Miracle-dz/UniFAR-FTUs huggingface dataset:
The Mammals of Coastal Texas: A Comparison between Mainland and Barrier Island Faunas

Biodiversity patterns of non-volant mammals on oceanic and land-bridge islands have been well documented, but little information is available about the biodiversity of barrier islands. We compared the mammalian fauna of the barrier islands of Texas with the adjacent mainland by using historical trapping records and recent field work. The barrier islands of Texas have a depauperate mammalian fauna when compared to that of the Texas coast. A number of factors have influenced this, including hurricane events, mainland species pool for the island to draw upon, and the degree of isolation of the island from the mainland. Differences in species richness and composition among islands can be attributed to the biotic province to which an island is adjacent and island area. Differences in species richness among island complexes can be attributed to island complex length. Regression analysis demonstrates a significant correlation between species richness and island area, and a highly correlated relationship between richness and island complex length. Two similarity indices indicate that the mammalian fauna of islands within each complex is more similar than the fauna between complexes. Two nested subset analyses reveal the islands contain a nested distribution of mainland species. It appears that the dearth of mammals on the islands has not resulted from a lack of colonization events, but rather the inability of mammals to survive in the harsher island environments.
Causal inference for multiple continuous exposures via the multivariate generalized propensity score

The generalized propensity score (GPS) is an extension of the propensity score for use with quantitative or continuous exposures (e.g., dose of medication or years of education). Current GPS methods allow estimation of the dose-response relationship between a single continuous exposure and an outcome. However, in many real-world settings, there are multiple exposures occurring simultaneously that could be causally related to the outcome. We propose a multivariate GPS method (mvGPS) that allows estimation of a dose-response surface that relates the joint distribution of multiple continuous exposure variables to an outcome. The method involves generating weights under a multivariate normality assumption on the exposure variables. Focusing on scenarios with two exposure variables, we show via simulation that the mvGPS method can achieve balance across sets of confounders that may differ for different exposure variables and reduces bias of the treatment effect estimates under a variety of data generating scenarios. We apply the mvGPS method to an analysis of the joint effect of two types of intervention strategies to reduce childhood obesity rates.
Direct Volume Rendering with Nonparametric Models of Uncertainty

We present a nonparametric statistical framework for the quantification, analysis, and propagation of data uncertainty in direct volume rendering (DVR). The state-of-the-art statistical DVR framework allows for preserving the transfer function (TF) of the ground truth function when visualizing uncertain data; however, the existing framework is restricted to parametric models of uncertainty. In this paper, we address the limitations of the existing DVR framework by extending the DVR framework for nonparametric distributions. We exploit the quantile interpolation technique to derive probability distributions representing uncertainty in viewing-ray sample intensities in closed form, which allows for accurate and efficient computation. We evaluate our proposed nonparametric statistical models through qualitative and quantitative comparisons with the mean-field and parametric statistical models, such as uniform and Gaussian, as well as Gaussian mixtures. In addition, we present an extension of the state-of-the-art rendering parametric framework to 2D TFs for improved DVR classifications. We show the applicability of our uncertainty quantification framework to ensemble, downsampled, and bivariate versions of scalar field datasets.
Active exploration for body model learning through self-touch on a humanoid robot with artificial skin

The mechanisms of infant development are far from understood. Learning about one's own body is likely a foundation for subsequent development. Here we look specifically at the problem of how spontaneous touches to the body in early infancy may give rise to first body models and bootstrap further development such as reaching competence. Unlike visually elicited reaching, reaching to own body requires connections of the tactile and motor space only, bypassing vision. Still, the problems of high dimensionality and redundancy of the motor system persist. In this work, we present an embodied computational model on a simulated humanoid robot with artificial sensitive skin on large areas of its body. The robot should autonomously develop the capacity to reach for every tactile sensor on its body. To do this efficiently, we employ the computational framework of intrinsic motivations and variants of goal babbling-as opposed to motor babbling-that prove to make the exploration process faster and alleviate the ill-posedness of learning inverse kinematics. Based on our results, we discuss the next steps in relation to infant studies: what information will be necessary to further ground this computational model in behavioral data.
Transfer Learning-based Road Damage Detection for Multiple Countries

Many municipalities and road authorities seek to implement automated evaluation of road damage. However, they often lack technology, know-how, and funds to afford state-of-the-art equipment for data collection and analysis of road damages. Although some countries, like Japan, have developed less expensive and readily available Smartphone-based methods for automatic road condition monitoring, other countries still struggle to find efficient solutions. This work makes the following contributions in this context. Firstly, it assesses the usability of the Japanese model for other countries. Secondly, it proposes a large-scale heterogeneous road damage dataset comprising 26620 images collected from multiple countries using smartphones. Thirdly, we propose generalized models capable of detecting and classifying road damages in more than one country. Lastly, we provide recommendations for readers, local agencies, and municipalities of other countries when one other country publishes its data and model for automatic road damage detection and classification. Our dataset is available at (this https URL).
What are Data Insights to Professional Visualization Users?

While many visualization researchers have attempted to define data insights, little is known about how visualization users perceive them. We interviewed 23 professional users of end-user visualization platforms (e.g., Tableau and Power BI) about their experiences with data insights. We report on seven characteristics of data insights based on interviewees’ descriptions. Grounded in these characteristics, we propose practical implications for creating tools that aim to automatically communicate data insights to users.
Blackmarket-Driven Collusion on Online Media: A Survey

Online media platforms have enabled users to connect with individuals and organizations, and share their thoughts. Other than connectivity, these platforms also serve multiple purposes, such as education, promotion, updates, and awareness. Increasing, the reputation of individuals in online media (aka social reputation) is thus essential these days, particularly for business owners and event managers who are looking to improve their publicity and sales. The natural way of gaining social reputation is a tedious task, which leads to the creation of unfair ways to boost the reputation of individuals artificially. Several online blackmarket services have developed a thriving ecosystem with lucrative offers to attract content promoters for publicizing their content online. These services are operated in such a way that most of their inorganic activities are going unnoticed by the media authorities, and the customers of the blackmarket services are less likely to be spotted. We refer to such unfair ways of bolstering social reputation in online media as collusion. This survey is the first attempt to provide readers a comprehensive outline of the latest studies dealing with the identification and analysis of blackmarket-driven collusion in online media. We present a broad overview of the problem, definitions of the related problems and concepts, the taxonomy of the proposed approaches, and a description of the publicly available datasets and online tools, and we discuss the outstanding issues. We believe that collusive entity detection is a newly emerging topic in anomaly detection and cyber-security research in general, and the current survey will provide readers with an easy-to-access and comprehensive list of methods, tools, and resources proposed so far for detecting and analyzing collusive entities on online media.
Random Surfing Revisited: Generalizing PageRank's Teleportation Model

We revisit the Random Surfer model, focusing on its--often overlooked--Teleportation component, and we introduce NCDawareRank; a novel ranking framework designed to exploit network meta-information as well as aspects of its higher-order structural organization in a way that preserves the mathematical structure and the attractive computational characteristics of PageRank. A rigorous theoretical exploration of the proposed model reveals a wealth of mathematical properties that entail tangible benefits in terms of robustness, computability, as well as modeling flexibility and expressiveness. A set of experiments on real-work networks verify the theoretically predicted properties of NCDawareRank, and showcase its effectiveness as a network centrality measure.
Discrete convolution statistic for hypothesis testing

Abstract The question of testing for equality in distribution between two linear models, each consisting of sums of distinct discrete independent random variables with unequal numbers of observations, has emerged from the biological research. In this case, the computation of classical statistics, which would not include all observations, results in loss of power, especially when sample sizes are small. Here, as an alternative that uses all data, the maximum likelihood estimator for the distribution of sum of discrete and independent random variables, which we call the convolution statistic, is proposed and its limiting normal covariance matrix determined. To challenge null hypotheses about the distribution of this sum, the generalized Wald’s method is applied to define a testing statistic whose distribution is asymptotic to a with as many degrees of freedom as the rank of such covariance matrix. Rank analysis also reveals a connection with the roots of the probability generating functions associated to the addend variables of the linear models. A simulation study is performed to compare the convolution test with Pearson’s and to provide usage guidelines.
SemEval-2020 Task 6: Definition Extraction from Free Text with the DEFT Corpus

Research on definition extraction has been conducted for well over a decade, largely with significant constraints on the type of definitions considered. In this work, we present DeftEval, a SemEval shared task in which participants must extract definitions from free text using a term-definition pair corpus that reflects the complex reality of definitions in natural language. Definitions and glosses in free text often appear without explicit indicators, across sentences boundaries, or in an otherwise complex linguistic manner. DeftEval involved 3 distinct subtasks: 1) Sentence classification, 2) sequence labeling, and 3) relation extraction.
Model selection for estimation of causal parameters

A popular technique for selecting and tuning machine learning estimators is cross-validation. Cross-validation evaluates overall model fit, usually in terms of predictive accuracy. In causal inference, the optimal choice of estimator depends not only on the fitted models, but also on assumptions the statistician is willing to make. In this case, the performance of different (potentially biased) estimators cannot be evaluated by checking overall model fit. We propose a model selection procedure that estimates the squared `2-deviation of a finitedimensional estimator from its target. The procedure relies on knowing an asymptotically unbiased ”benchmark estimator” of the parameter of interest. Under regularity conditions, we investigate bias and variance of the proposed criterion compared to competing procedures and derive a finite-sample bound for the excess risk compared to an oracle procedure. The resulting estimator is discontinuous and does not have a Gaussian limit distribution. Thus, standard asymptotic expansions do not apply. We derive asymptotically valid confidence intervals that take into account the model selection step. The performance of the approach for estimation and inference for average treatment effects is evaluated on simulated data sets, including experimental data, instrumental variables settings and observational data with selection on observables.
Momentum‐based accelerated mirror descent stochastic approximation for robust topology optimization under stochastic loads

Robust topology optimization (RTO) improves the robustness of designs with respect to random sources in real‐world structures, yet an accurate sensitivity analysis requires the solution of many systems of equations at each optimization step, leading to a high computational cost. To open up the full potential of RTO under a variety of random sources, this article presents a momentum‐based accelerated mirror descent stochastic approximation (AC‐MDSA) approach to efficiently solve RTO problems involving various types of load uncertainties. The proposed framework performs high‐quality design updates with highly noisy and biased stochastic gradients. The sample size is reduced to two (minimum for unbiased variance estimation) and is shown to be sufficient for evaluating stochastic gradients to obtain robust designs, thus drastically reducing the computational cost. The AC‐MDSA update formula based on entropic ℓ1‐norm is derived, which mimics the feasible space geometry. A momentum‐based acceleration scheme is integrated to accelerate the convergence, stabilize the design evolution, and alleviate step size sensitivity. Several 2D and 3D examples are presented to demonstrate the effectiveness and efficiency of the proposed AC‐MDSA to handle RTO involving various loading uncertainties. Comparison with other methods shows that the proposed AC‐MDSA is superior in computational cost, stability, and convergence speed.
Detection of Mutagenic Activity of Mycotoxins by Salmonella typhimurium/microsome Assay and Ultra-weak Chemiluminescence.

Nippon Elyo Shokuryo Gakkaishi (J. Jpn. Soc. Nutr. Food Sci.) 45, 169~173 (1992) Twenty-one mycotoxins were examined for mutagenic potency in the Salmonella typhimurium/ microsome assay. Bisfuranoid mycotoxins such as afiatoxin B1 and G1, showed potent mutagenicity, but B2 and sterigmatocystin showed weak mutagenicity. Aflatoxin G2, PR-toxin (an epoxide) and kojic acid gave negative results. Trichothecenes having an epoxide ring, such as T-2 toxin, nivalenol and fusarenon X, were also negative. The intensity of biochemiluminescence of the toxins by activation with S9 mix was also measured by a luminescence analyzer sensitive wavelengths of around 300-650nm (optimum 400nm). Significant emission was observed for bisfuranoid mycotoxins and PR-toxin in addition to cyclopiazonic acid (an indole). These results are nearly consistent with those obtained from bacterial assays.
MementoML: Performance of selected machine learning algorithm configurations on OpenML100 datasets

Finding optimal hyperparameters for the machine learning algorithm can often significantly improve its performance. But how to choose them in a time-efficient way? In this paper we present the protocol of generating benchmark data describing the performance of different ML algorithms with different hyperparameter configurations. Data collected in this way is used to study the factors influencing the algorithm's performance. This collection was prepared for the purposes of the study presented in the EPP study. We tested algorithms performance on dense grid of hyperparameters. Tested datasets and hyperparameters were chosen before any algorithm has run and were not changed. This is a different approach than the one usually used in hyperparameter tuning, where the selection of candidate hyperparameters depends on the results obtained previously. However, such selection allows for systematic analysis of performance sensitivity from individual hyperparameters. This resulted in a comprehensive dataset of such benchmarks that we would like to share. We hope, that computed and collected result may be helpful for other researchers. This paper describes the way data was collected. Here you can find benchmarks of 7 popular machine learning algorithms on 39 OpenML datasets. The detailed data forming this benchmark are available at: this https URL.
On the waveform of the scalar induced gravitational waves

The scalar induced gravitational waves (SIGWs) is a useful tool to probe the physics in the early universe. To study inflationary models with this tool, we need to know how the waveform of SIGWs is related to the shape of the scalar power spectrum. We propose two parameterizations to approximate the scalar power spectrum with either a sharp or a broad spike at small scales, and then use these two parameterizations to study the relation between the shapes of $\Omega_{GW}$ and the scalar power spectrum. We find that the waveform of SIGWs has a similar shape to the power spectrum. Away from the peak of the spike, the frequency relation $\Omega_{GW}(k)\sim \mathcal{P}_\zeta^2(k)$ holds independent of the functional form of the scalar power spectrum. We also give a physical explanation for this general relationship. The general relation is useful for determining the scalar power spectrum and probing inflationary physics with the waveform of SIGWs.
C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text Using Feature Engineering

In today’s interconnected and multilingual world, code-mixing of languages on social media is a common occurrence. While many Natural Language Processing (NLP) tasks like sentiment analysis are mature and well designed for monolingual text, techniques to apply these tasks to code-mixed text still warrant exploration. This paper describes our feature engineering approach to sentiment analysis in code-mixed social media text for SemEval-2020 Task 9: SentiMix. We tackle this problem by leveraging a set of hand-engineered lexical, sentiment, and metadata fea- tures to design a classifier that can disambiguate between “positive”, “negative” and “neutral” sentiment. With this model we are able to obtain a weighted F1 score of 0.65 for the “Hinglish” task and 0.63 for the “Spanglish” tasks.
Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives

In recent years, industrial robots have been installed in various industries to handle advanced manufacturing and high precision tasks. However, further integration of industrial robots is hampered by their limited flexibility, adaptability and decision making skills compared to human operators. Assembly tasks are especially challenging for robots since they are contact-rich and sensitive to even small uncertainties. While reinforcement learning (RL) offers a promising framework to learn contact-rich control policies from scratch, its applicability to high-dimensional continuous state-action spaces remains rather limited due to high brittleness and sample complexity. To address those issues, we propose different pruning methods that facilitate convergence and generalization. In particular, we divide the task into free and contact-rich sub-tasks, perform the control in Cartesian rather than joint space, and parameterize the control policy. Those pruning methods are naturally implemented within the framework of dynamic movement primitives (DMP). To handle contact-rich tasks, we extend the DMP framework by introducing a coupling term that acts like the human wrist and provides active compliance under contact with the environment. We demonstrate that the proposed method can learn insertion skills that are invariant to space, size, shape, and closely related scenarios, while handling large uncertainties. Finally we demonstrate that the learned policy can be easily transferred from simulations to real world and achieve similar performance on UR5e robot.
At Your Service: Coffee Beans Recommendation From a Robot Assistant

With advances in the field of machine learning, service robots are envisioned to become more present. The COVID-19 pandemic has accelerated this need. One such example would be coffee shops, which have become intrinsic to our everyday lives. Yet, serving an excellent cup of coffee is not trivial as a coffee blend typically comprises rich aromas, indulgent and unique flavours. Our work addresses this by proposing a computational model which recommends optimal coffee beans resulting from users' preferences. Given coffee properties (objective features), we apply different supervised learning techniques to predict coffee qualities (subjective features). We then consider an unsupervised learning method to analyse the relationship between coffee beans in the subjective feature space. Evaluated on a real coffee beans dataset based on digitised reviews, our results illustrate that the proposed computational model gives up to 92.7 percent recommendation accuracy for coffee prediction. From this, we propose how it can be deployed on a robot.
Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization

We introduce Auto-Surprise1, an automated recommender system library. Auto-Surprise is an extension of the Surprise recommender system library and eases the algorithm selection and configuration process. Compared to an out-of-the-box Surprise library, without hyper parameter optimization, AutoSurprise performs better, when evaluated with MovieLens, Book Crossing and Jester datasets. It may also result in the selection of an algorithm with significantly lower runtime. Compared to Surprise’s grid search, Auto-Surprise performs equally well or slightly better in terms of RMSE, and is notably faster in finding the optimum hyperparameters.
On the geometry of the symmetrized bidisc

We study the action of the automorphism group of the $2$ complex dimensional manifold symmetrized bidisc $\mathbb{G}$ on itself. The automorphism group is 3 real dimensional. It foliates $\mathbb{G}$ into leaves all of which are 3 real dimensional hypersurfaces except one, viz., the royal variety. This leads us to investigate Isaev's classification of all Kobayashi-hyperbolic 2 complex dimensional manifolds for which the group of holomorphic automorphisms has real dimension 3 studied by Isaev. Indeed, we produce a biholomorphism between the symmetrized bidisc and the domain \[\{(z_1,z_2)\in \mathbb{C} ^2 : 1+|z_1|^2-|z_2|^2>|1+ z_1 ^2 -z_2 ^2|, Im(z_1 (1+\overline{z_2}))>0\}\] in Isaev's list. Isaev calls it $\mathcal D_1$. The road to the biholomorphism is paved with various geometric insights about $\mathbb{G}$. Several consequences of the biholomorphism follow including two new characterizations of the symmetrized bidisc and several new characterizations of $\mathcal D_1$. Among the results on $\mathcal D_1$, of particular interest is the fact that $\mathcal D_1$ is a "symmetrization". When we symmetrize (appropriately defined in the context in the last section) either $\Omega_1$ or $\mathcal{D}^{(2)}_1$ (Isaev's notation), we get $\mathcal D_1$. These two domains $\Omega_1$ and $\mathcal{D}^{(2)}_1$ are in Isaev's list and he mentioned that these are biholomorphic to $\mathbb{D} \times \mathbb{D}$. We produce explicit biholomorphisms between these domains and $\mathbb{D} \times \mathbb{D}$.