Neural machine translation aims at building a single large neural network that can be trained to maximize translation performance . The encoder-decoder architecture with an attention mechanism achieves a translation performance comparable to the existing state-of-the-art phrase-based systems on the task of English-to-French translation . However , the use of large vocabulary becomes the bottleneck in both training and improving the performance . The Attention mechanism is one of the most popular methods used in the seq2seq ( Sequence to Sequence ) text summarization models . The current attention mechanisms usually use the hidden states of the encoder and the decoder to generate attention distributions . However , they ignore the information of the word waiting to be input into the decoder , leading to possible failures to obtain accurate attention distributions . Attention [ reference ] has recently been explored as a standalone method for sentence encoding , giving competitive results compared to Bi - LSTM encoders for neural machine translation [ reference ] . The attention mechanism allows parallelisation , and can play a similar role to the sentence - level state in S - LSTMs , which uses neural gates to integrate word - level information compared to hierarchical attention . S - LSTM further allows local communication between neighbouring words . This approach has yielded impressive performance across several large - scale inductive benchmarks . Attention mechanisms have become almost a de facto standard in many sequence - based tasks [ reference ][ reference ] . One of the benefits of attention mechanisms is that they allow for dealing with variable sized inputs , focusing on the most relevant parts of the input to make decisions . The neural counterpart to alignment , attention , which is a key part of our approach , was originally proposed and has been predominantly used in conjunction with LSTMs and to a lesser extent with CNNs . In contrast , our use of attention is purely based on word embeddings and our method essentially consists of feed - forward networks that operate largely independently of word order . section : Approach This system is designed in double-thread architecture . DMA , Cache and optimization techniques enhance the real time performance of the system . DMA is an important technique to increase data transfer rate and MPU efficiency . The paper introduces the application of DMA in EPON(Ethernet Passive Optical Network ) system , then discusses the design of DMA driver and simply introduces the memory allocation of DMA driver . Therefore , run-time information has also to be used in order to produce an efficient design . This introduces new challenges , especially for embedded system designers using a Direct Memory Access ( DMA ) module , who have to know in advance the memory transfer behavior of the whole system , in order to design and program their DMA efficiently . In this paper , we propose a mixed Hardware/Software optimization at system level . The authors present the performance simulation and design of block data transfer mechanism for the SMP based message passing system called SPAX ( Scalable Parallel Architecture computer based on X-bar network ) . For node-to-node block data transfer in SPAX , two schemes of memory-mapped transfer and DMA based transfer can be considered as a design alternative of network interface architecture . According to the simulation results , the DMA based scheme is more efficient for both latency and bandwidth rather than the memory mapped scheme in transferring the block data in SPAX . We also present the design of the block data transfer mechanism based on the DMA based transfer in detail , which is applied to SPAX . DMA requires a digital data base to maintain , store , retrieve , compare , and report on this MC&G digital data . This paper addresses current DMA efforts that will evolve into an on-line interactive , distributed , networked data base system and its associated environment for data handled by HQ DMA and the DMA Production Centers . ( Author ) Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms , there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques . This paper presents a novel open-source framework called deformable model array ( DMA ) for the segmentation of multiple and complex structures of interest in different imaging modalities . While most active contour algorithms can extract one region at a time , DMA allows integrating several deformable models to deal with multiple segmentation scenarios . Interrupt functions of M/S computer system are utilized to perform frame-real-time acquisition , processing and displaying of laser image . A spec ial DMA controller is designed to co-occupied two identical dual-port SRAMs wi th slave computer ADSP21020 , and successive image buffering , acquisition and pro cessing are implemented by exchanging enable rights of SRAMs synchronously . Fina lly , the experiment result analysis is given . This technique increases data throughput by 45 percent while reducing the host 's CPU load . They found that peer DMA forwarding can increase host based router throughput by up to 45 percent , supporting bandwidths of 480 Mbps . Peer DMA host based forwarding requires network interface cards with substantial shared memory resources , because packet queues are stored on the interfaces themselves , rather than in host RAM . Damping corrections recover more than 70 % of the charge penetration energy in all dimers , whereas higher order damping introduces only minor improvement . Electrostatic energies calculated by the numerical DMA are less accurate than those calculated by the analytic DMA . Analysis of bonding in the benzene dimer shows that EFP with inclusion of the electrostatic damping term performs very well compared to the high‐level coupled cluster singles , doubles , and perturbative triples method . The data memory is constructed of a 16-bit × 66-word internal RAM and 16-bit × 4k-word external RAM , and the addressing mode for these RAMs is powerful . The I/O facilities contain a 2- channel DMA controller , thus avoiding troublesome I/O handling of the program . The powerful program control facilities support a nested loop program , subroutine call and three kinds of interrupts . In this paper , existing FPGA-related Direct Memory Access ( DMA ) solutions have been evaluated , and a new DMA solution tailored for hyperspectral images has been proposed . Results show that the proposed DMA core , CubeDMA , handles targeted memory access patterns in more efficient manner than existing solutions while being resource efficient . Inside TandemNet , a language model is used to represent report text , which cooperates with the image model in a tandem scheme . We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction . In the testing stage , TandemNet can make accurate image prediction with an optional report text input . In this paper , the entity alignment algorithm is understood to be a neural network binary classification model , and we propose an entity alignment algorithm based on the dual-attention mechanism . The algorithm improves the entity vector training process , proposes a dual-attention mechanism , and applies an incremental learning mechanism . The experiments show that the improvements proposed in this paper effectively improve the classification accuracy of the algorithm , and the overall effect of the algorithm is better than that of the existing physical alignment algorithm . This will enable agents to have conversations with users about the objects , activities and events surrounding them . In this work , we present three main architectural explorations for the Audio Visual Scene-Aware Dialog ( AVSD ) : 1 ) investigating ` topics ' of the dialog as an important contextual feature for the conversation , 2 ) exploring several multimodal attention mechanisms during response generation , 3 ) incorporating an end-to-end audio classification ConvNet , AclNet , into our architecture . We discuss detailed analysis of the experimental results and show that our model variations outperform the baseline system presented for the AVSD task . In this paper , we propose a multitask model capable of jointly learning these multimodal tasks , and transferring knowledge of words and their grounding in visual objects across the tasks . The proposed model uses a novel Dual-Attention unit to disentangle the knowledge of words in the textual representations and visual concepts in the visual representations , and align them with each other . This disentangled task-invariant alignment of representations facilitates grounding and knowledge transfer across both tasks . In community question and answering ( Q&A ) systems , due to the diversity of words and syntactic structure , matching question pairs representing similar meaning is a challenging task . A novel model based on dual-layer attention mechanism for semantic matching is proposed in this work . Firstly , an attention-based preprocessing method is used on the word representation layer to reduce redundant information . In particular , we first present a flexible HIN ( heterogeneous information network ) framework for modeling the short texts , which can integrate any type of additional information as well as capture their relations to address the semantic sparsity . Then , we propose Heterogeneous Graph ATtention networks ( HGAT ) to embed the HIN for short text classification based on a dual-level attention mechanism , including node-level and type-level attentions . The attention mechanism can learn the importance of different neighboring nodes as well as the importance of different node ( information ) types to a current node . To acquire more semantically accurate inpainting images , this paper proposes a novel inpainting model named \textit{N}eural \textit{I}mage Inpainting \textit{G}uided with \textit{D}escriptive \textit{T}ext ( NIGDT ) . First , a dual multi-modal attention mechanism is designed to extract the explicit semantic information about corrupted regions . The mechanism is trained to combine the descriptive text and two complementary images through reciprocal attention maps . Following dedicated non-linear mappings for visual features at each level , word , and sentence embeddings , we obtain multiple instantiations of our common semantic space in which comparisons between any target text and the visual content is performed with cosine similarity . We guide the model by a multi-level multimodal attention mechanism which outputs attended visual features at each level . The best level is chosen to be compared with text content for maximizing the pertinence scores of image-sentence pairs of the ground truth . Due to the sparsity of handcrafted features , we discrete the features into a 2D point map and learn the structure inspired by PointNet . Finally , a multimodal attention fusion module is designed to attentively fuse the local structural and global semantic features , providing the hybrid features for the more reasonable scene recovery . The experimental results demonstrate the excellent generalization ability and more significant performance of our method in both quantitative and qualitative evaluations , compared with the state-of-the-art methods . This paper discusses machine attention based on multimodal information integration and synchronization . The aim is to introduce a computational model for robust multimodal attention mechanism in artificial systems . First , we introduce a new blind source separation algorithm based on temporal structures and high order statistics of the source signals . We propose a structure - aware seq2seq architecture to encode both the content and the structure of a table for table - to - text generation . The model consists of field - gating encoder and description generator with dual attention . We add a field gate to the encoder LSTM unit to incorporate the field information . Note that our model does not use any position embeddings because ELMo gives the positional information of the words in each sequence . subsubsection : Dual Attention Layer This layer fuses information from the passages to the question as well as from the question to the passages in a dual mechanism . and . We use DCN XiongZS17 as the dual attention mechanism to obtain question - to - passage representations : and passage - to - question ones : subsubsection : Modeling Encoder Layer subsection : Model Framework As shown in Figure [ reference ] , our model consists of three modules including two encoders and a dual - attention decoder equipped with a context selection gate network . The sentence encoder reads the input words and builds its corresponding representation . Finally , both sentence hidden states and relation hidden states are fed to the decoder . subsection : Dual - Attention Decoder Previous s2s models have developed some task - specific modifications on the decoder , such as to incorporate the copying mechanism and coverage mechanism . Motivated by this observation , in addition to reasoning about visual attention , we also address the problem of question attention . Specifically , we present a novel multi - modal attention model for VQA with the following two unique features : Co - Attention : We propose a novel mechanism that jointly reasons about visual attention and question attention , which we refer to as co - attention . We propose Sparse Sinkhorn Attention , a new efficient and sparse method for learning to attend . Our method is based on differentiable sorting of internal representations .
Neural machine translation aims at building a single large neural network that can be trained to maximize translation performance . The encoder-decoder architecture with an attention mechanism achieves a translation performance comparable to the existing state-of-the-art phrase-based systems on the task of English-to-French translation . However , the use of large vocabulary becomes the bottleneck in both training and improving the performance .
The Attention mechanism is one of the most popular methods used in the seq2seq ( Sequence to Sequence ) text summarization models . The current attention mechanisms usually use the hidden states of the encoder and the decoder to generate attention distributions . However , they ignore the information of the word waiting to be input into the decoder , leading to possible failures to obtain accurate attention distributions .
Attention [ reference ] has recently been explored as a standalone method for sentence encoding , giving competitive results compared to Bi - LSTM encoders for neural machine translation [ reference ] . The attention mechanism allows parallelisation , and can play a similar role to the sentence - level state in S - LSTMs , which uses neural gates to integrate word - level information compared to hierarchical attention . S - LSTM further allows local communication between neighbouring words .
This approach has yielded impressive performance across several large - scale inductive benchmarks . Attention mechanisms have become almost a de facto standard in many sequence - based tasks [ reference ][ reference ] . One of the benefits of attention mechanisms is that they allow for dealing with variable sized inputs , focusing on the most relevant parts of the input to make decisions .
The neural counterpart to alignment , attention , which is a key part of our approach , was originally proposed and has been predominantly used in conjunction with LSTMs and to a lesser extent with CNNs . In contrast , our use of attention is purely based on word embeddings and our method essentially consists of feed - forward networks that operate largely independently of word order . section : Approach
This system is designed in double-thread architecture . DMA , Cache and optimization techniques enhance the real time performance of the system .
DMA is an important technique to increase data transfer rate and MPU efficiency . The paper introduces the application of DMA in EPON(Ethernet Passive Optical Network ) system , then discusses the design of DMA driver and simply introduces the memory allocation of DMA driver .
Therefore , run-time information has also to be used in order to produce an efficient design . This introduces new challenges , especially for embedded system designers using a Direct Memory Access ( DMA ) module , who have to know in advance the memory transfer behavior of the whole system , in order to design and program their DMA efficiently . In this paper , we propose a mixed Hardware/Software optimization at system level .
The authors present the performance simulation and design of block data transfer mechanism for the SMP based message passing system called SPAX ( Scalable Parallel Architecture computer based on X-bar network ) . For node-to-node block data transfer in SPAX , two schemes of memory-mapped transfer and DMA based transfer can be considered as a design alternative of network interface architecture . According to the simulation results , the DMA based scheme is more efficient for both latency and bandwidth rather than the memory mapped scheme in transferring the block data in SPAX . We also present the design of the block data transfer mechanism based on the DMA based transfer in detail , which is applied to SPAX .
DMA requires a digital data base to maintain , store , retrieve , compare , and report on this MC&G digital data . This paper addresses current DMA efforts that will evolve into an on-line interactive , distributed , networked data base system and its associated environment for data handled by HQ DMA and the DMA Production Centers . ( Author )
Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms , there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques . This paper presents a novel open-source framework called deformable model array ( DMA ) for the segmentation of multiple and complex structures of interest in different imaging modalities . While most active contour algorithms can extract one region at a time , DMA allows integrating several deformable models to deal with multiple segmentation scenarios .
Interrupt functions of M/S computer system are utilized to perform frame-real-time acquisition , processing and displaying of laser image . A spec ial DMA controller is designed to co-occupied two identical dual-port SRAMs wi th slave computer ADSP21020 , and successive image buffering , acquisition and pro cessing are implemented by exchanging enable rights of SRAMs synchronously . Fina lly , the experiment result analysis is given .
This technique increases data throughput by 45 percent while reducing the host 's CPU load . They found that peer DMA forwarding can increase host based router throughput by up to 45 percent , supporting bandwidths of 480 Mbps . Peer DMA host based forwarding requires network interface cards with substantial shared memory resources , because packet queues are stored on the interfaces themselves , rather than in host RAM .
Damping corrections recover more than 70 % of the charge penetration energy in all dimers , whereas higher order damping introduces only minor improvement . Electrostatic energies calculated by the numerical DMA are less accurate than those calculated by the analytic DMA . Analysis of bonding in the benzene dimer shows that EFP with inclusion of the electrostatic damping term performs very well compared to the high‐level coupled cluster singles , doubles , and perturbative triples method .
The data memory is constructed of a 16-bit × 66-word internal RAM and 16-bit × 4k-word external RAM , and the addressing mode for these RAMs is powerful . The I/O facilities contain a 2- channel DMA controller , thus avoiding troublesome I/O handling of the program . The powerful program control facilities support a nested loop program , subroutine call and three kinds of interrupts .
In this paper , existing FPGA-related Direct Memory Access ( DMA ) solutions have been evaluated , and a new DMA solution tailored for hyperspectral images has been proposed . Results show that the proposed DMA core , CubeDMA , handles targeted memory access patterns in more efficient manner than existing solutions while being resource efficient .
Inside TandemNet , a language model is used to represent report text , which cooperates with the image model in a tandem scheme . We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction . In the testing stage , TandemNet can make accurate image prediction with an optional report text input .
In this paper , the entity alignment algorithm is understood to be a neural network binary classification model , and we propose an entity alignment algorithm based on the dual-attention mechanism . The algorithm improves the entity vector training process , proposes a dual-attention mechanism , and applies an incremental learning mechanism . The experiments show that the improvements proposed in this paper effectively improve the classification accuracy of the algorithm , and the overall effect of the algorithm is better than that of the existing physical alignment algorithm .
This will enable agents to have conversations with users about the objects , activities and events surrounding them . In this work , we present three main architectural explorations for the Audio Visual Scene-Aware Dialog ( AVSD ) : 1 ) investigating ` topics ' of the dialog as an important contextual feature for the conversation , 2 ) exploring several multimodal attention mechanisms during response generation , 3 ) incorporating an end-to-end audio classification ConvNet , AclNet , into our architecture . We discuss detailed analysis of the experimental results and show that our model variations outperform the baseline system presented for the AVSD task .
In this paper , we propose a multitask model capable of jointly learning these multimodal tasks , and transferring knowledge of words and their grounding in visual objects across the tasks . The proposed model uses a novel Dual-Attention unit to disentangle the knowledge of words in the textual representations and visual concepts in the visual representations , and align them with each other . This disentangled task-invariant alignment of representations facilitates grounding and knowledge transfer across both tasks .
In community question and answering ( Q&A ) systems , due to the diversity of words and syntactic structure , matching question pairs representing similar meaning is a challenging task . A novel model based on dual-layer attention mechanism for semantic matching is proposed in this work . Firstly , an attention-based preprocessing method is used on the word representation layer to reduce redundant information .
In particular , we first present a flexible HIN ( heterogeneous information network ) framework for modeling the short texts , which can integrate any type of additional information as well as capture their relations to address the semantic sparsity . Then , we propose Heterogeneous Graph ATtention networks ( HGAT ) to embed the HIN for short text classification based on a dual-level attention mechanism , including node-level and type-level attentions . The attention mechanism can learn the importance of different neighboring nodes as well as the importance of different node ( information ) types to a current node .
To acquire more semantically accurate inpainting images , this paper proposes a novel inpainting model named \textit{N}eural \textit{I}mage Inpainting \textit{G}uided with \textit{D}escriptive \textit{T}ext ( NIGDT ) . First , a dual multi-modal attention mechanism is designed to extract the explicit semantic information about corrupted regions . The mechanism is trained to combine the descriptive text and two complementary images through reciprocal attention maps .
Following dedicated non-linear mappings for visual features at each level , word , and sentence embeddings , we obtain multiple instantiations of our common semantic space in which comparisons between any target text and the visual content is performed with cosine similarity . We guide the model by a multi-level multimodal attention mechanism which outputs attended visual features at each level . The best level is chosen to be compared with text content for maximizing the pertinence scores of image-sentence pairs of the ground truth .
Due to the sparsity of handcrafted features , we discrete the features into a 2D point map and learn the structure inspired by PointNet . Finally , a multimodal attention fusion module is designed to attentively fuse the local structural and global semantic features , providing the hybrid features for the more reasonable scene recovery . The experimental results demonstrate the excellent generalization ability and more significant performance of our method in both quantitative and qualitative evaluations , compared with the state-of-the-art methods .
This paper discusses machine attention based on multimodal information integration and synchronization . The aim is to introduce a computational model for robust multimodal attention mechanism in artificial systems . First , we introduce a new blind source separation algorithm based on temporal structures and high order statistics of the source signals .
We propose a structure - aware seq2seq architecture to encode both the content and the structure of a table for table - to - text generation . The model consists of field - gating encoder and description generator with dual attention . We add a field gate to the encoder LSTM unit to incorporate the field information .
Note that our model does not use any position embeddings because ELMo gives the positional information of the words in each sequence . subsubsection : Dual Attention Layer This layer fuses information from the passages to the question as well as from the question to the passages in a dual mechanism .
and . We use DCN XiongZS17 as the dual attention mechanism to obtain question - to - passage representations : and passage - to - question ones : subsubsection : Modeling Encoder Layer
subsection : Model Framework As shown in Figure [ reference ] , our model consists of three modules including two encoders and a dual - attention decoder equipped with a context selection gate network . The sentence encoder reads the input words and builds its corresponding representation .
Finally , both sentence hidden states and relation hidden states are fed to the decoder . subsection : Dual - Attention Decoder Previous s2s models have developed some task - specific modifications on the decoder , such as to incorporate the copying mechanism and coverage mechanism .
Motivated by this observation , in addition to reasoning about visual attention , we also address the problem of question attention . Specifically , we present a novel multi - modal attention model for VQA with the following two unique features : Co - Attention : We propose a novel mechanism that jointly reasons about visual attention and question attention , which we refer to as co - attention .
We propose Sparse Sinkhorn Attention , a new efficient and sparse method for learning to attend . Our method is based on differentiable sorting of internal representations .
An Efficient Character-Level Neural Machine Translation A Novel Attention Mechanism Considering Decoder Input for Abstractive Text Summarization Sentence-State LSTM for Text Representation Graph Attention Networks A Decomposable Attention Model for Natural Language Inference Development of High Speed Intelligent Real-time Monitoring System Application of DMA in EPON system Direct Memory Access Optimization in Wireless Terminals for Reduced Memory Latency and Energy Consumption A quantitative approach to the design of block data transfer mechanism for an SMP-based message-passing system Defense Mapping Agency Digital Data Base Evolution Multi-object segmentation framework using deformable models for medical imaging analysis Research on image acquisition and processing of laser radar High-Speed Data Paths in Host-Based Routers Electrostatic energy in the effective fragment potential method: Theory and application to benzene dimer A high performance digital signal processor VLSI: MN 1900 series CubeDMA - Optimizing three-dimensional DMA transfers for hyperspectral imaging applications TandemNet: Distilling Knowledge from Medical Images Using Diagnostic Reports as Optional Semantic References Entity Alignment Algorithm Based on Dual-Attention and Incremental Learning Mechanism Leveraging Topics and Audio Features with Multimodal Attention for Audio Visual Scene-Aware Dialog Embodied Multimodal Multitask Learning A model based on dual-layer attention mechanism for semantic matching Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification Neural Image Inpainting Guided with Descriptive Text Multi-Level Multimodal Common Semantic Space for Image-Phrase Grounding Model-Free Distortion Rectification Framework Bridged by Distortion Distribution Map Multi-Modal Machine Attention: Sound Localization and Visual-Auditory Signal Synchronization Table-to-text Generation by Structure-aware Seq2seq Learning Multi-style Generative Reading Comprehension Faithful to the Original: Fact Aware Neural Abstractive Summarization Hierarchical Question-Image Co-Attention for Visual Question Answering Sparse Sinkhorn Attention
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