Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Parameters for Modeling Traffic Participants

Accurately modeling the behavior of traffic participants is essential for safely and efficiently navigating an autonomous vehicle through heavy traffic. We propose a method, based on the intelligent driver model, that allows us to accurately model individual driver behaviors from only a small number of frames using easily observable features. On average, this method makes prediction errors that have less than 1 meter difference from an oracle with full-information when analyzed over a 10-second horizon of highway driving. We then validate the efficiency of our method through extensive analysis against a competitive data-driven method such as Reinforcement Learning that may be of independent interest.
Realization of a Passive UHF RFID Sensor Platform for the Detection of Damages on a Concrete Reinforcement

Every year, roads are exposed to chemical stress due to deicing in winter. The chemical composition of road salt causes pitting corrosion of steel used in road constructions, especially in the reinforced concrete of bridges. Due to increasing cracks in the concrete, chloride ions intrude deeper and deeper into the structure over a longer period of time and attack the steel structure. In the worst case, this can lead to structural breakdown. However, early detection of damage in reinforced concrete can prevent such failure. Therefore, this paper deals with the implementation of a passive Ultra-High Frequency Radio-Frequency Identification (UHF RFID) sensor platform as a preventive measure. The sensor data are collected and transmitted to a reader that processes the information about the chloride concentration. For the presented RFID sensor platform, a WISP based Tag was improved and supplemented by additional circuit components. Several modifications and an additional circuit with high input impedance allows the connection of electrochemical probes (sensor). An Impinj Speedway R420 (UHF RFID reader) is used to read the measurement data from the sensor platform which is integrated in the concrete over a distance of 15 cm.
A Short-term Traffic Flow Forecasting Model Based on Spatial-temporal Attention Neural Network

In recent years, with the enrichment of information collection methods, traffic flow data are no longer limited to a single section or a certain region, and often presented in the form of large-scale road networks. Traffic flow data of road networks are mainly spatial-temporal sequential data. This paper combines graph theory with prediction of temporal sequences to propose the Spatial-temporal Attention Neural Network (STAtt) from the perspective of spatial-temporal feature fusion, aiming at providing data support and reference for traffic guidance and path planning. The Graph Attention Network (GAT) is used to describe the directivity, difference and variability of the roads' interactions, which is embedded into the loop unit to replace the gate structure to achieve the spatial and temporal information fusion. At the same time, the Sequence-to-Sequence architecture and temporal attention mechanism are used to enlarge the receptive field of the model and reduce the cumulative error of multi-step prediction. The results of the experiment show that the model is superior to other models in the short-time prediction task within 1 hour.
Deep Learning Method for Cell-Wise Object Tracking, Velocity Estimation and Projection of Sensor Data over Time

Current deep learning methods for environment segmentation and velocity estimation rely on Convolutional Recurrent Neural Networks to exploit spatio temporal relationships within obtained sensor data. In these approaches, scene dynamics are implicitly derived using ConvNets to correlate novel input and memorized data, which, for the task of recurrently processing sensor scans, suffer from architectural restrictions. In addition, these prior approaches lead to distorted data due to spatio temporal misalignments caused by the movement of external road users. In this work, we solve these issues with a novel Recurrent Neural Network unit. Within this unit, we track object encodings across consecutive frames on cell level by correlating key-query pairs derived from sensor inputs and memory states, respectively. We then use resulting tracking patterns to obtain scene dynamics and regress velocities. In a last step, the memory state of the Recurrent Neural Network is projected based on extracted velocity estimates to resolve the aforementioned spatio temporal misalignment.
FS-GRU: Continuous Perception and Prediction with inter Frame Feature Sharing

Real-time detection and motion forecasting are critical for self-driving vehicle (SDV). However, there are lots of redundant computations in the existing joint detection and prediction methods. To solve this problem, we propose feature sharing GRU (FS-GRU) which employs the convolutional gate recurrent unit (ConvGRU) to aggregate temporal context and share inter frame feature. The key component is an inter frame feature coordinate transform module with an auxiliary task returning the transformed target pose. Importantly, the feature sharing mechanism reduce so much computation cost that we can exploit long-term history information to better infer the intention of participants. The method was evaluated on large-scale public dataset, and show significant improvements over the state-of-the-art with higher accuracy and less runtime.
Time in Advance Go-Around Predictions for Decision Support in Air Traffic Management

Maintaining the safety and cost-effectiveness of air transport operations, while increasing capacity, will push the next generation of ATM systems towards digitization. In the medium term, a digitized system in the human-managed ATM environment will be able to provide reliable predictive analytics based on automated information processing, thus providing decision support to human operators. This paper details the development of a machine learning based solution for go-around predictions, exemplified at two major European airports. Go-arounds are standard, high workload, procedures by which an aircraft in the final approach phase can safely discontinue the approach. The proposed machine learning solution is aimed at increasing safety levels in airport operations by enhancing air traffic controllers situational awareness and helping them better plan and adapt to go-around scenarios. This work leverages on more than two years worth of ADS-B and successfully uses oversampling technique to combat the high imbalanced in the data. In addition, we performed a benchmark study with a selection of the most common classification models. The final type of model selected was LightGBM for which a feasibility study for predictions at 2NM, 4NM, 6NM, and 8NM distance from the runway threshold was performed. The results for both airports showed that although the models’ recall decreases with the distance from the threshold, we were able to maintain a high prediction precision between 90% at 2NM to 80% at 8NM. Finally, a study of the explainability of the probabilistic predictions was carried out by evaluating the most important features of the models.
Estimation of objects transverse parameters in off-axis and in-line Fresnel digital holography

In this report transverse parameters of objects registered with inline and off-axis Fresnel digital holography schemes were estimated: maximum transverse dimensions of objects, size and quantity of object resolution elements. By determining allowed locations of diffraction orders under reconstruction, new expressions for estimation of objects transverse parameters were obtained. In case of off-axis holography it is desirable that object should not overlap with zero-order and twin images. If object and twin images are located on opposite sides relative to zero-order under reconstruction, this is case of preventing of cyclic shift of twin image (PCS). If twin image is located on both sides relative to zero-order under reconstruction but don’t overlap with object image, this is case of assumption of cyclic shift of twin image (ACS). ACS case allows to register digital holograms of larger objects compared to PCS case. However, for example, for automatic image processing, separate display of object and twin images relative to zero-order is often required. It was found that ACS case allows to register holograms with distance between the object and hologram 1.5 times lesser than in PCS case. And maximum transverse dimension of object in ACS case is always greater than in PCS case by the half of hologram size. For verification of obtained estimates, off-axis digital Fresnel holograms with 2048x2048 pixels were optically registered. Contour images located behind static scatter were used as objects. Confirmations on transverse object parameters estimates, satisfying ACS and PCS cases, were derived. These results demonstrate correctness of obtained quantitative estimates.
Rollover-Free Trajectory Planning for Reconfigurable Vehicles Under Terrain Uncertainty

This paper presents a trajectory planning formulation that enables efficient collision and rollover-free motion planning for reconfigurable off-road vehicles, under terrain uncertainty. The corresponding motion planning problem is stated as a nonlinear optimal control problem (NOCP) subject to vehicle kinematics, obstacle avoidance and rollover stability constraints. The latter takes the form of chance constraint expressed for probability of the machine maintaining rollover stability in the presence of uncertainty in the terrain map. To achieve efficient solution to the NOCP, a novel linear model for uncertainty in rollover based on the properties of rotational transformations is derived. The novel model is then utilized to modify the rollover-stability constraint. To showcase its benefit, comparisons between the proposed model of probabilistic rollover stability measure and the naive noise model are provided through a Monte Carlo simulation. The formulation's practicality is demonstrated in simulation for a feller-buncher machine, traversing undulating 3-D terrain with uncertain map. Results demonstrate stable relocation of the machine and computational efficiency of the trajectory generation.
Efficient Calculation of Triangle Centrality in Big Data Networks

The notion of “centrality” within graph analytics has led to the creation of well-known metrics such as Google's Page Rank [1], which is an extension of eigenvector centrality [2]. Triangle centrality is a related metric [3] that utilizes the presence of triangles, which play an important role in network analysis, to quantitatively determine the relative “importance” of a node in a network. Efficiently counting and enumerating these triangles are a major backbone to understanding network characteristics, and linear algebraic methods have utilized the correspondence between sparse adjacency matrices and graphs to perform such calculations, with sparse matrix-matrix multiplication as the main computational kernel. In this paper, we use an intersection representation of graph data implemented as a sparse matrix, and engineer an algorithm to compute the triangle centrality of each vertex within a graph. The main computational task of calculating these sparse matrix-vector products is carefully crafted by employing compressed vectors as accumulators. As with other state-of-the-art algorithms [4], our method avoids redundant work by counting and enumerating each triangle exactly once. We present results from extensive computational experiments on large-scale real-world and synthetic graph in-stances that demonstrate good scalability of our method. We also present a shared memory parallel implementation of our algorithm.
Modelling Conductor Contacts with Rough Surfaces

The properties of the conductor joints with rough surfaces and random distribution of asperity heights and contact areas are studied. The equivalent circuit models are used to characterise the contacts of a single and multiple pairs of asperities. It is shown that a source and a termination load must be considered in the models used for the self-consistent analysis of the nonlinear products generated at the contacts of conductors with rough surfaces and random distribution of asperity heights.
Comparison of Autonomous Robot's Mapping Performance Based on Number of Lidars And Number of Tours

Autonomous mobility is increasing its popularity day by day. Mapping, localization, planning, and control are the main research topics of autonomous systems. The performance of localization directly depends on the map quality. This paper aims to show how to improve mapping performance, by increasing the measurements and number of tours. In this context, using the GMapping package of the ROS platform, the mapping performance of two lidars located in different regions and heights of the wheelchair, working separately and together, is examined. In addition, the mapping performance according to the number of tours in the mapped region is also tested. As a result, it is confirmed that the results obtained using two lidars are more successful than a single lidar. Additionally, loop closure's effect on increasing the map quality is shown in the paper.
Daily OD Demand Prediction in Urban Metro Transit System: A Convolutional LSTM Neural Network with Multi-factor Fusion Channel-wise Attention

The distribution of Metro passenger flow is an important indicator of the operation status of urban rail systems. The accurate short-term passenger flow prediction at the network level plays an important role in the management of smart urban rail transit. Compared with road systems, urban rail transit OD (Origin-Destination) distribution has more obvious characteristics such as high dimension and sparseness, which brings great challenges to Data-driven prediction models. Although the existing research has made some progress in mining spatial-temporal features, the model lacks objective consideration in terms of the rationality of prediction framework embedding, multi-source data fusion, and spatial-temporal feature mining integration. To solve the above problems, we propose a deep learning framework - Convolutional LSTM (Long Short-Term Memory) Prediction Model (STMACL) based on multi-factor fusion channel-wise attention mechanism. First, we design a multi-factor spatial-temporal feature parallel structure encoder to capture the intrinsic relationship between inter-station, date attributes, external factors and high-level spatial correlation of passenger flow distribution, and use the Convolutional LSTM to further abstract spatial-temporal features to output the prediction result. The proposed framework was tested on two large-scale real datasets in Shanghai Metro, and its performance outperformed other benchmark methods, enabling higher prediction accuracy at the network-level, with high flexibility, scalability and robustness.
Global Graph Clustering and Local Graph Exploration for Community Detection in Twitter

In this paper, the concepts and techniques for global graph clustering are examined, or the process of locating related clusters of vertices within a graph. We introduce the construction of a graph clustering technique based on an eigenvector embedding and a local graph clustering method based on stochastic exploration of the graph. Then, the developed implementations of both methods are presented and assessed in terms of performance. In addition, the difficulties associated with assessing clusterings and benchmarking cluster algorithms are explored where PageRank and EigEmbed algorithms are utilized. The experiments show that the EigEmbed outperformed PageRank across all experiments as it detected more communities with the same number of clusters. Ultimately, we apply both algorithms to a real-world graph representing Twitter network and the followers and tweets therein.
Complementary and situation sensitive object detection, review performance, and performance dependencies of common approaches

Object detection can be performed on different modalities like camera or lidar systems. Image-based approaches are highly sensitive to variations of illumination. On the other hand, lidar-based approaches fail on detecting small objects or objects at higher distances. The research in this field leads to a variety of different approaches with different advantages and drawbacks. Fusion-based approaches aim to utilize the advantages of the available modalities and specialized approaches. Usually, the performance of object detection approaches is measured over a set of situations. The performance measures are typically provided as average values over a test dataset. Local variations are not considered for comparison and not utilized. For a better understanding, this contribution analyzes performance variations based on different situation parameters to understand the complementary potential and improve the application of redundant and situated object detection systems.
Oriented object detection based on cross-scale information fusion

Due to the enormous size discrepancies between classes and within classes, as well as the high degree of resemblance across classes of oriented objects, traditional remote sensing object detection was made difficult. Although coping with huge scale differences and high inter-class similarity was made possible by multi-scale information fusion, the multiscale weight fusion technique neglected the impact of cross-scale on picture semantic feature extraction, leading to subpar detection performance. The performance of the delayed inference was caused by the rotating region proposal network, which produced high-quality ideas while expanding the network’s capacity. In this study, a cross-scale shift oriented object detection method was suggested. First, by creating a feature pyramid network, the multi-layer feature maps were successfully fused. First, the multi-layer feature maps were effectively fused by reconstructing a feature pyramid network. A cross-scale shift module was simultaneously introduced to FPN to enhance the correlation between multi-scale properties. Finally, to raise the quality of the bounding boxes produced, an oriented region proposal network (ORPN) was used. On remote sensing datasets from DOTA-V1.5, the proposed method fared better than the control group in terms of detection accuracy.
Molecular Hydrogen Kinematics in Cepheus A

We present the radial velocity structure of the molecular hydrogen outflows associated with the star-forming region Cepheus A. This structure is derived from the Doppler shift of the H2 v = 1–0 S(1) emission line obtained by Fabry-Pérot spectroscopy. The east and west regions of emission, called Cep A (E) and Cep A (W), show radial velocities in the range of -20 to 0 km s-1 with respect to the molecular cloud. Cep A (W) shows an increasing velocity with position offset from the core, indicating the existence of a possible accelerating mechanism. Cep A (E) has an almost constant mean radial velocity of -18 km s-1 along the region, although with a large dispersion in velocity, indicating the possibility of a turbulent outflow. A detailed analysis of the Cep A (E) region shows evidence for the presence of a Mach disk on that outflow. In addition, we argue that the presence of a velocity gradient in Cep A (W) is indicative of a C-shock in this region. Following Riera and coworkers, we analyzed the data using wavelet analysis to study the line width and central radial velocity distributions. We found that both outflows have complex spatial and velocity structure characteristic of a turbulent flow.
An Optimization of Impedance-Modulated Passive and Lossless Metasurfaces for Expansion of a Gaussian Beam

We study a reflectionless transformation of an incident Gaussian beam in which the transmitted beam increases its waist. A 2D problem is solved analytically and numerically. The incident beam is diffracted on a synthesized spatially-modulated omega-bianisotropic meta-surface. The electric impedance, magnetic admittance, and magnetoelectric coupling coefficient were obtained using numerical optimization based on analytical formulas assuming evanescent surface waves propagating over the metasurface. In two examples with different output beam widths, the optimization results were verified by full-wave simulations with a transmission efficiency of over 90% for a triple-impedance-layer representation of the structure. The proposed approach could be used efficiently to provide any other output beam profile if it satisfies the local power conservation principle.
Hybrid mean field and real space model for vacancy diffusion-mediated annealing of radiation defects

In a fusion or advanced fission reactor, high energy neutrons induce the formation of extended defect clusters in structural component materials, degrading their properties over time. Such damage can be partially recovered via a thermal annealing treatment. Therefore, for the design and operation of fusion and advanced fission nuclear energy systems it is critical to estimate and predict the annealing timescales for arbitrary configurations of defect clusters. In our earlier paper [I. Rovelli, S. L. Dudarev, and A. P. Sutton, J. Mech. Phys. Solids 103, 121 (2017)] we extended the Green function formulation by Gu, Xiang et al. [Y. Gu, Y. Xiang, S. S. Quek, and D. J. Srolovitz, J. Mech. Phys. Solids 83, 319 (2015)] for the climb of curved dislocations, to include the evaporation and growth of cavities and vacancy clusters, and take into account the effect of free surfaces. In this work, we further develop this model to include the effect of radiation defects that are below the experimental detection limit, via a mean field approach coupled with an explicit treatment of the evolution of discrete defect clusters distributed in real space. We show that randomly distributed small defects screen diffusive interactions between larger discrete clusters. The evolution of the coupled system is modelled self-consistently. We also simulate the evolution of defects in an infinite laterally extended thin film, using the Ewald summation of screened Yukawa-type diffusive propagators.
The Complexity of Verifying Boolean Programs as Differentially Private

We study the complexity of the problem of verifying differential privacy for while-like programs working over boolean values and making probabilistic choices. Programs in this class can be interpreted into finite-state discrete-time Markov Chains (DTMC). We show that the problem of deciding whether a program is differentially private for specific values of the privacy parameters is PSPACE-complete. To show that this problem is in PSPACE, we adapt classical results about computing hitting probabilities for DTMC. To show PSPACE-hardness we use a reduction from the problem of checking whether a program almost surely terminates or not. We also show that the problem of approximating the privacy parameters that a program provides is PSPACE-hard. Moreover, we investigate the complexity of similar problems also for several relaxations of differential privacy: Renyi differential privacy, concentrated differential privacy, and truncated concentrated differential privacy. For these notions, we consider gap-versions of the problem of deciding whether a program is private or not and we show that all of them are PSPACE-complete.