198 research outputs found
Graph Augmentation Clustering Network
Existing graph clustering networks heavily rely on a predefined graph and may
fail if the initial graph is of low quality. To tackle this issue, we propose a
novel graph augmentation clustering network capable of adaptively enhancing the
initial graph to achieve better clustering performance. Specifically, we first
integrate the node attribute and topology structure information to learn the
latent feature representation. Then, we explore the local geometric structure
information on the embedding space to construct an adjacency graph and
subsequently develop an adaptive graph augmentation architecture to fuse that
graph with the initial one dynamically. Finally, we minimize the Jeffreys
divergence between multiple derived distributions to conduct network training
in an unsupervised fashion. Extensive experiments on six commonly used
benchmark datasets demonstrate that the proposed method consistently
outperforms several state-of-the-art approaches. In particular, our method
improves the ARI by more than 9.39\% over the best baseline on DBLP. The source
codes and data have been submitted to the appendix
Deep Attention-guided Graph Clustering with Dual Self-supervision
Existing deep embedding clustering works only consider the deepest layer to
learn a feature embedding and thus fail to well utilize the available
discriminative information from cluster assignments, resulting performance
limitation. To this end, we propose a novel method, namely deep
attention-guided graph clustering with dual self-supervision (DAGC).
Specifically, DAGC first utilizes a heterogeneity-wise fusion module to
adaptively integrate the features of an auto-encoder and a graph convolutional
network in each layer and then uses a scale-wise fusion module to dynamically
concatenate the multi-scale features in different layers. Such modules are
capable of learning a discriminative feature embedding via an attention-based
mechanism. In addition, we design a distribution-wise fusion module that
leverages cluster assignments to acquire clustering results directly. To better
explore the discriminative information from the cluster assignments, we develop
a dual self-supervision solution consisting of a soft self-supervision strategy
with a triplet Kullback-Leibler divergence loss and a hard self-supervision
strategy with a pseudo supervision loss. Extensive experiments validate that
our method consistently outperforms state-of-the-art methods on six benchmark
datasets. Especially, our method improves the ARI by more than 18.14% over the
best baseline
Single-breath-hold photoacoustic computed tomography of the breast
We have developed a single-breath-hold photoacoustic computed tomography (SBH-PACT) system to reveal detailed angiographic structures in human breasts. SBH-PACT features a deep penetration depth (4 cm in vivo) with high spatial and temporal resolutions (255 µm in-plane resolution and a 10 Hz 2D frame rate). By scanning the entire breast within a single breath hold (~15 s), a volumetric image can be acquired and subsequently reconstructed utilizing 3D back-projection with negligible breathing-induced motion artifacts. SBH-PACT clearly reveals tumors by observing higher blood vessel densities associated with tumors at high spatial resolution, showing early promise for high sensitivity in radiographically dense breasts. In addition to blood vessel imaging, the high imaging speed enables dynamic studies, such as photoacoustic elastography, which identifies tumors by showing less compliance. We imaged breast cancer patients with breast sizes ranging from B cup to DD cup, and skin pigmentations ranging from light to dark. SBH-PACT identified all the tumors without resorting to ionizing radiation or exogenous contrast, posing no health risks
Convolutional Neural Networks with Dynamic Regularization
Regularization is commonly used for alleviating overfitting in machine
learning. For convolutional neural networks (CNNs), regularization methods,
such as DropBlock and Shake-Shake, have illustrated the improvement in the
generalization performance. However, these methods lack a self-adaptive ability
throughout training. That is, the regularization strength is fixed to a
predefined schedule, and manual adjustments are required to adapt to various
network architectures. In this paper, we propose a dynamic regularization
method for CNNs. Specifically, we model the regularization strength as a
function of the training loss. According to the change of the training loss,
our method can dynamically adjust the regularization strength in the training
procedure, thereby balancing the underfitting and overfitting of CNNs. With
dynamic regularization, a large-scale model is automatically regularized by the
strong perturbation, and vice versa. Experimental results show that the
proposed method can improve the generalization capability on off-the-shelf
network architectures and outperform state-of-the-art regularization methods.Comment: 7 pages. Accepted for Publication at IEEE TNNL
Split Time Series into Patches: Rethinking Long-term Series Forecasting with Dateformer
Time is one of the most significant characteristics of time-series, yet has
received insufficient attention. Prior time-series forecasting research has
mainly focused on mapping a past subseries (lookback window) to a future series
(forecast window), and time of series often just play an auxiliary role even
completely ignored in most cases. Due to the point-wise processing within these
windows, extrapolating series to longer-term future is tough in the pattern. To
overcome this barrier, we propose a brand-new time-series forecasting framework
named Dateformer who turns attention to modeling time instead of following the
above practice. Specifically, time-series are first split into patches by day
to supervise the learning of dynamic date-representations with Date Encoder
Representations from Transformers (DERT). These representations are then fed
into a simple decoder to produce a coarser (or global) prediction, and used to
help the model seek valuable information from the lookback window to learn a
refined (or local) prediction. Dateformer obtains the final result by summing
the above two parts. Our empirical studies on seven benchmarks show that the
time-modeling method is more efficient for long-term series forecasting
compared with sequence modeling methods. Dateformer yields state-of-the-art
accuracy with a 40% remarkable relative improvement, and broadens the maximum
credible forecasting range to a half-yearly level
Gradient-Guided Attentional Network for Radio Transient Localization With the Cluster-Feed Telescope
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