Hasil untuk "Semantics"

A. Majid, S. Levinson

M. Harm, Mark S. Seidenberg

Patrice Godefroid

J. Baker, C. Bond, J. Corbett et al.

Mike Belshe, R. Peon, M. Thomson

Bhargav Kulkarni, Henry Whiting, Pavel Panchekha

Rasterization is the process of determining the color of every pixel drawn by an application. Powerful rasterization libraries like Skia, CoreGraphics, and Direct2D put exceptional effort into drawing, blending, and rendering efficiently. Yet applications are still hindered by the inefficient sequences of operations that they ask these libraries to perform. Even Google Chrome, a highly optimized program co-developed with the Skia rasterization library, still produces inefficient instruction sequences even on the top 100 most visited websites. The underlying reason for this inefficiency is that rasterization libraries have complex semantics and opaque and non-obvious execution models. To address this issue, we introduce $μ$Skia, a formal semantics for the Skia 2D graphics library, and mechanize this semantics in Lean. $μ$Skia covers language and graphics features like canvas state, the layer stack, blending, and color filters, and the semantics itself is split into three strata to separate concerns and enable extensibility. We then identify four patterns of sub-optimal Skia code produced by Google Chrome, and then write replacements for each pattern. $μ$Skia allows us to verify the replacements are correct, including identifying numerous tricky side conditions. We then develop a high-performance Skia optimizer that applies these patterns to speed up rasterization. On 99 Skia programs gathered from the top 100 websites, this optimizer yields a speedup of 18.7% over Skia's most modern GPU backend, while taking at most 32 $μ$s for optimization. The speedups persist across a variety of websites, Skia backends, and GPUs. To provide true, end-to-end verification, optimization traces produced by the optimizer are loaded back into the $μ$Skia semantics and translation validated in Lean.

Daniel Quigley

Formal semantics and distributional semantics are distinct approaches to linguistic meaning: the former models meaning as reference via model-theoretic structures; the latter represents meaning as vectors in high-dimensional spaces shaped by usage. This paper proves that these frameworks are structurally compatible for intensional semantics. We establish that Kripke-style intensional models embed injectively into vector spaces, with semantic functions lifting to (multi)linear maps that preserve composition. The construction accommodates multiple index sorts (worlds, times, locations) via a compound index space, representing intensions as linear operators. Modal operators are derived algebraically: accessibility relations become linear operators, and modal conditions reduce to threshold checks on accumulated values. For uncountable index domains, we develop a measure-theoretic generalization in which necessity becomes truth almost everywhere and possibility becomes truth on a set of positive measure, a non-classical logic natural for continuous parameters.

Parmveer Atwal, Ryley McWilliams, Ramani Ramaseshen et al.

Current diagnostic modalities for rheumatoid arthritis (RA), such as Magnetic Resonance Imaging (MRI) and ultrasound (US), excel at visualizing structural pathology but are either resource-intensive or often limited to morphological assessment. In this work, we present the design and technical validation of a low-cost continuous-wave near-infrared (NIR) dual-mode optical probe for functional monitoring of joint inflammation. Unlike superficial imaging, NIR light penetrates approximately 3–5 cm and is tissue and wavelength dependent, enabling trans-illumination of the synovial volume. The system combines reflectance and transmission geometries to resolve the ambiguity between disease presence and disease severity. To validate the diagnostic logic, we employed mcxyzn Monte Carlo (MC) simulations to model the optical signature of RA progression from early onset to EULAR-OMERACT grade 2 pannus hypertrophy on a simplified finger model, based on several tissue models in the literature and supported by physical measurements on a multilayer silicone phantom and in vivo signal verification on human volunteers. Our results demonstrate a distinct functional dichotomy: reflectance geometry serves as a binary discriminator of synovial turbidity onset, while transmission flux serves as a monotonic proxy for pannus volume, exhibiting a quantifiable signal decay consistent with the Beer–Lambert law. Signal verification on a subject with confirmed RA pathology demonstrated a significant increase in the effective attenuation coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>µ</mi></mrow><mrow><mi>e</mi><mi>f</mi><mi>f</mi></mrow></msub><mo> </mo><mo>~</mo><mo> </mo><mn>0.59</mn></mrow></semantics></math></inline-formula> mm⁻¹) compared to the healthy baseline (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>µ</mi></mrow><mrow><mi>e</mi><mi>f</mi><mi>f</mi></mrow></msub><mo> </mo><mo>~</mo><mo> </mo><mn>0.47</mn><mo> </mo></mrow></semantics></math></inline-formula> mm⁻¹). Furthermore, simulation analysis revealed a critical “metric inversion” in darker skin phenotypes (Fitzpatrick V–VI), where the standard beam-broadening signature of inflammation is artificially suppressed by epidermal absorption. We conclude that while transmission flux remains a robust grading metric across diverse skin tones, morphological beam-shape metrics are not robust, particularly in high-absorption populations. By targeting the hemodynamic precursors of structural damage, this dual-mode probe design offers a potential pathway for longitudinal, quantitative monitoring of disease activity at the point of care, while the systematic use of the Monte Carlo framework provides insight into the measurement geometry most suitable for a given clinical endpoint, whether that be detecting the presence or severity of rheumatoid arthritis.

Parosh Abdulla, Elli Anastasiadi, Mohamed Faouzi Atig et al.

The Release-Acquire (RA) semantics and its variants are some of the most fundamental models of concurrent semantics for architectures, programming languages, and distributed systems. Several steps have been taken in the direction of testing such semantics, where one is interested in whether a single program execution is consistent with a memory model. The more general verification problem, i.e., checking whether all allowed program runs are consistent with a memory model, has still not been studied as much. The purpose of this work is to bridge this gap. We tackle the verification problem, where, given an implementation described as a register machine, we check if any of its runs violates the RA semantics or its Strong (SRA) and Weak (WRA) variants. We show that verifying WRA in this setup is in O([)n5 ], while verifying the RA and SRA is in both NP- and coNP-hard, and provide a PSPACE upper bound. This both answers some fundamental questions about the complexity of these problems, but also provides insights on the expressive power of register machines as a model.

Lanmiao Liu, Esam Ghaleb, Aslı Özyürek et al.

Creating a virtual avatar with semantically coherent gestures that are aligned with speech is a challenging task. Existing gesture generation research mainly focused on generating rhythmic beat gestures, neglecting the semantic context of the gestures. In this paper, we propose a novel approach for semantic grounding in co-speech gesture generation that integrates semantic information at both fine-grained and global levels. Our approach starts with learning the motion prior through a vector-quantized variational autoencoder. Built on this model, a second-stage module is applied to automatically generate gestures from speech, text-based semantics and speaker identity that ensures consistency between the semantic relevance of generated gestures and co-occurring speech semantics through semantic coherence and relevance modules. Experimental results demonstrate that our approach enhances the realism and coherence of semantic gestures. Extensive experiments and user studies show that our method outperforms state-of-the-art approaches across two benchmarks in co-speech gesture generation in both objective and subjective metrics. The qualitative results of our model, code, dataset and pre-trained models can be viewed at https://semgesture.github.io/.

Kyeong-Jin Sung, Jisu Hong, Se-Jin Jeon

To achieve efficient design and ensure the safety of concrete structures, the use of high-strength concrete, reinforcing steel, and prestressing tendons has been steadily increasing. In this study, for flexural design of prestressed concrete (PSC) structures employing high-strength strands with tensile strengths of 2160 MPa and 2360 MPa, the applicability of the current design-code equation for predicting the strand stress at flexural failure (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>f</mi><mrow><mi>p</mi><mi>s</mi></mrow></msub></mrow></semantics></math></inline-formula>)—which was originally proposed based on studies of conventional strands with tensile strengths of 1860 MPa or lower—was evaluated. Furthermore, an improved prediction equation was proposed. Section analyses based on stress–strain curves obtained from numerous tensile tests of high-strength strands were conducted, and the results were compared with the existing prediction equations specified in ACI 318 and the Korean KDS code. The comparison revealed that, for high-strength strands, the strand stress tends to be underestimated in the tension-controlled region and overestimated in the compression-controlled region. To address these issues, a new prediction equation was proposed that retains the form of the existing equation but incorporates correction factors reflecting the characteristics of high-strength strands. The performance of the proposed equation was evaluated not only for rectangular sections but also for T- and I-shaped sections, and its predictive accuracy was verified by comparing the predicted strand stresses and nominal flexural strengths with those obtained from section analyses. As a result, the proposed prediction equation demonstrated improved accuracy compared with the existing one, while maintaining an appropriate level of conservatism. Therefore, it is expected to enhance design efficiency for PSC structures employing high-strength strands.

Jay Prakash Goit

The current study investigates the effect of terrain features on wind resources in a region with extremely diverse terrain. To that end, a case study of Nepal based on annual wind data collected from 10 different sites is performed. The evaluation of mean wind speeds using Weibull probability density functions (PDFs) shows that complex-terrain sites exhibit greater variability in 10-min average wind speeds relative to the annual average wind speeds. This pattern is also evident in comparisons of short- and long-term average wind speeds. At the complex-terrain sites, the wind speeds exhibited strong short-term variations, suggesting that local terrain effects dominate over seasonal wind variation. Terrain complexity also strongly affected turbulence. The flat-terrain sites showed turbulence intensities below the lowest IEC category turbulence profile, while the complex-terrain sites exceeded the highest IEC profile. This indicates that the IEC standard may require modification based on site complexity parameters, such as the standard deviation of elevation fluctuations. The power law exponent (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>α</mi></semantics></math></inline-formula>), used to extrapolate wind speeds to higher elevations, deviated notably from the typical 1/7 value, even in flat terrain. Finally, a power potential analysis indicated that three sites with higher mean wind speeds achieved higher capacity factors.

Ivana Matas Ivanković

Multi-word prepositions (MWPs) are complex lexical units, typically formed from prepositions and nouns. Because many are still evolving semantically, morphologically, and syntactically, no definitive list of MWPs exists. This article examines three-word expressions with the structure preposition + noun + preposition. Using regular expressions, these patterns were applied to a Croatian language corpus to identify and analyze potential MWPs based on features such as frequency of use, variability of elements, semantics, and other relevant properties.

Louis Lemonnier

This thesis revolves around an area of computer science called "semantics". We work with operational semantics, equational theories, and denotational semantics. The first contribution of this thesis is a study of the commutativity of effects through the prism of monads. Monads are the generalisation of algebraic structures such as monoids, which have a notion of centre: the centre of a monoid is made of elements which commute with all others. We provide the necessary and sufficient conditions for a monad to have a centre. We also detail the semantics of a language with effects that carry information on which effects are central. Moreover, we provide a strong link between its equational theories and its denotational semantics. The second focus of the thesis is quantum computing, seen as a reversible effect. Physically permissible quantum operations are all reversible, except measurement; however, measurement can be deferred at the end of the computation, allowing us to focus on the reversible part first. We define a simply-typed reversible programming language performing quantum operations called "unitaries". A denotational semantics and an equational theory adapted to the language are presented, and we prove that the former is complete. Furthermore, we study recursion in reversible programming, providing adequate operational and denotational semantics to a Turing-complete, reversible, functional programming language. The denotational semantics uses the dcpo enrichment of rig join inverse categories. This mathematical account of higher-order reasoning on reversible computing does not directly generalise to its quantum counterpart. In the conclusion, we detail the limitations and possible future for higher-order quantum control through guarded recursion.

Pengcheng Li, Wan Chen, Kun Wang et al.

Climate change has led to significant fluctuations in marine ecosystems. As a component of the food web, the trophic diversity and spatiotemporal changes of fish communities are crucial for understanding ecosystems. In recent years, stable isotope analysis has been increasingly used as a comprehensive tool for quantitative assessment of trophic diversity to explore spatiotemporal variations in fish community diversity. This study is based on carbon (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>¹³C) and nitrogen (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>δ</mi></semantics></math></inline-formula>¹⁵N) stable isotope analysis using different biomass-weighted isotope diversity indices, including isotopic divergence index (<i>IDiv</i>), isotopic dispersion index (<i>IDis</i>), isotopic evenness index (<i>IEve</i>), and isotopic uniqueness index (<i>IUni</i>). The overall results indicate that <i>IDis</i>, <i>IEve</i>, and <i>IUni</i> values of fish communities were relatively low, while <i>IDiv</i> was relatively high in the Haizhou Bay ecosystem. <i>IDiv</i>, <i>IDis</i>, <i>IEve</i>, and <i>IUni</i> were lower in autumn than in spring; <i>IDiv</i> and <i>IDis</i> were relatively higher in offshore waters, while <i>IEve</i> and <i>IUni</i> were relatively higher in inshore waters. The changes in species composition and intensive pelagic–benthic coupling in Haizhou Bay may lead to significant spatiotemporal variations in the trophic diversity of fish communities in the area. These findings highlight the importance of incorporating trophic relationships into ecosystem models, which will help to enhance our understanding of the complexity of the trophic structure of fish communities.

Ala Amourah, Abdullah Alsoboh, Daniel Breaz et al.

Bi-univalent functions associated with the leaf-like domain within the open unit disk are investigated and a new subclass is introduced and studied in the research presented here. This is achieved by applying the subordination principle for analytic functions in conjunction with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="italic">q</mi></semantics></math></inline-formula>-calculus. The class is proved to be not empty. By proving its existence, generalizations can be given to other sets of functions. In addition, coefficient bounds are examined with a particular focus on <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>|</mo></mrow><msub><mi>α</mi><mn>2</mn></msub><mrow><mo>|</mo></mrow></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mrow><mo>|</mo></mrow><msub><mi>α</mi><mn>3</mn></msub><mrow><mo>|</mo></mrow></mrow></semantics></math></inline-formula> coefficients, and Fekete–Szegö inequalities are estimated for the functions in this new class. To support the conclusions, previous works are cited for confirmation.

Huiyan Han, Hongwei Zeng, Liqun Kuang et al.

Abstract Human activity recognition has a wide range of applications in various fields, such as video surveillance, virtual reality and human–computer intelligent interaction. It has emerged as a significant research area in computer vision. GCN (Graph Convolutional networks) have recently been widely used in these fields and have made great performance. However, there are still some challenges including over-smoothing problem caused by stack graph convolutions and deficient semantics correlation to capture the large movements between time sequences. Vision Transformer (ViT) is utilized in many 2D and 3D image fields and has surprised results. In our work, we propose a novel human activity recognition method based on ViT (HAR-ViT). We integrate enhanced AGCL (eAGCL) in 2s-AGCN to ViT to make it process spatio-temporal data (3D skeleton) and make full use of spatial features. The position encoder module orders the non-sequenced information while the transformer encoder efficiently compresses sequence data features to enhance calculation speed. Human activity recognition is accomplished through multi-layer perceptron (MLP) classifier. Experimental results demonstrate that the proposed method achieves SOTA performance on three extensively used datasets, NTU RGB+D 60, NTU RGB+D 120 and Kinetics-Skeleton 400.

Zhiqiang Fan, Fangyue Chen, Xiaokai Xia et al.

EEG-based emotion recognition is a task that uses scalp-EEG data to classify the emotion states of humans. The study of EEG-based emotion recognition can contribute to a large spectrum of application fields including healthcare and human–computer interaction. Recent studies in neuroscience reveal that the brain regions and their interactions play an essential role in the processing of different stimuli and the generation of corresponding emotional states. Nevertheless, such regional interactions, which have been proven to be critical in recognizing emotions in neuroscience, are largely overlooked in existing machine learning or deep learning models, which focus on individual channels in brain signals. Motivated by this, in this paper, we present RGNet, a model that is designed to learn the regional level representation of EEG signal for accurate emotion recognition. Specifically, after applying preprocessing and feature extraction techniques on raw signals, RGNet adopts a novel region-wise encoder to extract the features of channels located within each region as input to compute the regional level features, enabling the model to effectively explore the regional functionality. A graph is then constructed by considering each region as a node and connections between regions as edges, upon which a graph convolutional network is designed with spectral filtering and learned adjacency matrix. Instead of focusing on only the spatial proximity, it allows the model to capture more complex functional relationships. We conducted experiments from the perspective of region division strategies, region encoders and input feature types. Our model has achieved <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>98.64</mn><mo>%</mo></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.33</mn><mo>%</mo></mrow></semantics></math></inline-formula> for Deap and Dreamer datasets, respectively. The comparison studies show that RGNet outperforms the majority of the existing models for emotion recognition from EEG signals.

Mônica Maria de Jesus Silva, Claudia Benedita dos Santos, Maria José Clapis

ABSTRACT Objectives: to elaborate and analyze the Pregnancy Depression Risk Scale psychometric properties. Methods: methodological research, in six steps: theoretical model empirical definition; elaboration of scale items with literature review; consultation with five professional health experts and 15 pregnant women; content validity with six experts; pre-test-semantic validity with 24 pregnant women; scale factor structure definition with 350 pregnant women; pilot study with 100 pregnant women, totaling 489 pregnant women and 11 experts. Data were analyzed by content analysis, exploratory factor analysis, multitrait-multimethod analysis and internal consistency. Results: sixty-eight risk factors were identified for item formulation. The final version of the scale consisted of 24 items in five domains. The scale demonstrated satisfactory construct content, semantic, validity and reliability. Conclusions: the scale proved to be valid in terms of content and semantics, with a factor structure defined according to the adopted theoretical model and satisfactory psychometric properties.

Alexey Neilo, Sergey Bakurskiy, Nikolay Klenov et al.

We have theoretically studied the transport properties of the SIsN<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mi>S</mi><mi>O</mi></mrow></msub></semantics></math></inline-formula>F structure consisting of thick (S) and thin (s) films of superconductor, an insulator layer (I), a thin film of normal metal with spin–orbit interaction (SOI) (N<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mi>S</mi><mi>O</mi></mrow></msub></semantics></math></inline-formula>), and a monodomain ferromagnetic layer (F). The interplay between superconductivity, ferromagnetism, and spin–orbit interaction allows the critical current of this Josephson junction to be smoothly varied over a wide range by rotating the magnetization direction in the single F-layer. We have studied the amplitude of the spin valve effect and found the optimal ranges of parameters.