UrbanFM: Inferring Fine-Grained Urban Flows

Urban flow monitoring systems play important roles in smart city efforts around the world. However, the ubiquitous deployment of monitoring devices, such as CCTVs, induces a long-lasting and enormous cost for maintenance and operation. This suggests the need for a technology that can reduce the number of deployed devices, while preventing the degeneration of data accuracy and granularity. In this paper, we aim to infer the real-time and fine-grained crowd flows throughout a city based on coarse-grained observations. This task is challenging due to two reasons: the spatial correlations between coarse- and fine-grained urban flows, and the complexities of external impacts. To tackle these issues, we develop a method entitled UrbanFM based on deep neural networks. Our model consists of two major parts: 1) an inference network to generate fine-grained flow distributions from coarse-grained inputs by using a feature extraction module and a novel distributional upsampling module; 2) a general fusion subnet to further boost the performance by considering the influences of different external factors. Extensive experiments on two real-world datasets, namely TaxiBJ and HappyValley, validate the effectiveness and efficiency of our method compared to seven baselines, demonstrating the state-of-the-art performance of our approach on the fine-grained urban flow inference problem

Multi-source Semantic Graph-based Multimodal Sarcasm Explanation Generation

Multimodal Sarcasm Explanation (MuSE) is a new yet challenging task, which aims to generate a natural language sentence for a multimodal social post (an image as well as its caption) to explain why it contains sarcasm. Although the existing pioneer study has achieved great success with the BART backbone, it overlooks the gap between the visual feature space and the decoder semantic space, the object-level metadata of the image, as well as the potential external knowledge. To solve these limitations, in this work, we propose a novel mulTi-source sEmantic grAph-based Multimodal sarcasm explanation scheme, named TEAM. In particular, TEAM extracts the object-level semantic meta-data instead of the traditional global visual features from the input image. Meanwhile, TEAM resorts to ConceptNet to obtain the external related knowledge concepts for the input text and the extracted object meta-data. Thereafter, TEAM introduces a multi-source semantic graph that comprehensively characterize the multi-source (i.e., caption, object meta-data, external knowledge) semantic relations to facilitate the sarcasm reasoning. Extensive experiments on a public released dataset MORE verify the superiority of our model over cutting-edge methods.Comment: Accepted by ACL 2023 main conferenc

Positioning, Planning and Operation of Emergency Response Resources and Coordination between Jurisdictions

Railroad related rail incidents, particularly those involving hazardous material (hazmat), cause severe consequences and pose significant threats to safety, public health and the environment. Rail safety is a huge issue in Midwestern states such as Illinois, Wisconsin, and Minnesota. This project aims at strategically positioning and allocating emergency responders and resources in anticipation of potential accidents in a region that may be impacted by rail incidents. Mathematical models and solution techniques are developed to enable systematic analysis of the emergency response system associated with railroad incidents; e.g., to strategically position and allocate emergency responders and resources in anticipation of potential accidents along spatially distributed railroad networks. We consider the added complexity due to vulnerability of the emergency response system itself, such as the risk of disruptions to the transportation network for first-responders (e.g., blockage of railroad crossings). The outcomes from these tasks will provide fundamental understanding, operational guidelines, and practical tools to policy makers (e.g., federal and state agencies) to induce socio-economically favorable system that support safe and efficient railroad industry operations

Dynamic Simulations on the Arachidonic Acid Metabolic Network

Drug molecules not only interact with specific targets, but also alter the state and function of the associated biological network. How to design drugs and evaluate their functions at the systems level becomes a key issue in highly efficient and low–side-effect drug design. The arachidonic acid metabolic network is the network that produces inflammatory mediators, in which several enzymes, including cyclooxygenase-2 (COX-2), have been used as targets for anti-inflammatory drugs. However, neither the century-old nonsteriodal anti-inflammatory drugs nor the recently revocatory Vioxx have provided completely successful anti-inflammatory treatment. To gain more insights into the anti-inflammatory drug design, the authors have studied the dynamic properties of arachidonic acid (AA) metabolic network in human polymorphous leukocytes. Metabolic flux, exogenous AA effects, and drug efficacy have been analyzed using ordinary differential equations. The flux balance in the AA network was found to be important for efficient and safe drug design. When only the 5-lipoxygenase (5-LOX) inhibitor was used, the flux of the COX-2 pathway was increased significantly, showing that a single functional inhibitor cannot effectively control the production of inflammatory mediators. When both COX-2 and 5-LOX were blocked, the production of inflammatory mediators could be completely shut off. The authors have also investigated the differences between a dual-functional COX-2 and 5-LOX inhibitor and a mixture of these two types of inhibitors. Their work provides an example for the integration of systems biology and drug discovery

Hybrid CuCoO-GO enables ultrasensitive detection of antibiotics with enhanced laser desorption/ionization at nano-interfaces

The soaring concerns globally on antibiotic overuse have made calls for the development of rapid and sensitive detection methods urgent. Here we report that the hybrid CuCoO-GO matrix allows for sensitive detection of various antibiotics in combination with MALDI TOF MS. The new matrix is composed of few-layered GO nanosheets decorated with CuCoO nanoparticles with an average size of 10 nm, and exhibits excellent aqueous suspensibility. Accurate quantitation of the sulfonamide antibiotics in milk samples have been demonstrated using a CuCoO-GO matrix and a stable isotope (C13)-labeled analyte as the internal standard. Our experiments have achieved lower limits of detection (LOD) by several hundred fold for the detection of a panel of representative antibiotics, in comparison with the literature reports. Both intrabacterial and extrabacterial residual antibiotics can be sensitively detected with our method. We have further investigated the molecular mechanism of the enhanced desorption/ionization efficiency by the CuCoO-GO matrix with synchrotron radiation techniques for the first time. This work provides a sensitive matrix enabling MALDI-TOF MS to be applied in small molecular analysis, but also presents a distinct perspective on the mechanism behind the material functions