scholarly journals Ambiguity in language networks

2015 ◽  
Vol 32 (1) ◽  
Author(s):  
Ricard V. Solé ◽  
Luís F. Seoane
Keyword(s):  
Language Processing ◽  
Network Architecture ◽  
Essential Role ◽  
Scaling Laws ◽  
Network Growth ◽  
Computational Approaches ◽  
Universal Scaling ◽  
Different Levels ◽  
Relevant Component ◽  
Language Network

AbstractHuman language defines the most complex outcomes of evolution. The emergence of such an elaborated form of communication allowed humans to create extremely structured societies and manage symbols at different levels including, among others, semantics. All linguistic levels have to deal with an astronomic combinatorial potential that stems from the recursive nature of languages. This recursiveness is indeed a key defining trait. However, not all words are equally combined nor frequent. In breaking the symmetry between less and more often used and between less and more meaning-bearing units, universal scaling laws arise. Such laws, common to all human languages, appear on different stages from word inventories to networks of interacting words. Among these seemingly universal traits exhibited by language networks, ambiguity appears to be a specially relevant component. Ambiguity is avoided in most computational approaches to language processing, and yet it seems to be a crucial element of language architecture. Here we review the evidence both from language network architecture and from theoretical reasonings based on a least effort argument. Ambiguity is shown to play an essential role in providing a source of language efficiency, and is likely to be an inevitable byproduct of network growth.

Putting up barriers

2017 ◽  
Author(s):  
David J. Lobina
Keyword(s):  
Knowledge Base ◽  
Real Time ◽  
Language Processing ◽  
Cognitive Domain ◽  
Linguistic Knowledge ◽  
Levels Of Explanation ◽  
Real Time Processing ◽  
Time Processing ◽  
Neural Underpinnings ◽  
Different Levels

The study of cognitive phenomena is best approached in an orderly manner. It must begin with an analysis of the function in intension at the heart of any cognitive domain (its knowledge base), then proceed to the manner in which such knowledge is put into use in real-time processing, concluding with a domain’s neural underpinnings, its development in ontogeny, etc. Such an approach to the study of cognition involves the adoption of different levels of explanation/description, as prescribed by David Marr and many others, each level requiring its own methodology and supplying its own data to be accounted for. The study of recursion in cognition is badly in need of a systematic and well-ordered approach, and this chapter lays out the blueprint to be followed in the book by focusing on a strict separation between how this notion applies in linguistic knowledge and how it manifests itself in language processing.

A Densely Connected Network Based on U-Net for Medical Image Segmentation

2021 ◽  
Vol 17 (3) ◽  
pp. 1-14
Author(s):  
Zhenzhen Yang ◽  
Pengfei Xu ◽  
Yongpeng Yang ◽  
Bing-Kun Bao
Keyword(s):  
Feature Extraction ◽  
Image Segmentation ◽  
Loss Function ◽  
Network Architecture ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Cross Entropy ◽  
Loss Functions ◽  
Feature Maps ◽  
Different Levels

The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.

scholarly journals The Small and Efficient Language Network of Polyglots and Hyper-polyglots

2020 ◽  
Vol 31 (1) ◽  
pp. 62-76
Author(s):  
Olessia Jouravlev ◽  
Zachary Mineroff ◽  
Idan A Blank ◽  
Evelina Fedorenko
Keyword(s):  
Foreign Language ◽  
Language Processing ◽  
Default Mode Network ◽  
Individual Level ◽  
Neural Architecture ◽  
Initial Characterization ◽  
The One ◽  
Multiple Languages ◽  
Language Network

Abstract Acquiring a foreign language is challenging for many adults. Yet certain individuals choose to acquire sometimes dozens of languages and often just for fun. Is there something special about the minds and brains of such polyglots? Using robust individual-level markers of language activity, measured with fMRI, we compared native language processing in polyglots versus matched controls. Polyglots (n = 17, including nine “hyper-polyglots” with proficiency in 10–55 languages) used fewer neural resources to process language: Their activations were smaller in both magnitude and extent. This difference was spatially and functionally selective: The groups were similar in their activation of two other brain networks—the multiple demand network and the default mode network. We hypothesize that the activation reduction in the language network is experientially driven, such that the acquisition and use of multiple languages makes language processing generally more efficient. However, genetic and longitudinal studies will be critical to distinguish this hypothesis from the one whereby polyglots’ brains already differ at birth or early in development. This initial characterization of polyglots’ language network opens the door to future investigations of the cognitive and neural architecture of individuals who gain mastery of multiple languages, including changes in this architecture with linguistic experiences.

Universal scaling laws and the QCD scale anomaly

1994 ◽  
Vol 242 (4-6) ◽  
pp. 355-361
Author(s):  
Georges Ripka ◽  
Martine Jaminon
Keyword(s):  
Scaling Laws ◽  
Universal Scaling

Universal scaling laws of transients

2002 ◽  
Vol 47 (3) ◽  
pp. 181-183 ◽  
Author(s):  
A. A. Koronovskii ◽  
D. I. Trubetskov ◽  
A. E. Khramov ◽  
A. E. Khramova
Keyword(s):  
Scaling Laws ◽  
Universal Scaling

Similarity of Computations Across Domains Does Not Imply Shared Implementation: The Case of Language Comprehension

2021 ◽  
Vol 30 (6) ◽  
pp. 526-534
Author(s):  
Evelina Fedorenko ◽  
Cory Shain
Keyword(s):  
Language Processing ◽  
Language Comprehension ◽  
Fluid Intelligence ◽  
Linguistic Knowledge ◽  
Domain Specific ◽  
Cognitive Operations ◽  
High Level ◽  
General Circuits ◽  
The Mind ◽  
Language Network

Understanding language requires applying cognitive operations (e.g., memory retrieval, prediction, structure building) that are relevant across many cognitive domains to specialized knowledge structures (e.g., a particular language’s lexicon and syntax). Are these computations carried out by domain-general circuits or by circuits that store domain-specific representations? Recent work has characterized the roles in language comprehension of the language network, which is selective for high-level language processing, and the multiple-demand (MD) network, which has been implicated in executive functions and linked to fluid intelligence and thus is a prime candidate for implementing computations that support information processing across domains. The language network responds robustly to diverse aspects of comprehension, but the MD network shows no sensitivity to linguistic variables. We therefore argue that the MD network does not play a core role in language comprehension and that past findings suggesting the contrary are likely due to methodological artifacts. Although future studies may reveal some aspects of language comprehension that require the MD network, evidence to date suggests that those will not be related to core linguistic processes such as lexical access or composition. The finding that the circuits that store linguistic knowledge carry out computations on those representations aligns with general arguments against the separation of memory and computation in the mind and brain.

scholarly journals Efecto de la Escolarización en la Producción de Relaciones Causales y Contracausales: Evidencia Psicolingüística para el Ámbito Educativo

2016 ◽  
Vol 6 (3) ◽  
pp. 258
Author(s):  
Gabriela Mariel Zunino
Keyword(s):  
Empirical Evidence ◽  
Language Processing ◽  
Formal Education ◽  
Educational Process ◽  
Semantic Relations ◽  
Practical Application ◽  
Discourse Comprehension ◽  
Educational Processes ◽  
Different Characteristics ◽  
Different Levels

In order to promote the practical application of psycholinguistic data in educational fields and expecting that this transfer would enhance the development of both the pedagogical field and the investigation in experimental psycholinguistics, we present two experiments to analyse the production of semantic relations in discourse, especially the causality/countercausality dimension. We found that the pattern of causal advantage is cross-wise and consistent in subjects with different levels of formal education, so it could be a suitable scaffold to develop other aspects of discourse comprehension and production. We compare our results with previous findings about discourse comprehension and interpret the data in the framework of educational processes. To use of empirical evidence about language processing on educational fields allows not only to review specific issues such as the characteristics of teaching materials, but also to improve educational process in a comprehensive way, making possible to adapt different approaches to populations with different characteristics.

scholarly journals Dynamic Rounds Chaotic Block Cipher Based on Keyword Abstract Extraction

Entropy ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. 693 ◽  
Author(s):  
Juan Wang ◽  
Qun Ding
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Block Cipher ◽  
Design Method ◽  
Chaotic Sequence ◽  
Cryptographic Algorithm ◽  
Nist Tests ◽  
Feistel Structure ◽  
Different Levels ◽  
Chaotic Theory

According to the keyword abstract extraction function in the Natural Language Processing and Information Retrieval Sharing Platform (NLPIR), the design method of a dynamic rounds chaotic block cipher is presented in this paper, which takes into account both the security and efficiency. The cipher combines chaotic theory with the Feistel structure block cipher, and uses the randomness of chaotic sequence and the nonlinearity of chaotic S-box to dynamically generate encrypted rounds, realizing more numbers of dynamic rounds encryption for the important information marked by NLPIR, while less numbers of dynamic rounds encryption for the non-important information that is not marked. Through linear and differential cryptographic analysis, ciphertext information entropy, “0–1” balance and National Institute of Science and Technology (NIST) tests and the comparison with other traditional and lightweight block ciphers, the results indicate that the dynamic variety of encrypted rounds can achieve different levels of encryption for different information, which can achieve the purpose of enhancing the anti-attack ability and reducing the number of encrypted rounds. Therefore, the dynamic rounds chaotic block cipher can guarantee the security of information transmission and realize the lightweight of the cryptographic algorithm.

scholarly journals Universal scaling across biochemical networks on Earth

2019 ◽  
Vol 5 (1) ◽  
pp. eaau0149 ◽  
Author(s):  
Hyunju Kim ◽  
Harrison B. Smith ◽  
Cole Mathis ◽  
Jason Raymond ◽  
Sara I. Walker
Keyword(s):  
Scaling Laws ◽  
Network Size ◽  
Biochemical Networks ◽  
Biochemical Network ◽  
Reaction Networks ◽  
Biochemical Reaction Networks ◽  
Global Database ◽  
Universal Scaling ◽  
Planetary Scale ◽  
Domains Of Life

The application of network science to biology has advanced our understanding of the metabolism of individual organisms and the organization of ecosystems but has scarcely been applied to life at a planetary scale. To characterize planetary-scale biochemistry, we constructed biochemical networks using a global database of 28,146 annotated genomes and metagenomes and 8658 cataloged biochemical reactions. We uncover scaling laws governing biochemical diversity and network structure shared across levels of organization from individuals to ecosystems, to the biosphere as a whole. Comparing real biochemical reaction networks to random reaction networks reveals that the observed biological scaling is not a product of chemistry alone but instead emerges due to the particular structure of selected reactions commonly participating in living processes. We show that the topology of biochemical networks for the three domains of life is quantitatively distinguishable, with >80% accuracy in predicting evolutionary domain based on biochemical network size and average topology. Together, our results point to a deeper level of organization in biochemical networks than what has been understood so far.

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