arXiv subjects

Tengfei Cao

Publications and source records attributed to Tengfei Cao.

16 recordsLinked to original sources

arXiv2026

Relevant but Incomplete: Referential Dangling as a Paradigm-Level Failure Mode in Hard Prompt Compression

Hard prompt compression reduces long-context inference cost by independently scoring tokens, sentences, or chunks and retaining the highest-scoring units under a budget. We identify a structural failure in this procedure: independent selection can split dependent evidence pairs, retaining one member while deleting the other. When retained text contains an answer but deleted text defines the entity needed to interpret it, we call the result referential dangling. At a compression ratio of 0.30, Beaver, which ranks coherent chunks using Qwen3-0.6B embeddings, leaves the answer path incomplete in 34-54% of bridge examples across three multi-hop question answering datasets. On a shared HotpotQA bridge set, all six hard compressors we test exhibit dangling at rates up to 60%, and every document in LongBench-v2 Single-Document QA contains at least one dangling reference. On dangling examples evaluated with Qwen3-8B, reinserting the missing supporting paragraph while removing nonsupporting paragraphs to maintain the token budget improves accuracy by 29-34 percentage points (p < 0.0001), recovering at least 88% of the gap to contexts retaining both supporting paragraphs. Stronger answer models do not absorb the loss: on MuSiQue, GPT-5.5 is 8.8 points less accurate on compressed contexts than on contexts retaining both supporting paragraphs. Finally, we train a compact classifier to rank omitted sentences by whether they are needed to interpret retained text and reinsert the top-ranked candidates without support annotations at inference. On HotpotQA with Qwen3-8B, this automatic restoration improves accuracy by 4.7 points while changing the compression ratio only from 0.30 to 0.31. Hard compressors should optimize both relevance and referential completeness.

cs.CL↗

arXiv2026

Reversible fully spin polarization in strain-engineered two-dimensional fully compensated magnets

Achieving controllable spin polarization and its reversal in symmetry-compensated magnets. Here we demonstrate, using symmetry analysis and a minimal tight-binding model, that uniaxial strain removes these constraints by inducing inequivalence between magnetic sublattices in two-dimensional (2D) system, driving an altermagnetic (AM) state into a fully compensated ferrimagnetic (fFIM) state and enabling fully spin polarization. Furthermore, strain along orthogonal directions gives rise to two energetically degenerate fFIM states with opposite spin polarization, enabling reversible spin switching. More importantly, the two symmetry-related fFIM states can be regarded as distinct ferroelastic variants, suggesting that this model or mechanism can be extended to ferroelastic fFIM systems. The generality of this mechanism is confirmed by combining spin-group analysis, first-principles calculations, and Boltzmann transport theory in representative candidates, including AM Mn$_2$SeO and ferroelastic fFIM V$_2$SO. Our results reveal a universal symmetry-driven framework for strain-controlled and -reversible fully spin-polarized transport and identify strain-engineered AM and ferroelastic fFIM systems as a promising platform for volatile and nonvolatile spintronic applications.

cond-mat.mtrl-sci↗

arXiv2025

Toward Stable Semi-Supervised Remote Sensing Segmentation via Co-Guidance and Co-Fusion

Semi-supervised remote sensing (RS) image semantic segmentation offers a promising solution to alleviate the burden of exhaustive annotation, yet it fundamentally struggles with pseudo-label drift, a phenomenon where confirmation bias leads to the accumulation of errors during training. In this work, we propose Co2S, a stable semi-supervised RS segmentation framework that synergistically fuses priors from vision-language models and self-supervised models. Specifically, we construct a heterogeneous dual-student architecture comprising two distinct ViT-based vision foundation models initialized with pretrained CLIP and DINOv3 to mitigate error accumulation and pseudo-label drift. To effectively incorporate these distinct priors, an explicit-implicit semantic co-guidance mechanism is introduced that utilizes text embeddings and learnable queries to provide explicit and implicit class-level guidance, respectively, thereby jointly enhancing semantic consistency. Furthermore, a global-local feature collaborative fusion strategy is developed to effectively fuse the global contextual information captured by CLIP with the local details produced by DINOv3, enabling the model to generate highly precise segmentation results. Extensive experiments on six popular datasets demonstrate the superiority of the proposed method, which consistently achieves leading performance across various partition protocols and diverse scenarios. Project page is available at https://xavierjiezou.github.io/Co2S/.

cs.CV↗

arXiv2025

Control of magnetic transition, metal-semiconductor transition, and magnetic anisotropy in noncentrosymmetric monolayer Cr$_2$Ge$_2$Se$_3$Te$_3$

Recent advances in two-dimensional materials have greatly expanded the family of ferromagnetic materials. The well-known 2D ferromagnets, such as CrI$_3$, Cr$_2$Ge$_2$Te$_6$, and Fe$_3$GeTe$_2$ monolayers, are characterized by centrosymmetric crystal structures. In contrast, ferromagnetic ordering in 2D noncentrosymmetric materials remains an underexplored area. Here we report a Janus ferromagnet, Cr$_2$Ge$_2$Se$_3$Te$_3$ with inversion symmetry breaking, through first-principles calculations. This monolayer can undergo a ferromagnetic-antiferromagnetic transformation and a metal-semiconductor transition under different strains. Additionally, the strength of magnetocrystalline anisotropy energy (MAE) can be modulated by electric field or strain. In particular, the magnetization easy axis can be altered from in-plane to out-of-plane under strain. We find that Te$_3$ atoms play a key role in determining the MAE, where contributions are primarily from $p_z / p_y$ and $p_x / p_y$ orbitals. This study of Janus ferromagnetic materials has provided a promising platform for the research on the control of magnetism by strain or electric field.

cond-mat.mtrl-sci↗

arXiv2025

Sliding and superlubric moir\'e twisting ferroelectric transition in HfO2

Despite progress in HfO2 thin-film ferroelectrics, issues like high coercive fields persist, and the dynamics of twisted ferroelectricity remain largely unexplored. Here, we explore how sliding and twisting in bilayer HfO2 enables low barrier switching. Among 144 sliding configurations, two exhibit strong in-plane polarization (2360 pC/m) with a low switching barrier of 9.57 meV/f.u. Twisting generates polar textures associated with moir\'e patterns, which drive ferroelectricity via a soft zone-center mode, as revealed by machine-learning-assisted first-principles calculations. The in-plane (out-of-plane) polarization values for HfO2 at twist angles of 21.79{\deg}, 27.80{\deg}, and 46.83{\deg} are 430 (5.82), 367 (2.20), and 1057 (0.03) pC/m, respectively. For 21.79{\deg} and 27.80{\deg} twisting, switching barriers drop to 1.74 and 0.18 meV/f.u., indicating superlubric-like transitions. Notably, the 46.83{\deg} twisted bilayer shows an almost barrier-free polar evolution (0.03 meV /f.u.), attributed to sharply enhanced zone-center phonon linewidths. Our findings establish a moir\'e-engineered switching route for 2D ferroelectrics.

cond-mat.mtrl-sci↗

arXiv2024

Knowledge Transfer and Domain Adaptation for Fine-Grained Remote Sensing Image Segmentation

Fine-grained remote sensing image segmentation is essential for accurately identifying detailed objects in remote sensing images. Recently, vision transformer models (VTMs) pre-trained on large-scale datasets have demonstrated strong zero-shot generalization. However, directly applying them to specific tasks may lead to domain shift. We introduce a novel end-to-end learning paradigm combining knowledge guidance with domain refinement to enhance performance. We present two key components: the Feature Alignment Module (FAM) and the Feature Modulation Module (FMM). FAM aligns features from a CNN-based backbone with those from the pretrained VTM's encoder using channel transformation and spatial interpolation, and transfers knowledge via KL divergence and L2 normalization constraint. FMM further adapts the knowledge to the specific domain to address domain shift. We also introduce a fine-grained grass segmentation dataset and demonstrate, through experiments on two datasets, that our method achieves a significant improvement of 2.57 mIoU on the grass dataset and 3.73 mIoU on the cloud dataset. The results highlight the potential of combining knowledge transfer and domain adaptation to overcome domain-related challenges and data limitations. The project page is available at https://xavierjiezou.github.io/KTDA/.

cs.CV↗

arXiv2024

Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent

In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens. We conduct a thorough evaluation of Hunyuan-Large's superior performance across various benchmarks including language understanding and generation, logical reasoning, mathematical problem-solving, coding, long-context, and aggregated tasks, where it outperforms LLama3.1-70B and exhibits comparable performance when compared to the significantly larger LLama3.1-405B model. Key practice of Hunyuan-Large include large-scale synthetic data that is orders larger than in previous literature, a mixed expert routing strategy, a key-value cache compression technique, and an expert-specific learning rate strategy. Additionally, we also investigate the scaling laws and learning rate schedule of mixture of experts models, providing valuable insights and guidances for future model development and optimization. The code and checkpoints of Hunyuan-Large are released to facilitate future innovations and applications. Codes: https://github.com/Tencent/Hunyuan-Large Models: https://huggingface.co/tencent/Tencent-Hunyuan-Large

cs.CL↗

arXiv2024

Interlayer ferroelectric polarization modulated anomalous Hall effects in four-layer MnBi2Te4 antiferromagnets

Van der Waals (vdW) assembly could efficiently modulate the symmetry of two-dimensional (2D) materials that ultimately governs their physical properties. Of particular interest is the ferroelectric polarization being introduced by proper vdW assembly that enables the realization of novel electronic, magnetic and transport properties of 2D materials. Four-layer antiferromagnetic MnBi2Te4 (F-MBT) offers an excellent platform to explore ferroelectric polarization effects on magnetic order and topological transport properties of nanomaterials. Here, by applying symmetry analyses and density-functional-theory calculations, the ferroelectric interface effects on magnetic order, anomalous Hall effect (AHE) or even quantum AHE (QAHE) on the F-MBT are analyzed. Interlayer ferroelectric polarization in F-MBT efficiently violates the PT symmetry (the combination symmetry of central inversion (P) and time reverse (T) of the F-MBT by conferring magnetoelectric couplings, and stabilizes a specific antiferromagnetic order encompassing a ferromagnetic interface in the F-MBT. We predict that engineering an interlayer polarization in the top or bottom interface of F-MBT allows converting F-MBT from a trivial insulator to a Chern insulator. The switching of ferroelectric polarization at the middle interfaces results in a direction reversal of the quantum anomalous Hall current. Additionally, the interlayer polarization of the top and bottom interfaces can be aligned in the same direction, and the switching of polarization direction also reverses the direction of anomalous Hall currents. Overall, our work highlights the occurrence of quantum-transport phenomena in 2D vdW four-layer antiferromagnets through vdW assembly. These phenomena are absent in the bulk or thin-film in bulk-like stacking forms of MnBi2Te4.

cond-mat.mtrl-sci↗

arXiv2023

Three consecutive quantum anomalous Hall gaps in a metal-organic network

In the quantum anomalous Hall (QAH) effect, chiral edge states are present in the absence of magnetic fields due to the intrinsic band topology. In this work, we predict that a synthesized two-dimensional metal-organic material, a Fe(biphenolate)$_3$ network, can be a unique QAH insulator, in which there are three consecutive nontrivial bandgaps. Based on first-principles calculations with effective model analysis, we reveal such nontrivial topology is from the $3$d$_{xz}$ and $3$d$_{yz}$ orbitals of Fe atoms. Moreover, we further study the effect of substrates, and the results shows that the metallic substrates used in the experiments (Ag and Cu) are unfavorable for observing the QAH effect whereas a hexagonal boron nitride substrate with a large bandgap may be a good candidate, where the three consecutive QAH gaps appear inside the substrate gap. The presence of three consecutive bandgaps near the Fermi level will significantly facilitate observations of the QAH effect in experiments.

cond-mat.str-el↗

arXiv2023

Stabilizing polar phases in binary metal oxides by hole doping

The recent observation of ferroelectricity in the metastable phases of binary metal oxides, such as HfO2, ZrO2, Hf0.5Zr0.5O2, and Ga2O3, has garnered a lot of attention. These metastable ferroelectric phases are typically stabilized through epitaxial growth, alloying, or defect engineering. Here, we propose hole doping plays a key role in stabilizing the polar phases in binary metal oxides. Using first-principles density-functional-theory calculations, we show that holes in these oxides mainly occupy one of the two oxygen sublattices. This hole localization, which is more pronounced in the polar phase than in the nonpolar phase, lowers the electrostatic energy of the system, and makes the polar phase more stable at sufficiently large concentrations. We demonstrate that this electrostatic mechanism is responsible for stabilization of the ferroelectric phase of HfO2 aliovalently doped with elements that introduce holes to the system, such as La and N. Finally, we show that the spontaneous polarization in HfO2 is robust to hole doping, and a large polarization persists even under a high concentration of holes.

cond-mat.mtrl-sci↗

arXiv2023

Switchable anomalous Hall effects in polar-stacked 2D antiferromagnet MnBi2Te4

Van der Waals (vdW) assembly allows controlling symmetry of two-dimensional (2D) materials that determines their physical properties. Especially interesting is the recently demonstrated breaking inversion symmetry by polar layer stacking to realize novel electronic, magnetic, and transport properties of 2D vdW materials switchable by induced electric polarization. Here, based on symmetry analyses and density-functional calculations, we explore the emergence of the anomalous Hall effect (AHE) in antiferromagnetic MnBi2Te4 films assembled by polar layer stacking. We demonstrate that breaking PT symmetry in an MnBi2Te4 bilayer makes this 2D material magnetoelectric and produces a spontaneous AHE switchable by electric polarization. We find that reversable polarization at one of the interfaces in a three-layer MnBi2Te4 film drives a metal-insulator transition, as well as switching between an AHE and quantum AHE (QAHE). Finally, we predict that engineering an interlayer polarization in a three-layer MnBi2Te4 film allows converting MnBi2Te4 from a trivial insulator to a Chern insulator. Overall, our work emphasizes the emergence of quantum-transport phenomena in 2D vdW antiferromagnets by polar layer stacking, which do not exist in this material in the bulk or bulk-like thin-film forms.

cond-mat.mtrl-sci↗

arXiv2022

Capture Agent Free Biosensing using Porous Silicon Arrays and Machine Learning

Biosensors are an essential tool for medical diagnostics, environmental monitoring and food safety. Typically, biosensors are designed to detect specific analytes through functionalization with the appropriate capture agents. However, the use of capture agents limits the number of analytes that can be simultaneously detected and reduces the robustness of the biosensor. In this work, we report a versatile, capture agent free biosensor platform based on an array of porous silicon (PSi) thin films, which has the potential to robustly detect a wide variety of analytes based on their physical and chemical properties in the nanoscale porous media. The ability of this system to reproducibly classify, quantify, and discriminate three proteins is demonstrated to concentrations down to at least 0.02g/L (between 300nM and 450nM) by utilizing PSi array elements with a unique combination of pore size and buffer pH, employing linear discriminant analysis for dimensionality reduction, and using support vector machines as a classifier. This approach represents a significant step towards a low cost, simple and robust biosensor platform that is able to detect a vast range of biomolecules.

physics.med-ph↗

arXiv2021

Modeling Temperature, Frequency, and Strain Effects on the Linear Electro-Optic Coefficients of Ferroelectric Oxides

An electro-optic modulator offers the function of modulating the propagation of light in a material with electric field and enables seamless connection between electronics-based computing and photonics-based communication. The search for materials with large electro-optic coefficients and low optical loss is critical to increase the efficiency and minimize the size of electro-optic devices. We present a semi-empirical method to compute the electro-optic coefficients of ferroelectric materials by combining first-principles density-functional theory calculations with Landau-Devonshire phenomenological modeling. We apply the method to study the electro-optic constants, also called Pockels coefficients, of three paradigmatic ferroelectric oxides: BaTiO3, LiNbO3, and LiTaO3. We present their temperature-, frequency- and strain-dependent electro-optic tensors calculated using our method. The predicted electro-optic constants agree with the experimental results, where available, and provide benchmarks for experimental verification.

cond-mat.mtrl-sci↗

arXiv2021

Direct Observation and Control of Surface Termination in Perovskite Oxide Heterostructures

The interfacial behavior of quantum materials leads to emergent phenomena such as two dimensional electron gases, quantum phase transitions, and metastable functional phases. Probes for in situ and real time surface sensitive characterization are critical for active monitoring and control of epitaxial synthesis, and hence the atomic-scale engineering of heterostructures and superlattices. Termination switching, especially as an interfacial process in ternary complex oxides, has been studied using a variety of probes, often ex situ; however, direct observation of this phenomena is lacking. To address this need, we establish in situ and real time reflection high energy electron diffraction and Auger electron spectroscopy for pulsed laser deposition, which provide structural and compositional information of the surface during film deposition. Using this unique capability, we show, for the first time, the direct observation and control of surface termination in complex oxide heterostructures of SrTiO3 and SrRuO3. Density-functional-theory calculations capture the energetics and stability of the observed structures and elucidate their electronic behavior. This demonstration opens up a novel approach to monitor and control the composition of materials at the atomic scale to enable next-generation heterostructures for control over emergent phenomena, as well as electronics, photonics, and energy applications.

cond-mat.mtrl-sci↗

arXiv2020

Throughput Optimal Uplink Scheduling in Heterogeneous PLC and LTE Communication for Delay Aware Smart Grid Applications

Smart grid is an energy network that integrates advanced power equipment, communication technology and control technology. It can transmit two-way power and data among all components of the grid at the same time. The existing smart grid communication technologies include power line carrier (PLC) com-munication, industrial Ethernet, passive optical networks and wireless communi-cation, each of which have different advantages. Due to the complex application scenarios, massive sampling points and high transmission reliability require-ments, a single communication method cannot fully meet the communication re-quirements of smart grid, and heterogeneous communication modes are required. In addition, with the development of cellular technology, long term evolution (LTE)-based standards have been identified as a promising technology that can meet the strict requirements of various operations in smart grid. In this paper, we analyze the advantages and disadvantages of PLC and LTE communication, and design a network framework for PLC and LTE communication uplink heteroge-neous communication in smart grid. Then, we propose an uplink scheduling transmission method for sampling data with optimized throughput according to the requirements of system delay and reliability. Then, we use the formula deri-vation to prove the stability and solvability of the scheduling system in theory. Finally, the simulation results show that under the condition of satisfying the de-lay requirement, our proposed framework can optimally allocate the wireless communication resource and maximize the throughput of the uplink transmission system. Keywords: smart grid, heterogeneous communication, optimized throughput.

cs.NI↗

arXiv2018

Ultrahard carbon film from epitaxial two-layer graphene

Atomically thin graphene exhibits fascinating mechanical properties, although its hardness and transverse stiffness are inferior to those of diamond. To date, there hasn't been any practical demonstration of the transformation of multi-layer graphene into diamond-like ultra-hard structures. Here we show that at room temperature and after nano-indentation, two-layer graphene on SiC(0001) exhibits a transverse stiffness and hardness comparable to diamond, resisting to perforation with a diamond indenter, and showing a reversible drop in electrical conductivity upon indentation. Density functional theory calculations suggest that upon compression, the two-layer graphene film transforms into a diamond-like film, producing both elastic deformations and sp2-to-sp3 chemical changes. Experiments and calculations show that this reversible phase change is not observed for a single buffer layer on SiC or graphene films thicker than 3 to 5 layers. Indeed, calculations show that whereas in two-layer graphene layer-stacking configuration controls the conformation of the diamond-like film, in a multilayer film it hinders the phase transformation.

cond-mat.mtrl-sci↗