A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram

As part of the PhysioNet Computing in Cardiology Challenge 2021, our team HaoWan AIeC, proposed Mixed-Domain Self-Attention Resnet (MDARsn) to identify cardiac abnormalities from reduced-lead ECG.

Transition Motion Tensor: A Data-Driven Approach for Versatile and Controllable Agents in Physically Simulated Environments

This paper proposes the Transition Motion Tensor, a data-driven framework that creates novel and physically accurate transitions outside of the motion dataset.

CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion

In this paper, we present CARL, a quadruped agent that can be controlled with high-level directives and react naturally to dynamic environments.

A Trainable Reconciliation Method for Hierarchical Time-Series

In this paper, we propose a new general, flexible, and easy-to-implement reconciliation strategy based on an encoder-decoder neural network.

Demystifying data and AI for manufacturing: case studies from a major computer maker

We create a deep learning-based algorithm for visual inspection of product appearances, which requires significantly less defect training data compared to traditional approaches.

TrustMAE: A Noise-Resilient Defect Classification Framework using Memory-Augmented Auto-Encoders with Trust Regions

In this paper, we propose a framework called Trust MAE to address the problem of product defect classification. Instead of relying on defective images that are difficult to collect and laborious to label, our framework can accept datasets with unlabeled images.

Domain-Generalized Textured Surface Anomaly Detection

By observing normal and abnormal surface data across multiple source domains, our model is expected to be generalized to an unseen textured surface of interest, in which only a small number of normal data can be observed during testing.

A Dense Tensor Accelerator with Data Exchange Mesh for DNN and Vision Workloads

We propose a dense tensor accelerator called VectorMesh, a scalable, memory-efficient architecture that can support a wide variety of DNN and computer vision workloads.

MERIT: Tensor transform for memory-efficient vision processing on parallel architectures

We propose a mathematical formulation which can be useful for transferring the parallel algorithm optimization knowledge across computing platforms.

GrateTile: Efficient Sparse Tensor Tiling for CNN Processing

We propose GrateTile, an efficient, hardware friendly data storage scheme for sparse CNN feature maps (activations).

Configuration through Optimization for In-Memory Computing Hardware and Simulators

In this paper, we aim to simulate unknown IMC hardware with existing simulators to reduce the development time for a new simulator.

FedTrust: Towards Building
Secure Robust and Trustworthy Moderators for Federated Learning

Most Federated Learning (FL) systems are built upon a strong assumption of trust—clients fully trust the centralized moderator, which might not be feasible in practice. This work aims to mitigate the assumption by using appropriate cryptographic tools.

A Robust Collaborative Learning Framework Using Data Digests and Synonyms to Represent Absent Clients

We propose Collaborative Learning with Synonyms (CLSyn), a robust and versatile collaborative machine learning framework that can tolerate unexpected client absence during training while maintaining high model accuracy.

USAID: Intelligent Forecasting Competition

The competition focused on the global health topic, where we need to build an AI algorithm to provide an accurate prediction for contraceptive consumption in Africa, specifically Cote d’Ivoire.

Expanding Versatility of Agile Locomotion through Policy Transitions Using Latent State Representation

This paper proposes the transition-net, a robust transition strategy that expands the versatility of robot locomotion in the real-world setting.

Interpretable Estimation of the risk of Heart Failure Hospitalization from a 30-second Electrocardiogram

This study shows it is possible to estimate hospitalization for congestive heart failure by a 30 seconds single-lead electrocardiogram signal. Using a machine learning approach not only results in greater predictive power but also provides clinically meaningful interpretations.

A Benchmark for Machine-Learning based Non-Invasive Blood Pressure Estimation using Photoplethysmogram

Blood Pressure (BP) is an important cardiovascular health indicator. BP is usually monitored non-invasively with a cuff-based device, which can be bulky and inconvenient. Thus, continuous and portable BP monitoring devices, such as those based on a photoplethysmography (PPG) waveform, are desirable. In particular, Machine Learning (ML) based BP estimation approaches have gained considerable attention as they have the potential to estimate intermittent or continuous BP with only a single PPG measurement.

Streamlined Framework for Agile Forecasting Model Development towards Efficient Inventory Management

This paper proposes a framework for developing forecasting models by streamlining the connections between core components of the developmental process. The proposed framework enables swift and robust integration of new datasets, experimentation on different algorithms, and selection of the best models.

Development of a Deep Learning-Based Tool to Assist Wound Classification

This paper presents a deep learning-based wound classification tool that can assist medical personnel in non-wound care specialization to classify five key wound conditions, namely deep wound, infected wound, arterial wound, venous wound, and pressure wound, given color images captured using readily available cameras. The accuracy of the classification is vital for appropriate wound management.

BARN Challenge 2023 – Navigation in Tighly Constrained Spaces

Many existing navigation systems allow robots to move from one point to another in a collision-free manner, which may create the impression that navigation is a solved problem. However, autonomous mobile robots still struggle in many scenarios, e.g., colliding with or getting stuck in novel and tightly constrained spaces.