Miracle-dz/UniFAR-FTUs huggingface dataset:
The Triglyceride Glucose (TyG) Index as a Sensible Marker for Identifying Insulin Resistance and Predicting Diabetic Kidney Disease

Background Patients with insulin-resistant diabetes have the highest risk of kidney disease. The triglyceride glucose (TyG) index is considered a reliable and simple marker of insulin resistance. We studied the relationship between the TyG index, diabetic kidney disease (DKD), and related metabolic disorders in patients with type 2 diabetes. Material/Methods This retrospective study included a consecutive case series from January 2021 to October 2022 in the Department of Endocrinology at Hebei Yiling Hospital. In total, 673 patients with type 2 diabetes met the inclusion criteria. The TyG index was calculated by napierian logarithmic (ln) (fasting triglyceride×fasting glucose /2). Patient demographic and clinical indicators were obtained from medical records, and statistical analysis was conducted using SPSS version 23. Results The TyG index was significantly related to metabolic indicators (low-density lipoprotein, high-density lipoprotein, alanine aminotransferase, plasma albumin, serum uric acid, triglyceride, and fasting glucose) and urine albumin (P<0.01) but not with serum creatinine and estimated glomerular filtration rate. In multiple regression analysis, an increase in the TyG index was revealed to be an independent risk factor for DKD (OR: 1.699, P<0.001). Conclusions The TyG index was independently related to DKD and related metabolic disorders; therefore, the TyG index can be used as an early sensitive target for clinical guidance in the treatment of DKD with insulin resistance.
Adaptive iteration method for star centroid extraction under highly dynamic conditions

Star centroiding accuracy decreases significantly when star sensor works under highly dynamic conditions or star images are corrupted by severe noise, reducing the output attitude precision. Herein, an adaptive iteration method is proposed to solve this problem. Firstly, initial star centroids are predicted by traditional method, and then based on initial reported star centroids and angular velocities of the star sensor, adaptive centroiding windows are generated to cover the star area and then an iterative method optimizing the location of centroiding window is used to obtain the final star spot extraction results. Simulation results shows that, compared with traditional star image restoration method and Iteratively Weighted Center of Gravity method, AWI algorithm maintains higher extraction accuracy when rotation velocities or noise level increases.
Digital control of spatial coherence in vortex beams

A means to digitally generate a partially coherent beam with orbital angular momentum is presented. Our approach is based on encoding the randomness of broadband light passing through a spiral phase plate in a spatial light modulator. We illustrate the technique by generating partially coherent beams with orbital angular momentum content and different coherence lengths, with no moving optical elements. We study the cross correlation spectra which yields to good agreement with theory.
Contribution of velocity-vorticity correlations to the frictional drag in wall-bounded turbulent flows

The relationship between the frictional drag and the velocity-vorticity correlations in wall-bounded turbulent flows is derived from the mean vorticity equation. A formula for the skin friction coefficient is proposed and evaluated with regards to three canonical wall-bounded flows: turbulent boundary layer, turbulent channel flow, and turbulent pipe flow. The frictional drag encompasses four terms: advective vorticity transport, vortex stretching, viscous, and inhomogeneous terms. Drag-reduced channel flow with the slip condition is used to test the reliability of the formula. The advective vorticity transport and vortex stretching terms are found to dominate the contributions to the frictional drag.
TOGETHER ENOUGH: Collaborative Constructions of Adaptations to Climate Futures

The cave painting was an instrument of adaptation we no longer understand. What technologies of today can we use to understand our own concerns of, and adaptions to, a climate future analogous to the way pre-historic humans drew their conceptions onto the cave? TOGETHER ENOUGH uses machine learning-generated visions of climate futures to narrate our resilient adaptions to a environmentally threatening future. The collaborative visions were built into physical installed objects using recycled, organic, found materials from site-specific paradigms for physical exhibition as a form of human-AI collaboration. The outcome is a collaborative imagining of our climate future and our collective resilience and adaptions to this future from a technological and material perspective.
Utility of methylene blue mixed with autologous blood in preoperative localization of pulmonary nodules and masses

Abstract The value of CT-guided puncture with methylene blue mixed with autologous blood in preoperative localization of pulmonary nodules and masses was explored. A total of 113 patients with 146 nodules and masses were treated with methylene blue mixed with autologous blood for preoperative localization and thoracoscopic surgery in the Department of Thoracic Surgery, the First Affiliated Hospital of Fujian Medical University between October 2021 and October 2022. The localization effect, complications, and pathological conditions were observed. The localization success rate was 98.63% (144/146). The localization failed nodules and masses could still be located by looking for needle eyes and reading films. The whole group successfully completed thoracoscopic surgery. The average interval of operation after puncture was 22.16 ± 6.22 h. There was a small amount of suspicious hemothorax after puncture. There was no pneumothorax after puncture in the whole group. There were no hemoptysis, irritating dry cough, and other reactions. The overall complication rate was 2.65%, and no special treatment was given. It is safe and effective to use methylene blue mixed with autologous blood for CT-guided preoperative puncture and localization of small pulmonary nodules and masses.
Dispensing mifepristone for medical abortion in Canada: Pharmacists’ experiences of the first year

Background: Mifepristone for medical abortion was first dispensed by community pharmacists in Canada directly to patients in January 2017. We asked about pharmacists’ experiences over their first year dispensing mifepristone in order to evaluate the frequency of the new practice and assess availability in urban/rural pharmacies. Methods: From August to December 2019, we invited 433 community pharmacists who had completed a baseline survey at least 1 year prior to participate in a follow-up online survey. We summarized categorical data using counts and proportions and conducted a qualitative thematic analysis of open-ended responses. Results: Among 122 participants, 67.2% had dispensed the product, and 48.4% routinely stocked mifepristone. Pharmacists reported a mean of 26 and median of 3 (interquartile range, 1, 8) mifepristone prescriptions filled in their pharmacies in the previous year. Participants perceived that the benefits of making mifepristone available in pharmacies included increased abortion access for patients (n = 115; 94.3%), reduced pressure on the health care system (n = 104; 85.3%), increased rural and remote abortion access (n = 103; 84.4%) and increased interprofessional collaborations (n = 48; 39.3%). Few participants reported challenges to maintaining adequate stock of mifepristone, but these challenges included low demand (n = 24; 19.7%), short expiry dating (n = 12; 9.8%) and drug shortages (n = 8; 6.6%). The overwhelming majority, 96.7%, reported that their communities did not resist the provision of mifepristone by their pharmacy. Interpretation: Participating pharmacists reported many benefits and very few barriers to stocking and dispensing mifepristone. Both urban and rural communities responded positively to enhanced access to mifepristone in their community. Conclusions: Mifepristone is well accepted by pharmacists within the primary care system in Canada.
Deservingness on Trial: Neutralisation Techniques in Public Housing Jurisprudence

How do judges formulate their written decisions when rejecting plaintiffs’ requests in a welfare context? In this paper, based on our thematic analysis, we show how judges construct a nuanced concept of ‘welfare deservingness’ to narratively mitigate their own moral and emotional tensions when making decisions on remedies in public-housing cases. Deploying a notion borrowed from criminology—‘neutralisation techniques’—we discuss the material and symbolic implications of this concept, contributing to the theoretical discussion of both poverty law and legal professionalism. We claim that judges use ‘neutralisation techniques’ to negotiate, justify, and explain their decisions while attempting to avoid or lessen the dilemmas they typically face, given the scarcity of housing resources and their inability to grant material assistance. Employing these techniques, the judges create ‘deservingness spectrum’ that enables them to essentially subvert the binary division of accepted/denied cases. At one end of this spectrum, the denial of the plaintiff's ‘victim’ status is enacted through the negation of symbolic deservingness and, thus, the denial of housing is framed as a warranted sanction on the plaintiff's reproachable character. At the other end, the techniques allow the judges to recognise ‘symbolic deservingness’ while still not providing material aid. Judges are therefore able to preserve their professional status and sense of moral rectitude when making such unenviable housing decisions. More broadly, our analysis offers a novel lens through which to critically understand judicial decision-making in welfare jurisprudence.
Exploration of Hidden Research Directions in Oil and Gas Industry via Full Text Analysis of OnePetro Digital Library

Данное исследование проведено с целью представления возможностей современных подходов к извлечению информации из текстовых массивов. Задача данного исследования состоит в том, чтобы с помощью научного подхода к анализу текста дать ответы на следующие бизнес-вопросы: Какие важные направления исследований развились за прошедший год? Что нового в нефтегазовых технологиях? Авторы успешно применили технологию тематического моделирования (topic modeling) для решения поставленной задачи. Особое внимание в ходе создания тематической модели авторы уделили метрикам качества модели. В статье исследовано поведение метрик Perplexity и Sparsity для матриц Θ и Φ при регуляризации. Применение методики последовательной множественной регуляризации позволило разделить тематики на основные и шумовые, что существенно улучшило интерпретируемость модели тематик.
Hierarchy Builder: Organizing Textual Spans into a Hierarchy to Facilitate Navigation

Information extraction systems often producehundreds to thousands of strings on a specifictopic. We present a method that facilitatesbetter consumption of these strings, in an ex-ploratory setting in which a user wants to bothget a broad overview of what’s available, and achance to dive deeper on some aspects. The sys-tem works by grouping similar items together,and arranging the remaining items into a hierar-chical navigable DAG structure. We apply themethod to medical information extraction.
Split-NER: Named Entity Recognition via Two Question-Answering-based Classifications

In this work, we address the NER problem by splitting it into two logical sub-tasks: (1) Span Detection which simply extracts entity mention spans irrespective of entity type; (2) Span Classification which classifies the spans into their entity types. Further, we formulate both sub-tasks as question-answering (QA) problems and produce two leaner models which can be optimized separately for each sub-task. Experiments with four cross-domain datasets demonstrate that this two-step approach is both effective and time efficient. Our system, SplitNER outperforms baselines on OntoNotes5.0, WNUT17 and a cybersecurity dataset and gives on-par performance on BioNLP13CG. In all cases, it achieves a significant reduction in training time compared to its QA baseline counterpart. The effectiveness of our system stems from fine-tuning the BERT model twice, separately for span detection and classification. The source code can be found at https://github.com/c3sr/split-ner.
U-CREAT: Unsupervised Case Retrieval using Events extrAcTion

The task of Prior Case Retrieval (PCR) in the legal domain is about automatically citing relevant (based on facts and precedence) prior legal cases in a given query case. To further promote research in PCR, in this paper, we propose a new large benchmark (in English) for the PCR task: IL-PCR (Indian Legal Prior Case Retrieval) corpus. Given the complex nature of case relevance and the long size of legal documents, BM25 remains a strong baseline for ranking the cited prior documents. In this work, we explore the role of events in legal case retrieval and propose an unsupervised retrieval method-based pipeline U-CREAT (Unsupervised Case Retrieval using Events Extraction). We find that the proposed unsupervised retrieval method significantly increases performance compared to BM25 and makes retrieval faster by a considerable margin, making it applicable to real-time case retrieval systems. Our proposed system is generic, we show that it generalizes across two different legal systems (Indian and Canadian), and it shows state-of-the-art performance on the benchmarks for both the legal systems (IL-PCR and COLIEE corpora).
Unsupervised Subtitle Segmentation with Masked Language Models

We describe a novel unsupervised approach to subtitle segmentation, based on pretrained masked language models, where line endings and subtitle breaks are predicted according to the likelihood of punctuation to occur at candidate segmentation points. Our approach obtained competitive results in terms of segmentation accuracy across metrics, while also fully preserving the original text and complying with length constraints. Although supervised models trained on in-domain data and with access to source audio information can provide better segmentation accuracy, our approach is highly portable across languages and domains and may constitute a robust off-the-shelf solution for subtitle segmentation.
Learning to Generate Equitable Text in Dialogue from Biased Training Data

The ingrained principles of fairness in a dialogue system’s decision-making process and generated responses are crucial for user engagement, satisfaction, and task achievement. Absence of equitable and inclusive principles can hinder the formation of common ground, which in turn negatively impacts the overall performance of the system. For example, misusing pronouns in a user interaction may cause ambiguity about the intended subject. Yet, there is no comprehensive study of equitable text generation in dialogue. Aptly, in this work, we use theories of computational learning to study this problem. We provide formal definitions of equity in text generation, and further, prove formal connections between learning human-likeness and learning equity: algorithms for improving equity ultimately reduce to algorithms for improving human-likeness (on augmented data). With this insight, we also formulate reasonable conditions under which text generation algorithms can learn to generate equitable text without any modifications to the biased training data on which they learn. To exemplify our theory in practice, we look at a group of algorithms for the GuessWhat? visual dialogue game and, using this example, test our theory empirically. Our theory accurately predicts relative-performance of multiple algorithms in generating equitable text as measured by both human and automated evaluation.
kNN-TL: k-Nearest-Neighbor Transfer Learning for Low-Resource Neural Machine Translation

Transfer learning has been shown to be an effective technique for enhancing the performance of low-resource neural machine translation (NMT). This is typically achieved through either fine-tuning a child model with a pre-trained parent model, or by utilizing the out- put of the parent model during the training of the child model. However, these methods do not make use of the parent knowledge during the child inference, which may limit the translation performance. In this paper, we propose a k-Nearest-Neighbor Transfer Learning (kNN-TL) approach for low-resource NMT, which leverages the parent knowledge throughout the entire developing process of the child model. Our approach includes a parent-child representation alignment method, which ensures consistency in the output representations between the two models, and a child-aware datastore construction method that improves inference efficiency by selectively distilling the parent datastore based on relevance to the child model. Experimental results on four low-resource translation tasks show that kNN-TL outperforms strong baselines. Extensive analyses further demonstrate the effectiveness of our approach. Code and scripts are freely available at https://github.com/NLP2CT/kNN-TL.
RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue

Evaluating open-domain dialogue systems is challenging for reasons such as the one-to-many problem, i.e., many appropriate responses other than just the golden response. As of now, automatic evaluation methods need better consistency with humans, while reliable human evaluation can be time- and cost-intensive. To this end, we propose the Reference-Assisted Dialogue Evaluation (RADE) approach under the multi-task learning framework, which leverages the pre-created utterance as reference other than the gold response to relief the one-to-many problem. Specifically, RADE explicitly compares reference and the candidate response to predict their overall scores.Moreover, an auxiliary response generation task enhances prediction via a shared encoder.To support RADE, we extend three datasets with additional rated responses other than just a golden response by human annotation.Experiments on our three datasets and two existing benchmarks demonstrate the effectiveness of our method, where Pearson, Spearman, and Kendall correlations with human evaluation outperform state-of-the-art baselines.
Don’t Parse, Choose Spans! Continuous and Discontinuous Constituency Parsing via Autoregressive Span Selection

We present a simple and unified approach for both continuous and discontinuous constituency parsing via autoregressive span selection. Constituency parsing aims to produce a set of non-crossing spans so that they can form a constituency parse tree. We sort gold spans using a predefined order and leverage a pointer network to autoregressively select spans by that order. To deal with discontinuous spans, we consecutively select their subspans from left to right, label all but last subspans with special discontinuous labels and the last subspan as the whole discontinuous spans’ labels. We use simple heuristic to output valid trees so that our approach is able to predict all possible continuous and discontinuous constituency trees without sacrificing data coverage and without the need to use expensive chart-based parsing algorithms. Experiments on multiple continuous and discontinuous benchmarks show that our model achieves state-of-the-art or competitive performance.
CLCL: Non-compositional Expression Detection with Contrastive Learning and Curriculum Learning

Non-compositional expressions present a substantial challenge for natural language processing (NLP) systems, necessitating more intricate processing compared to general language tasks, even with large pre-trained language models. Their non-compositional nature and limited availability of data resources further compound the difficulties in accurately learning their representations. This paper addresses both of these challenges. By leveraging contrastive learning techniques to build improved representations it tackles the non-compositionality challenge. Additionally, we propose a dynamic curriculum learning framework specifically designed to take advantage of the scarce available data for modeling non-compositionality. Our framework employs an easy-to-hard learning strategy, progressively optimizing the model’s performance by effectively utilizing available training data. Moreover, we integrate contrastive learning into the curriculum learning approach to maximize its benefits. Experimental results demonstrate the gradual improvement in the model’s performance on idiom usage recognition and metaphor detection tasks. Our evaluation encompasses six datasets, consistently affirming the effectiveness of the proposed framework. Our models available at https://github.com/zhjjn/CLCL.git.
On the Evaluation of Neural Selective Prediction Methods for Natural Language Processing

We provide a survey and empirical comparison of the state-of-the-art in neural selective classification for NLP tasks. We also provide a methodological blueprint, including a novel metric called refinement that provides a calibrated evaluation of confidence functions for selective prediction. Finally, we supply documented, open-source code to support the future development of selective prediction techniques.
A flux-controlled model of meminductor and its application in chaotic oscillator

A meminductor is a new type of memory device developed from the memristor. We present a mathematical model of a flux-controlled meminductor and its equivalent circuit model for exploring the properties of the meminductor in a nonlinear circuit. We explore the response characteristics of the meminductor under the exciting signals of sinusoidal, square, and triangular waves by using theoretical analysis and experimental tests, and design a meminductor-based oscillator based on the model. Theoretical analysis and experiments show that the meminductor-based oscillator possesses complex bifurcation behaviors and can generate periodic and chaotic oscillations. A special phenomenon called the co-existent oscillation that can generate multiple oscillations (such as chaotic, periodic oscillations as well as stable equilibrium) with the same parameters and different initial conditions occurs. We also design an analog circuit to realize the meminductor-based oscillator, and the circuit experiment results are in accordance with the theory analysis.
Linguistic representations for fewer-shot relation extraction across domains

Recent work has demonstrated the positive impact of incorporating linguistic representations as additional context and scaffolds on the in-domain performance of several NLP tasks. We extend this work by exploring the impact of linguistic representations on cross-domain performance in a few-shot transfer setting. An important question is whether linguistic representations enhance generalizability by providing features that function as cross-domain pivots. We focus on the task of relation extraction on three datasets of procedural text in two domains, cooking and materials science. Our approach augments a popular transformer-based architecture by alternately incorporating syntactic and semantic graphs constructed by freely available off-the-shelf tools. We examine their utility for enhancing generalization, and investigate whether earlier findings, e.g. that semantic representations can be more helpful than syntactic ones, extend to relation extraction in multiple domains. We find that while the inclusion of these graphs results in significantly higher performance in few-shot transfer, both types of graph exhibit roughly equivalent utility.