Zobrazeno 1 - 10
of 25 862
pro vyhledávání: '"A. ARANA"'
Autor:
Abbasi, Naveed A., Arana, Kelvin, Gomez-Ponce, Jorge, Pal, Tathagat, Vasudevan, Vikram, Bist, Atulya, Serbetci, Omer Gokalp, Nam, Young Han, Zhang, Charlie, Molisch, Andreas F.
The growing demand for higher data rates and expanded bandwidth is driving the exploration of new frequency ranges, including the upper mid-band spectrum (6-24 GHz), which is a promising candidate for future Frequency Range 3 (FR3) applications. This
Externí odkaz:
http://arxiv.org/abs/2412.12306
Autor:
Bilal, Iman Munire, Fang, Zheng, Arana-Catania, Miguel, van Lier, Felix-Anselm, Velarde, Juliana Outes, Bregazzi, Harry, Carter, Eleanor, Airoldi, Mara, Procter, Rob
As academic literature proliferates, traditional review methods are increasingly challenged by the sheer volume and diversity of available research. This article presents a study that aims to address these challenges by enhancing the efficiency and s
Externí odkaz:
http://arxiv.org/abs/2412.08578
Wildfires pose a significantly increasing hazard to global ecosystems due to the climate crisis. Due to its complex nature, there is an urgent need for innovative approaches to wildfire prediction, such as machine learning. This research took a uniqu
Externí odkaz:
http://arxiv.org/abs/2411.09844
We completely characterize rational polygons whose billiard flow is weakly mixing in almost every direction as those which are not almost integrable, in the terminology of Gutkin, modulo some low complexity exceptions. This proves a longstanding conj
Externí odkaz:
http://arxiv.org/abs/2410.11117
Ultralight bosonic fields can form condensates, or clouds, around spinning black holes. When this system is under the influence of a secondary massive body, its tidal response can be quantified in the tidal Love numbers (TLNs). Although TLNs vanish f
Externí odkaz:
http://arxiv.org/abs/2410.00968
Autor:
Dcruz, Julian Gerald, Mahoney, Sam, Chua, Jia Yun, Soukhabandith, Adoundeth, Mugabe, John, Guo, Weisi, Arana-Catania, Miguel
Autonomous operations of robots in unknown environments are challenging due to the lack of knowledge of the dynamics of the interactions, such as the objects' movability. This work introduces a novel Causal Reinforcement Learning approach to enhancin
Externí odkaz:
http://arxiv.org/abs/2409.13423
Autor:
Sánchez, Francisco José Zamudio, Machorro, Javier Jiménez, Ovalle, Roxana Arana, Silverio, Hildegardo Martínez
This paper introduces the Relative Inequality Index at the Maximum (IDRM), a novel and intuitive measure designed to capture inequality within a population, such as income inequality. The index is based on the idea that individuals experience varying
Externí odkaz:
http://arxiv.org/abs/2409.07538
Autor:
Platanitis, Konstantinos, Arana-Catania, Miguel, Capicchiano, Leonardo, Upadhyay, Saurabh, Felicetti, Leonard
This paper presents a machine learning approach to estimate the inertial parameters of a spacecraft in cases when those change during operations, e.g. multiple deployments of payloads, unfolding of appendages and booms, propellant consumption as well
Externí odkaz:
http://arxiv.org/abs/2408.03445
Reservoir computing is a promising approach for harnessing the computational power of recurrent neural networks while dramatically simplifying training. This paper investigates the application of integrate-and-fire neurons within reservoir computing
Externí odkaz:
http://arxiv.org/abs/2407.20547
In this work we demonstrate the possibility of estimating the wind environment of a UAV without specialised sensors, using only the UAV's trajectory, applying a causal machine learning approach. We implement the causal curiosity method which combines
Externí odkaz:
http://arxiv.org/abs/2407.01154