Experimentally it is observed that , a linear relationship exist between stress speech subspace and speech subspace . After linear transformation on speech subspace , speech recognizer outperforms by 7.57 % ( 62.14 % to 69.71 % ) under angry stress condition . Moreover , a novel nonlinear transformation of the common drive model input is proposed , which ensures that the model force output equals the desired target force . On a physiological level , this corresponds to adjusting the rate coding model to the force generating capabilities of the simulated muscle , while from a control theoretic point of view , this step is equivalent to an exact linearizing transformation of the controlled neuromuscular system . Finally , an alternative analytical formulation of the EMG model is proposed , which renders the physiological meaning of the model more clear and facilitates a mathematical proof that muscle fibers in this model at no point in time represent a net current source or sink . To address this problem , a novel strategy named adaptive subspace feature matching ( ASFM ) is proposed in this paper in order to integrate both the marginal and conditional distributions within a unified framework ( without any labeled samples from target subjects ) . Specifically , we develop a linear transformation function which matches the marginal distributions of the source and target subspaces without a regularization term . This significantly decreases the time complexity of our domain adaptation procedure . We consider the problem of developing a linear transformation process to compensate for range-dependent bistatic clutter spectral dispersion . Clutter non-homogeneity is problematic in space-time adaptive processing ( STAP ) for airborne bistatic radar systems . Second ( network alignment ) , guided by the high-quality node representations , our proposed non-rigid point set alignment approach overcomes the bottleneck of the linear transformation assumption . We conduct extensive experiments that demonstrate the proposed non-rigid alignment method is ( 1 ) effective , outperforming both the state-of-the-art linear transformation-based methods and node representation based methods , and ( 2 ) efficient , with a comparable computational time between the proposed multi-network representation learning component and its single-network counterpart . Abstract Conventional geometrical methods for mining applications such as geologic mapping , cross-sections , logging , stereonets , contours and others are so sophisticated that often additional explanations are needed . After studying conventional geometrical methods , we decided to analyze both general slope curves and discontinuities of open pit mines used in excavations in rock mass to achieve meaningful 3D results by using linear mathematical transformation and isometric perspective methods . In addition , a new reorganization of discontinuities as a construction method has been developed according to their spatial orientations . The circuit consists of opamps , resistors , capacitors and a pair of semiconductor diodes connected in anti-parallel to synthesize the nonlinear component necessary for chaotic oscillations . The model is described by a continuous time three-dimensional autonomous system with hyperbolic sine nonlinearity , and may be viewed as a linear transformation of model MO15 previously introduced in [ Sprott , 2010 ] . The structure of the equilibrium points and the discrete symmetries of the model equations are discussed . Then these vectors are fed into a bi-channel convolutional neural network model and the convolution operations re performed on them . Finally , the outputs of the bi-channel convolution operations are combined together and fed into a series of linear transformation operations to get the final relation classification result . Our method integrates syntactic tree features and convolutional neural network architecture together and elaborates their advantages fully . Through the assignment scheme , if the higher-level DM is not satisfied with the resulting solutions of objective functions , the re-solving process is easily conducted by adjusting the level of relative satisfaction for the associated lower-level DMs . A linearization transformation approach is also presented to facilitate the solution process . To emphasize some important fuzzy goals , a weighting scheme is considered in this paper . As it can be seen , sentence embeddings are obtained by applying a max - pooling operation over the output of a BiLSTM encoder . These sentence embeddings are used to initialize the decoder LSTM through a linear transformation , and are also concatenated to its input embeddings at every time step . Note that there is no other connection between the encoder and the decoder , as we want all relevant information of the input sequence to be captured by the sentence embedding . We aim at cutting down high precision operations and parameters both in training and inference stages of DU - Net . The bit - width of weights can be reduced to one or two bits through sign function or symmetrical threshold , whereas the layerwise gradients and inputs are quantized with linear mapping . In previous XNOR - Net , a scaling factor was introduced to approximate the real - value weight . We introduce a novel architecture , the Synchronized Spectral CNN ( SyncSpecCNN ) to address these issues . The basic architecture of our neural network is similar to the fully convolutional segmentation network of , namely , we repeat the operation of convolving a vertex function by kernels and applying a non - linear transformation . However , our network combines processing in the primal and dual ( spectral ) domains . For their final performance on LFW ( ) the authors combine 50 responses ( regular and flipped ) . Both PCA and a Joint Bayesian model that effectively correspond to a linear transform in the embedding space are employed . Their method does not require explicit 2D / 3D alignment . More specifically , is firstly represented as a one - hot vector , and it is then mapped into a continuous space by a linear embedding matrix as Given the segment label , we use an RNN to map the acoustic segment to a fixed - dimensional vector representation , i.e. , where denotes the initial hidden state , denotes the duration of the segment and is a non - linear function . We take the final hidden state as the segment embedding vector , then can be represented as where corresponds to one layer or multiple layers of linear or non - linear transformation . In fact , it is flexible to include other relevant features as additional inputs to the function , e.g. , the duration feature which can be obtained by converting into another embedding vector . The projection of a 3D point into its 2D location in the image plane involves two steps . First , the 3D point is rotated and translated using a linear view transformation , under the assumption that the camera is still where and are the 3D rotation and translation components , respectively . Then , a non - linear perspective transformation is applied as where is the focal length in pixel units ( we assume that the and components of the focal length are equal ) and is the principal point that is set to the image center . A new landmark configuration was determined by simply transforming the training landmarks to the current displacement location while preserving the topological structure of the configuration of landmarks . Two assumptions are made : 1 ) In a new pair of images the deformation is approximately the same size and has only been spatially relocated in the image , and that by a simple affine transformation one can identify the optimal configuration on this new pair of images ; and 2 ) The deformation is of similar size and shape on the original pair of images . These are reasonable assumptions in many cases where one seeks to register tumor images at multiple time points following application of therapy and to evaluate changes in tumor size . The robustness investigation was carried out in three different registration campaigns using overlapping images extracted from google earth and image pair acquired from an Unmanned Aerial Vehicle ( UAV ) . Sum of Square Difference ( SSD ) and Bilinear interpolation models were used to establish the similarity measure between the images to be registered , resampling of the pixel-values and computation of non-integer coordinates respectively while Random Sampling Consensus ( RANSAC ) algorithm was used to exclude the outliers and to compute the transformation matrix using affine transformation function . The results obtained from this preliminary investigation shows that the processing speed of MSER is quite high for Automatic Image Registration with a relatively high accuracy . To overcome this problem , data augmentation ( DA ) is useful solution . In this paper , we investigate the effects of three DA approaches ; affine transformation-based approach , synthetic image-based approach , and generative adversarial network (GAN)-based approach , and evaluate the effectiveness of DA for pupil center point detection task using an off-the-shelf CNN model . We set eleven conditions of the training data , and evaluated the detection accuracy . Then , the feature points in the binary geometric image are extracted , and the topological structure is constructed . After that , the pattern elements are extracted with the affine transformation theory and bilinear interpolation algorithm . The identification of pattern elements is modeled as a supervised classification problem , and the convolutional neural network technique is adopted to recognize the pattern elements by collecting a large number of training samples . In this work , we investigate cellular simultaneous recurrent networks ( CSRNs ) to learn topological image mappings , particularly those of the affine transformations . While affine image transformation in conventional image processing is a relatively simple task , learning these transformations is an important part of having neural networks ( NNs ) function as generalized image processors . We introduce the CSRN and discuss its adaptation for image processing tasks . The invention discloses a method and device for positioning feature information of a target object . The content includes : dividing an image frame including a target object , determining a target object region , using a feature information classifier to scan the determined target object region , determining central point position information of a region that feature information to be positioned occupies in the target object region , performing affine transformation operation on the determined central point position information , and obtaining initial position information corresponding to the region that the feature information to be positioned occupies in the target object region , thereby improving precision of initial positioning ; performing iteration processing on the obtained initial position information , obtaining position information corresponding to the region that the feature information to be positioned occupies in the target object region , integrating the obtained position information corresponding to the region that the feature information to be positioned occupies in the target object region , and obtaining the feature information of the target object , thereby improving precision of positioning the feature information of the target object . CONCLUSIONS The proposed approach led to the successful visualisation of non-invasive fiducial markers using photography and MRI ( TIRM and TSE2D ( SPAIR ) sequences ) . This visualisation , combined with an affine transformation process provided a simple , cost-effective way to accurately co-register photographs and MR images of subcutaneous hematomas located on the thigh . Further investigation of the novel markers and the proposed co-visualisation approach holds potential to improve not only the forensic documentation of soft tissue lesions , but to also improve certain clinical applications , including the area of dermatology . Image edge blur is obtained by using the traditional nonlinear diffusion image de-noising method ; Shearlet is a new-style multi-scale geometry analysis tool . It creates Shearlet functions , which have different characteristics through zooming , shearing translating and other affine transforming methods and enables its capable of optimally sparse representation . The paper proposed discusses adaptive intelligent single particle optimizer based image de-noising in Shearlet domain . Specific topics include : Loading images from JAR files Describing shapes and the affine transformation in Java 2D Designing a class library of shapes Designing a component class for interactive drawing Designing a component class for displaying images Printing in Java 2D Implementing pan and zoom Implementing interactive rotate , shear , and flip Manipulating images at the pixel level using the Java 2D API Working with tiles in JAI to render large images Image manipulation in JAI usingaffine transformation , projections , and warping Image analysis in JAI , including edge detection , statistics , and region of interest computation ( ROI ) Remote imaging using RMI and JAI Internet imaging , including the use of Java 2D and JAI with servlets and JSP , the design of Web-based imaging applications , and the Internet Imaging Protocol ( IIP ) Using the new Image I/O framework to read and write images " Putting It All Together " sections help you put vital concepts and techniques into practice with interactive examples using actual applications . The same matching operations are performed , except that matching occurs between P , H , and the image . Since the textual and visual vectors have different dimensionality and belong to different spaces , we first map them to a mutual space using an affine transformation . We match textual and image vectors using a cosine operation , as before . This ensures that as the model is training , layers can continue learning on input distributions that exhibit less internal covariate shift , thus accelerating the training . Furthermore , the learned affine transform applied to these normalized activations allows the BN transform to represent the identity transformation and preserves the network capacity . subsection : Training and Inference with Batch - Normalized Networks For instance , we have tried a larger batch size of 8 to be able to use BatchNorm ( and take advantage of batch statistics ) , however due to the GPU memory limits this modification required to use a smaller image crop size , and resulted in worse performance . We have also experimented with more sophisticated data augmentation techniques , including random histogram matching , affine image transforms , and random image filtering , which did not demonstrate any additional improvements . We have tried several data post - processing techniques to fine tune the segmentation predictions with CRF , but did not find it beneficial ( it helped for some images , but made some other image segmentation results worse ) . The features of all nodes and edges are projected to a vector space of common dimension ( typically = 300 ) . The question nodes and edges use vector embeddings implemented as look - up tables , and the scene nodes and edges use affine projections : with the word embedding ( usually pretrained , see supplementary material ) , the embedding of dependencies , and weight matrices , and and biases . section : Processing graphs with neural networks The results of SNLI experiments are summarized in Table 1 . First , we can see that LSTM - based leaf transformation has a clear advantage over the affine - transformation - based one . It improves the performance substantially and also leads to faster convergence . We introduce a method that optimizes a linear projection for an information retrieval task : retrieving neighbors of input samples based on their low-dimensional visualization coordinates only . The simple linear projection makes the method easy to interpret , while the visualization task is made well-defined by the novel information retrieval criterion . The method has a further advantage : it projects input features , but the input neighborhoods it preserves can be given separately from the input features , e.g. by external data of sample similarities . The adaptive Parallel Subgradient Projection ( PSP ) technique improves the convergence speed , in noisy environment , of linear-projection-based algorithms ( e.g. , NLMS and APA ) , with low computational complexity . The technique utilizes weighted average of the metric projections onto a series of closed half-spaces which contain , with high probability , unknown system to be identified . In discrete time , the state transition is described at the finest resolution and the sampling frequencies of sensors decrease successively by a factor of two in resolution . We can build a discrete model of the system by using a linear projection operator to approximate the state space projection . This discrete model satisfies the requirements of discrete Kalman filtering , which actually offers an optimal estimation algorithm of the system . For preserving the low rank properties the same , we proposed an algorithm , called linear preserving projection based on low rank representations(LLRLPP),to reduce the dimension of data . It can preserve the low rank properties of the original data space in the resulting low dimensional embedding subspace and correctly learn the low-dimensional subspace . Principal component analysis(PCA ) and Singular value decomposition(SVD ) are two algebra methods used in linear projection analysis to reduce the dimensions in algebra space . The paper analyses the two methods in detail and deduces the relation of PCA to SVD when they are used in dimension reduction . We aim to mathematically obtain the probability of recovery failure in distributed sensing and sampling for a Multiple Sensor System ( MSS ) . In this system , sensors take samples while compressing the signal with linear projection operations using the idea of compressive sensing ( CS ) [ 1 ] . In particular , we show that a bound for per-sensor measurements ( PSM ) , the number of compressed measurements required at each sensor for good signal recovery . The monocular vision-based spacecraft relative navigation method is researched , and non-linear equations are established representing relationship between image characteristics and navigation information , based on linear projection principle . The non-linear problem is solved by transferring to optimization problem using quasi-Newton method with corresponding objective function . This article develops a novel subspace learning algorithm for visual tracking . Specifically , the authors first present a linear projection view to formulate subspace learning and then develop a novel framework , called Latent Subspace Projection Pursuit ( LSPP ) , to estimate the intrinsic dimension , removing corruptions and recovering the subspace structure for observed datasets . The authors evaluate the performance of their proposed method on various synthetic and real-world datasets , and the experimental results demonstrate that LSPP can achieve significant improvements in terms of performance and reduced computational complexity for visual tracking . The set2set model is specifically designed to operate on sets and should have more expressive power than simply summing the final node states . This model first applies a linear projection to each tuple ( ) and then takes as input the set of projected tuples . Then , after steps of computation , the set2set model produces a graph level embedding which is invariant to the order of the of the tuples . To see , suppose the projection of on the image is . If the network can localize in the image and estimate the depth , then we can recover and according to the following projection equation assuming a pinhole camera : where and denote the focal lengths of the camera , and is the principal point . If the object origin is the centroid of the object , we call the 2D center of the object . In the second group , the vertices need further encoding of residual errors . Also in this group , for those vertices associated with large residual errors under affine mapping , we encode their motion effectively using Newtonian motion estimates . This automatic segmentation enables our algorithm to he very effective in compressing time-dependent geometric data . It consists of three main components : Data Sharing Estimator , Affine Mapping Finder and Maximum Speedup Predictor . Data Sharing Estimator creates application-specific data dependency signatures used by Affine Mapping Finder to determine the appropriate thread mapping of application for a given architecture . To prevent excessive thread migration , Maximum Speedup Predictor estimates the speedup of the obtained mapping and ignores it if it causes no significant performance improvement . Ground control point ( GCP ) coordinates were derived from 1:25,000 and 1:50,000 scale topographic maps , and by using two different types of GPS receivers- a stand-alone hand-held and dual frequency DGPS receivers . GCPs derived from each source were used independently for the GCP-based geometric correction of IRS PAN sensor images by using affine mapping function . GCPs derived from maps yielded root mean squares ( RMS ) error from 15 to 35 m respectively . In this paper we introduce a class of constraint logic programs such that their termination can be proved by using affine level mappings . We show that membership to this class is decidable in polynomial time . The so-called image fusion process cuts into two steps . First an affine linear mapping is determined , so that the so-called sum of squared differences , between the source images is as small as possible . Furthermore , a trilinear interpolation is used to combine the information of the matched source images . In this paper , we investigate outsourcing of general computational problems which constitute the mathematical basics for problems emerged from various fields such as engineering and finance . To be specific , we propose affine mapping based schemes for the problem transformation and outsourcing so that the cloud is unable to learn any key information from the transformed problem . Meanwhile , the overhead for the transformation is limited to an acceptable level compared to the computational savings introduced by the outsourcing itself . We also show that the $ \U$-Lagrangian of $ \La$ has a Hessian which can be explicitly computed ; the second-order development of the $ \U$-Lagrangian provides a second-order development of $ \La$ along a characteristic smooth manifold : the set of symmetric matrices whose maximal eigenvalues have a fixed multiplicity . The same results can be obtained when we precompose $ \La$ with an affine matrix-valued mapping $ A$ , provided that this mapping satisfies a regularity condition ( { \em transversality condition } ) . We show that the Hessian of the $ \U$-Lagrangian of $ \La \circ A$ coincides with the reduced Hessian encountered in sequential quadratic programming . An autocalibration algorithm for calibration of magnetometer triads used to determine heading in small aerial vehicles is developed and its performance is analyzed . The algorithm estimates the 12 calibration parameters , which are elements of an affine transformation mapping measured , and thus , erroneous magnetometer measurements into corrected field measurements . The mapping corrects magnetometer output errors due to hard-iron biases , scale factor deviations , and nonorthogonality effects caused by soft irons or physical arrangement of the sensor axes . In this paper , we study the relation between the error estimate of the bilinear interpolation on a general quadrilateral and the geometric characters of the quadrilateral . Some explicit bounds of the interpolation error are obtained based on some sharp estimates of the integral over $ \frac{1}{|J|^{p-1}}$ for $ 1\leq p\leq\infty$ on the reference element , where $ J$ is the Jacobian of the nonaffine mapping . This allows us to introduce weak geometric conditions ( depending on $ p$ ) leading to interpolation error estimates in the $ W^{1,p}$ norm , for any $ p\in [ 1,\infty)$ , which can be regarded as a generalization of the regular decomposition property ( RDP ) condition introduced in [ G. Acosta and R. G. Duran , SIAM J. Numer . Abstract We present a new network topology to avoid overfitting in two-layered feedforward networks . We use two additional linear layers and principal component analysis to reduce the dimension of both inputs and internal representations and to transmit the essential information . Thereby neurons with small variance in the output are removed , which results in better generalization properties . In this paper , we propose three new methods to improve Matsui ’s branch-and-bound search algorithm which is known as the first generic algorithm for finding the best differential and linear trails . The three methods , named Reconstructing DDT and LAT According to Weight , Executing Linear Layer Operations in Minimal Cost and Merging Two 4-bit S-boxes into One 8-bit S-box respectively , can efficiently speed up the search process by reducing the search space as much as possible and reducing the cost of executing linear layer operations . We apply our improved algorithm to DESL and GIFT , which are still the hard instances for the automatic search methods . The parameters of the fuzzy layer of R-ELANFIS are randomly selected by incorporating the explicit knowledge representation using fuzzy membership functions . The parameters of the linear neural layer are determined by solving a constrained optimization problem in a regularized framework . Simulations on regression problems show that R-ELANFIS achieves similar or better generalization performance compared to well known kernel based regression methods and ELM based neuro-fuzzy systems . We propose a novel feature based domain adaptation technique using hidden layer factorisation . This method is fundamentally different from existing methods because we use the domain features to calculate a linear combination of linear layers . These linear layers can capture domain specific information and information common to different domains . The encoded low-level and high-level features are then combined by concatenation and convolution . Finally , a linear fully connected layer is used to evaluate the saliency of a queried region . A full resolution saliency map is obtained by querying the saliency of each local region of an image . Time-delay and linear layer ( design ) intelligent computing expert system models were developed for predicting shelf life of soft mouth melting milk cakes stored at 6C . Linear layer ( design ) model gave best outcomes ( MSE : 0.000293366 , RMSE : 0.017127919 , R : 0.996479613 ) . Regression equations were developed based on these outcomes , shelf life was predicted 49.54 days . In this paper , we propose a four stage method engineering process which aims at estimating impact of change . The process model described is a linear layered model . Impact sets are computed by analyzing dependency tractability relations with other connected method components . In this paper , the neural network composed of a chaotic neuron layer and a linear neuron layer is used to construct a block cipher that transforms the data from the plaintext form into the unintelligible form under the control of the key . Among them , the chaotic neuron layer realizes data diffusion , the linear neuron layer realizes data confusion , and the two layers are repeated for several times to strengthen the cipher . The decryption process is symmetric to the encryption process . Unfortunately , least squares estimation does not give a maximum likelihood ( ML ) estimate of the weights in the network . Logistic regression , on the other hand , gives ML estimates for single layer linear models only . This report describes how to obtain ML estimates of the weights in a multi-layer model , and compares LS-BP to ML-BP using several examples . On the Jobs dataset , straightforward logistic regression does remarkably well in estimating the ATT . However , being a linear model , LR can only ascribe a uniform policy - in this case , “ treat everyone ” . The more nuanced policies offered by non - linear methods achieve lower policy risk in the case of Causal Forests and CFR . Since the universal node is pointed at when the question is unanswerable and this node itself already contains information collected from both the passage and question during encoding and information interaction , we believe that this node is important in distinguishing whether the question is answerable . Finally , we pass this fixed vector through a linear layer to obtain the prediction whether the question is answerable . where is a sigmoid function , is a learnable weight matrix . Thus , a reasonable compromise was to add a small correction factor which is learned per word , such that : where is a matrix projecting a low - dimensional embedding vector back up to the dimensionality of the projected LSTM hidden state of . This amounts to adding a bottleneck linear layer , and brings the CNN Softmax much closer to our best result , as can be seen in Table [ reference ] , where adding a 128 - dim correction halves the gap between regular and the CNN Softmax . Aside from a big reduction in the number of parameters and incorporating morphological knowledge from words , the other benefit of this approach is that out - of - vocabulary ( OOV ) words can easily be scored . All these layers are separated by Rectifier Linear Units ( ReLu ) . Finally , a fully - connected linear layer projects to the output of the network , i.e. , the Q - values . The optimization employed to train the network is RMSProp ( with momentum parameter ) . use this - localized convolution to define a convolutional neural network on graphs . subsection : Layer - Wise Linear Model A neural network model based on graph convolutions can therefore be built by stacking multiple convolutional layers of the form of Eq . FWN is consisted with a Weight Layer and a nonlinear transformation , which decides the importance of each dimension in the part feature vector . Considering that a single linear Weight Layer might cause excessive response on some specific dimensions of the part vector , we add a nonlinear function to equalize the response of part feature vector , and the fused feature representation is where the and the are the global and part feature vectors . The and in Eq . First , the network structures are substantially different . Rainbow uses noisy linear layers and ReLU activations throughout the network , whereas Reactor uses standard linear layers and concatenated ReLU activations throughout . To overcome partial observability , Rainbow , inheriting this choice from DQN , uses frame stacking .
Experimentally it is observed that , a linear relationship exist between stress speech subspace and speech subspace . After linear transformation on speech subspace , speech recognizer outperforms by 7.57 % ( 62.14 % to 69.71 % ) under angry stress condition .
Moreover , a novel nonlinear transformation of the common drive model input is proposed , which ensures that the model force output equals the desired target force . On a physiological level , this corresponds to adjusting the rate coding model to the force generating capabilities of the simulated muscle , while from a control theoretic point of view , this step is equivalent to an exact linearizing transformation of the controlled neuromuscular system . Finally , an alternative analytical formulation of the EMG model is proposed , which renders the physiological meaning of the model more clear and facilitates a mathematical proof that muscle fibers in this model at no point in time represent a net current source or sink .
To address this problem , a novel strategy named adaptive subspace feature matching ( ASFM ) is proposed in this paper in order to integrate both the marginal and conditional distributions within a unified framework ( without any labeled samples from target subjects ) . Specifically , we develop a linear transformation function which matches the marginal distributions of the source and target subspaces without a regularization term . This significantly decreases the time complexity of our domain adaptation procedure .
We consider the problem of developing a linear transformation process to compensate for range-dependent bistatic clutter spectral dispersion . Clutter non-homogeneity is problematic in space-time adaptive processing ( STAP ) for airborne bistatic radar systems .
Second ( network alignment ) , guided by the high-quality node representations , our proposed non-rigid point set alignment approach overcomes the bottleneck of the linear transformation assumption . We conduct extensive experiments that demonstrate the proposed non-rigid alignment method is ( 1 ) effective , outperforming both the state-of-the-art linear transformation-based methods and node representation based methods , and ( 2 ) efficient , with a comparable computational time between the proposed multi-network representation learning component and its single-network counterpart .
Abstract Conventional geometrical methods for mining applications such as geologic mapping , cross-sections , logging , stereonets , contours and others are so sophisticated that often additional explanations are needed . After studying conventional geometrical methods , we decided to analyze both general slope curves and discontinuities of open pit mines used in excavations in rock mass to achieve meaningful 3D results by using linear mathematical transformation and isometric perspective methods . In addition , a new reorganization of discontinuities as a construction method has been developed according to their spatial orientations .
The circuit consists of opamps , resistors , capacitors and a pair of semiconductor diodes connected in anti-parallel to synthesize the nonlinear component necessary for chaotic oscillations . The model is described by a continuous time three-dimensional autonomous system with hyperbolic sine nonlinearity , and may be viewed as a linear transformation of model MO15 previously introduced in [ Sprott , 2010 ] . The structure of the equilibrium points and the discrete symmetries of the model equations are discussed .
Then these vectors are fed into a bi-channel convolutional neural network model and the convolution operations re performed on them . Finally , the outputs of the bi-channel convolution operations are combined together and fed into a series of linear transformation operations to get the final relation classification result . Our method integrates syntactic tree features and convolutional neural network architecture together and elaborates their advantages fully .
Through the assignment scheme , if the higher-level DM is not satisfied with the resulting solutions of objective functions , the re-solving process is easily conducted by adjusting the level of relative satisfaction for the associated lower-level DMs . A linearization transformation approach is also presented to facilitate the solution process . To emphasize some important fuzzy goals , a weighting scheme is considered in this paper .
As it can be seen , sentence embeddings are obtained by applying a max - pooling operation over the output of a BiLSTM encoder . These sentence embeddings are used to initialize the decoder LSTM through a linear transformation , and are also concatenated to its input embeddings at every time step . Note that there is no other connection between the encoder and the decoder , as we want all relevant information of the input sequence to be captured by the sentence embedding .
We aim at cutting down high precision operations and parameters both in training and inference stages of DU - Net . The bit - width of weights can be reduced to one or two bits through sign function or symmetrical threshold , whereas the layerwise gradients and inputs are quantized with linear mapping . In previous XNOR - Net , a scaling factor was introduced to approximate the real - value weight .
We introduce a novel architecture , the Synchronized Spectral CNN ( SyncSpecCNN ) to address these issues . The basic architecture of our neural network is similar to the fully convolutional segmentation network of , namely , we repeat the operation of convolving a vertex function by kernels and applying a non - linear transformation . However , our network combines processing in the primal and dual ( spectral ) domains .
For their final performance on LFW ( ) the authors combine 50 responses ( regular and flipped ) . Both PCA and a Joint Bayesian model that effectively correspond to a linear transform in the embedding space are employed . Their method does not require explicit 2D / 3D alignment .
More specifically , is firstly represented as a one - hot vector , and it is then mapped into a continuous space by a linear embedding matrix as Given the segment label , we use an RNN to map the acoustic segment to a fixed - dimensional vector representation , i.e. , where denotes the initial hidden state , denotes the duration of the segment and is a non - linear function . We take the final hidden state as the segment embedding vector , then can be represented as where corresponds to one layer or multiple layers of linear or non - linear transformation . In fact , it is flexible to include other relevant features as additional inputs to the function , e.g. , the duration feature which can be obtained by converting into another embedding vector .
The projection of a 3D point into its 2D location in the image plane involves two steps . First , the 3D point is rotated and translated using a linear view transformation , under the assumption that the camera is still where and are the 3D rotation and translation components , respectively . Then , a non - linear perspective transformation is applied as where is the focal length in pixel units ( we assume that the and components of the focal length are equal ) and is the principal point that is set to the image center .
A new landmark configuration was determined by simply transforming the training landmarks to the current displacement location while preserving the topological structure of the configuration of landmarks . Two assumptions are made : 1 ) In a new pair of images the deformation is approximately the same size and has only been spatially relocated in the image , and that by a simple affine transformation one can identify the optimal configuration on this new pair of images ; and 2 ) The deformation is of similar size and shape on the original pair of images . These are reasonable assumptions in many cases where one seeks to register tumor images at multiple time points following application of therapy and to evaluate changes in tumor size .
The robustness investigation was carried out in three different registration campaigns using overlapping images extracted from google earth and image pair acquired from an Unmanned Aerial Vehicle ( UAV ) . Sum of Square Difference ( SSD ) and Bilinear interpolation models were used to establish the similarity measure between the images to be registered , resampling of the pixel-values and computation of non-integer coordinates respectively while Random Sampling Consensus ( RANSAC ) algorithm was used to exclude the outliers and to compute the transformation matrix using affine transformation function . The results obtained from this preliminary investigation shows that the processing speed of MSER is quite high for Automatic Image Registration with a relatively high accuracy .
To overcome this problem , data augmentation ( DA ) is useful solution . In this paper , we investigate the effects of three DA approaches ; affine transformation-based approach , synthetic image-based approach , and generative adversarial network (GAN)-based approach , and evaluate the effectiveness of DA for pupil center point detection task using an off-the-shelf CNN model . We set eleven conditions of the training data , and evaluated the detection accuracy .
Then , the feature points in the binary geometric image are extracted , and the topological structure is constructed . After that , the pattern elements are extracted with the affine transformation theory and bilinear interpolation algorithm . The identification of pattern elements is modeled as a supervised classification problem , and the convolutional neural network technique is adopted to recognize the pattern elements by collecting a large number of training samples .
In this work , we investigate cellular simultaneous recurrent networks ( CSRNs ) to learn topological image mappings , particularly those of the affine transformations . While affine image transformation in conventional image processing is a relatively simple task , learning these transformations is an important part of having neural networks ( NNs ) function as generalized image processors . We introduce the CSRN and discuss its adaptation for image processing tasks .
The invention discloses a method and device for positioning feature information of a target object . The content includes : dividing an image frame including a target object , determining a target object region , using a feature information classifier to scan the determined target object region , determining central point position information of a region that feature information to be positioned occupies in the target object region , performing affine transformation operation on the determined central point position information , and obtaining initial position information corresponding to the region that the feature information to be positioned occupies in the target object region , thereby improving precision of initial positioning ; performing iteration processing on the obtained initial position information , obtaining position information corresponding to the region that the feature information to be positioned occupies in the target object region , integrating the obtained position information corresponding to the region that the feature information to be positioned occupies in the target object region , and obtaining the feature information of the target object , thereby improving precision of positioning the feature information of the target object .
CONCLUSIONS The proposed approach led to the successful visualisation of non-invasive fiducial markers using photography and MRI ( TIRM and TSE2D ( SPAIR ) sequences ) . This visualisation , combined with an affine transformation process provided a simple , cost-effective way to accurately co-register photographs and MR images of subcutaneous hematomas located on the thigh . Further investigation of the novel markers and the proposed co-visualisation approach holds potential to improve not only the forensic documentation of soft tissue lesions , but to also improve certain clinical applications , including the area of dermatology .
Image edge blur is obtained by using the traditional nonlinear diffusion image de-noising method ; Shearlet is a new-style multi-scale geometry analysis tool . It creates Shearlet functions , which have different characteristics through zooming , shearing translating and other affine transforming methods and enables its capable of optimally sparse representation . The paper proposed discusses adaptive intelligent single particle optimizer based image de-noising in Shearlet domain .
Specific topics include : Loading images from JAR files Describing shapes and the affine transformation in Java 2D Designing a class library of shapes Designing a component class for interactive drawing Designing a component class for displaying images Printing in Java 2D Implementing pan and zoom Implementing interactive rotate , shear , and flip Manipulating images at the pixel level using the Java 2D API Working with tiles in JAI to render large images Image manipulation in JAI usingaffine transformation , projections , and warping Image analysis in JAI , including edge detection , statistics , and region of interest computation ( ROI ) Remote imaging using RMI and JAI Internet imaging , including the use of Java 2D and JAI with servlets and JSP , the design of Web-based imaging applications , and the Internet Imaging Protocol ( IIP ) Using the new Image I/O framework to read and write images " Putting It All Together " sections help you put vital concepts and techniques into practice with interactive examples using actual applications .
The same matching operations are performed , except that matching occurs between P , H , and the image . Since the textual and visual vectors have different dimensionality and belong to different spaces , we first map them to a mutual space using an affine transformation . We match textual and image vectors using a cosine operation , as before .
This ensures that as the model is training , layers can continue learning on input distributions that exhibit less internal covariate shift , thus accelerating the training . Furthermore , the learned affine transform applied to these normalized activations allows the BN transform to represent the identity transformation and preserves the network capacity . subsection : Training and Inference with Batch - Normalized Networks
For instance , we have tried a larger batch size of 8 to be able to use BatchNorm ( and take advantage of batch statistics ) , however due to the GPU memory limits this modification required to use a smaller image crop size , and resulted in worse performance . We have also experimented with more sophisticated data augmentation techniques , including random histogram matching , affine image transforms , and random image filtering , which did not demonstrate any additional improvements . We have tried several data post - processing techniques to fine tune the segmentation predictions with CRF , but did not find it beneficial ( it helped for some images , but made some other image segmentation results worse ) .
The features of all nodes and edges are projected to a vector space of common dimension ( typically = 300 ) . The question nodes and edges use vector embeddings implemented as look - up tables , and the scene nodes and edges use affine projections : with the word embedding ( usually pretrained , see supplementary material ) , the embedding of dependencies , and weight matrices , and and biases . section : Processing graphs with neural networks
The results of SNLI experiments are summarized in Table 1 . First , we can see that LSTM - based leaf transformation has a clear advantage over the affine - transformation - based one . It improves the performance substantially and also leads to faster convergence .
We introduce a method that optimizes a linear projection for an information retrieval task : retrieving neighbors of input samples based on their low-dimensional visualization coordinates only . The simple linear projection makes the method easy to interpret , while the visualization task is made well-defined by the novel information retrieval criterion . The method has a further advantage : it projects input features , but the input neighborhoods it preserves can be given separately from the input features , e.g. by external data of sample similarities .
The adaptive Parallel Subgradient Projection ( PSP ) technique improves the convergence speed , in noisy environment , of linear-projection-based algorithms ( e.g. , NLMS and APA ) , with low computational complexity . The technique utilizes weighted average of the metric projections onto a series of closed half-spaces which contain , with high probability , unknown system to be identified .
In discrete time , the state transition is described at the finest resolution and the sampling frequencies of sensors decrease successively by a factor of two in resolution . We can build a discrete model of the system by using a linear projection operator to approximate the state space projection . This discrete model satisfies the requirements of discrete Kalman filtering , which actually offers an optimal estimation algorithm of the system .
For preserving the low rank properties the same , we proposed an algorithm , called linear preserving projection based on low rank representations(LLRLPP),to reduce the dimension of data . It can preserve the low rank properties of the original data space in the resulting low dimensional embedding subspace and correctly learn the low-dimensional subspace .
Principal component analysis(PCA ) and Singular value decomposition(SVD ) are two algebra methods used in linear projection analysis to reduce the dimensions in algebra space . The paper analyses the two methods in detail and deduces the relation of PCA to SVD when they are used in dimension reduction .
We aim to mathematically obtain the probability of recovery failure in distributed sensing and sampling for a Multiple Sensor System ( MSS ) . In this system , sensors take samples while compressing the signal with linear projection operations using the idea of compressive sensing ( CS ) [ 1 ] . In particular , we show that a bound for per-sensor measurements ( PSM ) , the number of compressed measurements required at each sensor for good signal recovery .
The monocular vision-based spacecraft relative navigation method is researched , and non-linear equations are established representing relationship between image characteristics and navigation information , based on linear projection principle . The non-linear problem is solved by transferring to optimization problem using quasi-Newton method with corresponding objective function .
This article develops a novel subspace learning algorithm for visual tracking . Specifically , the authors first present a linear projection view to formulate subspace learning and then develop a novel framework , called Latent Subspace Projection Pursuit ( LSPP ) , to estimate the intrinsic dimension , removing corruptions and recovering the subspace structure for observed datasets . The authors evaluate the performance of their proposed method on various synthetic and real-world datasets , and the experimental results demonstrate that LSPP can achieve significant improvements in terms of performance and reduced computational complexity for visual tracking .
The set2set model is specifically designed to operate on sets and should have more expressive power than simply summing the final node states . This model first applies a linear projection to each tuple ( ) and then takes as input the set of projected tuples . Then , after steps of computation , the set2set model produces a graph level embedding which is invariant to the order of the of the tuples .
To see , suppose the projection of on the image is . If the network can localize in the image and estimate the depth , then we can recover and according to the following projection equation assuming a pinhole camera : where and denote the focal lengths of the camera , and is the principal point . If the object origin is the centroid of the object , we call the 2D center of the object .
In the second group , the vertices need further encoding of residual errors . Also in this group , for those vertices associated with large residual errors under affine mapping , we encode their motion effectively using Newtonian motion estimates . This automatic segmentation enables our algorithm to he very effective in compressing time-dependent geometric data .
It consists of three main components : Data Sharing Estimator , Affine Mapping Finder and Maximum Speedup Predictor . Data Sharing Estimator creates application-specific data dependency signatures used by Affine Mapping Finder to determine the appropriate thread mapping of application for a given architecture . To prevent excessive thread migration , Maximum Speedup Predictor estimates the speedup of the obtained mapping and ignores it if it causes no significant performance improvement .
Ground control point ( GCP ) coordinates were derived from 1:25,000 and 1:50,000 scale topographic maps , and by using two different types of GPS receivers- a stand-alone hand-held and dual frequency DGPS receivers . GCPs derived from each source were used independently for the GCP-based geometric correction of IRS PAN sensor images by using affine mapping function . GCPs derived from maps yielded root mean squares ( RMS ) error from 15 to 35 m respectively .
In this paper we introduce a class of constraint logic programs such that their termination can be proved by using affine level mappings . We show that membership to this class is decidable in polynomial time .
The so-called image fusion process cuts into two steps . First an affine linear mapping is determined , so that the so-called sum of squared differences , between the source images is as small as possible . Furthermore , a trilinear interpolation is used to combine the information of the matched source images .
In this paper , we investigate outsourcing of general computational problems which constitute the mathematical basics for problems emerged from various fields such as engineering and finance . To be specific , we propose affine mapping based schemes for the problem transformation and outsourcing so that the cloud is unable to learn any key information from the transformed problem . Meanwhile , the overhead for the transformation is limited to an acceptable level compared to the computational savings introduced by the outsourcing itself .
We also show that the $ \U$-Lagrangian of $ \La$ has a Hessian which can be explicitly computed ; the second-order development of the $ \U$-Lagrangian provides a second-order development of $ \La$ along a characteristic smooth manifold : the set of symmetric matrices whose maximal eigenvalues have a fixed multiplicity . The same results can be obtained when we precompose $ \La$ with an affine matrix-valued mapping $ A$ , provided that this mapping satisfies a regularity condition ( { \em transversality condition } ) . We show that the Hessian of the $ \U$-Lagrangian of $ \La \circ A$ coincides with the reduced Hessian encountered in sequential quadratic programming .
An autocalibration algorithm for calibration of magnetometer triads used to determine heading in small aerial vehicles is developed and its performance is analyzed . The algorithm estimates the 12 calibration parameters , which are elements of an affine transformation mapping measured , and thus , erroneous magnetometer measurements into corrected field measurements . The mapping corrects magnetometer output errors due to hard-iron biases , scale factor deviations , and nonorthogonality effects caused by soft irons or physical arrangement of the sensor axes .
In this paper , we study the relation between the error estimate of the bilinear interpolation on a general quadrilateral and the geometric characters of the quadrilateral . Some explicit bounds of the interpolation error are obtained based on some sharp estimates of the integral over $ \frac{1}{|J|^{p-1}}$ for $ 1\leq p\leq\infty$ on the reference element , where $ J$ is the Jacobian of the nonaffine mapping . This allows us to introduce weak geometric conditions ( depending on $ p$ ) leading to interpolation error estimates in the $ W^{1,p}$ norm , for any $ p\in [ 1,\infty)$ , which can be regarded as a generalization of the regular decomposition property ( RDP ) condition introduced in [ G. Acosta and R. G. Duran , SIAM J. Numer .
Abstract We present a new network topology to avoid overfitting in two-layered feedforward networks . We use two additional linear layers and principal component analysis to reduce the dimension of both inputs and internal representations and to transmit the essential information . Thereby neurons with small variance in the output are removed , which results in better generalization properties .
In this paper , we propose three new methods to improve Matsui ’s branch-and-bound search algorithm which is known as the first generic algorithm for finding the best differential and linear trails . The three methods , named Reconstructing DDT and LAT According to Weight , Executing Linear Layer Operations in Minimal Cost and Merging Two 4-bit S-boxes into One 8-bit S-box respectively , can efficiently speed up the search process by reducing the search space as much as possible and reducing the cost of executing linear layer operations . We apply our improved algorithm to DESL and GIFT , which are still the hard instances for the automatic search methods .
The parameters of the fuzzy layer of R-ELANFIS are randomly selected by incorporating the explicit knowledge representation using fuzzy membership functions . The parameters of the linear neural layer are determined by solving a constrained optimization problem in a regularized framework . Simulations on regression problems show that R-ELANFIS achieves similar or better generalization performance compared to well known kernel based regression methods and ELM based neuro-fuzzy systems .
We propose a novel feature based domain adaptation technique using hidden layer factorisation . This method is fundamentally different from existing methods because we use the domain features to calculate a linear combination of linear layers . These linear layers can capture domain specific information and information common to different domains .
The encoded low-level and high-level features are then combined by concatenation and convolution . Finally , a linear fully connected layer is used to evaluate the saliency of a queried region . A full resolution saliency map is obtained by querying the saliency of each local region of an image .
Time-delay and linear layer ( design ) intelligent computing expert system models were developed for predicting shelf life of soft mouth melting milk cakes stored at 6C . Linear layer ( design ) model gave best outcomes ( MSE : 0.000293366 , RMSE : 0.017127919 , R : 0.996479613 ) . Regression equations were developed based on these outcomes , shelf life was predicted 49.54 days .
In this paper , we propose a four stage method engineering process which aims at estimating impact of change . The process model described is a linear layered model . Impact sets are computed by analyzing dependency tractability relations with other connected method components .
In this paper , the neural network composed of a chaotic neuron layer and a linear neuron layer is used to construct a block cipher that transforms the data from the plaintext form into the unintelligible form under the control of the key . Among them , the chaotic neuron layer realizes data diffusion , the linear neuron layer realizes data confusion , and the two layers are repeated for several times to strengthen the cipher . The decryption process is symmetric to the encryption process .
Unfortunately , least squares estimation does not give a maximum likelihood ( ML ) estimate of the weights in the network . Logistic regression , on the other hand , gives ML estimates for single layer linear models only . This report describes how to obtain ML estimates of the weights in a multi-layer model , and compares LS-BP to ML-BP using several examples .
On the Jobs dataset , straightforward logistic regression does remarkably well in estimating the ATT . However , being a linear model , LR can only ascribe a uniform policy - in this case , “ treat everyone ” . The more nuanced policies offered by non - linear methods achieve lower policy risk in the case of Causal Forests and CFR .
Since the universal node is pointed at when the question is unanswerable and this node itself already contains information collected from both the passage and question during encoding and information interaction , we believe that this node is important in distinguishing whether the question is answerable . Finally , we pass this fixed vector through a linear layer to obtain the prediction whether the question is answerable . where is a sigmoid function , is a learnable weight matrix .
Thus , a reasonable compromise was to add a small correction factor which is learned per word , such that : where is a matrix projecting a low - dimensional embedding vector back up to the dimensionality of the projected LSTM hidden state of . This amounts to adding a bottleneck linear layer , and brings the CNN Softmax much closer to our best result , as can be seen in Table [ reference ] , where adding a 128 - dim correction halves the gap between regular and the CNN Softmax . Aside from a big reduction in the number of parameters and incorporating morphological knowledge from words , the other benefit of this approach is that out - of - vocabulary ( OOV ) words can easily be scored .
All these layers are separated by Rectifier Linear Units ( ReLu ) . Finally , a fully - connected linear layer projects to the output of the network , i.e. , the Q - values . The optimization employed to train the network is RMSProp ( with momentum parameter ) .
use this - localized convolution to define a convolutional neural network on graphs . subsection : Layer - Wise Linear Model A neural network model based on graph convolutions can therefore be built by stacking multiple convolutional layers of the form of Eq .
FWN is consisted with a Weight Layer and a nonlinear transformation , which decides the importance of each dimension in the part feature vector . Considering that a single linear Weight Layer might cause excessive response on some specific dimensions of the part vector , we add a nonlinear function to equalize the response of part feature vector , and the fused feature representation is where the and the are the global and part feature vectors . The and in Eq .
First , the network structures are substantially different . Rainbow uses noisy linear layers and ReLU activations throughout the network , whereas Reactor uses standard linear layers and concatenated ReLU activations throughout . To overcome partial observability , Rainbow , inheriting this choice from DQN , uses frame stacking .
Linear transformation on speech subspace for analysis of speech under stress condition A Comprehensive Mathematical Model of Motor Unit Pool Organization, Surface Electromyography, and Force Generation A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG)-Based Emotion Recognition Range-dependence compensation for bistatic STAP using focusing matrices ORIGIN: Non-Rigid Network Alignment An approach to geometrical modelling of slope curves and discontinuities Coexistence of Multiple Attractors and Crisis Route to Chaos in a Novel Chaotic Jerk Circuit BiTCNN: A Bi-Channel Tree Convolution Based Neural Network Model for Relation Classification Considering decision decentralizations to solve bi-level multi-objective decision-making problems: A fuzzy approach Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond Quantized Densely Connected U-Nets for Efficient Landmark Localization SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation FaceNet: A unified embedding for face recognition and clustering Segmental Recurrent Neural Networks for End-to-End Speech Recognition 3D Face Morphable Models "In-the-Wild" A novel point-based nonrigid image registration scheme based on learning optimal landmark configurations PRELIMINARY INVESTIGATION OF THE ROBUSTNESS OF MAXIMALLY STABLE EXTREMAL REGIONS (MSER) MODEL FOR THE AUTOMATIC REGISTRATION OF OVERLAPPING IMAGES Effectiveness of Data Augmentation for CNN-Based Pupil Center Point Detection Calibration of a Structured Light Measurement System Using Binary Shape Coding Learning topological image transforms using cellular simultaneous recurrent networks Method and device for positioning feature information of target object Assessment of fiducial markers to enable the co-registration of photographs and MRI data. Adaptive Intelligent Single Particle Optimizer Based Image De-noising in Shearlet Domain Building Imaging Applications with Java Technology: Using Awt Imaging, Java 2d and Java Advanced Imaging (Jai) Grounded Textual Entailment Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 3D MRI brain tumor segmentation using autoencoder regularization Graph-Structured Representations for Visual Question Answering Learning to Compose Task-Specific Tree Structures Visualization by Linear Projections as Information Retrieval Acceleration of adaptive parallel projection algorithms by Pairwise Optimal WEight Realization Multiresolution modeling and estimation of multisensor data Feature Extraction Based on Low Rank Representation Linear Preserving Projections Linear Projection Based Dimension Reduction Analysis in Algebra Space Per-sensor measurements behavior of compressive sensing system for multiple measurements Algorithm of Spacecraft Relative Navigation based on Monocular Vision Latent Subspace Projection Pursuit with Online Optimization for Robust Visual Tracking Neural Message Passing for Quantum Chemistry PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes Compression of Dynamic 3D Geometry Data Using Iterative Closest Point Algorithm Hierarchical multicore thread mapping via estimation of remote communication Ground control for the geometric correction of PAN imagery from Indian remote sensing (IRS) satellites Recurrence with affine level mappings is P-time decidable for CLP(R) Image Fusion of 3D MR-Images to improve the spatial resolution CASO: Cost-Aware Secure Outsourcing of General Computational Problems The U-Lagrangian of the Maximum Eigenvalue Function Magnetometer Autocalibration Leveraging Measurement Locus Constraints On the Interpolation Error Estimates for Q1 Quadrilateral Finite Elements Two Strategies to Avoid Overfitting in Feedforward Networks Improving Matsui's Search Algorithm for the Best Differential/Linear Trails and its Applications for DES, DESL and GIFT Regularized extreme learning adaptive neuro-fuzzy algorithm for regression and classification Feature Based Domain Adaptation for Neural Network Language Models with Factorised Hidden Layers ELD-Net: An Efficient Deep Learning Architecture for Accurate Saliency Detection Development of Intelligent Computing Expert System Models for Shelf Life Prediction of Soft Mouth Melting Milk Cakes Dependency based Process Model for Impact Analysis: A Requirement Engineering Perspective A block cipher based on chaotic neural networks Maximum likelihood training of connectionist models: comparison with least squares back-propagation and logistic regression. Estimating individual treatment effect: generalization bounds and algorithms U-Net: Machine Reading Comprehension with Unanswerable Questions Exploring the Limits of Language Modeling Deep Reinforcement Learning with Double Q-Learning Semi-Supervised Classification with Graph Convolutional Networks Pose-Driven Deep Convolutional Model for Person Re-identification The Reactor: A Sample-Efficient Actor-Critic Architecture
233b88e8b76ee520e87887ae5b5705d2645ee870 d43035e267cf187baa7fb081692a03180057e5f6 776a7bb32d774bc05b2476f16cb37ba05cfab2bf d2fb9cbab15645def9b3fa8e2c427a7ef206813d a14c83bc132969baddc9869663b693466b4967ba 3924ebbd5a635f03bbb3a4e938f76c9f59dece08 fce2265939645ff092da6d152bc5291bd581e82d 746c5280f055b2fd1cb40fd987f1db670bccf325 0f0b599083b1e21cc7b063ac6399764568ace8e1 d7ce9117d685e652be5b49f106a7c2112db178f8 2c31f645f3b292631dee4ba105085e4347b17486 4f567a5f0a8ab016e17838990cee66e637fbcdbe e336d78a68a4ab3ebcee7c9d5c8d08ef9f37d279 47cf8e2eb479d39112599f57a3b035fb1bdf4104 49039d98951c4b199909a590a2b79fe5756f6ac9 1201d49e84357f7bf31ee4f4761fad350ac91d84 a1621b2de5f9a6fc47a72506318312b5e48fdfc7 7e9af2f23ef3e1f17c65454a3c8109194bfc5fa7 e515b563abf2f30705d4a030a5028135db3eb77c f2656fee501f6682199b3f76390243997c0ea222 9b131ed8404ac26b3f59d7d73f26b89c36f61b5f 4f56037d257090cb921075bf9f8cb96e892ad523 0bed0c3096a415cd9aaf0e1d6ba0bcc447a96005 833d49ee459e3bd173c7b18ae31329944b79cd51 61c3a4079698a584410c44622403b019aa596d39 3aebaaf888cba25be25097173d0b3af73d9ce7f9 af04e253a2739a2ffd277713a0dd223bd70f2107 c383cb4228f6fc6c8611f551f8301894256f8444 b8eeef4fbc83e0e81293c236b40cfd8da9f3df31 6c2740541ea588a82ea3380b5cd24bbbc5f7b155 2f82915a363e170955d81dcefef9e51c7f5f7a43 93d820bd9d7f5f9a33e3f71b01f09f7fa23fcd5b bbed1860c93da23ca756ad104306dc8a6d3bbb08 8ad6f4f433c05e2f98f0ff9464333e64f83bafa1 022da90a0f59966a588d5fc5a31c5f5755ba28ef 494831d8e8a49e4a08c6fbf59731eaa88687629a b66a4de338756f0fc0c3464e1d4045552e6203c7 ec33edbc5ad696a7bac05c51d8dcb3064cdd662f 001c177f7ba44fcd83b0a82bf2f7d2fa7f8e09b3 efd4c38f4fa3aa6c283083ff0043e253b028905a dcfef2fa8beb198a8c1a02ddf2cf82483722cf7c f2595276ce8fcd5333b992e53ac835cd96c18b29 d4e5176189e6ab1b89e627304c8951abbf0d493f 60475ed516556cebc78b40737cb6a04c38384d2f
["Computer Science"] ["Medicine","Biology"] ["Computer Science","Medicine"] ["Engineering"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering","Physics"] ["Geography"] ["Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science","Mathematics"] ["Computer Science"] ["Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Engineering"] ["Computer Science","Mathematics"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"]
2015 2019 2017 2015 2019 2008 2016 2018 2013 2019 2018 2017 2015 2016 2017 2013 2018 2018 2017 2013 2013 2015 2017 2001 2018 2015 2018 2017 2018 2009 2004 2004 2015 2005 2010 2010 2014 2017 2018 2002 2018 2003 2008 2003 2015 1999 2007 2008 1997 2019 2017 2019 2018 2011 2010 2009 1991 2017 2018 2016 2016 2017 2017 2017
NCC BigData NLPCC (1) Transactions of the Association for Computational Linguistics ECCV 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) INTERSPEECH 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Medical Imaging: Image Processing ICVS IJCNN COLING ICML BrainLes@MICCAI 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) AAAI WSOM EUSIPCO ICML Robotics: Science and Systems Bildverarbeitung f&uuml;r die Medizin ICML ArXiv ArXiv AAAI ICLR 2017 IEEE International Conference on Computer Vision (ICCV) ArXiv
https://api.semanticscholar.org/233b88e8b76ee520e87887ae5b5705d2645ee870 https://api.semanticscholar.org/d43035e267cf187baa7fb081692a03180057e5f6 https://api.semanticscholar.org/776a7bb32d774bc05b2476f16cb37ba05cfab2bf https://api.semanticscholar.org/d2fb9cbab15645def9b3fa8e2c427a7ef206813d https://api.semanticscholar.org/a14c83bc132969baddc9869663b693466b4967ba https://api.semanticscholar.org/3924ebbd5a635f03bbb3a4e938f76c9f59dece08 https://api.semanticscholar.org/fce2265939645ff092da6d152bc5291bd581e82d https://api.semanticscholar.org/746c5280f055b2fd1cb40fd987f1db670bccf325 https://api.semanticscholar.org/0f0b599083b1e21cc7b063ac6399764568ace8e1 https://www.semanticscholar.org/paper/160563abbd75265b19afc8b4169bab9e1eb33d97 https://www.semanticscholar.org/paper/4543052aeaf52fdb01fced9b3ccf97827582cef5 https://www.semanticscholar.org/paper/ade1b1bfe2abefbcfe324499aa284db2b8fc50a6 https://www.semanticscholar.org/paper/5aa26299435bdf7db874ef1640a6c3b5a4a2c394 https://www.semanticscholar.org/paper/aedffcebea081138a0f2bf2454f872700237fbf6 https://www.semanticscholar.org/paper/4b2443858fa92c374c1fb0f3ac3c59b2886adf9f https://api.semanticscholar.org/d7ce9117d685e652be5b49f106a7c2112db178f8 https://api.semanticscholar.org/2c31f645f3b292631dee4ba105085e4347b17486 https://api.semanticscholar.org/4f567a5f0a8ab016e17838990cee66e637fbcdbe https://api.semanticscholar.org/e336d78a68a4ab3ebcee7c9d5c8d08ef9f37d279 https://api.semanticscholar.org/47cf8e2eb479d39112599f57a3b035fb1bdf4104 https://api.semanticscholar.org/49039d98951c4b199909a590a2b79fe5756f6ac9 https://api.semanticscholar.org/1201d49e84357f7bf31ee4f4761fad350ac91d84 https://api.semanticscholar.org/a1621b2de5f9a6fc47a72506318312b5e48fdfc7 https://api.semanticscholar.org/7e9af2f23ef3e1f17c65454a3c8109194bfc5fa7 https://www.semanticscholar.org/paper/1235dd37312cb20aced0e97d953f6379d8a0c7d4 https://www.semanticscholar.org/paper/4d376d6978dad0374edfa6709c9556b42d3594d3 https://www.semanticscholar.org/paper/37a18be8c599b781cc28b6a62d8f11e8a6a75169 https://www.semanticscholar.org/paper/c7d007ba376faddf0046930ea7375ed59600cee9 https://www.semanticscholar.org/paper/027f9695189355d18ec6be8e48f3d23ea25db35d https://api.semanticscholar.org/e515b563abf2f30705d4a030a5028135db3eb77c https://api.semanticscholar.org/f2656fee501f6682199b3f76390243997c0ea222 https://api.semanticscholar.org/9b131ed8404ac26b3f59d7d73f26b89c36f61b5f https://api.semanticscholar.org/4f56037d257090cb921075bf9f8cb96e892ad523 https://api.semanticscholar.org/0bed0c3096a415cd9aaf0e1d6ba0bcc447a96005 https://api.semanticscholar.org/833d49ee459e3bd173c7b18ae31329944b79cd51 https://api.semanticscholar.org/61c3a4079698a584410c44622403b019aa596d39 https://api.semanticscholar.org/3aebaaf888cba25be25097173d0b3af73d9ce7f9 https://www.semanticscholar.org/paper/e24cdf73b3e7e590c2fe5ecac9ae8aa983801367 https://www.semanticscholar.org/paper/1c7e078611c9df412e6eb3a356f31a0da0c1f99c https://api.semanticscholar.org/af04e253a2739a2ffd277713a0dd223bd70f2107 https://api.semanticscholar.org/c383cb4228f6fc6c8611f551f8301894256f8444 https://api.semanticscholar.org/b8eeef4fbc83e0e81293c236b40cfd8da9f3df31 https://api.semanticscholar.org/6c2740541ea588a82ea3380b5cd24bbbc5f7b155 https://api.semanticscholar.org/2f82915a363e170955d81dcefef9e51c7f5f7a43 https://api.semanticscholar.org/93d820bd9d7f5f9a33e3f71b01f09f7fa23fcd5b https://api.semanticscholar.org/bbed1860c93da23ca756ad104306dc8a6d3bbb08 https://api.semanticscholar.org/8ad6f4f433c05e2f98f0ff9464333e64f83bafa1 https://api.semanticscholar.org/022da90a0f59966a588d5fc5a31c5f5755ba28ef https://api.semanticscholar.org/494831d8e8a49e4a08c6fbf59731eaa88687629a https://api.semanticscholar.org/b66a4de338756f0fc0c3464e1d4045552e6203c7 https://api.semanticscholar.org/ec33edbc5ad696a7bac05c51d8dcb3064cdd662f https://api.semanticscholar.org/001c177f7ba44fcd83b0a82bf2f7d2fa7f8e09b3 https://api.semanticscholar.org/efd4c38f4fa3aa6c283083ff0043e253b028905a https://api.semanticscholar.org/dcfef2fa8beb198a8c1a02ddf2cf82483722cf7c https://api.semanticscholar.org/f2595276ce8fcd5333b992e53ac835cd96c18b29 https://api.semanticscholar.org/d4e5176189e6ab1b89e627304c8951abbf0d493f https://api.semanticscholar.org/60475ed516556cebc78b40737cb6a04c38384d2f https://www.semanticscholar.org/paper/17409ae85c237983dc22a5e4750d8054e4b6edc9 https://www.semanticscholar.org/paper/27e98e09cf09bc13c913d01676e5f32624011050 https://www.semanticscholar.org/paper/2f2d8f8072e5cc9b296fad551f65f183bdbff7aa https://www.semanticscholar.org/paper/3b9732bb07dc99bde5e1f9f75251c6ea5039373e https://www.semanticscholar.org/paper/36eff562f65125511b5dfab68ce7f7a943c27478 https://www.semanticscholar.org/paper/fd5cbf72a0cc82542b543207a9d1ea382ca7cbcf https://www.semanticscholar.org/paper/052282998bc24db695891f755a00e3cebd3fd796