arXiv subjects

Seungwoo Lee

Publications and source records attributed to Seungwoo Lee.

At least 19 recordsLinked to original sources

arXiv2026

Fundamental limits of hot-carrier and photothermal plasmonic solar chemistry

Plasmonic catalysts convert sunlight through direct metal--adsorbate excitation, nonequilibrium hot carriers and photothermal heating, yet these channels lack a common efficiency limit analogous to the Shockley-Queisser (SQ) limit. Here, we formulate a channel-resolved detailed-balance theory for plasmonic solar chemistry. The theory enforces exclusive partition of absorbed power, microscopic reversibility, non-negative entropy production, electromagnetic passivity and causality, and one physical nanostructure shared by all wavelengths and operating states. A reduced tightly coupled cycle obeys a chemical diode law with closed-form stall and maximum-power free energies. It recovers a 33.68% single-threshold radiative limit and the 30.58% ideal single-junction water-splitting limit at a 1.23-eV chemical load, whereas a Fowler hot-carrier kernel lowers the limit to 8.53%. Finite one-structure benchmarks show that independent wavelength optimization overestimates shared-geometry performance by 6.6-33.8%, while cross-frequency correlations tighten semidefinite upper bounds by 21-56%. An exact multi-electron network yields a universal arrival-storage criterion and the low-flux scaling J_n\sim g^nτ^{n-1}. A source-conditioned gold/p-type gallium nitride case study and a bias- and separation-complete ledger then distinguish enhanced product formation from net solar-energy conversion. The resulting framework provides a thermodynamically closed benchmark for hot-carrier and photothermal plasmonic chemistry.

physics.optics↗

arXiv2026

See & Sniff: Learning Visuo-Olfactory Representations

While modern multimodal models integrate vision with language, audio, or touch, olfaction remains largely unexplored due to the lack of paired visuo-olfactory data. We introduce SmellNet-V, a scalable visuo-olfactory dataset built on the insight that odor identity is largely invariant to visual transformations within a semantic category. This allows us to synthetically pair smell-only samples with semantically aligned in-the-wild web images, converting a unimodal olfactory dataset into a cross-modal benchmark without costly co-collection. Building on this dataset, we propose See & Sniff, a self-supervised framework that learns joint visuo-olfactory representations via dense local alignment and naturally produces smell saliency maps for spatial grounding of odor sources. We further introduce pixel-level smell localization task and a benchmark for evaluation. Our method surpasses smell-only baselines by 7% in smell classification from smell alone and generalizes to cross-modal retrieval and smell localization, establishing visuo-olfactory learning as a new direction in multimodal perception.

cs.CV↗

arXiv2026

Transition Metal Dichalcogenides Multijunction Solar Cells Toward the Multicolor Limit

Transition metal dichalcogenides (TMDs) and other van der Waals (vdW) semiconductors enable transfer-printed, lattice-mismatch-free stacking of many photovoltaic junctions, motivating a re-examination of multijunction detailed-balance limits under realistic material and optical constraints. Here, we develop a unified thermodynamic framework for a multijunction photovoltaic device, which can define a clear set of device-window constraints, optical boundary conditions, and luminescence/entropy penalties and therefore define how closely any realistic multijunction photovoltaic device can approach multicolor limit. By applying it to a conservative TMD bandgap window (1.0-2.1~eV), we show that the accessible bandgap window imposes a large-junction number (N) efficiency limit: under full concentration, unconstrained ladders approach 84.5% at N=50, whereas the TMD window plateaus near 63.4%. This efficiency plateau is set by photons outside the bandgaps, so radiative quality and optics dominate beyond N=5 junctions for realistic transfer-printed device stacks. We identify an experimentally achievable N=5 ladder Eg~(2.10,1.78,1.50,1.24, 1.00)eV and map each rung to candidate vdW/TMD absorbers. Using reciprocity and luminescence thermodynamics, we quantify penalties from finite external radiative efficiency, two-sided emission, and luminescent coupling, and introduce the upward-emitted luminescence power as an indicator of entropy-loss proxy. Incorporating excitonic absorptance and nanophotonic thickness bounds yields practical thickness and light-management targets for transfer-printed stacks. Finally, inserting an idealized nonreciprocal multijunction model into the reciprocity-optimized ladders provides conservative efficiency advantage estimates, which are consistent with negligible benefit for single junctions but measurable efficiency gains for multijunction TMD devices.

physics.optics↗

arXiv2026

Fundamental Efficiency Limits of Transition-Metal Dichalcogenide Solar Cells with Carrier Multiplication and Hot-Carrier Effects

Detailed-balance limits for transition-metal dichalcogenide (TMD) solar cells have been reported, but existing TMD-specific limits do not simultaneously resolve thickness-dependent optics, carrier multiplication (CM), hot-carrier (HC) extraction, and finite cooling leakage. Here, we develop a generalized detailed-balance theory that provides an upper-bound framework. The model combines energy- and thickness-dependent absorptance a(E,d), exciton-resolved monolayer absorbance, an experimentally available CM quantum-yield limit (eta_CM <= 0.97), and an endoreversible HC engine with ideal energy-selective contacts and finite heat-leak coefficient kappa. The framework shows that CM and HC draw on the same above-gap photon-energy reservoir; therefore, CM does not raise the reversible HC thermodynamic limit. Instead, CM can protect finite-kappa performance only by shifting excess-energy utilization from a cooling-sensitive voltage channel into collected current. For optically thick TMDs under AM1.5G illumination, the SQ optimum lies near E_g = 1.3 eV, whereas the CM/HC-favored envelope shifts toward E_g = 1.0 eV with reversible efficiencies above 50%. For monolayer TMDs such as WSe2 (E_g = 1.63 eV), CM is essentially inactive because only about 3.7% of above-gap AM1.5G photons satisfy E > 2E_g, giving an idealized short-circuit-current gain of only about 0.6% before device nonidealities. Bulk-like TMDs can show large HC-related gains at d = 10-50 nm, but even kappa = 0.2 W m^-2 K^-1 implies about 100 W m^-2 heat leak for Delta T = 500 K. Thus, high-E_g monolayer TMDs are not promising one-sun CM candidates, whereas narrow-E_g, bulk-like TMD absorbers remain plausible beyond-SQ candidates only if energy-selective extraction and phonon-engineered cooling suppression are realized together.

physics.app-ph↗

arXiv2026

Gate-Reconfigurable Single- and Double-Dot Transport in Trilayer MoSe2

We report gate-controlled quantum-dot transport in a trilayer MoSe2 device that combines a graphite back gate beneath the active region, a separate global gate for conductive access regions, and local top finger gates. In the low-backgate regime, bias spectroscopy shows regular Coulomb-blockade diamonds characteristic of single-dot transport. As backgate is increased, additional low-bias structure develops beyond a simple single-dot pattern, indicating that the electrostatic landscape is reshaped and that a second dot becomes active in transport. In the higher-backgate regime, plunger-gate tuning and two-gate measurements establish a gate-reconfigurable double-dot configuration with two non-equivalent dots whose relative alignment and interdot coupling evolve with gate voltage. These results indicate that trilayer MoSe2 supports electrically reconfigurable single- and double-dot transport in the present device architecture.

cond-mat.mes-hall↗

arXiv2026

Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions

We address the problem of tactile localization, where the goal is to identify image regions that share the same material properties as a tactile input. Existing visuo-tactile methods rely on global alignment and thus fail to capture the fine-grained local correspondences required for this task. The challenge is amplified by existing datasets, which predominantly contain close-up, low-diversity images. We propose a model that learns local visuo-tactile alignment via dense cross-modal feature interactions, producing tactile saliency maps for touch-conditioned material segmentation. To overcome dataset constraints, we introduce: (i) in-the-wild multi-material scene images that expand visual diversity, and (ii) a material-diversity pairing strategy that aligns each tactile sample with visually varied yet tactilely consistent images, improving contextual localization and robustness to weak signals. We also construct two new tactile-grounded material segmentation datasets for quantitative evaluation. Experiments on both new and existing benchmarks show that our approach substantially outperforms prior visuo-tactile methods in tactile localization.

cs.CV↗

arXiv2026

Local control and lateral nanofocusing of hyperbolic phonon polaritons

Phonon polaritons in van der Waals crystals enable exceptional light confinement and control over low-loss nanolight propagation. The polariton wavelength can be controlled by the crystal geometry, isotopic composition, or surrounding environment -- for which substrate engineering is particularly effective. However, existing approaches of substrate nanopatterning are binary and offer limited leverage. Here, we demonstrate local control over the wavelength of phonon polaritons in hexagonal boron nitride by employing a sinusoidally corrugated gold surface to smoothly vary the gap between the van der Waals crystal and metallic substrate. The nonuniform gap provides a continuous and nearly threefold local variation of the polariton wavelength across the structure, verified by near-field optical microscopy. Our platform further enables lateral nanofocusing by gradually compressing and decompressing the wavelength of propagating polaritons by a factor of around 2.5 achieved solely through substrate geometry, consistent with our local control experiments and theoretical calculations. Our results push the boundaries of substrate engineering and showcase a powerful method for precise and local tailoring of polaritonic modes.

physics.optics↗

arXiv2026

Asymptotic stability and cut-off phenomenon for the underdamped Langevin dynamics

In this article, we provide detailed analysis of the long-time behavior of the underdamped Langevin dynamics. We first provide a necessary condition guaranteeing that the zero-noise dynamical system converges to its unique attractor. We also observed that this condition is sharp for a large class of linear models. We then prove the so-called cut-off phenomenon in the small-noise regime under this condition. This result provides the precise asymptotics of the mixing time of the process and of the distance between the distribution of the process and its stationary measure. The main difficulty of this work relies on the degeneracy of its infinitesimal generator which is not elliptic, thus requiring a new set of methods.

math.PR↗

arXiv2026

Eyring-Kramers Law for the Underdamped Langevin Process

Consider the underdamped Langevin process $(q(t),p(t))_{t\geq0}$ in $\R^d\times\R^d$. We derive the low-temperature asymptotic of its mean-transition time between basins of attraction for a double-well potential. This asymptotic is called Eyring-Kramers law and often relies in the literature on Potential theory tools which are ill-defined for hypoelliptic processes like the underdamped Langevin process. In this work, we implement a novel approach which circumvents the use of these traditional methods.

math.PR↗

arXiv2026

FastAV: Efficient Token Pruning for Audio-Visual Large Language Model Inference

In this work, we present FastAV, the first token pruning framework tailored for audio-visual large language models (AV-LLMs). While token pruning has been actively explored in standard large language models (LLMs) and vision-language models (LVLMs), its application to AV-LLMs has received little attention, even though multimodal integration substantially increases their token demands. To address this gap, we introduce a pruning strategy that utilizes attention weights to identify tokens emphasized at different stages and estimates their importance. Building on this analysis, FastAV applies a two-stage pruning strategy: (1) global pruning in intermediate layers to remove broadly less influential tokens, and (2) fine pruning in later layers considering the impact on next token generation. Notably, our method does not rely on full attention maps, which makes it fully compatible with efficient attention mechanisms such as FlashAttention. Extensive experiments demonstrate that FastAV reduces FLOPs by more than 40% on two representative AV-LLMs, while preserving or even improving model performance.

cs.LG↗

arXiv2026

FEATHer: Fourier-Efficient Adaptive Temporal Hierarchy Forecaster for Time-Series Forecasting

Time-series forecasting is fundamental in industrial domains like manufacturing and smart factories. As systems evolve toward automation, models must operate on edge devices (e.g., PLCs, microcontrollers) with strict constraints on latency and memory, limiting parameters to a few thousand. Conventional deep architectures are often impractical here. We propose the Fourier-Efficient Adaptive Temporal Hierarchy Forecaster (FEATHer) for accurate long-term forecasting under severe limits. FEATHer introduces: (i) ultra-lightweight multiscale decomposition into frequency pathways; (ii) a shared Dense Temporal Kernel using projection-depthwise convolution-projection without recurrence or attention; (iii) frequency-aware branch gating that adaptively fuses representations based on spectral characteristics; and (iv) a Sparse Period Kernel reconstructing outputs via period-wise downsampling to capture seasonality. FEATHer maintains a compact architecture (as few as 400 parameters) while outperforming baselines. Across eight benchmarks, it achieves the best ranking, recording 60 first-place results with an average rank of 2.05. These results demonstrate that reliable long-range forecasting is achievable on constrained edge hardware, offering a practical direction for industrial real-time inference.

cs.LG↗

arXiv2025

Resource-efficient algorithm for estimating the trace of quantum state powers

Estimating the trace of quantum state powers, $\text{Tr}(ρ^k)$, for $k$ identical quantum states is a fundamental task with numerous applications in quantum information processing, including nonlinear function estimation of quantum states and entanglement detection. On near-term quantum devices, reducing the required quantum circuit depth, the number of multi-qubit quantum operations, and the copies of the quantum state needed for such computations is crucial. In this work, inspired by the Newton-Girard method, we significantly improve upon existing results by introducing an algorithm that requires only $\mathcal{O}(\widetilde{r})$ qubits and $\mathcal{O}(\widetilde{r})$ multi-qubit gates, where $\widetilde{r} = \min\left\{\text{rank}(ρ), \left\lceil\ln\left({2k}/ε\right)\right\rceil\right\}$. This approach is efficient, as it employs the $\tilde{r}$-entangled copy measurement instead of the conventional $k$-entangled copy measurement, while asymptotically preserving the known sample complexity upper bound. Furthermore, we prove that estimating $\{\text{Tr}(ρ^i)\}_{i=1}^{\tilde{r}}$ is sufficient to approximate $\text{Tr}(ρ^k)$ even for large integers $k > \widetilde{r}$. This leads to a rank-dependent complexity for solving the problem, providing an efficient algorithm for low-rank quantum states while also improving existing methods when the rank is unknown or when the state is not low-rank. Building upon these advantages, we extend our algorithm to the estimation of $\text{Tr}(Mρ^k)$ for arbitrary observables and $\text{Tr}(ρ^k σ^l)$ for multiple quantum states.

quant-ph↗

arXiv2025

COIN: Confidence Score-Guided Distillation for Annotation-Free Cell Segmentation

Cell instance segmentation (CIS) is crucial for identifying individual cell morphologies in histopathological images, providing valuable insights for biological and medical research. While unsupervised CIS (UCIS) models aim to reduce the heavy reliance on labor-intensive image annotations, they fail to accurately capture cell boundaries, causing missed detections and poor performance. Recognizing the absence of error-free instances as a key limitation, we present COIN (COnfidence score-guided INstance distillation), a novel annotation-free framework with three key steps: (1) Increasing the sensitivity for the presence of error-free instances via unsupervised semantic segmentation with optimal transport, leveraging its ability to discriminate spatially minor instances, (2) Instance-level confidence scoring to measure the consistency between model prediction and refined mask and identify highly confident instances, offering an alternative to ground truth annotations, and (3) Progressive expansion of confidence with recursive self-distillation. Extensive experiments across six datasets show COIN outperforming existing UCIS methods, even surpassing semi- and weakly-supervised approaches across all metrics on the MoNuSeg and TNBC datasets. The code is available at https://github.com/shjo-april/COIN.

cs.CV↗

arXiv2025

3D DNA Origami-Enabled Molecularly Addressable Optical Nanocircuit

The optical nanocircuit concept provides a predictive framework analogous to an electric RLC circuit, where induced dipoles in plasmonic nanoparticle (NPs), ohmic losses in NPs, and dielectric gaps serve as inductors (L), capacitors (C), and resistors (R), respectively. This modular theory allows unprecedented design flexibility, expanding the range of achievable optical resonances in plasmonic clusters. However, existing experimental approaches, such as atomic force microscope tip-enabled nanomanipulation and electron-beam lithography, lack the critical accuracy in nanogap tuning and molecular loading required for applications like PRET. Here, we introduce a molecularly addressable optical nanocircuit enabled by DNA origami. First, we theoretically and experimentally confirmed that gold (Au) NPs and dye-loaded DNA origami can function as different circuit elements: R- and C-coupled L and R-coupled C, respectively. To assemble large Au NPs into designer optical nanocircuits, we utilized a mechanically robust 3D DNA origami design rather than conventionally used 2D origami sheet. This platform provided high reproducibility and accuracy in assembling a range of structures-from dimers to tetramers-with controlled symmetry, heterogeneity, and nanogap tunability. Together with ultrasmoothness and uniformity of Au NPs, we achieved the highest Q-factor for magnetic resonance of a nanoparticle-based optical nanocircuit (~19.2). Also, selective molecular cargo loading onto designated 3D DNA origami sites within plasmonic clusters enabled deterministic, predictive light-molecule coupling in optical nanocircuits. This resulted in 100-fold stronger PRET signal in dimeric clusters compared to monomeric NPs. Our approach opens promising directions in designing custom optical resonances for use in molecular sensing, nonlinear optics, and quantum photonics.

physics.optics↗

arXiv2025

DNA Nanotechnology for Superradiance

Superradiance, first proposed by Dicke in 1954, is a highly efficient quantum light source that differs from conventional spontaneous emission. Unlike typical spontaneous emission, where intensity scales linearly with the number of electric dipoles, superradiance exhibits an intensity that scales quadratically with the number of electric dipoles. Similarly, the decay rate also increases proportionally to the dipole numbers. This collective emission is especially powerful when it manifests as superfluorescence, a physical regime of superradiance where spontaneously emerging coherence is achieved by arranging excited dipoles in the same orientation within a volume much smaller than their emission wavelength. Numerous experimental strategies have been employed to generate superradiance, with one common approach being the use of stochastically formed aggregates of quantum dots and organic dyes. However, the inherent randomness in such systems prevents precise control over the number, spatial distribution, and relative orientation of the emitters. This often leads to non-uniform coupling strengths and parasitic dephasing effects, which make it difficult to predict the resulting quantum emission and limit its use in engineered devices. A deterministic platform that provides precise control over these parameters is therefore essential for realizing the full potential of superradiant systems. Here, we (i) specifically outline the advantages of DNA nanotechnology in tackling this challenge, (ii) discuss the reasons why superradiance has not yet been realized even with the state-of-the art DNA nanotechnology, and (iii) propose potential solutions for overcoming the current limitations.

quant-ph↗

arXiv2025

Bloch phonon-polaritons with anomalous dispersion in polaritonic Fourier crystals

The recently suggested concept of a polaritonic Fourier crystal (PFC) is based on a harmonically-corrugated mirror substrate for a thin pristine polaritonic crystal layer. The propagating polaritons in PFC experience a harmonic and mode-selective momentum modulation leading to a manifestation of Bloch modes with practically zero inter-mode scattering. PFC was first demonstrated for the hyperbolic phonon-polaritons in hexagonal boron nitride (hBN) within its Type II Reststrahlen band (RB-II) where the in-plane components of the dielectric permittivity tensor are isotropic and negative, while the out-of-plane component is positive. By contrast, a Type I Reststrahlen band (RB-I) is characterized by negative out-of-plane and positive in-plane permittivity components, and consequently, the inversion of field symmetry of phonon-polaritons compared to RB-II. Behavior of such RB-I modes in a polaritonic crystal is yet to be explored. Here, we employ a biaxial crystal alpha-phase molybdenum trioxide (α-MoO3) and near-field imaging to study polaritonic Bloch modes in a one-dimensional PFC within the RB-I where the mid-infrared phonon-polaritons in α-MoO3 have anomalous dispersion and negative phase velocity. Surprisingly, we observe a manifestation of Bloch waves as a dispersionless near-field pattern across the first Brillouin zone, in contrast to RB-II case demonstrated with in-plane isotropic hBN. We attribute this difference to the opposite field symmetry of the lowest-order phonon-polariton mode in the two RBs, leading to a different momentum modulation regime in the polaritonic Fourier crystal. Our results reveal the importance of mode symmetry for polaritonic crystals in general and for the emerging field of Fourier crystals in particular, which promise new ways to manipulate the nanolight.

physics.optics↗

arXiv2025

ARC-NeRF: Area Ray Casting for Broader Unseen View Coverage in Few-shot Object Rendering

Recent advancements in the Neural Radiance Field (NeRF) have enhanced its capabilities for novel view synthesis, yet its reliance on dense multi-view training images poses a practical challenge, often leading to artifacts and a lack of fine object details. Addressing this, we propose ARC-NeRF, an effective regularization-based approach with a novel Area Ray Casting strategy. While the previous ray augmentation methods are limited to covering only a single unseen view per extra ray, our proposed Area Ray covers a broader range of unseen views with just a single ray and enables an adaptive high-frequency regularization based on target pixel photo-consistency. Moreover, we propose luminance consistency regularization, which enhances the consistency of relative luminance between the original and Area Ray, leading to more accurate object textures. The relative luminance, as a free lunch extra data easily derived from RGB images, can be effectively utilized in few-shot scenarios where available training data is limited. Our ARC-NeRF outperforms its baseline and achieves competitive results on multiple benchmarks with sharply rendered fine details.

cs.CV↗

arXiv2025

PoseSyn: Synthesizing Diverse 3D Pose Data from In-the-Wild 2D Data

Despite considerable efforts to enhance the generalization of 3D pose estimators without costly 3D annotations, existing data augmentation methods struggle in real world scenarios with diverse human appearances and complex poses. We propose PoseSyn, a novel data synthesis framework that transforms abundant in the wild 2D pose dataset into diverse 3D pose image pairs. PoseSyn comprises two key components: Error Extraction Module (EEM), which identifies challenging poses from the 2D pose datasets, and Motion Synthesis Module (MSM), which synthesizes motion sequences around the challenging poses. Then, by generating realistic 3D training data via a human animation model aligned with challenging poses and appearances PoseSyn boosts the accuracy of various 3D pose estimators by up to 14% across real world benchmarks including various backgrounds and occlusions, challenging poses, and multi view scenarios. Extensive experiments further confirm that PoseSyn is a scalable and effective approach for improving generalization without relying on expensive 3D annotations, regardless of the pose estimator's model size or design.

cs.CV↗