Zobrazeno 1 - 10
of 54 685
pro vyhledávání: '"HAMAD, A."'
Autor:
Hamad, Fadi, Hinder, Oliver
We present an adaptive trust-region method for unconstrained optimization that allows inexact solutions to the trust-region subproblems. Our method is a simple variant of the classical trust-region method of Sorensen [1]. The method achieves the best
Externí odkaz:
http://arxiv.org/abs/2412.02079
Autor:
Sayyid-Ali, Abdur-Rahman Ibrahim, Rafay, Abdul, Soomro, Muhammad Abdullah, Alizai, Muhammad Hamad, Bhatti, Naveed Anwar
Batteryless IoT systems face energy constraints exacerbated by checkpointing overhead. Approximate computing offers solutions but demands manual expertise, limiting scalability. This paper presents CheckMate, an automated framework leveraging LLMs fo
Externí odkaz:
http://arxiv.org/abs/2411.17732
Autor:
Sudevan, Vidya, Zayer, Fakhreddine, Hassan, Taimur, Javed, Sajid, Karki, Hamad, De Masi, Giulia, Dias, Jorge
This paper delves into the potential of DU-VIO, a dehazing-aided hybrid multi-rate multi-modal Visual-Inertial Odometry (VIO) estimation framework, designed to thrive in the challenging realm of extreme underwater environments. The cutting-edge DU-VI
Externí odkaz:
http://arxiv.org/abs/2411.13988
This paper introduces the concept of employing neuromorphic methodologies for task-oriented underwater robotics applications. In contrast to the increasing computational demands of conventional deep learning algorithms, neuromorphic technology, lever
Externí odkaz:
http://arxiv.org/abs/2411.13962
Autor:
De Vincenzi, Marco, Pesé, Mert D., Bodei, Chiara, Matteucci, Ilaria, Brooks, Richard R., Hasan, Monowar, Saracino, Andrea, Hamad, Mohammad, Steinhorst, Sebastian
The growing reliance on software in vehicles has given rise to the concept of Software-Defined Vehicles (SDVs), fundamentally reshaping the vehicles and the automotive industry. This survey explores the cybersecurity and privacy challenges posed by S
Externí odkaz:
http://arxiv.org/abs/2411.10612
Autor:
Angdembe, Alisha, Iqbal, Wasim A, Hamad, Rebeen Ali, Casement, John, Consortium, AI-Multiply, Missier, Paolo, Reynolds, Nick, Henkin, Rafael, Barnes, Michael R
Understanding the temporal properties of longitudinal data is critical for identifying trends, predicting future events, and making informed decisions in any field where temporal data is analysed, including health and epidemiology, finance, geoscienc
Externí odkaz:
http://arxiv.org/abs/2411.03210
We consider the adaptive-rank integration of general time-dependent advection-diffusion partial differential equations (PDEs) with spatially variable coefficients. We employ a standard finite-difference method for spatial discretization coupled with
Externí odkaz:
http://arxiv.org/abs/2410.19662
The remarkable reasoning and code generation capabilities of large language models (LLMs) have spurred significant interest in applying LLMs to enable task automation in digital chip design. In particular, recent work has investigated early ideas of
Externí odkaz:
http://arxiv.org/abs/2410.23299
The early detection and nuanced subtype classification of non-small cell lung cancer (NSCLC), a predominant cause of cancer mortality worldwide, is a critical and complex issue. In this paper, we introduce an innovative integration of multi-modal dat
Externí odkaz:
http://arxiv.org/abs/2409.18715
Autor:
MohajerAnsari, Pedram, Domeke, Alkim, de Voor, Jan, Mitra, Arkajyoti, Johnson, Grace, Salarpour, Amir, Olufowobi, Habeeb, Hamad, Mohammad, Pesé, Mert D.
Ensuring autonomous vehicle (AV) security remains a critical concern. An area of paramount importance is the study of physical-world adversarial examples (AEs) aimed at exploiting vulnerabilities in perception systems. However, most of the prevailing
Externí odkaz:
http://arxiv.org/abs/2409.18248