High dimensional motion capture date are represented using a low dimensional representation by topology preserving network , which maps similar motion instances to the neighborhood points on the low dimensional motion manifold . Nonlinear manifold learning between a low dimensional manifold representation and high dimensional motion data provides a generative model to synthesize new motion sequence by controlling trajectory on the low dimensional motion manifold . We segment motion primitives by analyzing low dimensional representation of body poses through motion from motion captured data . A “ Generative-Predicational Model ” is proposed and applied to the generation of meanings of simple mathematical word-problems . The model suggests that a fundamental property of cognition is a generative process that takes arguments and that produces results , such as events , answers and inferences . This fundamental property , called predication , generates a task-environment i.e. , a problem and its corresponding problem-space i.e. , its solution . However , objective measurements can be made of some of the varied cognitive and perceptual processes used in the formulation of musical structure such as those related to auditory perception and rhythmic inferences . Such observations provide insight into the similarities and differences between composers ' perceptions , and can help us significantly in modeling the generative process . To make comparisons across creators and across generative contexts , the format of the experiment must constrain subjectcomposers in a comparable fashion . ABSTRACT In this work , we will look at embedded finite clauses without the complementizer que in Classical Portuguese . Our goal is to investigate , within a generative perspective , whether this kind of construction can be interpreted as a CP or an IP complement . Based on facts related to the position of the subject , to the ordering of adverbs and to the placement of clitics , we will show that embedded clauses without the complementizer que instantiate V movement to C , resulting , therefore , in the presence of the CP category . Thus , it is essential to design a representation to encode information in a set of instances robustly . Existing methods such as FV and VLAD are designed based on a generative perspective , and their performances fluctuate when difference types of instance vectors are used ( i.e. , they are not robust ) . The proposed D3 method effectively compares two sets as two distributions , and proposes a directional total variation distance ( DTVD ) to measure their dissimilarity . Silhouette data is represented more compactly by nonlinear dimensionality reduction that explores the underlying structure of the articulated action space and preserves explicit temporal orders in projection trajectories of motions . FCRF models temporal sequences in multiple interacting ways , thus increasing joint accuracy by information sharing , with the ideal advantages of discriminative models over generative ones ( e.g. , relaxing independence assumption between observations and the ability to effectively incorporate both overlapping features and long-range dependencies ) . The experimental results on two recent datasets have shown that the proposed framework can not only accurately recognize human activities with temporal , intra-and inter-person variations , but also is considerably robust to noise and other factors such as partial occlusion and irregularities in motion styles . News . The results show that ( i ) query expansion improves both methods , ( ii ) that our discriminative models outperform the generative ones , and ( iii ) our best results surpass the state-of-the-art results by 10 % precision on average . Abstract Generative models can be used for a wide range of tasks , and have the appealing ability to learn from both labelled and unlabelled data . In contrast , discriminative models can not learn from unlabelled data , but tend to outperform their generative counterparts in supervised tasks . We develop a framework to jointly train deep generative and discriminative models , enjoying the benefits of both . Conversational agents or chatbots ( short for chat robot ) are a branch of Natural Language Processing ( NLP ) that has arisen a lot of interest nowadays due to the extent number of applications in company services such as customer support or automatized FAQS and personal asistent services , for instance Siri or Cortana . There are three types : rule-based models , retrieval-based models and generative-based models . The difference between them is the freedom they have at the time of generating an answer given a question . document : SeqGAN : Sequence Generative Adversarial Nets with Policy Gradient As a new way of training generative models , Generative Adversarial Net ( GAN ) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real - valued data . However , it has limitations when the goal is for generating sequences of discrete tokens . SIG is a general-purpose signal processing , analysis , and display program . Its main purpose is to perform manipulations on time- and frequency-domain signals . Sponsored by SIG III , MGT . There is a widespread belief in the developing world that the global explosion of knowledge may ameliorate the socio-economic conditions of the rural populace . Several SIG reference specifications have been constituted by ISO and OGC to provide standard interfaces . This paper pays attention to construct and implement SIG 's resource layer , which is the most important part , studies its conception structure and required functions . A SIG data service node is designed based on OGC and NWGISS . Abstract-An approach of constructing a data node in spatial information grid ( SIG ) based on Web Service Resource Framework ( WSRF ) and Web Service Notification ( WSN ) is described in this paper . Attentions are paid to construct and implement SIG 's resource layer , which is the most important part . A study on this layer find out , it is impossible to require persistent interaction with the clients of the services in common SIG architecture because of inheriting " stateless " and " not persistent " limitations of Web Service . Firms that achieve sustainable , positive IS leadership are more likely to develop superior capabilities in IS and gain a considerable competitive advantage . This special issue features five excellent articles ( one conceptual and four research articles ) in line with the mission of the AIS SIG LEAD that contribute to the accumulation of knowledge in IS academia and practice . Topics covered by this special issue range from attributes of effective IS leadership and strategy , impact of IS leadership on firm performance through use of IT resources and management of IT projects , and emerging issues on IT project management , cloud computing , and Health Information Exchange . These standards allow medical devices to intercommunicate and exchange measurement data within a single system . The Bluetooth special interest group ( SIG ) introduced a Bluetooth Low Energy ( LE ) technology with reduced power consumption . The Bluetooth LE can be used in medical devices with low data transmission rates . Sponsored by SIG DL , SIG ED , SIG LAN , SIG STI . Virtual reference services , particularly in academic libraries , are coming to be regarded as essential . The robot is driven with the aid of a PCL from the X20 series , produced by Bernecker&Rainer . The propulsion is provided using 3 integrated regulating driving mechanisms , IclA D065 produced by SIG Positec , which communicate via the CAN bus . Furthermore , the robot uses sensors to read the positioning of its chassis and undercarriage in order to identify the traction quality of the surface of the terrain below and subsequently reduce skidding . The aim of the work was to create a standard framework that meets the special requirements of marine automation , such as redundant communication , high availability of central network management functions , multi-master capability and the support of a large number of process data and configuration parameters . Here the SIG CANopen Maritime was able to fall back on tried and tested concepts and experience from already implemented CANor CANopen-based marine automation systems of the companies involved [ 2 , 3 ] . The IRSPM A&A SIG , CIGAR Network and EGPA PSG XII are three research networks that focus on Public Sector Accounting . The Task Force is made up of 17 researchers from these networks . A Collaboration Between the K-12 Online Learning SIG and Distance Learning SIG : How Higher Education and K-12 Online Learning Research Can Impact Each Other . Performance evaluation on real environments is also supported by ALMware . To prove the usefulness of ALMware , we have implemented well-known but different types of ALM protocols , ALMI , NARADA , NICE and OMNI , and conducted various kinds of performance evaluation experiments on PlanetLab , which clearly demonstrate the usability of ALMware . We believe that this is the first effort made to develop this kind of ALM supporting middleware . To cope with this new situation the nurses have to be armed with proper knowledge and thereafter the proper education will play a very important role . In this manner we started a new European project called NICE , whose main objective is to develop a new nursing informatics curricula and the aim of this paper is to present it and its design . The performance of this system was promising but inefficient when opposed to the JPEG standard . In this work , we aim at improving the architecture design of the NICE system . Thus , we propose 3 different architectures and we show that the quality of the reconstruction is substantially enhanced . We present a distributed scheme for trust inference in peer-to-peer networks . Our work is in the context of the NICE system , which is a platform for implementing cooperative applications over the Internet . We describe a technique for efficiently storing user reputation information in a completely decentralized manner , and show how this information can be used to efficiently identify non-cooperative users in NICE . In this article , we address this problem and propose a locality-aware P2P overlay construction method , called Nearcast , which builds an efficient overlay multicast tree by letting each peer node choose physically closer nodes as its logical children . We have conducted extensive simulations to evaluate the performance of Nearcast in comparison with the existing RTT and NICE protocols . Also , Nearcast has been deployed on a wide-area network testbed to delivery video coursed to about 7200 users distributed across 100 collages in 32 cities in China . The experimental results show that Nearcast leads to lower link stress and shorter end-to-end latencies compared with the RTT and NICE protocols . Experimental Results From the NICE Contests . This paper announces and discusses the experimental results from the Noisy Iris Challenge Evaluation ( NICE ) , an iris biometric evaluation initiative that received worldwide participation and whose main innovation is the use of heavily degraded data acquired in the visible wavelength and uncontrolled setups , with subjects moving and at widely varying distances . The NICE contest included two separate phases : 1 ) the NICE.I evaluated iris segmentation and noise detection techniques and 2 ) the NICE : II evaluated encoding and matching strategies for biometric signatures . Product management 's ability to identify the requirements and match them with the company 's core SW resources and technological assets is becoming a core competence . NICE Systems is a worldwide leader of multimedia digital recording solutions . NICE products are used by 65 % of fortune 100 companies in 30,000 sites including contact centers , financial institutions , public safety sites , ATC ( air traffic control ) sites , CCTV ( closed circuit television ) security installations and government markets . However , since the network is typically not a bottleneck , user acceptance remains the limiting factor for the deployment of ALM . In this paper we present an in-depth study of the user-perceived performance of the NICE ALM protocol . We use the OverSim simulation framework to evaluate delay experienced by a user and bandwidth consumption on the user ’s access link in large multicast groups and under aggressive churn models . The fact that these components can be recovered as entire pieces opens up a more sustainable and high value recycling and reuse potential , in terms of waste management towards a circular economy . This work reports results from feasibility studies on the disassembly of NICE modules and recycling/reuse of their components , carried out in the frame of the European H2020FOF-13 - 2015 ‘ ECOSOLAR ’ project . There has been a lot of recent interest in the potential of inter-base station cooperation techniques to manage excessive out-of-cell interference in evolving cellular networks . In this paper , we propose and describe a signal processing approach based on a novel Network Interference Cancellation Engine ( NICE ) , which opportunistically cancels dominant out-of-cell interference by exchanging decoded dominant interferer data among cooperating cells and reconstructing interfering signals . Simulation results with NICE processing in cellular networks show that substantial improvements in signal-to-interference-plus-noise ratio may be achieved over current non-cooperative multi-antenna receiver processing techniques while allowing backhaul overhead to be reduced by approximately between one and two orders of magnitude relative to joint processing techniques employing the exchange of complex-valued received signal samples . This paper describes the running first prototype ( PT1 ) of the NICE Hans Christian Andersen system . NICE stands for Natural Interactive Communication for Edutainment ( http://www.niceproject.com/ ) . Data transmission topology ( tree is used in the paper ) can be easily implemented on top of the ring . The paper provides simulations of both HMRB and a hierarchical ALM protocol NICE , using p2psim network simulator . Results demonstrate that HMRB has higher bandwidth usage , shorter average path length and lower link stress in proportion with NICE . The problems of automatically constructing algebraic surface models from sets of 3D and 2D images and using these models in pose computation , motion and deformation estimation , and object recognition are addressed . It is proposed that a combination of constrained optimization and nonlinear least-squares estimation techniques be used to minimize the mean-squared geometric distance between a set of points or rays and a parameterized surface . In modeling tasks , the unknown parameters are the surface coefficients , while in pose and deformation estimation tasks they represent the transformation mapping the observer 's coordinate system onto the modeled surface 's own coordinate system . In this paper , we combine these two approaches by fitting parameters of a systems-level model of the SCS to neural activity and behavior measured from a nonhuman primate executing four types of reach-tograsp tasks . We applied a nonlinear least squares estimation to fit parameters of the model components that characterize cerebrocerebellar processing of movement error and muscles that are actuated by alpha motor neurons receiving commands from primary motor cortex ( M1 ) . Our fitted SCS model accurately reconstructs firing rate activity of six populations of M1 neurons and associated reaching trajectories . The estimation of TRX chains ' complex responses is needed . Two estimation algorithms are used , referred as linear and non-linear least squares estimation . These estimation algorithms need to use the information regarding the antenna array mutual coupling matrix and over-the-air ( OTA ) self-measurements between pairs of elements in the antenna array . In this thesis we use nonlinear and linear estimation techniques to model two common investment strategies : hedge funds and technical analysis . Our models provide transparent and low-cost alternatives to these two nontransparent , and in some cases prohibitively costly , financial approaches . We first revisited its formulation to verify its consistency with the criterion of minimization of mutual information . Then , we applied the nonlinear independent component estimation algorithm ( NICE ) to synthetic 2D images that resemble structural magnetic resonance imaging ( sMRI ) data . This data was generated by mixing spatial components that represent axial slices of sMRI tissue concentration images . In this paper we present an algorithm for multipoint joint processing of range measurements with trial results accumulation , evaluate its accuracy analytically in terms of range mean square error and finally validate it through an experiment . The methods used for analytical evaluation is matrix analysis and nonlinear least squares estimation theory . The approach for experimental validation is a software-defined radio ( SDR ) transceiver working on National Instruments ( NI ) The current tendency in the digital systems design is the identification and compensation of unwanted nonlinearities . In this paper , we employed a nonlinear noise estimation approach for electroencephalogram ( EEG ) signal based on a combination of linear predictive coding ( LPC ) and Volterra filter that is a new and good way to estimate noise in EEG signal . We initially used LPC filter to estimate the noise present in EEG signal ( correlated and uncorrelated noise ) plus the uncorrelated portion of the signal ( the part of the signal that has no linear relation to its past samples ) . This paper proposes a dynamic origin-destination ( OD ) estimation method to extract valuable point-to-point split-fraction information from automatic vehicle identification ( AVI ) counts without estimating market-penetration rates and identification rates of AVI tags . A nonlinear ordinary least-squares estimation model is presented to combine AVI counts , link counts , and historical demand information into a multiobjective optimization framework . A joint estimation formulation and a one-sided linear-penalty formulation are further developed to take into account possible identification and representativeness errors , and the resulting optimization problems are solved by using an iterative bilevel estimation procedure . While these models can provide better spectrum estimation than DFT , they may not be the best option because the parameter estimation is only suboptimal of least squares or the methods have very narrow convergence area . In this paper , the traditional nonlinear separable estimation has been modified and a new time-domain least-squares method was developed for the identification of multi-frequency signal parameters . Algorithm convergence was analyzed and improved through segmentation of data . We design two types of adversarial attacks ; one that minimizes the likelihood scores of in-distribution samples , while the other that maximizes the likelihood scores of out-of-distribution ones . We find that GLOW and RealNVP are extremely sensitive to both types of attacks . Finally , using a hybrid adversarial training procedure , we significantly boost the robustness of these generative models . Furthermore , the recovered informative latent variables will be in one-to-one correspondence with the true latent variables of the generating process , up to a trivial component-wise transformation . Second , we introduce a modification of the RealNVP invertible neural network architecture ( Dinh et al. ( 2016 ) ) which is particularly suitable for this type of problem : the General Incompressible-flow Network ( GIN ) . Experiments on artificial data and EMNIST demonstrate that theoretical predictions are indeed verified in practice . We created a database of pairs of satellite images and the corresponding map of the area . Our model translates the satellite image to the corresponding standard layer map image using three main model architectures : ( i ) a conditional Generative Adversarial Network ( GAN ) which compresses the images down to a learned embedding , ( ii ) a generator which is trained as a normalizing flow ( RealNVP ) model , and ( iii ) a conditional GAN where the generator translates via a series of convolutions to the standard layer of a map and the discriminator input is the concatenation of the real/generated map and the satellite image . Model ( iii ) was by far the most promising of three models .
High dimensional motion capture date are represented using a low dimensional representation by topology preserving network , which maps similar motion instances to the neighborhood points on the low dimensional motion manifold . Nonlinear manifold learning between a low dimensional manifold representation and high dimensional motion data provides a generative model to synthesize new motion sequence by controlling trajectory on the low dimensional motion manifold . We segment motion primitives by analyzing low dimensional representation of body poses through motion from motion captured data .
A “ Generative-Predicational Model ” is proposed and applied to the generation of meanings of simple mathematical word-problems . The model suggests that a fundamental property of cognition is a generative process that takes arguments and that produces results , such as events , answers and inferences . This fundamental property , called predication , generates a task-environment i.e. , a problem and its corresponding problem-space i.e. , its solution .
However , objective measurements can be made of some of the varied cognitive and perceptual processes used in the formulation of musical structure such as those related to auditory perception and rhythmic inferences . Such observations provide insight into the similarities and differences between composers ' perceptions , and can help us significantly in modeling the generative process . To make comparisons across creators and across generative contexts , the format of the experiment must constrain subjectcomposers in a comparable fashion .
ABSTRACT In this work , we will look at embedded finite clauses without the complementizer que in Classical Portuguese . Our goal is to investigate , within a generative perspective , whether this kind of construction can be interpreted as a CP or an IP complement . Based on facts related to the position of the subject , to the ordering of adverbs and to the placement of clitics , we will show that embedded clauses without the complementizer que instantiate V movement to C , resulting , therefore , in the presence of the CP category .
Thus , it is essential to design a representation to encode information in a set of instances robustly . Existing methods such as FV and VLAD are designed based on a generative perspective , and their performances fluctuate when difference types of instance vectors are used ( i.e. , they are not robust ) . The proposed D3 method effectively compares two sets as two distributions , and proposes a directional total variation distance ( DTVD ) to measure their dissimilarity .
Silhouette data is represented more compactly by nonlinear dimensionality reduction that explores the underlying structure of the articulated action space and preserves explicit temporal orders in projection trajectories of motions . FCRF models temporal sequences in multiple interacting ways , thus increasing joint accuracy by information sharing , with the ideal advantages of discriminative models over generative ones ( e.g. , relaxing independence assumption between observations and the ability to effectively incorporate both overlapping features and long-range dependencies ) . The experimental results on two recent datasets have shown that the proposed framework can not only accurately recognize human activities with temporal , intra-and inter-person variations , but also is considerably robust to noise and other factors such as partial occlusion and irregularities in motion styles .
News . The results show that ( i ) query expansion improves both methods , ( ii ) that our discriminative models outperform the generative ones , and ( iii ) our best results surpass the state-of-the-art results by 10 % precision on average .
Abstract Generative models can be used for a wide range of tasks , and have the appealing ability to learn from both labelled and unlabelled data . In contrast , discriminative models can not learn from unlabelled data , but tend to outperform their generative counterparts in supervised tasks . We develop a framework to jointly train deep generative and discriminative models , enjoying the benefits of both .
Conversational agents or chatbots ( short for chat robot ) are a branch of Natural Language Processing ( NLP ) that has arisen a lot of interest nowadays due to the extent number of applications in company services such as customer support or automatized FAQS and personal asistent services , for instance Siri or Cortana . There are three types : rule-based models , retrieval-based models and generative-based models . The difference between them is the freedom they have at the time of generating an answer given a question .
document : SeqGAN : Sequence Generative Adversarial Nets with Policy Gradient As a new way of training generative models , Generative Adversarial Net ( GAN ) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real - valued data . However , it has limitations when the goal is for generating sequences of discrete tokens .
SIG is a general-purpose signal processing , analysis , and display program . Its main purpose is to perform manipulations on time- and frequency-domain signals .
Sponsored by SIG III , MGT . There is a widespread belief in the developing world that the global explosion of knowledge may ameliorate the socio-economic conditions of the rural populace .
Several SIG reference specifications have been constituted by ISO and OGC to provide standard interfaces . This paper pays attention to construct and implement SIG 's resource layer , which is the most important part , studies its conception structure and required functions . A SIG data service node is designed based on OGC and NWGISS .
Abstract-An approach of constructing a data node in spatial information grid ( SIG ) based on Web Service Resource Framework ( WSRF ) and Web Service Notification ( WSN ) is described in this paper . Attentions are paid to construct and implement SIG 's resource layer , which is the most important part . A study on this layer find out , it is impossible to require persistent interaction with the clients of the services in common SIG architecture because of inheriting " stateless " and " not persistent " limitations of Web Service .
Firms that achieve sustainable , positive IS leadership are more likely to develop superior capabilities in IS and gain a considerable competitive advantage . This special issue features five excellent articles ( one conceptual and four research articles ) in line with the mission of the AIS SIG LEAD that contribute to the accumulation of knowledge in IS academia and practice . Topics covered by this special issue range from attributes of effective IS leadership and strategy , impact of IS leadership on firm performance through use of IT resources and management of IT projects , and emerging issues on IT project management , cloud computing , and Health Information Exchange .
These standards allow medical devices to intercommunicate and exchange measurement data within a single system . The Bluetooth special interest group ( SIG ) introduced a Bluetooth Low Energy ( LE ) technology with reduced power consumption . The Bluetooth LE can be used in medical devices with low data transmission rates .
Sponsored by SIG DL , SIG ED , SIG LAN , SIG STI . Virtual reference services , particularly in academic libraries , are coming to be regarded as essential .
The robot is driven with the aid of a PCL from the X20 series , produced by Bernecker&Rainer . The propulsion is provided using 3 integrated regulating driving mechanisms , IclA D065 produced by SIG Positec , which communicate via the CAN bus . Furthermore , the robot uses sensors to read the positioning of its chassis and undercarriage in order to identify the traction quality of the surface of the terrain below and subsequently reduce skidding .
The aim of the work was to create a standard framework that meets the special requirements of marine automation , such as redundant communication , high availability of central network management functions , multi-master capability and the support of a large number of process data and configuration parameters . Here the SIG CANopen Maritime was able to fall back on tried and tested concepts and experience from already implemented CANor CANopen-based marine automation systems of the companies involved [ 2 , 3 ] .
The IRSPM A&A SIG , CIGAR Network and EGPA PSG XII are three research networks that focus on Public Sector Accounting . The Task Force is made up of 17 researchers from these networks .
A Collaboration Between the K-12 Online Learning SIG and Distance Learning SIG : How Higher Education and K-12 Online Learning Research Can Impact Each Other .
Performance evaluation on real environments is also supported by ALMware . To prove the usefulness of ALMware , we have implemented well-known but different types of ALM protocols , ALMI , NARADA , NICE and OMNI , and conducted various kinds of performance evaluation experiments on PlanetLab , which clearly demonstrate the usability of ALMware . We believe that this is the first effort made to develop this kind of ALM supporting middleware .
To cope with this new situation the nurses have to be armed with proper knowledge and thereafter the proper education will play a very important role . In this manner we started a new European project called NICE , whose main objective is to develop a new nursing informatics curricula and the aim of this paper is to present it and its design .
The performance of this system was promising but inefficient when opposed to the JPEG standard . In this work , we aim at improving the architecture design of the NICE system . Thus , we propose 3 different architectures and we show that the quality of the reconstruction is substantially enhanced .
We present a distributed scheme for trust inference in peer-to-peer networks . Our work is in the context of the NICE system , which is a platform for implementing cooperative applications over the Internet . We describe a technique for efficiently storing user reputation information in a completely decentralized manner , and show how this information can be used to efficiently identify non-cooperative users in NICE .
In this article , we address this problem and propose a locality-aware P2P overlay construction method , called Nearcast , which builds an efficient overlay multicast tree by letting each peer node choose physically closer nodes as its logical children . We have conducted extensive simulations to evaluate the performance of Nearcast in comparison with the existing RTT and NICE protocols . Also , Nearcast has been deployed on a wide-area network testbed to delivery video coursed to about 7200 users distributed across 100 collages in 32 cities in China . The experimental results show that Nearcast leads to lower link stress and shorter end-to-end latencies compared with the RTT and NICE protocols .
Experimental Results From the NICE Contests . This paper announces and discusses the experimental results from the Noisy Iris Challenge Evaluation ( NICE ) , an iris biometric evaluation initiative that received worldwide participation and whose main innovation is the use of heavily degraded data acquired in the visible wavelength and uncontrolled setups , with subjects moving and at widely varying distances . The NICE contest included two separate phases : 1 ) the NICE.I evaluated iris segmentation and noise detection techniques and 2 ) the NICE : II evaluated encoding and matching strategies for biometric signatures .
Product management 's ability to identify the requirements and match them with the company 's core SW resources and technological assets is becoming a core competence . NICE Systems is a worldwide leader of multimedia digital recording solutions . NICE products are used by 65 % of fortune 100 companies in 30,000 sites including contact centers , financial institutions , public safety sites , ATC ( air traffic control ) sites , CCTV ( closed circuit television ) security installations and government markets .
However , since the network is typically not a bottleneck , user acceptance remains the limiting factor for the deployment of ALM . In this paper we present an in-depth study of the user-perceived performance of the NICE ALM protocol . We use the OverSim simulation framework to evaluate delay experienced by a user and bandwidth consumption on the user ’s access link in large multicast groups and under aggressive churn models .
The fact that these components can be recovered as entire pieces opens up a more sustainable and high value recycling and reuse potential , in terms of waste management towards a circular economy . This work reports results from feasibility studies on the disassembly of NICE modules and recycling/reuse of their components , carried out in the frame of the European H2020FOF-13 - 2015 ‘ ECOSOLAR ’ project .
There has been a lot of recent interest in the potential of inter-base station cooperation techniques to manage excessive out-of-cell interference in evolving cellular networks . In this paper , we propose and describe a signal processing approach based on a novel Network Interference Cancellation Engine ( NICE ) , which opportunistically cancels dominant out-of-cell interference by exchanging decoded dominant interferer data among cooperating cells and reconstructing interfering signals . Simulation results with NICE processing in cellular networks show that substantial improvements in signal-to-interference-plus-noise ratio may be achieved over current non-cooperative multi-antenna receiver processing techniques while allowing backhaul overhead to be reduced by approximately between one and two orders of magnitude relative to joint processing techniques employing the exchange of complex-valued received signal samples .
This paper describes the running first prototype ( PT1 ) of the NICE Hans Christian Andersen system . NICE stands for Natural Interactive Communication for Edutainment ( http://www.niceproject.com/ ) .
Data transmission topology ( tree is used in the paper ) can be easily implemented on top of the ring . The paper provides simulations of both HMRB and a hierarchical ALM protocol NICE , using p2psim network simulator . Results demonstrate that HMRB has higher bandwidth usage , shorter average path length and lower link stress in proportion with NICE .
The problems of automatically constructing algebraic surface models from sets of 3D and 2D images and using these models in pose computation , motion and deformation estimation , and object recognition are addressed . It is proposed that a combination of constrained optimization and nonlinear least-squares estimation techniques be used to minimize the mean-squared geometric distance between a set of points or rays and a parameterized surface . In modeling tasks , the unknown parameters are the surface coefficients , while in pose and deformation estimation tasks they represent the transformation mapping the observer 's coordinate system onto the modeled surface 's own coordinate system .
In this paper , we combine these two approaches by fitting parameters of a systems-level model of the SCS to neural activity and behavior measured from a nonhuman primate executing four types of reach-tograsp tasks . We applied a nonlinear least squares estimation to fit parameters of the model components that characterize cerebrocerebellar processing of movement error and muscles that are actuated by alpha motor neurons receiving commands from primary motor cortex ( M1 ) . Our fitted SCS model accurately reconstructs firing rate activity of six populations of M1 neurons and associated reaching trajectories .
The estimation of TRX chains ' complex responses is needed . Two estimation algorithms are used , referred as linear and non-linear least squares estimation . These estimation algorithms need to use the information regarding the antenna array mutual coupling matrix and over-the-air ( OTA ) self-measurements between pairs of elements in the antenna array .
In this thesis we use nonlinear and linear estimation techniques to model two common investment strategies : hedge funds and technical analysis . Our models provide transparent and low-cost alternatives to these two nontransparent , and in some cases prohibitively costly , financial approaches .
We first revisited its formulation to verify its consistency with the criterion of minimization of mutual information . Then , we applied the nonlinear independent component estimation algorithm ( NICE ) to synthetic 2D images that resemble structural magnetic resonance imaging ( sMRI ) data . This data was generated by mixing spatial components that represent axial slices of sMRI tissue concentration images .
In this paper we present an algorithm for multipoint joint processing of range measurements with trial results accumulation , evaluate its accuracy analytically in terms of range mean square error and finally validate it through an experiment . The methods used for analytical evaluation is matrix analysis and nonlinear least squares estimation theory . The approach for experimental validation is a software-defined radio ( SDR ) transceiver working on National Instruments ( NI )
The current tendency in the digital systems design is the identification and compensation of unwanted nonlinearities . In this paper , we employed a nonlinear noise estimation approach for electroencephalogram ( EEG ) signal based on a combination of linear predictive coding ( LPC ) and Volterra filter that is a new and good way to estimate noise in EEG signal . We initially used LPC filter to estimate the noise present in EEG signal ( correlated and uncorrelated noise ) plus the uncorrelated portion of the signal ( the part of the signal that has no linear relation to its past samples ) .
This paper proposes a dynamic origin-destination ( OD ) estimation method to extract valuable point-to-point split-fraction information from automatic vehicle identification ( AVI ) counts without estimating market-penetration rates and identification rates of AVI tags . A nonlinear ordinary least-squares estimation model is presented to combine AVI counts , link counts , and historical demand information into a multiobjective optimization framework . A joint estimation formulation and a one-sided linear-penalty formulation are further developed to take into account possible identification and representativeness errors , and the resulting optimization problems are solved by using an iterative bilevel estimation procedure .
While these models can provide better spectrum estimation than DFT , they may not be the best option because the parameter estimation is only suboptimal of least squares or the methods have very narrow convergence area . In this paper , the traditional nonlinear separable estimation has been modified and a new time-domain least-squares method was developed for the identification of multi-frequency signal parameters . Algorithm convergence was analyzed and improved through segmentation of data .
We design two types of adversarial attacks ; one that minimizes the likelihood scores of in-distribution samples , while the other that maximizes the likelihood scores of out-of-distribution ones . We find that GLOW and RealNVP are extremely sensitive to both types of attacks . Finally , using a hybrid adversarial training procedure , we significantly boost the robustness of these generative models .
Furthermore , the recovered informative latent variables will be in one-to-one correspondence with the true latent variables of the generating process , up to a trivial component-wise transformation . Second , we introduce a modification of the RealNVP invertible neural network architecture ( Dinh et al. ( 2016 ) ) which is particularly suitable for this type of problem : the General Incompressible-flow Network ( GIN ) . Experiments on artificial data and EMNIST demonstrate that theoretical predictions are indeed verified in practice .
We created a database of pairs of satellite images and the corresponding map of the area . Our model translates the satellite image to the corresponding standard layer map image using three main model architectures : ( i ) a conditional Generative Adversarial Network ( GAN ) which compresses the images down to a learned embedding , ( ii ) a generator which is trained as a normalizing flow ( RealNVP ) model , and ( iii ) a conditional GAN where the generator translates via a series of convolutions to the standard layer of a map and the discriminator input is the concatenation of the real/generated map and the satellite image . Model ( iii ) was by far the most promising of three models .
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AAAI CVPR AAAI ASIST IGARSS ASIST DCC SwSTE MMB/DFT ADS CVPR EMBC MLSP
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