Miracle-dz/UniFAR-FTUs huggingface dataset:
Treatment nonresponse in children receiving silver diamine fluoride for caries: A pilot study

Objectives: Silver diamine fluoride (SDF) is a nonsurgical therapy for the arrest and pre- vention of dental caries with demonstrated clinical efficacy. Approximately 20% of children receiving SDF fail to respond to treatment. The objective of this study was to develop a pre- dictive model of treatment nonresponse using machine learning. Methods: An observational pilot study (N=20) consisting of children with and without active decay and who did and did not respond to silver diamine fluoride provided salivary samples and plaque from infected and contralateral sites. 16S rRNA genes from samples were amplified and sequenced on an Illu- mina Miseq and analyzed using QIIME. The association between operational taxonomic units and treatment nonresponse was assessed using lasso regression and artificial neural networks. Results: Bivariate group comparisons of bacterial abundance indicate a number of genera were significantly different between nonresponders and those who responded to SDF therapy. No dif- ferences were found between nonresponders and caries-active subjects. Prevotella pallens and Veillonella denticariosi were retained in full lasso models and combined with clinical variables in a six-input multilayer perceptron. Discussion: The acidogenic and acid-tolerant nature of retained bacterial species may overcome the antimicrobial effects of SDF. Further research to validate the model in larger external samples is needed.
Investigating the potential benefit that requiring travellers to self-isolate on arrival may have upon the reducing of case importations during international outbreaks of influenza, SARS, Ebola virus disease and COVID-19

With the advent of rapid international travel, disease can now spread between nations faster than ever. As such, when outbreaks occur in foreign states, pressure mounts to reduce the risk of importing cases to the home nation. In a previous paper, we developed a model to investigate the potential effectiveness of deploying screening at airports during outbreaks of influenza, SARS, and Ebola. We also applied the model to the current COVID-19 outbreak. This model simulated the testing of travellers (assumed not to be displaying symptoms prior to boarding their flight) as they arrived at their destination. The model showed that the reduction in risk of case importation that screening alone could deliver was minimal across most scenarios considered, with outputs indicating that screening alone could detect at most 46.4%, 12.9%, and 4.0% of travellers infected with influenza, SARS and Ebola respectively, while the model also reported a detection rate of 12.0% for COVID-19. In this paper, we present a brief modification to this model allowing us to assess the added impact that quarantining incoming travelers for various periods may have on reducing the risk of case importation. Primary results show that requiring all travellers to undergo 5 days of self-isolation on arrival, after which they are tested again, has the potential to increase rates of detection to 100%, 87.6%, 81.7% and 41.3% for travellers infected with influenza, SARS, COVID-19 and Ebola respectively. Extending the period of self-isolation to 14 days increases these potential detection rates to 100%, 100%, 99.5% and 91.8% respectively.
Provable Acceleration of Neural Net Training via Polyak's Momentum

Incorporating a so-called "momentum" dynamic in gradient descent methods is widely used in neural net training as it has been broadly observed that, at least empirically, it often leads to significantly faster convergence. At the same time, there are very few theoretical guarantees in the literature to explain this apparent acceleration effect. In this paper we show that Polyak's momentum, in combination with over-parameterization of the model, helps achieve faster convergence in training a one-layer ReLU network on $n$ examples. We show specifically that gradient descent with Polyak's momentum decreases the initial training error at a rate much faster than that of vanilla gradient descent. We provide a bound for a fixed sample size $n$, and we show that gradient descent with Polyak's momentum converges at an accelerated rate to a small error that is controllable by the number of neurons $m$. Prior work [DZPS19] showed that using vanilla gradient descent, and with a similar method of over-parameterization, the error decays as $(1-\kappa_n)^t$ after $t$ iterations, where $\kappa_n$ is a problem-specific parameter. Our result shows that with the appropriate choice of parameters one has a rate of $(1-\sqrt{\kappa_n})^t$. This work establishes that momentum does indeed speed up neural net training.
Intermittent Demand Forecasting with Renewal Processes

Intermittency is a common and challenging problem in demand forecasting. We introduce a new, unified framework for building intermittent demand forecasting models, which incorporates and allows to generalize existing methods in several directions. Our framework is based on extensions of well-established model-based methods to discrete-time renewal processes, which can parsimoniously account for patterns such as aging, clustering and quasi-periodicity in demand arrivals. The connection to discrete-time renewal processes allows not only for a principled extension of Croston-type models, but also for an natural inclusion of neural network based models---by replacing exponential smoothing with a recurrent neural network. We also demonstrate that modeling continuous-time demand arrivals, i.e., with a temporal point process, is possible via a trivial extension of our framework. This leads to more flexible modeling in scenarios where data of individual purchase orders are directly available with granular timestamps. Complementing this theoretical advancement, we demonstrate the efficacy of our framework for forecasting practice via an extensive empirical study on standard intermittent demand data sets, in which we report predictive accuracy in a variety of scenarios that compares favorably to the state of the art.
Group cohesion under individual regulatory constraints

We consider a group consisting of N business units. We suppose there are regulatory constraints for each unit; more precisely, the net worth of each business unit is required to belong to a set of acceptable risks, assumed to be a convex cone. Because of these requirements, there are less incentives to operate under a group structure, as creating one single business unit, or altering the liability repartition among units, may allow to reduce the required capital. We analyse the possibilities for the group to benefit from a diversification effect and economise on the cost of capital. We define and study the risk measures that allow for any group to achieve the minimal capital, as if it were a single unit, without altering the liability of business units, and despite the individual admissibility constraints. We call these risk measures cohesive risk measures. In the commonotonic case, we show that they are tail expectations but calculated under a different probability.
Optical conductivity of the type-II Weyl semimetal 
WTe2
 under pressure

Tungsten ditelluride WTe$_2$ is a type-II Weyl semimetal with electronic properties highly sensitive to external pressure, as demonstrated by the superconductivity emerging under pressure. Here, we study the optical conductivity of the type-II Weyl semimetal WTe$_2$ under external pressure at room temperature. With increasing pressure, a pronounced spectral weight transfer occurs from the high-energy to the low-energy interband transitions, with drastic changes in the profile of the optical conductivity spectrum indicating a high sensitivity of the electronic band structure to external pressure. The detailed analysis of the pressure-dependent optical conductivity furthermore reveals anomalies at the pressures $\sim$2 and $\sim$4.5~GPa, where an electronic and a structural phase transition, respectively, were reported in the literature.
PublishInCovid19 at WNUT 2020 Shared Task-1: Entity Recognition in Wet Lab Protocols using Structured Learning Ensemble and Contextualised Embeddings

In this paper, we describe the approach that we employed to address the task of Entity Recognition over Wet Lab Protocols - a shared task in EMNLP WNUT-2020 Workshop. Our approach is composed of two phases. In the first phase, we experiment with various contextualised word embeddings (like Flair, BERT-based) and a BiLSTM-CRF model to arrive at the best-performing architecture. In the second phase, we create an ensemble composed of eleven BiLSTM-CRF models. The individual models are trained on random train-validation splits of the complete dataset. Here, we also experiment with different output merging schemes, including Majority Voting and Structured Learning Ensembling (SLE). Our final submission achieved a micro F1-score of 0.8175 and 0.7757 for the partial and exact match of the entity spans, respectively. We were ranked first and second, in terms of partial and exact match, respectively.
Addition of increasing doses of ricinoleic acid from castor oil (Ricinus communis L.) in horse diets: intake, digestibility, glucose and insulin dynamic

ABSTRACT Ricinoleic acid is obtained from castor bean and studies related to its effects on glucose and insulin dynamic are scarce in the literature. Eight male Crioulo breed horses (362 ± 16 of live weight, mean ± SD) were used in a replicated 4 × 4 Latin square design experiment that consisted of 10 days of adaptation and 7 days for data collection. The horses were allocated in each square to receive one of the following treatments: Control (0) and 1, 2, 3 and 4 g/d of ricinoleic acid (diet DM basis). Diets were composed of a commercial concentrate and Coast cross hay. Treatments did not affect DM and nutrient intake. Ricinoleic acid did not alter the nutrient digestibility, regardless of the dosage used. The quadratic effect was observed for glucose and insulin area under the curve. Ricinoleic acid quadratically affected glucose and insulin concentrations. Increasing dietary doses of ricinoleic acid altered glycemic and insulinemic responses of horses, and the present study recommends the dietary inclusion of 1.8 g of ricinoleic acid to improve horse's energetic metabolism.
X-SRL: A Parallel Cross-Lingual Semantic Role Labeling Dataset

Even though SRL is researched for many languages, major improvements have mostly been obtained for English, for which more resources are available. In fact, existing multilingual SRL datasets contain disparate annotation styles or come from different domains, hampering generalization in multilingual learning. In this work we propose a method to automatically construct an SRL corpus that is parallel in four languages: English, French, German, Spanish, with unified predicate and role annotations that are fully comparable across languages. We apply high-quality machine translation to the English CoNLL-09 dataset and use multilingual BERT to project its high-quality annotations to the target languages. We include human-validated test sets that we use to measure the projection quality, and show that projection is denser and more precise than a strong baseline. Finally, we train different SOTA models on our novel corpus for mono- and multilingual SRL, showing that the multilingual annotations improve performance especially for the weaker languages.
Environmental correlates to amphibian and reptile diversity in Costa Rica

Environmental Correlates to Amphibian and Reptile Diversity in Costa Rica. (May 2007) David Edelman Laurencio, B.S., Texas A&M University Chair of Advisory Committee: Dr. Lee A. Fitzgerald The study of species diversity patterns and their causes remains a central theme of ecology. Work conducted over the last few decades has shown that both historical and ecological factors are important in determining species diversity patterns. Additionally, different causal mechanisms are important at different spatial and temporal scales. At the regional scale, species diversity patterns can best be studied in terms of three diversity components (alpha, beta and gamma). This study used the amphibians and reptiles of Costa Rica to examine these species diversity components at the regional scale. To accomplish this, existing species lists were compiled from the literature. Additionally, three herpetofaunal surveys were conducted at under surveyed sites to fill knowledge gaps. A survey of Parque Nacional Carara, a transitional zone site on Costa Rica’s central Pacific coast, gave evidence of a rich herpetofauna, containing species from both the dry tropical forest to the north and the wet lowland forest to the south. Survey results show that Carara’s herpetofaunal assemblage is more similar to that of the wet forest than the dry forest, and suggest many species from both assemblages reach their range limit at or near the park. Surveys of four sites in the eastern Área de Conservación Guanacaste showed rich herpetofaunal diversity and validated the newly purchased
Human translocation as an alternative hypothesis to explain the presence of giant tortoises on remote islands in the south‐western Indian Ocean

Giant tortoises are known from several remote islands in the Indian Ocean (IO). Our present understanding of ocean circulation patterns, the age of the islands, and the life history traits of giant tortoises makes it difficult to comprehend how these animals arrived on such small, remote and geologically young (8–1.5 Ma) landmasses. For colonization to have occurred by dispersal, giant tortoises must either have originated in Madagascar or Africa and swum for hundreds of km against the ocean currents, or have launched themselves from the eastern IO margin and drifted with the currents over several thousands of km of open ocean. After these navigational feats, the tortoises would have needed to found new, viable populations on potentially inhospitable volcanic or coral outcrops. Geologically recent sea level changes are likely to have eliminated terrestrial life from islands like Aldabra, complicating the scenario. We reviewed information relating to IO geology, the evolution and ecology of giant tortoises, and the spread of humans within the region, and propose an alternative explanation: we posit that giant tortoises were introduced to the IO islands by early Austronesian sailors, possibly to establish provisioning stations for their journeys, just as European sailors did in more recent historical times.
Trajectory-Based Spatiotemporal Entity Linking

Trajectory-based spatiotemporal entity linking is to match the same moving object in different datasets based on their movement traces. It is a fundamental step to support spatiotemporal data integration and analysis. In this paper, we study the problem of spatiotemporal entity linking using effective and concise signatures extracted from their trajectories. This linking problem is formalized as a <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="hua-ieq1-3036633.gif"/></alternatives></inline-formula>-nearest neighbor (<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="hua-ieq2-3036633.gif"/></alternatives></inline-formula>-NN) query on the signatures. Four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) are investigated for signature construction. A simple yet effective dimension reduction strategy is developed together with a novel indexing structure called the WR-tree to speed up the search. A number of optimization methods are proposed to improve the accuracy and robustness of the linking. Our extensive experiments on real-world datasets verify the superiority of our approach over the state-of-the-art solutions in terms of both accuracy and efficiency.
UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers

A flurry of fuzzing tools (fuzzers) have been proposed in the literature, aiming at detecting software vulnerabilities effectively and efficiently. To date, it is however still challenging to compare fuzzers due to the inconsistency of the benchmarks, performance metrics, and/or environments for evaluation, which buries the useful insights and thus impedes the discovery of promising fuzzing primitives. In this paper, we design and develop UNIFUZZ, an open-source and metrics-driven platform for assessing fuzzers in a comprehensive and quantitative manner. Specifically, UNIFUZZ to date has incorporated 35 usable fuzzers, a benchmark of 20 real-world programs, and six categories of performance metrics. We first systematically study the usability of existing fuzzers, find and fix a number of flaws, and integrate them into UNIFUZZ. Based on the study, we propose a collection of pragmatic performance metrics to evaluate fuzzers from six complementary perspectives. Using UNIFUZZ, we conduct in-depth evaluations of several prominent fuzzers including AFL [1], AFLFast [2], Angora [3], Honggfuzz [4], MOPT [5], QSYM [6], T-Fuzz [7] and VUzzer64 [8]. We find that none of them outperforms the others across all the target programs, and that using a single metric to assess the performance of a fuzzer may lead to unilateral conclusions, which demonstrates the significance of comprehensive metrics. Moreover, we identify and investigate previously overlooked factors that may significantly affect a fuzzer's performance, including instrumentation methods and crash analysis tools. Our empirical results show that they are critical to the evaluation of a fuzzer. We hope that our findings can shed light on reliable fuzzing evaluation, so that we can discover promising fuzzing primitives to effectively facilitate fuzzer designs in the future.
Partially-Aligned Data-to-Text Generation with Distant Supervision

The Data-to-Text task aims to generate human-readable text for describing some given structured data enabling more interpretability. However, the typical generation task is confined to a few particular domains since it requires well-aligned data which is difficult and expensive to obtain. Using partially-aligned data is an alternative way of solving the dataset scarcity problem. This kind of data is much easier to obtain since it can be produced automatically. However, using this kind of data induces the over-generation problem posing difficulties for existing models, which tends to add unrelated excerpts during the generation procedure. In order to effectively utilize automatically annotated partially-aligned datasets, we extend the traditional generation task to a refined task called Partially-Aligned Data-to-Text Generation (PADTG) which is more practical since it utilizes automatically annotated data for training and thus considerably expands the application domains. To tackle this new task, we propose a novel distant supervision generation framework. It firstly estimates the input data's supportiveness for each target word with an estimator and then applies a supportiveness adaptor and a rebalanced beam search to harness the over-generation problem in the training and generation phases respectively. We also contribute a partially-aligned dataset (The data and source code of this paper can be obtained from this https URL by sampling sentences from Wikipedia and automatically extracting corresponding KB triples for each sentence from Wikidata. The experimental results show that our framework outperforms all baseline models as well as verify the feasibility of utilizing partially-aligned data.
Joint Inference of Structure and Diffusion in Partially Observed Social Networks

Access to complete data in large scale networks is often infeasible. Therefore, the problem of missing data is a crucial and unavoidable issue in analysis and modeling of real-world social networks. However, most of the research on different aspects of social networks do not consider this limitation. One effective way to solve this problem is to recover the missing data as a pre-processing step. The present paper tries to infer the unobserved data from both diffusion network and network structure by learning a model from the partially observed data. We develop a probabilistic generative model called "DiffStru" to jointly discover the hidden links of network structure and the omitted diffusion activities. The interrelations among links of nodes and cascade processes are utilized in the proposed method via learning coupled low dimensional latent factors. In addition to inferring the unseen data, the learned latent factors may also help network classification problems such as community detection. Simulation results on synthetic and real-world datasets show the excellent performance of the proposed method in terms of link prediction and discovering the identity and infection time of invisible social behaviors.
On the grain-sized distribution of turbulent dust growth

It has recently been shown that turbulence in the interstellar medium (ISM) can significantly accelerate the growth of dust grains by accretion of molecules, but the turbulent gas-density distribution also plays a crucial role in shaping the grain-size distribution. The growth velocity, i.e., the rate of change of the mean grain radius, is proportional to the local gas density if the growth species (molecules) are well-mixed in the gas. As a consequence, grain growth happens at vastly different rates in different locations, since the gas-density distribution of the ISM shows a considerable variance. Here, it is shown that grain-size distribution (GSD) rapidly becomes a reflection of the gas-density distribution, irrespective of the shape of the initial GSD. This result is obtained by modelling ISM turbulence as a Markov process, which in the special case of an Ornstein-Uhlenbeck process leads to a lognormal gas-density distribution, consistent with numerical simulations of isothermal compressible turbulence. This yields an approximately lognormal GSD; the sizes of dust grains in cold ISM clouds may thus not follow the commonly adopted power-law GSD with index -3.5, but corroborates the use of a log-nomral GSD for large grains, suggested by several studies. It is also concluded that the very wide range of gas densities obtained in the high Mach-number turbulence of molecular clouds must allow formation of a tail of very large grains reaching radii of several microns.
Spatially resolved measurements of the solar photospheric oxygen abundance

Aims. We report the results of a novel determination of the solar oxygen abundance using spatially resolved observations and inversions. We seek to derive the photospheric solar oxygen abundance with a method that is robust against uncertainties in the model atmosphere. Methods. We use observations with spatial resolution obtained at the Vacuum Tower Telescope to derive the oxygen abundance at 40 different spatial positions in granules and intergranular lanes. We first obtain a model for each location by inverting the Fe I lines with the NICOLE inversion code. These models are then integrated into a hierarchical Bayesian model that is used to infer the most probable value for the oxygen abundance that is compatible with all the observations. The abundance is derived from the [O I] forbidden line at 6300 Å taking into consideration all possible nuisance parameters that can affect the abundance. Results. Our results show good agreement in the inferred oxygen abundance for all the pixels analyzed, demonstrating the robustness of the analysis against possible systematic errors in the model. We find a slightly higher oxygen abundance in granules than in intergranular lanes when treated separately (log(ϵO) = 8.83 ± 0.02 vs. log(ϵO) = 8.76 ± 0.02), which is a difference of approximately 2-σ. This tension suggests that some systematic errors in the model or the radiative transfer still exist but are small. When taking all pixels together, we obtain an oxygen abundance of log(ϵO) = 8.80 ± 0.03, which is compatible with both granules and lanes within 1-σ. The spread of results is due to both systematic and random errors.