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The discovery of high-temperature superconductivity in La$_3$Ni$_2$O$_7$ and La$_4$Ni$_3$O$_{10}$ under pressure has drawn extensive attention. Herein, we report systematic investigations on the evolutions of structure, magnetism, and electrical resistance of Pr$_4$Ni$_3$O$_{10-\delta}$ polycrystalline samples under various pressures. Pr$_4$Ni$_3$O$_{10-\delta}$ exhibits density wave transitions on Ni and Pr sublattices at about 158 K and 4.3 K, respectively, and the density wave can be progressively suppressed by pressure. A structural transformation from the monoclinic $P2_1/a$ space group to the tetragonal $I4/mmm$ occurs at around 20 GPa. An apparent drop in resistance with evident magnetic field dependence is observed as pressure above 20 GPa, indicating the emergence of superconductivity in Pr$_4$Ni$_3$O$_{10-\delta}$ polycrystalline samples. The discovery of the signature of superconductivity in Pr$_4$Ni$_3$O$_{10-\delta}$ broadens the family of nickelate superconductors and provides a new platform for investigating the mechanisms of superconductivity in the Ruddlesden-Popper phases of nickelates., Comment: 12 pages with 3 figures in the main article and supplementary files

The Android ecosystem is profoundly fragmented due to the frequent updates of the Android system and the prevalent customizations by mobile device manufacturers. Previous research primarily focused on identifying and repairing evolution-induced API compatibility issues, with limited consideration of devices-specific compatibility issues (DSC issues). To fill this gap, we conduct an empirical study of 197 DSC issues collected from 94 open-source repositories on GitHub. We introduce a new perspective for comprehending these issues by categorizing them into two principal groups, Functionality Breaks, and OEM Features, based on their manifestations and root causes. The functionality break issues disrupt standard Android system behaviors, lead to crashes or unexpected behaviors on specific devices, and require developers to implement workarounds to preserve the original functionality. The OEM feature issues involve the introduction of device-specific functionalities or features beyond the basic Android system. The different nature of functionality break issues and OEM feature issues lead to unique challenges in addressing them. Common solutions for functionality break issues involve calling additional APIs, substituting problematic ones, or using specific parameters, while resolving OEM feature issues often relies on Android inter-component communication methods and reflection, with additional unconventional strategies. Such observations highlight the distinctive challenges in addressing DSC issues in Android apps and will facilitate the future development of testing and analysis tools targeting these issues. Our study demonstrates that Functionality break and OEM feature issues have different characteristics, and future research may need to investigate them separately., Comment: Accepted by ICSME'24

This paper presents the design and optimization of a LYSO crystal electromagnetic calorimeter (ECAL) for the DarkSHINE experiment, which aims to search for dark photons as potential mediators of dark forces. The ECAL design was evaluated through comprehensive simulations, focusing on optimizing dimensions, material selection, energy distribution, and energy resolution. The ECAL configuration consists of 21$\times$21$\times$11 LYSO crystals, each measuring 2.5$\times$2.5$\times$4 cm$^3$, arranged in a staggered layout to improve signal detection efficiency. A 4 GeV energy dynamic range was established to ensure accurate energy measurements without saturation, which is essential for background rejection and signal identification. A detailed digitization model was developed to simulate the scintillation, SiPM, and ADC behaviors, providing a more realistic representation of detector performance. Additionally, the study assessed radiation damage in the ECAL region, highlighting the necessity of radiation-resistant scintillators and silicon sensors.

Operator learning for Partial Differential Equations (PDEs) is rapidly emerging as a promising approach for surrogate modeling of intricate systems. Transformers with the self-attention mechanism$\unicode{x2013}$a powerful tool originally designed for natural language processing$\unicode{x2013}$have recently been adapted for operator learning. However, they confront challenges, including high computational demands and limited interpretability. This raises a critical question: Is there a more efficient attention mechanism for Transformer-based operator learning? This paper proposes the Position-induced Transformer (PiT), built on an innovative position-attention mechanism, which demonstrates significant advantages over the classical self-attention in operator learning. Position-attention draws inspiration from numerical methods for PDEs. Different from self-attention, position-attention is induced by only the spatial interrelations of sampling positions for input functions of the operators, and does not rely on the input function values themselves, thereby greatly boosting efficiency. PiT exhibits superior performance over current state-of-the-art neural operators in a variety of complex operator learning tasks across diverse PDE benchmarks. Additionally, PiT possesses an enhanced discretization convergence feature, compared to the widely-used Fourier neural operator.

Atomic structure and electronic band structure are fundamental properties for understanding the mechanism of superconductivity. Motivated by the discovery of pressure-induced high-temperature superconductivity at 80 K in the bilayer Ruddlesden-Popper nickelate La3Ni2O7, the atomic structure and electronic band structure of the trilayer nickelate La4Ni3O10 under pressure up to 44.3 GPa are investigated. A structural transition from the monoclinic P21/a space group to the tetragonal I4/mmm around 12.6-13.4 GPa is identified, accompanying with a drop of resistance below 7 K. Density functional theory calculations suggest that the bonding state of Ni 3dz2 orbital rises and crosses the Fermi level at high pressures, which may give rise to possible superconductivity observed in resistance under pressure in La4Ni3O10. The trilayer nickelate La4Ni3O10 shows some similarities with the bilayer La3Ni2O7 and has unique properties, providing a new platform to investigate the underlying mechanism of superconductivity in nickelates., Comment: 19 pages, 4 figures

Coalition is an important mean of multi-robot systems to collaborate on common tasks. An adaptive coalition strategy is essential for the online performance in dynamic and unknown environments. In this work, the problem of territory defense by large-scale heterogeneous robotic teams is considered. The tasks include exploration, capture of dynamic targets, and perimeter defense over valuable resources. Since each robot can choose among many tasks, it remains a challenging problem to coordinate jointly these robots such that the overall utility is maximized. This work proposes a generic coalition strategy called K-serial stable coalition algorithm. Different from centralized approaches, it is distributed and complete, meaning that only local communication is required and a K-serial Stable solution is ensured. Furthermore, to accelerate adaptation to dynamic targets and resource distribution that are only perceived online, a heterogeneous graph attention network based heuristic is learned to select more appropriate parameters and promising initial solutions during local optimization. Compared with manual heuristics or end-to-end predictors, it is shown to both improve online adaptability and retain the quality guarantee. The proposed methods are validated via large-scale simulations with 170 robots and hardware experiments of 13 robots, against several strong baselines such as GreedyNE and FastMaxSum., Comment: 8 pages, 10 figures, 1 table

Multi-agent systems can be extremely efficient when working concurrently and collaboratively, e.g., for transportation, maintenance, search and rescue. Coordination of such teams often involves two aspects: (i) selecting appropriate sub-teams for different tasks; (ii) designing collaborative control strategies to execute these tasks. The former aspect can be combinatorial w.r.t. the team size, while the latter requires optimization over joint state-spaces under geometric and dynamic constraints. Existing work often tackles one aspect by assuming the other is given, while ignoring their close dependency. This work formulates such problems as combinatorial-hybrid optimizations (CHO), where both the discrete modes of collaboration and the continuous control parameters are optimized simultaneously and iteratively. The proposed framework consists of two interleaved layers: the dynamic formation of task coalitions and the hybrid optimization of collaborative behaviors. Overall feasibility and costs of different coalitions performing various tasks are approximated at different granularities to improve the computational efficiency. At last, a Nash-stable strategy for both task assignment and execution is derived with provable guarantee on the feasibility and quality. Two non-trivial applications of collaborative transportation and dynamic capture are studied against several baselines., Comment: 8 pages, 7 figures, 1 table

The quantum excitations of macroscopic surface acoustic waves (SAWs) have been tailored to control, communicate and transduce stationary and flying quantum states. However, the limited lifetime of this hybrid quantum systems remains critical obstacles to extend their applications in quantum information processing. Here we present the potentials of thin film aluminum nitride to on-chip integrate phonons with superconducting qubits over previous bulk piezoelectric substrates. We have reported high-quality thin film GHz-SAW resonators with the highest internal quality factor Qi of 5 e4 at the single-phonon level. The internal loss of SAW resonators are systematically investigated with tuning the parameters of sample layout, power and temperature. Our results manifest that SAWs on piezoelectric films are readily integrable with standard fabrication of Josephson junction quantum circuits, and offer excellent acoustic platforms for the high-coherence quantum acoustodynamics architectures., Comment: 10 pages, 4 figures

This paper proposes a novel deep learning approach for learning operators in semigroup, with applications to modeling unknown autonomous dynamical systems using time series data collected at varied time lags. It is a sequel to the previous flow map learning (FML) works [T. Qin, K. Wu, and D. Xiu, J. Comput. Phys., 395:620--635, 2019], [K. Wu and D. Xiu, J. Comput. Phys., 408:109307, 2020], and [Z. Chen, V. Churchill, K. Wu, and D. Xiu, J. Comput. Phys., 449:110782, 2022], which focused on learning single evolution operator with a fixed time step. This paper aims to learn a family of evolution operators with variable time steps, which constitute a semigroup for an autonomous system. The semigroup property is very crucial and links the system's evolutionary behaviors across varying time scales, but it was not considered in the previous works. We propose for the first time a framework of embedding the semigroup property into the data-driven learning process, through a novel neural network architecture and new loss functions. The framework is very feasible, can be combined with any suitable neural networks, and is applicable to learning general autonomous ODEs and PDEs. We present the rigorous error estimates and variance analysis to understand the prediction accuracy and robustness of our approach, showing the remarkable advantages of semigroup awareness in our model. Moreover, our approach allows one to arbitrarily choose the time steps for prediction and ensures that the predicted results are well self-matched and consistent. Extensive numerical experiments demonstrate that embedding the semigroup property notably reduces the data dependency of deep learning models and greatly improves the accuracy, robustness, and stability for long-time prediction.

It is well known that it is difficult to have a reliable and robust framework to link multi-agent deep reinforcement learning algorithms with practical multi-robot applications. To fill this gap, we propose and build an open-source framework for multi-robot systems called MultiRoboLearn1. This framework builds a unified setup of simulation and real-world applications. It aims to provide standard, easy-to-use simulated scenarios that can also be easily deployed to real-world multi-robot environments. Also, the framework provides researchers with a benchmark system for comparing the performance of different reinforcement learning algorithms. We demonstrate the generality, scalability, and capability of the framework with two real-world scenarios2 using different types of multi-agent deep reinforcement learning algorithms in discrete and continuous action spaces., Comment: 6pages, 6 figures

To solve the problems of instability and thermal stress birefringence of voltage divided medium in optical voltage sensor (OVS), a half-wave voltage regulation method based on a central symmetry electrode and an arbitrary directional electric field is proposed in this paper. The method uses a center-symmetric copper foil electrode or the ITO (Indium Tin Oxide) transparent electrode to apply voltage, which can arbitrarily adjust the direction of the average electric field in the crystal, so as to improve the half-wave voltage of the bismuth germanate (Bi4Ge3O12, or BGO) crystal directly without additional SF6 gas or quartz glass. The internal electric field of the BGO crystal is simulated by the finite element method, and the half-wave voltage is calculated by the coupling wave theory of electro-optic effect. The results show that the maximum regulated half-wave voltage, considering the 1{\deg} angle, can reach 2.67e3 kV. And this is consistent to the experimental results 1.72e3 kV with difference of 35.6%. For the measurement of 110kV line voltage, that is, 63.5kV phase voltage, to ensure a 0.2 accuracy class, the angle error of incident light, and the polarization direction and the electric field direction is required to be less than 3.9', which can be achieved by the existing technologies.

The occupancy grid map is a critical component of autonomous positioning and navigation in the mobile robotic system, as many other systems' performance depends heavily on it. To guarantee the quality of the occupancy grid maps, researchers previously had to perform tedious manual recognition for a long time. This work focuses on automatic abnormal occupancy grid map recognition using the residual neural networks and a novel attention mechanism module. We propose an effective channel and spatial Residual SE(csRSE) attention module, which contains a residual block for producing hierarchical features, followed by both channel SE (cSE) block and spatial SE (sSE) block for the sufficient information extraction along the channel and spatial pathways. To further summarize the occupancy grid map characteristics and experiment with our csRSE attention modules, we constructed a dataset called occupancy grid map dataset (OGMD) for our experiments. On this OGMD test dataset, we tested few variants of our proposed structure and compared them with other attention mechanisms. Our experimental results show that the proposed attention network can infer the abnormal map with state-of-the-art (SOTA) accuracy of 96.23% for abnormal occupancy grid map recognition.

The RGB-Thermal (RGB-T) information for semantic segmentation has been extensively explored in recent years. However, most existing RGB-T semantic segmentation usually compromises spatial resolution to achieve real-time inference speed, which leads to poor performance. To better extract detail spatial information, we propose a two-stage Feature-Enhanced Attention Network (FEANet) for the RGB-T semantic segmentation task. Specifically, we introduce a Feature-Enhanced Attention Module (FEAM) to excavate and enhance multi-level features from both the channel and spatial views. Benefited from the proposed FEAM module, our FEANet can preserve the spatial information and shift more attention to high-resolution features from the fused RGB-T images. Extensive experiments on the urban scene dataset demonstrate that our FEANet outperforms other state-of-the-art (SOTA) RGB-T methods in terms of objective metrics and subjective visual comparison (+2.6% in global mAcc and +0.8% in global mIoU). For the 480 x 640 RGB-T test images, our FEANet can run with a real-time speed on an NVIDIA GeForce RTX 2080 Ti card., Comment: 7 pages, 5 figures

We study a novel problem that tackles learning based sensor scanning in 3D and uncertain environments with heterogeneous multi-robot systems. Our motivation is two-fold: first, 3D environments are complex, the use of heterogeneous multi-robot systems intuitively can facilitate sensor scanning by fully taking advantage of sensors with different capabilities. Second, in uncertain environments (e.g. rescue), time is of great significance. Since the learning process normally takes time to train and adapt to a new environment, we need to find an effective way to explore and adapt quickly. To this end, in this paper, we present a meta-learning approach to improve the exploration and adaptation capabilities. The experimental results demonstrate our method can outperform other methods by approximately 15%-27% on success rate and 70%-75% on adaptation speed., Comment: 6 pages, 9 figures