128 research outputs found
Dual Node and Edge Fairness-Aware Graph Partition
Fair graph partition of social networks is a crucial step toward ensuring
fair and non-discriminatory treatments in unsupervised user analysis. Current
fair partition methods typically consider node balance, a notion pursuing a
proportionally balanced number of nodes from all demographic groups, but ignore
the bias induced by imbalanced edges in each cluster. To address this gap, we
propose a notion edge balance to measure the proportion of edges connecting
different demographic groups in clusters. We analyze the relations between node
balance and edge balance, then with line graph transformations, we propose a
co-embedding framework to learn dual node and edge fairness-aware
representations for graph partition. We validate our framework through several
social network datasets and observe balanced partition in terms of both nodes
and edges along with good utility. Moreover, we demonstrate our fair partition
can be used as pseudo labels to facilitate graph neural networks to behave
fairly in node classification and link prediction tasks
In-Context Operator Learning with Prompts for Differential Equation Problems
This paper introduces a new neural-network-based approach, namely In-Context
Operator Networks (ICON), to simultaneously learn operators from the prompted
data and apply it to new questions during the inference stage, without any
weight update. Existing methods are limited to using a neural network to
approximate a specific equation solution or a specific operator, requiring
retraining when switching to a new problem with different equations. By
training a single neural network as an operator learner, we can not only get
rid of retraining (even fine-tuning) the neural network for new problems, but
also leverage the commonalities shared across operators so that only a few
demos in the prompt are needed when learning a new operator. Our numerical
results show the neural network's capability as a few-shot operator learner for
a diversified type of differential equation problems, including forward and
inverse problems of ordinary differential equations (ODEs), partial
differential equations (PDEs), and mean-field control (MFC) problems, and also
show that it can generalize its learning capability to operators beyond the
training distribution.Comment: The second and third authors contributed equall
A review of knowledge management about theoretical conception and designing approaches
Purpose - The main purpose of this paper is to conduct an in-depth theoretical review and analysis for the fields of knowledge management (KM) and investigate the future research trend about KM. Design/methodology/approach - At first, few theoretical basis about KM which include definitions and stages about KM have been summarized and analyzed. Then a comprehensive review about the major approaches for designing the KM system from different perspectives including knowledge representation and organization, knowledge sharing and performance measure for KM has been conducted. Findings - The contributions of this paper will be useful for both academics and practitioners for the study of KM. Originality/value - For this research, the focus is on conducting an in-depth theoretical review and analysis of KM
Modelling and Performance Analysis of the Over-the-Air Computing in Cellular IoT Networks
Ultra-fast wireless data aggregation (WDA) of distributed data has emerged as
a critical design challenge in the ultra-densely deployed cellular internet of
things network (CITN) due to limited spectral resources. Over-the-air computing
(AirComp) has been proposed as an effective solution for ultra-fast WDA by
exploiting the superposition property of wireless channels. However, the effect
of access radius of access point (AP) on the AirComp performance has not been
investigated yet. Therefore, in this work, the mean square error (MSE)
performance of AirComp in the ultra-densely deployed CITN is analyzed with the
AP access radius. By modelling the spatial locations of internet of things
devices as a Poisson point process, the expression of MSE is derived in an
analytical form, which is validated by Monte Carlo simulations. Based on the
analytical MSE, we investigate the effect of AP access radius on the MSE of
AirComp numerically. The results show that there exists an optimal AP access
radius for AirComp, which can decrease the MSE by up to 12.7%. It indicates
that the AP access radius should be carefully chosen to improve the AirComp
performance in the ultra-densely deployed CITN
Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases
Large Language Models (LLMs) have demonstrated remarkable performance in code
completion. However, due to the lack of domain-specific knowledge, they may not
be optimal in completing code that requires intensive domain knowledge for
example completing the library names. Although there are several works that
have confirmed the effectiveness of fine-tuning techniques to adapt language
models for code completion in specific domains. They are limited by the need
for constant fine-tuning of the model when the project is in constant
iteration.
To address this limitation, in this paper, we propose NM-LM, a
retrieval-augmented language model (R-LM), that integrates domain knowledge
into language models without fine-tuning. Different from previous techniques,
our approach is able to automatically adapt to different language models and
domains. Specifically, it utilizes the in-domain code to build the
retrieval-based database decoupled from LM, and then combines it with LM
through Bayesian inference to complete the code. The extensive experiments on
the completion of intra-project and intra-scenario have confirmed that NM-LM
brings about appreciable enhancements when compared to CodeGPT and UnixCoder. A
deep analysis of our tool including the responding speed, storage usage,
specific type code completion, and API invocation completion has confirmed that
NM-LM provides satisfactory performance, which renders it highly appropriate
for domain adaptive code completion. Furthermore, our approach operates without
the requirement for direct access to the language model's parameters. As a
result, it can seamlessly integrate with black-box code completion models,
making it easy to integrate our approach as a plugin to further enhance the
performance of these models.Comment: Accepted by ASE202
Examining how internet use and non-farm employment affect rural households’ income gap? Evidence from China
The objective of this study is to assess the effect of Internet use on the income disparity between rural households and to determine how Internet usage can be used to reduce this income gap. We use the Recentered Influence Function Regression (RIF) and data from the China Family Panel Studies (CFPS) conducted by the China Social Science Survey (CSSS) center at Peking University to make the results of regression estimation more reliable. The results reveal that Internet use can make rural households’ income gap shrink considerably, and that the degree of non-farm employment among rural families has a mediating effect between Internet use and the income disparity of farm households. In addition, the Eastern region experiences a stronger mitigating effect from Internet use, whereas ethnic minorities find out no such mitigating effect. This study expands the scope of income disparity theory, provides new ideas for the construction of digital villages, and identifies new empirical evidence and decision-making grounds for improving the livelihoods of rural households and narrowing the income gap between rural households
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