Miracle-dz/UniFAR-FTUs huggingface dataset:
History to Myths: Social Network Analysis for Comparison of Stories over Time

We discuss on how related stories can be compared by their characters. We investigate character graphs, or social networks, in order to measure evolution of character importance over time. To illustrate this, we chose the Siegfried-Sigurd myth that may come from a Merovingian king named Sigiberthus. The Nibelungenlied, the Völsunga saga and the History of the Franks are the three resources used.
Automatic Interlinear Glossing for Under-Resourced Languages Leveraging Translations

Interlinear Glossed Text (IGT) is a widely used format for encoding linguistic information in language documentation projects and scholarly papers. Manual production of IGT takes time and requires linguistic expertise. We attempt to address this issue by creating automatic glossing models, using modern multi-source neural models that additionally leverage easy-to-collect translations. We further explore cross-lingual transfer and a simple output length control mechanism, further refining our models. Evaluated on three challenging low-resource scenarios, our approach significantly outperforms a recent, state-of-the-art baseline, particularly improving on overall accuracy as well as lemma and tag recall.
Urszula Walińska at SemEval-2020 Task 8: Fusion of Text and Image Features Using LSTM and VGG16 for Memotion Analysis

In this paper, we describe the entry to the task of Memotion Analysis. The sentiment analysis of memes task, is motivated by a pervasive problem of offensive content spread in social media, up to the present time. In fact, memes are an important medium of expressing opinion and emotions, therefore they can be hateful at many times. In order to identify emotions expressed by memes we construct a tool based on neural networks and deep learning methods. It takes an advantage of a multi-modal nature of the task and performs fusion of image and text features extracted by models dedicated to this task. Moreover, we show that visual information might be more significant in the sentiment analysis of memes than textual one. Our solution achieved 0.346 macro F1-score in Task A – Sentiment Classification, which brought us to the 7th place in the official rank of the competition.
MolDiscovery: learning mass spectrometry fragmentation of small molecules

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods. A large number of mass spectra from different samples have been collected, and to identify small molecules from these spectra, database searches are needed, which is challenging. Here, the authors report molDiscovery, a mass spectral database search method that uses an algorithm to generate mass spectrometry fragmentations and learns a probabilistic model to match small molecules with their mass spectra.
Interactive Question Clarification in Dialogue via Reinforcement Learning

Coping with ambiguous questions has been a perennial problem in real-world dialogue systems. Although clarification by asking questions is a common form of human interaction, it is hard to define appropriate questions to elicit more specific intents from a user. In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query. We first formulate a collection partitioning problem to select a set of labels enabling us to distinguish potential unambiguous intents. We list the chosen labels as intent phrases to the user for further confirmation. The selected label along with the original user query then serves as a refined query, for which a suitable response can more easily be identified. The model is trained using reinforcement learning with a deep policy network. We evaluate our model based on real-world user clicks and demonstrate significant improvements across several different experiments.
CLaC at SMM4H 2020: Birth Defect Mention Detection

For the detection of personal tweets, where a parent speaks of a child’s birth defect, CLaC combines ELMo word embeddings and gazetteer lists from external resources with a GCNN (for encoding dependencies), in a multi layer, transformer inspired architecture. To address the task, we compile several gazetteer lists from resources such as MeSH and GI. The proposed system obtains . 69 for μF1 score in the SMM4H 2020 Task 5 where the competition average is . 65.
Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut?

Automatic identification of multiword expressions (MWEs), like ‘to cut corners’ (to do an incomplete job), is a pre-requisite for semantically-oriented downstream applications. This task is challenging because MWEs, especially verbal ones (VMWEs), exhibit surface variability. This paper deals with a subproblem of VMWE identification: the identification of occurrences of previously seen VMWEs. A simple language-independent system based on a combination of filters competes with the best systems from a recent shared task: it obtains the best averaged F-score over 11 languages (0.6653) and even the best score for both seen and unseen VMWEs due to the high proportion of seen VMWEs in texts. This highlights the fact that focusing on the identification of seen VMWEs could be a strategy to improve VMWE identification in general.
Gemini Surfactants : Synthesis, Interfacial and Solution-Phase Behavior, and Applications

Synthesis of gemini (dimeric) and related surfactants, Isao Ikeda models of gemini surfactants, H. Diamant and D. Andelman adsorption and surface tension behaviour of gemini surfactants at air/water, oil/water and solid/water interfaces, E. Franses, M.R. Infante, L. Perez, A. Pinazo and A. Prosser state of gemini surfactants in solution at concentrations below the CMC, Raoul Zana gemini (dimeric) surfactants in water - solubility, CMC and thermodynamics of micellization and interaction with water soluble polymers, Raoul Zana properties of micelles and of micellar solutions of gemini (dimeric) surfactants, Raoul Zana rheology of solutions of gemini surfactants M. In phase behaviour of gemini surfactants, Raoul Zana and M. In mixed micellization between dimeric (gemini) surfactants and conventional surfactants, Raoul Zana and Jiding Xia special gemini surfactants - Nonionic, Zwitterionic, fluorinated and amino-acid based, T. Davey structure-performance relationships in gemini surfactants, Yun-Peng Zhu applications of gemini surfactants, Jiding Xia and Raoul Zana.
Acetylcholine signaling genes are required for cocaine-stimulated egg laying in Caenorhabditis elegans

Despite the toxicity and addictive liability associated with cocaine abuse, its mode of action is not completely understood, and effective pharmacotherapeutic interventions remain elusive. The cholinergic effects of cocaine on acetylcholine receptors, synthetic enzymes, and degradative enzymes have been the focus of relatively little empirical investigation. Due to its genetic tractability and anatomical simplicity, the egg laying circuit of the hermaphroditic nematode, Caenorhabditis elegans, is a powerful model system to precisely examine the genetic and molecular targets of cocaine in vivo. Here, we report a novel cocaine-induced phenotype in Caenorhabditis elegans, cocaine-stimulated egg laying. In addition, we present the results of an in vivo candidate screen of synthetic enzymes, receptors, degradative enzymes, and downstream components of the intracellular signaling cascades of the main neurotransmitter systems that control Caenorhabditis elegans egg laying. Our results show that cocaine-stimulated egg laying is dependent on acetylcholine synthesis and synaptic release, functional nicotinic acetylcholine receptors, and the Caenorhabditis elegans acetylcholinesterases. Further, we show that cocaine-stimulated egg laying is not dependent on other neurotransmitters besides acetylcholine, including serotonin, dopamine, octopamine, and tyramine. Finally, our data show that cocaine-stimulated egg laying is increased in mutants for the C. elegans serotonin reuptake transporter as well as mutants for a 5-HT-gated chloride channel likely expressed in the locomotion circuit. Together, these results highlight serotonergic inhibition of egg laying behavior, functional connectivity between the egg laying and locomotion circuits in Caenorhabditis elegans, and possible discrete cholinergic and serotonergic effects of cocaine in the egg laying and locomotion circuits, respectively.
Differential impact of GABAA Receptors on Inhibitory connectivity for Superficial Pyramidal Neuron Responsiveness In Vivo

A diverse set of GABAA receptors (GABAARs) enable synaptic plasticity adaptations at inhibitory postsynaptic sites in collaboration with the scaffolding protein gephyrin. Early studies helped to identify distinctions between GABAAR subsets allocated within specific functional circuits, but their contribution to the changing dynamics of a microcircuit remains unclear. Here, using the whisker-barrel system we assessed the contribution of specific GABAAR subtypes to sensory processing in vivo. We monitored spontaneous and evoked Ca2+ transients in layer 2/3 pyramidal cells with the genetically encoded Ca2+ sensor RCaMP1.07. Using Gabra1 or Gabra2 global and conditional knockout mice, we uncovered that α1- and α2-GABAARs determine the sparseness of L2/3 pyramidal neuron encoding. In a cell-type dependent manner, α1-GABAARs affected neuronal excitability while α2-GABAARs influenced the reliability of neuronal responses after whisker stimulation. We also discerned that gephyrin and its diverse post-translational modifications (PTMs) facilitate microcircuit homeostasis. Our results underscore the relevance of the diversity of GABAARs within a cortical microcircuit.
Auxin requirements for a meristematic state in roots depend on a dual brassinosteroid function

The organization of the root meristem is maintained by a complex interplay between plant hormones signaling pathways that both interpret and determine their accumulation and distribution. Brassinosteroids (BR) and auxin signaling pathways control the number of meristematic cells in the Arabidopsis root, via an interaction that appears to involve contradicting molecular outcomes, with BR promoting auxin signaling input but also repressing its output. However, whether this seemingly incoherent effect is significant for meristem function is unclear. Here, we established that a dual effect of BR on auxin, with BR simultaneously promoting auxin biosynthesis and repressing auxin transcriptional output, is essential for meristem maintenance. Blocking BR-induced auxin synthesis resulted in rapid BR-mediated meristem loss. Conversely, plants with reduced BR levels were resistant to loss of auxin biosynthesis and these meristems maintained their normal morphology despite a 10-fold decrease in auxin levels. In agreement, injured root meristems which rely solely on local auxin synthesis, regenerated when both auxin and BR synthesis were inhibited. Use of BIN2 as a tool to selectively inhibit BR signaling, revealed meristems with distinct phenotypes depending on the perturbed tissue; meristem reminiscent of BR-deficient mutants or of high BR exposure. This enabled mapping BR-auxin interactions to the outer epidermis and lateral root cap tissues, and demonstrated the essentiality of BR signaling in these tissues for meristem maintenance. BR activity in internal tissues however, proved necessary to control BR homeostasis. Together, we demonstrate a basis for inter-tissue coordination and how a critical ratio between these hormones determines the meristematic state.
Non-targeting control for MISSION® shRNA library silences SNRPD3 leading to cell death or permanent growth arrest

In parallel with the expansion of RNA interference techniques evidence has been accumulating that RNAi analyses may be seriously biased due to off-target effects of gene-specific shRNAs. Our work points to another possible source of misinterpretations of shRNA-based data – off-target effects of non-targeting shRNA. We found that one such control for the MISSION® library (commercialized TRC library), SHC016, is cytotoxic. Using a lentiviral vector with inducible expression of SHC016 we proved that this shRNA induces apoptosis in murine cells and, depending on p53 status, senescence or mitotic catastrophe in human tumor cell lines. We identified SNRPD3, a core spliceosomal protein, as a major SHC016 target in several cell lines and confirmed in A549 and U251 cell lines that CRISPRi-knockdown of SNRPD3 mimics the effects of SHC016 expression. Our finding disqualifies non-targeting SHC016 shRNA and adds a new premise to the discussion about the sources of uncertainty of RNAi results.
Attitudes and practices of open data, preprinting, and peer-review—A cross sectional study on Croatian scientists

Attitudes towards open peer review, open data and use of preprints influence scientists’ engagement with those practices. Yet there is a relatively small number of validated questionnaires that measure these attitudes. The goal of our study was to construct and validate such a questionnaire. Using a sample of Croatian scientists (N=541), from a wide range of disciplines, we developed a questionnaire titled Attitudes towards Open data sharing, preprinting, and peer-review (ATOPP). The questionnaire has 21 item with a four-factor structure (attitudes towards: open data, preprint servers, open peer-review and open peer-review in small scientific communities). Based on the questionnaire, the attitudes of Croatian scientists towards these topics were generally neutral, with a median of 3.3 out of 5 of the total attitude score. Croatian scientist attitudes were lowest for open peer-review in small scientific communities (Md 2.0) and highest for open data (Md 3.9).
Characterisation and chemometric evaluation of 17 elements in ten seaweed species from Greenland

Several Greenland seaweed species have potential as foods or food ingredients, both for local consumption and export. However, knowledge regarding their content of beneficial and deleterious elements on a species specific and geographical basis is lacking. This study investigated the content of 17 elements (As, Ca, Cd, Cr, Cu, Fe, Hg, I, K, Mg, Mn, Na, Ni, P, Pb, Se and Zn) in 77 samples of ten species (Agarum clathratum, Alaria esculenta, Ascophyllum nodosum, Fucus distichus, Fucus vesiculosus, Hedophyllum nigripes, Laminaria solidungula, Palmaria palmata, Saccharina latissima and Saccharina longicruris). Element profiles differed between species but showed similar patterns within the same family. For five species, different thallus parts were investigated separately, and showed different element profiles. A geographic origin comparison of Fucus species indicated regional differences. The seaweeds investigated were especially good sources of macrominerals (K > Na > Ca > Mg) and trace minerals, such as Fe. Iodine contents were high, especially in macroalgae of the family Laminariaceae. None of the samples exceeded the EU maximum levels for Cd, Hg or Pb, but some exceeded the stricter French regulations, especially for Cd and I. In conclusion, these ten species are promising food items.
Demystifying Robots in the Co-Design of a Tutee Robot with Primary School Children

his paper contributes to the existing body of knowledge on the co-design of novel technologies with children. As part of a three year research project aiming to design and develop a robot tutee for use in mathematics education, we present the initial phases of our design approach with children, in which we draw on principles of Participatory Design and Co-design. As part of the early stages of this process, we included a demystifying phase (I), and a gradual introduction to the robot’s capabilities (II), in order to foster reasonable expectations in children and gather feasible design input. Drawing on the Time-Space-Structure framework, two primary schools were involved in the co-design process, where children in grades 2 and 4 participated in a set of workshops. We discuss the benefits and tensions of our approach, and reflect on its implications for mutual learning, hoping to inspire further exploration in this field.
Differential Contribution of Dopaminergic Transmission at D1- and D2-like Receptors to Cost/Benefit Evaluation for Motivation in Monkeys

It has been widely accepted that dopamine (DA) plays a major role in motivation, yet the specific contribution of DA signaling at D1-like receptor (D1R) and D2-like receptor (D2R) to cost-benefit trade-off remains unclear. Here, by combining pharmacological manipulation of DA receptors (DARs) and positron emission tomography imaging, we assessed the relationship between the degree of D1R/D2R blockade and changes in benefit- and cost-based motivation for goal-directed behavior of macaque monkeys. We found that the degree of blockade of either D1R or D2R was associated with a reduction of relative incentive effect of reward amount, where D2R blockade had a stronger effect. Workload-discounting was selectively increased by D2R antagonism, whereas delay-discounting was similarly increased after D1R and D2R blockades. These results provide fundamental insight into the specific actions of DARs in the regulation of the cost/benefit trade-off and important implications for motivational alterations in both neurological and psychiatric disorders.
Hippocampal Egr1-Dependent Neuronal Ensembles Negatively Regulate Motor Learning

Motor skills learning is classically associated with brain regions including cerebral and cerebellar cortices and basal ganglia nuclei. Less is known about the role of the hippocampus in the acquisition and storage of motor skills. Here, we show that mice receiving a long-term training in the accelerating rotarod display marked hippocampal transcriptional changes and reduced pyramidal neurons activity in the CA1 region when compared with naive mice. Then, we use mice in which neural ensembles are permanently labeled in an Egr1 activity-dependent fashion. Using these mice, we identify a subpopulation of Egr1-expressing pyramidal neurons in CA1 activated in short-term (STT) and long-term (LTT) trained mice in the rotarod task. When Egr1 is downregulated in the CA1 or these neuronal ensembles are depleted, motor learning is improved whereas their chemogenetic stimulation impairs motor learning performance. Thus, Egr1 organizes specific CA1 neuronal ensembles during the accelerating rotarod task that limit motor learning. These evidences highlight the role of the hippocampus in the control of this type of learning and we provide a possible underlying mechanism. SIGNIFICANCE STATEMENT It is a major topic in neurosciences the deciphering of the specific circuits underlying memory systems during the encoding of new information. However, the potential role of the hippocampus in the control of motor learning and the underlying mechanisms has been poorly addressed. In the present work we show how the hippocampus responds to motor learning and how the Egr1 molecule is one of the major responsible for such phenomenon controlling the rate of motor coordination performances.
Human-Computer Synergies in Prosthetic Interactions

Remote sighted assistance provides prosthetic support to people with visual impairments (PVI) through internet-mediated conversational interactions. In these interactions, PVI broadcast live video to remotely-located, sighted people who engage in speech interactions with PVI to create prosthetic support. These interactions can be quite nuanced, creative, and effective. In this paper, we present a design investigation of remote sighted assistance (RSA) in which computer vision capabilities are integrated into the prosthetic interaction, supporting the human participants in various ways. Our study involved creating design scenarios to identify and concretize future possibilities in order to articulate and analyze design rationale for these scenarios, that is to say, strengths and challenges of RSA integrated with CV. We discuss implications for the design of the next generation of remote sighted assistance.
FB-Tree: Highly Efficient Tree-Based Index for Encrypted Boolean Queries in Smart Cities

To expand capacity, many resource-constrained industrial devices encrypt and outsource their private data to public clouds, employing a searchable encryption (SE) scheme that provides efﬁcient search service directly to encrypted data. Current tree-based SE schemes can do this and support sublinear encrypted Boolean queries. However, they all suffer from log n overhead in a search procedure. To resolve the challenge, in this paper, we propose a new tree structure called the four-branch tree (FB-tree). Our key design is to build a tree node with four branches, which helps a search reach the destination nodes with fast jumps. Based on the index tree, we setup two systems for different requirements. The ﬁrst system for efﬁciency-ﬁrst settings achieves nearly optimal search complexity and adaptive security. The second, constructed via an oblivious RAM for security-ﬁrst environments, still achieves worst-case sublinear search complexity. FB-tree performance is extensively evaluated with several real datasets. The Experimental data demonstrate that the FB-tree-based systems outperform the state-of-the-art solutions in terms of efﬁciency and scalability when Boolean queries are issued.
Relay Selection for CoMP-NOMA Transmission in Satellite and UAV Cooperative Networks

This work considers a new wireless network architecture in which satellite (SAT) communicates with ground users via UAVs as aerial decode-and-forward (DF) relays. Uavs employ non orthogonal multiple access (NOMA) coordinated multi-point (CoMP) transmission to serve users assuming they experiencing the low signal to noise ratio (SNR) or the signal-to-interference plus noise ratio (SINR). In order to maximize the received sum-rate in terrestrial users, we employ a relay selection scheme. The problem of relay selection has been investigated in both optimal and semi-optimal conditions. The performance of our proposed method is validated through outage probability (OP) and sum-rate evaluation. Simulation results show the superiority of CoMP transmission state in both mentioned conditions compared with the non-CoMP situation.
Optimal Scheduling Of Interconnected Energy Hubs Considering Responsive Loads

In recent years, studying on the multi carrier energy system known as Energy Hub (EH) has been considered by many researchers. Usually the reaction of the electrical load of EH in response to the price has not been considered. In this paper, a new model is proposed to analyze the demand response impacts in the interconnected EHs system. In this regard, an optimization model for operation of two EHs including three input energies, two type storages and the electrical as well as the thermal loads is proposed. One of the hubs is residential and the other is commercial EH. The proposed model is applied to this system and the results in four scenarios are discussed. The simulation results showed the effectiveness of the proposed model to quantitatively evaluate the advantage of demand response for decreasing the unsupplied energy in EHs loads.