Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Give Feedback: Modeling Attributes Affecting Argument Persuasiveness in Student Essays

Argument persuasiveness is one of the most important dimensions of argumentative essay quality, yet it is little studied in automated essay scoring research. Using a recently released corpus of essays that are simultaneously annotated with argument components, argument persuasiveness scores, and attributes of argument components that impact an argument’s persuasiveness, we design and train the first set of neural models that predict the persuasiveness of an argument and its attributes in a student essay, enabling useful feedback to be provided to students on why their arguments are (un)persuasive in addition to how persuasive they are.
Deep View-Aware Metric Learning for Person Re-Identification

Person re-identification remains a challenging issue due to the dramatic changes in visual appearance caused by the variations in camera views, human pose, and background clutter. In this paper, we propose a deep view-aware metric learning (DVAML) model, where image pairs with similar and dissimilar views are projected into different feature subspaces, which can discover the intrinsic relevance between image pairs from different aspects. Additionally, we employ multiple metrics to jointly learn feature subspaces on which the relevance between image pairs are explicitly captured and thus greatly promoting the retrieval accuracy. Extensive experiment results on datasets CUHK01, CUHK03, and PRID2011 demonstrate the superiority of our method compared with state-of-the-art approaches.
Second-generation anti-depressants and risk of new-onset seizures in the elderly

Abstract Background: Anti-depressants are among the most widely-prescribed medications. It is unknown whether the risk of seizure during therapeutic use differs by drug. We ranked the seizure risk of popular anti-depressants. Methods: We conducted a population-based case–control study between April 2002 and March 2015 in Ontario, Canada. Cases were Ontario residents aged ≥65 years hospitalized for a first-ever seizure within 60 d of filling a prescription for one of nine second-generation anti-depressants, each dispensed more than 1 million times (range: 1,196,810 [fluvoxamine] to 19,849,930 [citalopram]) during the study period. For each case, we identified up to four seizure-free controls receiving a similar anti-depressant, and matched on age, sex, date and a pre-defined seizure-specific disease risk index. Results: We identified 5701 patients hospitalized with a first-ever seizure and matched them with 21,872 controls. Relative to bupropion, the risk of new-onset seizure during therapeutic use was highest for escitalopram (adjusted odds ratio [OR] 1.79; 95% confidence interval [CI] 1.42–2.25) and citalopram (OR 1.67; 95% CI 1.35–2.07), while no incremental risk was found for fluoxetine (OR 1.02; 95%CI 0.78–1.33) and duloxetine (OR 0.94; 95%CI 0.75–1.22). Other anti-depressants were associated with modest increase in seizure risk. Conclusions: The risk of seizure during therapeutic use among elderly patients varies among second-generation anti-depressants. Escitalopram and citalopram are associated with the highest risk. Prescribers should consider the seizure risk of individual anti-depressants and use discretion when selecting an anti-depressant, especially for patients with other risk factors for seizure. Frontline clinicians should be cognizant of this differential risk.
Increased Serpina3n release into circulation during glucocorticoid‐mediated muscle atrophy

Glucocorticoids (GC) play a major role in muscle atrophy. As skeletal muscle is a secretory organ, characterization of the muscle secretome elicited by muscle atrophy should allow to better understand the cellular mechanisms and to identify circulating biomarkers of this condition. Our project aimed to identify the changes in the muscle secretome associated with GC‐induced muscle atrophy and susceptible to translate into circulation.
Velocity Control Based on Active Disturbance Rejection for Air-Breathing Supersonic Vehicles

This paper investigates a velocity tracking control approach for air-breathing supersonic vehicles with uncertainties and external disturbances. Considering angle of attack is difficult to be precisely measured in practice, extended state observer technique is introduced into the state reconstruction design. In order to avoid possible oscillations in the design of the traditional extended state observer (TESO), a modified extended state observer (MESO) is developed, where a new smooth function is proposed to replace nonsmooth function of TESO. On the basis of it, an active disturbance rejection controller (ADRC) is designed for velocity control systems. Simultaneously, the closed-loop stability is rigorously proved by using Lyapunov theory. Finally, numeric simulations are conducted to validate the effectiveness of the proposed method.
Biochemical characterization and chemical inhibition of PfATP4-associated Na+-ATPase activity in Plasmodium falciparum membranes

The antimalarial activity of chemically diverse compounds, including the clinical candidate cipargamin, has been linked to the ATPase PfATP4 in the malaria-causing parasite Plasmodium falciparum. The characterization of PfATP4 has been hampered by the inability thus far to achieve its functional expression in a heterologous system. Here, we optimized a membrane ATPase assay to probe the function of PfATP4 and its chemical sensitivity. We found that cipargamin inhibited the Na+-dependent ATPase activity present in P. falciparum membranes from WT parasites and that its potency was reduced in cipargamin-resistant PfATP4-mutant parasites. The cipargamin-sensitive fraction of membrane ATPase activity was inhibited by all 28 of the compounds in the “Malaria Box” shown previously to disrupt ion regulation in P. falciparum in a cipargamin-like manner. This is consistent with PfATP4 being the direct target of these compounds. Characterization of the cipargamin-sensitive ATPase activity yielded data consistent with PfATP4 being a Na+ transporter that is sensitive to physiologically relevant perturbations of pH, but not of [K+] or [Ca2+]. With an apparent Km for ATP of 0.2 mm and an apparent Km for Na+ of 16–17 mm, the protein is predicted to operate at below its half-maximal rate under normal physiological conditions, allowing the rate of Na+ efflux to increase in response to an increase in cytosolic [Na+]. In membranes from a cipargamin-resistant PfATP4-mutant line, the apparent Km for Na+ is slightly elevated. Our study provides new insights into the biochemical properties and chemical sensitivity of an important new antimalarial drug target.
Variance Reduction in Black-box Variational Inference by Adaptive Importance Sampling

Overdispersed black-box variational inference employs importance sampling to reduce the variance of the Monte Carlo gradient in black-box variational inference. A simple overdispersed proposal distribution is used. This paper aims to investigate how to adaptively obtain better proposal distribution for lower variance. To this end, we directly approximate the optimal proposal in theory using a Monte Carlo moment matching step at each variational iteration. We call this adaptive proposal moment matching proposal (MMP). Experimental results on two Bayesian models show that the MMP can effectively reduce variance in black-box learning, and perform better than baseline inference algorithms.
ElimiNet: A Model for Eliminating Options for Reading Comprehension with Multiple Choice Questions

The task of Reading Comprehension with Multiple Choice Questions, requires a human (or machine) to read a given {passage, question} pair and select one of the n given options. The current state of the art model for this task first computes a question-aware representation for the passage and then selects the option which has the maximum similarity with this representation. However, when humans perform this task they do not just focus on option selection but use a combination of elimination and selection. Specifically, a human would first try to eliminate the most irrelevant option and then read the passage again in the light of this new information (and perhaps ignore portions corresponding to the eliminated option). This process could be repeated multiple times till the reader is finally ready to select the correct option. We propose ElimiNet, a neural network-based model which tries to mimic this process. Specifically, it has gates which decide whether an option can be eliminated given the {passage, question} pair and if so it tries to make the passage representation orthogonal to this eliminated option (akin to ignoring portions of the passage corresponding to the eliminated option). The model makes multiple rounds of partial elimination to refine the passage representation and finally uses a selection module to pick the best option. We evaluate our model on the recently released large scale RACE dataset and show that it outperforms the current state of the art model on 7 out of the 13 question types in this dataset. Further, we show that taking an ensemble of our elimination-selection based method with a selection based method gives us an improvement of 3.1% over the best-reported performance on this dataset.
Alternating-time Temporal Logic on Finite Traces

We develop a logic-based technique to analyse finite interactions in multi-agent systems. We introduce a semantics for Alternating-time Temporal Logic (for both perfect and imperfect recall) and its branching-time fragments in which paths are finite instead of infinite. We study validities of these logics and present optimal algorithms for their model-checking problems in the perfect recall case.
Using Contextual Bandits with Behavioral Constraints for Constrained Online Movie Recommendation

AI systems that learn through reward feedback about the actions they take are increasingly deployed in domains that have significant impact on our daily life. In many cases the rewards should not be the only guiding criteria, as there are additional constraints and/or priorities imposed by regulations, values, preferences, or ethical principles. We detail a novel online system, based on an extension of the contextual bandits framework, that learns a set of behavioral constraints by observation and uses these constraints as a guide when making decisions in an online setting while still being reactive to reward feedback. In addition, our system can highlight features of the context which are more predicted to be more rewarding and/or are in line with the behavioral constraints. Â We demonstrate the system by building an interactive interface for an online movie recommendation agent and show that our system is able to act within a set of behavior constraints without significantly degrading overall performance.
Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks

Latent factor models have been widely used for recommendation. Most existing latent factor models mainly utilize the rating information between users and items, although some recently extended models add some auxiliary information to learn a unified latent factor between users and items. The unified latent factor only represents the latent features of users and items from the aspect of purchase history. However, the latent features of users and items may stem from different aspects, e.g., the brand-aspect and category-aspect of items. In this paper, we propose a Neural network based Aspect-level Collaborative Filtering model (NeuACF) to exploit different aspect latent factors. Through modelling rich objects and relations in recommender system as a heterogeneous information network, NeuACF first extracts different aspect-level similarity matrices of users and items through different meta-paths and then feeds an elaborately designed deep neural network with these matrices to learn aspect-level latent factors. Finally, the aspect-level latent factors are effectively fused with an attention mechanism for the top-N recommendation. Extensive experiments on three real datasets show that NeuACF significantly outperforms both existing latent factor models and recent neural network models.
Learning to Converse with Noisy Data: Generation with Calibration

The availability of abundant conversational data on the Internet brought prosperity to the generation-based open domain conversation systems. In the training of the generation models, existing methods generally treat all the training data equivalently. However, the data crawled from the websites may contain many noises. Blindly training with the noisy data could harm the performance of the final generation model. In this paper, we propose a generation with calibration framework, that allows high- quality data to have more influences on the generation model and reduces the effect of noisy data. Specifically, for each instance in training set, we employ a calibration network to produce a quality score for it, then the score is used for the weighted update of the generation model parameters. Experiments show that the calibrated model outperforms baseline methods on both automatic evaluation metrics and human annotations.
Intrapulmonary Vein Ablation Without Stenosis: A Novel Balloon‐Based Direct Current Electroporation Approach

Background Current thermal ablation methods for atrial fibrillation, including radiofrequency and cryoablation, have a suboptimal success rate. To avoid pulmonary vein (PV) stenosis, ablation is performed outside of the PV, despite the importance of triggers inside the vein. We previously reported on the acute effects of a novel direct current electroporation approach with a balloon catheter to create lesions inside the PVs in addition to the antrum. In this study, we aimed to determine whether the effects created by this nonthermal ablation method were associated with irreversible lesions and whether PV stenosis or other adverse effects occurred after a survival period. Methods and Results Initial and survival studies were performed in 5 canines. At the initial study, the balloon catheter was inflated to contact the antrum and interior of the PV. Direct current energy was delivered between 2 electrodes on the catheter in ECG‐gated 100 μs pulses. A total of 10 PVs were treated demonstrating significant acute local electrogram diminution (mean amplitude decrease of 61.2±19.8%). After the survival period (mean 27 days), computed tomography imaging showed no PV stenosis. On histologic evaluation, transmural, although not circumferential, lesions were seen in each treated vein. No PV stenosis or esophageal injury was present. Conclusions Irreversible, transmural lesions can be created inside the PV without evidence of stenosis after a 27‐day survival period using this balloon‐based direct current ablation approach. These early data show promise for an ablation approach that could directly treat PV triggers in addition to traditional PV antrum ablation.
A Novel Fuzzy Algorithm to Introduce New Variables in the Drug Supply Decision-Making Process in Medicine

One of the main challenges in medicine is to guarantee an appropriate drug supply according to the real needs of patients. Closed-loop strategies have been widely used to develop automatic solutions based on feedback variables. However, when the variable of interest cannot be directly measured or there is a lack of knowledge behind the process, it turns into a difficult issue to solve. In this research, a novel algorithm to approach this problem is presented. The main objective of this study is to provide a new general algorithm capable of determining the influence of a certain clinical variable in the decision making process for drug supply and then defining an automatic system able to guide the process considering this information. Thus, this new technique will provide a way to validate a given physiological signal as a feedback variable for drug titration. In addition, the result of the algorithm in terms of fuzzy rules and membership functions will define a fuzzy-based decision system for the drug delivery process. The method proposed is based on a Fuzzy Inference System whose structure is obtained through a decision tree algorithm. A four-step methodology is then developed: data collection, preprocessing, Fuzzy Inference System generation, and the validation of results. To test this methodology, the analgesia control scenario was analysed. Specifically, the viability of the Analgesia Nociception Index (ANI) as a guiding variable for the analgesic process during surgical interventions was studied. Real data was obtained from fifteen patients undergoing cholecystectomy surgery.
PD-L1 Nanobody Competitively Inhibits the Formation of the PD-1/PD-L1 Complex: Comparative Molecular Dynamics Simulations

The anti-PD-L1 monoclonal antibody (mAb) targeting PD-1/PD-L1 immune checkpoint has achieved outstanding results in clinical application and has become one of the most popular anti-cancer drugs. The mechanism of molecular recognition and inhibition of PD-L1 mAbs is not yet clear, which hinders the subsequent antibody design and modification. In this work, the trajectories of PD-1/PD-L1 and nanobody/PD-L1 complexes were obtained via comparative molecular dynamics simulations. Then, a series of physicochemical parameters including hydrogen bond, dihedral angle distribution, pKa value and binding free energy, and so forth, were all comparatively analyzed to investigate the recognition difference between PD-L1 and PD-1 and nanobody. Both LR113 (the amino acid residues in PD-L1 are represented by the lower left sign of L) and LR125 residues of PD-L1 undergo significant conformational change after association with mAbs, which dominates a strong electrostatic interaction. Solvation effect analysis revealed that solvent-water enhanced molecular recognition between PD-L1 and nanobody. By combining the analyses of the time-dependent root mean squared fluctuation (RMSF), free energy landscape, clustering and energy decomposition, the potential inhibition mechanism was proposed that the nanobody competitively and specifically bound to the β-sheet groups of PD-L1, reduced the PD-L1’s flexibility and finally blocked the formation of PD-1/PD-L1 complex. Based on the simulation results, site-directed mutagenesis of ND99 (the amino acid residues in Nano are displayed by the lower left sign of N) and NQ116 in the nanobody may be beneficial for improving antibody activity. This work offers some structural guidance for the design and modification of anticancer mAbs based on the structure of the PD-1/PD-L1 complex.
Hashtag2Vec: Learning Hashtag Representation with Relational Hierarchical Embedding Model

Hashtags have always been important elements in many social network platforms and micro-blog services. Semantic understanding of hashtags is a critical and fundamental task for many applications on social networks, such as event analysis, theme discovery, information retrieval, etc. However, this task is challenging due to the sparsity, polysemy, and synonymy of hashtags. In this paper, we investigate the problem of hashtag embedding by combining the short text content with the various heterogeneous relations in social networks. Specifically, we first establish a network with hashtags as its nodes. Hierarchically, each of the hashtag nodes is associated with a set of tweets and each tweet contains a set of words. Then we devise an embedding model, called Hashtag2Vec, which exploits multiple relations of hashtag-hashtag, hashtag-tweet, tweet-word, and word-word relations based on the hierarchical heterogeneous network. In addition to embedding the hashtags, our proposed framework is capable of embedding the short social texts as well. Extensive experiments are conducted on two real-world datasets, and the results demonstrate the effectiveness of the proposed method.
Cost-Effective Active Learning for Hierarchical Multi-Label Classification

Active learning reduces the labeling cost by actively querying labels for the most valuable data. It is particularly important for multi-label learning, where the annotation cost is rather high because each instance may have multiple labels simultaneously. In many multi-label tasks, the labels are organized into hierarchies from coarse to fine. The labels at different levels of the hierarchy contribute differently to the model training, and also have diverse annotation costs. In this paper, we propose a multi-label active learning approach to exploit the label hierarchies for cost-effective queries. By incorporating the potential contribution of ancestor and descendant labels, a novel criterion is proposed to estimate the informativeness of each candidate query. Further, a subset selection method is introduced to perform active batch selection by balancing the informativeness and cost of each instance-label pair. Experimental results validate the effectiveness of both the proposed criterion and the selection method.
Antigen phagocytosis by B cells is required for a potent humoral response

Successful vaccines rely on activating a functional humoral response that results from promoting a proper germinal center (GC) reaction. Key in this process is the activation of follicular B cells that need to acquire antigens and to present them to cognate CD4 T cells. Here, we report that follicular B cells can phagocytose large antigen‐coated particles, a process thought to be exclusive of specialized antigen‐presenting cells such as macrophages and dendritic cells. We show that antigen phagocytosis by B cells is BCR‐driven and mechanistically dependent on the GTPase RhoG. Using Rhog−/− mice, we show that phagocytosis of antigen by B cells is important for the development of a strong GC response and the generation of high‐affinity class‐switched antibodies. Importantly, we show that the potentiation effect of alum, a common vaccine adjuvant, requires direct phagocytosis of alum–antigen complexes by B cells. These data suggest a new avenue for vaccination approaches by aiming to deliver 1–3 μm size antigen particles to follicular B cells.
Fractional-Order Memristor Emulator Circuits

This brief leads the synthesis of fractional-order memristor (FOM) emulator circuits. To do so, a novel fractional-order integrator (FOI) topology based on current-feedback operational amplifier and integer-order capacitors is proposed. Then, the FOI is substituting the integer-order integrator inside flux- or charge-controlled memristor emulator circuits previously reported in the literature and in both versions: floating and grounded. This demonstrates that FOM emulator circuits can also be configured at incremental or decremental mode and the main fingerprints of an integer-order memristor are also holding up for FOMs. Theoretical results are validated through HSPICE simulations and the synthesized FOM emulator circuits can easily be reproducible. Moreover, the FOM emulator circuits can be used for improving future applications such as cellular neural networks, modulators, sensors, chaotic systems, relaxation oscillators, nonvolatile memory devices, and programmable analog circuits.