COVID-19

Testing the equivalency of human "predators" and deep neural networks in the detection of cryptic moths.

Researchers have shown growing interest in using deep neural networks (DNNs) to efficiently test the effects of perceptual processes on the evolution of colour patterns and morphologies. Whether this is a valid approach remains unclear, as it is unknown whether the relative detectability of ecologically relevant stimuli to DNNs actually matches that of biological neural networks. To test this, we ...

BSODiag: A Global Diagnosis Framework for Batch Servers Outage in Large-scale Cloud Infrastructure Systems

Cloud infrastructure is the collective term for all physical devices within cloud systems. Failures within the cloud infrastructure system can severely compromise the stability and availability of cloud services. Particularly, batch servers outage, which is the most fatal failure, could result in the complete unavailability of all upstream services. In this work, we focus on the batch servers ou...

Longer Attention Span: Increasing Transformer Context Length with Sparse Graph Processing Techniques

Transformers have demonstrated great success in numerous domains including natural language processing and bioinformatics. This success stems from t...

A network-driven framework for enhancing gene-disease association studies in coronary artery disease

Over the last decade, genome-wide association studies (GWAS) have successfully identified numerous genetic variants associated with complex diseases...

Full-scale Representation Guided Network for Retinal Vessel Segmentation

The U-Net architecture and its variants have remained state-of-the-art (SOTA) for retinal vessel segmentation over the past decade. In this study, w...

Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation using Deep Learning-based Image Processing Techniques

Global Coconut (Cocos nucifera (L.)) cultivation faces significant challenges, including yield loss, due to pest and disease outbreaks. In particula...

A Robust Support Vector Machine Approach for Raman COVID-19 Data Classification

Recent advances in healthcare technologies have led to the availability of large amounts of biological samples across several techniques and applica...

A two-stage dual-task learning strategy for early prediction of pathological complete response to neoadjuvant chemotherapy for breast cancer using dynamic contrast-enhanced magnetic resonance images

Rationale and Objectives: Early prediction of pathological complete response (pCR) can facilitate personalized treatment for breast cancer patients....

Testing Conditional Mean Independence Using Generative Neural Networks

Conditional mean independence (CMI) testing is crucial for statistical tasks including model determination and variable importance evaluation. In th...

A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images

Early detection of COVID-19 is crucial for effective treatment and controlling its spread. This study proposes a novel hybrid deep learning model fo...

SketchYourSeg: Mask-Free Subjective Image Segmentation via Freehand Sketches

We introduce SketchYourSeg, a novel framework that establishes freehand sketches as a powerful query modality for subjective image segmentation acro...

Color Flow Imaging Microscopy Improves Identification of Stress Sources of Protein Aggregates in Biopharmaceuticals

Protein-based therapeutics play a pivotal role in modern medicine targeting various diseases. Despite their therapeutic importance, these products c...

FlatTrack: Eye-tracking with ultra-thin lensless cameras

Existing eye trackers use cameras based on thick compound optical elements, necessitating the cameras to be placed at focusing distance from the eye...

Efficient Sparse Flow Decomposition Methods for RNA Multi-Assembly

Decomposing a flow on a Directed Acyclic Graph (DAG) into a weighted sum of a small number of paths is an essential task in operations research and ...

UltraLightSqueezeNet: A Deep Learning Architecture for Malaria Classification with up to 54x fewer trainable parameters for resource constrained devices

Lightweight deep learning approaches for malaria detection have gained attention for their potential to enhance diagnostics in resource constrained ...

Effective Defect Detection Using Instance Segmentation for NDI

Ultrasonic testing is a common Non-Destructive Inspection (NDI) method used in aerospace manufacturing. However, the complexity and size of the ultr...

A Comprehensive Social Bias Audit of Contrastive Vision Language Models

In the domain of text-to-image generative models, biases inherent in training datasets often propagate into generated content, posing significant et...

Academic case reports lack diversity: Assessing the presence and diversity of sociodemographic and behavioral factors related to Post COVID-19 Condition

Understanding the prevalence, disparities, and symptom variations of Post COVID-19 Condition (PCC) for vulnerable populations is crucial to improvin...

Efficient Algorithm for Sparse Fourier Transform of Generalized $q$-ary Functions

Computing the Fourier transform of a $q$-ary function $f:\mathbb{Z}_{q}^n\rightarrow \mathbb{R}$, which maps $q$-ary sequences to real numbers, is a...

Saturation in Snapshot Compressive Imaging

Snapshot Compressive Imaging (SCI) maps three-dimensional (3D) data cubes, such as videos or hyperspectral images, into two-dimensional (2D) measure...