Miracle-dz/UniFAR-FTUs huggingface dataset:
Question Headline Generation for News Articles

In this paper, we introduce and tackle the Question Headline Generation (QHG) task. The motivation comes from the investigation of a real-world news portal where we find that news articles with question headlines often receive much higher click-through ratio than those with non-question headlines. The QHG task can be viewed as a specific form of the Question Generation (QG) task, with the emphasis on creating a natural question from a given news article by taking the entire article as the answer. A good QHG model thus should be able to generate a question by summarizing the essential topics of an article. Based on this idea, we propose a novel dual-attention sequence-to-sequence model (DASeq2Seq) for the QHG task. Unlike traditional sequence-to-sequence models which only employ the attention mechanism in the decoding phase for better generation, our DASeq2Seq further introduces a self-attention mechanism in the encoding phase to help generate a good summary of the article. We investigate two ways of the self-attention mechanism, namely global self-attention and distributed self-attention. Besides, we employ a vocabulary gate over both generic and question vocabularies to better capture the question patterns. Through the offline experiments, we show that our approach can significantly outperform the state-of-the-art question generation or headline generation models. Furthermore, we also conduct online evaluation to demonstrate the effectiveness of our approach using A/B test.
Depth-Aware Image Colorization Network

The color bleeding problem remains a challenging issue in image colorization. That is, different objects share the same color when they are nearby, leading to the boundary between objects looks unnatural. In this paper, we study how to combine depth information into a neural network and achieve better image colorization. The reasons to integrate depth information are twofold: (1) Depth information clearly provides boundary information between objects, and (2) depth information is commonly available as the development of RGB-D cameras. To the best of our knowledge, depth information was not considered in image colorization before. We evaluate the proposed method from both objective and subjective perspectives, and demonstrate that better colorization results can be obtained when depth information is further considered.
Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients

Artificial Neural Networks (ANNs) are currently being used as function approximators in many state-of-the-art Reinforcement Learning (RL) algorithms. Spiking Neural Networks (SNNs) have been shown to drastically reduce the energy consumption of ANNs by encoding information in sparse temporal binary spike streams, hence emulating the communication mechanism of biological neurons. Due to their low energy consumption, SNNs are considered to be important candidates as co-processors to be implemented in mobile devices. In this work, the use of SNNs as stochastic policies is explored under an energy-efficient first-to-spike action rule, whereby the action taken by the RL agent is determined by the occurrence of the first spike among the output neurons. A policy gradient-based algorithm is derived considering a Generalized Linear Model (GLM) for spiking neurons. Experimental results demonstrate the capability of online trained SNNs as stochastic policies to gracefully trade energy consumption, as measured by the number of spikes, and control performance. Significant gains are shown as compared to the standard approach of converting an offline trained ANN into an SNN.
Two clusters of residues contribute to the activity and substrate specificity of Fm1, a bifunctional oleate and linoleate desaturase of fungal origin

Polyunsaturated fatty acids (PUFAs) have important industrial, physiological, and nutritional properties. Plants use the sequential activities of FAD2 and FAD3 desaturases to convert 18:1Δ9 to the important PUFA 18:3Δ9,12,15, whereas the fungus Fusarium verticillioides 7600 uses the bifunctional desaturase Fm1 for both reactions. Here, we used a combination of sequence comparisons, structural modeling, and mutagenesis experiments to investigate Fm1's regioselectivity and identified two functionally relevant clusters of residues that contribute to Fm1 activity. We found that cluster I (Leu153, Phe157, and His194), located near the catalytic iron ions, predominantly affects activity, whereas cluster II (Tyr280, His284, and Leu287), located in a helix forming the entrance to the substrate-binding pocket, mainly specifies 15-desaturation. Individual or combined substitutions of cluster II residues substantially reduced 15-desaturation. The combination of F157W from cluster I with Y280L, H284V, and L287T from cluster II created an increased-activity variant that almost completely lost the ability to desaturate at C15 and acted almost exclusively as a 12-desaturase. No variants were identified in which 15-desaturation occurred in the absence of 12-desaturation. Fm1 displayed only traces of activity with C16 substrate, but several cluster I variants exhibited increased activity with both 18:1 and 16:1 substrates, converting 16:1Δ9 to 16:3Δ9,12,15, consistent with Fm1 performing sequential v + 3 desaturation reactions at C12 and then C15. We propose that cluster II residues interact with the substrate headgroup when the acyl chain contains both Δ9 and Δ12 double bonds, in which case C15 becomes positioned adjacent to the di-iron site enabling a second v + 3 desaturation.
Resistant Maltodextrin and Metabolic Syndrome: A Review

Abstract Resistant maltodextrin is a non-viscous dietary fiber that is fermentable in the colon by colonic bacteria. The objective of this review is to summarize the studies of resistant maltodextrin and its effect on metabolic profile, such as blood glucose, lipid profile, and body weight. Several studies support the idea that resistant maltodextrin may improve blood glucose, insulin sensitivity, lipid profile, and obesity. However, the use of resistant maltodextrin should be limited to minimize the adverse effect on the gastrointestinal system. This review provides information regarding the benefits of resistant maltodextrin on metabolic health as well as its proposed mechanism to enhance the knowledge of this novel fiber. Key teaching points Resistant maltodextrin is a novel non-viscous dietary fiber classified as resistant starch type V that is produced by debranching of the starch structure. Resistant maltodextrin is fermentable in the colon and thus produces short-chain fatty acid. Resistant maltodextrin helps to maintain blood and lipid profiles as well as promote satiety and reducing food intake. High intake of resistant maltodextrin may cause gastrointestinal discomfort due to the gas production and increased osmotic pressure.
Photo Squarization by Deep Multi-Operator Retargeting

Squared forms of photos are widely used in social media as album covers or thumbnails of image streams. In this study, we realize photo squarization by modeling Retargeting Visual Perception Issues, which reflect human perception preference toward image ratargeting. General image retargeting techniques deal with three common issues, namely, salient content, object shape, and scene composition, to preserve the important information of original image. We propose a new way based on multi-operator techniques to investigate human behavior in balancing the three issues. We establish a new dataset and observe human behavior by inviting investigators to retarget images to square manually. We propose a data-driven approach composed of perception and distillation modules by using deep learning techniques to predict human perception preference. The perception part learns the relations among the three issues, and the distillation part transfers the learned relations to a simple but effective network. Our study contributes to deep learning literature by optimizing a network index and lightening its running burden. Experimental results show that photo squarization results generated by the proposed model are consistent with human visual perception results.
A weakly supervised approach for estimating spatial density functions from high-resolution satellite imagery

We propose a neural network component, the regional aggregation layer, that makes it possible to train a pixel-level density estimator using only coarse-grained density aggregates, which reflect the number of objects in an image region. Our approach is simple to use and does not require domain-specific assumptions about the nature of the density function. We evaluate our approach on several synthetic datasets. In addition, we use this approach to learn to estimate high-resolution population and housing density from satellite imagery. In all cases, we find that our approach results in better density estimates than a commonly used baseline. We also show how our housing density estimator can be used to classify buildings as residential or non-residential.
High-Order Residual Convolutional Neural Network for Robust Crop Disease Recognition

Fast1 and robust recognition of crop diseases is the basis for crop disease prevention and control. It is also an important guarantee for crop yield and quality. Most crop disease recognition methods focus on improving the recognition accuracy on public datasets, but ignoring the anti-interference ability of the methods, which result in poor recognition accuracy when the real scene is applied. In this paper, we propose a high-order residual convolutional neural network (HOResNet) for accurate and robust recognizing crop diseases. Our HOResNet is capable of exploiting low-level features with object details and high-level features with abstract representation simultaneously in order to improve the anti-interference ability. Furthermore, in order to better verify the anti-interference ability of our approach, we introduce a new dataset, which contains 9,214 images of six diseases of Rice and Cucumber. This dataset is collected in the natural environment. The images in the dataset have different sizes, shooting angles, poses, backgrounds and illuminations. Extensive experimental results demonstrate that our approach achieves the highest accuracy on the datasets tested. In addition, when the input images are added to different levels of noise interference, our approach still obtains higher recognition accuracy than other methods.
Pim1 promotes cell proliferation and regulates glycolysis via interaction with MYC in ovarian cancer

Background Ovarian cancer (OC) is the leading cause of death among women with gynecologic malignancies. Recent studies have highlighted the role of Pim1, which belongs to a group of constitutively activated serine/threonine kinases, in cancer development. However, the effect of Pim1 in OC is largely unclear. Methods OC cell lines with Pim1 overexpression or knockdown were constructed with len-tivirus transduction. Cell Counting Kit-8, colony formation, glycolysis stress test and in vivo mice models were carried out to assess the effect of Pim1 on OC biological functions. Co-immunoprecipitation assay coupled with western blot were performed to explore the intrinsic mechanisms of Pim1 in OC. Bioinformatic analysis was then performed to evaluate the expression and prognostic value of Pim1. Results We present the first evidence that silencing or overexpressing Pim1 can suppress or promote, respectively, OC cell proliferation. Furthermore, we demonstrated that Pim1 can significantly enhance glycolysis in OC cells. In vivo experiments further confirmed that knockdown of Pim1 inhibits the growth of tumors derived from the SKOV3 cell line. To search for the underlying molecular mechanism, we examined the effect of Pim1 on MYC, a pivotal gene in glycolysis, and observed that Pim1-mediated phosphorylation of c-Myc activated the expression of glycolysis-associated key genes such as PGK1 and LDHA. Moreover, we found that the Pim1 inhibitor SMI4a induced chemosensitization to cisplatin. Clinically, Pim1 was also overexpressed in OC and correlated with poor overall survival by bioinformatics analysis. Conclusion Together, these results suggest that Pim1 contributes to proliferation and gly-colysis in OC via interaction with MYC and may serve as a potential target in the treatment of OC patients.
Development of Computational Thinking Skills through Educational Robotics

Computational thinking (CT) is an important concept in modern education. The scientific community is not only investigating the skills involved in CT but, is also trying to establish how these skills can be developed and through what technological means. Meanwhile, a few studies have investigated the effectiveness of educational robotics (ER) as technological means which can support the development of CT but, issues of measurement of CT (i.e., using validated instruments) seem to hinder the validity of these investigations. In this paper, two quasi-experimental studies were conducted to address students’ CT gains linked to their participation in ER activities. The first study was conducted at a primary school in the Eastern Mediterranean; 15 consented students participated in ER activities for five weeks. The second study included 16 students in a secondary school in the same region, who participated in ER activities for three months. Quantitative results, based on a valid measure of CT, showed that the students who participated in the ER interventions demonstrated significant improvement in their CT skills. This study extends the evidence of the potential of using ER to improve students’ CT skills in K-12 contexts.
The Effectiveness of China National Doctoral Dissertations Quality Inspection

National1 Doctoral Dissertations Quality Inspection (NDDQI), implemented by the Ministry of Education of China, is an important but questioned postgraduate education policy. Effectiveness of NDDQI is highly dependent on examiner's reports, which had not been fully demonstrated. This study analyzed 17249 examiner's reports in 2015 NDDQI with help of NLP techniques, and found that 16% of examiners would give five types of problematic reports (i.e. vague report, less-strict report, rigorous report, confused report and dilettante report) which makes 10% of all examiner reports. The existence of problematic reports will largely shake the NDDQI result; the number of unqualified theses may be underestimated. Hence, for the purpose of enhancing the effectiveness of NDDQI, it is suggested that a distinguishing module based on Natural Language Processing technology should also be added on the inspection platform. Meanwhile, this study also showed the flexibility and power of NLP techniques, and as well showed the possibility of applying NLP for policy analysis and large-scale social science research.
Multiresolution Graph Attention Networks for Relevance Matching

A large number of deep learning models have been proposed for the text matching problem, which is at the core of various typical natural language processing (NLP) tasks. However, existing deep models are mainly designed for the semantic matching between a pair of short texts, such as paraphrase identification and question answering, and do not perform well on the task of relevance matching between short-long text pairs. This is partially due to the fact that the essential characteristics of short-long text matching have not been well considered in these deep models. More specifically, these methods fail to handle extreme length discrepancy between text pieces and neither can they fully characterize the underlying structural information in long text documents. In this paper, we are especially interested in relevance matching between a piece of short text and a long document, which is critical to problems like query-document matching in information retrieval and web searching. To extract the structural information of documents, an undirected graph is constructed, with each vertex representing a keyword and the weight of an edge indicating the degree of interaction between keywords. Based on the keyword graph, we further propose a Multiresolution Graph Attention Network to learn multi-layered representations of vertices through a Graph Convolutional Network (GCN), and then match the short text snippet with the graphical representation of the document with an attention mechanism applied over each layer of the GCN. Experimental results on two datasets demonstrate that our graph approach outperforms other state-of-the-art deep matching models.
Using Word Embeddings for Information Retrieval: How Collection and Term Normalization Choices Affect Performance

Neural word embedding approaches, due to their ability to capture semantic meanings of vocabulary terms, have recently gained attention of the information retrieval (IR) community and have shown promising results in improving ad hoc retrieval performance. It has been observed that these approaches are sensitive to various choices made during the learning of word embeddings and their usage, often leading to poor reproducibility. We study the effect of varying following two parameters, viz., i) the term normalization and ii) the choice of training collection, on ad hoc retrieval performance with word2vec and fastText embeddings. We present quantitative estimates of similarity of word vectors obtained under different settings, and use embeddings based query expansion task to understand the effects of these parameters on IR effectiveness.
Big data and sentiment analysis to highlight decision behaviours: a case study for student population

ABSTRACT Starting from the assumption that the factors orienting University choice are heterogeneous and multidimensional, the study explores student’s motivations in higher education. To this aim, a big data analysis has been performed through ‘TalkWalker’, a tool based on the algorithms developed in the context of Social Data Intelligence, which allows understanding the sentiment of a group of people regarding a specific theme. The data have been extracted by drawing on published posts from anywhere in the world over a 12-month period from many online sources. According to the findings, the main variable capable of influencing the choice of University is training offer, followed by physical structure, work opportunities, prestige, affordability, communication, organisation, environmental sustainability. The study establishes an innovative research agenda for further studies by proposing the elaboration of a systems and process-based view for higher education. However, it presents the limitation of the superficial investigation, determined by the analysis of a large amount of data. Therefore, for future research, it might be appropriate to apply a different technique to realise a comparison and to check whether the size of the considered sample and the depth of the analysis technique can affect the results and the consequent considerations.
Drug delivery systems for elemene, its main active ingredient β-elemene, and its derivatives in cancer therapy

β-elemene is a noncytotoxic Class II antitumor drug extracted from the traditional Chinese medicine Curcuma wenyujin Y. H. Chen et C. Ling. β-elemene exerts its effects by inhibiting cell proliferation, arresting the cell cycle, inducing cell apoptosis, exerting antiangiogenesis and antimetastasis effects, reversing multiple-drug resistance (MDR), and enhancing the immune system. Elemene injection and oral emulsion have been used to treat various tumors, including cancer of the lung, liver, brain, breast, ovary, gastric, prostate, and other tissues, for >20 years. The safety of both elemene injection and oral emulsion in the clinic has been discussed. Recently, the secondary development of β-elemene has attracted the attention of researchers and made great progress. On the one hand, studies have been carried out on liposome-based systems (including solid lipid nanoparticles [SLNs], nanostructured lipid carriers [NLCs], long-circulating liposomes, active targeting liposomes, and multidrug-loaded liposomes) and emulsion systems (including microemulsions, self-emulsion drug delivery systems [SEDDSs], and active targeting microemulsion) to solve the issues of poor solubility in water, low bioavailability, and severe phlebitis, as well as to improve antitumor efficacy. The pharmacokinetics of different drug delivery systems of β-elemene are also summarized. On the other hand, a number of highly active anticancer β-elemene derivatives have been obtained through modification of the structure of β-elemene. This review focuses on the two drug delivery systems and derivatives of β-elemene for cancer therapy.
Cellular communication promotes mammosphere growth and collective invasion through microtubule-like structures and angiogenesis

Networks of nanotubes and microtubules are highly valued in cellular communication, and collective cancer movement has been revealed to be associated with cell information exchange. In the present study, cellular communication was demonstrated to participate in mammosphere growth, differentiation and collective invasion. By promoting differentiation, networks of cells and microtubule-like structures were verified. Analyses of cell cycle progression, stemness markers and gene expression indicated that mammospheres had collective characteristics of stemness and differentiation. Invasion assays revealed that networks of microtubule-like structures promoted collective invasion. Conversely, using anti-angiogenic intervention, the growth of stem-like mammospheres and cellular communication links were effectively inhibited. In vivo experiments revealed that cellular communication promoted tumor growth and metastasis through the formation of nodular fusion, cluttered microtubule-like structures and cancer stem cells, as well as vascular niches. In conclusion, the present results demonstrated that a network of cells and structures were largely present in mammosphere cellular communication in vitro and in vivo. Therefore, blocking cellular communication may prove beneficial in halting the progression of mammary tumors.
Comparison of Automatic Extraction of Research Highlights and Abstracts of Journal Articles

Many academic journals1 require authors to submit a list of bullet points, called highlights. Highlights provide concise information for readers to review the article quickly. However, some journals and old articles do not have the highlights. It is useful to develop an automated method for extracting highlights of these articles. In this paper, we study the important features of research highlight extraction and identify the differences between extraction of highlights and abstracts of journal articles. We use information science related journal articles as test data. We quantitatively evaluate 23 common unsupervised extractive text summarization methods. The results show that the application of extractive text summarization is suitable for research highlight extraction. In particular, TextRank obtains the highest recall and the title word method achieves the highest precision. The result could help researchers developing new methods of highlight extraction.