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"title": "Efficient tracing and performance analysis for large distributed systems",
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"abstract": "Distributed systems are notoriously difficult to implement and debug. One important tool for understanding the behavior of distributed systems is tracing. Unfortunately, effective tracing for modern distributed systems faces several challenges. First, many interesting behaviors in distributed systems only occur rarely, or at full production scale. Hence we need tracing mechanisms which impose minimal overhead, in order to allow always-on tracing of production instances. Second, for high-speed systems, messages can be delivered in significantly less time than the error of traditional time synchronization techniques such as network time protocol (NTP), necessitating time adjustment techniques with much higher precision. Third, distributed systems today may generate millions of events per second systemwide, resulting in traces consisting of billions of events. Such large traces can overwhelm existing trace analysis tools. These challenges make effective tracing difficult. We present techniques that address these three challenges. Our contributions include (1) a low-overhead tracing mechanism, which allows tracing of large systems without impacting their behavior or performance (0.14 ¿s/event), (2) a post hoc technique for producing highly accurate time synchronization across hosts (within 10/ts, compared to between 100 ¿s to 2 ms for NTP), and (3) incremental data processing techniques which facilitate analyzing traces containing billions of trace points on desktop systems. We have successfully applied these techniques to two distributed systems, a cooperative caching system and a distributed storage system, and from our experience, we believe our techniques are applicable to other distributed systems.",
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"abstract": "Image segmentation is an important component of image processing. The improvements of the segmentation efficiency and quality are the two significant issues for each segmentation algorithm. This paper proposed a segmentation algorithm based on the negative selection mechanism of the artificial immune system. The algorithm can extract the occluded target in an infrared image by using a template constructed from negative selection method. A segmentation algorithm combined with the information entropy and the clonal selection algorithm is introduced to avoid the drawbacks of deciding a segmentation threshold subjectively. The simulation results presented that the two proposed algorithms do have some advantages on the segmentation of the occluded target in an infrared image, especially the latter can acquire a stable result leading to an ideal effect.",
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"abstract": "Computer generated hologram (CGH) is a promising technology for realizing 3D displays. Large-scale CGH has an advantage that it resolves problems of existing 3D displays. However, the large-scale CGH generation requires a lot of memory space and computation time in proportion to pixel number. Further, in order to use CGH as a display, it needs to be generated in real time, and this is the reason why CGH does not suit to practical use. Computation of CGH is comprised of data-independent operations and current GPU has thousands of processing cores. Thus, acceleration of CGH generation can be expected by using GPU. To accelerate CGH generation processing, we adapt several parallelization and optimization techniques to the CGH program both for single node and multiple ones. The single node optimization techniques include the way of object decomposition, the reduction of data transfer amount between CPU and GPU, the kernel integration, stream processing, and the utilization of multi-GPU parallelism. The multi-node optimization includes inter-node data distribution method. The results show that we have achieved 134.7 times speed-up compared to sequential program execution by CPU.",
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"abstract": "Neighborhood communication is a very important issue in the development of wisdom city. In the digital age, virtual network is changing the way of our social living, it will bring us the new opportunity to solve the problem in the contemporary neighborhood communication. This article analysis the concept and the challenge of the contemporary neighborhood communication, and the development of the neighborhood communication in wisdom city, it summarize three trends to improve the quality of neighborhood communication by using the virtual network technology, including: reality-based network, digital communication space and the virtual community based on the intelligent hardware.",
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"abstract": "Haikou is the fulcrum city of \"One Belt and One Road\" strategy and the core city of Hainan Free Trade Port. Rail transit lines and theirs station planning and layout are an important part of urban development pattern in the future. In this paper, using GIS technology, considering urban planning, geological data, existing bus lines, traffic network conditions, large passenger flow distribution points, according to the October 2017 Didi order data, rail transit planning principles of Haikou urban rail transit lines and stations are planned.",
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"abstract": "Given a standard RGB color image, an approach to recovering the shading image, an intrinsic image that depends only on the geometry of the viewing situation, is presented. The approach is formulated for scenes that exhibit both diffuse and specular properties. It is discriminative in the sense that variations in intensity caused by material changes are first detected and then removed. Material changes are detected using a method based on color information alone. The method stems from a scheme proposed here to handle specularities under known scene illumination chromaticity. This scheme is also used to produce an image that is free of specular reflections. Any material changes are then removed from this specular-free image by using an inpainting technique. The performance of the proposed approach has been tested on synthetic images.",
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"abstract": "The set of physical and sensitive phenomena that interacts with the urban morphology acts on the resulting perception from the users of a place. Its study and representation provides elements beyond the aesthetics aspects that can allow a better understanding of the space and future urban projects. We aim to analyze the effects of three different wind representations in terms of perception and sense of presence in virtual reality (VR). We focus on the following conditions: (R) reference scene with the audiovisual representation of the mechanical effects of the wind on the elements of the context, (V) reference scene plus the visualization of the wind flow, present (among others) in the architecture field, (T) reference scene plus tactile restitution of wind and eventually (V+T) assembling all previous conditions. For the experiment, we present to the participants (R), then (V) followed by (T), and finish with (V+T). 37 participants evaluated 12 different stop points (divided into four routes in the same simulated street), where they had to determine the perceived wind force and direction concerning the four different conditions (each one corresponding to one route). At the end of each route, participants evaluated their sense of presence in the VR scene. Our analysis showed significant effects of tactile restitution over the visual effects used in the study, both for understanding wind properties and for increasing the sense of presence in the VR scene. In terms of wind direction, (T) reduced the estimation error by 27% compared to (V). Concerning wind force, the reduction was 9.8%. As far as presence was concerned, (T) increased the sense of presence by 12.2% compared to (V).",
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"abstract": "As lithographic technology drives the integrated-circuit feature size towards 0.1 micron and below, overlay and alignment tolerances are becoming increasingly severe. State-of-the-art aerial imaging overlay metrology systems are limited in accuracy due to inherent limitation on tool imaging resolution and aberrations inherent to the optical system. These in effect result in measurement inaccuracy exceeding industry requirements for next generation overlay tolerances. In this paper, a spectroscopic, diffraction based, technique is proposed as an alternative solution for overlay metrology and alignment measurement in sub 0.1 micron node. With one diffraction grating on the surface overlaying a second diffraction grating on a reference layer, the spectroscopic reflection is modulated by the relative position of the two gratings. Thus, the alignment error can be extracted from broadband diffraction efficiency of measurement pads constituent to the alignment target. This novel diffraction-based overlay metrology has inherent advantages over the traditional image-based overlay metrology: the targets are less sensitive to process variations, and spectroscopic diffraction measurements are less affected by the inherent aberration of the optical system. For these reasons, a diffraction-based spectroscopic metrology has higher potential to generate overlay data that is directly related to registration errors due to alignment and not due to the process or tool aberrations. As a consequence, this technology has a higher potential to generate accurate data to correct registration errors during the lithographic process. Feasibility and accuracy of the technique is studied through a set of experiments and of rigorous simulations on grating targets with various line-width and line-to-space ratios. Correlation to aerial imaging overlay measurements is demonstrated. The results suggest the technique is capable of achieving or exceeding overlay and alignment control accuracy requirements for sub 0.1 micron design rules.",
"abstracts": [
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"content": "As lithographic technology drives the integrated-circuit feature size towards 0.1 micron and below, overlay and alignment tolerances are becoming increasingly severe. State-of-the-art aerial imaging overlay metrology systems are limited in accuracy due to inherent limitation on tool imaging resolution and aberrations inherent to the optical system. These in effect result in measurement inaccuracy exceeding industry requirements for next generation overlay tolerances. In this paper, a spectroscopic, diffraction based, technique is proposed as an alternative solution for overlay metrology and alignment measurement in sub 0.1 micron node. With one diffraction grating on the surface overlaying a second diffraction grating on a reference layer, the spectroscopic reflection is modulated by the relative position of the two gratings. Thus, the alignment error can be extracted from broadband diffraction efficiency of measurement pads constituent to the alignment target. This novel diffraction-based overlay metrology has inherent advantages over the traditional image-based overlay metrology: the targets are less sensitive to process variations, and spectroscopic diffraction measurements are less affected by the inherent aberration of the optical system. For these reasons, a diffraction-based spectroscopic metrology has higher potential to generate overlay data that is directly related to registration errors due to alignment and not due to the process or tool aberrations. As a consequence, this technology has a higher potential to generate accurate data to correct registration errors during the lithographic process. Feasibility and accuracy of the technique is studied through a set of experiments and of rigorous simulations on grating targets with various line-width and line-to-space ratios. Correlation to aerial imaging overlay measurements is demonstrated. The results suggest the technique is capable of achieving or exceeding overlay and alignment control accuracy requirements for sub 0.1 micron design rules.",
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"Spatial Variables Measurement",
"Diffraction Gratings",
"Integrated Circuit Technology",
"Alignment Metrology",
"Overlay Metrology",
"Spectroscopic Diffraction",
"Lithographic Technology",
"Integrated Circuit",
"Diffraction Grating",
"Registration Error Correction",
"0 1 Micron",
"Metrology",
"Spectroscopy",
"Optical Diffraction",
"Optical Imaging",
"Diffraction Gratings",
"Optical Sensors",
"Image Resolution",
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"affiliation": "Nanometrics Inc., Milpitas, CA, USA",
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"affiliation": "Nanometrics Inc., Milpitas, CA, USA",
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"title": "Sensitivity and resolution limit of pulse-echo diffraction tomography imaging system",
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"abstract": "The objective of this paper is to present a comprehensive analysis of the resolving capability of diffraction tomography using finite-size receiving apertures based on the span of spatial spectral coverage. The analysis is performed on both the transmission and reflection modes, and can be generalized into various data acquisition configurations. In addition, the analysis provides an important link to other tomographic systems such as the classical X-ray tomography and synthetic aperture radar systems.",
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"content": "The objective of this paper is to present a comprehensive analysis of the resolving capability of diffraction tomography using finite-size receiving apertures based on the span of spatial spectral coverage. The analysis is performed on both the transmission and reflection modes, and can be generalized into various data acquisition configurations. In addition, the analysis provides an important link to other tomographic systems such as the classical X-ray tomography and synthetic aperture radar systems.",
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"keywords": [
"Computerised Tomography Sensitivity Resolution Limit Pulse Echo Diffraction Tomography Imaging Systems Resolving Capability Finite Size Receiving Apertures Spatial Spectral Coverage Transmission Modes Reflection Modes Data Acquisition Configurations Classical X Ray Tomography Synthetic Aperture Radar Systems"
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"authors": [
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"affiliation": "Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA",
"fullName": "H. Lee",
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"affiliation": "Dept. of Electr. & Comput. Eng., California Univ., Santa Barbara, CA, USA",
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"abstract": "Capturing structural information of a biological molecule is crucial to determine its function and understand its mechanics. X-ray Free Electron Lasers (XFEL) are an experimental method used to create diffraction patterns (images) that can reveal structural information. In this work we design, implement, and evaluate XPSI (X-ray Free Electron Laser-based Protein Structure Identifier), a framework capable of predicting three structural properties in molecules (i.e., orientation, conformation, and protein type) from their diffraction patterns. XPSI predicts these properties with high accuracy in challenging scenarios, such as recognizing orientations despite symmetries in diffraction patterns, distinguishing conformations even when they have similar structures, and identifying protein types under different noise conditions. Our framework shows low computational cost and high prediction accuracy compared to other machine learning methods such as random forest and neural networks.",
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"content": "Capturing structural information of a biological molecule is crucial to determine its function and understand its mechanics. X-ray Free Electron Lasers (XFEL) are an experimental method used to create diffraction patterns (images) that can reveal structural information. In this work we design, implement, and evaluate XPSI (X-ray Free Electron Laser-based Protein Structure Identifier), a framework capable of predicting three structural properties in molecules (i.e., orientation, conformation, and protein type) from their diffraction patterns. XPSI predicts these properties with high accuracy in challenging scenarios, such as recognizing orientations despite symmetries in diffraction patterns, distinguishing conformations even when they have similar structures, and identifying protein types under different noise conditions. Our framework shows low computational cost and high prediction accuracy compared to other machine learning methods such as random forest and neural networks.",
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"normalizedAbstract": "Capturing structural information of a biological molecule is crucial to determine its function and understand its mechanics. X-ray Free Electron Lasers (XFEL) are an experimental method used to create diffraction patterns (images) that can reveal structural information. In this work we design, implement, and evaluate XPSI (X-ray Free Electron Laser-based Protein Structure Identifier), a framework capable of predicting three structural properties in molecules (i.e., orientation, conformation, and protein type) from their diffraction patterns. XPSI predicts these properties with high accuracy in challenging scenarios, such as recognizing orientations despite symmetries in diffraction patterns, distinguishing conformations even when they have similar structures, and identifying protein types under different noise conditions. Our framework shows low computational cost and high prediction accuracy compared to other machine learning methods such as random forest and neural networks.",
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"title": "Automated Continual Learning of Defect Identification in Coherent Diffraction Imaging",
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"abstract": "X-ray Bragg coherent diffraction imaging (BCDI) is widely used for materials characterization. However, obtaining X-ray diffraction data is difficult and computationally intensive. Here, we introduce a machine learning approach to identify crystalline line defects in samples from the raw coherent diffraction data. To automate this process, we compose a workflow coupling coherent diffraction data generation with training and inference of deep neural network defect classifiers. In particular, we adopt a continual learning approach, where we generate training and inference data as needed based on the accuracy of the defect classifier instead of all training data generated a priori. The results show that our approach improves the accuracy of defect classifiers while using much fewer samples of data.",
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"abstract": "In this paper, the distribution model of the light intensity at the fiber end is established by superposition of the light intensity distribution of near Gaussian distribution and circular hole diffraction. Then, the coupling efficiency model of reflective fiber bundle is established based on the reflection principle of reflective surface. Then, this paper selects plastic fiber and designs relevant experiments to verify the above model. Finally, the reflective hydrogen sensor was tested by using Au-Pd core-shell nanoparticle film.",
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"abstract": "X-ray diffraction is a commonly used experimental science to detect the atomic and molecular structure of crystalline material. The process is called X-ray crystallography (XRC). Traditionally, it is done by human experts with some conjecture about what structure the crystalline material is likely to be. However, the study of crystal structure using X-ray diffraction patterns is applicable in many domains, such as chemistry, physics, biology, etc. It is tedious to have manual crystallography of X-ray diffraction patterns to determine a crystal structure with a massive amount of dataset. With the advent of high computational resources, deep learning techniques have taken classification to its peak. Convolution Neural Network (CNN) maps an input image into a high dimensional space and produce a low-cost function for image classification. In this paper, we deploy a variation of the Convolution Neural Network to predict crystal structure from X-ray diffraction patterns. We compare our approach with a wide range of conventional as well as modern Machine Learning based classification techniques for the structure prediction of a crystal. We report a cross-validation accuracy of 95.6% and Micro F1-score of 0.949 with our model for the proposed task which is significantly better than the other reported baseline methods.",
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"content": "X-ray diffraction is a commonly used experimental science to detect the atomic and molecular structure of crystalline material. The process is called X-ray crystallography (XRC). Traditionally, it is done by human experts with some conjecture about what structure the crystalline material is likely to be. However, the study of crystal structure using X-ray diffraction patterns is applicable in many domains, such as chemistry, physics, biology, etc. It is tedious to have manual crystallography of X-ray diffraction patterns to determine a crystal structure with a massive amount of dataset. With the advent of high computational resources, deep learning techniques have taken classification to its peak. Convolution Neural Network (CNN) maps an input image into a high dimensional space and produce a low-cost function for image classification. In this paper, we deploy a variation of the Convolution Neural Network to predict crystal structure from X-ray diffraction patterns. We compare our approach with a wide range of conventional as well as modern Machine Learning based classification techniques for the structure prediction of a crystal. We report a cross-validation accuracy of 95.6% and Micro F1-score of 0.949 with our model for the proposed task which is significantly better than the other reported baseline methods.",
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"abstract": "The well known Force Directed List Scheduling (FDLS) Algorithm uses a rigorous priority function called the Force of an operation. The force of an operation is governed by two components, namely the self-force of an operation and its successors' forces. The successor force in turn is governed by the self-force of all the descendants of the operation. FDLS is computationally intensive in its force calculations. For data flow dominated designs, a major portion of the FDLS execution time is spent in the computation of successor forces. However in this paper we observe that it is not always necessary to compute successor forces till the last successor level. We have shown in this paper that there usually exists a stabilization point after which successor force computations would not affect the quality of the schedule produced. This paper presents a concept of stability to show that it is possible to dynamically bound the successor force calculations in FDLS, up to a certain level of descendants. We have measured the performance of FDLS for a suite of high level synthesis benchmarks. Results presented in the paper show considerable reduction in execution time for the same schedule quality. This would allow a high-level synthesis tool to perform better design space exploration.",
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"content": "The well known Force Directed List Scheduling (FDLS) Algorithm uses a rigorous priority function called the Force of an operation. The force of an operation is governed by two components, namely the self-force of an operation and its successors' forces. The successor force in turn is governed by the self-force of all the descendants of the operation. FDLS is computationally intensive in its force calculations. For data flow dominated designs, a major portion of the FDLS execution time is spent in the computation of successor forces. However in this paper we observe that it is not always necessary to compute successor forces till the last successor level. We have shown in this paper that there usually exists a stabilization point after which successor force computations would not affect the quality of the schedule produced. This paper presents a concept of stability to show that it is possible to dynamically bound the successor force calculations in FDLS, up to a certain level of descendants. We have measured the performance of FDLS for a suite of high level synthesis benchmarks. Results presented in the paper show considerable reduction in execution time for the same schedule quality. This would allow a high-level synthesis tool to perform better design space exploration.",
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"abstract": "Graph drawing with spring embedders employs a V ×V computation phase over the graph's vertex set to compute repulsive forces. Here, the efficacy of forces diminishes with distance: a vertex can effectively only influence other vertices in a certain radius around its position. Therefore, the algorithm lends itself to an implementation using search data structures to reduce the runtime complexity. NVIDIA RT cores implement hierarchical tree traversal in hard-ware. We show how to map the problem of finding graph layouts with force-directed methods to a ray tracing problem that can subsequently be implemented with dedicated ray tracing hardware. With that, we observe speedups of 4× to 13× over a CUDA software implementation.",
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"abstract": "Efficient layout of complex network remains a challenging problem. Based on network embedding and an improved dimensionality reduction technique, we propose a new graph layout method in this work. First, the network embedding method is used to generate the low dimensional dense vector representation of nodes, called embedding, which captures a given node's one-order and high-order neighbourhood node information. And then an improved tsNET approach is proposed to project this embedding to 2D space resulting in a good graph layout. Finally, we evaluate our method comparing with state-of-the-art methods by measuring the layout quality, visual inspection and analyzing the computational complexity. The experiment demonstrates the superiority of our method, and it can produce excellent layouts.",
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"content": "Estimating the complete 3D point cloud from a partial input is a key challenge in 3D vision. Existing point cloud completion networks overlook the long-range, hierarchical features and object details of the incomplete point cloud. To this end, we propose Hierarchical Feature Fusion Network (HFF-Net) for precise and detailed point cloud completion. To succeed at this task, HFF-Net estimates the missing Point Agents (PAs) by designing a topology-aware transformer-based encoder-decoder network with Multi-level Feature Learning (MFL), which hierarchically exploits the various regional and detailed information. Further, to make better utilization of the hierar-chical information captured from MFL, we devise the Hier-archical Features Fusion (HFF) module to convert them into cross-regional features. Besides, the predicted PAs is utilized by a multi-resolution output module to recover the missing point cloud in a coarse-to-fine manner. Experiments indi-cate that HFF-Net performs favorably against state-of-the-art (SOTA) approaches on both the new-proposed and existing datasets.",
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"normalizedAbstract": "Estimating the complete 3D point cloud from a partial input is a key challenge in 3D vision. Existing point cloud completion networks overlook the long-range, hierarchical features and object details of the incomplete point cloud. To this end, we propose Hierarchical Feature Fusion Network (HFF-Net) for precise and detailed point cloud completion. To succeed at this task, HFF-Net estimates the missing Point Agents (PAs) by designing a topology-aware transformer-based encoder-decoder network with Multi-level Feature Learning (MFL), which hierarchically exploits the various regional and detailed information. Further, to make better utilization of the hierar-chical information captured from MFL, we devise the Hier-archical Features Fusion (HFF) module to convert them into cross-regional features. Besides, the predicted PAs is utilized by a multi-resolution output module to recover the missing point cloud in a coarse-to-fine manner. Experiments indi-cate that HFF-Net performs favorably against state-of-the-art (SOTA) approaches on both the new-proposed and existing datasets.",
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"affiliation": "School of Computer Science, Fudan University,Shanghai Key Laboratory of Data Science,China",
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"affiliation": "School of Computer Science, Fudan University,Shanghai Key Laboratory of Data Science,China",
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"affiliation": "The Academy for Engineering and Technology, Fudan University,China",
"fullName": "Wenming Chen",
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"affiliation": "School of Computer Science, Fudan University,Shanghai Key Laboratory of Data Science,China",
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{
"affiliation": "School of Optical-electrical Information and Computer Engineering, University of Shanghai for Science and Technology,China",
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"abstract": "3D dense captioning aims to describe individual objects in 3D scenes by natural language, where 3D scenes are usually represented as RGB-D scans or point clouds. However, only exploiting single modal information, e.g., point cloud, previous approaches fail to produce faithful descriptions. Though aggregating 2D features into point clouds may be beneficial, it introduces an extra computational burden, especially in the inference phase. In this study, we investigate a cross-modal knowledge transfer using Transformer for 3D dense captioning, namely X-Trans2Cap. Our proposed X-Trans2Cap effectively boost the performance of single-modal 3D captioning through the knowledge distillation enabled by a teacher-student framework. In practice, during the training phase, the teacher network exploits auxiliary 2D modality and guides the student network that only takes point clouds as input through the feature consistency constraints. Owing to the well-designed cross-modal feature fusion module and the feature alignment in the training phase, X-Trans2Cap acquires rich appearance information embedded in 2D images with ease. Thus, a more faithful caption can be generated only using point clouds during the inference. Qualitative and quantitative results confirm that X-Trans2Cap outperforms previous state-of-the-art by a large margin, i.e., about +21 and +16 CIDEr points on ScanRefer and Nr3D datasets, respectively.",
"abstracts": [
{
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"content": "3D dense captioning aims to describe individual objects in 3D scenes by natural language, where 3D scenes are usually represented as RGB-D scans or point clouds. However, only exploiting single modal information, e.g., point cloud, previous approaches fail to produce faithful descriptions. Though aggregating 2D features into point clouds may be beneficial, it introduces an extra computational burden, especially in the inference phase. In this study, we investigate a cross-modal knowledge transfer using Transformer for 3D dense captioning, namely X-Trans2Cap. Our proposed X-Trans2Cap effectively boost the performance of single-modal 3D captioning through the knowledge distillation enabled by a teacher-student framework. In practice, during the training phase, the teacher network exploits auxiliary 2D modality and guides the student network that only takes point clouds as input through the feature consistency constraints. Owing to the well-designed cross-modal feature fusion module and the feature alignment in the training phase, X-Trans2Cap acquires rich appearance information embedded in 2D images with ease. Thus, a more faithful caption can be generated only using point clouds during the inference. Qualitative and quantitative results confirm that X-Trans2Cap outperforms previous state-of-the-art by a large margin, i.e., about +21 and +16 CIDEr points on ScanRefer and Nr3D datasets, respectively.",
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"normalizedAbstract": "3D dense captioning aims to describe individual objects in 3D scenes by natural language, where 3D scenes are usually represented as RGB-D scans or point clouds. However, only exploiting single modal information, e.g., point cloud, previous approaches fail to produce faithful descriptions. Though aggregating 2D features into point clouds may be beneficial, it introduces an extra computational burden, especially in the inference phase. In this study, we investigate a cross-modal knowledge transfer using Transformer for 3D dense captioning, namely X-Trans2Cap. Our proposed X-Trans2Cap effectively boost the performance of single-modal 3D captioning through the knowledge distillation enabled by a teacher-student framework. In practice, during the training phase, the teacher network exploits auxiliary 2D modality and guides the student network that only takes point clouds as input through the feature consistency constraints. Owing to the well-designed cross-modal feature fusion module and the feature alignment in the training phase, X-Trans2Cap acquires rich appearance information embedded in 2D images with ease. Thus, a more faithful caption can be generated only using point clouds during the inference. Qualitative and quantitative results confirm that X-Trans2Cap outperforms previous state-of-the-art by a large margin, i.e., about +21 and +16 CIDEr points on ScanRefer and Nr3D datasets, respectively.",
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"affiliation": "The Chinese University of Hong Kong (Shenzhen), The Future Network of Intelligence Institute, Shenzhen Research Institute of Big Data",
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"content": "Vision-and-language navigation (VLN) combines cross-modal object references and scene descriptions to provide a breadcrumb trail to a goal location. Whereas existing VLN approaches often do not take full advantage of cross-modal object information, this work proposes a transformer network with perceptual cross-modal object data that fuses and aligns the two cue features of object reference to help agents capture object features. In our method, linguistic object processing provides semantic-level contextual information for visual object features. With this design, our model is able to leverage object features to assist the agent in substantially improving performance on the R2R and R4R benchmarks. Through extensive experiments on R2R and R4R, we demonstrate the effectiveness of the proposed model, and our method improves the absolute 1.6% in SPL on R2R and 2.1% in CLS on R4R. Our analysis shows that the network performs better when focusing on longer heavily object-referenced navigation instructions, which also indicates that our approach is better able to use object features and align them to references in the instructions.",
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"abstract": "Natural Language Understanding (NLU) aims to make sense of language by enabling computers to comprehend text in semantic level, which is a fundamental but challenging task in natural language processing. Recently, BERT has achieved state-of-the-art performances in NLU utilizing pretraining and fine-tuning techniques to capture the task-specific information without any task-specific structure. However, the existing models still cannot capture context-specific information, leading to poor performance when dealing with the pervasive ambiguity of language caused by the polysemous words. In this paper, we propose a Parameter-Adaptive Convolution Neural Network (PACNN) to capture the context-specific information which can deal with the polysemous word better. Instead of the convolution layers in existing models, the parameters of convolutional filters in PACNN, generated by a deconvolution (e.g. convolution transpose) neural network, are adaptable according to the input sentences, which can filter the local information via the global information and capture the context-specific meaning of the polysemous words. We empirically demonstrate the efficiency of the proposed PACNN by performing a series of experiments on the General Language Understanding Evaluation (GLUE) benchmark, a collection of popular datasets on different tasks, and the PACNN significantly outperforms all the baselines. Besides, our model can be appended to BERT to further improve its performances. As BERT and the proposed PACNN capture information from various aspects, the proposed BERT+PACNN achieves the best performances compared with BERT and other baselines. Furthermore, we visualize the task-specific information and context-specific information captured by BERT and the PACNN, separately, using Singular Value Decomposition (SVD) to demonstrate the efficiencies of the two models further.",
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