213 research outputs found
Condition-Adaptive Graph Convolution Learning for Skeleton-Based Gait Recognition
Graph convolutional networks have been widely applied in skeleton-based gait
recognition. A key challenge in this task is to distinguish the individual
walking styles of different subjects across various views. Existing
state-of-the-art methods employ uniform convolutions to extract features from
diverse sequences and ignore the effects of viewpoint changes. To overcome
these limitations, we propose a condition-adaptive graph (CAG) convolution
network that can dynamically adapt to the specific attributes of each skeleton
sequence and the corresponding view angle. In contrast to using fixed weights
for all joints and sequences, we introduce a joint-specific filter learning
(JSFL) module in the CAG method, which produces sequence-adaptive filters at
the joint level. The adaptive filters capture fine-grained patterns that are
unique to each joint, enabling the extraction of diverse spatial-temporal
information about body parts. Additionally, we design a view-adaptive topology
learning (VATL) module that generates adaptive graph topologies. These graph
topologies are used to correlate the joints adaptively according to the
specific view conditions. Thus, CAG can simultaneously adjust to various
walking styles and viewpoints. Experiments on the two most widely used datasets
(i.e., CASIA-B and OU-MVLP) show that CAG surpasses all previous skeleton-based
methods. Moreover, the recognition performance can be enhanced by simply
combining CAG with appearance-based methods, demonstrating the ability of CAG
to provide useful complementary information.The source code will be available
at https://github.com/OliverHxh/CAG.Comment: Accepted by TIP journa
Context-Sensitive Temporal Feature Learning for Gait Recognition
Although gait recognition has drawn increasing research attention recently,
it remains challenging to learn discriminative temporal representation, since
the silhouette differences are quite subtle in spatial domain. Inspired by the
observation that human can distinguish gaits of different subjects by
adaptively focusing on temporal clips with different time scales, we propose a
context-sensitive temporal feature learning (CSTL) network for gait
recognition. CSTL produces temporal features in three scales, and adaptively
aggregates them according to the contextual information from local and global
perspectives. Specifically, CSTL contains an adaptive temporal aggregation
module that subsequently performs local relation modeling and global relation
modeling to fuse the multi-scale features. Besides, in order to remedy the
spatial feature corruption caused by temporal operations, CSTL incorporates a
salient spatial feature learning (SSFL) module to select groups of
discriminative spatial features. Particularly, we utilize transformers to
implement the global relation modeling and the SSFL module. To the best of our
knowledge, this is the first work that adopts transformer in gait recognition.
Extensive experiments conducted on three datasets demonstrate the
state-of-the-art performance. Concretely, we achieve rank-1 accuracies of
98.7%, 96.2% and 88.7% under normal-walking, bag-carrying and coat-wearing
conditions on CASIA-B, 97.5% on OU-MVLP and 50.6% on GREW.Comment: Submitted to TPAM
Non-covalent interactions in electrochemical reactions and implications in clean energy applications
Understanding and controlling non-covalent interactions associated with solvent molecules and redox-inactive ions provide new opportunities to enhance the reaction entropy changes and reaction kinetics of metal redox centers, which can increase the thermodynamic efficiency of energy conversion and storage devices. Here, we report systematic changes in the redox entropy of one-electron transfer reactions including [Fe(CN)6]3-/4-, [Fe(H2O)6]3+/2+and [Ag(H2O)4]+/0induced by the addition of redox inactive ions, where approximately twenty different known structure making/breaking ions were employed. The measured reaction entropy changes of these redox couples were found to increase linearly with higher concentration and greater structural entropy (having greater structure breaking tendency) for inactive ions with opposite charge to the redox centers. The trend could be attributed to the altered solvation shells of oxidized and reduced redox active species due to non-covalent interactions among redox centers, inactive ions and water molecules, which was supported by Raman spectroscopy. Not only were these non-covalent interactions shown to increase reaction entropy, but they were also found to systematically alter the redox kinetics, where increasing redox reaction energy changes associated with the presence of water structure breaking cations were correlated linearly with the greater exchange current density of [Fe(CN)6]3-/4-.United States. Department of Energy. Office of Basic Energy Science (Award Number DE-SC0001299/DE-FG02-09ER46577)Hong Kong (China). Innovation and Technology Commission (Project No. ITS/ 020/16FP)United States. Department of Energy (Contract No. DE-AC02-5CH11231
Detecting HI Galaxies with Deep Neural Networks in the Presence of Radio Frequency Interference
In neutral hydrogen (HI) galaxy survey, a significant challenge is to
identify and extract the HI galaxy signal from observational data contaminated
by radio frequency interference (RFI). For a drift-scan survey, or more
generally a survey of a spatially continuous region, in the time-ordered
spectral data, the HI galaxies and RFI all appear as regions which extend an
area in the time-frequency waterfall plot, so the extraction of the HI galaxies
and RFI from such data can be regarded as an image segmentation problem, and
machine learning methods can be applied to solve such problems. In this study,
we develop a method to effectively detect and extract signals of HI galaxies
based on a Mask R-CNN network combined with the PointRend method. By simulating
FAST-observed galaxy signals and potential RFI impacts, we created a realistic
data set for the training and testing of our neural network. We compared five
different architectures and selected the best-performing one. This architecture
successfully performs instance segmentation of HI galaxy signals in the
RFI-contaminated time-ordered data (TOD), achieving a precision of 98.64% and a
recall of 93.59%.Comment: 17 pages, 9 figures, 1 tables. Accepted for publication in RA
A Dyad Model of Calling Behaviour with Tie Strength Dynamics
This paper investigates the dynamic relation between callers' social
ties and their wireless phone service consumption. We construct a large
pair-level panel dataset with information on the number of each pair's
common contacts, calling activities, prices, and each caller's
characteristics over a one-year time period. We estimate a dynamic model
that encapsulates the evolving relationship between each pair of
consumers. We find the amount of communications between a pair of
consumers increases with the strength of their tie, which is higher when
these two consumers share more common contacts. Our results support the
reciprocity rule in telephone calls, i.e. when individual A initiates
more (less) phone calls to individual B in one month, their social tie
will be strengthened (weakened) and individual B will make more (less)
calls to individual A in the subsequent months. We demonstrate the
implications of our results in evaluating the return of temporary price
promotions and designing price plans. Our results underscore the
importance of incorporating social network characteristics in the study
of telecommunications markets
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