Target-adaptive graph for cross-target stance detection

Target plays an essential role in stance detection of an opinionated review/claim, since the stance expressed in the text often depends on the target. In practice, we need to deal with targets unseen in the annotated training data. As such, detecting stance for an unknown or unseen target is an important research problem. This paper presents a novel approach that automatically identifies and adapts the target-dependent and target-independent roles that a word plays with respect to a specific target in stance expressions, so as to achieve cross-target stance detection. More concretely, we explore a novel solution of constructing heterogeneous target-adaptive pragmatics dependency graphs (TPDG) for each sentence towards a given target. An in-target graph is constructed to produce inherent pragmatics dependencies of words for a distinct target. In addition, another cross-target graph is constructed to develop the versatility of words across all targets for boosting the learning of dominant word-level stance expressions available to an unknown target. A novel graph-aware model with interactive Graphical Convolutional Network (GCN) blocks is developed to derive the target-adaptive graph representation of the context for stance detection. The experimental results on a number of benchmark datasets show that our proposed model outperforms state-of-the-art methods in cross-target stance detection

The Physical and Genetic Framework of the Maize B73 Genome

Maize is a major cereal crop and an important model system for basic biological research. Knowledge gained from maize research can also be used to genetically improve its grass relatives such as sorghum, wheat, and rice. The primary objective of the Maize Genome Sequencing Consortium (MGSC) was to generate a reference genome sequence that was integrated with both the physical and genetic maps. Using a previously published integrated genetic and physical map, combined with in-coming maize genomic sequence, new sequence-based genetic markers, and an optical map, we dynamically picked a minimum tiling path (MTP) of 16,910 bacterial artificial chromosome (BAC) and fosmid clones that were used by the MGSC to sequence the maize genome. The final MTP resulted in a significantly improved physical map that reduced the number of contigs from 721 to 435, incorporated a total of 8,315 mapped markers, and ordered and oriented the majority of FPC contigs. The new integrated physical and genetic map covered 2,120 Mb (93%) of the 2,300-Mb genome, of which 405 contigs were anchored to the genetic map, totaling 2,103.4 Mb (99.2% of the 2,120 Mb physical map). More importantly, 336 contigs, comprising 94.0% of the physical map (∼1,993 Mb), were ordered and oriented. Finally we used all available physical, sequence, genetic, and optical data to generate a golden path (AGP) of chromosome-based pseudomolecules, herein referred to as the B73 Reference Genome Sequence version 1 (B73 RefGen_v1)

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Influence of Learning Intervention on Online Learners’ Performance

With the expansion of the online learning scale and the rapid development of learning analysis techniques, learning systems can completely record learners’ online learning behavior. What types of learning intervention measures can influence learning performance? How is the intervention working? The above questions are the focus of online courses and emphasize improving the online learning effect. In this research, 96 undergraduates (Classes 1 and 2, Grade 2) majoring in civil engineering and enrolled in the Engineering Surveying online course at the College of Transportation Engineering, Huanghe Jiaotong University, Henan Province, were selected as the research objects and randomly divided into two groups (the experimental group (N = 48) and the control group (N = 48)). Then, conducted a 15-week sustained intervention study through quasi-experimental research, and verified the effectiveness of different types of intervention measures on the learners’ learning performance. Next, a one-way analysis of variance was conducted, based on the pretesting of the performance data, the quality of online discussion posts, the effective learning duration, and the online final test results. Research results showed no obvious differences between the experimental group and control group in the online final test results from the previous semester (P = 0.347). The online learners’ learning performance was influenced by three aspects of the learning intervention, that is, apparent differences in the posting quality in the learning prompt intervention (T = 8.23, P < 0.01), the online learning duration in the process monitoring intervention (T = 23.19, P < 0.01), and the online final test results in the achievement incentive intervention (T = 6.08, P < 0.01). The research results have an important reference value for implementing intervention strategies in the online learning environment, judging the effectiveness of such strategies according to curve changes in performance, and using the mass data of learners recorded by online learning platforms

A Study of Hydrogen Embrittlement of SA-372 J Class High Pressure Hydrogen Storage Seamless Cylinder (≥100 MPA)

The spinning process will lead to changes in the micro-structure and mechanical properties of the materials in different positions of the high-pressure hydrogen storage cylinder, which will show different hydrogen embrittlement resistance in the high-pressure hydrogen environment. In order to fully study the safety of hydrogen storage in large-volume seamless steel cylinders, this chapter associates the influence of the forming process with the deterioration of a high-pressure hydrogen cylinder (≥100 MPa). The anti-hydrogen embrittlement of SA-372 grade J steel at different locations of the formed cylinders was studied experimentally in three cylinders. The hydrogen embrittlement experiments were carried out according to method A of ISO 11114-4:2005. The relationship between tensile strength, microstructure, and hydrogen embrittlement is analyzed, which provides comprehensive and reliable data for the safety of hydrogen storage and transmission

Filling process and filling characteristic analysis of Paleogene Baxian sag in Bohai Bay basin, China

In addition to core, logging, and other previous research results, this paper determines the fault development and tectonic evolution process of the Baxian sag with the Paleogene rift stage based on 3D seismic data. The Paleogene tectonic evolution of the sag can be divided into three episodes and six evolution stages, and three types of faults are identified: intensely active normal, active normal, and weakly active normal. One first-order sequence, three second-order sequences, and fourteen third-order sequences of the Paleogene Baxian sag were created, and fifteen sequence boundaries were recognised. According to the rifting background and sedimentary facies development characteristics of each episode, five combination types of the depositional system associations were identified, including alluvial fan-fluvial and braided-delta-lacustrine in an early rifting episode, delta-lacustrine and nearshore subaqueous fan-lacustrine in the middle rifting episode, and fluvial-flood plain in the late rifting episode. Six response models of filling and the evolution process in Paleogene Baxian sag were concluded. The multi-episodes tectonic cycles of faulted lake basins resulted in complex paleogeomorphology and variable provenance supply, forming abundant sequence structure patterns and different filling and evolution processes of faulted lake basins. The stable rifting stage is favourable to form and preserve high-quality source rock, and develop various sedimentary facies and sandbody types, which is a potential area for exploration of a lithologic stratigraphic oil and gas reservoir.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

High-throughput screening for superhard carbon and boron nitride allotropes with superior stiffness and strength

In search of intrinsically superhard materials with superior stiffness and strength, we performed a comprehensive high-throughput hunting on hundreds of carbon and BN allotropes based on energetic and mechanical criteria. Our results suggest that at ambient pressure, an approximate linear relationship exists between the ideal strengths and elastic moduli in two allotrope regions with high elastic moduli, while no carbon (BN) allotrope can possess both superior stiffness and strength than diamond (c-BN). With further consideration of pressure induced stiffening and strengthening, it is interestingly found that the strength enhancement shows distinct characteristic trend, resulting in some intriguing ultrastiffening and strengthening phenomena. In particular, a superdense carbon allotrope termed as tI12-C was unexpectedly discovered to possess superior stiffness and strength than diamond under high pressure. Electronic structure analysis indicates that an increasing charge accumulation appearing in tI12-C under pressure is responsible for its ultra-stiffening and strengthening phenomena, differing from the appearance of abnormal charge depletions and the accompanied metallization in diamond under applied strain. These findings provide a fundamental basis for screening the novel superhard carbon and BN allotropes based on mechanical criteria, and highlight the importance to understand the effect of strain tunable electronic structure on mechanical response of materials.Web of Science13716415

An Electric Taxi Charging Station Planning Scheme Based on an Improved Destination Choice Method

WOS: 000498072600192The environmental crisis has prompted the development of electric vehicles as a green and environmentally friendly mode of travel. Since a reasonable layout of electric vehicle (EV) charging stations is the prerequisite for developing the EV industry, obtaining an optimal and efficient EV charging station planning scheme is a key issue. Although the Chinese government has carried out a plan to build EV charging piles in residential and working places, it cannot properly fulfill the task of matching the charging needs for public transportation vehicles such as electric taxis (ETs). How to evaluate the performance of fast charging stations (FCSs) and how to help find the optimal ET charging station planning scheme are new challenges. In this paper, an improved destination selection model is proposed to simulate the ET operation system and to help find the optimal ET charging station size with statistical analysis based on the charging need prediction. A numerical case study shows that the proposed method can address ET charging behavior well and can help to statistically determine the size of each ET charging station, which should satisfy the constraints on the preset proportion of the ET charging service requests.National Natural Science Foundation of ChinaNational Natural Science Foundation of China [71801224, 61203100]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities [16MS42]This research was funded by the National Natural Science Foundation of China grant number 71801224 and 61203100, and was also funded by the Fundamental Research Funds for the Central Universities grant number 16MS42

A Virus Propagation Model and Optimal Control Strategy in the Point-to-Group Network to Information Security Investment

Epidemiological dynamics is a vital method in studying the spread of computer network viruses. In this paper, an optimal control measure is proposed based on the SEIR virus propagation model in point-to-group information networks. First, considering the need for antivirus measures in reality, an optimal control problem is introduced, and then a controlled computer virus spread model in point-to-group information networks is established. Second, the optimal control measure is formulated by making a tradeoff between control cost and network loss caused by virus intrusion. Third, optimal control strategies are theoretically investigated by Pontryagin’s maximum principle and the Hamiltonian function. Finally, through numerical simulations, effective measures for controlling virus spread in point-to-group information networks are proposed