Generazione di dati eterogenei sugli alberi genetici
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. Inoltre, the pair consisting of the process and its derivative is time-homogeneous Markov...
Gli effetti dei carichi di pressione nella riduzione dimensionale dell'elasticità modelli
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: Ricerca di rete neurale adattativa alle attività mediche basata su Supernet
Apprendimento profondo (DL) has achieved remarkable progress in the field of medical imaging. Tuttavia, adapting DL models to medical tasks remains a significant challenge, dovuto principalmente a due fattori chiave: (1) selezione dell'architettura, as different tasks necessitate specialized model designs, E (2)...
Un processo decisionale tattico ad orizzonte esteso per la guida automatizzata sulla ricerca degli alberi di Monte Carlo
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 questo lavoro, we propose a satellite-terrestrial covert...
Azioni di calibro migliorate da RG apprese dalla macchina e classicamente perfette flussi gradienti
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...
Rilevazione multiscala di cambiamenti praticamente significativi in modo graduale serie temporali variabili
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 $t >t_0$. Spesso, these gradual changes are not of...