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Existing automated urban traffic management systems, designed to mitigate traffic congestion and reduce emissions in real time, face significant challenges in effectively adapting to rapidly evolving conditions. Predominantly reactive, these systems typically respond to incidents only after they have transpired. A promising solution lies in implementing real-time traffic simulation models capable of accurately modelling environmental changes. Central to these real-time traffic simulations are origin-destination (OD) demand matrices. However, the inherent variability, stochasticity, and unpredictability of traffic demand complicate the precise calibration of these matrices in the face of disruptions. This paper introduces a hybrid neural network (NN) architecture specifically designed for real-time OD demand calibration to enhance traffic simulations' accuracy and reliability under both recurrent and non-recurrent traffic conditions. The proposed hybrid NN predicts the OD demand to reconcile the discrepancies between actual and simulated traffic patterns. To facilitate real-time updating of the internal parameters of the NN, we develop a metamodel-based backpropagation method by integrating data from real-world traffic systems and simulated environments. This ensures precise predictions of the OD demand even in the case of abnormal or unpredictable traffic patterns. Furthermore, we incorporate offline pre-training of the NN using the metamodel to improve computational efficiency. Validation through a toy network and a Tokyo expressway corridor case study illustrates the model's ability to dynamically adjust to shifting traffic patterns across various disruption scenarios. Our findings underscore the potential of advanced machine learning techniques in developing proactive traffic management strategies, offering substantial improvements over traditional reactive systems.

Magnetic Weyl semimetal (MWS) is a unique topological state with open surface Fermi arc states and other exotic transport phenomena. However, most reported MWSs show multiple pairs of Weyl points and complicated Fermi surfaces, which increases the difficulty of the investigation into the intrinsic chiral transport property. In this wor, we successfully synthesized a soft magnetic Weyl semimetal Eu$_3$In$_2$As$_4$ with a single pair of Weyl points under magnetic fields. The Shubnikov de Haas (SdH) oscillation with a single frequency, as well as a linear hall resistance with the same carrier density, is observed up to 50 Tesla, indicating a single pair of Weyl points around the Fermi level with a massless fermion ($m^* = 0.121 m_0$, $\pi$ Berry phase). Such a single pair of Weyl points is further confirmed by the density functional theory calculations. The magnetic ordering and band topology can be easily tuned by the external magnetic field. The field-induced MWS Eu$_3$In$_2$As$_4$ with a single pair of Weyl points is a good platform to detect chiral transport properties, including possible quantum anomalous Hall effect.

Guaranteeing the safe operations of autonomous vehicles (AVs) is crucial for their widespread adoption and public acceptance. It is thus of a great significance to not only assess the AV against the standard safety tests, but also discover potential corner cases of the AV under test that could lead to unsafe behaviour or scenario. In this paper, we propose a novel framework to systematically explore corner cases that can result in safety concerns in a highway traffic scenario. The framework is based on an adaptive stress testing (AST) approach, an emerging validation method that leverages a Markov decision process to formulate the scenarios and deep reinforcement learning (DRL) to discover the desirable patterns representing corner cases. To this end, we develop a new reward function for DRL to guide the AST in identifying crash scenarios based on the collision probability estimate between the AV under test (i.e., the ego vehicle) and the trajectory of other vehicles on the highway. The proposed framework is further integrated with a new driving model enabling us to create more realistic traffic scenarios capturing both the longitudinal and lateral movements of vehicles on the highway. In our experiment, we calibrate our model using real-world crash statistics involving automated vehicles in California, and then we analyze the characteristics of the AV and the framework. Quantitative and qualitative analyses of our experimental results demonstrate that our framework outperforms other existing AST schemes. The study can help discover crash scenarios of AV that are unknown or absent in human driving, thereby enhancing the safety and trustworthiness of AV technology.

Superconductivity in V-based kagome metals has recently raised great interest as they exhibit the competing ground states associated with the flat bands and topological electronic structures. Here we report the discovery of superconductivity in Ta2V3.1Si0.9 with a superconducting transition temperature Tc of 7.5 K, much higher than those in previously reported kagome metals at ambient pressure. While the V ions form a two-dimensional breathing kagome structure, the length difference between two different V-V bonds is just 0.04, making it very close to the perfect kagome structure. Our results show that Ta2V3.1Si0.9 is a moderate-coupled superconductor with a large upper critical field that is close to the Pauli limit. DFT calculations give a van-Hove-singularity band located at Fermi energy, which may explain the relatively high Tc observed in this material., Comment: 24 pages, 6 figures

Single crystals of Sr3NaIrO6 and Sr3AgIrO6 have been successfully grown using hydroxides flux. Sr3NaIrO6 and Sr3AgIrO6 crystallize in the K4CdCl6-type structure with the space group R-3c (No.167). Sr3NaIrO6 and Sr3AgIrO6 are electrically insulating with estimated activation gaps of 0.68 eV and 0.80 eV, respectively. Sr3NaIrO6 and Sr3AgIrO6 show paramagnetic behavior down to 2 K., Comment: corrected errors for single crystal growth method

Advancing ocean science has a significant impact to the development of the world, from operating a safe navigation for vessels to maintaining a healthy and diverse ocean ecosystem. Various ocean software systems have been extensively adopted for different purposes, for instance, predicting hourly sea level elevation across shorelines, simulating large-scale ocean circulations, as well as integrating into Earth system models for weather forecasts and climate projections. Regardless of their significance, guaranteeing the trustworthiness of ocean software and modelling systems is a long-standing challenge. The testing of ocean software suffers a lot from the so-called oracle problem, which refers to the absence of test oracles mainly due to the nonlinear interactions of multiple physical variables and the high complexity in computation. In the ocean, observed tidal signals are distorted by non-deterministic physical variables, hindering us from knowing the "true" astronomical tidal constituents existing in the timeseries. In this paper, we present how to test tidal analysis and prediction (TAP) software based on metamorphic testing (MT), a simple yet effective testing approach to the oracle problem. In particular, we construct metamorphic relations from the periodic property of astronomical tide, and then use them to successfully detect a real-life defect in an open-source TAP software. We also conduct a series of experiments to further demonstrate the applicability and effectiveness of MT in the testing of TAP software. Our study not only justifies the potential of MT in testing more complex ocean software and modelling systems, but also can be expanded to assess and improve the quality of a broader range of scientific simulation software systems., Comment: 7 pages, 3 tables

Automated driving systems (ADS) are expected to be reliable and robust against a wide range of driving scenarios. Their decisions, first and foremost, must be well understood. Understanding a decision made by ADS is a great challenge, because it is not straightforward to tell whether the decision is correct or not, and how to verify it systematically. In this paper, a Sequential MetAmoRphic Testing Smart framework is proposed based on metamorphic testing, a mainstream software testing approach. In metamorphic testing, metamorphic groups are constructed by selecting multiple inputs according to the so-called metamorphic relations, which are basically the system's necessary properties; the violation of certain relations by some corresponding metamorphic groups implies the detection of erroneous system behaviors. The proposed framework makes use of sequences of metamorphic groups to understand ADS behaviors, and is applicable without the need of ground-truth datasets. To demonstrate its effectiveness, the framework is applied to test three ADS models that steer an autonomous car in different scenarios with another car either leading in front or approaching in the opposite direction. The conducted experiments reveal a large number of undesirable behaviors in these top-ranked deep learning models in the scenarios. These counter-intuitive behaviors are associated with how the core models of ADS respond to different positions, directions and properties of the other car in its proximity. Further analysis of the results helps identify critical factors affecting ADS decisions and thus demonstrates that the framework can be used to provide a comprehensive understanding of ADS before their deployment, Comment: 11 pages, 6 figures, 3 tables

The IARAI Traffic4cast competitions at NeurIPS 2019 and 2020 showed that neural networks can successfully predict future traffic conditions 1 hour into the future on simply aggregated GPS probe data in time and space bins. We thus reinterpreted the challenge of forecasting traffic conditions as a movie completion task. U-Nets proved to be the winning architecture, demonstrating an ability to extract relevant features in this complex real-world geo-spatial process. Building on the previous competitions, Traffic4cast 2021 now focuses on the question of model robustness and generalizability across time and space. Moving from one city to an entirely different city, or moving from pre-COVID times to times after COVID hit the world thus introduces a clear domain shift. We thus, for the first time, release data featuring such domain shifts. The competition now covers ten cities over 2 years, providing data compiled from over 10^12 GPS probe data. Winning solutions captured traffic dynamics sufficiently well to even cope with these complex domain shifts. Surprisingly, this seemed to require only the previous 1h traffic dynamic history and static road graph as input., Comment: Pre-print under review, submitted to Proceedings of Machine Learning Research

The IARAI competition Traffic4cast 2021 aims to predict short-term city-wide high-resolution traffic states given the static and dynamic traffic information obtained previously. The aim is to build a machine learning model for predicting the normalized average traffic speed and flow of the subregions of multiple large-scale cities using historical data points. The model is supposed to be generic, in a way that it can be applied to new cities. By considering spatiotemporal feature learning and modeling efficiency, we explore 3DResNet and Sparse-UNet approaches for the tasks in this competition. The 3DResNet based models use 3D convolution to learn the spatiotemporal features and apply sequential convolutional layers to enhance the temporal relationship of the outputs. The Sparse-UNet model uses sparse convolutions as the backbone for spatiotemporal feature learning. Since the latter algorithm mainly focuses on non-zero data points of the inputs, it dramatically reduces the computation time, while maintaining a competitive accuracy. Our results show that both of the proposed models achieve much better performance than the baseline algorithms. The codes and pretrained models are available at https://github.com/resuly/Traffic4Cast-2021.

Activity generation plays an important role in activity-based demand modelling systems. While machine learning, especially deep learning, has been increasingly used for mode choice and traffic flow prediction, much less research exploiting the advantage of deep learning for activity generation tasks. This paper proposes a novel activity pattern generation framework by incorporating deep learning with travel domain knowledge. We model each activity schedule as one primary activity tour and several secondary activity tours. We then develop different deep neural networks with entity embedding and random forest models to classify activity type, as well as to predict activity times. The proposed framework can capture the activity patterns for individuals in both training and validation sets. Results show high accuracy for the start time and end time of work and school activities. The framework also replicates the start time patterns of stop-before and stop-after primary work activity well. This provides a promising direction to deploy advanced machine learning methods to generate more reliable activity-travel patterns for transport demand systems and their applications., Comment: 21 pages, 12 figures

Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods under normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes or entire intersections are affected. This paper aims at tackling this problem and presents a novel methodology for optimizing the traffic signal timings in signalized urban intersections, under non-recurrent traffic incidents. With the purpose of producing fast and reliable decisions, we combine the fast running Machine Learning (ML) algorithms and the reliable Genetic Algorithms (GA) into a single optimization framework. As a benchmark, we first start with deploying a typical GA algorithm by considering the phase duration as the decision variable and the objective function to minimize the total travel time in the network. We fine tune the GA for crossover, mutation, fitness calculation and obtain the optimal parameters. Secondly, we train various machine learning regression models to predict the total travel time of the studied traffic network, and select the best performing regressor which we further hyper-tune to find the optimal training parameters. Lastly, we propose a new algorithm BGA-ML combining the GA algorithm and the extreme-gradient decision-tree, which is the best performing regressor, together in a single optimization framework. Comparison and results show that the new BGA-ML is much faster than the original GA algorithm and can be successfully applied under non-recurrent incident conditions., Comment: 26 pages, 28 figures. arXiv admin note: text overlap with arXiv:1906.05356

Pb$_2$CoOsO$_6$ is a newly synthesized polar metal in which inversion symmetry is broken by the magnetic frustration in an antiferromagnetic ordering of Co and Os sublattices. The coupled magnetic and structural transition occurs at 45 K at ambient pressure. Here we perform transport measurements and first-principles calculations to study the pressure effects on the magnetic/structural coupled transition of Pb$_2$CoOsO$_6$. Experimentally we monitor the resistivity anomaly at $T_N$ under various pressures up to 11 GPa in a cubic anvil cell apparatus. We find that $T_N$ determined from the resistivity anomaly first increases quickly with pressure in a large slope of $dT_N/dP$ = +6.8(8) K/GPa for $P < 4$ GPa, and then increases with a much reduced slope of 1.8(4) K/GPa above 4 GPa. Our first-principles calculations suggest that the observed discontinuity of $dT_N/dP$ around 4 GPa may be attributed to the vanishing of Os magnetic moment under pressure. Pressure substantially reduces the Os moment and completely suppresses it above a critical value, which relieves the magnetic frustration in the antiferromagnetic ordering of Pb$_2$CoOsO$_6$. The Co and Os polar distortions decrease with the increasing pressure and simultaneously vanish at the critical pressure. Therefore above the critical pressure a new centrosymmetric antiferromagnetic state emerges in Pb$_2$CoOsO$_6$, distinct from the one under ambient pressure, thus showing a discontinuity in $dT_N/dP$., Comment: 17 pages, 6 figures

We report the experimental and theoretical studies of a magnetic topological nodal line semimetal candidate HoSbTe. Single crystals of HoSbTe are grown from Sb flux, crystallizing in a tetragonal layered structure (space group: P4/nmm, no.129), in which the Ho-Te bilayer is separated by the square-net Sb layer. The magnetization and specific heat present distinct anomalies at 4 K related to an antiferromagnetic (AFM) phase transition. Meanwhile, with applying magnetic field perpendicular and parallel to the crystallographic c axis, an obvious magnetic anisotropy is observed. Electrical resistivity undergoes a bad-metal-like state below 200 K and reveals a plateau at about 8 K followed by a drop due to the AFM transition. In addition, with the first-principle calculations of band structure, we find that HoSbTe is a topological nodal line semimetal or a weak topological insulator with or without taking the spin-orbit coupling into account, providing a platform to investigate the interplay between magnetic and topological fermionic properties., Comment: 7 pages, 6 figures, 2tables

We report on the structural, magnetic, and electronic properties of two new double-perovskites synthesized under high pressure; Pb2CaOsO6 and Pb2ZnOsO6. Upon cooling below 80 K, Pb2CaOsO6 simultaneously undergoes a metal--insulator transition and develops antiferromagnetic order. Pb2ZnOsO6, on the other hand, remains a paramagnetic metal down to 2 K. The key difference between the two compounds lies in their crystal structure. The Os atoms in Pb2ZnOsO6 are arranged on an approximately face-centred cubic lattice with strong antiferromagnetic nearest-neighbor exchange couplings. The geometrical frustration inherent to this lattice prevents magnetic order from forming down to the lowest temperatures. In contrast, the unit cell of Pb2CaOsO6 is heavily distorted up to at least 500 K, including antiferroelectric-like displacements of the Pb and O atoms despite metallic conductivity above 80 K. This distortion relieves the magnetic frustration, facilitating magnetic order which in turn drives the metal--insulator transition. Our results suggest that the phase transition in Pb2CaOsO6 is spin-driven, and could be a rare example of a Slater transition., Comment: 14 pages, 9 figures. Accepted as a regular article in Phys. Rev. B

Background: The chronically instrumented pregnant sheep has been used as a model of human fetal development and responses to pathophysiologic stimuli. This is due to the unique amenability of the unanesthetized fetal sheep to the surgical placement and maintenance of catheters and electrodes, allowing repetitive blood sampling, substance injection, recording of bioelectrical activity, application of electric stimulation and in vivo organ imaging. Recently, there has been growing interest in pleiotropic effects of vagus nerve stimulation (VNS) on various organ systems such as innate immunity, metabolism, and appetite control. There is no approach to study this in utero and corresponding physiological understanding is scarce. New Method: Based on our previous presentation of a stable chronically instrumented unanesthetized fetal sheep model, here we describe the surgical instrumentation procedure allowing successful implantation of a cervical uni- or bilateral VNS probe with or without vagotomy. Results: In a cohort of 53 animals, we present the changes in blood gas, metabolic, and inflammatory markers during the postoperative period. We detail the design of a VNS probe which also allows recording from the nerve. We also present an example of vagus electroneurogram (VENG) recorded from the VNS probe and an analytical approach to the data. Comparison with Existing Methods: This method represents the first implementation of VENG/VNS in a large pregnant mammalian organism. Conclusions: This study describes a new surgical procedure allowing to record and manipulate chronically the vagus nerve activity in an animal model of human pregnancy., Comment: Accompanying data repository: https://doi.org/10.6084/m9.figshare.7228307.v2

Room-temperature ferrimagnetism was discovered for the anti-site-disordered perovskite Ca2MnOsO6 with Tc = 305 K. Ca2MnOsO6 crystallizes into an orthorhombic structure with a space group of Pnma, in which Mn and Os share the oxygen-coordinated-octahedral site at an equal ratio without a noticeable ordered arrangement. The material is electrically semiconducting with variable-range-hopping behavior. X-ray absorption spectroscopy confirmed the trivalent state of the Mn and the pentavalent state of the Os. X-ray magnetic circular dichroism spectroscopy reveals that the Mn and Os magnetic moments are aligned antiferromagnetically, thereby classifying the material as a ferrimagnet which is in accordance with band structure calculations. It is intriguing that the magnetic signal of the Os is very weak, and that the observed total magnetic moment is primarily due to the Mn. The Tc = 305 K is the second highest in the material category of so-called disordered ferromagnets such as CaRu1-xMnxO3, SrRu1-xCrxO3, and CaIr1-xMnxO3, and hence, may support the development of spintronic oxides with relaxed requirements concerning the anti-site disorder of the magnetic ions.

Polycrystalline Sr$_3$OsO$_6$, which is an ordered double-perovskite insulator, is synthesized via solid-state reaction under high-temperature and high-pressure conditions of 1200 $^\circ$C and 6 GPa. The synthesis enables us to conduct a comparative study of the bulk form of Sr$_3$OsO$_6$ toward revealing the driving mechanism of 1000 K ferromagnetism, which has recently been discovered for epitaxially grown Sr$_3$OsO$_6$ films. Unlike the film, the bulk is dominated by antiferromagnetism rather than ferromagnetism. Therefore, robust ferromagnetic order appears only when Sr$_3$OsO$_6$ is under the influence of interfaces. A specific heat capacity of 39.6(9) 10$^{-3}$ J mol$^{-1}$ K$^{-2}$ is found at low temperatures ($<$17 K). This value is remarkably high, suggesting the presence of possible fermionic-like excitations at the magnetic ground state. Although the bulk and film forms of Sr$_3$OsO$_6$ share the same lattice basis and electrically insulating state, the magnetism is entirely different between them.