Miracle-dz/UniFAR-FTUs huggingface dataset:
Technical report: Impact of evaluation metrics and sampling on the comparison of machine learning methods for biodiversity indicators prediction

Machine learning (ML) approaches are used more and more widely in biodiversity monitoring. In particular, an important application is the problem of predicting biodiversity indicators such as species abundance, species occurrence or species richness, based on predictor sets containing, e.g., climatic and anthropogenic factors. Considering the impressive number of different ML methods available in the litterature and the pace at which they are being published, it is crucial to develop uniform evaluation procedures, to allow the production of sound and fair empirical studies. However, defining fair evaluation procedures is challenging: because welldocumented, intrinsic properties of biodiversity indicators such as their zero-inflation and over-dispersion, it is not trivial to design good sampling schemes for cross-validation nor good evaluation metrics. Indeed, the classical Mean Squared Error (MSE) fails to capture subtle differences in the performance of different methods, particularly in terms of prediction of very small, or very large values (e.g., zero counts or large counts). In this report, we illustrate this phenomenon by comparing ten statistical and machine learning models on the task of predicting waterbirds abundance in the North-African area, based on geographical, meteorological and spatio-temporal factors. Our results highlight that differnte off-the-shelf evaluation metrics and cross-validation sampling approaches yield drastically different rankings of the metrics, and fail to capture interpretable conclusions.
Independent Ethical Assessment of Text Classification Models: A Hate Speech Detection Case Study

An independent ethical assessment of an artificial intelligence system is an impartial examination of the system's development, deployment, and use in alignment with ethical values. System-level qualitative frameworks that describe high-level requirements and component-level quantitative metrics that measure individual ethical dimensions have been developed over the past few years. However, there exists a gap between the two, which hinders the execution of independent ethical assessments in practice. This study bridges this gap and designs a holistic independent ethical assessment process for a text classification model with a special focus on the task of hate speech detection. The assessment is further augmented with protected attributes mining and counterfactual-based analysis to enhance bias assessment. It covers assessments of technical performance, data bias, embedding bias, classification bias, and interpretability. The proposed process is demonstrated through an assessment of a deep hate speech detection model.
A BCS-GDE Algorithm for Multi-objective Optimization of Combined Cooling, Heating and Power Model

District energy systems can not only reduce energy consumption but also set energy supply dispatching schemes according to demand. In this paper, the combined cooling heating and power economic emission dispatch (CCHPEED) model is established with the objective of economic cost, primary energy consumption, and pollutant emissions, as well as the decision-making strategie is proposed to meet the demand for energy supply. Besides, a generalized differential evolution with the best compromise solution processing mechanism (BCS-GDE) is proposed to solve the model, also, the best compromise solution processing mechanism is put forward in the algorithm for the first time. In the simulation, the resource dispatching is performed according to the different energy demands of hotels, offices, and residential buildings on the whole day. The simulation results show that the model established in this paper can reduce the economic cost, energy consumption, and pollutant emission, in which the maximum reduction rate of economic cost is 36%, the maximum reduction rate of primary energy consumption is 38%, and the maximum reduction rate of pollutant emission is 61%. Concurrently, BCS-GDE also has better convergence and diversity than the classic algorithms.
ANOMALYMAXQ: Anomaly-Structured Maximization to Query in Attributed Network

The detection of anomaly subgraphs naturally appears in various real-life tasks, yet label noise seriously interferes with the result. As a motivation for our work, we focus on inaccurate supervision and use prior knowledge to reduce effects of noise, like query graphs. Anomalies in attributed networks exhibit structured-properties, e.g., anomaly in money laundering with"ring structure"property. It is the main challenge to fast and approximate query anomaly in attributed networks. We propose a novel search method: 1) decomposing a query graph into stars; 2) sorting attributed vertices; and 3) assembling anomaly stars under the root vertex sequence into near query. We present ANOMALYMAXQ and perform on 68,411 company network (Tianyancha dataset),7.72m patent networks (Company patents) and so on. Extensive experiments show that our method has high robustness and fast response time. When running the patent dataset,the average running time to query the graph once is about 252 seconds.
Duality and Outermost Boundaries in Generalized Percolation Lattices

In this paper we consider a connected planar graph G and impose conditions that results in G having a percolation lattice-like cellular structure. Assigning each cell of G to be either occupied or vacant, we describe the outermost boundaries of star and plus connected components in G. We then consider the dual graph of G and impose conditions under which the dual is also a percolation lattice. Finally, using G and its dual, we construct vacant cell cycles surrounding occupied components and study left right crossings and bond percolation in rectangles.
Learning to Cluster via Same-Cluster Queries

We study the problem of learning to cluster data points using an oracle which can answer same-cluster queries. Different from previous approaches, we do not assume that the total number of clusters is known at the beginning and do not require that the true clusters are consistent with a predefined objective function such as the K-means. These relaxations are critical from the practical perspective and, meanwhile, make the problem more challenging. We propose two algorithms with provable theoretical guarantees and verify their effectiveness via an extensive set of experiments on both synthetic and real-world data.
Demonstrating REACT: a Real-time Educational AI-powered Classroom Tool

We present a demonstration of REACT, a new Real-time Educational AI-powered Classroom Tool that employs EDM techniques for supporting the decision-making process of educators. REACT is a data-driven tool with a user-friendly graphical interface. It analyzes students’ performance data and provides context-based alerts as well as recommendations to educators for course planning. Furthermore, it incorporates model-agnostic explanations for bringing explainability and interpretability in the process of decision making. This paper demonstrates a use case scenario of our proposed tool using a real-world data set, and presents the design of its architecture and user-interface. This demonstration focuses on the agglomerative clustering of students based on their performance (i.e., incorrect responses and hints used) during an in-class activity. This formation of clusters of students with similar strengths and weaknesses may help educators to improve their course planning by identifying at-risk students, forming study groups, or encouraging tutoring between students of different strengths.
Cross-Image Region Mining With Region Prototypical Network for Weakly Supervised Segmentation

Weakly supervised image segmentation trained with image-level labels usually suffers from inaccurate coverage of object areas during the generation of the pseudo groundtruth. This is because the object activation maps are trained with the classification objective and lack the ability to generalize. To improve the generality of the object activation maps, we propose a region prototypical network (RPNet) to explore the cross-image object diversity of the training set. Similar object parts across images are identified via region feature comparison. Object confidence is propagated between regions to discover new object areas while background regions are suppressed. Experiments show that the proposed method generates more complete and accurate pseudo object masks while achieving state-of-the-art performance on PASCAL VOC 2012 and MS COCO. In addition, we investigate the robustness of the proposed method on reduced training sets. The code is available at https://github.com/liuweide01/RPNet-Weakly-Supervised-Segmentation.
Trace formalism for motivic cohomology

The goal of this paper is to construct trace maps for the six functor formalism of motivic cohomology after Voevodsky, Ayoub, and Cisinski-D\'{e}glise. We also construct an $\infty$-enhancement of such a trace formalism. In the course of the $\infty$-enhancement, we need to reinterpret the trace formalism in a more functorial manner. This is done by using Suslin-Voevodsky's relative cycle groups.
A study on the mechanical, thermal properties and crystallization behavior of poly(lactic acid)/thermoplastic poly(propylene carbonate) polyurethane blends

Poly(lactic acid) (PLA) was toughened by the thermoplastic poly(propylene carbonate) polyurethane (PPCU), and the mechanical properties, crystallization behaviour and enzymatic degradation of the PLA/PPCU blends were investigated. The mechanical testing results showed that the impact strength and elongation at break of PLA/PPCU blends were remarkably higher than those of pure PLA. The impact strength was enhanced from 4.8 kJ m−2 for pure PLA to 102.8 kJ m−2 for the 50/50 PLA/PPCU blend. The impact testing showed obvious ductile fracture after PLA was blended with PPCU. It was found that the PLA matrix showed large plastic deformation (shear yielding) in the blend subjected to impact tests, which is an important energy-dissipation process and led to a toughened polymer blend. PPCU could act as an impact modifier for PLA. The maximum improvement in the cold crystallization temperature (Tcc) value was observed for PLA/PPCU blends where Tcc was increased from 113 °C to 128 °C. PPCU enhanced the crystallization degree of PLA, but the positive function of crystallization was weakened when the content of PPCU was increased from 10 to 50 wt%. Isothermal crystallization kinetics showed that PPCU improved the crystallinity of the PLA and reduced the crystallization rate of blends. And the incorporation of PPCU did not alter the crystal structure of the PLA matrix. The PLA/PPCU blends were high strength and high toughness, entirely biodegradable materials, which is important for manufacture and application.
A simple equilibration procedure leading to polynomial-degree-robust a posteriori error estimators for the curl-curl problem

We introduce two a posteriori error estimators for Nédélec finite element discretizations of the curl-curl problem. These estimators pertain to a new Prager–Synge identity and an associated equilibration procedure. They are reliable and efficient, and the error estimates are polynomial-degree-robust. In addition, when the domain is convex, the reliability constants are fully computable. The proposed error estimators are also cheap and easy to implement, as they are computed by solving divergence-constrained minimization problems over edge patches. Numerical examples highlight our key findings, and show that both estimators are suited to drive adaptive refinement algorithms. Besides, these examples seem to indicate that guaranteed upper bounds can be achieved even in non-convex domains.
Traces of links and simply connected 4‐manifolds

We study the set ŜM$\widehat{\mathcal {S}}_M$ of framed smoothly slice links which lie on the boundary of the complement of a 1‐handlebody in a closed, simply connected, smooth 4‐manifold M$M$ . We show that ŜM$\widehat{\mathcal {S}}_M$ is well defined and describe how it relates to exotic phenomena in dimension four. In particular, in the case when X$X$ is a smooth 4‐manifold‐with‐boundary, with a handle decompositions with no 1‐handles and homeomorphic to but not smoothly embeddable in D4$D^4$ , our results tell us that X$X$ is exotic if and only if there is a link L↪S3$L\hookrightarrow S^3$ which is smoothly slice in X$X$ , but not in D4$D^4$ . Furthermore, we extend the notion of high genus 2‐handles attachment, introduced by Hayden and Piccirillo, to prove that exotic 4‐disks that are smoothly embeddable in D4$D^4$ , and therefore possible counterexamples to the smooth 4‐dimensional Schönflies conjecture, cannot be distinguished from D4$D^4$ only by comparing the slice genus functions of links.
Global well-posedness for fractional Sobolev-Galpern type equations

This article is a comparative study on an initial-boundary value problem for a class of semilinear pseudo-parabolic equations with the fractional Caputo derivative, also called the fractional Sobolev-Galpern type equations. The purpose of this work is to reveal the influence of the degree of the source nonlinearity on the well-posedness of the solution. By considering four different types of nonlinearities, we derive the global well-posedness of mild solutions to the problem corresponding to the four cases of the nonlinear source terms. For the advection source function case, we apply a nontrivial limit technique for singular integral and some appropriate choices of weighted Banach space to prove the global existence result. For the gradient nonlinearity as a local Lipschitzian, we use the Cauchy sequence technique to show that the solution either exists globally in time or blows up at finite time. For the polynomial form nonlinearity, by assuming the smallness of the initial data we derive the global well-posed results. And for the case of exponential nonlinearity in two-dimensional space, we derive the global well-posedness by additionally using an Orlicz space.
ImitAL: Learning Active Learning Strategies from Synthetic Data

One of the biggest challenges that complicates applied supervised machine learning is the need for huge amounts of labeled data. Active Learning (AL) is a well-known standard method for efficiently obtaining labeled data by first labeling the samples that contain the most information based on a query strategy. Although many methods for query strategies have been proposed in the past, no clear superior method that works well in general for all domains has been found yet. Additionally, many strategies are computationally expensive which further hinders the widespread use of AL for large-scale annotation projects. We, therefore, propose ImitAL, a novel query strategy, which encodes AL as a learning-to-rank problem. For training the underlying neural network we chose Imitation Learning. The required demonstrative expert experience for training is generated from purely synthetic data. To show the general and superior applicability of \ImitAL{}, we perform an extensive evaluation comparing our strategy on 15 different datasets, from a wide range of domains, with 10 different state-of-the-art query strategies. We also show that our approach is more runtime performant than most other strategies, especially on very large datasets.
Understanding the factors related to the opioid epidemic using machine learning

In recent years, the US has experienced an opioid epidemic with an unprecedented number of drugs overdose deaths. Research finds such overdose deaths are linked to neighborhood-level traits, thus providing opportunity to identify effective interventions. Typically, techniques such as Ordinary Least Squares (OLS) or Maximum Likelihood Estimation (MLE) are used to document neighborhood-level factors significant in explaining such adverse outcomes. These techniques are, however, less equipped to ascertain non-linear relationships between confounding factors. Hence, in this study we apply machine learning based techniques to identify opioid risks of neighborhoods in Delaware and explore the correlation of these factors using Shapley Additive explanations (SHAP). We discovered that the factors related to neighborhoods’ environment, followed by education and then crime, were highly correlated with higher opioid risk. We also explored the change in these correlations over the years to understand the changing dynamics of the epidemic. Furthermore, we discovered that, as the epidemic has shifted from legal (i.e., prescription opioids) to illegal (e.g., heroin and fentanyl) drugs in recent years, the correlation of environment, crime and health related variables with the opioid risk has increased significantly while the correlation of economic and socio-demographic variables has decreased. The correlation of education related factors has been higher from the start and has increased slightly in recent years suggesting a need for increased awareness about the opioid epidemic.
Not All Linearizations Are Equally Data-Hungry in Sequence Labeling Parsing

Different linearizations have been proposed to cast dependency parsing as sequence labeling and solve the task as: (i) a head selection problem, (ii) finding a representation of the token arcs as bracket strings, or (iii) associating partial transition sequences of a transition-based parser to words. Yet, there is little understanding about how these linearizations behave in low-resource setups. Here, we first study their data efficiency, simulating data-restricted setups from a diverse set of rich-resource treebanks. Second, we test whether such differences manifest in truly low-resource setups. The results show that head selection encodings are more data-efficient and perform better in an ideal (gold) framework, but that such advantage greatly vanishes in favour of bracketing formats when the running setup resembles a real-world low-resource configuration.
Bounding the number of edges of matchstick graphs

We show that a matchstick graph with $n$ vertices has no more than $3n-c\sqrt{n-1/4}$ edges, where $c=\frac12(\sqrt{12} + \sqrt{2\pi\sqrt{3}})$. The main tools in the proof are the Euler formula, the isoperimetric inequality, and an upper bound for the number of edges in terms of $n$ and the number of non-triangular faces. We also find a sharp upper bound for the number of triangular faces in a matchstick graph.
Prediction of Students performance with Artificial Neural Network using Demographic Traits

Many researchers have studied student academic performance in supervised and unsupervised learning using numerous data mining techniques. Neural networks often need a greater collection of observations to achieve enough predictive ability. Due to the increase in the rate of poor graduates, it is necessary to design a system that helps to reduce this menace as well as reduce the incidence of students having to repeat due to poor performance or having to drop out of school altogether in the middle of the pursuit of their career. It is therefore necessary to study each one as well as their advantages and disadvantages, so as to determine which is more efficient in and in what case one should be preferred over the other. The study aims to develop a system to predict student performance with Artificial Neutral Network using the student demographic traits so as to assist the university in selecting candidates (students) with a high prediction of success for admission using previous academic records of students granted admissions which will eventually lead to quality graduates of the institution. The model was developed based on certain selected variables as the input. It achieved an accuracy of over 92.3 percent, showing Artificial Neural Network potential effectiveness as a predictive tool and a selection criterion for candidates seeking admission to a university.
The relative calibration of radial velocity for LAMOST medium resolution stellar spectra

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) started a median-resolution spectroscopic (MRS, R ∼7500) survey since October 2018. The main scientific goals of MRS, including binary stars, pulsators and other variable stars, were launched with a time-domain spectroscopic survey. However, the systematic errors, including the bias induced from wavelength calibration and the systematic difference between different spectrographs, have to be carefully considered during radial velocity measurement. In this work, we provide a technique to correct the systematics in the wavelength calibration based on the relative radial velocity measurements from LAMOST MRS spectra. We show that, for the stars with multi-epoch spectra, the systematic bias which is induced from the exposures on different nights can be corrected well for LAMOST MRS in each spectrograph. In addition, the precision of radial velocity zero-point of multi-epoch time-domain observations reaches below 0.5 km s−1. As a by-product, we also give the constant star candidates**, which can be the secondary radial-velocity standard star candidates of LAMOST MRS time-domain surveys.
Detecting Crowdsourced Test Report Consistency for Mobile Apps with Deep Image Understanding and Text Analysis

Crowdsourced testing, as a distinct testing paradigm, has attracted much attention in software testing, especially in mobile application (app) testing field. Compared with in-house testing, crowd-sourced testing outperforms because it utilize the diverse testing environments of different crowdworkers faced with the mobile testing fragmentation problem. However, crowdsourced testing also brings some problem. The crowdworkers involved are with different expertise, and they are not professional testers. Therefore, the reports they may submit are numerous and with uneven quality. App developers have to distinguish high-quality reports from low-quality ones to help the bug revealing and fixing. Some crowd-workers would submit inconsistent test reports, which means the textual descriptions are not focusing on the attached bug occurring screenshots. Such reports cause the waste on both time and human resources of app developing and testing. To solve such a problem, we propose ReCoDe in this paper, which is designed to detect the consistency of crowdsourced test reports via deep image-and-text fusion understanding. First, according to a pre-conducted survey, ReCoDe classifies the crowd-sourced test reports into 10 categories, which covers the vast majority of reported problems in the test reports. Then, for each category of bugs, we have distinct processing models. The models have a deep fusion understanding on both image information and textual descriptions. We also have conducted an experiment to evaluate