Prototype Theory Research Articles

Ferroelectric Content-Addressable Memory Cells with IGZO Channel: Impact of Retention Degradation on the Multibit Operation.

Indium gallium zinc oxide (IGZO)-based ferroelectric thin-film transistors (FeTFTs) are being vigorously investigated for being deployed in computing-in-memory (CIM) applications. Content-addressable memories (CAMs) are the quintessential example of CIM, which conduct a parallel search over a queue or stack to obtain the matched entries for a given input data. CAM cells offer the ability for massively parallel searches in a single clock cycle throughout an entire CAM array for the input query, thereby enabling pattern matching and searching functionality. Therefore, CAM cells are used extensively for pattern matching or search operations in data-centric computing. This paper investigates the impact of retention degradation on IGZO-based FeTFT on the multibit operation in content CAM cell applications. We propose a scalable multibit 1FeTFT-1T-based CAM cell composed of only one FeTFT and one transistor, thus significantly improving the density and energy efficiency compared with conventional complementary metal-oxide-semiconductor (CMOS)-based CAM. We successfully demonstrate the operations of our proposed CAM with storage and search by exploiting the multilevel states of the experimentally calibrated IGZO-based FeTFT devices. We also investigate the impact of retention degradation on the search operation. Our proposed IGZO-based 3-bit and 2-bit CAM cell shows 104 s and 106 s retention, respectively. The single-bit CAM cell shows lifelong (10 years) retention.

CuAAC ensembled 1,2,3-triazole linked nanogels for targeted drug delivery: a review.

Copper(i) catalyzed alkyne azide cycloaddition (CuAAC), the quintessential example of 'click chemistry', provides an adaptable and adequate platform for the synthesis of nanogels for sustained drug release at targeted sites because of their better biocompatibility. The coupling of drugs, carried out via various synthetic routes including CuAAC, into long-chain polymeric forms like nanogels has exhibited considerable assurance in therapeutic advancements and intracellular drug delivery due to the progression of water solubility, evacuation of precocious drug release, and improved upthrust of the pharmacokinetics of the nanogels, thereby rendering them as better and efficient drug carriers. The inefficiency of drug transmission to the target areas due to the resistance of complex biological barriers in vivo is a major hurdle that impedes the therapeutic translation of nanogels. This review compiles the data of nanogels synthesized specifically via CuAAC 'click' methodology, as scaffolds for targeted drug delivery and their assimilation into nanomedicine. In addition, it elaborates the ability of CuAAC to graft specific moieties and conjugating biomolecules like proteins and growth factors, onto orthogonally functionalized polymer chains with various chemical groups resulting in nanogels that are not only more appealing but also more effective at delivering drugs, thereby enhancing their site-specific target approach and initiating selective therapies.

Multi-Granularity Context Network for Efficient Video Semantic Segmentation.

Current video semantic segmentation tasks involve two main challenges: how to take full advantage of multi-frame context information, and how to improve computational efficiency. To tackle the two challenges simultaneously, we present a novel Multi-Granularity Context Network (MGCNet) by aggregating context information at multiple granularities in a more effective and efficient way. Our method first converts image features into semantic prototypes, and then conducts a non-local operation to aggregate the per-frame and short-term contexts jointly. An additional long-term context module is introduced to capture the video-level semantic information during training. By aggregating both local and global semantic information, a strong feature representation is obtained. The proposed pixel-to-prototype non-local operation requires less computational cost than traditional non-local ones, and is video-friendly since it reuses the semantic prototypes of previous frames. Moreover, we propose an uncertainty-aware and structural knowledge distillation strategy to boost the performance of our method. Experiments on Cityscapes and CamVid datasets with multiple backbones demonstrate that the proposed MGCNet outperforms other state-of-the-art methods with high speed and low latency.

Effect of doctors’ and patients’ individual characteristics on shared decision-making and its psychological mechanism: A perspective from implicit prototype theory

Effect of doctors’ and patients’ individual characteristics on shared decision-making and its psychological mechanism: A perspective from implicit prototype theory

Synonymy from a Prototype Theory Perspective and its Symbiosis with Polysemy: Towards a New Dictionary of Synonyms

This paper presents an attempt at providing a model for comprehensive, precise and systematic presentation of a word's synonymy within a dictionary entry, especially in the case of highly polysemous words. It is founded on the complementarity of polysemy and synonymy and a prototype-based view of the latter lexical relation. The proposed model is presented through four steps of tailoring an entry in a new dictionary of synonyms: the analysis of a word's polysemous structure, the compilation of a word's synonym sets viewed as prototype-based categories, the definition of synonyms within a set and the structure and organisation of an individual synonym set and the whole entry. Comprehensiveness of synonymy presentation according to the model is reflected in the fact that an entry lists synonym sets corresponding to different senses of the given headword as well as in the tendency for each set to offer a wide range of synonyms. Systematicity and precision in synonymy presentation are achieved primarily due to the application of prototype-based principles to the structuring and organizing of synonym sets and the defining of synonyms. The model also opens up the possibility of a transparent diagram-based visual repre­sentation of a word's synonymy in an electronic dictionary offering a more convenient visual organisation of large amounts of information. Keywords: polysemy, synonymy, symbiosis, complementarity, prototype theory, model, dictionary entry, synonym set, sense defining, entry struc­ture

The Development of the Twelve Flora Images in Mid-Qing Dynasty of China

The traditional Chinese “Twelve Flora” refer to the images of twelve flower gods corresponding to the twelve months of the Chinese lunar calendar. They are usually composed of celebrities in Chinese history and are the subject matter of traditional Chinese paintings, closely related to traditional flower and festival cultures. This paper mainly uses the research methods of iconography, formal analysis and prototype theory to study the images of the Twelve Flora in the middle of the Qing Dynasty. While examining the visual images, it uses the method of mutual verification of texts to sort out how the image of the Twelve Flora of the Qing Dynasty was initially formed and formed a relatively fixed creative mode.

Rethinking and Improving Few-Shot Segmentation From a Contour-Aware Perspective

Existing few-shot segmentation approaches basically adopt the idea of comparing the semantic prototype vector of the query image and support images, and then obtaining the segmentation result. However, recent studies have shown that a single feature vector in feature map cannot accurately represent pixel-level categories, thus leading to poor segmentation of object boundary and semantic ambiguity. To address this common problem, we propose a novel contour-aware network (CTANet) for few-shot segmentation in this paper. Unlike the usual practice of classifying each pixel separately, CTANet regards all pixels within the same contour as a whole, which can take advantage of the internal consistency of objects to obtain a more accurate representation of category information. To obtain the accurate object contour, our network consists of a contour generation module and a contour refinement module, where the former exploits multiple levels of features to generate a primary contour map and the latter learns to refine the primary contour map. Furthermore, a novel contour-aware mixed loss is proposed to fuse the common BCE loss and our contour-aware loss to supervise the training process on two levels, pixel-level and contour-level. Extensive experiments demonstrate that our CTANet achieves a new state-of-the-art performance on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \text{PASCAL-5}^{i}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ \text{COCO-20}^{i}$</tex-math></inline-formula> . Hopefully, our new perspective could provide more clues for future research on few-shot segmentation. Our code is freely available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hardtogetA/CTANet</uri> .

Beyond Word Embeddings: Heterogeneous Prior Knowledge Driven Multi-Label Image Classification

Multi-Label Image Classification (MLIC) is a fundamental yet challenging task which aims to recognize multiple labels from given images. The key to solve MLIC lies in how to accurately model the correlation between labels. Recent studies often adopt Graph Convolutional Network (GCN) to model label dependencies with word embeddings as prior knowledge. However, classical word embeddings typically contain redundant information due to the imperfect distributional hypothesis it relies on, which may degrade model generalizability. To tackle this problem, we propose a novel deep learning framework termed Visual-Semantic based Graph Convolutional Network (VSGCN), which alleviates the negative impact of redundant information by utilizing heterogeneous sources of prior knowledge. Specifically, we construct both visual prototype and semantic prototype for each label as heterogeneous prior label representations, which are further mapped to multi-label classifiers via two Multi-Head GCNs separately. The Multi-Head GCN mechanism proposed in this paper aims to guide the information propagation between prototypes for each label, which constructs multiple correlation graphs to simultaneously model the label correlation in different subspaces. Notably, we alleviate the negative influence of needless information by decreasing the inconsistency of predictions that come from visual space and semantic space. Extensive experiments conducted on various multi-label image datasets demonstrate the superiority of our proposed method.

GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning

Generalized Zero-Shot Learning (GZSL) aims to recognize images not only for seen classes but also for unseen ones by transferring semantic-visual relationships from the seen to the unseen classes. It is an intuitive solution to take the advantage of generative models to hallucinate realistic unseen samples based on the knowledge learned from the seen classes. However, due to the generation shifts, the synthesized samples by most existing methods may drift from the real distribution of the unseen data. To address this issue, we propose a novel flow-based generative framework that consists of multiple conditional affine coupling layers for learning unseen data generation. Specifically, we investigate and address three essential problems that trigger the generation shifts, <i>i.e.</i>, <i>semantic inconsistency</i>, <i>variance collapse</i>, and <i>structure disorder</i>. First, to improve the reflection of the semantic information in the generated samples, we proactively embed the semantic information into the transformation in each conditional affine coupling layer. Second, to promote the intrinsic feature variance of the unseen classes, we introduce a boundary sample mining strategy with entropy maximization to discover ambiguous visual variants of semantic prototypes and hereby calibrate the decision boundary of the classifiers. Third, a relative positioning strategy is proposed to revise the attribute embeddings, guiding which to fully preserve the inter-class geometric structure and further avoid structure disorder in the semantic space. Extensive experimental results on four GZSL benchmark datasets demonstrate that GSMFlow achieves the state-of-the-art performance on GZSL.

Towards Cross-lingual Representation of Prototypical Lexical Knowledge

In order to be concretely effective, many Natural Language Processing (NLP) applications require the availability of lexical resources providing varied, broadly shared, and language-unbounded (i.e., not limited to any specific language or linguistic system) lexical information. However, state-of-the-art knowledge models typically focus on specific levels of semantic analysis rather than adopting such a comprehensive and cross-lingual approach to lexical knowledge. This is often due to the theoretical paradigms on which such resources are based, each addressing the semantic phenomenon from a (de)finite perspective. In this paper, we first suggest a maximalist approach to lexical semantics to pursue through the idea of semantic prototype and linguistic representativeness as easily applicable to textual corpora. Starting from this conceptual framework, we thus propose a novel corpus-based automatable methodology for knowledge modeling based on a multilingual word alignment mechanism. This model enables the retrieval and encoding of prototypical, language-unbounded, and naturally disambiguated lexical knowledge in the form of diversified conceptual links between words and their senses. Results from a simple implementation of the proposal show relevant outcomes that are not found in other resources. Finally, different application opportunities of the proposed model will be presented.

The convergence and divergence of extension and intension on semantic change

AbstractDespite the fact that semantic change studies have intensively argued that intensional readings develop from the literal reading as a whole, diachronic prototype semantics proposes that intensional readings arise from the extensional subsets of the literal reading. This study empirically explored this proposal by carrying out a corpus-based diachronic study. It is proved from the semantic change of Chinesepòthat: (1) There exists a corresponding relationship between extensional usages and intensional readings of a lexical item. (2) Extension and intension both converge and diverge on semantic change. Their convergence lies in the fact that extensional usages give rise to intensional readings. Extensional usages, though nuanced, motivate the emergence and development of intensional readings. Their divergence is reflected in the independent development of extensional usages and intensional readings. The subsistence or dying out of extensional usages does not constrain the appearance or disappearance of intensional readings. (3) Semantic change involves three stages, namely the extensional stage, the intensional stage, and the grammaticalization stage. These three stages constitute an interweaving continuum in the process of semantic change.

C-AlO[formula omitted], c-HAlO[formula omitted], and c-(HN)OAlH spectroscopic constants and anharmonic frequencies

Aluminum forms strong bonds to oxygen, nitrogen, and hydrogen, but these motifs are relatively rare in the interstellar medium (ISM). However, no chemical rationale accounts for this observation. The cyclic molecules AlO2, HAlO2, and (HN)OAlH are quintessential examples of the aforementioned phenomenon, and the latter two are isoelectronic with each other. Matrix-isolation spectroscopic data and prior theoretical computations exist for cyclic AlO2, although it has yet to be observed astronomically. However, high-level theoretical data provided herein generate useful predictions for and insights into spectral data for these three molecules. Anharmonic vibrational frequencies and rotational constants are determined in this work for these molecules via quartic force fields at the CCSD(T)-F12b/cc-pVTZ-F12 level of theory and with canonical CCSD(T) for consideration of basis set convergence, core electron correlation, and relativity (CcCR). The present work finds that these aluminum oxides are weak-to-moderate infrared emitters but are much stronger microwave emitters with dipole moments of 4.95 D, 4.55 D, and 3.76 D respective of cyclic AlO2 (c-AlO2), cyclic HAlO2 (c-HAlO2), and cyclic (HN)OAlH (c-(HN)OAlH). Correlation to argon-matrix experiments for c-AlO2 is within the expected matrix shift for the ν3 bend computed here to be 527.8 cm−1. Astrophysical detection of these molecules could imply their role in the creation or degradation of aluminum-containing nanocrystals and interstellar dust of importance for the formation of rocky bodies, and the spectral data computed in this work should be able to assist in such classification.

Tuning the Topological θ -Angle in Cold-Atom Quantum Simulators of Gauge Theories

The topological θ-angle in gauge theories engenders a series of fundamental phenomena, including violations of charge-parity (CP) symmetry, dynamical topological transitions, and confinement-deconfinement transitions. At the same time, it poses major challenges for theoretical studies, as it implies a sign problem in numerical simulations. Analog quantum simulators open the promising prospect of treating quantum many-body systems with such topological terms, but, contrary to their digital counterparts, they have not yet demonstrated the capacity to control the θ-angle. Here, we demonstrate how a tunable topological θ-term can be added to a prototype theory with U(1) gauge symmetry, a discretized version of quantum electrodynamics in one spatial dimension. As we show, the model can be realized experimentally in a single-species Bose-Hubbard model in an optical superlattice with three different spatial periods, thus requiring only standard experimental resources. Through numerical calculations obtained from the time-dependent density-matrix renormalization group method and exact diagonalization, we benchmark the model system, and illustrate how salient effects due to the θ-term can be observed. These include charge confinement, the weakening of quantum many-body scarring, as well as the disappearance of Coleman’s phase transition due to explicit breaking of CP symmetry. This work opens the door towards studying the rich physics of topological gauge-theory terms in large-scale cold-atom quantum simulators.4 MoreReceived 15 April 2022Revised 19 September 2022Accepted 26 September 2022DOI:https://doi.org/10.1103/PRXQuantum.3.040316Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasColor confinementColor deconfinementConfinementQuantum simulationPhysical Systems1-dimensional systemsBose gasesTechniquesBose-Hubbard modelDensity matrix renormalization groupExact diagonalizationGauge symmetriesLattice gauge theoryPerturbation theoryAtomic, Molecular & OpticalCondensed Matter, Materials & Applied Physics

Stability in the Unstable: How Social Enterprises Survive and Adapt in the VUCA World

Social enterprises are rightfully considered a quintessential example of organizations with a high level of social impact. The founders of these projects are striving to create sustainable financial models and strike a balance between social mission and commercial performance. How do social entrepreneurs manage to live and work in a world where fast-paced change has become the norm? What innovations and strategies help them get through the tough times?

The Impact of Transformative Tourism Experiences on Prosocial Behaviors of College Students: Multiple Chain Mediating Effects of Dispositional Awe and Social Connectedness

As sustainable behaviors that contribute to the development of human society, prosocial behaviors are an important part of the moral cultivation of college students and have attracted the growing attention of higher education in recent years. It has been indicated by previous studies that transformative tourism experiences can have a profound impact on individual prosocial behaviors. Therefore, how transformative tourism experiences play a role in strengthening college students’ prosocial behaviors has become a topic worthy of note. Based on the self-determination theory, the awe prototype theory, and the transformative tourism research, this study constructed a mechanism model of the transformative tourism experiences affecting college students’ prosocial behaviors. Four hundred and fifty-four valid questionnaires were collected through questionnaire surveys, with the structural equation model and bootstrap analysis method used for the empirical test. The results showed that transformative tourism experiences became one of the important ways to strengthen college students’ prosocial behaviors and that the multiple chain intermediary effects of awe and social connectedness between transformative tourism experiences and the college students’ prosocial behaviors were significant. This study provided a new way to cultivate college students’ prosocial behaviors and promote the sustainable development of human society, and it provided a theoretical basis for the social education function played by research tourism in higher education.

The Main Problems of Cognitive Linguistics

The article highlights the main problems of cognitive linguistics, its goals, object and subject of research, theoretical and methodological principles, based on the analysis of scientific works of famous scientists who have made a significant contribution to the development of cognitive science. The theoretical and methodological basis of cognitive linguistics is based on such influential theories as frame semantics, conceptual semantics, prototype theory, the theory of relevance, the theory of cognitive models and cognitive metaphor, the theory of mental spaces. The main principles of cognitive linguistics are anthropocentrism, interdisciplinarity and recognition of the cognitive and communicative functions of the language as the main ones. The main problems of cognitive linguistics are the problems of knowledge structures’ representation, the verbalization of concepts and their typology, the cognitive-discursive approach to the analysis of language units and text, the method of cognitive modeling, etc.

Drum Rhythms and Golden Scriptures: Reasons for Mormon Conversion Within Haiti's Culture of Vodou

Drum Rhythms and Golden Scriptures: Reasons for Mormon Conversion Within Haiti's Culture of Vodou

Opening the Black Box: Finding Osgood’s Semantic Factors in Word2vec Space

State-of-the-art models of artificial intelligence are developed in the black-box paradigm, in which sensitive information is limited to input-output interfaces, while internal representations are not interpretable. The resulting algorithms lack explainability and transparency, requested for responsible application. This paper addresses the problem by a method for finding Osgood’s dimensions of affective meaning in multidimensional space of a pre-trained word2vec model of natural language. Three affective dimensions are found based on eight semantic prototypes, composed of individual words. Evaluation axis is found in 300-dimensional word2vec space as a difference between positive and negative prototypes. Potency and activity axes are defined from six process-semantic prototypes (perception, analysis, planning, action, progress, and evaluation), representing phases of a generalized circular process in that plane. All dimensions are found in simple analytical form, not requiring additional training. Dimensions are nearly orthogonal, as expected for independent semantic factors. Osgood’s semantics of any word2vec object is then retrieved by a simple projection of the corresponding vector to the identified dimensions. The developed approach opens the possibility for interpreting the inside of black box-type algorithms in natural affective-semantic categories, and provides insights into foundational principles of distributive vector models of natural language. In the reverse direction, the established mapping opens machine-learning models as rich sources of data for cognitive-behavioral research and technology.

Prototypical associations in the production of words in English as a foreign language by L2 learners

Abstract This ongoing study attempts to explore the role semantic prototypes play in response to the production of words of a semantic category by two groups of learners differing in age and language level. Our main objectives point to i) the identification of the number of words produced in English regarding semantic prototypes by the groups of informants; ii) the analysis and comparison of the similarities and the differences of the prototypical associations provided by learners of different ages and different language levels; iii) the evidence of the universality of prototypes through the convergences and/ or divergences collected in the sample. The data collection instruments considered for this ongoing study are a background questionnaire and a productive semantic categorization task. Findings yielded that (i) there is evidence of prototype responses, and they are exclusively associated with the basic level of categorization; (ii) children retrieved fewer prototypical and non-prototypical words than adolescents in a lexical availability test; (iii) convergences and divergences are shown in the words retrieved by the two groups differing in age and language level. This study aims to evolve the dynamics of prototypical associations in language, and their linguistic, social, and cultural implications in communication when learning a foreign language.

Redesign Driven by Manufacturing Data for Next-Generation Modernization of Legacy Products

Abstract As the life cycles of many products advance, obsolescence of original components can pose challenges for a product’s maintainability and serviceability. Information about the original design intent of the product system may not be available. For example, it may not be obvious how to identify old components by reverse engineering or to select and configure the optimal components for best system performance. Thus, time spent to redesign an older legacy product with high-fidelity simulations may be difficult to justify. Increasingly more performance measurements are available in digital twins of product systems generated during manufacturing and testing. This paper introduces a redesign from manufacturing data mapping (RfMDM) method that can infer optimal new components to modernize legacy product systems. This RfMDM method does not rely upon time-consuming redesign efforts. The inputs come from a basic understanding of the subsystem of concern and qualitative scoring estimates of the influence of design rules on design criteria and simulation fidelity. RfMDM transforms manufacturing data to determine the significance and required quality of each design variable of each obsolete component. This enables estimation of optimal new component specifications for selection and a first article redesign and test to enable product line improvements over time more efficiently. Recognizing that radiofrequency interference effects in printed circuit boards present a complex and quintessential example of legacy subsystems with potentially obsolete components, this paper demonstrates the RfMDM method with a theoretical example subsystem of an active low-pass filter on a printed circuit board.