Sequence Compression Research Articles

GenCoder: A Novel Convolutional Neural Network Based Autoencoder for Genomic Sequence Data Compression.

Revolutionary advances in DNA sequencing technologies fundamentally change the nature of genomics. Today's sequencing technologies have opened into an outburst in genomic data volume. These data can be used in various applications where long-term storage and analysis of genomic sequence data are required. Data-specific compression algorithms can effectively manage a large volume of data. In recent times, deep learning has achieved great success in many compression tools and is gradually being used in genomic sequence compression. Significantly, autoencoder has been applied in dimensionality reduction, compact representations of data, and generative model learning. It can use convolutional layers to learn essential features from input data, which is better for image and series data. Autoencoder reconstructs the input data with some loss of information. Since accuracy is critical in genomic data, compressed genomic data must be decompressed without any information loss. We introduce a new scheme to address the loss incurred in the decompressed data of the autoencoder. This paper proposes a novel algorithm called GenCoder for reference-free compression of genomic sequences using a convolutional autoencoder and regenerating the genomic sequences from a latent code produced by the autoencoder, and retrieving original data losslessly. Performance evaluation is conducted on various genomes and benchmarked datasets. The experimental results on the tested data demonstrate that the deep learning model used in the proposed compression algorithm generalizes well for genomic sequence data and achieves a compression gain of 27% over the best state-of-the-art method.

Failure analysis of strain clamps on a ± 800 kV transmission line

During X-ray testing of a ± 800 kV ultra-high voltage direct current transmission line tension clamp, it was found that the internal steel core had broken. To investigate the cause of the steel core fracture, the failed clamp was dissected and analyzed, including chemical material testing, mechanical performance testing, fracture morphology inspection, and analysis of the compression process of the tension clamp. A preliminary judgment was proposed on the cause of the internal steel core fracture of the tension clamp, And verification experiments and simulation analysis were conducted to preliminarily determine the cause of the steel core fracture, and it was ultimately concluded that the main reason for the steel core fracture was the incorrect compression sequence of the large cross-section wire tension clamp.

Through the compression glass: language complexity and the linguistic structure of compressed strings

Abstract Against the backdrop of the sociolinguistic-typological complexity debate which is all about measuring, comparing and explaining language complexity, this article investigates how Kolmogorov-based information theoretic complexity relates to linguistic structures. Specifically, the linguistic structure of text which has been compressed with the text compression algorithm gzip will be analysed. One implementation of Kolmogorov-based language complexity is the compression technique (Ehret, Katharina. 2021. An information-theoretic view on language complexity and register variation: Compressing naturalistic corpus data. Corpus Linguistics and Linguistic Theory (2). 383–410) which employs gzip to measure language complexity in naturalistic text samples. In order to determine what type of structures compression algorithms like gzip capture, and how these compressed strings relate to linguistically meaningful structures, gzip’s lexicon output is retrieved and subjected to an in-depth analysis. As a case study, the compression technique is applied to the English version of Lewis Carroll’s Alice’s Adventures in Wonderland and its lexicon output is extracted. The results show that gzip-like algorithms sometimes capture linguistically meaningful structures which coincide, for instance, with lexical words or suffixes. However, many compressed sequences are linguistically unintelligible or simply do not coincide with any linguistically meaningful structures. Compression algorithms like gzip thus crucially capture purely formal structural regularities. As a consequence, information theoretic complexity, in this context, is a linguistically agnostic, purely structural measure of regularity and redundancy in texts.

Statistical shape analysis and computational modeling reveal novel relationships between tibiofemoral bony geometry and knee mechanics in young, female athletes

Young female athletes participating in sports requiring rapid changes of direction are at heightened risk of suffering traumatic knee injury, especially noncontact rupture of the anterior cruciate ligament (ACL). Clinical studies have revealed that geometric features of the tibiofemoral joint are associated with increased risk of suffering noncontact ACL injury. However, the relationship between three-dimensional (3D) tibiofemoral geometry and knee mechanics in young female athletes is not well understood. We developed a statistically augmented computational modeling workflow to determine relationships between 3D geometry of the knee and tibiofemoral kinematics and ACL force in response to an applied loading sequence of compression, valgus, and anterior force, which is known to load the ACL. This workflow included 3D characterization of tibiofemoral bony geometry via principal component analysis and multibody dynamics models incorporating subject-specific knee geometries. A combination of geometric features of both the tibia and the femur that spanned all three anatomical planes was related to increased ACL force and to increased kinematic coupling (i.e., anterior, medial, and distal tibial translations and internal tibial rotation) in response to the applied loads. In contrast, a uniplanar measure of tibiofemoral geometry that is associated with ACL injury risk, sagittal plane slope of the lateral tibial plateau subchondral bone, was not related to ACL force. Thus, our workflow may aid in developing mechanics-based ACL injury screening tools for young, active females based on a unique combination of bony geometric features that are related to increased ACL loading.

GNN-LSTM-based fusion model for structural dynamic responses prediction

With the rapid growth of deep learning technology, the potential for its use in structural engineering has substantially increased in recent years. This study proposes an innovative deep-learning fusion network architecture based on the graph neural network (GNN) and long short-term memory (LSTM) network. The proposed fusion model can accurately predict nonlinear floor acceleration, velocity, and displacement responses of typical steel moment-resisting frames (SMRF) with 4 through 7 stories subjected to strong ground motions. The fusion model framework overcomes a major drawback in existing deep learning models as it can predict the dynamic responses of various structures. This has widened the range of potential applications of structural surrogate models generated through deep learning technology on the design and analysis of building structures with different geometries. Additionally, this paper presents two LSTM-optimized learning strategies, namely packing padded sequences and sequences compression strategies. These strategies improve the model’s performance significantly without modifying its architecture. Finally, this study reveals that the model’s internal graph embedding is highly correlated with certain critical structural parameters, such as the first natural period and the height of a building. This shows that the proposed fusion model is interpretable and has the ability to extract vital information that influences structural dynamic responses.

DNA-QLC: an efficient and reliable image encoding scheme for DNA storage

BackgroundDNA storage has the advantages of large capacity, long-term stability, and low power consumption relative to other storage mediums, making it a promising new storage medium for multimedia information such as images. However, DNA storage has a low coding density and weak error correction ability.ResultsTo achieve more efficient DNA storage image reconstruction, we propose DNA-QLC (QRes-VAE and Levenshtein code (LC)), which uses the quantized ResNet VAE (QRes-VAE) model and LC for image compression and DNA sequence error correction, thus improving both the coding density and error correction ability. Experimental results show that the DNA-QLC encoding method can not only obtain DNA sequences that meet the combinatorial constraints, but also have a net information density that is 2.4 times higher than DNA Fountain. Furthermore, at a higher error rate (2%), DNA-QLC achieved image reconstruction with an SSIM value of 0.917.ConclusionsThe results indicate that the DNA-QLC encoding scheme guarantees the efficiency and reliability of the DNA storage system and improves the application potential of DNA storage for multimedia information such as images.

Enhanced Index Based DNA Sequence Compression Algorithm

Enhanced Index Based DNA Sequence Compression Algorithm

A compressive seeding algorithm in conjunction with reordering-based compression.

Seeding is a rate-limiting stage in sequence alignment for next-generation sequencing reads. The existing optimization algorithms typically utilize hardware and machine-learning techniques to accelerate seeding. However, an efficient solution provided by professional next-generation sequencing compressors has been largely overlooked by far. In addition to achieving remarkable compression ratios by reordering reads, these compressors provide valuable insights for downstream alignment that reveal the repetitive computations accounting for more than 50% of seeding procedure in commonly used short read aligner BWA-MEM at typical sequencing coverage. Nevertheless, the exploited redundancy information is not fully realized or utilized. In this study, we present a compressive seeding algorithm, named CompSeed, to fill the gap. CompSeed, in collaboration with the existing reordering-based compression tools, finishes the BWA-MEM seeding process in about half the time by caching all intermediate seeding results in compact trie structures to directly answer repetitive inquiries that frequently cause random memory accesses. Furthermore, CompSeed demonstrates better performance as sequencing coverage increases, as it focuses solely on the small informative portion of sequencing reads after compression. The innovative strategy highlights the promising potential of integrating sequence compression and alignment to tackle the ever-growing volume of sequencing data. CompSeed is available at https://github.com/i-xiaohu/CompSeed.

A randomized optimal k-mer indexing approach for efficient parallel genome sequence compression

Next Generation Sequencing (NGS) technology generates massive amounts of genome sequence that increases rapidly over time. As a result, there is a growing need for efficient compression algorithms to facilitate the processing, storage, transmission, and analysis of large-scale genome sequences. Over the past 31 years, numerous state-of-the-art compression algorithms have been developed. The performance of any compression algorithm is measured by three main compression metrics: compression ratio, time, and memory usage. Existing k-mer hash indexing systems take more time, due to the decision-making process based on compression results. In this paper, we propose a two-phase reference genome compression algorithm using optimal k-mer length (RGCOK). Reference-based compression takes advantage of the inter-similarity between chromosomes of the same species. RGCOK achieves this by finding the optimal k-mer length for matching, using a randomization method and hashing. The performance of RGCOK was evaluated on three different benchmark data sets: novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Homo sapiens, and other species sequences using an Amazon AWS virtual cloud machine. Experiments showed that the optimal k-mer finding time by RGCOK is around 45.28 min, whereas the time for existing state-of-the-art algorithms HiRGC, SCCG, and HRCM ranges from 58 min to 8.97 h.

The study on the properties of a metamaterial composed of star-shaped auxetic and curved-beam multi-stable structure

This article presents the design of a new metamaterial with adjustable Poisson’s ratio and multi-stability, which combines two common metamaterial structures: star-shaped auxetic and multi-stability. The range of negative Poisson’s ratio variation for the unit structure is 0.2433. The negative Poisson’s ratio shows an increasing trend followed by a decrease with the increase in compression displacement. The peak compression displacement is 12 mm, and the peak negative Poisson’s ratio is −0.1499. Simultaneously, the structure exhibits good shape memory properties, with a shape memory recovery ratio of R rx = 94.18% in the y-axis direction and R ry = 91.20% in the x-axis direction. The shape memory recovery time is 35.10 s. The combination of experimental testing and finite element analysis was employed to verify the functionality of the adjustable Poisson’s ratio in the structure. Special attention was given to investigating the structural parameters such as the type of curved-beam, the angle of the star-shaped structure, the width of the star-shaped structure, and the connection method for their effects on the shape memory effect and the range, peak value, and peak compression displacement of the negative Poisson’s ratio in the unit structure. Based on the characteristics of the novel metamaterial, including a significant increase in stiffness upon compression and controllable deformation sequence, it can be used as a buffering device in protective engineering and civil engineering applications.

Lossy Compression of Individual Sequences Revisited: Fundamental Limits of Finite-State Encoders.

We extend Ziv and Lempel's model of finite-state encoders to the realm of lossy compression of individual sequences. In particular, the model of the encoder includes a finite-state reconstruction codebook followed by an information lossless finite-state encoder that compresses the reconstruction codeword with no additional distortion. We first derive two different lower bounds to the compression ratio, which depend on the number of states of the lossless encoder. Both bounds are asymptotically achievable by conceptually simple coding schemes. We then show that when the number of states of the lossless encoder is large enough in terms of the reconstruction block length, the performance can be improved, sometimes significantly so. In particular, the improved performance is achievable using a random-coding ensemble that is universal, not only in terms of the source sequence but also in terms of the distortion measure.

Low Complexity Coding Unit Decision for Video-Based Point Cloud Compression.

With growing demand for point cloud coding, Video-based Point Cloud Compression (V-PCC) is released for dynamic point clouds, relying on mature 2D video coding techniques. However, the huge computational complexity of 2D video codec is inherited by V-PCC, thereby resulting in a notably time-consuming encoding process for the projection videos. To accelerate the compression, this paper proposes a low complexity coding unit decision algorithm for V-PCC intra coding. First, the 2D sequences (occupancy, geometry, and attribute sequences) are projected from same 3D point could frames in V-PCC. By exploring the strong correlations among them, the cross-projection information is creatively proposed for improving the predication performance of CU partition. Second, considering the disparate coding losses generated by incorrect partitioning decisions of different CUs, we develop a rate-distortion-oriented learning approach aimed at increasing the decision accuracy of the CUs, severely affecting coding performance. Third, to accommodate the particular coding architecture of V-PCC intra configuration, we further devise an overall framework, including targeted feature extraction and partitioning decision for intra and inter coding of geometry and attribute sequences. The final experimental results strongly demonstrate the effectiveness of our proposed algorithm. The time consumption of total projection sequence compression can be reduced by 57.80%, while the coding losses on Geom.BD-TotalRate (D1 and D2) and Attr.BD-TotalRate Luma component are only 0.08%, 0.33%, and 0.16%, respectively, which can be negligible. To the best of our knowledge, the proposed algorithm achieves state-of-the-art performance for accelerating the projection sequence compression in V-PCC All-Intra configuration.

Deep fusible skinning of animation sequences

Animation compression is a key process in replicating and streaming animated 3D models. Linear Blend Skinning (LBS) facilitates the compression of an animated sequence while maintaining the capability of real-time streaming by deriving vertex to proxy bone assignments and per frame bone transformations. We introduce a innovative deep learning approach that learns how to assign vertices to proxy bones with persistent labeling. This is accomplished by learning how to correlate vertex trajectories to bones of fully rigged animated 3D models. Our method uses these pretrained networks on dynamic characteristics (vertex trajectories) of an unseen animation sequence (a sequence of meshes without skeleton or rigging information) to derive an LBS scheme that outperforms most previous competent approaches by offering better approximation of the original animation sequence with fewer bones, therefore offering better compression and smaller bandwidth requirements for streaming. This is substantiated by a thorough comparative performance evaluation using several error metrics, and compression/bandwidth measurements. In this paper, we have also introduced a persistent bone labeling scheme that (i) improves the efficiency of our method in terms of lower error values and better visual outcome and (ii) facilitates the fusion of two (or more) LBS schemes by an innovative algorithm that combines two arbitrary LBS schemes. To demonstrate the usefulness and potential of this fusion process, we have combined the outcome of our deep skinning method with that of Rignet—which is a state-of-the-art method that performs rigging on static meshes—with impressive results.

Brain-imaging evidence for compression of binary sound sequences in human memory.

According to the language-of-thought hypothesis, regular sequences are compressed in human memory using recursive loops akin to a mental program that predicts future items. We tested this theory by probing memory for 16-item sequences made of two sounds. We recorded brain activity with functional MRI and magneto-encephalography (MEG) while participants listened to a hierarchy of sequences of variable complexity, whose minimal description required transition probabilities, chunking, or nested structures. Occasional deviant sounds probed the participants' knowledge of the sequence. We predicted that task difficulty and brain activity would be proportional to the complexity derived from the minimal description length in our formal language. Furthermore, activity should increase with complexity for learned sequences, and decrease with complexity for deviants. These predictions were upheld in both fMRI and MEG, indicating that sequence predictions are highly dependent on sequence structure and become weaker and delayed as complexity increases. The proposed language recruited bilateral superior temporal, precentral, anterior intraparietal, and cerebellar cortices. These regions overlapped extensively with a localizer for mathematical calculation, and much less with spoken or written language processing. We propose that these areas collectively encode regular sequences as repetitions with variations and their recursive composition into nested structures.

Remote Expert DVT Triaging of Novice-User Compression Sonography with AI-Guidance

Compression ultrasonography of the leg is established for triaging proximal lower extremity deep vein thrombosis (DVT). AutoDVT, a machine-learning software, provides a tool for nonspecialists in acquiring compression sequences to be reviewed by an expert for patient triage. The purpose of this study was to test image acquisition and remote triaging in a clinical setting. Patients with a suspected DVT were recruited at 2 centers in Germany and Greece. Enrolled patients underwent an artificial intelligence-guided two-point compression examination by a nonspecialist using a handheld ultrasound device prior to a standard scan. Images collected by the software were uploaded for blind review by 5 qualified physicians. All reviewers rated the quality of all sequences on the American College of Emergency Physicians (ACEP) image quality scale (score 1-5, ≥ 3 defined as adequate imaging quality) and for an ACEP score ≥3, chose "Compressible", "Incompressible", or "Other". Sensitivity and specificity were calculated for adequate quality scans with an assessment as "Compressible" or "Incompressible". We define this group as diagnostic quality. To simulate a triaging clinical algorithm, a post hoc analysis was performed merging the "incomplete", the "low quality", and the "Incompressible" into a high-risk group for proximal DVT. Seventy-three patients (average age 64.2years, 44% females) were eligible for inclusion and scanned by 3 nonultrasound-qualified healthcare professionals. Three patients were excluded from further analysis due to incomplete scans. Sixty two of 70 (88.57%) of the completed scans were judged to be of adequate image quality with an average ACEP score of 3.35. Forty seven of 62 adequate AutoDVT scans were assessed as diagnostic quality, of which 8 were interpreted as positive for proximal DVT by the reviewers resulting in a sensitivity of 100% and specificity of 95.12%. When simulating a triaging algorithm, 34/73 (46.58%) of patients would be triaged as high risk and 8 would be confirmed as positive for proximal DVT (6 in the diagnostic and 2 in the low-quality cohort). Of 39/73 patients triaged as low risk, all were negative for proximal DVT in standard duplex; thus, this triaging algorithm could potentially save 53.42% of standard duplex scans. Machine learning software was able to aid nonexperts in acquiring valid ultrasound images of venous compressions and allowed remote triaging. This strategy allows faster diagnosis and treatment of high-risk patients and can spare the need for multiple unnecessary duplex scans, the vast majority being negative.

Bamboo fiber‐reinforced epoxy composites fabricated by vacuum‐assisted resin transfer molding (VARTM): Effect of molding sequence and fiber content

AbstractFabricating bamboo fiber‐reinforced epoxy composites (BFREs) using the vacuum‐assisted resin transfer molding (VARTM) process has many advantages, such as low cost, high product quality, controllability, and broad applicability. However, the immature fabrication process seriously affects product performance and hinders the full exploitation of the advantages of the VARTM process. The effects on the physical‐mechanical and thermal properties of BFREs by molding sequence and fiber content were investigated in this study. The molding sequence of fiber compression followed by resin injection was beneficial to ensure hermeticity, thus reducing the voids in the BFREs. The mechanical properties of BFREs gradually increased with increasing fiber content. With 50 wt% fiber content, the bending strength and modulus, shear strength, and impact toughness of BFREs reached 71.27 MPa, 6.78 GPa, 11.68 MPa, and 5.45 kJ/m2, respectively. Although the thermal stability of the BFREs was slightly lower than that of pure epoxy, the bamboo fibers' high rigidity evidentially increased the epoxy's dynamic mechanical properties. The life cycle assessment indicated that the BFREs effectively reduced the environmental impacts and production energy consumption compared to the glass fiber correspondent. The VARTM‐fabricated BFREs have great potential for construction, transportation, household items, packaging, sports equipment, and leisure goods applications.Highlights Fiber compression followed by resin injection could ensure great hermeticity. Micro‐CT accurately measured the void characteristics of the composites. Voids had detrimental effects on the properties of the composites. Dense bamboo fiber preforms enhanced the mechanical properties of epoxy. Bamboo fibers reinforcing epoxy reduced the impact on the environment.

A Method for Generating Geometric Image Sequences for Non-Isomorphic 3D-Mesh Sequence Compression

As virtual reality and 3D-modeling technology continue to advance, the amount of digital geometric media data is growing at an explosive rate. For example, 3D meshes, an important type of digital geometric media, can precisely record geometric information on a model’s surface. However, as the complexity and precision of 3D meshes increase, it becomes more challenging to store and transmit them. The traditional method of compressing non-isomorphic 3D-mesh sequences through frame-by-frame compression is inefficient and destroys the inter-frame correlations of the sequences. To tackle these issues, this study investigates the generation of time-dependent geometric image sequences for compressing non-isomorphic 3D-mesh sequences. Two methods are proposed for generating such sequences: one through image registration and the other through parametrization-geometry cooperative registration. Based on the experimental compression results of the video-coding algorithms, it was observed that the proposed geometric image-sequence-generation method offers superior objective and subjective qualities, as compared to the traditional method.

Sequence Compression and Alignment-Based Process Alarm Prediction

Sequence Compression and Alignment-Based Process Alarm Prediction

Association of Training in Basic Life Support with the Evolution of Cardiopulmonary Resuscitation Performed by Firefighters.

This study aimed to compare the results of professional technical and anthropometric anamnesis data with the transmission of external chest compressions performed by military firefighters at different execution times. The objective was to evaluate the performance and perceived effort of the sequence of external chest compressions performed in two minutes, as well as the evolution of the technique over time. This was a descriptive, correlational study involving adult firefighters who were members of a specific firefighter group, comprising a population of 105 individuals with a voluntary sample of 44 participants. The study used a Bayesian statistical approach to provide probabilistic expressions. The participants had an average work experience of 17 years, an average age of 38.6 years, an average weight of 81.48 kilograms, an average height of 176 centimeters, and an average of 2.5 qualifications. The results indicated that the firefighters performed external chest compressions with excellent technique and a moderate level of perceived effort in a two-minute evaluation. The evaluation of the evolution of the technique over time showed that the participants were able to maintain high-quality compressions for an average of 6 minutes, with a maximum of 20 uninterrupted minutes. The study underscores the critical role of professional firefighters in performing and maintaining high-quality external chest compressions, which has the potential to reduce morbidity and mortality in cases of cardiorespiratory arrest.

Proposed Extensions to the Third Edition of JPEG XS (ISO/IEC 21122) Standard

At its 94th meeting (17–21 January 2022), International Organization for Standardization (ISO)/IEC SC29WG1 (also known as JPEG Committee) decided to create a third edition of the JPEG XS standard (ISO/IEC 21122) for lightweight, low-latency image coding. While existing coding tools in the standard already support the compression of natural content and color filter array (CFA) RAW Bayer pattern data well, this third edition will extend the previous editions with coding tools that improve its performance on the compression of screen content and image sequences with static background. The first experiments demonstrate that the proposed coding tools can improve the average peak signal-to-noise ratio (PSNR) performance of JPEG XS on such sequences of more than 10 dB, and approximately 3 dB on computer-generated content. In this work, the authors will present the proposed coding tools and will evaluate their performance objectively by PSNR.