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Topological crystalline insulators (TCIs) host topological phases of matter protected by crystal symmetries. Topological surface states in three-dimensional TCIs have been predicted and observed in IV-VI SnTe-class semiconductors. Despite the prediction of a two-dimensional (2D) TCI characterized by two pairs of edge states inside the bulk gap, materials challenges have thus far prevented its experimental realization. Here we report the growth and characterization of bilayer SnTe on the 2$H$-NbSe$_2$ substrate by molecular beam epitaxy and scanning tunneling microscopy. We experimentally observe two anticorrelated, periodically modulated pairs of conducting edge states along the perimeters of the sample with a large band gap exceeding $0.2$ eV. We identify these states with a 2D TCI through first principles calculations. Finally, we probe the coupling of adjacent topological edge states and demonstrate the resulting energy shift driven by a combination of electrostatic interactions and tunneling coupling. Our work opens the door to investigations of tunable topological states in 2D TCIs, of potential impact for spintronics and nanoelectronics applications at room temperature.

Deep Neural Networks (DNNs) for Autonomous Driving Systems (ADS) are typically trained on real-world images and tested using synthetic simulator images. This approach results in training and test datasets with dissimilar distributions, which can potentially lead to erroneously decreased test accuracy. To address this issue, the literature suggests applying domain-to-domain translators to test datasets to bring them closer to the training datasets. However, translating images used for testing may unpredictably affect the reliability, effectiveness and efficiency of the testing process. Hence, this paper investigates the following questions in the context of ADS: Could translators reduce the effectiveness of images used for ADS-DNN testing and their ability to reveal faults in ADS-DNNs? Can translators result in excessive time overhead during simulation-based testing? To address these questions, we consider three domain-to-domain translators: CycleGAN and neural style transfer, from the literature, and SAEVAE, our proposed translator. Our results for two critical ADS tasks -- lane keeping and object detection -- indicate that translators significantly narrow the gap in ADS test accuracy caused by distribution dissimilarities between training and test data, with SAEVAE outperforming the other two translators. We show that, based on the recent diversity, coverage, and fault-revealing ability metrics for testing deep-learning systems, translators do not compromise the diversity and the coverage of test data, nor do they lead to revealing fewer faults in ADS-DNNs. Further, among the translators considered, SAEVAE incurs a negligible overhead in simulation time and can be efficiently integrated into simulation-based testing. Finally, we show that translators increase the correlation between offline and simulation-based testing results, which can help reduce the cost of simulation-based testing., Comment: Accepted for publication by the International Conference on Automated Software Engineering (ASE 2024)

This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Na\"ive Bayes, K-Mean Clustering, and Random Forest. The models, particularly Na\"ive Bayes and Random Forest, demonstrate high effectiveness, as shown through data visualizations. The research concludes that integrating these analytical methods significantly enhances the accuracy and reliability of crop yield predictions, offering vital contributions to agricultural data science.

This paper presents the results of developing a multi-layer Neural Network (NN) to represent diesel engine emissions and integrating this NN into control design. Firstly, a NN is trained and validated to simultaneously predict oxides of nitrogen (N Ox) and Soot using both transient and steady-state data. Based on the input-output correlation analysis, inputs to NN with the highest influence on the emissions are selected while keeping the NN structure simple. Secondly, a co-simulation framework is implemented to integrate the NN emissions model with a model of a diesel engine airpath system built in GT-Power and used to identify a low-order linear parameter-varying (LPV) model for emissions prediction. Finally, an economic supervisory model predictive controller (MPC) is developed using the LPV emissions model to adjust setpoints to an inner-loop airpath tracking MPC. Simulation results are reported illustrating the capability of the resulting controller to reduce N Ox, meet the target Soot limit, and track the adjusted intake manifold pressure and exhaust gas recirculation (EGR) rate targets.

This paper explores the synergies between integrated power and thermal management (iPTM) and battery charging in an electric vehicle (EV). A multi-objective model predictive control (MPC) framework is developed to optimize the fast charging performance while enforcing the constraints in the power and thermal loops. The approach takes into account the coupling of the battery and cabin thermal management. The case study of a commercial EV demonstrates that the proposed method can effectively meet the requirements of fast charging and thermal management when accurate preview information is available. However, failure to predict the charging event can result in performance degradation with longer charging time. A time-varying weighting strategy is proposed to enhance charging performance in the presence of uncertainty. This strategy leverages the battery state-of-charge (SOC) and adjusts the priority of the multi-objective MPC at different phases during charging. Simulated results using a commercial EV use case show improved robustness in charging time using the proposed strategy., Comment: The 62nd Conference on Decision and Control (CDC), December 13-15, 2023, Singapore

Progress in layered van der Waals materials has resulted in the discovery of ferromagnetic and ferroelectric materials down to the monolayer limit. Recently, evidence of the first purely two-dimensional multiferroic material was reported in monolayer NiI$_2$. However, probing multiferroicity with scattering-based and optical bulk techniques is challenging on 2D materials, and experiments on the atomic scale are needed to fully characterize the multiferroic order at the monolayer limit. Here, we use scanning tunneling microscopy (STM) supported by theoretical calculations based on density functional theory (DFT) to probe and characterize the multiferroic order in monolayer NiI$_2$. We demonstrate that the type-II multiferroic order displayed by NiI$_2$, arising from the combination of a magnetic spin spiral order and a strong spin-orbit coupling, allows probing the multiferroic order in the STM experiments. Moreover, we directly probe the magnetoelectric coupling of NiI$_2$ by external electric field manipulation of the multiferroic domains. Our findings establish a novel point of view to analyse magnetoelectric effects at the microscopic level, paving the way towards engineering new multiferroic orders in van der Waals materials and their heterostructures.

Designer heterostructures, where the desired physics emerges from the controlled interactions between different components, represent one of the most powerful strategies to realize unconventional electronic states. This approach has been particularly fruitful in combining magnetism and superconductivity to create exotic superconducting states. In this work, we use a heterostructure platform combining supramolecular metal complexes (SMCs) with a quasi-2D van der Waals (vdW) superconductor NbSe$_2$. Our scanning tunneling microscopy (STM) measurements demonstrate the emergence of Yu-Shiba-Rusinov (YSR) bands arising from the interaction between the SMC magnetism and the NbSe$_2$ superconductivity. Using X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism (XMCD) measurements, we show the presence of antiferromagnetic coupling between the SMC units. These result in the emergence of an unconventional $3\times3$ reconstruction in the magnetic ground state that is directly reflected in real space modulation of the YSR bands. The combination of flexible molecular building blocks, frustrated magnetic textures, and superconductivity in heterostructures establishes a fertile starting point to fabricating tunable quantum materials, including unconventional superconductors and quantum spin liquids.

Control co-design (CCD) explores physical and control design spaces simultaneously to optimize a system's performance. A commonly used CCD framework aims to achieve open-loop optimal control (OLOC) trajectory while optimizing the physical design variables subject to constraints on control and design parameters. In this study, in contrast with the conventional CCD methods based on OLOC schemes, we present a CCD formulation that explicitly considers a feedback controller. In the formulation, we consider two control laws based on proportional linear and quadratic state feedback, where the control gain is optimized. The simulation results show that the OLOC trajectory could be approximated by a feedback controller. While the total energy generated from the CCD with a feedback controller is slightly lower than that of the CCD with OLOC, it results in a much simpler control structure and more robust performance in the presence of uncertainties and disturbances, making it suitable for real-time control. The study in this paper investigates the performance of optimal hydrokinetic turbine design with a feedback controller in the presence of uncertainties and disturbances to demonstrate the benefits and highlight challenges associated with incorporating the feedback controller explicitly in the CCD stage., Comment: 2023 American Control Conference (ACC), May 31 - June 2, 2023, San Diego, CA, USA

Decentralized cooperative multi-agent deep reinforcement learning (MARL) can be a versatile learning framework, particularly in scenarios where centralized training is either not possible or not practical. One of the critical challenges in decentralized deep MARL is the non-stationarity of the learning environment when multiple agents are learning concurrently. A commonly used and efficient scheme for decentralized MARL is independent learning in which agents concurrently update their policies independently of each other. We first show that independent learning does not always converge, while sequential learning where agents update their policies one after another in a sequence is guaranteed to converge to an agent-by-agent optimal solution. In sequential learning, when one agent updates its policy, all other agent's policies are kept fixed, alleviating the challenge of non-stationarity due to simultaneous updates in other agents' policies. However, it can be slow because only one agent is learning at any time. Therefore it might also not always be practical. In this work, we propose a decentralized cooperative MARL algorithm based on multi-timescale learning. In multi-timescale learning, all agents learn simultaneously, but at different learning rates. In our proposed method, when one agent updates its policy, other agents are allowed to update their policies as well, but at a slower rate. This speeds up sequential learning, while also minimizing non-stationarity caused by other agents updating concurrently. Multi-timescale learning outperforms state-of-the-art decentralized learning methods on a set of challenging multi-agent cooperative tasks in the epymarl(Papoudakis et al., 2020) benchmark. This can be seen as a first step towards more general decentralized cooperative deep MARL methods based on multi-timescale learning.

Two-dimensional magnetic materials provide an ideal platform to explore collective many-body excitations associated with spin fluctuations. In particular, it should be feasible to explore, manipulate and ultimately design magnonic excitations in two-dimensional van der Waals magnets in a controllable way. Here we demonstrate the emergence of moir\'e magnon excitations, stemming from the interplay of spin-excitations in monolayer CrBr$_3$ and the moir\'e pattern stemming from the lattice mismatch with the underlying substrate. The existence of moir\'e magnons is further confirmed via inelastic quasiparticle interference, showing the appearance of a dispersion pattern correlated with the moir\'e length scale. Our results provide a direct visualization in real-space of the dispersion of moir\'e magnons, demonstrating the versatility of moir\'e patterns in creating emerging many-body excitations.

Two-dimensional (2D) ferroelectric materials provide a promising platform for the electrical control of quantum states. In particular, due to their 2D nature, they are suitable for influencing the quantum states of deposited molecules via the proximity effect. Here, we report electrically controllable molecular states in phthalocyanine molecules adsorbed on monolayer ferroelectric material SnTe. In particular, we demonstrate that the strain and ferroelectric order in SnTe creates a transition between two distinct orbital orders in the adsorbed phthalocyanine molecules. By controlling the polarization of the ferroelectric domain using scanning tunneling microscopy (STM), we have successfully demonstrated that orbital order can be manipulated electrically. Our results show how ferroelastic coupling in 2D systems allows control of molecular states, providing a starting point for ferroelectrically switchable molecular orbital ordering and ultimately, electrical control of molecular magnetism.

This paper investigates options to complement a diesel engine airpath feedback controller with a feedforward. The control objective is to track the intake manifold pressure and exhaust gas recirculation (EGR) rate targets by manipulating the EGR valve and variable geometry turbine (VGT) while satisfying state and input constraints. The feedback controller is based on rate-based Model Predictive Control (MPC) that provides integral action for tracking. Two options for the feedforward are considered one based on a look-up table that specifies the feedforward as a function of engine speed and fuel injection rate, and another one based on a (non-rate-based) MPC that generates dynamic feedforward trajectories. The controllers are designed and verified using a high-fidelity engine model in GT-Power and exploit a low-order rate-based linear parameter-varying (LPV) model for prediction which is identified from transient response data generated by the GT-Power model. It is shown that the combination of feedforward and feedback MPC has the potential to improve the performance and robustness of the control design. In particular, the feedback MPC without feedforward can lose stability at low engine speeds, while MPC-based feedforward results in the best transient response. Mechanisms by which feedforward is able to assist in stabilization and improve performance are discussed., Comment: 10th IFAC Symposium on Robust Control Design (ROCOND), August 30-September 2, 2022, Kyoto, Japan

This paper introduces a control co-design (CCD) framework to simultaneously explore the physical parameters and control spaces for a hydro-kinetic turbine (HKT) rotor optimization. The optimization formulation incorporates a coupled dynamic-hydrodynamic model to maximize the rotor power efficiency for various time-variant flow profiles. The open-loop optimal control is applied for maximum power tracking, and the blade element momentum theory (BEMT) is used to model the hydrodynamics. Case studies with different control constraints are investigated for CCD. Sensitivity analyses were conducted with respect to different flow profiles and initial geometries. Comparisons are made between CCD and the sequential process, with physical design followed by a control design process under the same conditions. The results demonstrate the benefits of CCD and reveal that, with control constraints, CCD leads to increased energy production compared to the design obtained from the sequential design process., Comment: Proceedings of the 41st International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2022), June 5-10, 2022, Hamburg, Germany

This paper presents the results of a model predictive controller (MPC) development for diesel engine air-path regulation. The control objective is to track the intake manifold pressure and exhaust gas recirculation (EGR) rate targets by manipulating the EGR valve and variable geometry turbine (VGT) while satisfying state and control constraints. The MPC controller is designed and verified using a high-fidelity engine model in GT-Power. The controller exploits a low-order rate-based linear parameter-varying (LPV) model for prediction which is identified from transient response data generated by the GT-Power model. It is shown that transient engine thermal dynamics influence the airpath dynamics, specifically the intake manifold pressure response, however, MPC demonstrates robustness against inaccuracies in modeling these thermal dynamics. In particular, we show that MPC can be successfully implemented using a rate-based prediction model with two inputs (EGR and VGT positions) identified from data with steady-state wall temperature dynamics, however, closed-loop performance can be improved if a prediction model (i) is identified from data with transient thermal dynamics, and (ii) has the fuel injection rate as extra model input. Further, the MPC calibration process across the engine operating range to achieve improved performance is addressed. As the MPC calibration is shown to be sensitive to the operating conditions, a fast calibration process is proposed., Comment: 2022 American Control Conference (ACC), June 8-10, 2022, Atlanta, GA, USA

Much of recent Deep Reinforcement Learning success is owed to the neural architecture's potential to learn and use effective internal representations of the world. While many current algorithms access a simulator to train with a large amount of data, in realistic settings, including while playing games that may be played against people, collecting experience can be quite costly. In this paper, we introduce a deep reinforcement learning architecture whose purpose is to increase sample efficiency without sacrificing performance. We design this architecture by incorporating advances achieved in recent years in the field of Natural Language Processing and Computer Vision. Specifically, we propose a visually attentive model that uses transformers to learn a self-attention mechanism on the feature maps of the state representation, while simultaneously optimizing return. We demonstrate empirically that this architecture improves sample complexity for several Atari environments, while also achieving better performance in some of the games.

Unconventional superconductors represent one of the fundamental directions in modern quantum materials research. In particular, nodal superconductors are known to appear naturally in strongly correlated systems, including cuprate superconductors and heavy-fermion systems. Van der Waals materials hosting superconducting states are well known, yet nodal monolayer van der Waals superconductors have remained elusive. Here, using low-temperature scanning tunneling microscopy (STM) and spectroscopy (STS) experiments, we show that pristine monolayer 1H-TaS$_2$ realizes a nodal superconducting state. By including non-magnetic disorder, we drive the nodal superconducting state to a conventional gapped s-wave state. Furthermore, we observe the emergence of many-body excitations close to the gap edge, signalling a potential unconventional pairing mechanism. Our results demonstrate the emergence of nodal superconductivity in a van der Waals monolayer, providing a building block for van der Waals heterostructures exploiting unconventional superconducting states.