Miracle-dz/UniFAR-FTUs huggingface dataset:
Editorial Manager(tm) Jcns Manuscript Draft Title: Cortical Architecture for the Organization of Spatial and Object Attention

This paper presents a model of the potential role of the early visual cortex in mediating the interaction of information processing in the dorsal and ventral streams. The model is inspired by a number of recent neurophysiological studies reporting delayed contextual modulations in the early visual cortex which has implicated the continual interaction between the early visual cortex and higher visual areas during visual processing. This model uses recurrent connections among cortical areas to coordinate the communication and computations in the two processing streams, achieving routing control for invariant object recognition and parallel-serial visual search in one uni ed framework. It also establishes the links between attentional processes and the functional tasks of selective routing and visual search. In this model, parallel and serial visual search can be implemented using a single mechanism. The computational viability of this hypothesis is tested and veri ed by large-scale network simulations.
UVA Wahoos at SemEval-2019 Task 6: Hate Speech Identification using Ensemble Machine Learning

With the growth in the usage of social media, it has become increasingly common for people to hide behind a mask and abuse others. We have attempted to detect such tweets and comments that are malicious in intent, which either targets an individual or a group. Our best classifier for identifying offensive tweets for SubTask A (Classifying offensive vs. nonoffensive) has an accuracy of 83.14% and a f1- score of 0.7565 on the actual test data. For SubTask B, to identify if an offensive tweet is targeted (If targeted towards an individual or a group), the classifier performs with an accuracy of 89.17% and f1-score of 0.5885. The paper talks about how we generated linguistic and semantic features to build an ensemble machine learning model. By training with more extracts from different sources (Facebook, and more tweets), the paper shows how the accuracy changes with additional training data.
FERMI at SemEval-2019 Task 5: Using Sentence embeddings to Identify Hate Speech Against Immigrants and Women in Twitter

This paper describes our system (Fermi) for Task 5 of SemEval-2019: HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter. We participated in the subtask A for English and ranked first in the evaluation on the test set. We evaluate the quality of multiple sentence embeddings and explore multiple training models to evaluate the performance of simple yet effective embedding-ML combination algorithms. Our team - Fermi’s model achieved an accuracy of 65.00% for English language in task A. Our models, which use pretrained Universal Encoder sentence embeddings for transforming the input and SVM (with RBF kernel) for classification, scored first position (among 68) in the leaderboard on the test set for Subtask A in English language. In this paper we provide a detailed description of the approach, as well as the results obtained in the task.
On Inductive Verification and Synthesis

We study possibilities of using symbol elimination in program verification and synthesis. We consider programs for which a property is given, which is supposed to hold for all states reachable from the initial states. If it can not be proven that such a formula is an inductive invariant, the task is to find conditions to strengthen the property in order to make it an inductive invariant. We propose a method for property-directed invariant generation and analyze its properties.
TakeLab at SemEval-2019 Task 4: Hyperpartisan News Detection

In this paper, we demonstrate the system built to solve the SemEval-2019 task 4: Hyperpartisan News Detection (Kiesel et al., 2019), the task of automatically determining whether an article is heavily biased towards one side of the political spectrum. Our system receives an article in its raw, textual form, analyzes it, and predicts with moderate accuracy whether the article is hyperpartisan. The learning model used was primarily trained on a manually prelabeled dataset containing news articles. The system relies on the previously constructed SVM model, available in the Python Scikit-Learn library. We ranked 6th in the competition of 42 teams with an accuracy of 79.1% (the winning team had 82.2%).
Saagie at Semeval-2019 Task 5: From Universal Text Embeddings and Classical Features to Domain-specific Text Classification

This paper describes our contribution to SemEval 2019 Task 5: Hateval. We propose to investigate how domain-specific text classification task can benefit from pretrained state of the art language models and how they can be combined with classical handcrafted features. For this purpose, we propose an approach based on a feature-level Meta-Embedding to let the model choose which features to keep and how to use them.
AndrejJan at SemEval-2019 Task 7: A Fusion Approach for Exploring the Key Factors pertaining to Rumour Analysis

The viral spread of false, unverified and misleading information on the Internet has attracted a heightened attention of an interdisciplinary research community on the phenomenon. This paper contributes to the research efforts of automatically determining the veracity of rumourous tweets and classifying their replies according to stance. Our research objective was to investigate the interplay between a number of phenomenological and contextual features of rumours, in particular, we explore the extent to which network structural characteristics, metadata and user profiles could complement the linguistic analysis of the written content for the task at hand. The current findings strongly demonstrate that supplementary sources of information play significant role in classifying the veracity and the stance of Twitter interactions deemed to be rumourous.
ApproxSymate: path sensitive program approximation using symbolic execution

Approximate computing, a technique that forgoes quantifiable output accuracy in favor of performance gains, is useful for improving the energy efficiency of error-resilient software, especially in the embedded setting. The identification of program components that can tolerate error plays a crucial role in balancing the energy vs. accuracy trade off in approximate computing. Manual analysis for approximability is not scalable and therefore automated tools which employ static or dynamic analysis have been proposed. However, static techniques are often coarse in their approximations while dynamic efforts incur high overhead. In this work we present ApproxSymate, a framework for automatically identifying program approximations using symbolic execution. ApproxSymate first statically computes symbolic error expressions for program components and then uses a dynamic sensitivity analysis to compute their approximability. A unique feature of this tool is that it explores the previously not considered dimension of program path for approximation which enables safer transformations. Our evaluation shows that ApproxSymate averages about 96% accuracy in identifying the same approximations found in manually annotated benchmarks, outperforming existing automated techniques.
nlpUP at SemEval-2019 Task 6: A Deep Neural Language Model for Offensive Language Detection

This paper presents our submission for the SemEval shared task 6, sub-task A on the identification of offensive language. Our proposed model, C-BiGRU, combines a Convolutional Neural Network (CNN) with a bidirectional Recurrent Neural Network (RNN). We utilize word2vec to capture the semantic similarities between words. This composition allows us to extract long term dependencies in tweets and distinguish between offensive and non-offensive tweets. In addition, we evaluate our approach on a different dataset and show that our model is capable of detecting online aggressiveness in both English and German tweets. Our model achieved a macro F1-score of 79.40% on the SemEval dataset.
USF at SemEval-2019 Task 6: Offensive Language Detection Using LSTM With Word Embeddings

In this paper, we present a system description for the SemEval-2019 Task 6 submitted by our team. For the task, our system takes tweet as an input and determine if the tweet is offensive or non-offensive (Sub-task A). In case a tweet is offensive, our system identifies if a tweet is targeted (insult or threat) or non-targeted like swearing (Sub-task B). In targeted tweets, our system identifies the target as an individual or group (Sub-task C). We used data pre-processing techniques like splitting hashtags into words, removing special characters, stop-word removal, stemming, lemmatization, capitalization, and offensive word dictionary. Later, we used keras tokenizer and word embeddings for feature extraction. For classification, we used the LSTM (Long short-term memory) model of keras framework. Our accuracy scores for Sub-task A, B and C are 0.8128, 0.8167 and 0.3662 respectively. Our results indicate that fine-grained classification to identify offense target was difficult for the system. Lastly, in the future scope section, we will discuss the ways to improve system performance.
SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text

In this paper, we present the SemEval-2019 Task 3 - EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading “Why don’t you ever text me! ” we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score. Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class.
Homotopy type of spaces of curves with constrained curvature on flat surfaces

Let $S$ be a complete flat surface, such as the Euclidean plane. We determine the homeomorphism class of the space of all curves on $S$ which start and end at given points in given directions and whose curvatures are constrained to lie in a given open interval, in terms of all parameters involved. Any connected component of such a space is either contractible or homotopy equivalent to an $n$-sphere, and every $n\geq 1$ is realizable. Explicit homotopy equivalences between the components and the corresponding spheres are constructed.
Stop PropagHate at SemEval-2019 Tasks 5 and 6: Are abusive language classification results reproducible?

This paper summarizes the participation of Stop PropagHate team at SemEval 2019. Our approach is based on replicating one of the most relevant works on the literature, using word embeddings and LSTM. After circumventing some of the problems of the original code, we found poor results when applying it to the HatEval contest (F1=0.45). We think this is due mainly to inconsistencies in the data of this contest. Finally, for the OffensEval the classifier performed well (F1=0.74), proving to have a better performance for offense detection than for hate speech.
CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM

This paper describes our system at SemEval 2019, Task 3 (EmoContext), which focused on the contextual detection of emotions in a dataset of 3-round dialogues. For our final system, we used a neural network with pretrained ELMo word embeddings and POS tags as input, GRUs as hidden units, an attention mechanism to capture representations of the dialogues, and an SVM classifier which used the learned network representations to perform the task of multi-class classification.This system yielded a micro-averaged F1 score of 0.7072 for the three emotion classes, improving the baseline by approximately 12%.
GSI-UPM at SemEval-2019 Task 5: Semantic Similarity and Word Embeddings for Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter

This paper describes the GSI-UPM system for SemEval-2019 Task 5, which tackles multilingual detection of hate speech on Twitter. The main contribution of the paper is the use of a method based on word embeddings and semantic similarity combined with traditional paradigms, such as n-grams, TF-IDF and POS. This combination of several features is fine-tuned through ablation tests, demonstrating the usefulness of different features. While our approach outperforms baseline classifiers on different sub-tasks, the best of our submitted runs reached the 5th position on the Spanish sub-task A.
Inferring Hand Motion from Multi-Cell Recordings in Motor Cortex using a Kalman Filter

This paper develops a control-theoretic approach to the problem of decoding neural activity in motor cortex. Our goal is to infer the position and velocity of a subject's hand from the neural spiking activity of 25 cells simultaneously recorded in primary motor cortex. We propose to model the encoding and decoding of the neural data using a Kalman lter. Towards that end we specify a measurement model that assumes the ring rate of a cell within 50ms is a stochastic linear function of position, velocity, and acceleration of the hand. This model is learned from training data along with a system model that encodes how the hand moves. Experimental results show that the reconstructed trajectories are superior to those obtained by linear ltering. Additionally, the Kalman lter provides insight into the neural encoding of hand motion. For example, analysis of the measurement model suggests that, while the neural ring is closely related to the position and velocity of the hand, the acceleration is redundant. Furthermore, the Kalman lter framework is exploited to recover the optimal lag time between hand movement and neural ring.
Enhancement of Oral Bioavailability and Anti-hyperuricemic Activity of Isoliquiritigenin via Self-Microemulsifying Drug Delivery System

The aim of this study was to develop a self-microemulsifying drug delivery system (SMEDDS) for enhancement of the oral bioavailability of isoliquiritigenin (ISL) as well as evaluate its in vivo anti-hyperuricemic effect in rats. The ISL-loaded self-microemulsifying drug delivery system (ISL-SMEDDS) was comprised of ethyl oleate (EO, oil phase), Tween 80 (surfactant), and PEG 400 (co-surfactant). The ISL-SMEDDS exhibited an acceptable narrow size distribution (44.78 ± 0.35 nm), negative zeta potential (− 10.67 ± 0.86 mV), and high encapsulation efficiency (98.17 ± 0.24%). The in vitro release study indicated that the release rates of the formulation were obviously higher in different release media (HCl, pH 1.2; PBS, pH 6.8; double-distilled water, pH 7.0) compared with the ISL solution. The oral bioavailability of the ISL-SMEDDS was enhanced by 4.71 times in comparison with the free ISL solution. More importantly, ISL-SMEDDS significantly reduced uric acid level by inhibiting xanthine oxidase (XOD) activity in the model rats. Collectively, the prepared ISL-SMEDDS proved to be potential carriers for enhancing the solubility and oral bioavailability of ISL, as well as ameliorating its anti-hyperuricemic effect.
Assessment of humoral and cell-mediated immune responses to pertussis vaccination: a systematic review protocol

Introduction Globally, some studies show a resurgence of pertussis. The risks and benefits of using whole-cell pertussis (wP) or acellular pertussis (aP) vaccines in the control of the disease have been widely debated. Better control of pertussis will require improved understanding of the immune response to pertussis vaccines. Improved understanding and assessment of the immunity induced by pertussis vaccines is thus imperative. Several studies have documented different immunological outcomes to pertussis vaccination from an array of assays. We propose to conduct a systematic review of the different immunological assays and outcomes used in the assessment of the humoraland cell-mediated immune response following pertussis vaccination. Methods and analysis The primary outcomes for consideration are quality and quantity of immune responses (humoral and cell-mediated) post-pertussis vaccination. Of interest as secondary outcomes are types of immunoassays used in assessing immune responses post-pertussis vaccination, types of biological samples used in assessing immune responses post-pertussis vaccination, as well as the types of antigens used to stimulate these samples during post-pertussis vaccination immune response assessments. Different electronic databases (including PubMed, Cochrane, EBSCO Host, Scopus and Web of Science) will be accessed for peer-reviewed published and grey literature evaluating immune responses to pertussis vaccines between 1990 and 2019. The quality of included articles will be assessed using standardised risk and quality assessment tools specific to the study design used in each article. Data extraction will be done using a data extraction form. The extracted data will be analysed using STATA V.14. 0 and RevMan V.5. 3 software. A subgroup analysis will be conducted based on the study population, type of vaccine (wP or aP) and type of immune response (cell-mediated or humoral). Guidelines for reporting systematic reviews in the revised 2009 Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement will be used in this study. Ethics and dissemination Ethics approval is not required for this study as it is a systematic review. We will only make use of data already available in the public space. Findings will be reported via publication in a peer-reviewed journal and presented at scientific meetings and workshops. Trial registration number CRD42018102455.
Fifth-order time-domain Raman spectroscopy of photoactive yellow protein for visualizing vibrational coupling in its excited state

2D impulsive stimulated Raman spectroscopy unveiled couplings of low- and high-frequency vibrations of photoexcited PYP. We report fifth-order time-domain Raman spectroscopy of photoactive yellow protein (PYP), with the aim to visualize vibrational coupling in its excited state. After the ultrashort actinic pump pulse prepared the vibrational coherence and population in the excited state, the evolving vibrational structure was tracked by time-resolved impulsive stimulated Raman spectroscopy using sub–7-fs pulses. The obtained fifth-order time-domain Raman data were translated to a two-dimensional (2D) frequency-frequency correlation map, which visualizes the correlation between low- and high-frequency vibrational modes of the excited state. The 2D map of PYP reveals a cross peak, indicating the coupling between the phenolic C─O stretch mode of the chromophore and the low-frequency modes (~160 cm−1), assignable to the intermolecular motions involving the surrounding hydrogen-bonded amino acids. The unveiled coupling suggests the importance of the low-frequency vibrational motion in the primary photoreaction of PYP, highlighting the unique capability of this spectroscopic approach for studying ultrafast reaction dynamics.
Long-term Exposure to Low Air Pollutant Concentrations and the Relationship with All-Cause Mortality and Stroke in Older Men.

BACKGROUND Long-term air pollution exposure has been associated with increased risk of mortality and stroke. Less is known about the risk at lower concentrations. The association of long-term exposure to PM2.5, PM2.5 absorbance, NO2, and NOx with all-cause mortality and stroke was investigated in a cohort of men aged ≥ 65 years who lived in metropolitan Perth, Western Australia. METHODS Land use regression models were used to estimate long-term exposure to air pollutants at participant's home address (n = 11,627) over 16 years. Different metrics of exposure were assigned: baseline; year before the outcome event; and average exposure across follow-up period. The Mortality Register and Hospital Morbidity Data from the Western Australia Data Linkage System were used to ascertain mortality and stroke cases. Hazard ratios (HRs) and 95% confidence intervals were estimated using Cox proportional hazard models, adjusting for age, smoking, education, and body mass index for all-cause mortality. For fatal and hospitalized stroke, the models included variables controlled for all-cause mortality plus hypertension. RESULTS Fifty-four percent of all-participants died, 3% suffered a fatal stroke, and 14% were hospitalized stroke cases. PM2.5 absorbance increased the risk of all-cause mortality with adjusted HR of 1.12 (1.02-1.23) for baseline and average exposures, and 1.14 (1.02-1.24) for past-year exposure. There were no associations between PM2.5 absorbance, NO2, and NOx and stroke outcomes. However, PM2.5 was associated with reduced risks of fatal stroke. CONCLUSION Long-term exposure to PM2.5 absorbance was associated with all-cause mortality among older men exposed to low concentrations; and exposure to PM2.5 was associated with reduced risk of fatal stroke.
Nitrogen along the Hydrological Gradient of Marsh Sediments in a Subtropical Estuary: Pools, Processes, and Fluxes

Knowledge on the distribution of nitrogen (N) pools, processes, and fluxes along hydrological gradients provides a comprehensive perspective to understand the underlying causal mechanisms in intertidal flats, and thus improve predictions and climate adaptation strategies. We used a space-for-time substitution method to quantify N pools, processes, and fluxes along a hydrological gradient. Further, we linked N pools and processes and investigated not only surface but also subsurface sediments. Our results showed a gradual decrease in total N (TN) and mineralization rates (PNmin), but an increase in potential rates of nitrification (PNR) and denitrification (PDNR) under an elevated hydrological gradient, except for TN and PNmin in the subsurface sediment, which accumulated on the interaction zone between the high and middle tidal flats. Most sedimentary ammonium N (NH4+) and nitrate N (NO3−) concentrations were similar; however, NH4+ accumulated on the subsurface of the middle tidal flat. NO3− fluxes (from −0.54 to −0.35 mmol m−2 h−1) were uptake fluxes in the intertidal flats, but NH4+ fluxes (−2.48–3.54 mmol m−2 h−1) changed from uptake to efflux in the seaward direction. Structural equation modeling of the effects of inundation frequency, underground biomass, total carbon (TC), electrical conductivity (EC), and clay proportion on the N processes revealed that these accounted for 67%, 82%, and 17% of the variance of PDNR, PNmin, and PNR, respectively. Inundation frequency, underground biomass, TC, EC, and PNmin effects on N pools accounted for 53%, 69%, and 98% of the variance of NH4+, NO3−, and TN, respectively. This suggests that future sea level rise may decrease N storage due to increase in coupled nitrification–denitrification and decrease in N mineralization, and the NH4+ flux may change from sink to source in intertidal ecosystems.