Sequence-to-sequence models have achieved impressive results on various tasks . However , they are unsuitable for tasks that require incremental predictions to be made as more data arrives . In particular , we provide tests to investigate ( i ) if models systematically recombine known parts and rules ( ii ) if models can extend their predictions beyond the length they have seen in the training data ( iii ) if models ' composition operations are local or global ( iv ) if models ' predictions are robust to synonym substitutions and ( v ) if models favour rules or exceptions during training . To demonstrate the usefulness of this evaluation paradigm , we instantiate these five tests on a highly compositional data set which we dub PCFG SET and apply the resulting tests to three popular sequence-to-sequence models : a recurrent , a convolution-based and a transformer model . We provide an in-depth analysis of the results , which uncover the strengths and weaknesses of these three architectures and point to potential areas of improvement . CVT can be applied to a variety of tasks and neural architectures , but we focus on sequence modeling tasks where the prediction modules are attached to a shared Bi - LSTM encoder . We propose auxiliary prediction modules that work well for sequence taggers , graph - based dependency parsers , and sequence - to - sequence models . We evaluate our approach on English dependency parsing , combinatory categorial grammar supertagging , named entity recognition , part - of - speech tagging , and text chunking , as well as English to Vietnamese machine translation . Intuitively , the ideal solution for graph - to - sequence tasks is to build a more powerful encoder which is able to learn the input representation regardless of its inherent structure . To cope with graph - to - sequence problems , a simple and straightforward approach is to directly convert more complex structured graph data into sequences iyer2016summarizing , gomez2016automatic , liu2017retrosynthetic , and apply sequence models to the resulting sequences . However , the Seq2Seq model often fails to perform as well as hoped on these problems , in part because it inevitably suffers significant information loss due to the conversion of complex structured data into a sequence , especially when the input data is naturally represented as graphs . Most words have multiple senses or meanings , and it is generally necessary to use the context in which a word occurs to determine which of its senses or meanings is meant in a given sentence . Even though TreeRNNs are more effective at composing meanings in principle , this ambiguity can give simpler sequence - based sentence - encoding models an advantage : when a sequence - based model first processes a word , it has direct access to a state vector that summarizes the left context of that word , which acts as a cue for disambiguation . In contrast , when a standard tree - structured model first processes a word , it only has access to the constituent that the word is merging with , which is often just a single additional word . convert graphs into sequences by following random walks on the graph then learns node embeddings using sequence - based methods . map graphs to graph vectors then classify using an output neural network . For sentence modeling , CNNs perform excellently in extracting n - gram features at different positions of a sentence through convolutional filters , and can learn short and long - range relations through pooling operations . CNNs have been successfully combined with both sequence - based model and tree - structured model in sentence modeling . The other popular neural network architecture – RNN – is able to handle sequences of any length and capture long - term dependencies . On the other hand , Autism patients had significantly difference STFT at bandwidth ( STFTBW ) . At electrodes FP1 , F3 and T5 with ( p<0.01 ) and F7 , T3 and O1 with ( p<0.05 ) had significantly differences . In addition our findings suggested that STFT-BW can discriminate 82.4 % between normal and Autism subject with mahalanobis distance . The TUT-T5 code calculates , for a one-energy model , a regionwise distribution of capture probabilities in a twodimensional quarter-cell by a Monte Carlo method . If desired , as many as 32 regions can be treated , all of different material content ; however , the content of each region must be uniform . Empirical results show that today 's models struggle at our task , even those with billions of parameters . The best model , a finetuned T5 , writes advice that is at least as helpful as human-written advice in only 9 % of cases . This low performance reveals language understanding errors that are hard to spot outside of a generative setting , showing much room for progress . Half black screen was shown in Philips T5-NT MRI Scanner and with its message . This trouble was associated with environment temperature , checking machine and find the bad parts TC-420 board at the best power up time . By contrast , our knowledge of OFF-pathway ( T5 ) has been incomplete . Here , we present a conclusive and comprehensive connectome that , for the first time , integrates detailed connectivity information for inputs to both the T4 and T5 pathways in a single EM dataset covering the entire optic lobe . With novel reconstruction methods using automated synapse prediction suited to such a large connectome , we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5 . This simulation assisted study investigates the energy and cost implications of a large office located in India specifically at 9.9252 ° N , 78.1198 ° E. The energy savings and payback time of T5&CFL and LED luminaire with control schemes is presented , study shows the best results with scheduled occupancy with a photo sensor for window zones . For this to launch , a grouping of luminaires is carried out after accurate simulation of the building . Some co-factors such as Mn(2 + ) stimulated the cleavage of double-stranded closed-circular plasmid DNA . Such endonucleolytic cleavage of circular double-stranded DNA can not be readily explained by the threading model proposed for the cleavage of substrates with free 5'-ends as the hole observed in the crystal structure of T5 exonuclease is too small to permit the passage of double-stranded DNA . We suggest that such a substrate may gain access to the active site of the enzyme by a process which does not involve threading . The geometric model was created in Autodesk  Maya 3D 2013 , and the mesh process in HiperMesh and MeshMixer ( v11.0.544 Autodesk ) . Results : The T5-T10 thoracic segment model is presented with its passive components , bones , intervertebral discs and flavum , intertransverse and supraspinous ligaments , in different views , as well as the volumetric mesh . Life cycle assessment ( LCA ) methodology has been used to evaluate the performance of the following lighting systems : compact fluorescent lamps ( CFL ) , light-emitting diode ( LED ) lamps , and fluorescent tubes ( T5 type ) . This work covers the singularity of the French electricity mix for indoor workplaces lighting and describes the best strategy for lamp replacement . The 3.6 GHz SPARC T5 processor is Oracle 's next generation CMT SoC processor implemented in TSMC 's 28 nm process with 1.5 billion transistors . Significant performance improvements were made by doubling the previous generations number of cores to 16 and L3 cache size to 8 MB while increasing bandwidth by nearly 3 × . In recent years , the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields . Many biologically inspired visual models are proposed , and among them , the Hierarchical Max-pooling model ( HMAX ) is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal ( PIT ) layer of the primate visual cortex , which could generate a series of position- and scale- invariant features . However , it could be improved with attention modulation and memory processing , which are two important properties of the primate visual cortex . For the facial expression perception , we propose a novel approach which exploits convolutional deep belief networks ( CDBN ) to spontaneously locate the places containing the most discriminative information and synchronously complete the feature learning and feature selection . For the facial identity perception , we propose an approach which adopts the hierarchical max-pooling ( HMAX ) model to encode notable characteristics of facial components and utilizes a new memory formation integrating the preliminary decision , expression modulation and final decision process . We evaluate our model through a series of experiments and the experimental results demonstrate its rationality and effectiveness . Recently , many computational models have been proposed to simulate visual cognition process . For example , the hierarchical Max-Pooling ( HMAX ) model was proposed according to the hierarchical and bottom-up structure of V1 to V4 in the ventral pathway of primate visual cortex , which could achieve position- and scale-tolerant recognition . In our previous work , we have introduced memory and association into the HMAX model to simulate visual cognition process . However , rich structural information is overlooked . Thus a hierarchical pooling procedure is desired to preserve the graph structure during the graph representation learning . There are some recent works on hierarchically learning graph representation analogous to the pooling step in conventional convolutional neural ( CNN ) networks . Recurrent neural networks have proven to be very effective in learning distributed representations and can be trained efficiently on natural language inference tasks . We build on top of one such model and propose a hierarchy of BiLSTM and max pooling layers that implements an iterative refinement strategy and yields state of the art results on the SciTail dataset as well as strong results for SNLI and MultiNLI . We can show that the sentence embeddings learned in this way can be utilized in a wide variety of transfer learning tasks , outperforming InferSent on 7 out of 10 and SkipThought on 8 out of 9 SentEval sentence embedding evaluation tasks . The experimental results demonstrated that the Bi - LSTM with max pooling achieved the best performance . Talman et al . designed a hierarchical Bi - LSTM max pooling ( HBMP ) model to encode sentences . This model applied parameters of one Bi - LSTM to initialize the next Bi - LSTM to convey information , which shown better results than the model with a single Bi - LSTM . Recently , text-to-speech ( TTS ) models such as FastSpeech and ParaNet have been proposed to generate mel-spectrograms from text in parallel . Despite the advantages , the parallel TTS models can not be trained without guidance from autoregressive TTS models as their external aligners . This paper implements a lightweight crypto core that unifies a variety of algorithms while meeting the hardware area requirement of constrained devices . The cryptographic core consists of an Elliptic Curve Diffie-Hellman ( ECDH ) protocol for key exchange , a unified architecture of two encryption/decryption lightweight ciphers ( PRESENT and a newly proposed algorithm ) and a unified architecture of four HopperBlum ( HB type ) authentication protocols ( HB , HB+ , HBMP , and HBMP+ ) . All the unified hardware architectures share resources in other to minimize the total hardware area of the core . A segmentation algorithm is used to identify shots from video data . In this paper , we propose a histogram-based moment-preserving ( HBMP ) clustering algorithm for segmenting video data . This algorithm is a hybrid of the shot change detection approach and the clustering approach . Learning-based driving solution , a new branch for autonomous driving , is expected to simplify the modeling of driving by learning the underlying mechanisms from data . To improve the tactical decision-making for learning-based driving solution , we introduce hierarchical behavior and motion planning ( HBMP ) to explicitly model the behavior in learning-based solution . Due to the coupled action space of behavior and motion , it is challenging to solve HBMP problem using reinforcement learning ( RL ) for long-horizon driving tasks .
Sequence-to-sequence models have achieved impressive results on various tasks . However , they are unsuitable for tasks that require incremental predictions to be made as more data arrives .
In particular , we provide tests to investigate ( i ) if models systematically recombine known parts and rules ( ii ) if models can extend their predictions beyond the length they have seen in the training data ( iii ) if models ' composition operations are local or global ( iv ) if models ' predictions are robust to synonym substitutions and ( v ) if models favour rules or exceptions during training . To demonstrate the usefulness of this evaluation paradigm , we instantiate these five tests on a highly compositional data set which we dub PCFG SET and apply the resulting tests to three popular sequence-to-sequence models : a recurrent , a convolution-based and a transformer model . We provide an in-depth analysis of the results , which uncover the strengths and weaknesses of these three architectures and point to potential areas of improvement .
CVT can be applied to a variety of tasks and neural architectures , but we focus on sequence modeling tasks where the prediction modules are attached to a shared Bi - LSTM encoder . We propose auxiliary prediction modules that work well for sequence taggers , graph - based dependency parsers , and sequence - to - sequence models . We evaluate our approach on English dependency parsing , combinatory categorial grammar supertagging , named entity recognition , part - of - speech tagging , and text chunking , as well as English to Vietnamese machine translation .
Intuitively , the ideal solution for graph - to - sequence tasks is to build a more powerful encoder which is able to learn the input representation regardless of its inherent structure . To cope with graph - to - sequence problems , a simple and straightforward approach is to directly convert more complex structured graph data into sequences iyer2016summarizing , gomez2016automatic , liu2017retrosynthetic , and apply sequence models to the resulting sequences . However , the Seq2Seq model often fails to perform as well as hoped on these problems , in part because it inevitably suffers significant information loss due to the conversion of complex structured data into a sequence , especially when the input data is naturally represented as graphs .
Most words have multiple senses or meanings , and it is generally necessary to use the context in which a word occurs to determine which of its senses or meanings is meant in a given sentence . Even though TreeRNNs are more effective at composing meanings in principle , this ambiguity can give simpler sequence - based sentence - encoding models an advantage : when a sequence - based model first processes a word , it has direct access to a state vector that summarizes the left context of that word , which acts as a cue for disambiguation . In contrast , when a standard tree - structured model first processes a word , it only has access to the constituent that the word is merging with , which is often just a single additional word .
convert graphs into sequences by following random walks on the graph then learns node embeddings using sequence - based methods . map graphs to graph vectors then classify using an output neural network .
For sentence modeling , CNNs perform excellently in extracting n - gram features at different positions of a sentence through convolutional filters , and can learn short and long - range relations through pooling operations . CNNs have been successfully combined with both sequence - based model and tree - structured model in sentence modeling . The other popular neural network architecture – RNN – is able to handle sequences of any length and capture long - term dependencies .
On the other hand , Autism patients had significantly difference STFT at bandwidth ( STFTBW ) . At electrodes FP1 , F3 and T5 with ( p<0.01 ) and F7 , T3 and O1 with ( p<0.05 ) had significantly differences . In addition our findings suggested that STFT-BW can discriminate 82.4 % between normal and Autism subject with mahalanobis distance .
The TUT-T5 code calculates , for a one-energy model , a regionwise distribution of capture probabilities in a twodimensional quarter-cell by a Monte Carlo method . If desired , as many as 32 regions can be treated , all of different material content ; however , the content of each region must be uniform .
Empirical results show that today 's models struggle at our task , even those with billions of parameters . The best model , a finetuned T5 , writes advice that is at least as helpful as human-written advice in only 9 % of cases . This low performance reveals language understanding errors that are hard to spot outside of a generative setting , showing much room for progress .
Half black screen was shown in Philips T5-NT MRI Scanner and with its message . This trouble was associated with environment temperature , checking machine and find the bad parts TC-420 board at the best power up time .
By contrast , our knowledge of OFF-pathway ( T5 ) has been incomplete . Here , we present a conclusive and comprehensive connectome that , for the first time , integrates detailed connectivity information for inputs to both the T4 and T5 pathways in a single EM dataset covering the entire optic lobe . With novel reconstruction methods using automated synapse prediction suited to such a large connectome , we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5 .
This simulation assisted study investigates the energy and cost implications of a large office located in India specifically at 9.9252 ° N , 78.1198 ° E. The energy savings and payback time of T5&CFL and LED luminaire with control schemes is presented , study shows the best results with scheduled occupancy with a photo sensor for window zones . For this to launch , a grouping of luminaires is carried out after accurate simulation of the building .
Some co-factors such as Mn(2 + ) stimulated the cleavage of double-stranded closed-circular plasmid DNA . Such endonucleolytic cleavage of circular double-stranded DNA can not be readily explained by the threading model proposed for the cleavage of substrates with free 5'-ends as the hole observed in the crystal structure of T5 exonuclease is too small to permit the passage of double-stranded DNA . We suggest that such a substrate may gain access to the active site of the enzyme by a process which does not involve threading .
The geometric model was created in Autodesk  Maya 3D 2013 , and the mesh process in HiperMesh and MeshMixer ( v11.0.544 Autodesk ) . Results : The T5-T10 thoracic segment model is presented with its passive components , bones , intervertebral discs and flavum , intertransverse and supraspinous ligaments , in different views , as well as the volumetric mesh .
Life cycle assessment ( LCA ) methodology has been used to evaluate the performance of the following lighting systems : compact fluorescent lamps ( CFL ) , light-emitting diode ( LED ) lamps , and fluorescent tubes ( T5 type ) . This work covers the singularity of the French electricity mix for indoor workplaces lighting and describes the best strategy for lamp replacement .
The 3.6 GHz SPARC T5 processor is Oracle 's next generation CMT SoC processor implemented in TSMC 's 28 nm process with 1.5 billion transistors . Significant performance improvements were made by doubling the previous generations number of cores to 16 and L3 cache size to 8 MB while increasing bandwidth by nearly 3 × .
In recent years , the interdisciplinary research between neuroscience and computer vision has promoted the development in both fields . Many biologically inspired visual models are proposed , and among them , the Hierarchical Max-pooling model ( HMAX ) is a feedforward model mimicking the structures and functions of V1 to posterior inferotemporal ( PIT ) layer of the primate visual cortex , which could generate a series of position- and scale- invariant features . However , it could be improved with attention modulation and memory processing , which are two important properties of the primate visual cortex .
For the facial expression perception , we propose a novel approach which exploits convolutional deep belief networks ( CDBN ) to spontaneously locate the places containing the most discriminative information and synchronously complete the feature learning and feature selection . For the facial identity perception , we propose an approach which adopts the hierarchical max-pooling ( HMAX ) model to encode notable characteristics of facial components and utilizes a new memory formation integrating the preliminary decision , expression modulation and final decision process . We evaluate our model through a series of experiments and the experimental results demonstrate its rationality and effectiveness .
Recently , many computational models have been proposed to simulate visual cognition process . For example , the hierarchical Max-Pooling ( HMAX ) model was proposed according to the hierarchical and bottom-up structure of V1 to V4 in the ventral pathway of primate visual cortex , which could achieve position- and scale-tolerant recognition . In our previous work , we have introduced memory and association into the HMAX model to simulate visual cognition process .
However , rich structural information is overlooked . Thus a hierarchical pooling procedure is desired to preserve the graph structure during the graph representation learning . There are some recent works on hierarchically learning graph representation analogous to the pooling step in conventional convolutional neural ( CNN ) networks .
Recurrent neural networks have proven to be very effective in learning distributed representations and can be trained efficiently on natural language inference tasks . We build on top of one such model and propose a hierarchy of BiLSTM and max pooling layers that implements an iterative refinement strategy and yields state of the art results on the SciTail dataset as well as strong results for SNLI and MultiNLI . We can show that the sentence embeddings learned in this way can be utilized in a wide variety of transfer learning tasks , outperforming InferSent on 7 out of 10 and SkipThought on 8 out of 9 SentEval sentence embedding evaluation tasks .
The experimental results demonstrated that the Bi - LSTM with max pooling achieved the best performance . Talman et al . designed a hierarchical Bi - LSTM max pooling ( HBMP ) model to encode sentences . This model applied parameters of one Bi - LSTM to initialize the next Bi - LSTM to convey information , which shown better results than the model with a single Bi - LSTM .
Recently , text-to-speech ( TTS ) models such as FastSpeech and ParaNet have been proposed to generate mel-spectrograms from text in parallel . Despite the advantages , the parallel TTS models can not be trained without guidance from autoregressive TTS models as their external aligners .
This paper implements a lightweight crypto core that unifies a variety of algorithms while meeting the hardware area requirement of constrained devices . The cryptographic core consists of an Elliptic Curve Diffie-Hellman ( ECDH ) protocol for key exchange , a unified architecture of two encryption/decryption lightweight ciphers ( PRESENT and a newly proposed algorithm ) and a unified architecture of four HopperBlum ( HB type ) authentication protocols ( HB , HB+ , HBMP , and HBMP+ ) . All the unified hardware architectures share resources in other to minimize the total hardware area of the core .
A segmentation algorithm is used to identify shots from video data . In this paper , we propose a histogram-based moment-preserving ( HBMP ) clustering algorithm for segmenting video data . This algorithm is a hybrid of the shot change detection approach and the clustering approach .
Learning-based driving solution , a new branch for autonomous driving , is expected to simplify the modeling of driving by learning the underlying mechanisms from data . To improve the tactical decision-making for learning-based driving solution , we introduce hierarchical behavior and motion planning ( HBMP ) to explicitly model the behavior in learning-based solution . Due to the coupled action space of behavior and motion , it is challenging to solve HBMP problem using reinforcement learning ( RL ) for long-horizon driving tasks .
An Online Sequence-to-Sequence Model Using Partial Conditioning Compositionality Decomposed: How do Neural Networks Generalise? Semi-Supervised Sequence Modeling with Cross-View Training Graph2Seq: Graph to Sequence Learning with Attention-based Neural Networks A Fast Unified Model for Parsing and Sentence Understanding Gated Graph Sequence Neural Networks A C-LSTM Neural Network for Text Classification Analysis of EEG background activity in Autism disease patients with bispectrum and STFT measure TUT-T5--A TWO DIMENSIONAL MONTE CARLO CALCULATION OF CAPTURE PROBABILITIES FOR THE IBM-704 Evaluating Machines by their Real-World Language Use Malfunction Servicing of Philips T5-NT MRI Set Comparisons between the ON- and OFF-edge motion pathways in the Drosophila brain Office Lighting Simulation: Energy implications with scheduled occupancy and daylight harvesting Unusually wide co-factor tolerance in a metalloenzyme; divalent metal ions modulate endo?exonuclease activity in T5 exonuclease Three-dimensional geometric model of the middle segment of the thoracic spine based on graphical images for finite element analysis Life Cycle Assessment of Lighting Systems and Light Loss Factor: A Case Study for Indoor Workplaces in France A 3.6 GHz 16-Core SPARC SoC Processor in 28 nm Enhanced HMAX model with feedforward feature learning for multiclass categorization A biologically inspired model mimicking the memory and two distinct pathways of face perception Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment Graph Convolutional Networks with EigenPooling Sentence embeddings in NLI with iterative refinement encoders Attention Boosted Sequential Inference Model Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search The Design and Implementation of a Hardware Crypto Core for Securing Pervasive Devices A histogram-based moment-preserving clustering algorithm for video segmentation Learning hierarchical behavior and motion planning for autonomous driving
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EMNLP ArXiv ACL ICLR ArXiv ISSCC ArXiv
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