that is able to connect the bulk of the Internet without congesting the nucleus , with self-similar properties and critical exponents predicted from percolation theory ; and ( iii ) dendrite-like structures , usually isolated nodes that are connected to the rest of the network through the nucleus only . We show that our method of decomposition is robust and provides insight into the underlying structure of the Internet and its functional consequences . This leads to new organizational procedures and manufacturing systems . Their characteristics are : autonomous manufacturing cells , fractal units , adaptive systems , dynamic and agile management , self-optimization and self-control , customer-specific order management , communication networks . Particulary “ manufacturing on demand ” and the order management in production networks require new methods . Section [ reference ] reviews this large body of related techniques . Hybrid designs could certainly integrate any of them with a fractal architecture ; we leave open the question of the degree to which such hybrids are synergistic . Our main contribution is twofold : We introduce FractalNet , the first simple alternative to ResNet . The channel count corresponds to the size of the filter set in the preceding layer . As the fractal is expanded , we collapse neighboring joins into a single layer which spans multiple columns , as shown on the right side of Figure [ reference ] . The layer merges all of its input feature blobs into a single output blob . Contrast with plain networks , which are not trainable if made too deep ( Table [ reference ] ) . table Fractal structure as a training apparatus ( CIFAR - 100 ++ ) . Plain networks perform well if moderately deep , but exhibit worse convergence during training if instantiated with great depth . In other words , both of the spatial dimensions and feature dimensions remain the same across all intermediate layers . To render Figure [ reference ] ( b ) equivalent to a ResNet with residual blocks , one possible technique is to make : be the input image or a function of . , and . Thus , the state transition becomes .
These blocks are solved and discretized separately , while their connection is realized in the discrete system during run-time . In this paper a 2D wave field simulation program based on block based modeling is demonstrated . Elementary block models are solved with the FTM and can be connected together to create complex 2D geometries .
It serves as a starting point for the more general idea of block-based physical modeling , where arbitrary discrete-time state space representations can communicate via wave variables . An example in musical acoustics shows the application of block-based modeling to multidimensional physical systems .
In this study , the sorting options considered were by location and Fe grade . The effectiveness of each method was evaluated using the block model sub-sets to select the maximum tonnage of ore at a specified target grade . Block model data from an anonymous Pilbara iron ore project with about one million blocks averaging 1.132 kt were used .
There have been rapid developments in model-based clustering of graphs , also known as block modelling , over the last ten years or so . We review different approaches and extensions proposed for different aspects in this area , such as the type of the graph , the clustering approach , the inference approach , and whether the number of groups is selected or estimated . We also review models that combine block modelling with topic modelling and/or longitudinal modelling , regarding how these models deal with multiple types of data . How different approaches cope with various issues will be summarised and compared , to facilitate the demand of practitioners for a concise overview of the current status of these areas of literature .
Special attention is paid to geometric relationships to provide for easy upgrading and restructuring the blocks . To illustrate the research results there are given the main stages of the block-modelling with the help of Solid Works . The technique is multi-purpose and can be realized by means of other CAD applications , as well as for researching guiding devices of various design concepts
To this end we mainly built upon the idea of blockmodels . We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically . We show that in the degree-corrected blockmodel , node degrees display an interesting scaling property , which is reminiscent of what is observed in real-world fractal networks .
The cadastral spatial feature models are first defined based on geography markup language . An application schema is then defined , in which the cadastral features are described as CityModel , StreetModel , BlockModel , LandModel and BuildingModel , and the relationships between them are defined as CityMember , StreetMember , BlockMember and Landmember . The creation of a GML document is discussed in details .
We propose a dynamic edge exchangeable network model that can capture sparse connections observed in real temporal networks , in contrast to existing models which are dense . The model achieved superior link prediction accuracy on multiple data sets when compared to a dynamic variant of the blockmodel , and is able to extract interpretable time-varying community structures from the data . In addition to sparsity , the model accounts for the effect of social influence on vertices ' future behaviours .
Structure of 4-core embedded parallel processor is presented and FPGA validation platform that is called FPEP is built . To evaluate performance of multi-core processor platform , three feasible parallel algorithms with OpenMP on SUSAN that is a classic image processing algorithm : direct parallel SUSAN , image block processing and parallel processing of multiple images are proposed , and performance testing of these three parallel algorithms execute on Intel 4-core platform and FPGA validation platform of FPEP.Experimental results demonstrate that speedup ratio of three parallel algorithms on both 4-Core platforms is approximate 3.0,and speedup ratio of parallel processing of multiple images on FPGA validation platform of FPEP is approximate 4.0 .
In order to overcome this problem , it shall be optimized to meet the real-time requirement . A modified real-time imaging tracking algorithm based on the available position prediction relevant tracking algorithm is presented by utilizing the image block processing to decrease the calculation load of traditional correlation tracking algorithm and improve the real-time performance . The modified algorithm was simulated and verified in the experiment .
In order to improve the reconstruction speed , some improvement in the selection of the measurement matrix and the design of the reconstruction algorithm is made . The wavelet transform is used to sparse decompose the image , and the very sparse random projection matrix is used as the measurement matrix , after the image block processing we use the OMP algorithm to reconstruct the image . The experimental result shows that this method could reduce the algorithm time and improved the reconstruction speed greatly .
Abstract This paper describes a binary image representation scheme , called Image Block Representation and presents new algorithms for a number of basic binary image processing and analysis operations , which are rapidly implemented on block-represented binary images . The main purpose of the Image Block Representation is to provide an efficient binary image representation that permits the execution of operations on image areas instead of image points .
This correspondence firstly gives comprehensive study to fast algorithms for geometric moments , including image transform , Delta method , computation via corner points , method based on Green′s theorem and image block representation . Then an efficient fast algorithm for the computation of regular moment is proposed .
Some of these binary images are substituted by an ideal image , which is called “ half-intensity ” image . The remaining binary images are represented using the image block representation concept and their moments are computed fast using block techniques . The proposed method computes approximated moment values with an error of 2–3 % from the exact values and operates in real time ( i.e. , video rate ) .
We consider the problem associated to recovering the block structure of an Ising model given independent observations on the binary hypercube . This new model , called the Ising blockmodel , is a perturbation of the mean field approximation of the Ising model known as the Curie-Weiss model : the sites are partitioned into two blocks of equal size and the interaction between those of the same block is stronger than across blocks , to account for more order within each block . We study probabilistic , statistical and computational aspects of this model in the high-dimensional case when the number of sites may be much larger than the sample size .
spacefromasequence oftwo-dimensional camera images . Thegoals aretodetermine theobjects ' movement towardorawayfromthecamera aswellasleft/right andup/down movementintheimage plane andtobuild models oftheblocks . Themovementinformation isusedaspartofahierarchical matching process that determines thecorrespondence ofblocks between scenes .
Based on our previous work on an assembly planning framework using Function Blocks ( FBs ) , a novel approach to assembly planning and control is developed , which enables adaptive decision making besides effective plan execution . Following our previous work , this paper reports the latest development of design and simulation of an FB communication network in Matlab-Simulink environment , and validates the methodology through an example .
The final predicted results are inferred by jointly taking both the forward and backward consistencies into account . Moreover , we embed the motion dynamics and the visual content into a single framework via the FB-Net architecture , which significantly differs from learning each component throughout the videos separately . We evaluate our FB-Net on the large-scale KTH and UCF101 datasets . The experiments show that it can introduce considerable margin improvements with respect to most recent leading studies .
This paper deals with the development of reconfigurable control systems following the component-based International Industrial Standard IEC61499 . In this standard , a function block is an event-triggered component owning data and an application is a FB network that has to satisfy temporal properties . This network is totally changed or modified if a reconfiguration scenario is applied at run-time .
The proposed solution greatly reduces signal swing at the 1st integrator , by not affecting the STF . Thus , it takes the advantages of the FF-topology , by keeping the advantages of the FB-topology .
To overcome this constraint , a fuzzy Bayesian belief network ( FBBN ) has been increasingly used for risk assessment . Further project-specific studies based on FBBN are recommended to justify its broader application . Beyond fuzzy methods , the Credal network – an extended form of Bayesian network- is found to have potential for risk assessment under uncertainty .
SwiftNet , which is a set of models discovered by GRAM , outperforms MobileNet-V2 by 2.15x higher accuracy density and 2.42x faster with similar accuracy . Compared with FBNet , SwiftNet reduces the search cost by 26x and achieves 2.35x higher accuracy density and 1.47x speedup while preserving similar accuracy . SwiftNetcan obtain 63.28 % top-1 accuracy on ImageNet-1 K with only 53 M MACs and 2.07 M parameters .
FBNets ( Facebook-Berkeley-Nets ) , a family of models discovered by DNAS surpass state-of-the-art models both designed manually and generated automatically . FBNet-B achieves 74.1 % top-1 accuracy on ImageNet with 295 M FLOPs and 23.1 ms latency on a Samsung S8 phone , 2.4x smaller and 1.5x faster than MobileNetV2 - 1.3 with similar accuracy . Despite higher accuracy and lower latency than MnasNet , we estimate FBNet-B 's search cost is 420x smaller than MnasNet 's , at only 216 GPU-hours .
This is an important property for causality inference since several network unfoldings , as in the case of Dynamic BNs , are not needed . This makes FBNs robust to the underlying model . The proposed method is tested using multivariate autoregressive ( MVAR ) and non-linear ( NL ) systems with the variable model order d. Networks are estimated from the MVAR(d ) and NL(d ) systems directly and also from a magnetoencephalographic (MEG)-simulated environment where beamforming is implemented for source inference .
CPU times are used for a variety of purposes : to compare the speed of two different processors , to gauge how CPU-intensive a program is , and to measure the amount of processing time being allocated to different programs in a multitasking environment [ 2 ] . Overview of Fractal Block Coding : In this section we describe a generic fractal block coding scheme based on those in [ 12 ] [ 13 ] , and we provide some heuristic motivation for the scheme . A more complete overview of fractal coding techniques can be found in [ 14 ] [ 15 ] .
A video coding method is proposed which is based upon fractal block coding . The method utilizes a novel three-dimensional partitioning of input frames for which a number of efficient block-matching search methods can be used , and permits spatio-temporal splitting of the input blocks to improve overall-encoding quality .
Practical realization of the model-based emergency forecasting in the pulse system for the case of multiparameter variation in the admissible wide range associates with the problem of a priori one to one information ( about system dynamics ) forming . The fractal diagram is proposed to use for the purpose of the system dynamics presentation . This diagram allows establishing one to one correspondence between the parameter and phase subspaces by line up stages
Practical realization of model-based emergency forecasting in pulse systems for the case of multi-parameter variation in a possible wide range associates with the problem of a-priori forming of single-statement information on system dynamics . The fractal diagram is proposed to use for the purpose of system dynamics presentation . This diagram allows one-to-one correspondence between the both , parameter and phase , spaces establishing by line up stages .
Quantization simply causes shifts in estimates . The results suggest that one should not place much reliance in the absolute value of a fractal estimate , but that the estimates do vary monotonically with D and might be useful descriptors in tasks such as image segmentation and description .
A basis is provided for an image-analysis algorithm for automatic recognition , considered in this article from the viewpoint of formalizing the process of distinguishing clusters either of constant or similar signal intensity , their number and size , and two-level decision-making . Such an approach is based on the fractal hypothesis , which consists of assuming that the number of constant-intensity pixels in a cluster has a power dependence on its rank and that the pixels in the cluster itself have a fractal spatial distribution . The rationale of the hypothesis is associated with harmony of the variability and stability of the image as a system .
This paper introduces a fractal partition class defined over a unit wrapped space . The cells of a fractal partition have self-similarity property and can be used as a set of tiles that can tile Rn space periodically or quasi-periodically with non-uniform tiling density . An algorithm for generating set of fractal cells using Voronoi diagram computation is proposed with several applications in computer graphics and computational chemistry .
Self similarity concepts of image compression are used in fractal video compression . However selfsimilarity means that fractal picture is consists of duplicates of itself that are interpreted and indicated by a change . More computational complexity is present in fractal video compression for reducing this complexity different technique has been implemented .
This paper proposes the fractal features classification for liver biopsy images using probabilistic neural network ( PNN ) . Fractal set has the properties of self-similarity and self-affinity . It can be used to estimate the fractal dimension ( FD ) from two-dimensional ( 2D ) images , including the normal and cancerous liver tissue images .
We study a map of the Internet ( at the autonomous systems level ) , by introducing and using the method of k-shell decomposition and the methods of percolation theory and fractal geometry , to find a model for the structure of the Internet . In particular , our analysis uses information on the connectivity of the network shells to separate , in a unique ( no parameters ) way , the Internet into three subcomponents : ( i ) a nucleus that is a small ( ≈100 nodes ) , very well connected globally distributed subgraph ; ( ii ) a fractal subcomponent that is able to connect the bulk of the Internet without congesting the nucleus , with self-similar properties and critical exponents predicted from percolation theory ; and ( iii ) dendrite-like structures , usually isolated nodes that are connected to the rest of the network through the nucleus only . We show that our method of decomposition is robust and provides insight into the underlying structure of the Internet and its functional consequences .
This leads to new organizational procedures and manufacturing systems . Their characteristics are : autonomous manufacturing cells , fractal units , adaptive systems , dynamic and agile management , self-optimization and self-control , customer-specific order management , communication networks . Particulary “ manufacturing on demand ” and the order management in production networks require new methods .
Section [ reference ] reviews this large body of related techniques . Hybrid designs could certainly integrate any of them with a fractal architecture ; we leave open the question of the degree to which such hybrids are synergistic . Our main contribution is twofold : We introduce FractalNet , the first simple alternative to ResNet .
The channel count corresponds to the size of the filter set in the preceding layer . As the fractal is expanded , we collapse neighboring joins into a single layer which spans multiple columns , as shown on the right side of Figure [ reference ] . The layer merges all of its input feature blobs into a single output blob .
Contrast with plain networks , which are not trainable if made too deep ( Table [ reference ] ) . table Fractal structure as a training apparatus ( CIFAR - 100 ++ ) . Plain networks perform well if moderately deep , but exhibit worse convergence during training if instantiated with great depth .
In other words , both of the spatial dimensions and feature dimensions remain the same across all intermediate layers . To render Figure [ reference ] ( b ) equivalent to a ResNet with residual blocks , one possible technique is to make : be the input image or a function of . , and . Thus , the state transition becomes .
Wave Field Simulation with the Functional Transformation Method Block-Based Physical Modeling with Applications in Musical Acoustics Block model data reduction and sensitivity analysis A review of stochastic block models and extensions for graph clustering Computer geometric modeling of the guiding device with continuous transfer area Exponential random graph models for networks with community structure Modeling Cadastral Spatial Features Based on Geography Markup Language in GIS: A Case Study in Shanghai A Dynamic Edge Exchangeable Model for Sparse Temporal Networks Multi-Core Embedded Processor Based on FPGA and Parallelization of SUSAN Algorithm Fast template matching tracking algorithm based on image separation block processing A Method of Seabed Soil Image Reconstruction Based on Compressed Sensing Fast algorithms for basic processing and analysis operations on block-represented binary images AN EFFICIENT FAST ALGORITHM FOR THE COMPUTATION OF REGULAR MOMENTS Parameterized real-time moment computation on gray images using block techniques Exact recovery in the Ising blockmodel Computer Tracking ofObjects MovinginSpace Design and simulation of an adaptive and collaborative assembly cell Video Forecasting with Forward-Backward-Net: Delving Deeper into Spatiotemporal Consistency A formal approach to check and schedule reconfigurable embedded control systems Integrator swing reduction in feedback compensated Sigma-Delta modulators Current research trends and application areas of fuzzy and hybrid methods to the risk assessment of construction projects SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search Fourier Bayesian networks: A novel approach for network structure inference with application to brain connectivity studies from magnetoencephalographic recordings Study of Encoding and Decoding Time for Varying Image Size Fractal block coding of digital video An influence of parameter variation on model-based emergency forecasting in the pulse system AN INFLUENCE OF PARAMETER VARIATION ON MODEL-BASED EMERGENCY FORECASTIING IN THE PULSE SYSTEM Can the fractal dimension of images be measured? Fractal-clustering analysis of video information Generating Fractal Tiles using Voronoi Diagrams A review on fractal compression and motion estimation techniques Fractal features classification for liver biopsy images using neural network-based classifier A model of Internet topology using k-shell decomposition Manufacturing on Demand in Production Networks FractalNet: Ultra-Deep Neural Networks without Residuals Image Super-Resolution via Dual-State Recurrent Networks
a6ef1b734189e7f017ff989f5ece595c3d4a918a 988612d38e42e3abbea1c6a64fa7b35331c461f8 0ea3b6e872586e83fd3f87508f390fcfb6e06ec9 d7db38e9504bdcd7cf249b41e3c58daaad156f0a 72160f0f6ea4103effeb35ce50e174ea6ed71b46 983f7d390d753caf5decaabe14b72d5408072a9a 4ccf776419b4d7f2bfb3de32b39b6e27b8b5197f e65a5d022c1214f0b2ace87e0097f120ec219a9d b34237cb6a96d067b496dc757ecb2fdb9b08cd67 c8c73ef93291b9d3ebc145f6d57f183502622f83 5dc97961607d8ab98a022e2bca8e5a94360fd4ab 4c13cb54470039960455dc6be851a386b6a9b8ab 7dae0a4aeea8c52b24988fbd7d3298e301013edd e1c6b38c6847b29dac17d38f2f68ae5133dd040f 5b99db33876b337cb063307ecc653f216ab1c678 ba6398744222770c5a94ef29602046300674155f 7ad8261a973d5c0377f3a21ac1c6db972d05a71d 40ff3276e62f03fe216d8592d2fc994d8eead010 71cefe9075c91744966fb1229d8b2f2f680730cc 647d9121ecc73f3e1a07c1e46c428f82d0b64676 a9dc3ac344266727c0d8d69108c6ce1b4b5dd824 2dd259eefa6dc3246cb63ee481b447864da7830c 45532bffbfbb5553da0b2d0844e95a1b37e59147 c704bdca65c86c7ef6c73e2b63e65342f3612cfe 66277c9d975c3fa26fde2ede440ec07c238ccc23 200a5de66b359772e14f8e7c51b96ddc7b54996e 8c48418cea2763369ffdae6290bb8a704ab73a87 f4d01da016c1bfd53fa92ab06bca15f92b4d4976 b1bb4eecd33f44d0dfe2cf546807154303def6e9 1dc0ffd78147f9b167de6c3116a1f494d93b47be 2a48eeb9e9e4ba110f52d6e8c6cb5766c6e1beeb 877bba138b150bec449934935f13316256db982b 56bcc84b5a926c7f49fa83448deff14e781e4d74 6e502397fc9f21c013f74efc85ca681dc5d4824c 834b36a65fe7cb72602f8b11702dffa970087cf0
["Computer Science","Engineering"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Mathematics","Physics","Computer Science","Medicine"] ["Engineering"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science","Engineering"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Physics"] ["Mathematics","Computer Science"] ["Biology","Computer Science"] ["Computer Science"] ["Computer Science","Physics","Medicine"] ["Computer Science"]
2006 2008 2017 2019 2013 2013 2005 2017 2008 2009 2012 1996 2001 2011 2019 1979 2010 2018 2008 2013 2017 2019 2018 2012 2008 1994 2003 2016 1994 2010 2007 2018 2010 2006 1997 2017 2018
ACM Multimedia ETFA ISCAS ISVD RICE ICLR 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
https://api.semanticscholar.org/a6ef1b734189e7f017ff989f5ece595c3d4a918a https://api.semanticscholar.org/988612d38e42e3abbea1c6a64fa7b35331c461f8 https://api.semanticscholar.org/0ea3b6e872586e83fd3f87508f390fcfb6e06ec9 https://api.semanticscholar.org/d7db38e9504bdcd7cf249b41e3c58daaad156f0a https://api.semanticscholar.org/72160f0f6ea4103effeb35ce50e174ea6ed71b46 https://api.semanticscholar.org/983f7d390d753caf5decaabe14b72d5408072a9a https://api.semanticscholar.org/4ccf776419b4d7f2bfb3de32b39b6e27b8b5197f https://api.semanticscholar.org/e65a5d022c1214f0b2ace87e0097f120ec219a9d https://api.semanticscholar.org/b34237cb6a96d067b496dc757ecb2fdb9b08cd67 https://api.semanticscholar.org/c8c73ef93291b9d3ebc145f6d57f183502622f83 https://api.semanticscholar.org/5dc97961607d8ab98a022e2bca8e5a94360fd4ab https://api.semanticscholar.org/4c13cb54470039960455dc6be851a386b6a9b8ab https://api.semanticscholar.org/7dae0a4aeea8c52b24988fbd7d3298e301013edd https://api.semanticscholar.org/e1c6b38c6847b29dac17d38f2f68ae5133dd040f https://api.semanticscholar.org/5b99db33876b337cb063307ecc653f216ab1c678 https://api.semanticscholar.org/ba6398744222770c5a94ef29602046300674155f https://api.semanticscholar.org/7ad8261a973d5c0377f3a21ac1c6db972d05a71d https://api.semanticscholar.org/40ff3276e62f03fe216d8592d2fc994d8eead010 https://api.semanticscholar.org/71cefe9075c91744966fb1229d8b2f2f680730cc https://api.semanticscholar.org/647d9121ecc73f3e1a07c1e46c428f82d0b64676 https://api.semanticscholar.org/a9dc3ac344266727c0d8d69108c6ce1b4b5dd824 https://api.semanticscholar.org/2dd259eefa6dc3246cb63ee481b447864da7830c https://api.semanticscholar.org/45532bffbfbb5553da0b2d0844e95a1b37e59147 https://api.semanticscholar.org/c704bdca65c86c7ef6c73e2b63e65342f3612cfe https://api.semanticscholar.org/66277c9d975c3fa26fde2ede440ec07c238ccc23 https://api.semanticscholar.org/200a5de66b359772e14f8e7c51b96ddc7b54996e https://api.semanticscholar.org/8c48418cea2763369ffdae6290bb8a704ab73a87 https://api.semanticscholar.org/f4d01da016c1bfd53fa92ab06bca15f92b4d4976 https://api.semanticscholar.org/b1bb4eecd33f44d0dfe2cf546807154303def6e9 https://api.semanticscholar.org/1dc0ffd78147f9b167de6c3116a1f494d93b47be https://api.semanticscholar.org/2a48eeb9e9e4ba110f52d6e8c6cb5766c6e1beeb https://api.semanticscholar.org/877bba138b150bec449934935f13316256db982b https://api.semanticscholar.org/56bcc84b5a926c7f49fa83448deff14e781e4d74 https://api.semanticscholar.org/6e502397fc9f21c013f74efc85ca681dc5d4824c https://api.semanticscholar.org/834b36a65fe7cb72602f8b11702dffa970087cf0 https://www.semanticscholar.org/paper/d0156126edbfc524c8d108bdc0cf811cfe3129aa https://www.semanticscholar.org/paper/175f74a09241b6cb5101a2a09978095720db7d5f