arXiv subjects

Bin Gao

Publications and source records attributed to Bin Gao.

At least 37 records · Page 2Linked to original sources

arXiv2025

Normalized tensor train decomposition

Tensors with unit Frobenius norm are fundamental objects in many fields, including scientific computing and quantum physics, which are able to represent normalized eigenvectors and pure quantum states. While the tensor train decomposition provides a powerful low-rank format for tackling high-dimensional problems, it does not intrinsically enforce the unit-norm constraint. To address this, we introduce the normalized tensor train (NTT) decomposition, which aims to approximate a tensor by unit-norm tensors in tensor train format. The low-rank structure of NTT decomposition not only saves storage and computational cost but also preserves the underlying unit-norm structure. We prove that the set of fixed-rank NTT tensors forms a smooth manifold, and the corresponding Riemannian geometry is derived, paving the way for geometric methods. We propose NTT-based methods for low-rank tensor recovery, high-dimensional eigenvalue problem, estimation of stabilizer rank, and calculation of the minimum output Rényi 2-entropy of quantum channels. Numerical experiments demonstrate the superior efficiency and scalability of the proposed NTT-based methods.

math.NA↗

arXiv2025

Interacting spin and charge density waves in kagome metal FeGe

Unveiling the interplay between spin density wave (SDW) and charge density wave (CDW) orders in correlated electron materials is important to obtain a comprehensive understanding of their electronic, structural, and magnetic properties. Kagome lattice materials are interesting because their flat electronic bands, Dirac points, and van Hove singularities can enable a variety of exotic electronic and magnetic phenomena. The kagome metal FeGe, which exhibits a CDW order deep within an A-type antiferromagnetic (AFM) phase, was found to respond dramatically to post-growth annealing - with the ability to tune the CDW repeatedly from long-range order to no (or extremely weak) order. Additionally, neutron scattering studies suggest that incommensurate magnetic peaks that onsets at $T_{Canting}$ = $T_{SDW} \approx$ 60 K in the system arise from a SDW order instead of the AFM double cone structure. Here we use inelastic neutron scattering to show two distinct spin excitations exist below $T_{Canting}$ corresponding to two coexisting magnetic orders in the system in both sets of annealed samples with and without CDW. While CDW order or no order can dramatically affect the onset temperature of $T_{Canting}$ and elastic incommensurate magnetic scattering, its impact on low-energy spin fluctuations is more limited. In both samples, a pair of gapless incommensurate spin excitations arising from the SDW order wavevector coexist with gapped commensurate spin waves from the A-type AFM order across $T_{Canting}$. Low-energy spin excitations for both samples couple dynamically to the lattice through enhanced magnetic scattering intensity on cooling below $T_{CDW}$, regardless the status of the static long-range CDW order. The incommensurate SDW order in the long-range CDW ordered sample also induces a tiny in-plane lattice distortion of the kagome lattice that is absent in the no CDW ordered sample.

cond-mat.str-el↗

arXiv2025

A space-decoupling framework for optimization on bounded-rank matrices with orthogonally invariant constraints

Imposing additional constraints on low-rank optimization has garnered growing interest. However, the geometry of coupled constraints hampers the well-developed low-rank structure and makes the problem intricate. To this end, we propose a space-decoupling framework for optimization on bounded-rank matrices with orthogonally invariant constraints. The "space-decoupling" is reflected in several ways. We show that the tangent cone of coupled constraints is the intersection of tangent cones of each constraint. Moreover, we decouple the intertwined bounded-rank and orthogonally invariant constraints into two spaces, leading to optimization on a smooth manifold. Implementing Riemannian algorithms on this manifold is painless as long as the geometry of additional constraints is known. In addition, we unveil the equivalence between the reformulated problem and the original problem. Numerical experiments on real-world applications -- spherical data fitting, graph similarity measuring, low-rank SDP, model reduction of Markov processes, reinforcement learning, and deep learning -- validate the superiority of the proposed framework.

math.OC↗

arXiv2025

CORB-Planner: Corridor as Observations for RL Planning in High-Speed Flight

Reinforcement learning (RL) has shown promise in a large number of robotic control tasks. Nevertheless, its deployment on unmanned aerial vehicles (UAVs) remains challenging, mainly because of reliance on accurate dynamic models and platform-specific sensing, which hinders cross-platform transfer. This paper presents the CORB-Planner (Corridor-as-Observations for RL B-spline planner), a real-time, RL-based trajectory planning framework for high-speed autonomous UAV flight across heterogeneous platforms. The key idea is to combine B-spline trajectory generation with the RL policy producing successive control points with a compact safe flight corridor (SFC) representation obtained via heuristic search. The SFC abstracts obstacle information in a low-dimensional form, mitigating overfitting to platform-specific details and reducing sensitivity to model inaccuracies. To narrow the sim-to-real gap, we adopt an easy-to-hard progressive training pipeline in simulation. A value-based soft decomposed-critic Q (SDCQ) algorithm is used to learn effective policies within approximately ten minutes of training. Benchmarks in simulation and real-world tests demonstrate real-time planning on lightweight onboard hardware and support maximum flight speeds up to 8.2m/s in dense, cluttered environments without external positioning. Compatibility with various UAV configurations (quadrotors, hexarotors) and modest onboard compute underlines the generality and robustness of CORB-Planner for practical deployment.

cs.RO↗

arXiv2025

Signatures of three-state Potts nematicity in spin excitations of the van der Waals antiferromagnet FePSe$_3$

In two-dimensional (2D) nearly square-lattice quantum materials, electron correlations can induce an electronic nematic phase with twofold rotational ($C_2$) symmetry that profoundly impacts their properties. For 2D materials with threefold rotational ($C_3$) symmetry, such as the honeycomb lattice, a vestigial three-state Potts nematic order has been observed in the van der Waals antiferromagnet (AFM) FePSe$_3$ via optical and thermodynamic methods under uniaxial strain. Here, we use neutron scattering to study the magnetic order and spin excitations of FePSe$_3$ under uniaxial strain. In the AFM ordered state, we find that $\sim$0.6% tensile strain significantly suppresses one zigzag domain and promotes the other two, lowering the AFM order and spin waves to $C_2$ symmetry. The broken $C_3$ symmetry in spin excitations persists slightly above $T_{\rm{N}}\approx 108.6$ K, where the zigzag AFM order is absent. Our results thus provide direct evidence of magnetoelastic coupling and suggest that the three-state Potts nematicity in paramagnetic spin excitations arises from the vestigial order associated with the low-temperature zigzag AFM order.

cond-mat.str-el↗

arXiv2025

Thermoelectric evidence of the electronic structure changes from the charge-density-wave transition in FeGe

Kagome metals provide a material platform for probing new correlated quantum phenomena due to the naturally incorporated linear dispersions, flat bands, and Van Hove singularities in their electronic structures. Among these quantum phenomena is the charge density wave (CDW), or the distortion of the lattice structure due to the motion of correlated electrons through the material. CDWs lower the energy of the compound, creating an energy gap that facilitates behaviors akin to superconductivity, nonlinear transport, or other quantum correlated phenomena. The kagome metal FeGe has been shown to host a CDW transition at approximately 100 K, and its occurrence is strongly influenced by the sample annealing conditions. However, a notable gap in the literature is the lack of clear thermoelectric transport evidence for electronic structure changes associated with this CDW transition. Here we present evidence of electron behavior modification due to annealing disorder via thermoelectric measurements on FeGe crystals presenting a CDW transition and those without a CDW. The observed Nernst effect and Seebeck effect under sufficient annealing demonstrate modified electrical transport properties resulting from induced disorder, including a change in carrier sign and an enhancement of the Nernst effect due to the CDW. Our results provide evidence of multiple phase transitions, which confirms the influence of CDW on the thermal properties of FeGe and demonstrates the suppression of CDW with sufficient disordering.

cond-mat.str-el↗

arXiv2025

Distributed Retraction-Free and Communication-Efficient Optimization on the Stiefel Manifold

Optimization problems on the Stiefel manifold, ranging from principal component analysis to enhancing neural network robustness, are ubiquitous in machine learning. The Landing algorithm avoids computationally expensive retraction operations on manifolds, making it highly competitive for large-scale problems. This paper extends this method to distributed settings, introducing *EF-Landing*, the first retraction-free and communication-efficient algorithm for distributed stochastic optimization on the Stiefel manifold. By incorporating communication compression and error feedback, EF-Landing ensures convergence and constraint feasibility while significantly reducing communication overhead. We provide sharp convergence guarantees, demonstrating that EF-Landing achieves the same asymptotic linear speedup convergence rate as existing methods without communication compression. Furthermore, our analysis is highly versatile, applying to both deterministic and stochastic settings and encompassing algorithms based on gradient descent or momentum-based gradient descent. We also generalize EF-Landing to operate on block-wise Stiefel manifolds, enabling greater flexibility for structured constraints. Extensive numerical experiments validate our theoretical results.

math.OC↗

arXiv2025

Dichotomy of flat bands in the van der Waals ferromagnet Fe$_5$GeTe$_2$

Quantum materials with bands of narrow bandwidth near the Fermi level represent a promising platform for exploring a diverse range of fascinating physical phenomena, as the high density of states within the small energy window often enables the emergence of many-body physics. On one hand, flat bands can arise from strong Coulomb interactions that localize atomic orbitals. On the other hand, quantum destructive interference can quench the electronic kinetic energy. Although both have a narrow bandwidth, the two types of flat bands should exhibit very distinct spectral properties arising from their distinctive origins. So far, the two types of flat bands have only been realized in very different material settings and chemical environments, preventing a direct comparison. Here, we report the observation of the two types of flat bands within the same material system--an above-room-temperature van der Waals ferromagnet, Fe$_{5-x}$GeTe$_2$, distinguishable by a switchable iron site order. The contrasting nature of the flat bands is also identified by the remarkably distinctive temperature-evolution of the spectral features, indicating that one arises from electron correlations in the Fe(1) site-disordered phase, while the other geometrical frustration in the Fe(1) site-ordered phase. Our results therefore provide a direct juxtaposition of the distinct formation mechanism of flat bands in quantum materials, and an avenue for understanding the distinctive roles flat bands play in the presence of magnetism, topology, and lattice geometrical frustration, utilizing sublattice ordering as a key control parameter.

cond-mat.str-el↗

arXiv2025

External Large Foundation Model: How to Efficiently Serve Trillions of Parameters for Online Ads Recommendation

Ads recommendation is a prominent service of online advertising systems and has been actively studied. Recent studies indicate that scaling-up and advanced design of the recommendation model can bring significant performance improvement. However, with a larger model scale, such prior studies have a significantly increasing gap from industry as they often neglect two fundamental challenges in industrial-scale applications. First, training and inference budgets are restricted for the model to be served, exceeding which may incur latency and impair user experience. Second, large-volume data arrive in a streaming mode with data distributions dynamically shifting, as new users/ads join and existing users/ads leave the system. We propose the External Large Foundation Model (ExFM) framework to address the overlooked challenges. Specifically, we develop external distillation and a data augmentation system (DAS) to control the computational cost of training/inference while maintaining high performance. We design the teacher in a way like a foundation model (FM) that can serve multiple students as vertical models (VMs) to amortize its building cost. We propose Auxiliary Head and Student Adapter to mitigate the data distribution gap between FM and VMs caused by the streaming data issue. Comprehensive experiments on internal industrial-scale applications and public datasets demonstrate significant performance gain by ExFM.

cs.IR↗

arXiv2025

Detection of subsurface structures with a vehicle-based atom gravity gradiometer

High-precision mobile gravity gradiometers are very useful in geodesy and geophysics. Atom gravity gradiometers (AGGs) could be among the most accurate mobile gravity gradiometers but are currently constrained by the trade-off between portability and sensitivity. Here, we present a high-sensitivity mobile AGG featuring an ultra-compact sensor head with a volume of only 94 L. In the laboratory, it achieves a sensitivity of 77 E/$\sqrt{Hz}$ (1 E=1$\times10^{-9}$/s$^2$) and a long-term stability of better than 0.5 E. We integrated the instrument in a minivan, enabling efficient mobile field surveys with excellent maneuverability in confined spaces. Using this vehicular system, we surveyed the gravitational field over a set of subsurface structures within a small wooded area, successfully resolving their structural signatures with a signal-to-noise ratio of 57 and quantifying the water depth in a reservoir with an accuracy of $\pm$0.23 m. Compared with previous observations using a CG-5 gravimeter, the superior spatial resolution inherent in gradiometry is clearly demonstrated. This work paves the way for bring AGGs to practical field applications.

physics.ins-det↗

arXiv2025

NuGrounding: A Multi-View 3D Visual Grounding Framework in Autonomous Driving

Multi-view 3D visual grounding is critical for autonomous driving vehicles to interpret natural languages and localize target objects in complex environments. However, existing datasets and methods suffer from coarse-grained language instructions, and inadequate integration of 3D geometric reasoning with linguistic comprehension. To this end, we introduce NuGrounding, the first large-scale benchmark for multi-view 3D visual grounding in autonomous driving. We present a Hierarchy of Grounding (HoG) method to construct NuGrounding to generate hierarchical multi-level instructions, ensuring comprehensive coverage of human instruction patterns. To tackle this challenging dataset, we propose a novel paradigm that seamlessly combines instruction comprehension abilities of multi-modal LLMs (MLLMs) with precise localization abilities of specialist detection models. Our approach introduces two decoupled task tokens and a context query to aggregate 3D geometric information and semantic instructions, followed by a fusion decoder to refine spatial-semantic feature fusion for precise localization. Extensive experiments demonstrate that our method significantly outperforms the baselines adapted from representative 3D scene understanding methods by a significant margin and achieves 0.59 in precision and 0.64 in recall, with improvements of 50.8% and 54.7%.

cs.CV↗

arXiv2025

Anomalous properties of spark plasma sintered boron nitride solids

Hexagonal boron nitride (h-BN) is a brittle ceramic with a layered structure, however, recent experiments have suggested that inter-layer structural engineering could be key to new structural and functional properties. Here we report the scalable bulk synthesis of high-density crystalline h-BN solids, by using high-temperature spark plasma sintering (SPS) of h-BN powders, which show high values of mechanical strength, ductility, dielectric constant, thermal conductivity, and exceptional neutron radiation shielding capability. Through exhaustive characterizations we reveal that SPS induces non-basal plane crystallinity, twisting of layers, and facilitates inter-grain fusion with a high degree of in-plane alignment across macroscale dimensions, resulting in near-theoretical density and improved properties. Our findings highlight the importance of material design, via new approaches such as layer twisting and interlayer interconnections, to create novel ceramics with properties that could go beyond their intrinsic limits.

cond-mat.mtrl-sci↗

arXiv2025

Anomalous Electrical Transport in the Kagome Magnet YbFe$_6$Ge$_6$

Two-dimensional (2D) kagome metals offer a unique platform for exploring electron correlation phenomena derived from quantum many-body effects. Here, we report a combined study of electrical magnetotransport and neutron scattering on YbFe$_6$Ge$_6$, where the Fe moments in the 2D kagome layers exhibit an $A$-type collinear antiferromagnetic order below $T_{\rm{N}} \approx 500$ K. Interactions between the Fe ions in the layers and the localized Yb magnetic ions in between reorient the $c$-axis aligned Fe moments to the kagome plane below $T_{\rm{SR}} \approx 63$ K. Our magnetotransport measurements show an intriguing anomalous Hall effect (AHE) that emerges in the spin-reorientated collinear state, accompanied by the closing of the spin anisotropy gap as revealed from inelastic neutron scattering. The gapless spin excitations and the Yb-Fe interaction are able to support a dynamic scalar spin chirality, which explains the observed AHE. Therefore, our study demonstrates spin fluctuations may provide an additional scattering channel for the conduction electrons and give rise to AHE even in a collinear antiferromagnet.

cond-mat.str-el↗

arXiv2025

Charge Dynamics of an Unconventional Three-Dimensional Charge Density Wave in Kagome FeGe

We report on the charge dynamics of kagome FeGe, an antiferromagnet with a charge density wave (CDW) transition at $T_{\mathrm{CDW}} \simeq 105$ K, using polarized infrared spectroscopy and band structure calculations. We reveal pronounced optical anisotropy along the $a$- and $c$-axis, as well as an unusual response associated with three-dimensional CDW order. Above $T_{\mathrm{CDW}}$, there is a notable transfer of spectral weight (SW) from high to low energies, promoted by the magnetic splitting-induced shift in bands. Across the CDW transition, we observe a sudden SW transfer from low to high energies over a broad range, along with the emergence of new excitations around 1200 cm$^{-1}$. These results contrast with observations from other kagome metals like CsV$_3$Sb$_5$, where the nesting of VHSs leads to a clear CDW gap feature. Instead, our findings can be accounted for by a $2\times2\times2$ CDW ground state driven by a first-order structural transition involving large partial Ge1-dimerization. Our study thus unveils a complex interplay among structure, magnetism, and charge order, offering valuable insights for a comprehensive understanding of CDW order in FeGe.

cond-mat.str-el↗

arXiv2025

Bilevel reinforcement learning via the development of hyper-gradient without lower-level convexity

Bilevel reinforcement learning (RL), which features intertwined two-level problems, has attracted growing interest recently. The inherent non-convexity of the lower-level RL problem is, however, to be an impediment to developing bilevel optimization methods. By employing the fixed point equation associated with the regularized RL, we characterize the hyper-gradient via fully first-order information, thus circumventing the assumption of lower-level convexity. This, remarkably, distinguishes our development of hyper-gradient from the general AID-based bilevel frameworks since we take advantage of the specific structure of RL problems. Moreover, we design both model-based and model-free bilevel reinforcement learning algorithms, facilitated by access to the fully first-order hyper-gradient. Both algorithms enjoy the convergence rate $O(ε^{-1})$. To extend the applicability, a stochastic version of the model-free algorithm is proposed, along with results on its iteration and sample complexity. In addition, numerical experiments demonstrate that the hyper-gradient indeed serves as an integration of exploitation and exploration.

math.OC↗

arXiv2025

LancBiO: dynamic Lanczos-aided bilevel optimization via Krylov subspace

Bilevel optimization, with broad applications in machine learning, has an intricate hierarchical structure. Gradient-based methods have emerged as a common approach to large-scale bilevel problems. However, the computation of the hyper-gradient, which involves a Hessian inverse vector product, confines the efficiency and is regarded as a bottleneck. To circumvent the inverse, we construct a sequence of low-dimensional approximate Krylov subspaces with the aid of the Lanczos process. As a result, the constructed subspace is able to dynamically and incrementally approximate the Hessian inverse vector product with less effort and thus leads to a favorable estimate of the hyper-gradient. Moreover, we propose a provable subspace-based framework for bilevel problems where one central step is to solve a small-size tridiagonal linear system. To the best of our knowledge, this is the first time that subspace techniques are incorporated into bilevel optimization. This successful trial not only enjoys $\mathcal{O}(ε^{-1})$ convergence rate but also demonstrates efficiency in a synthetic problem and two deep learning tasks.

math.OC↗

arXiv2025

Optimization on product manifolds under a preconditioned metric

Since optimization on Riemannian manifolds relies on the chosen metric, it is appealing to know that how the performance of a Riemannian optimization method varies with different metrics and how to exquisitely construct a metric such that a method can be accelerated. To this end, we propose a general framework for optimization problems on product manifolds endowed with a preconditioned metric, and we develop Riemannian methods under this metric. Generally, the metric is constructed by an operator that aims to approximate the diagonal blocks of the Riemannian Hessian of the cost function. We propose three specific approaches to design the operator: exact block diagonal preconditioning, left and right preconditioning, and Gauss--Newton type preconditioning. Specifically, we tailor new preconditioned metrics and adapt the proposed Riemannian methods to the canonical correlation analysis and the truncated singular value decomposition problems, which provably accelerate the Riemannian methods. Additionally, we adopt the Gauss--Newton type preconditioning to solve the tensor ring completion problem. Numerical results among these applications verify that a delicate metric does accelerate the Riemannian optimization methods.

math.OC↗

arXiv2025

Riemannian Optimization for Holevo Capacity

Computing the classical capacity of a noisy quantum channel is crucial for understanding the limits of communication over quantum channels. However, its evaluation remains challenging due to the difficulty of computing the Holevo capacity and the even greater difficulty of regularization. In this work, we formulate the computation of the Holevo capacity as an optimization problem on a product manifold constructed from probability distributions and their corresponding pure input states for a quantum channel. A Riemannian gradient descent algorithm is proposed to solve the problem, providing lower bounds on the classical capacity of general quantum channels and outperforming existing methods in numerical experiments in both efficiency and scale.

quant-ph↗