Dictionary Of Patterns Research Articles

Interpretable online network dictionary learning for inferring long-range chromatin interactions.

Dictionary learning (DL), implemented via matrix factorization (MF), is commonly used in computational biology to tackle ubiquitous clustering problems. The method is favored due to its conceptual simplicity and relatively low computational complexity. However, DL algorithms produce results that lack interpretability in terms of real biological data. Additionally, they are not optimized for graph-structured data and hence often fail to handle them in a scalable manner. In order to address these limitations, we propose a novel DL algorithm called online convex network dictionary learning (online cvxNDL). Unlike classical DL algorithms, online cvxNDL is implemented via MF and designed to handle extremely large datasets by virtue of its online nature. Importantly, it enables the interpretation of dictionary elements, which serve as cluster representatives, through convex combinations of real measurements. Moreover, the algorithm can be applied to data with a network structure by incorporating specialized subnetwork sampling techniques. To demonstrate the utility of our approach, we apply cvxNDL on 3D-genome RNAPII ChIA-Drop data with the goal of identifying important long-range interaction patterns (long-range dictionary elements). ChIA-Drop probes higher-order interactions, and produces data in the form of hypergraphs whose nodes represent genomic fragments. The hyperedges represent observed physical contacts. Our hypergraph model analysis has the objective of creating an interpretable dictionary of long-range interaction patterns that accurately represent global chromatin physical contact maps. Through the use of dictionary information, one can also associate the contact maps with RNA transcripts and infer cellular functions. To accomplish the task at hand, we focus on RNAPII-enriched ChIA-Drop data from Drosophila Melanogaster S2 cell lines. Our results offer two key insights. First, we demonstrate that online cvxNDL retains the accuracy of classical DL (MF) methods while simultaneously ensuring unique interpretability and scalability. Second, we identify distinct collections of proximal and distal interaction patterns involving chromatin elements shared by related processes across different chromosomes, as well as patterns unique to specific chromosomes. To associate the dictionary elements with biological properties of the corresponding chromatin regions, we employ Gene Ontology (GO) enrichment analysis and perform multiple RNA coexpression studies.

Investigating the Geoenvironmental and Climatic Impacts on the Facades of Historical Houses in Killit (Dereiçi) Village, Mardin

Killit (Dereici), which is largely abandoned today, is an Assyrian village thought to have a population of more than 20,000 spread around the world. The Kilit (Dereiçi) village of Savur stands out as a rare example in the world with its multi-identity structure. The street façades of this Assyrian village, which has such cultural importance, can be clearly seen today, due to various factors. The aim of the study is to investigate the damage patterns and the factors affecting the durability of Kilit (Dereici) street facades (Old Bazaar). For this purpose, observational detection method was used and the findings were presented based on the dictionary of stone deterioration patterns prepared by ICOMOS-ISCS,2008. As a result of the study, the most intense deteriorations observed on the street facades; cement-induced faulty repairs made between the joints of the stone blocks on the facades and ‘crack, disintegration, erosion, ‘missing part, discolouration’ on the stone surfaces exposed to climate impact for a long time.

Desain Kamus Semantik Pola Kalimat dalam Al-Qur'an

This study aims to formulate the design of a semantic dictionary of Arabic sentence patterns in the Qur'an. The research also aims to complement research on existing Al-Qur'an Arabic dictionary designs, especially for Al-Qur'an Arabic lessons in Indonesia. This research is qualitative with a literature study approach. Where the research data were obtained from the primary reading sources of the Al-Qur'an and the book Mu'jam al-Furūq al-Dalāliyyah fī al-Qur'ān al-Karīm and then developed into dictionary designs. The results of the study show that the design of this semantic dictionary can be considered a book dictionary design that supports the understanding of a book through the presentation of the meaning of the vocabulary, syntax, and morphology recorded in the book. The design of the semantic dictionary of sentence patterns in the Qur'an that the researcher offers is by writing the column rows, numbers, names of surahs and verses, the sentence patterns that appear, and the text of the verses.

An extension of the ADVISE Meta modeling framework and its application for an early-stage security analysis of a public transport supervision system

Early-stage security analysis can be used for a preliminary assessment of the security level of a system, thus providing useful insights to guide the whole system’s development. In this paper, we focus on a specific meta-level modeling framework for security analysis, ADVISE Meta, which allows representing a system using generic built-in blocks and relationships constituting the ontology of the framework, and to automatically derive complex low-level stochastic models representing attack steps and adversaries. In this paper, we extend the ADVISE Meta ontology to enlarge the variety of the possible attack paths and adversaries that can be represented in the framework, by modeling (i) attack patterns available in the CAPEC database, a comprehensive dictionary of known patterns of attack, and (ii) the adversaries’ profiles defined in the Threat Agent Library (TAL), a reference library which describes the characteristics of threat agents. The paper provides a detailed description of the whole process for extending the ADVISE Meta ontology, and the application of the extended modeling framework for an early-stage security analysis of a public transport supervision system. The framework enables a variety of security-oriented analyses, in particular to assess the probability that a given adversary can successfully reach a specific goal, to analyze the most probable attack path that adversaries can follow to reach a goal, to perform sensitivity analysis at varying of attack patterns and adversaries’ profiles, to compare different architectural solutions, and to identify the system’s components that can be more probably attacked by adversaries.

Abstract LB151: Exploring the tumor microenvironment in a spatial context with SpaCET

Abstract Uncovering the composition and structure of the tumor microenvironment is critical to a mechanistic understanding of tumorigenesis and therapeutic resistance. Spatial transcriptomics (ST) technology has enabled profiling the molecular features of tumor tissue with position information. However, the spatial probe of various ST strategies with a 10~100 μm diameter might capture a mixture of transcripts from multiple cells or cell lineages. Cell type deconvolution in ST data of tumor tissues remains challenging for existing methods, which are designed to decompose general ST or bulk data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. Without the need of inputting cell references, SpaCET estimates malignant and non-malignant cell abundance by using a gene pattern dictionary of copy number alterations in common malignancies and a hierarchical atlas of immune/stromal cells, respectively. SpaCET provides higher accuracy than existing methods based on eight ST datasets on seven cancer types with matched double-blind histopathology annotations as ground truth. Furthermore, SpaCET can reveal the potential intercellular interactions at the tumor-immune interface by integrating inferred cell fractions with the ligand-receptor interaction network. We expect that SpaCET will be a valuable tool for spatial cancer biology. Citation Format: Beibei Ru, Jinlin Huang, Yu Zhang, Kenneth Aldape, Peng Jiang. Exploring the tumor microenvironment in a spatial context with SpaCET [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB151.

An evaluation of existing text de-identification tools for use with patient progress notes from Australian general practice

IntroductionDigitized patient progress notes from general practice represent a significant resource for clinical and public health research but cannot feasibly and ethically be used for these purposes without automated de-identification. Internationally, several open-source natural language processing tools have been developed, however, given wide variations in clinical documentation practices, these cannot be utilized without appropriate review. We evaluated the performance of four de-identification tools and assessed their suitability for customization to Australian general practice progress notes. MethodsFour tools were selected: three rule-based (HMS Scrubber, MIT De-id, Philter) and one machine learning (MIST). 300 patient progress notes from three general practice clinics were manually annotated with personally identifying information. We conducted a pairwise comparison between the manual annotations and patient identifiers automatically detected by each tool, measuring recall (sensitivity), precision (positive predictive value), f1-score (harmonic mean of precision and recall), and f2-score (weighs recall 2x higher than precision). Error analysis was also conducted to better understand each tool’s structure and performance. ResultsManual annotation detected 701 identifiers in seven categories. The rule-based tools detected identifiers in six categories and MIST in three. Philter achieved the highest aggregate recall (67%) and the highest recall for NAME (87%). HMS Scrubber achieved the highest recall for DATE (94%) and all tools performed poorly on LOCATION. MIST achieved the highest precision for NAME and DATE while also achieving similar recall to the rule-based tools for DATE and highest recall for LOCATION. Philter had the lowest aggregate precision (37%), however preliminary adjustments of its rules and dictionaries showed a substantial reduction in false positives. ConclusionExisting off-the-shelf solutions for automated de-identification of clinical text are not immediately suitable for our context without modification. Philter is the most promising candidate due to its high recall and flexibility however will require extensive revising of its pattern matching rules and dictionaries.

Estimation of cell lineages in tumors from spatial transcriptomics data

Spatial transcriptomics (ST) technology through in situ capturing has enabled topographical gene expression profiling of tumor tissues. However, each capturing spot may contain diverse immune and malignant cells, with different cell densities across tissue regions. Cell type deconvolution in tumor ST data remains challenging for existing methods designed to decompose general ST or bulk tumor data. We develop the Spatial Cellular Estimator for Tumors (SpaCET) to infer cell identities from tumor ST data. SpaCET first estimates cancer cell abundance by integrating a gene pattern dictionary of copy number alterations and expression changes in common malignancies. A constrained regression model then calibrates local cell densities and determines immune and stromal cell lineage fractions. SpaCET provides higher accuracy than existing methods based on simulation and real ST data with matched double-blind histopathology annotations as ground truth. Further, coupling cell fractions with ligand-receptor coexpression analysis, SpaCET reveals how intercellular interactions at the tumor-immune interface promote cancer progression.

Industry update, December 2022.

Industry update, December 2022.

Reconstruction of Radar Pulse Repetition Pattern via Semantic Coding of Intercepted Pulse Trains

The timing structure of multiple successive radar pulses constructs a high-dimensional pattern for intercepted pulse trains, which is called as pulse repetition interval (PRI) pattern. By treating the radar as a machine that uses differently permuted pulses to communicate with surroundings, this pattern acts as the grammatical structure of its language. Compared with the discrete PRI set that is conventionally used for pulse train description, PRI pattern contains richer and more condensed structural information about radar pulse train, which is helpful for pulse deinterleaving and radar recognition. This article introduces the semantic coding theory to reveal and reconstruct PRI pattern from the intercepted radar pulse train. We first define the coding complexity of a pulse train, which is divided into two parts: 1) the complexity of encoding a PRI pattern dictionary with each element consisting of several successive PRIs; and 2) encoding the intercepted pulse train based on this dictionary. The coding complexity is then minimized by optimizing the components in dictionary, and the PRI timing patterns are finally obtained from the dictionary when the minimization is reached. The effectiveness of semantic coding model and PRI pattern reconstruction method is verified in the simulation part.

A pattern accumulated compression method for trajectories constrained by urban road networks

In an edge computing framework, a moving object in an urban road network can collect both its own and others’ trajectories to answer spatial–temporal queries. How to store the ever increasing amount of trajectories with limited storage space is a great challenge. We observe that the traces of moving objects in urban road networks usually exhibit great repetition in terms of spatial and velocity distributions. So we propose two strategies, Pattern Accumulated Compression based on Average Velocity (PAC-AV) and Pattern Accumulated Compression based on Representative Velocity (PAC-RV), to compress trajectories online on the mobile devices. The former mainly exploits the path pattern to compress trajectories while the latter exploits both the path pattern and the velocity pattern to compress trajectories. Both PAC-AV and PAC-RV compress the trajectories by two stages. First, we extract the spatial as well as velocity components according to a velocity deviation threshold. Second, we compress the components into a series of identifiers leveraging a Path Pattern Dictionary (PPD) and a Velocity Pattern Dictionaries (VPD). Our method not only greatly cuts down the storage space but also directly supports the velocity-related queries on the compressed trajectories. Experimental results demonstrate the effectiveness and efficiency of our method.

Memory-augmented appearance-motion network for video anomaly detection

Video anomaly detection is a promising yet challenging task, where only normal events are observed in the training phase. Without any explicit classification boundary between normal and abnormal events, anomaly detection can be turned into an outlier detection problem by regarding any event that does not conform to the normal patterns as an anomaly. Most of the existing works mainly focus on improving the representation of normal events, while ignore the relationship between normal and abnormal events. Besides, the lack of restrictions on classification boundaries also leads to performance degradation. To address the above problems, we design a novel autoencoder-based Memory-Augmented Appearance-Motion Network (MAAM-Net), which consists of a novel end-to-end network to learn appearance and motion feature of a given input frame, a fused memory module to build a bridge for normal and abnormal events, a well-designed margin-based latent loss to relieve the computation costs, and a pointed Patch-based Stride Convolutional Detection (PSCD) algorithm to eliminate the degradation phenomenon. Specifically, the memory module is embedded between the encoder and decoder, which serves as a sparse dictionary of normal patterns, therefore it can be further employed to reintegrate abnormal events during inference. To further distort the reintegration quality of abnormal events, the margin-based latent loss is leveraged to enforce the memory module to select a sparse set of critical memory items. Last but not least, the simple yet effective detection method focuses on patches rather than the overall frame responses, which can benefit from the distortion of abnormal events. Extensive experiments and ablation studies on three anomaly detection benchmarks, i.e., UCSD Ped2, CUHK Avenue, and ShanghaiTech, demonstrate the effectiveness and efficiency of our proposed MAAM-Net. Notably, we achieve superior AUC performances on UCSD Ped2 (0.977), CHUK Avenue (0.909), and ShanghaiTech (0.713). The code is publicly available at https://github.com/Owen-Tian/MAAM-Net.

Video anomaly detection by fusing self-attention and autoencoder

Video anomaly detection by fusing self-attention and autoencoder

A Digital Processing in Memory Architecture Using TCAM for Rapid Learning and Inference Based on a Spike Location Dependent Plasticity

In this paper, we present a digital processing in memory (DPIM) configured as a stride edge-detection search frequency neural network (SE-SFNN) which is trained through spike location dependent plasticity (SLDP), a learning mechanism reminiscent of spike timing dependent plasticity (STDP). This mechanism allows for rapid online learning as well as a simple memory-based implementation. In particular, we employ a ternary data scheme to take advantage of ternary content addressable memory (TCAM). The scheme utilizes a ternary representation of the image pixels, and the TCAMs are used in a two-layer format to significantly reduce the computation time. The first layer applies several filtering kernels, followed by the second layer, which reorders the pattern dictionaries of TCAMs to place the most frequent patterns at the top of each supervised TCAM dictionary. Numerous TCAM blocks in both layers operate in a massively parallel fashion using digital ternary values. There are no complicated multiply operations performed, and learning is performed in a feedforward scheme. This allows rapid and robust learning as a trade-off with the parallel memory block size. Furthermore, we propose a method to reduce the TCAM memory size using a two-tiered minor to major promotion (M2MP) of frequently occurring patterns. This reduction scheme is performed concurrently during the learning operation without incurring a preconditioning overhead. We show that with minimal circuit overhead, the required memory size is reduced by 84.4%, and the total clock cycles required for learning also decrease by 97.31 % while the accuracy decreases only by 1.12%. We classified images with 94.58% accuracy on the MNIST dataset. Using a 100 MHz clock, our simulation results show that the MNIST training takes about 6.3 ms dissipating less than 4 mW of average power. In terms of inference speed, the trained hardware is capable of processing 5,882,352 images per second.

A Pattern Dictionary Method for Anomaly Detection.

In this paper, we propose a compression-based anomaly detection method for time series and sequence data using a pattern dictionary. The proposed method is capable of learning complex patterns in a training data sequence, using these learned patterns to detect potentially anomalous patterns in a test data sequence. The proposed pattern dictionary method uses a measure of complexity of the test sequence as an anomaly score that can be used to perform stand-alone anomaly detection. We also show that when combined with a universal source coder, the proposed pattern dictionary yields a powerful atypicality detector that is equally applicable to anomaly detection. The pattern dictionary-based atypicality detector uses an anomaly score defined as the difference between the complexity of the test sequence data encoded by the trained pattern dictionary (typical) encoder and the universal (atypical) encoder, respectively. We consider two complexity measures: the number of parsed phrases in the sequence, and the length of the encoded sequence (codelength). Specializing to a particular type of universal encoder, the Tree-Structured Lempel–Ziv (LZ78), we obtain a novel non-asymptotic upper bound, in terms of the Lambert W function, on the number of distinct phrases resulting from the LZ78 parser. This non-asymptotic bound determines the range of anomaly score. As a concrete application, we illustrate the pattern dictionary framework for constructing a baseline of health against which anomalous deviations can be detected.

Comparative Evaluation of NLP-Based Approaches for Linking CAPEC Attack Patterns from CVE Vulnerability Information

Vulnerability and attack information must be collected to assess the severity of vulnerabilities and prioritize countermeasures against cyberattacks quickly and accurately. Common Vulnerabilities and Exposures is a dictionary that lists vulnerabilities and incidents, while Common Attack Pattern Enumeration and Classification is a dictionary of attack patterns. Direct identification of common attack pattern enumeration and classification from common vulnerabilities and exposures is difficult, as they are not always directly linked. Here, an approach to directly find common links between these dictionaries is proposed. Then, several patterns, which are combinations of similarity measures and popular algorithms such as term frequency–inverse document frequency, universal sentence encoder, and sentence BERT, are evaluated experimentally using the proposed approach. Specifically, two metrics, recall and mean reciprocal rank, are used to assess the traceability of the common attack pattern enumeration and classification identifiers associated with 61 identifiers for common vulnerabilities and exposures. The experiment confirms that the term frequency–inverse document frequency algorithm provides the best overall performance.

Likelihood-constrained coupled space learning for motion synthesis

When considering the human skeleton as a hierarchical structure (i.e. an upside-down tree structure), human motion can be treated as the rotation movements of joints around their parent joints, such that a motion sequence can be represented by the joint angle changing processes (referred to as motion patterns) of the joints between its start and end frames. With this motion representation mode, we convert motion synthesis to a motion pattern generation task, given a start-end frame pair. Conforming to such a motion synthesis scheme, we propose a coupled space learning model within which we enforce the sparse representation of the start-end frame pair on the motion frame dictionary to reconstruct its corresponding motion patterns well with a motion pattern dictionary. In addition, as motion naturalness is an important evaluation metric of motions, we propose to use the likelihood of a motion with a pre-estimated motion probability distribution to measure its naturalness and add the motion naturalness measurement into the coupled space learning model to constrain the generated motion patterns to reconstruct highly natural motions. The superior performances of both accuracy and naturalness of the synthesized motions demonstrate the effectiveness of the coupled space learning with the motion likelihood constraint.

Reconstruction of 3D greyscale image for reservoir rock from a single image based on pattern dictionary

Most methods that model 3D porous media from 2D images are based on binary images. In this paper, we propose a method for reconstructing 3D greyscale isotropic porous media images from a single image. Our proposed method incorporates a fast-sampling procedure to control the continuity and variability between adjoining reconstruction layers, a new similarity calculation method to obtain the most similar patterns from a pattern dictionary, and a central area simulation procedure to solve the block effect problem. The reconstruction results from application of our proposed method to a real reservoir 3D model obtained via computed tomography (CT) and a comparison with the original CT structure demonstrate that our proposed method can reproduce properties such as autocorrelation function, linear function, shape distribution, average shape factor, average pore radius size, average throat radius size, average pore volume, permeability and grey histogram. Further, the comparison results indicate that the statistical characteristics of the reconstructions match the training image and the CT model perfectly.

Meaning and Grammar in the light of Corpus Pattern Analysis

Abstract This article presents an interview with Professor Patrick Hanks, a British lexicographer and corpus linguist, who proposes a new approach – a corpus-driven, phraseological approach – to lexicography. Hanks has developed a procedure called Corpus Pattern Analysis (CPA), which is the foundation of The Pattern Dictionary of English Verbs (in progress). The basic aim of CPA is to explore the relationship between word meaning and patterns of word use. Hanks maintains that the verb is ‘the pivot of the clause’. Verb meanings – or, rather, the meanings of clauses – are mapped onto phraseological patterns, rather than just being listed for words in isolation. Associated with this is the Theory of Norms and Exploitations (TNE), which was discussed in his monograph published by MIT Press in 2013. In this interview, Professor Hanks outlines his vision of a phraseological dictionary of the future (an alternative to WordNet). He discusses meaning and grammar in the light of Corpus Pattern Analysis. Specifically, he explains the procedure of Corpus Pattern Analysis, and the objectives of the Pattern Dictionary of English Verbs (PDEV), including the light that such work can shed on the relationship between language and logic. He discusses the relationship between PDEV and English grammar, his evaluation of generative linguistics, and his views on Noam Chomsky. He also highlights the contributions made by linguists of the past to the understanding of the nature of meaning in language. He goes on to suggest that any new bilingual dictionary with Chinese as a source language should be based on careful analyses of actual language use, both in Chinese and in the various target languages.

Adaptive multiple subtraction using a hybrid ℓ2-ℓ1 pattern-learning approach

Adaptive multiple subtraction (AMS) is a critical and challenging task for multiple attenuation. Recently, pattern-learning-based AMS approaches, including pattern coding and decoding steps based on the [Formula: see text] norm, have achieved impressive results. However, the traditional pattern-learning approach might hurt the primaries under the minimum energy condition of the [Formula: see text]-norm-based decoding. Thus, we adopt a hybrid [Formula: see text]-[Formula: see text] pattern-learning-based AMS approach, which includes the [Formula: see text]-norm-based multiband coding and the [Formula: see text]-norm-based decoding steps. In the coding stage, the pattern dictionary is obtained by using the [Formula: see text]-norm-based principal component analysis, which has been proven to be an effective feature extraction method. Subsequently, in the decoding stage, the learned patterns from the predicted multiple are used to estimate the multiples in the recorded data, whereas the primaries are treated as additive noise. In general, the primaries are better represented by a Laplacian than by a Gaussian distribution. Consequently, the proposed method uses the [Formula: see text] norm to decode the multiples contained in the recorded data and then uses an alternating split Bregman algorithm to solve the decoding problem. We validate the approach on synthetic and field data sets, and our method yields better results compared with the [Formula: see text]-norm-based matching filter and the traditional pattern-learning approach.

Blind Separation of Ground-Roll Using Interband Morphological Similarity and Pattern Coding

Ground-roll is a common coherent noise in land seismic records. It has a low frequency, low velocity, and, yet, strong energy, which often conceals important information about reflections. Various approaches were proposed to suppress or remove ground-roll from reflections. The main difficulty of this task is the accurate separation of the ground-roll and reflections without damaging the morphological structure and frequency characteristics of both waves. In this article, we directly aim at the separation in the low-frequency band, where ground-roll and reflections are overlapped, and the recovery of low-frequency reflections deteriorated by ground-roll noise. First, we explore the morphological self-similarity of different frequency bands in seismic data and then utilize this similarity to synthesize pseudolow-band reflections from higher frequency band reflections that contain no ground-roll contamination. After the pseudolow-band reflections are generated, the pattern coding method is then applied to learn patterns from pseudolow-band reflections. The learned pattern dictionary is used to reconstruct the low-band reflections from the mixture of ground-roll and low-band reflections. After the whole scheme, recovered low-band reflections and clean reflections in other frequency bands are combined to form the recovered reflections. Detailed analysis is conducted to illustrate the validity of the scheme, and experiments on both synthetic data and real-land records show promising results.