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As an empirical tool in materials science and engineering, the iconic phase diagram owes its robustness and practicality to the topological characteristics rooted in the celebrated Gibbs phase law (F = C - P + 2). When crossing the phase diagram boundary, the structure transition occurs abruptly, bringing about an instantaneous change in physical properties and limited controllability on the boundaries (F = 1). Here, we expand the sharp phase boundary to an amorphous transition region (F = 2) by partially disrupting the long-range translational symmetry, leading to a sequential crystalline-amorphous-crystalline (CAC) transition in a pressurized In2Te5 single crystal. Through detailed in-situ synchrotron diffraction, we elucidate that the phase transition stems from the rotation of immobile blocks [In2Te2]2+, linked by hinge-like [Te3]2- trimers. Remarkably, within the amorphous region, the amorphous phase demonstrates a notable 25 % increase of the superconducting transition temperature (Tc), while the carrier concentration remains relatively constant. Furthermore, we propose a theoretical framework revealing that the unconventional boost in amorphous superconductivity might be attributed to an intensified electron correlation, triggered by a disorder-augmented multifractal behavior. These findings underscore the potential of disorder and prompt further exploration of unforeseen phenomena on the phase boundaries., Comment: 14 pages, 4 figures, Accepted for publication in Advanced Materials

Graph Neural Networks (GNNs) have excelled in learning from graph-structured data, especially in understanding the relationships within a single graph, i.e., intra-graph relationships. Despite their successes, GNNs are limited by neglecting the context of relationships across graphs, i.e., inter-graph relationships. Recognizing the potential to extend this capability, we introduce Relating-Up, a plug-and-play module that enhances GNNs by exploiting inter-graph relationships. This module incorporates a relation-aware encoder and a feedback training strategy. The former enables GNNs to capture relationships across graphs, enriching relation-aware graph representation through collective context. The latter utilizes a feedback loop mechanism for the recursively refinement of these representations, leveraging insights from refining inter-graph dynamics to conduct feedback loop. The synergy between these two innovations results in a robust and versatile module. Relating-Up enhances the expressiveness of GNNs, enabling them to encapsulate a wider spectrum of graph relationships with greater precision. Our evaluations across 16 benchmark datasets demonstrate that integrating Relating-Up into GNN architectures substantially improves performance, positioning Relating-Up as a formidable choice for a broad spectrum of graph representation learning tasks., Comment: 16 pages, 6 figures, 9 tables

Noncentrosymmetric magnets usually host intriguing magnetic interactions inherent the crystal structure with broken inversion symmetry, which can give rise to rich magnetic behaviors. We report herein the high-pressure synthesis, crystal structure, magnetizations and magnetic structure of a so-called Nowotny chimney ladder compound Cr4Ge7. Our analysis on the powder neutron diffraction data revises the crystal structure as a noncentrosymmetric space group (P-4c2, No.116). It exhibits two magnetic orders within the temperature range of 2 - 400 K. The first order at ~ 207 K associated with a small magnetic moment of ~ 0.75 miuB is assigned to a commensurate ferromagnetic structure with a propagation vector k = (0, 0, 0). The weak itinerant ferromagnet nature should be caused by the complex Cr spin orders from different Wyckoff positions. The second order at ~ 18 K is assumed to arise from a competition between the Dzyaloshinskii-Moria and Heisenberg interactions. The results provide an excellent platform for study on intricate interactions between various magnetic exchanges as well as for the exploration of high temperature exotic magnetic properties which host potential applications in next-generation spintronics., Comment: 21 pages, 5 figures, 2 tables; Supporting Information is not included

In the domain of large-scale software development, the demands for dynamic and multifaceted static code analysis exceed the capabilities of traditional tools. To bridge this gap, we present CodeFuse-Query, a system that redefines static code analysis through the fusion of Domain Optimized System Design and Logic Oriented Computation Design. CodeFuse-Query reimagines code analysis as a data computation task, support scanning over 10 billion lines of code daily and more than 300 different tasks. It optimizes resource utilization, prioritizes data reusability, applies incremental code extraction, and introduces tasks types specially for Code Change, underscoring its domain-optimized design. The system's logic-oriented facet employs Datalog, utilizing a unique two-tiered schema, COREF, to convert source code into data facts. Through Godel, a distinctive language, CodeFuse-Query enables formulation of complex tasks as logical expressions, harnessing Datalog's declarative prowess. This paper provides empirical evidence of CodeFuse-Query's transformative approach, demonstrating its robustness, scalability, and efficiency. We also highlight its real-world impact and diverse applications, emphasizing its potential to reshape the landscape of static code analysis in the context of large-scale software development.Furthermore, in the spirit of collaboration and advancing the field, our project is open-sourced and the repository is available for public access

Nickelate superconductors have attracted a great deal of attention over the past few decades due to their similar crystal and electronic structures with high-temperature cuprate superconductors. Here, we report the superconductivity in a pressurized Ruddlesden-Popper phase single crystal, La4Ni3O10 (n = 3), and its interplay with the density wave order in the phase diagram. With increasing pressure, the density wave order as indicated by the anomaly in the resistivity is progressively suppressed, followed by the emergence of the superconductivity around 25 K. Our angle-resolved photoemission spectroscopy measurements reveal that the electronic structure of La4Ni3O10 is very similar to that of La3Ni2O7, suggesting unified electronic properties of nickelates in Ruddlesden-Popper phases. Moreover, theoretical analysis unveils that antiferromagnetic (AFM) super-exchange interactions can serve as the effective pairing interaction for the emergence of superconductivity (SC) in pressurized La4Ni3O10. Our research provides a new platform for the investigation of the unconventional superconductivity mechanism in Ruddlesden-Popper trilayer perovskite nickelates., Comment: 21 pages, 4 figures

Lu5Rh6Sn18 is one of the caged-type quasiskutterudite superconductors with superconducting transition temperature Tc = 4.12 K. Here, we investigate the effect of pressure on the superconductivity in Lu5Rh6Sn18 by combining high pressure electrical transport, synchrotron x-ray diffraction (XRD) and chemical doping. Application of high pressure can enhance both the metallicity and the superconducting transition temperature in Lu5Rh6Sn18. Tc is found to show a continuous increase reaching up to 5.50 K at 11.4 GPa. Our high pressure synchrotron XRD measurements demonstrate the stability of the pristine crystal structure up to 12.0 GPa. In contrast, Tc is suppressed after the substitution of La ions in Lu sites, inducing negative chemical pressure. Our study provides valuable insights into the improvement of superconductivity in caged compounds., Comment: 9 pages, 8 figures

In complex systems, the interplay between network structure and noise often leads to emergent phenomena. This study explores the effects of uneven coupling and asymmetric noise on the dynamics of feed-forward loop (FFL) motifs, essential components of networks. We investigate how these variations in network motifs influence noise-induced synchrony and regularity. Using mathematical models and simulations, we uncover that the interaction of asymmetric noise and uneven coupling can significantly impact the behavior of FFL motifs, promoting synchronization and enhancing output regularity. These findings contribute to a deeper understanding of the role of network motifs in shaping network dynamics, shedding light on the intricate interplay between structure, noise, and emergent behavior in complex systems.

While the stochastic dynamics of two coupled oscillators have been extensively studied, most of the research has focused on stochastic input with equal intensity. However, this is often not the case in biological systems. In this study, we investigate the effects of intrinsic noise with unequal intensity on a pair of diffusively coupled lambda-omega oscillators that are quiescent without noise but become excitable with the addition of a noise stimulus. Our findings demonstrate that unequal noise can play a constructive role in inducing phase synchronization (PS) in coupled oscillators. Additionally, we show that PS can be optimized by moving the model closer to the bifurcation point/excitation threshold. This study emphasizes the significance of examining asymmetric stochasticity between oscillators and provides valuable insight into how unequal noise intensity can be utilized to promote PS.

Package delivery via the UAVs is a promising transport mode to provide efficient and green logistic services, especially in urban areas or complicated topography. However, the energy storage limit of the UAV makes it difficult to perform long-distance delivery tasks. In this paper, we propose a novel multimodal logistics framework, in which the UAVs can call on ground vehicles to provide hitch services to save their own energy and extend their delivery distance. This multimodal logistics framework is formulated as a two-stage model to jointly consider the incentive mechanism design for ground vehicles and path planning for UAVs. In Stage I, to deal with the motivations for ground vehicles to assist UAV delivery, a dynamic pricing scheme is proposed to best balance the vehicle response time and payments to ground vehicles. It shows that a higher price should be decided if the vehicle response time is long to encourage more vehicles to offer a ride. In Stage II, the task allocation and path planning of the UAVs over traffic network is studied based on the vehicle response time obtained in Stage I. To address pathfinding with restrictions and the performance degradation of the pathfinding algorithm due to the rising number of conflicts in multi-agent pathfinding, we propose the suboptimal conflict avoidance-based path search (CABPS) algorithm, which has polynomial time complexity. Finally, we validate our results via simulations. It is shown that our approach is able to increase the success rate of UAV package delivery. Moreover, we estimate the delivery time of the UAV in a pessimistic case, it is still twice as fast as the delivery time of the ground vehicle only.

Deep cooperative multi-agent reinforcement learning has demonstrated its remarkable success over a wide spectrum of complex control tasks. However, recent advances in multi-agent learning mainly focus on value decomposition while leaving entity interactions still intertwined, which easily leads to over-fitting on noisy interactions between entities. In this work, we introduce a novel interactiOn Pattern disenTangling (OPT) method, to disentangle the entity interactions into interaction prototypes, each of which represents an underlying interaction pattern within a subgroup of the entities. OPT facilitates filtering the noisy interactions between irrelevant entities and thus significantly improves generalizability as well as interpretability. Specifically, OPT introduces a sparse disagreement mechanism to encourage sparsity and diversity among discovered interaction prototypes. Then the model selectively restructures these prototypes into a compact interaction pattern by an aggregator with learnable weights. To alleviate the training instability issue caused by partial observability, we propose to maximize the mutual information between the aggregation weights and the history behaviors of each agent. Experiments on single-task, multi-task and zero-shot benchmarks demonstrate that the proposed method yields results superior to the state-of-the-art counterparts. Our code is available at https://github.com/liushunyu/OPT., Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)

Despite the promising results achieved, state-of-the-art interactive reinforcement learning schemes rely on passively receiving supervision signals from advisor experts, in the form of either continuous monitoring or pre-defined rules, which inevitably result in a cumbersome and expensive learning process. In this paper, we introduce a novel initiative advisor-in-the-loop actor-critic framework, termed as Ask-AC, that replaces the unilateral advisor-guidance mechanism with a bidirectional learner-initiative one, and thereby enables a customized and efficacious message exchange between learner and advisor. At the heart of Ask-AC are two complementary components, namely action requester and adaptive state selector, that can be readily incorporated into various discrete actor-critic architectures. The former component allows the agent to initiatively seek advisor intervention in the presence of uncertain states, while the latter identifies the unstable states potentially missed by the former especially when environment changes, and then learns to promote the ask action on such states. Experimental results on both stationary and non-stationary environments and across different actor-critic backbones demonstrate that the proposed framework significantly improves the learning efficiency of the agent, and achieves the performances on par with those obtained by continuous advisor monitoring., Comment: Accepted by IEEE Transactions on Systems, Man and Cybernetics: Systems (TSMC)

The real-time transient stability assessment (TSA) plays a critical role in the secure operation of the power system. Although the classic numerical integration method, \textit{i.e.} time-domain simulation (TDS), has been widely used in industry practice, it is inevitably trapped in a high computational complexity due to the high latitude sophistication of the power system. In this work, a data-driven power system estimation method is proposed to quickly predict the stability of the power system before TDS reaches the end of simulating time windows, which can reduce the average simulation time of stability assessment without loss of accuracy. As the topology of the power system is in the form of graph structure, graph neural network based representation learning is naturally suitable for learning the status of the power system. Motivated by observing the distribution information of crucial active power and reactive power on the power system's bus nodes, we thus propose a distribution-aware learning~(DAL) module to explore an informative graph representation vector for describing the status of a power system. Then, TSA is re-defined as a binary classification task, and the stability of the system is determined directly from the resulting graph representation without numerical integration. Finally, we apply our method to the online TSA task. The case studies on the IEEE 39-bus system and Polish 2383-bus system demonstrate the effectiveness of our proposed method., Comment: 8 pages, 6 figures, 4 tables

This paper describes the Around View Monitoring (AVM) system for hydraulic excavators that prevents the safety accidents caused by blind spots and increases the operational efficiency. To verify the developed system, experiments were conducted with its prototype. The experimental results demonstrate its applicability in the field with the following values: 7m of a visual range, 15fps of image refresh rate, 300ms of working information data reception rate, and 300ms of surface condition data reception rate., Comment: 9 pages, 11 figures

We report herein the nontrivial topological states in an intrinsic type-II superconductor BaSn5 (Tc ~ 4.4 K) probed via measuring the magnetizations, specific heat, de Haas-van Alphen (dHvA) effect and performing first principles calculations. The first principles calculations reveal a topological nodal ring structure centering at the H point in the kz = {\pi} plane of the Brillouin zone (BZ), which could be gapped by spin-orbit coupling (SOC), yielding rather small gaps below and above the Fermi level about 0.04 eV and 0.14 eV, respectively. The SOC also results in a pair of Dirac points along the {\Gamma}-A direction and located ~ 0.2 eV above the Fermi level. The analysis of the dHvA quantum oscillations supports the calculations by revealing nontrivial Berry phase originated from three hole and one electron pockets related to the bands forming the Dirac cones. Our study thus provides an excellent avenue for investigating the interplay between superconductivity and nontrivial topological states., Comment: 15 pages, 6 figures, 1 tables

Through either elements intercalation or application of pressure, transition metal dichalcogenide 1T-TiSe2 exhibits superconductivity in proximity to a charge density wave (CDW) quantum critical point (QCP), thus providing an ideal avenue to study the correlation between the two symmetry-breaking exotic quantum electronic states. We report herein that, in addition to the well-known superconducting dome that emerges within the low pressure range of 2 - 4 GPa and peaks with the maximal Tc of about 1.8 K, the pressure induces another separate superconducting transition starting around 15 GPa with a substantially higher Tc that reaches 5.6 K at about 21.5 GPa. The high-pressure X-ray diffraction and Raman spectroscopy measurements unveil that the superconductivity reentrant is caused by a first-order structural phase transition (from P-3m1 space group to Pnma space group), which is also supported by the density functional theory calculation. A comparative theoretical calculation also reveals that the conventional phonon-mediated mechanism can account for the superconductivity of 1T-TiSe2 under low pressure, while the electron-phonon coupling of 4O-TiSe2 under high pressure is too weak to induce the superconductivity with a Tc as high as 5.6 K. This implies that the emergent superconductivity in the 4O-TiSe2 may have an unconventional origin. Our finding would open a new window toward the discovery of more exotic quantum states in transition metal dichalcogenides via high pressure., Comment: 15 pages, 4 figures

The rich nontrivial topological phases rooted in the interplay between magnetism and topology in the layered antiferromagnet EuCd2As2 have captured vast attention, especially the ideal Weyl semimetal state realized in the spin-polarized ferromagnetic (FM) structure driven by a moderate external magnetic field. In this work, combining high-pressure magnetotransport measurements, structure chracterizations and first principles calculations, we find that application of pressure can also realize the ideal Weyl state in EuCd2As2 through driving the in-plane antiferromagnetic state across an intermediate in-plane FM state then into the out-of-plane FM state. Our high-pressure angle dispersive X-ray diffraction and X-ray absorption near-edge spectroscopy measurements excluded structure transition and/or change of Eu2+ valence state as the sources for the magnetic phase transitions. Alternatively, the apparently reduced axial ratio (c/a) and compressed Eu-layer space distance should play important roles. Our result provides an alternative way to realize the ideal Weyl semimetal state in EuCd2As2 and would be instructive for the exploration of exotic topological properties in such layered magnetic topological phase with strongly competing magnetic exchanges by using high pressure., Comment: main text 16 pages,4 figures and SI 8 pages, 4 figures