Efficient Data Representation Research Articles

Representing DNA for machine learning algorithms: A primer on one-hot, binary, and integer encodings.

This short paper presents an educational approach to teaching three popular methods for encoding DNA sequences: one-hot encoding, binary encoding, and integer encoding. Aimed at bioinformatics and computational biology students, our learning intervention focuses on developing practical skills in implementing these essential techniques for efficient representation and analysis of genetic data. The primary goal of this study is to enhance students' understanding and practical application of DNA encoding methods, which are crucial for various computational analyses in bioinformatics. Our intervention consists of three key components: (1) a conceptual framework that contextualizes these encoding methods within broader bioinformatics applications, (2) an interactive Jupyter Notebook with Python code examples (https://github.com/yashmgupta/Representing-DNA/tree/main), and (3) a user-friendly Streamlit application for visualizing encoded sequences (https://dnaencoding.streamlit.app/) that also enables students to input their own DNA sequences and visualize the different encoding methods, further enhancing their understanding and practical experience. By combining conceptual overview with practical coding and visualization tools, our approach provides a comprehensive foundation for students to leverage these key DNA sequence encoding methods in their future work. This study contributes to bioinformatics education by offering effective, hands-on learning resources that bridge the gap between theoretical knowledge and practical application in DNA sequence analysis, preparing students for advanced research and data analysis projects in the field.

Drivers of global irrigation expansion: the role of discrete global grid choice

Abstract. Global statistical irrigation modeling relies on geospatial data and traditionally adopts a discrete global grid based on longitude–latitude reference. However, this system introduces area distortion, which may lead to biased results. We propose using the ISEA3H geodesic grid based on hexagonal cells, enabling efficient and distortion-free representation of spherical data. To understand the impact of discrete global grid choice, we employ a non-parametric statistical framework, utilizing random forest methods, to identify the main drivers of historical global irrigation expansion using, among other data, outputs from the global dynamic vegetation model Lund-Potsdam-Jena managed Land (LPJml). Irrigation is critical for food security amidst growing populations, changing consumption patterns, and climate change. It significantly boosts crop yields but also alters the water cycle and global water resources. Understanding past irrigation expansion and its drivers is vital for global change research, resource assessment, and the prediction of future trends. We compare predictive accuracy, simulated irrigation patterns, and identification of irrigation drivers between the two grid systems. Using the ISEA3H geodesic grid system increases the predictive accuracy by up to 28 % compared to the longitude–latitude grid. The model identifies population density, potential productivity increase, evaporation, precipitation, and water discharge as key drivers of historical global irrigation expansion. Gross domestic product (GDP) per capita also shows some influence. We conclude that the geodesic discrete global grid system significantly affects predicted irrigation patterns and identification of drivers and thus has the potential to enhance statistical modeling, which warrants further exploration in future research across related fields. This analysis lays the foundation for comprehending historical global irrigation expansion.

Adaptive boundary-aware artificial immune recognition system for data classification

In this paper, we introduce AIBARS (Adaptive Immune Boundary-Aware Recognition System), a novel learning algorithm designed to improve upon the traditional Artificial Immune Recognition System (AIRS). AIBARS addresses key limitations of AIRS by integrating a boundary-aware stimulation measure, an antigen-specific stopping criterion, and a more directed mutation approach. These innovations enable AIBARS to effectively navigate complex decision boundaries and reduce time complexity, particularly in high-dimensional datasets. Experimental results show that AIBARS achieves a 5-10% improvement in classification accuracy compared to AIRS. Additionally, AIBARS reduces memory cells by 20-30%, leading to more efficient data representation and a 15-25% reduction in computational costs. The algorithm also reduces processing time by 10-20%, making it a more efficient approach overall. Evaluations were conducted using a variety of datasets, including public domain datasets from the UCI Machine Learning Repository and synthetic datasets. The results indicate that AIBARS offers significant performance and efficiency gains over AIRS, while substantially reducing computational expenses.

A Hierarchical Matrix Factorization-Based Method for Intelligent Industrial Fault Diagnosis.

Data-driven fault diagnosis, identifying abnormality causes using collected industrial data, is one of the challenging tasks for intelligent industry safety management. It is worth noting that practical industrial data are usually related to a mixture of several physical attributes, such as the operating environment, product quality and working conditions. However, the traditional models may not be sufficient to leverage the coherent information for diagnostic performance enhancement, due to their shallow architecture. This paper presents a hierarchical matrix factorization (HMF) that relies on a succession of matrix factoring to find an efficient representation of industrial data for fault diagnosis. Specifically, HMF consecutively decomposes data into several hierarchies. The intermediate hierarchies play the role of analysis operators which automatically learn implicit characteristics of industrial data; the final hierarchy outputs high-level and discriminative features. Furthermore, HMF is also extended in a nonlinear manner by introducing activation functions, referred as NHMF, to deal with nonlinearities in practical industrial processes. The applications of HMF and NHMF to fault diagnosis are evaluated by the multiple-phase flow process. The experimental results show that our models achieve competitive performance against the considered shallow and deep models, consuming less computing time than deep models.

PanVA: Pangenomic Variant Analysis.

Genomics researchers increasingly use multiple reference genomes to comprehensively explore genetic variants underlying differences in detectable characteristics between organisms. Pangenomes allow for an efficient data representation of multiple related genomes and their associated metadata. However, current visual analysis approaches for exploring these complex genotype-phenotype relationships are often based on single reference approaches or lack adequate support for interpreting the variants in the genomic context with heterogeneous (meta)data. This design study introduces PanVA, a visual analytics design for pangenomic variant analysis developed with the active participation of genomics researchers. The design uniquely combines tailored visual representations with interactions such as sorting, grouping, and aggregation, allowing users to navigate and explore different perspectives on complex genotype-phenotype relations. Through evaluation in the context of plants and pathogen research, we show that PanVA helps researchers explore variants in genes and generate hypotheses about their role in phenotypic variation.

Optimal qubit mapping search for encoding classical data into matrix product state representation with minimal loss

Matrix product state (MPS) offers a framework for encoding classical data into quantum states, enabling the efficient utilization of quantum resources for data representation and processing. This research paper investigates techniques to enhance the efficiency and accuracy of MPS representations specifically designed for encoding classical data. Based on the observations that MPS truncation error depends on the pattern of the classical data, we devised an algorithm that finds optimal qubit mapping for given classical data, thereby improving the efficiency and fidelity of the MPS representation. Furthermore, we evaluate the impact of the optimized MPS in the context of quantum classifiers, demonstrating their enhanced performance compared to the conventional mapping. This improvement confirms the efficacy of the proposed techniques for encoding classical data into quantum states. MPS representation combined with optimal qubit mapping can pave a new way for more efficient and accurate quantum data representation and processing.

The Application and Exploration of Data Visualization Technology in Artistic Expression and Exhibition

Embarking on the transformation of virtual art museums, the confluence of data visualization technology and deep learning emerges as a catalyst for redefining artistic expression. Driven by the desire to change traditional approaches, this work utilizes a sophisticated deep learning model, the self-attention-based cycle-consistent generative adversarial network (SA-CCGAN), for transformative purposes. The objective of proposed Modernizing Virtual Art: The Collaboration of Data Visualization Technology and Self-Attention-Based Cycle-Consistent Generative Adversarial Network for Artistic Expression (MVA: DVT & SA-CCGAN) is to elevate artistic expression and exhibition practices within the virtual space, marking a paradigm shift in traditional approaches. SA-CCGAN, renowned for its ability to generate realistic and coherent artistic representations, serves as the cornerstone of this transformative endeavor. The workflow intricately incorporates SA-CCGAN into the virtual art museum context, enhancing stylized outcomes and capturing global geometric features. This yields a computationally efficient representation of high-dimensional visual data, revolutionizing digital art presentation. The fusion of data visualization technology, creativity, and meticulous design not only signals innovation but also has the potential to redefine the virtual art landscape, fostering accessibility, engagement, and a deeper cultural exchange in digital art exploration.

Real-Time Object Detection Using a Lightweight Two-Stage Detection Network with Efficient Data Representation

In this paper, we introduce a novel fast object detection framework, designed to meet the needs of real-time applications such as autonomous driving and robot navigation. Traditional processing methods often trade-off between accuracy and processing speed. To address this issue, we propose a hybrid data representation method that combines the computational efficiency of voxelization with the detail capture capability of direct data processing to optimize overall performance. Our detection framework comprises two main components: a Rapid Region Proposal Network (RPN) and a Refinement Detection Network (RefinerNet). The RPN is used to generate high-quality candidate regions, while the RefinerNet performs detailed analysis on these regions to improve detection accuracy. Additionally, we have implemented a variety of network optimization techniques, including lightweight network layers, network pruning, and model quantization, to increase processing speed and reduce computational resource consumption. Extensive testing on the KITTI and the NEXET datasets has proven the effectiveness of our method in enhancing the accuracy of object detection and real-time processing speed. The experimental results show that, compared to existing technologies, our method performs exceptionally well across multiple evaluation metrics, especially in meeting the stringent requirements of real-time applications in terms of processing speed.

Knowledge-integrated Multi-modal Movie Turning Point Identification

The rapid development of artificial intelligence provides rich technologies and tools for the automated understanding of literary works. As a comprehensive carrier of storylines, movies are natural multimodal data sources that provide sufficient data foundations, and how to fully leverage the benefits of data remains a sustainable research hotspot. In addition, the efficient representation of multi-source data also poses new challenges for information fusion technology. Therefore, we propose a knowledge-enhanced turning points identification (KTPi) method for multimodal scene recognition. First, the BiLSTM method is used to encode scene text and integrate contextual information into scene representations to complete text sequence modeling. Then, the graph structure is used to model all scenes, which strengthens long-range semantic dependencies between scenes and enhances scene representations using graph convolution network. After, the self-supervised method is used to obtain the optimal number of neighboring nodes in sparse graph. Next, actor and verb knowledge involved in the scene text are added to the multimodal data to enhance the diversity of scene feature expressions. Finally, the teacher-student network strategy is used to train the KTPi model. Experimental results show that KTPi outperforms baseline methods in scene role recognition tasks, and ablation experiments show that incorporating knowledge into multimodal model can improve its performance.

ConBGAT: a novel model combining convolutional neural networks, transformer and graph attention network for information extraction from scanned image.

Extracting information from scanned images is a critical task with far-reaching practical implications. Traditional methods often fall short by inadequately leveraging both image and text features, leading to less accurate and efficient outcomes. In this study, we introduce ConBGAT, a cutting-edge model that seamlessly integrates convolutional neural networks (CNNs), Transformers, and graph attention networks to address these shortcomings. Our approach constructs detailed graphs from text regions within images, utilizing advanced Optical Character Recognition to accurately detect and interpret characters. By combining superior extracted features of CNNs for image and Distilled Bidirectional Encoder Representations from Transformers (DistilBERT) for text, our model achieves a comprehensive and efficient data representation. Rigorous testing on real-world datasets shows that ConBGAT significantly outperforms existing methods, demonstrating its superior capability across multiple evaluation metrics. This advancement not only enhances accuracy but also sets a new benchmark for information extraction in scanned image.

Extending 3D geometric file formats for geospatial applications

This study addresses the representation and exchange of geospatial geometric 3D models, which is a common requirement in various applications like outdoor mixed reality, urban planning, and disaster risk management. Over the years, multiple file formats have been developed to cater to diverse needs, offering a wide range of supported features and target areas of application. However, classic exchange formats like the JavaScript Object Notation and the Extensible Markup Language have been predominantly favored as a basis for exchanging geospatial information, leaving out common geometric information exchange formats such as Wavefront’s OBJ, Stanford’s PLY, and OFF. To bridge this gap, our research proposes three novel extensions for the mentioned geometric file formats, with a primary focus on minimizing storage requirements while effectively representing geospatial data and also allowing to store semantic meta-information. The extensions, named GeoOBJ, GeoOFF, and GeoPLY, offer significant reductions in storage needs, ranging from 14 to 823% less compared to standard file formats, while retaining support for an adequate number of semantic features. Through extensive evaluations, we demonstrate the suitability of these proposed extensions for geospatial information representation, showcasing their efficacy in delivering low storage overheads and seamless incorporation of critical semantic features. The findings underscore the potential of GeoOBJ, GeoOFF, and GeoPLY as viable solutions for efficient geospatial data representation, empowering various applications to operate optimally with minimal storage constraints.

Advancements in number representation for high-precision computing

Efficient representation of data is a fundamental prerequisite for addressing computational problems effectively using computers. The continual improvement in methods for representing numbers in computers serves as a critical step in expanding the scope and capabilities of computing systems. In this research, we conduct a comprehensive review of both fundamental and advanced techniques for representing numbers in computers. Additionally, we propose a novel model capable of representing rational numbers with absolute precision, catering to specific high precision applications. Specifically, we adopt fractional positional notation coupled with explicit codification of the periodic parts, thereby accommodating the entire rational number set without any loss of accuracy. We elucidate the properties and hardware representation of this proposed format and provide the results of extensive experiments to demonstrate its expressiveness and minimal codification error when compared to other real number representation formats. This research contributes to the advancement of numerical representation in computer systems, empowering them to handle complex computations with heightened accuracy, making them more reliable and versatile in a wide range of applications.

Improved Whale Optimization Algorithm with Deep Learning-Driven Retinal Fundus Image Grading and Retrieval

Several Deep Learning (DL) and medical image Machine Learning (ML) methods have been investigated for efficient data representations of medical images, such as image classification, Content-Based Image Retrieval (CBIR), and image segmentation. CBIR helps medical professionals make decisions by retrieving similar cases and images from electronic medical image databases. CBIR needs expressive data representations for similar image identification and knowledge discovery in massive medical image databases explored by distinct algorithmic methods. In this study, an Improved Whale Optimization Algorithm with Deep Learning-Driven Retinal Fundus Image Grading and Retrieval (IWOADL-RFIGR) approach was developed. The presented IWOADL-RFIGR method mainly focused on retrieving and classifying retinal fundus images. The proposed IWOADL-RFIGR method used the Bilateral Filtering (BF) method to preprocess the retinal images, a lightweight Convolutional Neural Network (CNN) based on scratch learning with Euclidean distance-based similarity measurement for image retrieval, and the Least Square Support Vector Machine (LS-SVM) model for image classification. Finally, the IWOA was used as a hyperparameter optimization technique to improve overall performance. The experimental validation of the IWOADL-RFIGR model on a benchmark dataset exhibited better performance than other models.

Towards efficient image-based representation of tabular data

Towards efficient image-based representation of tabular data

Reference-based genome compression using the longest matched substrings with parallelization consideration

BackgroundA large number of researchers have devoted to accelerating the speed of genome sequencing and reducing the cost of genome sequencing for decades, and they have made great strides in both areas, making it easier for researchers to study and analyze genome data. However, how to efficiently store and transmit the vast amount of genome data generated by high-throughput sequencing technologies has become a challenge for data compression researchers. Therefore, the research of genome data compression algorithms to facilitate the efficient representation of genome data has gradually attracted the attention of these researchers. Meanwhile, considering that the current computing devices have multiple cores, how to make full use of the advantages of the computing devices and improve the efficiency of parallel processing is also an important direction for designing genome compression algorithms.ResultsWe proposed an algorithm (LMSRGC) based on reference genome sequences, which uses the suffix array (SA) and the longest common prefix (LCP) array to find the longest matched substrings (LMS) for the compression of genome data in FASTA format. The proposed algorithm utilizes the characteristics of SA and the LCP array to select all appropriate LMSs between the genome sequence to be compressed and the reference genome sequence and then utilizes LMSs to compress the target genome sequence. To speed up the operation of the algorithm, we use GPUs to parallelize the construction of SA, while using multiple threads to parallelize the creation of the LCP array and the filtering of LMSs.ConclusionsExperiment results demonstrate that our algorithm is competitive with the current state-of-the-art algorithms in compression ratio and compression time.

FlexBlock: A Flexible DNN Training Accelerator With Multi-Mode Block Floating Point Support

When training deep neural networks (DNNs), expensive floating point arithmetic units are used in GPUs or custom neural processing units (NPUs). To reduce the burden of floating point arithmetic, community has started exploring the use of more efficient data representations, e.g., block floating point (BFP). The BFP format allows a group of values to share an exponent, which effectively reduces the memory footprint and enables cheaper fixed point arithmetic for multiply-accumulate (MAC) operations. However, existing BFP-based DNN accelerators are targeted for a specific precision, making them less versatile. In this paper, we present FlexBlock, a DNN training accelerator with three BFP modes, possibly different among activation, weight, and gradient tensors. By configuring FlexBlock to a lower BFP precision, the number of MACs handled by the core increases by up to 4× in 8-bit mode or 16× in 4-bit mode compared to 16-bit mode. To reach this theoretical upper bound, FlexBlock maximizes the core utilization at various precision levels or layer types, and allows dynamic precision control to keep throughput at its peak without sacrificing training accuracy. We evaluate the effectiveness of FlexBlock using representative DNNs on CIFAR, ImageNet and WMT14 datasets. As a result, training in FlexBlock significantly improves training speed by 1.5 5.3× and energy efficiency by 2.4 7.0× compared to other training accelerators.

A methodology to determine the optimal train-set size for autoencoders applied to energy systems

In the latest years, deep learning has been massively used to face problems that have not been solved by means of classical approaches. In particular, an autoencoder is a popular unsupervised artificial neural network that learns efficient data representations (encoding) by training the network to ignore features with a small content of information. Even though autoencoders over-perform classical techniques in several applications like anomaly detection, dimensionality reduction, features denoising, and missing values imputation, the literature does not provide a commonly accepted methodology to define the optimal amount of data needed to train the model. This paper proposes a procedure to determine the optimal train-set size to minimize the reconstruction error of an autoencoder with pre-defined structure and hyper-parameters that will be trained to encode the normal behavior of energy generation systems. This procedure exploits the outcome of learning curves, a powerful tool to track algorithms performance while the train-set dimension varies. Afterward, the procedure is applied to three real case studies where two types of autoencoders are trained to learn the normal behavior of a YANMAR combined heat and power unit with the scope of detecting incoming anomalies. In the end, the outcomes of the procedure are explained and, under the constraint of a daily retraining frequency, 6 weeks are identified as the optimal train-set size for both autoencoders.

Automated Tongue Diagnosis: A Deep Autoencoder Neural Network and Clustering-Based Image Segmentation Approach

Automated tongue diagnosis has gained significant attention in recent years as a non-invasive and cost-effective method for disease detection and monitoring. In this project, propose a novel approach for automated tongue diagnosis using a combination of Deep Autoencoder Neural Network (DAENN) and clustering-based image segmentation techniques. The objective is to develop a reliable and efficient system that can accurately analyze tongue images and provide diagnostic information. The proposed methodology involves several key steps. First, high-resolution images of the tongue are acquired using a digital camera. Preprocessing techniques are then applied to enhance image quality and remove noise. Next, a clustering-based image segmentation algorithm is employed to extract the tongue region from the background. This step ensures that subsequent analysis is focused on the relevant area of interest. After tongue region extraction, relevant features are extracted from the segmented image. These features capture important visual characteristics such as color distribution, texture, and shape. The DAENN is then trained using these features to learn an efficient representation of the input data. The network's encoder-decoder architecture enables it to compress the features into a lower-dimensional latent space and reconstruct the original features. By leveraging advanced machine learning techniques, the system can accurately analyze tongue images and assist healthcare professionals in making informed decisions. The evaluation of the proposed system on a diverse dataset demonstrates promising results, showcasing its potential as an effective tool for automated tongue diagnosis. Keywords—deep autoencoder, Tongue segmentation, Pattern recognition, colour intensity

Improving BERT model for requirements classification by bidirectional LSTM-CNN deep model

In the last decade, requirements classification has emerged as hot research topic in Requirements Engineering (RE). Early identification of software requirements helps the development team in the design of software systems. Manual identification and classification of these requirements is time-consuming and labor-intensive task. To address this issue, machine and deep learning techniques have been studied for automatic classification of requirements. Furthermore, an efficient word embedding representation of the input data is a major concern in automatic approaches. This research work presents a novel requirements classification model that integrates Bidirectional Encoder Representations from Transformers (BERT) with Bidirectional Long-short Term Memory (BiLSTM) with Convolutional Neural Network (CNN) layer called BERT-BiCNN. In BERT-BiCNN, BERT is used as word embedding layer that extract the full combination of contextual semantics. BiLSTM obtains contextual information. The CNN mechanism has been employed to reduce the dimensionality of feature space by selecting the important features. The effectiveness of BERT-BiCNN is evaluated on the PROMISE dataset. On comparative analysis, it can be concluded that proposed approach outperforms six recent deep learning based architectures in binary and multi-class classification.

Shearlet-based intensity classification loop filter for video coding

The new Versatile Video Coding standard (VVC) exhibits a significantly higher coding efficiency in comparison to its predecessor High Efficiency Video Coding (HEVC). Since the finalization of VVC in July 2020 many new in-loop compression tools were suggested, especially with data-driven approaches. The question arises if non-data-driven in-loop filtering approaches can further increase coding efficiency beyond the capability of VVC. In this paper we approach the task of filtering images corrupted by quantization noise from the perspective of applied harmonic analysis, an area of applied mathematics focusing on the efficient representation, analysis and encoding of data. The shearlet-based intensity classification loop filter (SCLF) is a novel non-data-driven approach using an overcomplete and sparsifying transform, the shearlet transform. The basic idea is to achieve a signal-noise separation by applying a shearlet transform. Shearlets can identify important structures of natural images such as edges in the sparse transform domain. Each shearlet transform coefficient is classified into different classes. All coefficients with similar intensities are grouped into the same class, which eventually gives a partition of the set of all shearlet coefficients. This separates important information from noise and ensures an accurate reconstruction of original information by performing an inverse shearlet transform combined with Wiener filtering separately for each class. SCLF effectively removes compression artifacts and therefore restores subjective visual quality. Simulation results show that by adding SCLF to the in-loop stage yet further average bit rate reductions of up to 1.5% are achieved for JVET common test sequences.