A new semi-supervised learning model based on k-means and transductive support vector machine is proposed to improve the accuracy of P2P traffic identification . The semi-supervised cluster algorithm of k-means is used to calculate the number of positive instances in a training set , and then the TSVM model is trained based on the number of positive instances . The model may further be refined by carrying out experiments with varied hyper-parameters . Further studies may be conducted by considering semi-supervised or unsupervised learning techniques . Recently , much work has been devoted to kernel learning , that is , the problem of finding a good kernel matrix for a given task . This can be done in a semi-supervised learning setting by using a large set of unlabeled data and a ( typically small ) set of i.i.d . labeled data . The underlying assumption is that either view alone is sufficient to predict the target Y accurately . This provides a natural semi-supervised learning setting in which unlabeled data can be used to eliminate hypothesis from either view , whose predictions tend to disagree with predictions based on the other view . This work explicitly formalizes an information theoretic , multi-view assumption and studies the multi-view paradigm in the PAC style semisupervised framework of Balcan and Blum [ 2006 ] . The proposed approach combines a machine learning-based method with a conditional random fields ( CRF ) model and a rule-based method in a post-processing phase to handle 8 PHI types with disambiguity . The CRF-based classification phase and the rule-based post-processing phase are then conducted in a semi-supervised learning manner . As compared to the existing works , our work has the merits of PHI identification such as : ( 1 ) . In a series of papers by Dai and colleagues [ 1,2 ] , a feature map ( or kernel ) was introduced for semi- and unsupervised learning . This feature map is build from the output of an ensemble of classifiers trained without using the ground-truth class labels . At learning tasks where humans typically outperform computers , neuromorphic learning machines can have potential advantages in learning in terms of power and complexity compared to mainstream technologies . Here , we present Synaptic Sampling Machines ( S2 M ) , a class of stochastic neural networks that use stochasticity at the connections ( synapses ) to implement energy efficient semi- and unsupervised learning for weakly or unlabeled data . Stochastic synapses play the dual role of a regularizer during learning and a mechanism for implementing stochasticity in neural networks . Moreover , datasets used for analysis contain only true-positive interactions , and experimentally validated negative samples are unavailable . To overcome these limitations , we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information . The proposed method initially determines similarity measures , such as similarities among samples and local correlations among the labels of the samples , by integrating biological information . Application of machine learning technique to detect malicious nodes in OBS network is relatively new . As finding sufficient amount of labeled data to perform supervised learning is difficult , semi-supervised method of learning ( SSML ) can be leveraged . In this paper , we studied the classical self-training algorithm ( ST ) which uses SSML paradigm . subsection : Model Training We train LSTM , TD - LSTM and TC - LSTM in an end - to - end way in a supervised learning framework . The loss function is the cross - entropy error of sentiment classification . Furthermore , with the help of unsupervised pre - training , we can get good feature representation which guides the learning towards basins of attraction of minima that support better generalisation from the training data . Usually , these deep models have two stages in learning : the first stage performs model initialisation via unsupervised learning ( i.e. , the restricted Boltzmann machine or stacked denoising auto - encoders ) to make the model catch the input data distribution ; the second stage involves a fine tuning of the initialised model via supervised learning with back - propagation . The novelty of our deep learning models lies in the first layer initialisation , where the input raw features are high dimensional and sparse binary features converted from the raw categorical features , which makes it hard to train traditional DNNs in large scale . Second , the challenge of performing semi - supervised learning using labeled and unlabeled data in CNN - based methods remains . Usually , the unsupervised data is used as a pre - training step before supervised learning . Our method uses all the data simultaneously . While we consider transductive learning in the above formulation , in many cases , it is desirable to learn a classifier that can generalize to unobserved instances , especially for large - scale tasks . For example , machine reading systems very frequently encounter novel entities on the Web and it is not practical to train a semi - supervised learning system on the entire Web . However , since learning graph embeddings is transductive in nature , it is not straightforward to do it in an inductive setting . In our experiments , we found that the algorithm converges rapidly . This semi - supervised learning is essentially based on self - training , a popular strategy taken by many semi - supervised learning methods . For any self - training based methods , preventing model drift is of paramount importance . The core idea of using mutual information to induce representation can be applied to other methods like VAE [ reference ] , which is a promising area of future work . Other possible extensions to this work include : learning hierarchical latent representations , improving semi - supervised learning with better codes [ reference ] , and using InfoGAN as a high - dimensional data discovery tool . A Proof of Lemma 5.1 Present work . We propose node2vec , a semi - supervised algorithm for scalable feature learning in networks . We optimize a custom graph - based objective function using SGD motivated by prior work on natural language processing . LINE showed worse performance than expected , which can be explained by its inability to reuse samples , a feat that can be easily done using the random walk methods . Even in our other two networks , where we have a mix of equivalences present , the semi - supervised nature of node2vec can help us infer the appropriate degree of exploration necessary for feature learning . In the case of PPI network , the best exploration strategy ( , ) turns out to be virtually indistinguishable from DeepWalk ’s uniform ( , ) exploration giving us only a slight edge over DeepWalk by avoiding redudancy in already visited nodes through a high value , but a convincing 23.8 % gain over LINE in Macro - F scores . Though we are free to set the search parameters based on the underlying task and domain at no additional cost , learning the best settings of our search parameters adds an overhead . However , as our experiments confirm , this overhead is minimal since node2vec is semi - supervised and hence , can learn these parameters efficiently with very little labeled data . subsection : Link prediction However , the generated data is greatly deviated from the real data , which can not make the trained model to achieve the state - of - the - art results on real dataset such as the popular ICDAR2015 Incidental Scene Text benchmark . In order to solve this problem , used semi - supervised method to finetune model on real data and obtained good results . The segmentation based methods have also been used in text detection recently . However , currently we have no influence over which characteristics are learned in the unsupervised setting which means that the model can also learn characteristics or features that are meaningless or not interpretable by humans . In the future , this can be mitigated by combining our approach with semi - supervised approaches , in which we can supply a limited amount of labels for the characteristics we are interested in to exert more control over which data - generating factors are learned while still being able to discover “ new ” generating factors which do not have to be known or specified beforehand . bibliography : References However , acquiring labels is costly , motivating the need for effective semi - supervised learning techniques that leverage unlabeled examples . A widely successful semi - supervised learning strategy for neural NLP is pre - training word vectors Mikolov2013DistributedRO . More recent work trains a Bi - LSTM sentence encoder to do language modeling and then incorporates its context - sensitive representations into supervised models dai2015semi , peters2018deep . A key disadvantage of pre - training is that the first representation learning phase does not take advantage of labeled data – the model attempts to learn generally effective representations rather than ones that are targeted towards a particular task . Older semi - supervised learning algorithms like self - training do not suffer from this problem because they continually learn about a task on a mix of labeled and unlabeled data . Self - training has historically been effective for NLP yarowsky1995unsupervised , mcclosky2006effective , but is less commonly used with neural models . b ) We show that tri - training can serve as a strong and robust semi - supervised learning baseline for the current generation of NLP models . c ) A solution is to develop a data generation pipeline . Once the scope of an assistant has been defined , a mix of crowdsourcing and semi - supervised machine learning is used to generate thousands of high - quality training examples , mimicking user data collection without compromising on privacy . The aim of this section is to describe the data generation pipeline , and to demonstrate its impact on the performance of the NLU . The error back-propagation method , in which a constant gain is introduced , can be reduced to the ordinary error back-propagation method without introducing the gain . When the dynamic gain is introduced , the method can not be reduced to the steepest descent method , as well as the momentum method , without introducing the gain . Furthermore , it is shown that there exists a characteristic superellipse that determines the behavior of the gain . We show that , contrary to popular belief , standard implementations of fixed momentum methods do no more than act to rescale the learning rate . We achieve this by showing that the momentum method converges to a gradient flow , with a momentum-dependent time-rescaling , using the method of modified equations from numerical analysis . Further we show that the momentum method admits an exponentially attractive invariant manifold on which the dynamic reduces to a gradient flow with respect to a modified loss function , equal to the original one plus a small perturbation . We present a novel momentum-based method for maintaining balance of humanoid robots . By controlling the desired ground reaction force ( GRF ) and center of pressure ( CoP ) at each support foot , our method can naturally deal with non-level and non-stationary ground at each foot-ground contact , as well as different frictional properties . While momentum-based methods , in conjunction with the stochastic gradient descent , are widely used when training machine learning models , there is little theoretical understanding on the generalization error of such methods . In practice , the momentum parameter is often chosen in a heuristic fashion with little theoretical guidance . The debris flow , induced by intensive rainfall on 24 September 2008 , was a large scale turbulent one . We analyzed the risk of Qinglin debris flow using the debris flow risk assessment model of momentum principle . Results illuminate that Qinglin gully has the characteristics of high frequency and large scale and severe risks after the earthquake . This study provides a comprehensive examination of the feasibility of a simple onedimensional Boussinesq-type model equation for such types of flow problems . This equation , which allows for curvature of the free surface and a non-hydrostatic pressure distribution , is derived using the momentum principle together with the assumption of a constant centrifugal term at a vertical section . Besides , two Boussinesq-type model equations that incorporate different degrees of corrections for the effects of the curvature of the streamline are investigated in this work . The second part reviews different approaches to match analysis in more detail , using the performance analysis classification of theoretical and practical performance analysis . The review includes the most actual theoretical performance analysis techniques in table tennis : performance indices , simulative approaches , momentum analysis , footwork analysis , and finally an approach which uses top expert knowledge for a comprehensive technical-tactical analysis . In the section of practical performance analysis , the paper presents two “ best practice ” examples describing performance analysis procedures of the two most successful countries of the last Olympics : China and Japan . We use the training recipe from weiss - etAl:2015:ACL for each training stage of our model . Specifically , we use averaged stochastic gradient descent with momentum , and we tune the learning rate , learning rate schedule , momentum , and early stopping time using a separate held - out corpus for each task . We tune again with a different set of hyperparameters for training with the global objective . Weights are initialzed to small random values drawn from a zero - mean normal distribution with standard deviation 0.01 . Momentum is used to speed up learning . The momentum starts off at a value of 0.5 and is increased linearly to 0.99 over the first 500 epochs , after which it stays at 0.99 . Motion compensation ( MoCo ) is pivotal in the processing of airborne synthetic aperture radar ( SAR ) data . Motion errors are space variant according to the data acquisition geometry , which can be split into range-variant and azimuth-variant components . To perform the first second program chip card ( C2 ) inserted in the terminal ( TE ) in addition to the chip card ( C1 ) for storing data relating to the owner of the first card , the second card ( C2 ) , the command and the exchange of responses between the first card ( C1 ) terminal ( DEM [ ISX ] , CP1 ) relays , command and exchange of response between the two cards ( DEM [ SIX = IPA ] ; CP2 ) to communicate with terminals over . For example , the terminal is a mobile radiotelephone terminal having a SIM card as the first card , it does not require any SIM Toolkit protocol interface between the terminal and the second card . These charge pumps are widely used in phase-locked loop ( PLL ) and have been compared for a number of parameters , mainly current mismatch , power consumption , voltage swing and the design complexity . We observed the current mismatch between the charging and discharging currents at control voltage of 0.9 V to be 3.88 % , 2.7 % and 3.55 % for CP1 , CP3 and CP4 , respectively , while it is 6.96 % for CP2 at control voltage of 1.3 V. At frequency of 50 MHz , CP4 consumes 377 μW power using 200 μA current source , CP3 consumes 1840μWusing 100μAcurrent source , CP1 consumes 704 μW using 80 μA current source , while CP2 consumes 756 μW power for bias voltage of 0.47 V. The voltage swing for CP1 , CP2 , CP3 and CP4 is obtained to be 0.2 , 1.275 , 0.9 and 0.3 V , respectively , at 50 MHz frequency . Corporate 's Spin Offs is originated by one or more individuals ( Spin-Off entrepreneurs)C that are looking for the development and capture of a high potential idea , that is based on the experienced acquired within the parent company[1 , 2 ] . In a collaborative effort between CORFO1- Corporación de Fomento a la Producción , CPC2 - Confederación de la Producción y del Comercio , y el IDB3 - entrusted Universidad del Desarrollo with a study to evaluate and identify the best practices that incentive the Corporative Spin Off in Chile . The program included the participation of a total of 46 companies , through five independents agencies , identifying the more relevant characteristics that the companies has to have in order to increase the probabilities of success in the implementation of corporative Spin Offs . Value-Based Communication Preservation ( VBCP ) is a behavior-based , computationally efficient approach to maintaining line-of-sight RF communication between members of robot teams in the context of other tasks . The goal of VBCP is , at each time step , to reactively choose a direction in which to move that provides the best communication quality of service with the rest of the team . VBCP uses information about other robots , real-time quality of service measurements and an a priori map of the environment to approximate an optimal direction in an efficient manner . Here , VBCP maintains communication between members of a robotic team while traversing an urban environment in formation . The system setting in this paper is general , i.e. , the velocity coupling and position coupling ( VCPC ) between agents , respectively , are set to be general and nonequal ( up to rescaling ) , together with distinct damping and stiffness gains for the VCPC , respectively . This paper has two primary contributions on consensus : 1 ) further necessary and sufficient conditions are established to guarantee the DCR problems of the system , which have enriched the previous results and 2 ) the patterns of the convergence rate contours for the DCR are characterized , in terms of the damping and stiffness gains , which are closely related to the characteristics of the spectra of the two Laplacian matrices of the VCPC . Additionally , this paper has a contribution on matrix theory , i.e. , the sufficient conditions for the simultaneous upper-triangularization of two independent Laplacian matrices , particularly from an easily verifiable topological perspective on the corresponding digraphs of these Laplacian matrices . Comparison of keyword similarity results shows that CP/CV provides significantly better ( upto 33 % ) results than existing structure-based schemes . Also , even if CP/CV does not assume the availability of an appropriate corpus to extract keyword frequency information , our approach matches ( and slightly improves on ) the performance of information-based approaches . All the infections currently reported , worldwide , are caused by either of the genetic variants of CPV2 viz . , CPV2a , CPv2b and CPV2c . This study describes a novel , combinatorial approach for rapid and direct detection of CPV2 from canine fecal samples followed by typing to determine the variants ( CPV2a , CPV2b and CPV2c ) involved in the infection . The combined approach adapts a SYBR green based real-time PCR assay for the detection followed by the mini-sequencing assay of the resultant PCR amplicons for the variant typing . We present Momentum Contrast ( MoCo ) for unsupervised visual representation learning . From a perspective on contrastive learning as dictionary look-up , we build a dynamic dictionary with a queue and a moving-averaged encoder . The alternative prospective triggering prolongs the acquisition roughly twofold . Retrospective motion compensation ( MoCo ) aims mainly at correction of phase errors or detects motion directly on the DW images . However , image-based motion detection is challenging as DWI predominantly shows tissue with restricted diffusion and has decreasing signal-to-noise ratio with increasing b -value [ 2 ] . However , despite a permanent increase of mobile internet accessibility and availability of mobile devices over the past years , a mobile learning environment that is easy to use , widely accepted by teachers and learners , uses widespread off-the-shelf software , and that covers various application scenarios and mobile devices , is not yet available . In this paper , we address this issue by presenting an approach and technical framework called " Mobile Contributions " ( " MoCo " ) . MoCo supports learners to create and send contributions through various channels ( including third-party solutions like Twitter , SMS and Facebook ) , which are collected and stored in a central repository for processing , filtering and visualization on a shared display . However , when visualizing moving targets , the image quality deteriorates quickly due to phase incoherence in the compounding process . The aim of this study was therefore to contrast different scan sequences by computer simulations in order to enable a Doppler-based motion-compensation ( MoCo ) method in 3D DWC . A “ round-trip ” scan sequence was designed to estimate the Doppler velocity by combining the phase shifts obtained in the forward and backward trips respectively . Hence , a crucial problem in most airborne SAR systems is the compensation of motion errors ; if not corrected , the image quality will considerably degrade . A first-order motion compensation ( MoCo ) technique was developed at our institute , based on a precise knowledge of the position and the velocity of the transmitter and the receiver . The problem arose while proceeding with our processing algorithm , since the reconstructed image was better focused but not completely focused . Our paper proposes a second-order MoCo technique that will considerably improve the reconstruction of the SAR image . While the first-order MoCo considered the range and azimuth times , the second-order MoCo approximation will consider the frequencies . Experiments with different sets of bistatic airborne SAR data are performed based on this solution , and some promising results are given . However , the second-order MoCo is inaccurate due to the wavelength-dependent scaling of the azimuth signal after the modified Stolt mapping . Although a third-step MoCo was introduced to make up for this drawback , it is limited in dealing with high-frequency motion errors because it is applied block-wise in the time domain . A MoCo approach is proposed in this paper integrated in the traditional omega-K algorithm , which implements the aperture-dependent second-order MoCo after the range compression and before the Stolt mapping .
A new semi-supervised learning model based on k-means and transductive support vector machine is proposed to improve the accuracy of P2P traffic identification . The semi-supervised cluster algorithm of k-means is used to calculate the number of positive instances in a training set , and then the TSVM model is trained based on the number of positive instances .
The model may further be refined by carrying out experiments with varied hyper-parameters . Further studies may be conducted by considering semi-supervised or unsupervised learning techniques .
Recently , much work has been devoted to kernel learning , that is , the problem of finding a good kernel matrix for a given task . This can be done in a semi-supervised learning setting by using a large set of unlabeled data and a ( typically small ) set of i.i.d . labeled data .
The underlying assumption is that either view alone is sufficient to predict the target Y accurately . This provides a natural semi-supervised learning setting in which unlabeled data can be used to eliminate hypothesis from either view , whose predictions tend to disagree with predictions based on the other view . This work explicitly formalizes an information theoretic , multi-view assumption and studies the multi-view paradigm in the PAC style semisupervised framework of Balcan and Blum [ 2006 ] .
The proposed approach combines a machine learning-based method with a conditional random fields ( CRF ) model and a rule-based method in a post-processing phase to handle 8 PHI types with disambiguity . The CRF-based classification phase and the rule-based post-processing phase are then conducted in a semi-supervised learning manner . As compared to the existing works , our work has the merits of PHI identification such as : ( 1 ) .
In a series of papers by Dai and colleagues [ 1,2 ] , a feature map ( or kernel ) was introduced for semi- and unsupervised learning . This feature map is build from the output of an ensemble of classifiers trained without using the ground-truth class labels .
At learning tasks where humans typically outperform computers , neuromorphic learning machines can have potential advantages in learning in terms of power and complexity compared to mainstream technologies . Here , we present Synaptic Sampling Machines ( S2 M ) , a class of stochastic neural networks that use stochasticity at the connections ( synapses ) to implement energy efficient semi- and unsupervised learning for weakly or unlabeled data . Stochastic synapses play the dual role of a regularizer during learning and a mechanism for implementing stochasticity in neural networks .
Moreover , datasets used for analysis contain only true-positive interactions , and experimentally validated negative samples are unavailable . To overcome these limitations , we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information . The proposed method initially determines similarity measures , such as similarities among samples and local correlations among the labels of the samples , by integrating biological information .
Application of machine learning technique to detect malicious nodes in OBS network is relatively new . As finding sufficient amount of labeled data to perform supervised learning is difficult , semi-supervised method of learning ( SSML ) can be leveraged . In this paper , we studied the classical self-training algorithm ( ST ) which uses SSML paradigm .
subsection : Model Training We train LSTM , TD - LSTM and TC - LSTM in an end - to - end way in a supervised learning framework . The loss function is the cross - entropy error of sentiment classification .
Furthermore , with the help of unsupervised pre - training , we can get good feature representation which guides the learning towards basins of attraction of minima that support better generalisation from the training data . Usually , these deep models have two stages in learning : the first stage performs model initialisation via unsupervised learning ( i.e. , the restricted Boltzmann machine or stacked denoising auto - encoders ) to make the model catch the input data distribution ; the second stage involves a fine tuning of the initialised model via supervised learning with back - propagation . The novelty of our deep learning models lies in the first layer initialisation , where the input raw features are high dimensional and sparse binary features converted from the raw categorical features , which makes it hard to train traditional DNNs in large scale .
Second , the challenge of performing semi - supervised learning using labeled and unlabeled data in CNN - based methods remains . Usually , the unsupervised data is used as a pre - training step before supervised learning . Our method uses all the data simultaneously .
While we consider transductive learning in the above formulation , in many cases , it is desirable to learn a classifier that can generalize to unobserved instances , especially for large - scale tasks . For example , machine reading systems very frequently encounter novel entities on the Web and it is not practical to train a semi - supervised learning system on the entire Web . However , since learning graph embeddings is transductive in nature , it is not straightforward to do it in an inductive setting .
In our experiments , we found that the algorithm converges rapidly . This semi - supervised learning is essentially based on self - training , a popular strategy taken by many semi - supervised learning methods . For any self - training based methods , preventing model drift is of paramount importance .
The core idea of using mutual information to induce representation can be applied to other methods like VAE [ reference ] , which is a promising area of future work . Other possible extensions to this work include : learning hierarchical latent representations , improving semi - supervised learning with better codes [ reference ] , and using InfoGAN as a high - dimensional data discovery tool . A Proof of Lemma 5.1
Present work . We propose node2vec , a semi - supervised algorithm for scalable feature learning in networks . We optimize a custom graph - based objective function using SGD motivated by prior work on natural language processing .
LINE showed worse performance than expected , which can be explained by its inability to reuse samples , a feat that can be easily done using the random walk methods . Even in our other two networks , where we have a mix of equivalences present , the semi - supervised nature of node2vec can help us infer the appropriate degree of exploration necessary for feature learning . In the case of PPI network , the best exploration strategy ( , ) turns out to be virtually indistinguishable from DeepWalk ’s uniform ( , ) exploration giving us only a slight edge over DeepWalk by avoiding redudancy in already visited nodes through a high value , but a convincing 23.8 % gain over LINE in Macro - F scores .
Though we are free to set the search parameters based on the underlying task and domain at no additional cost , learning the best settings of our search parameters adds an overhead . However , as our experiments confirm , this overhead is minimal since node2vec is semi - supervised and hence , can learn these parameters efficiently with very little labeled data . subsection : Link prediction
However , the generated data is greatly deviated from the real data , which can not make the trained model to achieve the state - of - the - art results on real dataset such as the popular ICDAR2015 Incidental Scene Text benchmark . In order to solve this problem , used semi - supervised method to finetune model on real data and obtained good results . The segmentation based methods have also been used in text detection recently .
However , currently we have no influence over which characteristics are learned in the unsupervised setting which means that the model can also learn characteristics or features that are meaningless or not interpretable by humans . In the future , this can be mitigated by combining our approach with semi - supervised approaches , in which we can supply a limited amount of labels for the characteristics we are interested in to exert more control over which data - generating factors are learned while still being able to discover “ new ” generating factors which do not have to be known or specified beforehand . bibliography : References
However , acquiring labels is costly , motivating the need for effective semi - supervised learning techniques that leverage unlabeled examples . A widely successful semi - supervised learning strategy for neural NLP is pre - training word vectors Mikolov2013DistributedRO . More recent work trains a Bi - LSTM sentence encoder to do language modeling and then incorporates its context - sensitive representations into supervised models dai2015semi , peters2018deep .
A key disadvantage of pre - training is that the first representation learning phase does not take advantage of labeled data – the model attempts to learn generally effective representations rather than ones that are targeted towards a particular task . Older semi - supervised learning algorithms like self - training do not suffer from this problem because they continually learn about a task on a mix of labeled and unlabeled data . Self - training has historically been effective for NLP yarowsky1995unsupervised , mcclosky2006effective , but is less commonly used with neural models .
b ) We show that tri - training can serve as a strong and robust semi - supervised learning baseline for the current generation of NLP models . c )
A solution is to develop a data generation pipeline . Once the scope of an assistant has been defined , a mix of crowdsourcing and semi - supervised machine learning is used to generate thousands of high - quality training examples , mimicking user data collection without compromising on privacy . The aim of this section is to describe the data generation pipeline , and to demonstrate its impact on the performance of the NLU .
The error back-propagation method , in which a constant gain is introduced , can be reduced to the ordinary error back-propagation method without introducing the gain . When the dynamic gain is introduced , the method can not be reduced to the steepest descent method , as well as the momentum method , without introducing the gain . Furthermore , it is shown that there exists a characteristic superellipse that determines the behavior of the gain .
We show that , contrary to popular belief , standard implementations of fixed momentum methods do no more than act to rescale the learning rate . We achieve this by showing that the momentum method converges to a gradient flow , with a momentum-dependent time-rescaling , using the method of modified equations from numerical analysis . Further we show that the momentum method admits an exponentially attractive invariant manifold on which the dynamic reduces to a gradient flow with respect to a modified loss function , equal to the original one plus a small perturbation .
We present a novel momentum-based method for maintaining balance of humanoid robots . By controlling the desired ground reaction force ( GRF ) and center of pressure ( CoP ) at each support foot , our method can naturally deal with non-level and non-stationary ground at each foot-ground contact , as well as different frictional properties .
While momentum-based methods , in conjunction with the stochastic gradient descent , are widely used when training machine learning models , there is little theoretical understanding on the generalization error of such methods . In practice , the momentum parameter is often chosen in a heuristic fashion with little theoretical guidance .
The debris flow , induced by intensive rainfall on 24 September 2008 , was a large scale turbulent one . We analyzed the risk of Qinglin debris flow using the debris flow risk assessment model of momentum principle . Results illuminate that Qinglin gully has the characteristics of high frequency and large scale and severe risks after the earthquake .
This study provides a comprehensive examination of the feasibility of a simple onedimensional Boussinesq-type model equation for such types of flow problems . This equation , which allows for curvature of the free surface and a non-hydrostatic pressure distribution , is derived using the momentum principle together with the assumption of a constant centrifugal term at a vertical section . Besides , two Boussinesq-type model equations that incorporate different degrees of corrections for the effects of the curvature of the streamline are investigated in this work .
The second part reviews different approaches to match analysis in more detail , using the performance analysis classification of theoretical and practical performance analysis . The review includes the most actual theoretical performance analysis techniques in table tennis : performance indices , simulative approaches , momentum analysis , footwork analysis , and finally an approach which uses top expert knowledge for a comprehensive technical-tactical analysis . In the section of practical performance analysis , the paper presents two “ best practice ” examples describing performance analysis procedures of the two most successful countries of the last Olympics : China and Japan .
We use the training recipe from weiss - etAl:2015:ACL for each training stage of our model . Specifically , we use averaged stochastic gradient descent with momentum , and we tune the learning rate , learning rate schedule , momentum , and early stopping time using a separate held - out corpus for each task . We tune again with a different set of hyperparameters for training with the global objective .
Weights are initialzed to small random values drawn from a zero - mean normal distribution with standard deviation 0.01 . Momentum is used to speed up learning . The momentum starts off at a value of 0.5 and is increased linearly to 0.99 over the first 500 epochs , after which it stays at 0.99 .
Motion compensation ( MoCo ) is pivotal in the processing of airborne synthetic aperture radar ( SAR ) data . Motion errors are space variant according to the data acquisition geometry , which can be split into range-variant and azimuth-variant components .
To perform the first second program chip card ( C2 ) inserted in the terminal ( TE ) in addition to the chip card ( C1 ) for storing data relating to the owner of the first card , the second card ( C2 ) , the command and the exchange of responses between the first card ( C1 ) terminal ( DEM [ ISX ] , CP1 ) relays , command and exchange of response between the two cards ( DEM [ SIX = IPA ] ; CP2 ) to communicate with terminals over . For example , the terminal is a mobile radiotelephone terminal having a SIM card as the first card , it does not require any SIM Toolkit protocol interface between the terminal and the second card .
These charge pumps are widely used in phase-locked loop ( PLL ) and have been compared for a number of parameters , mainly current mismatch , power consumption , voltage swing and the design complexity . We observed the current mismatch between the charging and discharging currents at control voltage of 0.9 V to be 3.88 % , 2.7 % and 3.55 % for CP1 , CP3 and CP4 , respectively , while it is 6.96 % for CP2 at control voltage of 1.3 V. At frequency of 50 MHz , CP4 consumes 377 μW power using 200 μA current source , CP3 consumes 1840μWusing 100μAcurrent source , CP1 consumes 704 μW using 80 μA current source , while CP2 consumes 756 μW power for bias voltage of 0.47 V. The voltage swing for CP1 , CP2 , CP3 and CP4 is obtained to be 0.2 , 1.275 , 0.9 and 0.3 V , respectively , at 50 MHz frequency .
Corporate 's Spin Offs is originated by one or more individuals ( Spin-Off entrepreneurs)C that are looking for the development and capture of a high potential idea , that is based on the experienced acquired within the parent company[1 , 2 ] . In a collaborative effort between CORFO1- Corporación de Fomento a la Producción , CPC2 - Confederación de la Producción y del Comercio , y el IDB3 - entrusted Universidad del Desarrollo with a study to evaluate and identify the best practices that incentive the Corporative Spin Off in Chile . The program included the participation of a total of 46 companies , through five independents agencies , identifying the more relevant characteristics that the companies has to have in order to increase the probabilities of success in the implementation of corporative Spin Offs .
Value-Based Communication Preservation ( VBCP ) is a behavior-based , computationally efficient approach to maintaining line-of-sight RF communication between members of robot teams in the context of other tasks . The goal of VBCP is , at each time step , to reactively choose a direction in which to move that provides the best communication quality of service with the rest of the team . VBCP uses information about other robots , real-time quality of service measurements and an a priori map of the environment to approximate an optimal direction in an efficient manner . Here , VBCP maintains communication between members of a robotic team while traversing an urban environment in formation .
The system setting in this paper is general , i.e. , the velocity coupling and position coupling ( VCPC ) between agents , respectively , are set to be general and nonequal ( up to rescaling ) , together with distinct damping and stiffness gains for the VCPC , respectively . This paper has two primary contributions on consensus : 1 ) further necessary and sufficient conditions are established to guarantee the DCR problems of the system , which have enriched the previous results and 2 ) the patterns of the convergence rate contours for the DCR are characterized , in terms of the damping and stiffness gains , which are closely related to the characteristics of the spectra of the two Laplacian matrices of the VCPC . Additionally , this paper has a contribution on matrix theory , i.e. , the sufficient conditions for the simultaneous upper-triangularization of two independent Laplacian matrices , particularly from an easily verifiable topological perspective on the corresponding digraphs of these Laplacian matrices .
Comparison of keyword similarity results shows that CP/CV provides significantly better ( upto 33 % ) results than existing structure-based schemes . Also , even if CP/CV does not assume the availability of an appropriate corpus to extract keyword frequency information , our approach matches ( and slightly improves on ) the performance of information-based approaches .
All the infections currently reported , worldwide , are caused by either of the genetic variants of CPV2 viz . , CPV2a , CPv2b and CPV2c . This study describes a novel , combinatorial approach for rapid and direct detection of CPV2 from canine fecal samples followed by typing to determine the variants ( CPV2a , CPV2b and CPV2c ) involved in the infection . The combined approach adapts a SYBR green based real-time PCR assay for the detection followed by the mini-sequencing assay of the resultant PCR amplicons for the variant typing .
We present Momentum Contrast ( MoCo ) for unsupervised visual representation learning . From a perspective on contrastive learning as dictionary look-up , we build a dynamic dictionary with a queue and a moving-averaged encoder .
The alternative prospective triggering prolongs the acquisition roughly twofold . Retrospective motion compensation ( MoCo ) aims mainly at correction of phase errors or detects motion directly on the DW images . However , image-based motion detection is challenging as DWI predominantly shows tissue with restricted diffusion and has decreasing signal-to-noise ratio with increasing b -value [ 2 ] .
However , despite a permanent increase of mobile internet accessibility and availability of mobile devices over the past years , a mobile learning environment that is easy to use , widely accepted by teachers and learners , uses widespread off-the-shelf software , and that covers various application scenarios and mobile devices , is not yet available . In this paper , we address this issue by presenting an approach and technical framework called " Mobile Contributions " ( " MoCo " ) . MoCo supports learners to create and send contributions through various channels ( including third-party solutions like Twitter , SMS and Facebook ) , which are collected and stored in a central repository for processing , filtering and visualization on a shared display .
However , when visualizing moving targets , the image quality deteriorates quickly due to phase incoherence in the compounding process . The aim of this study was therefore to contrast different scan sequences by computer simulations in order to enable a Doppler-based motion-compensation ( MoCo ) method in 3D DWC . A “ round-trip ” scan sequence was designed to estimate the Doppler velocity by combining the phase shifts obtained in the forward and backward trips respectively .
Hence , a crucial problem in most airborne SAR systems is the compensation of motion errors ; if not corrected , the image quality will considerably degrade . A first-order motion compensation ( MoCo ) technique was developed at our institute , based on a precise knowledge of the position and the velocity of the transmitter and the receiver . The problem arose while proceeding with our processing algorithm , since the reconstructed image was better focused but not completely focused . Our paper proposes a second-order MoCo technique that will considerably improve the reconstruction of the SAR image . While the first-order MoCo considered the range and azimuth times , the second-order MoCo approximation will consider the frequencies . Experiments with different sets of bistatic airborne SAR data are performed based on this solution , and some promising results are given .
However , the second-order MoCo is inaccurate due to the wavelength-dependent scaling of the azimuth signal after the modified Stolt mapping . Although a third-step MoCo was introduced to make up for this drawback , it is limited in dealing with high-frequency motion errors because it is applied block-wise in the time domain . A MoCo approach is proposed in this paper integrated in the traditional omega-K algorithm , which implements the aperture-dependent second-order MoCo after the range compression and before the Stolt mapping .
P2P Traffic Identification via k-Means Based Transductive Support Vetor Machine Classification of non-small cell lung cancer using one-dimensional convolutional neural network Transfer Learning by Kernel Meta-Learning An Information Theoretic Framework for Multi-view Learning A Hybrid Semi-supervised Learning Approach to Identifying Protected Health Information in Electronic Medical Records Comment on "Ensemble Projection for Semi-supervised Image Classification" Stochastic neuromorphic learning machines for weakly labeled data Predicting Drug–Target Interactions With Multi-Information Fusion Semi-supervised learning approach using modified self-training algorithm to counter burst header packet flooding attack in optical burst switching network Effective LSTMs for Target-Dependent Sentiment Classification Deep Learning over Multi-field Categorical Data - - A Case Study on User Response Prediction Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro Revisiting Semi-Supervised Learning with Graph Embeddings Learning a Discriminative Null Space for Person Re-identification InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets node2vec: Scalable Feature Learning for Networks Sliding Line Point Regression for Shape Robust Scene Text Detection Inferencing based on unsupervised learning of disentangled representations Semi-Supervised Sequence Modeling with Cross-View Training Strong Baselines for Neural Semi-supervised Learning under Domain Shift Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces Analysis of the error back-propagation learning algorithms with gain Analysis Of Momentum Methods Ground reaction force control at each foot: A momentum-based humanoid balance controller for non-level and non-stationary ground On the Stability and Convergence of Stochastic Gradient Descent with Momentum Characteristics and risk analysis of Qinglin debris flow induced by “5.12” Wenchuan Earthquake in Beichuan County, Sichuan, China A one-dimensional Boussinesq-type momentum model for steady rapidly varied open channel flows Table tennis match analysis: a review Globally Normalized Transition-Based Neural Networks Improving neural networks by preventing co-adaptation of feature detectors Improvements to the Frequency Division-Based Subaperture Algorithm for Motion Compensation in Wide-Beam SAR Preparation and execution of the program in the terminal of the additional chip card Comparison of low current mismatch CMOS charge pumps for analog PLLS using 180 nm technology Identification of best practices to achieve innovation, corporate entrepreneurship and spinoff in Chilean companies Value-Based Communication Preservation for Mobile Robots The Designated Convergence Rate Problems of Consensus or Flocking of Double-Integrator Agents With General Nonequal Velocity and Position Couplings: Further Results and Patterns of Convergence Rate Contours CP/CV: concept similarity mining without frequency information from domain describing taxonomies Detection and Typing of CPV with Real-Time PCR and Mini-Sequencing Momentum Contrast for Unsupervised Visual Representation Learning Motion Compensation for Free-Breathing Diffusion-Weighted Imaging (MoCo DWI) in Whole Body Integrated PET-MRI Enabling and Evaluating Mobile Learning Scenarios with Multiple Input Channels Motion compensation and sequence optimization for 3D diverging wave compounding: A simulation study Second-order motion compensation in bistatic airborne SAR based on a geometrical approach A motion compensation approach integrated in the omega-K algorithm for airborne SAR
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ICML Unsupervised and Transfer Learning COLT IMCOM ICCD COLING ECIR 2017 IEEE International Conference on Computer Vision (ICCV) ICML 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS KDD 2018 24th International Conference on Pattern Recognition (ICPR) ESANN EMNLP ACL ArXiv IROS IGARSS (2) ACL ArXiv IEEM DARS CIKM CRIWG IGARSS
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