Parallelism is often required for performance. In these situations an excess of non-determinism is harmful as it means the program can have several different behaviours or even different results. Even in domains such as high-performance computing where parallelism is crucial for performance, the computed value should be deterministic. Unfortunately, non-determinism in programs also allows dynamic scheduling

This paper introduces a new algorithm (Gravity & Magnetic North Vector correction – Double SLERP, or “GMV-D”) to estimate the orientation of a MEMS Magnetic/Angular-Rate/Gravity (MARG) sensor module using sensor fusion in the context of a real-time hand tracking application, for human–computer interaction purposes. Integrated MEMS MARG modules are affordable, small, light and consume minimal power

In this work we present a large-scale comparison of 21 learning and aggregation methods proposed in the ensemble learning, social choice theory (SCT), information fusion and uncertainty management (IF-UM) and collective intelligence (CI) fields, based on a large collection of 40 benchmark datasets. The results of this comparison show that Bagging-based approaches reported performances comparable with

In this paper, the distributed fusion estimation problem is studied for a class of nonlinear networked systems subject to unknown-but-bounded (UBB) noises. A bit rate constraint is introduced to quantify the limited bandwidth of the communication channel, under which a bit rate allocation protocol is further designed by solving a certain off-line optimization problem. Based on the received data from

Convolutional neural networks (CNNs) have shown promising results and have outperformed classical machine learning techniques in tasks such as image classification and object recognition. Their human-brain alike structure enabled them to learn sophisticated features while passing images through their layers. However, their lack of explainability led to the demand for interpretations to justify their

Neurodevelopmental Disorders (NDD) are a group of conditions with onset in the developmental period characterized by deficits in the cognitive and social areas. Conversational agents have been increasingly explored to support therapeutic interventions for people with NDD. This survey provides a structured view of the crucial design features of these systems, the types of therapeutic goals they address

Data redundancy is ubiquitous in the inputs and intermediate results of Deep Neural Networks (DNN). It offers many significant opportunities for improving DNN performance and efficiency and has been explored in a large body of work. These studies have scattered in many venues across several years. The targets they focus on range from images to videos and texts, and the techniques they use to detect

Entity alignment is critical for multiple knowledge graphs (KGs) integration. Although researchers have made significant efforts to explore the relational embeddings between different KGs, existing approaches may not describe multi-modal knowledge well in some tasks, e.g., entity alignment. In this paper, we propose DFMKE, a dual fusion multi-modal knowledge graph embedding framework, to address entity

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Ensemble diversity is an important characteristic of Multiple Classifier Systems (MCS), which aim at improving the overall performance of a classification system by combining the response of several models. While diversity may be introduced through various manipulations at the data level and the model level, some MCSs incorporate local information in order to increase it and/or take advantage of it

Reducing the vibration responses caused by the unbalance of the rotor is a crucial technology to ensure the safe and efficient operation of high-speed rotating machinery. For the flexible rotor system, the unbalance responses of rotors vary in different radial directions due to the anisotropic stiffness. The holospectrum technique using multi-sensor fusion provides ideas to solve this issue. The standard

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Federated learning is a machine learning paradigm that emerges as a solution to the privacy-preservation demands in artificial intelligence. As machine learning, federated learning is threatened by adversarial attacks against the integrity of the learning model and the privacy of data via a distributed approach to tackle local and global learning. This weak point is exacerbated by the inaccessibility

Emotion ontologies have been developed to capture affect, a concept that encompasses discrete emotions and feelings, especially for research on sentiment analysis, which analyzes a customer's attitude towards a company or a product. However, there have been limited efforts to adapt and employ these ontologies. This research surveys and synthesizes emotion ontology studies to develop a Framework of

This paper surveys and organizes research works in a new paradigm in natural language processing, which we dub “prompt-based learning”. Unlike traditional supervised learning, which trains a model to take in an input x and predict an output y as P(y|x), prompt-based learning is based on language models that model the probability of text directly. To use these models to perform prediction tasks, the

The recent serious cases of spreading false information have posed a significant threat to the social stability and even national security, urgently require all circles to respond adequately. Therefore, this survey illustrates how to fight against false information from its propagation process by (1) exploring the drivers of information infectivity from the content, media, user, structural and temporal

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Alongside huge volumes of research on deep learning models in NLP in the recent years, there has been also much work on benchmark datasets needed to track modeling progress. Question answering and reading comprehension have been particularly prolific in this regard, with over 80 new datasets appearing in the past two years. This study is the largest survey of the field to date. We provide an overview

The booming development of remote sensing images in many visual tasks has led to an increasing demand for obtaining images with more precise details. However, it is impractical to directly supply images that are simultaneously rich in spatial, spectral, and temporal information. One feasible solution is to fuse the information from multiple images. Since deep learning has achieved impressive achievements

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 37, September 2022.

Abstract not available

The micro-electro-mechanical technology facilitates the development of low-cost orientation estimation solutions using inertial and magnetic sensors. However, the magnetic disturbances challenge the robust pitch and roll measurements on some occasions, such as the quadrotor, ship, and railway. This work analyzes the coupling mechanism of the magnetic information on the roll and pitch estimates. Subsequently

Gait can reflect locomotion and physical condition and thus is used to assess people's health. Traditional high-precision gait analysis devices are expensive and are limited to laboratory conditions, so the trend is to use wearable devices to analyze gait. The existing wearable inertial-based gait-analysis methods have stride-length and foot-clearance estimation accuracy of 2.0 cm to 5.0 cm (in root

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.

Proceedings of the National Academy of Sciences, Volume 119, Issue 38, September 2022.