Hasil untuk "Semantics"

Ulrich Plass

A. Wierzbicka

C. Fillmore

David L. Martin, M. Paolucci, Sheila A. McIlraith et al.

Claudia Maienborn, K. Heusinger, P. Portner

S. Abramsky, B. Coecke

Particular focus in this paper is on quantum information protocols, which exploit quantum-mechanical effects in an essential way. The particular examples we shall use to illustrate our approach will be teleportation (Benett et al., 1993), logic-gate teleportation (Gottesman and Chuang,1999), and entanglement swapping (Zukowski et al., 1993). The ideas illustrated in these protocols form the basis for novel and potentially very important applications to secure and fault-tolerant communication and computation (2001,1999,2000).

I. Niemelä

L. Meteyard, Sara Rodriguez Cuadrado, B. Bahrami et al.

J. Ardila

P. Baroni, Martin Wigbertus Antonius Caminada, M. Giacomin

Thomas Verma, J. Pearl

Dependency knowledge of the form “ x is independent of y once z is known” invariably obeys the four graphoid axioms, examples include probabilistic and database dependencies. Often, such knowledge can be represented efficiently with graphical structures such as undirected graphs and directed acyclic graphs (DAGs). In this paper we show that the graphical criterion called d-separation is a sound rule for reading independencies from any DAG based on a causal input list drawn from a graphoid. The rule may be extended to cover DAGs that represent functional dependencies as well as conditional dependencies.

P. Liang, Michael I. Jordan, D. Klein

Suppose we want to build a system that answers a natural language question by representing its semantics as a logical forxm and computing the answer given a structured database of facts. The core part of such a system is the semantic parser that maps questions to logical forms. Semantic parsers are typically trained from examples of questions annotated with their target logical forms, but this type of annotation is expensive.Our goal is to instead learn a semantic parser from question–answer pairs, where the logical form is modeled as a latent variable. We develop a new semantic formalism, dependency-based compositional semantics (DCS) and define a log-linear distribution over DCS logical forms. The model parameters are estimated using a simple procedure that alternates between beam search and numerical optimization. On two standard semantic parsing benchmarks, we show that our system obtains comparable accuracies to even state-of-the-art systems that do require annotated logical forms.

F. Pulvermüller

How brain structures and neuronal circuits mechanistically underpin symbolic meaning has recently been elucidated by neuroimaging, neuropsychological, and neurocomputational research. Modality-specific 'embodied' mechanisms anchored in sensorimotor systems appear to be relevant, as are 'disembodied' mechanisms in multimodal areas. In this paper, four semantic mechanisms are proposed and spelt out at the level of neuronal circuits: referential semantics, which establishes links between symbols and the objects and actions they are used to speak about; combinatorial semantics, which enables the learning of symbolic meaning from context; emotional-affective semantics, which establishes links between signs and internal states of the body; and abstraction mechanisms for generalizing over a range of instances of semantic meaning. Referential, combinatorial, emotional-affective, and abstract semantics are complementary mechanisms, each necessary for processing meaning in mind and brain.

Sampo Pyysalo, Filip Ginter, Hans Moen et al.

Ahmed Métwalli, Fares Fathy, Esraa Khatab et al.

Ant Colony Optimization (ACO) is a widely adopted metaheuristic for solving complex combinatorial problems; however, performance is often deteriorated by premature convergence and limited exploration in later iterations. Eclipse Randomness–Ant Colony Optimization (ER-ACO) is introduced as a lightweight ACO variant in which an exponentially fading randomness factor is integrated into the state-transition mechanism. Strong early-stage exploration is enabled, and a smooth transition to exploitation is induced, improving convergence behavior and solution quality. Low computational overhead is maintained while exploration and exploitation are dynamically balanced. ER-ACO is positioned within real-time healthcare logistics, with a focus on Emergency Medical Services (EMS) routing and hospital resource scheduling, where rapid and adaptive decision-making is critical for patient outcomes. These systems face dynamic constraints such as fluctuating traffic conditions, urgent patient arrivals, and limited medical resources. Experimental evaluation on benchmark instances indicates that solution cost is reduced by up to 14.3% relative to the slow-fade configuration (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>γ</mi><mo>=</mo><mn>1</mn></mrow></semantics></math></inline-formula>) in the 20-city TSP sweep, and faster stabilization is indicated under the same iteration budget. Additional comparisons against Standard ACO on TSP/QAP benchmarks indicate consistent improvements, with unchanged asymptotic complexity and negligible measured overhead at the tested scales. TSP/QAP benchmarks are used as controlled proxies to isolate algorithmic behavior; EMS deployment is treated as a motivating application pending validation on EMS-specific datasets and formulations. These results highlight ER-ACO’s potential as a lightweight optimization engine for smart healthcare systems, enabling real-time deployment on edge devices for ambulance dispatch, patient transfer, and operating room scheduling.

Pujue Wang, Jiayi Cheng, Hongyun Liu

Data-driven approaches have emerged as powerful tools for analyzing process data. This study focuses on two data-driven methods: n-gram chi-square feature selection for extracting key action segments and K-medoids clustering combined with Dynamic Time Warping (DTW) distance for identifying behavioral patterns. To address the limitations that arise when applying these methods to complex tasks where ambiguous raw actions often hinder interpretation, this study introduces distance-based effectiveness indicators to enhance both data-driven methods for analyzing actions in the context of complex problem-solving. The research examines how representing action sequences through state effectiveness (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>d</mi></mrow><mrow><mi>s</mi></mrow></msub></mrow></semantics></math></inline-formula>) and transition effectiveness (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="sans-serif">Δ</mi><msub><mrow><mi>d</mi></mrow><mrow><mi>s</mi><mo>→</mo><msup><mrow><mi>s</mi></mrow><mrow><mo>′</mo></mrow></msup></mrow></msub></mrow></semantics></math></inline-formula>) indicators outperforms the use of raw actions alone within the complex collaborative problem-solving Balance Beam task. Results consistently demonstrated that effectiveness indicators significantly improved the sensitivity of n-gram feature selection, the performance of clustering, and the interpretability of both n-grams and resulting clusters. Specifically, state effectiveness representations (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>d</mi></mrow><mrow><mi>s</mi></mrow></msub><mo>→</mo><msub><mrow><mi>d</mi></mrow><mrow><msup><mrow><mi>s</mi></mrow><mrow><mo>′</mo></mrow></msup></mrow></msub></mrow></semantics></math></inline-formula>) yielded the best outcomes. These findings advocate for the integration of effectiveness indicators into data-driven process analytics to more effectively capture and explain behavioral patterns of problem-solving.

Linkai Li, Xiaohuang Hu, Yuheng Xing et al.

Inspired by the well-known experimental connections between <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>X</mi><mo>(</mo><mn>3872</mn><mo>)</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mi>Z</mi><mrow><mi>c</mi><mi>s</mi></mrow></msub><mrow><mo>(</mo><mn>4220</mn><mo>)</mo></mrow></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>Y</mi><mo>(</mo><mn>4620</mn><mo>)</mo></mrow></semantics></math></inline-formula>, we systematically study the recently reported strange partner of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>T</mi><mrow><mi>c</mi><mi>c</mi></mrow></msub></semantics></math></inline-formula>, the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>1</mn><mo>+</mo></msup></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>c</mi><mi>c</mi><mover accent="true"><mi>q</mi><mo>¯</mo></mover><mover accent="true"><mi>s</mi><mo>¯</mo></mover></mrow></semantics></math></inline-formula> system, and its orbital excitation state <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>1</mn><mo>−</mo></msup></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>c</mi><mi>c</mi><mover accent="true"><mi>q</mi><mo>¯</mo></mover><mover accent="true"><mi>s</mi><mo>¯</mo></mover></mrow></semantics></math></inline-formula>. A chiral quark model incorporating SU(3) symmetry is considered to study these two systems. To better investigate their spatial structure, we introduce a precise few-body calculation method, the Gaussian Expansion Method (GEM). In our calculations, we include all possible physical channels, including molecular states and diquark structures, and consider channel coupling effects. To identify the stable structures in the system (bound states and resonance states) we employ a powerful resonance search method, the Real-Scaling Method (RSM). According to our results, in the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>1</mn><mo>+</mo></msup></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>c</mi><mi>c</mi><mover accent="true"><mi>q</mi><mo>¯</mo></mover><mover accent="true"><mi>s</mi><mo>¯</mo></mover></mrow></semantics></math></inline-formula> system, we obtain two bound states with energies of 3890 MeV and 3940 MeV, as well as two resonance states with energies of 3975 MeV and 4090 MeV. The decay channels of these two resonance states are <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><msubsup><mi>D</mi><mi>s</mi><mo>∗</mo></msubsup></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>D</mi><mo>∗</mo></msup><msub><mi>D</mi><mi>s</mi></msub></mrow></semantics></math></inline-formula>, respectively. In the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mn>1</mn><mo>−</mo></msup></semantics></math></inline-formula><inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>c</mi><mi>c</mi><mover accent="true"><mi>q</mi><mo>¯</mo></mover><mover accent="true"><mi>s</mi><mo>¯</mo></mover></mrow></semantics></math></inline-formula> system, we obtain only one resonance state, with an energy of 4570 MeV, and two main decay channels: <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>D</mi><msubsup><mi>D</mi><mrow><mi>s</mi><mn>1</mn></mrow><mo>∗</mo></msubsup></mrow></semantics></math></inline-formula> and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>D</mi><mo>∗</mo></msup><msubsup><mi>D</mi><mrow><mi>s</mi><mn>1</mn></mrow><mo>′</mo></msubsup></mrow></semantics></math></inline-formula>. We strongly suggest that experimental groups use our predictions to search for these stable structures.

David M Kahn, Jan Hoffmann, Runming Li

As is evident in the programming language literature, many practitioners favor specifying dynamic program behavior using big-step over small-step semantics. Unlike small-step semantics, which must dwell on every intermediate program state, big-step semantics conveniently jumps directly to the ever-important result of the computation. Big-step semantics also typically involves fewer inference rules than their small-step counterparts. However, in exchange for ergonomics, big-step semantics gives up power: Small-step semantics describes program behaviors that are outside the grasp of big-step semantics, notably divergence. This work presents a little-known extension of big-step semantics with inductive definitions that captures diverging computations without introducing error states. This big-stop semantics is illustrated for typed, untyped, and effectful variants of PCF, as well as a while-loop-based imperative language. Big-stop semantics extends the standard big-step inference rules with a few additional rules to define an evaluation judgment that is equivalent to the reflexive-transitive closure of small-step transitions. This simple extension contrasts with other solutions in the literature that sacrifice ergonomics by introducing many additional inference rules, global state, and/or less-commonly-understood reasoning principles like coinduction. The ergonomics of big-stop semantics is exemplified via concise Agda proofs for some key results and compilation theorems.

Ryo Takemura

We investigate the completeness of intuitionistic logic with respect to Prawitz's proof-theoretic validity. As an intuitionistic natural deduction system, we apply atomic second-order intuitionistic propositional logic. By developing phase semantics with proof-terms introduced by Okada & Takemura (2007), we construct a special phase model whose domain consists of closed terms. We then discuss how our phase semantics can be regarded as proof-theoretic semantics, and we prove completeness with respect to proof-theoretic semantics via our phase semantics.

Malik Muhammad Waqar, Hassan Ali, Heng Zhou et al.

In shrimp farming, determining the physical traits of shrimp is vital for assessing their health and growth. One of the critical traits is their size, as it serves as a key indicator of growth rates, biomass, and effective feed management. However, the accurate measurement of shrimp size is challenged by factors such as their naturally curved body posture, frequent overlapping among individuals, and their tendency to blend with the background, all of which hinder precise size estimation. Traditional methods for measuring the size of shrimp involve manual sampling, which is labor-intensive and time consuming. In contrast, image processing and classical computer vision techniques provide some reasonable results but often suffer from inaccuracies, making them unsuitable for large-scale monitoring. To address this problem, this paper proposes a dual-segmentation deep learning-based framework for accurately estimating shrimp size. It integrates instance segmentation using the RTMDet-m model with an enhanced semantic segmentation model to effectively predict the centerline of the shrimp’s body, enabling precise size measurements. The first stage employs the RTMDet-m model for the instance segmentation of shrimp, achieving an average precision (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>AP</mi><mn>50</mn></msub></semantics></math></inline-formula>) of 96% with fewer parameters and the highest frames per second (FPS) count among state-of-the-art models. The second stage utilizes our custom segmentation model for centerline predictive module, attaining the highest FPS and F1-score of 88.3%. The proposed framework achieves the lowest mean absolute error of 1.02 cm and a root mean square error of 1.27 cm in shrimp size estimation compared to the baseline methods discussed in comparative study sections. Our proposed dual-segmentation framework outperforms both traditional and deep learning based methods used for measuring shrimp size.