arXiv subjects

Yunzhi Li

Publications and source records attributed to Yunzhi Li.

8 recordsLinked to original sources

arXiv2026

Exactness of Symmetry-Broken Self-Interaction Correction in the Strongly-Correlated or Classical Limit: Harmonium as a Demonstration

Strong electron correlation is an important challenge to both wavefunction and density functional theory. It has been argued that the Perdew-Zunger 1981 self-interaction correction to any density functional approximation, after symmetry breaking, can correctly describe the ground-state energy in the strongly-correlated limit in which each electron is described by a highly-localized and non-overlapped one-electron spin orbital. It has also been argued that the classical limit, in which Planck's constant tends to zero, is the strongly-correlated limit of quantum mechanics, where standard density functionals fail badly, as demonstrated by the exactly-solvable problem of harmonium (two Coulomb-interacting electrons bound by a spherically-symmetric harmonic-oscillator external potential). Here we combine these two ideas and demonstrate that, for harmonium, symmetry-broken self-interaction correction is exact in the limit where Planck's constant tends to zero, and usefully accurate for all values between 0 and the physical value (1 in atomic units). We also show that the Planck-constant-dependent symmetric ground-state density can be restored by spherical averaging of the broken-symmetry density.

cond-mat.str-el↗

arXiv2026

Not Just Pockets: Understanding Phone-Carrying Behaviors of Wheelchair Users for Mobile Context-Awareness

Smartphone-based context-awareness holds significant promise for wheelchair users -- from detecting everyday accessibility barriers to enabling ability-based adaptations. Such capabilities often build on passive context inference through mobile sensing, yet their accuracy hinges on how and where phones are carried and the resulting signal quality. While prior work documents phone-carrying behaviors in the general population, patterns specific to wheelchair users remain underexplored. Through a mixed-methods approach combining a survey of 91 and interviews with 15 wheelchair users, we systematically investigate their phone-carrying locations and influencing factors. Our findings reveal distinct patterns extending beyond pocket storage to diverse wheelchair-mounted accessories and around-body placements, shaped by the interplay of physical ability, wheelchair design, and everyday contexts, including social, activity, and device factors. Grounded in these findings, we articulate how carrying location can serve as a proxy for user context to enable novel context-aware experiences, and discuss design implications for developing inclusive and effective mobile context-aware applications.

cs.HC↗

arXiv2026

Your Data Manifold is Secretly a Reward Model: Shell-LCC for Text-to-Video Generation

Recent text-to-video (T2V) diffusion models rely heavily on auxiliary reward signals (e.g., via reward models or DPO) to align generated content with human aesthetics and improve realism. These signals, however, incur substantial computational overhead, require costly human annotations, and often yield limited improvement in fine-grained local details. In this paper, we argue that your data manifold is secretly a reward model. By explicitly modeling the manifold structure of high-quality Supervised Fine-Tuning (SFT) data and encouraging video latents to lie on this manifold, we derive dense, differentiable, and nearly cost-free reward signals that significantly improve video quality, particularly in mitigating low-level distortions. Our modeling builds upon Local Coordinate Coding (LCC), which captures the `skeleton' of the manifold. However, directly applying LCC suffers from mean regression, pulling latents toward the geometric mean and losing high-frequency details. We therefore extend it to Shell Local Coordinate Coding (Shell-LCC), which models the manifold `surface' as an isotropic shell to align with the true high-density region. Experiments demonstrate that our approach improves realism, enhances high-frequency details, reduces over-smoothing artifacts, and alleviates motion blur.

cs.CV↗

arXiv2026

Spin-adapted neural network backflow for symmetry-preserving simulations of strongly correlated electrons

Strongly correlated molecules often contain dense manifolds of low-lying spin states, making total-spin symmetry essential for predictive electronic-structure theory. Neural-network quantum states provide flexible variational wavefunctions, but commonly used fermionic architectures do not enforce this symmetry and can therefore converge to spin-contaminated states with misleading energies and properties. Here we introduce a spin-adapted neural-network backflow (SA-NNBF) ansatz in second quantization, which combines configuration-dependent spatial orbitals with a compressed spin eigenfunction. A projected tensor compression scheme for spin eigenfunctions and a particle-hole representation make variational Monte Carlo calculations with SA-NNBF practical for active spaces containing more than one hundred electrons. Across hydrogen chains and iron-sulfur clusters, SA-NNBF eliminates spin contamination and consistently achieves lower variational energies than standard NNBF with a comparable number of parameters. For the CAS(113e,76o) active-space model of FeMoco, SA-NNBF yields a highly compact spin-adapted variational state, achieving an energy competitive with recent spin-adapted DMRG calculations at bond dimension $D=10000$ while using orders of magnitude fewer parameters. Our work establishes a general framework for developing spin-symmetry-preserving neural-network quantum states for chemically realistic strongly correlated electrons.

physics.chem-ph↗

arXiv2026

Clifford disentanglers for entanglement reduction in molecular electronic structure simulations

Entanglement is a key bottleneck limiting the efficiency of tensor-network and quantum simulations of molecular electronic structures. Here, we systematically assess and extend Clifford disentanglers as a structure-preserving approach to entanglement reduction: they can modify the entanglement structure of qubit wavefunctions while retaining the Pauli-string form of qubit Hamiltonians. To enable a practical search over Clifford transformations, we classify Clifford operators by their action on the Schmidt spectrum across a bipartition, reducing the two- and four-qubit search spaces to 20 and 91392 representatives, respectively. Embedded in an iterative Clifford-augmented matrix product state framework, these transformations reduce the energy errors at fixed bond dimension for the molecular test cases studied and mitigate the dependence on orbital orderings and fermion-to-qubit mappings. We further show that Clifford disentanglers can also benefit quantum simulations such as the shallow-circuit variational quantum eigensolver calculations. Together, these results establish Clifford disentanglers as a useful structure-preserving entanglement-engineering tool for tensor-network and quantum simulations of molecular electronic structure, while also clarifying their correlation dependence and motivating future developments.

quant-ph↗

arXiv2025

Measuring multi-site pulse transit time with an AI-enabled mmWave radar

Pulse Transit Time (PTT) is a measure of arterial stiffness and a physiological marker associated with cardiovascular function, with an inverse relationship to diastolic blood pressure (DBP). We present the first AI-enabled mmWave system for contactless multi-site PTT measurement using a single radar. By leveraging radar beamforming and deep learning algorithms our system simultaneously measures PTT and estimates diastolic blood pressure at multiple sites. The system was evaluated across three physiological pathways - heart-to-radial artery, heart-to-carotid artery, and mastoid area-to-radial artery -- achieving correlation coefficients of 0.73-0.89 compared to contact-based reference sensors for measuring PTT. Furthermore, the system demonstrated correlation coefficients of 0.90-0.92 for estimating DBP, and achieved a mean error of -1.00-0.62 mmHg and standard deviation of 4.97-5.70 mmHg, meeting the FDA's AAMI guidelines for non-invasive blood pressure monitors. These results suggest that our proposed system has the potential to provide a non-invasive measure of cardiovascular health across multiple regions of the body.

physics.med-ph↗

arXiv2025

Exact Constraint of Density Functional Approximations at the Semiclassical Limit

We introduce the semiclassical limit to electronic systems by taking the limit $\hbar\rightarrow 0$ in the solution of Schrödinger equations. We show that this limit is closely related to one type of strong correlation that is particularly challenging from conventional multi-configurational perspective but can be readily described through semiclassical analysis. Furthermore, by studying the performance of density functional approximations (DFAs) in the semiclassical limit, we find that mainstream DFAs have erroneous divergent energy behaviors as $\hbar \rightarrow 0$, violating the exact constraint of finite energy. Importantly, by making connection of the significantly underestimated DFA energies of many strongly correlated transition-metal diatomic molecules to their rather small estimated $\hbar_{\text{eff}}$, we demonstrate the usefulness of our semiclassical analysis and its promise for inspiring better DFAs.

physics.comp-ph↗

arXiv2024

WheelPoser: Sparse-IMU Based Body Pose Estimation for Wheelchair Users

Despite researchers having extensively studied various ways to track body pose on-the-go, most prior work does not take into account wheelchair users, leading to poor tracking performance. Wheelchair users could greatly benefit from this pose information to prevent injuries, monitor their health, identify environmental accessibility barriers, and interact with gaming and VR experiences. In this work, we present WheelPoser, a real-time pose estimation system specifically designed for wheelchair users. Our system uses only four strategically placed IMUs on the user's body and wheelchair, making it far more practical than prior systems using cameras and dense IMU arrays. WheelPoser is able to track a wheelchair user's pose with a mean joint angle error of 14.30 degrees and a mean joint position error of 6.74 cm, more than three times better than similar systems using sparse IMUs. To train our system, we collect a novel WheelPoser-IMU dataset, consisting of 167 minutes of paired IMU sensor and motion capture data of people in wheelchairs, including wheelchair-specific motions such as propulsion and pressure relief. Finally, we explore the potential application space enabled by our system and discuss future opportunities. Open-source code, models, and dataset can be found here: https://github.com/axle-lab/WheelPoser.

cs.GR↗