Miracle-dz/UniFAR-FTUs huggingface dataset:
Cannot Predict Comment Volume of a News Article before (a few) Users Read It

Many news outlets allow users to contribute comments on topics about daily world events. News articles are the seeds that spring users' interest to contribute content, i.e., comments. An article may attract an apathetic user engagement (several tens of comments) or a spontaneous fervent user engagement (thousands of comments). In this paper, we study the problem of predicting the total number of user comments a news article will receive. Our main insight is that user-to-user interaction factors contribute the most to an accurate prediction, while news article specific factors have surprisingly little influence. This appears to be an interesting and understudied phenomenon: collective social behavior at a news outlet shapes user response and may even downplay the content of an article. We compile and analyze a large number of features, both old and novel from literature. The features span a broad spectrum of facets including news article and comment contents, temporal dynamics, sentiment/linguistic features, and user behaviors. We show that the early arrival rate of comments is the best indicator of the eventual number of comments. We conduct an in-depth analysis of this feature across several dimensions, such as news outlets and news article categories. We show that the relationship between the early rate and the final number of comments as well as the prediction accuracy vary considerably across news outlets and news article categories (e.g., politics, sports, or health).
Hate speech detection and racial bias mitigation in social media based on BERT model

Disparate biases associated with datasets and trained classifiers in hateful and abusive content identification tasks have raised many concerns recently. Although the problem of biased datasets on abusive language detection has been addressed more frequently, biases arising from trained classifiers have not yet been a matter of concern. In this paper, we first introduce a transfer learning approach for hate speech detection based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers) and evaluate the proposed model on two publicly available datasets that have been annotated for racism, sexism, hate or offensive content on Twitter. Next, we introduce a bias alleviation mechanism to mitigate the effect of bias in training set during the fine-tuning of our pre-trained BERT-based model for hate speech detection. Toward that end, we use an existing regularization method to reweight input samples, thereby decreasing the effects of high correlated training set’ s n-grams with class labels, and then fine-tune our pre-trained BERT-based model with the new re-weighted samples. To evaluate our bias alleviation mechanism, we employed a cross-domain approach in which we use the trained classifiers on the aforementioned datasets to predict the labels of two new datasets from Twitter, AAE-aligned and White-aligned groups, which indicate tweets written in African-American English (AAE) and Standard American English (SAE), respectively. The results show the existence of systematic racial bias in trained classifiers, as they tend to assign tweets written in AAE from AAE-aligned group to negative classes such as racism, sexism, hate, and offensive more often than tweets written in SAE from White-aligned group. However, the racial bias in our classifiers reduces significantly after our bias alleviation mechanism is incorporated. This work could institute the first step towards debiasing hate speech and abusive language detection systems.
MLM: A Benchmark Dataset for Multitask Learning with Multiple Languages and Modalities

In this paper, we introduce the MLM (Multiple Languages and Modalities) dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. The generation process and inclusion of semantic data provide a resource that further tests the ability for multitask systems to learn relationships between entities. The dataset is designed for researchers and developers who build applications that perform multiple tasks on data encountered on the web and in digital archives. A second version of MLM provides a geo-representative subset of the data with weighted samples for countries of the European Union. We demonstrate the value of the resource in developing novel applications in the digital humanities with a motivating use case and specify a benchmark set of tasks to retrieve modalities and locate entities in the dataset. Evaluation of baseline multitask and single task systems on the full and geo-representative versions of MLM demonstrate the challenges of generalising on diverse data. In addition to the digital humanities, we expect the resource to contribute to research in multimodal representation learning, location estimation, and scene understanding.
Feature Selection Methods for Cost-Constrained Classification in Random Forests

Cost-sensitive feature selection describes a feature selection problem, where features raise individual costs for inclusion in a model. These costs allow to incorporate disfavored aspects of features, e.g. failure rates of as measuring device, or patient harm, in the model selection process. Random Forests define a particularly challenging problem for feature selection, as features are generally entangled in an ensemble of multiple trees, which makes a post hoc removal of features infeasible. Feature selection methods therefore often either focus on simple pre-filtering methods, or require many Random Forest evaluations along their optimization path, which drastically increases the computational complexity. To solve both issues, we propose Shallow Tree Selection, a novel fast and multivariate feature selection method that selects features from small tree structures. Additionally, we also adapt three standard feature selection algorithms for cost-sensitive learning by introducing a hyperparameter-controlled benefit-cost ratio criterion (BCR) for each method. In an extensive simulation study, we assess this criterion, and compare the proposed methods to multiple performance-based baseline alternatives on four artificial data settings and seven real-world data settings. We show that all methods using a hyperparameterized BCR criterion outperform the baseline alternatives. In a direct comparison between the proposed methods, each method indicates strengths in certain settings, but no one-fits-all solution exists. On a global average, we could identify preferable choices among our BCR based methods. Nevertheless, we conclude that a practical analysis should never rely on a single method only, but always compare different approaches to obtain the best results.
A Data-Driven Control-Theoretic Paradigm for Pandemic Mitigation with Application to Covid-19

In this paper, we introduce a new control-theoretic paradigm for mitigating the spread of a virus. To this end, our discrete-time controller, aims to reduce the number of new daily deaths, and consequently, the cumulative number of deaths. In contrast to much of the existing literature, we do not rely on a potentially complex virus transmission model whose equations must be customized to the "particulars" of the pandemic at hand. For new viruses such as Covid-19, the epidemiology driving the modelling process may not be well known and model estimation with limited data may be unreliable. With this motivation in mind, the new paradigm described here is data-driven and, to a large extent, we avoid modelling difficulties by concentrating on just two key quantities which are common to pandemics: the doubling time, denoted by $d(k)$ and the peak day denoted by $\theta(k)$. Our numerical studies to date suggest that our appealingly simple model can provide a reasonable fit to real data. Given that time is of the essence during the ongoing global health crisis, the intent of this paper is to introduce this new paradigm to control practitioners and describe a number of new research directions suggested by our current results.
The transuranic elements and the island of stability

Since the 1930s the synthesis of nuclides too unstable to exist naturally on Earth has stretched the periodic table to 118 elements. While the lighter transuranic elements have found uses, the isotopes of those past lawrencium, the superheavy elements, are too unstable to exist outside the laboratory. In the 1970s, leading element discoverers Glenn Seaborg at the University of California, Berkeley, USA, and Georgy Flerov, at the Joint Institute for Nuclear Research in Dubna, USSR, took interest in a supposed ‘island of stability’, leading from the nuclear shell model of Maria Goeppert Mayer and Hans Jensen, and predicted elements with so-called magic numbers of protons and neutrons would be far more stable. This review shall look at the historical developments that led to the field of element discovery, the attempts to discover superheavy elements in nature based on the island of stability, and the subsequent successful synthesis of elements and the implications of their half-lives and properties. This article is part of the theme issue ‘Mendeleev and the periodic table’.
The role of objective and subjective measures in material similarity learning

Establishing a robust measure for material similarity that correlates well with human perception is a long-standing problem. A recent work presented a deep learning model trained to produce a feature space that aligns with human perception by gathering human subjective measures. The resulting metric outperforms objective existing ones. In this work, we aim to understand whether this increased performance is a result of using human perceptual data or is due to the nature of feature learnt by deep learning models. We train similar networks with objective measures (BRDF similarity or classification task) and show that these networks can predict human judgements as well, suggesting that the non-linear features learnt by convolutional network might be a key to model material perception.
Non-photorealistic Radiance Remapping

We present a color mapping method that corresponds to intensity and considers the interaction of light among objects in a 3-dimensional scene. Previous methods that map color according to geometric specifications or a rendered result do not reproduce the interreflection of surrounding objects. Using a path tracer, the proposed method replaces the radiance with an arbitrary one-dimensional texture in the sampled light transport path. The texture appearing on the object is calculated by a weighted average of the modified estimations. The proposed method can generate color variation in physically based intensity. It hence can reproduce physical events such as caustics or color bleeding while retaining the flexibility of rendering within a path tracing framework.
Towards CSR as a Regulatory Framework and Governance Tool in Disclosure of Corporate Social and Environmental Impact: The Example of Poland

Despite recognition of corporate social responsibility (CSR) as a global normative concept, differences exist in how the idea is understood. CSR has, so far, not put enough emphasis on national institutions. The aim of this paper is to present and evaluate CSR activities in Poland from 2002 to 2015. Additionally, we will discuss – based on Poland’s case – whether voluntary, market-driven CSR is a sufficient governance tool for emerging economies. The results of the study CSR in Poland support the argument for a pro-regulation attitude via CSR and mandatory nonfinancial information disclosure in reporting as tools to improve company governance and empower civil society in emerging economies. Voluntarybased and market-driven CSR models are not compatible with the realities of the emerging Polish economies.
Perspective on dietary isothiocyanates in the prevention, development and treatment of cancer

Epidemiological evidence has highlighted the association of specific diets and a lower incidence of cancer. Foremost, the Mediterranean diet provides high levels of polyphenolics and a high consumption of healthier fats, e.g., as from olive oil. In the Mediterranean region the consumption of vegetables is elevated providing a class of compounds, the isothiocyanates (ITCs) as found in the cabbage family. The ITCs have raised great interest for their health benefits over the past few decades. Some of the key ITC compounds, sulforaphane, phenethylisothiocyanate and benzyl isothiocyanate, have been studied in vitro and in vivo and the data support their promise for cancer chemoprevention, as anti-tumor agents, and for chemoprotection of normal tissues and organs. Along with other polyphenolic compounds in the diet, in general, they also possess key anti-inflammatory properties thus satisfying the criteria for compounds that could intervene in cancer initiation and progression. In this review we provide a larger overview of the advantages of including ITCs in the diet as food or as supplements and speculate on what could constitute a valuable therapeutic strategy for improving and sustaining good health and countering cancer disease in humans.
Modelling Private New Housing Starts In Australia

Kew words: Housing starts, completions, economic aggregates residential property cycles, causal relationships, time series models. A number of causal and non causal approaches to forecasting private new housing starts in Australia are considered in this paper. Simple time series models are developed in Excel and more complex ARIMA models are estimated using the popular econometric software SHAZAM. Combining models is also discussed.
A Survey of Weed Management in Organic Small Grains and Forage Systems in the Northwest United States

A lack of information regarding weed control, relative to conventional systems, has left organic growers largely on their own when devising weed management systems for organic crops. As interest in organic weed management increases, researchers need more information regarding the type and number of weed control practices undertaken on organic farms. A survey of certified organic growers was conducted in five states in the northwest United States to identify organic weed management programs and what grower and farm-operation characteristics were factors in weed management program design. Three types of weed management programs, with varying diversity in weed control practices, were identified. Stepwise binary logistic regression indicated that the likelihood of an organic grower using a more-diverse weed management program increased if the grower engaged in grain production and as the number of crops produced on an organic farm operation in 1 yr increased. The probability of operating a more-diverse weed management program also increased as a grower's education level increased. Organic hectarage operated was positively correlated with weed management program diversity, and with the adoption of cultural controls. Additionally, awareness of weeds as a factor causing yield loss was correlated with increased weed management program diversity. An increased awareness among researchers of the differing needs and abilities of organic growers in managing weeds on their farms will improve communication and outreach efforts when assisting growers with designing organic weed management programs.
A Study of Differently Expressed Brain Genes for Calcium Channel Gene Knockout and Wild-Type Mice by Statistical and Hierarchical Clustering

Brain science research becomes an important issue because there are still many unknown mystery in the human brain. The researchers make efforts to answer the questions of how are the functions of these learning and memory genes from human brain. Presently, microarray is the most mature technology of studying whole genome from specific organs, and hoping to discover new marker genes from them. Consequently, the data used in this paper are microarray data obtained from the α1H T-type calcium channel with gene knockout mice. It was proven by experiments that the express of calcium channel was very high with hippocampus. And hippocampus is related to situated learning and spatial memory. The main purpose of this paper is to select some target genes related to learning, memory, or brain diseases in order to perform follow-up verification with these genes. In this paper, a new selection brain learning and memory maker genes analysis system is proposed. At first, by using linear regression analysis to select up-regulated and down-regulated genes, and then using analysis of variance (ANOVA) to detect genetic differences between these genes. We take these genes as input variables for unsupervised classification models (Hierarchical clustering) to find successful clustering result. The accurate rate of this Hierarchical clustering obtained in this experiment are100%. These select marker genes can be used for further research in brain science by biological evolution. This paper also provides a friendly user graphic interface systems for visualization. Thus, it also provides an useful information for biological experiments to comparison with.
EFEK ANTIOKSIDAN EKSTRAK ETANOL 70% BIJI ALPUKAT (PERSEA AMERICANA MILL) DENGAN METODE DPPH

Avocado is traditionally used to treat various diseases among others, menorrhagia, diabetes, diarrhea, hypertension and others. In everyday use avocado seed is often not utilized. This study aims to know the effects of antioxidant of 70% ethanolic extract of the avocado seed (Persea Americana Mill). The method used is the in vitro antioxidant with DPPH. The extract was dissolved in methanol and made in a variety of concentrations, namely 10, 30, 50 and 70 ppm respectively as much as 10 ml. Into each solution was added to 1 mL of 1 mM DPPH solution and incubated at 37 ° C for 30 minutes, then measured at a wavelength of 515 nm. As the blank is used methanol and DPPH 1mM. BHT (butylated hydroxytoluene/BHT with concentration of 2 , 4 , 6 , 8 ug / mL were used as a comparator The results show the 70% ethanolic extract of avocado seed can inhibit free radicals with IC50 41,5ppm.
PERCEPTIONS OF RISK FROM DEER-RELATED VEHICLE ACCIDENTS: IMPLICATIONS FOR PUBLIC PREFERENCES FOR DEER HERD SIZE

Many areas of the eastern United States have large white-tailed deer (Odocoileus virginianus) populations in close proximity to people. Deer often occur in suburban areas and occasionally in urban areas. As a consequence, more people are experiencing negative interactions with deer (Connelly et al. 1987:1-4, Decker and Gavin 1987). Motor-vehicle collisions involving deer are a safety concern anywhere deer and people coexist. The incidence of deer-related vehicle accidents (DRVA's) has increased as deer populations (and traffic volumes) have grown in Michigan (Allen and McCullough 1976, Hansen 1983, Langenau and Rabe 1987:21), Pennsylvania (Naze 1989; D. S. deCalesta, U.S. For. Serv., Warren, Pa., pers. commun., 1991), Connecticut (M. R. Ellingwood and J. V. Spignesi, Conn. Wildl. Div., Hartford, pers. commun., 1991), Illinois (J. H. Witham, Ill. Dep. Energy and Nat. Resour., unpubl. data) and New York (J. R. Palmeteer, N.Y. State Dep. Environ. Conserv. [NYSDEC], Delmar, unpubl. data). Deer-related vehicle accidents occur not only when a deer and vehicle collide, but also when accidents result from motorists' attempts to avoid deer. Human fatalities occur in a small proportion of DRVA's, but the cost of vehicle damage is substantial. A study of incidence and costs of reported and unreported DRVA's in Tompkins County, New York, during 1988 yielded a conservative estimate of 57,000 DRVA's statewide, causing $50 million in vehicle damage (Decker et al. 1990). Public awareness of DRVA's may intensify in areas where the density of deer and motorists creates a greater incidence of accidents. Associated perceptions about the threat of deer to public safety may influence public attitudes and behaviors toward deer, including public preferences regarding the size of deer populations. The prevalence of various vehiclemounted "deer whistles" intended to frighten deer away from moving vehicles is evidence of people's concern, although the effectiveness of such devices is disputed (Schwatbach 1989, Bomford and O'Brien 1990, Romin and Dalton 1992). Studies from New York indicate that more people are concerned about deer-car collisions than about Lyme disease (Connelly et al. 1987:24-25; Decker and Gavin 1987; R. J. Stout, unpubl. data), even though New York has the greatest number of reported cases of Lyme disease nationwide (D. White, N.Y. State Tick-borne Dis. Inst., Albany, pers. commun., 1991). Perceptions of the threat of DRVA's to public safety may influence people's (especially motorists') preferences regarding the size of deer populations and support for various deer management objectives.
Combined effect of durian (Durio zibethinus Murr.) and β-glucan on glycaemic response and food intake in male rats

Durian (Durio zibethinus Murr.) is notorious for its stench. However, durian has a smooth texture and creamy taste, and is rich in macronutrients, bioactive compounds, and volatile compounds. These qualities could be useful in combination with other food components such as β-glucan. β-Glucan is a type of soluble fibre that has been approved by the European Food Safety Authority (EFSA) for beneficially reducing the postprandial glucose response. The aims of this study were to determine the total polyphenol content in durian and to determine the combined effects of durian and β-glucan on glycaemic response and food intake in rats. The total polyphenols content in the durians (variety D24) was 131.40 (22.20) mg GAE/100 g fresh weight. Durian significantly reduced (p < 0.05) postprandial glucose responses at 15, 30, 60, and 120 mins compared with control. A combination of durian and β-glucan significantly (p < 0.05) reduced glucose responses at 30, 60 and 90 mins compared with control. A combination of durian and β-glucan significantly (p < 0.05) reduced glucose responses at 120 mins compared with control and β-glucan. Food intake of the durian + β-glucan group was significantly (p < 0.05) lower than the control, durian and β-glucan groups. This study suggests that a combination of durian and β-glucan potentially reduces glycaemic response and food intake but this needs to be tested in humans.
Transgenic Maize Plants Obtained by Pollen-mediated Transformation

The genetic transformation was achieved by pollen_mediated approach on maize ( Zea mays L.) inbred lines Tai 9101 and Zong 31. Plasmid DNA of pGLⅡ_RC_1 was mixed with fresh pollen of maize inbreds in sucrose solution. The pollens were treated by ultrasonication and collected, pollinated on silks of maize ears. Transformants were confirmed by dot blot hybridization, PCR amplification and PCR_Southern blot hybridization. The pollen_mediated transformation approach could circumvent the tedious tissue culture procedures like in particle bombardment and Agrobacterium infection, etc. This approach is simple, easy to operate, and could be widely used in practice.