They present a new way of improving single-threaded performance by running a thread on groups of cores ( compositions ) and with the ability of changing the processor topology on the fly , they can better adapt themselves to any task at hand . However , these potential performance improvements are made difficult due to two main challenges : the difficulty of determining a processor configuration that leads to the optimal performance and knowing how to tackle hardware bottlenecks that may impede the performance of composition . This thesis first demonstrates that ahead-of-time thread and core partitioning used to improve the performance of multi-threaded programs can be automated . Existing literature offers a wide choice of frameworks and tools that exploit model composition following different approaches . This chapter provides an insight into the composition approach used by SIMTHESys ( a framework for the development of modeling languages and the solution of multiformalism models ) in order to compose easily and consistently primitives belonging to different ( custom ) modeling languages . A case study is presented to illustrate the effectiveness of the proposed composition formalism . We also propose a procedure to approximate the postprocessor such that its evaluation is virtually cost-free . The analysis is illustrated for a particular class of composition methods . Key to achieving this objective is the removal , or deforestation , of intermediate objects in a program execution . We first show that by encoding data types using Bohm-Berarducci encodings ( often referred to as Church encodings ) , and combining these with partial evaluation for function composition we achieve deforestation . This allows us to implement optimisations themselves as libraries , with minimal dependence on an underlying optimising compiler . We argue for syntactic verb cluster formation in certain restructuring configurations , the result of which is interpreted via function composition . This cluster formation can be diagnosed by its semantic consequences . We propose that the process of cluster formation takes place whenever two lexical verbs are part of the same phasal Spell-Out domain , which we relate to Richards ’ ( 2010 ) distinctness proposal . Our analysis entails that ( i ) some instances of head movement have semantic effects and hence can not take place at PF ; ( ii ) the set of rules of semantic composition must include function composition ; and ( iii ) it provides additional support for the notion of distinctness and extends its application to head movement . Business processes built from Web services need a more adaptable composition solution . In this paper , a rule-driven approach is proposed to control the executions of business processes via agents ' behavior rules , which can be generated automatically and modified dynamically to enable the adaptations of business processes . Business processes built from web services need a more adaptable composition solution . In this paper , an approach based on agents is proposed to control the executions of business processes via agent behavior rules , which can be generated automatically and modified dynamically to enable the adaptations of business process . subsubsection : This is modified version of a paper originally published at NAACL 2016 that contains a corrigendum at the end , with improved results after fixing an implementation bug in the RNNG composition function . section : Introduction Chris Dyer⁢ ♠ ♣ Adhiguna Kuncoro ♠ Miguel Ballesteros ♢ Noah A. Smith ♡ School of Computer Science , Carnegie Mellon University , Pittsburgh , PA , USA NLP Group , Pompeu Fabra University , Barcelona , Spain Google DeepMind , London , UK Computer Science & Engineering , University of Washington , Seattle , WA , USA { cdyer , akuncoro}@cs.cmu.edumiguel.ballesteros@upf.edu , nasmith@cs.washington.edu section : RNNG Composition Function and Implementation Error The composition function reduces a completed constituent into a single vector representation using a bidirectional LSTM ( Figure [ reference ] ) over embeddings of the constituent ’s children as well as an embedding of the resulting nonterminal symbol type . We begin with an ablation study to discover the importance of the composition function in § [ reference ] . Based on the findings , we augment the RNNG composition function with a novel gated attention mechanism ( leading to the GA - RNNG ) to incorporate more interpretability into the model in § [ reference ] . Using the GA - RNNG , we proceed by investigating the role that individual heads play in phrasal representation ( § [ reference ] ) and the role that nonterminal category labels play ( § [ reference ] ) . Unlike previous recursive models , the tree structure for NTI is relaxed , i.e. , NTI does not require the input sequences to be parsed syntactically ; and therefore it is flexible and can be directly applied to a wide range of NLP tasks beyond sentence modeling . Furthermore , we propose different variants of node composition function and attention over tree for our NTI models . When a sequential leaf node transformer such as LSTM is chosen , the NTI network forms a sequence - tree hybrid model taking advantage of both conditional and compositional powers of sequential and recursive models . Recently , zhu2015long introduced S - LSTM which extends LSTM units to compose tree nodes in a recursive fashion . In this paper , we introduce a novel attentive node composition function that is based on S - LSTM . Our NTI model does not rely on either a parser output or a fine - grained supervision of non - leaf nodes , both required in previous work . Attentive Non - leaf Node Function ( ANF ) : Some NLP applications ( e.g. , QA and machine translation ) would benefit from a dynamic query dependent composition function . We introduce ANF as a new non - leaf node function . Surprisingly , attention degree for the single word expression like ” stone ” , ” wall ” and ” leaves ” is lower to compare with multiword phrases . Sequence models lack this property as they have no explicit composition module to produce such mutiword phrases . Finally , the most interesting pattern is that the model attends over higher level ( low depth ) tree nodes with rich semantics when considering a ( c ) longer phrase or ( d ) full sentence . Our network ’s structure , connections and layer types , is defined by . A network consisting of a single convolutional layer is the base case : We define successive fractals recursively : where denotes composition and a join operation . When drawn in the style of Figure [ reference ] , corresponds to the number of columns , or width , of network . subsection : ResNet Architecture The Residual Network in is a functional composition of residual blocks ( ResBlocks ) , each encoding the update rule ( [ reference ] ) . Fig [ reference ] shows the schematic illustration of the ResBlock . The mapping from the output of the acoustic model to likely word sequences is done via a Viterbi search in a weighted Finite State Transducer ( wFST ) , called ASR decoding graph in the following . Formally , the decoding graph may be written as the composition of four wFSTs , where denotes transducer composition ( see section [ reference ] ) , represents Hidden Markov Models ( HMMs ) modeling context - dependent phones , represents the context - dependency , is the lexicon and is the LM , typically a bigram or a trigram model represented as a wFST . Determinization and minimization operations are also applied at each step in order to compute equivalent optimized transducers with less states , allowing the composition and the inference to run faster . Consequently , the states and transitions of the complete HCLG decoding graph are not computed at the time of creation , but rather at runtime during the inference , notably speeding up the building of the decoding graph . Additionally , employing lazy composition allows to break the decoding graph into two pieces ( HCL on one hand , and G on the other ) . The sum of the sizes of these pieces is typically several times smaller than the equivalent , statically - composed HCLG . The recurrence relation is for . Inference net is equivalent to the composition of the same elementary function : where the operator denotes a function composition and denotes the - fold product of . Reconstruction net takes the input hidden state and outputs the target image ( high - resolution ) . Unlike in computer vision , where convolutional neural networks are predominant , there are multiple ways to encode a sentence using neural networks . Hence , we investigate the impact of the sentence encoding architecture on representational transferability , and compare convolutional , recurrent and even simpler word composition schemes . Our experiments show that an encoder based on a bi - directional LSTM architecture with max pooling , trained on the Stanford Natural Language Inference ( SNLI ) dataset , yields state - of - the - art sentence embeddings compared to all existing alternative unsupervised approaches like SkipThought or FastSent , while being much faster to train . To overcome this limitation , a Multi-Level Translation Method based on the Constructive Process Theory has been proposed . This paper describes the benefits of using this method in the Japanese-to-English machine translation system ALT-J/E. In comparison with conventional compositional methods , the Multi-Level Translation Method emphasizes the importance of the meaning contained in expression structures as a whole . It is shown to be capable of translating typical written Japanese based on the meaning of the text in its context , with comparative ease . Learning new structures 8 . A DOP model for compositional semantic representations 9 . Speech understanding and dialogue processing 10 . By overcoming data sparsity problems , as well as providing information about semantic relatedness which is not available in discrete representations , distributed representations have proven useful in many NLP tasks . Recent work has shown how compositional semantic representations can successfully be applied to a number of monolingual applications such as sentiment analysis . At the same time , there has been some initial success in work on learning shared word-level representations across languages . In this paper we suggest a typed compositional semantics for nominal compounds of the form [ Adj Noun ] that models adjectives as higher-order polymorphic functions , and where types are assumed to represent concepts in an ontology that reflects our commonsense view of the world and the way we talk about it in ordinary language . In addition to [ Adj Noun ] compounds our proposal seems also to suggest a plausible explanation for well known adjective ordering restrictions . In such a framework , certain types of ambiguities in natural language are explained by the notion of polymorphism . In this paper we suggest such a typed compositional semantics for nominal compounds of the form ( Adj Noun ) where adjectives are modeled as higher-order polymorphic functions . In addition to ( Adj Noun ) compounds our proposal seems also to suggest a plausible explanation for well known adjective ordering restrictions . The thesis offers an implementation of a new approach for automatic modelling and music information retrieval . The model is based on a visual compositional hierarchical model , which provides good results in the area of categorization of visual objects in pictures . We translate the approach to the music domain by implementing and visualizing the model . This result can be used to estimate the pose and structure of the face and the illumination conditions for each frame in a video sequence in the presence of multiple point and extended light sources . We propose a new inverse compositional estimation approach for this purpose . We then synthesize images using the face model estimated from the training data corresponding to the conditions in the probe sequences . Although there is enormous evidence that reference levels influence preferences , conjoint models , one of the most successful marketing research tools , assume that preferences depend on the absolute levels of attributes . In this paper we investigate the relevance of reference effects in two settings , compositional or self-explicated models in experimental studies 1 and 2 , and decompositional or choice-based models in experimental study 3 . In particular , we introduce a simple modification of the traditional self-explicated conjoint model which permits dependence of preference on reference levels . Semantic composition in distributional semantic models ( DSMs ) offers a powerful tool to represent word meaning in context . In this paper , we investigate methods to utilize compositional DSMs to improve word sense discrimination and word sense disambiguation . In this work , we rely on a previously proposed multiplicative model of composition . To handle semantic constraints , support for a peer review approach to checking expressions is a desirable feature for tools . Other desirable features include a mechanism for supporting the hierarchy that exists in terminologies to maintain the context , and further support around the SNOMED Compositional Grammar to highlight syntax problems . Further work on the result display of Snapper 's constraint checker would improve its usability , especially for more complex expressions . High-level supervisory control software for automotive applications ( e.g. , drive-by-wire ) presents many challenges to making performance guarantees , which are a necessary part of the software 's certification for deployment . The features of such systems demand that a compositional , or modular , approach to reasoning about performance be devised and applied . We discuss one such analytical approach as an alternative to simulation and testing . How can we explain the origins of our uniquely human compositional system of communication ? Recently there has been a resurgence of interest in this and other questions surrounding the evolution of human language and the origins of syntax in particular ( Bickerton 1990 ; Pinker & Bloom 1990 ; Newmeyer 1991 ; Hurford et al. 1998).1 Much of this is due to an explicit attempt to relate models of our innate linguistic endowment with neo-Darwinian evolutionary theory . Deep models can also learn hierarchical feature representations from images . In the Natural Language Processing ( NLP ) field , the belief that compositional models can also be used to text - related tasks is more recent . The increasing availability of text data motivates research for models able to improve accuracy in different language tasks . In this paper , we present the first experiments using neural network models for the task of error detection in learner writing . We perform a systematic comparison of alternative compositional architectures and propose a framework for error detection based on bidirectional LSTMs . Experiments on the CoNLL - 14 shared task dataset show the model is able to outperform other participants on detecting errors in learner writing . Our Own shared task also evaluated grammatical error detection of a number of different error types , though most systems were error - type specific and the best approach was heavily skewed towards article and preposition errors . We extend this line of research , working towards general error detection systems , and investigate the use of neural compositional models on this task . The related area of grammatical error correction has also gained considerable momentum in the past years , with four recent shared tasks highlighting several emerging directions . Did What dataset . The effectiveness of multiplicative interaction is demonstrated by an ablation study , and by comparing to alternative compositional operators for implementing the gated - attention . citecolor = darkblue Finally , in kiperwasser2016ef we present an Easy - First parser based on a novel hierarchical - LSTM tree encoding . In contrast to these , the approach we present in this work results in much simpler feature functions , without resorting to elaborate network architectures or compositional tree representations . Work by vinlays2014grammar employs a sequence - to - sequence with attention architecture for constituency parsing . Zhu add spatial attention to the standard LSTM model for pointing and grounded QA . Andreas propose a compositional scheme that consists of a language parser and a number of neural modules networks . The language parser predicts which neural module network should be instantiated to answer the question . In contrast , our approach does not suffer from these limitations . section : Advantage of Compositional Codes In this section , we formally describe the compositional coding approach and analyze its merits for compressing word embeddings . For the compositional approach , the number of vectors required to construct an embedding vector is . Both the binary and compositional approaches have significantly fewer vectors in the embedding matrix . The compositional coding approach provides a better balance with shorter codes and lower computational cost . Superimposition is a composition technique that has been applied successfully in many areas of software development . Although superimposition is a general-purpose concept , it has been (re)invented and implemented individually for various kinds of software artifacts . The aim of this paper is to present and discuss the latest accomplishments in voxel-based craniofacial CBCT superimpositions along with structure discriminations . Methods : We propose a CBCT superimposition method based on joint embedding of subsets extracted from CBCT images . The subset is defined at local extremes of the first-order difference of Gaussian-smoothed volume images to reduce the data involved in the computation . Combining CBCT imaging and intraoral scans with an intention to prepare comprehensive treatment plans is common in clinical practice . Segmentation and superimposition of models is indispensable when planning complicated prosthetic reconstructions . The authors of this publication evaluated registration discrepancies of virtual dental arch models obtained by means of CBCT imaging at different segmentation thresholds with intraoral dental scans . FrFT is a new tool which has a dominant advantage to detect LFM-signal . However , under the multipath environment , the LFM signal is distortion on account of the noncorrelation superimposition of the multipath component , which results in performance degradation of the detector based on FrFT . In this paper , an improved algorithm based on Active Virtual time reversal and Fractional Fourier Transform is proposed . The only information required is the 3D image of the skull , age , gender and Body Mass Index of the individual . The second technique is an automated skull-photo superimposition method which calculates the matching degree of a large number of photos with a skull . We set out to investigate this issue by implementing an efficient algorithm for structure superimposition enabling routine searches for alternative alignments . RESULTS The algorithm is based on optimum superimposition of structures and dynamic programming . The implementation is tested and validated using published results . Results The T 2 landmark differences in hand tracing between the operators ranged from 0.61 mm to 1.65 mm for the three types of superimposition . There were no significant differences in accuracy between hand and automated superimposition ( p > 0.05 ) . Conclusions Computer-aided cephalometric superimposition provides comparably accurate results to those of traditional hand tracing and will provide a powerful tool for academic research . RESULTS The optimised practice consisted of : anode towards the head , broad focus , no OID or grid , 80 kVp , 32 mAs and 130 cm SID . CONCLUSION Images of suitable quality for laterally assessing spinal conditions using Cobb or superimposition measurements were produced from an effective dose of 0.05 mSv , which is 83 % less than the average effective dose used in the UK for lateral thoracic/lumbar exposures . This optimisation procedure can be adopted and use for optimisation of other radiographic techniques . Morphological attribute profiles were used because the structural and contextual information of images was effective in distinguishing the classes with similar spectral characteristics . Moreover , a road superimposition strategy that considers road hierarchy was developed because OSM data provide road information with high completeness in the urban area . Experiments were conducted on the data captured over Wuhan city , and three state-of-the-art approaches were adopted for comparison . Consequently they require a different set of primitives for tool support . This paper describes a form of tool composition called cotools which provides many of the benefits of UNIX pipes in terms of being able to reuse existing tools in tool composites , and allowing tools to operate concurrently ad incrementally on data . However , the tool language and the composition model is tailored to provide these benefits in a database-centered environment where tools do not directly communicate with each other . We trace the evolution of the representation of management in cropping and grazing systems models , from fixed annual schedules of identical actions in single paddocks toward flexible scripts of rules . Attempts to define higher-level organizing concepts in management policies , and to analyse them to identify optimal plans , have focussed on questions relating to grazing management owing to its inherent complexity . " Data augmentation is usually adopted to increase the amount of training data , prevent overfitting and improve the performance of deep models . However , in practice , random data augmentation , such as random image cropping , is low-efficiency and might introduce many uncontrolled background noises . In this paper , we propose Weakly Supervised Data Augmentation Network ( WS-DAN ) to explore the potential of data augmentation . Small ( handheld ) to improve the cropping system by obtaining the coverage of a wide range of content for the smaller size of the device display , the invention is metadata aggregation ( metadata aggregation ) and the corresponding video , e.g. , the late proposed a method that starts from the operation ( post-production ) program exchange and storage ( archiving ) in , where , ( a ) the video is , for example , movement ( motion ) detection , mopol sludge ( morphology ) filters , edge detection by using such ( edge ​​detection ) , and passed to a video analyzer ( video analysis ) to transfer the video , ( b ) , and the video and metadata distinction is combined to extract important features of ( features ) in the context in which meta and classification of sensitive information from the data , a dynamic fit to the ( fitted ) chain of the feature extraction steps adapted to the delivered video content , Is used to vaporize , ( c ) extracted important features are the object ( object ) are combined to limit ( define ) the regions ( ROI ) of interest to be searched from the ( consecutive ) video consecutive frames by the tracking , tracing the object identifies the new position and deformation of each initialized in the video frames successive ROI and sikimyeo returns this information to the feature extraction , obtaining a permanent communication ( permanent communication ) between said feature extraction and said object tracking thereby , and ( d ) , one or several ROI are extracted and each video frame is input to the cropping step ( video frame by video frame ) , so by using , sorting the supplied ROI by priority based on the ( e ) the weight information ( weighting information ) preferably configured image part ( well composed image part ) the cropping and , ( f ) the croissant the region ( s ) ping is scaled to the desired small screen size It is . This report describes new findings supplementing previous project reports on driving simulation studies to develop detection algorithms and effective advisory , alarm , and alerting stimuli for a vehicle-based drowsy driver detection/warning system . Part I describes attempts to further optimize previously-reported driver drowsiness detection algorithms by the use of time-on-task variables , assessment of the predictive power of algorithms , use of prior time segment data along with current data , and " cropping " of outliers to minimize their possible spurious effects . Part II reports an experimental study to identify effective advisory , alarm , and alertness-maintaining stimuli to be used in a drowsy driver detection system . A fuzzy match index for scale-invariant feature transform ( SIFT ) features is proposed in this study that cumulatively involves all the test SIFT keypoints in the decision-making process . The new fuzzy SIFT classifier is adapted successfully for robust face recognition from complex backgrounds without using any face cropping tools and using only a single training template . The further incorporation of entropy weights ensures that the facial features have a greater role in the soft decision-making as compared with the background features . This work is motivated by recent evidence from the cognitive science community demonstrating the efficacy of component-based facial representations . The component-based framework presented in this paper consists of the following major steps : 1 ) landmark extraction using Active Shape Models ( ASM ) , 2 ) alignment and cropping of components using Procrustes Analysis , 3 ) representation of components with Multiscale Local Binary Patterns ( MLBP ) , 4 ) per-component measurement of facial similarity , and 5 ) fusion of per-component similarities . We demonstrate on three public datasets and an operational dataset consisting of face images of 8000 subjects , that the proposed component-based representation provides higher recognition accuracies over holistic-based representations . The present invention relates to an image display system , the image display system comprises a separating apparatus , the separating apparatus emits N signals divided images , wherein N number of divided image is formed by dividing the source image and do not overlap each other ; transmitting N number of divided image N transmission line ; the N dummy pixel insertion member , the N dummy pixel interpolation means generates the dummy pixels and dummy pixel interpolation to the outside of the image to produce an expanded image based on the divided image ; and N of the extended image application space signal processing image processing means . The image display system further comprises means N number of divided image cropping and image synthesis means , wherein the N number of divided image parts cut out from the respective images by the spatial processing to remove image components dummy pixels , in order to cut out the divided input image an image the same size , and the cut-out image synthesis means put together the divided images to generate an image of the same size of the source image resynthesis . Thus , one of the main bottlenecks in processing data from photographic capture-recapture surveys is in cropping to an area of interest so that matching algorithms can identify the individual . Here , we describe the development and testing of an automated cropping program . The methods and techniques we describe are broadly applicable to any system where raw photos must be cropped down to a specific area of interest before pattern recognition software can be used for individual identification . Random Erasing is parameter learning free , easy to implement , and can be integrated with most of the CNN - based recognition models . Albeit simple , Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping , and yields consistent improvement over strong baselines in image classification , object detection and person re - identification . Code is available at : . [ reference ] . subsection : Comparison with Random Cropping Random cropping is an effective data augmentation approach , it reduces the contribution of the background in the CNN decision , and can base learning models on the presence of parts of the object instead of focusing on the whole object . We experienced the need for such a mechanism in an industry project with Deutsche Bahn , where we do not merely model a highly variable system , but a system with highly variable subsystems . Based on MPL concepts and delta-oriented oriented programming , we present a novel solution to the design challenges arising from having to manage and interoperate multiple subsystems with multiple variants : how to reference variants , how to avoid name or type clashes , and how to keep variants interoperable .
They present a new way of improving single-threaded performance by running a thread on groups of cores ( compositions ) and with the ability of changing the processor topology on the fly , they can better adapt themselves to any task at hand . However , these potential performance improvements are made difficult due to two main challenges : the difficulty of determining a processor configuration that leads to the optimal performance and knowing how to tackle hardware bottlenecks that may impede the performance of composition . This thesis first demonstrates that ahead-of-time thread and core partitioning used to improve the performance of multi-threaded programs can be automated .
Existing literature offers a wide choice of frameworks and tools that exploit model composition following different approaches . This chapter provides an insight into the composition approach used by SIMTHESys ( a framework for the development of modeling languages and the solution of multiformalism models ) in order to compose easily and consistently primitives belonging to different ( custom ) modeling languages . A case study is presented to illustrate the effectiveness of the proposed composition formalism .
We also propose a procedure to approximate the postprocessor such that its evaluation is virtually cost-free . The analysis is illustrated for a particular class of composition methods .
Key to achieving this objective is the removal , or deforestation , of intermediate objects in a program execution . We first show that by encoding data types using Bohm-Berarducci encodings ( often referred to as Church encodings ) , and combining these with partial evaluation for function composition we achieve deforestation . This allows us to implement optimisations themselves as libraries , with minimal dependence on an underlying optimising compiler .
We argue for syntactic verb cluster formation in certain restructuring configurations , the result of which is interpreted via function composition . This cluster formation can be diagnosed by its semantic consequences .
We propose that the process of cluster formation takes place whenever two lexical verbs are part of the same phasal Spell-Out domain , which we relate to Richards ’ ( 2010 ) distinctness proposal . Our analysis entails that ( i ) some instances of head movement have semantic effects and hence can not take place at PF ; ( ii ) the set of rules of semantic composition must include function composition ; and ( iii ) it provides additional support for the notion of distinctness and extends its application to head movement .
Business processes built from Web services need a more adaptable composition solution . In this paper , a rule-driven approach is proposed to control the executions of business processes via agents ' behavior rules , which can be generated automatically and modified dynamically to enable the adaptations of business processes .
Business processes built from web services need a more adaptable composition solution . In this paper , an approach based on agents is proposed to control the executions of business processes via agent behavior rules , which can be generated automatically and modified dynamically to enable the adaptations of business process .
subsubsection : This is modified version of a paper originally published at NAACL 2016 that contains a corrigendum at the end , with improved results after fixing an implementation bug in the RNNG composition function . section : Introduction
Chris Dyer⁢ ♠ ♣ Adhiguna Kuncoro ♠ Miguel Ballesteros ♢ Noah A. Smith ♡ School of Computer Science , Carnegie Mellon University , Pittsburgh , PA , USA NLP Group , Pompeu Fabra University , Barcelona , Spain Google DeepMind , London , UK Computer Science & Engineering , University of Washington , Seattle , WA , USA { cdyer , akuncoro}@cs.cmu.edumiguel.ballesteros@upf.edu , nasmith@cs.washington.edu section : RNNG Composition Function and Implementation Error The composition function reduces a completed constituent into a single vector representation using a bidirectional LSTM ( Figure [ reference ] ) over embeddings of the constituent ’s children as well as an embedding of the resulting nonterminal symbol type .
We begin with an ablation study to discover the importance of the composition function in § [ reference ] . Based on the findings , we augment the RNNG composition function with a novel gated attention mechanism ( leading to the GA - RNNG ) to incorporate more interpretability into the model in § [ reference ] . Using the GA - RNNG , we proceed by investigating the role that individual heads play in phrasal representation ( § [ reference ] ) and the role that nonterminal category labels play ( § [ reference ] ) .
Unlike previous recursive models , the tree structure for NTI is relaxed , i.e. , NTI does not require the input sequences to be parsed syntactically ; and therefore it is flexible and can be directly applied to a wide range of NLP tasks beyond sentence modeling . Furthermore , we propose different variants of node composition function and attention over tree for our NTI models . When a sequential leaf node transformer such as LSTM is chosen , the NTI network forms a sequence - tree hybrid model taking advantage of both conditional and compositional powers of sequential and recursive models .
Recently , zhu2015long introduced S - LSTM which extends LSTM units to compose tree nodes in a recursive fashion . In this paper , we introduce a novel attentive node composition function that is based on S - LSTM . Our NTI model does not rely on either a parser output or a fine - grained supervision of non - leaf nodes , both required in previous work .
Attentive Non - leaf Node Function ( ANF ) : Some NLP applications ( e.g. , QA and machine translation ) would benefit from a dynamic query dependent composition function . We introduce ANF as a new non - leaf node function .
Surprisingly , attention degree for the single word expression like ” stone ” , ” wall ” and ” leaves ” is lower to compare with multiword phrases . Sequence models lack this property as they have no explicit composition module to produce such mutiword phrases . Finally , the most interesting pattern is that the model attends over higher level ( low depth ) tree nodes with rich semantics when considering a ( c ) longer phrase or ( d ) full sentence .
Our network ’s structure , connections and layer types , is defined by . A network consisting of a single convolutional layer is the base case : We define successive fractals recursively : where denotes composition and a join operation . When drawn in the style of Figure [ reference ] , corresponds to the number of columns , or width , of network .
subsection : ResNet Architecture The Residual Network in is a functional composition of residual blocks ( ResBlocks ) , each encoding the update rule ( [ reference ] ) . Fig [ reference ] shows the schematic illustration of the ResBlock .
The mapping from the output of the acoustic model to likely word sequences is done via a Viterbi search in a weighted Finite State Transducer ( wFST ) , called ASR decoding graph in the following . Formally , the decoding graph may be written as the composition of four wFSTs , where denotes transducer composition ( see section [ reference ] ) , represents Hidden Markov Models ( HMMs ) modeling context - dependent phones , represents the context - dependency , is the lexicon and is the LM , typically a bigram or a trigram model represented as a wFST . Determinization and minimization operations are also applied at each step in order to compute equivalent optimized transducers with less states , allowing the composition and the inference to run faster .
Consequently , the states and transitions of the complete HCLG decoding graph are not computed at the time of creation , but rather at runtime during the inference , notably speeding up the building of the decoding graph . Additionally , employing lazy composition allows to break the decoding graph into two pieces ( HCL on one hand , and G on the other ) . The sum of the sizes of these pieces is typically several times smaller than the equivalent , statically - composed HCLG .
The recurrence relation is for . Inference net is equivalent to the composition of the same elementary function : where the operator denotes a function composition and denotes the - fold product of . Reconstruction net takes the input hidden state and outputs the target image ( high - resolution ) .
Unlike in computer vision , where convolutional neural networks are predominant , there are multiple ways to encode a sentence using neural networks . Hence , we investigate the impact of the sentence encoding architecture on representational transferability , and compare convolutional , recurrent and even simpler word composition schemes . Our experiments show that an encoder based on a bi - directional LSTM architecture with max pooling , trained on the Stanford Natural Language Inference ( SNLI ) dataset , yields state - of - the - art sentence embeddings compared to all existing alternative unsupervised approaches like SkipThought or FastSent , while being much faster to train .
To overcome this limitation , a Multi-Level Translation Method based on the Constructive Process Theory has been proposed . This paper describes the benefits of using this method in the Japanese-to-English machine translation system ALT-J/E. In comparison with conventional compositional methods , the Multi-Level Translation Method emphasizes the importance of the meaning contained in expression structures as a whole . It is shown to be capable of translating typical written Japanese based on the meaning of the text in its context , with comparative ease .
Learning new structures 8 . A DOP model for compositional semantic representations 9 . Speech understanding and dialogue processing 10 .
By overcoming data sparsity problems , as well as providing information about semantic relatedness which is not available in discrete representations , distributed representations have proven useful in many NLP tasks . Recent work has shown how compositional semantic representations can successfully be applied to a number of monolingual applications such as sentiment analysis . At the same time , there has been some initial success in work on learning shared word-level representations across languages .
In this paper we suggest a typed compositional semantics for nominal compounds of the form [ Adj Noun ] that models adjectives as higher-order polymorphic functions , and where types are assumed to represent concepts in an ontology that reflects our commonsense view of the world and the way we talk about it in ordinary language . In addition to [ Adj Noun ] compounds our proposal seems also to suggest a plausible explanation for well known adjective ordering restrictions .
In such a framework , certain types of ambiguities in natural language are explained by the notion of polymorphism . In this paper we suggest such a typed compositional semantics for nominal compounds of the form ( Adj Noun ) where adjectives are modeled as higher-order polymorphic functions . In addition to ( Adj Noun ) compounds our proposal seems also to suggest a plausible explanation for well known adjective ordering restrictions .
The thesis offers an implementation of a new approach for automatic modelling and music information retrieval . The model is based on a visual compositional hierarchical model , which provides good results in the area of categorization of visual objects in pictures . We translate the approach to the music domain by implementing and visualizing the model .
This result can be used to estimate the pose and structure of the face and the illumination conditions for each frame in a video sequence in the presence of multiple point and extended light sources . We propose a new inverse compositional estimation approach for this purpose . We then synthesize images using the face model estimated from the training data corresponding to the conditions in the probe sequences .
Although there is enormous evidence that reference levels influence preferences , conjoint models , one of the most successful marketing research tools , assume that preferences depend on the absolute levels of attributes . In this paper we investigate the relevance of reference effects in two settings , compositional or self-explicated models in experimental studies 1 and 2 , and decompositional or choice-based models in experimental study 3 . In particular , we introduce a simple modification of the traditional self-explicated conjoint model which permits dependence of preference on reference levels .
Semantic composition in distributional semantic models ( DSMs ) offers a powerful tool to represent word meaning in context . In this paper , we investigate methods to utilize compositional DSMs to improve word sense discrimination and word sense disambiguation . In this work , we rely on a previously proposed multiplicative model of composition .
To handle semantic constraints , support for a peer review approach to checking expressions is a desirable feature for tools . Other desirable features include a mechanism for supporting the hierarchy that exists in terminologies to maintain the context , and further support around the SNOMED Compositional Grammar to highlight syntax problems . Further work on the result display of Snapper 's constraint checker would improve its usability , especially for more complex expressions .
High-level supervisory control software for automotive applications ( e.g. , drive-by-wire ) presents many challenges to making performance guarantees , which are a necessary part of the software 's certification for deployment . The features of such systems demand that a compositional , or modular , approach to reasoning about performance be devised and applied . We discuss one such analytical approach as an alternative to simulation and testing .
How can we explain the origins of our uniquely human compositional system of communication ? Recently there has been a resurgence of interest in this and other questions surrounding the evolution of human language and the origins of syntax in particular ( Bickerton 1990 ; Pinker & Bloom 1990 ; Newmeyer 1991 ; Hurford et al. 1998).1 Much of this is due to an explicit attempt to relate models of our innate linguistic endowment with neo-Darwinian evolutionary theory .
Deep models can also learn hierarchical feature representations from images . In the Natural Language Processing ( NLP ) field , the belief that compositional models can also be used to text - related tasks is more recent . The increasing availability of text data motivates research for models able to improve accuracy in different language tasks .
In this paper , we present the first experiments using neural network models for the task of error detection in learner writing . We perform a systematic comparison of alternative compositional architectures and propose a framework for error detection based on bidirectional LSTMs . Experiments on the CoNLL - 14 shared task dataset show the model is able to outperform other participants on detecting errors in learner writing .
Our Own shared task also evaluated grammatical error detection of a number of different error types , though most systems were error - type specific and the best approach was heavily skewed towards article and preposition errors . We extend this line of research , working towards general error detection systems , and investigate the use of neural compositional models on this task . The related area of grammatical error correction has also gained considerable momentum in the past years , with four recent shared tasks highlighting several emerging directions .
Did What dataset . The effectiveness of multiplicative interaction is demonstrated by an ablation study , and by comparing to alternative compositional operators for implementing the gated - attention . citecolor = darkblue
Finally , in kiperwasser2016ef we present an Easy - First parser based on a novel hierarchical - LSTM tree encoding . In contrast to these , the approach we present in this work results in much simpler feature functions , without resorting to elaborate network architectures or compositional tree representations . Work by vinlays2014grammar employs a sequence - to - sequence with attention architecture for constituency parsing .
Zhu add spatial attention to the standard LSTM model for pointing and grounded QA . Andreas propose a compositional scheme that consists of a language parser and a number of neural modules networks . The language parser predicts which neural module network should be instantiated to answer the question .
In contrast , our approach does not suffer from these limitations . section : Advantage of Compositional Codes In this section , we formally describe the compositional coding approach and analyze its merits for compressing word embeddings .
For the compositional approach , the number of vectors required to construct an embedding vector is . Both the binary and compositional approaches have significantly fewer vectors in the embedding matrix . The compositional coding approach provides a better balance with shorter codes and lower computational cost .
Superimposition is a composition technique that has been applied successfully in many areas of software development . Although superimposition is a general-purpose concept , it has been (re)invented and implemented individually for various kinds of software artifacts .
The aim of this paper is to present and discuss the latest accomplishments in voxel-based craniofacial CBCT superimpositions along with structure discriminations . Methods : We propose a CBCT superimposition method based on joint embedding of subsets extracted from CBCT images . The subset is defined at local extremes of the first-order difference of Gaussian-smoothed volume images to reduce the data involved in the computation .
Combining CBCT imaging and intraoral scans with an intention to prepare comprehensive treatment plans is common in clinical practice . Segmentation and superimposition of models is indispensable when planning complicated prosthetic reconstructions . The authors of this publication evaluated registration discrepancies of virtual dental arch models obtained by means of CBCT imaging at different segmentation thresholds with intraoral dental scans .
FrFT is a new tool which has a dominant advantage to detect LFM-signal . However , under the multipath environment , the LFM signal is distortion on account of the noncorrelation superimposition of the multipath component , which results in performance degradation of the detector based on FrFT . In this paper , an improved algorithm based on Active Virtual time reversal and Fractional Fourier Transform is proposed .
The only information required is the 3D image of the skull , age , gender and Body Mass Index of the individual . The second technique is an automated skull-photo superimposition method which calculates the matching degree of a large number of photos with a skull .
We set out to investigate this issue by implementing an efficient algorithm for structure superimposition enabling routine searches for alternative alignments . RESULTS The algorithm is based on optimum superimposition of structures and dynamic programming . The implementation is tested and validated using published results .
Results The T 2 landmark differences in hand tracing between the operators ranged from 0.61 mm to 1.65 mm for the three types of superimposition . There were no significant differences in accuracy between hand and automated superimposition ( p > 0.05 ) . Conclusions Computer-aided cephalometric superimposition provides comparably accurate results to those of traditional hand tracing and will provide a powerful tool for academic research .
RESULTS The optimised practice consisted of : anode towards the head , broad focus , no OID or grid , 80 kVp , 32 mAs and 130 cm SID . CONCLUSION Images of suitable quality for laterally assessing spinal conditions using Cobb or superimposition measurements were produced from an effective dose of 0.05 mSv , which is 83 % less than the average effective dose used in the UK for lateral thoracic/lumbar exposures . This optimisation procedure can be adopted and use for optimisation of other radiographic techniques .
Morphological attribute profiles were used because the structural and contextual information of images was effective in distinguishing the classes with similar spectral characteristics . Moreover , a road superimposition strategy that considers road hierarchy was developed because OSM data provide road information with high completeness in the urban area . Experiments were conducted on the data captured over Wuhan city , and three state-of-the-art approaches were adopted for comparison .
Consequently they require a different set of primitives for tool support . This paper describes a form of tool composition called cotools which provides many of the benefits of UNIX pipes in terms of being able to reuse existing tools in tool composites , and allowing tools to operate concurrently ad incrementally on data . However , the tool language and the composition model is tailored to provide these benefits in a database-centered environment where tools do not directly communicate with each other .
We trace the evolution of the representation of management in cropping and grazing systems models , from fixed annual schedules of identical actions in single paddocks toward flexible scripts of rules . Attempts to define higher-level organizing concepts in management policies , and to analyse them to identify optimal plans , have focussed on questions relating to grazing management owing to its inherent complexity . "
Data augmentation is usually adopted to increase the amount of training data , prevent overfitting and improve the performance of deep models . However , in practice , random data augmentation , such as random image cropping , is low-efficiency and might introduce many uncontrolled background noises . In this paper , we propose Weakly Supervised Data Augmentation Network ( WS-DAN ) to explore the potential of data augmentation .
Small ( handheld ) to improve the cropping system by obtaining the coverage of a wide range of content for the smaller size of the device display , the invention is metadata aggregation ( metadata aggregation ) and the corresponding video , e.g. , the late proposed a method that starts from the operation ( post-production ) program exchange and storage ( archiving ) in , where , ( a ) the video is , for example , movement ( motion ) detection , mopol sludge ( morphology ) filters , edge detection by using such ( edge ​​detection ) , and passed to a video analyzer ( video analysis ) to transfer the video , ( b ) , and the video and metadata distinction is combined to extract important features of ( features ) in the context in which meta and classification of sensitive information from the data , a dynamic fit to the ( fitted ) chain of the feature extraction steps adapted to the delivered video content , Is used to vaporize , ( c ) extracted important features are the object ( object ) are combined to limit ( define ) the regions ( ROI ) of interest to be searched from the ( consecutive ) video consecutive frames by the tracking , tracing the object identifies the new position and deformation of each initialized in the video frames successive ROI and sikimyeo returns this information to the feature extraction , obtaining a permanent communication ( permanent communication ) between said feature extraction and said object tracking thereby , and ( d ) , one or several ROI are extracted and each video frame is input to the cropping step ( video frame by video frame ) , so by using , sorting the supplied ROI by priority based on the ( e ) the weight information ( weighting information ) preferably configured image part ( well composed image part ) the cropping and , ( f ) the croissant the region ( s ) ping is scaled to the desired small screen size It is .
This report describes new findings supplementing previous project reports on driving simulation studies to develop detection algorithms and effective advisory , alarm , and alerting stimuli for a vehicle-based drowsy driver detection/warning system . Part I describes attempts to further optimize previously-reported driver drowsiness detection algorithms by the use of time-on-task variables , assessment of the predictive power of algorithms , use of prior time segment data along with current data , and " cropping " of outliers to minimize their possible spurious effects . Part II reports an experimental study to identify effective advisory , alarm , and alertness-maintaining stimuli to be used in a drowsy driver detection system .
A fuzzy match index for scale-invariant feature transform ( SIFT ) features is proposed in this study that cumulatively involves all the test SIFT keypoints in the decision-making process . The new fuzzy SIFT classifier is adapted successfully for robust face recognition from complex backgrounds without using any face cropping tools and using only a single training template . The further incorporation of entropy weights ensures that the facial features have a greater role in the soft decision-making as compared with the background features .
This work is motivated by recent evidence from the cognitive science community demonstrating the efficacy of component-based facial representations . The component-based framework presented in this paper consists of the following major steps : 1 ) landmark extraction using Active Shape Models ( ASM ) , 2 ) alignment and cropping of components using Procrustes Analysis , 3 ) representation of components with Multiscale Local Binary Patterns ( MLBP ) , 4 ) per-component measurement of facial similarity , and 5 ) fusion of per-component similarities . We demonstrate on three public datasets and an operational dataset consisting of face images of 8000 subjects , that the proposed component-based representation provides higher recognition accuracies over holistic-based representations .
The present invention relates to an image display system , the image display system comprises a separating apparatus , the separating apparatus emits N signals divided images , wherein N number of divided image is formed by dividing the source image and do not overlap each other ; transmitting N number of divided image N transmission line ; the N dummy pixel insertion member , the N dummy pixel interpolation means generates the dummy pixels and dummy pixel interpolation to the outside of the image to produce an expanded image based on the divided image ; and N of the extended image application space signal processing image processing means . The image display system further comprises means N number of divided image cropping and image synthesis means , wherein the N number of divided image parts cut out from the respective images by the spatial processing to remove image components dummy pixels , in order to cut out the divided input image an image the same size , and the cut-out image synthesis means put together the divided images to generate an image of the same size of the source image resynthesis .
Thus , one of the main bottlenecks in processing data from photographic capture-recapture surveys is in cropping to an area of interest so that matching algorithms can identify the individual . Here , we describe the development and testing of an automated cropping program . The methods and techniques we describe are broadly applicable to any system where raw photos must be cropped down to a specific area of interest before pattern recognition software can be used for individual identification .
Random Erasing is parameter learning free , easy to implement , and can be integrated with most of the CNN - based recognition models . Albeit simple , Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping , and yields consistent improvement over strong baselines in image classification , object detection and person re - identification . Code is available at : .
[ reference ] . subsection : Comparison with Random Cropping Random cropping is an effective data augmentation approach , it reduces the contribution of the background in the CNN decision , and can base learning models on the presence of parts of the object instead of focusing on the whole object .
We experienced the need for such a mechanism in an industry project with Deutsche Bahn , where we do not merely model a highly variable system , but a system with highly variable subsystems . Based on MPL concepts and delta-oriented oriented programming , we present a novel solution to the design challenges arising from having to manage and interoperate multiple subsystems with multiple variants : how to reference variants , how to avoid name or type clashes , and how to keep variants interoperable .
From software to hardware: making dynamic multi-core processors practical Multiformalism Modeling Compositionality in SIMTHESys On the Numerical Integration of Ordinary Differential Equations by Processed Methods Specialising Parsers for Queries Interpreting verb clusters A Rule-Based Approach to Supporting Adaptable Web Service Composition An agent-based approach to composing web services to support adaptable business processes Recurrent Neural Network Grammars What Do Recurrent Neural Network Grammars Learn About Syntax? Neural Tree Indexers for Text Understanding FractalNet: Ultra-Deep Neural Networks without Residuals Deep Residual Networks with Exponential Linear Unit Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces Deeply-Recursive Convolutional Network for Image Super-Resolution Supervised Learning of Universal Sentence Representations from Natural Language Inference Data Toward an MT System without Pre-Editing - Effects of New Methods in ALT-J/E - Beyond Grammar: An Experience-Based Theory of Language Multilingual Distributed Representations without Word Alignment Concerning Olga, the Beautiful Little Street Dancer: Adjectives as Higher-Order Polymorphic Functions Polymorphism as a Model for Ambiguity in Natural Language Hierarchical compositional model for chord estimation Integrating Illumination, Motion, and Shape Models for Robust Face Recognition in Video Reference Dependence and Conjoint Analysis Utilizing Semantic Composition in Distributional Semantic Models for Word Sense Discrimination and Word Sense Disambiguation Mapping Existing Medical Terminologies to SNOMED CT: An Investigation of the Novice User's Experience Issues in performance certification for high-level automotive control software Language evolution without natural selection: From vocabulary to syntax in a population of learners Squeezed Very Deep Convolutional Neural Networks for Text Classification Compositional Sequence Labeling Models for Error Detection in Learner Writing Gated-Attention Readers for Text Comprehension Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations Hierarchical Question-Image Co-Attention for Visual Question Answering Compressing Word Embeddings via Deep Compositional Code Learning FEATUREHOUSE: Language-independent, automated software composition Superimposition of Cone-Beam Computed Tomography Images by Joint Embedding Surface Registration Accuracy of Clinically Obtained Intraoral Optical Scans with Manually Threshold Segmented CBCT Data An new LFM detection algorithm based on ADTR-FrFT for active sonar system A Computer Application for 3D Craniofacial Reconstruction and Automatic Skull-Photo Identification Optimum superimposition of protein structures: ambiguities and implications. The application and accuracy of feature matching on automated cephalometric superimposition A systematic procedure to optimise dose and image quality for the measurement of inter-vertebral angles from lateral spinal projections using Cobb and superimposition methods. Fusing High-Spatial-Resolution Remotely Sensed Imagery and OpenStreetMap Data for Land Cover Classification Over Urban Areas View-based tool integration in database-centered environment Modelling the manager: Representing rule-based management in farming systems simulation models See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification A method of adapting video images to small screen sizes RESEARCH ON VEHICLE-BASED DRIVER STATUS/PERFORMANCE MONITORING: SEVENTH SEMI-ANNUAL RESEARCH REPORT Fuzzy match index for scale-invariant feature transform (SIFT) features with application to face recognition with weak supervision Component-Based Representation in Automated Face Recognition Image display system, display apparatus, image re-synthesis apparatus, image re-synthesis method, and recording medium An automated program to find animals and crop photographs for individual recognition Random Erasing Data Augmentation Same Same But Different: Interoperability of Software Product Line Variants
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