Zobrazeno 1 - 10
of 7 693
pro vyhledávání: '"Qadeer, A."'
Publikováno v:
Eur. Phys. J. C 83 (2023) 522
The objective of this manuscript is to investigate the traversable wormhole solutions in the background of the $f(R, \phi)$ theory of gravity, where $R$ is the Ricci scalar and $\phi$ is the scalar potential respectively. For this reason, we use the
Externí odkaz:
http://arxiv.org/abs/2404.02946
Contemporary large-scale visual language models (VLMs) exhibit strong representation capacities, making them ubiquitous for enhancing image and text understanding tasks. They are often trained in a contrastive manner on a large and diverse corpus of
Externí odkaz:
http://arxiv.org/abs/2311.03964
Autor:
Qadeer, Saad, Engel, Andrew, Howard, Amanda, Tsou, Adam, Vargas, Max, Stinis, Panos, Chiang, Tony
Despite their immense promise in performing a variety of learning tasks, a theoretical understanding of the limitations of Deep Neural Networks (DNNs) has so far eluded practitioners. This is partly due to the inability to determine the closed forms
Externí odkaz:
http://arxiv.org/abs/2310.18612
This paper studies the problem of safe control of sampled-data systems under bounded disturbance and measurement errors with piecewise-constant controllers. To achieve this, we first propose the High-Order Doubly Robust Control Barrier Function (HO-D
Externí odkaz:
http://arxiv.org/abs/2309.08050
In this work, we present a learning method for lateral and longitudinal motion control of an ego-vehicle for vehicle pursuit. The car being controlled does not have a pre-defined route, rather it reactively adapts to follow a target vehicle while mai
Externí odkaz:
http://arxiv.org/abs/2308.08380
Deep learning models for self-driving cars require a diverse training dataset to manage critical driving scenarios on public roads safely. This includes having data from divergent trajectories, such as the oncoming traffic lane or sidewalks. Such dat
Externí odkaz:
http://arxiv.org/abs/2308.01424
Publikováno v:
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC), Bilbao, Spain, 2023, pp. 2893-2900
In this work, we propose a learning based neural model that provides both the longitudinal and lateral control commands to simultaneously navigate multiple vehicles. The goal is to ensure that each vehicle reaches a desired target state without colli
Externí odkaz:
http://arxiv.org/abs/2307.16727
Publikováno v:
AIMS Mathematics, Vol 9, Iss 7, Pp 19597-19625 (2024)
In this paper, we have delved into the intricate dynamics of a discrete-time Hepatitis B virus (HBV) model, shedding light on its local dynamics, topological classifications at equilibrium states, and pivotal epidemiological parameters such as the ba
Externí odkaz:
https://doaj.org/article/2fdcd662178f4bb0950dcb2411f2f279
We study the problem of defending a Cyber-Physical System (CPS) consisting of interdependent components with heterogeneous sensitivity to investments. In addition to the optimal allocation of limited security resources, we analyze the impact of an or
Externí odkaz:
http://arxiv.org/abs/2302.05411
Autor:
Shamshair Ali, Rubina Ghazal, Nauman Qadeer, Oumaima Saidani, Fatimah Alhayan, Anum Masood, Rabia Saleem, Muhammad Attique Khan, Deepak Gupta
Publikováno v:
Alexandria Engineering Journal, Vol 103, Iss , Pp 88-97 (2024)
In an era dominated by the Internet of Things (IoT), protecting interconnected devices from botnets has become essential. This study introduces an innovative hybrid deep learning model that synergizes LSTM Auto Encoders and Multilayer Perceptrons in
Externí odkaz:
https://doaj.org/article/6bf020167fef4681b009d604e408465d