1,721,017 research outputs found
Explainable AI-aided feature selection and model reduction for DRL-based V2X resource allocation
No full textArtificial intelligence (AI) is expected to significantly enhance radio resource management (RRM) in sixth-generation (6G) networks. However, the lack of explainability in complex deep learning (DL) models poses a challenge for practical implementation. This paper proposes a novel explainable AI (XAI)-based framework for feature selection and model complexity reduction in a model-agnostic manner. Applied to a multi-agent deep reinforcement learning (MADRL) setting, our approach addresses the joint sub-band assignment and power allocation problem in cellular vehicle-to-everything (V2X) communications. We propose a novel two-stage systematic explainability framework leveraging feature relevance-oriented XAI to simplify the DRL agents. While the former stage generates a state feature importance ranking of the trained models using Shapley additive explanations (SHAP)-based importance scores, the latter stage exploits these importance-based rankings to simplify the state space of the agents by removing the least important features from the model’s input. Simulation results demonstrate that the XAI-assisted methodology achieves ~97% of the original MADRL sum-rate performance while reducing optimal state features by ~28%, average training time by ~11%, and trainable weight parameters by ~46% in a network with eight vehicular pairs.</p
A practical and highly efficient transfer hydrogenation of aryl azides using a [Ru(p-cymene)Cl-2](2) catalyst and sodium borohydride
Get PDFColeri, Sinem/0000-0002-7502-3122WOS: 000453646100010Various aniline derivatives were synthesized by selective reduction of aryl azides in the presence of a dichloro(p-cymene)ruthenium(II) dimer ([Ru(p-cymene)Cl-2](2)) via hydrolysis of sodium borohydride. The hydrogenation reactions were carried out in aqueous media at room temperature. Most of the reactions were completed within 10 min with quantitative yields. (C) 2018 Academie des sciences. Published by Elsevier Masson SAS. All rights reserved.Duzce University Research FundDuzce University [2014.05.03.274]This research was supported by Duzce University Research Fund (grant no. 2014.05.03.274)
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Digital twin-assisted explainable AI for robust beam prediction in mmWave MIMO systems
No full textIn line with the AI-native 6G vision, explainability and robustness are crucial for building trust and ensuring reliable performance in millimeter-wave (mmWave) systems. Efficient beam alignment is essential for initial access, but deep learning (DL) solutions face challenges, including high data collection overhead, hardware constraints, lack of explainability, and susceptibility to adversarial attacks. This paper proposes a robust and explainable DL-based beam alignment engine (BAE) for mmWave multiple-input multiple-output (MIMO) systems. The BAE uses received signal strength indicator (RSSI) measurements from wide beams to predict the best narrow beam, reducing the overhead of exhaustive beam sweeping. To overcome the challenge of real-world data collection, this work leverages a site-specific digital twin (DT) to generate synthetic channel data closely resembling real-world environments. A model refinement via transfer learning is proposed to fine-tune the pre-trained model residing in the DT with minimal real-world data, effectively bridging mismatches between the digital replica and real-world environments. To reduce beam training overhead and enhance transparency, the framework uses deep Shapley additive explanations (SHAP) to rank input features by importance, prioritizing key spatial directions and minimizing beam sweeping. It also incorporates the Deep k-nearest neighbors (DkNN) algorithm, providing a credibility metric for detecting out-of-distribution inputs and ensuring robust, transparent decision-making. Experimental results show that the proposed framework reduces real-world data needs by 70%, beam training overhead by 62%, and improves outlier detection robustness by up to 8.5×, achieving near-optimal spectral efficiency and transparent decision making compared to traditional softmax based DL models.</p
Statistical Analysis of Geometric Algorithms in Vehicular Visible Light Positioning
Get PDFVehicular visible light positioning (VLP) methods find relative locations of
vehicles by estimating the positions of intensity-modulated head/tail lights of
one vehicle (target) with respect to another (ego). Estimation is done in two
steps: 1) relative bearing or range of the transmitter-receiver link is
measured over the received signal on the ego side, and 2) target position is
estimated based on those measurements using a geometric algorithm that
expresses position coordinates in terms of the bearing-range parameters. The
primary source of statistical error for these non-linear algorithms is the
channel noise on the received signals that contaminates parameter measurements
with varying levels of sensitivity. In this paper, we present two such
geometric vehicular VLP algorithms that were previously unexplored, compare
their performance with state-of-the-art algorithms over simulations, and
analyze theoretical performance of all algorithms against statistical channel
noise by deriving the respective Cramer-Rao lower bounds. The two newly
explored algorithms do not outperform existing state-of-the-art, but we present
them alongside the statistical analyses for the sake of completeness and to
motivate further research in vehicular VLP. Our main finding is that direct
bearing-based algorithms provide higher accuracy against noise for estimating
lateral position coordinates, and range-based algorithms provide higher
accuracy in the longitudinal axis due to the non-linearity of the respective
geometric algorithms.Comment: Technical report. 7 pages, 4 figure
Wireless Channel Modeling Based on Extreme Value Theory for Ultra-Reliable Communications
Get PDFA key building block in the design of ultra-reliable communication systems is
a wireless channel model that captures the statistics of rare events occurring
due to significant fading. In this paper, we propose a novel methodology based
on extreme value theory (EVT) to statistically model the behavior of extreme
events in a wireless channel for ultra-reliable communication. This methodology
includes techniques for fitting the lower tail distribution of the received
power to the generalized Pareto distribution (GPD), determining the optimum
threshold over which the tail statistics are derived, ascertaining the optimum
stopping condition on the number of samples required to estimate the tail
statistics by using GPD, and finally, assessing the validity of the derived
Pareto model. Based on the data collected within the engine compartment of Fiat
Linea under various engine vibrations and driving scenarios, we demonstrate
that the proposed methodology provides the best fit to the collected data,
significantly outperforming the conventional extrapolation-based methods.
Moreover, the usage of the EVT in the proposed method decreases the required
number of samples for estimating the tail statistics by about .Comment: 13 pages, 10 figure
Non-Stationary Wireless Channel Modeling Approach Based on Extreme Value Theory for Ultra-Reliable Communications
No full textA proper channel modeling methodology that characterizes the statistics of
extreme events is key in the design of a system at an ultra-reliable regime of
operation. The strict constraint of ultra-reliability corresponds to the packet
error rate (PER) in the range of within the acceptable
latency on the order of milliseconds. Extreme value theory (EVT) is a robust
framework for modeling the statistical behavior of extreme events in the
channel data. In this paper, we propose a methodology based on EVT to model the
extreme events of a non-stationary wireless channel for the ultra-reliable
regime of operation. This methodology includes techniques for splitting the
channel data sequence into multiple groups concerning the environmental factors
causing non-stationarity, and fitting the lower tail distribution of the
received power in each group to the generalized Pareto distribution (GPD). The
proposed approach also consists of optimally determining the time-varying
threshold over which the tail statistics are derived as a function of time, and
assessing the validity of the derived Pareto model. Finally, the proposed
approach chooses the best model with minimum complexity that represents the
time variation behavior of the non-stationary channel data sequence. Based on
the data collected within the engine compartment of Fiat Linea under various
engine vibrations and driving scenarios, we demonstrate the capability of the
proposed methodology in providing the best fit to the extremes of the
non-stationary data, significantly outperforming the channel modeling approach
in characterizing the extreme events with the stationary channel assumption
- …
