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Found 34 papers, 4 papers with code
Self-Consistency Training for Hamiltonian Prediction
This merit addresses the data scarcity difficulty, and distinguishes the task from other property prediction formulations with unique benefits: (1) self-consistency training enables the model to be trained on a large amount of unlabeled data, hence substantially enhances generalization; (2) self-consistency training is more efficient than labeling data with DFT for supervised training, since it is an amortization of DFT calculation over a set of molecular structures.
Communication-Constrained STL Task Decomposition through Convex Optimization
In this work, we propose a method to decompose signal temporal logic (STL) tasks for multi-agent systems subject to constraints imposed by the communication graph.
End-to-End Crystal Structure Prediction from Powder X-Ray Diffraction
XtalNet represents a significant advance in CSP, enabling the prediction of complex structures from PXRD data without the need for external databases or manual intervention.
On Approximate Opacity of Stochastic Control Systems
Based on this new system relation, we show that one can verify opacity for stochastic control systems using their abstractions (modeled as finite gMDPs).
Reconstruction of Cortical Surfaces with Spherical Topology from Infant Brain MRI via Recurrent Deformation Learning
Moreover, this mapping is possible only if the topology of the surface mesh is homotopic to a sphere.
Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning
Orbital-free density functional theory (OFDFT) is a quantum chemistry formulation that has a lower cost scaling than the prevailing Kohn-Sham DFT, which is increasingly desired for contemporary molecular research.
Controller Synthesis of Collaborative Signal Temporal Logic Tasks for Multi-Agent Systems via Assume-Guarantee Contracts
This paper considers the problem of controller synthesis of signal temporal logic (STL) specifications for large-scale multi-agent systems, where the agents are dynamically coupled and subject to collaborative tasks.
Secure-by-Construction Synthesis for Control Systems
These controllers are synthesized based on a concept of so-called (augmented) control barrier functions, which we introduce and discuss in detail.
Addressing Class Variable Imbalance in Federated Semi-supervised Learning
Most of the current research is focusing on class imbalance with a fixed number of classes, while little attention is paid to data imbalance with a variable number of classes.
Brain Tissue Segmentation Across the Human Lifespan via Supervised Contrastive Learning
In this paper, we make the first attempt to segment brain tissues across the entire human lifespan (0-100 years of age) using a unified deep learning model.
Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives
Our proposal performs better in terms of name disambiguation accuracy compared with baselines and the ablation experiments demonstrate the improvement by feature selection and the meta-path level attention in our method.
Abstraction-Based Verification of Approximate Pre-Opacity for Control Systems
In this paper, we first introduce a concept of approximate pre-opacity by capturing the security level of control systems with respect to the measurement precision of the intruder.
Incorporating Interactive Facts for Stock Selection via Neural Recursive ODEs
Stock selection attempts to rank a list of stocks for optimizing investment decision making, aiming at minimizing investment risks while maximizing profit returns.
Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles
Federated learning (FL) is emerging as a new paradigm to train machine learning (ML) models in distributed systems, including vehicles in V2X networks.
On Understanding the Influence of Controllable Factors with a Feature Attribution Algorithm: a Medical Case Study
Feature attribution XAI algorithms enable their users to gain insight into the underlying patterns of large datasets through their feature importance calculation.
Explainable Artificial Intelligence (XAI)
Feature Importance
Compositional Synthesis of Signal Temporal Logic Tasks via Assume-Guarantee Contracts
In this paper, we focus on the problem of compositional synthesis of controllers enforcing signal temporal logic (STL) tasks over a class of continuous-time nonlinear interconnected systems.
An Investigation of the Impact of COVID-19 Non-Pharmaceutical Interventions and Economic Support Policies on Foreign Exchange Markets with Explainable AI Techniques
Since the onset of the the COVID-19 pandemic, many countries across the world have implemented various non-pharmaceutical interventions (NPIs) to contain the spread of virus, as well as economic support policies (ESPs) to save their economies.
Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model
The key problem in the protein sequence representation learning is to capture the co-evolutionary information reflected by the inter-residue co-variation in the sequences.
Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer
The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential role in drug discovery.
Learning MRI Artifact Removal With Unpaired Data
Retrospective artifact correction (RAC) improves image quality post acquisition and enhances image usability.
Learning Multi-Site Harmonization of Magnetic Resonance Images Without Traveling Human Phantoms
Harmonization improves data consistency and is central to effective integration of diverse imaging data acquired across multiple sites.
Compositional Verification of Initial-State Opacity for Switched Systems
In this work, we propose a compositional framework for the verification of approximate initial-state opacity for networks of discrete-time switched systems.
CCGL: Contrastive Cascade Graph Learning
Second, it learns a generic model for graph cascade tasks via self-supervised contrastive pre-training using both unlabeled and labeled data.
Ranked #1 on
Information Cascade Popularity Prediction
on Weibo
Evaluating the Correctness of Explainable AI Algorithms for Classification
In this work, we develop a method to quantitatively evaluate the correctness of XAI algorithms by creating datasets with known explanation ground truth.
Symbolic Models for Infinite Networks of Control Systems: A Compositional Approach
The proposed approach is based on the notion of alternating simulation functions.
Exploring the Regulatory Function of the N-terminal Domain of SARS-CoV-2 Spike Protein Through Molecular Dynamics Simulation
We performed molecular dynamics simulation on the S protein with a focus on the function of its N-terminal domains (NTDs).
An Investigation of COVID-19 Spreading Factors with Explainable AI Techniques
We compute the top one and two measures that are most effective for the countries and regions studied during the period.
Explainable AI for Classification using Probabilistic Logic Inference
The overarching goal of Explainable AI is to develop systems that not only exhibit intelligent behaviours, but also are able to explain their rationale and reveal insights.
Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks
In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images.
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test
This study provides convincing evidence that the proposed method could objectively create a powerful test statistic based on Q-Q plots and this method could be modified to construct many more powerful test statistics for other applications in the future.
Information-based Adaptive Stimulus Selection to Optimize Communication Efficiency in Brain-Computer Interfaces
Stimulus-driven brain-computer interfaces (BCIs), such as the P300 speller, rely on using a sequence of sensory stimuli to elicit specific neural responses as control signals, while a user attends to relevant target stimuli that occur within the sequence.
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable.
Local Gaussian Processes for Efficient Fine-Grained Traffic Speed Prediction
In this work, we address their efficiency issues by proposing local GPs to learn from and make predictions for correlated subsets of data.
A Generic Inverted Index Framework for Similarity Search on the GPU - Technical Report
We propose a novel generic inverted index framework on the GPU (called GENIE), aiming to reduce the programming complexity of the GPU for parallel similarity search of different data types.
