Miracle-dz/UniFAR-FTUs huggingface dataset:
TwoResNet: Two-level resolution neural network for traffic forecasting on freeway networks

Traffic forecasting problems of freeway networks are heavily tackled by deep learning methods because it requires learning highly complex correlations between variables both in time and space. Adopting a graph convolutional network (GCN) becomes a standard to extract spatial correlations; therefore, most works have achieved great prediction accuracy by implanting it into their architecture. However, the conventional GCN has the drawback that receptive field size should be small, i.e., barely refers to traffic features of remote sensors, resulting in inaccurate long-term prediction. We suggest a forecasting model called two-level resolutions deep neural network (TwoResNet) that overcomes the limitation. It consists of two resolution blocks. The low-resolution block predicts traffics on a macroscopic scale, such as regional traffic changes. On the other hand, the high-resolution block predicts traffics on a microscopic scale by using GCN to extract spatial correlations, referring to the regional changes produced by the low-resolution block. This process allows the GCN to refer to the traffic features from remote sensors. As a result, TwoResNet achieves competitive prediction accuracy compared to state-of-the-art methods, especially showing excellent performance for long-term predictions.
Polarised structures in the radio lobes of B2 0258+35

Context. The contribution of active galactic nuclei to the magnetisation of the Universe can be constrained by knowing their duty cycles, jet and magnetic field morphologies, and the physical processes dominating their interaction with the surrounding environment. Aims. The magnetic field morphology and strength of radio lobes of AGN has an influence on the mechanisms for the propagation of cosmic rays into intergalactic space. Using the source B2 0258+35 we want to investigate the interaction of its radio lobes with the surrounding environment and examine the underlying physical effects. Methods. Published H I and radio continuum data at λ21 cm were combined with newly reduced archival Westerbork Radio Synthesis Telescope polarisation data at the same wavelength to investigate the polarised emission in the radio lobes of B2 0258+35. We assumed energy equipartition between the cosmic rays and the magnetic field to calculate their pressure and investigate the physical processes leading to the detected emission. Results. We detected a unique S-shaped diffuse polarised structure. The lobes have a pressure of p = 1.95 ± 0.4 × 10−14 dyn cm−2. The calculated total magnetic field strengths are low (Beq = 1.21 ± 0.12 μG). We observe depolarisation in the northern lobe, which might originate from the H I-disc in the foreground. In addition we see an anti-correlation between the pressure and the fractional polarisation along the S-shaped structure. Therefore we consider magnetic draping and magnetic field compression as possible effects that might have created the observed S-shape. Conclusions. Our results suggest that magnetic draping can be effectively used to explain the observed polarised structures. This is likely due to the combination of a relatively low magnetic field strength, enabling super-Alfvénic motion of the rising lobes (with MA = 2.47 − 3.50), and the coherency of the surrounding magnetic field. Moreover, the draped layer tends to suppress any mixing of the material between the radio lobes and the surrounding environment, but can enhance the mixing and re-acceleration efficiencies inside the lobes, providing an explanation for the average flat spectral index observed in the lobes.
A Robust Deep Unsupervised Image Segmentation Model With Application in Mining Industry

Segmentation of rock and coal images is significant in the mining industry. Unfortunately, there are limited pub-lished work in this field. Deep convolutional neural networks (CNNs) demonstrated considerable promise in supervised and unsupervised image segmentation. In this paper, we introduce an unsupervised CNN-based model for segmentation of the rock and coal images which can have a huge application in the mining industry and related fields. Due to the lack of training labels in unsupervised image segmentation, we mutually optimize the pixel labels, features and model parameters based on an iterative steepest descent approach. Ground truth estimation and model parameters are iterated to assign identical labels to the similar features and assign the same label to the objects belonging to the same object using the texture information. In addition, we collected a new dataset of rock and coal images from open web sources and local mines to assess the effectiveness of the proposed approach. We annotated the images in this dataset, and made publicly available for researchers. Our model is quite competitive with existing state-of-the-art unsupervised image segmentation algorithms, according to the experimental results on two datasets.
Binary Red Supergiants: A New Method for Detecting B-type Companions

With the exception of a few well-known and studied systems, the binary population of red supergiants (RSGs) remains relatively uncharacterized. Famous systems such as VV Cep, 31 Cyg, and ζ Aur contain RSG + B star binaries, and here we explore whether B stars are the main type of companion we expect from an evolutionary point of view. Using the Geneva evolutionary models, we find that this is indeed the case. However, few such systems are known, and we use model spectra to determine how easy such binaries would be to detect observationally. We find that it should be quite difficult to hide a B-type companion given a reasonable signal-to-noise in the optical/blue portion of the spectrum. We next examine spectra of Magellanic Cloud RSGs and newly acquired spectra of Galactic RSGs looking for new systems and refining our conclusions about what types of stars could be hidden in the spectra. Finally, we develop a set of photometric criteria that can help select likely binaries in the future without the overhead of large periodic or spectroscopic surveys.
Cooperative Collision Avoidance for Coalitions of Connected and Autonomous Vehicles

In this paper, we discuss collision avoidance for Connected and Autonomous Vehicles (CAVs) on a highway. CAVs are clustered into coalitions each managed by a leader. Within a coalition, collision avoidance is addressed using a Monte Carlo Tree Search (MCTS)-based approach. We propose algorithms for collision avoidance across coalitions. After an initial assessment of the impact of a potential collision on an affected coalition, leaders cooperate to define action plans that are free of intra-coalition and inter-coalition conflicts. The algorithms were validated through extensive realistic simulations in a multi-agent-based traffic simulator. Experimental results discuss the reliability and scalability of the algorithms for coalitions of different sizes. Moreover, we present an analysis to select the optimal coalition size and the optimal number of coalitions given a total number of CAVs.
Empirical Investigation of Properties of Lane-free Automated Vehicle Traffic

A new traffic paradigm has been recently proposed for automated vehicles, characterized by two combined principles: lane-free roads and vehicle nudging; the latter implying that vehicles may be influenced (nudged) by other vehicles around and even behind them. This paper presents and employs an ad-hoc vehicle movement strategy for Connected and Automated Vehicles (CAVs) driving in a lane-free environment, to investigate empirically (via simulation) some properties of the new traffic paradigm, such as the emerging fundamental diagram, traffic flow capacity, the impact of vehicle nudging, the impact of road width and more. The vehicle movement strategy is inspired by an adaptive cruise control scheme, albeit with significant modifications to account for lane-free driving, nudging, and respect of road boundaries. The movement strategy relies on real-time information to decide on longitudinal and lateral accelerations. Specifically, an “aura” is defined around each vehicle that depends on the vehicle dimensions, position, and relative speeds with surrounding vehicles. Virtual forces from surrounding vehicles are activated if these vehicles intersect with the corresponding aura. The proposed vehicle movement strategy is employed for traffic simulation on a ring-road, using a recently developed microscopic simulator called TrafficFluid-Sim, which builds on the SUMO simulation infrastructure. The achieved results demonstrate: safe (crash-free) driving at all densities; high traffic flows and capacity; increase of traffic flows and capacity via vehicle nudging; as well as via small road widening.
Chimera patterns in conservative systems and ultracold atoms with mediated nonlocal hopping

Chimera patterns, characterized by coexisting regions of phase coherence and incoherence, have so far been studied in non-conservative systems with dissipation. Here, we show that the formation of chimera patterns can also be observed in conservative Hamiltonian systems with nonlocal hopping in which both energy and particle number are conserved. Effective nonlocality can be realized in a physical system with only local coupling if different time scales exist, which can be illustrated by a minimal conservative model with an additional mediating channel. Finally, we show that the patterns should be observable in ultracold atomic systems. Nonlocal spatial hopping over up to tens of lattice sites with independently tunable hopping strength and on-site nonlinearity can be implemented in a two-component Bose-Einstein condensate with a spin-dependent optical lattice, where the untrapped component serves as the matter-wave mediating field. The present work highlights the connections between chimera patterns, nonlinear dynamics, condensed matter, and ultracold atoms.
Shear thinning in dilute and semidilute solutions of polystyrene and DNA

The viscosity of dilute and semidilute unentangled deoxyribonucleic acid (DNA) solutions, in steady simple shear flow, has been measured across a range of temperatures and concentrations. For polystyrene solutions, measurements of viscosity have been carried out in the semidilute unentangled regime, while results of prior experimental measurements in the dilute regime have been used for the purpose of data analysis, and for comparison with the behavior of DNA solutions. Interpretation of the shear rate dependence of viscosity in terms of suitably defined nondimensional variables is shown to lead to master plots, independent of temperature and concentration, in each of the two concentration regimes. In the case of semidilute unentangled solutions, defining the Weissenberg number in terms of a concentration dependent large scale relaxation time is found not to lead to data collapse across different concentrations. On the other hand, the use of an alternative relaxation time, with the concentration dependence of a single correlation blob, suggests the existence of universal shear thinning behavior at large shear rates.The viscosity of dilute and semidilute unentangled deoxyribonucleic acid (DNA) solutions, in steady simple shear flow, has been measured across a range of temperatures and concentrations. For polystyrene solutions, measurements of viscosity have been carried out in the semidilute unentangled regime, while results of prior experimental measurements in the dilute regime have been used for the purpose of data analysis, and for comparison with the behavior of DNA solutions. Interpretation of the shear rate dependence of viscosity in terms of suitably defined nondimensional variables is shown to lead to master plots, independent of temperature and concentration, in each of the two concentration regimes. In the case of semidilute unentangled solutions, defining the Weissenberg number in terms of a concentration dependent large scale relaxation time is found not to lead to data collapse across different concentrations. On the other hand, the use of an alternative relaxation time, with the concentration dependenc.
Kalman Filter Driven Estimation of Community Structure in Time Varying Graphs

Community detection is an NP-hard graph problem that has been the subject of decades of research. Moreover, efficient methods are needed for time-varying graphs. In this paper we propose and evaluate a method of approximating the latent block structure within a time-varying graph using a Kalman filter. The method described breaks a stream of graph updates into samples of sufficient size, each one forming a graph $G_{t}$, and has the desirable feature that it accurately updates its representation of the latent block structure using a relatively small amount of information: the prior $t-1$ predicted block structure and the current datastream sample $G_{t}$. This paper details the underlying system of linear equations, used here to represent community detection, that achieves 97 % accuracy estimating the latent block representation as the community structure changes. This is demonstrated for synthetic graphs generated by a hybrid mixed-model stochastic block model from the DARPAIMIT Graph Challenge with time-varying block structure.
Zone Based Selective Neighbours to Mitigate Flooding & Reliable Routing for WSN

Growing demand in wireless Mobile Adhoc Networks due to their immense potential and effectiveness in varied applications had motivated researchers to control and optimize various parameters for efficient routing in mobile environment. The rapidly changing environment of the network due to mobility of nodes results in unpredictable topology and thus introduces primary challenge to design better and an efficient routing protocol. The network in such case lacks fixed infrastructure and common head centric design. MANET routing schemes use flooding to propagate packets in the network resulting retransmissions, collisions, and congestions which significantly degrade the performance of the network. Using GPS, knowing the geographical position of the sensor nodes, the protocol performance can be improved while reducing the number of retransmissions. The proposed work is a future extension of efficient flooding based on selective neighbours where the surrounding region limiting the transmission range is divided into eight quadrants or zones with the source at the centre called zone based selective neighbours (ZBSN). Flooding based on selective four neighbours suffers from few selection of hoping or forwarding nodes when the node density is low and does not meet the selection criteria around the source or forwarder node and may miss the chance of approaching the destination or requires large number of hops. This neighbour selection scheme uses the modified approach of the Adhoc on Demand Distance Vector (AODV) protocol to reduce the flooding of Route request (RREQ) packets, control the network overall traffic, improves link stability and the residual energy reducing the overheads by 24%.
Existential fulfillment and burnout among principals and teachers

The aim of the current study was to determine the prevalence of burnout in primary education teachers and primary school principals and to explore the relationship of existential fulfillment to self‐reported burnout scores. Random samples of 215 teachers and 514 principals participated in a cross‐sectional study using an anonymous, mailed survey. Two questionnaires were used in both studies. The Dutch version of the Maslach Burnout Inventory for teachers was used to assess the teachers’ and principals’ burnout level. Second, existential fulfillment was measured by the Längle, Orgler and Kundi Existence Scale. The results of the present study indicated that teachers’ and principals’ existential fulfillment is related to their burnout level. Lack of existential fulfillment was an important burnout determinant. Unfortunately, this concept has been neglected to date. Existential fulfillment may be a significant and usable point of departure for devising psychological interventions aimed at teachers and principals.
3D Convolutional Neural Network for Speech Emotion Recognition With Its Realization on Intel CPU and NVIDIA GPU

Due to the high level of precision and remarkable capabilities to solve the intricate problems in industry and academia, convolutional neural networks (CNNs) are presented. Speech emotion recognition is an interesting application for CNNs in the field of audio processing. In this paper, a speech emotion recognition system based on a 3D CNN is suggested to analyze and classify the emotions. In the proposed method, the three-dimensional reconstructed phase spaces of the speech signals were calculated. Then, emotion-related patterns formed in these spaces were converted into 3D tensors. Accordingly, a 3D CNN for speech emotion recognition applied to two datasets, EMO-DB and eNTERFACE05, using a speaker-independent technique achieved 90.40% and 82.20% accuracy, respectively. By employing gender recognition, the accuracy rates on EMO-DB increased to 94.42% and on eNTERFACE05 rose to 88.47%. Realization of the introduced 3D CNN on both Intel CPU and NVIDIA GPU is also explored. The results of the implemented 3D CNN without and with regard to gender recognition show that GPU-based running is faster for the EMO-DB and eNTERFACE05 datasets than CPU-based executions (using Python).
DAG Scheduling Considering Parallel Execution for High-Load Processing on Clustered Many-core Processors

In recent years, high computational power has been required for computer platforms to support complex systems such as self-driving systems. Clustered many-core processors and directed acyclic graphs (DAGs), which can represent dependencies and parallelism of task processing, have attracted much attention as solutions to this problem. Previous studies on scheduling DAGs on multi-core processors have attempted to reduce the makespan (i.e., time it takes for a task to complete) by increasing the number of processes that can be executed in parallel. However, in self-driving systems, such as those utilizing clustered many-core processors, it is impossible to sufficiently increase the utilization of processor cores due to high-load processing. In this paper, a scheduling method is proposed to improve the utilization of processor cores by parallel executing high-load processes in parallel across multiple cores. The proposed method can reduce the makespan of DAGs performing high-load processing on clustered many-core processors.
Infra2Go: A Mobile Development Platform for Connected, Cooperative and Autonomous Driving

Connected, cooperative autonomous driving and mobility promises increased comfort and safety for public transportation and logistics in urban and suburban regions. Stationary roadside infrastructure equipped with intelligent perception sensors and communication units has the potential to increase the field of view and mitigate occlusions in perception, but is limited to fixed places in the development process. We present a mobile development platform for connected roadside infrastructure that can be used in an extensible and flexible fashion for various purposes: as a traffic monitoring device, as a reference sensor platform to benchmark in-vehicle perception, as a test platform for collaborative perception algorithms and to operate as connected edge computation infrastructure to support vehicles in decision making in real world settings. In this work, we present an overview over the Infra2Go concept and give details about the hardware and software architecture. We evaluate our platform by deploying it in an outdoor real world scenario for cooperative perception between the platform and a vehicle equipped with an on-board V2X communication unit. The platform is used to track vulnerable road users (VRU) and transmits the information via V2X communication for cooperative perception (Cooperative Perception Messages) to an autonomous vehicle in order to extend it's sensor field of view.
Citywide reconstruction of traffic flow using the vehicle-mounted moving camera in the CARLA driving simulator

Estimating traffic flow is essential in planning road development, routing, navigation, autonomous driving, and other applications in road infrastructure management. Recently, cameras and portable devices mounted on moving vehicles have been proposed to estimate citywide traffic flow. Nevertheless, the efficacy of such algorithms has not been proven on a wide scale under varying environmental conditions. This paper modifies existing algorithms to estimate traffic flow from vehicle-mounted cameras in the driving simulator CARLA and conducts a comprehensive analysis of seven different traffic environments to assess the effectiveness of recently proposed techniques. The proposed algorithm achieves the highest accuracy, up to 84.2% evaluated using ground-truth data. With the proliferation of moving cameras (such as dashcams and smartphones) and inexpensive edge devices, the potential for the use of moving camera sensors in conjunction with edge devices for traffic flow estimation is promising. Our study provides more insight into how these techniques perform in different traffic environments.
Quasi-two-dimensional Fermi surface topography of the delafossite PdRhO2

We report on a combined study of the de Haas-van Alphen effect and angle-resolved photoemission spectroscopy on single crystals of the metallic delafossite ${\mathrm{PdRhO}}_{2}$ rounded off by ab initio band structure calculations. A high-sensitivity torque magnetometry setup with superconducting quantum interference device readout and synchrotron-based photoemission with a light spot size of $50\phantom{\rule{0.222222em}{0ex}}\ensuremath{\mu}\mathrm{m}$ enabled high-resolution data to be obtained from samples as small as $150\ifmmode\times\else\texttimes\fi{}100\ifmmode\times\else\texttimes\fi{}20\phantom{\rule{0.222222em}{0ex}}{(\ensuremath{\mu}\mathrm{m})}^{3}$. The Fermi surface shape is nearly cylindrical with a rounded hexagonal cross section enclosing a Luttinger volume of 1.00(1) electrons per formula unit.
Maritime Vessel Detection and Tracking under UAV Vision

Unmanned Aerial Vehicles (UAVs) are playing an important role in the development of smart maritime. However, images crowned with small-sized and highly dense cause the accuracy to decrease for ship detection under UAV vision. Aiming at the problem, this paper proposes an improved YOLOv5 to detect ships accurately under UAV vision and combines with deepsort to realize ship tracking. Firstly, we add a detection layer to make full use of shallow features with rich detail information in the part of feature fusion. Then, the coordinate attention is introduced in YOLOv5 to focus on more important feature information. The test results show that the accuracy, recall and average precision of the proposed SA-YOLOv5 are improved by 3.4%, 0.3% and 1.0% compared with YOLOv5. Finally, the deepsort is used as the tracker to realize the real-time ship tracking under UAV vision.