Error, consistency and triviality Tiefensee, Christine; Wheeler, Gregory
Noûs (Bloomington, Indiana),
September 2022, Letnik:
56, Številka:
3
Journal Article
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In this paper, we present a new semantic challenge to the moral error theory. Its first component calls upon moral error theorists to deliver a deontic semantics that is consistent with the ...error‐theoretic denial of moral truths by returning the truth‐value false to all moral deontic sentences. We call this the ‘consistency challenge’ to the moral error theory. Its second component demands that error theorists explain in which way moral deontic assertions can be seen to differ in meaning despite necessarily sharing the same intension. We call this the ‘triviality challenge’ to the moral error theory. Error theorists can either meet the consistency challenge or the triviality challenge, we argue, but are hard pressed to meet both.
Image-level weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. Most of advanced solutions exploit class activation map (CAM). However, CAMs ...can hardly serve as the object mask due to the gap between full and weak supervisions. In this paper, we propose a self-supervised equivariant attention mechanism (SEAM) to discover additional supervision and narrow the gap. Our method is based on the observation that equivariance is an implicit constraint in fully supervised semantic segmentation, whose pixel-level labels take the same spatial transformation as the input images during data augmentation. However, this constraint is lost on the CAMs trained by image-level supervision. Therefore, we propose consistency regularization on predicted CAMs from various transformed images to provide self-supervision for network learning. Moreover, we propose a pixel correlation module (PCM), which exploits context appearance information and refines the prediction of current pixel by its similar neighbors, leading to further improvement on CAMs consistency. Extensive experiments on PASCAL VOC 2012 dataset demonstrate our method outperforms state-of-the-art methods using the same level of supervision. The code is released online.
An introduction to the linguistic study of meaning, this book outlines the meaning potential (semantics) of English and how language knowledge is put to use (pragmatics). As well as gaining a ...systematic overview of meaning in English, readers can learn how to argue for analyses. Among the significant concepts introduced are denotation, sense relations, event types, explicature, implicature, presupposition, metaphor, reference, speech acts and (at an elementary level) Generalised Quantifier Theory. Sense relations - such as antonymy and hyponymy - are presented as summarising patterns of entailment. The sense of a word is seen as the contributions it makes to the entailments carried by sentences.
This textbook is compact and self-contained, offering:
Discussion of many examplesA theoretically informed approachAccessible organisation and styleDetailed treatment of selected areas of EnglishConsolidation and extension through exercises
The chapters cover adjective, noun and verb meanings, situation types, figurative language, tense, aspect, modality, quantification, topic and focus. Explanations of entailment, compositionality and scope provide a foundation for subsequent study of formal semantics.
Recent advances in adaptive object detection have achieved compelling results in virtue of adversarial feature adaptation to mitigate the distributional shifts along the detection pipeline. Whilst ...adversarial adaptation significantly enhances the transferability of feature representations, the feature discriminability of object detectors remains less investigated. Moreover, transferability and discriminability may come at a contradiction in adversarial adaptation given the complex combinations of objects and the differentiated scene layouts between domains. In this paper, we propose a Hierarchical Transferability Calibration Network (HTCN) that hierarchically (local-region/image/instance) calibrates the transferability of feature representations for harmonizing transferability and discriminability. The proposed model consists of three components: (1) Importance Weighted Adversarial Training with input Interpolation (IWAT-I), which strengthens the global discriminability by re-weighting the interpolated image-level features; (2) Context-aware Instance-Level Alignment (CILA) module, which enhances the local discriminability by capturing the underlying complementary effect between the instance-level feature and the global context information for the instance-level feature alignment; (3) local feature masks that calibrate the local transferability to provide semantic guidance for the following discriminative pattern alignment. Experimental results show that HTCN significantly outperforms the state-of-the-art methods on benchmark datasets.
Driving style analysis plays a pivotal role in intelligent vehicle design. This paper presents a novel framework for driving style analysis based on primitive driving patterns. To this end, a ...Bayesian nonparametric approach based on a hidden semi-Markov model (HSMM) is introduced to extract the primitive driving patterns from muti-dimensional time-series driving data without prior knowledge of these driving patterns. For the Bayesian nonparametric approach, a hierarchical Dirichlet process (HDP) is applied to learn the unknown smooth dynamical modes in the HSMM, called primitive driving patterns. Two other types of Bayesian nonparametric approaches (HDP-HMM and sticky HDP-HMM) are developed as comparatives in order to show the advantages of the HDP-HSMM. The naturalistic car-following data of 18 drivers are collected from the University of Michigan Safety Pilot Model Deployment database. For each driver, 75 primitive driving patterns are semantically predefined according to their physical and psychological perception thresholds. The individual driving styles are then semantically analyzed based on the distribution over primitive driving patterns, and the similarity of driving styles among drivers is then evaluated. Experimental results demonstrate that the utilization of driving primitive pattern provides a semantically interpretable way to analyze driver's behavior and driving style.
The subject of the research is the processes of meaning-making in public and socio-political discourse, related to constructing and interpreting the image of Russia. The study deals with the ...specificity of semantic description of the country's image, with a particular focus on a thesaurus – a repository of data, knowledge and concepts an individual possesses in relation to a reflected fact of life. With the emergence of the cognitive approach the researchers' observations shift towards the confrontation of cultures and the resulting ideological opposition as prerequisites for constructing a particular image of Russia. In contrast to traditional linguistics, describing metaphorical images of Russia in terms of "semantic components" or "semantic fields", cognitive linguistics views constructing of the country's image as an effect of metaphorical framing which involves applying a number of coherent procedures: naming, categorization, framing, narrating. Under proliferation and competition of ideologies, it is not the frequency of a metaphorical model of the image of Russia that serves as an indicator of its productivity but rather its potential to produce new lexical units, frames or slots. The current study exemplifies the role of ideologemes and beliefs, reflecting different value systems, in constructing the auto- and hetero-image of Russia. The paper dwells on framing in the processes of activating society's emotional states "fostering" various Russia-related sentiments. While constructing the auto- and hetero-image of Russia, political actors compete fiercely for their right to frame ideas dominating the public and political discourse and ensuring their accepted meaning. Under the current ideological opposition, the instrumentarium of metaphorical framing and reframing can serve not only as a means of competitive practices but also a means of conflict resolution in discussing the ideological and political agendas associated with attitudes to Russia.
Abstract
The word for grey has been studied in different languages as part of the definition of basic colour terms, and in
historical linguistics some studies have addressed grey as their main focus. ...The Finnish basic colour term
harmaa
(‘grey’) has been addressed before in data from early to mid-twentieth century and as a basic colour term, but in contemporary language
harmaa
has not been analysed with corpus methods before. This study presents the semantics of
harmaa
in written communicational use of language, internet conversation, in the framework of Cognitive
Grammar and access semantics, using qualitative corpus analysis. The study results show that the shade or set of shades that
harmaa
profiles in different contexts vary, and surface textures, mood, or evaluative aspects can also be
profiled. However, more studies are needed to reveal the semantics of
harmaa
in different interactional
situations.
We present Convolutional Oriented Boundaries (COB), which produces multiscale oriented contours and region hierarchies starting from generic image classification Convolutional Neural Networks (CNNs). ...COB is computationally efficient, because it requires a single CNN forward pass for multi-scale contour detection and it uses a novel sparse boundary representation for hierarchical segmentation; it gives a significant leap in performance over the state-of-the-art, and it generalizes very well to unseen categories and datasets. Particularly, we show that learning to estimate not only contour strength but also orientation provides more accurate results. We perform extensive experiments for low-level applications on BSDS, PASCAL Context, PASCAL Segmentation, and NYUD to evaluate boundary detection performance, showing that COB provides state-of-the-art contours and region hierarchies in all datasets. We also evaluate COB on high-level tasks when coupled with multiple pipelines for object proposals, semantic contours, semantic segmentation, and object detection on MS-COCO, SBD, and PASCAL; showing that COB also improves the results for all tasks.
Abstract Creation of large diachronic text corpora triggered studies of evolution of natural languages using quantitative methods in the last decade. For the first time, it became possible to raise ...questions about general patterns of lexical semantic change. In the work by W. Hamilton et al., 2016, a hypothesis was formulated about a more rapid change in the meanings of polysemic words. In this paper, we consider how frequency of words influence variations of semantic metrics. We use explicit word vector representations and Jensen-Shannon divergence as a measure of change in the distribution and semantics of a word. The obtained results show that changes in the frequency ratio of a word in different meanings can themselves lead to a change in a word distribution, even in the absence of any changes in its meaning. Therefore, the observations made in the work by W. Hamilton et al about correlation between the rate of change of words and their local cluster coefficient in the semantic connection network (considered as a quantitative measure of polysemy) can be fully explained by the effect described in the article.
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