Miracle-dz/UniFAR-FTUs huggingface dataset:
On the application of the Schoenberg quasi-interpolant for complexity reduction in trajectory generation

The paper extends previous work on trajectory generation for UAV (Unmanned Aerial Vehicles) using B-spline curves to parameterize an associated flat output. Typical constraints and costs (such as those involving input bounds and trajectory length) lead to nonlinear formulations in terms of the control points weighting the B-spline curve. This complexity adversely affects the computation time and conservatism of the result. To mitigate these effects we use the Schoenberg operator to provide a quasi-interpolant of the original nonlinear functions. These improvements come at the price of an approximation error which requires a tightening of the original constraints (either from a theoretical bound or via an iterative procedure). The obtained results are exemplified over a fixed wing UAV model and they can be applied for any optimization-based trajectory planning problem.
Simulating the cold dark matter-neutrino dipole with TianNu

Measurements of neutrino mass in cosmological observations rely on two point statistics that are hindered by significant degeneracies with the optical depth and galaxy bias. The relative velocity effect between cold dark matter and neutrinos induces a large scale dipole into the matter density field and may be able to provide orthogonal constraints to standard techniques. We numerically investigate this dipole in the TianNu Simulation, which contains cold dark matter and 50 meV neutrinos. We first compute the dipole using a new linear response technique where we treat the displacement caused by the relative velocity as a phase in Fourier space and then integrate the matter power spectrum over redshift. Then, we compute the dipole numerically in real space using the simulation density and velocity fields. We find excellent agreement between the linear response and N-body methods. Utilizing the dipole as an observational tool will require two tracers of the matter distribution that are differently biased with respect to the neutrino density.
RLP: Power Management Based on a Latency-Aware Roofline Model

The ever-growing power draw in high-performance computing (HPC) clusters and the rising energy costs enforce a pressing urge for energy-efficient computing. Consequently, advanced infrastructure orchestration is required to regulate power dissipation efficiently. In this work, we propose a novel approach for managing power consumption at runtime based on the well-known roofline model and call it Roofline Power (RLP) management. The RLP employs rigorously selected but generally available hardware performance events to construct rooflines, with minimal overheads. In particular, RLP extends the original roofline model to include the memory access latency metric for the first time. The extension identifies whether execution is bandwidth, latency, or compute-bound, and improves the modeling accuracy. We evaluated the RLP model on server-grade CPUs and a GPU with real-world HPC workloads in two scenarios: optimization with and without power capping. Compared to system default settings, RLP reduces the energy-to-solution up to 22% with negligible performance degradation. The other scenario accelerates the execution up to 14.7% under power capping. In addition, RLP outperforms other state-of-the-art techniques in generality and effectiveness.
CaliBrainVR: Using Psycho-physiological Measures to Calibrate Virtual Reality Training

The use of extended reality (XR) in training has surged with technological advancements. To meet the demand for adaptive and effective XR training, biofeedback measures like heart rate, eye gaze, and pupil dilation have become crucial. Even though, XR providers have developed headsets equipped with physiological sensors, the accuracy of interpreting the signals has yet to consider inter-individual differences in physiology. Nevertheless, it is important to measure an individual's respon-sivity in terms of biometrics (i.e., the degree to which a person's physiological signal changes in response to changes in their cognitive state) to build accurate XR assessment and adaptive XR training. Currently, we present CaliBrainVR, a virtual reality “calibration” application that aims to create tailored user profiles using eye-tracking and heart rate data outlining the individual's responsivity. The present text describes the virtual environment of the application, the data logging and preprocessing and the results of the initial validation stage. Finally, we outline the future development of the application and its current impact on training and assessment.
Accurate and Efficient Distributed COVID-19 Spread Prediction based on a Large-Scale Time-Varying People Mobility Graph

Compared to previous epidemics, COVID-19 spreads much faster in people gatherings. Thus, we need not only more accurate epidemic spread prediction considering the people gatherings but also more time-efficient prediction for taking actions (e.g., allocating medical equipments) in time. Motivated by this, we analyzed a time-varying people mobility graph of the United States (US) for one year and the effectiveness of previous methods in handling time-varying graphs. We identified several factors that influence COVID-19 spread and observed that some graph changes are transient, which degrades the effectiveness of the previous graph repartitioning and replication methods in distributed graph processing since they generate more time overhead than saved time. Based on the analysis, we propose an accurate and time-efficient Distributed Epidemic Spread Prediction system (DESP). First, DESP incorporates the factors into a previous prediction model to increase the prediction accuracy. Second, DESP conducts repartitioning and replication only when a graph change is stable for a certain time period (predicted using machine learning) to ensure the operation improves time-efficiency. We conducted extensive experiments on Amazon AWS based on real people movement datasets. Experimental results show DESP reduces communication time by up to 52%, while enhancing accuracy by up to 24% compared to existing methods.
Sources of the Ultra-High Energy Cosmic Rays (UHECRs)

We estimate the mean free path of protons by calculating the total cross section of the reaction p + gCM! p + + n, where gCM represents the photons of the cosmic ray microwave radiation. Based on the BlandfordZnajek (BZ) mechanism for electromagnetically extracting rotational energy from a black hole, we find that at least two candidates of local Seyfert galaxies (NGC 1365 and NGC 3783) are sources of the UHECRs. Between the event horizon and a few gravitational radii rg of the Kerr hole electromagnetic forces can accelerate protons well above 10 19 eV.
A Painless Deterministic Policy Gradient Method for Learning-based MPC

The combination of Reinforcement Learning (RL) and Model Predictive Control (MPC) has gained a lot of interest in the recent literature as a way of computing the optimal policies from MPC schemes based on inaccurate models. In that context, the Deterministic Policy Gradient (DPG) methods are often observed to be the most reliable class of RL methods to improve the MPC closed-loop performance. The DPG methods are fairly easy to formulate when used with compatible function approximation as an advantage function. However, this formulation requires an additional value function approximation, often carried out using Deep Neural Networks (DNNs). In this paper, we propose to estimate the required value function approximation as a first-order expansion of the value function estimate from the MPC scheme providing the policy. The proposed approach drastically simplifies the use of DPG methods for learning-based MPC as no additional structure for approximating the value function needs to be constructed. We illustrate the proposed approach with two numerical examples of varying complexity.
Early-Life Income Shocks and Old-Age Mortality: Evidence from World War I Veterans’ Bonus *

In 1936, the US government enacted the later-known Bonus Act, which triggered cash transfers to about 3 million veterans who had served in World War I. The large and unexpected nature of transfers provides an opportunity to examine the impact of family income shocks on children’s long-term outcomes. This paper studies the long-run benefits of veterans’ bonus receipt on their children’s old-age longevity. We employ data from Social Security Administration death records over the years 1975-2005 linked to the full-count 1940 census and implement regressions that compare the longevity of children of veterans versus non-veterans across various ages of exposure to the bonus receipt. We find that those exposed during in-utero and early-life reveal significant improvements in longevity of about 5.6-7.5 months. Our balancing tests fail to provide concerning evidence regarding the endogenous dynamic differences in individual and family characteristics based on veteran-status that vary across cohorts. Further analyses suggest stronger effects among children of low-educated mothers and those with low socioeconomic index fathers. We also show that increases in house values, rise in homeownership, and potential improvements in neighborhood characteristics are candidate mechanisms of impact.
Adaptive MPC for trajectory tracking with online adaption of the vehicle model’s yaw intensification

This paper presents an Adaptive Model Predictive Controller (AMPC) for vehicle trajectory tracking. The general goal is to adapt the vehicle model’s yaw intensification, to compensate for lost tracking accuracy caused by model errors and condition changes. The approach combines a classic Model Predictive Controller (MPC) with an online learning algorithm based on a trajectory-dynamic lookup table. This lookup table is updated periodically according to the trajectory dynamics and the tracking performance of the controller. In contrast to current research, the proposed AMPC is able to adapt repeatedly to condition changes and transfer learned behavior to unknown trajectories. The feasibility of this approach is evaluated via simulation experiments in CarMaker and Matlab/Simulink on several closed loop circuits with additional weight to emulate condition changes.
A wake loss model asymmetry induced by the circulation of a vertical axis wind turbine

The purpose of this research is to develop a theoretical method for predicting the wake region downstream of the flow from a top view of a vertical axis wind turbine (VAWT) and to examine the wake loss effects of the VAWT. Previous literature has reported that the wake region of the VAWT is not symmetric, and its downstream is similar to a circular cylinder wake street. The airflow of the VAWT is turbulent, and the inviscid incompressible flow can be applied to its range. Therefore, the current study analyzed the flow over a cylinder with circulation to investigate the velocity, pressure coefficient, and tip speed ratio and to observe the similarity between the cylinder and the VAWT. The results of this study demonstrate remarkable similarity, particularly with respect to the asymmetry of the wake region due to the distinct velocity speed in two opposite locations of the VAWT. Moreover, the pressure coefficient functionality and curvature are similar to the available measurements of the VAWT. This model can be further developed and examined under varied conditions to provide a theoretical wake loss model for VAWTs. This will lead to the expansion of VAWTs’ applications in green, sustainable, and clean energy production.
Classification of Arrhythmia ECG Signals using Convolutional Neural Network

The electrocardiogram (ECG) has been established as a reliable tool for monitoring cardiovascular health. Vast amount of ECG recordings can pose a challenge for its processing and analysis and seeking out experts to analyze such a large amount of ECG data can deplete valuable medical resources. Recently, there has been a growing interest in automatic methods for accurate heartbeat categorization. Our motivation behind this work is to improve efficiency and accuracy for arrhythmia classification based on convolutional neural networks (CNNs). We first pre-process the input signals by normalizing them using a z-score. Following the data segmentation, the under-represented classes are oversampled using random duplication. By randomly duplicating the samples of underrepresented categories and eliminating instances from the overrepresented categories, this approach minimizes the imbalance in the training data. We propose an efficient 12-layer 1D-CNN to classify cardiac arrhythmia into five classes. We demonstrate the oversampling technique’s impact on the proposed CNN’s performance by comparing its performance before and after its application.
Dimensionality reduction of hyperspectral images using an ICA-based stochastic second-order optimization algorithm

Hyperspectral imaging is one of the advanced remote sensing techniques whose goal is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials or detecting processes. However, the high dimensional nature of hyperspectral images makes their analysis complex. Various methods have been developed to reduce the dimension of hyperspectral images. Most commonly used dimension reduction techniques are Principal Component Analysis (PCA) and Independent Component Analysis (ICA). PCA is a method to reduce the dimensionality by removing the correlation among the bands, while ICA finds additively independent components. FastICA is one of the most used ICA algorithms. It is based on maximizing a loss derived from the fourth order statistical moment (kurtosis) or negentropy, which are both non-convex functions. Moreover, FastICA can find irrelevant stationary points (no maxima) and is not scalable as it uses at each iteration the whole set of pixels. In this paper, we present a stochastic second-order Taylor-based algorithm adapted to such ICA non-convex loss functions. Our algorithm guarantees ascent, hence it usually identifies (local) maxima. Moreover, the algorithm since it is stochastic, is scalable. Detailed numerical simulations show the superior performance of our method compared to FastICA.
Execution Examination of Distinctive Edge Detection Algorithms

Edge detection or segmentation is a rudiment innovation as it can evaluate sharpness and analyze object boundaries. That’s why it carries an influential Figure in the image processing era. However, the approach of partitioning an image into discontinuous parts is called edge detection. It defines the change of intensity associated with the image boundary. Edge detection can be done using a variety of approaches. This research proposed an innovative method to measure performance of four edge detection techniques using quality assessment metrics on satellite images and Gaussian noise-influenced satellite images. This paper comprises well-known edge detection technologies like Canny, Prewitt, Scharr, and Robert operators. Furthermore, the Image Quality Assessment (IQA) metric is an image’s essential characteristic for measuring image quality. For evaluating image quality, we mainly consider SSIM, MSE, PSNR, and RMSE. The execution of the Canny and Prewitt methods on the satellite dataset has been experimentally validated. However, Canny edge detection achieves better results when the Gaussian Noise effect is applied to the same dataset.
A Macro-Micro Population-Based Co-Evolutionary Multi-Objective Algorithm for Community Detection in Complex Networks [Research Frontier]

Recently, multi-objective evolutionary algorithms (MOEAs) have shown promising performance in terms of community detection in complex networks. However, most studies have focused on designing different strategies to achieve good community detection performance based on a single population. Unlike these studies, this study proposes a macro-micro population-based co-evolutionary multi-objective algorithm called MMCoMO for community detection in complex networks to obtain a better trade-off between exploration and exploitation. This algorithm employs two populations, i.e., macro-population and micro-population, for co-evolution to obtain better community structures. In particular, the macro-population prefers exploration and is responsible for quickly determining approximate partitions of the network to obtain good rough community structures as early as possible, whereas the micro-population favors exploitation and is responsible for searching for good fine community structures through the local search process. Thus, these two populations can be used to improve each other through interactions in the co-evolutionary process. In particular, a guiding strategy is designed using the elite (non-dominated) solutions of the macro-population to guide the micro-population, and an influencing strategy is further designed using the elite solutions of the micro-population to positively influence the macro-population. Experiments on synthetic networks and 14 real-world networks demonstrate the superiority of the proposed algorithm over several state-of-the-art community detection algorithms.
Gateway-Based Certification Approach to Include IoT Nodes in a Trusted Edge/Cloud Environment

Nowadays, Internet of Things (IoT) devices are gaining more and more popularity in several fields including smart cities and home automation. IoT devices are small components with limited functionalities, low costs and often low or missing security features. Therefore, hackers could use IoT devices to conduct attacks to digital systems based on IoT devices. A possible solution to solve such an issue is to employ Edge strategies for managing IoT devices. The advantage of using this approach is two folds. Firstly, unsecured devices are isolated from the trusted network. Secondly, IoT devices are certified and data signed. In this work, we propose a system for certifying IoT devices and verifying their identity. Experiments proved the goodness of the proposed approach.
Optimal Path Tracking: Benchmarking an NMPC for a Wide-Span Autonomous Agricultural Machine

In this article the development of an optimal controller for a special type of tractor, commonly referred to as gantry tractor, is presented. A Model Predictive Controller (MPC) based on a single track model controls the global kinematic behavior of the tractor. The formulation of the optimal control problem aims at guaranteeing an extremely accurate path tracking. In a subsequent step, the controls for the individual four wheels are calculated following a no-slip hypothesis. A high frequency controller corrects the lateral behavior of the vehicle to ensure higher tracking accuracy. The performance of the MPC is compared to that of a geometric path tracking controller in terms of their respective tracking accuracy.
FaultyRank: A Graph-based Parallel File System Checker

Similar to local file system checkers such as e2fsck for Ext4, a parallel file system (PFS) checker ensures the file system's correctness. The basic idea of file system checkers is straightforward: important metadata are stored redundantly in separate places for cross-checking; inconsistent metadata will be repaired or overwritten by its ‘more correct' counterpart, which is defined by the developers. Unfortunately, implementing the idea for PFSes is non-trivial due to the system complexity. Although many popular parallel file systems already contain dedicated checkers (e.g., LFSCK for Lustre, BeeGFS-FSCK for BeeGFS, mmfsck for GPFS), the existing checkers often cannot detect or repair inconsistencies accurately due to one fundamental limitation: they rely on a fixed set of consistency rules predefined by developers, which cannot cover the various failure scenarios that may occur in practice.In this study, we propose a new graph-based method to build PFS checkers. Specifically, we model important PFS metadata into graphs, then generalize the logic of cross-checking and repairing into graph analytic tasks. We design a new graph algorithm, FaultyRank, to quantitatively calculate the correctness of each metadata object. By leveraging the calculated correctness, we are able to recommend the most promising repairs to users. Based on the idea, we implement a prototype of FaultyRank on Lustre, one of the most widely used parallel file systems, and compare it with Lustre's default file system checker LFSCK. Our experiments show that FaultyRank can achieve the same checking and repairing logic as LFSCK. Moreover, it is capable of detecting and repairing complicated PFS consistency issues that LFSCK can not handle. We also show the performance advantage of FaultyRank compared with LFSCK. Through this study, we believe FaultyRank opens a new opportunity for building PFS checkers effectively and efficiently.
Vision of the Seas: Open Visual Perception Framework for Autonomous Sailing Vessels

We propose an Open Visual Perception framework for autonomous navigation and obstacle detection in marine sailing vessels. The proposed framework lays the groundwork for perceiving marine objects needed for vessel navigation and obstacle avoidance using only image processing as the primary method. Various methods are explored to extract features such as waterline, terrain, and other vessels. The proposed framework also incorporates image stabilization techniques without the need for an Inertial Measurement Unit to compensate for ocean wave oscillations. The work also introduces the system architecture of an autonomous marine vessel, besides the components of the open visual perception framework. Results demonstrate the capabilities of obstacle detection, horizon line identification, and water texture detection.
Online 3D Geometry Reconstruction for Direct Energy Deposition Based on Melt Pool Images

Direct energy deposition (DED) is an additive manufacturing technique developed to build and repair metallic objects with complex shapes. The deposition process involves a high-power laser that locally melts a metallic substrate, creating the so-called melt pool which is enriched by metal powder particles emitted by nozzles. Due to the printing process complexity, anomalies may occur. In the present paper, an innovative approach able to quickly detect geometrical defects is presented. A color camera is mounted coaxially to the laser beam providing images of the melt pool. The proposed algorithm combines all the images acquired for each deposited layer and estimates its actual geometrical profile. This profile can be compared to a reference one, for example, retrieved by sectioning the part CAD file or better from a thermo-mechanical simulation of the part printing process. From this comparison, the operator can evaluate the possible out-of-tolerance geometrical deviations at an early stage and without stopping the printing process, avoiding waste of energy, time, and raw materials. Estimated layer profiles could also be combined to generate a full 3D reconstruction of the built part. The proposed approach has been tested on real printed samples belonging to an experimental campaign aimed at evaluating the machine performance while realizing objects with significant overhang features. Some samples present evident geometrical deviations that helped in optimizing the algorithm parameters under variable conditions. Results have been validated by comparing the reconstructed profiles to the real ones, measured with an external high-resolution scanner.