Miracle-dz/UniFAR-FTUs huggingface dataset:
Vocabulary: Common or Basic?

Neither linguistics nor psychology offers a single, unified notion of simplicity, and therefore the simplest “core” layer of vocabulary is hard to define in theory and hard to pinpoint in practice. In section 1 we briefly survey the main approaches, and distinguish two that are highly relevant to lexicography: we will call these common and basic. In sections 2 and 3 we compare these approaches, and in section 4 we point the reader to Kolmogorov complexity, unfamiliar as it may be to most working psychologists, lexicographers, and educators, as the best formal means to deal with core vocabulary.
Gene Action Governing the Inheritance of Stomatal Conductance in Four Wheat Crosses Under High Temperature Stress Condition

The knowledge pertaining to gene action and interactions involved in the inheritance of a character in different generations is crucial for determining the breeding strategies in crop improvement program. In the present study, the gene action of stomatal conductance was determined in four wheat populations under high elevated temperatures coupled with late sowing at experimental farm, ICAR-Indian Agricultural Research Institute, New Delhi, India. Steady-state SC-1 leaf porometer was used to record stomatal conductance on adaxial and abaxial leaf surface at late boot (Z 49–50), early milk (Z 73), and late milk (Z 77) growth stages. Evidence for nuclear genetic control of stomatal conductance was strong, with large and repeatable genetic difference observed for parents and progeny across all the four crosses. Mean stomatal conductance for genotypes, GW 322 and GW 366, was consistently low at late boot, early milk, and late milk under timely sown, late sown, and very late sown condition, whereas the converse was true for the high-conducting parents, KAUZ/AA//KAUZ and RAC 875. Additive and additive x additive epistatic effects were large and reasonably consistent at three stages and in all crosses. Detection of epistasis and evidence of transgressive segregation suggested that variation for stomatal conductance was under oligo or polygenic control. Thus, it is conceivable that independent alleles at two or more loci could be pyramided into a single family for increased or decreased stomatal conductance. Additive-based gene action also facilitates with simple selection at early generation to improve stomatal conductance in expected direction. This is the first report on estimates of gene action for stomatal conductance of flag leaf under heat stress condition during reproductive and grain filling stage.
Which is Making the Contribution: Modulating Unimodal and Cross-modal Dynamics for Multimodal Sentiment Analysis

Multimodal sentiment analysis (MSA) draws increasing attention with the availability of multimodal data. The boost in performance of MSA models is mainly hindered by two problems. On the one hand, recent MSA works mostly focus on learning cross-modal dynamics, but neglect to explore an optimal solution for unimodal networks, which determines the lower limit of MSA models. On the other hand, noisy information hidden in each modality interferes the learning of correct cross-modal dynamics. To address the above-mentioned problems, we propose a novel MSA framework \textbf{M}odulation \textbf{M}odel for \textbf{M}ultimodal \textbf{S}entiment \textbf{A}nalysis ({$ M^3SA $}) to identify the contribution of modalities and reduce the impact of noisy information, so as to better learn unimodal and cross-modal dynamics. Specifically, modulation loss is designed to modulate the loss contribution based on the confidence of individual modalities in each utterance, so as to explore an optimal update solution for each unimodal network. Besides, contrary to most existing works which fail to explicitly filter out noisy information, we devise a modality filter module to identify and filter out modality noise for the learning of correct cross-modal embedding. Extensive experiments on publicly datasets demonstrate that our approach achieves state-of-the-art performance.
SentNoB: A Dataset for Analysing Sentiment on Noisy Bangla Texts

In this paper, we propose an annotated sentiment analysis dataset made of informally written Bangla texts. This dataset comprises public comments on news and videos collected from social media covering 13 different domains, including politics, education, and agriculture. These comments are labeled with one of the polarity labels, namely positive, negative, and neutral. One significant characteristic of the dataset is that each of the comments is noisy in terms of the mix of dialects and grammatical incorrectness. Our experiments to develop a benchmark classification system show that hand-crafted lexical features provide superior performance than neural network and pretrained language models. We have made the dataset and accompanying models presented in this paper publicly available at https://git. io/JuuNB.
Research on Testing Technology of CMOS Camera Module

After the completion of the CMOS(Complementary Metal-Oxide-Semiconductor Transistor) camera module assembly,in order to test the image quality of module. In this paper, based on the ISO12233 standard chart proposing a CMOS camera module modulation transfer function MTF (modulation transfer function) of test method, MTF average value calculated for each module with high contrast and grayscale method, and validation processing to detect unqualified imaging module. The results show that MTF detection method used in this paper can quickly and accurately detect the quality camera module, and stable and reliable, playing an important role to found the quality problems and improve the efficiency of production.
Enhancing JSON Schema Discovery by Uncovering Hidden Data

Schema discovery is finding the structure of data. It helps users understand the meaning of data and write queries to manipulate it. This is typically easy for relational databases, but complex for non-relational (NoSQL) databases with JavaScript Object Notation (JSON) documents. JSON is a representation of documents that contain objects stored in the form of nested key-value pairs. For relational databases, the schema is predefined because the data they contain is structured, but for NoSQL databases, data is usually unstructured or semi-structured. In a collection of JSON documents, the structure of one document can be completely different from another. Several algorithms were developed to discover schemas from JSON documents, but they provide the physical structure and semantic information that is insufficient for data understanding and analysis. In this paper, we enumerate the major techniques used to extract a schema from JSON documents and present the next challenge: uncovering hidden data disguised as metadata. This challenge needs to be addressed within the field of JSON schema discovery to enhance the quality of the discovered schemas.
Probing Pre-trained Language Models for Semantic Attributes and their Values

Pretrained Language Models (PTLMs) yield state-of-the-art performance on many Natural Language Processing tasks, including syntax, semantics and commonsense reasoning. In this paper, we focus on identifying to what extent do PTLMs capture semantic attributes and their values, e.g. the relation between rich and high net worth. We use PTLMs to predict masked tokens using patterns and lists of items from Wikidata in order to verify how likely PTLMs encode semantic attributes along with their values. Such inferences based on semantics are intuitive for us humans as part of our language understanding. Since PTLMs are trained on large amount of Wikipedia data, we would assume that they can generate similar predictions. However, our ﬁndings reveal that PTLMs perform still much worse than humans on this task. We show an analysis which explains how to exploit our methodology to integrate better context and semantics into PTLMs using knowledge bases.
Generating Mammography Reports from Multi-view Mammograms with BERT

Writing mammography reports can be errorprone and time-consuming for radiologists. In this paper we propose a method to generate mammography reports given four images, corresponding to the four views used in screening mammography. To the best of our knowledge our work represents the first attempt to generate the mammography report using deep-learning. We propose an encoder-decoder model that includes an EfficientNet-based encoder and a Transformerbased decoder. We demonstrate that the Transformer-based attention mechanism can combine visual and semantic information to localize salient regions on the input mammograms and generate a visually interpretable report. The conducted experiments, including an evaluation by a certified radiologist, show the effectiveness of the proposed method. Our code is available at https://github. com/sberbank-ai-lab/mammo2text.
Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation

In parataxis languages like Chinese, word meanings are constructed using specific wordformations, which can help to disambiguate word senses. However, such knowledge is rarely explored in previous word sense disambiguation (WSD) methods. In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD. We first construct a large-scale Chinese lexical sample WSD dataset with word-formations. Then, we propose a model FormBERT to explicitly incorporate word-formations into sense disambiguation. To further enhance generalizability, we design a word-formation predictor module in case word-formation annotations are unavailable. Experimental results show that our method brings substantial performance improvement over strong baselines.1
Improved Word Sense Disambiguation with Enhanced Sense Representations

Current state-of-the-art supervised word sense disambiguation (WSD) systems (such as GlossBERT and bi-encoder model) yield sur-prisingly good results by purely leveraging pre-trained language models and short dictionary deﬁnitions (or glosses) of the different word senses. While concise and intuitive, the sense gloss is just one of many ways to provide information about word senses. In this paper, we focus on enhancing the sense representations via incorporating synonyms, example phrases or sentences showing usage of word senses, and sense gloss of hypernyms. We show that incorporating such additional information boosts the performance on WSD. With the pro-posed enhancements, our system achieves an F1 score of 82.0% on the standard benchmark test dataset of the English all-words WSD task, surpassing previous published scores on this benchmark dataset.
Competing Independent Modules for Knowledge Integration and Optimization

This paper presents a neural framework of untied independent modules, used here for integrating off the shelf knowledge sources such as language models, lexica, POS information, and dependency relations. Each knowledge source is implemented as an independent component that can interact and share information with other knowledge sources. We report proof of concept experiments for several standard sentiment analysis tasks and show that the knowledge sources interoperate effectively without interference. As a second use-case, we show that the proposed framework is suitable for optimizing BERT-like language models even without the help of external knowledge sources. We cast each Transformer layer as a separate module and demonstrate performance improvements from this explicit integration of the different information encoded at the different Transformer layers .
An OT-ET Analysis of Polish Singular-Plural Pairs

Optimality Theory (OT) and Exemplar Theory (ET) are two enchanting theories to many scholars, but each still faces criticism and remaining persistent problems. Application of both theories to areas in linguistics where conflicts may arise has been attempted, but still the suitability of combining the two theories to resolve contradictions awaits further analysis and verification. This article takes Polish singular-plural pairs as the object of study and argues in favor of an OT-ET combined model of analyzing the linguistic phenomenon. First, an underlying representation is identified to be the input in an OT analysis. Then two main changes are recognized between the input and output, and are regarded as instances of positional neutralization, and their relevant constraints and constraint hierarchies are presented. Following this, challenges are posed to OT despite its merits. It turns out that the combined OT-ET model works well, with historical development, underspecification, constraint hierarchy, and resemblance to existing word clouds, among others, all playing relevant parts. The current study adds to the extensiveness of language data analyzed for or against combining OT and ET, and sketches the analysis pattern of thus doing, with a view to offering more real-life language materials for an OT-ET combined model.
Knowledge-Guided Paraphrase Identification

Paraphrase identification (PI), a fundamental task in natural language processing, is to identify whether two sentences express the same or similar meaning, which is a binary classification problem. Recently, BERT-like pretrained language models have been a popular choice for the frameworks of various PI models, but almost all existing methods consider general domain text. When these approaches are applied to a specific domain, existing models cannot make accurate predictions due to the lack of professional knowledge. In light of this challenge, we propose a novel framework, namely Knowing, which can leverage the external unstructured Wikipedia knowledge to accurately identify paraphrases. We propose to mine outline knowledge of concepts related to given sentences from Wikipedia via BM25 model. After retrieving related outline knowledge, Knowing makes predictions based on both the semantic information of two sentences and the outline knowledge. Besides, we propose a gating mechanism to aggregate the semantic information-based prediction and the knowledge-based prediction. Extensive experiments are conducted on two public datasets: PARADE (a computer science domain dataset) and clinicalSTS2019 (a biomedical domain dataset). The results show that the proposed Knowing outperforms state-ofthe-art methods.
P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion

Few-shot knowledge graph completion is to infer the unknown facts (i.e., query head-tail entity pairs) of a given relation with only a few observed reference entity pairs. Its general process is to ﬁrst encode the implicit relation of an entity pair and then match the relation of a query entity pair with the relations of the reference entity pairs. Most existing methods have thus far encoded an entity pair and matched entity pairs by using the direct neighbors of concerned entities. In this paper, we propose the P-INT model for effective few-shot knowledge graph completion. First, P-INT infers and leverages the paths that can expressively encode the relation of two entities. Second, to capture the ﬁne grained matches, P-INT calculates the interactions of paths instead of mixing them for each entity pair. Extensive experimental results demonstrate that P-INT out-performs the state-of-the-art baselines by 11.2– 14.2% in terms of Hits@1. Our codes and datasets are online now 1 .
TWEETSUMM - A Dialog Summarization Dataset for Customer Service

In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue. The goal of the present article is advancing the automation of this task. We introduce the first large scale, high quality, customer care dialog summarization dataset with close to 6500 human annotated summaries. The data is based on real-world customer support dialogs and includes both extractive and abstractive summaries. We also introduce a new unsupervised, extractive summarization method specific to dialogs.
HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition

Nested Named Entity Recognition (NNER) has been extensively studied, aiming to identify all nested entities from potential spans (i.e., one or more continuous tokens). However, recent studies for NNER either focus on tedious tagging schemas or utilize complex structures, which fail to learn effective span representations from the input sentence with highly nested entities. Intuitively, ex-plicit span representations will contribute to NNER due to the rich context information they contain. In this study, we propose a Hi erarchical Trans former (HiT RANS ) network for the NNER task, which decomposes the input sentence into multi-grained spans and enhances the representation learning in a hierarchical manner. Speciﬁcally, we ﬁrst utilize a two-phase module to generate span representations by aggregating context information based on a bottom-up and top-down transformer network. Then a label prediction layer is designed to recognize nested entities hierarchically, which naturally explores semantic dependencies among different spans. Experiments on GENIA, ACE-2004, ACE-2005 and NNE datasets demonstrate that our proposed method achieves much better performance than the state-of-the-art approaches
InVeRo-XL: Making Cross-Lingual Semantic Role Labeling Accessible with Intelligible Verbs and Roles

Notwithstanding the growing interest in cross-lingual techniques for Natural Language Processing, there has been a surprisingly small number of efforts aimed at the development of easy-to-use tools for cross-lingual Semantic Role Labeling. In this paper, we fill this gap and present InVeRo-XL, an off-the-shelf state-of-the-art system capable of annotating text with predicate sense and semantic role labels from 7 predicate-argument structure inventories in more than 40 languages. We hope that our system – with its easy-to-use RESTful API and Web interface – will become a valuable tool for the research community, encouraging the integration of sentence-level semantics into cross-lingual downstream tasks. InVeRo-XL is available online at http://nlp.uniroma1. it/invero.
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization

Meta-learning algorithms such as MAML, Reptile, and FOMAML have led to improved performance of several neural models. The primary difference between standard gradient descent and these meta-learning approaches is that they contain as a small component the gradient for maximizing dot-product between gradients of batches, leading to improved generalization. Previous work has shown that aligned gradients are related to generalization, and have also used the Reptile algorithm in a single-task setting to improve generalization. Inspired by these approaches for a single task setting, this paper proposes to use the ﬁnite differences ﬁrst-order algorithm to calculate this gradient from dot-product of gradients, al-lowing explicit control on the weightage of this component relative to standard gradients. We use this gradient as a regularization technique, leading to more aligned gradients between different batches. By using the ﬁnite differences approximation, our approach does not suffer from O ( n 2 ) memory usage of naively calculating the Hessian and can be easily applied to large models with large batch sizes. Our approach achieves state-of-the-art performance on the Gigaword dataset, and shows performance improvements on several datasets such as SQuAD-v2.0, Quasar-T, NewsQA and all the SuperGLUE datasets, with a range of models such as BERT, RoBERTa and ELECTRA. Our method also outperforms previous approaches of Reptile and FOMAML when used as a regularization technique, in both single and multi-task settings. Our method is model agnostic, and introduces no extra trainable weights.
Communication with Ambient Light using Digital Micromirror Devices

Passive visible light communication (VLC) takes advantage of the pervasive nature of ambient light in our environment for wireless transmissions. The design of transmitters in passive VLC predominately uses liquid crystal displays (LCDs). While LCDs are an economical choice with low power consumption, they lack some key properties that are desirable for passive VLC. For example, LCDs absorb more than half of the incident light, leaving only a small portion to be used for communication. In addition, since the direction of ambient can change over time, the relative positions of the LCDs and receivers have to be changed constantly to maintain the correct alignment. To overcome these shortcomings, we propose the use of a novel transmitter with integrated optical fibres and digital micro-mirror devices (DMDs). DMDs are able to reflect up to 97% of the incident light, while the accompanying optical fibres aim to capture ambient light from various angles and guide them to the DMDs in a fixed direction. This design is a first step towards the goal of decoupling the direction of ambient light from the direction of the optical link, while achieving the same communication characteristics as LCDs with a much smaller device. We also design an App to allow users
TWT: Table with Written Text for Controlled Data-to-Text Generation

Large pre-trained neural models have recently shown remarkable progress in text generation. In this paper, we propose to generate text conditioned on the structured data (table) and a preﬁx (the written text) by leveraging the pre-trained models. We present a new data-to-text dataset, T able with W ritten T ext (TWT), by repurposing two existing datasets: ToTTo and TabFact. TWT contains both factual and logical statements that are faithful to the structured data, aiming to serve as a useful benchmark for controlled text generation. Compared with existing data-to-text task settings, TWT is more intuitive, the preﬁx (usually provided by the user) controls the topic of the generated text. Existing methods usually output hallucinated text that is not faithful on TWT. Therefore, we design a novel approach with table-aware attention visibility and copy mechanism over the table. Experimental results show that our approach outperforms state-of-the-art methods under both automatic and human evaluation metrics.
Association of sleep apnoea risk and aortic enlargement in Marfan syndrome

Background Marfan syndrome (MFS) is a connective tissue disorder characterised by complex aortic pathology and a high prevalence of obstructive sleep apnoea (OSA). OSA produces intrathoracic transmural stresses that may accelerate aortic injury. The current study was designed to examine the associations between OSA risk and markers of aortic enlargement in MFS. Method Consecutive patients with MFS were recruited at Johns Hopkins if they completed a STOP-BANG survey. Composite survey scores were categorised into those with low OSA risk (STOP-BANG <3) and high OSA risk (STOP-BANG ≥3). Participants’ aortic data were collated to ascertain aortic root diameter, dilatation and prior aortic root replacement. Regression analyses were used to examine associations between OSA risk strata and these aortic parameters. Results Of the 89 participants studied, 28% had a high OSA risk and 32% had aortic grafts. Persons with high OSA risk had greater aortic root diameter (mm) (ß=4.13, SE=1.81, p=0.027) and aortic root dilatation (ß=2.80, SE=1.34, p=0.046) compared with those with low OSA risk . In addition, the odds of prior aortic root replacement was three times greater in those with high OSA risk compared with those with low OSA risk. Conclusion In MFS, high OSA risk is associated with aortic enlargement and a threefold increased risk of having had prior aortic root replacement. These findings invite further exploration of the relationship between OSA and aortic disease in MFS, and studies to clarify whether targeted interventions for OSA might mitigate aortic disease progression in MFS. Registration number IRB00157483.