How the cerebellum may monitor sensory information for spatial representation

The cerebellum has already been shown to participate in the navigation function. We propose here that this structure is involved in maintaining a sense of direction and location during self-motion by monitoring sensory information and interacting with navigation circuits to update the mental representation of space. To better understand the processing performed by the cerebellum in the navigation function, we have reviewed: the anatomical pathways that convey self-motion information to the cerebellum; the computational algorithm(s) thought to be performed by the cerebellum from these multi-source inputs; the cerebellar outputs directed toward navigation circuits and the influence of self-motion information on space-modulated cells receiving cerebellar outputs. This review highlights that the cerebellum is adequately wired to combine the diversity of sensory signals to be monitored during self-motion and fuel the navigation circuits. The direct anatomical projections of the cerebellum toward the head-direction cell system and the parietal cortex make those structures possible relays of the cerebellum influence on the hippocampal spatial map. We describe computational models of the cerebellar function showing that the cerebellum can filter out the components of the sensory signals that are predictable, and provides a novelty output. We finally speculate that this novelty output is taken into account by the navigation structures, which implement an update over time of position and stabilize perception during navigation

Scintillation Properties of CsPbBr3 Nanocrystals Prepared by Ligand-Assisted Reprecipitation and Dual Effect of Polyacrylate Encapsulation toward Scalable Ultrafast Radiation Detectors

Lead halide perovskite nanocrystals (LHP-NCs) embedded in polymeric hosts are gaining attention as scalable and low-cost scintillation detectors for technologically relevant applications. Despite rapid progress, little is currently known about the scintillation properties and stability of LHP-NCs prepared by the ligand assisted reprecipitation (LARP) method, which allows mass scalability at room temperature unmatched by any other type of nanostructure, and the implications of incorporating LHP-NCs into polyacrylate hosts are still largely debated. Here, we show that LARP-synthesized CsPbBr3 NCs are comparable to particles from hot-injection routes and unravel the dual effect of polyacrylate incorporation, where the partial degradation of LHP-NCs luminescence is counterbalanced by the passivation of electron-poor defects by the host acrylic groups. Experiments on NCs with tailored surface defects show that the balance between such antithetical effects of polymer embedding is determined by the surface defect density of the NCs and provide guidelines for further material optimization

Lexical Semantic Change through Large Language Models: a Survey

Lexical Semantic Change (LSC) is the task of identifying, interpreting, and assessing the possible change over time in the meanings of a target word. Traditionally, LSC has been addressed by linguists and social scientists through manual and time-consuming analyses, which have thus been limited in terms of the volume, genres, and time-frame that can be considered. In recent years, computational approaches based on Natural Language Processing have gained increasing attention to automate LSC as much as possible. Significant advancements have been made by relying on Large Language Models (LLMs), which can handle the multiple usages of the words and better capture the related semantic change. In this article, we survey the approaches based on LLMs for LSC, and we propose a classification framework characterized by three dimensions: meaning representation, time-awareness, and learning modality. The framework is exploited to (i) review the measures for change assessment, (ii) compare the approaches on performance, and (iii) discuss the current issues in terms of scalability, interpretability, and robustness. Open challenges and future research directions about the use of LLMs for LSC are finally outlined

Knowledge-driven entity recognition and disambiguation in biomedical text

Entity recognition and disambiguation (ERD) for the biomedical domain are notoriously difficult problems due to the variety of entities and their often long names in many variations. Existing works focus heavily on the molecular level in two ways. First, they target scientific literature as the input text genre. Second, they target single, highly specialized entity types such as chemicals, genes, and proteins. However, a wealth of biomedical information is also buried in the vast universe of Web content. In order to fully utilize all the information available, there is a need to tap into Web content as an additional input. Moreover, there is a need to cater for other entity types such as symptoms and risk factors since Web content focuses on consumer health. The goal of this thesis is to investigate ERD methods that are applicable to all entity types in scientific literature as well as Web content. In addition, we focus on under-explored aspects of the biomedical ERD problems -- scalability, long noun phrases, and out-of-knowledge base (OOKB) entities. This thesis makes four main contributions, all of which leverage knowledge in UMLS (Unified Medical Language System), the largest and most authoritative knowledge base (KB) of the biomedical domain. The first contribution is a fast dictionary lookup method for entity recognition that maximizes throughput while balancing the loss of precision and recall. The second contribution is a semantic type classification method targeting common words in long noun phrases. We develop a custom set of semantic types to capture word usages; besides biomedical usage, these types also cope with non-biomedical usage and the case of generic, non-informative usage. The third contribution is a fast heuristics method for entity disambiguation in MEDLINE abstracts, again maximizing throughput but this time maintaining accuracy. The fourth contribution is a corpus-driven entity disambiguation method that addresses OOKB entities. The method first captures the entities expressed in a corpus as latent representations that comprise in-KB and OOKB entities alike before performing entity disambiguation.Die Erkennung und Disambiguierung von Entitäten für den biomedizinischen Bereich stellen, wegen der vielfältigen Arten von biomedizinischen Entitäten sowie deren oft langen und variantenreichen Namen, große Herausforderungen dar. Vorhergehende Arbeiten konzentrieren sich in zweierlei Hinsicht fast ausschließlich auf molekulare Entitäten. Erstens fokussieren sie sich auf wissenschaftliche Publikationen als Genre der Eingabetexte. Zweitens fokussieren sie sich auf einzelne, sehr spezialisierte Entitätstypen wie Chemikalien, Gene und Proteine. Allerdings bietet das Internet neben diesen Quellen eine Vielzahl an Inhalten biomedizinischen Wissens, das vernachlässigt wird. Um alle verfügbaren Informationen auszunutzen besteht der Bedarf weitere Internet-Inhalte als zusätzliche Quellen zu erschließen. Außerdem ist es auch erforderlich andere Entitätstypen wie Symptome und Risikofaktoren in Betracht zu ziehen, da diese für zahlreiche Inhalte im Internet, wie zum Beispiel Verbraucherinformationen im Gesundheitssektor, relevant sind. Das Ziel dieser Dissertation ist es, Methoden zur Erkennung und Disambiguierung von Entitäten zu erforschen, die alle Entitätstypen in Betracht ziehen und sowohl auf wissenschaftliche Publikationen als auch auf andere Internet-Inhalte anwendbar sind. Darüber hinaus setzen wir Schwerpunkte auf oft vernachlässigte Aspekte der biomedizinischen Erkennung und Disambiguierung von Entitäten, nämlich Skalierbarkeit, lange Nominalphrasen und fehlende Entitäten in einer Wissensbank. In dieser Hinsicht leistet diese Dissertation vier Hauptbeiträge, denen allen das Wissen von UMLS (Unified Medical Language System), der größten und wichtigsten Wissensbank im biomedizinischen Bereich, zu Grunde liegt. Der erste Beitrag ist eine schnelle Methode zur Erkennung von Entitäten mittels Lexikonabgleich, welche den Durchsatz maximiert und gleichzeitig den Verlust in Genauigkeit und Trefferquote (precision and recall) balanciert. Der zweite Beitrag ist eine Methode zur Klassifizierung der semantischen Typen von Nomen, die sich auf gebräuchliche Nomen von langen Nominalphrasen richtet und auf einer selbstentwickelten Sammlung von semantischen Typen beruht, die die Verwendung der Nomen erfasst. Neben biomedizinischen können diese Typen auch nicht-biomedizinische und allgemeine, informationsarme Verwendungen behandeln. Der dritte Beitrag ist eine schnelle Heuristikmethode zur Disambiguierung von Entitäten in MEDLINE Kurzfassungen, welche den Durchsatz maximiert, aber auch die Genauigkeit erhält. Der vierte Beitrag ist eine korpusgetriebene Methode zur Disambiguierung von Entitäten, die speziell fehlende Entitäten in einer Wissensbank behandelt. Die Methode wandelt erst die Entitäten, die in einem Textkorpus ausgedrückt aber nicht notwendigerweise in einer Wissensbank sind, in latente Darstellungen um und führt anschließend die Disambiguierung durch

Evaluating and improving lexical language understanding in neural machine translation

Lexical understanding is an inalienable component of the translation process. In order to correctly map the meaning of a linguistic unit to the appropriate target language expression, the meaning of its constituent words has first to be identified and disambiguated, followed by the application of compositional operations. This thesis examines the competency of contemporary neural machine translation (NMT) models on two core aspects of lexical understanding – word sense disambiguation (WSD) and coreference resolution (CoR), both of which are well-established and much-studied natural language processing (NLP) tasks. Certain linguistic properties that are under-specified in a source language (e.g. the grammatical gender of a noun in English) may need to be stated explicitly in the chosen target language (e.g. German). Doing so correctly requires the accurate resolution of the associated ambiguities. While recent modeling advances appear to suggest that both WSD and CoR are largely solved challenges in machine translation, the work conducted within the scope of this thesis demonstrates that this is not yet the case. In particular, we show that NMT systems are prone to relying on surface-level heuristics and data biases to guide their lexical disambiguation decisions, rather than engaging in deep language understanding by correctly recognizing and leveraging contextual disambiguation triggers. As part of our investigation, we introduce a novel methodology for predicting WSD errors a translation model is likely to make and utilize this knowledge to craft adversarial attacks with the aim to elicit disambiguation errors in model translations. Additionally, we create a set of challenging CoR benchmarks that uncover the inability of translation systems to identify referents of pronouns in contexts that presuppose commonsense reasoning, caused by their pathological over-reliance on data biases. At the same time, we develop initial solutions for the identified model deficiencies. As such, we show that fine-tuning on de-biased data and modifying the learning objective of a model can significantly improve disambiguation performance by counteracting the harmful impact of data biases. We furthermore propose a novel extension to the popular transformer architecture that is found to strengthen its WSD capabilities and robustness to adversarial WSD attacks by facilitating the accessibility of lexical features across all layers of the model and increasing the extent to which contextual information is encapsulated with its latent representations. Despite the so effected improvements to WSD and CoR, both tasks remain far from solved, posing a veritable challenge for the current generation of NMT models, as well as for large language models that have risen to prominence within NLP in recent years

An exploration of the integration of speech with co-speech gesture with non-invasive brain stimulation

The current PhD project focuses on the integration of gesture with their co-occurring speech with the use of non-invasive brain stimulation. The project investigated ‘where’ and ‘when’ gesture-speech integration takes place. Building on the paradigm of Kelly et al., (2010) which provides a reaction time index of automatic gesture-speech integration, it was tested whether left middle temporal gyrus (pMTG) as well as left Inferior frontal gyrus (LIFG) are causally involved in gesture-speech integration. A follow-up study investigated the time window for this integration of gesture and speech in pMTG. This study found that gesture has a priming effect on the semantic retrieval of speech. This effect only manifested itself after gesture had been clearly understood and before the semantic analysis of speech. Based on the common coding hypothesis, this finding was interpreted in terms of gesture and speech originating from a common coding system, with both LIFG and pMTG as its neural underpining, enabling bi-directional influences between both domains

Dances with Words: Issues in the Translation of Japanese Literature into English

Chapter One: Literary Translation Studies, Japanese-to-English Translation, and Izu no odoriko This introductory chapter explores aspects of Translation Studies relevant to Japanese-to-English literary translation. I employ extended metaphors from the case study, Kawabata Yasunari's novella Izu no odoriko,to re-illuminate perennial TS issues such as equivalence, 'style' and disambiguation, contrasting the translating approaches of Edward G. Seidensticker and J. Martin Holman. The chapter concludes with an outline of the investigative path I followed in analysing the sourcetext (ST) and comparing it with the target texts (TTs): the English translations. I explain the thesis's systematic corpus approach in using an NVivo database to establish a set of potentially problematic translation issues that arise out of the interaction of source language-target language (SL-TL) features. Chapter Two: A Taxonomy of Japanese Paradigmatic Features and the Issues Arising for Translation into English The Japanese and English languages have significant lexical and morpho-syntactic differences, which I contend give rise to potentially problematic translation issues. The chapter begins by differentiating cultural and linguistic features and explaining why the thesis will focus on the latter. The rest of the chapter presents a detailed analysis of ST exemplars of the most significant of the paradigmatic (lexical) features. Seidensticker and Holman's translations are analysed to determine how they have addressed the translation issues arising from these features. Chapter Three: A Taxonomy of Japanese Syntagmatic Features and the Issues Arising for Translation into English This chapter continues the analysis of linguistic differences between Japanese and English in the context of literary translation. Here the focus is on the syntagmatic (structural)features of Japanese in comparison with English, again examining examples from the ST and comparing how the translators address the issues arising in their translating decisions. Chapter 4: 'Shall We Dance?' Translation Acts in the English Translations of Izu no odoriko and Beyond The focus moves to the features of the translators' overall translation strategies, and how they apply these strategies in their translating decisions: so-called 'translation acts'. Conducting a close reading of the ST and TTs of a pivotal scene in Izu no odoriko, I draw on previous academics' frameworks to create a simple rubric for categorising the manifestation of these strategies at the discourse level. The chapter concludes by drawing together the theoretical and empirical strands of the thesis and demonstrating the relevance of this discussion to the English translation of Japanese literature. While acknowledging the necessarily subjective nature of the translational act, and the sophisticated techniques the translators employ to deal with complex issues, I propose that my analytic framework urges more care in the preservation of semantic and formal elements than can be observed in aspects of the translations examined