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Article ; Online: Identifying and prioritizing sustainability indicators for China's assessing demolition waste management using modified Delphi-analytic hierarchy process method.

Han, Dongchen / Kalantari, Mohsen / Rajabifard, Abbas

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA

2023 Volume 41, Issue 11, Page(s) 1649–1660

Abstract: Addressing the sustainability issues arising from construction and demolition waste management (DWM) has gained little traction due to the lack of incentives, stringent regulations, and systematic guidance. This study aims to empower systematic decision- ... ...

Abstract	Addressing the sustainability issues arising from construction and demolition waste management (DWM) has gained little traction due to the lack of incentives, stringent regulations, and systematic guidance. This study aims to empower systematic decision-making concerning DWM alternative selection by developing a sustainability assessment framework by coupling a modified Delphi method with the multicriteria decision analysis technique. First, the study identifies a comprehensive inventory of indicators across three dimensions of sustainability in the context of DWM. Next, the study combines a modified Delphi method with the analytic hierarchy process to validate and prioritize the selected sustainability indicators. For the first time, insights regarding the DWM sustainability indicators from China's construction industry practitioners' perspectives are elicited using a mixed method comprising online semistructured interviews and two rounds of questionnaire surveys. Experts participating in the research are mostly based in Guangzhou and Shenzhen, where local governments exhaust all efforts in promoting carbon-neutral and sustainable development. The findings reveal that eight sustainability indicators were regarded as the determinants for the sustainability performance of DWM, with the global warming potential (32%), energy efficiency (16.1%) and land use (13.5%) receiving the highest preference scores (weights) based on the experts' judgment. Notably, the economic factors like the total cost (6.54%) appeared not highly prioritized by the local experts as typically did in the previous studies from developing countries.
MeSH term(s)	Analytic Hierarchy Process ; Waste Management ; Construction Industry ; China
Language	English
Publishing date	2023-04-26
Publishing country	England
Document type	Journal Article
ZDB-ID	1480483-9
ISSN	1096-3669 ; 1399-3070 ; 0734-242X
ISSN (online)	1096-3669 ; 1399-3070
ISSN	0734-242X
DOI	10.1177/0734242X231166309
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The development of an integrated BIM-based visual demolition waste management planning system for sustainability-oriented decision-making.

Han, Dongchen / Kalantari, Mohsen / Rajabifard, Abbas

Journal of environmental management

2023 Volume 351, Page(s) 119856

Abstract: In light of the suboptimal resource efficiency persisting in current demolition waste management (DWM) practices caused by inattentive and profit-driven decision-making due to the lack of tailored stringent legislation, monetary incentives, and ... ...

Abstract	In light of the suboptimal resource efficiency persisting in current demolition waste management (DWM) practices caused by inattentive and profit-driven decision-making due to the lack of tailored stringent legislation, monetary incentives, and benchmarking frameworks, this study aims to facilitate sustainability-oriented decision-making at the demolition planning stage. A practical Building Information Modelling (BIM)-based visual DWM planning system is designed, wherein the system seamlessly accommodates inventory analysis and Multi-Criteria Decision-Aiding (MCDA) algorithms into various interconnected modules. Moreover, this research proposes bespoke algorithms and colour coding schemes to quantify and visualise the recycling value of building components for augmenting the visual guidance of sustainable building design and selective demolition planning. Furthermore, a pilot case study demonstrates the system's applicability in a real-world demolition project. The findings unveil that improving the recycling rate substantially offsets carbon emissions and demolition waste disposal expenditures. The increment in beneficial impacts outweighs the additional energy consumption and costs for implementing sustainable DWM strategies based on the predefined geographical settings. This BIM-based system reforms the conventional demolition planning and DWM decision-making workflow by tackling technical barriers concerning data richness, interoperability, and result interpretation. It equips the users with intuitive visual design guidance and parallel scenario analysis when crafting sustainability-oriented DWM schemes. In summary, this research contributes to familiarising industry practitioners with sustainable DWM schematic design and circular economy principles. Moreover, it prompts the development of customised BIM libraries as repositories for updating and capitalising on DWM-related information that can be adapted to different regional contexts.
MeSH term(s)	Construction Materials ; Waste Management ; Recycling ; Refuse Disposal ; Construction Industry ; Industrial Waste
Chemical Substances	Industrial Waste
Language	English
Publishing date	2023-12-27
Publishing country	England
Document type	Journal Article
ZDB-ID	184882-3
ISSN	1095-8630 ; 0301-4797
ISSN (online)	1095-8630
ISSN	0301-4797
DOI	10.1016/j.jenvman.2023.119856
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Article ; Online: Span-based single-stage joint entity-relation extraction model.

Han, Dongchen / Zheng, Zhaoqian / Zhao, Hui / Feng, Shanshan / Pang, Haiting

PloS one

2023 Volume 18, Issue 2, Page(s) e0281055

Abstract: Extracting entities and relations from the unstructured text has attracted increasing attention in recent years. The existing work has achieved considerable results, yet it is difficult to solve entity overlap and exposure bias. To address cascading ... ...

Abstract	Extracting entities and relations from the unstructured text has attracted increasing attention in recent years. The existing work has achieved considerable results, yet it is difficult to solve entity overlap and exposure bias. To address cascading errors, exposure bias, and entity overlap in existing entity relation extraction approaches, we propose a joint entity relation extraction model (SMHS) based on a span-level multi-head selection mechanism, transforming entity relation extraction into a span-level multi-head selection problem. Our model uses span-tagger and span-embedding to construct span semantic vectors, utilizes LSTM and multi-head self-attention mechanism for span feature extraction, multi-head selection mechanism for span-level relation decoding, and introduces span classification task for multi-task learning to decode out the relation triad in a single-stage. Experiments on the classic English dataset NYT and the publicly available Chinese relationship extraction dataset DuIE 2.0 show that this method achieves better results than the baseline method, which verifies the effectiveness of this method. Source code and data are published here(https://github.com/Beno-waxgourd/NLP.git).
MeSH term(s)	Semantics ; Software ; Learning ; Text Messaging
Language	English
Publishing date	2023-02-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0281055
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Article ; Online: Salinity and pH related microbial nitrogen removal in the largest coastal lagoon of Chinese mainland (Pinqing Lagoon).

Han, Dongchen / Hong, Yiguo / Wu, Jiapeng / Xian, Hanbiao / Dong, Xuhui / Liu, Honglei / Ye, Fei / Wang, Yu

Marine pollution bulletin

2024 Volume 201, Page(s) 116181

Abstract: Coastal lagoon is critical habitat for human and provides a wide range of ecosystem services. These vital habitats are now threatened by waste discharge and eutrophication. Previous studies suggest that the pollution mitigation of coastal lagoon relies ... ...

Abstract	Coastal lagoon is critical habitat for human and provides a wide range of ecosystem services. These vital habitats are now threatened by waste discharge and eutrophication. Previous studies suggest that the pollution mitigation of coastal lagoon relies on the water exchange with open sea, and the role of microbial processes inside the lagoon is overlooked. This study takes the Pinqing Lagoon which is the largest coastal lagoon in Chinese mainland as example. The distribution of nutrients, microbial activity of nitrogen removal and community structure of denitrifying bacteria in sediment are analyzed. The results showed that the nutrient in sediment represented by DIN (1.65-12.78 mg kg
MeSH term(s)	Humans ; Ecosystem ; Denitrification ; Nitrogen/analysis ; Salinity ; China ; Hydrogen-Ion Concentration
Chemical Substances	Nitrogen (N762921K75)
Language	English
Publishing date	2024-02-22
Publishing country	England
Document type	Journal Article
ZDB-ID	2001296-2
ISSN	1879-3363 ; 0025-326X
ISSN (online)	1879-3363
ISSN	0025-326X
DOI	10.1016/j.marpolbul.2024.116181
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Book ; Online: FLatten Transformer

Han, Dongchen / Pan, Xuran / Han, Yizeng / Song, Shiji / Huang, Gao

Vision Transformer using Focused Linear Attention

2023

Abstract: The quadratic computation complexity of self-attention has been a persistent challenge when applying Transformer models to vision tasks. Linear attention, on the other hand, offers a much more efficient alternative with its linear complexity by ... ...

Abstract	The quadratic computation complexity of self-attention has been a persistent challenge when applying Transformer models to vision tasks. Linear attention, on the other hand, offers a much more efficient alternative with its linear complexity by approximating the Softmax operation through carefully designed mapping functions. However, current linear attention approaches either suffer from significant performance degradation or introduce additional computation overhead from the mapping functions. In this paper, we propose a novel Focused Linear Attention module to achieve both high efficiency and expressiveness. Specifically, we first analyze the factors contributing to the performance degradation of linear attention from two perspectives: the focus ability and feature diversity. To overcome these limitations, we introduce a simple yet effective mapping function and an efficient rank restoration module to enhance the expressiveness of self-attention while maintaining low computation complexity. Extensive experiments show that our linear attention module is applicable to a variety of advanced vision Transformers, and achieves consistently improved performances on multiple benchmarks. Code is available at https://github.com/LeapLabTHU/FLatten-Transformer. Comment: ICCV 2023
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	005
Publishing date	2023-08-01
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Pseudo-Q

Jiang, Haojun / Lin, Yuanze / Han, Dongchen / Song, Shiji / Huang, Gao

Generating Pseudo Language Queries for Visual Grounding

2022

Abstract: Visual grounding, i.e., localizing objects in images according to natural language queries, is an important topic in visual language understanding. The most effective approaches for this task are based on deep learning, which generally require expensive ... ...

Abstract	Visual grounding, i.e., localizing objects in images according to natural language queries, is an important topic in visual language understanding. The most effective approaches for this task are based on deep learning, which generally require expensive manually labeled image-query or patch-query pairs. To eliminate the heavy dependence on human annotations, we present a novel method, named Pseudo-Q, to automatically generate pseudo language queries for supervised training. Our method leverages an off-the-shelf object detector to identify visual objects from unlabeled images, and then language queries for these objects are obtained in an unsupervised fashion with a pseudo-query generation module. Then, we design a task-related query prompt module to specifically tailor generated pseudo language queries for visual grounding tasks. Further, in order to fully capture the contextual relationships between images and language queries, we develop a visual-language model equipped with multi-level cross-modality attention mechanism. Extensive experimental results demonstrate that our method has two notable benefits: (1) it can reduce human annotation costs significantly, e.g., 31% on RefCOCO without degrading original model's performance under the fully supervised setting, and (2) without bells and whistles, it achieves superior or comparable performance compared to state-of-the-art weakly-supervised visual grounding methods on all the five datasets we have experimented. Code is available at https://github.com/LeapLabTHU/Pseudo-Q. Comment: Accepted by CVPR2022
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2022-03-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Agent Attention

Han, Dongchen / Ye, Tianzhu / Han, Yizeng / Xia, Zhuofan / Song, Shiji / Huang, Gao

On the Integration of Softmax and Linear Attention

2023

Abstract: The attention module is the key component in Transformers. While the global attention mechanism offers high expressiveness, its excessive computational cost restricts its applicability in various scenarios. In this paper, we propose a novel attention ... ...

Abstract	The attention module is the key component in Transformers. While the global attention mechanism offers high expressiveness, its excessive computational cost restricts its applicability in various scenarios. In this paper, we propose a novel attention paradigm, Agent Attention, to strike a favorable balance between computational efficiency and representation power. Specifically, the Agent Attention, denoted as a quadruple $(Q, A, K, V)$, introduces an additional set of agent tokens $A$ into the conventional attention module. The agent tokens first act as the agent for the query tokens $Q$ to aggregate information from $K$ and $V$, and then broadcast the information back to $Q$. Given the number of agent tokens can be designed to be much smaller than the number of query tokens, the agent attention is significantly more efficient than the widely adopted Softmax attention, while preserving global context modelling capability. Interestingly, we show that the proposed agent attention is equivalent to a generalized form of linear attention. Therefore, agent attention seamlessly integrates the powerful Softmax attention and the highly efficient linear attention. Extensive experiments demonstrate the effectiveness of agent attention with various vision Transformers and across diverse vision tasks, including image classification, object detection, semantic segmentation and image generation. Notably, agent attention has shown remarkable performance in high-resolution scenarios, owning to its linear attention nature. For instance, when applied to Stable Diffusion, our agent attention accelerates generation and substantially enhances image generation quality without any additional training. Code is available at https://github.com/LeapLabTHU/Agent-Attention.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-12-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: OT-Attack

Han, Dongchen / Jia, Xiaojun / Bai, Yang / Gu, Jindong / Liu, Yang / Cao, Xiaochun

Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization

2023

Abstract: Vision-language pre-training (VLP) models demonstrate impressive abilities in processing both images and text. However, they are vulnerable to multi-modal adversarial examples (AEs). Investigating the generation of high-transferability adversarial ... ...

Abstract	Vision-language pre-training (VLP) models demonstrate impressive abilities in processing both images and text. However, they are vulnerable to multi-modal adversarial examples (AEs). Investigating the generation of high-transferability adversarial examples is crucial for uncovering VLP models' vulnerabilities in practical scenarios. Recent works have indicated that leveraging data augmentation and image-text modal interactions can enhance the transferability of adversarial examples for VLP models significantly. However, they do not consider the optimal alignment problem between dataaugmented image-text pairs. This oversight leads to adversarial examples that are overly tailored to the source model, thus limiting improvements in transferability. In our research, we first explore the interplay between image sets produced through data augmentation and their corresponding text sets. We find that augmented image samples can align optimally with certain texts while exhibiting less relevance to others. Motivated by this, we propose an Optimal Transport-based Adversarial Attack, dubbed OT-Attack. The proposed method formulates the features of image and text sets as two distinct distributions and employs optimal transport theory to determine the most efficient mapping between them. This optimal mapping informs our generation of adversarial examples to effectively counteract the overfitting issues. Extensive experiments across various network architectures and datasets in image-text matching tasks reveal that our OT-Attack outperforms existing state-of-the-art methods in terms of adversarial transferability.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-12-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: GSVA

Xia, Zhuofan / Han, Dongchen / Han, Yizeng / Pan, Xuran / Song, Shiji / Huang, Gao

Generalized Segmentation via Multimodal Large Language Models

2023

Abstract: Generalized Referring Expression Segmentation (GRES) extends the scope of classic RES to referring to multiple objects in one expression or identifying the empty targets absent in the image. GRES poses challenges in modeling the complex spatial ... ...

Abstract	Generalized Referring Expression Segmentation (GRES) extends the scope of classic RES to referring to multiple objects in one expression or identifying the empty targets absent in the image. GRES poses challenges in modeling the complex spatial relationships of the instances in the image and identifying non-existing referents. Recently, Multimodal Large Language Models (MLLMs) have shown tremendous progress in these complicated vision-language tasks. Connecting Large Language Models (LLMs) and vision models, MLLMs are proficient in understanding contexts with visual inputs. Among them, LISA, as a representative, adopts a special [SEG] token to prompt a segmentation mask decoder, e.g., SAM, to enable MLLMs in the RES task. However, existing solutions to of GRES remain unsatisfactory since current segmentation MLLMs cannot properly handle the cases where users might reference multiple subjects in a singular prompt or provide descriptions incongruent with any image target. In this paper, we propose Generalized Segmentation Vision Assistant (GSVA) to address this gap. Specifically, GSVA reuses the [SEG] token to prompt the segmentation model towards supporting multiple mask references simultaneously and innovatively learns to generate a [REJ] token to reject the null targets explicitly. Experiments validate GSVA's efficacy in resolving the GRES issue, marking a notable enhancement and setting a new record on the GRES benchmark gRefCOCO dataset. GSVA also proves effective across various classic referring expression segmentation and comprehension tasks. Comment: 17 pages, 6 figures, 9 tables
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	004
Publishing date	2023-12-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Dynamic Perceiver for Efficient Visual Recognition

Han, Yizeng / Han, Dongchen / Liu, Zeyu / Wang, Yulin / Pan, Xuran / Pu, Yifan / Deng, Chao / Feng, Junlan / Song, Shiji / Huang, Gao

2023

Abstract: Early exiting has become a promising approach to improving the inference efficiency of deep networks. By structuring models with multiple classifiers (exits), predictions for ``easy'' samples can be generated at earlier exits, negating the need for ... ...

Abstract	Early exiting has become a promising approach to improving the inference efficiency of deep networks. By structuring models with multiple classifiers (exits), predictions for ``easy'' samples can be generated at earlier exits, negating the need for executing deeper layers. Current multi-exit networks typically implement linear classifiers at intermediate layers, compelling low-level features to encapsulate high-level semantics. This sub-optimal design invariably undermines the performance of later exits. In this paper, we propose Dynamic Perceiver (Dyn-Perceiver) to decouple the feature extraction procedure and the early classification task with a novel dual-branch architecture. A feature branch serves to extract image features, while a classification branch processes a latent code assigned for classification tasks. Bi-directional cross-attention layers are established to progressively fuse the information of both branches. Early exits are placed exclusively within the classification branch, thus eliminating the need for linear separability in low-level features. Dyn-Perceiver constitutes a versatile and adaptable framework that can be built upon various architectures. Experiments on image classification, action recognition, and object detection demonstrate that our method significantly improves the inference efficiency of different backbones, outperforming numerous competitive approaches across a broad range of computational budgets. Evaluation on both CPU and GPU platforms substantiate the superior practical efficiency of Dyn-Perceiver. Code is available at https://www.github.com/LeapLabTHU/Dynamic_Perceiver.
Keywords	Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2023-06-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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