GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection

In this paper we present GumDrop, Georgetown University's entry at the DISRPT 2019 Shared Task on automatic discourse unit segmentation and connective detection. Our approach relies on model stacking, creating a heterogeneous ensemble of classifiers, which feed into a metalearner for each final task. The system encompasses three trainable component stacks: one for sentence splitting, one for discourse unit segmentation and one for connective detection. The flexibility of each ensemble allows the system to generalize well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking (DISRPT2019

TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Models

With the promotion of chatgpt to the public, Large language models indeed showcase remarkable common sense, reasoning, and planning skills, frequently providing insightful guidance. These capabilities hold significant promise for their application in urban traffic management and control. However, LLMs struggle with addressing traffic issues, especially processing numerical data and interacting with simulations, limiting their potential in solving traffic-related challenges. In parallel, specialized traffic foundation models exist but are typically designed for specific tasks with limited input-output interactions. Combining these models with LLMs presents an opportunity to enhance their capacity for tackling complex traffic-related problems and providing insightful suggestions. To bridge this gap, we present TrafficGPT, a fusion of ChatGPT and traffic foundation models. This integration yields the following key enhancements: 1) empowering ChatGPT with the capacity to view, analyze, process traffic data, and provide insightful decision support for urban transportation system management; 2) facilitating the intelligent deconstruction of broad and complex tasks and sequential utilization of traffic foundation models for their gradual completion; 3) aiding human decision-making in traffic control through natural language dialogues; and 4) enabling interactive feedback and solicitation of revised outcomes. By seamlessly intertwining large language model and traffic expertise, TrafficGPT not only advances traffic management but also offers a novel approach to leveraging AI capabilities in this domain. The TrafficGPT demo can be found in https://github.com/lijlansg/TrafficGPT.git

Enhanced Quadratic Video Interpolation

With the prosperity of digital video industry, video frame interpolation has arisen continuous attention in computer vision community and become a new upsurge in industry. Many learning-based methods have been proposed and achieved progressive results. Among them, a recent algorithm named quadratic video interpolation (QVI) achieves appealing performance. It exploits higher-order motion information (e.g. acceleration) and successfully models the estimation of interpolated flow. However, its produced intermediate frames still contain some unsatisfactory ghosting, artifacts and inaccurate motion, especially when large and complex motion occurs. In this work, we further improve the performance of QVI from three facets and propose an enhanced quadratic video interpolation (EQVI) model. In particular, we adopt a rectified quadratic flow prediction (RQFP) formulation with least squares method to estimate the motion more accurately. Complementary with image pixel-level blending, we introduce a residual contextual synthesis network (RCSN) to employ contextual information in high-dimensional feature space, which could help the model handle more complicated scenes and motion patterns. Moreover, to further boost the performance, we devise a novel multi-scale fusion network (MS-Fusion) which can be regarded as a learnable augmentation process. The proposed EQVI model won the first place in the AIM2020 Video Temporal Super-Resolution Challenge.Comment: Winning solution of AIM2020 VTSR Challenge (in conjunction with ECCV 2020

Source Apportionment of Gaseous and Particulate PAHs from Traffic Emission Using Tunnel Measurements in Shanghai, China

Understanding sources and contributions of gaseous and particulate PAHs from traffic-related pollution can provide valuable information for alleviating air contamination from traffic in urban areas. On-road sampling campaigns were comprehensively conducted during 2011–2012 in an urban tunnel of Shanghai, China. 2–3 rings PAHs were abundant in the tunnel\u27s gas and particle phases. Diagnostic ratios of PAHs were statistically described; several were significantly different between the gas and particle phases. Principal component analysis (PCA), positive matrix factorization (PMF), bivariate correlation analysis and multiple linear regression analysis (MLRA) were applied to apportion sources of gaseous and particulate PAHs in the tunnel. Main sources of the gaseous PAHs included evaporative emission of fuel, high-temperature and low-temperature combustion of fuel, accounting for 50–51%, 30–36% and 13–20%, respectively. Unburned fuel particles (56.4–78.3%), high-temperature combustion of fuel (9.5–26.1%) and gas-to-particle condensation (12.2–17.5%) were major contributors to the particulate PAHs. The result reflected, to a large extent, PAH emissions from the urban traffic of Shanghai. Improving fuel efficiency of local vehicles will greatly reduce contribution of traffic emission to atmospheric PAHs in urban areas. Source apportionment of PM10 mass was also performed based on the organic component data. The results showed that high-temperature combustion of fuel and gas-to-particle condensation contributed to 15–18% and 7–8% of PM10 mass, respectively, but 55–57% of the particle mass was left unexplained. Although the results from the PCA and PMF models were comparable, the PMF method is recommended for source apportionment of PAHs in real traffic conditions. In addition, the combination of multivariate statistical method and bivariate correlation analysis is a useful tool to comprehensively assess sources of PAHs

Analysis of Effect of Schisandra in the Treatment of Myocardial Infarction Based on Three-Mode Gene Ontology Network

Schisandra chinensis is a commonly used traditional Chinese medicine, which has been widely used in the treatment of acute myocardial infarction in China. However, it has been difficult to systematically clarify the major pharmacological effect of Schisandra, due to its multi-component complex mechanism. In order to solve this problem, a comprehensive network analysis method was established based-on “component–gene ontology–effect” interactions. Through the network analysis, reduction of cardiac preload and myocardial contractility was shown to be the major effect of Schisandra components, which was further experimentally validated. In addition, the expression of NCOR2 and NFAT in myocyte were experimentally confirmed to be associated with Schisandra in the treatment of AMI, which may be responsible for the preservation effect of myocardial contractility. In conclusion, the three-mode gene ontology network can be an effective network analysis workflow to evaluate the pharmacological effects of a multi-drug complex system

Global land surface temperature influenced by vegetation cover and PM2.5 from 2001 to 2016

Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM2.5 increased by 0.17 K, 0.04, and 1.02 �g/m3 in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72�N and 48�S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM2.5 on LST was more complex. On the whole, LST increased with a small increase in PM2.5 concentrations but decreased with a marked increase in PM2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature

Wet and Dry Atmospheric Depositions of Inorganic Nitrogen during Plant Growing Season in the Coastal Zone of Yellow River Delta

The ecological problems caused by dry and wet deposition of atmospheric nitrogen have been widespread concern in the world. In this study, wet and dry atmospheric depositions were monitored in plant growing season in the coastal zone of the Yellow River Delta (YRD) using automatic sampling equipment. The results showed that SO42- and Na+ were the predominant anion and cation, respectively, in both wet and dry atmospheric depositions. The total atmospheric nitrogen deposition was ~2264.24 mg m−2, in which dry atmospheric nitrogen deposition was about 32.02%. The highest values of dry and wet atmospheric nitrogen deposition appeared in May and August, respectively. In the studied area, NO3-–N was the main nitrogen form in dry deposition, while the predominant nitrogen in wet atmospheric deposition was NH4+–N with ~56.51% of total wet atmospheric nitrogen deposition. The average monthly attribution rate of atmospheric deposition of NO3-–N and NH4+–N was ~31.38% and ~20.50% for the contents of NO3-–N and NH4+–N in 0–10 cm soil layer, respectively, suggested that the atmospheric nitrogen was one of main sources for soil nitrogen in coastal zone of the YRD

The Development and Assessment on the Social Studies Handbook for Supporting Teacher’s Lesson Planning and Improvement : A Content Structure of Handbook which can be Applied to Pre-service and In-service Teacher Education

The purposes of this paper are to develop the draft of handbook for planning, teaching and accessing the class of social studies and evaluate effects of the handbook for teacher training and their professional development. The structure of the first draft was designed based on Kolb’s learning theory. The present results suggested that the usefulness of the contents structure was perceived by (1) pre-service teachers and (2) in-service teachers, and the possibility for application was also recognized by the teacher educator as (3) university professor who teach methods courses, (4) senior supervisor who is in charge of designing the professional development programs and (5) younger supervisor who is in charge of tutoring the novice teacher, but they illustrated their different types of the significances, limits and utilization according to their purposes and as well as their responsibility. The authors implicated the alterative design of the handbook based on Korthagen’s reflective learning model for meeting their purposes and solving the structural problems inherit in the handbook

Education for Disaster Prevention in England : Analysis of Secondary Geography Textbooks

The purpose of this study is to clarify the aims, contents and activities of education for disaster prevention on geographical education in England, by analysis of secondary textbooks. Thereby, we want to contribute the improvement of Japanese geographical education. As a result, we clarified the following: 1) when students study a natural disaster in UK, they learn a plurality of cases including domestic and foreign regions, 2) all textbooks contain activities that students can form attitudes and decide what to do for disaster prevention. On the basis of these results, we suggest improvements of education for disaster prevention. The first is to develop a clear understanding of a natural disaster with multi-area including Japan. The second is to place contents and activities for forming a value relevant to awareness of disaster prevention into Japanese geography textbook. The third is to set an activity to learn practical strategies for disaster prevention in the real world

Assessing the impact of diagenesis on foraminiferal geochemistry from a low latitude, shallow-water drift deposit

Due to their large heat and moisture storage capabilities, the tropics are fundamental in modulating both regional and global climate. Furthermore, their thermal response during past extreme warming periods, such as super interglacials, is not fully resolved. In this regard, we present high-resolution (analytical) foraminiferal geochemical (δ18O and Mg/Ca) records for the last 1800 kyr from the shallow (487 m) Inner Sea drift deposits of the Maldives archipelago in the equatorial Indian Ocean. Considering the diagenetic susceptibility of these proxies, in carbonate-rich environments, we assess the integrity of a suite of commonly used planktonic and benthic foraminifera geochemical datasets (Globigerinoides ruber (white), Globigerinita glutinata (with bulla), Pulleniatina obliquiloculata (with cortex) and Cibicides mabahethi) and their use for future paleoceanographic reconstructions. Using a combination of spot Secondary Ion Mass Spectrometer, Electron Probe Micro-Analyzer and Scanning Electron Microscope image data, it is evident that authigenic overgrowths are present on both the external and internal test (shell) surfaces, yet the degree down-core as well as the associated bias is shown to be variable across the investigated species and proxies. Given the elevated authigenic overgrowth Mg/Ca (∼12–22 mmol/mol) and δ18O values (closer to the benthic isotopic compositions) the whole-test planktonic G. ruber (w) geochemical records are notably impacted beyond ∼627.4 ka (24.7 mcd). Yet, considering the setting (i.e. bottom water location) for overgrowth formation, the benthic foraminifera δ18O record is markedly less impacted with only minor diagenetic bias beyond ∼790.0 ka (28.7 mcd). Even though only the top of the G. ruber (w) and C. mabahethi records (whole-test data) would be suitable for paleo-reconstructions of absolute values (i.e. sea surface temperature, salinity, seawater δ18O), the long-term cycles, while dampened, appear to be preserved. Furthermore, planktonic species with thicker-tests (i.e. P. obliquiloculata (w/c)) might be better suited, in comparison to thinner-test counter-parts (i.e. G. glutinata (w/b), G. ruber (w)), for traditional whole- test geochemical studies in shallow, carbonate-rich environments. A thicker test equates to a smaller overall bias from the authigenic overgrowth. Overall, if the diagenetic impact is constrained, as done in this study, these types of diagenetically altered geochemical records can still significantly contribute to studies relating to past tropical seawater temperatures, latitudinal scale ocean current shifts and South Asian Monsoon dynamics