Traditional Alignment Research Articles

Research on Chinese Audio and Text Alignment Algorithm Based on AIC-FCM and Doc2Vec

‘‘Audiobook” is a multimedia-based reading technology that has emerged in recent years. Realizing the alignment of e-book text and book audio is the most important part of its processing. This article describes an audio and text alignment algorithm using deep learning and neural network technology to improve the efficiency and quality of audiobook production. The algorithm first uses dual-threshold endpoint detection technology to segment long audio into short audio with sentence dimensions and recognizes it as short text. The threshold is calculated by AIC-FCM optimized based on simulated annealing genetic algorithm. Then the algorithm uses Doc2vec optimized by the threshold prediction method based on the average length of the short text to calculate the text similarity. Finally, proofread and output the text sequence and audio segment aligned in the time dimension to meet the needs of audiobook production. Experiments show that compared to traditional audio and text alignment algorithms, the proposed algorithm is closer to the ideal segmentation result in long audio segmentation, and the alignment effect is basically the same as Doc2vec and the time complexity is reduced by about 35%.

Fast Two-position Initial Alignment of SINS Based on CKF

In practical vehicle applications, the initial alignment environment of SINS is more complicated, and the coarse alignment accuracy is not high due to carrier sloshing, which leads to the filtering problem of nonlinear system in fine alignment. The traditional two-position alignment method based on EKF is no longer applicable. In this paper, a fast two-position alignment method of SINS based on CKF is proposed. Use two CKFs for state estimation at two locations respectively, then the error parameters are solved through the spatial relationship between the two positions. The simulation shows that using CKF improves accuracy compared to EKF. Verified by experiment, the two-position alignment method based on CKF can better adapt to the nonlinear system, which can be applied in practical engineering.

A Coarse Alignment Method Based on Vector Observation and Truncated Vectorized $\boldsymbol{\kappa}$-Matrix for Underwater Vehicle

This paper focuses on the coarse alignment problem for the strapdown inertial navigation system (SINS) applied to the underwater vehicle. The coarse alignment is one of the crucial technologies of underwater navigation and determines whether SINS can work normally. However, the traditional coarse alignment methods suffer from the complicated noises hidden in the inertial sensors’ outputs. To solve this problem, a new coarse alignment algorithm based on vector observation and truncated vectorized <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix is proposed in this paper. Different from previous methods based on the optimal quaternion, the proposed algorithm makes the matrix state-space formulae of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix vectorized, and truncates the resulting state vectors by implementing the linear dependence within the components of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix. On one hand, based on the vectorized and truncated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix, a linear reduced model of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\kappa}$</tex-math></inline-formula> -matrix can be applied to the estimation process, and the measurement errors of inertial sensors can be easily restrained using the estimation algorithm. On the other hand, the proposed approach makes up for the drawbacks of the Optimal-REQUEST algorithm which has a conservative covariance matrix and scalar gain. The simulation test and practical experiment demonstrate that the proposed approach can reduce random noise effectively. Therefore, the performance of coarse alignment algorithm can be improved more enormously than the traditional algorithms.

Characterization of the LED filament lamp for luminous intensity calibration

Light-emitting diode (LED) is now the main type of light source in global lighting marketing. However, traditional photometry with the incandescent standard lamp is not sufficient to implement high-level LED photometric measurement, since a considerable spectral mismatch error may exist if the photometer has a large general V(λ) mismatch index . The LED standard lamp is proposed to be the complementary transfer standard for photometry to reduce this measurement uncertainty. National Institute of Metrology, China (NIM) has previously reported a type of LED filament lamp for total luminous flux. In this article, NIM demonstrates another type of LED filament lamp for luminous intensity calibration. The LED filaments are sealed in a T90 cylinder glass bulb as emitters, and have a spectrum similar to the reference white LED spectrum considered in Commission on Illumination technical committee 2–90. The LED lamp uses the E27 screw base as the connector, and is compatible with the traditional alignment approaches. The warm-up time is less than 12 min. The output change of a group of 12 lamps, over nearly two years of storage and 1 h operation every day during the whole long-term test, is found to be ±0.2% or less for most of the lamps. The ageing effect of one lamp over a continuous 6500 h of burning is estimated to be −0.014% per 100 h, and it is significantly lower than 1% per 100 h, the typical drift rate of the traditional incandescent luminous intensity lamp. In the aspect of the coincidence with inverse square law, the LED lamp has a similar performance to the WI41/G lamp, the traditional luminous intensity lamp.

Limb alignment changes with knee flexion: A study based on CAS data

BackgroundRecent evidence has questioned the value of standing limb alignment for predicting the adduction moment and forces exerted on healthy and prosthetic knees. The purpose of this study was to assess the lower limb alignment of OA knee patients at various knee flexion angles.The main hypothesis was that lower limb alignment measured throughout knee flexion does not significantly differ between patients displaying different extension alignment (neutral, varus or valgus). Methods206 arthritic knee patients undergoing computer-assisted total (CAS) knee prosthesis were included. Frontal limb alignment was assessed in a systematic manner by CAS at three knee positions: extension, 90 degrees of flexion and maximal flexion. The HKA angle at each knee position and the change in HKA angle between two knee positions (delta value) were reported and compared. ResultsA large proportion of OA patients had significant variation in their lower limb alignment (32% with Δ HKA > 5°). The extended limb deformity tended to reduce with knee flexion: mean of 5° and 6° deformity reduction for varus and valgus patients, 40% and 66% of varus and valgus patients progressed to neutral alignment with 90° knee flexion. Forty percent of neutral extended lower limb did not maintain their neutral alignment but rather progressed to either varus or valgus at 90° knee flexion. ConclusionsLimb alignment in extension is a poor predictor of limb alignment in flexion in OA patients.Only considering the traditional frontal alignment of an extended lower limb for planning knee arthroplasty or osteotomy is likely insufficient.

Rapid Transfer Alignment for Large and Time-Varying Attitude Misalignment Angles

Transfer alignment is an effective method to estimate the attitude difference between two inertial platforms. Traditional transfer alignment methods are either meant for small misalignments or use nonlinear filtering. Linear methods for large misalignments use quaternion attitude representation, but their performance degrades when the misalignment is not constant or slowly varying. This work proposes applying rapid transfer alignment to the inertial stabilization of a Satcom On-The-Move antenna. In this application case, the two inertial platforms are connected by a kinematic chain, meaning the attitude difference between the inertial platforms is both large and time-varying. This work proposes a method to estimate this attitude difference, with a Kalman filter used for the estimation of the unknown angles. To that effect, a new propagation model is developed to take into account the misalignment variations using master and slave rotation rates. Simulation results show the performance of the proposed solution in a Satcom antenna inertial stabilization case.

DendroSSR: SSRs and sequence alignment as tools for building phylogeny trees.

This study introduces a new method to construct phylogenetic trees by combining both of the Simple Sequence Repeats (SSRs) and sequence alignments. The purpose of this work is to present the DendroSSR program and show it via a case study involving diverse Aspergillus species. To show how the DendroSSR program works to resolve complicated species relationships in phylogenetic trees, we employed the Aspergillus species as an example of a research case. The DendroSSR employs a technique containing multiple phases beginning with, detecting SSRs, computing SSRs similarities, sequences alignment, building a distance matrix based on SSRs similarity and sequences alignments, and then hierarchical clustering, and presenting the findings in a dendrogram. Sometimes sequence alignments alone may not give adequate information to generate a phylogenetic tree to resolve complicated species relationships. Therefore, establishing a distance matrix that is formed of addition of SSRs similarity across sequences to the traditional sequence alignment helps the process substantially and resolves the connections of complex species on phylogenetic trees. Additionally, it may be hard to distinguish complex relationships across species when studying conserved sequences, which could lead to an incomplete representation of their evolutionary relationships. These limitations are addressed by DendroSSR, which offers a technique to produce phylogenetic trees by incorporating SSRs similarity across species into the approach of generating phylogenetic trees. As it is known, SSRs are extensively scattered across the genomes of species and exhibit a great variation. Therefore, SSRs may support the knowledge gathered from sequence alignments by providing more information on genetic variation and even evolutionary relationships. The use of DendroSSR analysis might be considered for creating phylogenetic trees as a complementary or secondary strategy among the species under examination in circumstances where traditional phylogenetic analysis fails to clarify the species complex phylogenetic relationships.

Модификация алгоритма выравнивания коротких прочтений для повышения качества пайплайна обработки данных полногеномного секвенирования человека

This study emphasizes the importance of aligning short reads in the analysis of human whole-genome sequencing data. The alignment process involves determining the positions of short genetic sequences relative to a known reference genome sequence of the human genome. Traditional alignment methods use a linear reference sequence, but this can lead to incorrect alignment, especially when short reads contain genetic variations. In this work, the index file of the reference sequence was modified using the minimap2 tool. Experimental results showed that adding information about frequently occurring genetic variations to the minimap2 index increases the number of correctly identified genetic variants, which affects the quality of subsequent data analysis.

Compression-Complexity Measures for Analysis and Classification of Coronaviruses

Finding a vaccine or specific antiviral treatment for a global pandemic of virus diseases (such as the ongoing COVID-19) requires rapid analysis, annotation and evaluation of metagenomic libraries to enable a quick and efficient screening of nucleotide sequences. Traditional sequence alignment methods are not suitable and there is a need for fast alignment-free techniques for sequence analysis. Information theory and data compression algorithms provide a rich set of mathematical and computational tools to capture essential patterns in biological sequences. In this study, we investigate the use of compression-complexity (Effort-to-Compress or ETC and Lempel-Ziv or LZ complexity) based distance measures for analyzing genomic sequences. The proposed distance measure is used to successfully reproduce the phylogenetic trees for a mammalian dataset consisting of eight species clusters, a set of coronaviruses belonging to group I, group II, group III, and SARS-CoV-1 coronaviruses, and a set of coronaviruses causing COVID-19 (SARS-CoV-2), and those not causing COVID-19. Having demonstrated the usefulness of these compression complexity measures, we employ them for the automatic classification of COVID-19-causing genome sequences using machine learning techniques. Two flavors of SVM (linear and quadratic) along with linear discriminant and fine K Nearest Neighbors classifer are used for classification. Using a data set comprising 1001 coronavirus sequences (causing COVID-19 and those not causing COVID-19), a classification accuracy of 98% is achieved with a sensitivity of 95% and a specificity of 99.8%. This work could be extended further to enable medical practitioners to automatically identify and characterize coronavirus strains and their rapidly growing mutants in a fast and efficient fashion.

Strobealign: flexible seed size enables ultra-fast and accurate read alignment

Read alignment is often the computational bottleneck in analyses. Recently, several advances have been made on seeding methods for fast sequence comparison. We combine two such methods, syncmers and strobemers, in a novel seeding approach for constructing dynamic-sized fuzzy seeds and implement the method in a short-read aligner, strobealign. The seeding is fast to construct and effectively reduces repetitiveness in the seeding step, as shown using a novel metric E-hits. strobealign is several times faster than traditional aligners at similar and sometimes higher accuracy while being both faster and more accurate than more recently proposed aligners for short reads of lengths 150nt and longer. Availability: https://github.com/ksahlin/strobealign

A novel conditional weighting transfer Wasserstein auto-encoder for rolling bearing fault diagnosis with multi-source domains

Transfer learning based on a single source domain to a target domain has received a lot of attention in the cross-domain fault diagnosis tasks of rolling bearing. However, the practical issues often contain multiple source domain data, and the information contained in the target domain is quite different from one source domain to another. Therefore, the transfer pattern from multiple source domains to the target domain undoubtedly has brighter application prospects. Based on these discussions, a multi-source domain transfer learning approach called conditional weighting transfer Wasserstein auto-encoder is developed to deal with the challenges of cross-domain fault diagnosis. Different from the traditional distribution alignment idea of directly aligning the source and target domains, the proposed framework adopts an indirect latent alignment idea to achieve better feature alignment, that is, indirectly aligning the feature distribution of source and target in the latent feature space with the help of Gaussian prior distribution. Furthermore, considering the variability of different source domains containing information about the target domain, an ingenious conditional weighting strategy is designed to quantify the similarity of different source domains to target domain, and further help the proposed model to minimize the discrepancy in conditional distribution. The cross-domain fault diagnosis tasks adequately verify that the proposed framework can sufficiently transfer knowledge from all source domains to the target domain, and has extensive application prospects.

IMCSA: Providing Better Sequence Alignment Space for Industrial Control Protocol Reverse Engineering

Nowadays, with the wide application of industrial control facilities, industrial control protocol reverse engineering has significant security implications. The reverse method of industrial protocol based on sequence alignment is the current mainstream method because of its high accuracy. However, this method will incur a huge time overhead due to unnecessary alignments during the sequence alignment process. In this paper, we optimize the traditional sequence alignment method by combining the characteristics of industrial control protocols. We improve the frequent sequence mining algorithm, Apriori, to propose a more efficient Bag-of-Words generation algorithm for finding keywords. Then, we precluster the messages based on the generated Bag-of-Words to improve the similarity of the message within a cluster. Finally, we propose an industrial control protocol message preclustering model for sequence alignment, namely, IMCSA. We evaluate it over five industrial control protocols, and the results show that IMCSA can generate clusters with higher message similarity, which will greatly reduce the invalid alignments existing in the sequence alignment stage and ultimately improve the overall efficiency.

MTSv: rapid alignment-based taxonomic classification and high-confidence metagenomic analysis.

As the size of reference sequence databases and high-throughput sequencing datasets continue to grow, it is becoming computationally infeasible to use traditional alignment to large genome databases for taxonomic classification of metagenomic reads. Exact matching approaches can rapidly assign taxonomy and summarize the composition of microbial communities, but they sacrifice accuracy and can lead to false positives. Full alignment tools provide higher confidence assignments and can assign sequences from genomes that diverge from reference sequences; however, full alignment tools are computationally intensive. To address this, we designed MTSv specifically for alignment-based taxonomic assignment in metagenomic analysis. This tool implements an FM-index assisted q-gram filter and SIMD accelerated Smith-Waterman algorithm to find alignments. However, unlike traditional aligners, MTSv will not attempt to make additional alignments to a TaxID once an alignment of sufficient quality has been found. This improves efficiency when many reference sequences are available per taxon. MTSv was designed to be flexible and can be modified to run on either memory or processor constrained systems. Although MTSv cannot compete with the speeds of exact k-mer matching approaches, it is reasonably fast and has higher precision than popular exact matching approaches. Because MTSv performs a full alignment it can classify reads even when the genomes share low similarity with reference sequences and provides a tool for high confidence pathogen detection with low off-target assignments to near neighbor species.

Irish Unification After Brexit: Old and New Political Identities?

AbstractThis article contends that the outcome of the prolonged dispute about the future constitutional status of Northern Ireland (NI) will be shaped by the emerging dynamic between ‘old’ and ‘new’ political identities in NI. The ‘old’ identities conceived political outcomes as defined by two monolithic ethno‐cultural blocs: nationalism (alongside republicanism) and unionism (alongside loyalism). Nationalism and unionism formed the ‘two communities model’ of consociational governance enshrined in the Belfast/Good Friday Agreement (B/GFA). Today, despite the Agreement being in existence for over twenty years, growing numbers of citizens identify with neither ethno‐cultural category. Recent elections indicate that more pluralistic ‘new’ political identities are gaining ground at the expense of traditional alignments. The implications for NI's constitutional future are likely to be profound. The emerging constituency of non‐aligned voters will have a decisive impact on the final outcome of any border poll on Irish unification. Such voters typically support the cross‐community Alliance Party, the Greens, or the left People Before Profit (PBP) party. Crucially, these parties are concerned as much with economic and social issues as constitutional questions. In the context of growing political fluidity, the result of any future border poll remains contingent.

Person Entity Alignment Method Based on Multimodal Information Aggregation

Entity alignment is used to determine whether entities from different sources refer to the same object in the real world. It is one of the key technologies for constructing large-scale knowledge graphs and is widely used in the fields of knowledge graphs and knowledge complementation. Because of the lack of semantic connection between the visual modality face attribute of the person entity and the text modality attribute and relationship information, it is difficult to model the visual and text modality into the same semantic space, and, as a result, that the traditional multimodal entity alignment method cannot be applied. In view of the scarcity of multimodal person relation graphs datasets and the difficulty of the multimodal semantic modeling of person entities, this paper analyzes and crawls open-source semi-structured data from different sources to build a multimodal person entity alignment dataset and focuses on using the facial and semantic information of multimodal person entities to improve the similarity of entity structural features which are modeled using the graph convolution layer and the dynamic graph attention layer to calculate the similarity. Through verification on the self-made multimodal person entity alignment dataset, the method proposed in this paper is compared with other entity alignment models which have a similar structure. Compared with AliNet, the probability that the first item in the candidate pre-aligned entity set is correct is increased by 12.4% and average ranking of correctly aligned entities in the candidate pre-aligned entity set decreased by 32.8, which proves the positive effect of integrating multimodal facial information, applying dynamic graph attention and a layer-wise gated network to improve the alignment effect of person entities.

Group Appeals of Parties in Times of Economic and Identity Conflicts and Realignment.

Party-group relations are today characterized by various forms of alignments. These include the persistence of traditional class alignments, the realignment of economic groups due to identity politics and alignments of groups at the centre of identity politics. This study analyses the group-based messaging of parties in relation to these three groups. We argue that, contrary to the catch-all party thesis, positive and negative group appeals of parties are (still) structured by parties' support among social groups and by groups either liked or disliked by their voters. Our argument is tested through a content analysis of election materials in Germany, Switzerland and the Netherlands, combined with survey data. The results indicate that group appeals are indeed shaped by electoral support among social groups and attitudes towards them. Results also suggest that the former effect is present for traditionally aligned economic and identity politics groups, but not for realigned economic groups.

Pattern matching for high precision detection of LINE-1s in human genomes

BackgroundLong interspersed element 1 (LINE-1 or L1) retrotransposons are mobile elements that constitute 17–20% of the human genome. Strong correlations between abnormal L1 expression and several human diseases have been reported. This has motivated increasing interest in accurate quantification of the number of L1 copies present in any given biologic specimen. A main obstacle toward this aim is that L1s are relatively long DNA segments with regions of high variability, or largely present in the human genome as truncated fragments. These particularities render traditional alignment strategies, such as seed-and-extend inefficient, as the number of segments that are similar to L1s explodes exponentially. This study uses the pattern matching methodology for more accurate identification of L1s. We validate experimentally the superiority of pattern matching for L1 detection over alternative methods and discuss some of its potential applications.ResultsPattern matching detected full-length L1 copies with high precision, reasonable computational time, and no prior input information. It also detected truncated and significantly altered copies of L1 with relatively high precision. The method was effectively used to annotate L1s in a target genome and to calculate copy number variation with respect to a reference genome. Crucial to the success of implementation was the selection of a small set of k-mer probes from a set of sequences presenting a stable pattern of distribution in the genome. As in seed-and-extend methods, the pattern matching algorithm sowed these k-mer probes, but instead of using heuristic extensions around the seeds, the analysis was based on distribution patterns within the genome. The desired level of precision could be adjusted, with some loss of recall.ConclusionPattern matching is more efficient than seed-and-extend methods for the detection of L1 segments whose characterization depends on a finite set of sequences with common areas of low variability. We propose that pattern matching may help establish correlations between L1 copy number and disease states associated with L1 mobilization and evolution.

Aligning and extending technologies of parallel corpora for the Kazakh language

The paper presents the two-stage alignment and extending methods of parallel corpora for the Kazakh language. The Kazakh language is agglutinative with rich morphology and related to the Turkic language group. So, the traditional alignment methods for similar languages do not work for the Kazakh language. The alignment is used primarily to ensure that the fragment corresponding to the original is found in the translation. After that, identical fragments of parallel texts are compared with each other. At the initial stage, the question is what needs to be leveled. It is possible to align word by word, but this often becomes almost impossible for several reasons: sets of lexemes and expressions do not match in different languages. Considering the linguistic peculiarities of languages, the developed technologies and ways of universal alignment of parallel text may not work in languages with agglutination. It means that the form of the word is formed by additional affixes and auxiliary words that carry semantic and morphological information. The approach presented in this paper is to use a two-stage alignment, which uses a bilingual dictionary of synonyms. The evaluation with the use of the English-Kazakh corpus verifies that our method shows an average of 89 % correct alignment. The second method is designed to expand the parallel corpus due to the lack of natural parallel corpora of the Kazakh-English language pair with good quality. The developed method uses a combinatorial method taking into account the semantic and grammatical features of the Kazakh language. Different tenses of the Kazakh language are used for sentence generation, and different endings for parts of speech are also considered.

A high-accuracy initial alignment method based on backtracking process for strapdown inertial navigation system

When the alignment time is short, the traditional initial alignment method of strapdown inertial navigation system has very low accuracy, and even the attitude angle error is difficult to converge. We propose a high-accuracy initial alignment method for strapdown inertial navigation system based on the backtracking process, which reuses short-time measurement data to improve the accuracy and speed of initial alignment. We introduce the backtracking process into coarse alignment and fine alignment of strapdown inertial navigation system. In the coarse alignment, the instantaneous acceleration error caused by the backtracking process is estimated and compensated. In the fine alignment, the optimal initialization method of the parameters in the Kalman filter is given. The initial alignment experimental results of strapdown inertial navigation system prove the effectiveness of the proposed method. Compared with the previous initial alignment method, the accuracy and speed of the proposed method are improved.

In Search for Leadership Authenticity at the Intersection of Tradition &amp; Modernity

This paper conjectures that the Authentic Leadership literature has fallen into a sample trap– primarily drawing its evidence base from modern Western, Educated, Industrialized, Rich, and Democratic (WEIRD) contexts. In seeking to address this bias this paper addresses Authentic Leadership at the intersection of tradition and modernity and seeks to advance understandings of Authentic Leadership by investigating insider-outsider dynamics that arise where traditional cultural groups interface with more modern WEIRD groups. It also addresses the importance of out-group legitimacy – this is particularly important in relation to the intersection of tradition and modernity where significant power disparities are regularly present between modern and traditional groups. This paper combines the concepts of insider perceptions of legitimacy and outsider perceptions of legitimacy into a typology that provides a framework for understanding authentic leadership roles at the intersection of tradition and modernity. This paper contributes to the Authentic Leadership literature, first by challenging an underlying assumption within Authentic Leadership, namely that the role of the individualised self is foundational in achieving Authentic Leadership. Instead this paper looks to broaden this concept to fluid alignment which includes degrees, not only of internalised self-alignment, but also socio-political or normative congruence with the in-group context. Whist this may not be foundational in WEIRD highly individualised modern contexts in less or non-WEIRD contexts traditional alignment can be foundational for achieving legitimacy (perceived authenticity) with leaders frequently becoming paragons of traditional authenticity. Secondly this paper challenges the sole focus on internal leader follower (in-group) legitimacy, asserting that this should be considered in relation to an outsider external (out-group) legitimacy. This is particularly relevant at the intersection of tradition and modernity where power inequalities between traditional and modern groups has historically been and continue to be substantial.