Representation Of Patterns Research Articles

Evaluating statistical homogeneity of cone penetration test (CPT) data profile using auto-correlation function

Cone penetration test (CPT) data profile is often numerically modelled using random field theory and based implicitly on a fundamental assumption: statistical homogeneity of the preprocessed stationary CPT data profile. In classical random field theory, statistical homogeneity evaluation may only be achieved if the CPT data profile is infinite in length. While in engineering practice, it is often the case that a CPT data profile has limited sounding depth. This leads to a critical question that whether the CPT data profile satisfies statistical homogeneity assumption to enable proper random field modelling. This paper proposes a novel non-parametric method for evaluating statistical homogeneity of a single CPT data profile. A novel unified representation of CPT data profile pattern is developed in this study using discrete cosine transform (DCT)-based auto-correlation function (ACF). Statistical homogeneity evaluation of a CPT data profile is reformulated as statistical analysis of pattern similarity between the original CPT data profile and its partial segment profiles. The proposed method is general since it is distribution-free and does not require a parametric correlation function. The proposed method performs well on both simulated and real examples and can identify the possible locations of heterogeneity in a CPT profile.

Intelligent phenotype-detection and gene expression profile generation with generative adversarial networks

Gene expression analysis is valuable for cancer type classification and identifying diverse cancer phenotypes. The latest high-throughput RNA sequencing devices have enabled access to large volumes of gene expression data. However, we face several challenges, such as data security and privacy, when we develop machine learning-based classifiers for categorizing cancer types with these datasets. To address these issues, we propose IP3G (Intelligent Phenotype-detection and Gene expression profile Generation with Generative adversarial network), a model based on Generative Adversarial Networks. IP3G tackles two major problems: augmenting gene expression data and unsupervised phenotype discovery. By converting gene expression profiles into 2-Dimensional images and leveraging IP3G, we generate new profiles for specific phenotypes. IP3G learns disentangled representations of gene expression patterns and identifies phenotypes without labeled data. We improve the objective function of the GAN used in IP3G by employing the earth mover distance and a novel mutual information function. IP3G outperforms clustering methods like k-Means, DBSCAN, and GMM in unsupervised phenotype discovery, while also surpassing SVM and CNN classification accuracy by up to 6% through gene expression profile augmentation. The source code for the developed IP3G is accessible to the public on GitHub.

Концепция иерархической структуры ментального опыта обучающегося программированию

The article raises the problem of insufficient attention to such a pedagogical category as the student's mental experience and its impact on cognitive processes. The possibility of considering the student's mental experience as an element of the content of education and as a resource for building an effective process of teaching programming at a university is discussed. Due to the fact that modern programming is built within the framework of technological processes, the activities of programmers are regulated. The program code is compiled in a formalized programming language, limited by syntactic rules and semantic constructions. Design patterns are used to facilitate the development of complex information systems. The pattern can be set both for the elementary level of abstraction - using structural operators of a programming language, and for the highest level - building an information system architecture. As part of the development of design patterns, representations of patterns are formed in the mental experience of the student. In the process of solving a programming problem, in the presence of an appropriate mental representation, a detailed analysis of the pattern execution algorithm does not occur in the cognitive processes of the student, the decision is formed directly on the basis of associative linking of information stimuli and elements of mental experience. The article lists the theses of the concept of the hierarchical structure of mental experience, which can be considered as a theoretical basis for the development of pedagogical technologies aimed at improving the efficiency of mastering professional competencies.

Urban shrinkage in the regional multiscale context: Spatial divergence and interaction

The consensus on the requirements of specific policies that address the ongoing population loss of cities in certain regions is growing. To design policies that can cope with this issue, a comprehensive representation of regional shrinkage patterns needs to be obtained that involves spatial heterogeneity and interaction of shrinking cities at various scales. This study explores the contributing factors of urban shrinkage at different scales and their interaction relationships in Northeast China. The results indicate that painful structural reforms, locational marginalization, and shortage of surplus agricultural labor are the key reasons for the shrinkage of prefecture-level cities, county-level cities and towns, respectively. Moreover, factors of a city, traditional industries, surplus agricultural labor, educational resources, and geological conditions, can influence the demographic trajectories of cities at other scales through spatial spillover effects. The findings offer a basis for reassessing the conventional wisdom of a simplistic association between identifying scale and urban shrinkage.

On the fragility of the Italian economic territories under SARS-COV2 lockdown policies

AbstractWe leverage a granular representation of mobility patterns before and during the first wave of SARS-COV2 in Italy to investigate the economic consequences of various forms of lockdown policies when accounting for mobility restrictions between and within local jurisdictions, i.e. municipalities, provinces and regions. We provide an analytical characterization of the rate of economic losses using a network-based spectral method. The latter treats the spread of contagion of economic losses due to commuting restrictions as a dynamical system stability problem. Our results indicate that the interplay between lower level of smartworking and the polarization of commuting flows to fewer local labor hubs in the South of Italy makes Southern territories extremely important in spreading economic losses. We estimate an economic contraction of total income derived from commuting restrictions in the range of 10–30% depending on the economic assumptions. However, alternative policies proposed during the second wave of SARS-COV2 can pose a greater risk to Northern areas due to their higher degree of mobility between jurisdictions than Southern ones. The direction of economic losses tend to propagate from large to medium-small jurisdictions across all alternative lockdown policies we tested. Our study shows how complex mobility patterns can have unequal consequences to economic losses across the country and call for more tailored implementation of restrictions to balance the containment of contagion with the need to sustain economic output.

A fairness assessment of mobility-based COVID-19 case prediction models.

In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models' performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups.

Compensation of accuracy by increased data “thickness” for high timeliness in fault diagnosis of chemical process

BackgroundFault diagnosis rate (FDR) and fault diagnosis timeliness (FDT) are critical to a fault diagnosis model applied to industrial processes. Generally, high FDR requires process data of longer time, which means a low timeliness; while, the time-length reduction of data will result in a decreased FDR as learning features from less data is more difficult. MethodsTo address this problem, an idea is proposed that the time-length reduction of process data could be compensated by enhancing the data “thickness”, i.e., the shortage of feature information of data could be compensated by investigating more patterns from multiple different aspects of the data. Based on this, the multiple pattern representation-convolutional neural network (MPR-CNN) is proposed, in which multiple pattern representation algorithms are used first to pre-extract features from data. The resulting three-dimensional matrix contains richer features, which could be learned more efficiently by the CNN. Significant FindingsThe diagnosis results from a continuous and a batch chemical process show that MPR-CNN significantly improves the fault diagnosis performance, which achieved 96.1% of FDR in the Tennessee Eastman process and 98.1% of FDR in a semi-batch crystallization process, and outperforms other CNN structure-based models.

Categorization of formulas for calculation of crack width and spacing in reinforced concrete elements

AbstractOver the last century, over one hundred crack width formulas have been developed to calculate the width and spacing of cracks in reinforced and prestressed concrete elements. It is unclear which formulas are the most accurate. An extensive comparison study is required to determine which formulas accurately describe the crack patterns, consisting of the crack width and spacing. To make such a study possible, this paper proposes categorizing formulas. The categorization of the formulas is based on their applicability, crack pattern representation, and background. The categorization presents an overview of the different assumptions and application areas for describing crack patterns.

FPRnet: A lightweight multi-domain multi-stream network for complex horizontal oil-water two-phase flow pattern recognition

The complicated spatio-temporal structure of horizontal oil-water flows renders the identification of flow patterns exceedingly challenging, owing to the presence of interfacial waves and droplet entrainment. In this study, a multi-domain fusion feature (MFF) as flow pattern descriptors (FPD) and a lightweight multi-stream networks (LMN) as flow pattern classifier (FPC) are proposed to identify the flow patterns of horizontal oil-water two-phase flow. MFF can map time series into different domains and compactly encode them into higher dimensional expressions, thus displaying implicit flow pattern signals incorporating nonlinear, time-frequency, and probabilistic transition domain features. Finally, the comparison of LMN, variational and other networks is evaluated on the horizontal oil-water flows pattern identification task. Extensive comparison experiments show that the time-frequency feature exhibits optimal characterization for the D O/W flow pattern, the URP feature is most accurately characterized for the D W/O and D O/W flow patterns, and the MTF feature exhibits superior characterization for the remaining three flow patterns. The 98.40 % recognition accuracy demonstrates that MFF has a good representation of flow patterns and the proposed LMN outperforms existing classifiers in flow pattern recognition.

The effect of musical training and language background on vocal imitation of pitch in speech and song.

Vocal imitation plays a critical function in the development and use of both language and music. Previous studies have reported more accurate imitation for sung pitch than spoken pitch, which might be attributed to the structural differences in acoustic signals and/or the distinct mental representations of pitch patterns across speech and music. The current study investigates the interaction between bottom-up (i.e., acoustic structure) and top-down (i.e., participants' language and musical background) factors on pitch imitation by comparing speech and song imitation accuracy across four groups: English and Mandarin speakers with or without musical training. Participants imitated pitch sequences that were characteristic of either song or speech, derived from pitch patterns in English and Mandarin spoken sentences. Overall, song imitation was more accurate than speech imitation, and this advantage was larger for English than Mandarin pitch sequences, regardless of participants' musical and language experiences. This effect likely reflects the perceptual salience of linguistic tones in Mandarin relative to English speech. Music and language knowledge were associated with optimal imitation of different acoustic features. Musicians were more accurate in matching absolute pitch across syllables and musical notes compared to nonmusicians. By contrast, Mandarin speakers were more accurate at imitating fine-grained changes within and across pitch events compared to English speakers. These results suggest that different top-down factors (i.e., language and musical background) influence pitch imitation ability for different dimensions of bottom-up features (i.e., absolute pitch and relative pitch patterns). (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Time-dependent memory transformation in hippocampus and neocortex is semantic in nature

Memories undergo a time-dependent neural reorganization, which is assumed to be accompanied by a transformation from detailed to more gist-like memory. However, the nature of this transformation and its underlying neural mechanisms are largely unknown. Here, we report that the time-dependent transformation of memory is semantic in nature, while we find no credible evidence for a perceptual transformation. Model-based MRI analyses reveal time-dependent increases in semantically transformed representations of events in prefrontal and parietal cortices, while specific pattern representations in the anterior hippocampus decline over time. Posterior hippocampal memory reinstatement, in turn, increases over time and is linked to the semantic gist of the original memory, without a statistically significant link to perceptual details. These findings indicate that qualitative changes in memory over time, associated with distinct representational changes in the neocortex and within the hippocampus, reflect a semantic transformation, which may promote the integration of memories into abstract knowledge structures.

FNRegion: A fast NAM‐based region extraction algorithm

AbstractRegion extraction is usually used by many computer vision tasks as a pre‐processing step to extract image features. However, how to efficiently extract effective regions remains a challenging problem. In this paper, inspired by the non‐symmetry and anti‐packing pattern representation model (NAM) and the FatRegion algorithm, a fast NAM‐based region extraction algorithm which is called FNRegion is proposed. A NAM‐based homogeneous block generation algorithm is first presented to represent an image as a combination of multiple homogeneous blocks, each of which is a square region with visually indistinguishable intra‐region colour difference. Then, these homogeneous blocks are merged into larger regions according to their colour and shape information. To group these regions into larger ones in order to progressively build a region tree, a distance function is defined using variety of regional information to measure the distance between adjacent regions. Also, a multi‐feature region merging algorithm with linear complexity both in time and space is presented.The proposed algorithm has been evaluated on multiple public datasets in comparison with the state‐of‐the‐art region extraction algorithms. The experimental results show that in the case of almost the same or even less running time as other fast region extraction algorithms, the proposed algorithm is able to extract higher‐quality regions.

Hesitant fuzzy time series forecasting model of higher order based on one and two-factor aggregate logical relationship

Fuzzy time series forecasting is a methodology that incorporates fuzzy logic to handle uncertainty in time series data. It allows for more flexible and accurate predictions by considering the linguistic terms and membership functions associated with each data point, enabling a better representation of complex patterns and trends within the time series. A time series forecasting model examines the relationships between present and past observations for future measurement. This article proposes a hesitant fuzzy time series forecasting (FTSF) technique for higher order based on one and two-factor aggregate fuzzy relationships. The proposed method presents a new approach to partitioning intervals into an unequal and equal length. The hesitant fuzzy sets are defined for each observation, and fuzzification is based on their maximum score function. Further, aggregated triangular fuzzy numbers are defined using an aggregation operator corresponding to the fuzzified datum. In order to enhance forecasting performance, this work also introduces a novel defuzzification rule based on aggregated fuzzy logical relationship groups (AFLRGs) for higher order one and two-factor fuzzy time series. The implementation of the proposed approach is verified on two data sets to the forecasting of enrollment data and TAIFEX and TAIEX stock index data sets, and the forecasting error is tested in term of different statistical parameters. Thus, the model validates the effectiveness of the proposed method with higher forecasting accuracy rate in the environment hesitant fuzzy information.

Characterizing Tidal Currents and Guangdong Coastal Current Over the Northern South China Sea Shelf Using Himawari‐8 Geostationary Satellite Observations

AbstractObtaining high spatiotemporal resolution surface currents with wide coverage is of great importance for understanding marine physical and biogeochemical processes, as well as practical applications. This study utilizes the sequential hourly images from the Himawari‐8 geostationary satellite to map the semidiurnal M2 surface tidal current pattern over the shallow shelf (<100 m) of the northern South China Sea. The comparison between the satellite‐derived tidal currents and the High‐Frequency Radar observations shows good agreement. The satellite‐derived tidal currents are also fairly comparable to the prediction of the regional tidal model from Oregon State University. The detided daily mean surface flows provide an apparent representation of the spatial pattern of the Guangdong Coastal Current (GCC) and its synoptic variability, well consistent with the simultaneous sea surface temperature and salinity satellite observations. The observed surface flows are compared to the Copernicus Marine Environment Monitoring Service daily model currents, and the model findings are in good agreement with the observations, indicating the sensitivity of the GCC to rapid changes of northeasterly winds. This study highlights the potential of the Himawari‐8 geostationary satellite in measuring sea surface currents over a broad, shallow continental shelf.

Snowman: A Symbol of Self-Realization and Unity of Opposites

This literature review delves into the symbolic aspects associated with snowman. The study examines the symbolism connected to both the process of constructing snowman and the eventual melting of snow. Furthermore, it explores how snowman can be seen as representations of life's patterns and natural processes. Particularly, the analysis scrutinizes snowman as integrative symbols within a broader dramatic context. The research also reveals how the cyclical processes of nature parallel the concept of individualization, leading to self-understanding and Self-realization. Through a comprehensive analysis of existing literature, this study investigates the symbolism of snowman, shedding light on their relevance in enhancing our understanding of human nature.

Mapping historical forest biomass for stock-change assessments at parcel to landscape scales

Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship scenarios. Carbon accounting frameworks based exclusively on national forest inventories are limited to broad-scale estimates, but model-based approaches that combine these inventories with remotely sensed data can yield contiguous fine-resolution maps of forest biomass and carbon stocks across landscapes over time. Here we describe a fundamental step in building a map-based stock-change framework: mapping historical forest biomass at fine temporal and spatial resolution (annual, 30 m) across all of New York State (USA) from 1990 to 2019, using freely available data and open-source tools. Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA) data, and off-the-shelf LiDAR collections we developed three modeling approaches for mapping historical forest aboveground biomass (AGB): training on FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps (indirect), and an ensemble averaging predictions from the direct and indirect models. Model prediction surfaces (maps) were tested against FIA estimates at multiple scales. All three approaches produced viable outputs, yet tradeoffs were evident in terms of model complexity, map accuracy, saturation, and fine-scale pattern representation. The resulting map products can help identify where, when, and how forest carbon stocks are changing as a result of both anthropogenic and natural drivers alike. These products can thus serve as inputs to a wide range of applications including stock-change assessments, monitoring reporting and verification frameworks, and prioritizing parcels for protection or enrollment in improved management programs.

Radar Active Jamming Recognition under Open World Setting

To address the issue that conventional methods cannot recognize unknown patterns of radar jamming, this study adopts the idea of zero-shot learning (ZSL) and proposes an open world recognition method, RCAE-OWR, based on residual convolutional autoencoders, which can implement the classification of known and unknown patterns. In the supervised training phase, a residual convolutional autoencoder network structure is first constructed to extract the semantic information from a training set consisting solely of known jamming patterns. By incorporating center loss and reconstruction loss into the softmax loss function, a joint loss function is constructed to minimize the intra-class distance and maximize the inter-class distance in the jamming features. Moving to the unsupervised classification phase, a test set containing both known and unknown patterns is fed into the trained encoder, and a distance-based recognition method is utilized to classify the jamming signals. The results demonstrate that the proposed model not only achieves sufficient learning and representation of known jamming patterns but also effectively identifies and classifies unknown jamming signals. When the jamming-to-noise ratio (JNR) exceeds 10 dB, the recognition rate for seven known jamming patterns and two unknown jamming patterns is more than 92%.

Computational thinking and repetition patterns in early childhood education: Longitudinal analysis of representation and justification

This paper provides a longitudinal analysis of the understanding of repetition patterns by 24 Spanish children ages 3, 4 and 5, through representation and the type of justification. A mixed quantitative and qualitative study is conducted to establish bridges between algebraic thinking and computational thinking by teaching repetition patterns in technological contexts. The data are obtained using: a) participant observations; b) audio-visual and photographic records; and c) written representations, in drawing format, from the students. The analysis involves, on the one hand, a statistical analysis of the representations of patterns, and on the other, an interpretive analysis to describe the type of justification that children use in technological contexts: “elaboration”, “validation”, “inference” and “prediction or decision-making”. The results show that: a) with respect to the representation of patterns, errors decreased by 27.3% in 3-to-5-year-olds, with understanding and correct representation of repetition patterns gaining prominence in more than 50% of the sample from the age of 4; b) on the type of justification used, it is evident that in 3-and-4-year-olds, “elaboration” predominates, and at 5, progress is made towards “validation”. We conclude that it is necessary to design learning sequences connected with theory and upheld through practice, and that foster the active role of the teacher as a promoter of teaching situations that help spur the beginning of computational and algebraic thinking.

Different Methods in Assessing El Niño Flavors Lead to Opposite Results

AbstractEl Niño‐Southern Oscillation (ENSO) flavors have been defined to characterize ENSO events and their teleconnections. Studying El Niño flavor evolution during the Holocene period can provide valuable insights into changes over long time scales. We investigated ENSO flavor evolution using simulations spanning the last 6,000 years and present‐day observations. Two approaches to computing ENSO flavors, in agreement in the present, lead to opposite trends in the last 6,000 years. The methods also differ significantly in their representation of ENSO flavor patterns. However, incorporating the sensitivity of the methods to calibration periods and mean state changes yields similar interpretations of ENSO variability changes. Both methods suggest an increase in El Niño variability spreading to the west and east tropical Pacific over the past 6,000 years. Standardizing El Niño flavor definitions is necessary for meaningful comparisons between studies and robust climate variability analysis.

Enhancing Object Detection for VIPs Using YOLOv4_Resnet101 and Text-to-Speech Conversion Model

Vision impairment affects an individual’s quality of life, posing challenges for visually impaired people (VIPs) in various aspects such as object recognition and daily tasks. Previous research has focused on developing visual navigation systems to assist VIPs, but there is a need for further improvements in accuracy, speed, and inclusion of a wider range of object categories that may obstruct VIPs’ daily lives. This study presents a modified version of YOLOv4_Resnet101 as backbone networks trained on multiple object classes to assist VIPs in navigating their surroundings. In comparison to the Darknet, with a backbone utilized in YOLOv4, the ResNet-101 backbone in YOLOv4_Resnet101 offers a deeper and more powerful feature extraction network. The ResNet-101’s greater capacity enables better representation of complex visual patterns, which increases the accuracy of object detection. The proposed model is validated using the Microsoft Common Objects in Context (MS COCO) dataset. Image pre-processing techniques are employed to enhance the training process, and manual annotation ensures accurate labeling of all images. The module incorporates text-to-speech conversion, providing VIPs with auditory information to assist in obstacle recognition. The model achieves an accuracy of 96.34% on the test images obtained from the dataset after 4000 iterations of training, with a loss error rate of 0.073%.