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In this paper, we develop a new algorithm for pattern synthesis of large linear arrays. In our algorithm, the large linear array is divided into multiple disjoint nonuniformly-spaced subarrays and is only weighted at subarray level. Unlike the existing methods, the subarray synthesis is implemented in two stages. Firstly, the optimization of subarray partition and subarray weights are performed by using the K-means approach. Based on the determined subarray partition and subarray weights, the subarray element positions are further optimized by the iterative convex programming method. Compared with the state-of-the-art methods, the proposed algorithm can synthesize lower peak sidelobe level and yield smaller pattern matching error. Numerical results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The field of chatbots has witnessed a remarkable evolution in recent years, marked by a transition from simplistic rule-based structures to sophisticated systems employing advanced natural language processing (NLP) techniques. While most languages benefit from NLP support, the majority of chatbot research and development has been conducted in English, leaving a notable scarcity of comparable works in Arabic. This scarcity is attributed to the myriad challenges posed by the linguistically intricate nature of Arabic, encompassing orthographic variations and diverse dialects. This study systematically reviews articles that represent implementations of Arabic chatbots, revealing a discernible shift from rule-based frameworks to the predominant adoption of machine learning (ML) and deep learning (DL) methods. The results highlight the dynamic trajectory of chatbot technology, with a notable emphasis on the pivotal role of DL, as evidenced by a significant peak in 2023. Looking forward, the study anticipates a more sophisticated future for chatbot development, driven by ongoing advancements in artificial intelligence (AI) and NLP, offering valuable insights into the current state of Arabic chatbot research and laying the foundation for continued exploration in this evolving and dynamic field. [ABSTRACT FROM AUTHOR]

This paper proposes an all-sky star map identification algorithm that can simultaneously achieve high identification probability, low algorithm complexity, and small databases for well photometric and intrinsic parameters-calibrated star sensors. The proposed algorithm includes three main steps. First, a binary radial pattern table is constructed offline. Then, the maximum value matching of the radial pattern is performed between the star spots and the guide stars, and the star pairs (i.e., the minimum star map) after radial pattern matching undergo a coarse matching through angular distance cross-validation. Finally, a reference star map is designed based on the identified star pairs, and the matching of all the star spots in the field of view is realized. Simulation and analysis results show that the database required by the proposed algorithm for 5,000 guide stars is not larger than 200 KB. Also, when false and missing star spots account for 50% of all guide stars and the star spot extraction error is 0.5 pixel (the corresponding pointing error is 26″), the average star map identification time of the proposed algorithm is less than 2 ms, and its identification probability is higher than 98%. The results demonstrate that the proposed algorithm performs better than similar algorithms. [ABSTRACT FROM AUTHOR]

Blockchain received a vast amount of attention in recent years and is still growing. The second generation of blockchain, such as Ethereum, allows execution of almost any program in EVM, making it a global protocol for distributed applications. The code deployment and each operation performed in EVM cost the network fee called gas, whose price varies and can be significant. That is why code optimization and well-chosen algorithms are crucial in programming on the blockchain. This paper evaluates the gas usage of several exact pattern matching algorithms on the EVM. We also propose an efficient implementation of the algorithms in the Solidity/YUL language. We evaluate the gas fees of all the algorithms for different parameters (such as pattern length, alphabet size, and text size). We show a significant gas fee and execution time reduction with up to 22-fold lower gas usage and 55-fold speed-up compared to StringUtils (a popular Solidity string library). [ABSTRACT FROM AUTHOR]

The primary objective of a depalletizing system is to automate the process of detecting and locating specific variable-shaped objects on a pallet, allowing a robotic system to accurately unstack them. Although many solutions exist for the problem in industrial and manufacturing settings, the application to small-scale scenarios such as retail vending machines and small warehouses has not received much attention so far. This paper presents a comparative analysis of four different computer vision algorithms for the depalletizing task, implemented on a Raspberry Pi 4, a very popular single-board computer with low computer power suitable for the IoT and edge computing. The algorithms evaluated include the following: pattern matching, scale-invariant feature transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifier. Each technique is described and their implementations are outlined. Their evaluation is performed on the task of box detection and localization in the test images to assess their suitability in a depalletizing system. The performance of the algorithms is given in terms of accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The results reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of a depalletizing system. [ABSTRACT FROM AUTHOR]

The recently proposed DEtection TRansformer (DETR) has established a fully end-to-end paradigm for object detection. However, DETR suffers from slow training convergence, which hinders its applicability to various detection tasks. We observe that DETR's slow convergence is largely attributed to the difficulty in matching object queries to relevant regions due to the unaligned semantics between object queries and encoded image features. With this observation, we design Semantic-Aligned-Matching DETR++ (SAM-DETR++) to accelerate DETR's convergence and improve detection performance. The core of SAM-DETR++ is a plug-and-play module that projects object queries and encoded image features into the same feature embedding space, where each object query can be easily matched to relevant regions with similar semantics. Besides, SAM-DETR++ searches for multiple representative keypoints and exploits their features for semantic-aligned matching with enhanced representation capacity. Furthermore, SAM-DETR++ can effectively fuse multi-scale features in a coarse-to-fine manner on the basis of the designed semantic-aligned matching. Extensive experiments show that the proposed SAM-DETR++ achieves superior convergence speed and competitive detection accuracy. Additionally, as a plug-and-play method, SAM-DETR++ can complement existing DETR convergence solutions with even better performance, achieving 44.8% AP with merely 12 training epochs and 49.1% AP with 50 training epochs on COCO val 2017 with ResNet-50. Codes are available at https://github.com/ZhangGongjie/SAM-DETR. [ABSTRACT FROM AUTHOR]

The instanceof pattern matching can improve the code quality and readability by removing the redundant typecasting and simplifying the design in different scenarios. However, existing works do not provide sufficient support for refactoring instanceof pattern matching. This paper first identifies several cases that cannot be well handled by existing IDEs. Based on these observations, we propose a novel approach called ReInstancer to refactor instanceof pattern matching automatically. ReInstancer conducts program analysis for multi‐branch statements to obtain pattern variables. After analyzing these patterns, the multi‐branch statements are optimized and finally refactored into switch statements or expressions. ReInstancer is evaluated by 20 real‐world projects with more than 7,700 instanceof pattern matching. The experimental results demonstrate that a total of 3,558 instanceof expressions and 228 multi‐branch statements are refactored within 10.8 s on average for each project. ReInstancer improves the code quality by reducing redundant typecasting, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

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Sentiment analysis is one of the core tasks in natural language processing, and its main goal is to identify and classify the sentiment tendencies contained in texts. Traditional sentiment analysis methods and shallow models often fail to capture the rich semantic information and contextual relationships in text, while increasing the network depth is prone to problems such as network degradation, which has some limitations in terms of accuracy and performance. Based on this foundation, a sentiment analysis model called BERT-Invos (Bidirectional Encoder Representations from Transformers (BERT)-based stacked involutions) is introduced. The model utilizes the dynamic pre-training language model BERT to encode text, enabling rich semantic features and contextual understanding. In addition, the model employs stacked involutions with varying dimensions to extract features and perceive local information, gradually learning different scales and hierarchical representations of the input text. Furthermore, the proposed method incorporates nonlinear activation functions such as ReLU6 and H-Swish to enhance the model's expression capability and performance, ultimately delivering classification results. In the experiment, a financial news sentiment dataset was utilized for model validation and comparison against other models. The results revealed that the model achieved an accuracy of 96.34% in sentiment analysis tasks, with precision, recall, and F1 score reaching 96.37%, 96.34%, and 96.34%, respectively. Additionally, the loss value could be minimized to 0.07 with stable convergence, thereby enhancing the accuracy of sentiment classification and reducing loss rates. This improvement facilitates better capturing of local patterns in text and addresses the issue of degradation in deep neural networks. Regarding the deep architecture of the proposed model, future work will focus on further exploring optimization techniques for model compression and deployment. [ABSTRACT FROM AUTHOR]

• An innovative star identification algorithm is proposed for lost-in-space scenarios. • A unique approach is utilized, which involves singular values in the form of Gaussian mixture models to generate distinctive features. • The matching process involving candidate filtering, similarity comparison, and validation is proposed to enhance identification capabilities. This paper presents an innovative star identification algorithm specifically designed for lost-in-space scenarios in satellite missions. The algorithm introduces a unique approach by utilizing singular values to generate Gaussian mixture models as distinctive features for star identification. By employing a matching process involving candidate filtering, similarity comparison, and validation, the algorithm demonstrates remarkable performance in precisely identifying stars while minimizing false identifications even in demanding environmental conditions. Extensive simulations are conducted to evaluate the algorithm's effectiveness under various scenarios with positional error and the presence of false stars. The algorithm exhibits reliable and consistent performance, making it highly suitable for dynamic scenarios and contributing to the reduction of hardware burden in star sensor systems. Furthermore, the proposed algorithm offers prominent performance across varying field of view sizes, thereby enhancing its practicality and usability in a wide range of missions. [ABSTRACT FROM AUTHOR]

The rise of machine learning and cloud technologies has led to a remarkable influx of data within modern cyber-physical systems. However, extracting meaningful information from this data has become a significant challenge due to its volume and complexity. Timed pattern matching has emerged as a powerful specification-based runtime verification and temporal data analysis technique to address this challenge. In this paper, we provide a comprehensive tutorial on timed pattern matching that ranges from the underlying algebra and pattern specification languages to performance analyses and practical case studies. Analogous to textual pattern matching, timed pattern matching is the task of finding all time periods within temporal behaviors of cyber-physical systems that match a predefined pattern. Originally we introduced and solved several variants of the problem using the name of match sets, which has evolved into the concept of timed relations over the past decade. Here we first formalize and present the algebra of timed relations as a standalone mathematical tool to solve the pattern matching problem of timed pattern specifications. In particular, we show how to use the algebra of timed relations to solve the pattern matching problem for timed regular expressions and metric compass logic in a unified manner. We experimentally demonstrate that our timed pattern matching approach performs and scales well in practice. We further provide in-depth insights into the similarities and fundamental differences between monitoring and matching problems as well as regular expressions and temporal logic formulas. Finally, we illustrate the practical application of timed pattern matching through two case studies, which show how to extract structured information from temporal datasets obtained via simulations or real-world observations. These results and examples show that timed pattern matching is a rigorous and efficient technique in developing and analyzing cyber-physical systems. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) perform well in text analysis tasks. Their unique structure allows them to capture complex patterns and dependencies in text, making them ideal for processing natural language tasks. At the same time, XGBoost (version 1.6.2.) outperforms other machine learning methods on heterogeneous tabular data. However, traditional graph neural networks mainly study isomorphic and sparse data features. Therefore, when dealing with tabular data, traditional graph neural networks encounter challenges such as data structure mismatch, feature selection, and processing difficulties. To solve these problems, we propose a novel architecture, XGNN, which combines the advantages of XGBoost and GNNs to deal with heterogeneous features and graph structures. In this paper, we use GAT for our graph neural network model. We can train XGBoost and GNN end-to-end to fit and adjust the new tree in XGBoost based on the gradient information from the GNN. Extensive experiments on node prediction and node classification tasks demonstrate that the performance of our proposed new model is significantly improved for both prediction and classification tasks and performs particularly well on heterogeneous tabular data. [ABSTRACT FROM AUTHOR]

Efficient algorithms for online string pattern matching have been deemed crucial for cloud database retrieval. However, the potential leaking of search content has been recognized as a threat to users' privacy. Existing string pattern matching algorithms have not been designed to consider the protection of users' search content. Although searchable encryption schemes can protect users' search content, they have been associated with high index construction costs and inefficient retrieval. Two pattern matching algorithms were proposed to protect users' search content: PMDPF (pattern matching based on distributed point function) and JPMDPF (jumping pattern matching based on distributed point function). In the PMDPF algorithm, a pattern string was provided to protect the search content, and a distributed point function combined with a fingerprint function was utilized to construct true value tables, which were then sent to two independent servers. Character comparison operations were transformed into table lookup operations to conceal the characters that needed to be checked. To enhance search efficiency, a jump pattern matching algorithm (JPMDPF) was proposed, based on the distributed point function. In this approach, character skipping was employed to induce the JPMDPF algorithm, which achieved a significant improvement in search efficiency over the PMDPF algorithm, albeit with the trade-off of revealing more information. On average, the search efficiency of JPMDPF was increased by approximately m-fold, where m represented the length of the pattern string. Concurrently, there was a significant reduction in the probability of misjudgments due to collisions within the fingerprinting function. The experimental results demonstrate that the search efficiency of the PMDPF algorithm is approximately 5% higher than that of the classical algorithm based on the fingerprint function. It outperforms existing searchable encryption schemes. The search time of the PMDPF algorithm is found to be 4.2 times that of JPMDPF when the length of the search content is 4.

Abstract The technique known as 4D-STEM has recently emerged as a powerful tool for the local characterization of crystalline structures in materials, such as cathode materials for Li-ion batteries or perovskite materials for photovoltaics. However, the use of new detectors optimized for electron diffraction patterns and other advanced techniques requires constant adaptation of methodologies to address the challenges associated with crystalline materials. In this study, we present a novel image-processing method to improve pattern matching in the determination of crystalline orientations and phases. Our approach uses sub-pixel adaptive image processing to register and reconstruct electron diffraction signals in large 4D-STEM datasets. By using adaptive prominence and linear filters, we can improve the quality of the diffraction pattern registration. The resulting data compression rate of 103 is well-suited for the era of big data and provides a significant enhancement in the performance of the entire ACOM data processing method. Our approach is evaluated using dedicated metrics, which demonstrate a high improvement in phase recognition. Several features are extracted from the registered data to map properties such as the spot count, and various virtual dark fields, which are used to enhance the handling of the results maps. Our results demonstrate that this data preparation method not only enhances the quality of the resulting image but also boosts the confidence level in the analysis of the outcomes related to determining crystal orientation and phase. Additionally, it mitigates the impact of user bias that may occur during the application of the method through the manipulation of parameters.

Abstract Side‐scan sonar is a lightweight acoustic sensor that is frequently deployed on autonomous underwater vehicles (AUVs) to provide high‐resolution seafloor images. However, using side‐scan images to perform simultaneous localization and mapping (SLAM) remains a challenge when there is a lack of 3D bathymetric information and discriminant features in the side‐scan images. To tackle this, the authors propose a feature‐based SLAM framework using side‐scan sonar, which is able to automatically detect and robustly match keypoints between paired side‐scan images. The authors then use the detected correspondences as constraints to optimise the AUV pose trajectory. The proposed method is evaluated on real data collected by a Hugin AUV, using as a ground truth reference both manually‐annotated keypoints and a 3D bathymetry mesh from multibeam echosounder (MBES). Experimental results demonstrate that this approach is able to reduce drifts from the dead‐reckoning system. The framework is made publicly available for the benefit of the community.

Abstract In natural environments, head movements are required to search for objects outside the field of view (FoV). Here we investigate the power of a salient target in an extended visual search array to facilitate faster detection once this item comes into the FoV by a head movement. We conducted two virtual reality experiments using spatially clustered sets of stimuli to observe target detection and head and eye movements during visual search. Participants completed search tasks with three conditions: (1) target in the initial FoV, (2) head movement needed to bring the target into the FoV, (3) same as condition 2 but the periphery was initially hidden and appeared after the head movement had brought the location of the target set into the FoV. We measured search time until participants found a more salient (O) or less salient (T) target among distractors (L). On average O’s were found faster than T’s. Gaze analysis showed that saliency facilitation occurred due to the target guiding the search only if it was within the initial FoV. When targets required a head movement to enter the FoV, participants followed the same search strategy as in trials without a visible target in the periphery. Moreover, faster search times for salient targets were only caused by the time required to find the target once the target set was reached. This suggests that the effect of stimulus saliency differs between visual search on fixed displays and when we are actively searching through an extended visual field.

Information-Centric Networking (ICN) is the emerging next-generation internet paradigm. The Low Earth Orbit (LEO) satellite mega-constellation based on ICN can achieve seamless global coverage and provide excellent support for Internet of Things (IoT) services. Additionally, in-network caching, typically characteristic of ICN, plays a paramount role in network performance. Therefore, the in-network caching policy is one of the hotspot problems. Especially, compared to caching traditional internet content, in-networking caching IoT content is more challenging, since the IoT content lifetime is small and transient. In this paper, firstly, the framework of the LEO satellite mega-constellation Information-Centric Networking for IoT (LEO-SMC-ICN-IoT) is proposed. Then, introducing the concept of "viscosity", the proposed Caching Algorithm based on the Random Forest (CARF) policy of satellite nodes combines both content popularity prediction and satellite nodes location prediction, for achieving good cache matching between the satellite nodes and content. And using the matching rule, the Random Forest (RF) algorithm is adopted to predict the matching relationship among satellite nodes and content for guiding the deployment of caches. Especially, the content is cached in advance at the future satellite to maintain communication with the current ground segment at the time of satellite switchover. Additionally, the policy considers both the IoT content lifetime and the freshness. Finally, a simulation platform with LEO satellite mega-constellation based on ICN is developed in Network Simulator 3 (NS-3). The simulation results show that the proposed caching policy compared with the Leave Copy Everywhere (LCE), the opportunistic (OPP), the Leave Copy down (LCD), and the probabilistic algorithm which caches each content with probability 0.5 (prob 0.5) yield a significant performance improvement, such as the average number of hops, i.e., delay, cache hit rate, and throughput. [ABSTRACT FROM AUTHOR]

Therapy for personality disorder can be understood as a process of enhancing attachment security. This study tests the theory that individuals in a secure forensic mental health setting with a diagnosis of personality disorder who have undertaken psychological therapy will report secure patterns of attachment in their relationship with their therapist. It uses a novel pattern matching approach that assesses the behavioural, cognitive and affective markers of attachment security in a group of patients undergoing therapy. Eight behavioural, cognitive and affective characteristics of secure attachment were identified. Ten patients completed a semi-structured interview about their experiences in therapy and key relationships, and their responses were reviewed for evidence of these eight characteristics. Nine out of ten participants were judged to have demonstrated a pattern of six or more of the eight characteristics of secure attachment. This study supports the theory that treatment for this group of patients works by developing patient-therapist attachments. A focus on attachment issues should, therefore, be a core aspect of therapy. [ABSTRACT FROM AUTHOR]

Object-oriented micro-video background music recommendation is a complicated task where the matching degree between videos and background music is a major issue. However, music selections in user-generated content (UGC) are prone to selection bias caused by historical preferences of uploaders. Since historical preferences are not fully reliable and may reflect obsolete behaviors, over-reliance on them should be avoided as knowledge and interests dynamically evolve. In this article, we propose a Deconfounded Cross-Modal matching model to mitigate such bias. Specifically, uploaders' personal preferences of music genres are identified as confounders that spuriously correlate music embeddings and background music selections, causing the learned system to over-recommend music from majority groups. To resolve such confounders, backdoor adjustment is utilized to deconfound the spurious correlation between music embeddings and prediction scores. We further utilize Monte Carlo estimator with batch-level average as the approximations to avoid integrating the entire confounder space calculated by the adjustment. Furthermore, we design a teacher–student network to utilize the matching of music videos, which is professionally generated content (PGC) with specialized matching, to better recommend content-matching background music. The PGC data are modeled by a teacher network to guide the matching of uploader-selected UGC data of student network by Kullback–Leibler–based knowledge transfer. Extensive experiments on the TT-150k-genre dataset demonstrate the effectiveness of the proposed method. The code is publicly available on https://github.com/jing-1/DecCM [ABSTRACT FROM AUTHOR]

This article demonstrates a method using tools from the field of Natural Language Processing (NLP) to aid in analyzing theatrical texts and similar works. The method deploys pre-trained large language model neural networks to gather metadata for a text that is amenable to downstream statistical analyses surfacing patterns of interest in character dialogue. We specifically focus on Shakespeare's works, collecting metadata in the form of sentiment and emotion scores for each line of his plays. In addition to sentiment and emotion scores produced by NLP models, we also directly gather metadata such as genre, line length, and character gender. We show how these metadata may be used to illuminate a number of interesting patterns in Shakespearean character which may be difficult to detect from a direct reading of the texts. We use these metadata to expose statistically significant relationships in Shakespeare between character gender and the emotional content of that character's dialogue, controlling for genre. We also present here the publicly available dataset that we have compiled to perform these analyses. The data collects text from Shakespeare's plays along with a variety of metadata useful for this and other forms of analysis of Shakespeare's works. The methodology demonstrated here may be extended to other varieties of metadata provided by large NLP models. [ABSTRACT FROM AUTHOR]

Complex frameworks for defining programming languages aim to generate various tools (e.g. interpreters, symbolic execution engines, deductive verifiers, etc.) using only the formal definition of a language. When used at an industrial scale, these tools are constantly updated, and at the same time, it is required to be trustworthy. Ensuring the correctness of such a framework is practically impossible. A solution is to generate proof objects as correctness artefacts that can be checked by an external trusted checker. A logic suitable for developing such frameworks is matching logic. K framework is a canonical example having matching logic-based foundation. Since the (symbolic) configurations of the programs are represented by matching logic patterns, the algorithms computing the dynamics of these configurations can be seen as pattern transformers and a proof object should be generated for the relationship between these patterns. In this paper, we show that conjunctions and disjunctions of patterns, produced by semantics or analysis rules, can be safely normalized using unification and antiunification algorithms. We also provide a prototype implementation of our proof object generation technique and a checker for certifying the generated objects. [ABSTRACT FROM AUTHOR]

Technological advances in signal processing and enormous data storage capabilities have made large speech datasets readily available. These speech data often being unlabelled and unannotated their effective use is a research hot topic. In particular, query by example spoken term detection (QbE-STD) constitutes a potential way to take advantage of these resources. Given a speech stream, QbE-STD techniques aim to detect the presence of a given spoken query, in an unsupervised way. QbE-STD is considered a pattern-matching problem, and the dynamic time-warping (DTW) algorithm is state-of-the-art. This paper proposes an alternative to the DTW and its variants by leveraging deep learning models. The proposed architecture uses convolutions to extract short-term dependencies and an attention mechanism to extract long-term dependencies, from the speech archive. Thus, a Fully Connected Convolutional Neural Network (FC-CNN) is trained to detect the presence of a target spoken query in a speech stream. During the training stage, the model is trained on raw speech, in a self-supervised way, while during the test stage and without prior knowledge on the target query, the system is expected to decide whether the query exists or not within the speech archive. Experiments were carried on a dataset built on the Google speech commands corpus. The obtained results show the potential of our suggestion in indexing speech databases. [ABSTRACT FROM AUTHOR]

Sustained casing pressure (SCP) and/or annular pressure build-up (APB) can pose severe threats to the safe operation of high-pressure, high-production gas wells, leading to premature well abandonment, unplanned workovers, significant production losses, and exorbitant detection costs. The techniques for addressing these issues broadly fall into two categories of rig-based and rig-less workovers. The rig-less workovers are the most cost-effective, outperforming surefooted rig-based workovers because there are many techniques for detecting downhole leaks, especially those utilizing rig-less data. As an established technique used elsewhere in the petroleum industry, downhole chokes have been proposed to mitigate SCP. However, some critical questions remain unanswered. For this reason and to facilitate field applicability, essential parameters, such as choke depth, are optimized in this work under various leaking circumstances in the tubing string. By using commercial software, the evolution of the annulus pressure with one single leak is analysed under the influences of certain parameters, such as leakage depth, casing pressure relief, production shifts, and initial gas column height in the tubing/casing annulus; then, the patterns of fluid exchange between the tubing and casing string and the annular pressure rise are investigated for two successive leaks. Finally, based on the resulting insights, the adaptability levels of downhole throttling technology for mitigating SCP are investigated under various scenarios with either single or double leaks. The results show that the key to success is to land below the leak for a single leak and below the lower leak for double leaks. The conclusions provide a low-cost, rigid measure for abating SCP. [ABSTRACT FROM AUTHOR]

Domain ontologies facilitate the organization, sharing, and reuse of subject areas. Building a software ontology is labor-intensive and time-consuming. In the process of obtaining a software ontology, it is required to analyze a huge number of scientific publications relevant to the software being modeled. The process of ontology replenishing with information from a huge number of scientific publications can be facilitated and accelerated through the use of lexical-syntactic patterns of ontological design. In this paper, we consider the possibility of automated construction of scientific subject area ontologies based on a heterogeneous patterns system of ontological design. This system includes ontological design patterns and is intended for ontology developers. System also includes automatically built lexical and syntactic patterns, which help to automatic replenishment of the ontology with information extracted from natural language texts. [ABSTRACT FROM AUTHOR]

The increasing availability of large geological datasets together with modern methods of data analysis facilitate a data science approach to geology in which inferences are drawn from geological data using automated methods based on statistics and machine learning. Such methods offer the potential for faster and less subjective interpretations of geological data than are possible from a human interpreter, but translating the understanding of a trained geologist to an algorithm is not straightforward. In this paper, we present automated workflows for detecting geological folds from map data using both unsupervised and supervised machine learning. For the unsupervised case, we use regular expression matching to identify map patterns suggestive of folds along lines crossing the map. We then use the hdbscan clustering algorithm to cluster these possible fold identifications into a smaller number of distinct folds, the number of which is not known a priori. For the supervised learning case, we use synthetic models of folds to train a convolutional neural network to identify folds using map and topographic data. We test both methods on synthetic and real datasets. [ABSTRACT FROM AUTHOR]

Side‐scan sonar is a lightweight acoustic sensor that is frequently deployed on autonomous underwater vehicles (AUVs) to provide high‐resolution seafloor images. However, using side‐scan images to perform simultaneous localization and mapping (SLAM) remains a challenge when there is a lack of 3D bathymetric information and discriminant features in the side‐scan images. To tackle this, the authors propose a feature‐based SLAM framework using side‐scan sonar, which is able to automatically detect and robustly match keypoints between paired side‐scan images. The authors then use the detected correspondences as constraints to optimise the AUV pose trajectory. The proposed method is evaluated on real data collected by a Hugin AUV, using as a ground truth reference both manually‐annotated keypoints and a 3D bathymetry mesh from multibeam echosounder (MBES). Experimental results demonstrate that this approach is able to reduce drifts from the dead‐reckoning system. The framework is made publicly available for the benefit of the community. [ABSTRACT FROM AUTHOR]

Blinking contributes to the health and protection of the eye and also holds potential in the context of muscle or nerve disorder diagnosis. Traditional methods of classifying eye blinking as open or closed are insufficient, as they do not capture medical-relevant aspects like closure speed, duration, or percentage. The issue could be solved by reliably detecting blinking intervals in high-temporal recordings. Our research demonstrates the reliable detection of blinking events through data-driven analysis of the eye aspect ratio. In an unsupervised manner, we establish an eye state prototype to identify blink intervals and measure inter-eye synchronicity between moments of peak closure. Additionally, our research shows that manually defined prototypes yield comparable results. Our results demonstrate inter-eye synchronicity up to 4.16 ms. We anticipate that medical professionals could utilize our methods to identify or define disease-specific prototypes as potential diagnostic tools. [ABSTRACT FROM AUTHOR]

It is widely recognized that we need to prepare students to think with data. This study investigates student interactions with digital data graphs and seeks to identify what might prompt them to shift toward using their graphs as thinking tools in the authentic activity of doing science. Drawing from video screencast data of three small groups engaged in sensor-based and computer simulation-based experiments in high school physics classes, exploratory qualitative methods are used to identify the student interactions with their graphs and what appeared to prompt shifts in those interactions. Analysis of the groups, one from a 9th grade class and two from 11th/12th grade combined classes, revealed that unexpected data patterns and graphical anomalies sometimes, but not always, preceded deeper engagement with the graphs. When shifts toward deeper engagement did occur, transcripts revealed that the students perceived the graphical patterns to be misaligned with the actions they had taken to produce those data. Misalignments between the physical, digital, and conceptual worlds of the investigations played an important role in these episodes, appearing to motivate students to revise either their experimental procedures or their conceptions of the phenomena being explored. If real-time graphs can help foster a sense in students that there should be alignments between their data production and data representations, it is suggested that pedagogy leverage this as a way to support deeper student engagement with graphs. [ABSTRACT FROM AUTHOR]

As a knowledge representation method, knowledge graph is widely used in intelligent question answering systems and recommendation systems. At present, the research on knowledge graph mainly focuses on information query and retrieval based on knowledge graph. In some domain knowledge graphs, specific subgraph structures (patterns) have specific physical meanings. Aiming at this problem, this paper proposes a method and framework of knowledge graph pattern mining based on gat. Firstly, the patterns with specific physical meaning were transformed into subgraph structures containing topological structures and entity attributes. Secondly, the subgraph structure of the pattern is regarded as the query graph, and the knowledge graph is regarded as the data graph, so that the problem is transformed into an approximate subgraph matching problem. Then, the improved relational graph attention network is used to fuse the adaptive edge deletion mechanism to realize the approximate subgraph matching of subgraph structure and attribute, so as to obtain the best matching subgraph. The proposed method is trained in an end-to-end manner. The approximate subgraph matching is realized on the existing data set, and the research work of key pattern mining of complex geological structure knowledge graph is carried out. [ABSTRACT FROM AUTHOR]

In natural environments, head movements are required to search for objects outside the field of view (FoV). Here we investigate the power of a salient target in an extended visual search array to facilitate faster detection once this item comes into the FoV by a head movement. We conducted two virtual reality experiments using spatially clustered sets of stimuli to observe target detection and head and eye movements during visual search. Participants completed search tasks with three conditions: (1) target in the initial FoV, (2) head movement needed to bring the target into the FoV, (3) same as condition 2 but the periphery was initially hidden and appeared after the head movement had brought the location of the target set into the FoV. We measured search time until participants found a more salient (O) or less salient (T) target among distractors (L). On average O's were found faster than T's. Gaze analysis showed that saliency facilitation occurred due to the target guiding the search only if it was within the initial FoV. When targets required a head movement to enter the FoV, participants followed the same search strategy as in trials without a visible target in the periphery. Moreover, faster search times for salient targets were only caused by the time required to find the target once the target set was reached. This suggests that the effect of stimulus saliency differs between visual search on fixed displays and when we are actively searching through an extended visual field. [ABSTRACT FROM AUTHOR]

Purpose: Wiswesser Line Notation (WLN) is a old line notation for encoding chemical compounds for storage and processing by computers. Whilst the notation itself has long since been surpassed by SMILES and InChI, distribution of WLN during its active years was extensive. In the context of modernising chemical data, we present a comprehensive WLN parser developed using the OpenBabel toolkit, capable of translating WLN strings into various formats supported by the library. Furthermore, we have devised a specialised Finite State Machine l, constructed from the rules of WLN, enabling the recognition and extraction of chemical strings out of large bodies of text. Available open-access WLN data with corresponding SMILES or InChI notation is rare, however ChEMBL, ChemSpider and PubChem all contain WLN records which were used for conversion scoring. Our investigation revealed a notable proportion of inaccuracies within the database entries, and we have taken steps to rectify these errors whenever feasible. Scientific contribution: Tools for both the extraction and conversion of WLN from chemical documents have been successfully developed. Both the Deterministic Finite Automaton (DFA) and parser handle the majority of WLN rules officially endorsed in the three major WLN manuals, with the parser showing a clear jump in accuracy and chemical coverage over previous submissions. The GitHub repository can be found here: https://github.com/Mblakey/wiswesser. [ABSTRACT FROM AUTHOR]

Simple Summary: Long Interspersed Element-1 (LINE-1 or L1) is an autonomous transposable element, meaning that its DNA sequences are able to replicate themselves throughout the human genome. This activity may lead to genomic instability and is associated with several different diseases. Additionally, L1s are also capable of replicating other non-autonomous sequences, thereby increasing their disruptive impact. Although there are different tools available that may be used for L1 detection, the heuristics involved affect their accuracy. L1PD (LINE-1 Pattern Detection) uses a novel pattern-matching approach to detect L1s in human genomes, using a fixed set of k-mer probes of length 50 that were generated using the human reference genome GRCh38. This research aims to improve L1PD by using longer probes and testing whether this leads to better results. Additionally, experiments were performed to test the effectiveness of L1PD in detecting L1s in other species, such as dogs, horses, and cows. The results showed that longer probes did improve precision and recall of L1s, not only in humans but in the other species as well. Long Interspersed Element-1 (LINE-1 or L1) is an autonomous transposable element that accounts for 17% of the human genome. Strong correlations between abnormal L1 expression and diseases, particularly cancer, have been documented by numerous studies. L1PD (LINE-1 Pattern Detection) had been previously created to detect L1s by using a fixed pre-determined set of 50-mer probes and a pattern-matching algorithm. L1PD uses a novel seed-and-pattern-match strategy as opposed to the well-known seed-and-extend strategy employed by other tools. This study discusses an improved version of L1PD that shows how increasing the size of the k-mer probes from 50 to 75 or to 100 yields better results, as evidenced by experiments showing higher precision and recall when compared to the 50-mers. The probe-generation process was updated and the corresponding software is now shared so that users may generate probes for other reference genomes (with certain limitations). Additionally, L1PD was applied to other non-human genomes, such as dogs, horses, and cows, to further validate the pattern-matching strategy. The improved version of L1PD proves to be an efficient and promising approach for L1 detection. [ABSTRACT FROM AUTHOR]

To reduce harmonic components, balance system impedance, and stabilize driving voltage, an additional matching circuit is required for ultrasonic motors (USMs) driver. However, the performance of inductor or capacitor matching can be seriously weakened with changes in driving frequency. Therefore, this paper presents a simple and effective LC matching method against driving frequency adjustment for USMs. First, the driving scheme of the USM is proposed and the electromechanical coupling model is analyzed. Subsequently, the output characteristics of the full-bridge inverter are derived theoretically when the driving frequency deviates from the mechanical resonant frequency. Then, the impedance circular transform method is proposed, which can intuitively analyze the effect of matching parameters on the voltage amplitude. A matching objective function is established that can consider both the voltage stabilization and harmonic suppression. The matching parameters are solved using random weight particle swarm optimization. Simulations and experiments demonstrate that within the operating frequency of the USM, the proposed matching method can effectively prevent overvoltage and suppress harmonic components. Furthermore, compared with the existing resonant matching method, the proposed matching method can realize more stable driving capability at different frequencies. The proposed method could be useful for USMs' variable-frequency driver design. [ABSTRACT FROM AUTHOR]

The geometry of 4-string contact interaction of closed string field theory is characterized using machine learning. We obtain Strebel quadratic differentials on 4-punctured spheres as a neural network by performing unsupervised learning with a custom-built loss function. This allows us to solve for local coordinates and compute their associated mapping radii numerically. We also train a neural network distinguishing vertex from Feynman region. As a check, 4-tachyon contact term in the tachyon potential is computed and a good agreement with the results in the literature is observed. We argue that our algorithm is manifestly independent of number of punctures and scaling it to characterize the geometry of n-string contact interaction is feasible. [ABSTRACT FROM AUTHOR]

Two recent lower bounds on the compressibility of repetitive sequences, δ ≤ γ , have received much attention. It has been shown that a length-n string S over an alphabet of size σ can be represented within the optimal O (δ log n log σ δ log n ) space, and further, that within that space one can find all the occ occurrences in S of any length-m pattern in time O (m log n + o c c log ϵ n) for any constant ϵ > 0 . Instead, the near-optimal search time O (m + (o c c + 1) log ϵ n) has been achieved only within O (γ log n γ) space. Both results are based on considerably different locally consistent parsing techniques. The question of whether the better search time could be supported within the δ -optimal space remained open. In this paper, we prove that both techniques can indeed be combined to obtain the best of both worlds: O (m + (o c c + 1) log ϵ n) search time within O (δ log n log σ δ log n ) space. Moreover, the number of occurrences can be computed in O (m + log 2 + ϵ n) time within O (δ log n log σ δ log n ) space. We also show that an extra sublogarithmic factor on top of this space enables optimal O (m + o c c) search time, whereas an extra logarithmic factor enables optimal O(m) counting time. [ABSTRACT FROM AUTHOR]

The main objective of this article is to highlight the importance of training in digital tools at the university level to foster the development of innovative and efficient data analysis from a scientific perspective. In an increasingly digitised world, the acquisition of digital skills has become a fundamental requirement for success in various disciplines, especially in conducting academic studies and research. The implementation of Computer-Aided Qualitative Data Analysis (CAQDAS) software, such as the ATLAS.ti platform, for text analysis not only enriches the educational experience but also prepares students to excel in an ever-evolving digital environment and raise the quality of their research. We present here a practical example of textual analysis in ATLAS.ti that can serve as a reference guide for similar studies based on content analysis of interviews. Various qualitative and quantitative data analysis options and techniques are explored that allow researchers to identify patterns, trends and relationships in the texts analysed, which contributes to a deeper understanding of the topics under study by transcending traditional methods of text analysis. [ABSTRACT FROM AUTHOR]

We introduce fast sensor diagnosis and focuses on intelligent offloading skills to enhance the sensor data screening efficiency. This study proposed the adaptive offloading model based on statistics-based prediction feedback and sensor candidate filtering. For the statistics-based filtering, sliding sensor grids and compounded sensor context were devised. This study also proposed hybrid prediction model using support vector machine (SVM) and k-nearest neighbors (KNN) machine training for the adaptive offloading. Therefore, the sensor information that is highly likely to be the cause of the actual device faults can be selected and transmitted, resulting in improved offloading performance. The test results through Google Colab show that the fault prediction accuracy of proposed models is 95%. [ABSTRACT FROM AUTHOR]