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Found 15 papers, 7 papers with code
A Tri-Level Optimization Model for Interdependent Infrastructure Network Resilience Against Compound Hazard Events
Here, our notional operator may choose proxy actions to operate an interdependent system comprised of fuel terminals and gas stations (functioning as supplies) and a transportation network with traffic flow (functioning as demand) to minimize unmet demand at gas stations.
Cognition-Mode Aware Variational Representation Learning Framework for Knowledge Tracing
The Knowledge Tracing (KT) task plays a crucial role in personalized learning, and its purpose is to predict student responses based on their historical practice behavior sequence.
Focused Specific Objects NeRF
Most NeRF-based models are designed for learning the entire scene, and complex scenes can lead to longer learning times and poorer rendering effects.
No Length Left Behind: Enhancing Knowledge Tracing for Modeling Sequences of Excessive or Insufficient Lengths
Knowledge tracing (KT) aims to predict students' responses to practices based on their historical question-answering behaviors.
Counterfactual Monotonic Knowledge Tracing for Assessing Students' Dynamic Mastery of Knowledge Concepts
Since students' mastery of knowledge concepts is often unlabeled, existing KT methods rely on the implicit paradigm of historical practice to mastery of knowledge concepts to students' responses to practices to address the challenge of unlabeled concept mastery.
qecGPT: decoding Quantum Error-correcting Codes with Generative Pre-trained Transformers
Our framework is general and can be applied to any error model and quantum codes with different topologies such as surface codes and quantum LDPC codes.
Managed Geo-Distributed Feature Store: Architecture and System Design
Companies are using machine learning to solve real-world problems and are developing hundreds to thousands of features in the process.
Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation
Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms.
Multi-Factors Aware Dual-Attentional Knowledge Tracing
In this paper, we propose Multi-Factors Aware Dual-Attentional model (MF-DAKT) which enriches question representations and utilizes multiple factors to model students' learning progress based on a dual-attentional mechanism.
TS4Net: Two-Stage Sample Selective Strategy for Rotating Object Detection
At present, most of the rotating object detection datasets focus on the field of remote sensing, and these images are usually shot in high-altitude scenes.
Boltzmann machines as two-dimensional tensor networks
Using numerical experiments, we demonstrate that the proposed algorithm is much more accurate than the state-of-the-art machine learning methods in estimating the partition function of restricted Boltzmann machines and deep Boltzmann machines, and have potential applications in training deep Boltzmann machines for general machine learning tasks.
Magnon-mediated interlayer coupling in an all-antiferromagnetic junction
The interlayer coupling mediated by fermions in ferromagnets brings about parallel and anti-parallel magnetization orientations of two magnetic layers, resulting in the giant magnetoresistance, which forms the foundation in spintronics and accelerates the development of information technology.
Materials Science
Expectation Synchronization Synthesis in Non-Markovian Open Quantum Systems
In this article, we investigate the problem of engineering synchronization in non-Markovian quantum systems.
Quantum Physics
Contracting Arbitrary Tensor Networks: general approximate algorithm and applications in graphical models and quantum circuit simulations
We present a general method for approximately contracting tensor networks with an arbitrary connectivity.
Computational Physics Statistical Mechanics Strongly Correlated Electrons Quantum Physics
Solving Statistical Mechanics on Sparse Graphs with Feedback Set Variational Autoregressive Networks
We propose a method for solving statistical mechanics problems defined on sparse graphs.
