Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction of monetary penalties for data protection cases in multiple languages

As the use of personal data becomes further entrenched in the function of societal interaction, the regulation of such data continues to grow as an important area of law. Nevertheless, it is unfortunately the case that data protection authorities have limited resources to address an increasing number of investigations. The leveraging of appropriate data-driven models, coupled with the automation of decision making, has the potential to help in such circumstances. In this paper, we evaluate machine learning models in the literature (such as Support Vector Machine (SVM), Random Forest, and Multinomial Naive Bayes (MNB) classifiers) for natural language processing in order to predict whether a monetary penalty was levied based on a description of case facts. We tested these models on a novel data set collected from the data protection authority of Macao across the three languages (i.e., Chinese, English, and Portuguese). Our experimental results show that the machine learning models provide the necessary predictability in order to automate the evaluation of data protection cases. In particular, SVM has consistent performance across three languages and achieving an AUROC of 0.725, 0.762, and 0.748 for Chinese, English, and Portuguese, respectively. We further evaluated the interpretability of the results independently for each of the languages and found that the salient texts that were identified are shared across the three languages.
ASSESSMENT OF THE PERFORMANCE OF A NOVEL REGIONAL LOW-MAGNITUDE GMPE FOR SOUTHERN ITALY

Performance of a novel low-magnitude regional ground-motion prediction equation (GMPE) is assessed in the paper by comparing GMPE predictions with independent ground-motion data and other regional, European, and global GMPEs through the log-likelihood method. The novel GMPE was developed using 2270 waveforms coming from 319 earthquakes occurred in Southern Italy till the end of year 2014, with local magnitudes ranging between 1.5 and 4.2, recorded at 60 stations with hypocentral distances varying from 3 km to about 100 km. This GMPE is able to predict peak ground acceleration and 5% damped spectral acceleration at 11 oscillator periods ranging from 0.1 to 3 s with reference to stiff ground conditions (i.e. ground category B according to the Italian Building Code and Eurocode 8). The GMPE coefficients were computed through a nonlinear weighted damped least-squares algorithm, attributing a higher weight to accelerometric data recorded at stations whose soil category was defined using purposely-assembled geotechnical and geophysical measurements. Predictions of the GMPE are compared herein against an independent accelerometric dataset built out of 326 waveforms from 55 low-magnitude earthquakes occurred in Southern Italy in 2015, 2016, and 2017. The comparison shows that the novel GMPE performs better than any other GMPE considered in this study (overall five attenuation models). Applications of the proposed GMPE are also illustrated in the paper with reference to low-magnitude earthquakes recorded by the regional seismic network operating in Southern Italy.
Signatures of Gait Movement Variability in CKD Patients Scheduled for Hemodialysis Indicate Pathological Performance Before and After Hemodialysis: A Prospective, Observational Study

Background: The frailty status of hemodialysis patients is well-known, but the role of the therapy in the frailty process is not yet clear. Nowadays gait analysis in nephrology is neglected, although gait performance is known to be related to frailty and kidney function. We hypothesized that gait quality and physical activity level is already affected before, and does not change because of the start of hemodialysis. Methods: Fourteen patients (72.3 ± 5.7 years old) in a pre-dialysis program underwent an instrumental gait analysis and their physical activity was monitored for a week. This protocol was repeated 3, 6, 12, and 24 months after the first hemodialysis session. Results: At baseline, our sample showed a conservative gait with pathologic gait variability, high dual-task cost, and a sedentary lifestyle. No statistically significant change was found in any parameter in the analyzed period, but there was a tendency toward an improvement of gait quality and physical activity in the first year of treatment, and a decline in the second year. Conclusion: Elderly patients in the pre-dialysis stage show a conservative gait, however variability was in a pathological range and did not change post-hemodialysis. This hints toward changes in the central nervous system due to the kidney disease. This finding suggests the importance of gait analysis in the early stages of renal disease in the diagnosis of changes in the nervous system due to kidney failure that affect gait. Early detection of these changes would potentially allow a prevention program tailored to this population to be developed.
The Neurovascular Unit in Dementia: An Opinion on Current Research and Future Directions

Dementia is a burgeoning public health crisis, with 50 million people currently affected worldwide (Prince et al., 2015). As the population ages, this figure is set to rise dramatically by 40% over the next 12 years (Prince et al., 2015). Dementia is an umbrella term for several disorders which result in the progressive loss of memory or other cognitive functions (Scott and Barrett, 2007). It remains an incurable disease, and current therapeutics have limited efficacy at slowing disease progression for one third of patients (Rockwood et al., 2008). Of the dementia sub-types, Alzheimer’s disease (AD) remains the most prevalent, accounting for ∼60–70% cases (Alzheimer’s-Society, 2016). Vascular dementia (VaD) is the second most common form and is responsible for ∼20% of cases, with a further 10% being a combination of these two diseases (Alzheimer’s-Society, 2016). However, in practise these distinctions are somewhat arbitrary given the significant overlap in altered vascular structure and function in both of these major sub-types (Kalaria and Ballard, 1999). At least 30% of patients with AD have evidence of cerebrovascular disease on post-mortem examination, and almost all have evidence of cerebral amyloid angiopathy, microvascular degeneration, and white matter lesions (Kalaria and Ballard, 1999). Similarly, one-third of patients with VaD exhibit pathology consistent with AD (e.g., hippocampal or temporal lobe atrophy) (Kalaria and Ballard, 1999). Longitudinal studies have demonstrated that vascular risk factors (e.g., hypertension), significantly increase the risk of both AD and VaD (Rius-Pérez et al., 2018). In genetically at-risk individuals positive for apolipoprotein E4 (APOE4), atherosclerosis can increase the risk of AD by three-fold (Hoffmann et al., 2010). This article provides an opinion on the current evidence on the role of the neurovascular unit in dementia, for further information, several recent reviews are available on this topic (Nelson et al., 2016; Kisler et al., 2017).
The miR-130a-3p/TGF-βRII Axis Participates in Inhibiting the Differentiation of Fibroblasts Induced by TGF-β1

Pulmonary fibrosis (PF) is a chronic progressive interstitial lung disease that has a poor prognosis. Abnormal activation of transforming growth factor-β1 (TGF-β1) plays a crucial role in fibroblast differentiation. Mesenchymal stem cells (MSCs) are currently being considered for the treatment of PF, but the regulatory mechanisms are poorly understood. We co-cultured bone marrow-derived MSCs and mouse lung fibroblasts (MLg) in the presence of TGF-β1, and studied the protein/mRNA expression of fibrosis markers and related signaling pathways. The effects of miR-130a-3p and TGF-β receptor II (TGF-βRII) on the differentiation of MLg induced by TGF-β1 were studied using immunofluorescence assay, Western blot, and quantitative real-time PCR techniques, respectively. Our results showed that MSCs reversed the overexpression of fibrosis markers and TGF-β1/Smad signaling pathway proteins and mRNAs after TGF-β1 treatment and increased the level of miR-130a-3p. TGF-βRII was identified as a target of miR-130a-3p and was evaluated by dual-luciferase reporter assay. The miR-130a-3p/TGF-βRII axis could suppress the differentiation of lung fibroblasts via the TGF-β1/Smad signaling pathway, thereby reducing the process of PF.
Calcium in the Backstage of Malaria Parasite Biology

The calcium ion (Ca2+) is a ubiquitous second messenger involved in key biological processes in prokaryotes and eukaryotes. In Plasmodium species, Ca2+ signaling plays a central role in the parasite life cycle. It has been associated with parasite development, fertilization, locomotion, and host cell infection. Despite the lack of a canonical inositol-1,4,5-triphosphate receptor gene in the Plasmodium genome, pharmacological evidence indicates that inositol-1,4,5-triphosphate triggers Ca2+ mobilization from the endoplasmic reticulum. Other structures such as acidocalcisomes, food vacuole and mitochondria are proposed to act as supplementary intracellular Ca2+ reservoirs. Several Ca2+-binding proteins (CaBPs) trigger downstream signaling. Other proteins with no EF-hand motifs, but apparently involved with CaBPs, are depicted as playing an important role in the erythrocyte invasion and egress. It is also proposed that a cross-talk among kinases, which are not members of the family of Ca2+-dependent protein kinases, such as protein kinases G, A and B, play additional roles mediated indirectly by Ca2+ regulation. This statement may be extended for proteins directly related to invasion or egress, such as SUB1, ERC, IMC1I, IMC1g, GAP45 and EBA175. In this review, we update our understanding of aspects of Ca2+-mediated signaling correlated to the developmental stages of the malaria parasite life cycle.
Biomarker Discovery in Atherosclerotic Diseases Using Quantitative Nuclear Magnetic Resonance Metabolomics

Despite great progress in the management of atherosclerosis (AS), its subsequent cardiovascular disease (CVD) remains the leading cause of morbidity and mortality. This is probably due to insufficient risk detection using routine lipid testing; thus, there is a need for more effective approaches relying on new biomarkers. Quantitative nuclear magnetic resonance (qNMR) metabolomics is able to phenotype holistic metabolic changes, with a unique advantage in regard to quantifying lipid-protein complexes. The rapidly increasing literature has indicated that qNMR-based lipoprotein particle number, particle size, lipid components, and some molecular metabolites can provide deeper insight into atherogenic diseases and could serve as novel promising determinants. Therefore, this article aims to offer an updated review of the qNMR biomarkers of AS and CVD found in epidemiological studies, with a special emphasis on lipoprotein-related parameters. As more researches are performed, we can envision more qNMR metabolite biomarkers being successfully translated into daily clinical practice to enhance the prevention, detection and intervention of atherosclerotic diseases.
CCL2: An Important Mediator Between Tumor Cells and Host Cells in Tumor Microenvironment

Tumor microenvironment (TME) formation is a major cause of immunosuppression. The TME consists of a considerable number of macrophages and stromal cells that have been identified in multiple tumor types. CCL2 is the strongest chemoattractant involved in macrophage recruitment and a powerful initiator of inflammation. Evidence indicates that CCL2 can attract other host cells in the TME and direct their differentiation in cooperation with other cytokines. Overall, CCL2 has an unfavorable effect on prognosis in tumor patients because of the accumulation of immunosuppressive cell subtypes. However, there is also evidence demonstrating that CCL2 enhances the anti-tumor capability of specific cell types such as inflammatory monocytes and neutrophils. The inflammation state of the tumor seems to have a bi-lateral role in tumor progression. Here, we review works focusing on the interactions between cancer cells and host cells, and on the biological role of CCL2 in these processes.
Action Sounds Informing Own Body Perception Influence Gender Identity and Social Cognition

Sensory information can temporarily affect mental body representations. For example, in Virtual Reality (VR), visually swapping into a body with another sex can temporarily alter perceived gender identity. Outside of VR, real-time auditory changes to walkers’ footstep sounds can affect perceived body weight and masculinity/femininity. Here, we investigate whether altered footstep sounds also impact gender identity and relation to gender groups. In two experiments, cisgender participants (26 females, 26 males) walked with headphones which played altered versions of their own footstep sounds that sounded more typically male or female. Baseline and post-intervention measures quantified gender identity [Implicit Association Test (IAT)], relation to gender groups [Inclusion of the Other-in-the-Self (IOS)], and perceived masculinity/femininity. Results show that females felt more feminine and closer to the group of women (IOS) directly after walking with feminine sounding footsteps. Similarly, males felt more feminine after walking with feminine sounding footsteps and associated themselves relatively stronger with “female” (IAT). The findings suggest that gender identity is temporarily malleable through auditory-induced own body illusions. Furthermore, they provide evidence for a connection between body perception and an abstract representation of the Self, supporting the theory that bodily illusions affect social cognition through changes in the self-concept.
Global Carbon Dioxide Removal Potential of Waste Materials From Metal and Diamond Mining

There is growing urgency for CO2 removal strategies to slow the increase of, and potentially lower, atmospheric CO2 concentrations. Enhanced weathering, whereby the natural reactions between CO2 and silicate minerals that produce dissolved bicarbonate ions are accelerated, has the potential to remove substantial CO2 on decadal to centennial timescales. The global mining industry produces huge volumes of fine wastes that could be utilised as feedstock for enhanced weathering. We have compiled a global database of the enhanced weathering potential of mined metal and diamond commodity tailings from silicate-hosted deposits. Our data indicate that all deposit types, notably mafic and ultramafic rock-hosted operations and high tonnage Cu-hosting deposits, have the potential to capture ~1.1–4.5 Gt CO2 annually, between 31 and 125% of the industry's primary emissions. However, current knowledge suggests that dissolution rates of many minerals are relatively slow, such that only a fraction (~3–21%) of this potential may be realised on timescales of <50 years. Field trials in mine settings are urgently needed and, if this prediction is confirmed, then methodologies for accelerating weathering reactions will need to be developed.
Do Reading and Arithmetic Fluency Share the Same Cognitive Base?

We examined the role of different cognitive-linguistic skills in reading and arithmetic fluency, and whether the effects of these skills are mediated by reading and arithmetic accuracy. One hundred twenty-six English-speaking Grade 1 children (67 females, 59 males; Mage = 6.41 years) were followed from the beginning of Grade 1 (Time 1) to the end of Grade 1 (Time 2). At Time 1, they were assessed on measures of non-verbal IQ, speed of processing, working memory, phonological awareness, rapid automatized naming (RAN), and number sense. At Time 2, they were assessed on measures of reading and arithmetic accuracy as well as on measures of reading and arithmetic fluency. Results of path analysis showed first that when reading and arithmetic fluency were included in the model as separate outcomes, RAN was predictive of both and that speed of processing and working memory were predictive of only arithmetic fluency. Second, RAN, speed of processing, and working memory had both direct and indirect effects (via reading and arithmetic accuracy) on the covariation of reading and arithmetic fluency. Irrespective of how reading and arithmetic fluency were treated in the analyses, the effects of non-verbal IQ, phonological awareness, and number sense were all indirect. Taken together, these findings suggest that reading and arithmetic fluency draw on a broader network of cognitive-linguistic skills, whose effects can sometimes be indirect through reading and arithmetic accuracy.
Reversibility of Antipsychotic-Induced Weight Gain: A Systematic Review and Meta-Analysis

Background and Aims Weight gain is a major adverse effect of antipsychotic medication, negatively affecting physical and mental well-being. The objective of this study was to explore if dose reduction, discontinuation, switch to a partial agonist, or switch from polypharmacy to monotherapy will lead to weight loss. Methods Controlled and uncontrolled studies reporting the effects of discontinuation, dose reduction, switch to a partial agonist, or switch from polypharmacy to monotherapy on weight were included. Primary outcome was difference in weight compared to maintenance groups based on controlled studies. Secondary outcome was change in weight from initiation of one of the included interventions until follow-up in a pre-post analysis. Results We identified 40 randomized controlled trials and 15 uncontrolled studies including 12,279 individuals. The effect of the interventions, i.e. dose reduction, drug discontinuation, or switch to a partial agonis, reduced the weight with 1.5 kg (95% CI −2.03 to −0.98; P < 0.001) compared to maintenance treatment. The weight change from pre to post was a reduction of 1.13 kg (95% CI −1.36 to −0.90; P < 0.001). Conclusion We found a significant but small reduction in weight, suggesting that antipsychotic-induced weight gain can be reversed to some degree. Only a few studies were designed to address the question as primary outcome, which limits the generalizability of our findings.
Amherst685 at SemEval-2021 Task 7: Joint Modeling of Classification and Regression for Humor and Offense

This paper describes our submission to theSemEval’21: Task 7- HaHackathon: Detecting and Rating Humor and Offense. In this challenge, we explore intermediate finetuning, backtranslation augmentation, multitask learning, and ensembling of different language models. Curiously, intermediate finetuning and backtranslation do not improve performance, while multitask learning and ensembling do improve performance. We explore why intermediate finetuning and backtranslation do not provide the same benefit as other natural language processing tasks and offer insight into the errors that our model makes. Our best performing system ranks 7th on Task 1bwith an RMSE of 0.5339
Grenzlinie at SemEval-2021 Task 7: Detecting and Rating Humor and Offense

This paper introduces the result of Team Grenzlinie’s experiment in SemEval-2021 task 7: HaHackathon: Detecting and Rating Humor and Offense. This task has two subtasks. Subtask1 includes the humor detection task, the humor rating prediction task, and the humor controversy detection task. Subtask2 is an offensive rating prediction task. Detection task is a binary classification task, and the rating prediction task is a regression task between 0 to 5. 0 means the task is not humorous or not offensive, 5 means the task is very humorous or very offensive. For all the tasks, this paper chooses RoBERTa as the pre-trained model. In classification tasks, Bi-LSTM and adversarial training are adopted. In the regression task, the Bi-LSTM is also adopted. And then we propose a new approach named compare method. Finally, our system achieves an F1-score of 95.05% in the humor detection task, F1-score of 61.74% in the humor controversy detection task, 0.6143 RMSE in humor rating task, 0.4761 RMSE in the offensive rating task on the test datasets.
Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations

Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL). Recent work has proposed the use of external knowledge to improve the efficiency of RL agents for TBGs. In this paper, we posit that to act efficiently in TBGs, an agent must be able to track the state of the game while retrieving and using relevant commonsense knowledge. Thus, we propose an agent for TBGs that induces a graph representation of the game state and jointly grounds it with a graph of commonsense knowledge from ConceptNet. This combination is achieved through bidirectional knowledge graph attention between the two symbolic representations. We show that agents that incorporate commonsense into the game state graph outperform baseline agents.
macech at SemEval-2021 Task 5: Toxic Spans Detection

Toxic language is often present in online forums, especially when politics and other polarizing topics arise, and can lead to people becoming discouraged from joining or continuing conversations. In this paper, we use data consisting of comments with the indices of toxic text labelled to train an RNN to deter-mine which parts of the comments make them toxic, which could aid online moderators. We compare results using both the original dataset and an augmented set, as well as GRU versus LSTM RNN models.
SINAI at SemEval-2021 Task 5: Combining Embeddings in a BiLSTM-CRF model for Toxic Spans Detection

This paper describes the participation of SINAI team at Task 5: Toxic Spans Detection which consists of identifying spans that make a text toxic. Although several resources and systems have been developed so far in the context of offensive language, both annotation and tasks have mainly focused on classifying whether a text is offensive or not. However, detecting toxic spans is crucial to identify why a text is toxic and can assist human moderators to locate this type of content on social media. In order to accomplish the task, we follow a deep learning-based approach using a Bidirectional variant of a Long Short Term Memory network along with a stacked Conditional Random Field decoding layer (BiLSTM-CRF). Specifically, we test the performance of the combination of different pre-trained word embeddings for recognizing toxic entities in text. The results show that the combination of word embeddings helps in detecting offensive content. Our team ranks 29th out of 91 participants.
StereoRel: Relational Triple Extraction from a Stereoscopic Perspective

Relational triple extraction is critical to understanding massive text corpora and constructing large-scale knowledge graph, which has attracted increasing research interest. However, existing studies still face some challenging issues, including information loss, error propagation and ignoring the interaction between entity and relation. To intuitively explore the above issues and address them, in this paper, we provide a revealing insight into relational triple extraction from a stereoscopic perspective, which rationalizes the occurrence of these issues and exposes the shortcomings of existing methods. Further, a novel model is proposed for relational triple extraction, which maps relational triples to a three-dimension (3-D) space and leverages three decoders to extract them, aimed at simultaneously handling the above issues. A series of experiments are conducted on five public datasets, demonstrating that the proposed model outperforms the recent advanced baselines.