Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust adaptive global nonlinear sliding mode controller for a quadrotor under external disturbances and uncertainties

The present paper proposes an adaptive global nonlinear sliding mode controller (AGNSMC) for the tracking problem of a quadrotor subjected to external disturbances. In order to eliminate the reaching phase and to guarantee the sliding mode of the quadrotor states in the initial time, a novel control law is developed. The upper bounds of disturbances affected the quadrotor dynamics are rejected based on adaptive laws for the both attitude and position subsystem. The tracking performance is enhanced by using the suggested controller. The stability of quadrotor is guaranteed and the global sliding mode surfaces converge to origin values in a finite time. To show the robustness of the proposed control approach against the external disturbances, simulation results are presented and compared with the results of the super-twisting-integral sliding mode controller.
Frontoparietal, Cerebellum Network Codes for Accurate Intention Prediction in Altered Perceptual Conditions

Integrating and predicting intentions and actions of others are crucial components of social interactions, but the behavioral and neural underpinnings of such mechanisms in altered perceptual conditions remain poorly understood. We demonstrated that expertise was necessary to successfully understand and evaluate communicative intent in spatially and temporally altered visual representations of music plays, recruiting frontoparietal regions and several sub-areas of the cerebellum. Functional connectivity between these brain areas revealed widespread organization, especially in the cerebellum. This network may be essential to assess communicative intent in ambiguous or complex visual scenes.
ProCbA: Protein Function Prediction based on Clique Analysis

Protein function prediction based on protein-protein interactions (PPI) is one of the most important challenges of the Post-Genomic era. Due to the fact that determining protein function by experimental techniques can be costly, function prediction has become an important challenge for computational biology and bioinformatics. Some researchers utilize graph- (or network-) based methods using PPI networks for un-annotated proteins. The aim of this study is to increase the accuracy of the protein function prediction using two proposed methods. To predict protein functions, we propose a Protein Function Prediction based on Clique Analysis (ProCbA) and Protein Function Prediction on Neighborhood Counting using functional aggregation (ProNC-FA). Both ProCbA and ProNC-FA can predict the functions of unknown proteins. In addition, in ProNC-FA which is not including new algorithm; we try to address the essence of incomplete and noisy data of PPI era in order to achieving a network with complete functional aggregation. The experimental results on MIPS data and the 17 different explained datasets validate the encouraging performance and the strength of both ProCbA and ProNC-FA on function prediction. Experimental result analysis as can be seen in Section IV, the both ProCbA and ProNC-FA are generally able to outperform all the other methods.
QuantifyPolarity, a new tool-kit for measuring planar polarized protein distributions and cell properties in developing tissues

The coordination of cells or structures within the plane of a tissue is known as planar polarization. It is often governed by the asymmetric distribution of planar polarity proteins within cells. A number of quantitative methods have been developed to provide a readout of planar polarization of protein distributions. However, the quantification of planar polarization can be affected by different cell geometries. Hence, we developed a novel planar polarity quantification method based on Principal Component Analysis (PCA) that is shape insensitive. Here, we compare this method with other state-of-the-art methods on simulated models and biological datasets. We found that the PCA method performs robustly in quantifying planar polarity independently of variation in cell geometry. Furthermore, we designed a user-friendly graphical user interface called QuantifyPolarity, equipped with three different methods for automated quantification of polarity. QuantifyPolarity also provides image analysis tools to quantify cell morphology and packing geometry, allowing the relationship of these characteristics to planar polarization to be investigated. This all-in-one tool enables experimentalists with no prior computational expertise to perform high-throughput cell polarity and shape analysis automatically and efficiently.
FiberAI: A Deep Learning model for automated analysis of nascent DNA Fibers

All life forms undergo cell division and are dependent on faithful DNA replication to maintain the stability of their genomes. Both intrinsic and extrinsic factors can stress the replication process and multiple checkpoint mechanisms have evolved to ensure genome stability. Understanding these molecular mechanisms is crucial for preventing and treating genomic instability associated diseases including cancer. DNA replicating fiber fluorography is a powerful technique that directly visualizes the replication process and a cell’s response to replication stress. Analysis of DNA-fiber microscopy images provides quantitative information about replication fitness. However, a bottleneck for high throughput DNA-fiber studies is that quantitative measurements are laborious when performed manually. Here we introduce FiberAI, which uses state-of-the art deep learning frameworks to detect and quantify DNA-fibers in high throughput microscopy images. FiberAI efficiently detects DNA fibers, achieving a bounding box average precision score of 0.91 and a segmentation average precision score of 0.90. We then use FiberAI to measure the integrity of replication checkpoints. FiberAI is publicly available and allows users to view model predicted selections, add their own manual selections, and easily analyze multiple image sets. Thus, FiberAI can help elucidate DNA replication processes by streamlining DNA-fiber analyses.
How ancient forest fragmentation and riparian connectivity generate high levels of genetic diversity in a microendemic Malagasy tree

Understanding landscape changes is central to predicting evolutionary trajectories and defining conservation practices. While human-driven deforestation is intense throughout Madagascar, exceptions in areas like the Loky-Manambato region (North) raise questions. Such regions also harbor a rich and endemic flora, whose evolutionary origin remains poorly understood. We assessed the genetic diversity of an endangered microendemic Malagasy olive species (Noronhia spinifolia Hong-Wa) to better understand the vegetation dynamic in the Loky-Manambato region and its influence on past evolutionary processes. We characterized 72 individuals sampled across eight forests through nuclear and mitochondrial restriction associated sequencing data (RADseq) and chloroplast microsatellites (cpSSR). Combined population and landscape genetics analyses indicate that N. spinifolia diversity is largely explained by the current forest cover, highlighting a long-standing habitat mosaic in the region. This sustains a major and long-term role of riparian corridors in maintaining connectivity across those antique mosaic-habitats, calling for the study of organismal interactions that promote gene flow.
CLaC at SemEval-2020 Task 5: Muli-task Stacked Bi-LSTMs

We consider detection of the span of antecedents and consequents in argumentative prose a structural, grammatical task. Our system comprises a set of stacked Bi-LSTMs trained on two complementary linguistic annotations. We explore the effectiveness of grammatical features (POS and clause type) through ablation. The reported experiments suggest that a multi-task learning approach using this external, grammatical knowledge is useful for detecting the extent of antecedents and consequents and performs nearly as well without the use of word embeddings.
Deeply Felt Affect: The Emergence of Valence in Deep Active Inference

The positive-negative axis of emotional valence has long been recognized as fundamental to adaptive behavior, but its origin and underlying function have largely eluded formal theorizing and computational modeling. Using deep active inference, a hierarchical inference scheme that rests on inverting a model of how sensory data are generated, we develop a principled Bayesian model of emotional valence. This formulation asserts that agents infer their valence state based on the expected precision of their action model—an internal estimate of overall model fitness (“subjective fitness”). This index of subjective fitness can be estimated within any environment and exploits the domain generality of second-order beliefs (beliefs about beliefs). We show how maintaining internal valence representations allows the ensuing affective agent to optimize confidence in action selection preemptively. Valence representations can in turn be optimized by leveraging the (Bayes-optimal) updating term for subjective fitness, which we label affective charge (AC). AC tracks changes in fitness estimates and lends a sign to otherwise unsigned divergences between predictions and outcomes. We simulate the resulting affective inference by subjecting an in silico affective agent to a T-maze paradigm requiring context learning, followed by context reversal. This formulation of affective inference offers a principled account of the link between affect, (mental) action, and implicit metacognition. It characterizes how a deep biological system can infer its affective state and reduce uncertainty about such inferences through internal action (i.e., top-down modulation of priors that underwrite confidence). Thus, we demonstrate the potential of active inference to provide a formal and computationally tractable account of affect. Our demonstration of the face validity and potential utility of this formulation represents the first step within a larger research program. Next, this model can be leveraged to test the hypothesized role of valence by fitting the model to behavioral and neuronal responses.
RNA 3D modeling with FARFAR2, online

Understanding the three-dimensional structure of an RNA molecule is often essential to understanding its function. Sampling algorithms and energy functions for RNA structure prediction are improving, due to the increasing diversity of structural data available for training statistical potentials and testing structural data, along with a steady supply of blind challenges through the RNA Puzzles initiative. The recent FARFAR2 algorithm enables near-native structure predictions on fairly complex RNA structures, including automated selection of final candidate models and estimation of model accuracy. Here, we describe the use of a publicly available webserver for RNA modeling for realistic scenarios using FARFAR2, available at https://rosie.rosettacommons. org/farfar2. We walk through two cases in some detail: a simple model pseudoknot from the frameshifting element of beet western yellows virus modeled using the “basic interface” to the webserver, and a replication of RNA-Puzzle 20, a metagenomic twister sister ribozyme, using the “advanced interface. ” We also describe example runs of FARFAR2 modeling including two kinds of experimental data: a c-di-GMP riboswitch modeled with low resolution restraints from MOHCA-seq experiments and a tandem GA motif modeled with 1H NMR chemical shifts.
Read and Reason with MuSeRC and RuCoS: Datasets for Machine Reading Comprehension for Russian

The paper introduces two Russian machine reading comprehension (MRC) datasets, called MuSeRC and RuCoS, which require reasoning over multiple sentences and commonsense knowledge to infer the answer. The former follows the design of MultiRC, while the latter is a counterpart of the ReCoRD dataset. The datasets are included in RussianSuperGLUE, the Russian general language understanding benchmark. We provide a comparative analysis and demonstrate that the proposed tasks are relatively more complex as compared to the original ones for English. Besides, performance results of human solvers and BERT-based models show that MuSeRC and RuCoS represent a challenge for recent advanced neural models. We thus hope to facilitate research in the field of MRC for Russian and prompt the study of multi-hop reasoning in a cross-lingual scenario.
The SADID Evaluation Datasets for Low-Resource Spoken Language Machine Translation of Arabic Dialects

Low-resource Machine Translation recently gained a lot of popularity, and for certain languages, it has made great strides. However, it is still difficult to track progress in other languages for which there is no publicly available evaluation data. In this paper, we introduce benchmark datasets for Arabic and its dialects. We describe our design process and motivations and analyze the datasets to understand their resulting properties. Numerous successful attempts use large monolingual corpora to augment low-resource pairs. We try to approach augmentation differently and investigate whether it is possible to improve MT models without any external sources of data. We accomplish this by bootstrapping existing parallel sentences and complement this with multilingual training to achieve strong baselines.
Neuroskeletal Effects of Chronic Bioelectric Nerve Stimulation in Health and Diabetes

Background/Aims Bioelectric nerve stimulation (eStim) is a novel clinical paradigm that can promote nerve regeneration after trauma, including within the context of diabetes. However, its ability to prevent the onset of diabetic peripheral neuropathy (DPN) has not yet been evaluated. Beyond the nerve itself, DPN has emerged as a potential contributor to sarcopenia and bone disease; thus, we hypothesized that eStim could serve as a strategy to simultaneously promote neural and musculoskeletal health in diabetes. Methods To address this question, an eStim paradigm pre-optimized to promote nerve regeneration was applied to the sciatic nerve, which directly innervates the tibia and lower limb, for 8-weeks in control and streptozotocin-induced type 1 diabetic (T1D) rats. Metabolic, gait, nerve and bone assessments were used to evaluate the progression of diabetes and the effect of sciatic nerve eStim on neuropathy and musculoskeletal disease, while also considering the effects of cuff placement and chronic eStim in otherwise healthy animals. Results Rats with T1D exhibited increased mechanical allodynia in the hindpaw, reduced muscle mass, decreased cortical and cancellous bone volume fraction (BVF), reduced cortical bone tissue mineral density (TMD), and decreased bone marrow adiposity. T1D also had an independent effect on gait. Placement of the cuff electrode alone was sufficient to alter gait patterns and to promote unilateral reductions in tibia length, cortical BVF, and bone marrow adiposity. Alterations in gait patterns and left-right balance to tibia length were restored with eStim, but it did not prevent T1D-induced changes in muscle, bone, marrow adiposity or mechanical sensitivity. Beyond this, chronic eStim resulted in an independent, bilateral reduction in cortical TMD. Conclusion Overall, these results provide new insight into the pathogenesis of diabetic neuroskeletal disease and its regulation by eStim. Though eStim did not prevent neural or musculoskeletal complications in T1D, our results demonstrate that clinical applications of peripheral neuromodulation ought to consider the impact of device placement and eStim on long-term skeletal health in both healthy individuals and those with metabolic disease. This includes monitoring for compounded bone loss to prevent unintended consequences including decreased bone mineral density and increased fracture risk.
Response to thermal and infection stresses in an American vector of visceral leishmaniasis

The phlebotomine sand fly Lutzomyia longipalpis is the primary insect vector of visceral leishmaniasis in the Americas. For ectothermic organisms such as sand flies, the ambient temperature is a critical factor influencing all aspects of their life. However, the impact of temperature has been ignored in previous investigations of stress-induced responses by the vector, such as taking a blood meal or during Leishmania infection. Therefore, this study explored the interaction of Lu. longipalpis with temperature by evaluating sand fly behaviour across a thermal gradient after sugar or blood-feeding, and infection with Leishmania mexicana. Thermographic recordings of sand fly females fed on mice were analysed, and the gene expression of heat shock proteins HSP70 and HSP90(83) was evaluated when insects were exposed to extreme temperatures or infected. The results showed that 72h after blood ingestion females of Lu. longipalpis became less active and preferred relatively low temperatures. However, at later stages of blood digestion females increased their activity and remained at higher temperatures prior to taking a second blood meal; this behaviour seems to be correlated with the evolution of their oocysts and voracity for a second blood meal. No changes in the temperature preferences of female sand flies were recorded in the presence of a gut infection by Le. mexicana, indicating that this parasite has not triggered behavioural immunity in Lu. longipalpis. Real-time imaging showed that the body temperature of female flies feeding on mice increased to the same temperature as the host within a few seconds after landing. The body temperature of females remained around 35 ± 0.5 °C until the end of blood-feeding, revealing a lack of thermoregulatory behaviour. Analysis of expression of heat shock proteins revealed insects increased expression of HSP90(83) when exposed to higher temperatures, such as during blood feeding. Our findings suggest that Lu. longipalpis interacts with the environmental temperature by using its behaviour to avoid temperature-related physiological damage during the gonotrophic cycle. However, the expression of certain heat shock proteins might be triggered to mitigate against thermal stress in situations where a behavioural response is not the best option.
Multi-grained Chinese Word Segmentation with Weakly Labeled Data

In contrast with the traditional single-grained word segmentation (SWS), where a sentence corresponds to a single word sequence, multi-grained Chinese word segmentation (MWS) aims to segment a sentence into multiple word sequences to preserve all words of different granularities. Due to the lack of manually annotated MWS data, previous work train and tune MWS models only on automatically generated pseudo MWS data. In this work, we further take advantage of the rich word boundary information in existing SWS data and naturally annotated data from dictionary example (DictEx) sentences, to advance the state-of-the-art MWS model based on the idea of weak supervision. Particularly, we propose to accommodate two types of weakly labeled data for MWS, i.e., SWS data and DictEx data by employing a simple yet competitive graph-based parser with local loss. Besides, we manually annotate a high-quality MWS dataset according to our newly compiled annotation guideline, consisting of over 9,000 sentences from two types of texts, i.e., canonical newswire (NEWS) and non-canonical web (BAIKE) data for better evaluation. Detailed evaluation shows that our proposed model with weakly labeled data significantly outperforms the state-of-the-art MWS model by 1.12 and 5.97 on NEWS and BAIKE data in F1.
Story Generation with Rich Details

Automatically generated stories need to be not only coherent, but also interesting. Apart from realizing a story line, the text also needs to include rich details to engage the readers. We propose a model that features two different generation components: an outliner, which proceeds the main story line to realize global coherence; a detailer, which supplies relevant details to the story in a locally coherent manner. Human evaluations show our model substantially improves the informativeness of generated text while retaining its coherence, outperforming various baselines.
End-to-End Emotion-Cause Pair Extraction with Graph Convolutional Network

Emotion-cause pair extraction (ECPE), which aims at simultaneously extracting emotion-cause pairs that express emotions and their corresponding causes in a document, plays a vital role in understanding natural languages. Considering that most emotions usually have few causes mentioned in their contexts, we present a novel end-to-end Pair Graph Convolutional Network (PairGCN) to model pair-level contexts so that to capture the dependency information among local neighborhood candidate pairs. Moreover, in the graphical network, contexts are grouped into three types and each type of contexts is propagated by its own way. Experiments on a benchmark Chinese emotion-cause pair extraction corpus demonstrate the effectiveness of the proposed model.
Tree Representations in Transition System for RST Parsing

The transition-based systems in the past studies propose a series of actions, to build a right-heavy binarized tree for the RST parsing. However, the nodes of the binary-nuclear relations (e.g., Contrast) have the same nuclear type with those of the multi-nuclear relations (e.g., Joint) in the binary tree structure. In addition, the reduce action only construct binary trees instead of multi-branch trees, which is the original RST tree structure. In our paper, we design a new nuclear type for the multi-nuclear relations, and a new action to construct a multi-branch tree. We enrich the feature set by extracting additional refined dependency feature of texts from the Bi-Affine model. We also compare the performance of two approaches for RST parsing in the transition-based system: a joint action of reduce-shift and nuclear type (i.e., Reduce-SN) vs a separate one that applies Reduce action first and then assigns nuclear type. We find that the new devised nuclear type and action are more capable of capturing the multi-nuclear relation and the joint action is more suitable than the separate one. Our multi-branch tree structure obtains the state-of-the-art performance for all the 18 coarse relations.
The interaction of p38 with its upstream kinase MKK6

Mitogen-activated protein kinase (MAPK; p38, ERK and JNK) cascades are evolutionarily conserved signaling pathways that regulate the cellular response to a variety of extracellular stimuli, such as growth factors and interleukins. The MAPK p38 is activated by its specific upstream MAPK kinases, MKK6 and MKK3. However, a comprehensive molecular understanding of how these cognate upstream kinases bind and activate p38 is still missing. Here, we combine NMR spectroscopy and isothermal titration calorimetry to define the binding interface between full-length MKK6 and p38. We show that p38 engages MKK6 not only via its hydrophobic docking groove, but also helix αF, a secondary structural element that plays a key role in organizing the kinase core. We also show that, unlike MAPK phosphatases, the p38 conserved docking (CD) site is much less affected by MKK6 binding. Finally, we demonstrate that these interactions with p38 are conserved independent of the MKK6 activation state. Together, our results reveal differences between specificity markers of p38 regulation by upstream kinases, which do not effectively engage the CD site, and downstream phosphatases, which require the CD site for productive binding.