Zobrazeno 1 - 10
of 6 630
pro vyhledávání: '"A Cinque"'
Unsupervised domain adaptation remains a critical challenge in enabling the knowledge transfer of models across unseen domains. Existing methods struggle to balance the need for domain-invariant representations with preserving domain-specific feature
Externí odkaz:
http://arxiv.org/abs/2411.15557
Autor:
Cinque, Fabrizio, Orsingher, Enzo
We present a systematic study of higher-order Airy-type differential equations providing the explicit form of the solutions, deriving their power series expansions and a probabilistic interpretation. Under suitable convergence hypotheses, we compute
Externí odkaz:
http://arxiv.org/abs/2410.07729
This paper works on Binary Neural Networks (BNNs), a promising avenue for efficient deep learning, offering significant reductions in computational overhead and memory footprint to full precision networks. However, the challenge of energy-intensive t
Externí odkaz:
http://arxiv.org/abs/2410.00050
This paper introduces the Neural Transcoding Vision Transformer (\modelname), a generative model designed to estimate high-resolution functional Magnetic Resonance Imaging (fMRI) samples from simultaneous Electroencephalography (EEG) data. A key feat
Externí odkaz:
http://arxiv.org/abs/2409.11836
Action anticipation is the task of forecasting future activity from a partially observed sequence of events. However, this task is exposed to intrinsic future uncertainty and the difficulty of reasoning upon interconnected actions. Unlike previous wo
Externí odkaz:
http://arxiv.org/abs/2407.02309
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 3771-3780
Deepfake detection aims to contrast the spread of deep-generated media that undermines trust in online content. While existing methods focus on large and complex models, the need for real-time detection demands greater efficiency. With this in mind,
Externí odkaz:
http://arxiv.org/abs/2406.04932
Autor:
Barletta, Marco, Cinque, Marcello, Di Martino, Catello, Kalbarczyk, Zbigniew T., Iyer, Ravishankar K.
Publikováno v:
54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), Brisbane, Australia, 2024, pp. 1-14
In this paper, we i) analyze and classify real-world failures of Kubernetes (the most popular container orchestration system), ii) develop a framework to perform a fault/error injection campaign targeting the data store preserving the cluster state,
Externí odkaz:
http://arxiv.org/abs/2404.11169
With the increasing use of multicore platforms to realize mixed-criticality systems, understanding the underlying shared resources, such as the memory hierarchy shared among cores, and achieving isolation between co-executing tasks running on the sam
Externí odkaz:
http://arxiv.org/abs/2404.01910
The adoption of cloud computing technologies in the industry is paving the way to new manufacturing paradigms. In this paper we propose a model to optimize the orchestration of workloads with differentiated criticality levels on a cloud-enabled facto
Externí odkaz:
http://arxiv.org/abs/2403.19042
Autor:
Cinque, Fabrizio, Orsingher, Enzo
We study Cauchy problems of fractional differential equations in both space and time variables by expressing the solution in terms of ``stochastic composition" of the solutions to two simpler problems. These Cauchy sub-problems respectively concern t
Externí odkaz:
http://arxiv.org/abs/2402.13691