Miracle-dz/UniFAR-FTUs huggingface dataset:
Plasma biomarkers of neurodegeneration in patients with Parkinson's disease and dementia with Lewy bodies and at-risk individuals of Lewy body disease in the NaT-PROBE cohort

Background: Comorbid Alzheimer's disease (AD) neuropathology is common in Lewy body disease (LBD); however, AD comorbidity in the prodromal phase of LBD remains unclarified. This study investigated AD comorbidity in the prodromal and symptomatic phases of LBD by analysing plasma biomarkers in patients with Parkinson's disease (PD) and dementia with Lewy bodies (DLB) and at-risk individuals of LBD (NaT-PROBE cohort). Methods: Patients with PD (PD group, n=84) and DLB (DLB group, n=16) and individuals with LBD with [≥]2 (high-risk group, n=82) and without (low-risk group, n=37) prodromal symptoms were enrolled. Plasma amyloid-beta (A{beta}) composite was measured using immunoprecipitation-mass spectrometry assays. Plasma phosphorylated tau 181 (p-tau181), neurofilament light chain (NfL), and -synuclein (aSyn) were measured using a single-molecule array. Results: p-tau181 levels were higher in the PD and DLB groups than in the low-risk group. A{beta} composite level was higher in the DLB group than in the high-risk group. AD-related biomarker levels were not elevated in the high-risk group. NfL levels were higher in the high-risk, PD, and DLB groups than in the low-risk group. In the PD group, A{beta} composite was associated with cognitive function, p-tau181 with motor function and non-motor symptoms, and NfL with cognitive and motor functions and non-motor symptoms. In the high-risk group, NfL was associated with metaiodobenzylguanidine scintigraphy abnormalities. Conclusions: The PD and DLB groups exhibited comorbid AD neuropathology, though not in the prodromal phase. Elevated plasma NfL levels, even without elevated AD-related plasma biomarker levels, may indicate aSyn-induced neurodegeneration in the prodromal phase of LBD.
Validierung und Reliabilitätsprüfung des „Athlete Fear Avoidance Questionnaire (AFAQ)“

Zusammenfassung Hintergrund Verletzungen im Sport können für die Karriere eines Athleten schwerwiegende Folgen haben. Psychologische Faktoren spielen auf dem Weg von der Verletzung zum Wiedereinstieg in den kompetitiven Sport eine wichtige Rolle. Diese Faktoren werden oft ungenügend beurteilt, wodurch sich die Ausfallszeit verlängern kann. Mit dem Athlete Fear Avoidance Questionnaire (AFAQ) kann das Angstvermeidungsverhalten bei verletzten Athleten gemessen werden. Zum jetzigen Zeitpunkt gibt es noch keine deutsche validierte Version des Fragebogens. Ziel Das Ziel dieser Studie ist, die Test-Retest-Reliabilität, die interne Konsistenz sowie die Konstruktvalidität der transkulturell übersetzten deutschen Version des AFAQ (AFAQ_D) bei verletzen Athletinnen und Athleten zu untersuchen. Methode Die deutsche Version des AFAQ_D wurde mit dem TRAPD-Model übersetzt. Die Konstruktvalidität wurde mit Korrelationen zum Fear Avoidance Beliefs Questionnaire (FABQ) und zur Tampa Scale for Kinesiophobia (TSK) untersucht. Die interne Konsistenz wurde mittels Cronbach’s Alpha und die Test-Retest-Reliabilität mit dem Interklassen-Koeffizienten berechnet. Ergebnisse Es wurden 64 Athletinnen und Athleten im Alter zwischen 16 und 36 Jahren (Ø 23,15 Jahre, ♀ 17%, ♂ 83%) nach verschiedenen Verletzungen eingeschlossen. Der AFAQ_D ergab gute Werte in der Reliabilität mit einem Cronbach’s Alpha von 0,76 (95%CI; 0,67–0,84) bei der ersten beziehungsweise 0,82 (95%CI; 0,76–0,88) bei der zweiten Messung und einem Interklassen-Koeffizienten von 0,798 (95%CI; 0,674–0,876). Die Validität ergab eine moderate Korrelation zwischen dem AFAQ_D und der TSK (r= 0,57, p<0.01) und eine schwache und nicht signifikante Korrelation zwischen dem AFAQ_D und dem FABQ (r = 0,13, p=0.31). Schlussfolgerung Die deutsche Version vom AFAQ ist größtenteils ein valider und reliabler Fragebogen zur Beurteilung der Angstvermeidung, als auch der psychologischen Bereitschaft von Athletinnen und Athleten nach einer Verletzung. Die Korrelationen mit TSK und FABQ sind grob mit der Originalstudie vergleichbar.
Der Einfluss von Vitaminen auf das Darmmikrobiom

Das Darmmikrobiom ist ein komplexes Ökosystem von Mikroorganismen, das sich besonders im unteren Magen-Darm-Trakt befindet. Es besteht aus Bakterien, Viren, Protozoen und Pilzen und ist für die menschliche Gesundheit und das Wohlbefinden von entscheidender Bedeutung. Faktoren wie Gene, Alter, Umwelt und die Ernährung beeinflussen die Zusammensetzung des Mikrobioms. Neue Daten zeigen, dass auch Vitamine hier eine förderliche Rolle spielen.
A consolidated checklist and new avifaunal records for southern Tigray, Ethiopia

Summary. We recorded 17 bird taxa new for southern Tigray across a mosaic of habitats. Some of these records document substantial geographical extensions of previously known ranges. Many are Acacia woodland and bushland species but were documented in habitats subject to considerable human disturbance. New records for Tigray include White-breasted Cormorant Phalacrocorax lucidus, Pygmy Falcon Polihierax semitorquatus, Three-banded Courser Rhinoptilus cinctus mayaudi, Orange-breasted Bushshrike Chlorophoneus sulfureopectus, House Sparrow Passer domesticus indicus and White-headed Buffalo Weaver Dinemellia dinemelli. In addition, the endemic subspecies erlangeri of Blanford's Lark Calandrella blanfordi, previously known only from the central highlands of Ethiopia, was recorded for the first time in southern Tigray. Focusing on Hugumbirda and Gra-Kahsu State Forest and human-dominated landscapes in southern Tigray, we found key drivers of habitat modification in the area to be the expansion of settlements, farmland and bare land.
Diet and growth of vegetarian and vegan children

The prevalence of plant-based diets, that is, vegetarian (without meat and fish) and vegan (plant foods only) diets, is increasing also among children and adolescents, and energy and nutrient requirements are highest during this age. Hence the question emerges whether and, if so, how much animal-source food should be included in a healthy and sustainable diet. Recent studies, published 2018–2023, mostly showed comparable anthropometrics between omnivore and vegetarian children, however, fat mass was lower in vegetarians or vegans. Results on marker of iron status were inconsistent. Vitamin B12 status was lower without supplementation, but did not differ between groups when supplementation prevalence was high. Blood lipid profile seems to be more favourable in plant-based groups. Bone mineral density was lower in vegetarians and vegans, but differences attenuated after adjustment. Nevertheless, the long-term clinical relevance of these results remains unclear. Energy intake did not differ between groups and was in the recommended range, although protein intake was lower in vegetarians and vegans. Reported calcium intakes did not differ or were lower in vegetarians compared with omnivores, and were lowest in vegans. More favourable intakes in subjects on plant-based diets were found for fibre, sugar, folate, magnesium and iron. All but one study were cross-sectional, and longitudinal studies of both vegetarian and vegan children/adolescents are required to prospectively examine associations of plant-based diets with health. Professional societies should develop country-specific food-based dietary guidelines adapted to the special dietary habits for children on plant-based diets.
Frank at NADI 2023 Shared Task: Trio-Based Ensemble Approach for Arabic Dialect Identification

We present our system designed for Subtask 1 in the shared task NADI on Arabic Dialect Identification, which is part of ArabicNLP 2023. In our approach, we utilized models such as: MARBERT, MARBERTv2 (A) and MARBERTv2 (B). Subsequently, we created a majority voting ensemble of these models. We used MARBERTv2 with different hyperparameters, which significantly improved the overall performance of the ensemble model. In terms of performance, our systems achieved a competitive an F1 score of 84.76. Overall, our system secured the 5^\text{th} position out of 16 participating teams.
Scent and Sensibility: Perception Shifts in the Olfactory Domain

In this work, we investigate olfactory perception shifts, analysing how the description of the smells emitted by specific sources has changed over time. We first create a benchmark of selected smell sources, relying upon existing historical studies related to olfaction. We also collect an English text corpus by retrieving large collections of documents from freely available resources, spanning from 1500 to 2000 and covering different domains. We label such corpus using a system for olfactory information extraction inspired by frame semantics, where the semantic roles around the smell sources in the benchmark are marked. We then analyse how the roles describing Qualities of smell sources change over time and how they can contribute to characterise perception shifts, also in comparison with more standard statistical approaches.
CUNI at WMT23 General Translation Task: MT and a Genetic Algorithm

This paper presents the contributions of Charles University teams to the WMT23 General translation task (English to Czech and Czech to Ukrainian translation directions). Our main submission, CUNI-GA, is a result of applying a novel n-best list reranking and modification method on translation candidates produced by the two other submitted systems, CUNI-Transformer and CUNI-DocTransformer (document-level translation only used for the en \rightarrow cs direction). Our method uses a genetic algorithm and MBR decoding to search for optimal translation under a given metric (in our case, a weighted combination of ChrF, BLEU, COMET22-DA, and COMET22-QE-DA). Our submissions are first in the constrained track and show competitive performance against top-tier unconstrained systems across various automatic metrics.
Evaluating ChatGPT and Bard AI on Arabic Sentiment Analysis

Large Language Models (LLMs) such as ChatGPT and Bard AI have gained much attention due to their outstanding performance on a range of NLP tasks. These models have demonstrated remarkable proficiency across various languages without the necessity for full supervision. Nevertheless, their performance in low-resource languages and dialects, like Arabic dialects in comparison to English, remains to be investigated. In this paper, we conduct a comprehensive evaluation of three LLMs for Dialectal Arabic Sentiment Analysis: namely, ChatGPT based on GPT-3.5 and GPT-4, and Bard AI. We use a Saudi dialect Twitter dataset to assess their capability in sentiment text classification and generation. For classification, we compare the performance of fully fine-tuned Arabic BERT-based models with the LLMs in few-shot settings. For data generation, we evaluate the quality of the generated new sentiment samples using human and automatic evaluation methods. The experiments reveal that GPT-4 outperforms GPT-3.5 and Bard AI in sentiment analysis classification, rivaling the top-performing fully supervised BERT-based language model. However, in terms of data generation, compared to manually annotated authentic data, these generative models often fall short in producing high-quality Dialectal Arabic text suitable for sentiment analysis.
Treating General MT Shared Task as a Multi-Domain Adaptation Problem: HW-TSC’s Submission to the WMT23 General MT Shared Task

This paper presents the submission of Huawei Translate Services Center (HW-TSC) to the WMT23 general machine translation (MT) shared task, in which we participate in Chinese↔English (zh↔en) language pair. We use Transformer architecture and obtain the best performance via a variant with larger parameter size. We perform fine-grained pre-processing and filtering on the provided large-scale bilingual and monolingual datasets. We mainly use model enhancement strategies, including Regularized Dropout, Bidirectional Training, Data Diversification, Forward Translation, Back Translation, Alternated Training, Curriculum Learning and Transductive Ensemble Learning. Our submissions obtain competitive results in the final evaluation.
Low-Resource Deontic Modality Classification in EU Legislation

In law, it is important to distinguish between obligations, permissions, prohibitions, rights, and powers. These categories are called deontic modalities. This paper evaluates the performance of two deontic modality classification models, LEGAL-BERT and a Fusion model, in a low-resource setting. To create a generalized dataset for multi-class classification, we extracted random provisions from European Union (EU) legislation. By fine-tuning previously researched and published models, we evaluate their performance on our dataset against fusion models designed for low-resource text classification. We incorporate focal loss as an alternative for cross-entropy to tackle issues of class imbalance. The experiments indicate that the fusion model performs better for both balanced and imbalanced data with a macro F1-score of 0.61 for imbalanced data, 0.62 for balanced data, and 0.55 with focal loss for imbalanced data. When focusing on accuracy, our experiments indicate that the fusion model performs better with scores of 0.91 for imbalanced data, 0.78 for balanced data, and 0.90 for imbalanced data with focal loss.
Frank at ArAIEval Shared Task: Arabic Persuasion and Disinformation: The Power of Pretrained Models

In this work, we present our systems developed for “ArAIEval” shared task of ArabicNLP 2023 (CITATION). We used an mBERT transformer for Subtask 1A, which targets persuasion in Arabic tweets, and we used the MARBERT transformer for Subtask 2A to identify disinformation in Arabic tweets. Our persuasion detection system achieved micro-F1 of 0.745 by surpassing the baseline by 13.2%, and registered a macro-F1 of 0.717 based on leaderboard scores. Similarly, our disinformation system recorded a micro-F1 of 0.816, besting the naïve majority by 6.7%, with a macro-F1 of 0.637. Furthermore, we present our preliminary results on a variety of pre-trained models. In terms of overall ranking, our systems placed 7^\text{th} out of 16 and 12^\text{th} out of 17 teams for Subtasks 1A and 2A, respectively.
On the Effects of Structural Modeling for Neural Semantic Parsing

Semantic parsing aims to map natural language sentences to predefined formal languages, such as logic forms and programming languages, as the semantic annotation. From the theoretic views of linguistic and programming language, structures play an important role in both languages, which had motivated semantic parsers since the task was proposed in the beginning. But in the neural era, semantic parsers treating both natural and formal language as sequences, such as Seq2Seq and LLMs, have got more attentions. On the other side, lots of neural progress have been made for grammar induction, which only focuses on natural languages. Although closely related in the sense of structural modeling, these techniques hadn’t been jointly analyzed on the semantic parsing testbeds. To gain the better understanding on structures for semantic parsing, we design a taxonomy of structural modeling methods, and evaluate some representative techniques on semantic parsing, including both compositional and i.i. d. generalizations. In addition to the previous opinion that structures will help in general, we find that (1) structures must be designed for the specific dataset and generalization level, and (2) what really matters is not the structure choice of either source or target side, but the choice combination of both sides. Based on the finding, we further propose a metric that can evaluate the structure choice, which we believe can boost the automation of grammar designs for specific datasets and domains.
Changing usage of Low Saxon auxiliary and modal verbs

We investigate the usage of auxiliary and modal verbs in Low Saxon dialects from both Germany and the Netherlands based on word vectors, and compare developments in the modern language to Middle Low Saxon. Although most of these function words have not been affected by lexical replacement, changes in usage that likely at least partly result from contact with the state languages can still be observed.
UM6P & UL at WojoodNER shared task: Improving Multi-Task Learning for Flat and Nested Arabic Named Entity Recognition

In this paper, we present our submitted system for the WojoodNER Shared Task, addressing both flat and nested Arabic Named Entity Recognition (NER). Our system is based on a BERT-based multi-task learning model that leverages the existing Arabic Pretrained Language Models (PLMs) to encode the input sentences. To enhance the performance of our model, we have employed a multi-task loss variance penalty and combined several training objectives, including the Cross-Entropy loss, the Dice loss, the Tversky loss, and the Focal loss. Besides, we have studied the performance of three existing Arabic PLMs for sentence encoding. On the official test set, our system has obtained a micro-F1 score of 0.9113 and 0.9303 for Flat (Sub-Task 1) and Nested (Sub-Task 2) NER, respectively. It has been ranked in the 6th and the 2nd positions among all participating systems in Sub-Task 1 and Sub-Task 2, respectively.
Computational Narrative Understanding: A Big Picture Analysis

This paper provides an overview of outstanding major research goals for the field of computational narrative understanding. Storytelling is an essential human practice, one that provides a sense of personal meaning, shared sense of community, and individual enjoyment. A number of research domains have increasingly focused on storytelling as a key mechanism for explaining human behavior. Now is an opportune moment to provide a vision of the contributions that computational narrative understanding can make towards this collective endeavor and the challenges facing the field. In addition to providing an overview of the elements of narrative, this paper outlines three major lines of inquiry: understanding the multi-modality of narrative; the temporal patterning of narrative (narrative “shape”); and socio-cultural narrative schemas, i.e. collective narratives. The paper concludes with a call for more inter-disciplinary working groups and deeper investment in building cross-cultural and multi-modal narrative datasets.
nanoT5: Fast & Simple Pre-training and Fine-tuning of T5 Models with Limited Resources

State-of-the-art language models like T5 have revolutionized the NLP landscape, but their computational demands hinder a large portion of the research community. To address this challenge, we present nanoT5, a specially-optimized PyTorch framework for efficient pre-training and fine-tuning of T5 models. Drawing on insights from optimizer differences and prioritizing efficiency, nanoT5 allows a T5-Base model to be pre-trained on a single GPU in just 16 hours, without any loss in performance. With the introduction of this open-source framework, we hope to widen the accessibility to language modelling research and cater to the community’s demand for more user-friendly T5 (Encoder-Decoder) implementations. We make our contributions, including configurations, codebase, pre-training insights, and pre-trained models, available to the public.
PTUK-HULAT at ArAIEval Shared Task Fine-tuned Distilbert to Predict Disinformative Tweets

Disinformation involves the dissemination of incomplete, inaccurate, or misleading information; it has the objective, goal, or purpose of deliberately or intentionally lying to others aboutthe truth. The spread of disinformative information on social media has serious implications, and it causes concern among internet users in different aspects. Automatic classification models are required to detect disinformative posts on social media, especially on Twitter. In this article, DistilBERT multilingual model was fine-tuned to classify tweets either as dis-informative or not dis-informative in Subtask 2A of the ArAIEval shared task. The system outperformed the baseline and achieved F1 micro 87% and F1 macro 80%. Our system ranked 11 compared with all participants.
Stock Forecasting with Decision Support System

In recent years, with the development of the national economy, the stock market has played an increasingly important role, and accurate prediction of the stock market has important socio-economic value. The stock market has a massive amount of data, which is characterized by non-stationary, nonlinear, and cyclical characteristics, posing a huge challenge for accurate stock prediction. and research on stock prediction has important academic value and practical significance. This study designed a stock prediction system based on LSTM deep neural network. The system includes four modules: user, stock, user operation, and backend management, and each module has been designed in detail. First, web crawler are used to crawl the historical data of stocks in recent years from financial websites, and these data are cleaned and preprocessed as data sets; Then these data sets are fed to the bidirectional LSTM neural network for training. The adam optimizer is used. The hyperbolic tangent function is used as the activation function, and the loss function uses the mean square error as the evaluation index; Using the attention model to predict the price of a certain period of time, taking the price increase over a period of time as the focus, and then summarizing the average is the predicted value. The system testing results indicate that all functions of stock prediction have achieved the expected goals, and the system runs well. The implementation of this system can provide a good decision-making reference for investing in stocks.
SCO-DUMA: Reading Comprehension with Looking Origin and Comparing Options

Multiple-choice reading comprehension (MCRC) is a task of choosing the correct option from candidates given an article and a question. Recent researchs propose a new model with combining a part-of-speech (POS) embedding layer in the pre-training language model and a DUal Multi-head Attention (DUMA) matching layer to get the state-of-the-art. However, there is still distance between the model’s performance and human’s performance. We propose two mechanisms base on DUMA to futher advance the performance. In the interaction layer, we propose a option interaction mechanism and a gated self-comparing mechanism to imitate the human’s behavior of re-reading passage and comparing the options. Our mechanisms on ALBERT-Base has a 1.4% improvement on DREAM and a 2.1% improvement on RACE.
The possible impact on the arctic ocean of the proposed transfer of water from the northern rivers of the USSR to the south

Abstract A critic of excessive interbasin diversion of water from the northern to the southern watershed of Eurasia questions some of the estimates of water requirements in the southern regions of the USSR, particularly the amount of water needed to maintain the level of the Caspian Sea. He expresses concern about the impact of reduced runoff into the Arctic Ocean, stressing the role of freshwater in the eaily appearance of young ice in autumn, the heat of river waters in speeding spring breakup and, the role of river waters in generating a well defined gradient current draining water out of the Arctic Ocean into the North Atlantic. (For other perspectives on the north‐south diversion problem, see Soviet Geography, December 1978.)