Publications
Multimodal representation learning via maximization of local mutual information [e-print]
Summary
Summary
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn useful image representations by taking advantage of the rich information contained in the free text that describes the findings in the image...
Priority scheduling for multi-function apertures with hard- and soft-time constraints
Summary
Summary
A multi-function aperture (MFA) is an antenna array that supports multiple RF signals for a diverse set of activities. An MFA may support multiple activities simultaneously if they are compatible, and platforms may utilize multiple MFAs to meet field-of-regard and frequency range requirements. Efficient MFA utilization requires a Resource Manager...
Using oculomotor features to predict changes in optic nerve sheath diameter and ImPACT scores from contact-sport athletes
Summary
Summary
There is mounting evidence linking the cumulative effects of repetitive head impacts to neuro-degenerative conditions. Robust clinical assessment tools to identify mild traumatic brain injuries are needed to assist with timely diagnosis for return-to-field decisions and appropriately guide rehabilitation. The focus of the present study is to investigate the potential...
Speaker separation in realistic noise environments with applications to a cognitively-controlled hearing aid
Summary
Summary
Future wearable technology may provide for enhanced communication in noisy environments and for the ability to pick out a single talker of interest in a crowded room simply by the listener shifting their attentional focus. Such a system relies on two components, speaker separation and decoding the listener's attention to...
Learning emergent discrete message communication for cooperative reinforcement learning
Summary
Summary
Communication is a important factor that enables agents work cooperatively in multi-agent reinforcement learning (MARL). Most previous work uses continuous message communication whose high representational capacity comes at the expense of interpretability. Allowing agents to learn their own discrete message communication protocol emerged from a variety of domains can increase...
More than a fair share: Network Data Remanence attacks against secret sharing-based schemes
Summary
Summary
With progress toward a practical quantum computer has come an increasingly rapid search for quantum-safe, secure communication schemes that do not rely on discrete logarithm or factorization problems. One such encryption scheme, Multi-path Switching with Secret Sharing (MSSS), combines secret sharing with multi-path switching to achieve security as long as...
Beyond expertise and roles: a framework to characterize the stakeholders of interpretable machine learning and their needs
Summary
Summary
To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them. In this paper, we eschew prior expertise- and role-based categorizations of interpretability stakeholders in favor of a more granular framework that decouples...
Seasonal Inhomogeneous Nonconsecutive Arrival Process Search and Evaluation
Summary
Summary
Time series often exhibit seasonal patterns, and identification of these patterns is essential to understanding thedata and predicting future behavior. Most methods train onlarge datasets and can fail to predict far past the training data. This limitation becomes more pronounced when data is sparse. This paper presents a method to...
Automatic detection of influential actors in disinformation networks
Summary
Summary
The weaponization of digital communications and social media to conduct disinformation campaigns at immense scale, speed, and reach presents new challenges to identify and counter hostile influence operations (IO). This paper presents an end-to-end framework to automate detection of disinformation narratives, networks, and influential actors. The framework integrates natural language...
The Speech Enhancement via Attention Masking Network (SEAMNET): an end-to-end system for joint suppression of noise and reverberation [early access]
Summary
Summary
This paper proposes the Speech Enhancement via Attention Masking Network (SEAMNET), a neural network-based end-to-end single-channel speech enhancement system designed for joint suppression of noise and reverberation. It formalizes an end-to-end network architecture, referred to as b-Net, which accomplishes noise suppression through attention masking in a learned embedding space. A...