arXiv subjects

Tao Ouyang

Publications and source records attributed to Tao Ouyang.

At least 19 recordsLinked to original sources

arXiv2026

Phonon-Localization-Driven Decoupling of Dual-Channel Transport for Record-Low Intrinsic Lattice Thermal Conductivity

A fundamental bottleneck in pushing the intrinsic lattice thermal conductivity of inorganic crystalline solids to its lowest limit arises from the inherent competition between the particle-like propagation ($κ_{\mathrm{L}}^{\mathrm{P}}$) and wave-like tunneling ($κ_{\mathrm{L}}^{\mathrm{C}}$) channels. Herein, we demonstrate that phonon localization provides a robust pathway to decouple the dual-channel transport, achieving record-low $κ_{\mathrm{L}}$ in quasi-1D ternary helical crystals. Despite the structural complexity leading to densely populated phonon branches and thus inducing abundant coherent phonons, the weak interchain interactions and heavy elements compress numerous branches into highly localized, nearly dispersionless flat bands. Such strong localization simultaneously suppresses both the diagonal and off-diagonal components of the group velocity, thereby synergistically suppressing $κ_{\mathrm{L}}^{\mathrm{P}}$ and $κ_{\mathrm{L}}^{\mathrm{C}}$. Taking InSeI as an example, the interchain room-temperature $κ_{\mathrm{L}}^{\mathrm{P}}$ and $κ_{\mathrm{L}}^{\mathrm{C}}$ are 0.145 and 0.053 W/mK, respectively, yielding an ultralow total $κ_{\mathrm{L}}$ of 0.198 W/mK. Weaker interchain interactions further drive the room-temperature $κ_{\mathrm{L}}$ of GaSeI and AlSeI to record lows of 0.086 and 0.089 W/mK, respectively; these values even drop to 0.058 and 0.059 W/mK at 900 K. These findings provide useful insights into exploring the thermal conductivity limit in crystals.

cond-mat.mtrl-sci↗

arXiv2026

GALA: Generative Aligned Learning for Adaptive Multimodal Representation in the Taobao Shangou Recommender System

Modern recommender systems in food delivery increasingly leverage multimodal signals, including images, text, and user interaction histories, to enhance user experience, yet effective fusion of these heterogeneous modalities remains challenging, hindering both the joint modeling of multimodal signals and adaptation to evolving user intent. In mainstream two-stage approaches, the separation between content-semantic pretraining of image-text encoders and behavior-driven ranking models limits alignment between semantic understanding and user behavior patterns. To address these issues, we present GALA, a three-stage pipeline whose core innovation lies in an intermediate "generative RL alignment" stage that constructs multimodal pretraining data from user behavior and refines it via conversion-based rewards, effectively bridging the pretraining-fine-tuning gap to align with downstream objectives. GALA comprises three stages: first, behavior-aware triplet pretraining on query-image-text pairs from search logs to early capture user intent and content preferences; second, a novel intermediate stage that refines multimodal embeddings through reward-driven optimization (GRPO) to dynamically align them with user behavior and bridge the pretraining-fine-tuning gap; and finally, integration of multimodal and ID embeddings via adaptive gating with a hybrid loss, preserving multimodal contributions under long-term ID-dominant training. GALA has been deployed in the production environment at Taobao Shangou, serving over 200 million daily active users. Compared with state-of-the-art (SOTA) methods, it delivers consistent offline gains of +0.12/+0.20 AUC along with better PCOC metrics. Large-scale online A/B tests further report a 0.55 percent increase in order volume, confirming GALA's effectiveness at industrial scale and its robustness across diverse demand patterns.

cs.IR↗

arXiv2026

High-Throughput Discovery of Semimetallic Borophenes with Diverse Dirac States Via Transferable Tight-Binding Approach

Borophene has attracted extensive interest due to its structural flexibility and emergent topological electronic states. However, semimetallic borophenes hosting robust Dirac states remain rare among the large number of predicted allotropes. Here, we develop a transferable tight-binding framework for planar borophenes and combine it with a graph- and group-theory-based random generation strategy to perform high-throughput screening of 522 borophene candidates. Eight previously unreported semimetallic borophenes are identified, hosting diverse topological band crossings, including type-I and type-III Dirac cones, Dirac nodal lines, and quadratic nodal points. Notably, quadratic nodal-point semimetals are predicted in borophene for the first time. Symmetry analysis reveals crystalline-symmetry-protected Dirac states, while first-principles calculations confirm their dynamical and thermal stability. These findings establish borophene as a versatile platform for engineering emergent Dirac physics in two dimensions.

cond-mat.mtrl-sci↗

arXiv2026

The ground state of CuInP$_2$S$_6$ thin films: A study of the deep potential method

The two-dimensional ferroelectric (FE) material CuInP$_2$S$_6$ (CIPS) has garnered considerable interest due to its out-of-plane ferroelectricity at room temperature. However, a notable discrepancy exists between experiments and density functional theory (DFT) calculations regarding the ground state of CIPS thin films: experiments suggest a state with net polarization, while DFT predicts an antiferroelectric (AFE) state as the lowest-energy state. Here, we investigate the stability of polarization states in CIPS thin films by combining first-principles calculations with the deep potential (DP) method. Our results reveal that for films thicker than the bilayer, an AFE state that has intralayer AFE ordering in the inner layers and intralayer FE ordering in the two surface layers has the lowest electronic energy. This state is significantly lower than the uniform FE state. In addition, we find that a ferrielectric (FiE) state with pure intralayer FE ordering is very close to the AFE state in energy. By using the DP model, we calculated the phonon free energy of CIPS thin films. For the monolayer, the intralayer FE ordering possesses a lower phonon free energy than the intralayer AFE ordering. This energetic preference for the intralayer FE ordering maintains as the thickness grows. Consequently, when the phonon-free energy is incorporated, the FiE state becomes energetically favorable over the AFE state for multilayers CIPS. Our findings demonstrate that the inclusion of vibrational entropy stabilizes the FiE state as the ground state in multilayers CIPS at finite temperatures, reconciling the previous discrepancy between experimental observations and DFT predictions. This insight is vital for understanding the FE properties of CIPS and its potential applications in devices.

cond-mat.mtrl-sci↗

arXiv2025

CoDrone: Autonomous Drone Navigation Assisted by Edge and Cloud Foundation Models

Autonomous navigation for Unmanned Aerial Vehicles faces key challenges from limited onboard computational resources, which restrict deployed deep neural networks to shallow architectures incapable of handling complex environments. Offloading tasks to remote edge servers introduces high latency, creating an inherent trade-off in system design. To address these limitations, we propose CoDrone - the first cloud-edge-end collaborative computing framework integrating foundation models into autonomous UAV cruising scenarios - effectively leveraging foundation models to enhance performance of resource-constrained unmanned aerial vehicle platforms. To reduce onboard computation and data transmission overhead, CoDrone employs grayscale imagery for the navigation model. When enhanced environmental perception is required, CoDrone leverages the edge-assisted foundation model Depth Anything V2 for depth estimation and introduces a novel one-dimensional occupancy grid-based navigation method - enabling fine-grained scene understanding while advancing efficiency and representational simplicity of autonomous navigation. A key component of CoDrone is a Deep Reinforcement Learning-based neural scheduler that seamlessly integrates depth estimation with autonomous navigation decisions, enabling real-time adaptation to dynamic environments. Furthermore, the framework introduces a UAV-specific vision language interaction module incorporating domain-tailored low-level flight primitives to enable effective interaction between the cloud foundation model and the UAV. The introduction of VLM enhances open-set reasoning capabilities in complex unseen scenarios. Experimental results show CoDrone outperforms baseline methods under varying flight speeds and network conditions, achieving a 40% increase in average flight distance and a 5% improvement in average Quality of Navigation.

cs.RO↗

arXiv2025

CoEdge-RAG: Optimizing Hierarchical Scheduling for Retrieval-Augmented LLMs in Collaborative Edge Computing

Motivated by the imperative for real-time responsiveness and data privacy preservation, large language models (LLMs) are increasingly deployed on resource-constrained edge devices to enable localized inference. To improve output quality, retrieval-augmented generation (RAG) is an efficient technique that seamlessly integrates local data into LLMs. However, existing edge computing paradigms primarily focus on single-node optimization, neglecting opportunities to holistically exploit distributed data and heterogeneous resources through cross-node collaboration. To bridge this gap, we propose CoEdge-RAG, a hierarchical scheduling framework for retrieval-augmented LLMs in collaborative edge computing. In general, privacy constraints preclude accurate a priori acquisition of heterogeneous data distributions across edge nodes, directly impeding RAG performance optimization. Thus, we first design an online query identification mechanism using proximal policy optimization (PPO), which autonomously infers query semantics and establishes cross-domain knowledge associations in an online manner. Second, we devise a dynamic inter-node scheduling strategy that balances workloads across heterogeneous edge nodes by synergizing historical performance analytics with real-time resource thresholds. Third, we develop an intra-node scheduler based on online convex optimization, adaptively allocating query processing ratios and memory resources to optimize the latency-quality trade-off under fluctuating assigned loads. Comprehensive evaluations across diverse QA benchmarks demonstrate that our proposed method significantly boosts the performance of collaborative retrieval-augmented LLMs, achieving performance gains of 4.23\% to 91.39\% over baseline methods across all tasks.

cs.DC↗

arXiv2025

Unique Hierarchical Rotational Dynamics Induces Ultralow Lattice Thermal Conductivity in Cyanide-bridged Framework Materials

The pursuit of materials combining light constituent elements with ultralow lattice thermal conductivity ($κ_{\mathrm{L}}$) is crucial to advancing technologies like thermoelectrics and thermal barrier coatings, yet it remains a formidable challenge to date. Herein, we achieve ultralow $κ_{\mathrm{L}}$ in lightweight cyanide-bridged framework materials (CFMs) through the rational integration of properties such as the hierarchical vibrations exhibited in superatomic structures and rotational dynamics exhibited in perovskites. Unique hierarchical rotation behavior leads to multiple negative peaks in Grüneisen parameters across a wide frequency range, thereby inducing pronounced negative thermal expansion and strong cubic anharmonicity in CFMs. Meanwhile, the synergistic effect between large four-phonon scattering phase space (induced by phonon quasi-flat bands and wide bandgaps) and strong quartic anharmonicity (associated with rotation modes) leads to giant quartic anharmonic scattering rates in these materials. Consequently, the $κ_{\mathrm{L}}$ of these CFMs decreases by one to two orders of magnitude compared to the known perovskites or perovskite-like materials with equivalent average atomic masses. For instance, the Cd(CN)$_{2}$, NaB(CN)$_{4}$, LiIn(CN)$_{4}$, and AgX(CN)$_{4}$ (X = B, Al, Ga, In) exhibit ultralow room-temperature $κ_{\mathrm{L}}$ values ranging from 0.35 to 0.81 W/mK. This work not only establishes CFMs as a novel and rich platform for studying extreme phonon anharmonicity, but also provides a new paradigm for achieving ultralow thermal conductivity in lightweight materials via the conscious integration of hierarchical and rotational dynamics.

cond-mat.mtrl-sci↗

arXiv2025

First-principles predictions of the diversity in atomic structures and electronic properties of the reconstructed Si(111)-7x7 surface

The 7x7 reconstruction of Si(111) surface is widely understood by the dimer-adatom-stacking-fault model (DAS), but the predicted metallicity of DAS contradicts experimental signs of insulation. It is still challenge to predict DAS-like reconstructions by traditional method to solve such a puzzle. Here, we show that low-energy reconstructions of Si(111)-7x7 surface with (DAS-d8-T12, DAS-d8-T9H3-A, DAS-d8-T9H3-B and DAS-d8-T6H6) and without (AB-d10-T12, AB-d10-T9H3, AA-d10-T12 and AA-d10-T9H3) stacking-fault can be quickly discovered by graph theory as implemented in RG2 code for crystal structure prediction. They exhibit comparable stability to the DAS (DAS-d8-T12) model and similar STM patterns, offering a plausible explanation for the observed Si(111)-7x7 reconstruction. All these reconstructions exhibit metallic behavior in the nonmagnetic (NM) state with isolated narrow bands crossing the Fermi level in varying occupancy. And they are further confirmed as ferromagnetic (FM) metals (DAS-d8-T9H3-B), half-metals (DAS-d8-T12, AB-d10-T9H3, AA-d10-T12 and AA-d10-T9H3), half-semimetals (DAS-d8-T9H3-A and DAS-d8-T6H6) and even insulators (AB-d10-T12), depending their occupancies of the NM band structures. These findings not only demonstrate the rich electromagnetic phases of reconstructed Si(111) surfaces and their potential for spintronic applications, but also provide a plausible physical explanation for the metal-insulator transition observed on the Si(111) surface.

cond-mat.mes-hall↗

arXiv2025

Combining intrinsic and sliding-induced polarizations for multistates in two dimensional ferroelectrics

Going beyond the bistability paradigm of the charge polarizations in ferroelectrics is highly desired for ferroelectric (FE) memory devices toward ultra-high-density information storage. Here, we propose to build multistates by combining the intrinsic and sliding-induced polarizations. The physics is that there is at least one order of magnitude difference in the energy barriers between these two types of polarization, which leads to a significant difference in the electric fields for reversing the polarization. This difference, along with the symmetry breaking, allows for a unique flipping mechanism involving layer-by-layer sliding followed by layer-by-layer flipping during the transformation of the multistates. As a result, six and ten switchable states can be achieved for the 1T" bilayers and trilayers, respectively. We further illustrate the concept in H-stacking bilayers and trilayers of 1T" transition-metal dichalcogenides by first-principles calculations. Our study provides a new route to design novel polarization states for developing next-generation memory devices.

cond-mat.mtrl-sci↗

arXiv2024

Online Optimization of DNN Inference Network Utility in Collaborative Edge Computing

Collaborative Edge Computing (CEC) is an emerging paradigm that collaborates heterogeneous edge devices as a resource pool to compute DNN inference tasks in proximity such as edge video analytics. Nevertheless, as the key knob to improve network utility in CEC, existing works mainly focus on the workload routing strategies among edge devices with the aim of minimizing the routing cost, remaining an open question for joint workload allocation and routing optimization problem from a system perspective. To this end, this paper presents a holistic, learned optimization for CEC towards maximizing the total network utility in an online manner, even though the utility functions of task input rates are unknown a priori. In particular, we characterize the CEC system in a flow model and formulate an online learning problem in a form of cross-layer optimization. We propose a nested-loop algorithm to solve workload allocation and distributed routing iteratively, using the tools of gradient sampling and online mirror descent. To improve the convergence rate over the nested-loop version, we further devise a single-loop algorithm. Rigorous analysis is provided to show its inherent convexity, efficient convergence, as well as algorithmic optimality. Finally, extensive numerical simulations demonstrate the superior performance of our solutions.

cs.DC↗

arXiv2024

Doping induced multiferroicity and quantum anomalous Hall effect in $α$-In$_2$Se$_3$ thin films

In flat-band materials, the strong Coulomb interaction between electrons can lead to exotic physical phenomena. Recently, $α$-In$_2$Se$_3$ thin films were found to possess ferroelectricity and flat bands. In this work, using first-principles calculations, we find that for the monolayer, there is a Weyl point at $Γ$ in the flat band, where the inclusion of the spin-orbit coupling opens a gap. Shifting the Fermi level into the spin-orbit gap gives rise to nontrivial band topology, which is preserved for the bilayer regardless of the interlayer polarization couplings. We further calculate the Chern number and edge states for both the monolayer and bilayer, for which the results suggest that they become quantum anomalous Hall insulators under appropriate dopings. Moreover, we find that the doping-induced magnetism for In$_2$Se$_3$ bilayer is strongly dependent on the interlayer polarization coupling. Therefore, doping the flat bands in In$_2$Se$_3$ bilayer can also yield multiferroicity, where the magnetism is electrically tunable as the system transforms between different polarization states. Our study thus reveals that multiferroicity and nontrivial band topology can be unified into one material for designing multifunctional electronic devices.

cond-mat.mtrl-sci↗

arXiv2023

BeeFlow: Behavior Tree-based Serverless Workflow Modeling and Scheduling for Resource-Constrained Edge Clusters

Serverless computing has gained popularity in edge computing due to its flexible features, including the pay-per-use pricing model, auto-scaling capabilities, and multi-tenancy support. Complex Serverless-based applications typically rely on Serverless workflows (also known as Serverless function orchestration) to express task execution logic, and numerous application- and system-level optimization techniques have been developed for Serverless workflow scheduling. However, there has been limited exploration of optimizing Serverless workflow scheduling in edge computing systems, particularly in high-density, resource-constrained environments such as system-on-chip clusters and single-board-computer clusters. In this work, we discover that existing Serverless workflow scheduling techniques typically assume models with limited expressiveness and cause significant resource contention. To address these issues, we propose modeling Serverless workflows using behavior trees, a novel and fundamentally different approach from existing directed-acyclic-graph- and state machine-based models. Behavior tree-based modeling allows for easy analysis without compromising workflow expressiveness. We further present observations derived from the inherent tree structure of behavior trees for contention-free function collections and awareness of exact and empirical concurrent function invocations. Based on these observations, we introduce BeeFlow, a behavior tree-based Serverless workflow system tailored for resource-constrained edge clusters. Experimental results demonstrate that BeeFlow achieves up to 3.2X speedup in a high-density, resource-constrained edge testbed and 2.5X speedup in a high-profile cloud testbed, compared with the state-of-the-art.

cs.DC↗

arXiv2023

Vertical Ferroelectricity in Van der Waals Materials: Models and Devices

Ferroelectricity has a wide range of applications in functional electronics and is extremely important for the development of next-generation information storage technology, but it is difficult to achieve due to its special symmetry requirements. In this letter, based on van derWaals stacking, a generic model is proposed for realizing ferroelectric devices, where a freely movable center layer is packaged in two fixed and symmetrically stacked layers. In this model, the ferroelectric phase transition can be realized between the two equivalent and eccentric ground stacking-states with opposite polarizations. By means of first-principles calculations, taking the h-BN/h-BN/h-BN and h-BN/Graphene/h-BN as feasible models, we carefully evaluate the magnitude of ferroelectricity. The corresponding polarizations are estimated as 1.83 and 1.35 pC/m, respectively, which are comparable to the sliding ferroelectricity. Such a new tri-layer model of vertical ferroelectricity can be constructed by arbitrary van derWaals semiconducting materials, and usually holds low switching barrier. Optimized material combinations with remarkable polarization are highly expectable to be discovered from the huge candidate set for future information storage.

cond-mat.mtrl-sci↗

arXiv2023

HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association

Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally. However, for many existing FL systems, clients need to frequently exchange model parameters of large data size with the remote cloud server directly via wide-area networks (WAN), leading to significant communication overhead and long transmission time. To mitigate the communication bottleneck, we resort to the hierarchical federated learning paradigm of HiFL, which reaps the benefits of mobile edge computing and combines synchronous client-edge model aggregation and asynchronous edge-cloud model aggregation together to greatly reduce the traffic volumes of WAN transmissions. Specifically, we first analyze the convergence bound of HiFL theoretically and identify the key controllable factors for model performance improvement. We then advocate an enhanced design of HiFlash by innovatively integrating deep reinforcement learning based adaptive staleness control and heterogeneity-aware client-edge association strategy to boost the system efficiency and mitigate the staleness effect without compromising model accuracy. Extensive experiments corroborate the superior performance of HiFlash in model accuracy, communication reduction, and system efficiency.

cs.NI↗

arXiv2022

Flat-band based ferromagnetic semiconducting state in the graphitic C$_4$N$_3$ monolayer

A new set of lattice-models based on the hexagonal $\sqrt{N}\times\sqrt{N}$ super-cells of the well-known honeycomb lattice with single-hole defect (HL-D-1/2N) are proposed to realize the nontrivial isolated flat-bands. Through performing both tight-binding and density functional theory calculations, we demonstrate that the experimentally realized graphitic carbon nitride (Adv. Mater., 22, 1004, 2010; Nat. Commun., 9, 3366, 2018), the HL-D-1/8 based C$_4$N$_3$, is a perfect system to host such flat bands. For the flat high-energy P-6m2 C$_4$N$_3$ structure, it displays the ferromagnetic half-metallicity which is not related to the isolated flat bands. However, the P-6m2 C$_4$N$_3$ structure is dynamically unstable. Using a structure searching method based on group and graph theory, we find that a new corrugated Pca21 C4N3 structure has the lowest energy among all known C$_4$N$_3$ structures. This Pca21 C$_4$N$_3$ structure is an intrinsic ferromagnetic half-semiconductor (Tc$\approx$241 K) with one semiconducting spin-channel (1.75 eV) and one insulating spin-channel (3.64 eV), which is quite rare in the two-dimensional (2D) systems. Its ferromagnetic semiconducting property originates from the isolated p$_z$-state flat-band as the corrugation shift the flat band upward to the Fermi level. Interestingly, this Pca21 C$_4$N$_3$ structure is found to be piezoelectric and ferroelectric, which makes C$_4$N$_3$ an unusual transition-metal-free 2D multiferroic.

cond-mat.mtrl-sci↗

arXiv2022

Collaboration in Participant-Centric Federated Learning: A Game-Theoretical Perspective

Federated learning (FL) is a promising distributed framework for collaborative artificial intelligence model training while protecting user privacy. A bootstrapping component that has attracted significant research attention is the design of incentive mechanism to stimulate user collaboration in FL. The majority of works adopt a broker-centric approach to help the central operator to attract participants and further obtain a well-trained model. Few works consider forging participant-centric collaboration among participants to pursue an FL model for their common interests, which induces dramatic differences in incentive mechanism design from the broker-centric FL. To coordinate the selfish and heterogeneous participants, we propose a novel analytic framework for incentivizing effective and efficient collaborations for participant-centric FL. Specifically, we respectively propose two novel game models for contribution-oblivious FL (COFL) and contribution-aware FL (CAFL), where the latter one implements a minimum contribution threshold mechanism. We further analyze the uniqueness and existence for Nash equilibrium of both COFL and CAFL games and design efficient algorithms to achieve equilibrium solutions. Extensive performance evaluations show that there exists free-riding phenomenon in COFL, which can be greatly alleviated through the adoption of CAFL model with the optimized minimum threshold.

cs.AI↗

arXiv2022

I4/mcm-Si$_{48}$: An Ideal Topological Nodal-Line Semimetal

Topological semimetals (TSMs) have attracted numerous attention due to their exotic physical properties and great application potentials. Silicon-based TMSs are of particularly importance because of their high abundance, nontoxicity and natural compatibility with current semiconductor industry. In this work, an ideal low-energy topological nodal-line semimetal (TNLSM) silicon (I4/mcm-Si$_{48}$) with a clean band crossing at Fermi level is screened from thousands of silicon allotropes by the transferable tight-binding and DFT-HSE calculations. The results of formation energy, phonon dispersion, ab initio molecular dynamics and elastic constants show that I4/mcm-Si48 possesses good stability and is more stable than several synthetized silicon structures. By analyzing the symmetry, it reveals that the topological nodal-line of I4/mcm-Si48 is protected by mirror symmetry and inversion, time-reversal and SU(2) spin-rotation symmetries, and the nearly flat drumhead-like surface spectrum is observed. Furthermore, I4/mcm-Si48 exhibits exotic photoelectric properties and the Dirac fermions with high Fermi velocity (3.4$\sim$4.36$\times$10$^5$ m/s) can be excited by low energy photons. Our study provides a promising topological nodal-line semimetal for fundamental research and potential practical applications in semiconductor-compatible high-speed photoelectric devices.

cond-mat.mtrl-sci↗

arXiv2022

Separating Data via Block Invalidation Time Inference for Write Amplification Reduction in Log-Structured Storage

Log-structured storage has been widely deployed in various domains of storage systems, yet its garbage collection incurs write amplification (WA) due to the rewrites of live data. We show that there exists an optimal data placement scheme that minimizes WA using the future knowledge of block invalidation time (BIT) of each written block, yet it is infeasible to realize in practice. We propose a novel data placement algorithm for reducing WA, SepBIT, that aims to infer the BITs of written blocks from storage workloads and separately place the blocks into groups with similar estimated BITs. We show via both mathematical and production trace analyses that SepBIT effectively infers the BITs by leveraging the write skewness property in practical storage workloads. Trace analysis and prototype experiments show that SepBIT reduces WA and improves I/O throughput, respectively, compared with state-of-the-art data placement schemes. SepBIT is currently deployed to support the log-structured block storage management at Alibaba Cloud.

cs.DC↗