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Found 11 papers, 2 papers with code
Pressure-driven switching of magnetism in layered CrCl3
Layered transition-metal compounds with controllable magnetic behaviors provide many fascinating opportunities for the fabrication of high-performance magneto-electric and spintronic devices.
Materials Science
Object Hider: Adversarial Patch Attack Against Object Detectors
Additionally, we have applied the proposed methods to competition "Adversarial Challenge on Object Detection" that is organized by Alibaba on the Tianchi platform and won top 7 in 1701 teams.
Calibration of manganin pressure gauge for diamond-anvil cells
However, the ruby scale can often hardly be used for programmably-controlled DAC devices, especially the piezoelectric-driving cells, where a continuous pressure calibration is required.
Applied Physics Materials Science
TransRefer3D: Entity-and-Relation Aware Transformer for Fine-Grained 3D Visual Grounding
Existing works usually adopt dynamic graph networks to indirectly model the intra/inter-modal interactions, making the model difficult to distinguish the referred object from distractors due to the monolithic representations of visual and linguistic contents.
Target-Driven Structured Transformer Planner for Vision-Language Navigation
Vision-language navigation is the task of directing an embodied agent to navigate in 3D scenes with natural language instructions.
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining.
Towards Semi-supervised Universal Graph Classification
To tackle class shifts, we estimate the certainty of unlabeled graphs using multiple subgraphs, which facilities the discovery of unlabeled data from unknown categories.
Learning on Graphs under Label Noise
Since this regularization term cannot utilize label information, it can enhance the robustness of node representations to label noise.
Dynamic Hypergraph Structure Learning for Traffic Flow Forecasting
This paper studies the problem of traffic flow forecasting, which aims to predict future traffic conditions on the basis of road networks and traffic conditions in the past.
A Survey on Graph Neural Networks in Intelligent Transportation Systems
However, most of the research in this area is still concentrated on traffic forecasting, while other ITS domains, such as autonomous vehicles and urban planning, still require more attention.
COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting
Toward this end, this paper proposes Conjoint Spatio-Temporal graph neural network (abbreviated as COOL), which models heterogeneous graphs from prior and posterior information to conjointly capture high-order spatio-temporal relationships.
