CNN Layers Research Articles

Fabric Fault and Extra Thread Detection using Convolutional Neural Network

A planar substance made of textile fibers is called fabric. The main reason why defective fabrics are produced is loom malfunctions. A specialized computer vision system called a fabric inspection system is used to find fabric flaws in order to ensure product quality. In this paper we classify the defect by using Convolutional Neural Network. Utilizing a special type of class-based ensemble convolutional neural network architecture, the defect recognition system is built. The experiment is carried out using several textile fiber kinds. There is four layers in CNN to classify the defect that is Convolution, ReLU, Pooling, Fully Connected layer. A number of well-known CNN architectures, such as Inception, ResNet, VGG, MobileNet, DenseNet, and Xception to classify the defect are tested in this study. Finally, The study demonstrates the result by classification and proves how accurately the defects are identified.

Agamotto: A Performance Optimization Framework for CNN Accelerator With Row Stationary Dataflow

We propose a software/hardware co-design framework called Agamotto for the complete design automation and performance optimization of the row stationary-based CNN accelerator. We design a scalable accelerator template whose critical design parameters can be configured. Based on the hardware template, Agamotto estimates the performance of the numerous possible hardware implementations for the target FPGA device and CNN model using the latency modeling tool. It chooses the best hardware design and generates the instructions and optimal runtime variables for each target CNN layer. As a result, Agamotto can generate the best hardware design within 61.67 seconds, achieving up to 2.8x higher hardware utilization than the original accelerator. In addition, experimental results show that the performance estimation is accurate, showing only 4.8% difference against the FPGA runtime for the end-to-end CNN model execution. The accelerator implemented on the Xilinx VCU118 evaluation board achieves 402 giga operations per second (GOPS) at 200 MHz, resulting in 13 frames per second (FPS) for the end-to-end execution of VGG-16. It is flexible enough to run more complex CNN models such as ResNet-50 and DarkNet-53, achieving 29.3 FPS and 16.9 FPS, respectively.

Design of a Music Recommendation Device Using Mini-Xception CNN and Facial Recognition

Due to the emerging developments in Artificial Intelligence and Machine Learning Technologies, various prediction systems are been developed based on human emotions and real time aspects of human psychology as well. Facial recognition system is one such mechanism which is the most vibrant strategy used for predicting human emotions. It is extensively applied in surveillance systems, fault identification and other security related aspects. Based on the human emotions researchers have already proposed several music recommendation systems. This paper aims to propose a Facial recognition-based music recommendation system to treat the psychology patients. This helps to recover the patients from mental stress, anxiety, and depression. The suggested method aims to take into account the limitations of the face recognition system in current frameworks, such as the requirement to lower the processing delay for deep feature extraction and the necessity to design a Mini exception technique based on Deep Convolutional Neural Network (DCNN) architecture. The FER- 2013 image dataset, which consists of 35000 face photos with automated labelling is considered. It is used to determine how well the proposed approach would detect the various emotion classes. In comparison to other states of methods, the Mini exception technique utilised in CNN layers acts as a lightweight system. The proposed solution has a 92% accuracy rate and removes the barrier between the current frameworks. The suggested music is taken from a music database and then further mapped in accordance with the algorithm's output.

Attention-based Speech Emotion Recognition Approach for Medical Application

Abstract: Presently AI is used in various medical fields. Mental health is an important part of the overall health of a person and speech is the primary form for expression of emotion. Thus, Speech Emotion Recognition can be used to understand emotions of the person and help doctors focus on the cure. Speech Emotion recognition is analysis and classification of speech signals to detect the underlying emotions. This paper proposes a model for speech emotion recognition using an attention mechanism. The model is developed using Mel-frequency cepstral coefficients (MFCCs), and a combination of 2D CNN layers and LSTM recurrent layers for temporal aggregation. The proposed model is evaluated using a dataset of speech recordings containing eight emotion categories. The results show that the model achieves 89% accuracy. The attention mechanism is found to improve the recognition performance by focusing on relevant emotional information and ignoring irrelevant information. This research has potential applications in clinical settings in detection as well as treatment for mental health issues.

Optimized deep learning-based cricket activity focused network and medium scale benchmark

The recognition of different activities in sports has gained attention in recent years for its applications in various athletic events, including soccer and cricket. Cricket, in particular, presents a challenging task for automatic activity recognition methods due to its closely overlapped activities such as cover drive, and pull short, to name a few. Existing methods often rely on hand-crafted features as the limited availability of public data has restricted the scope of research to only the significant categories of cricket activities. To this end, we proposed a cricket activities dataset and an intuitive end-to-end deep learning model for cricket activity recognition. The data is collected from online sources and pre-processed through cleaning, resizing, and organizing. Similarly, an intuitive deep model is designed with a combination of time-distributed 2D CNN layers and LSTM cells for extracting and learning the spatiotemporal information from the input sequences. For benchmarking, we evaluated the model on our cricket datasets and four standard datasets namely UCF101, HMDB51, YouTube action, and Kinetics. The quantitative results show that the proposed model outperforms different variants of recurrent neural networks and achieved an accuracy of 92%, recall of 91%, and F1 score of 91%. Our code and dataset is publicly available for further research on https://drive.google.com/file/d/1c9qcAz4q00qvx4yFA3pSudWFczm1cWUL/view?usp=sharing.

Towards COVID-19 fake news detection using transformer-based models

The COVID-19 pandemic has resulted in a surge of fake news, creating public health risks. However, developing an effective way to detect such news is challenging, especially when published news involves mixing true and false information. Detecting COVID-19 fake news has become a critical task in the field of natural language processing (NLP). This paper explores the effectiveness of several machine learning algorithms and fine-tuning pre-trained transformer-based models, including Bidirectional Encoder Representations from Transformers (BERT) and COVID-Twitter-BERT (CT-BERT), for COVID-19 fake news detection. We evaluate the performance of different downstream neural network structures, such as CNN and BiGRU layers, added on top of BERT and CT-BERT with frozen or unfrozen parameters. Our experiments on a real-world COVID-19 fake news dataset demonstrate that incorporating BiGRU on top of the CT-BERT model achieves outstanding performance, with a state-of-the-art F1 score of 98%. These results have significant implications for mitigating the spread of COVID-19 misinformation and highlight the potential of advanced machine learning models for fake news detection.

A deep actor critic reinforcement learning framework for learning to rank

In this paper, we propose a Deep Reinforcement learning based approach for Learning to rank task. Reinforcement Learning has been applied in the ranking task with good success, but the existing Policy Gradient based approaches suffer from noisy gradients and high variance, resulting in unstable learning. The natural policy gradient methods like REINFORCE perform Monte Carlo sampling, thus taking samples randomly, which leads to high variance. As the action space becomes large, i.e., with a very large number of documents, traditional RL techniques lack the complex model required in the scenario to deal with a large number of items. We propose a Deep Reinforcement learning based approach for learning to rank task in this paper to address these issues. By combining Deep learning with the Reinforcement Learning framework, our approach can learn a complex function as deep neural networks can provide significant function approximation. We used Actor-Critic framework where the critic network can reduce variance by utilizing techniques such as clipped delayed policy updates, clipped double q learning, etc. Also, due to the enormous space of the web, the most relevant results are needed to be returned for the corresponding query from within a large action space. Policy gradient algorithms have been effectively applied to problems in large action spaces(items) with deep neural networks as they don’t rely on finding value for each action(item) as in value-based methods. Further, we use an actor-network with a CNN layer in the ranking process to capture the sequential patterns among the documents. We utilize the TD3 method to train our Reinforcement Learning agent with a listwise loss function, which performs delayed policy updates resulting in value estimates with lower variance. To the best of our knowledge, this is the first Deep reinforcement learning method applied in Learning to Rank for document retrieval. We performed experiments on the various Letor datasets and showed that our method outperforms various state-of-the-art baselines.

Multi gait recognition using Clustering based Faster Regions-Convolutional Neural Network

Gait recognition is the process of recognizing a person based on their walking style. Each person’s walking gait is distinctive and cannot be imitated by others. However, the walking motion of a person will be changed based on their behaviour but their walking pattern doesn’t change. In this paper, a novel Clustering based Faster RCNN has been proposed to identify the single, double and multi-gait. The gait images from the publicly available dataset are pre-processed using Multi scale Retinex (MSR) to reduce the noise artifacts. The Faster RCNN is used for extracting the relevant features from the gait images via the two modules namely CNN and RPN. The CNN layers extract the most relevant features as feature maps and RPN is used for creating the bounding boxes for the extracted features. Fuzzy K-means clustering is used to group the features based on their labels, and it specifies the features acquired using CNN and RPN as input. Finally, the Fast RCNN is employed for classifying the gait images into suspicious and non-suspicious walking pattern. The proposed Clustering based Faster RCNN net achieves the high accuracy rate of 98.74% and 99.19% for suspicious and non-suspicious walking pattern respectively. The proposed Clustering based Faster RCNN model was compared with other traditional models like CNN, U-net, Fab net and Fast R-CNN. The proposed Clustering based Faster RCNN model improves the overall accuracy of 8.86% 33.77% 3.12% and 5.48% better than mmGait, LSTM Net, STDNN and RNN respectively.

Image-free single-pixel object detection.

Recently developed image-free sensing techniques have achieved remarkable performance in various vision tasks. However, existing image-free methods still cannot simultaneously obtain the category, location, and size information of all objects. In this Letter, we report a novel image-free single-pixel object detection (SPOD) technique. SPOD enables efficient and robust multi-object detection directly from a small number of measurements, eliminating the requirement for complicated image reconstruction. Different from the conventional full-size pattern sampling method, the reported small-size optimized pattern sampling method achieves higher image-free sensing accuracy with fewer pattern parameters (∼1 order of magnitude). Moreover, instead of simply stacking CNN layers, we design the SPOD network based on the transformer architecture. It can better model global features and reinforce the network's attention to the targets in the scene, thus improving the object detection performance. We demonstrate the effectiveness of SPOD on the Voc dataset, which achieves a detection accuracy of 82.41% mAP at a sampling rate of 5% with a refresh rate of 63 f.p.s.

Animal Species Recognition with Deep Convolutional Neural Networks from Ecological Camera Trap Images.

Accurate identification of animal species is necessary to understand biodiversity richness, monitor endangered species, and study the impact of climate change on species distribution within a specific region. Camera traps represent a passive monitoring technique that generates millions of ecological images. The vast numbers of images drive automated ecological analysis as essential, given that manual assessment of large datasets is laborious, time-consuming, and expensive. Deep learning networks have been advanced in the last few years to solve object and species identification tasks in the computer vision domain, providing state-of-the-art results. In our work, we trained and tested machine learning models to classify three animal groups (snakes, lizards, and toads) from camera trap images. We experimented with two pretrained models, VGG16 and ResNet50, and a self-trained convolutional neural network (CNN-1) with varying CNN layers and augmentation parameters. For multiclassification, CNN-1 achieved 72% accuracy, whereas VGG16 reached 87%, and ResNet50 attained 86% accuracy. These results demonstrate that the transfer learning approach outperforms the self-trained model performance. The models showed promising results in identifying species, especially those with challenging body sizes and vegetation.

REMAP: A Spatiotemporal CNN Accelerator Optimization Methodology and Toolkit Thereof

Designing CNN accelerators is getting more difficult owing to the fast-increasing types of CNN models. Some approaches use constant dataflow and micro-architecture that have lower design comlexity. However, these accelerators are difficult to adapt with the highly-diverse CNN models and often suffer from low PE utilization. Some other accelerators resort to reconfigurable devices such as FPGA and CGRA to support flexible dataflows in order to to fit diverse CNN layers. However, layer-by-layer processing may require more energy for frequent reconfiguration and off-chip DDR access. In this work, we introduce a reconfigurable pipeline accelerator (RPA) that can reduce the latency and DDR access by pipelining the compuptation of CNN layers. Although there has been several researches that try to speedup the design process by automatically exploring sub-set of the accelerator design space, identifying an available automated design tool that can effectively find the complete and optimal design scheme remains a problem, especially for the novel RPA architecture type. Unfortunately, comprehensive exploration of the whole design space faces an excessive large searching space. To tackle this problem, we propose REMAP, a toolkit for designing CNN accelerators based on the Monte Carlo Tree Search (MCTS) method. To efficiently search the huge design space, we propose several methods to improve searching efficiency. Evaluations show that REMAP significantly outperforms some state-of-the-art approaches; compared with GAMMA, it achieves an average speed increase of 14.75×, and an energy reduction of 45.45%; it also achieves a speed increase of 32.6× against ConfuciuX on MobileNetV2 and ResNet50. We also show a FPGA accelerator implementation which is based on REMAP’s search result, and it achieves high performance in real-time CNN tasks. This indicates that REMAP can provide high-quality design exploration with valuable insights and useful architecture design guidances.

Web-Informed-Augmented Fake News Detection Model Using Stacked Layers of Convolutional Neural Network and Deep Autoencoder

Today, fake news is a growing concern due to its devastating impacts on communities. The rise of social media, which many users consider the main source of news, has exacerbated this issue because individuals can easily disseminate fake news more quickly and inexpensive with fewer checks and filters than traditional news media. Numerous approaches have been explored to automate the detection and prevent the spread of fake news. However, achieving accurate detection requires addressing two crucial aspects: obtaining the representative features of effective news and designing an appropriate model. Most of the existing solutions rely solely on content-based features that are insufficient and overlapping. Moreover, most of the models used for classification are constructed with the concept of a dense features vector unsuitable for short news sentences. To address this problem, this study proposed a Web-Informed-Augmented Fake News Detection Model using Stacked Layers of Convolutional Neural Network and Deep Autoencoder called ICNN-AEN-DM. The augmented information is gathered from web searches from trusted sources to either support or reject the claims in the news content. Then staked layers of CNN with a deep autoencoder were constructed to train a probabilistic deep learning-base classifier. The probabilistic outputs of the stacked layers were used to train decision-making by staking multilayer perceptron (MLP) layers to the probabilistic deep learning layers. The results based on extensive experiments challenging datasets show that the proposed model performs better than the related work models. It achieves 26.6% and 8% improvement in detection accuracy and overall detection performance, respectively. Such achievements are promising for reducing the negative impacts of fake news on communities.

Evaluation of SEU impact on convolutional neural networks based on BRAM and CRAM in FPGAs

This study evaluates the sensitivity of FPGA-based CNN systems and the efficacy of selective hardening approaches with laser injection and proton irradiation. The LeNet-5 CNN architecture was taken as a case study. First, the entire CNN with block random access memory (BRAM) attached is evaluated by laser test scanning, and errors generated from each layer are detected to determine the most critical layer. Second, the same network is evaluated without BRAM, considering only the configuration RAM (CRAM) and errors generated from each layer are detected. Third, the impact of laser scan errors from BRAM and CRAM are compared, while tracing the layers in which these errors are generated. Then, proton testing is performed on the entire CNN, and errors from each layer are detected and compared to validate the laser scanning test. Experimental results from laser and proton demonstrate that CRAM errors have a larger impact on CNN layers than BRAM errors. Regarding layer criticality, the second convolutional layer (C2) appears to be the most critical one because it generates most CRAM errors. This was validated by serial and parallel designs, where both confirm similar trends. Accordingly, the partial triple modular redundancy (TMR) approach is only applied to the critical portions, leading to around 40 % reliability improvement with less than 20 % overhead.

Air Quality Index Prediction

We address air index forecast by inferring, using deep and machine learning algorithms, the concentration per hour of air pollutants (such ozone, Particulate Matter (2.5), and Sulphur). DL is one of the most popular methods, may efficiently apply optimization algorithms to train a model on large amounts of information and data. The air quality prediction model is trained using time series data. In our approach, the sequence module makes use of deep CNN, and time series data is progressively fed into the CNN model for training. In CNN, there are many different functioning layers, including RNN, and LSTM. The CNN layer can be utilized to successfully extract the features that are sequential of time series. Advanced features outperform normal features when analyzing data of time series. CNN's pooling layer oversees down sampling. CNN is good at forecasting air quality, according to test data. Key Words: AQP, CNN, PM, RNN, DL

A hybrid deep learning approach for phenotype prediction from clinical notes

Identifying patient cohorts from clinical notes in secondary electronic health records is a fundamental task in clinical information management. However, with the growing number of clinical notes, it becomes challenging to analyze the data manually for phenotype detection. Automatic extraction of clinical concepts would helps to identify the patient phenotypes correctly. This paper proposes a novel hybrid model for automatically extracting patient phenotypes using natural language processing and deep learning models to determine the patient phenotypes without dictionaries and human intervention. The model is based on a neural bidirectional sequence model (BiLSTM or BiGRU) and a CNN layer for phenotypes identification. An extra CNN layer is run parallel to the hybrid model to extract more features related to each phenotype. We used pre-trained embeddings such as FastText and Word2vec separately as the input layers to evaluate other embedding's performance. Experimental results using MIMIC III database in internal comparison demonstrate that the proposed model achieved significant performance improvement over existing models. The enhanced version of our model with an extra CNN layer obtained a relatively higher F1-score than the original hybrid model. We also showed that BiGRU layer with FastText embedding had better performance than BiLSTM layer to identify patient phenotypes.

SE-CNN: Convolution Neural Network Acceleration via Symbolic Value Prediction

CNNs are difficult to achieve inter-layer parallelism because of the data dependence between layers. In the paper, we propose Symbolic-Execution CNN (SE-CNN), which breaks data dependence between CNN layers via value prediction. Our insight is that in post-trained CNNs, only a subset (less than 10%) of neurons are activated (producing non-zero values) to identify patterns in inputs while most of the other neurons remain silent (producing zeros). This is because given an input image there are only a limited number of features presented. Thus, within the CNN, the neurons that are sensitive to the given features are more exercised than the others. Based on this insight, SE-CNN works in two successive phases: A parallel computation phase and a serial correction phase. In the parallel computation phase, each CNN layer starts computation simultaneously based on predicted inputs: we predict most of the neurons having zeros as inputs. For non-zero input neurons we predict their inputs for the next input image the same as the previous ones. In the serial correction phase, each layer compares the predicted inputs with the real ones to correct its computation results if necessary. If a neuron has predicted correctly its input during the parallel phase, thus the corresponding neuron passes its serial phase. Otherwise the neuron will amend its prediction with a light-weight result amendment mechanism based on the real inputs. We implement SE-CNN on top of the streaming processor of a state-of-the-art general purpose GPU (GPGPU) architecture, adding marginal hardware overheads in area and power consumption. We also provide application programming interfaces (APIs) so that CNNs that have already been implemented can directly enjoy the benefits of our technique. We utilize GPGPU-sim as our experimental platform, benchmarked with 9 well-accepted CNNs from recent years’ ILSVRC contests. Experimental results show that compared to other three state-of-the-art GPU based CNN acceleration mechanisms, SE-CNN can averagely achieve <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.4\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10.4\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$7.9\times $ </tex-math></inline-formula> (maximally <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$22.0\times $ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$18.7\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16.4\times $ </tex-math></inline-formula> ) CNN execution speedup while maintaining over 95% of classification accuracy.

Performance Analysis of Deep Convolutional Network Architectures for Classification of Over-Volume Vehicles

The number of vehicle accidents has increased in recent years due to overloaded goods carriers. Off-road driving, mountain roads, and sharp edges on a road are the main causes of an imbalance in overloaded trucks. In rural areas, where smaller roads cannot accommodate high volume vehicles, such vehicles cause many problems for cars, bikes, bicycles, and other small vehicles, as well as an increase in traffic congestion in those areas. This has become a major problem in the daily lives of drivers in rural areas as well as major urban areas. Solutions are needed to detect over-volume goods carriers and alert drivers to slow down or control the volume in their trucks. This work mainly focuses on a solution that uses deep CNN models. In this work, different deep convolutional architectures are evaluated for their ability to classify goods based on their volume. The model implemented is based on a dataset-specific transfer learning process with CNN layers generated in ImageNet in which only dense layers are learned. The primary objective of this work is to identify a classification method that exhibits proven results with respect to the accuracy parameters. In this work, different deep architectures were tested, and among the efficient networks, Net-B3 was found to perform with 95% accuracy on average. The different architectures were evaluated based on their accuracy, confusion matrix, ROC curve, and AUC score with a real-time dataset.

CNN-BiLSTM-Attention: A multi-label neural classifier for short texts with a small set of labels

We propose a CNN-BiLSTM-Attention classifier to classify online short messages in Chinese posted by users on government web portals, so that a message can be directed to one or more government offices. Our model leverages every bit of information to carry out multi-label classification, to make use of different hierarchical text features and the labels information. In particular, our designed method extracts label meaning, the CNN layer extracts local semantic features of the texts, the BiLSTM layer fuses the contextual features of the texts and the local semantic features, and the attention layer selects the most relevant features for each label. We evaluate our model on two public large corpuses, and our high-quality handcraft e-government multi-label dataset, which is constructed by the text annotation tool doccano and consists of 29920 data points. Experimental results show that our proposed method is effective under common multi-label evaluation metrics, achieving micro-f1 of 77.22%, 84.42%, 87.52%, and marco-f1 of 77.68%, 73.37%, 83.57% on these three datasets respectively, confirming that our classifier is robust. We conduct ablation study to evaluate our label embedding method and attention mechanism. Moreover, case study on our handcraft e-government multi-label dataset verifies that our model integrates all types of semantic information of short messages based on different labels to achieve text classification.

Optimization of Deep Neural Networks Using DAPP (DNN Acceleration Using Ping-Pong) Approach

Abstract: Deep Neural Networks (DNNs) are one of the leading classification algorithms. Deep Learning has achieved remarkable milestones such as Google-Net and Alpha-Go. They have shown promising results in pattern recognition for images and text, language translation, sound recognition, and many more. DNN has been widely accepted and also employed for pattern matching and image recognition. All these applications are possible as these networks emulate functioning of human brain and hence name “Neural” Network.However, to provide competent results, these millions of neurons in neural networks needs to be trained. For which billions of operations are to be carried out. Training of these many neurons and with these many operations is a time-consuming affair. Hence choice of network and its parameter play an important role both in providing accurate trained network and time taken for training. If the network is deep and has plethora of neurons, the time taken is considerably high as training works sequentially on batches of dataset using Sequential Back-propagation Algorithm.To accelerate the training there are many hardware solutions like use of GPU, FPGA and ASICs. However because of popularity of DNN there is increase in demand in mobile and IoT platform devices. These are resource constrained devices, where power and size of these device, restricts usage and implementation of deep NN (Neural Network).Simulation of DAPP is done on MNIST and CIFAR-10 datasets using System-C. Additionally, this technique has been adapted for multi-core architectures. The design shows a reduction in time by 38% for 3 layers of CNN and 92% for 10 layers of CNN, while maintaining the accuracy of networks. This generic methodology has been implemented for Vanilla RNN and LSTM networks. An improvement of 38% for Vanilla RNN and 40% for LSTM has been demonstrated by this methodology.

Content-Based Image Search While Maintaining Privacy with Cloud Image Repository

If you don't know the names of the images, it is important to pick a set of photos that look like informational images and use a search structure that uses the CBIR rule. In general, the CBIR framework breaks down the visual aspects. B. Diversity, image boundaries, surfaces, and nomenclature consistency between input photos and dataset images. The rendering strategy is CNN and the restoration method is cosine similarity. This white paper addresses the problem of large-scale image recovery with a focus on improving accuracy and flexibility. We focus on factors that can affect search performance, such as: B. Varying lighting conditions, object size and structure, and possibly obstacles and crowded facilities. These factors are particularly large and track very large data sets with great variability. Suggest REMAP. REMAP is the original CNN-based global descriptor that started and learned on Trio Bungle and is added to an ever-evolving deep set of features from several CNN layers. REMAP is broadly stable across different semantic levels of visual reflection and retains the associated absolute identifiers.