Modality-Guided Subnetwork for Salient Object Detection

Recent RGBD-based models for saliency detection have attracted research attention. The depth clues such as boundary clues, surface normal, shape attribute, etc., contribute to the identification of salient objects with complicated scenarios. However, most RGBD networks require multi-modalities from the input side and feed them separately through a two-stream design, which inevitably results in extra costs on depth sensors and computation. To tackle these inconveniences, we present in this paper a novel fusion design named modality-guided subnetwork (MGSnet). It has the following superior designs: 1) Our model works for both RGB and RGBD data, and dynamically estimating depth if not available. Taking the inner workings of depth-prediction networks into account, we propose to estimate the pseudo-geometry maps from RGB input - essentially mimicking the multi-modality input. 2) Our MGSnet for RGB SOD results in real-time inference but achieves state-of-the-art performance compared to other RGB models. 3) The flexible and lightweight design of MGS facilitates the integration into RGBD two-streaming models. The introduced fusion design enables a cross-modality interaction to enable further progress but with a minimal cost.

Transform and Bitstream Domain Image Classification

Classification of images within the compressed domain offers significant benefits. These benefits include reduced memory and computational requirements of a classification system. This paper proposes two such methods as a proof of concept: The first classifies within the JPEG image transform domain (i.e. DCT transform data); the second classifies the JPEG compressed binary bitstream directly. These two methods are implemented using Residual Network CNNs and an adapted Vision Transformer. Top-1 accuracy of approximately 70% and 60% were achieved using these methods respectively when classifying the Caltech C101 database. Although these results are significantly behind the state of the art for classification for this database (~95%), it illustrates the first time direct bitstream image classification has been achieved. This work confirms that direct bitstream image classification is possible and could be utilised in a first pass database screening of a raw bitstream (within a wired or wireless network) or where computational, memory and bandwidth requirements are severely restricted.

Stochastic Assignment for Deploying Multiple Marsupial Robots

Marsupial robot teams consist of carrier robots that transport and deploy multiple passenger robots, such as a team of ground robots that carry and deploy multiple aerial robots, to rapidly explore complex environments. We specifically address the problem of planning the deployment times and locations of the carrier robots to best meet the objectives of a mission while reasoning over uncertain future observations and rewards. While prior work proposed optimal, polynomial-time solutions to single-carrier robot systems, the multiple-carrier robot deployment problem is fundamentally harder as it requires addressing conflicts and dependencies between deployments of multiple passenger robots. We propose a centralized heuristic search algorithm for the multiple-carrier robot deployment problem that combines Monte Carlo Tree Search with a dynamic programming-based solution to the Sequential Stochastic Assignment Problem as a rollout action-selection policy. Our results with both procedurally-generated data and data drawn from the DARPA Subterranean Challenge Urban Circuit show the viability of our approach and substantial exploration performance improvements over alternative algorithms.

Application of Machine Learning to Sleep Stage Classification

Sleep studies are imperative to recapitulate phenotypes associated with sleep loss and uncover mechanisms contributing to psychopathology. Most often, investigators manually classify the polysomnography into vigilance states, which is time-consuming, requires extensive training, and is prone to inter-scorer variability. While many works have successfully developed automated vigilance state classifiers based on multiple EEG channels, we aim to produce an automated and open-access classifier that can reliably predict vigilance state based on a single cortical electroencephalogram (EEG) from rodents to minimize the disadvantages that accompany tethering small animals via wires to computer programs. Approximately 427 hours of continuously monitored EEG, electromyogram (EMG), and activity were labeled by a domain expert out of 571 hours of total data. Here we evaluate the performance of various machine learning techniques on classifying 10-second epochs into one of three discrete classes: paradoxical, slow-wave, or wake. Our investigations include Decision Trees, Random Forests, Naive Bayes Classifiers, Logistic Regression Classifiers, and Artificial Neural Networks. These methodologies have achieved accuracies ranging from approximately 74% to approximately 96%. Most notably, the Random Forest and the ANN achieved remarkable accuracies of 95.78% and 93.31%, respectively. Here we have shown the potential of various machine learning classifiers to automatically, accurately, and reliably classify vigilance states based on a single EEG reading and a single EMG reading.

Efficacy the of Confinement Policies on the COVID-19 Spread Dynamics in the Early Period of the Pandemic

In this study, we propose a clustering-based approach on time-series data to capture COVID-19 spread patterns in the early period of the pandemic. We analyze the spread dynamics based on the early and post stages of COVID-19 for different countries based on different geographical locations. Furthermore, we investigate the confinement policies and the effect they made on the spread. We found that implementations of the same confinement policies exhibit different results in different countries. Specifically, lockdowns become less effective in densely populated regions, because of the reluctance to comply with social distancing measures. Lack of testing, contact tracing, and social awareness in some countries forestall people from self-isolation and maintaining social distance. Large labor camps with unhealthy living conditions also aid in high community transmissions in countries depending on foreign labor. Distrust in government policies and fake news instigate the spread in both developed and under-developed countries. Large social gatherings play a vital role in causing rapid outbreaks almost everywhere. While some countries were able to contain the spread by implementing strict and widely adopted confinement policies, some others contained the spread with the help of social distancing measures and rigorous testing capacity. An early and rapid response at the beginning of the pandemic is necessary to contain the spread, yet it is not always sufficient.

Data-driven Clustering in Ad-hoc Networks based on Community Detection

High demands for industrial networks lead to increasingly large sensor networks. However, the complexity of networks and demands for accurate data require better stability and communication quality. Conventional clustering methods for ad-hoc networks are based on topology and connectivity, leading to unstable clustering results and low communication quality. In this paper, we focus on two situations: time-evolving networks, and multi-channel ad-hoc networks. We model ad-hoc networks as graphs and introduce community detection methods to both situations. Particularly, in time-evolving networks, our method utilizes the results of community detection to ensure stability. By using similarity or human-in-the-loop measures, we construct a new weighted graph for final clustering. In multi-channel networks, we perform allocations from the results of multiplex community detection. Experiments on real-world datasets show that our method outperforms baselines in both stability and quality.

MultiHead MultiModal Deep Interest Recommendation Network

With the development of information technology, human beings are constantly producing a large amount of information at all times. How to obtain the information that users are interested in from the large amount of information has become an issue of great concern to users and even business managers. In order to solve this problem, from traditional machine learning to deep learning recommendation systems, researchers continue to improve optimization models and explore solutions. Because researchers have optimized more on the recommendation model network structure, they have less research on enriching recommendation model features, and there is still room for in-depth recommendation model optimization. Based on the DIN\cite{Authors01} model, this paper adds multi-head and multi-modal modules, which enriches the feature sets that the model can use, and at the same time strengthens the cross-combination and fitting capabilities of the model. Experiments show that the multi-head multi-modal DIN improves the recommendation prediction effect, and outperforms current state-of-the-art methods on various comprehensive indicators.

On Reciprocally Rotating Magnetic Actuation of a Robotic Capsule in Unknown Tubular Environments

Active wireless capsule endoscopy (WCE) based on simultaneous magnetic actuation and localization (SMAL) techniques holds great promise for improving diagnostic accuracy, reducing examination time and relieving operator burden. To date, the rotating magnetic actuation methods have been constrained to use a continuously rotating permanent magnet. In this paper, we first propose the reciprocally rotating magnetic actuation (RRMA) approach for active WCE to enhance patient safety. We first show how to generate a desired reciprocally rotating magnetic field for capsule actuation, and provide a theoretical analysis of the potential risk of causing volvulus due to the capsule motion. Then, an RRMA-based SMAL workflow is presented to automatically propel a capsule in an unknown tubular environment. We validate the effectiveness of our method in real-world experiments to automatically propel a robotic capsule in an ex-vivo pig colon. The experiment results show that our approach can achieve efficient and robust propulsion of the capsule with an average moving speed of $2.48 mm/s$ in the pig colon, and demonstrate the potential of using RRMA to enhance patient safety, reduce the inspection time, and improve the clinical acceptance of this technology.

COVID-19 personal protective equipment detection using real-time deep learning methods

The exponential spread of COVID-19 in over 215 countries has led WHO to recommend face masks and gloves for a safe return to school or work. We used artificial intelligence and deep learning algorithms for automatic face masks and gloves detection in public areas. We investigated and assessed the efficacy of two popular deep learning algorithms of YOLO (You Only Look Once) and SSD MobileNet for the detection and proper wearing of face masks and gloves trained over a data set of 8250 images imported from the internet. YOLOv3 is implemented using the DarkNet framework, and the SSD MobileNet algorithm is applied for the development of accurate object detection. The proposed models have been developed to provide accurate multi-class detection (Mask vs. No-Mask vs. Gloves vs. No-Gloves vs. Improper). When people wear their masks improperly, the method detects them as an improper class. The introduced models provide accuracies of (90.6% for YOLO and 85.5% for SSD) for multi-class detection. The systems' results indicate the efficiency and validity of detecting people who do not wear masks and gloves in public.

Using Clinical Notes with Time Series Data for ICU Management

Monitoring patients in ICU is a challenging and high-cost task. Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources. There has been continuous progress in machine learning research for ICU management, and most of this work has focused on using time series signals recorded by ICU instruments. In our work, we show that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management. While the time-series data is measured at regular intervals, doctor notes are charted at irregular times, making it challenging to model them together. We propose a method to model them jointly, achieving considerable improvement across benchmark tasks over baseline time-series model. Our implementation can be found at \url{https://github.com/kaggarwal/ClinicalNotesICU}.