In this paper we discuss new energy efficient paradigms for optical networks . With the increasing number of high bandwidth applications , devices used in backbone networks such as optical networks increase . Energy consumption of optical networks is an important issue that has to be addressed . In this paper , we propose a new approach to handle the design of optical networks in scenarios related to the discovery of the most suitable node locations . In general , the node locations of backbone networks are previously defined , and they are posed as input for optimization algorithms . However , different node locations can offer better tradeoffs regarding capital expenditure and network performance . In this paper , we propose a time-efficient face alignment algorithm while maintain a sufficient algorithmic accuracy . Specifically , we adopt MobileNet-V2 as our backbone architecture to deal with easy samples , accompanied by a ResNet branch to handle hard examples . This combination leads to a low-latency and yet agreeable-performance design as our extensive experiment shows . For this purpose , following the common approach in multilingual NMT , we prepend a special token to the beginning of both the source text and the MT output indicating the required amount of post-editing . Following the best submissions to the WMT 2018 APE shared task , our backbone architecture is based on multi-source Transformer to encode both the MT output and the corresponding source text . We participated both in the English-German and English-Russian subtasks . X-ray Computed Tomography ( CT ) is widely used in clinical applications such as diagnosis and image-guided interventions . In this paper , we propose a new deep learning based model for CT image reconstruction with the backbone network architecture built by unrolling an iterative algorithm . However , unlike the existing strategy to include as many data-adaptive components in the unrolled dynamics model as possible , we find that it is enough to only learn the parts where traditional designs mostly rely on intuitions and experience . Finally , the growth of subscription ratio and its implication on the backbone network architecture is studied and possible solutions are suggested . It is shown that careful system design and network planning is required along with a suitable backbone network architecture in order to fully utilize the great potential of optical subcarrier multiplexing in PCS . This article introduces an application framework to handle this kind of services and a routing protocol , the DBD ( Distributed Beaconless Dissemination ) , that enhances the dissemination of live video flows on multimedia highway VANETs . DBD uses a backbone-based approach to create and maintain persistent and high quality routes during the video delivery in opportunistic Vehicle to Vehicle ( V2V ) scenarios . It also improves the performance of the IEEE 802.11p MAC layer , by solving the Spurious Forwarding ( SF ) problem , while increasing the packet delivery ratio and reducing the forwarding delay . In this paper , we tend to study the impact of implementing machine learning ( ML ) algorithms and methods and their efficiency in the IoT domain . As well as explore how these algorithms help in founding efficient backbone solutions to analyze and estimate the huge amounts of data that are expected to arise in the coming few years due to the rapid growth on demands for IoT based applications . With the widespread use of broadband access technologies and the development of high-speed Internet backbones , the requirement for high-performance metropolitan area networks ( MANs ) is increasing . Traditional ring- or star-based metro networks are costly to scale up to high speed and can not recover from multiple failures , while backbone solutions are too expensive to fit into the cost-sensitive metro market . This paper proposes a virtual fully connected ( VFC ) architecture for metro networks to provide high-performance node-to-node all-optical transportation . The multilayer model will be described , along with two main scalability options appropriate for building-sized networks up to l arge campus networks . Five different backbone designs with different performance and scalability are also presented . In this paper t he term backbone is used to represent the switches and links in the core of the network through which all traffic passes on its way from client to server . We study three different models taken from the bibliography in a twofold fashion . First , we calculate the hydrogen bonds in 2gb1 , an ( α + β)‐protein , and see how different backbone representations and potentials can mimic the effects of real hydrogen bonds both in helices and sheets . Second , we use an evolutionary method for protein fragment assembly to locate the global energy minimum for a set of small β‐proteins with these models . Previous work using regular secondary structures and regular divisions of the backbone dihedral angle data has shown that these rotamers are sensitive to the protein 's local backbone conformation . In this preliminary study , we demonstrate a method for combining a more general backbone structure model with an objective clustering algorithm to investigate the effects of backbone structures on side chain rotamer classes and distributions . For the local structure classification , we use the Structural Building Blocks ( SBB ) categories , which represent all types of secondary structure , including regular structures , capping structures , and loops . All experiment settings remain unchanged with the previous experiments . For fair comparison , VGG - 16 is used as backbone model . The results of all methods are summarized in the bottom part of Table [ reference ] . However , face detection remains a challenging task due to occlusion , illumination , makeup , as well as pose and scale variance , as shown in the benchmark dataset WIDER FACE . Current state - of - the - art CNN - based face detectors attempt to address these challenges by employing more powerful backbone models , exploiting feature pyramid - style architectures to combine features from multiple detection feature maps , designing denser anchors and utilizing larger contextual information . These methods and techniques have been shown to be successful to build a robust face detector , and improve the performance towards human - level for most images . For clarity , we differentiate between : ( i ) the convolutional backbone architecture used for feature extraction over an entire image , and ( ii ) the network head for bounding - box recognition ( classification and regression ) and mask prediction that is applied separately to each RoI. We denote the backbone architecture using the nomenclature network - depth - features . We evaluate ResNet [ reference ] and ResNeXt [ reference ] networks of depth 50 or 101 layers . Network backbone . Table 6 illustrates the details of our backbone architecture ( f θ in the main paper ) . For both variants , we use a ResNet - 50 [ reference ] until the final convolutional layer of the 4 - th stage . Inspired by the observation that a human glances at an image and instantly knows how many and where the objects are in the image , we formulate instance - aware clustering by parallelly inferring the instance number and pixel - wise instance location , discarding the requirement of time - consuming region proposal generation . We modify a same backbone architecture to incorporate two sibling branches as the generator of NAN for location - sensitive learning , where denotes the network parameters for the generator and denotes the pre - defined instance location coordinate number . As multi - scale features integrating both global and local contextual information are crucial for increasing location prediction accuracy , we further augment the pixel - wise instance location prediction branch with a M ulti - S cale F usion subsection : Architecture SVDNet mostly follows the backbone networks , , CaffeNet and ResNet - 50 . The only difference is that SVDNet uses the Eigenlayer as the second last FC layer , as shown in Fig . How to design a more efficient network architecture is the key to further improving the performance of CNNs . In the past few years , several backbone networks , e.g. , , have made significant advances in numerous vision tasks with state - of - the - art performance . Earlier architectures such as AlexNet and VGGNet stack convolutional operators , making the data - driven learning of multi - scale features feasible . This question captures the essence of the problem we address here . We study the intertwined propagation of two competing " memes " ( or viruses , rumors , products etc . ) in a composite network . A key novelty is the use of a composite network , which in its simplest model is defined as a single set of nodes with two distinct types of edges interconnecting them . Combining networks to form a composite network for joint operations in the current business context is desirable in order to limit capital and operational expenditures , but currently requires complying with existing value chains and business models . However , commonalities between the two most popular infrastructures , mobile cellular and TV broadcast , are such that a common framework for services and QoS management can be built , without the expense of starting from scratch in the design of a universal system able to connect users for communications and entertainment . On the one hand , it proposes a neighborly search method to eliminate the burrs based on the burr growing point during the process of data preprocessing . On the other hand , it puts forward a composite connection method to connect the breakpoints after deeply analyses the causes and types of the disconnection of contour lines . These technological methods mentioned above have been proved in CAD environment by using VBA program , which shows that the composite connection method is not only practicable but also fast and accurate . Therefore , the methods proposed in this paper are both helpful to perfect and improve the data accuracy of contour line after the automatically vectored and beneficial to its research . The intrinsic diversity and complexity of home application environment determines that home network should have composite network architecture as well as dynamic and robust distributed application framework . In this paper , a distributed home network architecture composed of some different types of networks is described firstly . In particular , we will show the instantiation of a composite network service using a previously instantiated service . When terminating the composite network service , the initial instantiated network service resumes its operation without disruption . Consequently , we need to insert a number of repeaters into the network to recover the signal power , which causes the flooding problem in the network and lowering of the network performance . In this paper , to solve the flooding problem , an automatic repeat MAC protocol has been devised and applied to a powerline communication network in order to form a composite communication network for DAS . A mathematical performance analysis has been carried out to measure the performance characteristics of the proposed protocol applied to various powerline communication network environments . Existing knowledge of gene regulation can be presented using gene connectivity networks . However , these composite connectivity networks do not specify the range of biological conditions of the activity of each link in the network . ResultsWe present a novel method that utilizes the expression and binding patterns of the neighboring nodes of each link in existing experimentally-based , literature-derived gene transcriptional regulatory networks and extend them in silico using TF-gene binding motifs and a compendium of large expression data from Saccharomyces cerevisiae . AbstractA unified vector sorting algorithm ( VSA ) is proposed , which sortsN arbitrary numbers with clog2N-bits on an SIMD multi-processor system ( SMMP ) with $ $ p = \frac{{N^{1 + \varepsilon } } } {u}$$ processors and a composite interconnected network in $ $ T = \frac{c}{\varepsilon } \left ( { 4\log _ 2 N - 2\log _ 2 u + 10u } \right)$$ time , wherec is an arbitrary positive constant . When ε is an arbitrary small positive constant andu = log2N , it is anO(logN ) algorithm and $ $ p = \frac{{N^{1 + \varepsilon } } } { { log_2 N}}$$ ; when $ $ \varepsilon = \frac{1}{{log N}}$$ andu=2log2N , it is an optimal algorithm ( $ $ p = \frac{N}{{log_2 N}}$$ , T = O(log2N),pT = O(N logN ) ) ; whereu=1,c=1 and ε=0.5 ( a constant ) . This thesis proposes a network coding based and easily-realizable composite network topologic model . It is composed of disjoint multi-path routing ( DMR ) and braided multi-path routing ( BMR ) in wireless sensor network , and network coding technology is also employed in our multi-path routing . A simplified version of this network , the Spatial Encoder is sufficient to match state-of-the-art performance on the Hockey Fights and Movies datasets . However , on the Violent Flows dataset , the Spatiotemporal Encoder outperforms the Spatial Encoder . Despite its obvious advantages , the limitations of the analog combining architecture makes necessary to develop specific transmission schemes . For instance , in the case of perfect channel state information ( CSI ) at the receiver , and correlation CSI at the transmitter , the space and time encoders must operate separately ( the former works in the RF domain and the latter works in baseband ) , and at different time scales : the spatial encoder or RF beamformer must remain fixed during the transmission of a probably large number of symbols , whereas the time encoder can work at the symbol rate . In this paper we propose a transmission scheme for this scenario with the goal of minimizing the pairwise error probability ( PEP ) . Next , when subjected to only spatial coding to the difference video signal , a second compressed video signals . For producing a first decoded version of the original video signal , the first compressed video signal , compensating temporal decoding motion and spatial decoding ( 504,506,508 ) ( 510,512 , SW subjected to both ) , the second compressed video signal , it is possible to apply only the spatial decoding . Each pixel of each picture of the second decoded video signal , it is possible to add to the corresponding balanced pixel of picture temporally and spatially aligned in the first decoded video signal ( 514 ) . In our proposed approach , a convolutional long-term encoder is used to encode the whole given motion sequence into a long-term hidden variable , which is used with a decoder to predict the remainder of the sequence . The decoder itself also has an encoder-decoder structure , in which the short-term encoder encodes a shorter sequence to a short-term hidden variable , and the spatial decoder maps the long and short-term hidden variable to motion predictions . By using such a model , we are able to capture both invariant and dynamic information of human motion , which results in more accurate predictions . Then , the temporal encoder-decoder is realized with the help of Convolutional Long Short Term Memory ( ConvLSTM ) , gated with the tanh and sigmoid activation functions . Finally , the spatial decoder is implemented using three layers of deconvolutional layers . The proposed model is trained only on the dataset comprises the normal classes by minimizing the reconstruction error . This provides an architectural prior for dissociating positional from non-positional features in the latent distribution of VAEs , yet without providing any explicit supervision to this effect . We show that this architecture , which we term the Spatial Broadcast decoder , improves disentangling , reconstruction accuracy , and generalization to held-out regions in data space . It provides a particularly dramatic benefit when applied to datasets with small objects . We also emphasize a method for visualizing learned latent spaces that helped us diagnose our models and may prove useful for others aiming to assess data representations . Finally , we show the Spatial Broadcast Decoder is complementary to state-of-the-art ( SOTA ) disentangling techniques and when incorporated improves their performance . Though the reconstructed high resolution output of these methods is of rich semantics , it can not faithfully recover the fine image details owing to lack of desired precise low-level information . To overcome the problem , we propose to improve the spatial decoding process through embedding possibly lost low-level information in a principled way . To this end , we make the following three contributions . Nonvolatile memory array is organized into a plurality of interleaved even and odd blocks . When a block is selected for operation , the spatial decoding circuit and effective scheme a set of word line voltage to the word line pass block . The plurality of blocks are organized array odd block and even block pair is adjacent . The spatial component(i.e . spatial encoder/decoder ) uses Convolutional Neural Network ( CNN ) and carries information about scenes and objects . The temporal component(i.e . The embedded spatial information bit stream obtained by the embedded signal decoder is an encoded spatial information stream and inputted to a spatial information decoder(703 ) . The spatial information decoder decodes the encoded spatial information stream . A multichannel producing member(704 ) receives the down-mixed audio signal and spatial information obtained by decoding and outputs a multichannel audio signal(705 ) . When Multi-Input Multi-Output ( MIMO ) radar and communication transceivers are co-existing and operating simultaneously in the same frequency band , interference can be managed by designing signal spaces that facilitate spectrum sharing . In this paper , a spatial precoder-decoder design based on Interference Alignment ( IA ) is proposed assuming that an interference channel is shared by Kc communication users and Kr radar users . In order to find an IA based precoder-decoder solution guaranteeing desired multiplexing gain or diversity order , the design problem is formulated using a rank minimization criterion with rank constraint for the communication subsystem and rank minimization criterion with l0 norm constraint for the radar , respectively . In this paper , we aim to achieve better detection performance by building a more powerful backbone from existing backbones like ResNet and ResNeXt . Specifically , we propose a novel strategy for assembling multiple identical backbones by composite connections between the adjacent backbones , to form a more powerful backbone named Composite Backbone Network ( CBNet ) . In this way , CBNet iteratively feeds the output features of the previous backbone , namely high-level features , as part of input features to the succeeding backbone , in a stage-by-stage fashion , and finally the feature maps of the last backbone ( named Lead Backbone ) are used for object detection . We show that CBNet can be very easily integrated into most state-of-the-art detectors and significantly improve their performances . For example , it boosts the mAP of FPN , Mask R-CNN and Cascade R-CNN on the COCO dataset by about 1.5 to 3.0 percent . Meanwhile , experimental results show that the instance segmentation results can also be improved . Specially , by simply integrating the proposed CBNet into the baseline detector Cascade Mask R-CNN , we achieve a new state-of-the-art result on COCO dataset ( mAP of 53.3 ) with single model , which demonstrates great effectiveness of the proposed CBNet architecture . Code will be made available on this https URL .
In this paper we discuss new energy efficient paradigms for optical networks . With the increasing number of high bandwidth applications , devices used in backbone networks such as optical networks increase . Energy consumption of optical networks is an important issue that has to be addressed .
In this paper , we propose a new approach to handle the design of optical networks in scenarios related to the discovery of the most suitable node locations . In general , the node locations of backbone networks are previously defined , and they are posed as input for optimization algorithms . However , different node locations can offer better tradeoffs regarding capital expenditure and network performance .
In this paper , we propose a time-efficient face alignment algorithm while maintain a sufficient algorithmic accuracy . Specifically , we adopt MobileNet-V2 as our backbone architecture to deal with easy samples , accompanied by a ResNet branch to handle hard examples . This combination leads to a low-latency and yet agreeable-performance design as our extensive experiment shows .
For this purpose , following the common approach in multilingual NMT , we prepend a special token to the beginning of both the source text and the MT output indicating the required amount of post-editing . Following the best submissions to the WMT 2018 APE shared task , our backbone architecture is based on multi-source Transformer to encode both the MT output and the corresponding source text . We participated both in the English-German and English-Russian subtasks .
X-ray Computed Tomography ( CT ) is widely used in clinical applications such as diagnosis and image-guided interventions . In this paper , we propose a new deep learning based model for CT image reconstruction with the backbone network architecture built by unrolling an iterative algorithm . However , unlike the existing strategy to include as many data-adaptive components in the unrolled dynamics model as possible , we find that it is enough to only learn the parts where traditional designs mostly rely on intuitions and experience .
Finally , the growth of subscription ratio and its implication on the backbone network architecture is studied and possible solutions are suggested . It is shown that careful system design and network planning is required along with a suitable backbone network architecture in order to fully utilize the great potential of optical subcarrier multiplexing in PCS .
This article introduces an application framework to handle this kind of services and a routing protocol , the DBD ( Distributed Beaconless Dissemination ) , that enhances the dissemination of live video flows on multimedia highway VANETs . DBD uses a backbone-based approach to create and maintain persistent and high quality routes during the video delivery in opportunistic Vehicle to Vehicle ( V2V ) scenarios . It also improves the performance of the IEEE 802.11p MAC layer , by solving the Spurious Forwarding ( SF ) problem , while increasing the packet delivery ratio and reducing the forwarding delay .
In this paper , we tend to study the impact of implementing machine learning ( ML ) algorithms and methods and their efficiency in the IoT domain . As well as explore how these algorithms help in founding efficient backbone solutions to analyze and estimate the huge amounts of data that are expected to arise in the coming few years due to the rapid growth on demands for IoT based applications .
With the widespread use of broadband access technologies and the development of high-speed Internet backbones , the requirement for high-performance metropolitan area networks ( MANs ) is increasing . Traditional ring- or star-based metro networks are costly to scale up to high speed and can not recover from multiple failures , while backbone solutions are too expensive to fit into the cost-sensitive metro market . This paper proposes a virtual fully connected ( VFC ) architecture for metro networks to provide high-performance node-to-node all-optical transportation .
The multilayer model will be described , along with two main scalability options appropriate for building-sized networks up to l arge campus networks . Five different backbone designs with different performance and scalability are also presented . In this paper t he term backbone is used to represent the switches and links in the core of the network through which all traffic passes on its way from client to server .
We study three different models taken from the bibliography in a twofold fashion . First , we calculate the hydrogen bonds in 2gb1 , an ( α + β)‐protein , and see how different backbone representations and potentials can mimic the effects of real hydrogen bonds both in helices and sheets . Second , we use an evolutionary method for protein fragment assembly to locate the global energy minimum for a set of small β‐proteins with these models .
Previous work using regular secondary structures and regular divisions of the backbone dihedral angle data has shown that these rotamers are sensitive to the protein 's local backbone conformation . In this preliminary study , we demonstrate a method for combining a more general backbone structure model with an objective clustering algorithm to investigate the effects of backbone structures on side chain rotamer classes and distributions . For the local structure classification , we use the Structural Building Blocks ( SBB ) categories , which represent all types of secondary structure , including regular structures , capping structures , and loops .
All experiment settings remain unchanged with the previous experiments . For fair comparison , VGG - 16 is used as backbone model . The results of all methods are summarized in the bottom part of Table [ reference ] .
However , face detection remains a challenging task due to occlusion , illumination , makeup , as well as pose and scale variance , as shown in the benchmark dataset WIDER FACE . Current state - of - the - art CNN - based face detectors attempt to address these challenges by employing more powerful backbone models , exploiting feature pyramid - style architectures to combine features from multiple detection feature maps , designing denser anchors and utilizing larger contextual information . These methods and techniques have been shown to be successful to build a robust face detector , and improve the performance towards human - level for most images .
For clarity , we differentiate between : ( i ) the convolutional backbone architecture used for feature extraction over an entire image , and ( ii ) the network head for bounding - box recognition ( classification and regression ) and mask prediction that is applied separately to each RoI. We denote the backbone architecture using the nomenclature network - depth - features . We evaluate ResNet [ reference ] and ResNeXt [ reference ] networks of depth 50 or 101 layers .
Network backbone . Table 6 illustrates the details of our backbone architecture ( f θ in the main paper ) . For both variants , we use a ResNet - 50 [ reference ] until the final convolutional layer of the 4 - th stage .
Inspired by the observation that a human glances at an image and instantly knows how many and where the objects are in the image , we formulate instance - aware clustering by parallelly inferring the instance number and pixel - wise instance location , discarding the requirement of time - consuming region proposal generation . We modify a same backbone architecture to incorporate two sibling branches as the generator of NAN for location - sensitive learning , where denotes the network parameters for the generator and denotes the pre - defined instance location coordinate number . As multi - scale features integrating both global and local contextual information are crucial for increasing location prediction accuracy , we further augment the pixel - wise instance location prediction branch with a M ulti - S cale F usion
subsection : Architecture SVDNet mostly follows the backbone networks , , CaffeNet and ResNet - 50 . The only difference is that SVDNet uses the Eigenlayer as the second last FC layer , as shown in Fig .
How to design a more efficient network architecture is the key to further improving the performance of CNNs . In the past few years , several backbone networks , e.g. , , have made significant advances in numerous vision tasks with state - of - the - art performance . Earlier architectures such as AlexNet and VGGNet stack convolutional operators , making the data - driven learning of multi - scale features feasible .
This question captures the essence of the problem we address here . We study the intertwined propagation of two competing " memes " ( or viruses , rumors , products etc . ) in a composite network . A key novelty is the use of a composite network , which in its simplest model is defined as a single set of nodes with two distinct types of edges interconnecting them .
Combining networks to form a composite network for joint operations in the current business context is desirable in order to limit capital and operational expenditures , but currently requires complying with existing value chains and business models . However , commonalities between the two most popular infrastructures , mobile cellular and TV broadcast , are such that a common framework for services and QoS management can be built , without the expense of starting from scratch in the design of a universal system able to connect users for communications and entertainment .
On the one hand , it proposes a neighborly search method to eliminate the burrs based on the burr growing point during the process of data preprocessing . On the other hand , it puts forward a composite connection method to connect the breakpoints after deeply analyses the causes and types of the disconnection of contour lines . These technological methods mentioned above have been proved in CAD environment by using VBA program , which shows that the composite connection method is not only practicable but also fast and accurate . Therefore , the methods proposed in this paper are both helpful to perfect and improve the data accuracy of contour line after the automatically vectored and beneficial to its research .
The intrinsic diversity and complexity of home application environment determines that home network should have composite network architecture as well as dynamic and robust distributed application framework . In this paper , a distributed home network architecture composed of some different types of networks is described firstly .
In particular , we will show the instantiation of a composite network service using a previously instantiated service . When terminating the composite network service , the initial instantiated network service resumes its operation without disruption .
Consequently , we need to insert a number of repeaters into the network to recover the signal power , which causes the flooding problem in the network and lowering of the network performance . In this paper , to solve the flooding problem , an automatic repeat MAC protocol has been devised and applied to a powerline communication network in order to form a composite communication network for DAS . A mathematical performance analysis has been carried out to measure the performance characteristics of the proposed protocol applied to various powerline communication network environments .
Existing knowledge of gene regulation can be presented using gene connectivity networks . However , these composite connectivity networks do not specify the range of biological conditions of the activity of each link in the network . ResultsWe present a novel method that utilizes the expression and binding patterns of the neighboring nodes of each link in existing experimentally-based , literature-derived gene transcriptional regulatory networks and extend them in silico using TF-gene binding motifs and a compendium of large expression data from Saccharomyces cerevisiae .
AbstractA unified vector sorting algorithm ( VSA ) is proposed , which sortsN arbitrary numbers with clog2N-bits on an SIMD multi-processor system ( SMMP ) with $ $ p = \frac{{N^{1 + \varepsilon } } } {u}$$ processors and a composite interconnected network in $ $ T = \frac{c}{\varepsilon } \left ( { 4\log _ 2 N - 2\log _ 2 u + 10u } \right)$$ time , wherec is an arbitrary positive constant . When ε is an arbitrary small positive constant andu = log2N , it is anO(logN ) algorithm and $ $ p = \frac{{N^{1 + \varepsilon } } } { { log_2 N}}$$ ; when $ $ \varepsilon = \frac{1}{{log N}}$$ andu=2log2N , it is an optimal algorithm ( $ $ p = \frac{N}{{log_2 N}}$$ , T = O(log2N),pT = O(N logN ) ) ; whereu=1,c=1 and ε=0.5 ( a constant ) .
This thesis proposes a network coding based and easily-realizable composite network topologic model . It is composed of disjoint multi-path routing ( DMR ) and braided multi-path routing ( BMR ) in wireless sensor network , and network coding technology is also employed in our multi-path routing .
A simplified version of this network , the Spatial Encoder is sufficient to match state-of-the-art performance on the Hockey Fights and Movies datasets . However , on the Violent Flows dataset , the Spatiotemporal Encoder outperforms the Spatial Encoder .
Despite its obvious advantages , the limitations of the analog combining architecture makes necessary to develop specific transmission schemes . For instance , in the case of perfect channel state information ( CSI ) at the receiver , and correlation CSI at the transmitter , the space and time encoders must operate separately ( the former works in the RF domain and the latter works in baseband ) , and at different time scales : the spatial encoder or RF beamformer must remain fixed during the transmission of a probably large number of symbols , whereas the time encoder can work at the symbol rate . In this paper we propose a transmission scheme for this scenario with the goal of minimizing the pairwise error probability ( PEP ) .
Next , when subjected to only spatial coding to the difference video signal , a second compressed video signals . For producing a first decoded version of the original video signal , the first compressed video signal , compensating temporal decoding motion and spatial decoding ( 504,506,508 ) ( 510,512 , SW subjected to both ) , the second compressed video signal , it is possible to apply only the spatial decoding . Each pixel of each picture of the second decoded video signal , it is possible to add to the corresponding balanced pixel of picture temporally and spatially aligned in the first decoded video signal ( 514 ) .
In our proposed approach , a convolutional long-term encoder is used to encode the whole given motion sequence into a long-term hidden variable , which is used with a decoder to predict the remainder of the sequence . The decoder itself also has an encoder-decoder structure , in which the short-term encoder encodes a shorter sequence to a short-term hidden variable , and the spatial decoder maps the long and short-term hidden variable to motion predictions . By using such a model , we are able to capture both invariant and dynamic information of human motion , which results in more accurate predictions .
Then , the temporal encoder-decoder is realized with the help of Convolutional Long Short Term Memory ( ConvLSTM ) , gated with the tanh and sigmoid activation functions . Finally , the spatial decoder is implemented using three layers of deconvolutional layers . The proposed model is trained only on the dataset comprises the normal classes by minimizing the reconstruction error .
This provides an architectural prior for dissociating positional from non-positional features in the latent distribution of VAEs , yet without providing any explicit supervision to this effect . We show that this architecture , which we term the Spatial Broadcast decoder , improves disentangling , reconstruction accuracy , and generalization to held-out regions in data space . It provides a particularly dramatic benefit when applied to datasets with small objects . We also emphasize a method for visualizing learned latent spaces that helped us diagnose our models and may prove useful for others aiming to assess data representations . Finally , we show the Spatial Broadcast Decoder is complementary to state-of-the-art ( SOTA ) disentangling techniques and when incorporated improves their performance .
Though the reconstructed high resolution output of these methods is of rich semantics , it can not faithfully recover the fine image details owing to lack of desired precise low-level information . To overcome the problem , we propose to improve the spatial decoding process through embedding possibly lost low-level information in a principled way . To this end , we make the following three contributions .
Nonvolatile memory array is organized into a plurality of interleaved even and odd blocks . When a block is selected for operation , the spatial decoding circuit and effective scheme a set of word line voltage to the word line pass block . The plurality of blocks are organized array odd block and even block pair is adjacent .
The spatial component(i.e . spatial encoder/decoder ) uses Convolutional Neural Network ( CNN ) and carries information about scenes and objects . The temporal component(i.e .
The embedded spatial information bit stream obtained by the embedded signal decoder is an encoded spatial information stream and inputted to a spatial information decoder(703 ) . The spatial information decoder decodes the encoded spatial information stream . A multichannel producing member(704 ) receives the down-mixed audio signal and spatial information obtained by decoding and outputs a multichannel audio signal(705 ) .
When Multi-Input Multi-Output ( MIMO ) radar and communication transceivers are co-existing and operating simultaneously in the same frequency band , interference can be managed by designing signal spaces that facilitate spectrum sharing . In this paper , a spatial precoder-decoder design based on Interference Alignment ( IA ) is proposed assuming that an interference channel is shared by Kc communication users and Kr radar users . In order to find an IA based precoder-decoder solution guaranteeing desired multiplexing gain or diversity order , the design problem is formulated using a rank minimization criterion with rank constraint for the communication subsystem and rank minimization criterion with l0 norm constraint for the radar , respectively .
In this paper , we aim to achieve better detection performance by building a more powerful backbone from existing backbones like ResNet and ResNeXt . Specifically , we propose a novel strategy for assembling multiple identical backbones by composite connections between the adjacent backbones , to form a more powerful backbone named Composite Backbone Network ( CBNet ) . In this way , CBNet iteratively feeds the output features of the previous backbone , namely high-level features , as part of input features to the succeeding backbone , in a stage-by-stage fashion , and finally the feature maps of the last backbone ( named Lead Backbone ) are used for object detection . We show that CBNet can be very easily integrated into most state-of-the-art detectors and significantly improve their performances . For example , it boosts the mAP of FPN , Mask R-CNN and Cascade R-CNN on the COCO dataset by about 1.5 to 3.0 percent . Meanwhile , experimental results show that the instance segmentation results can also be improved . Specially , by simply integrating the proposed CBNet into the baseline detector Cascade Mask R-CNN , we achieve a new state-of-the-art result on COCO dataset ( mAP of 53.3 ) with single model , which demonstrates great effectiveness of the proposed CBNet architecture . Code will be made available on this https URL .
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CEC CCBR WMT (3) Pacific Symposium on Biocomputing 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) IEEE Transactions on Pattern Analysis and Machine Intelligence 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv 2017 IEEE International Conference on Computer Vision (ICCV) IEEE transactions on pattern analysis and machine intelligence MobiHoc ECCV Workshops (2) ICVS SPAWC
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