Miracle-dz/UniFAR-FTUs huggingface dataset:
A Context-Aware Path Forecasting Method for Connected Autonomous Vehicles

Forecasting the future paths of surrounding vehicles of a Connected Autonomous Vehicle (CAV) can enhance connectivity and efficiency of vehicular networks, and accurate motion forecasting of nearby vulnerable road users can advance road safety and urban mobility. This task needs a high-level situational awareness for the CAV. Early methods rely solely on vehicle kinematics and overlook the uncertainty within agents behavior and the effects of surrounding context on the behavior of nearby agents, resulting in lower performance or infeasible predictions. In the current work, we introduce a novel context-aware forecasting approach for CAVs that benefits from inverse reinforcement learning (IRL) to condition the future motions of nearby agents on scene-based state sequences defined using a Markov Decision Process. More precisely, we map the images of the surrounding context and the behavior history of agents into rewards and learn optimal expert behaviors using IRL. We validate the path forecasting efficiency of our model using two large motion prediction benchmarks with different scenes and achieve state-of-the-art results in terms of FDE and ADE metrics.
Real Time Fire and Smoke Detection Techniques Using Improved Transfer Learning Algorithms

Classical fire and smoke detection systems cannot be used outdoors and do not guarantee the necessary availability for real-time applications. The objective of this research paper is to use a deep neural network to detect fire and smoke in outdoor environments using an embedded system with a camera. Recently, after significant progress in programming structures and, above all, in the field of machine learning, embedded systems are now able to perform real-time edge computing. This work proposes improved transfer learning approaches to perform computer vision fire and smoke detection using a convolutional neural network (CNN). Three transfer learning models (VGG-16, VGG-19, and YOLOv5) have been enhanced and used in order to detect once fire or smoke appears in any camera, then the system can detect them and immediately send an alert to the corresponding supervisors. The performances of the proposed algorithms are verified on a common database; then on real scenarios.
Determination of the Plasma Density in a Plasma Antenna Based on Image Analysis and LIVPD Graphs

This paper introduces an innovative method for quantitatively determining plasma density in plasma columns, with particular applicability to reconfigurable antennas. This method significantly enhances the speed of capturing density variations, particularly in surface wave-excited antennas. By utilizing images of excited plasma tubes, the proposed approach extracts a light intensity versus plasma density (LIVPD) graph, creating a direct correspondence between visible light intensity and plasma density. The methodology, encompassing LIVPD graph extraction and plasma density evaluation, is comprehensively elucidated. Rigorous analytical and experimental validations confirm a minor deviation of approximately 8.5%. The versatility of the method is demonstrated through two practical applications, reaffirming its accuracy in challenging scenarios. Additionally, this study includes a comparative analysis that highlights the superiority of the method in terms of sampling rate, measurement points, and non-intrusiveness over conventional techniques. This paper thus presents a robust method with promising potential for accurate plasma density determination in diverse antenna applications.
On Reward Shaping Methods in Deep Reinforcement Learning for Radio Resource Management in Wireless Networks

This paper provides a comprehensive study on the learning models' power violation, sum-rate performance while taking into consideration power constraint, and computational efficiency in terms of training and execution times over a dynamic wireless channel. We propose a reward shaping method and modify learning models with the output scaling strategy to enforce them to fully respect the power constraints while optimizing the sum-rate performance. The proposed approach reaches close-to-optimal accuracy, i.e., up to 99.15%, while satisfying the predefined power constraint of the base station. Moreover, learning models are shown to be more computationally efficient compared to the traditional algorithm. However, solving the power allocation problem within the Orthogonal Frequency Division Multiplexing (OFDM) symbol duration of $16.7\mu \mathrm{s}$ is a remaining challenge.
A Survey on Using Spatio-Temporal Networks for Rainfall Prediction

Precipitation has a great impact on people's lives and social and economic development, so researchers have conducted a lot of research on accurate precipitation prediction. However, due to the nonlinear and dynamic characteristics of precipitation, traditional methods such as numerical weather prediction (NWP) and optical flow methods cannot achieve satisfactory results. There has been some progress in rainfall prediction using deep learning, but there is still room for improvement. This paper summarizes the application of spatiotemporal sequence models in rainfall forecasting by arranging the review papers published between 2016 and 2022, summarizes the short-term forecasting methods and long-term forecasting methods, and analyzes the structure, advantages and disadvantages of the models. Therefore, this paper can provide useful insights into the field of deep learning rainfall prediction to aid future research.
Electric Vehicle Route Simulation: A Preliminary Approach

This article presents a NetLogo-based multi-agent simulator developed to optimize route and task planning using electric vehicles for travel between branches of Société Générale. The simulator takes into account constraints linked to the limited autonomy of electric vehicle batteries in the dense urban context of branches. We carried out simulation tests to evaluate the simulator's performance and generate optimized routes, minimizing distances travelled and CO2 emissions. This simulator represents a promising first step in the search for innovative solutions for planning electric vehicle routes between branches.
Quantum Optimization in Engineering: A Brief Review

Since its beginning, quantum computing has opened up a plethora of new perspectives and advantages. , including its ability to exponentially accelerate computing processes. A large amount of research has been devoted to developing innovative methods for solving a variety of engineering problems by designing quantum versions of numerous mathematical processes. In this paper, we review the state of the art in these new techniques from the standpoint of quantum computer creation and efficiency enhancement, with an emphasis on the most widespread mathematical optimization techniques for engineering applications. It also emphasizes the obstacles and constraints associated with the use of quantum computing, as well as the major prospects for future contributions. This paper is providing a useful resource for engineering researchers who are likely to seek solutions from quantum computing.
International Rewards for Gender Equality Reforms in Autocracies

How do international audiences perceive, and respond to, gender equality reforms in autocracies? For autocrats, the post-Cold War rewards associated with democracy create incentives to make reforms that will be viewed as democratic but not threaten their political survival. We theorize women’s rights as one such policy area, contrasting it with more politically costly reforms to increase electoral competition. A conjoint survey experiment with development and democracy promotion professionals demonstrates how autocracies enhance their reputations and prospects for foreign aid using this strategy. While increasing electoral competition significantly improves perceived democracy and support for aid, increasing women’s economic rights is also highly effective. Gender quotas exhibit a significant (though smaller) effect on perceived democracy. A follow-up survey of the public and elite interviews replicate and contextualize the findings. Relevant international elites espouse a broad, egalitarian conception of democracy, and autocrats accordingly enjoy considerable leeway in how to burnish their reputations.
Benefits of FAIMS to Improve the Proteome Coverage of Deteriorated and/or Cross-Linked TMT 10-Plex FFPE Tissue and Plasma-Derived Exosomes Samples

The proteome characterization of complex, deteriorated, or cross-linked protein mixtures as paired clinical FFPE or exosome samples isolated from low plasma volumes (250 µL) might be a challenge. In this work, we aimed at investigating the benefits of FAIMS technology coupled to the Orbitrap Exploris 480 mass spectrometer for the TMT quantitative proteomics analyses of these complex samples in comparison to the analysis of protein extracts from cells, frozen tissue, and exosomes isolated from large volume plasma samples (3 mL). TMT experiments were performed using a two-hour gradient LC-MS/MS with or without FAIMS and two compensation voltages (CV = −45 and CV = −60). In the TMT experiments of cells, frozen tissue, or exosomes isolated from large plasma volumes (3 mL) with FAIMS, a limited increase in the number of identified and quantified proteins accompanied by a decrease in the number of peptides identified and quantified was observed. However, we demonstrated here a noticeable improvement (>100%) in the number of peptide and protein identifications and quantifications for the plasma exosomes isolated from low plasma volumes (250 µL) and FFPE tissue samples in TMT experiments with FAIMS in comparison to the LC-MS/MS analysis without FAIMS. Our results highlight the potential of mass spectrometry analyses with FAIMS to increase the depth into the proteome of complex samples derived from deteriorated, cross-linked samples and/or those where the material was scarce, such as FFPE and plasma-derived exosomes from low plasma volumes (250 µL), which might aid in the characterization of their proteome and proteoforms and in the identification of dysregulated proteins that could be used as biomarkers.
A U-Net Based Architecture for Automatic Music Transcription

Automatic Music Transcription (AMT) is a complex matter involving several researchers. Recently, due to the powerful Deep Learning techniques, many effective solutions have been proposed. However, there is still room for improvement. To this purpose, in this paper, we propose an architecture based on two U-Net models exploiting Convolutional Neural Networks (CNNs) and a Bidirectional Long-Short Term Memory (BiLSTM) unit, aiming at improving the wave to MIDI transcription performance. This couple of U-Nets act as onset and offset detectors, respectively, whose information are jointly used along with the input mel spectrogram into a third model to find all the active notes in each time-frame. Some numerical results, obtained on the well known MAPS dataset, show the effectiveness of the proposed idea and the advantages over similar state-of-the-art approaches.
Mutual Coupling Reduction Between Two Tightly Packed Half-Split Cylindrical Dielectric Resonator Antennas

An efficient and advanced decoupling technique is presented to significantly reduce the mutual coupling in the ${E}$ -plane between two tightly spaced half-split cylindrical dielectric resonator antennas (CDRAs). The proposed design offers around 59% reduction in the overall electrical footprint compared to the reference two-port full CDRA antenna exhibiting the same mutual coupling performance. This is possible by the vertical placement of copper-cladded FR-4 substrate with air gaps between two halves and maintains the boundary condition to support the dominant $\mathrm {\textbf {HEM}}_{\textbf {11}\boldsymbol {\delta }}$ mode of CDRA. A significant reduction in mutual coupling is obtained precisely at the antenna resonance by controlling the dimensions of the decoupling structure, leading to its minimum value. The proposed technique appears flexible and provides a comprehensive design guideline to tune the return loss and mutual coupling minima. A detailed analysis is performed with an equivalent circuit model to explain the decoupling mechanism and its precise control for optimal performance. Measured S-parameters show a −10-dB impedance matching bandwidth of 16% (4.54–5.34 GHz) and −44 dB mutual coupling at 4.9 GHz, reaching −62.3 dB at 4.86 GHz. Within the operating bandwidth, the measured gain lies between 4.6 and 6 dBi, and the envelope correlation coefficient is below 0.053. The results indicate that the two-port antenna is a good candidate for multiple-input multiple-output (MIMO) communication.
Impact of the Membrane Structure of the Stationary Phase on Steric Exclusion Chromatography (SXC) of Lentiviral Vectors

For steric exclusion chromatography (SXC), hydrophilic stationary phases are used to capture the target molecule in the presence of polyethylene glycol. The influence of the structure and pore size of the stationary phase on the process requirements are not yet well understood. To better understand the SXC process, membranes with different pore sizes that served as a stationary phase were compared for the purification of lentiviral vectors (LVs). A design of experiments (DoE) was performed to assess the combined impact of PEG concentration and membrane pore size on the purification performance. A visualization experiment showed that the LVs were captured on the first membrane layer for a pore size up to 2.2 µm, and for a pore size larger than 2.2 µm, LVs were also partly found on the second and third membrane layers. Moreover, we could observe that increasing membrane pore size requires a higher PEG concentration to achieve comparable LV recoveries. Using five membrane layers as a stationary phase was sufficient to achieve good performance, supporting the visualized capture results. In conclusion, we could show that each stationary phase has its optimal PEG buffer compositions for SXC, depending on the membrane structure and pore size.
Explainable Symbolic Regression Model for Tool Wear Diagnosis

In precision machining, predicting the tool health can improve productivity, job quality, and reduce machine downtime and energy consumption. While deep learning (DL) algorithms have garnered recent interest, they lack the physics understanding associated with machining processes. To address this limitation, we present a symbolic regression framework for tool wear diagnostics. The method explores analytical symbolic expressions using health indicators and cutting settings. Tool health indicators are computed from wavelet subspaces of vibration signals by applying a distance metric to the wavelet coefficients. These indicators are strongly correlated with tool wear measurements, making them suitable for tool wear diagnostics. We applied the developed framework to the IEEE PHM 2010 data, which comprises three sets of run-to-failure machining tests conducted with three tools. The framework predicted tool wear with an $R^{2}$ of 0.947 and a mean absolute error (MAE) of 0.006 mm across test sets. The results demonstrate the effectiveness of the symbolic regression approach for tool wear diagnostics, showcasing the richness of information in the indicators and the quality of the developed model $\mathbf{for}$ tool wear estimation.
Reinforcement Learning-Based Collision Avoidance of a Connected and Automated Vehicle at Merging Roads

Although autonomous driving technology has made significant advancements, autonomous vehicles tend to be defensive in complex environments such as merging roads, intersections, and roundabouts. Connected autonomous vehicles (CAVs) are expected to behave more naturally even in such complex environments leveraging information obtained through vehicle connectivity, such as neighboring vehicles' position and velocity. This study presents a reinforcement learning-based framework for collision avoidance of a CAV at merging roads. The key idea is to redesign the reward function of previous relevant work so that collision avoidance is realized more in a human-like and predictive manner.
REE and trace element variations in accessory minerals and hornblende from the strongly zoned McMurry Meadows Pluton, California

ABSTRACT The zoned McMurry Meadows Pluton has been examined for REE and trace element variations in hornblende, sphene, apatite, allanite and zircon. Mineral separates (17), were analysed by INAA from four granitoids spanning the compositional range of the pluton (60%–75% SiO2). All of the phases examined exhibit significant compositional variations, with sphene having the largest changes in chondrite normalised REE patterns. Compositional variations in these minerals are related to paragenetic sequence and, as the whole rocks become more evolved, increases in partition coefficients; not subsolidus re-equilibration. Hornblende is only a dominant site for REE in granites where sphene is a later crystallising phase, otherwise allanite (LREE only) and sphene are the dominant whole rock sites for REE. Zircon and apatite normally contribute less than 10% each to the whole rock abundance of REE. Three zircon samples contain only small compositional differences and are interpreted as having crystallised from the bulk magma prior to differentiation. Zr variation in the pluton is nonlinear, first increasing and then decreasing with whole rock fractionation. A simple process, analogous to “restite unmixing” applied to the Zr variation, defines a bulk magma composition of about 63% SiO2, before differentiation of the zoned pluton. The modelled bulk magma need only have evolved by about 2·5% silica and still have produced the entire compositional range (60–75% SiO2) observed in the pluton.
Deep Learning-Based Localization and Outlier Removal Integration Model for Indoor Surveillance

Directional antenna technologies are crucial to enhance high-speed data transmission in emerging wireless communications such as mmWave. These technologies can enable high-accuracy radio positioning by exploiting spatial-temporal signal processing in wideband beamforming space. However, the radio positioning technique potentially poses surveillance risks to mobile users, particularly being tracked illegally. This work presents a novel scheme to track a user in a building based on passively received signals. The scheme includes a Deep Convolutional Neural Network (DCNN) localization module to train on the accumulated channel impulse responses (CIR) and corresponding Angle-Delay profiles. The user's estimated locations from the DCNN localization are then refined with an Unscented Kalman filter (UKF) data fusion module to eliminate outlier data points. Simulations indicate that the proposed scheme can accurately regenerate the user trajectory, even without the attacker's physical intrusion into the building. This poses a new concern of surveillance risks in directional wireless communications, given their expected popularity in 5G and beyond.
Modeling the finite viscoelasticity of human brain tissue based on microstructural information

Continuum‐mechanics‐based computational models are a valuable tool to advance our understanding of mechanics‐related physiological and pathological processes in the brain. Current numerical predictions of brain tissue behavior are mainly based on phenomenological material models. Such models rely on parameters that often lack physical interpretation and only provide adequate estimates for brain regions with a similar microstructure as those used for calibration. These issues can be overcome by establishing advanced constitutive models that are microstructurally motivated and account for regional and temporal changes through microstructural parameters. In a previous study, we have proposed a microstructure‐informed finite viscoelastic constitutive model for porcine brain tissue based on experimental insights into the link between macroscopic deformations and cell displacements during compression tests. Here, we test whether the model can be applied to the material response of human brain tissue from different brain regions during compression and tension tests. We calibrate the model using a combination of cyclic loadings in compression and tension as well as stress relaxation experiments in compression. Furthermore, we include cell densities and the percentage area covered by cell nuclei from microstructural analyses of the tested samples into the model. While it was not possible to identify one set of material parameters for all four considered brain regions, we successfully applied the model to tissue from the cerebellar white matter. Our results show that the microstructure‐based time constant that we had determined based on experiments on porcine brain tissue is also valid for human tissue. Furthermore, the microstructure‐informed material model is capable of capturing the pronounced compression‐tension asymmetry of brain tissue.
Analytical Approach for Optimal Deployment of Drone Base Stations in Cellular Networks

Reliable mobile communications is of critical importance, and should be maintained even in case of extremely crowded events or emergency scenarios. In such scenarios the deployment of drone-mounted base stations offers an agile and cost-efficient way to sustain coverage and/or provide capacity relief. In this paper we develop an analytical method to estimate the blocking and coverage probabilities of drone-assisted cellular networks using information that is readily available from network planning tools. We demonstrate how this method can be used to determine the minimum required number of drones and their corresponding locations for a given target performance level.
Diversity Enabled Wireless Transmissions with Random Arrivals and Hard Delay Constraints

Hard delay-constrained wireless communication has attracted considerable attention recently, as it is expected to play an essential role in time-sensitive networks (TSN) and deterministic networks (DetNet), However, it has been a long challenge to develop techniques that satisfy the constraints on both bounded delay and average power. In this paper, we investigate the tradeoff between delay and throughput in a delay-bounded wireless system with random arrivals, which characterizes the fundamental performance limitations of delay-bounded communication over fading channels. In particular, for diversity-enabled wireless systems with the finite average power, we present the sufficient condition of the arrival process with which the hard delay constraint can be satisfied. Moreover, based on the proposed sufficient condition, a cross-layer policy is conceived to achieve the bounded delay. Through markovian queueing analysis, the average power consumption of this policy is derived as a function of latency threshold and throughput. By this means, the latency-throughput tradeoff is characterized given the average power.