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Found 15 papers, 4 papers with code
RASP: A Drone-based Reconfigurable Actuation and Sensing Platform Towards Ambient Intelligent Systems
Realizing consumer-grade drones that are as useful as robot vacuums throughout our homes or personal smartphones in our daily lives requires drones to sense, actuate, and respond to general scenarios that may arise.
SISSA: Real-time Monitoring of Hardware Functional Safety and Cybersecurity with In-vehicle SOME/IP Ethernet Traffic
Scalable service-Oriented Middleware over IP (SOME/IP) is an Ethernet communication standard protocol in the Automotive Open System Architecture (AUTOSAR), promoting ECU-to-ECU communication over the IP stack.
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach
Using COVID-19 as a testbed domain, our experiments demonstrate a significant alignment between the susceptibility scores estimated by our computational modeling and human judgments, confirming the effectiveness of this latent modeling approach.
Task-Agnostic Low-Rank Adapters for Unseen English Dialects
Large Language Models (LLMs) are trained on corpora disproportionally weighted in favor of Standard American English.
Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications
Recent literature has suggested the potential of using large language models (LLMs) to make classifications for tabular tasks.
MIDDAG: Where Does Our News Go? Investigating Information Diffusion via Community-Level Information Pathways
We present MIDDAG, an intuitive, interactive system that visualizes the information propagation paths on social media triggered by COVID-19-related news articles accompanied by comprehensive insights, including user/community susceptibility level, as well as events and popular opinions raised by the crowd while propagating the information.
Textureless Deformable Surface Reconstruction with Invisible Markers
Leveraging the markers, we design a multi-camera system that captures surface deformation under the UV light and the visible light in a time multiplexing fashion.
DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules
We show that DADA is effective for both single task and instruction finetuned language models, offering an extensible and interpretable framework for adapting existing LLMs to different English dialects.
SMoA: Sparse Mixture of Adapters to Mitigate Multiple Dataset Biases
Recent studies reveal that various biases exist in different NLP tasks, and over-reliance on biases results in models' poor generalization ability and low adversarial robustness.
Cardiac Adipose Tissue Segmentation via Image-Level Annotations
To lessen the need for pixel-wise labeling, we develop a two-stage deep learning framework for cardiac adipose tissue segmentation using image-level annotations on OCT images of human cardiac substrates.
Semantic-Oriented Unlabeled Priming for Large-Scale Language Models
Due to the high costs associated with finetuning large language models, various recent works propose to adapt them to specific tasks without any parameter updates through in-context learning.
Automatic Symmetry Discovery with Lie Algebra Convolutional Network
Existing equivariant neural networks require prior knowledge of the symmetry group and discretization for continuous groups.
Lie Algebra Convolutional Neural Networks with Automatic Symmetry Extraction
We propose to learn the symmetries during the training of the group equivariant architectures.
Approximate Network Symmetry
We define a new measure of network symmetry that is capable of capturing approximate global symmetries of networks.
Physics and Society
Weighted Community Detection and Data Clustering Using Message Passing
In the community detection problem in weighted and directed networks, we show that our algorithm significantly outperforms existing algorithms.
