A reasonable cost measurement that integrates with Scrum would help provide an additional feedback loop . We adapted earned value management ( EVM ) , using values defined in Scrum . The result is called AgileEVM ( agile earned value management ) and is a simplified set of earned value calculations . From the values in Scrum , we derived a release date estimate using mean velocity and from this equation , generated an equivalent equation using traditional EVM techniques , thus establishing the validity of using EVM with the Scrum framework . Finally , we used this technique on two projects to further test our hypothesis . The whole system is complex and expensive . Using Electronic System-Level ( ESL ) software SystemVue and filter 's S parameter measured by VNA , filters ' EVM could be simulated for different communication standards . The Pearson produce-moment correlation coefficient between the simulation and measurement results is used to validate the correctness and applicability of the methodology . This paper presents a SigmaDelta DAC based transmitter ( TX ) baseband for 802.11n / 802.16e with channel filtering and gain control performed in the digital domain . The effect of circuit imperfections on TX EVM is analyzed for a robust line-up with gain control and the design of the high speed SigmaDelta modulator is described . The SigmaDelta DAC fabricated in a 90 nm CMOS process operates up to 3.6 GHz while drawing 16 mW from a 1.3 V supply . The T/R switch incorporates an impedance-transformation network to provide a receive S11 of -15dB at 2.4GHz and a sensitivity of -73dBm for a 54Mb/s 802.11 g signal . For 64QAM OFDM at 2.4GHz , the TX EVM is -25dB at an output power of + 16dBm . Such visual results have possible applications in non-contact human physiological parameter measurement , such as heart rate estimation . Unfortunately , the linear EVM is sensitive to noise and frequencies of the changes should be customized , which generates a limitation of applications . This paper presents an advanced EVM for magnifying the signal amplitude in the presence of relatively high noise as well as unknown the frequencies of changes in video . In this article , based on the working principle of vector signal analyzer and theories of error vector magnitude ( EVM ) , Combined signal demodulation experiment of vector signal analyzer , converting some types of digital modulation signal EVM into root mean square of error vector magnitude(EVMRMS ) . According to the relationship between EVMRMS and bit error rate(BER ) , analyzing the relationship between vector signal analyzer EVM and BER , and the simulation results are carried out . Therefore , in this work to substantiate the EVM improvement , a designed 26–30 GHz CMOS six-port receiver is used which consists of LNA , coupled line bandpass filter , six-port correlator , diode power detectors and two instrumentation amplifiers for recovering the baseband I/Q data . The EVM improvement process is assessed in terms of 64QAM modulation , and according to obtained results , EVM is reduced from 61 % when using constant DC bias to 22 % achieved with an LNA bias control algorithm for low input power ( − 60 dBm ) at the center frequency of 28 GHz . Moreover , the EVM is become less sensitive to input RF power , and also the dynamic range is improved more than 20 dB. A novel pseudo real — time 3D visualisation system is proposed in the system development and optimisation . The proposed system has been developed using the TI ( Texas Instruments ) DM642 EVM board which is a standalone Digital Media Processing board . The system also includes a stereo video capture module consisting of two PAL cameras and the X3D-19 DISPLAY AD 3D display unit for visualisation of 3D video output . Using these values , respective probability density functions are calculated and plotted , as handy tool for risk analysis of future projects of similar characteristics . By input of variables from other projects , decision making processes can be made for a whole project or its components using EVM Analysis in project evaluation and review techniques . This framework , as a quantitative approach , depends on the assumption of normality in projects completion time , it can help greatly in determining the completion time probability for veritable projects using any of the contract forms under consideration . The main contribution of this research paper is the real time implementation of two novel EVM ( Error Vector Magnitude ) based Multiuser Scheduling algorithms such as EVM based Strict priority scheduler ( ESPS ) , EVM based Round robin scheduler ( ERRS ) . The proposed EVM based algorithms are evaluated by implementing it on the FPGA ( Field Programmable Gate Array ) based WARP ( Wireless open- Access Research Platform ) board . Resources were allocated depending upon EVM . Firstly , the impact of channel quality indicator(CQI ) prediction error on system throughput is evaluated emphatically ; Secondly , we study the effect of different EVM on bit error rate , block error rate and frame error rate of 64QAM at different rates under the EVM allowed when the peak rate is reached . And this paper redicts EVM standards for higher order modulation . Since the dialog success is related to catching the want of the user , dialog state tracking is a necessary component for spoken dialog systems . This paper proposes a neural dialog state tracker with the attention mechanism for focusing on valuable words and the hierarchical softmax for efficient training of the tracker . In addition , the proposed tracker combines a natural language understanding module and a dialog state module in an end-to-end style . These qualities thus makes the Z-loss an appealing candidate to train very efficiently large output networks such as word-language models or other extreme classification problems . On the One Billion Word ( Chelba et al. , 2014 ) dataset , we are able to train a model with the Z-loss 40 times faster than the log-softmax and more than 4 times faster than the hierarchical softmax . Extreme multi-label classification ( XMLC ) is the problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels . Large label spaces can be efficiently handled by organizing labels as a tree , like in the hierarchical softmax ( HSM ) approach . In this paper , we investigate Probabilistic Label Trees ( PLTs ) that have been recently devised for tackling XMLC problems . We focus on a particular subclass of these algorithms that use probabilistic classifiers in the tree nodes . Examples of such algorithms are hierarchical softmax ( HSM ) , designed for multi-class classification , and probabilistic label trees ( PLTs ) that generalize HSM to multi-label problems . If the tree structure is given , learning of PLT can be solved with provable regret guaranties [ Wydmuch A decision network ( D-Net ) is further adopted to identify the quality of the input low-resolution image and adaptively select suitable sub-modules to perform SR . U-Net and D-Net together constitute the proposed RAN model , and are jointly trained using a new hierarchical loss function on SR tasks of multiple factors . Experimental evaluations demonstrate that the proposed RAN compares favorably against the state-of-the-art methods and its performance can well generalize across different upscaling factors . In this paper , we present an image-constrained particle filter approach to track 3D human motion from monocular video clips with the assistance of a pre-captured motion library . We propose two novel particle filtering criteria and design a hierarchical likelihood function . The top layer of the function consists of the particle filtering criteria , and the bottom layer consists of the likelihood functions based on image contours and edge features . In this paper , a series of approaches will be used to approximate the continuous diversity of gene expression across a series of pluripotent , totipotent , and fibroblast cellular subtypes . We will use a series of previously-collected datasets and analyze them using three complementary approaches : the computation of distances based on the subsampling of diversity , assessing the separability of individual genes for a specific cell line both within and between cell types , and a hierarchical soft classification technique that will assign a membership value for specific genes in specific cell types given a number of different criteria . These approaches will allow us to assess the observed gene-expression diversity in these datasets , as well as assess how well a priori cell types characterize their constituent populations . These vectors have proven to be useful in a variety of machine learning tasks . In this work , we propose new methods to increase the speed of the Word2Vec skip gram with hierarchical softmax architecture on multi-core shared memory CPU systems , and on modern NVIDIA GPUs with CUDA . We accomplish this on multi-core CPUs by batching training operations to increase thread locality and to reduce accesses to shared memory . We then propose new heterogeneous NVIDIA GPU CUDA implementations of both the skip gram hierarchical softmax and negative sampling techniques that utilize shared memory registers and in-warp shuffle operations for maximized performance . Our GPU skip gram with negative sampling approach produces a higher quality of word vectors than previous GPU implementations , and our flexible skip gram with hierarchical softmax implementation achieves a factor of 10 speedup of the existing methods . Neural network language models ( NNLMs ) have attracted a lot of attention recently . In this paper , we present a training method that can incrementally train the hierarchical softmax function for NNMLs . We split the cost function to model old and update corpora separately , and factorize the objective function for the hierarchical softmax . We propose a new approach to conditional probability estimation for ordinal labels . First , we present a specialized hierarchical softmax variant inspired by k-d trees that leverages the inherent spatial structure of ( potentially-multivariate ) ordinal labels . We then adapt ideas from signal processing on noisy graphs to develop a novel regularizer for such hierarchical softmax models . The approach splits the control architecture into two separate layers . The hierarchical top layer is used to implement a strategy that addresses the desired transparency , and the lower layer ensures that no “ virtual ” energy is generated . This means that any bilateral controller can be implemented in a passive manner . In order to exploit the assumption of a hierarchical generative process over datasets we will use a ‘ parameter - transfer approach ’ [ see ][] transfer_survey to extend the variational autoencoder model of variational_autoencoder . figure Left : basic hierarchical model , where the plate encodes the fact that the context variable is shared across each item in a given dataset . Center : full neural statistician model with three latent layers . subsection : Base Model Though many different variants of neural networks have been proposed for the problem of sequence tagging , we find that most of the models can be described with the hierarchical framework illustrated in Figure [ reference ] . A character - level layer takes a sequence of characters ( represented as embeddings ) as input , and outputs a representation that encodes the morphological information at the character level . More precisely , the computational complexity is where is the number of classes and the dimension of the text representation . In order to improve our running time , we use a hierarchical softmax based on the Huffman coding tree . During training , the computational complexity drops to . There , we have Each of is un - normalized log - probability for each output word , computed as where are the softmax parameters . is constructed by a concatenation or average of word vectors extracted from . In practice , hierarchical softmax is preferred to softmax for fast training . In our work , the structure of the hierarical softmax is a binary Huffman tree , where short codes are assigned to frequent words . The main challenge when is very large ( in the order of one million in this paper ) is the fact that computing all inner products between and all embeddings becomes prohibitively slow during training ( even when exploiting matrix - matrix multiplications and modern GPUs ) . Several approaches have been proposed to cope with the scaling issue : importance sampling , Noise Contrastive Estimation ( NCE ) , self normalizing partition functions or Hierarchical Softmax – they all offer good solutions to this problem . The importance of skipped connections for biomedical image segmentation tasks have been empirically evaluated with U - Net and residual networks [ reference ] . A deep contour - aware network called Deep ContourAware Networks ( DCAN ) was proposed in 2016 , which can extract multi - level contextual features using a hierarchical architecture for accurate gland segmentation of histology images and shows very good performance for segmentation [ reference ] . Furthermore , Nabla - Net : a deep dig - like convolutional architecture was proposed for segmentation in 2017 section : Discussions The Necessity of the Hierarchical Architecture The hierarchical architecture in LeakGAN serves as the mechanism of incorporating leaked information from D into G. However , in the body part , we have n't shown whether the explotation of hierachical architecture is a must . Recent research in computer vision and pattern recognition has highlighted the capabilities of Convolutional Neural Networks ( CNNs ) to solve challenging tasks such as classification , segmentation and object detection , achieving state - of - the - art performances . This success has been attributed to the ability of CNNs to learn a hierarchical representation of raw input data , without relying on handcrafted features . As the inputs are processed through the network layers , the level of abstraction of the resulting features increases . A very natural and intuitive alternative organizes classifiers in a hierarchical manner according to the divide - and - conquer strategy . Although hierarchical classification has been proven effective for conventional linear classifiers , few attempts have been made to exploit category hierarchies in deep CNN models . Since deep CNN models are large models themselves , organizing them hierarchically imposes the following challenges . First , instead of a handcrafted category hierarchy , how can we learn such a category hierarchy from the training data itself so that cascaded inferences in a hierarchical classifier will not degrade the overall accuracy while dedicated fine category classifiers exist for hard - to - distinguish categories ? Second , a hierarchical CNN classifier consists of multiple CNN models at different levels . section : Conclusion We propose deep convolutional architectures for matching natural language sentences , which can nicely combine the hierarchical modeling of individual sentences and the patterns of their matching . Empirical study shows our models can outperform competitors on a variety of matching tasks . For underactuated bipeds , control design based on input-output linearization can result in internal dynamics whose stability and convergence rate to the desired gait affect the closed-loop stability and the overall convergence rate . Because the stability and the convergence rate of the internal dynamics can only be affected by the output function design , this paper proposes a general output function design to allow for greater freedom in output function optimization and better control performance as compared with previous studies . The proposed output function design and optimization , as well as the input-output linearizing controller , are simulated on a planar bipedal robot to validate the fast convergence rate to desired gait and the high robustness to external disturbances of the proposed method . We can use technology to accurately and effectively detect falls and provide emergency first aid to falling individuals . In this study , we examined human movements for effective and efficient detection of fall action , and we performed fall detection using one-dimensional LBP ( 1D-Local Binary Pattern ) and ELM ( Extreme Leaning Machine ) method . Although only acceleration data is used in our application , these methods have achieved very good results and these results are presented at the end of the article . The proposed method consists of several steps : a ) recognizing sound signals and associating them with known classes while also being able to identify the unknown classes ; b ) detecting the hidden unknown classes among the rejected sound samples ; c ) learning those novel detected classes and updating the classifier . The experimental results illustrate the effectiveness of the developed approach in detecting unknown sound classes compared to extreme value machine ( EVM ) and Weibull-calibrated SVM ( W-SVM ) . The ILC is an immense e + e - machine planned since 2004 by a large international collaboration , to be potentially built in Japan [ 1 ] . The gigantic size of the whole research infrastructure , the involved human , technical and financial resources , and the pressure of new emerging and potentially soon to be competitive accelerator technologies , make the final building decision quite difficult . Additionally to the known identities , we introduce the concepts of known unknowns ( known , but uninteresting persons ) and unknown unknowns ( people never seen before ) to the biometric community . We compare three algorithms for assessing similarity in a deep feature space under an open-set protocol : thresholded verification-like scores , linear discriminant analysis ( LDA ) scores , and an extreme value machine ( EVM ) probabilities . Our findings suggest that thresholding EVM probabilities , which are open-set by design , outperforms thresholding verification-like scores . Finally , a hardware-in-the-loop ( HIL ) simulation was performed to verify the proposed controller . The HIL platform was composed of a dynamometer-simulated engine , a real transmission , a virtual electric machine , and a virtual vehicle , which was constructed based on the nonlinear dynamics of tires , road , and vehicle body . The HIL simulation results showed the effectiveness of the proposed robust mode transition control . Automatic extraction of protein-protein interaction ( PPI ) information from scientific literature is important for building PPI databases , studying biological networks and discovering new biological knowledge through automatic hypothesis generation . In this paper , we present a new method for PPI extraction based on a mixture of logistic models . The method automatically clusters interaction words ( words that describe the interactions of protein pairs ) into groups with similar grammatical properties . Under this framework we aim to combine the strengths of three classifiers in an ensemble approach . The three classifiers we consider are Bayesian Networks , Support Vector Machines , and mixture of logistic models defined by interaction word . The three classifiers and ensemble approach are evaluated on three benchmark corpora and one corpus that is introduced in this study . This paper investigated the performance of GEE in R ( version 3.0.2 ) , SAS ( version 9.4 ) , and SPSS ( version 22.0.0 ) using simulated data under default settings . Multivariate logistic distributions were used in the simulation to generate correlated ordinal data . The simulation study demonstrated substantial bias in the parameter estimates and numerical issues for data sets with relative small number of subjects . Moreover , finding the optimal number of linearly combined distributions using the Akaike information criterion is investigated . The results show that good fitting can be obtained by using either exponential or log-normal distributions mixture . Even though , the fitting is done for a representative case with a tempo-spatial consideration , the model is yet applicable in general for LTE and other cellular systems in a wider sense . In this paper , a systematic approach was introduced for measuring the performance of multistage systems via estimating the overall ROC curve . Two new ROC models sharing the same properties of classical ROC curves were proposed , inspired by the Gaussian and logistic distributions . The models were then experimented on a recently introduced multistage system for epileptic spike classification from electroencephalogram data . First , utilizing a novel star-tree data structure , we forecast the bid for each sample using non-linear regression by gradient boosting decision trees . Then we employ a mixture-of-log-normal model to generate campaign-level bid distribution based on the sample-level forecasted distributions . The experiment results of a system developed with our approach show that it can accurately forecast the bid distributions for various campaigns running on the world 's largest NGD advertising exchange system , outperforming two baseline methods in term of forecasting errors . Moreover , inference networks are prone to component collapsing , impairing the collection of coherent topics . To overcome these problems , we propose two new text topic models based on the categorical distribution Gumbel-Softmax ( GSDTM ) and on mixtures of Logistic-Normal distributions ( LMDTM ) . We also provide a study on the impact of different modeling choices on the generated topics , observing a trade-off between topic coherence and document reconstruction . The patterns of change in the annual household income in the countries of the European Community during the years 1994 - 1999 are explored . The income is modelled by mixtures of multivariate log-normal distributions , and the mixture components are interpreted as representing one subpopulation with steady increments and others with various levels of volatility . The method is extended to models for a combination of log-normal and categorical variables . For the possibility to estimate the parameters of a mixture model consistently on the basis of observations from the mixture , identifiability is a necessary condition . In this study , we give some results on the identifiability of multivariate logistic mixture models . Quantum mechanics has been used to relate this decrease to degradation , and device failure . Although the lifetime , and degradation of a device are typically used to characterize its reliability , in this paper we model the distribution of hot-electron activation energies , which has appeal because it exhibits a two-point discrete mixture of logistic distributions . The logistic mixture presents computational problems that are addressed in simulation .
A reasonable cost measurement that integrates with Scrum would help provide an additional feedback loop . We adapted earned value management ( EVM ) , using values defined in Scrum . The result is called AgileEVM ( agile earned value management ) and is a simplified set of earned value calculations . From the values in Scrum , we derived a release date estimate using mean velocity and from this equation , generated an equivalent equation using traditional EVM techniques , thus establishing the validity of using EVM with the Scrum framework . Finally , we used this technique on two projects to further test our hypothesis .
The whole system is complex and expensive . Using Electronic System-Level ( ESL ) software SystemVue and filter 's S parameter measured by VNA , filters ' EVM could be simulated for different communication standards . The Pearson produce-moment correlation coefficient between the simulation and measurement results is used to validate the correctness and applicability of the methodology .
This paper presents a SigmaDelta DAC based transmitter ( TX ) baseband for 802.11n / 802.16e with channel filtering and gain control performed in the digital domain . The effect of circuit imperfections on TX EVM is analyzed for a robust line-up with gain control and the design of the high speed SigmaDelta modulator is described . The SigmaDelta DAC fabricated in a 90 nm CMOS process operates up to 3.6 GHz while drawing 16 mW from a 1.3 V supply .
The T/R switch incorporates an impedance-transformation network to provide a receive S11 of -15dB at 2.4GHz and a sensitivity of -73dBm for a 54Mb/s 802.11 g signal . For 64QAM OFDM at 2.4GHz , the TX EVM is -25dB at an output power of + 16dBm .
Such visual results have possible applications in non-contact human physiological parameter measurement , such as heart rate estimation . Unfortunately , the linear EVM is sensitive to noise and frequencies of the changes should be customized , which generates a limitation of applications . This paper presents an advanced EVM for magnifying the signal amplitude in the presence of relatively high noise as well as unknown the frequencies of changes in video .
In this article , based on the working principle of vector signal analyzer and theories of error vector magnitude ( EVM ) , Combined signal demodulation experiment of vector signal analyzer , converting some types of digital modulation signal EVM into root mean square of error vector magnitude(EVMRMS ) . According to the relationship between EVMRMS and bit error rate(BER ) , analyzing the relationship between vector signal analyzer EVM and BER , and the simulation results are carried out .
Therefore , in this work to substantiate the EVM improvement , a designed 26–30 GHz CMOS six-port receiver is used which consists of LNA , coupled line bandpass filter , six-port correlator , diode power detectors and two instrumentation amplifiers for recovering the baseband I/Q data . The EVM improvement process is assessed in terms of 64QAM modulation , and according to obtained results , EVM is reduced from 61 % when using constant DC bias to 22 % achieved with an LNA bias control algorithm for low input power ( − 60 dBm ) at the center frequency of 28 GHz . Moreover , the EVM is become less sensitive to input RF power , and also the dynamic range is improved more than 20 dB.
A novel pseudo real — time 3D visualisation system is proposed in the system development and optimisation . The proposed system has been developed using the TI ( Texas Instruments ) DM642 EVM board which is a standalone Digital Media Processing board . The system also includes a stereo video capture module consisting of two PAL cameras and the X3D-19 DISPLAY AD 3D display unit for visualisation of 3D video output .
Using these values , respective probability density functions are calculated and plotted , as handy tool for risk analysis of future projects of similar characteristics . By input of variables from other projects , decision making processes can be made for a whole project or its components using EVM Analysis in project evaluation and review techniques . This framework , as a quantitative approach , depends on the assumption of normality in projects completion time , it can help greatly in determining the completion time probability for veritable projects using any of the contract forms under consideration .
The main contribution of this research paper is the real time implementation of two novel EVM ( Error Vector Magnitude ) based Multiuser Scheduling algorithms such as EVM based Strict priority scheduler ( ESPS ) , EVM based Round robin scheduler ( ERRS ) . The proposed EVM based algorithms are evaluated by implementing it on the FPGA ( Field Programmable Gate Array ) based WARP ( Wireless open- Access Research Platform ) board . Resources were allocated depending upon EVM .
Firstly , the impact of channel quality indicator(CQI ) prediction error on system throughput is evaluated emphatically ; Secondly , we study the effect of different EVM on bit error rate , block error rate and frame error rate of 64QAM at different rates under the EVM allowed when the peak rate is reached . And this paper redicts EVM standards for higher order modulation .
Since the dialog success is related to catching the want of the user , dialog state tracking is a necessary component for spoken dialog systems . This paper proposes a neural dialog state tracker with the attention mechanism for focusing on valuable words and the hierarchical softmax for efficient training of the tracker . In addition , the proposed tracker combines a natural language understanding module and a dialog state module in an end-to-end style .
These qualities thus makes the Z-loss an appealing candidate to train very efficiently large output networks such as word-language models or other extreme classification problems . On the One Billion Word ( Chelba et al. , 2014 ) dataset , we are able to train a model with the Z-loss 40 times faster than the log-softmax and more than 4 times faster than the hierarchical softmax .
Extreme multi-label classification ( XMLC ) is the problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels . Large label spaces can be efficiently handled by organizing labels as a tree , like in the hierarchical softmax ( HSM ) approach . In this paper , we investigate Probabilistic Label Trees ( PLTs ) that have been recently devised for tackling XMLC problems .
We focus on a particular subclass of these algorithms that use probabilistic classifiers in the tree nodes . Examples of such algorithms are hierarchical softmax ( HSM ) , designed for multi-class classification , and probabilistic label trees ( PLTs ) that generalize HSM to multi-label problems . If the tree structure is given , learning of PLT can be solved with provable regret guaranties [ Wydmuch
A decision network ( D-Net ) is further adopted to identify the quality of the input low-resolution image and adaptively select suitable sub-modules to perform SR . U-Net and D-Net together constitute the proposed RAN model , and are jointly trained using a new hierarchical loss function on SR tasks of multiple factors . Experimental evaluations demonstrate that the proposed RAN compares favorably against the state-of-the-art methods and its performance can well generalize across different upscaling factors .
In this paper , we present an image-constrained particle filter approach to track 3D human motion from monocular video clips with the assistance of a pre-captured motion library . We propose two novel particle filtering criteria and design a hierarchical likelihood function . The top layer of the function consists of the particle filtering criteria , and the bottom layer consists of the likelihood functions based on image contours and edge features .
In this paper , a series of approaches will be used to approximate the continuous diversity of gene expression across a series of pluripotent , totipotent , and fibroblast cellular subtypes . We will use a series of previously-collected datasets and analyze them using three complementary approaches : the computation of distances based on the subsampling of diversity , assessing the separability of individual genes for a specific cell line both within and between cell types , and a hierarchical soft classification technique that will assign a membership value for specific genes in specific cell types given a number of different criteria . These approaches will allow us to assess the observed gene-expression diversity in these datasets , as well as assess how well a priori cell types characterize their constituent populations .
These vectors have proven to be useful in a variety of machine learning tasks . In this work , we propose new methods to increase the speed of the Word2Vec skip gram with hierarchical softmax architecture on multi-core shared memory CPU systems , and on modern NVIDIA GPUs with CUDA . We accomplish this on multi-core CPUs by batching training operations to increase thread locality and to reduce accesses to shared memory . We then propose new heterogeneous NVIDIA GPU CUDA implementations of both the skip gram hierarchical softmax and negative sampling techniques that utilize shared memory registers and in-warp shuffle operations for maximized performance . Our GPU skip gram with negative sampling approach produces a higher quality of word vectors than previous GPU implementations , and our flexible skip gram with hierarchical softmax implementation achieves a factor of 10 speedup of the existing methods .
Neural network language models ( NNLMs ) have attracted a lot of attention recently . In this paper , we present a training method that can incrementally train the hierarchical softmax function for NNMLs . We split the cost function to model old and update corpora separately , and factorize the objective function for the hierarchical softmax .
We propose a new approach to conditional probability estimation for ordinal labels . First , we present a specialized hierarchical softmax variant inspired by k-d trees that leverages the inherent spatial structure of ( potentially-multivariate ) ordinal labels . We then adapt ideas from signal processing on noisy graphs to develop a novel regularizer for such hierarchical softmax models .
The approach splits the control architecture into two separate layers . The hierarchical top layer is used to implement a strategy that addresses the desired transparency , and the lower layer ensures that no “ virtual ” energy is generated . This means that any bilateral controller can be implemented in a passive manner .
In order to exploit the assumption of a hierarchical generative process over datasets we will use a ‘ parameter - transfer approach ’ [ see ][] transfer_survey to extend the variational autoencoder model of variational_autoencoder . figure Left : basic hierarchical model , where the plate encodes the fact that the context variable is shared across each item in a given dataset . Center : full neural statistician model with three latent layers .
subsection : Base Model Though many different variants of neural networks have been proposed for the problem of sequence tagging , we find that most of the models can be described with the hierarchical framework illustrated in Figure [ reference ] . A character - level layer takes a sequence of characters ( represented as embeddings ) as input , and outputs a representation that encodes the morphological information at the character level .
More precisely , the computational complexity is where is the number of classes and the dimension of the text representation . In order to improve our running time , we use a hierarchical softmax based on the Huffman coding tree . During training , the computational complexity drops to .
There , we have Each of is un - normalized log - probability for each output word , computed as where are the softmax parameters . is constructed by a concatenation or average of word vectors extracted from . In practice , hierarchical softmax is preferred to softmax for fast training . In our work , the structure of the hierarical softmax is a binary Huffman tree , where short codes are assigned to frequent words .
The main challenge when is very large ( in the order of one million in this paper ) is the fact that computing all inner products between and all embeddings becomes prohibitively slow during training ( even when exploiting matrix - matrix multiplications and modern GPUs ) . Several approaches have been proposed to cope with the scaling issue : importance sampling , Noise Contrastive Estimation ( NCE ) , self normalizing partition functions or Hierarchical Softmax – they all offer good solutions to this problem .
The importance of skipped connections for biomedical image segmentation tasks have been empirically evaluated with U - Net and residual networks [ reference ] . A deep contour - aware network called Deep ContourAware Networks ( DCAN ) was proposed in 2016 , which can extract multi - level contextual features using a hierarchical architecture for accurate gland segmentation of histology images and shows very good performance for segmentation [ reference ] . Furthermore , Nabla - Net : a deep dig - like convolutional architecture was proposed for segmentation in 2017
section : Discussions The Necessity of the Hierarchical Architecture The hierarchical architecture in LeakGAN serves as the mechanism of incorporating leaked information from D into G. However , in the body part , we have n't shown whether the explotation of hierachical architecture is a must .
Recent research in computer vision and pattern recognition has highlighted the capabilities of Convolutional Neural Networks ( CNNs ) to solve challenging tasks such as classification , segmentation and object detection , achieving state - of - the - art performances . This success has been attributed to the ability of CNNs to learn a hierarchical representation of raw input data , without relying on handcrafted features . As the inputs are processed through the network layers , the level of abstraction of the resulting features increases .
A very natural and intuitive alternative organizes classifiers in a hierarchical manner according to the divide - and - conquer strategy . Although hierarchical classification has been proven effective for conventional linear classifiers , few attempts have been made to exploit category hierarchies in deep CNN models . Since deep CNN models are large models themselves , organizing them hierarchically imposes the following challenges . First , instead of a handcrafted category hierarchy , how can we learn such a category hierarchy from the training data itself so that cascaded inferences in a hierarchical classifier will not degrade the overall accuracy while dedicated fine category classifiers exist for hard - to - distinguish categories ? Second , a hierarchical CNN classifier consists of multiple CNN models at different levels .
section : Conclusion We propose deep convolutional architectures for matching natural language sentences , which can nicely combine the hierarchical modeling of individual sentences and the patterns of their matching . Empirical study shows our models can outperform competitors on a variety of matching tasks .
For underactuated bipeds , control design based on input-output linearization can result in internal dynamics whose stability and convergence rate to the desired gait affect the closed-loop stability and the overall convergence rate . Because the stability and the convergence rate of the internal dynamics can only be affected by the output function design , this paper proposes a general output function design to allow for greater freedom in output function optimization and better control performance as compared with previous studies . The proposed output function design and optimization , as well as the input-output linearizing controller , are simulated on a planar bipedal robot to validate the fast convergence rate to desired gait and the high robustness to external disturbances of the proposed method .
We can use technology to accurately and effectively detect falls and provide emergency first aid to falling individuals . In this study , we examined human movements for effective and efficient detection of fall action , and we performed fall detection using one-dimensional LBP ( 1D-Local Binary Pattern ) and ELM ( Extreme Leaning Machine ) method . Although only acceleration data is used in our application , these methods have achieved very good results and these results are presented at the end of the article .
The proposed method consists of several steps : a ) recognizing sound signals and associating them with known classes while also being able to identify the unknown classes ; b ) detecting the hidden unknown classes among the rejected sound samples ; c ) learning those novel detected classes and updating the classifier . The experimental results illustrate the effectiveness of the developed approach in detecting unknown sound classes compared to extreme value machine ( EVM ) and Weibull-calibrated SVM ( W-SVM ) .
The ILC is an immense e + e - machine planned since 2004 by a large international collaboration , to be potentially built in Japan [ 1 ] . The gigantic size of the whole research infrastructure , the involved human , technical and financial resources , and the pressure of new emerging and potentially soon to be competitive accelerator technologies , make the final building decision quite difficult .
Additionally to the known identities , we introduce the concepts of known unknowns ( known , but uninteresting persons ) and unknown unknowns ( people never seen before ) to the biometric community . We compare three algorithms for assessing similarity in a deep feature space under an open-set protocol : thresholded verification-like scores , linear discriminant analysis ( LDA ) scores , and an extreme value machine ( EVM ) probabilities . Our findings suggest that thresholding EVM probabilities , which are open-set by design , outperforms thresholding verification-like scores .
Finally , a hardware-in-the-loop ( HIL ) simulation was performed to verify the proposed controller . The HIL platform was composed of a dynamometer-simulated engine , a real transmission , a virtual electric machine , and a virtual vehicle , which was constructed based on the nonlinear dynamics of tires , road , and vehicle body . The HIL simulation results showed the effectiveness of the proposed robust mode transition control .
Automatic extraction of protein-protein interaction ( PPI ) information from scientific literature is important for building PPI databases , studying biological networks and discovering new biological knowledge through automatic hypothesis generation . In this paper , we present a new method for PPI extraction based on a mixture of logistic models . The method automatically clusters interaction words ( words that describe the interactions of protein pairs ) into groups with similar grammatical properties .
Under this framework we aim to combine the strengths of three classifiers in an ensemble approach . The three classifiers we consider are Bayesian Networks , Support Vector Machines , and mixture of logistic models defined by interaction word . The three classifiers and ensemble approach are evaluated on three benchmark corpora and one corpus that is introduced in this study .
This paper investigated the performance of GEE in R ( version 3.0.2 ) , SAS ( version 9.4 ) , and SPSS ( version 22.0.0 ) using simulated data under default settings . Multivariate logistic distributions were used in the simulation to generate correlated ordinal data . The simulation study demonstrated substantial bias in the parameter estimates and numerical issues for data sets with relative small number of subjects .
Moreover , finding the optimal number of linearly combined distributions using the Akaike information criterion is investigated . The results show that good fitting can be obtained by using either exponential or log-normal distributions mixture . Even though , the fitting is done for a representative case with a tempo-spatial consideration , the model is yet applicable in general for LTE and other cellular systems in a wider sense .
In this paper , a systematic approach was introduced for measuring the performance of multistage systems via estimating the overall ROC curve . Two new ROC models sharing the same properties of classical ROC curves were proposed , inspired by the Gaussian and logistic distributions . The models were then experimented on a recently introduced multistage system for epileptic spike classification from electroencephalogram data .
First , utilizing a novel star-tree data structure , we forecast the bid for each sample using non-linear regression by gradient boosting decision trees . Then we employ a mixture-of-log-normal model to generate campaign-level bid distribution based on the sample-level forecasted distributions . The experiment results of a system developed with our approach show that it can accurately forecast the bid distributions for various campaigns running on the world 's largest NGD advertising exchange system , outperforming two baseline methods in term of forecasting errors .
Moreover , inference networks are prone to component collapsing , impairing the collection of coherent topics . To overcome these problems , we propose two new text topic models based on the categorical distribution Gumbel-Softmax ( GSDTM ) and on mixtures of Logistic-Normal distributions ( LMDTM ) . We also provide a study on the impact of different modeling choices on the generated topics , observing a trade-off between topic coherence and document reconstruction .
The patterns of change in the annual household income in the countries of the European Community during the years 1994 - 1999 are explored . The income is modelled by mixtures of multivariate log-normal distributions , and the mixture components are interpreted as representing one subpopulation with steady increments and others with various levels of volatility . The method is extended to models for a combination of log-normal and categorical variables .
For the possibility to estimate the parameters of a mixture model consistently on the basis of observations from the mixture , identifiability is a necessary condition . In this study , we give some results on the identifiability of multivariate logistic mixture models .
Quantum mechanics has been used to relate this decrease to degradation , and device failure . Although the lifetime , and degradation of a device are typically used to characterize its reliability , in this paper we model the distribution of hot-electron activation energies , which has appeal because it exhibits a two-point discrete mixture of logistic distributions . The logistic mixture presents computational problems that are addressed in simulation .
AgileEVM - earned value management in Scrum Projects Using SystemVue to extend VNA's application to EVM measurement A 3.6GHz, 16mW ΣΔ DAC for a 802.11n / 802.16e transmitter with 30dB digital power control in 90nm CMOS A Fully Integrated RF Front-End with Independent RX/TX Matching and +20dBm Output Power for WLAN Applications PCA-based magnification method for revealing small signals in video Study of Digital Modulation Signal Error Vector Magnitude Based on Vector Signal Analyzer Dynamic range improvement of six-port receiver through analysis of output DC offset Image-based rendering for visualisation of 3D scenes in near real-time Stochastic Risk Analysis Framework for Building Construction Projects Real-time evaluation of EVM based Strict priority and round robin schedulers Impact of EVM on Network Performance An End-to-End Neural Dialog State Tracking for Task-Oriented Dialogs The Z-loss: a shift and scale invariant classification loss belonging to the Spherical Family A no-regret generalization of hierarchical softmax to extreme multi-label classification On the computational complexity of the probabilistic label tree algorithms Resolution-Aware Network for Image Super-Resolution An Image-Constrained Particle Filter for 3D Human Motion Tracking Collective properties of cellular identity: a computational approach Efficient and accurate Word2Vec implementations in GPU and shared-memory multicore architectures Incrementally Learning the Hierarchical Softmax Function for Neural Language Models Leaf-Smoothed Hierarchical Softmax for Ordinal Prediction Bilateral Telemanipulation With Time Delays: A Two-Layer Approach Combining Passivity and Transparency Towards a Neural Statistician Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks Bag of Tricks for Efficient Text Classification Distributed Representations of Sentences and Documents Exploring the Limits of Language Modeling Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation Long Text Generation via Adversarial Training with Leaked Information V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition Convolutional Neural Network Architectures for Matching Natural Language Sentences Optimization of Output Functions with Nonholonomic Virtual Constraints in Underactuated Bipedal Walking Control Detection of Fall Event with Extreme Learning Machine and One Dimensional Local Binary Pattern Open-set Evolving Acoustic Scene Classification System The International Linear Collider - A Polish perspective Toward Open-Set Face Recognition Hardware-in-the-Loop Simulation of Robust Mode Transition Control for a Series–Parallel Hybrid Electric Vehicle Mixture of logistic models and an ensemble approach for protein-protein interaction extraction A Statistical Approach for Information Extraction of Biological Relationships GEE for longitudinal ordinal data: Comparing R-geepack, R-multgee, R-repolr, SAS-GENMOD, SPSS-GENLIN Empirical Statistical Model for LTE Downlink Channel Occupancy On the overall ROC of multistage systems Bid landscape forecasting in online ad exchange marketplace Topic Modeling using Variational Auto-Encoders with Gumbel-Softmax and Logistic-Normal Mixture Distributions Stability of household income in European countries in the 1990s Identifiability of multivariate logistic mixture models Statistical Models for Hot Electron Degradation in Nano-Scaled MOSFET Devices
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AGILE FUZZ-IEEE NeurIPS HPEC AAAI AAAI ICLR ICLR EACL ICML ArXiv ArXiv AAAI 2016 Fourth International Conference on 3D Vision (3DV) 2015 IEEE International Conference on Computer Vision (ICCV) NIPS ACC BCB KDD IJCNN
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