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The impact of building operations on urban heat/cool islands under urban densification: a comparison between naturally-ventilated and air-conditioned buildings
Many cities are suffering the effects of urban heat islands (UHI) or urban cool islands (UCI) due to rapid urban expansion and numerous infrastructure developments. This paper presents a lumped urban-building thermal coupling model which captures the fundamental physical mechanism for thermal interactions between buildings and their urban environment. The benefits of the model are its simplicity and high computational efficiency for practical use in investigating the diurnal urban air temperature change and its asymmetry in a city with both naturally-ventilated (NV) and air-conditioned (AC) buildings. Our model predicts a lower urban heat island and higher urban cool island intensity in a city with naturally-ventilated buildings than for a city with air-conditioned buildings. During the urban densification (from a low-rise, low-density city to a high-rise, high-density one), the increases in the time constant and internal heat gain give rise to asymmetric warming phenomena, which become more obvious in a city with air-conditioned buildings rather than naturally-ventilated ones. Unlike previous studies, we found that a low-rise, low-density city experiences a stronger urban cool island effect than a high-rise, high-density city due to less heat being emitted into the urban atmosphere. The urban cool/heat island effect will firstly increase/decrease, and then rapidly decrease/increase and ultimately disappear/dominate with increasing time constant in the process of urbanization/urban densification
An Experimental Study on Shear Performance of Adhesive Interface between Steel Plates and CFRP
CFRP (Carbon Fiber Reinforced Polymer) are widely used in steel structural reinforcement. For steel structures strengthened with CFRP, except the cases the structures have defects before strengthening, the adhesive interface is the weakest part and CFRP debonding is the most common failure mode. In order to investigate the failure mechanism of CFRP strengthened steel structures, this paper presents an experimental study on shear performance of adhesive interface between steel plate and CFRP by twin shear model. Six steel plates strengthened with CFRP are divided into two groups, one has no damage, another has a gap at the mid. The specimens are tested under tensile loadings. The study results show that, the plates with a gap failed for CFRPs debonding, the cracking loading and breaking loading are 14.85kN, and 17.88kN respectively; the strain-loading curves had long linear stages, two strains decrease and other strains of another side increased rapidly at the cracking loading, then they both rose until the plates failed
AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning
WiFi sensing technology has shown superiority in smart homes among various
sensors for its cost-effective and privacy-preserving merits. It is empowered
by Channel State Information (CSI) extracted from WiFi signals and advanced
machine learning models to analyze motion patterns in CSI. Many learning-based
models have been proposed for kinds of applications, but they severely suffer
from environmental dependency. Though domain adaptation methods have been
proposed to tackle this issue, it is not practical to collect high-quality,
well-segmented and balanced CSI samples in a new environment for adaptation
algorithms, but randomly-captured CSI samples can be easily collected.
{\color{black}In this paper, we firstly explore how to learn a robust model
from these low-quality CSI samples, and propose AutoFi, an annotation-efficient
WiFi sensing model based on a novel geometric self-supervised learning
algorithm.} The AutoFi fully utilizes unlabeled low-quality CSI samples that
are captured randomly, and then transfers the knowledge to specific tasks
defined by users, which is the first work to achieve cross-task transfer in
WiFi sensing. The AutoFi is implemented on a pair of Atheros WiFi APs for
evaluation. The AutoFi transfers knowledge from randomly collected CSI samples
into human gait recognition and achieves state-of-the-art performance.
Furthermore, we simulate cross-task transfer using public datasets to further
demonstrate its capacity for cross-task learning. For the UT-HAR and Widar
datasets, the AutoFi achieves satisfactory results on activity recognition and
gesture recognition without any prior training. We believe that the AutoFi
takes a huge step toward automatic WiFi sensing without any developer
engagement.Comment: The paper has been accepted by IEEE Internet of Things Journa
Convolutional Embedding for Edit Distance
Edit-distance-based string similarity search has many applications such as
spell correction, data de-duplication, and sequence alignment. However,
computing edit distance is known to have high complexity, which makes string
similarity search challenging for large datasets. In this paper, we propose a
deep learning pipeline (called CNN-ED) that embeds edit distance into Euclidean
distance for fast approximate similarity search. A convolutional neural network
(CNN) is used to generate fixed-length vector embeddings for a dataset of
strings and the loss function is a combination of the triplet loss and the
approximation error. To justify our choice of using CNN instead of other
structures (e.g., RNN) as the model, theoretical analysis is conducted to show
that some basic operations in our CNN model preserve edit distance.
Experimental results show that CNN-ED outperforms data-independent CGK
embedding and RNN-based GRU embedding in terms of both accuracy and efficiency
by a large margin. We also show that string similarity search can be
significantly accelerated using CNN-based embeddings, sometimes by orders of
magnitude.Comment: Accepted by the 43rd International ACM SIGIR Conference on Research
and Development in Information Retrieval, 202
PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Images
As image generation technology advances, AI-based image generation has been
applied in various fields and Artificial Intelligence Generated Content (AIGC)
has garnered widespread attention. However, the development of AI-based image
generative models also brings new problems and challenges. A significant
challenge is that AI-generated images (AIGI) may exhibit unique distortions
compared to natural images, and not all generated images meet the requirements
of the real world. Therefore, it is of great significance to evaluate AIGIs
more comprehensively. Although previous work has established several human
perception-based AIGC image quality assessment (AIGCIQA) databases for
text-generated images, the AI image generation technology includes scenarios
like text-to-image and image-to-image, and assessing only the images generated
by text-to-image models is insufficient. To address this issue, we establish a
human perception-based image-to-image AIGCIQA database, named PKU-I2IQA. We
conduct a well-organized subjective experiment to collect quality labels for
AIGIs and then conduct a comprehensive analysis of the PKU-I2IQA database.
Furthermore, we have proposed two benchmark models: NR-AIGCIQA based on the
no-reference image quality assessment method and FR-AIGCIQA based on the
full-reference image quality assessment method. Finally, leveraging this
database, we conduct benchmark experiments and compare the performance of the
proposed benchmark models. The PKU-I2IQA database and benchmarks will be
released to facilitate future research on
\url{https://github.com/jiquan123/I2IQA}.Comment: 18 page
Understanding and Improving Proximity Graph based Maximum Inner Product Search
The inner-product navigable small world graph (ip-NSW) represents the
state-of-the-art method for approximate maximum inner product search (MIPS) and
it can achieve an order of magnitude speedup over the fastest baseline.
However, to date it is still unclear where its exceptional performance comes
from. In this paper, we show that there is a strong norm bias in the MIPS
problem, which means that the large norm items are very likely to become the
result of MIPS. Then we explain the good performance of ip-NSW as matching the
norm bias of the MIPS problem - large norm items have big in-degrees in the
ip-NSW proximity graph and a walk on the graph spends the majority of
computation on these items, thus effectively avoids unnecessary computation on
small norm items. Furthermore, we propose the ip-NSW+ algorithm, which improves
ip-NSW by introducing an additional angular proximity graph. Search is first
conducted on the angular graph to find the angular neighbors of a query and
then the MIPS neighbors of these angular neighbors are used to initialize the
candidate pool for search on the inner-product proximity graph. Experiment
results show that ip-NSW+ consistently and significantly outperforms ip-NSW and
provides more robust performance under different data distributions.Comment: 8 pages, 8 figure
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