KERT: Automatic Extraction and Ranking of Topical Keyphrases from Content-Representative Document Titles

We introduce KERT (Keyphrase Extraction and Ranking by Topic), a framework for topical keyphrase generation and ranking. By shifting from the unigram-centric traditional methods of unsupervised keyphrase extraction to a phrase-centric approach, we are able to directly compare and rank phrases of different lengths. We construct a topical keyphrase ranking function which implements the four criteria that represent high quality topical keyphrases (coverage, purity, phraseness, and completeness). The effectiveness of our approach is demonstrated on two collections of content-representative titles in the domains of Computer Science and Physics.Comment: 9 page

An Information-theoretic Visual Analysis Framework for Convolutional Neural Networks

Despite the great success of Convolutional Neural Networks (CNNs) in Computer Vision and Natural Language Processing, the working mechanism behind CNNs is still under extensive discussions and research. Driven by a strong demand for the theoretical explanation of neural networks, some researchers utilize information theory to provide insight into the black box model. However, to the best of our knowledge, employing information theory to quantitatively analyze and qualitatively visualize neural networks has not been extensively studied in the visualization community. In this paper, we combine information entropies and visualization techniques to shed light on how CNN works. Specifically, we first introduce a data model to organize the data that can be extracted from CNN models. Then we propose two ways to calculate entropy under different circumstances. To provide a fundamental understanding of the basic building blocks of CNNs (e.g., convolutional layers, pooling layers, normalization layers) from an information-theoretic perspective, we develop a visual analysis system, CNNSlicer. CNNSlicer allows users to interactively explore the amount of information changes inside the model. With case studies on the widely used benchmark datasets (MNIST and CIFAR-10), we demonstrate the effectiveness of our system in opening the blackbox of CNNs

Identification of sources of elevated concentrations of polycyclic aromatic hydrocarbons in an industrial area in Tianjin, China

The concentrations of 16 polycyclic aromatic hydrocarbons (PAHs) were determined by gas chromatography equipped with a mass spectrometry detector in 105 topsoil samples from an industrial area around Bohai Bay, Tianjin in the North of China. Results demonstrated that concentrations of PAHs in 104 soil samples from this area ranged from 68.7 to 5,590 ng g (-aEuro parts per thousand 1) dry weight with a mean of a16PAHs 814 +/- 813 ng g (-aEuro parts per thousand 1), which suggests that there exists mid to high levels of PAH contamination. The concentration of a16PAHs in one soil sample from Tianjin Port was exceptionally high (48,700 ng g (-aEuro parts per thousand 1)). Ninety-three of the 105 soil samples were considered to be contaminated with PAHs (> 200 ng g (-aEuro parts per thousand 1)), and 25 were heavily polluted (> 1,000 ng g (-aEuro parts per thousand 1)). The sites with high PAHs concentration are mainly distributed around chemical industry parks and near highways. Two low molecular weight PAHs, naphthalene and phenanthrene, were the dominant components in the soil samples, which accounted for 22.1% and 10.7% of the a16PAHs concentration, respectively. According to the observed molecular indices, house heating in winter, straw stalk combustion in open areas after harvest, and petroleum input were common sources of PAHs in this area, while factory discharge and vehicle exhaust were the major sources around chemical industrial parks and near highways. Biological processes were probably another main source of low molecular weight PAHs

Regional differences and sources of organochlorine pesticides in soils surrounding chemical industrial parks

Concentrations of organochlorine pesticides (OCPs; dichlorodiphenyltrichloroethanes (DDTs), hexachlorocyclohexanes (HCHs), hexachlorobenzene (HCB)) were investigated in 105 soil samples collected in vicinity of the chemical industrial parks in Tianjin, China. OCP concentrations significantly varied in the study area, high HCH and DDT levels were found close to the chemical industrial parks. The intensity of agricultural activity and distance from the potential OCP emitters have important influences on the OCP residue distributions. Principal component analysis indicates that HCH pollution is a mix of historical technical HCH and current lindane pollution and DDT pollution input is only due to technical DDT sources. The significant correlations of OCP compounds reveal that HCHs, DDTs and HCB could have some similar sources of origin

SD4Match: Learning to Prompt Stable Diffusion Model for Semantic Matching

In this paper, we address the challenge of matching semantically similar keypoints across image pairs. Existing research indicates that the intermediate output of the UNet within the Stable Diffusion (SD) can serve as robust image feature maps for such a matching task. We demonstrate that by employing a basic prompt tuning technique, the inherent potential of Stable Diffusion can be harnessed, resulting in a significant enhancement in accuracy over previous approaches. We further introduce a novel conditional prompting module that conditions the prompt on the local details of the input image pairs, leading to a further improvement in performance. We designate our approach as SD4Match, short for Stable Diffusion for Semantic Matching. Comprehensive evaluations of SD4Match on the PF-Pascal, PF-Willow, and SPair-71k datasets show that it sets new benchmarks in accuracy across all these datasets. Particularly, SD4Match outperforms the previous state-of-the-art by a margin of 12 percentage points on the challenging SPair-71k dataset

Citizen Willingness to Pay for the Implementation of Urban Green Infrastructure in the Pilot Sponge Cities in China

Urban green infrastructure has been widely used to in cities to solve stormwater problems caused by extreme weather events and urbanization around the world. However, the lack of a long-term funding mechanism for performing urban green infrastructure's functions has limited wider implementation. Factors influencing citizen attitudes and willingness to pay for urban green infrastructure vary from city to city. This study estimated the public's willingness to pay for urban green infrastructure, as well as compared the selected influencing factors of willingness to pay in different Chinese pilot sponge cities. The results show that 60% to 75% of all respondents in the cities were willing to support the implementation of urban green infrastructure in sponge cities, with those most willing to pay around 0-5 RMB/month (0-0.72 USD/month). The respondents' educational level was a significant influencing factor for their willingness to pay in all six cities, but age, gender and family monthly income correlated differently with respondents' willingness to pay in different cities. Previous knowledge of the sponge city concept and sponge city construction in the community were not significantly correlated with residents' willingness to pay. We conclude that local governments in China need to provide more information to the general public about the multiple ecosystem services, e.g., educational and recreational benefits, that urban green infrastructure can provide. In doing so, it will help a shift to urban green infrastructure as the solution to dealing with urban stormwater problems

Efficient Convolutional Neural Network for FMCW Radar Based Hand Gesture Recognition

FMCW radar could detect object's range, speed and Angleof-Arrival, advantages are robust to bad weather, good range resolution, and good speed resolution. In this paper, we consider the FMCW radar as a novel interacting interface on laptop. We merge sequences of object's range, speed, azimuth information into single input, then feed to a convolution neural network to learn spatial and temporal patterns. Our model achieved 96% accuracy on test set and real-time test.Comment: Poster in Ubicomp 201

LiCamGait: Gait Recognition in the Wild by Using LiDAR and Camera Multi-modal Visual Sensors

LiDAR can capture accurate depth information in large-scale scenarios without the effect of light conditions, and the captured point cloud contains gait-related 3D geometric properties and dynamic motion characteristics. We make the first attempt to leverage LiDAR to remedy the limitation of view-dependent and light-sensitive camera for more robust and accurate gait recognition. In this paper, we propose a LiDAR-camera-based gait recognition method with an effective multi-modal feature fusion strategy, which fully exploits advantages of both point clouds and images. In particular, we propose a new in-the-wild gait dataset, LiCamGait, involving multi-modal visual data and diverse 2D/3D representations. Our method achieves state-of-the-art performance on the new dataset. Code and dataset will be released when this paper is published

A multi-step solvent-free mechanochemical route to indium(III) complexes

La Química mecánica está bien establecida en la fase sólida de síntesis de materiales inorgánicos, pero rara vez ha sido empleada para la síntesis molecular. En los últimos años, ha habido interés en los nacientes métodos sintéticos "más verde" con menos solvente, rendimientos más altos y tiempos de reacción más cortos, todo lo cual es especialmente atractivo para los productos de química fina y los catalizadores inorgánicos de la industria. Aquí, demostramos que el grupo principal de complejo indio (III) ligado con bis(imino)acenafteno (bian) son fácilmente accesibles a través de un enfoque de molienda de química mecánica. La metodología sintética reporta, y no sólo evita, la utilización de grandes cantidades de disolventes y reactivos para metales de transición ligando la síntesis, pero también reduce drásticamente los tiempos de reacción. Estos nuevos grupos, principalmente complejos, muestran el potencial de ser reducidos al complejo indio (I), que pueden ser empleadas como en productos fotosensibles en catálisis orgánicas cataliza y materiales funcionales.Mechanochemistry is well-established in the solid-phase synthesis of inorganic materials but has rarely been employed for molecular syntheses. In recent years, there has been nascent interest in ‘greener’ synthetic methods with less solvent, higher yields, and shorter reaction times being especially appealing to the fine chemicals and inorganic catalyst industries. Herein, we demonstrate that main-group indium(III) complexes featuring bis(imino)acenaphthene (BIAN) ligands are readily accessible through a mechanochemical milling approach. The synthetic methodology reported herein not only bypasses the use of large solvent quantities and transition metal reagents for ligand synthesis, but also reduces reaction times dramatically. These new main-group complexes exhibit the potential to be reduced to indium(I) compounds, which may be employed as photosensitizers in organic catalyses and functional materials.Trabajo financiado por: Nanyang Technological University (Singapur). Ayuda M4080552, para Felipe García García Ministry of Education (Singapur). Ayuda nivel 1. M4011441, para Felipe García García Nanyang Technological University (Singapur). Ayuda M4081012, para Han Sen Soo Ministry of Education (Singapur). Ayuda nivel 1M4011144, para Han Sen Soo Nanyang. Ayuda Profesor Ayudante M4081154, para Han Sen SoopeerReviewe