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Supervised multi-modal learning involves mapping multiple modalities to a target label. Previous studies in this field have concentrated on capturing in isolation either the inter-modality dependencies (the relationships between different modalities and the label) or the intra-modality dependencies (the relationships within a single modality and the label). We argue that these conventional approaches that rely solely on either inter- or intra-modality dependencies may not be optimal in general. We view the multi-modal learning problem from the lens of generative models where we consider the target as a source of multiple modalities and the interaction between them. Towards that end, we propose inter- & intra-modality modeling (I2M2) framework, which captures and integrates both the inter- and intra-modality dependencies, leading to more accurate predictions. We evaluate our approach using real-world healthcare and vision-and-language datasets with state-of-the-art models, demonstrating superior performance over traditional methods focusing only on one type of modality dependency.
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Dependent Misconfigurations in 5G/4.5G Radio Resource Control
In this paper, we study an important, yet unexplored problem of configuration dependencies in 5G/4.5G radio resource control (RRC). Different from the previous studies in 3G/4G networks, 5G/4.5G allows more than one cells to serve a mobile device, resulting in more configuration dynamics and complexity that vary with all the serving cells. We analyze inter-dependency among configurations, categorize dependent misconfigurations, uncover their root causes, and quantify negative performance impacts. Specifically, we formulate configuration updates into a delta state machine (DSM) and unveil two types of dependent misconfigurations among states (inter-state) and within a state (intra-state); They stem from structural dependency and cross-parameter dependency. We further show that such misconfigurations incur service disruption and performance degradation. Our findings have been largely validated with three US operators and one Chinese operator; Our study has uncovered 644 instances of problematic dependencies.
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- Award ID(s):
- 1750953
- PAR ID:
- 10558113
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM on Networking
- Volume:
- 1
- Issue:
- CoNEXT1
- ISSN:
- 2834-5509
- Page Range / eLocation ID:
- 1 to 20
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3595288
