Scaling Effect of Fused ASTER-MODIS Land Surface Temperature in an Urban Environment

There is limited research in land surface temperatures (LST) simulation using image fusion techniques, especially studies addressing the downscaling effect of LST image fusion. LST simulation and associated downscaling effect can potentially benefit the thermal studies requiring both high spatial and temporal resolutions. This study simulated LSTs based on observed Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LST imagery with Spatial and Temporal Adaptive Reflectance Fusion Model, and investigated the downscaling effect of LST image fusion at 15, 30, 60, 90, 120, 250, 500, and 1000 m spatial resolutions. The study area partially covered the City of Los Angeles, California, USA, and surrounding areas. The reference images (observed ASTER and MODIS LST imagery) were acquired on 04/03/2007 and 07/01/2007, with simulated LSTs produced for 4/28/2007. Three image resampling methods (Cubic Convolution, Bilinear Interpolation, and Nearest Neighbor) were used during the downscaling and upscaling processes, and the resulting LST simulations were compared. Results indicated that the observed ASTER LST and simulated ASTER LST images (date 04/28/2007, spatial resolution 90 m) had high agreement in terms of spatial variations and basic statistics based on a comparison between the observed and simulated ASTER LST maps. Urban developed lands possessed higher LSTs with lighter tones and mountainous areas showed dark tones with lower LSTs. The Cubic Convolution and Bilinear Interpolation resampling methods yielded better results over Nearest Neighbor resampling method across the scales from 15 to 1000 m. The simulated LSTs with image fusion can be used as valuable inputs in heat related studies that require frequent LST measurements with fine spatial resolutions, e.g., seasonal movements of urban heat islands, monthly energy budget assessment, and temperature-driven epidemiology. The observation of scale-independency of the proposed image fusion method can facilitate with image selections of LST studies at various locations

Access-based consumption, behaviour change and future mobility: insights from visions of car sharing in Greater London

The way in which people choose to travel has changed throughout history and adaptations have taken place in order to provide the most convenient, efficient and cost-effective method(s) of transport possible. This research explores two trends—technological and socio-economic change—by discussing the effects of their application in the renewed drive to promote car clubs in Greater London through the introduction of new technologies and innovative ways in which a car can be used and hired, thus helping to generate new insights for car sharing. A mixed methods approach was used, combining secondary data analysis obtained from a car club member survey of 5898 people with in-depth, semi-structured interviews. Our findings show that there is an opportunity to utilise car clubs as a tool for facilitating a step change away from private vehicle ownership in the city. In addition, the results suggest that car club operators are seeking to deliver a mode of transport that is able to compete with private car ownership. In terms of policy implications, such findings would suggest that compromise is necessary, and an operator/authority partnership would offer the most effective way of delivering car clubs in a manner that benefits all Londoners

Spatio-Temporal Analysis of the Relationship Between WNV Dissemination and Environmental Variables in Indianapolis, USA.

Background: This study developed a multi-temporal analysis on the relationship between West Nile Virus (WNV) dissemination and environmental variables by using an integrated approach of remote sensing, GIS, and statistical techniques. WNV mosquito cases in seven months (April-October) of the six years (2002–2007) were collected in Indianapolis, USA. Epidemic curves were plotted to identify the temporal outbreaks of WNV. Spatial-temporal analysis and k-mean cluster analysis were further applied to determine the high-risk areas. Finally, the relationship between environmental variables and WNV outbreaks were examined by using Discriminant Analysis. Results: The results show that the WNV epidemic curve reached its peak in August for all years in the study area except in 2007, where the peak was reached in July. WNV dissemination started from the central longitudinal corridor of the city and spread out to the east and west. Different years and seasons had different high-risk areas, but the southwest and southeast corners show the highest risk for WNV infection due to their high percentages of agriculture and water sources. Conclusion: Major environmental factors contributing to the outbreak of WNV in Indianapolis were the percentages of agriculture and water, total length of streams, and total size of wetlands. This study provides important information for urban public health prevention and management. It also contributes to the optimization of mosquito control and arrangement of future sampling efforts

Synthesising the Existing Literature on the Market Acceptance of Autonomous Vehicles and the External Underlying Factors

In recent years, the level of acceptance of autonomous vehicles (AVs) has changed with the advent of new sensor technologies and the proportional increase in market perception of these vehicles. Our study provides an overview of the relevant existing studies in order to consolidate current knowledge and pave the way for future studies in this area. The paper first reviews studies investigating the market acceptance of AVs. We identify the nonbehavioural factors that account for the level of acceptance and examine these in detail by cross-referencing the results of relevant papers published between 2014 and 2021 to reach a consensus on the perceived benefits and concerns. The findings showed that previous studies have found legal liability, safety, privacy, security, traffic conditions, and cost to be key external factors influencing the acceptance or rejection of AVs, and that the upsides of adopting AVs in regard to improving traffic conditions and safety outweigh the risks identified in relation to these areas. This resulted in an overall weighted average of 65% market acceptance of AVs among the 11,057 people surveyed in this regard. However, the remaining respondents were not very favourably disposed towards adopting AVs because of unresolved issues related to data privacy, security breaches, and legal liability in the event of accidents. In addition, our evaluation showed that the worldwide market purchasing power for an AV, based on 2022 prices, is around $38k, which is significantly below the current anticipated price of$100k

Resource Allocation for Uplink Cell-Free Massive MIMO enabled URLLC in a Smart Factory

Smart factories need to support the simultaneous communication of multiple industrial Internet-of-Things (IIoT) devices with ultra-reliability and low-latency communication (URLLC). Meanwhile, short packet transmission for IIoT applications incurs performance loss compared to traditional long packet transmission for human-to-human communications. On the other hand, cell-free massive multiple-input and multiple-output (CF mMIMO) technology can provide uniform services for all devices by deploying distributed access points (APs). In this paper, we adopt CF mMIMO to support URLLC in a smart factory. Specifically, we first derive the lower bound (LB) on achievable uplink data rate under the finite blocklength (FBL) with imperfect channel state information (CSI) for both maximum-ratio combining (MRC) and full-pilot zero-forcing (FZF) decoders. \textcolor{black}{The derived LB rates based on the MRC case have the same trends as the ergodic rate, while LB rates using the FZF decoder tightly match the ergodic rates}, which means that resource allocation can be performed based on the LB data rate rather the exact ergodic data rate under FBL. The \textcolor{black}{log-function method} and successive convex approximation (SCA) are then used to approximately transform the non-convex weighted sum rate problem into a series of geometric program (GP) problems, and an iterative algorithm is proposed to jointly optimize the pilot and payload power allocation. Simulation results demonstrate that CF mMIMO significantly improves the average weighted sum rate (AWSR) compared to centralized mMIMO. An interesting observation is that increasing the number of devices improves the AWSR for CF mMIMO whilst the AWSR remains relatively constant for centralized mMIMO.Comment: Accepted by Transactions on Communication

Intriguing Properties of Text-guided Diffusion Models

Text-guided diffusion models (TDMs) are widely applied but can fail unexpectedly. Common failures include: (i) natural-looking text prompts generating images with the wrong content, or (ii) different random samples of the latent variables that generate vastly different, and even unrelated, outputs despite being conditioned on the same text prompt. In this work, we aim to study and understand the failure modes of TDMs in more detail. To achieve this, we propose SAGE, an adversarial attack on TDMs that uses image classifiers as surrogate loss functions, to search over the discrete prompt space and the high-dimensional latent space of TDMs to automatically discover unexpected behaviors and failure cases in the image generation. We make several technical contributions to ensure that SAGE finds failure cases of the diffusion model, rather than the classifier, and verify this in a human study. Our study reveals four intriguing properties of TDMs that have not been systematically studied before: (1) We find a variety of natural text prompts producing images that fail to capture the semantics of input texts. We categorize these failures into ten distinct types based on the underlying causes. (2) We find samples in the latent space (which are not outliers) that lead to distorted images independent of the text prompt, suggesting that parts of the latent space are not well-structured. (3) We also find latent samples that lead to natural-looking images which are unrelated to the text prompt, implying a potential misalignment between the latent and prompt spaces. (4) By appending a single adversarial token embedding to an input prompt we can generate a variety of specified target objects, while only minimally affecting the CLIP score. This demonstrates the fragility of language representations and raises potential safety concerns.Comment: Code will be available at: https://github.com/qihao067/SAG

Evaluating transit-served areas with non-traditional data: An exploratory study of Shenzhen, China

In this study, transit-served areas (TSAs) are defined as areas within a reasonable distance (e.g., 800 meters) of transit services. TSAs have two key dimensions: physical features (e.g., land-use density and mix) and performance (regarding human behaviors). Non-traditional data (NTD) (e.g., social media check-ins and cellular network data) can supplement traditional data (TD) (e.g., interviews and censuses) to enhance studies and monitoring of TSAs. A case study of Shenzhen, China, illustrates how to combine NTD and TD to evaluate the features and performance of 167 TSAs along metro lines. It finds that NTD can be used to formulate new indicators to measure and monitor the two dimensions of TSAs; the features and performance of different TSAs vary significantly; point of interest (POI) efficiency, or the average users attracted by each POI, can be a useful indicator to differentiate TSAs’ performance; the POI efficiency of a single TSA can vary across days and the POI efficiency of an extremely efficient or inefficient TSA can be totally different across days; and the combination of NTD and TD can effectively help locate extreme TSAs and explain factors contributing to the extremity

InstMove: Instance Motion for Object-centric Video Segmentation

Despite significant efforts, cutting-edge video segmentation methods still remain sensitive to occlusion and rapid movement, due to their reliance on the appearance of objects in the form of object embeddings, which are vulnerable to these disturbances. A common solution is to use optical flow to provide motion information, but essentially it only considers pixel-level motion, which still relies on appearance similarity and hence is often inaccurate under occlusion and fast movement. In this work, we study the instance-level motion and present InstMove, which stands for Instance Motion for Object-centric Video Segmentation. In comparison to pixel-wise motion, InstMove mainly relies on instance-level motion information that is free from image feature embeddings, and features physical interpretations, making it more accurate and robust toward occlusion and fast-moving objects. To better fit in with the video segmentation tasks, InstMove uses instance masks to model the physical presence of an object and learns the dynamic model through a memory network to predict its position and shape in the next frame. With only a few lines of code, InstMove can be integrated into current SOTA methods for three different video segmentation tasks and boost their performance. Specifically, we improve the previous arts by 1.5 AP on OVIS dataset, which features heavy occlusions, and 4.9 AP on YouTubeVIS-Long dataset, which mainly contains fast-moving objects. These results suggest that instance-level motion is robust and accurate, and hence serving as a powerful solution in complex scenarios for object-centric video segmentation.Comment: Accepted to CVPR 202