Artificial Intelligence
the dataset is related to an app-based framework for multivariate next-day price prediction using
GRU attention networks with rolling averages.
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Decentralized Collaborative Simultaneous Localization and Mapping (C-SLAM) is essential to enable multi-robot missions in unknown environments without relying on pre-existing localization and communication infrastructure. This technology is anticipated to play a key role in the exploration of the Moon, Mars, and other planets. In this work, we introduce a novel dataset collected during C-SLAM experiments involving three robots operating on a Mars analogue terrain.
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The benchmarking dataset, GenAI on the Edge, contains performance metrics from evaluating Large Language Models (LLMs) on edge devices, utilizing a distributed testbed of Raspberry Pi devices orchestrated by Kubernetes (K3s). It includes performance data collected from multiple runs of prompt-based evaluations with various LLMs, leveraging Prometheus and the Llama.cpp framework. The dataset captures key metrics such as resource utilization, token generation rates/throughput, and detailed inference timing for stages such as Sample, Prefill, and Decode.
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This study introduces a multimodal dataset collected to investigate the psychophysiological relationship between perceived fear and muscle activity in climbers. The dataset includes physiological, motion, and subjective data from 19 climbers during \textit{lead} and \textit{top rope climbing} ascent styles, which differ in perceived risk due to varying fall distances. Physiological data were recorded using EMG, ECG, and IMU sensors. Additionally, subjective fear ratings were collected at distinct phases of the climb.
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This is a wheat breeding phenotyping and yield dataset, including canopy height (CH, m), canopy volume (CV, m3), and leaf area index (LAI) collected in the field; vegetation index (VI) generated by multispectral data acquired by UAV remote sensing; trial site weather (Weather); and yield (Yield, kg). The data comes from field trials.
Data acquisition and processing are described in the relevant part of the manuscript.
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These are the codes and models used in our experiments regarding our submitted article “Cheby-KANs:
Advanced Kolmogorov-Arnold Networks for Applying Geometric Deep Learning in Quantum Chemistry
Applications”. The code is developed using python programming language. In our paper we hae
developed the B-spline based KANs with a more powerful and much faster polynomials “shifted-
Chebyshev polynomials” of the first kind. Also, we integrated our new architecture with geometric deep
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We proposes a new task in the field of Answering Subjective Induction Question on Products (SUBJPQA). The answer to this kind of question is non-unique, but can be interpreted from many perspectives. For example, the answer to ‘whether the phone is heavy’ has a variety of different viewpoints. A satisfied answer should be able to summarize these subjective opinions from multiple sources and provide objective knowledge, such as the weight of a phone.
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Tourism is increasing worldwide and has many benefits for countries and cities, such as creating jobs, increasing company revenue, and improving government tax collection. As such, tourism is an unstoppable trend followed by countries and municipalities that try to stimulate this activity. However, unexpected impacts of this, in principle, wealthy activity must be observed.
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Two publicly available datasets, the PASS and EmpaticaE4Stress databases, were utilised in this study. They were chosen because they both used the same Empatica E4 device, which allowed the acquisition of a variety of signals, including PPG and EDA. The dataset consists of in 1587 30-second PPG segments. Each segment has been filtered and normalized using a 0.9–5 Hz band-pass and min-max normalization scheme.
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