相关论文: Utilizing a Capture-Recapture Strategy to Accelera…
Recent deep network-based compressive sensing (CS) methods have achieved great success. However, most of them regard different sampling matrices as different independent tasks and need to train a specific model for each target sampling…
The ongoing COVID-19 pandemic has already taken millions of lives and damaged economies across the globe. Most COVID-19 deaths and economic losses are reported from densely crowded cities. It is comprehensible that the effective control and…
There is an especially strong need in modern large-scale data analysis to prioritize samples for manual inspection. For example, the inspection could target important mislabeled samples or key vulnerabilities exploitable by an adversarial…
Quantitative tools are increasingly appealing for decision support in healthcare, driven by the growing capabilities of advanced AI systems. However, understanding the predictive uncertainties surrounding a tool's output is crucial for…
We present a general framework for adaptive allocation of viral tests in social contact networks. We pose and solve several complementary problems. First, we consider the design of a social sensing system whose objective is the early…
TREC-COVID is a community evaluation designed to build a test collection that captures the information needs of biomedical researchers using the scientific literature during a pandemic. One of the key characteristics of pandemic search is…
Controlling recurrent infectious diseases is a vital yet complicated problem. In this paper, we propose a novel active screening model (ACTS) and algorithms to facilitate active screening for recurrent diseases (no permanent immunity) under…
The COVID19 pandemic has had a detrimental impact on the health and welfare of the worlds population. An important strategy in the fight against COVID19 is the effective screening of infected patients, with one of the primary screening…
Branch and bound algorithms have been developed for reliability analysis of coherent systems. They exhibit a set of advantages; in particular, they can find a computationally efficient representation of a system failure or survival event,…
We propose a novel coded integrated passive sensing and communication (CIPSAC) system with orthogonal frequency division multiplexing (OFDM), where a multi-antenna base station (BS) passively senses the parameters of the targets and decodes…
While uniform sampling has been widely studied in the matrix completion literature, CUR sampling approximates a low-rank matrix via row and column samples. Unfortunately, both sampling models lack flexibility for various circumstances in…
Accurate disease detection is of paramount importance for effective medical treatment and patient care. However, the process of disease detection is often associated with extensive medical testing and considerable costs, making it…
Monitoring plant health is crucial for maintaining agricultural productivity and food safety. Disruptions in the plant's normal state, caused by diseases, often interfere with essential plant activities, and timely detection of these…
Distinguishing cause from effect is a scientific challenge resisting solutions from mathematics, statistics, information theory and computer science. Compression-Complexity Causality (CCC) is a recently proposed interventional measure of…
In this paper, we develop a method to estimate the infection-rate of a disease, over a region, as a field that varies in space and time. To do so, we use time-series of case-counts of symptomatic patients as observed in the areal units that…
In this work, we develop a novel data-driven model predictive controller using advanced techniques in the field of machine learning. The objective is to regulate control signals to adjust the desired internal room setpoint temperature,…
The objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on chest CT scans acquired in different imaging centers using various…
Accurate intent classification is critical for efficient routing in customer service, ensuring customers are connected with the most suitable agents while reducing handling times and operational costs. However, as companies expand their…
Electronic Health Record (EHR) coding involves automatically classifying EHRs into diagnostic codes. While most previous research treats this as a multi-label classification task, generating probabilities for each code and selecting those…
The current COVID-19 pandemic is now getting contained, albeit at the cost of morethan2.3million human lives. A critical phase in any pandemic is the early detection of cases to develop preventive treatments and strategies. In the case of…