Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Utility of Functional Near-Infrared Spectroscopy for Assessment and Prediction of Suicidality: A Systematic Review

Introduction: Suicide is a pressing psychiatric concern worldwide with no established biomarker. While there is some evidence of the clinical utility of functional near-infrared spectroscopy (fNIRS) in assessing and predicting suicidality, no systematic review of such evidence has been conducted to date. Therefore, this review aimed to systematically review and gather evidence from existing studies that used fNIRS signals to assess suicidality and its associated changes in the brain, and those that examined how such signals correlated with suicide symptomatology. Methods: PubMed, EMBASE, and Cochrane Library databases were used in a systematic literature search for English-language articles published between 2000 and December 19, 2020 that focused on the utility of fNIRS for (i) assessing suicidality and its associated changes in the brain, and (ii) correlating with suicide symptomatology. Studies were included if they utilised fNIRS to evaluate variations in fNIRS-measured cerebral hemodynamic responses in patients with different mental disorders (e.g., major depressive disorder, schizophrenia), as well as in healthy controls, of any age group. Quality of evidence was assessed using the Newcastle-Ottawa quality assessment scale. Results: A total of 7 cross-sectional studies were included in this review, all of which had acceptable quality. Across all studies, fNIRS demonstrated reduced cerebral hemodynamic changes in suicidal individuals when compared to non-suicidal individuals. One study also demonstrated the potential of fNIRS signals in correlating with the severity of suicidality. Conclusions: This review provides a comprehensive, updated review of evidence supporting the clinical utility of fNIRS in the assessment and prediction of suicidality. Further studies involving larger sample sizes, standardised methodology, and longitudinal follow-ups are needed.
High Sitting Time Is a Behavioral Risk Factor for Blunted Improvement in Depression Across 8 Weeks of the COVID-19 Pandemic in April–May 2020

The COVID-19 pandemic has elicited increased sedentary behaviors, decreased moderate-to-vigorous physical activity (MVPA), and worsened mental health, yet the longitudinal impact of these changes and their inter-relations remains unknown. Our purpose was to examine associations between changes in self-reported activity behaviors and mental health over an 8-week period following the COVID-19 outbreak. Participants from all 50 states and the District of Colombia were recruited through convenience and snowball sampling at baseline April 3–10, 2020. Prospective data from 2,327 US adults with ≥2 responses (63.8% female; 74.3% response rate) were collected weekly via online survey for eight consecutive weeks (April 3–June 3, 2020). Primary exposures were self-reported time spent sitting, viewing screens and in MVPA, with primary outcomes being depressive symptoms, anxiety symptoms, and positive mental health (PMH). A significant sitting-by-time interaction (p < 0.05) showed slightly higher marginal effects for depressive symptoms for the 90th-percentile of sitting time than the 10th-percentile at baseline (5.8 [95% confidence interval = 5.5–6.2] vs. 5.7 [5.4–6.1]), with the difference magnifying over time (week 8: 3.5 [3.2–3.9] vs. 2.7 [2.4–2.9]). No other interactions over time were significant. Screen time was negatively associated with PMH and positively associated with depressive and anxiety symptoms (p < 0.05). Sitting time was negatively associated with PMH (p < 0.05). Rapid changes in sitting patterns (e.g., due to a pandemic) may have lasting effects on depressive symptoms. Strategies targeting those most affected (i.e., young adults, females) and/or focused on reducing sitting time may be critical for preventing long-term mental health effects resulting from COVID-19 or other large-scale behavior changes in the general population.
Genetic and Morphological Evidence to Split the Coris julis Species Complex (Teleostei: Labridae) Into Two Sibling Species: Resurrection of Coris melanura () Redescription of Coris julis ()

The genus Coris contains 28 valid species, most of them with an Indo-Pacific distribution and only two species recognized from the eastern Atlantic: Coris atlantica and C. julis. This last species has a large variability in its coloration, which caused that during the first half of XIX century the sexes were considered as different species. Two liveries have been described for C. julis: (i) primary or “giofredi,” which corresponds to females and primary-phase males and is considered common for Atlantic and Mediterranean populations; and (ii) the secondary or “julis” which corresponds to secondary-phase males, which is different for Atlantic and Mediterranean populations. In this study we demonstrate the existence of two sibling species within the C. julis species complex. Morphological and molecular analyses showed that this species complex is composed of two species: (i) C. julis, distributed mainly in the Mediterranean, from which the species was originally described; and (ii) C. melanura, a species described by Lowe in 1839 which is resurrected here, mainly distributed in the eastern Atlantic and western Mediterranean. We also discuss the possible speciation event to understand the contemporary distribution patterns of Coris species in the eastern Atlantic and Mediterranean.
Persisting Effects of Ayahuasca on Empathy, Creative Thinking, Decentering, Personality, and Well-Being

Background: Naturalistic and placebo-controlled studies have suggested that ayahuasca, a traditional Amazonian beverage, could be helpful in the treatment of psychopathologies like depression and anxiety disorders by changing otherwise disturbed cognitive and emotional processes. To better understand its full therapeutic potential, one way is to study the effects on processes like flexible thinking, empathy, and well-being, which are normally compromised in stress-related psychopathologies. Materials and Methods: Volunteers attending ayahuasca ceremonies were asked to complete a test battery at three separate occasions: baseline, the morning after, and 1 week after the ceremony. We included the constructs of creative thinking (measured by Picture Concept Test), empathy (Multifaceted Empathy Test), satisfaction with life (Satisfaction with Life Scale), decentering (Experiences Questionnaire), and personality (Big Five Inventory) into the test battery. Additionally, the psychedelic experience was quantified with the Persisting Effects Questionnaire, the Ego Dissolution Scale, and Visual Analogue Scales. Results: In total, 43 attendees (males = 22; females = 21) completed parts of the baseline assessment, 20 (males = 12; females = 8) completed assessments in the morning after the ceremony, and 19 (males = 14; females = 5) completed assessments at the 1-week follow-up. At one and 7 days post-ceremony, cognitive empathy, satisfaction with life, and decentering increased, while divergent thinking (Fluency corrected for Originality) decreased, when compared to baseline. Implicit emotional empathy increased at 1-week follow-up, whereas ratings of the trait neuroticism decreased. Conclusion: The study suggests that a single ingestion of ayahuasca in a social setting is associated with enhancement of subjective well-being, an enhanced ability to take an objective and non-judging stance towards the self (decentering), and the ability to correctly recognize emotions in others, compared to baseline, lasting up to 1 week post-ceremony. To understand the therapeutic potential related to these effects, further research with clinical populations is needed in which these effects can be assessed, including its link with therapeutic outcomes. Together, this will increase our understanding of the effectiveness and breadth of future therapeutic options.
R&D Investments, Debt Capital, and Ownership Concentration: A Three-Way Interaction and Lag Effects on Firm Performance in China's Pharmaceutical Industry

The existing literature has yet to provide consistent evidence on the relationship between R&D investments and firm performance. The current study attempted to fill this gap in the literature by examining the effect of lag structure and the moderating role of financial governance, in terms of debt capital and ownership concentration, on the returns of R&D. Analyzing a sample of China's pharmaceutical firms from 2009 to 2018, we found that the effect of R&D upon growth begins in the second year after R&D spending and increases thereafter. There exists a vigorous debate about the choice between debt and ownership structure. To fill this gap, we proposed a three-way interactive effect. The results suggest that firms that invest heavily in R&D may achieve their highest performance when the use of debt capital and the extent of ownership concentration are both low. This study contributes to the R&D investments and financial governance literature by reconciling previous mixed evidence about the returns of R&D and the debt–equity choices on R&D investment decisions.
Telling the Seasons Underground: The Circadian Clock and Ambient Temperature Shape Light Exposure and Photoperiodism in a Subterranean Rodent

Living organisms anticipate the seasons by tracking the proportion of light and darkness hours within a day—photoperiod. The limits of photoperiod measurement can be investigated in the subterranean rodents tuco-tucos (Ctenomys aff. knighti), which inhabit dark underground tunnels. Their exposure to light is sporadic and, remarkably, results from their own behavior of surface emergence. Thus, we investigated the endogenous and exogenous regulation of this behavior and its consequences to photoperiod measurement. In the field, animals carrying biologgers displayed seasonal patterns of daily surface emergence, exogenously modulated by temperature. In the laboratory, experiments with constant lighting conditions revealed the endogenous regulation of seasonal activity by the circadian clock, which has a multi-oscillatory structure. Finally, mathematical modeling corroborated that tuco-tuco’s light exposure across the seasons is sufficient for photoperiod encoding. Together, our results elucidate the interrelationship between the circadian clock and temperature in shaping seasonal light exposure patterns that convey photoperiod information in an extreme photic environment.
Rule-based Blockchain Knowledge Graphs: Declarative AI for Solving Industrial Blockchain Challenges

Blockchains have an incredible impact on our economic systems. Over the last years, more and more illicit activities have taken place not only via the traditional ﬁnancial space but also via cryptocur-rencies, where tracking illicit activities is more complex. Yet, several blockchain toolkits have been introduced that explore blockchain data. There is a clear industrial need to bring together many of those valuable methods into a principled approach that upholds central aspects such as the ability to provide explainable analytics. In this paper, we report on an international industrial and academic collaboration that uses a declarative, rule-based approach on top of a Knowledge Graph system and describes the use-case of analyzing the Petya ransomware.
On the Extraction and Use of Arguments in Recommender Systems: A Case Study in the E-participation Domain (Long paper)

In this paper, we present ongoing work on the automatic extraction of arguments from textual content, and on the use of interconnected argument structures by recommender systems. Differently to the majority of existing argument mining methods –which only consider ‘premise’ and ‘claim’ as the components of an argument, and ‘support’ and ‘attack’ as the possible relations between argument components–, we propose an argumentation model based on a detailed taxonomy of argumentative relations. Moreover, we provide a lexicon of English and Spanish linguistic connectors categorized in our taxonomy. As a proof of concept, we apply a simple, yet effective method that makes use of the built taxonomy and lexicon to extract argument graphs from citizen proposals and debates of an e-participation platform. We then describe how the extracted graphs could be exploited to generate and explain argument-based recommendations.
Coupled or Decoupled Evaluation for Group Recommendation Methods?

Group recommendations are a sub-domain of recommender systems (RS), where the final recommendations should comply with preferences of all members of the group. Usually, group recommendations are built on top of common "single-user" RS via aggregating models or predictions for multiple users with some notions of fairness and relevance in mind. So far, group recommendations were usually evaluated off-line either as a tightly coupled pair with the underlying RS or in a decoupled fashion, where the relevance scores estimated by underlying RS serves as a ground truth. Both evaluation types may suffer from different biases that provide illicit advantages to some classes of group recommending strategies. In experimental part, we evaluate several recent group recommendation models and show that the evaluation process itself significantly affects their perceived usability. While coupled evaluation favors group RS that tend to select per-user best items, decoupled evaluation favors strategies aiming to find items with (some degree of) overall agreement. We further evaluate methods w.r. t several variants of inverse propensity based de-biasing scenario in order to reduce the popularity bias of coupled evaluations. Also in this case, if groups of similar users are considered, the magnitude of de-biasing has a determining effect on the ordering of individual methods.
MOCHI: an Offline Evaluation Framework for Educational Recommendations

Evaluating recommendation algorithms with long-term independently-measured rewards, such as educational recommender systems, has proven to be a difficult task, especially using offline data. While many use model-based evaluation strategies to evaluate such recommender systems, we argue that these strategies are unreliable, particularly due to biases introduced via simulation and reward estimation models. In this paper, we showcase this argument by experimenting with a state-of-the-art model-based evaluation model and presenting its flaws. Next, we propose MOCHI, an offline model-free evaluation framework that can be used on sparser collected data with longer trajectories. We experiment with MOCHI and show how it can be used to effectively evaluate educational recommender policies with long-term goals.
Multilingual Simultaneous Sentence End and Punctuation Prediction (short paper)

This paper describes the model and its corresponding setup, proposed by the Unbabel & INESC-ID team for the 1st Shared Task on Sentence End and Punctuation Prediction in NLG Text (SEPP-NLG 2021). The shared task covers 4 languages (English, German, French and Italian) and includes two subtasks: sub-task 1 – detecting the end of a sentence, and subtask 2 – predicting a range of punctuation marks. Our team proposes a single multilingual and multitask model that is able to produce suitable results for all the languages and subtasks involved. The results show that it is possible to achieve state-of-the-art results using one single multilingual model for both tasks and multiple languages. Using a single multilingual model to solve the task for multiple languages is of particular importance, since training a different model for each language is a cumbersome and time-consuming process. Finally, the code for the shared task is publicly available for reproducible purposes at https://github.com/Unbabel/ caption/tree/shared-task .
(Linked) Data Quality Assessment: An Ontological Approach

The eﬀective functioning of data-intensive applications usually requires that the dataset should be of high quality. The quality depends on the task they will be used for. However, it is possible to identify task-independent data quality dimensions which are solely re-lated to data themselves and can be extracted with the help of rule mining/pattern mining. In order to assess and improve data quality, we propose an ontological approach to report data quality violated triples. Our goal is to provide data stakeholders with a set of methods and techniques to guide them in assessing and improving data quality.
Enhancing Gazetteers for Named Entity Recognition in Conversational Recommender Systems (Long paper)

Named Entity Recognition (NER) is a crucial building block of a conversational agent, but remains challenging in real-word settings. It is particularly challenging for domains where the entities are linguistically complex and resemble common phrases (e.g. music and movies). While gazetteer features have been shown to improve NER performance, their utility is undermined by pervasive spurious entity matching. We propose a framework for gazetteer knowledge integration that incorporates external knowledge about entity popularity (e.g. a song’s play count) to reduce spurious entity matching and improve the robustness of gazetteer features. Our experimental evaluations show that using unfiltered gazetteers degrades performance, but that incorporating external information improves it compared to a baseline model that doesn’t use gazetteer information. Further, our framework can efficiently adapt to new entities in gazetteers without additional training, which is crucial for rapidly growing domains like music.
Towards Automated Schema Optimization

Non-relational systems are essential to manage large amounts of semi-and/or unstructured data. To use the optimal data storage at a given time, it may be necessary to change the data model during the lifetime of an application. This paper oﬀers a visionary approach providing an automated schema migration and optimization between diﬀerent NoSQL data stores. By means of data and query analyses, optimizations of all existing cardinalities can be achieved with respect to good query performances with minimal redundancy. First performance measurements prove the increase in performance.
The Cholesterol Factor: Balancing Accuracy and Health in Recipe Recommendation Through a Nutrient-Specific Metric

Whereas many food recommender systems optimize for users’ preferences, health is another but often overlooked objective. This paper aims to recommend relevant recipes that avoid nutrients that contribute to high levels of cholesterol, such as saturated fat and sugar. We introduce a novel metric called ‘The Cholesterol Factor’, based on nutritional guidelines from the Norwegian Directorate of Health, that can balance accuracy and health through linear re-weighting in post-filtering. We tested popular recommender approaches by evaluating a recipe dataset from AllRecipes.com, in which a CF-based SVD method outperformed content-based and hybrid methods. Although we found that increasing the healthiness of a recommended recipe set came at the cost of Precision and Recall metrics, only putting little weight (10-15%) on our Cholesterol Factor can significantly improve the healthiness of a recommendation set with minimal accuracy losses.
UZH OnPoint at Swisstext-2021: Sentence End and Punctuation Prediction in NLG Text Through Ensembling of Different Transformers (short paper)

This paper presents our solutions for the Swiss-Text 2021 shared task “Sentence End and Punctuation Prediction in NLG Text”. We engaged with both subtasks (i.e., sentence end detection and full-punctuation prediction) and built systems for English, German, French and Italian. To tackle the punctuation prediction problem, we ensemble multiple differently trained Transformer models (BERT, CamemBERT, Electra, Longformer, MPNet, XLM-RoBERTa, XLNet) and leverage their re-sults using a sliding window method during inference time. As a result, we achieve an F1 score of the positive class of 0.94 for English, 0.96 for German, 0.93 for French, and 0.93 for Italian for the subtask 1 “sentence end detec-tion” on the respective test sets. Furthermore, Macro F1 results on test sets for subtask 2 “full-punctuation prediction” for English, German, French and Italian are 0.78, 0.81, 0.78, 0.76 respectively.
On Evaluating Session-Based Recommendation with Implicit Feedback

Session-based recommendation systems are used in environments where system recommendation actions are interleaved with user choice reactions. Domains include radio-style song recommendation, session-aware related-items in a shopping context, and next video recommendation. In many situations, interactions logged from a production policy can be used to train and evaluate such session-based recommendation systems. This paper presents several concerns with interpreting logged interactions as reflecting user preferences and provides possible mitigation to those concerns.
A General Aspect-Term-Extraction Model for Multi-Criteria Recommendations (Long paper)

In recent years, increasingly large quantities of user reviews have been made available by several e-commerce platforms. This content is very useful for recommender systems (RSs), since it reflects the users’ opinion of the items regarding several aspects. In fact, they are especially valuable for RSs that are able to exploit multi-faceted user ratings. However, extracting aspect-based ratings from unstructured text is not a trivial task. Deep Learning models for aspect extraction have proven to be effective, but they need to be trained on large quantities of domain-specific data, which are not always available. In this paper, we explore the possibility of transferring knowledge across domains for automatically extracting aspects from user reviews, and its implications in terms of recommendation accuracy. We performed different experiments with several Deep Learning-based Aspect Term Extraction (ATE) techniques and Multi-Criteria recommendation algorithms. Results show that our framework is able to improve recommendation accuracy compared to several baselines based on single-criteria recommendation, despite the fact that no labeled data in the target domain was used when training the ATE model.
Understanding Misinformation: Perspectives on Emerging Issues

The ever-increasing volume of information disseminated by technological advances leads to new challenges with respect to misinfor-mation and fake news. Misinformation could potentially affect people in their daily life, and the choices they are making, be it medical, financial, or the upcoming elections. Technological solutions to identify, verify, and manage misinformation aim to combat the harmful effects of misinfor-mation. In this research proposal, we explore the use of machine learning algorithms in combination with symbolic approaches to identify misin-formation in German news texts in order to help users spot deceptive articles. Based on linguistic aspects of the text, news stories are classified as misleading or truthful. In particular, we plan to compare it to existing symbolic and sub-symbolic approaches for misinformation detection.