Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Uncertainty for Deep Reinforcement Learning

Uncertainty is ubiquitous in games, both in the agents playing games and often in the games themselves. Working with uncertainty is therefore an important component of successful deep reinforcement learning agents. While there has been substantial effort and progress in understanding and working with uncertainty for supervised learning, the body of literature for uncertainty aware deep reinforcement learning is less developed. While many of the same problems regarding uncertainty in neural networks for supervised learning remain for reinforcement learning, there are additional sources of uncertainty due to the nature of an interactable environment. In this work, we provide an overview motivating and presenting existing techniques in uncertainty aware deep reinforcement learning. These works show empirical benefits on a variety of reinforcement learning tasks. This work serves to help to centralize the disparate results and promote future research in this area.
GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search

Current Graph Neural Networks (GNNs) suffer from the over-smoothing problem, which results in indistinguishable node representations and low model performance with more GNN layers. Many methods have been put forward to tackle this problem in recent years. However, existing tackling over-smoothing methods emphasize model performance and neglect the over-smoothness of node representations. Additional, different approaches are applied one at a time, while there lacks an overall framework to jointly leverage multiple solutions to the over-smoothing challenge. To solve these problems, we propose GraTO, a framework based on neural architecture search to automatically search for GNNs architecture. GraTO adopts a novel loss function to facilitate striking a balance between model performance and representation smoothness. In addition to existing methods, our search space also includes DropAttribute, a novel scheme for alleviating the over-smoothing challenge, to fully leverage diverse solutions. We conduct extensive experiments on six real-world datasets to evaluate GraTo, which demonstrates that GraTo outperforms baselines in the over-smoothing metrics and achieves competitive performance in accuracy. GraTO is especially effective and robust with increasing numbers of GNN layers. Further experiments bear out the quality of node representations learned with GraTO and the effectiveness of model architecture. We make the code of GraTo available at Github (https://github.com/fxsxjtu/GraTO).
An empirical model of the Gaia DR3 selection function

Interpreting and modelling astronomical catalogues requires an understanding of the catalogues' completeness or selection function: objects of what properties had a chance to end up in the catalogue. Here we set out to empirically quantify the completeness of the overall Gaia DR3 catalogue. This task is not straightforward because Gaia is the all-sky optical survey with the highest angular resolution to date and no consistent ``ground truth'' exists to allow direct comparisons. However, well-characterised deeper imaging enables an empirical assessment of Gaia's $G$-band completeness across parts of the sky. On this basis, we devised a simple analytical completeness model of Gaia as a function of the observed $G$ magnitude and position over the sky, which accounts for both the effects of crowding and the complex Gaia scanning law. Our model only depends on a single quantity: the median magnitude $M_{10}$ in a patch of the sky of catalogued sources with $\texttt{astrometric_matched_transits}$ $\leq 10$. $M_{10}$ reflects elementary completeness decisions in the Gaia pipeline and is computable from the Gaia DR3 catalogue itself and therefore applicable across the whole sky. We calibrate our model using the Dark Energy Camera Plane Survey (DECaPS) and test its predictions against Hubble Space Telescope observations of globular clusters. We find that our model predicts Gaia's completeness values to a few per cent across the sky. We make the model available as a part of the $\texttt{gaiasf}$ Python package built and maintained by the GaiaUnlimited project: $\texttt{https://github.com/gaia-unlimited/gaiaunlimited}$
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding

Generalized text representations are the foundation of many natural language understanding tasks. To fully utilize the different corpus, it is inevitable that models need to understand the relevance among them. However, many methods ignore the relevance and adopt a single-channel model (a coarse paradigm) directly for all tasks, which lacks enough rationality and interpretation. In addition, some existing works learn downstream tasks by stitches skill block (a fine paradigm), which might cause irrational results due to its redundancy and noise. In this work, we first analyze the task correlation through three different perspectives, , data property, manual design, and model-based relevance, based on which the similar tasks are grouped together. Then, we propose a hierarchical framework with a coarse-to-fine paradigm, with the bottom level shared to all the tasks, the mid-level divided to different groups, and the top-level assigned to each of the tasks. This allows our model to learn basic language properties from all tasks, boost performance on relevant tasks, and reduce the negative impact from irrelevant tasks. Our experiments on 13 benchmark datasets across five natural language understanding tasks demonstrate the superiority of our method.
Autophagy-related genes genetically interact with Pmk1 MAPK signaling in fission yeast

Apart from the highly conserved role in the cellular degradation process, autophagy also appears to play a key role in cellular proliferation. Here, we describe the genetic interaction of autophagy-related genes and Pmk1 MAPK signaling in fission yeast. atg1 deletion cells (Δ atg1 ) exhibit the vic (viable in the presence of immunosuppressant and Cl - ) phenotype, indicative of Pmk1 signaling inhibition. Moreover, the Δ atg1 Δ pmk1 double mutant resembles the single Δ pmk1 mutant, suggesting that Atg1 functions in the Pmk1 pathway. In addition, the growth defect induced by overexpression of Pck2, an upstream activator of Pmk1 MAPK was alleviated by the deletion of atg1 + . Finally, the deletion of autophagy-related genes recapitulates Pmk1 MAPK signaling inhibition. Our data suggest a novel role for autophagy in MAPK signaling regulation.
Investigating the functional role of a buried interchain aromatic cluster in Escherichia coli GrpE dimer

Aromatic clusters in the core of proteins are often involved in imparting structural stability to proteins. However, their functional importance is not always clear. In this study, we investigate the thermosensing role of a phenylalanine cluster present in the GrpE homodimer. GrpE, which acts as a nucleotide exchange factor for the molecular chaperone DnaK, is well known for its thermosensing activity resulting from temperature‐dependent structural changes that allow control of chaperone function. Using mutational analysis, we show that an interchain phenylalanine cluster in a four‐helix bundle of the GrpE homodimer assists in the thermosensing ability of the co‐chaperone. Substitution of aromatic residues with hydrophobic ones in the core of the four‐helix bundle reduces the thermal stability of the bundle and that of a connected coiled‐coil domain, which impacts thermosensing. Cell growth assays and SEM images of the mutants show filamentous growth of Escherichia coli cells at 42°C, which corroborates with the defect in thermosensing. Our work suggests that the interchain edge‐to‐face aromatic cluster is important for the propagation of the structural signal from the coiled‐coil domain to the four‐helical bundle of GrpE, thus facilitating GrpE‐mediated thermosensing in bacteria.
Diagnosing Alzheimer’s Disease Specifically and Sensitively With pLG72 and Cystine/Glutamate Antiporter SLC7A11 AS Blood Biomarkers

Abstract Background Reliable blood biomarkers for Alzheimer’s disease (AD) have been lacking. The D-amino acids oxidase modulator (named pLG72) modulates glutamate N-methyl-D-aspartate receptor activity. The cystine/glutamate antiporter contains a SLC7A11 subunit, which mediates glutamate release. This study aimed to determine the accuracy of pLG72 protein and SLC7A11 mRNA in diagnosing AD. Methods This study enrolled 130 healthy controls and 109 unmatched AD patients; among them, 40 controls and 70 patients were selected to match by age. We measured their pLG72 protein in plasma and SLC7A11 mRNA in white blood cells. Results AD patients had markedly higher pLG72 levels and SLC7A11 mRNA ΔCT values than healthy controls (in both unmatched and matched cohorts; all 4 P values <.001). The receiver operating characteristics analysis in the unmatched cohorts demonstrated that the pLG72 level had a high specificity (0.900) at the optimal cutoff value of 2.3285, the ΔCT of SLC7A11 mRNA displayed an excellent sensitivity (0.954) at the cutoff of 12.185, and the combined value of pLG72 and SLC7A11 ΔCT determined a favorable area under the curve (AUC) (0.882) at the cutoff of 21.721. The AUC of the combined value surpassed that of either biomarker. The specificity, sensitivity, and AUC of the matched cohort were like those of the unmatched cohort. Conclusions The findings suggest that pLG72 protein and SLC7A11 mRNA can distinguish AD patients from healthy controls with excellent specificity and sensitivity, respectively. The combination of pLG72 and SLC7A11 yields better AUC than either, suggesting the superiority of simultaneously measuring both biomarkers in identifying AD patients.
Electrophysiologically Determined Spectral Responses in Lobesia botrana (Lepidoptera: Tortricidae)

Abstract Electrophysiological methods were used to test the visual sensitivity of European grapevine moth, Lobesia botrana (Lepidoptera:Tortricidae) to wavelengths ranging from 300 to 700 nm. For male and females tested, a main, peak response occurred in the 460–540 nm range (blue-green wavelengths) with females having a generally lower response to wavelengths in that range. A second smaller peak was observed for both sexes at the 340–420 nm range. A general linear model indicated that males, virgin females, and mated females did not react differently to changes in wavelength. No moths showed any obvious sensitivity to wavelengths between 580 and 700 nm. Based on our retinal recording data we suggest that UV light traps (≤480 nm) could be utilized alongside pheromone traps when monitoring L. botrana in high risk areas.
Necessary Morphological Patches Extraction for Automatic Micro-Expression Recognition

Micro expressions are usually subtle and brief facial expressions that humans use to hide their true emotional states. In recent years, micro-expression recognition has attracted wide attention in the fields of psychology, mass media, and computer vision. The shortest micro expression lasts only 1/25 s. Furthermore, different from macro-expressions, micro-expressions have considerable low intensity and inadequate contraction of the facial muscles. Based on these characteristics, automatic micro-expression detection and recognition are great challenges in the field of computer vision. In this paper, we propose a novel automatic facial expression recognition framework based on necessary morphological patches (NMPs) to better detect and identify micro expressions. Micro expression is a subconscious facial muscle response. It is not controlled by the rational thought of the brain. Therefore, it calls on a few facial muscles and has local properties. NMPs are the facial regions that must be involved when a micro expression occurs. NMPs were screened based on weighting the facial active patches instead of the holistic utilization of the entire facial area. Firstly, we manually define the active facial patches according to the facial landmark coordinates and the facial action coding system (FACS). Secondly, we use a LBP-TOP descriptor to extract features in these patches and the Entropy-Weight method to select NMP. Finally, we obtain the weighted LBP-TOP features of these NMP. We test on two recent publicly available datasets: CASME II and SMIC database that provided sufficient samples. Compared with many recent state-of-the-art approaches, our method achieves more promising recognition results.
Master thesis : From mission analysis to systems engineering of the OUFTI-Next nanosatellite

OUFTI-Next is the new CubeSat project of the University of Liège. This mission was imagined after the success of OUFTI-1. The goal of this nanosatellite is to detect hydric stress of agricultural fields around the world. It is equipped with a Mid-Wavelength InfraRed (MWIR) detector. It will be a world premiere with such a small satellite (3U or 30 cm × 10 cm × 10 cm). From the data, the temperature of the crop will be extracted and the irrigation status assessed. This satellite is a technology demonstrator for an ambitious project. The final goal is indeed to create a smart irrigation program with a daily revisit over a location. It will provide tools for farmers to improve the irrigation, increase the yield of their fields and spare less drinkable water. With only one satellite, it is unfortunately impossible. OUFTI-Next’s mission is no less important because it will demonstrate that the integration of a MWIR detector is feasible. This master thesis is the continuity of a feasibility study done last year (2016-2017). From the requirements, primordial aspects of the satellite are developed. Orbits, communication, power budget, attitude strategy, . are typical topics introduced in this work. It offers an overview of the satellite and a link between different subjects addressed in other master theses (the detector’s cooling system, the optical design and the thermal aspect). At the end, some configurations, thought as simple as possible, are introduced and discussed. All subsystems are reviewed with the will to find an optimal configuration. Of course, concessions are done and assumptions made. At this stage of the development, it is natural that some information is missing.
A Survey on Recent Approaches to Question Difficulty Estimation from Text

Question Difficulty Estimation from Text (QDET) is the application of Natural Language Processing techniques to the estimation of a value, either numerical or categorical, which represents the difficulty of questions in educational settings. We give an introduction to the field, build a taxonomy based on question characteristics, and present the various approaches that have been proposed in recent years, outlining opportunities for further research. This survey provides an introduction for researchers and practitioners into the domain of question difficulty estimation from text and acts as a point of reference about recent research in this topic to date.
Intensive Care Based Interventions to Reduce Family Member Stress Disorders: A Systematic Review of the Literature

Abstract Background Increasing awareness of the emotional impact of an Intensive Care Unit (ICU) hospitalization on patients and their families has led to a rise in studies seeking to mitigate Post Intensive Care Syndrome (PICS) for both groups. In efforts to decrease symptoms of anxiety and depression, ICUs have implemented a variety of programs to reduce family distress. Methods We conducted a systematic review of experimental studies which aimed to reduce stress related disorders in family members after the experience of having a patient admitted to the ICU. Multiple databases were searched for randomized controlled trials or nonrandomized comparative trials which targeted family members or surrogate decision makers. A total of 17 studies were identified for inclusion in the review representing 3471 participants. Results We describe those interventions which we qualitatively assigned as “not passive,” or those which actively engaged the family to express themselves, as more likely to be successful in both the available pediatric and adult literature than interventions which we identified as “passive. ” Studies which described active engagement of family members demonstrated comparative improvements in symptoms of depression, anxiety, and PTSD, as well as reduced hospital costs in the case of two studies. Discussion This review may serve to aid in the development of future interventions targeted at reducing family stress and PICS following an ICU hospitalization.
Thermal degradation studies and hybrid neural network modelling of eutectic phase change material composites

Using a thermogravimetric analyzer, the thermal stability of pure eutectic phase change material (PEPCM) (LiNO3 + NaCl) and composite eutectic PCM (CEPCM) mixture (ie, PCM containing 9% expanded graphite [EG]) was examined. PEPCM and CEPCM degradation kinetics were studied using model free kinetics methods. The activation energy of both PCM samples was evaluated using the Kissinger‐Akahira‐Sunose (KAS), Flynn‐Wall‐Ozawa (FWO), Starink, Friedman and Vyazovkin kinetic models. The calculated activation energies for Vyazovkin, Frideman, Ozawa, KAS and Starink techniques for PEPCM were 80.62‐149.2, 108.1‐180.18, 83.74‐136.17, 73.55‐127.02 and 74.64‐149.9 kJ/mol, respectively. Likewise, the activation energy of CEPCM vary between 59.4‐161.41, 83.97‐188.69, 57.1‐147.32, 54.19‐137.43 and 55‐160.65 kJ/mol. Hybrid neural networks such as ANN‐PSO and ANFIS were used to model the degradation of PCM samples. The type of PCM, the heating rate, and the temperature were applied as input parameters, while the sample's mass loss was utilized as an output parameter. The created hybrid models are capable of effectively predicting experimental TGA data.
Distinct effects of intracellular vs. extracellular acidic pH on the cardiac metabolome during ischemia and reperfusion

Tissue ischemia results in intracellular pH (pHIN) acidification, and while accumulation of metabolites such as lactate is a known driver of acidic pHIN, less is known about how acidic pHIN regulates metabolism. Furthermore, acidic extracellular (pHEX) during early reperfusion confers cardioprotection, but how this impacts metabolism is unclear. Herein we employed LCMS based targeted metabolomics to analyze perfused mouse hearts exposed to: (i) control perfusion, (ii) hypoxia, (iii) ischemia, (iv) enforced acidic pHIN, (v) control reperfusion, and (vi) acidic pHEX (6.8) reperfusion. Surprisingly little overlap was seen between metabolic changes induced by hypoxia, ischemia, and acidic pHIN. Acidic pHIN elevated metabolites in the top half of glycolysis, and enhanced glutathione redox state. Acidic pHEX reperfusion induced substantial metabolic changes in addition to those seen in control reperfusion. This included elevated metabolites in the top half of glycolysis, prevention of purine nucleotide loss, and an enhancement in glutathione redox state. These data led to parallel hypotheses regarding potential roles for methylglyoxal inhibiting the mitochondrial permeability transition pore, and for acidic inhibition of ecto-5’-nucleotidase, as potential mediators of cardioprotection by acidic pHEX reperfusion. However, neither hypothesis was supported by subsequent experiments. In contrast, analysis of cardiac effluents revealed complex effects of pHEX on metabolite transport, suggesting that mildly acidic pHEX may protect in part by enhancing succinate release during reperfusion. Overall, each intervention had distinct and overlapping metabolic effects, suggesting acidic pH is an independent metabolic regulator regardless which side of the cell membrane it is imposed. HIGHLIGHTS Hypoxia, ischemia and acidic pHIN each induce unique cardiac metabolic profiles. Acidic pHEX at reperfusion prevents purine loss and enhances succinate release.
Isolation and characterization of autophagic bodies from yeast

Autophagy is a major cellular degradation pathway that is highly conserved among eukaryotes. The identification of cargos captured by autophagosomes is critical to our understanding of the physiological significance of autophagy in cells. In the yeast S. cerevisiae, cells deficient in the vacuolar lipase Atg15 accumulate autophagic bodies (ABs) within the vacuole following the induction of autophagy. As ABs contain cytosolic components including proteins, RNAs, and lipids, their purification allows the identification of material targeted by autophagy for degradation. In this study, we demonstrate a method to purify intact ABs from vacuoles that retain membrane integrity and contain autophagic cargos. This technique offers a valuable tool for the identification of the cargos of autophagy, examination of autophagic cargo selectivity, and biochemical characterization of autophagosome membranes.
Statistics of Three-Body Experiments

A large number of three-body interactions involving one initial binary have been studied by a numerical regularization technique. In each set of experiments some parameters have fixed values, whereas others are selected by uniform sampling of the corresponding distribution functions. Similar statistical results are obtained for different random number sequences at a lower integration accuracy. This experimental approach permits an approximate determination of the final distributions of eccentricity, velocity and lifetime. These results show a strong dependence on the total angular momentum, total energy and the mass range, whereas other parameters are usually of secondary importance.
A Positive Correlation between CRISPR-Cas Contents and Cell 1 Cycle Durations Across the Bacteria Domain

19 CRISPR-Cas systems provide adaptive immunity for prokaryotic cells by recognizing and 20 eliminating the recurrent genetic invaders whose sequences had been captured in a prior 21 infection and stored in the CRISPR arrays as spacers. However, the biological/environmental 22 factors determining the efficiency of this immune system have yet to be fully characterized. 23 Recent studies in cultured bacteria showed that slowing the growth rate of bacterial cells 24 could promote their acquisition of novel spacers. This study examined the relationship 25 between the CRISPR-Cas content and the minimal doubling time across the bacteria and the 26 archaea domains. Every completely sequenced genome could be used to predict a minimal 27 doubling time. With a large dataset of 4142 bacterial samples, we found that the predicted 28 minimal doubling times are positively correlated with spacer number and other parameters of 29 the CRISPR-Cas systems, like array number, Cas gene cluster number, and Cas gene number. 30 Different datasets gave different results. Weak results were obtained in analyzing bacterial 31 empirical minimal doubling times and the archaea domain. Still, the conclusion of more 32 spacers in slowly-grown prokaryotes was supported. In addition, we found that the minimal 33 doubling times are negatively correlated with the occurrence of prophages, and the spacer 34 numbers per array are negatively associated with the number of prophages. These 35 observations support the existence of an evolutionary trade-off between bacterial growth and 36 adaptive defense against virulent phages. 37