Hasil untuk "Semantics"

L. Rizzi

G. Lakoff

T. Odlin

Yang Liu, Kaikai Guo, Xiaoyue Li et al.

The confined space of coal mines characterized by curved tunnels with rough surfaces and a variety of deployed production equipment induces severe signal attenuation and interruption, which significantly degrades the accuracy of conventional channel estimation algorithms applied in coal mine wireless communication systems. To address these challenges, we propose a modified Bilinear Generalized Approximate Message Passing (mBiGAMP) algorithm enhanced by intelligent reflecting surface (IRS) technology to improve channel estimation accuracy in coal mine scenarios. Due to the presence of abundant coal-carrying belt conveyors, we establish a hybrid channel model integrating both fast-varying and quasi-static components to accurately model the unique propagation environment in coal mines. Specifically, the fast-varying channel captures the varying signal paths affected by moving conveyors, while the quasi-static channel represents stable direct links. Since this hybrid structure necessitates an augmented factor graph, we introduce two additional factor nodes and variable nodes to characterize the distinct message-passing behaviors and then rigorously derive the mBiGAMP algorithm. Simulation results demonstrate that the proposed mBiGAMP algorithm achieves superior channel estimation accuracy in dynamic conveyor-affected coal mine scenarios compared with other state-of-the-art methods, showing significant improvements in both separated and cascaded channel estimation. Specifically, when the NMSE is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>, the SNR of mBiGAMP is improved by approximately 5 dB, 6 dB, and 14 dB compared with the Dual-Structure Orthogonal Matching Pursuit (DS-OMP), Parallel Factor (PARAFAC), and Least Squares (LS) algorithms, respectively. We also verify the convergence behavior of the proposed mBiGAMP algorithm across the operational signal-to-noise ratios range. Furthermore, we investigate the impact of the number of pilots on the channel estimation performance, which reveals that the proposed mBiGAMP algorithm consumes fewer number of pilots to accurately recover channel state information than other methods while preserving estimation fidelity.

Miruna-Ioana Belciu, Alin Velea

Chalcogenide glasses (ChGs) are a class of amorphous materials presenting remarkable mechanical, optical, and electrical properties, making them promising candidates for advanced photonic and optoelectronic applications. With the increasing integration of artificial intelligence in modern materials design, we are able to systematically select, prepare, and optimize appropriate compositions for desired applications in a manner that was unachievable before. This study employs various machine learning models to reliably predict the refractive index at 20 °C using a small dataset of 541 samples extracted from the SciGlass database. The input for the algorithms consists of a selected set of physico-chemical features computed for the chemical composition of each entry. Additionally, these algorithms served as inner models for an ensemble logistic regression estimator that achieved a superior <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> value of 0.8985. SHAP feature analysis of the second-best model, CatBoostRegressor (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi mathvariant="normal">R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> = 0.8920), revealed the importance of elemental density, atomic weight, ground state atomic gap, and fraction of p valence electrons in tuning the value of the refractive index of a chalcogenide compound.

James Abraham, Nigel D. Shepherd, Chris Littler et al.

The morphology, structure, and composition of CVD-grown molybdenum disulfide (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>MoS</mi><mn>2</mn></msub></semantics></math></inline-formula>) films were investigated under varying precursor vapor pressures. Increasing sulfur vapor pressure transformed the film morphology from well-defined triangular domains to structures dominated by sulfur-terminated zigzag edges. These morphological changes were accompanied by notable variations in both structural and electrical properties. Non-uniform precursor vapor distribution promoted the formation of intrinsic point defects. To elucidate this behavior, a thermodynamic model was developed to link growth parameters to native defect formation. The analysis considered molybdenum and sulfur vacancies in both neutral and charged states, with equilibrium concentrations determined from the corresponding defect formation reactions. Sulfur vapor pressure emerged as the dominant factor controlling defect concentrations. The model validated experimental observations, with films grown under optimum and sulfur-rich conditions, yielding a carrier concentration of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>9.6</mn><mo>×</mo><msup><mn>10</mn><mn>11</mn></msup></mrow></semantics></math></inline-formula> <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>cm</mi><mrow><mo>−</mo><mn>2</mn></mrow></msup></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>7.5</mn><mo>×</mo><msup><mn>10</mn><mn>11</mn></msup></mrow></semantics></math></inline-formula> <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>cm</mi><mrow><mo>−</mo><mn>2</mn></mrow></msup></semantics></math></inline-formula>, respectively. The major difference in the field-effect transistor (FET) performance of devices fabricated under these two conditions was the degradation of the field-effect mobility and the current switching ratio. The degradation observed is attributed to increased carrier scattering at charged vacancy defect sites.

Yu. L. Obolenskaya

The article seeks to determine the extent to which the national cultural code of the Spaniards is influenced by the designations of people’s place of residence or birth. The historical, cultural, etymological, psycholinguistic and conceptual analysis of such nominations as capital, ciudad, villa, pueblo, aldea, burgo reveals their role in the formation of the national and social identity of the Spaniards, as well as in their mythological, conceptual and linguistic picture of the world. The history and semantics of Spanish toponyms, the mythologems associated with them, as well as the structure of Spanish cities and the way of life there, which are reflected in the language people use, further justify the multicultural nature of their linguistic consciousness. Nearly 80 % of the country’s population perceive themselves as city residents, which is decisive for the social self-identification of Spaniards, as not only the residents of Madrid and other capitals of autonomous communities suffer from the ‘capital resident complex’, but also those who inhabit the ancient capitals of both Christian kingdoms and Muslim Spain of the «Al Andalus» era. The Spanish concepts of ciudad, villa, pueblo, capital and the history of the mythologem of Madrid are regarded as deep-rooted historical and cultural phenomena. Mentioning the symbolic cities of Sagunto, Numancia and Zaragoza, which also play a crucial role in the worldview of the Spanish, is equal to describing the heroism and resilience of the Spanish people. The «carnival element» and the love for puns, which clearly characterize both the national linguistic consciousness and the way the Spaniards speak, nurtured another mythologem — a small Andalusian town of Lepe acquired the status of «capital of jokes».

Ruslan Khandogin, Nina S. Proner

Artificial Intelligence (AI) plays an increasingly prominent role in various spheres of life in today’s world, including generation of a variety of visual content from selfie stream processing to creating works of digital art. The present paper raises the question of whether AI is capable of creating real art or it just imitates its external form. The paper examines the specificity of prompts: from concrete named ones to interpretive descriptive queries in linguistic, artistic and socio-cultural contexts. The article dwells upon some important aspects of evaluating the quality of keyword extraction algorithms and their relation to artistic practice. The authors rely on semiotic analysis to uncover encoded meanings and imports in the text. The article emphasises that the literary text is at the top of the hierarchy of cultural texts; it is characterised by intentionality and coherence and represents a complex semantic field where key words and images interact with the explicit and implicit contexts. The study examines and analyses the visualised images of Cheshire Cat, Cat Behemoth and Tomcat Murr created by the authors with the use of three generative neural networks: Stable Diffusion, Dall‑E and Kandinsky. Understanding and visualising the literary text by generative systems and models realising specific algorithms requires the ability to reveal its multilayered semantics and connection with the cultural context, which ultimately helps to understand the in-depth meanings of the work and its place in culture. Consideration of the operational quality of algorithms for keyword system extraction and image generation is deemed possible from the point of view of their structural organisation. Generative algorithms create an imitative reality, while the immanence of the artistic value determines the uniqueness and meanings of the created figurative world. The article can be useful to anyone interested in the substance and specificity of digital art, the relationship between technological innovations and socio-cultural context, the creation and visualisation of artistic images in generative AI models, their conceptualisation and interpretation.

Neng Dong, Shuanglin Yan, Liyan Zhang et al.

Visible-infrared person re-identification (VIReID) retrieves pedestrian images with the same identity across different modalities. Existing methods learn visual content solely from images, lacking the capability to sense high-level semantics. In this paper, we propose an Embedding and Enriching Explicit Semantics (EEES) framework to learn semantically rich cross-modality pedestrian representations. Our method offers several contributions. First, with the collaboration of multiple large language-vision models, we develop Explicit Semantics Embedding (ESE), which automatically supplements language descriptions for pedestrians and aligns image-text pairs into a common space, thereby learning visual content associated with explicit semantics. Second, recognizing the complementarity of multi-view information, we present Cross-View Semantics Compensation (CVSC), which constructs multi-view image-text pair representations, establishes their many-to-many matching, and propagates knowledge to single-view representations, thus compensating visual content with its missing cross-view semantics. Third, to eliminate noisy semantics such as conflicting color attributes in different modalities, we design Cross-Modality Semantics Purification (CMSP), which constrains the distance between inter-modality image-text pair representations to be close to that between intra-modality image-text pair representations, further enhancing the modality-invariance of visual content. Finally, experimental results demonstrate the effectiveness and superiority of the proposed EEES.

G. A. Kavvos

The study of modal logic has witnessed tremendous development following the introduction of Kripke semantics. However, recent developments in programming languages and type theory have led to a second way of studying modalities, namely through their categorical semantics. We show how the two correspond.

Jianhong Zhao, Jinhui Kang, Yongwang Zhao

CIRCT, an open-source EDA framework akin to LLVM for software, is a foundation for various hardware description languages. Despite its crucial role, CIRCT's lack of formal semantics challenges necessary rigorous hardware verification. Thus, this paper introduces K-CIRCT, the first formal semantics in {\K} for a substantial CIRCT subset adequate for simulating a RISC-V processor. Our semantics are structured into multiple layers: (1) MLIR static semantics, which covers fundamental MLIR concepts across domains; (2) CIRCT common semantics, featuring a generic hardware model that captures key hardware features across dialects; and (3) composable and extensible semantics for specific dialects, formalized individually using a unified approach. This approach has been applied to formalize CIRCT core dialects. We validated our semantics through full-rule coverage tests and assessed its practicality using the popular RISC-V hardware design, riscv-mini.

Xavier Rull

Questions can be open-ended (waiting for an affirmative or negative answer) but they can also have a confirmatory interpretation (i.e. there is an assumption that may be confirmed). In the latter case, question tags (like oh) may appear in the questions. This paper reports all the question tags in Catalan and the precise syntactic contexts in which they occur (at the beginning or at the end; although in principle they can all occur in two syntactic contexts, not all of them appear everywhere). Special emphasis is placed on the north-western dialects.

Alexander Moskvin, Evgenii Vasinovich, Anton Shadrin

This is a simple but realistic microscopic theory of spontaneous spin reorientation in rare-earth perovskites, orthoferrites <i>R</i>FeO<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>3</mn></msub></semantics></math></inline-formula> and orthochromites <i>R</i>CrO<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mn>3</mn></msub></semantics></math></inline-formula>, induced by the 4f-3d interaction, namely, the interaction of the well-isolated ground-state Kramers doublet or non-Kramers quasi-doublet of the 4f ion with an effective magnetic field induced by 3d sublattice. Both the temperature and the nature of the spin-reorientation transition are the result of competition between the second- and fourth-order spin anisotropy of the 3d sublattice, the crystal field for 4f ions, and 4f-3d interaction.

Heejin Kook, Chanhyuk Park

Wastewater treatment plants (WWTPs) contribute to the release of significant quantities of microplastics into the aquatic environment. The facile identification of microplastics and an understanding of their occurrence and transport through WWTPs are essential for improving microplastic retention. Potential microplastic treatment technologies for both polymeric and ceramic membrane filtrations were systematically investigated to inform decisions on the optimal choice of membrane for effective microplastic retention. A blocking filtration model, based on a simple linear regression fitting, was used in experiments on the filtration of microplastic suspensions to determine the relative importance of individual fouling mechanisms. Unlike the commonly applied spectroscopic techniques, the facile identification approaches, that are closely related to the amounts of particles within wastewater samples, attempted to identify tiny microplastics (<1.0 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>m) by comparing them against silica particles for reference. A larger decline in the normalized permeate flux was observed for 0.1 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>m polystyrene microplastics, while standard pore blocking appeared to be the dominant fouling mechanism for all membranes. More microplastics based on turbidity and total solids were removed using the ceramic membrane than the other polymeric membranes. However, fewer microplastics, based on the particle size distribution analysis, were removed using the ceramic membrane as the pore size measurements gave a relatively large pore size for the ceramic membrane, compared with other polymeric membranes; even though a nominal pore size of 0.1 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>m for all membranes were provided by the suppliers. The contribution of microplastic-containing synthetic wastewaters to overall flux decline was significantly greater than those of identical microplastic suspensions because of the aggregation of larger microplastics with dissolved organic matter in synthetic wastewater, leading to the formation of a cake layer on the membrane surface. Despite the challenges associated with the facile identification approaches, our findings provided deeper insights and understanding of how microplastics behave in membrane filtration, which could enable the application of potential microplastic treatment technologies.

Neofytos Dimitriou, Ognjen Arandjelović

Normalization as a layer within neural networks has over the years demonstrated its effectiveness in neural network optimization across a wide range of different tasks, with one of the most successful approaches being that of batch normalization. The consensus is that better estimates of the BatchNorm normalization statistics (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>μ</mi></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>σ</mi><mn>2</mn></msup></semantics></math></inline-formula>) in each mini-batch result in better optimization. In this work, we challenge this belief and experiment with a new variant of BatchNorm known as GhostNorm that, despite independently normalizing batches within the mini-batches, i.e., <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>μ</mi></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>σ</mi><mn>2</mn></msup></semantics></math></inline-formula> are independently computed and applied to groups of samples in each mini-batch, outperforms BatchNorm consistently. Next, we introduce sequential normalization (SeqNorm), the sequential application of the above type of normalization across two dimensions of the input, and find that models trained with SeqNorm consistently outperform models trained with BatchNorm or GhostNorm on multiple image classification data sets. Our contributions are as follows: (i) we uncover a source of regularization that is unique to GhostNorm, and not simply an extension from BatchNorm, and illustrate its effects on the loss landscape, (ii) we introduce sequential normalization (SeqNorm) a new normalization layer that improves the regularization effects of GhostNorm, (iii) we compare both GhostNorm and SeqNorm against BatchNorm alone as well as with other regularization techniques, (iv) for both GhostNorm and SeqNorm models, we train models whose performance is consistently better than our baselines, including ones with BatchNorm, on the standard image classification data sets of CIFAR–10, CIFAR-100, and ImageNet ((<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>+</mo><mn>0.2</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>+</mo><mn>0.7</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>+</mo><mn>0.4</mn><mo>%</mo></mrow></semantics></math></inline-formula>), and (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>+</mo><mn>0.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>+</mo><mn>1.7</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>+</mo><mn>1.1</mn><mo>%</mo></mrow></semantics></math></inline-formula>) for GhostNorm and SeqNorm, respectively).

Zhe Yang, Kun Jiang, Miaomiao Lou et al.

Abstract Background Health data from different specialties or domains generallly have diverse formats and meanings, which can cause semantic communication barriers when these data are exchanged among heterogeneous systems. As such, this study is intended to develop a national health concept data model (HCDM) and develop a corresponding system to facilitate healthcare data standardization and centralized metadata management. Methods Based on 55 data sets (4640 data items) from 7 health business domains in China, a bottom-up approach was employed to build the structure and metadata for HCDM by referencing HL7 RIM. According to ISO/IEC 11179, a top-down approach was used to develop and standardize the data elements. Results HCDM adopted three-level architecture of class, attribute and data type, and consisted of 6 classes and 15 sub-classes. Each class had a set of descriptive attributes and every attribute was assigned a data type. 100 initial data elements (DEs) were extracted from HCDM and 144 general DEs were derived from corresponding initial DEs. Domain DEs were transformed by specializing general DEs using 12 controlled vocabularies which developed from HL7 vocabularies and actual health demands. A model-based system was successfully established to evaluate and manage the NHDD. Conclusions HCDM provided a unified metadata reference for multi-source data standardization and management. This approach of defining health data elements was a feasible solution in healthcare information standardization to enable healthcare interoperability in China.

Valerio D’Alessandro, Sergio Montelpare, Renato Ricci

This paper present recent advances in the development of local correlation based laminar–to–turbulent transition modeling relying on the Spalart–Allmaras equation. Such models are extremely important for the flow regimes involved in wind energy applications. Indeed, fully turbulent flow models are not completely reliable to predict the aerodynamic force coefficients. This is particularly significant for the wind turbine blade sections. In this paper, we focus our attention on two different transitional flow models for Reynolds–Averaged Navier–Stokes (RANS) equations. It is worth noting that this is a crucial aspect because standard RANS models assume a fully turbulent regime. Thus, our approaches couple the well–known <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula>– technique and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo form="prefix">log</mo><mi>γ</mi></mrow></semantics></math></inline-formula> equation with the Spalart–Allmaras turbulence model in order to overcome the common drawbacks of standard techniques. The effectiveness, efficiency, and robustness of the above-mentioned methods are tested and discussed by computing several flow fields developing around airfoils operating at Reynolds numbers typical of wind turbine blade sections.

Gonçalo Carnaz, Vitor Beires Nogueira, Mário Antunes

Organizations have been challenged by the need to process an increasing amount of data, both structured and unstructured, retrieved from heterogeneous sources. Criminal investigation police are among these organizations, as they have to manually process a vast number of criminal reports, news articles related to crimes, occurrence and evidence reports, and other unstructured documents. Automatic extraction and representation of data and knowledge in such documents is an essential task to reduce the manual analysis burden and to automate the discovering of names and entities relationships that may exist in a case. This paper presents <i>SEMCrime</i>, a framework used to extract and classify named-entities and relations in Portuguese criminal reports and documents, and represent the data retrieved into a graph database. A <i>5WH1</i> (Who, What, Why, Where, When, and How) information extraction method was applied, and a graph database representation was used to store and visualize the relations extracted from the documents. Promising results were obtained with a prototype developed to evaluate the framework, namely a name-entity recognition with an F-Measure of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.73</mn></mrow></semantics></math></inline-formula>, and a 5W1H information extraction performance with an F-Measure of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.65</mn></mrow></semantics></math></inline-formula>.

James Y. Huang, Kuan-Hao Huang, Kai-Wei Chang

Pre-trained language models have achieved huge success on a wide range of NLP tasks. However, contextual representations from pre-trained models contain entangled semantic and syntactic information, and therefore cannot be directly used to derive useful semantic sentence embeddings for some tasks. Paraphrase pairs offer an effective way of learning the distinction between semantics and syntax, as they naturally share semantics and often vary in syntax. In this work, we present ParaBART, a semantic sentence embedding model that learns to disentangle semantics and syntax in sentence embeddings obtained by pre-trained language models. ParaBART is trained to perform syntax-guided paraphrasing, based on a source sentence that shares semantics with the target paraphrase, and a parse tree that specifies the target syntax. In this way, ParaBART learns disentangled semantic and syntactic representations from their respective inputs with separate encoders. Experiments in English show that ParaBART outperforms state-of-the-art sentence embedding models on unsupervised semantic similarity tasks. Additionally, we show that our approach can effectively remove syntactic information from semantic sentence embeddings, leading to better robustness against syntactic variation on downstream semantic tasks.

Oriol Corcoll

Automatic image cropping techniques are commonly used to enhance the aesthetic quality of an image; they do it by detecting the most beautiful or the most salient parts of the image and removing the unwanted content to have a smaller image that is more visually pleasing. In this thesis, I introduce an additional dimension to the problem of cropping, semantics. I argue that image cropping can also enhance the image's relevancy for a given entity by using the semantic information contained in the image. I call this problem, Semantic Image Cropping. To support my argument, I provide a new dataset containing 100 images with at least two different entities per image and four ground truth croppings collected using Amazon Mechanical Turk. I use this dataset to show that state-of-the-art cropping algorithms that only take into account aesthetics do not perform well in the problem of semantic image cropping. Additionally, I provide a new deep learning system that takes not just aesthetics but also semantics into account to generate image croppings, and I evaluate its performance using my new semantic cropping dataset, showing that using the semantic information of an image can help to produce better croppings.