Concept Classes Research Articles

A workflow for analyzing cultural schemas in texts

ABSTRACT Concept class analysis (CoCA) is a method for recovering cultural schemas in texts using a combination of word embedding and community detection models. Like survey-based forms of schematic class analysis (SCA), however, interpreting results can be difficult. Some of these interpretive difficulties are applicable across types of SCA, while others are unique to CoCA. In this paper, we propose a complete workflow for interpreting and analyzing CoCA output. We use the case of social identity schemas in a collection of over 13,000 U.S. political blog posts to outline a number of interpretive and analytical strategies and a robustness check to make sense of the cultural schemas recovered from texts.

Explaining systematic polysemy: kinds and individuation

ABSTRACT Polysemy is a phenomenon involving single lexical items with multiple related senses. Much theorizing about it has focused on developing linguistic accounts that are responsive to various compositional and representational challenges in semantics and psychology. We focus on an underexplored question: Why does systematic polysemy cluster in the ways it does? That is, why do we see certain regular patterns of sense multiplicity, but not others? Drawing on an independently motivated view of kind cognition – i.e. the formal structures for different classes of kind representations – we argue for an answer centered on conceptual individuation. Specifically, we argue that classes of kind concepts vary in what they individuate (i.e. counting as one and specifying what makes it the same or different from others). By elucidating these differences, we can explain why a range of patterns of systematic polysemy are found cross-linguistically and why other patterns are not attested. Overall, our account provides an explanatory framework addressing an important question at the interface between language and mind and opens new avenues for future theoretical and empirical research.

Permutation-invariant linear classifiers

Invariant concept classes form the backbone of classification algorithms immune to specific data transformations, ensuring consistent predictions regardless of these alterations. However, this robustness can come at the cost of limited access to the original sample information, potentially impacting generalization performance. This study introduces an addition to these classes—the permutation-invariant linear classifiers. Distinguished by their structural characteristics, permutation-invariant linear classifiers are unaffected by permutations on feature vectors, a property not guaranteed by other non-constant linear classifiers. The study characterizes this new concept class, highlighting its constant capacity, independent of input dimensionality. In practical assessments using linear support vector machines, the permutation-invariant classifiers exhibit superior performance in permutation experiments on artificial datasets and real mutation profiles. Interestingly, they outperform general linear classifiers not only in permutation experiments but also in permutation-free settings, surpassing unconstrained counterparts. Additionally, findings from real mutation profiles support the significance of tumor mutational burden as a biomarker.

Heterogeneous pseudobulk simulation enables realistic benchmarking of cell-type deconvolution methods

BackgroundComputational cell type deconvolution enables the estimation of cell type abundance from bulk tissues and is important for understanding tissue microenviroment, especially in tumor tissues. With rapid development of deconvolution methods, many benchmarking studies have been published aiming for a comprehensive evaluation for these methods. Benchmarking studies rely on cell-type resolved single-cell RNA-seq data to create simulated pseudobulk datasets by adding individual cells-types in controlled proportions.ResultsIn our work, we show that the standard application of this approach, which uses randomly selected single cells, regardless of the intrinsic difference between them, generates synthetic bulk expression values that lack appropriate biological variance. We demonstrate why and how the current bulk simulation pipeline with random cells is unrealistic and propose a heterogeneous simulation strategy as a solution. The heterogeneously simulated bulk samples match up with the variance observed in real bulk datasets and therefore provide concrete benefits for benchmarking in several ways. We demonstrate that conceptual classes of deconvolution methods differ dramatically in their robustness to heterogeneity with reference-free methods performing particularly poorly. For regression-based methods, the heterogeneous simulation provides an explicit framework to disentangle the contributions of reference construction and regression methods to performance. Finally, we perform an extensive benchmark of diverse methods across eight different datasets and find BayesPrism and a hybrid MuSiC/CIBERSORTx approach to be the top performers.ConclusionsOur heterogeneous bulk simulation method and the entire benchmarking framework is implemented in a user friendly package https://github.com/humengying0907/deconvBenchmarking and https://doi.org/10.5281/zenodo.8206516, enabling further developments in deconvolution methods.

Learning Quantum Processes Without Input Control

We introduce a general statistical learning theory for processes that take as input a classical random variable and output a quantum state. Our setting is motivated by the practical situation in which one desires to learn a quantum process governed by classical parameters that are out of one’s control. This framework is applicable, for example, to the study of astronomical phenomena, disordered systems and biological processes not controlled by the observer. We provide an algorithm for learning with high probability in this setting with a finite amount of samples, even if the concept class is infinite. To do this, we review and adapt existing algorithms for shadow tomography and hypothesis selection, and combine their guarantees with the uniform convergence on the data of the loss functions of interest. As a byproduct, we obtain sufficient conditions for performing shadow tomography of classical-quantum states with a number of copies, which depends on the dimension of the quantum register, but not on the dimension of the classical one. We give concrete examples of processes that can be learned in this manner, based on quantum circuits or physically motivated classes, such as systems governed by Hamiltonians with random perturbations or data-dependent phase shifts. Published by the American Physical Society 2024

Infinite-dimensional reservoir computing

Reservoir computing approximation and generalization bounds are proved for a new concept class of input/output systems that extends the so-called generalized Barron functionals to a dynamic context. This new class is characterized by the readouts with a certain integral representation built on infinite-dimensional state–space systems. It is shown that this class is very rich and possesses useful features and universal approximation properties. The reservoir architectures used for the approximation and estimation of elements in the new class are randomly generated echo state networks with either linear or ReLU activation functions. Their readouts are built using randomly generated neural networks in which only the output layer is trained (extreme learning machines or random feature neural networks). The results in the paper yield a recurrent neural network-based learning algorithm with provable convergence guarantees that do not suffer from the curse of dimensionality when learning input/output systems in the class of generalized Barron functionals and measuring the error in a mean-squared sense.

Natural Concepts and the Economics of Cognition and Communication

AbstractThis article takes a cognitive approach to natural concepts. The aim is to introduce criteria that are evaluated with respect to how they support the cognitive economy of humans when using concepts in reasoning and communicating with them. I first present the theory of conceptual spaces as a tool for expressing the criteria. Then I introduce the central idea that natural concepts correspond to convex regions of a conceptual space. I argue that this criterion has far-reaching consequences as regards natural concepts. Partly following earlier work, I present some other criteria that further delimit the class of natural concepts. One of these is coherence, which does not seem to have been discussed previously. Finally, I show that convexity and other criteria make it possible to ensure that people mean the same thing when they communicate using concepts. Apart from its philosophical interest, the analysis presented in the article will be relevant for tasks of conceptual engineering in artificial systems that work with concepts.

TACO: A Turkish database for abstract concepts.

The organization of abstract concepts reflects different dimensions, grounded in the brain regions coding for the corresponding experience. Normative measures of linguistic stimuli offer noteworthy insights into the organization of conceptual knowledge, but studies differ in the dimensions and classes of concepts considered. Additionally, most of the available information has been collected in English, without considering possible linguistic and cultural differences. Here, we aimed to create a comprehensive Turkish database for abstract concepts (TACO), including rarely investigated classes such as political concepts. We included 503 words-78 concrete (fruits, animals, tools) and 425 abstract (emotions, social, mental states, theoretical, quantity, space, political)-rated by 134 Turkish speakers for familiarity, imageability, age of acquisition, valence, arousal, quantity, space, theoretical, social, mental state, and political dimensions. We calculated dominance and exclusivity, indicating the dimension receiving the highest mean score for each word, and the position of the word along the unidimensional-multidimensional continuum, respectively. A principal component analysis (PCA) was conducted on the semantic dimensions. The results showed that mental state was the dominant dimension for most concepts. Moderate to low levels of exclusivity indicated that the concepts were multidimensional. PCA revealed three components: Component 1 captured the juxtaposition between social/mental state and magnitude polarities, Component 2 highlighted affective components, and Component 3 grouped together political and theoretical dimensions. The introduction of political concepts provided insights into the multidimensional nature of this unexplored class, closely intertwined with the theoretical dimension. TACO constitutes the first comprehensive Turkish database covering several abstract dimensions, paving the way for cross-linguistic and cross-cultural studies of semantic representations.

Paradigmatic Properties of Foreign Language Lexical Units in Russian and Uzbek Languages (Based on the Material of Internet Media)

The purpose of this article is to identify and describe the paradigmatic properties of foreign language lexical units in the Russian and Uzbek languages, which are actively functioning in the Internet media. The article examines both the theoretical concepts of "lexical system", "paradigmatics" and synonymous paradigms, structures of LSP, where the core (lexeme-concept or group of lexeme-concepts), the center (classes of basic concepts, words with their paradigmatic and syntagmatic relations) and the periphery are distinguished.

Ontology Attention Layer for Medical Named Entity Recognition

Named entity recognition (NER) is particularly challenging for medical texts due to the high domain specificity, abundance of technical terms, and sparsity of data in this field. In this work, we propose a novel attention layer, called the “ontology attention layer”, that enhances the NER performance of a language model for clinical text by utilizing an ontology consisting of conceptual classes related to the target entity set. The proposed layer computes the relevance between each input token and the classes in the ontology and then fuses the encoded token vectors and the class vectors to enhance the token vectors by explicit superior knowledge. In our experiments, we apply the proposed layer to various language models for an NER task based on a Chinese clinical dataset to evaluate the performance of the layer. We also investigate the influence of the granularity of the classes utilized in the ontology attention layer. The experimental results show that the proposed ontology attention layer improved F1 scores by 0.4% to 0.5%. The results suggest that the proposed method is an effective approach to improving the NER performance of existing language models for clinical datasets.

A Study of Teaching Strategies for Integrating Mathematical Culture into High School Mathematics Concepts Classes in the APOS Perspective

A Study of Teaching Strategies for Integrating Mathematical Culture into High School Mathematics Concepts Classes in the APOS Perspective

Knowledge graph representation method for semantic 3D modeling of Chinese grottoes

The integration of 3D geometric models with semantic information significantly improves the applicability and comprehensibility of cultural heritage. The semantic 3D modeling of Chinese grottoes poses challenges for individuals without expertise in cultural heritage due to gaps in domain knowledge and discrepancies in understanding. However, the existing domain ontology and knowledge graph provide an insufficient representation of the knowledge of Chinese grottoes. To overcome these obstacles, we propose a knowledge graph representation method to provide explicit knowledge for participants at different stages of semantic 3D modeling of Chinese grottoes, which includes schema layer construction and data layer construction. On the schema layer, we design a domain ontology named ChgOnto (Chinese Grottoes Ontology) that consists of four high-level concept classes: spatial object, informational object, digital device, and temporal object. Among the classes in the ChgOnto, the components (e.g., cliff wall, cave roof, cliff wall footing), elements (e.g., primary Buddha statue, pedestal, decoration), the properties (e.g., length, width, depth) of caves and niches in Chinese grottoes as well as the spatial relationships between them are all precisely defined. ChgOnto also reuse the classes from the renowned CIDOC CRM ontology in the cultural heritage field and GeoSPARQL in the geospatial domain, facilitating integration between the two subjects. Considering the schema layer as the conceptual data model, the data layer extracts knowledge from unstructured text through natural language processing tools to instantiate the abstract classes and fill the properties of the schema layer. Finally, the knowledge required for semantic 3D modeling of Chinese grottoes is expressed in the data layer by a knowledge graph in a fixed expression form. Dazu Rock Carvings, a World Heritage site in China, is selected as a case study to validate the practicality and effectiveness of the proposed method. The results reveal that our method offers a robust knowledge-sharing platform for the semantic 3D modeling of Chinese grottoes and demonstrates excellent scalability. The method proposed in this paper can also serve as an informative reference for other types of cultural heritage.

Quantum Alphatron: quantum advantage for learning with kernels and noise

At the interface of machine learning and quantum computing, an important question is what distributions can be learned provably with optimal sample complexities and with quantum-accelerated time complexities. In the classical case, Klivans and Goel discussed the Alphatron, an algorithm to learn distributions related to kernelized regression, which they also applied to the learning of two-layer neural networks. In this work, we provide quantum versions of the Alphatron in the fault-tolerant setting. In a well-defined learning model, this quantum algorithm is able to provide a polynomial speedup for a large range of parameters of the underlying concept class. We discuss two types of speedups, one for evaluating the kernel matrix and one for evaluating the gradient in the stochastic gradient descent procedure. We also discuss the quantum advantage in the context of learning of two-layer neural networks. Our work contributes to the study of quantum learning with kernels and from samples.

Compression schemes for concept classes induced by three types of discrete undirected graphical models

Sample compression schemes were first proposed by Littlestone and Warmuth in 1986. Undirected graphical model is a powerful tool for classification in statistical learning. In this paper, we consider labelled compression schemes for concept classes induced by discrete undirected graphical models. For the undirected graph of two vertices with no edge, where one vertex takes two values and the other vertex can take any finite number of values, we propose an algorithm to establish a labelled compression scheme of size VC dimension of associated concept class. Further, we extend the result to other two types of undirected graphical models and show the existence of labelled compression schemes of size VC dimension for induced concept classes. The work of this paper makes a step forward in solving sample compression problem for concept class induced by a general discrete undirected graphical model.

On the non-efficient PAC learnability of conjunctive queries

This note serves three purposes: (i) we provide a self-contained exposition of the fact that conjunctive queries are not efficiently learnable in the Probably-Approximately-Correct (PAC) model, paying clear attention to the complicating fact that this concept class lacks the polynomial-size fitting property, a property that is tacitly assumed in much of the computational learning theory literature; (ii) we establish a strong negative PAC learnability result that applies to many restricted classes of conjunctive queries (CQs), including acyclic CQs for a wide range of notions of acyclicity; (iii) we show that CQs (and UCQs) are efficiently PAC learnable with membership queries.

A note on hardness of computing recursive teaching dimension

In this short note, we show that the problem of computing the recursive teaching dimension (RTD) for a concept class (given explicitly as input) requires nΩ(log⁡n)-time, assuming the exponential time hypothesis (ETH). This matches the running time nO(log⁡n) of the brute-force algorithm for the problem.

Effective Concept Classes of PAC and PACi Incomparable Degrees, Joins and Embedding of Degrees

AbstractThe Probably Approximately Correct (PAC) learning is a machine learning model introduced by Leslie Valiant in 1984. The PACi reducibility refers to the PAC reducibility independent of size and computation time. This reducibility in PAC learning resembles the reducibility in Turing computability. The ordering of concept classes under PAC reducibility is nonlinear, even when restricted to particular concrete examples.Due to the resemblance to Turing Reducibility, we suspected that there could be incomparable PACi and PAC degrees for the PACi and PAC reducibilities as in Turing incomparable degrees. In 1957 Friedberg and in 1956 Muchnik independently solved the Post problem by constructing computably enumerable sets A and B of incomparable degrees using the priority construction method. We adapt this idea to PACi and PAC reducibilities and construct two effective concept classes C and D such that C is not reducible to D and vice versa. When considering PAC reducibility it was necessary to work on the size of an effective concept class, thus we use Kolmogorov complexity to obtain the size. The non-learnability of concept classes in the PAC learning model is explained by the existence of PAC incomparable degrees.Analogous to the Turing jump, we give a jump operation on effective concept classes for the zero jump. To define the zero jump operator for PACi degrees the join of all the effective concept classes is constructed and proved that it is a greatest element. There are many properties proven for existing degrees. Thus we can explore proving those properties to PACi and PAC degrees. But if we prove an embedding from those degrees to PACi and PAC degrees then those properties will be true for PACi and PAC degrees without explicitly proving them.Abstract prepared by Dodamgodage Gihnee M. Senadheera and taken directly from the thesisE-mail: senadheerad@winthrop.eduURL: https://www.proquest.com/docview/2717762461/abstract/ACD19F29A8774AF6PQ/1?accountid=13864

Research on the Intelligent Construction of UAV Knowledge Graph Based on Attentive Semantic Representation

Accurate target recognition of unmanned aerial vehicles (UAVs) in the intelligent warfare mode relies on a highly standardized UAV knowledge base, and thus it is crucial to construct a knowledge graph suitable for UAV multi-source information fusion. However, due to the lack of domain knowledge and the cumbersome and inefficient construction techniques, the intelligent construction approaches of knowledge graphs for UAVs are relatively backward. To this end, this paper proposes a framework for the construction and application of a standardized knowledge graph from large-scale UAV unstructured data. First, UAV concept classes and relations are defined to form specialized ontology, and UAV knowledge extraction triples are labeled. Then, a two-stage knowledge extraction model based on relational attention-based contextual semantic representation (UASR) is designed based on the characteristics of the UAV knowledge extraction corpus. The contextual semantic representation is then applied to the downstream task as a key feature through the Multilayer Perceptron (MLP) attention method, while the relation attention mechanism-based approach is used to calculate the relational-aware contextual representation in the subject–object entity extraction stage. Extensive experiments were carried out on the final annotated dataset, and the model F1 score reached 70.23%. Based on this, visual presentation is achieved based on the UAV knowledge graph, which lays the foundation for the back-end application of the UAV knowledge graph intelligent construction technology.

Interpretable early warning recommendations in interactive learning environments: a deep-neural network approach based on learning behavior knowledge graph

Early warning recommendation is crucial for tracking learning behavior and represents a significant issue in interactive learning environments. However, an interactive learning environment-based learning process may not always achieve expected goals, leading to inefficient or ineffective learning behavior and negative emotions. Additionally, many learners fail assessments due to these issues. To address this problem, this study proposes relevant test problems for interpretable early warning recommendations based on massive learning behavior instances and potential relationships. We design an applicable learning analysis model, namely a deep-neural network based on the knowledge graph of learning behavior, and verify its feasibility and reliability through extensive experiments and data analysis. Our results demonstrate that the interactive learning process must match multi-factor analysis at different temporal sequences to determine key temporal sequences or intervals. This is limited by the classification of learning contents and interpretable concepts, which provide effective reference for subsequent learning content with similar concept classes and knowledge structures. Our approach recommends effective learning behavior in appropriate temporal sequences as soon as possible or constructs feasible intervention measures to improve learners’ participation. This research deepens and expands early warning by proposing a feasible new method and obtaining key conclusions with vital practical significance.

Sustainable Fertilizers: Publication Landscape on Wastes as Nutrient Sources, Wastewater Treatment Processes for Nutrient Recovery, Biorefineries, and Green Ammonia Synthesis.

The ability of modern agriculture to meet future food demand imposed by accelerating growth of the world's population is a major challenge, and fertilizers play a key role by replacing nutrients in agricultural soil. Given the need for fertilizers, their cost in nonrenewable resources and energy, and the consequences of the greenhouse gas emissions required to make them, people have begun to explore ways to make fertilizer manufacturing and use more sustainable. Using data from the CAS Content Collection, this review examines and analyzes the academic and patent literature on sustainable fertilizers from 2001 to 2021. The breakdown of journal and patent literature publication over time on this topic, country or region of publications, the substances included in published research, among other things allow us to understand the general progress in the field as well as the classes of materials and concepts driving innovation. We hope that this bibliometric analysis and literary review will assist researchers in relevant industries to discover and implement ways to supplement conventional fertilizers and nutrient sources while improving the efficiency and sustainability of waste management and ammonia production.