Static Semantics Research Articles

Morpho-orthographic segmentation on visual word recognition in Brazilian Portuguese speakers

ObjectiveStudies have demonstrated a morphological decomposition process in the initial stages of visual word recognition based on orthographically defined morphemes and independent of semantic and lexical information. The present study sought to investigate this phenomenon in adult Brazilian Portuguese-speaking readers.MethodParticipants performed a lexical decision task preceded by primes at two Stimulus Onset Asynchrony (SOA): 33 or 250 ms. The primes had one of the following relations with the target word: morphological (porteiro [DOORKEEPER]–PORTA [DOOR]), morpho-orthographic (cordeiro [LAMB]–CORDA [ROPE]), orthographic (abril [APRIL]–ABRIU [OPENED]), semantic (abelha [BEE]–MEL [HONEY]), or no relation (pessoa [PERSON]–DADO [DICE]).ResultsPriming effects were observed for the morphological and semantic conditions at both SOAs but not for the orthographic and morpho-orthographic conditions.ConclusionOur results suggest that semantic representations mediate morphological priming in Brazilian Portuguese since the early stages of visual word recognition.

Construction of consistent SysML models applied to the CPS

With the increasing complexity of cyber-physical systems (CPS), it is interesting to decompose a CPS into sub-systems. This provides greater modularity and flexibility so that each system can be developed independently, making it easier to maintain. Also, it can improve its fault tolerance. However, this decomposition of the system can lead to inconsistency. This paper proposes an approach for early verification of cyber-physical systems decomposition using SysML. We address the limitations of SysML as a semi-formal language by introducing syntax and static semantics for its structural diagrams. The aim is to verify structural consistency before defining behavioral aspects. For that, the proposed approach verifies a set of structural consistency rules within a refinement relation to ensure that sub-components offer at least the same services as the abstract block and require the same services. Furthermore, the sub-blocks must satisfy all the requirements that the abstract block is supposed to verify. We used the CyCab as a case study to demonstrate the effectiveness of this approach.

Vulnerability to semantic and phonological interference in normal aging and amnestic mild cognitive impairment (aMCI).

To determine whether the increased vulnerability to semantic interference previously observed in amnestic mild cognitive impairment (aMCI) is specifically associated with semantic material or if it also affects other types of material, suggesting generalized executive and inhibitory impairment. Seventy-two participants divided into two groups (33 aMCI, 39 normal control [NC]) matched for age and education were included. They completed a comprehensive neuropsychological examination, the French version of the Loewenstein Acevedo Scale for Semantic Interference and Learning (LASSI-L; semantic interference test), and a homologous experimental phonological test, the phonological interference and learning test. Independent sample t tests, mixed analysis of variance (ANOVA), and analysis of covariance (ANCOVA) on memory and interference scores were conducted to compare memory and interference in both conditions for both groups. For memory scores, results revealed significant main effects of group (NC > aMCI) and condition (semantic > phonological) and significant interactions (poorer performance in the semantic condition for aMCI). aMCI committed more phonological false recognition errors, were disproportionately more vulnerable to retroactive semantic interference, and showed a higher percentage of intrusion errors associated with proactive semantic interference than NC. To our knowledge, this is the first study to compare vulnerability to interference in aMCI and normal aging with two similarly designed semantic and phonological word list learning tasks. Taken together, our results suggest that aMCI present with broad difficulties in source memory and inhibition, but that impaired deep semantic processing results in additional semantic intrusion errors during proactive interference and impacts their ability to show good recall after an interference list (greater semantic retroactive interference). Results are discussed according to the level-of-processing and activation/monitoring theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Insertional code-switching as interactional resource in Mandarin–English bilingual conversation

Aims and Objectives: This study investigates the interactional relevance of insertional code-switching, that is, insertion of other-language words/phrases, in Mandarin–English bilingual conversation. It focuses on a next speaker’s (dis)alignment with an insertion in an adjacency pair and its import to the ongoing interaction. Methodology: This study adopts the framework of conversation analysis, which seeks to describe the underlying organization of talk-in-interaction. It features analyses warranted by techniques and signals in conversation that are demonstrably relevant to conversation participants. Data and Analysis: The data come from CallFriend Mandarin Corpus. Speakers are native speakers of Mandarin Chinese who resided in the United States at the time of recording. First, all instances of insertional code-switching were extracted with the extended context, filtering out those not occurring in an adjacency pair. Next, sequential analysis was conducted to examine the interactional import of a next speaker’s alignment and disalignment, respectively, when insertional code-switching occurs in an adjacency pair. Findings: This study discovers that a next speaker’s alignment with an insertion is sequentially preferred over disalignment. When different language choice over the same word occurs, speakers orient to it as problematic and then repair it, such that, alignment is achieved. Disalignment is used to claim the current speaker’s more authoritative status in the matter concerned. Originality: A large bulk of literature on insertional code-switching has focused on its syntactic constraints and semantic conditions; its interactional functions in bilingual conversation have not received as much attention. This study reveals that insertional code-switching works as an extra resource for bilingual speakers to achieve interactional alignment. Significance: This study challenges the view that insertional code-switching is not sequentially implicative by showing that it does place sequential constraints on the next speaker. It further argues that such sequential implicativeness can be exploited to establish finely calibrated alignment and index epistemic status.

First-Order Temporal Logic on Finite Traces: Semantic Properties, Decidable Fragments, and Applications

Formalisms based on temporal logics interpreted over finite strict linear orders, known in the literature as finite traces , have been used for temporal specification in automated planning, process modelling, (runtime) verification and synthesis of programs, as well as in knowledge representation and reasoning. In this article, we focus on first-order temporal logic on finite traces . We first investigate preservation of equivalences and satisfiability of formulas between finite and infinite traces, by providing a set of semantic and syntactic conditions to guarantee when the distinction between reasoning in the two cases can be blurred. Moreover, we show that the satisfiability problem on finite traces for several decidable fragments of first-order temporal logic is ExpSpace -complete, as in the infinite trace case, while it decreases to NExpTime when finite traces bounded in the number of instants are considered. This leads also to new complexity results for temporal description logics over finite traces. Finally, we investigate applications to planning and verification, in particular by establishing connections with the notions of insensitivity to infiniteness and safety from the literature.

A Mereological Reading of the Dictum de Omni et Nullo

Abstract When Aristotle introduces the complete moods, he refers back to the dictum de omni et nullo, a semantic condition for universal affirmations and negations. There recently has been renewed interest in the question whether the dictum validates the assertoric syllogistic. I rehearse evidence that Aristotle provides a mereological semantics for universal affirmations and negations, and note that this semantics entails a nonstandard reading of the dictum, under which the dictum, in the presence of a minimal logical apparatus, indeed validates the assertoric syllogistic. I argue that this mereological validation offers advantages over recent discussions in Morison, Malink, Ebert and Vlasits.

Hierarchical Perceptual Graph Attention Network for Knowledge Graph Completion

Knowledge graph completion (KGC), the process of predicting missing knowledge through known triples, is a primary focus of research in the field of knowledge graphs. As an important graph representation technique in deep learning, graph neural networks (GNNs) perform well in knowledge graph completion, but most existing graph neural network-based knowledge graph completion methods tend to aggregate neighborhood information directly and individually, ignoring the rich hierarchical semantic structure of KGs. As a result, how to effectively deal with multi-level complex relations is still not well resolved. In this study, we present a hierarchical knowledge graph completion technique that combines both relation-level and entity-level attention and incorporates a weight matrix to enhance the significance of the embedded information under different semantic conditions. Furthermore, it updates neighborhood information to the central entity using a hierarchical aggregation approach. The proposed model enhances the capacity to capture hierarchical semantic feature information and is adaptable to various scoring functions as decoders, thus yielding robust results. We conducted experiments on a public benchmark dataset and compared it with several state-of-the-art models, and the experimental results indicate that our proposed model outperforms existing models in several aspects, proving its superior performance and validating the effectiveness of the model.

THE USE OF INTERROGATIVE PRONOUNS AS INDEFINITE IN CONTEMPORARY SERBIAN

The paper aims at identifying the semantic conditions allowing the use of interrogative pronouns instead of indefinite ones without change in meaning, in the case of the Serbian language. To this end, first of all, a pattern of the meaning of indefiniteness characteristic of indefinite pronouns is outlined based on observations on the language of four contem- porary dramas. The paper uses a componential analysis borrowed from another author and adapted for the purposes of the present study, highlighting relevant differential features in the semantic invariant, as well as a syntactic-grammatical analysis of the context, dividing it into two domains. The variants of the meaning of indefiniteness thus identified are applied to the uses of interrogatives in the role of indefinite pronouns extracted from the electronic corpus of Serbian. The pattern of differential features enabling the use in question is highlighted

Do Counterexamples on the Kāraka Rules A. 1.4.33–36 in the Kāśikāvr̥tti Serve Any Purpose?

Ajotikar et al. (2016) claim that most of the counterexamples provided in the Kāśikāvr̥tti conform to the distinctive feature of a counterexample, namely, having all the conditions stated in the rule except one (ekāṅgavikalatā). Ajotikar (2021) discusses how a variant reading for a counterexample helps understand the relation between two operational rules. This article adds one more aspect to the importance of counterexamples. However, there are some cases where the purpose of the counterexample of a complex semantic condition is not clear. In this article, I study counterexamples provided on the sūtras, A. 1.4.33-36, in the kāraka section, on which Patañjali did not comment. These sūtras are chosen for discussion because the counterexamples available on these sūtras are first provided in the Kāśikāvr̥tti. When it comes to the issue of complex semantic conditions (priyamāṇa, jñīpsyamāna, īpsita or uttamarṇa) stated in A. 1.4.33-36, it is difficult to justify the usefulness of the available counterexamples. After carefully examining Bhatr̥hari’s views along with Helārāja’s explanation, it is evident that these counterexamples must have been included in order to fulfil the criteria of a vr̥tti. A vr̥tti typically includes an example, a counterexample and a supplementary word, which are necessary to complete the meaning of the rule. However, these counterexamples fail to justify the significance of the semantic conditions stated in the rule for which they are provided. Hence they do not serve any purpose.

Detection of illicit phrasal movement in Huntington’s disease

ABSTRACT The role of the basal ganglia has been a longstanding issue in neural language models. Huntington’s disease (HD) shows primary impairment in the striatum and has previously been shown to affect the processing of phrase-structural hierarchies that are built by phrasal movement (e.g. in passives). Here we asked patients with HD to judge the acceptability of sentences containing different types of illicit phrasal movement, which were contrasted with semantic violations involving no movement. A logistic mixed-effects regression showed that patients had a profound impairment in judging incorrect but not correct sentences across all types of illicit movement, while the semantic condition was also affected, but significantly less so. Adding neuropsychological variables to the model did not improve predictions. These results demonstrate a loss of cognitive control, worsening with disease progression, over phrase-structural hierarchies, which extends to forms of meaning built at sentential levels.

On the functional brain networks involved in tool-related action understanding

Tool-use skills represent a significant cognitive leap in human evolution, playing a crucial role in the emergence of complex technologies. Yet, the neural mechanisms underlying such capabilities are still debated. Here we explore with fMRI the functional brain networks involved in tool-related action understanding. Participants viewed images depicting action-consistent (e.g., nail-hammer) and action-inconsistent (e.g., scarf-hammer) object-tool pairs, under three conditions: semantic (recognizing the tools previously seen in the pairs), mechanical (assessing the usability of the pairs), and control (looking at the pairs without explicit tasks). During the observation of the pairs, task-based left-brain functional connectivity differed within conditions. Compared to the control, both the semantic and mechanical conditions exhibited co-activations in dorsal (precuneus) and ventro-dorsal (inferior frontal gyrus) regions. However, the semantic condition recruited medial and posterior temporal areas, whereas the mechanical condition engaged inferior parietal and posterior temporal regions. Also, when distinguishing action-consistent from action-inconsistent pairs, an extensive frontotemporal neural circuit was activated. These findings support recent accounts that view tool-related action understanding as the combined product of semantic and mechanical knowledge. Furthermore, they emphasize how the left inferior parietal and anterior temporal lobes might be considered as hubs for the cross-modal integration of physical and conceptual knowledge, respectively.

Modulating Complex Sentence Processing in Aphasia Through Attention and Semantic Networks.

Lexical processing impairments such as delayed and reduced activation of lexical-semantic information have been linked to syntactic processing disruptions and sentence comprehension deficits in individuals with aphasia (IWAs). Lexical-level deficits can also preclude successful lexical encoding during sentence processing and amplify the processing costs of similarity-based interference during syntactic retrieval. We investigate whether two manipulations to engage attention and pre-activate semantic features of a target (to-be-retrieved) noun will (a) boost lexical activation during initial lexical encoding and (b) facilitate syntactic dependency linking through improved resolution of interference in IWAs and neurologically unimpaired age-matched controls (AMCs). Eye-tracking-while-listening with a visual world paradigm was used to investigate whether semantic and attentional manipulations modulated initial lexical processing and downstream syntactic retrieval of the direct-object noun in object-relative sentences. In the attention and semantic manipulations, the AMC group showed no changes in initial lexical access levels; however, gaze patterns revealed clear facilitations in dependency linking and interference resolution. In the IWA group, the attentional cue increased and maintained activation of N1 with modest facilitations in dependency linking. In the semantic condition, IWA results showed a greater degree of facilitation during dependency linking. The results suggest that attention and semantic activation are parameters that may be manipulated to strengthen encoding of lexical representations to facilitate retrieval (i.e., dependency linking) and mitigate similarity-based interference. In IWAs, these manipulations may help to reduce lexical processing deficits that can preclude successful encoding.

Cross-language morphological transfer in similar-script bilinguals

The current study explored cross-language morphological transfer mechanisms using a similar-script morphological translation priming paradigm in highly proficient unbalanced Turkish (first language; L1)–English (second language; L2) bilinguals. Using noncognate English and Turkish stimuli that shared a similar meaning with no form overlap (e.g., ice [Eng.] – buz [Tur.]), in Experiment 1, L2 English stem targets (e.g., ICE) were primed by affixed L1 nonwords (e.g., buzca [iceish]), nonaffixed L1 nonwords (e.g., buznak [iceald]), and unrelated L1 nonwords (e.g., tuşku [keyment]). The results revealed priming effects in both the affixed and nonaffixed nonword conditions relative to the unrelated control, and significantly larger priming in the affixed than the nonaffixed condition. In addition, enhanced cross-language morphological transfer effects were evidenced in bilinguals with an earlier age of L2 acquisition. In Experiment 2, English stem targets (e.g., ICE) were primed by nonaffixed L1 nonwords including translated stems (e.g., buznak [iceald]), semantically related stems (e.g., suzur [waterew]), and unrelated L1 nonwords (e.g., tuşzur [keyew]). The results showed significantly larger priming effects in the translated condition compared with the semantic and unrelated control conditions, with no priming in the semantic condition relative to the unrelated condition, suggesting that cross-language morphological priming effects were specifically due to the lexico-semantic relationship between the embedded word and its translation equivalent.

Comparative Analysis of the Semantic Conditions of LoD3 3D Building Model Based on Aerial Photography and Terrestrial Photogrammetry

3D modeling of buildings is an important method in mapping and modeling the built environment. In this study, we analyzed the differences between the semantic state of actual buildings and 3D models of LoD3 buildings generated using aerial and terrestrial photogrammetric methods. We also evaluated the accuracy of the visual representation as well as the suitability of the building geometry and texture. Our method involves collecting aerial and terrestrial photographic data and processing it using SFM (structure from motion) technology. The photogrammetric data was then processed using image matching algorithms and 3D reconstruction techniques to generate 3D models of LoD3 buildings. The actual semantic state of the building was identified through field surveys and reference data collection. The 3D building model was successfully modeled from 1201 photos and 19 ground control points. The results of the evaluation of the geometry accuracy test, dimensions and semantic completeness of the 3D model, the use of aerial photographs and terrestrial photogrammetry in LoD3 3D modeling are assessed from the results of the automatic 3D modeling process using SfM (Structure from Motion) technology that produces 3D building models in Level of Detail (LoD) 3 with Root Mean Square Error values <0.5 meters and has semantic completeness of the building in accordance with the original object based on the City Geography Markup Language (CityGML) standard. The facade formed from the modeling almost follows the original model such as doors, windows, hallways, etc.

The Korean Speech Recognition Sentences: A Large Corpus for Evaluating Semantic Context and Language Experience in Speech Perception.

The aim of this study was to develop and validate a large Korean sentence set with varying degrees of semantic predictability that can be used for testing speech recognition and lexical processing. Sentences differing in the degree of final-word predictability (predictable, neutral, and anomalous) were created with words selected to be suitable for both native and nonnative speakers of Korean. Semantic predictability was evaluated through a series of cloze tests in which native (n = 56) and nonnative (n = 19) speakers of Korean participated. This study also used a computer language model to evaluate final-word predictabilities; this is a novel approach that the current study adopted to reduce human effort in validating a large number of sentences, which produced results comparable to those of the cloze tests. In a speech recognition task, the sentences were presented to native (n = 23) and nonnative (n = 21) speakers of Korean in speech-shaped noise at two levels of noise. The results of the speech-in-noise experiment demonstrated that the intelligibility of the sentences was similar to that of related English corpora. That is, intelligibility was significantly different depending on the semantic condition, and the sentences had the right degree of difficulty for assessing intelligibility differences depending on noise levels and language experience. This corpus (1,021 sentences in total) adds to the target languages available in speech research and will allow researchers to investigate a range of issues in speech perception in Korean. https://doi.org/10.23641/asha.24045582.

Spatio-Temporal Adaptive Network With Bidirectional Temporal Difference for Action Recognition

Action Recognition is a fundamental task in computer vision field, with a wide range of applications in autonomous driving, security monitoring, etc. However, previous action recognition approaches usually suffer from the inappropriate spatio-temporal modeling or high computational consumption (e.g., 3D CNN). In this paper, we propose a novel Spatio-Temporal Adaptive Network (STANet) with bidirectional temporal difference, consisting of a Temporal Adaptive module (TA) and a Spatial Adaptive (SA) module, to sufficiently extract the crucial motion information and model the spatial pivotal appearance information from both forward and backward perspectives, respectively. Specifically, the Temporal Adaptive module uses bidirectional temporal differences to learn valuable motion trends and balance the static semantics and dynamic motion for a certain action during information fusion; while the Spatial Adaptive module uses the bidirectional temporal difference to obtain the spatio-channel attention to stress the discriminative position-relevant and semantic-relevant appearance features. Extensive experiments conducted on widely-used action recognition benchmarks UCF-101, HMDB-51, Something-Something V1, and Kinetics-400 prove the effectiveness of the proposed methods compared to other state-of-the-art approaches.

A K-Net-based hybrid semantic segmentation method for extracting lake water bodies

Lakes have a crucial impact on natural disaster prevention, resource recycling, maintenance of agricultural production and daily life. The traditional way of acquiring lake water body information lacks efficiency, is dangerous, and is not suitable for lake water body information acquisition and real-time monitoring. For this reason, the automated lake water body extraction method based on deep learning semantic segmentation model is gradually becoming a mainstream method. However, most of the semantic segmentation models used for lake extraction today express features through static semantics, while ignoring the extraction relationships of different convolutional kernels for these features. In order to better extract lake water bodies from remote sensing images, this paper proposes a hybrid semantic segmentation method based on K-Net, which achieves high accuracy extraction of lake water bodies by introducing dynamic semantic kernels to iteratively refine the feature information. The superiority of the K-Net-based hybrid model on a Google remote sensing image dataset of lakes is validated. The experimental results show that (1) the hybrid model is able to achieve accurate extraction of lake water bodies, with the UperNet + K-Net model using Swin-l performing the best among all six evaluation metrics, with mean intersection over union (mIoU) reaching 97.77%; and that (2) after incorporating the K-Net module, all tested models obtain a larger mIoU than before.

Dual-Aligned Feature Confusion Alleviation for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) aims to recognize both seen and unseen samples by leveraging the connections between semantic and visual representations. Recently, a majority of GZSL methods focus on generating visual features for unseen categories conditioned on category-level semantic attributes. However, there is a considerable gap between generated features and real unseen features since the generator is trained with only seen samples. The final classifier may get confused by the unfaithful generated features and make misclassification. To alleviate this issue, we propose a dual-aligned feature confusion alleviation (DFCA) framework that simultaneously generates faithful and discriminative features for unseen categories. Specifically, our DFCA attains the faithfulness via a conditional invertible neural network (cINN) and aligns the generated visual features and reconstructed semantic conditions with their real counterparts, respectively. To further encourage distinguishable synthetic features, we learn discriminative category-level semantic conditions for cINN with an attributes mapping layer. To verify the proposed method, we conduct extensive experiments on five widely used benchmarks. Experimental results show that our method outperforms previous state-of-the-arts and successfully alleviates features confusion problem in GZSL. For instance, our method achieves the best performance in terms of seen accuracy, unseen accuracy and harmonic mean accuracy on FLO.

Positive cross-linguistic influence in the representation and processing of sentence-final particle le by L2 and heritage learners of Chinese.

This study investigates the representation and processing of written Chinese sentences subject to a semantic condition (i.e., "direction of change") attached to the sentence-final particle (SFP) le in Mandarin Chinese. Three groups of bilingual speakers of Chinese and English who differ in their onset age of bilingualism and proficiency of English were studied. It was anticipated that there would be a positive cross-linguistic influence (CLI) from English due to similarities between the SFP le and the English adverb already in terms of direct semantic transfer. An acceptability judgment (AJ) task and a self-paced reading (SPR) task were conducted to elicit judgment and processing difficulty with and without semantic violations. The participants included English-dominant second language (L2) learners (n = 18) and heritage learners (n = 19) who had advanced proficiency in Chinese, as well as monolingually raised Mandarin speakers from China as a baseline control group (n = 18). The results indicated that sensitivity to violations of the semantic condition varied depending on factors such as the specific structure (noun vs. verb phrase), the task type (offline vs. online), and the type of bilingual speaker (early vs. late). Among the three groups of bilinguals, the heritage learners demonstrated a representation of the semantic condition that resembled the target language across different sentence structures, whereas the L2 learners did not. Furthermore, the heritage learners exhibited earlier sensitivity to violations during online processing compared to the baseline control group. These exceptional results can be attributed to the heritage learners' early exposure to and positive CLI between the SFP le in Mandarin and the English adverb already.

Monitoring Arithmetic Temporal Properties on Finite Traces

We study monitoring of linear-time arithmetic properties against finite traces generated by an unknown dynamic system. The monitoring state is determined by considering at once the trace prefix seen so far, and all its possible finite-length, future continuations. This makes monitoring at least as hard as satisfiability and validity. Traces consist of finite sequences of assignments of a fixed set of variables to numerical values. Properties are specified in a logic we call ALTLf, combining LTLf (LTL on finite traces) with linear arithmetic constraints that may carry lookahead, i.e., variables may be compared over multiple instants of the trace. While the monitoring problem for this setting is undecidable in general, we show decidability for (a) properties without lookahead, and (b) properties with lookahead that satisfy the abstract, semantic condition of finite summary, studied before in the context of model checking. We then single out concrete, practically relevant classes of constraints guaranteeing finite summary. Feasibility is witnessed by a prototype implementation.