Blog
Generating heterogeneous data on gene trees
We introduce GenPhylo, a Python module that simulates nucleotide sequence data along a phylogeny avoiding the restriction of continuous-time Markov processes. GenPhylo uses directly a general Markov model and therefore naturally incorporates heterogeneity across lineages. We solve the challenge of generating transition...
FailLite: Failure-Resilient Model Serving for Resource-Constrained Edge Environments
Model serving systems have become popular for deploying deep learning models for various latency-sensitive inference tasks. While traditional replication-based methods have been used for failure-resilient model serving in the cloud, such methods are often infeasible in edge environments due to significant resource...
Positive-tone Nanolithography of Antimony Trisulfide with Femtosecond Laser Wet-etching
Antimony trisulfide ($Sb_{2}S_{3}$), as an emerging material for integrated photonic devices, has attracted significant attention due to its high index, low loss, and phase-changing property in the optical regime. Tuttavia, conventional lithography-based fabrication methods involve complex, time-consuming, multistep processes, rendering the photonic...
The area of spheres in the Brownian plane
We consider the area of spheres centered at the distinguished point in the Brownian plane. As a function of the radius, the resulting process has continuously differentiable sample paths. Furthermore, the pair consisting of the process and its derivative is time-homogeneous Markov...
The effects of pressure loads in the dimension reduction of elasticity models
We study the dimensional reduction from three to two dimensions in hyperelastic materials subject to a live load, modeled as a constant pressure force. Our results demonstrate that this loading has a significant impact in higher-order scaling regimes, namely those associated with...
MedNNS: Supernet-based Medical Task-Adaptive Neural Network Search
Deep learning (DL) has achieved remarkable progress in the field of medical imaging. Tuttavia, adapting DL models to medical tasks remains a significant challenge, primarily due to two key factors: (1) architecture selection, as different tasks necessitate specialized model designs, E (2)...
An Extended Horizon Tactical Decision-Making for Automated Driving Based on Monte Carlo Tree Search
This paper introduces COR-MCTS (Conservation of Resources – Monte Carlo Tree Search), a novel tactical decision-making approach for automated driving focusing on maneuver planning over extended horizons. Traditional decision-making algorithms are often constrained by fixed planning horizons, typically up to 6 seconds...
Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications
An integration of satellites and terrestrial networks is crucial for enhancing performance of next generation communication systems. Tuttavia, the networks are hindered by the long-distance path loss and security risks in dense urban environments. In this work, we propose a satellite-terrestrial covert...
Machine-learned RG-improved gauge actions and classically perfect gradient flows
Extracting continuum properties of quantum field theories from discretized spacetime is challenging due to lattice artifacts. Renormalization-group (RG)-improved lattice actions can preserve continuum properties, but are in general difficult to parameterize. Machine learning (ML) with gauge-equivariant convolutional neural networks provides a way...
Multiscale detection of practically significant changes in a gradually varying time series
In many change point problems it is reasonable to assume that compared to a benchmark at a given time point $t_0$ the properties of the observed stochastic process change gradually over time for $t >t_0$. Often, these gradual changes are not of...