Miracle-dz/UniFAR-FTUs huggingface dataset:
Arterial Blood Gas Analysis in Breath-Hold Divers at Depth

The present study aimed to evaluate the partial pressure of arterial blood gases in breath-hold divers performing a submersion at 40 m. Eight breath-hold divers were enrolled for the trials held at “Y-40 THE DEEP JOY” pool (Montegrotto Terme, Padova, Italy). Prior to submersion, an arterial cannula in the radial artery of the non-dominant limb was positioned. All divers performed a sled-assisted breath-hold dive to 40 m. Three blood samplings occurred: at 10 min prior to submersion, at 40 m depth, and within 2 min after diver’s surfacing and after resuming normal ventilation. Blood samples were analyzed immediately on site. Six subjects completed the experiment, without diving-related problems. The theoretically predicted hyperoxia at the bottom was observed in 4 divers out of 6, while the other 2 experienced a reduction in the partial pressure of oxygen (paO2) at the bottom. There were no significant increases in arterial partial pressure of carbon dioxide (paCO2) at the end of descent in 4 of 6 divers, while in 2 divers paCO2 decreased. Arterial mean pH and mean bicarbonate (HCO3−) levels exhibited minor changes. There was a statistically significant increase in mean arterial lactate level after the exercise. Ours was the first attempt to verify real changes in blood gases at a depth of 40 m during a breath-hold descent in free-divers. We demonstrated that, at depth, relative hypoxemia can occur, presumably caused by lung compression. Also, hypercapnia exists at depth, to a lesser degree than would be expected from calculations, presumably because of pre-dive hyperventilation and carbon dioxide distribution in blood and tissues.
Improving the Coverage and the Generalization Ability of Neural Word Sense Disambiguation through Hypernymy and Hyponymy Relationships

In Word Sense Disambiguation (WSD), the predominant approach generally involves a supervised system trained on sense annotated corpora. The limited quantity of such corpora however restricts the coverage and the performance of these systems. In this article, we propose a new method that solves these issues by taking advantage of the knowledge present in WordNet, and especially the hypernymy and hyponymy relationships between synsets, in order to reduce the number of different sense tags that are necessary to disambiguate all words of the lexical database. Our method leads to state of the art results on most WSD evaluation tasks, while improving the coverage of supervised systems, reducing the training time and the size of the models, without additional training data. In addition, we exhibit results that significantly outperform the state of the art when our method is combined with an ensembling technique and the addition of the WordNet Gloss Tagged as training corpus.
Towards Robust Detection of Adversarial Infection Vectors: Lessons Learned in PDF Malware

Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents and other media formats. These infection vectors hide embedded malicious code to the victim users, thus facilitating the use of social engineering techniques to infect their machines. In the last decade, machine-learning algorithms provided an effective defense against such threats, being able to detect malware embedded in various infection vectors. However, the existence of an arms race in an adversarial setting like that of malware detection has recently questioned their appropriateness for this task. In this work, we focus on malware embedded in PDF files, as a representative case of how such an arms race can evolve. We first provide a comprehensive taxonomy of PDF malware attacks, and of the various learning-based detection systems that have been proposed to detect them. Then, we discuss more sophisticated attack algorithms that craft evasive PDF malware oriented to bypass such systems. We describe state-of-the-art mitigation techniques, highlighting that designing robust machine-learning algorithms remains a challenging open problem. We conclude the paper by providing a set of guidelines for designing more secure systems against the threat of adversarial malicious PDF files.
The “Dark Side” Effects of Social Capital on Harmful Drinking among Chinese Community Residents: A Multilevel Study

Background: To determine the effects of social capital on harmful drinking (HD) among Chinese community residents using a multilevel study. Methods: A cross-sectional study conducted from 2017–2018. In total, 13,610 participants were randomly interviewed from 29 districts of 3 cities in China with a multi-stage sampling procedure. Social capital, including social cohesion, membership in social organizations, and frequency of social participation, were assessed using validated scales. HD was assessed using the CAGE four-item questionnaire. Multilevel models were developed to determine whether social capital was related to HD when socioeconomic and demographic covariates were controlled. Results: In general, the prevalence of HD was 8.18%, and more specifically, 13.77% for men and 2.74% for women. After controlling for covariates and stratifying by gender, compared to residents in the low individual-level membership of social organizations, we found that the odds ratio (OR) for HD was 1.30 with a 95% confidence interval (CI) of 1.07–1.56 among men and 1.95 (95% CI: 1.29–2.97) among women. Compared to residents in the low individual-level frequency of social participation groups, the odds ratio of HD among women was 1.58 (95% CI: 1.10–2.26). There was no association between district-level social capital and HD. Conclusions: A high level of social capital may promote HD among the residents of Chinese neighborhoods. Intervention to modify social capital under the Chinese drinking culture may help reduce HD.
Autonomy Supportive Contexts, Autonomous Motivation, and Self-Efficacy Predict Academic Adjustment of First-Year University Students

Aim. The purpose of this study was to provide a better understanding of undergraduate students' academic adjustment in the first year of higher education, by testing a predictive model based on self-determination theory with the inclusion of self-efficacy. The model posits that perceived autonomous forms of support from parents and teachers foster autonomous motivation and self-efficacy, which in turn predict academic adjustment. Method. A two-wave prospective design was adopted. Freshman students at an Italian university (N = 388; 73.5% females, M age = 21.38 years ± 4.84) completed measures of autonomous motivation, perceived autonomy support from parents and teachers, self-efficacy, and intention to drop out from university at the start of their academic year. Students' past performance and socioeconomic background were also measured. At the end of the first semester, information about number of course modules passed and credits attained for each student were collected from the department office and matched with the data collected in the first wave by an identification number. Results. Findings of structural equation modeling analysis supported the proposed model for first-year university students, after controlling for the influence of past performance and socioeconomic background. Specifically, autonomous motivation and self-efficacy predicted dropout intention and academic adjustment a few months later. Autonomous motivation and self-efficacy were encouraged by autonomy supportive behaviors provided by teachers and parents. Conclusion. According to our findings, in order to promote higher degree of academic adjustment in freshman students, interventions should aim to encourage autonomous motivation and self-efficacy through autonomous supportive behavior from the university and the family contexts.
Spectral Signatures in Backdoor Attacks

A recent line of work has uncovered a new form of data poisoning: so-called \emph{backdoor} attacks. These attacks are particularly dangerous because they do not affect a network's behavior on typical, benign data. Rather, the network only deviates from its expected output when triggered by a perturbation planted by an adversary. In this paper, we identify a new property of all known backdoor attacks, which we call \emph{spectral signatures}. This property allows us to utilize tools from robust statistics to thwart the attacks. We demonstrate the efficacy of these signatures in detecting and removing poisoned examples on real image sets and state of the art neural network architectures. We believe that understanding spectral signatures is a crucial first step towards designing ML systems secure against such backdoor attacks
Smile Mimicry and Emotional Contagion in Audio-Visual Computer-Mediated Communication

We investigate whether smile mimicry and emotional contagion are evident in non-text-based computer-mediated communication (CMC). Via an ostensibly real-time audio-visual CMC platform, participants interacted with a confederate who either smiled radiantly or displayed a neutral expression throughout the interaction. Automatic analyses of expressions displayed by participants indicated that smile mimicry was at play: A higher level of activation of the facial muscle that characterizes genuine smiles was observed among participants who interacted with the smiling confederate than among participants who interacted with the unexpressive confederate. However, there was no difference in the self-reported level of joviality between participants in the two conditions. Our findings demonstrate that people mimic smiles in audio-visual CMC, but that even though the diffusion of emotions has been documented in text-based CMC in previous studies, we find no convincing support for the phenomenon of emotional contagion in non-text-based CMC.
Drought and Phytophthora Are Associated With the Decline of Oak Species in Southern Italy

Forest decline induced by climate change is a global phenomenon that affects many tree species, mainly in drought-prone areas as the Mediterranean region. In southern Italy, several oak species have shown decline symptoms and elevated mortality since the 2000s due to drought stress. However, it remains to be answered whether decline occurred alone or whether a pathogen was also involved. To this aim, we compared two coexisting oak species in a forest located in southern Italy which are assumed to be less (Quercus cerris) and more tolerant to drought (Quercus pubescens). We sampled fifteen couples of neighboring declining (D) and non-declining (ND) trees of both species. Wood cores were taken from all trees to perform dendrochronological analyses to detect the decline onset and link it to potential climatic drivers. Carbon isotope ratios (d13C) were analyzed in wood of the two vigor classes to compare their water-use efficiency. Phytophthora presence was also assessed in soil samples from ten D-ND couples of trees per species. The oak species most affected by drought-induced decline in terms of leaf shedding and mortality was Q. cerris, i.e., the least tolerant to drought. In both species, the D trees showed a reduced growth rate compared with ND trees from 2000 onward when drought and warming intensified. Q. pubescens showed higher growth sensitivity to precipitation, temperature and drought than Q. cerris. This sensitivity to climate was magnified in D trees whose growth decreased in response to warm and dry conditions during the prior winter and the late summer. The Q. pubescens D trees were more efficient in their water use than ND trees before the growth divergence between D and ND trees amplified. In the studied area, Phytophthora quercina was isolated from 40% of the sampled trees, and tended to be more frequent amongst ND than amongst D trees. Our data suggests that droughts and warm summer conditions triggered oak decline. The high prevalence of P. quercina in the studied area warrants further study as a potential predisposing factor.
Metode penelitian: kuantitatif, kualitatif, dan penelitian gabungan

Buku ini mempertegas ihwal penelitian sebagai suatu sistem yang terintegratif. DImana ketepatan hasil penelitian bukan hanya ditentukan oleh satu aspek, melainkan dipengaruhi oleh berbagai faktor 'di dalam' dan 'di luar' objek penelitian. Cakupan materi penyajian model metode penelitian yang komplet dan konseptual ini sangat dianjurkan tidak hanya sebagai textbook bagi para mahasiswa juga bahan penyegar bagi praktisi.
Colder Weather and Fewer Sunlight Hours Increase Alcohol Consumption and Alcoholic Cirrhosis Worldwide

Risk of alcoholic cirrhosis is determined by genetic and environmental factors. We aimed to investigate if climate has a causal effect on alcohol consumption and its weight on alcoholic cirrhosis. We collected extensive data from 193 sovereign countries as well as 50 states and 3,144 counties in the United States. Data sources included World Health Organization, World Meteorological Organization, and the Institute on Health Metrics and Evaluation. Climate parameters comprised Koppen‐Geiger classification, average annual sunshine hours, and average annual temperature. Alcohol consumption data, pattern of drinking, health indicators, and alcohol‐attributable fraction (AAF) of cirrhosis were obtained. The global cohort revealed an inverse correlation between mean average temperature and average annual sunshine hours with liters of annual alcohol consumption per capita (Spearman’s rho −0.5 and −0.57, respectively). Moreover, the percentage of heavy episodic drinking and total drinkers among population inversely correlated with temperature −0.45 and −0.49 (P < 0.001) and sunshine hours −0.39 and −0.57 (P < 0.001). Importantly, AAF was inversely correlated with temperature −0.45 (P < 0.001) and sunshine hours −0.6 (P < 0.001). At a global level, all included parameters in the univariable and multivariable analysis showed an association with liters of alcohol consumption and drinkers among population once adjusted by potential confounders. In the multivariate analysis, liters of alcohol consumption associated with AAF. In the United States, colder climates showed a positive correlation with the age‐standardized prevalence of heavy and binge drinkers. Conclusion: These results suggest that colder climates may play a causal role on AAF mediated by alcohol consumption.
Morning Bright Light Treatment for Sleep-Wake Disturbances in Primary Biliary Cholangitis: A Pilot Study

Patients with Primary Biliary Cholangitis (PBC) exhibit delayed sleep-wake habits, disturbed night sleep and daytime sleepiness/fatigue. Such combination of symptoms is reminiscent of delayed sleep-wake phase disorder (DSPD), which benefits from morning light treatment. The aim of the present pilot study was to test the effect of morning light treatment in a group of 13 well-characterized patients with PBC [all females; (mean ± SD) 53 ± 10 years]. Six healthy individuals (4 females, 57 ± 14 years) and 7 patients with cirrhosis (1 female, 57 ± 12 years) served as controls and diseased controls, respectively. At baseline, all participants underwent an assessment of quality of life, diurnal preference, sleep quality/timing (subjective plus actigraphy), daytime sleepiness, and urinary 6-sulphatoxymelatonin (aMT6s) rhythmicity. Then they underwent a 15-day course of morning bright light treatment, immediately after getting up (light box, 10,000 lux, 45 min) whilst monitoring sleep-wake patterns and aMT6s rhythmicity. At baseline, both patients with PBC and patients with cirrhosis had significantly worse subjective sleep quality compared to controls. In patients with PBC, light treatment resulted in an improvement in subjective sleep quality and a reduction in daytime sleepiness. In addition, both their sleep onset and get-up time were significantly advanced. Finally, the robustness of aMT6s rhythmicity (i.e., strength of the cosinor fit) increased after light administration but post-hoc comparisons were not significant in any of the groups. In conclusion, a brief course of morning bright light treatment had positive effects on subjective sleep quality, daytime sleepiness, and sleep timing in patients with PBC. This unobtrusive, side-effect free, non-pharmacological treatment is worthy of further study.
Cross-Domain Priming From Mathematics to Relative-Clause Attachment: A Visual-World Study in French

Human language processing must rely on a certain degree of abstraction, as we can produce and understand sentences that we have never produced or heard before. One way to establish syntactic abstraction is by investigating structural priming. Structural priming has been shown to be effective within a cognitive domain, in the present case, the linguistic domain. But does priming also work across different domains? In line with previous experiments, we investigated cross-domain structural priming from mathematical expressions to linguistic structures with respect to relative clause attachment in French (e.g., la fille du professeur qui habitait à Paris/the daughter of the teacher who lived in Paris). Testing priming in French is particularly interesting because it will extend earlier results established for English to a language where the baseline for relative clause attachment preferences is different form English: in English, relative clauses (RCs) tend to be attached to the local noun phrase (low attachment) while in French there is a preference for high attachment of relative clauses to the first noun phrase (NP). Moreover, in contrast to earlier studies, we applied an online-technique (visual world eye-tracking). Our results confirm cross-domain priming from mathematics to linguistic structures in French. Most interestingly, different from less mathematically adept participants, we found that in mathematically skilled participants, the effect emerged very early on (at the beginning of the relative clause in the speech stream) and is also present later (at the end of the relative clause). In line with previous findings, our experiment suggests that mathematics and language share aspects of syntactic structure at a very high-level of abstraction.
Resting State Vagally-Mediated Heart Rate Variability Is Associated With Neural Activity During Explicit Emotion Regulation

Resting state vagally mediated heart rate variability (vmHRV) is related to difficulties in emotion regulation (ER). The prefrontal cortex (PFC) provides inhibitory control over the amygdala during ER. Previous studies linked vmHRV with activity in the ventromedial PFC (vmPFC) during implicit ER. To date no study examined the relation between vmHRV and brain activity during explicit ER. vmHRV was measured during a 7 min baseline at T1 2–5 days preceding T2. At T2 n = 24 participants (50% female, Mage = 24.6 years) viewed neutral or emotional pictures of pleasant or unpleasant valence and were instructed to intensify or to reduce their present emotion using two ER strategies (reappraisal and response modulation) or to passively view the picture. Participants rated the valence of their emotional state from pleasant to unpleasant after ER. Whole-brain fMRI data were collected using a 1.5-T-scanner. We observed an association between resting state vmHRV and brain activation in the PFC and the amygdala during ER of unpleasant emotions. Groups based on vmHRV showed significant differences in the modulation of amygdala activity as a function of ER strategy. In participants with high vmHRV amygdala activity was modulated only when using reappraisal and for low vmHRV participants only when using response modulation. Similar, dorsomedial PFC activity in high vmHRV participants was increased when using reappraisal and in low vmHRV participants when using response modulation to regulate unpleasant emotions. These results suggest that individuals with low vmHRV might have difficulties in recruiting prefrontal brain areas necessary for the modulation of amygdala activity during explicit ER.
Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities

We present a method that "meta" classifies whether segments predicted by a semantic segmentation neural network intersect with the ground truth. For this purpose, we employ measures of dispersion for predicted pixel-wise class probability distributions, like classification entropy, that yield heat maps of the input scene’s size. We aggregate these dispersion measures segment-wise and derive metrics that are well correlated with the segment-wise IoU of prediction and ground truth. This procedure yields an almost plug and play post-processing tool to rate the prediction quality of semantic segmentation networks on segment level. This is especially relevant for monitoring neural networks in online applications like automated driving or medical imaging where reliability is of utmost importance. In our tests, we use publicly available state-of-the-art networks trained on the Cityscapes dataset and the BraTS2017 dataset and analyze the predictive power of different metrics as well as different sets of metrics. To this end, we compute logistic LASSO regression fits for the task of classifying IoU = 0 vs. IoU > 0 per segment and obtain AUROC values of up to 91.55%. We complement these tests with linear regression fits to predict the segment-wise IoU and obtain prediction standard deviations of down to 0.130 as well as R2 values of up to 84.15%. We show that these results clearly outperform standard approaches.
Changes in Cortical Dopamine D1 Receptor Binding Associated with Cognitive Training

Working memory is a key function for human cognition, dependent on adequate dopamine neurotransmission. Here we show that the training of working memory, which improves working memory capacity, is associated with changes in the density of cortical dopamine D1 receptors. Fourteen hours of training over 5 weeks was associated with changes in both prefrontal and parietal D1 binding potential. This plasticity of the dopamine D1 receptor system demonstrates a reciprocal interplay between mental activity and brain biochemistry in vivo.