Supervised learning in a one-layered , hard-limited perceptron can be formulated into a set of linear inequalities containing the unknown weight coefficients . Solving these inequalities under a generalized separability condition by a noniterative method then achieves the goal of supervised learning . Third , we achieve state-of-the-art performance on DAVIS 2017 video segmentation and JHMDB keypoint tracking tasks , outperforming all previous self-supervised learning approaches by a significant margin . Fourth , in order to shed light on the potential of self-supervised learning on the task of video correspondence flow , we probe the upper bound by training on additional data , \ie more diverse videos , further demonstrating significant improvements on video segmentation . In this paper , we present the first work to exploit unlabeled 360-degree data for image representation learning . We propose middle-out , a new self-supervised learning task , which leverages the spatial configuration of normal field-of-view images sampled from a 360-degree image as supervisory signal . We train a Siamese ConvNet model to identify the middle image among three shuffled images sampled from a panorama by perspective projection . In this study , we propose a new method for Li-ion battery health assessment based on multi-layer characteristic integration and deep learning . The proposed method realizes multi-layer characteristic fusion ( charge current , discharge voltage , charge time under constant voltage etc . ) of relative input parameters by unsupervised self-learning both in the data-layer and the feature-layer , in which the effect of multi-feature fusion can be improved by refactoring learning . Based on the feature extracted by the fusion of multi-layer self-learning , the health assessment of Li-ion battery under multi-feature fusion can be realized using the logistic regression model . Secondly , a fuzzy classifier is constructed to solve the ambiguity of sentiment intensity . Furthermore , two different selfsupervised methods using pseudo-labeled training data are proposed to learn the optimum parameters of the fuzzy classifier . Experimental results in four different datasets prove that our model improves 6.25 % more accuracy on average than the competitive baselines in sentiment classification of Chinese reviews . As a result , these algorithms are poorly suited to the task of tracking unstructured roads typical in desert environments . In this paper , we propose a road following algorithm that operates in a selfsupervised learning regime , allowing it to adapt to changing road conditions while making no assumptions about the general structure or appearance of the road surface . An application of optical flow techniques , paired with one-dimensional template matching , allows identification of regions in the current camera image that closely resemble the learned appearance of the road in the recent past . Only short range traversable regions can be detected using traditional methods based on stereo vision because of the limited image resolution and baseline of stereo vision . In this paper , we propose a novel method to detect long range traversable regions without using any supervised or self-supervised learning process . Superpixels are clustered using an improved spectral clustering algorithm to segment the image effectively , and after integrating short range traversable region detection based on u-v-disparity , the traversable region can be extended to long range naturally . But the prediction performance will degenerate very fast as the new point gets away from the convex hull ( extrapolation ) . In this paper , we suggest regarding the prediction not just a mathematical extrapolation , but a mathematics-assisted physical problem , and propose a supervised self-learning scheme , adaptive space transformation ( AST ) , for the prediction . AST tries to automatically detect an underlying invariant relation with the known data under the supervision of physicists . We propose an unsupervised approach to predict depth maps from images captured by a wireless endoscopic capsule . Recent advances in deep learning have shown that accurate depth maps can be predicted from a single image , where the deep network is trained via unsupervised or self-supervised learning by using monocular video sequences or stereo image pairs . However , directly applying these techniques to endoscopic images does not yield satisfactory results owing to the inherent difficulties of the wireless capsule imaging such as dim lighting and low-resolution of images , which are different from normal imaging conditions . In this paper , we suggest a new methodology . Our approach is focused on background extraction , as a complimentary operation to standard foreground object segmentation , using self-organizing feature selective property of unsupervised self-learning paradigm based on the competitive algorithm . The results of our studies show that background segmentation can be achievable in efficient manner . This guarantees that the augmented dataset generally supports a better characterization of the class distributions during embedding learning . The second aspect , on the other hand , supports the usage of supervised learning [ reference ] , a different mechanism from [ reference ] which leverages unlabeled GAN images for regularization . section : Baseline Deep Re - ID Model Since these 2D images do not have any 3D annotations , it is not straightforward to exploit them in 3D face model learning . We design a novel self - supervised learning method that is able to train a 3D face model with weak supervision from 2D images . In particular , the proposed method takes the sparse annotated 2D landmarks as input and fully leverage the consistency within the 2D - to - 2D and 3D - to - 3D self - mapping procedure as supervision . • We introduce a new method that is able to train 3D face models with 2D face images by self - supervised learning . The devised multiple forms of self - supervision are effective and data efficient . For example , recent works show that the temporal information from videos can be used as a plentiful source of supervision to learn embeddings that are useful for various tasks [ reference ][ reference ] . Other works show that deep features learned from egomotion supervision perform better than features using class - labels as supervision for many tasks [ reference ] . Analogous to these recent works in self - supervised learning , our method of extracting training data and correspondence labels from existing RGB - D reconstructions online is fully automatic , and does not require any manual labor or human supervision . section : Learning From Reconstructions Specifically , designing models with tractable learning , sampling , inference and evaluation is crucial in solving this task . We extend the space of such models using real - valued non - volume preserving ( real NVP ) transformations , a set of powerful , stably invertible , and learnable transformations , resulting in an unsupervised learning algorithm with exact log - likelihood computation , exact and efficient sampling , exact and efficient inference of latent variables , and an interpretable latent space . We demonstrate its ability to model natural images on four datasets through sampling , log - likelihood evaluation , and latent variable manipulations . section : Discussion We described Paragraph Vector , an unsupervised learning algorithm that learns vector representations for variable - length pieces of texts such as sentences and documents . The vector representations are learned to predict the surrounding words in contexts sampled from the paragraph . Given a corrupted input image , the model synthesizes a spatially varying linear filter which , when applied to the input image , reconstructs the desired output . The model parameters are learned using supervised or self - supervised training . We test this model on three tasks : non - uniform motion blur removal , lossy - compression artifact reduction and single image super resolution . During training , we use a loss defined over the transformed image ( rather than the predicted flow ) . This is closely related to so - called self - supervised techniques that learn to predict optical flow and depth from unlabeled video data . Specifically , for the reconstruction tasks we consider such as image super - resolution , the forward degradation process can be easily simulated to generate a large quantity of training data without manual collection or annotation . Several CNN architectures have been developed for image generation . Fully convolutional networks can be trained in the supervised learning manner to generate an image from an input image . The generative CNN model stacks four convolution layers upon five fully connected layers to generate images from object description . The performance of each sub - head is assessed independently , and best and average performances are reported . paragraph : Unsupervised learning analysis . IIC is highly capable of discovering clusters in unlabelled data that accurately correspond to the underlying semantic classes , and outperforms all competing baselines at this task ( t : img_clus_iid ) , with significant margins of and in the case of STL10 and CIFAR10 . This suggests that collection of a larger - scale dataset to study visual pretraining may greatly benefit the field . Our findings also suggest a bright future for unsupervised or self - supervised representation learning approaches . It seems the scale of data can overpower noise in the label space . The experiment is conducted on EEG database for emotions i.e. , DEAP data set . In present work , the data set used contains EEG recording from 4 participants ( S01 , S02 , S03 , S04 ) from 15 channel ( FP1 , FP2 , F3 , P3 , F4 , T7 , T8 , P4 , O1 , PZ , PO3 , O2 , P7 , CP2 , and C4 ) having 40 trails each . The paper explore the capability of proposed features namely Mean , Standard deviation , Variance , Shannon entropy , Hjorth parameters and Band power . Data standardization helps to provide a relatively balanced representation between larger-scale synoptic systems ( e.g. anticyclones ) and smaller-scale synoptic features ( e.g. thermal lows ) , and also improves the level of pattern self-organization on the SOM across seasons . The additional documentation in this paper encourages a wider application of CP2 in environmental research . Abstract With advances in next-generation sequencing technologies , numerous novel transcripts in a large number of organisms have been identified . With the goal of fast , accurate assessment of the coding ability of RNA transcripts , we upgraded the coding potential calculator CPC1 to CPC2 . CPC2 runs ∼1000 times faster than CPC1 and exhibits superior accuracy compared with CPC1 , especially for long non-coding transcripts . The near infrared fingerprint of persimmon was acquired by principal component analysis(PCA ) , Cand support vector machine ( SVM ) methods were used to further identify the persimmon separately . The result of PCA indicated that the score map made by the scores of PCI , CPC2 and PC3 was used , and 8 principal components ( PCs ) were selected as the input of support vector machine ( SVM ) based on the reliabilities of PCs of 99 . 888%.51 persimmon samples were used for calibration and the remaining 15 persimmon samples were used for validation . The general case of VBCP on weighted graphs is known to be NP-hard , and we show that the special case on unweighted graphs is also NP-hard for any fixed integer ri¾?1 . We present an analytical lower bound on the cardinality of an optimal solution to VBCP , as well as formulate VBCP as a linear 0 - 1 program with an exponential number of constraints . Facet-inducing inequalities for the convex hull of feasible solutions to VBCP are also identified . The adaptive robust control ( ARC ) algorithm and the feedforward PID algorithm are introduced into the VCPC system for the purpose of obtaining better pressure tracking performance . The effectiveness of the proposed VCPC system is validated through the compared experiments . Electromagnetic interference is considered and experiment results are carefully analyzed . In this paper , we propose a concept propagation ( CP ) scheme , which relies on the semantical relationships between concepts implied by the structure of the hierarchy to annotate each concept-node with a concept-vector ( CV ) . We refer to this approach as CP/CV . 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 . | Canine parvovirus causes an acute enteric disease in dogs . 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 . The real-time PCR assay reported here was standardized and validated with plasmid constructs bearing target DNA of CPV2a , 2b , 2c ; and CPV-2b genome as source of templates . The assay could detect all three variants of CPV2 with a lower limit of detection ( LOD ) of one copy per reaction . Subsequently , the real-time PCR with viral genomic DNA extracted from tissue culture fluid ( TCF ) post viral culture showed a LOD of 5fg of genomic DNA per reaction . In this study , we constructed PERSIANN-CDR using the newest version of GPCP ( V2.3 ) . We compared the PERSIANN-CDR dataset that is constructed using GPCP V2.3 ( from here on referred to as PERSIANN-CDR V2.3 ) with the PERSIANN-CDR constructed using GPCP V2.2 ( from here on PERSIANN-CDR V2.2 ) , at monthly and daily scales for the period from 2009 to 2013 . First , we discuss the changes between PERSIANN-CDR V2.3 and V2.2 over the land and ocean . Since Convolutional codes and Turbo codes are widely used in wireless communication systems , TI DSPs integrate two high-performance embedded coprocessors ( enhanced Viterbi decoder coprocessor and enhanced Turbo decoder coprocessor ) that significantly speed up channel-decoding operations on-chip . This application note presents the strategies and programming methodology to optimize VCP2 and TCP2 BER performance on TI DSPs . It is assumed the reader is familiar with Convolutional and Turbo coding theory . It allows them to discover and acquire large repertoires of skills through self-generation , self-selection , self-ordering and self-experimentation of learning goals . We present the unsupervised multi-goal reinforcement learning formal framework as well as an algorithmic approach called intrinsically motivated goal exploration processes ( IMGEP ) to enable similar properties of autonomous learning in machines . The IMGEP algorithmic architecture relies on several principles : 1 ) self-generation of goals as parameterized reinforcement learning problems ; 2 ) selection of goals based on intrinsic rewards ; 3 ) exploration with parameterized time-bounded policies and fast incremental goal-parameterized policy search ; 4 ) systematic reuse of information acquired when targeting a goal for improving other goals . Exploration is a cornerstone both for machine learning algorithms and for science in general to discover novel solutions , phenomena and behaviors . Intrinsically motivated goal exploration processes ( IMGEPs ) were shown to enable autonomous agents to efficiently explore the diversity of effects they can produce on their environment . With IMGEPs , agents self-define their own experiments by imagining goals , then try to achieve them by leveraging their past discoveries . Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments . These exploration algorithms have been shown to allow real world robots to acquire skills such as tool use in high-dimensional continuous state and action spaces . Using a continuous GOL as a testbed , we show that recent intrinsically-motivated machine learning algorithms ( POP-IMGEPs ) , initially developed for learning of inverse models in robotics , can be transposed and used in this novel application area . These algorithms combine intrinsically-motivated goal exploration and unsupervised learning of goal space representations . Goal space representations describe the interesting features of patterns for which diverse variations should be discovered . A Study Using Intrinsically Motivated Exploration Architectures . This paper introduces an intrinsically motivated sensorimotor exploration architecture which considers social reinforcement and motor constraint awareness . We argue that proprioception provides a way to deal with redundancy in the sensorimotor model and that social reinforcement provides clues to the learner of sensorimotor regions where it might be interesting to explore . The experimental results show that using both mechanisms , proprioception and social reinforcement , improves the performance of intrinsically motivated exploration architectures . We provide theoretical guarantees of R3L exploration finding successful solutions , as well as bounds for its sampling complexity . We experimentally demonstrate the method outperforms classic and intrinsic exploration techniques , requiring only a fraction of exploration samples and achieving better asymptotic performance . This article studies how developmental phonetic learning can be guided by pure curiosity-driven exploration , also called intrinsically motivated exploration . Phonetic learning refers here to learning how to control a vocal tract to reach acoustic goals . Several intrinsically motivated learning techniques have been proposed [ 10 ] [ 11 ] [ 12 ] . In this article , we will build on a particular intrinsically motivated , goal-oriented technique initiated by Baranes and Oudeyer [ 7 ] , which defines the interest of an area of the sensorimotor space as the progress of the competence in reaching self-assigned goals in this area . This method has yielded excellent results in experiment with motor spaces of high dimension . Our algorithm builds an internal representation of the state space that handles the computation of curiosity signals using the learned predictive models and extends the Continuous-Actor-Critic-Learning-Automaton to use extrinsic and intrinsic feedback . Evaluation of our algorithm on simple and complex robotic control tasks shows a significant performance gain for the intrinsically motivated goal reaching agent compared to agents that are only motivated by extrinsic rewards . In order to address these limitations , there is a need for adaptive methods that can solve the problem in an online and robust manner . In this paper , we propose a novel developmental method that utilizes the adversarial self-play between an intrinsically motivated preference exploration component , and a policy coverage set optimization component that robustly evolves a convex coverage set of policies to solve the problem using preferences proposed by the former component . We show experimentally the effectiveness of the proposed method in comparison to state-of-the-art multi-objective reinforcement learning methods in stationary and non-stationary environments . It allows them to discover and acquire large repertoires of skills through self-generation , self-selection , self-ordering and self-experimentation of learning goals . We present the unsupervised multi-goal reinforcement learning formal framework as well as an algorithmic approach called intrinsically motivated goal exploration processes ( IMGEP ) to enable similar properties of autonomous learning in machines . The IMGEP algorithmic architecture relies on several principles : 1 ) self-generation of goals as parameterized reinforcement learning problems ; 2 ) selection of goals based on intrinsic rewards ; 3 ) exploration with parameterized time-bounded policies and fast incremental goal-parameterized policy search ; 4 ) systematic reuse of information acquired when targeting a goal for improving other goals . We present a particularly efficient form of IMGEP that uses a modular representation of goal spaces as well as intrinsic rewards based on learning progress . IMGEPs were shown to enable efficient discovery and learning of diverse repertoires of skills in high-dimensional robots . In this article , we show that the IMGEP framework can also be used in an entirely different application area : automated discovery of self-organized patterns in complex morphogenetic systems . We also introduce a new IMGEP algorithm where goal representations are learned online and incrementally ( past approaches used precollected training data with batch learning ) . For experimentation , we use Lenia , a continuous game-of-life cellular automaton . To achieve this , this problem is transformed into a surrogate continuous bandit problem where the teacher samples environments in order to maximize absolute learning progress of its student . We present a new algorithm modeling absolute learning progress with Gaussian mixture models ( ALP-GMM ) . We also adapt existing algorithms and provide a complete study in the context of DRL .
Supervised learning in a one-layered , hard-limited perceptron can be formulated into a set of linear inequalities containing the unknown weight coefficients . Solving these inequalities under a generalized separability condition by a noniterative method then achieves the goal of supervised learning .
Third , we achieve state-of-the-art performance on DAVIS 2017 video segmentation and JHMDB keypoint tracking tasks , outperforming all previous self-supervised learning approaches by a significant margin . Fourth , in order to shed light on the potential of self-supervised learning on the task of video correspondence flow , we probe the upper bound by training on additional data , \ie more diverse videos , further demonstrating significant improvements on video segmentation .
In this paper , we present the first work to exploit unlabeled 360-degree data for image representation learning . We propose middle-out , a new self-supervised learning task , which leverages the spatial configuration of normal field-of-view images sampled from a 360-degree image as supervisory signal . We train a Siamese ConvNet model to identify the middle image among three shuffled images sampled from a panorama by perspective projection .
In this study , we propose a new method for Li-ion battery health assessment based on multi-layer characteristic integration and deep learning . The proposed method realizes multi-layer characteristic fusion ( charge current , discharge voltage , charge time under constant voltage etc . ) of relative input parameters by unsupervised self-learning both in the data-layer and the feature-layer , in which the effect of multi-feature fusion can be improved by refactoring learning . Based on the feature extracted by the fusion of multi-layer self-learning , the health assessment of Li-ion battery under multi-feature fusion can be realized using the logistic regression model .
Secondly , a fuzzy classifier is constructed to solve the ambiguity of sentiment intensity . Furthermore , two different selfsupervised methods using pseudo-labeled training data are proposed to learn the optimum parameters of the fuzzy classifier . Experimental results in four different datasets prove that our model improves 6.25 % more accuracy on average than the competitive baselines in sentiment classification of Chinese reviews .
As a result , these algorithms are poorly suited to the task of tracking unstructured roads typical in desert environments . In this paper , we propose a road following algorithm that operates in a selfsupervised learning regime , allowing it to adapt to changing road conditions while making no assumptions about the general structure or appearance of the road surface . An application of optical flow techniques , paired with one-dimensional template matching , allows identification of regions in the current camera image that closely resemble the learned appearance of the road in the recent past .
Only short range traversable regions can be detected using traditional methods based on stereo vision because of the limited image resolution and baseline of stereo vision . In this paper , we propose a novel method to detect long range traversable regions without using any supervised or self-supervised learning process . Superpixels are clustered using an improved spectral clustering algorithm to segment the image effectively , and after integrating short range traversable region detection based on u-v-disparity , the traversable region can be extended to long range naturally .
But the prediction performance will degenerate very fast as the new point gets away from the convex hull ( extrapolation ) . In this paper , we suggest regarding the prediction not just a mathematical extrapolation , but a mathematics-assisted physical problem , and propose a supervised self-learning scheme , adaptive space transformation ( AST ) , for the prediction . AST tries to automatically detect an underlying invariant relation with the known data under the supervision of physicists .
We propose an unsupervised approach to predict depth maps from images captured by a wireless endoscopic capsule . Recent advances in deep learning have shown that accurate depth maps can be predicted from a single image , where the deep network is trained via unsupervised or self-supervised learning by using monocular video sequences or stereo image pairs . However , directly applying these techniques to endoscopic images does not yield satisfactory results owing to the inherent difficulties of the wireless capsule imaging such as dim lighting and low-resolution of images , which are different from normal imaging conditions .
In this paper , we suggest a new methodology . Our approach is focused on background extraction , as a complimentary operation to standard foreground object segmentation , using self-organizing feature selective property of unsupervised self-learning paradigm based on the competitive algorithm . The results of our studies show that background segmentation can be achievable in efficient manner .
This guarantees that the augmented dataset generally supports a better characterization of the class distributions during embedding learning . The second aspect , on the other hand , supports the usage of supervised learning [ reference ] , a different mechanism from [ reference ] which leverages unlabeled GAN images for regularization . section : Baseline Deep Re - ID Model
Since these 2D images do not have any 3D annotations , it is not straightforward to exploit them in 3D face model learning . We design a novel self - supervised learning method that is able to train a 3D face model with weak supervision from 2D images . In particular , the proposed method takes the sparse annotated 2D landmarks as input and fully leverage the consistency within the 2D - to - 2D and 3D - to - 3D self - mapping procedure as supervision .
• We introduce a new method that is able to train 3D face models with 2D face images by self - supervised learning . The devised multiple forms of self - supervision are effective and data efficient .
For example , recent works show that the temporal information from videos can be used as a plentiful source of supervision to learn embeddings that are useful for various tasks [ reference ][ reference ] . Other works show that deep features learned from egomotion supervision perform better than features using class - labels as supervision for many tasks [ reference ] . Analogous to these recent works in self - supervised learning , our method of extracting training data and correspondence labels from existing RGB - D reconstructions online is fully automatic , and does not require any manual labor or human supervision . section : Learning From Reconstructions
Specifically , designing models with tractable learning , sampling , inference and evaluation is crucial in solving this task . We extend the space of such models using real - valued non - volume preserving ( real NVP ) transformations , a set of powerful , stably invertible , and learnable transformations , resulting in an unsupervised learning algorithm with exact log - likelihood computation , exact and efficient sampling , exact and efficient inference of latent variables , and an interpretable latent space . We demonstrate its ability to model natural images on four datasets through sampling , log - likelihood evaluation , and latent variable manipulations .
section : Discussion We described Paragraph Vector , an unsupervised learning algorithm that learns vector representations for variable - length pieces of texts such as sentences and documents . The vector representations are learned to predict the surrounding words in contexts sampled from the paragraph .
Given a corrupted input image , the model synthesizes a spatially varying linear filter which , when applied to the input image , reconstructs the desired output . The model parameters are learned using supervised or self - supervised training . We test this model on three tasks : non - uniform motion blur removal , lossy - compression artifact reduction and single image super resolution .
During training , we use a loss defined over the transformed image ( rather than the predicted flow ) . This is closely related to so - called self - supervised techniques that learn to predict optical flow and depth from unlabeled video data . Specifically , for the reconstruction tasks we consider such as image super - resolution , the forward degradation process can be easily simulated to generate a large quantity of training data without manual collection or annotation .
Several CNN architectures have been developed for image generation . Fully convolutional networks can be trained in the supervised learning manner to generate an image from an input image . The generative CNN model stacks four convolution layers upon five fully connected layers to generate images from object description .
The performance of each sub - head is assessed independently , and best and average performances are reported . paragraph : Unsupervised learning analysis . IIC is highly capable of discovering clusters in unlabelled data that accurately correspond to the underlying semantic classes , and outperforms all competing baselines at this task ( t : img_clus_iid ) , with significant margins of and in the case of STL10 and CIFAR10 .
This suggests that collection of a larger - scale dataset to study visual pretraining may greatly benefit the field . Our findings also suggest a bright future for unsupervised or self - supervised representation learning approaches . It seems the scale of data can overpower noise in the label space .
The experiment is conducted on EEG database for emotions i.e. , DEAP data set . In present work , the data set used contains EEG recording from 4 participants ( S01 , S02 , S03 , S04 ) from 15 channel ( FP1 , FP2 , F3 , P3 , F4 , T7 , T8 , P4 , O1 , PZ , PO3 , O2 , P7 , CP2 , and C4 ) having 40 trails each . The paper explore the capability of proposed features namely Mean , Standard deviation , Variance , Shannon entropy , Hjorth parameters and Band power .
Data standardization helps to provide a relatively balanced representation between larger-scale synoptic systems ( e.g. anticyclones ) and smaller-scale synoptic features ( e.g. thermal lows ) , and also improves the level of pattern self-organization on the SOM across seasons . The additional documentation in this paper encourages a wider application of CP2 in environmental research .
Abstract With advances in next-generation sequencing technologies , numerous novel transcripts in a large number of organisms have been identified . With the goal of fast , accurate assessment of the coding ability of RNA transcripts , we upgraded the coding potential calculator CPC1 to CPC2 . CPC2 runs ∼1000 times faster than CPC1 and exhibits superior accuracy compared with CPC1 , especially for long non-coding transcripts .
The near infrared fingerprint of persimmon was acquired by principal component analysis(PCA ) , Cand support vector machine ( SVM ) methods were used to further identify the persimmon separately . The result of PCA indicated that the score map made by the scores of PCI , CPC2 and PC3 was used , and 8 principal components ( PCs ) were selected as the input of support vector machine ( SVM ) based on the reliabilities of PCs of 99 . 888%.51 persimmon samples were used for calibration and the remaining 15 persimmon samples were used for validation .
The general case of VBCP on weighted graphs is known to be NP-hard , and we show that the special case on unweighted graphs is also NP-hard for any fixed integer ri¾?1 . We present an analytical lower bound on the cardinality of an optimal solution to VBCP , as well as formulate VBCP as a linear 0 - 1 program with an exponential number of constraints . Facet-inducing inequalities for the convex hull of feasible solutions to VBCP are also identified .
The adaptive robust control ( ARC ) algorithm and the feedforward PID algorithm are introduced into the VCPC system for the purpose of obtaining better pressure tracking performance . The effectiveness of the proposed VCPC system is validated through the compared experiments . Electromagnetic interference is considered and experiment results are carefully analyzed .
In this paper , we propose a concept propagation ( CP ) scheme , which relies on the semantical relationships between concepts implied by the structure of the hierarchy to annotate each concept-node with a concept-vector ( CV ) . We refer to this approach as CP/CV . 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 .
| Canine parvovirus causes an acute enteric disease in dogs . 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 . The real-time PCR assay reported here was standardized and validated with plasmid constructs bearing target DNA of CPV2a , 2b , 2c ; and CPV-2b genome as source of templates . The assay could detect all three variants of CPV2 with a lower limit of detection ( LOD ) of one copy per reaction . Subsequently , the real-time PCR with viral genomic DNA extracted from tissue culture fluid ( TCF ) post viral culture showed a LOD of 5fg of genomic DNA per reaction .
In this study , we constructed PERSIANN-CDR using the newest version of GPCP ( V2.3 ) . We compared the PERSIANN-CDR dataset that is constructed using GPCP V2.3 ( from here on referred to as PERSIANN-CDR V2.3 ) with the PERSIANN-CDR constructed using GPCP V2.2 ( from here on PERSIANN-CDR V2.2 ) , at monthly and daily scales for the period from 2009 to 2013 . First , we discuss the changes between PERSIANN-CDR V2.3 and V2.2 over the land and ocean .
Since Convolutional codes and Turbo codes are widely used in wireless communication systems , TI DSPs integrate two high-performance embedded coprocessors ( enhanced Viterbi decoder coprocessor and enhanced Turbo decoder coprocessor ) that significantly speed up channel-decoding operations on-chip . This application note presents the strategies and programming methodology to optimize VCP2 and TCP2 BER performance on TI DSPs . It is assumed the reader is familiar with Convolutional and Turbo coding theory .
It allows them to discover and acquire large repertoires of skills through self-generation , self-selection , self-ordering and self-experimentation of learning goals . We present the unsupervised multi-goal reinforcement learning formal framework as well as an algorithmic approach called intrinsically motivated goal exploration processes ( IMGEP ) to enable similar properties of autonomous learning in machines . The IMGEP algorithmic architecture relies on several principles : 1 ) self-generation of goals as parameterized reinforcement learning problems ; 2 ) selection of goals based on intrinsic rewards ; 3 ) exploration with parameterized time-bounded policies and fast incremental goal-parameterized policy search ; 4 ) systematic reuse of information acquired when targeting a goal for improving other goals .
Exploration is a cornerstone both for machine learning algorithms and for science in general to discover novel solutions , phenomena and behaviors . Intrinsically motivated goal exploration processes ( IMGEPs ) were shown to enable autonomous agents to efficiently explore the diversity of effects they can produce on their environment . With IMGEPs , agents self-define their own experiments by imagining goals , then try to achieve them by leveraging their past discoveries .
Intrinsically motivated goal exploration algorithms enable machines to discover repertoires of policies that produce a diversity of effects in complex environments . These exploration algorithms have been shown to allow real world robots to acquire skills such as tool use in high-dimensional continuous state and action spaces .
Using a continuous GOL as a testbed , we show that recent intrinsically-motivated machine learning algorithms ( POP-IMGEPs ) , initially developed for learning of inverse models in robotics , can be transposed and used in this novel application area . These algorithms combine intrinsically-motivated goal exploration and unsupervised learning of goal space representations . Goal space representations describe the interesting features of patterns for which diverse variations should be discovered .
A Study Using Intrinsically Motivated Exploration Architectures . This paper introduces an intrinsically motivated sensorimotor exploration architecture which considers social reinforcement and motor constraint awareness .
We argue that proprioception provides a way to deal with redundancy in the sensorimotor model and that social reinforcement provides clues to the learner of sensorimotor regions where it might be interesting to explore . The experimental results show that using both mechanisms , proprioception and social reinforcement , improves the performance of intrinsically motivated exploration architectures .
We provide theoretical guarantees of R3L exploration finding successful solutions , as well as bounds for its sampling complexity . We experimentally demonstrate the method outperforms classic and intrinsic exploration techniques , requiring only a fraction of exploration samples and achieving better asymptotic performance .
This article studies how developmental phonetic learning can be guided by pure curiosity-driven exploration , also called intrinsically motivated exploration . Phonetic learning refers here to learning how to control a vocal tract to reach acoustic goals .
Several intrinsically motivated learning techniques have been proposed [ 10 ] [ 11 ] [ 12 ] . In this article , we will build on a particular intrinsically motivated , goal-oriented technique initiated by Baranes and Oudeyer [ 7 ] , which defines the interest of an area of the sensorimotor space as the progress of the competence in reaching self-assigned goals in this area . This method has yielded excellent results in experiment with motor spaces of high dimension .
Our algorithm builds an internal representation of the state space that handles the computation of curiosity signals using the learned predictive models and extends the Continuous-Actor-Critic-Learning-Automaton to use extrinsic and intrinsic feedback . Evaluation of our algorithm on simple and complex robotic control tasks shows a significant performance gain for the intrinsically motivated goal reaching agent compared to agents that are only motivated by extrinsic rewards .
In order to address these limitations , there is a need for adaptive methods that can solve the problem in an online and robust manner . In this paper , we propose a novel developmental method that utilizes the adversarial self-play between an intrinsically motivated preference exploration component , and a policy coverage set optimization component that robustly evolves a convex coverage set of policies to solve the problem using preferences proposed by the former component . We show experimentally the effectiveness of the proposed method in comparison to state-of-the-art multi-objective reinforcement learning methods in stationary and non-stationary environments .
It allows them to discover and acquire large repertoires of skills through self-generation , self-selection , self-ordering and self-experimentation of learning goals . We present the unsupervised multi-goal reinforcement learning formal framework as well as an algorithmic approach called intrinsically motivated goal exploration processes ( IMGEP ) to enable similar properties of autonomous learning in machines . The IMGEP algorithmic architecture relies on several principles : 1 ) self-generation of goals as parameterized reinforcement learning problems ; 2 ) selection of goals based on intrinsic rewards ; 3 ) exploration with parameterized time-bounded policies and fast incremental goal-parameterized policy search ; 4 ) systematic reuse of information acquired when targeting a goal for improving other goals . We present a particularly efficient form of IMGEP that uses a modular representation of goal spaces as well as intrinsic rewards based on learning progress .
IMGEPs were shown to enable efficient discovery and learning of diverse repertoires of skills in high-dimensional robots . In this article , we show that the IMGEP framework can also be used in an entirely different application area : automated discovery of self-organized patterns in complex morphogenetic systems . We also introduce a new IMGEP algorithm where goal representations are learned online and incrementally ( past approaches used precollected training data with batch learning ) . For experimentation , we use Lenia , a continuous game-of-life cellular automaton .
To achieve this , this problem is transformed into a surrogate continuous bandit problem where the teacher samples environments in order to maximize absolute learning progress of its student . We present a new algorithm modeling absolute learning progress with Gaussian mixture models ( ALP-GMM ) . We also adapt existing algorithms and provide a complete study in the context of DRL .
Characteristics of a deterministic supervised learning scheme Self-supervised Learning for Video Correspondence Flow Self-supervised Representation Learning Using 360° Data Li-ion Battery Health Estimation Based on Multi-layer Characteristic Fusion and Deep Learning A multi-granularity fuzzy computing model for sentiment classification of Chinese reviews Adaptive Road Following using Self-Supervised Learning and Reverse Optical Flow Long range traversable region detection based on superpixels clustering for mobile robots Adaptive space transformation: An invariant based method for predicting aerodynamic coefficients of hypersonic vehicles Learning Depth from Endoscopic Images Background Segmentation in Color Image Using Self-Organizing Feature Selection Camera Style Adaptation for Person Re-identification Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions Density estimation using Real NVP Distributed Representations of Sentences and Documents Image Reconstruction with Predictive Filter Flow Deep Identity-aware Transfer of Facial Attributes Invariant Information Clustering for Unsupervised Image Classification and Segmentation Revisiting Unreasonable Effectiveness of Data in Deep Learning Era Classification of Human Emotions using Multiwavelet Transform based Features and Random Forest Technique Insights into the implementation of synoptic weather‐type classification using self‐organizing maps: an Australian case study CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features NIR Spectroscopy Identification of Persimmon Varieties Based on PCA-SVM Minimum vertex blocker clique problem The design of low cost valve-and-pump compounded pressure control system for the hydro viscous clutch CP/CV: concept similarity mining without frequency information from domain describing taxonomies Detection and Typing of CPV with Real-Time PCR and Mini-Sequencing Evaluation of PERSIANN-CDR Constructed Using GPCP V2.2 and V2.3 and A Comparison with TRMM 3B42 V7 and CPC Unified Gauge-Based Analysis in Global Scale Tuning VCP2 and TCP2 Bit Error Rate Performance Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning Intrinsically Motivated Exploration for Automated Discovery of Patterns in Morphogenetic Systems Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems Social Reinforcement in Artificial Prelinguistic Development: A Study Using Intrinsically Motivated Exploration Architectures Social reinforcement in intrinsically motivated sensorimotor exploration for embodied agents with constraint awareness Reinforcement Learning with Probabilistically Complete Exploration Curiosity-driven phonetic learning Intrisically Motivated Goal Space Creation for Autonomous Goal-Directed Exploration in High-Dimens Curiosity-driven exploration enhances motor skills of continuous actor-critic learner Evolving Robust Policy Coverage Sets in Multi-Objective Markov Decision Processes Through Intrinsically Motivated Self-Play Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning Intrinsically Motivated Exploration for Automated Discovery of Patterns in Morphogenetic Systems Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments
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ACM Multimedia Robotics: Science and Systems IROS 3DV 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR ICML ArXiv ArXiv 2019 IEEE/CVF International Conference on Computer Vision (ICCV) 2017 IEEE International Conference on Computer Vision (ICCV) CCTA (2) CIKM ICDL-EPIROB ICDL-EPIROB
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