Levels Of Representation Research Articles

Low-Cost and Contactless Survey Technique for Rapid Pavement Texture Assessment Using Mobile Phone Imagery

Collecting pavement texture information is crucial to understand the characteristics of a road surface and to have essential data to support road maintenance. Traditional texture assessment techniques often require expensive equipment and complex operations. To ensure cost sustainability and reduce traffic closure times, this study proposes a rapid, cost-effective, and non-invasive surface texture assessment technique. This technology consists of capturing a set of images of a road surface with a mobile phone; then, the images are used to reconstruct the 3D surface with photogrammetric processing and derive the roughness parameters to assess the pavement texture. The results indicate that pavement images taken by a mobile phone can reconstruct the 3D surface and extract texture features with accuracy, meeting the requirements of a time-effective documentation. To validate the effectiveness of this technique, the surface structure of the pavement was analyzed in situ using a 3D structured light projection scanner and rigorous photogrammetry with a high-end reflex camera. The results demonstrated that increasing the point cloud density can enhance the detail level of the real surface 3D representation, but it leads to variations in road surface roughness parameters. Therefore, appropriate density should be chosen when performing three-dimensional reconstruction using mobile phone images. Mobile phone photogrammetry technology performs well in detecting shallow road surface textures but has certain limitations in capturing deeper textures. The texture parameters and the Abbott curve obtained using all three methods are comparable and fall within the same range of acceptability. This finding demonstrates the feasibility of using a mobile phone for pavement texture assessments with appropriate settings.

OTM-HC: Enhanced Skeleton-Based Action Representation via One-to-Many Hierarchical Contrastive Learning

Human action recognition has become crucial in computer vision, with growing applications in surveillance, human–computer interaction, and healthcare. Traditional approaches often use broad feature representations, which may miss subtle variations in timing and movement within action sequences. Our proposed One-to-Many Hierarchical Contrastive Learning (OTM-HC) framework maps the input into multi-layered feature vectors, creating a hierarchical contrast representation that captures various granularities within a human skeleton sequence temporal and spatial domains. Using sequence-to-sequence (Seq2Seq) transformer encoders and downsampling modules, OTM-HC can distinguish between multiple levels of action representations, such as instance, domain, clip, and part levels. Each level contributes significantly to a comprehensive understanding of action representations. The OTM-HC model design is adaptable, ensuring smooth integration with advanced Seq2Seq encoders. We tested the OTM-HC framework across four datasets, demonstrating improved performance over state-of-the-art models. Specifically, OTM-HC achieved improvements of 0.9% and 0.6% on NTU60, 0.4% and 0.7% on NTU120, and 0.7% and 0.3% on PKU-MMD I and II, respectively, surpassing previous leading approaches across these datasets. These results showcase the robustness and adaptability of our model for various skeleton-based action recognition tasks.

Neural processing of Mandarin and Cantonese lexical tone alternations: implications for the nature of phonological representations

This study provides neurocognitive evidence to shed further insight into the architecture of phonological representations. We tap into these representations by focusing on the neural processing of phonological alternations. Traditional generative accounts postulate abstract representations which are transformed by phonological processes into different alternants. Recent psycholinguistically-based frameworks posit that different alternants are listed in parallel in the abstract representation. Exemplar-based theories posit that alternation is a manifestation of frequency effects among concrete phonetic forms stored in memory. However, it is unclear the extent to which phonological representations of different levels of abstractedness are neurocognitive realities, beyond formal entities for linguistic analysis. This study tests the hypothesis that phonological representations are neurocognitive primitives that modulate speech processing and lexical access. We examined the processing of two surface-similar, but distributionally distinct lexical tone alternation patterns in Mandarin and Cantonese. With a cross-modal priming paradigm, differential neurophysiological components (LPC vs. N400) associated with the processing of alternation violations were identified cross-linguistically. Results support our hypothesis, suggesting that cross-linguistically distinct abstract phonological representations differentially modulate phonotactic detection, lexical access, and phonological restructuring processes. Results also lend theoretical insight into the multiform nature of the phonological representation, which encompasses both abstract and concrete levels of representations.

Machine learning for air quality index (AQI) forecasting: shallow learning or deep learning?

In this study, several machine learning (ML) models consisting of shallow learning (SL) models (e.g., random forest (RF), K-nearest neighbor (KNN), weighted K-nearest neighbor (WKNN), support vector machine (SVM), artificial neural network (ANN), and deep learning (DL) models (e.g., long short-term memory (LSTM), gated recurrent unit (GRU), recurrent neural network (RNN), and convolutional neural network (CNN)) have been employed for predicting air pollution and its classification. The models were selected based on factors such as prediction accuracy, model generalization, model complexity, and training time. Our study focuses on analyzing and predicting the air quality index (AQI) using daily PM10 concentration as natural pollutants and nine meteorological parameters from March 2013 to February 2022 in Zabol. We also utilized the information gain (IG) method for feature selection. Several measures including accuracy, F1 score, precision, recall, and the area under the curve (AUC), are computed to assess model performance. This study demonstrates the efficacy of DL models, particularly CNN, in predicting the AQI with remarkable accuracy. Our findings reveal that all models effectively classify air quality levels, with an AUC of 0.95 for the good class in both DL and ANN models, significantly outperforming SL models. The AUC values for the hazardous and moderate classes of DL models were also impressive, at 0.90 and 0.83, respectively, underscoring their effectiveness in critical classifications. In terms of performance, CNN achieved an accuracy of 0.60, leading the models, while RF followed closely at 0.58. RNN, GRU, ANN, and SVM each reached an accuracy of 0.57, demonstrating a competitive edge. LSTM and WKNN recorded an accuracy of 0.55, and KNN was slightly lower at 0.53. These results highlight the superior capabilities of DL models in addressing complex air quality classifications, providing invaluable insights for policymakers. By leveraging these advanced techniques, stakeholders can implement more effective strategies to combat air pollution and safeguard public health. It is worth noting that irregular monitoring of air quality data may affect the robustness of our predictions, highlighting the need for more consistent data collection to ensure an accurate representation of pollution levels.

Analisis Semiotika Feminisme Radikal dalam Film Poor Things

The issue of feminism has been a topic of intense and in-depth discussion in various circles, involving a variety of perspectives in academic, social, and cultural studies. Social dynamics that occur such as social class injustice based on gender, oppression of women, sexism, patriarchal ideology that defines women as objects of sexuality and so on make this issue raised in any media, one of which is film. In this study, researchers are interested in analyzing the film Poor Things directed by Yorgos Lanthimos from a script by Tony McNamara which highlights women having freedom over their bodies, the setting of this film was taken in the Victorian era where at that time women were considered a second class that functioned to satisfy male sexual urges. This research aims to explain the meaning of the semiotic code of radical feminism in Poor Things directed by Yorgos Lanthimos from a script by Tony McNamara which highlights women having freedom over their bodies. The method used in this research is a qualitative approach with John Fiske's semiotic analysis based on codes that are divided into three levels, namely the level of reality, representation and ideology. The results show the values of radical feminism at the level of reality through the codes of appearance, makeup, costumes, speech, environment and behavior. At the representation level, the values of feminism are shown through the codes of camera, character, action, conflict and dialog. At the ideological level, radical feminism found in the movie Poor Things is included in patriarchal ideology. In the movie Poor Things, the level of ideology displayed is patriarchy where women are considered as a second class that functions to satisfy men's sexual urges.

A novel enhancing method for terahertz imaging of integrated circuits flaw detection

This research paper addresses the current gap in understanding the intricate damage types within packaged integrated circuits (ICs). We introduce a novel and multiscale latent low-rank representation (M-LatLRR) methodology, meticulously tailored to enhance the quality of terahertz (THz) IC images and reveal latent features. By integrating THz imaging with our proposed M-LatLRR method, we aim to facilitate the precise identification of damage types within inside the packaged ICs. Firstly, the multiscale Gaussian functions are used to remove the blur and the components are obtained via averaging. Secondly, the image matrices are utilized to extract base matrices and detail matrices. With LatLRR, the multiscale detail matrices are extracted at several representation levels. The final enhanced image is reconstructed by average strategy for dealing with the detail and pre-enhancement parts. The M-LatLRR framework is universal and can be effectively applied to extract multi-level features of packaged IC images. In a comparative analysis, our method demonstrates superior capabilities in determining the failure types of fractured wire bonds, cracks, and delamination of the dielectric layer, outperforming alternative methodologies.

“Did you watch the Disney parade?” pedagogical translanguaging for evoking students’ perezhivanie to integrate representations in linguistically diverse science classroom

Learning content subjects like science requires culturally and linguistically diverse students to learn abstract representations. Previous studies focused on how content teachers can use translanguaging to promote emotional engagement of students with homogenous cultural and linguistic composition. As students in culturally and linguistically diverse classrooms brought different emotive experiences to classrooms, it was important for teachers to leverage semiotic resources to evoke shared memory that reconcile backgrounds of different students. This bridged the gap between students’ lived experiences and learning of abstract content representations. In the current case study involving a seventh-­grade mixed medium of instruction (MMI) science classroom, we examined how a science teacher capitalised on culturally and linguistically diverse students’ shared emotive experiences to translate across different representational levels in science. Drawing on the socio-­cultural concept of perezhivanie, our findings showed that the science teacher leveraged the class’s collective experience of watching the Disney parade and linked it to water molecules moving around dissociated salts in blackboard drawings. We argue for the importance of reconciling lived experiences among students with diverse cultural and linguistic background, in order to facilitate their equitable access to content subjects.

Towards better visualizations of urban sound environments: insights from interviews

Urban noise maps and noise visualizations traditionally provide macroscopic representations of noise levels across cities. However, those representations fail at accurately gauging the sound perception associated with these sound environments, as perception highly depends on the sound sources involved. This paper aims at analyzing the need for the representations of sound sources, by identifying the urban stakeholders for whom such representations are assumed to be of importance. Through spoken interviews with various urban stakeholders, we have gained insight into current practices, the strengths and weaknesses of existing tools and the relevance of incorporating sound sources into existing urban sound environment representations. Three distinct use of sound source representations emerged in this study: 1) noise-related complaints for industrials and specialized citizens, 2) soundscape quality assessment for citizens, and 3) guidance for urban planners. Findings also reveal diverse perspectives for the use of visualizations, which should use indicators adapted to the target audience, and enable data accessibility.

The Cognitive Map Mechanism in Digital Reading: An Experimental Study on the Influence of Textual Representation on Cognitive Effects

ABSTRACTThe Cognitive Map Mechanism explains that different textual representations influence cognitive effects by affecting how readers mentally organize and navigate information. The application of this mechanism in digital reading remains underexplored. We conducted a lab experiment(N = 148) manipulating low/medium/high textual representation through non−/micro−/combined‐annotation modes. Our aim was to clarify how the cognitive map based digital reading variations impact reading comprehension, performance calibration, and reading speed. Sufficient results showed that high‐level textual representation provides superior cognitive effects in all dimensions. It is interesting to discover that medium‐level textual representation impaired cognitive effects compared to low‐level representation. Specifically, metacognition negatively moderated the influence of textual representation level on reading speed, suggesting that texts with both micro‐annotations and graphic organizers can provide comprehensive cues and visual correlations to build cognitive map, thereby enhancing cognitive effects. Our study contributes novel insights for future research and practical implementation of cognitive‐oriented strategies in digital context.

Hurricane surge and inundation in the Bahamas, part 1: Storm surge model

AbstractA storm surge model has been developed as part of a pilot study for The Commonwealth of the Bahamas. The hydrodynamic model, TELEMAC‐2D, is used to simulate the response of water level to tide and the wind and atmospheric pressure fields of hurricanes and subsequent inundation over land. The model is used by The Bahamas Department of Meteorology to forecast storm surge and flooding over the islands of Grand Bahama and Eleuthera for incoming hurricanes to assist in preparation for and management of hurricane surge events and has been used in a flood risk assessment, reported in a companion paper. The model has been optimised to run quickly while also resolving the bathymetry and topography that affect the development and propagation of storm surge. Wind fields are generated within TELEMAC‐2D based on hurricane warning bulletins. The model has been validated for its representation of water level and against tide gauge measurements during four historical hurricanes: Irene, Sandy, Matthew and Dorian. Factors contributing to uncertainty in forecast predictions are discussed and recommendations are provided to improve the performance in future. The pilot study provides a template for future expansion to cover the other inhabited islands of The Bahamas.

Which classifiers are connected to others? An optimal connection framework for multi-layer ensemble systems

Ensemble learning is a powerful machine learning strategy that combines multiple models e.g. classifiers to improve predictions beyond what any single model can achieve. Until recently, traditional ensemble methods typically use only one layer of models which limits the exploration of different aspects in the classifiers’ predictions. On the other hand, the rise of deep learning has introduced multi-layer architectures that can learn complex functions by transforming data into multiple levels of representation. This characteristic of deep learning suggests that multi-layer ensembles may potentially provide better performance compared to single-layer ensembles. However, a problem which might arise is that in the subsequent layers, not all the inputs to a classifier are desirable, leading to lower performance. In this paper, we introduce a novel multi-layer ensemble of classifiers named COME in which each classifier at a specific layer is connected to multiple classifiers in the previous layer. These connections signify the use of the previous-layer-classifiers’ outputs as inputs for training the current layer's classifier. Each classifier can be connected to different classifiers in the previous layer, which allows inputs in each layer to be optimally selected. We propose a binary encoding scheme to encode the topology of the proposed multi-layer ensemble with defined connections between layers. Differential Evolution, a popular evolutionary computation method, is used as the optimisation algorithm to search for the optimal set of connections. Experimental results on 30 datasets from the UCI Machine Learning Repository and OpenML demonstrate that our proposed ensemble outperforms many state-of-the-art ensemble learning algorithms.

Female representation across mythologies

Social groups have been studied throughout history to understand how different configurations impact those within them. Along with this came the interest in investigating social groups of both fictional and mythological works. Over the last decade these social groups have been studied through the lens of network science allowing for a new level of comparison between these stories. We use this approach to focus on the attributes of the characters within these networks, specifically looking at their gender. With this we review how the female populations within various narratives and to some extent the societies they are based in are portrayed. Through this we find that although there is not a trend of all narratives of the same origin having similar levels of representation some are noticeably better than others. We also observe which narratives overall prioritise important female characters and which do not.

Fixed fantasies of loss and reunion in a mother-baby treatment of intergenerational trauma

ABSTRACT Mother-baby pairs referred to the Anni Bergman Parent-Infant Outreach Program, in New York, are offered clinical services in urgent circumstances. Mothers are typically contending with crises stemming from poverty, loss, mental illness, and the various forms of social neglect, abuse, and racism built into New York City’s legal, educational, and foster-care systems. While dyadic interventions aim to develop parents’ attunement and reflective functioning towards their babies, the nature of the trauma these mothers have endured often shuts down the critical functions necessary for growth and change. In fact, aspects of their psychic life, in a unique survival mode, actively foreclose on new ways of relating; rather they remain tethered to early trauma with varied levels of representation. For example, the mother whom I write about in this paper, Janine, was transfixed by her own birth history in a way that preserved the force of her earliest pain and occluded the reality of her baby. A central therapeutic question is how to find a way into a mother’s closed system and build upon her healthy, loving wishes to care for her child. The therapist's capacity to enter these psychic voids and revive herself through reverie creates a path for intervention.

The Operation of Moe: A Discourse Practice of Cyber Nationalism

Unlike the previous cyber-nationalism events characterized by anger and hatred, the "Panda Ya Ya" incident highlights a special form of "soft emotion." This paper focuses on the common representation of this soft emotion in both self-media and official media discourse practices: moe. Through Fairclough's discourse analysis, the specific mechanisms of its operation are explored to understand the emotional motivations in cyber nationalism. The study finds that at the levels of representation and style, the "moe discourse" in the Ya Ya incident is co-created by self-media and official media as: floating factual evidence, repeatedly performed legitimacy, and normalized political figures. However, there are characteristic differences in "time-space representation" and register between the two. The operation of moe discourse is premised on human participation, with the relationship between moe objects and humans essentially being "agented agency." Finally, the soft emotion manifested by moe discourse exhibits more significant network characteristics, relying on more universal human values, and more closely resembling the fulfillment of emotional needs. This can be viewed as a new, more powerful "commonality" resource in the development of cyber-nationalism. However, the operation of soft emotions in cyber-nationalism often remains in a state of "unfinished" without a directed outcome, becoming a kind of "emotional machine running idle."

Языковая личность синхронного переводчика: тезаурусный (лингвокогнитивный) уровень

The paper aims to identify the representatives of the thesaurus (linguocognitive) level of the professional linguistic personality of a simultaneous interpreter through verbal means. The author conducts the research within the framework of the linguo-functional approach. To achieve the stated aim, the researcher examines the phenomenon of the linguistic personality as such, distinguishes between the related concepts of “linguistic personality” and “linguistic identity”, and offers a theoretical justification for the formation of a working definition of the thesaurus (linguocognitive) level and the language units included in it. The scientific novelty of the research lies in the fact that the thesaurus (linguocognitive) level of the linguistic personality of a simultaneous interpreter is considered from the perspective of the linguo-functional approach. The author proposes a methodology for exploring the thesaurus (linguocognitive) level to determine and analyze representatives reflecting the individual’s linguistic model of the world. Examples of analyzing representatives at different levels (the level of verbal representation, the level of conceptual sphere representation, the level of extralinguistic representation) demonstrate the breadth of the thesaurus level of a simultaneous interpreter in a communication situation of translation, taking into account the manifestation of available background and special knowledge in translation.

A new high-resolution Coastal Ice-Ocean Prediction System for the East Coast of Canada

The Coastal Ice Ocean Prediction System for the East Coast of Canada (CIOPS-E) was developed and implemented operationally at Environment and Climate Change Canada (ECCC) to support a variety of critical marine applications. These include support for ice services, search and rescue, environmental emergency response and maritime safety. CIOPS-E uses a 1/36° horizontal grid (~ 2 km) to simulate sea ice and ocean conditions over the northwest Atlantic Ocean and the Gulf of St. Lawrence (GSL). Forcing at lateral open boundaries is taken from ECCC’s data assimilative Regional Ice-Ocean Prediction System (RIOPS). A spectral nudging method is applied offshore to keep mesoscale features consistent with RIOPS. Over the continental shelf and GSL, the CIOPS-E solution is free to evolve according to the model dynamics. Overall, CIOPS-E significantly improves the representation of tidal and sub-tidal water levels compared to ECCC’s lower resolution systems: RIOPS (~ 6 km) and the Regional Marine Prediction System – GSL (RMPS-GSL, 5 km). Improvements in the GSL are due to the higher resolution and a better representation of bathymetry, boundary forcing and dynamics in the upper St. Lawrence Estuary. Sea surface temperatures show persistent summertime cold bias, larger in CIOPS-E than in RIOPS, as the latter is constrained by observations. The seasonal cycle of sea ice extent and volume, unconstrained in CIOPS-E, compares well with observational estimates, RIOPS and RMPS-GSL. A greater number of fine-scale features are found in CIOPS-E with narrow leads and more intense ice convergence zones, compared to both RIOPS and RMPS-GSL.

Practice Reshapes the Geometry and Dynamics of Task-tailored Representations.

Extensive practice makes task performance more efficient and precise, leading to automaticity. However, theories of automaticity differ on which levels of task representations (e.g., low-level features, stimulus-response mappings, or high-level conjunctive memories of individual events) change with practice, despite predicting the same pattern of improvement (e.g., power law of practice). To resolve this controversy, we built on recent theoretical advances in understanding computations through neural population dynamics. Specifically, we hypothesized that practice optimizes the neural representational geometry of task representations to minimally separate the highest-level task contingencies needed for successful performance. This involves efficiently reaching conjunctive neural states that integrate task-critical features nonlinearly while abstracting over non-critical dimensions. To test this hypothesis, human participants (n = 40) engaged in extensive practice of a simple, context-dependent action selection task over 3 days while recording EEG. During initial rapid improvement in task performance, representations of the highest-level, context-specific conjunctions of task-features were enhanced as a function of the number of successful episodes. Crucially, only enhancement of these conjunctive representations, and not lower-order representations, predicted the power-law improvement in performance. Simultaneously, over sessions, these conjunctive neural states became more stable earlier in time and more aligned, abstracting over redundant task features, which correlated with offline performance gain in reducing switch costs. Thus, practice optimizes the dynamic representational geometry as task-tailored neural states that minimally tesselate the task space, taming their high-dimensionality.

Process and implementation evaluation of a virtual hospital model of care for low back pain (Back@Home).

Low back pain was the sixth most common reason for an ED visit in 2022-2023 in Australia, one-third of these patients were subsequently admitted to hospital. Therefore, we have assessed whether some patients could be diverted to alternate clinical pathways, via admission to a virtual hospital (rpavirtual), and be cared for remotely in their own homes. Ethics approval was granted for protocols X21-0278 & 2021/ETH10967 and X21-0094 & 2021/ETH00591. We conducted a mixed-method process evaluation, using the RE-AIM framework (reach, effectiveness, adoption, implementation and maintenance) to answer key questions regarding the Back@Home model of care. This preliminary evaluation describes a cohort of the first 50 patients who received care between 13 February and 31 July 2023. The service had high levels of reach and adoption, very low levels of ED representation, and no AEs. Virtual care cost a median of AU$2215 (interquartile range = AU$1724-AU$2855) per admission. Patients admitted virtually had the same high satisfaction with care as traditionally admitted patients and reported less pain and better physical function. Preliminary findings suggest that this model of care is a safe, acceptable, and feasible alternative to hospitalisation for non-serious low back pain, in a select cohort of patients meeting inclusion criteria. Further data collection will inform whether Back@Home has had an impact on length of stay or traditional admission rates.

Green chemistry for all: three principles of Inclusive Green and Sustainable Chemistry Education

Abstract The three principles of Inclusive Green and Sustainable Chemistry Education (IGSCE) are presented to guide the reflection, design, and implementation of potentially inclusive materials and approaches. These principles refer to (i) embracing student-centered learning, (ii) promoting teaching in the five levels of representation in chemistry, and (iii) adapting the curriculum to empower students to apply their academic skills effectively to real-life situations through supportive teaching and social guidance. Educational elements conducive to potentially inclusive classrooms and their interconnections are identified and discussed. These include using the Triangular Bipyramid Metaphor (TBM) to facilitate academic inclusivity for individuals with and without disabilities, such as those who are deaf and blind. Further, the importance of ensuring that all students, regardless of their abilities, can fully participate in the educational experience is highlighted, aligning with Sustainable Development Goal 4 (SDG #4) to achieve inclusive education and lifelong learning opportunities. Green chemistry should be available to everyone, not just a few. It promotes sustainable development and deserves global recognition and support. The change agents targeted by these three principles of IGSCE include, but are not limited to, educators, researchers, teachers, and students in secondary and university education.

Investigating Dayue : An LFG-OT approach to its subcategorization and structure

ABSTRACT: This paper investigates the subcategorization and structure of dayue , taking a Lexical Functional Grammar–Optimality Theory (LFG-OT) approach to account for the different uses of this word. It argues that dayue can be identified in the category of either an adverb or a preposition, depending on the function it takes and the structure it has. As an adverb, dayue modifies the clausal element or the quantifier phrase directly following it. As a preposition, dayue takes a noun phrase (NP) or an adpositional phrase as the complement, occurring in the adjunctive position. There may be ambiguities due to the subcategorization of, and the argument structure associated with, dayue , which result in the same linguistic form being generated as optimal outputs for different input meanings. However, each can be argued to correspond to a different c-structure. An LFG-OT approach is adopted in this paper to account for the structure of dayue . LFG proposes different levels of representation, and OT is involved in the theoretical model by evaluating linguistic forms according to the interaction and ranking of different kinds of linguistic constraints. Faithfulness constraints ensure faithful mappings among the lexical, functional, and syntactic levels of representation. Markedness constraints require linguistic structures to be well formed. Economy constraints pursue a concise, efficient way of expression. Alignment constraints map arguments with constituent structures. 摘要: 本文探讨了"大约"的子类别和结构，采用词汇功能语法–优选理论 (LFG-OT)方法来解释该词的不同用法。文章认为，"大约"可以归为副词或介词类别，取决于其作用于句式中之功能和结构。作为副词，"大约"直接修饰其后的从句成分或量化短语。作为介词，"大约"接受名词短语或介词短语作为补语，在附加位置出现。由于"大约"的子类别和相关的论元结构可能产生歧义，导致不同输入含义生成同样语言形式为最佳输出，而每个歧义对应不同的成分结构。本文采用词汇功能语法–优选理论方法来解释"大约"的结构。词汇功能语法使用不同语言层次表现语法，而优选理论根据不同种类的制约条件之间的相互作用和排名来评估语言形式。忠实性制约条件确保词汇、功能和句法层次之间的忠实映像。标记制约条件要求语言结构良好。经济制约条件追求简洁高效的表达方式。对齐制约条件将论元映像到成分结构中。