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Noise-enhanced applications in open quantum walk (QW) have recently seen a surge due to their ability to improve performance. However, verifying the success of open QW is challenging, as mixed-state tomography is a resource-intensive process, and implementing all required measurements is almost impossible due to various physical constraints. To address this challenge, we present a neural-network-based method for reconstructing mixed states with a high fidelity (~97.5%) while costing only 50% of the number of measurements typically required for open discrete-time QW in one dimension. Our method uses a neural density operator that models the system and environment, followed by a generalized natural gradient descent procedure that significantly speeds up the training process. Moreover, we introduce a compact interferometric measurement device, improving the scalability of our photonic QW setup that enables experimental learning of mixed states. Our results demonstrate that highly expressive neural networks can serve as powerful alternatives to traditional state tomography.

Open domain question answering systems frequently rely on information retrieved from large collections of text (such as the Web) to answer questions. However, such collections of text often contain conflicting information, and indiscriminately depending on this information may result in untruthful and inaccurate answers. To understand the gravity of this problem, we collect a human-annotated dataset, Question Answering with Conflicting Contexts (QACC), and find that as much as 25% of unambiguous, open domain questions can lead to conflicting contexts when retrieved using Google Search. We evaluate and benchmark three powerful Large Language Models (LLMs) with our dataset QACC and demonstrate their limitations in effectively addressing questions with conflicting information. To explore how humans reason through conflicting contexts, we request our annotators to provide explanations for their selections of correct answers. We demonstrate that by finetuning LLMs to explain their answers, we can introduce richer information into their training that guide them through the process of reasoning with conflicting contexts.

Dissipation is usually considered a negative factor for observing quantum effects and for harnessing them for quantum technologies. Here we propose a scheme for speeding up the generation of quantum entanglement between two coupled qubits by introducing a strong dissipation channel to one of these qubits. The maximal entanglement is conditionally established by evenly distributing a single excitation between these two qubits. When the excitation is initially held by the dissipative qubit, the dissipation accelerates the excitation re-distribution process for the quantum state trajectory without quantum jumps. Our results show that the time needed to conditionally attain the maximal entanglement is monotonously decreased as the dissipative rate is increased. We further show that this scheme can be generalized to accelerate the production of the W state for the three-qubit system, where one NH qubit is symmetrically coupled to two Hermitian qubits., Comment: 2 figures

The Schwinger model serves as a benchmark for testing non-perturbative algorithms in quantum chromodynamics (QCD), emphasizing its similarities to QCD in strong coupling regimes, primarily due to the phenomena such as confinement and charge screening. However, classical algorithms encounter challenges when simulating the Schwinger model, such as the "sign problem" and the difficulty in handling large-scale systems. These limitations motivate the exploration of alternative simulation approaches, including quantum computing techniques, to overcome the obstacles. While existing variational quantum algorithms (VQAs) methods for simulating the Schwinger model primarily rely on mathematical gradient-based optimization, which sometimes fail to provide intuitive and physically-guided optimization pathways. In contrast, the Variational Quantum Imaginary Time Evolution (VQITE) method offers a physically-inspired optimization approach. Therefore, we introduce that VQITE holds promise as a potent tool for simulating the Schwinger model. However, the standard VQITE method is not sufficiently stable, as it encounters difficulties with the non-invertible matrix problem. To address this issue, we have proposed a regularized version of the VQITE, which we have named the Regularized-VQITE (rVQITE) method, as it incorporates a truncation-based approach. Through numerical simulations, we demonstrate that our proposed rVQITE approach achieves better performance and exhibits faster convergence compared to other related techniques. We employ the rVQITE method to simulate the phase diagrams of various physical observables in the Schwinger model, and the resulting phase boundaries are in agreement with those obtained from an exact computational approach.

Autonomous vehicle navigation is a key challenge in artificial intelligence, requiring robust and accurate decision-making processes. This research introduces a new end-to-end method that exploits multimodal information from a single monocular camera to improve the steering predictions for self-driving cars. Unlike conventional models that require several sensors which can be costly and complex or rely exclusively on RGB images that may not be robust enough under different conditions, our model significantly improves vehicle steering prediction performance from a single visual sensor. By focusing on the fusion of RGB imagery with depth completion information or optical flow data, we propose a comprehensive framework that integrates these modalities through both early and hybrid fusion techniques. We use three distinct neural network models to implement our approach: Convolution Neural Network - Neutral Circuit Policy (CNN-NCP) , Variational Auto Encoder - Long Short-Term Memory (VAE-LSTM) , and Neural Circuit Policy architecture VAE-NCP. By incorporating optical flow into the decision-making process, our method significantly advances autonomous navigation. Empirical results from our comparative study using Boston driving data show that our model, which integrates image and motion information, is robust and reliable. It outperforms state-of-the-art approaches that do not use optical flow, reducing the steering estimation error by 31%. This demonstrates the potential of optical flow data, combined with advanced neural network architectures (a CNN-based structure for fusing data and a Recurrence-based network for inferring a command from latent space), to enhance the performance of autonomous vehicles steering estimation.

Autonomous driving holds great potential to transform road safety and traffic efficiency by minimizing human error and reducing congestion. A key challenge in realizing this potential is the accurate estimation of steering angles, which is essential for effective vehicle navigation and control. Recent breakthroughs in deep learning have made it possible to estimate steering angles directly from raw camera inputs. However, the limited available navigation data can hinder optimal feature learning, impacting the system's performance in complex driving scenarios. In this paper, we propose a shared encoder trained on multiple computer vision tasks critical for urban navigation, such as depth, pose, and 3D scene flow estimation, as well as semantic, instance, panoptic, and motion segmentation. By incorporating diverse visual information used by humans during navigation, this unified encoder might enhance steering angle estimation. To achieve effective multi-task learning within a single encoder, we introduce a multi-scale feature network for pose estimation to improve depth learning. Additionally, we employ knowledge distillation from a multi-backbone model pretrained on these navigation tasks to stabilize training and boost performance. Our findings demonstrate that a shared backbone trained on diverse visual tasks is capable of providing overall perception capabilities. While our performance in steering angle estimation is comparable to existing methods, the integration of human-like perception through multi-task learning holds significant potential for advancing autonomous driving systems. More details and the pretrained model are available at https://hi-computervision.github.io/uni-encoder/.

Recently, movable antennas (MAs) have garnered immense attention due to their capability to favorably alter channel conditions through agile movement. In this letter, we delve into a spectrum sharing system enabled by unmanned aerial vehicle (UAV) mounted MAs, thereby introducing a new degree of freedom vertically alongside the horizontal local mobility for MAs. Our objective is to maximize the minimum beamforming gain for secondary users (SUs) while ensuring that interference to the primary users (PUs) remains below a predefined threshold, which necessitates a joint optimization involving the UAV's height, the antenna weight vector (AWV), and the antenna position vector (APV). However, the formulated optimization problem is non-convex and challenging to solve optimally. To tackle this issue, we propose an alternating optimization algorithm that optimizes the UAV's height, APV and AWV in an iterative manner, thus yielding a near-optimal solution. Numerical results demonstrate the superiority of the proposed scheme as well as its ability to deliver full beamforming gain to SUs with reduced computational complexity.

This paper addresses the problem of guiding a quadrotor through a predefined sequence of waypoints in cluttered environments, aiming to minimize the flight time while avoiding collisions. Previous approaches either suffer from prolonged computational time caused by solving complex non-convex optimization problems or are limited by the inherent smoothness of polynomial trajectory representations, thereby restricting the flexibility of movement. In this work, we present a safe reinforcement learning approach for autonomous drone racing with time-optimal flight in cluttered environments. The reinforcement learning policy, trained using safety and terminal rewards specifically designed to enforce near time-optimal and collision-free flight, outperforms current state-of-the-art algorithms. Additionally, experimental results demonstrate the efficacy of the proposed approach in achieving both minimum flight time and obstacle avoidance objectives in complex environments, with a commendable $66.7\%$ success rate in unseen, challenging settings., Comment: 7 pages, 3 figures

The North-South asymmetry in the number density and bulk velocity of stars in the solar neighborhood provides valuable insights into the formation and evolution of the Milky Way disk. Our objective is to investigate the wave-like disk oscillations of mono-age stellar populations in the Solar neighbourhood using data from Gaia Data Release 3. We have selected a comprehensive sample of main sequence turn off stars. The ages of these stars can be accurately determined using isochrone fitting methods. Our findings indicate that the north-south density and mean vertical velocity asymmetries remain consistent across all age groups.The uniformity of perturbations across all subsamples suggests that all populations are responding to the same external influence, which likely affects them irrespective of their age. Moreover, the fact that these perturbations appear consistently implies they could be either ongoing or recent. Regarding vertical velocity dispersions, we observe that older stars exhibit larger dispersions., Comment: 6 pages, 4 figures, accepted for publication in MNRAS Letters

Safe learning is central to AI-enabled robots where a single failure may lead to catastrophic results. Barrier-based method is one of the dominant approaches for safe robot learning. However, this method is not scalable, hard to train, and tends to generate unstable signals under noisy inputs that are challenging to be deployed for robots. To address these challenges, we propose a novel Attention BarrierNet (ABNet) that is scalable to build larger foundational safe models in an incremental manner. Each head of BarrierNet in the ABNet could learn safe robot control policies from different features and focus on specific part of the observation. In this way, we do not need to one-shotly construct a large model for complex tasks, which significantly facilitates the training of the model while ensuring its stable output. Most importantly, we can still formally prove the safety guarantees of the ABNet. We demonstrate the strength of ABNet in 2D robot obstacle avoidance, safe robot manipulation, and vision-based end-to-end autonomous driving, with results showing much better robustness and guarantees over existing models., Comment: 18 pages

The recent observation of the fractional quantum anomalous Hall effect in moir\'e fractional Chern insulators (FCI) provides opportunities for investigating zero magnetic field anyons. So far, both experimental and theoretical results suggest that filling > 1/3 FCI states in the first Chern band share features with those of the lowest Landau level (LL). To create the possibility of realizing non-Abelian anyons, one route is to engineer higher flat Chern bands that mimic higher LLs. Here, we investigate the interaction, topology, and ferromagnetism of the second moir\'e miniband in twisted MoTe2 bilayer (tMoTe2). Around filling factor v = -3, i.e., half-filling of the second miniband, we uncover spontaneous ferromagnetism and an incipient Chern insulator state. By measuring the anomalous Hall effect as a function of twist angle, we find that the Chern numbers (C) of the top two moir\'e flat bands have opposite sign (C = -+1) at twist angles above 3.1{\deg} but the same sign (C = -1) around 2.6{\deg}. This observation is consistent with the recently predicted twist-angle dependent band topology, resulting from the competition between moir\'e ferroelectricity and piezoelectricity. As we increase the magnetic field, only the small twist-angle device (2.6{\deg}) experiences a topological phase transition with an emergent C = -2 state. This is attributed to a Zeeman field-induced band crossing between opposite valleys, with the determined C = -1 for the top two bands. Our results lay a firm foundation for understanding the higher flat Chern bands, which is essential for the prediction or discovery of non-Abelian FCIs., Comment: 24 pages, 4 figures

In moir\'e materials with flat electronic bands and suitable quantum geometry, strong correlations can give rise to novel topological states of matter. The nontrivial band topology of twisted molybdenum ditelluride (tMoTe$_2$) -- responsible for its fractional quantum anomalous Hall (FQAH) states -- is predicted to arise from a layer-pseudospin skyrmion lattice. Tracing the layer polarization of wavefunctions within the moir\'e unit cell can thus offer crucial insights into the band topology. Here, we use scanning tunneling microscopy and spectroscopy (STM/S) to probe the layer-pseudospin skyrmion textures of tMoTe$_2$. We do this by simultaneously visualizing the moir\'e lattice structure and the spatial localization of its electronic states. We find that the wavefunctions associated with the topological flat bands exhibit a spatially-dependent layer polarization within the moir\'e unit cell. This is in excellent agreement with our theoretical modeling, thereby revealing a direct microscopic connection between the structural properties of tMoTe$_2$ and its band topology. Our work enables new pathways for engineering FQAH states with strain, as well as future STM studies of the intertwined correlated and topological states arising in gate-tunable devices., Comment: 7 pages, 4 figures, Extended Data, 9 figures, Supplementary Information, 8 pages, 5 figures

We present a quantum theory of phonon magnetic moment in doped Dirac semimetals. Our theory is based on an emergent gauge field approach to the electron-phonon coupling, applicable to both gapless and gapped systems. We find that the magnetic moment is directly proportional to the electrical Hall conductivity through the phonon Hall viscosity. Our theory is combined with the first-principles calculations, allowing us to quantitatively implement it to realistic materials. Magnetic moments are found to be on the order of Bohr magneton for certain phonon modes in graphene and $\text{Cd}_3 \text{As}_2$. Our results provide practical guidance for the dynamical generation of large magnetization in the topological quantum materials., Comment: 7 pages, 3 figures, supplemental materials included

In this paper, we use the concepts of quantum entanglement and coherence to analyze the Unruh and anti-Unruh effects based on the model of Unruh-DeWitt detector. For the first time, we find that (i) the Unruh effect reduces quantum entanglement but enhances quantum coherence; (ii) the anti-Unruh effect enhances quantum entanglement but reduces quantum coherence. This surprising result refutes the notion that the Unruh effect can only destroy quantum entanglement and coherence simultaneously, and that the anti-Unruh can only protect quantum resources. Consequently, it opens up a new source for discovering experimental evidence supporting the existence of the Unruh and anti-Unruh effects., Comment: 12 pages, 2 figures

Within the framework of nonrelativistic QCD (NRQCD), we calculate the fragmentation function for a charm quark into an $S$-wave fully-charmed tetraquark, denoted as $T_{4c}$. The charm-to-$T_{4c}$ fragmentation function is expressed as a sum of products of the perturbatively calculable short-distance coefficients and the nonperturbative long-distance matrix elements (LDMEs). The short-distance coefficients are ascertained through the perturbative matching procedure at lowest order in $\alpha_{s}$ expansion. The LDMEs are approximated using the $T_{4c}$ four-body wave functions at the origin, which have been evaluated by various phenomenological potential models in literature. Incorporating the celebrated QCD factorization and the charm-to-$T_{4c}$ fragmentation function, we predict the $T_{4c}$ production rate at high transverse momentum $p_T$ regime in colliders. %After implementing appropriate kinematic constraints, Both the differential distribution over $p_T$ and the integrated cross sections are predicted at the \texttt{LHC}. The cross sections for $T_{4c}$ states production can reach several femtobarns to several hundreds femtobarns, suggesting a substantial potential for $T_{4c}$ event production at the \texttt{LHC}. Additionally, we estimate for the photoproduction of $T_{4c}$ in electron-proton ($ep$) collisions. It is observed that the cross sections for these processes are moderate at the \texttt{HERA} and \texttt{EIC}, and relatively small at the \texttt{EicC}. Given the luminosities of these colliders, the prospect of detecting these fully-charmed tetraquarks at $ep$ colliders is somewhat challenging., Comment: 22 pages, 2 figures, 4 tables; significant enhancement aligns with the published edition

Recent experimental discovery of fractional Chern insulators at zero magnetic field in moir\'e superlattices has sparked intense interests in bringing Landau level physics to flat Chern bands. In twisted MoTe$_2$ bilayers (tMoTe$_2$), recent theoretical and experimental studies have found three consecutive flat Chern bands at twist angle $\sim 2^\circ$. In this work, we investigate whether higher Landau level physics can be found in these consecutive Chern bands. At twist angles $2.00^\circ$ and $1.89^\circ$, we identify four consecutive $C = 1$ bands for the $K$ valley in tMoTe$_2$. By constructing Wannier functions directly from density functional theory (DFT) calculations, a six-orbital model is developed to describe the consecutive Chern bands, with the orbitals forming a honeycomb lattice. Exact diagonalization on top of Hartree-Fock calculations are carried out with the Wannier functions. Especially, when the second moir\'e miniband is half-filled, signatures of non-Abelian states are found. Our Wannier-based approach in modelling moir\'e superlattices is faithful to DFT wave functions and can serve as benchmarks for continuum models. The possibility of realizing non-Abelian anyons at zero magnetic field also opens up a new pathway for fault-tolerant quantum information processing.

Recent work has shown that stabilizing an affine control system while optimizing a quadratic cost subject to state and control constraints can be mapped to a sequence of Quadratic Programs (QPs) using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). One of the main challenges in this method is that the QPs could easily become infeasible under safety and spatio-temporal constraints with tight control bounds. In our own recent work, we defined Auxiliary-Variable Adaptive CBFs (AVCBFs) to improve the feasibility of the CBF-based QP, while avoiding extensive parameter tuning. In this paper, we consider spatio-temporal constraints as finite-time reachability requirements. In order to satisfy these requirements, we generalize AVCBFs to Auxiliary-Variable Adaptive Control Lyapunov Barrier Functions (AVCLBFs) that work for systems and constraints with arbitrary relative degrees. We show that our method has fewer conflicts with safety and input constraints, and outperforms the state of the art in term of adaptivity and feasibility in solving the QP. We illustrate our approach on an optimal control problem for a unicycle., Comment: 8 pages, 4 figures. arXiv admin note: text overlap with arXiv:2310.00238

Complex quantum information tasks in a gravitational background require multipartite entanglement for effective processing. Therefore, it is necessary to investigate the properties of multipartite entanglement in a relativistic setting. In this paper, we study genuine N-partite entanglement of massless Dirac fields in the Schwarzschild-de Sitter (SdS) spacetime, characterized by the presence of a black hole event horizon (BEH) and a cosmological event horizon (CEH). We obtain the general analytical expression of genuine N-partite entanglement shared by n observers near BEH and m (n+m = N) observers near CEH. It is shown that genuine N-partite entanglement monotonically decreases with the decrease of the mass of the black hole, suggesting that the Hawking effect of the black hole destroys quantum entanglement. It is interesting to note that genuine N-partite entanglement is a non-monotonic function of the cosmological constant, meaning that the Hawking effect of the expanding universe can enhance quantum entanglement. This result contrasts with multipartite entanglement in single-event horizon spacetime, offering a new perspective on the Hawking effect in multi-event horizon spacetime., Comment: 14 pages, 3 figures

We study the magnetized strangelets in the baryon density-dependent quark mass model, including the effects of both confinement and lead-order perturbation interactions. The properties of magnetized strangelets are investigated under the the field strength 2*10^17 G, where the anisotropy caused by the strong magnetic field is insignificant can be treated approximately as an isotropic system. The consideration of anomalous magnetic moments in the energy spectrum naturally solves the difficulty of infrared divergence encountered in integrating the density of states. The Coulomb interaction is accounted for a self-consistent treatment. The energy per baryon, mechanically stable radius, strangeness and electric charge of magnetized strangelets are presented, where their dependence on the field strength and parameter of confinement and perturbation are investigated.

In recent years, drones have found increased applications in a wide array of real-world tasks. Model predictive control (MPC) has emerged as a practical method for drone flight control, owing to its robustness against modeling errors/uncertainties and external disturbances. However, MPC's sensitivity to manually tuned parameters can lead to rapid performance degradation when faced with unknown environmental dynamics. This paper addresses the challenge of controlling a drone as it traverses a swinging gate characterized by unknown dynamics. This paper introduces a parameterized MPC approach named hyMPC that leverages high-level decision variables to adapt to uncertain environmental conditions. To derive these decision variables, a novel policy search framework aimed at training a high-level Gaussian policy is presented. Subsequently, we harness the power of neural network policies, trained on data gathered through the repeated execution of the Gaussian policy, to provide real-time decision variables. The effectiveness of hyMPC is validated through numerical simulations, achieving a 100\% success rate in 20 drone flight tests traversing a swinging gate, demonstrating its capability to achieve safe and precise flight with limited prior knowledge of environmental dynamics., Comment: To be published in Unmanned Systems

Searching for exotic spin-dependent interactions that beyond the standard model has been of interest for past decades and is crucial for unraveling the mysteries of the universe. Previous laboratory searches primarily focus on searching for either static or modulated energy-level shifts caused by exotic spin-dependent interactions. Here, we introduce a theoretical model based on thermal motion of particles, providing another efficient way to search for exotic spin-dependent interactions. The theoretical model indicates that as the exotic spin-dependent interactions are related with the relative displacements and velocities of atoms, atoms undergoing thermal motion would experience a fluctuating energy-level shift induced by the exotic interactions. Moreover, the resulting exotic energy-level shift noise could be sensed by high-sensitivity instruments. By using the model and taking the high-sensitivity atomic magnetometer as an example, we set the most stringent laboratory experiment constraints on eight different kinds of exotic spin- and velocity-dependent interactions, with five of which at the force range below 1 cm have not been covered previously. Furthermore, this theoretical model can be easily applied in other fields of quantum sensing, such as atomic clocks, atom interferometers and NV-diamond sensors, to further improve the laboratory constraints on exotic spin-dependent interactions.

We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to safely navigate through a conflict area (e.g., traffic intersections, merging roadways, roundabouts). Previous studies have shown that such a network can be targeted by adversarial attacks causing traffic jams or safety violations ending in collisions. We focus on attacks targeting the V2X communication network used to share vehicle data and consider as well uncertainties due to noise in sensor measurements and communication channels. To combat these, motivated by recent work on the safe control of CAVs, we propose a trust-aware robust event-triggered decentralized control and coordination framework that can provably guarantee safety. We maintain a trust metric for each vehicle in the network computed based on their behavior and used to balance the tradeoff between conservativeness (when deeming every vehicle as untrustworthy) and guaranteed safety and security. It is important to highlight that our framework is invariant to the specific choice of the trust framework. Based on this framework, we propose an attack detection and mitigation scheme which has twofold benefits: (i) the trust framework is immune to false positives, and (ii) it provably guarantees safety against false positive cases. We use extensive simulations (in SUMO and CARLA) to validate the theoretical guarantees and demonstrate the efficacy of our proposed scheme to detect and mitigate adversarial attacks., Comment: arXiv admin note: substantial text overlap with arXiv:2305.16818

For different alternating-sign multi-pulse trains electric fields with oscillation, the effects of the electric field pulse number and the relative phase of the combined electric field on pair production are investigated by solving quantum Vlasov equation. It is found that the number density of created particles in the combined electric fields is increased by more than one order of magnitude compared to the results without oscillating structure for both zero transverse momentum and full momentum space. In the case of zero transverse momentum, the created particles longitudinal momentum spectrum are monochromatic for large pulse numbers and some suitable relative phases. The number density depends nonlinearly on the relative phase that enables the optimal relative phase parameters for the number density. Moreover, for the full momentum space, the created particles number density and momentum spectrum under different multi-pulse trains electric fields are given and discussed. We also find that the number density as a function of pulse number satisfies the power law with index 5.342 for the strong but slowly varying electric field with large pulse numbers., Comment: 24 pages, 14 figures

The genuine entanglement of Dirac fields for an N-partite system is investigated in Schwarzschild spacetime and the analysis is carried out using the single-mode approximation. Due to the Hawking effect, quantum entanglement is divided into two parts physically accessible and inaccessible entanglement. We obtain a general analytic expression of genuine N-partite entanglement that includes all accessible and inaccessible entanglement in a Schwarzschild black hole. Unlike bosonic entanglement, the accessible N-partite entanglement of Dirac fields monotonically decreases to a nonzero value with the Hawking temperature. Interestingly, the inaccessible N-partite entanglement is a monotonic or non-monotonic function of the Hawking temperature, depending on the ratio between accessible and inaccessible modes, in contrast to bipartite or tripartite entanglement that is only a monotonic function of the Hawking temperature. Finally, we obtain two restrictive relationships for the quantum information of the black hole. This conclusion provides a new understanding of Hawking effect of the black hole., Comment: 14 pages, 1 figure

Motivated by recent experimental observations of opposite Chern numbers in $R$-type twisted MoTe$_2$ and WSe$_2$ homobilayers, we perform large-scale density-functional-theory (DFT) calculations with machine learning force fields to investigate moir\'e band topology from large to small twist angles in both materials. We find that the Chern numbers of the moir\'e frontier bands change sign as a function of twist angle, and this change is driven by the competition between moir\'{e} ferroelectricity and piezoelectricity. Our large-scale calculations, enabled by machine learning methods, reveal crucial insights into interactions across different scales in twisted bilayer systems. The interplay between atomic-level relaxation effects and moir\'e-scale electrostatic potential variation opens new avenues for the design of intertwined topological and correlated states, including the possibility of mimicking higher Landau-level physics in the absence of a magnetic field., Comment: 8 pages, 5 figures

We study the redistribution of the fermionic steering and the relation among fermionic Bell nonlocality, steering, and entanglement in the background of the Garfinkle-Horowitz-Strominger dilaton black hole. We analyze the meaning of the fermionic steering in terms of the Bell inequality in curved spacetime. We find that the fermionic steering, which is previously found to survive in the extreme dilaton black hole, cannot be considered to be nonlocal. We also find that the dilaton gravity can redistribute the fermionic steering, but cannot redistribute Bell nonlocality, which means that the physically inaccessible steering is also not nonlocal. Unlike the inaccessible entanglement, the inaccessible steering may increase non-monotonically with the dilaton. Furthermore, we obtain some monogamy relations between the fermionic steering and entanglement in dilaton spacetime. In addition, we show the difference between the fermionic and bosonic steering in curved spacetime., Comment: 21 pages, 4 figures

Incorporating both flexible and rigid components in robot designs offers a unique solution to the limitations of traditional rigid robotics by enabling both compliance and strength. This paper explores the challenges and solutions for controlling soft-rigid hybrid robots, particularly addressing the issue of self-contact. Conventional control methods prioritize precise state tracking, inadvertently increasing the system's overall stiffness, which is not always desirable in interactions with the environment or within the robot itself. To address this, we investigate the application of Control Barrier Functions (CBFs) and High Order CBFs to manage self-contact scenarios in serially connected soft-rigid hybrid robots. Through an analysis based on Piecewise Constant Curvature (PCC) kinematics, we establish CBFs within a classical control framework for self-contact dynamics. Our methodology is rigorously evaluated in both simulation environments and physical hardware systems. The findings demonstrate that our proposed control strategy effectively regulates self-contact in soft-rigid hybrid robotic systems, marking a significant advancement in the field of robotics., Comment: 6 pages, 6 figures, submitted to IEEE Robosoft 2024 Conference

As autonomous driving technology matures, end-to-end methodologies have emerged as a leading strategy, promising seamless integration from perception to control via deep learning. However, existing systems grapple with challenges such as unexpected open set environments and the complexity of black-box models. At the same time, the evolution of deep learning introduces larger, multimodal foundational models, offering multi-modal visual and textual understanding. In this paper, we harness these multimodal foundation models to enhance the robustness and adaptability of autonomous driving systems, enabling out-of-distribution, end-to-end, multimodal, and more explainable autonomy. Specifically, we present an approach to apply end-to-end open-set (any environment/scene) autonomous driving that is capable of providing driving decisions from representations queryable by image and text. To do so, we introduce a method to extract nuanced spatial (pixel/patch-aligned) features from transformers to enable the encapsulation of both spatial and semantic features. Our approach (i) demonstrates unparalleled results in diverse tests while achieving significantly greater robustness in out-of-distribution situations, and (ii) allows the incorporation of latent space simulation (via text) for improved training (data augmentation via text) and policy debugging. We encourage the reader to check our explainer video at https://www.youtube.com/watch?v=4n-DJf8vXxo&feature=youtu.be and to view the code and demos on our project webpage at https://drive-anywhere.github.io/., Comment: Project webpage: https://drive-anywhere.github.io Explainer video: https://www.youtube.com/watch?v=4n-DJf8vXxo&feature=youtu.be

Game-theoretic motion planners are a powerful tool for the control of interactive multi-agent robot systems. Indeed, contrary to predict-then-plan paradigms, game-theoretic planners do not ignore the interactive nature of the problem, and simultaneously predict the behaviour of other agents while considering change in one's policy. This, however, comes at the expense of computational complexity, especially as the number of agents considered grows. In fact, planning with more than a handful of agents can quickly become intractable, disqualifying game-theoretic planners as possible candidates for large scale planning. In this paper, we propose a planning algorithm enabling the use of game-theoretic planners in robot systems with a large number of agents. Our planner is based on the reality of locality of information and thus deploys local games with a selected subset of agents in a receding horizon fashion to plan collision avoiding trajectories. We propose five different principled schemes for selecting game participants and compare their collision avoidance performance. We observe that the use of Control Barrier Functions for priority ranking is a potent solution to the player selection problem for motion planning., Comment: to be published in IROS 2023 conference proceedings

Audio coding is an essential module in the real-time communication system. Neural audio codecs can compress audio samples with a low bitrate due to the strong modeling and generative capabilities of deep neural networks. To address the poor high-frequency expression and high computational cost and storage consumption, we proposed an integrated framework that utilizes a neural network to model wide-band components and adopts traditional signal processing to compress high-band components according to psychological hearing knowledge. Inspired by auditory perception theory, a perception-based loss function is designed to improve harmonic modeling. Besides, generative adversarial network (GAN) compression is proposed for the first time for neural audio codecs. Our method is superior to prior advanced neural codecs across subjective and objective metrics and allows real-time inference on desktop and mobile.

Diffusion models have risen as a promising approach to data-driven planning, and have demonstrated impressive robotic control, reinforcement learning, and video planning performance. Given an effective planner, an important question to consider is replanning -- when given plans should be regenerated due to both action execution error and external environment changes. Direct plan execution, without replanning, is problematic as errors from individual actions rapidly accumulate and environments are partially observable and stochastic. Simultaneously, replanning at each timestep incurs a substantial computational cost, and may prevent successful task execution, as different generated plans prevent consistent progress to any particular goal. In this paper, we explore how we may effectively replan with diffusion models. We propose a principled approach to determine when to replan, based on the diffusion model's estimated likelihood of existing generated plans. We further present an approach to replan existing trajectories to ensure that new plans follow the same goal state as the original trajectory, which may efficiently bootstrap off previously generated plans. We illustrate how a combination of our proposed additions significantly improves the performance of diffusion planners leading to 38\% gains over past diffusion planning approaches on Maze2D, and further enables the handling of stochastic and long-horizon robotic control tasks. Videos can be found on the anonymous website: \url{https://vis-www.cs.umass.edu/replandiffuser/}.

In the framework of nonrelativistic QCD (NRQCD) factorization, we compute both the polarized and the unpolarized decay widths for the processes $\eta_b(\chi_{bJ})\to J/\psi J/\psi$, accurate up to next-to-next-to-leading-order (NNLO) in $\alpha_s$. For the first time, we confirm that the NRQCD factorization does hold at NNLO for the process involving triple quarkonia. We find the radiative corrections are considerable. In particular for $\chi_{b2}$, both $\mathcal{O}(\alpha_s)$ and $\mathcal{O}(\alpha_s^2)$ corrections are sizable and negative, and can significantly reduce the leading order prediction. At NNLO, the branching fractions are $8.2\times 10^{-7}$, $6.2\times 10^{-6}$, $7.2\times 10^{-7}$ and $2.7\times 10^{-6}$ for $\eta_b$, $\chi_{b0}$, $\chi_{b1}$ and $\chi_{b2}$ decay, respectively. Our theoretical predictions are consistent with the upper limits measured by the {\tt Belle} Collaboration. Moreover, we investigate the dependence of the theoretical predictions on the ratio of the charm quark mass and the bottom quark mass. By fixing $m_b$ and varying $m_c$ from $1.25$ to $1.9$ GeV, we find the branching fraction can change a factor of $2$, $3$, and $6$ for $\eta_b$, $\chi_{b0}$, and $\chi_{b1}$, respectively. In the phenomenological analysis, with the integrate luminosity $\mathcal{L}=100\,{\rm fb}^{-1}$, we expect about $(5-10)\times 10^3$ $\eta_b(\chi_{bJ})\to J/\psi J/\psi\to \ell \bar{\ell}\ell \bar{\ell}$ events produced at the {\tt LHC}, thus it might be hopeful to search for these processes. On the other hand, there are less than $100$ $\eta_b(\chi_{bJ})\to J/\psi J/\psi$ signal events at the B factory, so it seems the experimental measurements on these channels are quite challenging based on current dataset., Comment: 17 pages, 3 figures, 4 tables; The affiliation and acknowledgement of the first author are updated

This paper studies safe driving interactions between Human-Driven Vehicles (HDVs) and Connected and Automated Vehicles (CAVs) in mixed traffic where the dynamics and control policies of HDVs are unknown and hard to predict. In order to address this challenge, we employ event-triggered Control Barrier Functions (CBFs) to estimate the HDV model online, construct data-driven and state-feedback safety controllers, and transform constrained optimal control problems for CAVs into a sequence of event-triggered quadratic programs. We show that we can ensure collision-free between HDVs and CAVs and demonstrate the robustness and flexibility of our framework on different types of human drivers in lane-changing scenarios while guaranteeing safety with human-in-the-loop interactions.

This paper studies safety and feasibility guarantees for systems with tight control bounds. It has been shown that stabilizing an affine control system while optimizing a quadratic cost and satisfying state and control constraints can be mapped to a sequence of Quadratic Programs (QPs) using Control Barrier Functions (CBF) and Control Lyapunov Functions (CLF). One of the main challenges in this method is that the QP could easily become infeasible under safety constraints of high relative degree, especially under tight control bounds. Recent work focused on deriving sufficient conditions for guaranteeing feasibility. The existing results are case-dependent. In this paper, we consider the general case. We define a feasibility constraint and propose a new type of CBF to enforce it. Our method guarantees the feasibility of the above mentioned QPs, while satisfying safety requirements. We demonstrate the proposed method on an Adaptive Cruise Control (ACC) problem for a heterogeneous platoon with tight control bounds, and compare our method to existing CBF-CLF approaches. The results show that our proposed approach can generate gradually transitioned control (without abrupt changes) with guaranteed feasibility and safety., Comment: 8 pages, 2 figures. arXiv admin note: text overlap with arXiv:2304.00372

This paper addresses the problem of safety-critical control for non-affine control systems. It has been shown that optimizing quadratic costs subject to state and control constraints can be sub-optimally reduced to a sequence of quadratic programs (QPs) by using Control Barrier Functions (CBFs). Our recently proposed High Order CBFs (HOCBFs) can accommodate constraints of arbitrary relative degree. The main challenges in this approach are that it requires affine control dynamics and the solution of the CBF-based QP is sub-optimal since it is solved point-wise. To address these challenges, we incorporate higher-order CBFs into neural ordinary differential equation-based learning models as differentiable CBFs to guarantee safety for non-affine control systems. The differentiable CBFs are trainable in terms of their parameters, and thus, they can address the conservativeness of CBFs such that the system state will not stay unnecessarily far away from safe set boundaries. Moreover, the imitation learning model is capable of learning complex and optimal control policies that are usually intractable online. We illustrate the effectiveness of the proposed framework on LiDAR-based autonomous driving and compare it with existing methods., Comment: 8 pages, accepted in IEEE CDC 2023