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Na Yu

Publications and source records attributed to Na Yu.

At least 19 recordsLinked to original sources
arXiv2026

Application of Algorithms in Energy-Efficient Design Platforms for Green Building

During green building design, computer-aided energy assessment is widely used to improve efficiency and achieve overall optimization. This paper presents a platform that combines Building Information Modeling (BIM), sensor operational data, and advanced simulation workflows using robust algorithms. The platform uses a multi-layer service architecture with dynamic energy simulation and evolutionary multi-objective optimization, connected via a high-performance C++ core and adaptive agent models. A mid-rise office building was selected as the case study. Five representative areas were chosen to collect data on building envelope characteristics and occupancy patterns. After preprocessing, missing sensor data accounted for 3.2% of annual records, and all variables were standardized using 15-minute interpolation. After 40 optimization rounds, annual energy consumption per square meter dropped by 29.3% from 315 kWh/m2 to 223 kWh/m2. The lifecycle cost increase for occupants was limited to 3.7%, and discomfort hours were reduced to under 70 hours per year. Analysis of Pareto optimal solutions shows that the envelope U-value ranges from 1.05 to 1.57 W/m2K, and nighttime ventilation rate ranges from 2.1 to 3.6 h-1, both closely linked to energy performance. The results confirm that the integrated algorithm framework offers good scalability, strong performance, and technical feasibility for green building design. This platform provides a reliable decision-support tool for design engineers and sustainability practitioners, enabling accurate, data-driven delivery of energy-efficient buildings.

cs.AI↗
arXiv2026

Rare-Earth-Tuned Evolution from d- to f-Orbital Dominance and Giant Anomalous Hall Effect in Topological RGaGe (R = Ce, Pr, Nd) Semimetals

The family of noncentrosymmetric rare-earth germanides RGaGe (R = Ce, Pr, Nd) provides a rich materials platform to explore the intertwined physics of strong magnetism, electronic correlations, and topological band structures. Through a combination of crystal growth, characterization, and first-principles calculations, we reveal that these compounds exhibit a pronounced uniaxial magnetic anisotropy, leading to distinct ground states: RGaGe orders ferromagnetically with moments along the crystallographic c-axis, and shows an antiferromagnetic-like structure in the ab-plane. A key finding is a significantly enhanced intrinsic anomalous Hall conductivity (AHC) compared to their well-known RAlGe counterparts, which even reaches as high as 948 {\Omega}-1 cm-1 at 2 K in PrGaGe. Our theoretical analysis predicts that this AHC originates from a robust Weyl semimetallic state driven by inversion symmetry breaking, where Weyl points near the Fermi level couple strongly to the magnetic order. Importantly, this topological state persists above the magnetic ordering temperature, confirming its intrinsic electronic origin. Our calculation also reveals that, while the near-Fermi-level states in CeGaGe and PrGaGe are dominated by d-orbital contributions, NdGaGe exhibits significant f-orbital involvement, signaling a progressive evolution from d- to f-orbital dominated topology. These results establish the RGaGe system as a tunable platform for systematically extending the RAlGe-related family, showcasing a large anomalous Hall response and orbital evolution near the Fermi level, and advancing the understanding of the interplay between topology and magnetism in quantum materials.

cond-mat.mtrl-sci↗
arXiv2026

Breaking the Trade-off: Bulk 2D Ising Superconductivity with High Tc and Giant Interlayer Spacing via a Unique Chain Intercalation in (BaS)1/3TaS2

Two-dimensional (2D) transition metal dichalcogenides (TMDs) are promising platforms for low dimensional superconductivity. However, in conventional intercalated systems, achieving a high superconducting transition temperature (Tc) often comes at the expense of reduced interlayer spacing and weakened 2D character. Here, we overcome this long-standing compromise through a unique chain-like intercalation strategy. We report the synthesis and properties of a new polymorph, (BaS)1/3TaS2, in which a distinctive Ba-S-S-Ba chain structure is inserted between TaS2 bilayers. This unique configuration breaks the bulk c axis mirror symmetry while achieving exceptional interlayer decoupling, with an inter-bilayer spacing of 12.75 {\AA}-more than three times that of pristine 2H-TaS2. By suppressing interlayer electronic coupling, this structural evolution allows local inversion symmetry breaking within individual TaS2 layers to dominate. This prevents compensation of the Ising spin-orbit fields typical of centrosymmetric bulk phases, enabling robust 2D Ising superconductivity. Remarkably, the compound exhibits an enhanced Tc without sacrificing its large interlayer spacing, thereby breaking the conventional trade-off between large spacing/high anisotropy and high Tc. Comprehensive transport, magnetic, and thermodynamic measurements confirm its robust superconducting state. Our work establishes a versatile intercalation framework for designing bulk-like 2D Ising superconductors, providing a new route to reconcile competing material demands and expanding the scope of Ising superconductivity research.

cond-mat.supr-con↗
arXiv2026

Integrated laser heating stage with active geometry modulation for simultaneous in-situ X-ray transmission and evolved gas analysis of molten liquids

We report the design and development of a compact, integrated laser heating stage tailored for in situ high-temperature X ray transmission studies of molten oxides. In horizontal beam geometries, widely used in both laboratory and synchrotron facilities, the natural spreading (wetting) of molten samples on substrates significantly reduces the effective vertical optical path length, detrimental to signal quality in transmission-mode measurements. To overcome this limitation, we introduced a thermocouple assisted active geometry modulation technique. This method mechanically lifts the spreading melt into a liquid bridge via surface tension, optimizing the transmission path length while simultaneously enabling in situ temperature monitoring. The device features a triple fiber coupled laser head with high power density, a precision closed loop Proportional Integral Derivative temperature control system, and an atmosphere controlled vacuum chamber coupled with a mass spectrometer. This integration allows for simultaneous evolved gas analysis, enabling the correlation of structural phase transitions with chemical volatilization or reaction dynamics. Validated by tracking the melting kinetics of a multicomponent glass precursor, this versatile setup provides a comprehensive solution for high quality data acquisition in X ray transmission experiments across various sources.

physics.app-ph↗
arXiv2025

Sliding two-dimensional superconductivity and charge-density-wave state in a bulk crystal

Superconductivity in the two-dimensional (2D) limit is a fertile ground for exotic quantum phenomena-many of which remain elusive in their 3D counterparts. While studies of 2D superconductivity have predominantly focused on mono- or few-layer systems, we demonstrate an alternative route-interlayer sliding in bulk crystals. Through a precisely controlled growth strategy, we engineer interlayer sliding in bulk 3R-NbSe2, deliberately disrupting [001] mirror symmetry and drastically suppressing interlayer coupling. Remarkably, this structural manipulation stabilizes Ising-type superconductivity coexisting with an unconventional charge-density-wave (CDW) state akin to that of monolayer 2H-NbSe2. The sliding phase exhibits a pronounced suppression of the upper critical field at low temperatures, revealing a delicate competition between Ising and Rashba spin-orbit coupling (SOC) in the globally noncentrosymmetric lattice. Intriguingly, the superconducting state displays two-fold symmetry, a signature that may arise from asymmetric SOC or a multi-component pairing order parameter. Our work establishes interlayer sliding as a symmetry-breaking tool to promote 2D superconductivity in bulk materials-without resorting to extrinsic intercalation or doping. More broadly, this approach sets a paradigm for unlocking hidden quantum states in layered materials, offering a new dimension in design of quantum matter.

cond-mat.supr-con↗
arXiv2024

Disorder-broadened phase boundary with enhanced amorphous superconductivity in pressurized In2Te5

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.

cond-mat.supr-con↗
arXiv2024

Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships

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.

cs.LG↗
arXiv2024

Noncentrosymmetric Nowotny Chimney Ladder Ferromagnet Cr4Ge7 with a High Curie Temperature of ~ 207 K

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.

cond-mat.str-el↗
arXiv2024

Principles and Practices of Large-Scale Code Analysis at Ant Group: A Data- and Logic-Oriented Approach

Large-scale software development requires dynamic and multifaceted static code analysis that extends beyond the capabilities of traditional tools. Existing tools like CodeQL lack cross-language analysis capabilities and can be time-consuming and resource-intensive. We present CodeFuse-Query, a data system tailored for large-scale code analysis. First, CodeFuse-Query adopts a Logic-Oriented Computation Design, employing Datalog with a two-tiered schema, COREF, to convert source code into data facts, and Godel to express complex analysis tasks in logical terms. Furthermore, CodeFuse-Query adopts a Domain-Optimized System Design. This approach optimizes resource utilization, prioritizes data reusability, applies incremental code extraction, and introduces task-type characteristics specifically for code changes, underscoring its domain-optimized design. We present empirical results demonstrating CodeFuse-Query's robustness, scalability, and efficiency in large-scale real-world scenarios at Ant Group, where it serves as a core static analysis infrastructure. Deployed in production environments, CodeFuse-Query processes up to 10 billion lines of code daily across more than 300,000 distinct analysis tasks. CodeFuse-Query has been open-sourced.

cs.SE↗
arXiv2023

Superconductivity in trilayer nickelate La4Ni3O10 under pressure

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 under I4/mmm space group. The susceptibility measurements confirm bulk superconductivity with a volume fraction exceeding 80%. 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.

cond-mat.supr-con↗
arXiv2023

Effect of physical and chemical pressure on the superconductivity of caged-type quasiskutterudite Lu5Rh6Sn18

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.

cond-mat.supr-con↗
arXiv2023

Noise-induced synchronization and regularity in feed-forward-loop motifs

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.

math.DS↗
arXiv2023

The inter-play between asymmetric noise and coupling in a super-critical Hopf bifurcation

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.

math.DS↗
arXiv2022

Incentive Mechanism and Path Planning for UAV Hitching over Traffic Networks

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.

eess.SY↗
arXiv2022

Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning

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.

cs.LG↗
arXiv2022

Ask-AC: An Initiative Advisor-in-the-Loop Actor-Critic Framework

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.

cs.LG↗
arXiv2022

Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power System

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.

eess.SY↗
arXiv2022

Nontrivial topological states in BaSn5 superconductor probed by de Haas-van Alphen quantum oscillations

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.

cond-mat.supr-con↗