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Recently, Multimodal LLMs (MLLMs) have shown a great ability to understand images. However, like traditional vision models, they are still vulnerable to adversarial images. Meanwhile, Chain-of-Thought (CoT) reasoning has been widely explored on MLLMs, which not only improves model's performance, but also enhances model's explainability by giving intermediate reasoning steps. Nevertheless, there is still a lack of study regarding MLLMs' adversarial robustness with CoT and an understanding of what the rationale looks like when MLLMs infer wrong answers with adversarial images. Our research evaluates the adversarial robustness of MLLMs when employing CoT reasoning, finding that CoT marginally improves adversarial robustness against existing attack methods. Moreover, we introduce a novel stop-reasoning attack technique that effectively bypasses the CoT-induced robustness enhancements. Finally, we demonstrate the alterations in CoT reasoning when MLLMs confront adversarial images, shedding light on their reasoning process under adversarial attacks.

Urban-scale quantification of window views can inform housing selection and valuation, landscape management, and urban planning. However, window views are numerous in high-rise, high-density urban areas and current automatic assessments of window views are inaccurate and time-consuming. Thus, both accurate and efficient assessment of window views is significant in improving the automation for urban-scale window view applications. The paper presents an automatic, accurate, and efficient assessment of window view indices (WVIs) of greenery, sky, waterbody, and construction using 3D color City Information Models (CIMs). The workflow includes: i) 3D semantic segmentation of photorealistic CIM and Digital Surface Model (DSM), and ii) batch computation of WVIs. Experimental results showed the estimated WVIs were more accurate (RMSE < 0.01), and the proposed method was more efficient (3.68 times faster) than Li et al.'s (2022) 2D semantic segmentation. Thus, the proposed method can facilitate large-scale WVI assessment and update in healthy high-rise, high-density urban development.

This paper explores the application of automated machine learning (AutoML) techniques to the construction industry, a sector vital to the global economy. Traditional ML model construction methods were complex, time-consuming, reliant on data science expertise, and expensive. AutoML shows the potential to automate many tasks in ML construction and to create outperformed ML models. This paper aims to verify the feasibility of applying AutoML to industrial datasets for the smart construction domain, with a specific case study demonstrating its effectiveness. Two data challenges that were unique to industrial construction datasets are focused on, in addition to the normal steps of dataset preparation, model training, and evaluation. A real-world application case of construction project type prediction is provided to illustrate the accessibility of AutoML. By leveraging AutoML, construction professionals without data science expertise can now utilize software to process industrial data into ML models that assist in project management. The findings in this paper may bridge the gap between data-intensive smart construction practices and the emerging field of AutoML, encouraging its adoption for improved decision-making, project outcomes, and efficiency

Many existing 3D semantic segmentation methods, deep learning in computer vision notably, claimed to achieve desired results on urban point clouds. Thus, it is significant to assess these methods quantitatively in diversified real-world urban scenes, encompassing high-rise, low-rise, high-density, and low-density urban areas. However, existing public benchmark datasets primarily represent low-rise scenes from European cities and cannot assess the methods comprehensively. This paper presents a benchmark dataset of high-rise urban point clouds, namely High-Rise, High-Density urban scenes of Hong Kong (HRHD-HK). HRHD-HK arranged in 150 tiles contains 273 million colorful photogrammetric 3D points from diverse urban settings. The semantic labels of HRHD-HK include building, vegetation, road, waterbody, facility, terrain, and vehicle. To our best knowledge, HRHD-HK is the first photogrammetric dataset that focuses on HRHD urban areas. This paper also comprehensively evaluates eight popular semantic segmentation methods on the HRHD-HK dataset. Experimental results confirmed plenty of room for enhancing the current 3D semantic segmentation of point clouds, especially for city objects with small volumes. Our dataset is publicly available at https://doi.org/10.25442/hku.23701866.v2., Comment: The paper has been published in the Proceedings of 2023 IEEE International Conference on Image Processing Challenges and Workshops

Owing to its high cost-effectiveness and suitable band gap, polycrystalline SnSe shows promising thermoelectric performance. However, its high figure-of-merit ZT is always realized at high temperatures of >800 K, which limits the whole-temperature thermoelectric efficiency. Here, we report a promising average ZT of >0.45 from 500 to 800 K in NiSe-alloyed SnSe fabricated by step-by-step solvothermal synthesis, mechanical alloying, oxidation removal, and sparkle plasma sintering processes. The introduced p-type NiSe nanophases construct NiSe–SnSe interfaces to effectively filter the low-energy carriers, leading to a significantly improved Seebeck coefficient within the entire temperature range. Moreover, the highly conductive NiSe nanophases simultaneously act as “expressways” to accelerate the carrier transport, especially at low temperatures, leading to enhanced electrical conductivity and, in turn, an improved power factor. On the other hand, NiSe–SnSe interfaces further contribute to the observed low thermal conductivity. This work provides a unique NiSe-alloying strategy to fabricate polycrystalline SnSe with improved average ZT.

Achieving high-stability, long span-life, and fast sodiation reaction kinetics in sodium ion battery (SIB) can significantly promote its practical application. Here, we use freeze-drying and carbonization to prepare a flexible N-doped silk wadding-derived carbon/SnOx@reduced graphene oxide (N-SWC/SnOx@rGO) film as an ultra-stable anode for a half/full SIB. A superior capacity of 572.2 mA h g−1 at 0.1 A g−1 and long span-life over 1000 cycles are obtained in the N-SWC/SnOx@rGO film anodes. Moreover, a super-stable capacity of 245.7 mA h g−1 can be secured in the assembled SIB full cell, outperforming the current studies. After the detailed structural and performance characterization, we attribute these superior capacities to the following unique structural characteristics: 1) SnOx nanoparticles (<100 nm) are attached to the surface of SWC to provide more active sites for Na+; 2) rGO and SWC form a double-layer conductive system, which can tremendously promote the transmission efficiency of electrons in N-SWC/SnOx@rGO film, thereby greatly accelerating the reaction kinetics of SIB; 3) the N-SWC/SnOx@rGO film contains a conductive network with voids, which can increase the contact area between electrolyte solution and SnOx and in turn effectively shorten the transmission path of Na+. This work provides a new perspective for the preparation of ultra-stable flexible SIB anodes.

This paper presents a deep learning-based point cloud processing method named FloorPP-Net for the task of Scan-to-BIM (building information model). FloorPP-Net first converts the input point cloud of a building story into point pillars (PP), then predicts the corners and edges to output the floor plan. Altogether, FloorPP-Net establishes an end-to-end supervised learning framework for the Scan-to-Floor-Plan (Scan2FP) task. In the 1st International Scan-to-BIM Challenge held in conjunction with CVPR 2021, FloorPP-Net was ranked the second runner-up in the floor plan reconstruction track. Future work includes general edge proposals, 2D plan regularization, and 3D BIM reconstruction.

The toll-adjustment mechanism (TAM) is a hybrid of a price cap regulation mechanism and a revenue sharing mechanism. It is one solution to saving private investors from severe traffic demand risk and the government from heavy fiscal burden, while ensuring the private investor a reasonable but not excessive rate of return in a public-private partnership (PPP) concession contract. This research models TAM as a real option to assess the value of flexibility of the right (but not obligation) to toll adjustments. A hypothetical case study derived from a real-life project (the Western Harbour Crossing in Hong Kong) is illustrated in detail to demonstrate the application of the framework developed and to validate the effectiveness and robustness of the framework. Outcomes of the research can help the government to design reasonable concession contracts and help the private investors to make sound investment decisions through effective management of the traffic demand risk. Therefore, a win-win prospect can be achieved in PPP concession contracts for both parties

Purpose Energy is a resource of strategic importance for high density cities. International trade reshapes the urban economy and industrial structure of a city, which will indirectly affect energy use. As an international trade hub, Hong Kong relies on the import and export of services. Energy performance in the international trading of these services needs to be properly understood and assessed for Hong Kong’s urban renewal efforts. Design/methodology/approach This study evaluates Hong Kong’s embodied energy in service trades based on an input-output analysis. The three criteria used for assessment include trading areas, industry sector, and trade balance. Findings Analyzed by region, results show that Mainland China and the USA are the two largest sources of embodied energy in imports of services, while Mainland China and Japan are the two largest destinations of exports. In terms of net embodied energy transfer, Hong Kong mainly receives net energy import from Mainland China and the USA and supplies net energy export to Japan, the UK and Taiwan. Among industry sectors, Manufacturing services, Transport and Travel contribute most significantly to the embodied energy in Hong Kong’s imported services, while Transport and Travel contribute most to the energy embodied in exported services. Originality/value This study identifies the characteristics of energy consumption of service trading and establishes a feasible approach to analyze energy performance of service trade in energy-deficient Hong Kong for the first time. It provides necessary understanding and foundation for developing energy strategies in a service-based, high density urban economy.

Min-ting Wang1, Yun Jin1, Yun-xia Yang2, Chun-yan Zhao1, Hong-Yun Yang1, Xue-fan Xu1, Xuan Qin1, Zhao-dan Wang2, Zhi-Rong Zhang1, Yan-lin Jian3, Yuan Huang11Key Laboratory of Drug Targeting and Drug Delivery System, Ministry of Education, West China School of Pharmacy, 2West China School of Preclinical and Forensic Medicine, Sichuan University, Chengdu, People’s Republic of China; 3Shenzhen Second People’s Hospital, Shenzhen, People’s Republic of ChinaAbstract: We aimed to evaluate whether the enhancement of the liver accumulation and anti-inflammatory activity of dexamethasone acetate (DXMA) could be achieved by incorporating it into nanostructured lipid carrier (NLCs). DXMA-NLCs were prepared using a film dispersion-ultrasonication method and characterized in terms of particle size, PDI, zeta potential, differential scanning calorimetry, drug loading capacity, encapsulation efficiency, and in vitro release. The biodistribution and pharmacokinetics of DXMA-NLCs in mice were significantly different from those of the DXMA solution (DXMA-sol). The peak concentration of DXMA-NLCs was obtained half an hour after intravenous administration. More than 55.62% of the total administrated dose was present in the liver. An increase of 2.57 fold in the area under the curve was achieved when compared with that of DXMA-sol. DXMA-NLCs exhibited a significant anti-inflammatory and hepatoprotective effect on carrageenan-induced rats and carbon tetrachloride-induced mice compared with DXMA-sol. However, the effect was not in proportion to the dosage. The intermediate and low dosages presented better effects than DXMA-sol. All results indicate that NLCs, as a novel carrier for DXMA, has potential for the treatment of liver diseases, increasing the cure efficiency and decreasing the side effects on other tissues.Keywords: dexamethasone acetate, nanostructured lipid carrier, liver targeting

Min-ting Wang1, Yun Jin1, Yun-xia Yang2, Chun-yan Zhao1, Hong-Yun Yang1, Xue-fan Xu1, Xuan Qin1, Zhao-dan Wang2, Zhi-Rong Zhang1, Yan-lin Jian3, Yuan Huang11Key Laboratory of Drug Targeting and Drug Delivery System, Ministry of Education, West China School of Pharmacy, 2West China School of Preclinical and Forensic Medicine, Sichuan University, Chengdu, People’s Republic of China; 3Shenzhen Second People’s Hospital, Shenzhen, People’s Republic of ChinaAbstract: We aimed to evaluate whether the enhancement of the liver accumulation and anti-inflammatory activity of dexamethasone acetate (DXMA) could be achieved by incorporating it into nanostructured lipid carrier (NLCs). DXMA-NLCs were prepared using a film dispersion-ultrasonication method and characterized in terms of particle size, PDI, zeta potential, differential scanning calorimetry, drug loading capacity, encapsulation efficiency, and in vitro release. The biodistribution and pharmacokinetics of DXMA-NLCs in mice were significantly different from those of the DXMA solution (DXMA-sol). The peak concentration of DXMA-NLCs was obtained half an hour after intravenous administration. More than 55.62% of the total administrated dose was present in the liver. An increase of 2.57 fold in the area under the curve was achieved when compared with that of DXMA-sol. DXMA-NLCs exhibited a significant anti-inflammatory and hepatoprotective effect on carrageenan-induced rats and carbon tetrachloride-induced mice compared with DXMA-sol. However, the effect was not in proportion to the dosage. The intermediate and low dosages presented better effects than DXMA-sol. All results indicate that NLCs, as a novel carrier for DXMA, has potential for the treatment of liver diseases, increasing the cure efficiency and decreasing the side effects on other tissues.Keywords: dexamethasone acetate, nanostructured lipid carrier, liver targeting