679 research outputs found
Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks
Representation learning on networks aims to derive a meaningful vector
representation for each node, thereby facilitating downstream tasks such as
link prediction, node classification, and node clustering. In heterogeneous
text-rich networks, this task is more challenging due to (1) presence or
absence of text: Some nodes are associated with rich textual information, while
others are not; (2) diversity of types: Nodes and edges of multiple types form
a heterogeneous network structure. As pretrained language models (PLMs) have
demonstrated their effectiveness in obtaining widely generalizable text
representations, a substantial amount of effort has been made to incorporate
PLMs into representation learning on text-rich networks. However, few of them
can jointly consider heterogeneous structure (network) information as well as
rich textual semantic information of each node effectively. In this paper, we
propose Heterformer, a Heterogeneous Network-Empowered Transformer that
performs contextualized text encoding and heterogeneous structure encoding in a
unified model. Specifically, we inject heterogeneous structure information into
each Transformer layer when encoding node texts. Meanwhile, Heterformer is
capable of characterizing node/edge type heterogeneity and encoding nodes with
or without texts. We conduct comprehensive experiments on three tasks (i.e.,
link prediction, node classification, and node clustering) on three large-scale
datasets from different domains, where Heterformer outperforms competitive
baselines significantly and consistently.Comment: KDD 2023. (Code: https://github.com/PeterGriffinJin/Heterformer
Deep Learning-Based Frequency Offset Estimation
In wireless communication systems, the asynchronization of the oscillators in
the transmitter and the receiver along with the Doppler shift due to relative
movement may lead to the presence of carrier frequency offset (CFO) in the
received signals. Estimation of CFO is crucial for subsequent processing such
as coherent demodulation. In this brief, we demonstrate the utilization of deep
learning for CFO estimation by employing a residual network (ResNet) to learn
and extract signal features from the raw in-phase (I) and quadrature (Q)
components of the signals. We use multiple modulation schemes in the training
set to make the trained model adaptable to multiple modulations or even new
signals. In comparison to the commonly used traditional CFO estimation methods,
our proposed IQ-ResNet method exhibits superior performance across various
scenarios including different oversampling ratios, various signal lengths, and
different channel
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