Improved overlap speech diarization of meeting recordings using long-term conversational features

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We construct a measure on the thick points of a Brownian loop soup in a bounded domain D

D

of the plane with given intensity theta>0

\theta >0

, which is formally obtained by exponentiating the square root of its occupation field. The measure is construct ...

This paper considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate with ...

Speech recognition-based applications upon the advancements in artificial intelligence play an essential role to transform most aspects of modern life. However, speech recognition in real-life conditions (e.g., in the presence of overlapping speech, varyin ...

Since the birth of Information Theory, researchers have defined and exploited various information measures, as well as endowed them with operational meanings. Some were born as a "solution to a problem", like Shannon's Entropy and Mutual Information. Other ...

In high dimension, low sample size (HDLSS) settings, classifiers based on Euclidean distances like the nearest neighbor classifier and the average distance classifier perform quite poorly if differences between locations of the underlying populations get m ...

Early and accurate detection of epileptic seizures is an extremely important therapeutic goal due to the severity of complications it can prevent. To this end, a low-power machine learning-based seizure detection implemented on an FPGA is proposed in this ...

Rationale: Given the expanding number of COVID-19 cases and the potential for new waves of infection, there is an urgent need for early prediction of the severity of the disease in intensive care unit (ICU) patients to optimize treatment strategies.Objecti ...

Effective fall-detection and classification systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is based on wearable sensors. While there is a vast amount of acade ...

Deep neural networks have achieved impressive results in many image classification tasks. However, since their performance is usually measured in controlled settings, it is important to ensure that their decisions remain correct when deployed in noisy envi ...

A simple predictive biomarker for fatty liver disease is required for individuals with insulin resistance. Here, we developed a supervised machine learning-based classifier for fatty liver disease using fecal 16S rDNA sequencing data. Based on the Kangbuk ...

In sensed buildings, information related to occupant movement helps optimize important func-tionalities such as caregiving, energy management, and security enhancement. Typical sensing approaches for occupant tracking rely on mobile devices and cameras. Th ...

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...

We find the complete set of conditions satisfied by the forward 2 -> 2 scattering amplitude in unitary and causal theories. These are based on an infinite set of energy dependent quantities (the arcs) which are dispersively expressed as moments of a positi ...

This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to ...

Network information theory studies the communication of information in a network and considers its fundamental limits. Motivating from the extensive presence of the networks in the daily life, the thesis studies the fundamental limits of particular network ...

Training models that perform well under distribution shifts is a central challenge in machine learning. In this paper, we introduce a modeling framework where, in addition to training data, we have partial structural knowledge of the shifted test distribut ...

Understanding the origins of militarized conflict is a complex, yet important undertaking. Existing research seeks to build this understanding by considering bi-lateral relationships between entity pairs (dyadic causes) and multi-lateral relationships amon ...

We present a new method for electrochemical sensing, which compensates the fouling effect of propofol through machine learning (ML) model. Direct and continuous monitoring of propofol is crucial in the development of automatic systems for control of drug i ...

We introduce the first passage set (FPS) of constant level -a of the two-dimensional continuum Gaussian free field (GFF) on finitely connected domains. Informally, it is the set of points in the domain that can be connected to the boundary by a path on whi ...

In this thesis, I focus on monitoring of patients suffering from cardiovascular and neurological diseases through the use of wearable devices. The main diseases considered in this thesis are atrial fibrillation (AF), myocardial infarction (MI), and epileps ...

Improved overlap speech diarization of meeting recordings using long-term conversational features

Chat with Graph Search

Multiplicative chaos of the Brownian loop soup

Impact of Redundancy on Resilience in Distributed Optimization and Learning

Sparse Autoencoders for Speech Modeling and Recognition

A Functional Perspective on Information Measures

On Generalizations of Some Distance Based Classifiers for HDLSS Data

Hardware-Friendly Random Forest Classification of iEEG Signals for Implantable Seizure Detection

Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU

Classification of fall directions via wearable motion sensors

Robustness and invariance properties of image classifiers

Machine learning-derived gut microbiome signature predicts fatty liver disease in the presence of insulin resistance

A framework for occupancy detection and tracking using floor-vibration signals

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Positive moments for scattering amplitudes

Network Classifiers Based On Social Learning

Feedback and Common Information: Bounds and Capacity for Gaussian Networks

Robust Generalization despite Distribution Shift via Minimum Discriminating Information

Classifying Dyads for Militarized Conflict Analysis

Continuous monitoring of propofol in human serum with fouling compensation by support vector classifier

First passage sets of the 2D continuum Gaussian free field

Monitoring of Cardiovascular and Neurological Diseases Using Wearable Devices