Semantic Distinctions Research Articles

Modal semantics for reasoning with probability and uncertainty

Abstract This paper belongs to the field of probabilistic modal logic, focusing on a comparative analysis of two distinct semantics: one rooted in Kripke semantics and the other in neighbourhood semantics. The primary distinction lies in the following: The latter allows us to adequately express belief functions (lower probabilities) over propositions, whereas the former does not. Thus, neighbourhood semantics is more expressive. The main part of the work is a section in which we study the modal equivalence between probabilistic Kripke models and a subclass of belief neighbourhood models, namely additive ones. We study how to obtain modally equivalent structures.

Analysing A/O Possession in Māori-Language Tweets

This article contributes the first corpus-based study of possession in Māori, the indigenous language of Aotearoa New Zealand. Like most Polynesian languages, Māori has a dual possessive system involving a choice between the so-called A and O categories. While Māori grammars describe these categories in terms of the inherent semantic relationship between the possessum and possessor, there have been no large-scale corpus analyses demonstrating their use in natural contexts. Social media provide invaluable opportunities for such linguistic studies, capturing contemporary language use while alleviating the burden of gathering data through traditional means. We operationalise semantic distinctions to investigate possession in Māori-language tweets, focusing on the [possessum a/o possessor] construction (e.g., te tīmatanga o te wiki ‘the beginning of the week’). In our corpus comprising 2500 tweets produced by more than 200 individuals, we find that users leverage a wide array of noun types encompassing many different semantic relationships. We observe not only the expected predominance of the O category, but also a tendency for examples described by Māori grammars as A-marked to instead be O-marked (59%). Although the A category persists in the corpus, our findings suggest that language change could be underway. Our primary dataset can be explored interactively online.

Not so peculiar after all: On the normal position of arguments of German experiencer-object verbs

The present paper argues that seemingly erratic linearisation patterns of experiencer-object verbs in German can be accounted for by considering well-known linearisation constraints. The analysis is based on two Two-Alternative Forced-Choice experiments: The first study tests experiencer-object verbs with inanimate subjects, the second one tests experiencer-object verbs with animate subjects. If the subject is inanimate, experiencer-object verbs selecting a dative object (e.g., behagen ‘to please’) prefer an object-before-subject linearisation while the ones selecting an accusative object (e.g., bezaubern ‘to charm’) lean towards subject before object. With animate subjects, accusative-object experiencer-object verbs prefer subject before object, while there is no clear preference for dative-object experiencer-object verbs. An explorative investigation reveals verb-specific differences that call into question the case-based classes. We argue that linearisation preferences of experiencer-object verbs in German should be analysed by coupling free base generation with violable linearisation constraints. We provide an analysis along these lines, which is based on a semantic distinction and does not require case-based constraints.

Aspectual reduplication in Sign Language of the Netherlands: reconsidering phonological constraints and aspectual distinctions

Abstract This study investigates the use of predicate reduplication to express aspectual meaning in Sign Language of the Netherlands (NGT). The study focuses on three aspect types that have been found to be encoded by reduplication across sign languages – habitual, continuative, and iterative – and addresses potential phonological restrictions. Naturalistic corpus data and data elicited from six deaf NGT signers were taken into account. The results suggest that (i) predicate reduplication can express all three aspect types, but it is optional; (ii) reduplication expressing habitual and continuative aspect appears to be phonologically constrained; and (iii) such phonological constraints do not apply to iterative reduplication, whose form is different from the other two aspects, in that the reduplication cycles are separated by pauses. Since there is no formal difference between habituals and continuatives in the data, it is suggested that this semantic distinction may not be grammaticalized in the language, and that, possibly, the inflectional system of NGT instead more broadly distinguishes imperfective/perfective viewpoint. While this latter suggestion is in line with findings reported for many spoken languages, the results are different from what has previously been described for NGT as well as for other sign languages. Potential explanations for these differences can be found in both methodological and sociolinguistic factors.

Revisiting the Transferability of Few-Shot Image Classification: A Frequency Spectrum Perspective.

Few-shot learning, especially few-shot image classification (FSIC), endeavors to recognize new categories using only a handful of labeled images by transferring knowledge from a model trained on base categories. Despite numerous efforts to address the challenge of deficient transferability caused by the distribution shift between the base and new classes, the fundamental principles remain a subject of debate. In this paper, we elucidate why a decline in performance occurs and what information is transferred during the testing phase, examining it from a frequency spectrum perspective. Specifically, we adopt causality on the frequency space for FSIC. With our causal assumption, non-causal frequencies (e.g., background knowledge) act as confounders between causal frequencies (e.g., object information) and predictions. Our experimental results reveal that different frequency components represent distinct semantics, and non-causal frequencies adversely affect transferability, resulting in suboptimal performance. Subsequently, we suggest a straightforward but potent approach, namely the Frequency Spectrum Mask (FRSM), to weight the frequency and mitigate the impact of non-causal frequencies. Extensive experiments demonstrate that the proposed FRSM method significantly enhanced the transferability of the FSIC model across nine testing datasets.

How do childhood and children's rights mean what they mean? Innovating the debate around the social semantics of childhood and children's rights through an interdisciplinary approach

AbstractThis article discusses children's rights as social semantics, approaching them as a form of self‐description of a paradoxical relationship that has emerged from the late twentieth century within several social systems, a relationship between generational order and children's position as holders of human rights. Charles Taylor's theory on the evolution of the semantics of human value is combined with a wide interdisciplinary array of contributions from Childhood Studies, Social Work, Pedagogy, Studies on Constitutionalism to propose an innovative social ontology of children's rights. Although the UNCRC has been the object of critical scrutiny since the early 1990s, the authors are not aware of any previous attempt to approach children's rights as social semantics in an attempt to illuminate the dynamic and paradoxical coupling within discourses on childhood between a fundamental social process, the reproduction of generational order and a fundamental social institution, human rights as codified in western modernity. The authors argue that while describing a paradoxical coexistence between intergenerational order and human rights, the semantics of children's rights maintains its unity as a cultural form because another semantic distinction, between human rights and personal rights continue to generate social meaning. It is hoped that the scholarly debate will benefit from the contribution of this article to enrich the debate around the social ontology of childhood and children's rights.

Herding Games and Socialisation into Pastoral Linguacultural Practices

Herding Games and Socialisation into Pastoral Linguacultural Practices

The loss of synthetic past tense forms in Slovene in the context of the hypothesised Central European convergence area

Summary One of the salient morphosyntactic features of the hypothesised Central European convergence area is allegedly a simple three-tense verb system (i. e. past, present, and future) without any formal and semantic distinction between the different past tense forms. The simplification of the originally more complex past tense system has been claimed to be contact-induced, which is mirrored in the loss of synthetic past tense forms in the linguistic history of German and Slavic. The article verifies the hypothesis according to which the loss of the preterit (i. e. a synthetic past tense form) in Upper German caused the disappearance of synthetic past tense forms, i. e. the aorist and the imperfect, in Slavic, more specifically Slovene. First, the process of the loss of the aorist and the imperfect in three medieval Slovene texts is presented, viz. the Freising Fragments (c. 972–1039), the Klagenfurt/Celovec or Rateče Manuscript (2nd half of the 14th century), and the Castelmonte/Stara Gora Manuscript (end of the 15th century). Second, the use of the preterit and the perfect in the Early New High German text Ain newes lied von den kraynneriſchen bauren (c. 1515) from the South Bavarian-Austrian speech territory, more precisely Lower Styria, is analysed. The historical linguistic analysis of the cited Slovene and German linguistic material (the latter originating from the Slovene linguistic area as well) shows that in medieval Slovene the aorist (and most probably the imperfect, too) was lost no later than by the end of the 15th century, while the formal and semantic distinction between the preterit on one hand and the perfect on the other is still preserved in Early New High German documented in the “Slovene-speaking lands” in the second decade of the 16th century. From these facts one cannot but conclude that the loss of synthetic past tense forms in Slovene could not have been influenced by (the demonstrably later) loss of the preterit in South Bavarian-Austrian German dialects, which were historically (and in some regions still are) in geographical and, consequently, sociolinguistic contact with Slovene.

An emergent ontology for digitally modeling existing buildings: examining Kyoto’s Nishiki market

PurposeThis project examines digital modeling strategies for existing buildings. In this context, it aims to question assumptions about the need for geometric accuracy and the efficacy of predefined ontologies. As a counterpoint to prevailing digital modeling strategies, this project proposes a digital modeling approach using a project-specific, emergent ontology.Design/methodology/approachNishiki Market, in Kyoto, Japan, is studied as a test case. The emergent-ontology modeling process is introduced with an initial minimal set of operations including basic fold and trim operations applicable to surfaces. As the model develops iteratively, new situations are encountered for which existing rules are insufficient. In response, the model maker’s subjective judgment is invoked to introduce new operations, and ontological rules are allowed to expand.FindingsThe emergent-ontology approach, when executed on the Nishiki Market test case, enables representation of specific architectural qualities, highlighting semantic distinctions between digitally modeled elements of real-world features. The modeling approach generated project-specific knowledge, informing disciplinary understanding. Ontological emergence enabled semantic relationships to be disclosed and newly constructed.Originality/valueThe project proposes a novel methodology using an emergent ontology for digitally modeling existing buildings. Instead of remaining within the limitations a predefined ontology, the model maker’s subjective decisions shape the model’s ongoing development. This interpretive approach allows project-specific knowledge generation while challenging prevailing assumptions about accuracy and consistency in digital models of existing buildings.

Гендерная маркированность молдавского варианта румынского языка как современная тенденция его развития

The aim of the study is to determine the characteristics of the phenomenon of feminization in the Moldavian variant of the Romanian language, manifested in the use of feminine forms for job titles and positions traditionally used in the masculine gender. The scientific novelty of the study lies in comparing the Moldavian and Romanian language variants in the context of feminization, demonstrating the different development of the same language depending on geopolitical and sociocultural conditions. In the modern Moldavian variant of the Romanian language, there is a trend towards an increased use of gender-specific job titles, indicating social, political, and economic changes in the region and a striving to adhere to European standards regarding gender balance. A key outcome of the study is identifying the trend of feminization in the Moldavian language, signaling a change in language standards. An analysis of lexical differences between the Moldavian and Romanian language variants has revealed significant semantic and morphological distinctions in the naming of professions, highlighting the influence of language standards and sociocultural aspects on professional terminology and gender norms.

Detecting the Background-Similar Objects in Complex Transportation Scenes

With the development of intelligent transportation systems, most human objects can be accurately detected in normal road scenes. However, the detection accuracy usually decreases sharply when the pedestrians are merged into the background with very similar colors or textures. In this paper, a camouflaged object detection method is proposed to detect the pedestrians or vehicles from the highly similar background. Specifically, we design a guide-learning-based multi-scale detection network (GLNet) to distinguish the weak semantic distinction between the pedestrian and its similar background, and output an accurate segmentation map to the autonomous driving system. The proposed GLNet mainly consists of a backbone network for basic feature extraction, a guide-learning module (GLM) to generate the principal prediction map, and a multi-scale feature enhancement module (MFEM) for prediction map refinement. Based on the guide learning and coarse-to-fine strategy, the final prediction map can be obtained with the proposed GLNet which precisely describes the position and contour information of the pedestrians or vehicles. Extensive experiments on four benchmark datasets, e.g., CHAMELEON, CAMO, COD10K, and NC4K, demonstrate the superiority of the proposed GLNet compared with several existing state-of-the-art methods.

Graph-based multi-level feature fusion network for diabetic retinopathy grading using ultra-wide-field images

Diabetic Retinopathy (DR) grading plays an important role for early intervention in managing the disease. However, conventional grading methods predominantly rely on traditional color fundus photography, which may not fully capture crucial pathological information. In contrast, Ultra-wide-field (UWF) represents a novel non-contact and non-invasive modality, capable of generating images with an unprecedented field of view, ranging from 180∘ to 200∘. This offers distinct advantages over conventional color fundus photography for DR grading. Furthermore, given the subtle distinctions in imaging characteristics among adjacent levels of DR, the grading task becomes exceptionally challenging. Therefore, we propose a new multi-level feature fusion network based on the graph convolution to accomplish the DR grading task using UWF images. Specifically, we first use the progressive multi-level constraint module to iteratively refine features by imposing constraints on model features at different levels. This process serves to minimize the inter-individual distances within the intra-class feature space, ultimately reducing the overlap of feature spaces between adjacent classes. Subsequently, since features at different network levels exhibit distinct semantics and distributions, conventional aggregation techniques, such as splicing and summing, may introduce redundant information and noise interference. To address this issue, we introduce a graph-based feature fusion module, capable of capturing spatial relationships and effectively fusing multi-layered features. Experimental results evaluated on an in-house UWF dataset with 1234 images and a publicly available fundus dataset with 3662 images illustrate that the proposed approach outperforms the state-of-the-art methods and shows promise for DR grading, with a respective accuracy of 0.81 and 0.84.

Exploiting negative correlation for unsupervised anomaly detection in contaminated time series

Anomaly detection in time series data is crucial for many fields such as healthcare, meteorology, and industrial fault detection. However, traditional unsupervised time series anomaly detection methods suffer from biased anomaly measurement under contaminated training data. Most of existing methods employ hard strategies for contamination calibration by assigning pseudo-label to training data. These hard strategies rely on threshold selection and result in suboptimal performance. To address this problem, in this paper, we propose a novel unsupervised anomaly detection framework for contaminated time series (NegCo), which builds an effective soft contamination calibration strategy by exploiting the observed negative correlation between semantic representation and anomaly detection inherent within the autoencoder framework. We innovatively redefine anomaly detection in data contamination scenarios as an optimization problem rooted in this negative correlation. To model this negative correlation, we introduce a dual construct: morphological similarity captures semantic distinctions relevant to normality, while reconstruction consistency quantifies deviations indicative of anomalies. Firstly, the morphological similarity is effectively measured based on the representative normal samples generated from the center of the learned Gaussian distribution. Then, an anomaly measurement calibration loss function is designed based on negative correlation between morphological similarity and reconstruction consistency, to calibrate the biased anomaly measurement caused by contaminated samples. Extensive experiments on various time series datasets show that the proposed NegCo outperforms state-of-the-art baselines, achieving an improvement of 6.2% to 26.8% in Area Under the Receiver Operating Characteristics (AUROC) scores, particularly in scenarios with heavily contaminated training data.

Appearance flow estimation for online virtual clothing warping via optimal feature linear assignment

Clothing warping to spatially align source garments with the corresponding body parts is crucial in clothing media tasks such as virtual try-on and pose-guided person generation. Recent pioneering work has utilized flow fields with additional dimensions of freedom to simulate clothing warping flexibly. However, current methods for estimating appearance flow typically rely on calculating just the local cost volume which contains multiple noisy matching points, potentially leading to a mismatch between clothing and body parts. To address this issue, we propose a novel appearance flow estimation network(Warping-Flow) for clothing warping based on optimal features linear assignment. Specially, two remarkable contributions are made to improve feature matching precision. First, a local context feature aggregation module is proposed to enhance the semantic feature distinction of the source cloth and target pose. Second, Warping-Flow estimates a hard attention mask through the cost volume to filter irrelevant features, followed by the optimal linear assignment algorithm to normalize the cost volume to a discrete permutation matrix that explicitly models the most contributing bipartite matches. Experiments conducted on the VITON and VITON-HD datasets demonstrate that Warping-Flow outperforms existing state-of-the-art algorithms, particularly in cases involving complex clothing deformation. Furthermore, Warping-Flow can serve as a plug-in to improve existing garment media technologies.

Почему прошлое ирреально и что ирреального в прошлом: когнитивный аспект типологической взаимосвязи категорий ирреалиса и прошедшего времени глагола

The article deals with cross-linguistic patterns of past tense verb forms functioning under the category of irrealis. Approaching language as a means and process of understanding and organizing the world, we examine ways in which the irreal meaning of the past tense is grounded in human experience. Theory of language does not seem to offer a consistent explanation as to why the past is irreal or why irreality is construed in past tense verbs. With this in mind, the investigation of the preterit as an irrealis marker across languages builds on a cognitive interpretation of the contrastive analysis. In other words, typological regularities of the irreal past are explained in terms of embodied reasoning in which a human engages when interacting with the world and others. It is hypothesized that the meaning of past tense is rooted in the subject’s experience with perspectival alternatives arising with her abstractions from and reflections on interactions with things that are no longer in a perceptual field, which requires coordinations (interpretations) of varied perceptual vantage points and temporal perspectives under uncertainty. It follows that the past as a domain of mediated, secondary observations (“hindsight observations”) is conceptualized as a domain of hypothetical entities and actions on which the meaning of irreality builds. Research data is mainly represented by corpus-based examples from literary and spoken discourse of Russian, English, and Bulgarian. The cognitive analysis reveals that past-tense marking of irrealis is accounted for by the prevalently relational, inter-subjective semantics through which a discourse of the past is constructed and made sense of relative to displaced and displaceable reference points, aka other subjects’ perspectives. This semantic distinction helps understand the difference between grammatical forms and meanings that underlie such categories as subjunctive, palliative, desiderative, dubitative, apprehensive, and other irreal moods across languages. In particular, non-preterit markers of irrealis in English and Russian subjunctives are apparently linked to a more linear and/or egocentric reasoning about temporal changes, whereby reality is identified with the “inner moving force” or the subject’s interests and desires that she can hardly control (as in imperative mood, for example), that is why such grammatical expressions of irreality are usually more emotional, colloquial and logically basic. Preterit markers, by contrast, are embedded in a more participatory, dialogical discourse that involves engagement in open-ended meaning-making wherein no definitive answer, decision, or choice is required, but rather options for acting are left open (as in counterfactuals and optatives, for example). Therefore, such expressions of irreality are usually more polite and logically challenging.

ГОРОЖАНИН И ПАРК: ПОВСЕДНЕВНЫЕ ПРАКТИКИ ВЗАИМОДЕЙСТВИЯ С ПРИРОДОЙ НА ПРИМЕРЕ СОВЕТСКОГО ЧЕЛЯБИНСКА

The article considers the formation of urbanized practices in green spaces using the example of soviet Chelyabinsk. In the 1930s–1960s, Chelyabinsk was a fast-growing industrial city, where a large relict pine forest was preserved. For soviet managers and new city dwellers, the forest became a “testing ground” for mastering new practices of interaction with nature in conditions of accelerated urbanization. Especially when in the 1930s a part of the forest was repurposed into the Park of Culture and Recreation. Based on archived and journalistic materials, it is shown that the semantic and functional distinction between the park and the forest led to the formation of two types of practices of interaction between city residents and nature. Contrasting the park with the forest, party leaders insisted on the development of infrastructure for the “cultural”, “organized” recreation of new Soviet citizens, while the forest became a space for interaction with the natural world unregulated by the authorities. During the 1960s the forests public image transformed, from a wild suburban forest to a fragment of pristine nature, miraculously surviving in an industrial city, with the Park of Culture and Recreation forming the “grand entrance”. It is concluded that urban practices of interaction with nature developed in two ways. The first is the “acculturation” of the forest area through political actions to establish an ideologically approved recreation for new citizens. The second way was constituted by the formation of citizens’ ecological consciousness in the context of a greenery deficit in the industrial areas of the city.

THE PRAGMATIC ASPECTS OF LINGUISTIC NEGATION

This paper is an attempt to give a general explanation of pragmatics of linguistic negation. After a brief survey of classical accounts of negation pragmatic theories (as speech act theory, argumentation theory and polyphony) the main pragmatic uses of negation (illocutionary negation, external negation and majoring negation) are discussed within relevance theory. The question of relevance of negative utterances is raised, and a general inferential schema (based on so-called invited interference) is proposed and tested for the main uses which are discussed in the paper. The analysis proposed in the framework of relevance theory has thus been verified by the main uses of negation. It has the advantage of not making any morphologic or semantic distinctions between the different types of negation and the different uses are explained from different pragmatic processes depending either on the linguistic environment, or on the context, or on both.
 The analysis is both simple and realistic. Simple because no complexification of the linguistic system is necessary. Linguistic negation treatment allows, on that count, interesting confrontations from one language to another. Realistic too, because it comes from the principle, admitted by psycholinguists, that positive information is more quickly treated than negative information. It follows from this principle that pragmatic inferences on negative utterances resort to positive premises.

CLFR-Det: Cross-level feature refinement detector for tiny-ship detection in SAR images

Ship detection in synthetic aperture radar (SAR) images is an important and active topic, due to the characteristics of SAR images involving all-time and all-weather imaging. However, complex backgrounds caused by speckle noise and inshore land, coupled with information deficiency of tiny ships, pose a great challenge for tiny-ship detection in SAR images. To tackle this problem, we present a cross-level feature refinement detector (CLFR-Det) that utilizes features reflecting different levels and distinct semantics (classification and localization). To enrich the semantic information of tiny ships, our approach incorporates a cross-level modulated deformable convolution to aggregate features from relevant positions across multi-level feature maps. This is supported by a spatially-informed multi-scale feature refinement mechanism that combines the features for classification and those for localization. We implement a uniform IoU-weighted adaptive training sample selection method for equitably distributing the impact of positive samples from targets of various sizes during the training process. A generalized IoU loss between ground-truth and preliminary bounding box is further proposed to supervise the learning process of the CLFR-Det, with uncertainty weights incorporated to dynamically depict the levels of disparate losses, enabling adequate training across different tasks. Also, we construct a novel tiny SAR ship detection dataset to comprehensively evaluate the effectiveness of our system, in conjunction with the use of publicly available SSDD and HRSID datasets. Experimental investigations demonstrate that CLFR-Det generally surpasses state-of-the-art performance for multi-scale ship detection, particularly for the detection of tiny ships.

Renovation sets and their applications in higher-order argumentation frameworks

Abstract The acceptability of attacks plays a key role in defining semantics of higher-order argumentation frameworks (HO-AF). Several approaches have been put forward to describe the recursive acceptability of attacks. The diversity, on the one hand, leads to the variety of HO-AF semantics. However, on the other hand, it also brings difficulties in the formal examination of the relationship among these distinct semantics and the exploration of new semantics. This work aims at exploring the underlying commons in existing HO-AF semantics and examining the role of such commons in the study of HO-AF semantics. To this end, we introduce novel notion of renovation sets to represent the acceptability of attacks, and identify several types of renovation sets for different kinds of acceptability. We then use renovation sets to represent and compare HO-AF semantics built in four different approaches. The proposal of renovation sets provides a general tool for the study of HO-AFs. The representation demonstrates a method of using renovation sets to establish HO-AF semantics. The comparison shows the feasibility of investigating the relationship between different HO-AF semantics with the help of renovation sets.

Emojis as Pictures

I argue that emojis are essentially little pictures, rather than words, gestures, expressives, or diagrams. 🎁 means that the world looks like that, from some viewpoint. I flesh out a pictorial semantics in terms of geometric projection with abstraction and stylization. Since such a semantics delivers only very minimal contents I add an account of pragmatic enrichment, driven by coherence and non-literal interpretation. The apparent semantic distinction between emojis depicting entities (like 🚗) and those depicting facial expressions (like 😊) I analyze as a difference between truth-conditional and use-conditional pictorial content: 🚗 depicts what the world of evaluation looks like, while 😊 depicts what the utterance context looks like.