arXiv Query: search_query=abs:skin AND (abs:reflectance OR abs:polarization OR abs:polarized OR abs:"appearance capture" OR abs:subsurface OR abs:spectral) AND (abs:face OR abs:human OR abs:capture OR abs:rendering)&id_list=&start=0&max_results=60

A 2D autocorrelation-based frequency estimator reflecting spatial tissue distribution to improve Ultrasound H-scan tissue characterization

2026-09-25T01:45:48Z

H-scan is a promising quantitative ultrasound technique that estimates the frequency content of backscattered signals and maps the estimated frequencies onto a red/blue color scale to reflect underlying tissue properties. Although it relies on matched filters tuned to different frequencies, the broad spectral bandwidth of ultrasound produces noisy, granular displays. Here, we introduce an adaptive frequency estimator designed to suppress the noise within homogeneous regions while preserving sharpness across tissue boundaries. The method combines 2D autocorrelation with a matched filter. In the first stage, a matched-filter-based estimation yields an a priori map of the spatial distribution of frequencies. The local heterogeneity of these estimates then defines a 2D weighting function that guides a second estimation stage. Drawing on the concept of Loupas's blood velocity estimator, we apply autocorrelation over a 2D spatial kernel to recover the axial frequency components, employing a weighted summation that accounts for the spatial frequency distribution within the kernel. We benchmarked the proposed estimator against conventional approaches, including the short-time Fourier transform, the H-scan matched filter, and standard autocorrelation, using both Field II simulations and in vivo data from human subjects with hepatic steatosis. In simulation, our adaptive estimator reduced the noisy texture in homogeneous regions while retaining clear boundary delineation, whereas the other estimators could achieve only one of these objectives. Applied to the in vivo human liver, the estimator improved H-scan image quality by lowering noise and enhancing the discrimination of steatotic liver from adjacent gallbladder and skin layers.

GenMC: Real-Time Generative Monte Carlo Surrogate for Quantitative Photoacoustic Imaging

2026-09-23T15:22:11Z

Photoacoustic (PA) imaging provides molecular and functional information about tissue, such as blood oxygen saturation (sO2), yet its clinical translation is hindered by inaccurate quantification. A major source of error is the spectral colouring effect, in which wavelength-dependent optical attenuation distorts the local optical fluence. Monte Carlo (MC) simulation is the gold standard for modelling light transport, but its computational demand precludes real-time use. Here, GenMC is presented, a deep generative framework based on a conditional generative adversarial network that estimates optical fluence distributions from tissue anatomy and literature-derived optical properties, with anatomical priors obtained from co-registered ultrasound images. Trained on MC-generated synthetic datasets, GenMC produces high-fidelity fluence maps in under 30 ms per frame, a four-orders-of-magnitude speed-up over conventional MC simulation, and reaches peak signal-to-noise ratios of up to 36.24 dB in vivo, outperforming UNet and Pix2Pix baselines. Validation in blood-mimicking phantoms and in 37 human volunteers spanning Fitzpatrick skin types III-V demonstrates improved accuracy, robustness, and physiological consistency of sO2 estimation. By enabling real-time, accurate, and reproducible quantification of tissue oxygenation, GenMC addresses a critical barrier to quantitative PA imaging and offers a general strategy for rapid, high-fidelity approximation of light transport in tissue.

Pre- and Post-Exercise Monitoring of Physiological Skin States in Sportsmen Using 77 GHz FMCW Radar and Wavelet Scattering Transform

2026-09-19T18:00:03Z

This paper presents a contactless radar sensing framework that classifies physiologically-induced changes in the superficial skin layer following intense fixed-duration physical activity along with water intake restriction. A 77 GHz frequency-modulated continuous wave (FMCW) radar is used to capture high-resolution range profiles from the subject's chest, seated in front of the radar, before and after sports activity. Since the electromagnetic penetration into biological tissue is inherently shallow at millimeter-wave frequencies, the sensing is confined primarily to the stratum corneum and upper epidermis. We demonstrate that exercise-induced thermoregulation, perspiration residue deposition, and transient shifts in superficial tissue hydration produce measurable alterations in quasi-static radar reflectivity. We utilize single-chirp snapshots of duration 102.4 micro sec in order to suppress confounders such as slow-varying cardiopulmonary motion, followed by lightweight feature extraction via the Wavelet Scattering Transform (WST) and classification using light-weight machine learning models suitable for edge nodes. Leave-one-subject-out cross-validation on a dataset of 15 sportsmen measured pre- and post-exercise yields a mean classification accuracy of 91.55% with a 95% confidence interval of [84.27, 98.83]%. Interpretability analysis reveals that low-order scattering coefficients (capturing overall signal energy and amplitude stability) dominate discriminative power, consistent with quasi-static changes in skin surface permittivity. The results establish a proof-of-concept for radar-based, edge deployable classification of post-exercise skin states, opening new avenues for non-contact physiological monitoring in sports science.

Online Multimodal Workload Assessment in Contact-Rich Physical Human-Robot Interaction

2026-09-16T02:30:23Z

Contact-rich physical human--robot interaction (pHRI) imposes time-varying demands associated with physical interaction, motor regulation, and physiological response, motivating continuous assessment of interaction workload. This paper presents an online multimodal assessment framework that integrates interaction wrench, planar tool-center-point (TCP) kinematics, and skin conductance level (SCL) into four interpretable workload-related factors. Their relative contributions are adjusted using path curvature to reflect changes in motion demand and task progression to account for gradual physiological variation over time. The framework was evaluated with 24 participants across 18 controlled combinations of temperature, acoustic noise, and illuminance under two admittance-control modes. Strict leave-one-subject-out (LOSO) evaluation used standardized pupil diameter ($\mathrm{PD}_z$) as an independent physiological reference and included comparisons with static variants and representative state-of-the-art learning-based baselines. The proposed framework achieves a cohort-mean $30\,\mathrm{s}$ block-wise Spearman correlation of $ρ_{30}=0.308$ with the physiological reference, with positive subject-level correspondence in 23 of 24 participants. Its overall performance is comparable to the state-of-the-art learning-based baseline. At the same time, our framework keeps the assessment process transparent through explicit workload-related factors and defined weighting rules, while outperforming the corresponding fixed-weight formulation. The framework also maintains consistent performance across the two tested admittance-control modes. These results support a transparent and interpretable approach to continuous interaction workload assessment in contact-rich pHRI.

High-Bandwidth Biomimetic Finger for Tactile-Transparent Remote Texture Sensing

2026-08-13T06:57:59Z

High-fidelity tactile feedback is essential for robotic teleoperation, enabling precise manipulation and critical decision-making. While biomimetic fingertip sensors can capture surface-texture features, how their design shapes the tactile transparency of rendered feedback remains poorly understood. This paper presents a biomimetic fingertip replicating the human finger's multilayer mechanical gradient and fingerprint morphology, with an embedded high-sensitivity inertial measurement unit capturing texture-induced vibrations for remote vibrotactile rendering. Three variants are compared with the human fingertip through temporal and spectral analyses and a user study spanning three perceptual dimensions, establishing a transmission chain from fingertip design through signal characteristics to tactile transparency. The multilayer mechanical gradient yields the clearest improvements in signal intensity and texture-feature representation, whereas a stiffer skin layer amplifies vibration intensity at the expense of feature representation. The perceptual dimensions draw on distinct signal attributes: roughness on energy scaling, granularity on spectral shape, and repetitivity on characteristic-peak representation. These findings offer design principles for application-specific fingertips in robotic teleoperation.

Robot Learning to Communicate through Projected Visual Abstractions

2026-07-24T15:52:36Z

Humans routinely communicate through abstractions of their bodies, including shadows, silhouettes, and reflections. Yet robots remain largely confined to expressing themselves through their physical morphology. Enabling robots to communicate through such projected visual abstractions requires reasoning not only about bodily motion but also about how that motion is transformed into an external representation perceived by an observer. Among these abstractions, shadows provide a particularly compelling example because they emerge directly from the robot's embodiment while remaining visually distinct from the body itself. Here, we present a robotic system capable of dynamic shadow expression using a 21-degree-of-freedom dexterous hand with compliant soft skin and a learned shadow self-model. The soft-skinned embodiment reduces light leakage to produce visually continuous silhouettes, while the differentiable self-model learns the mapping between hand configurations and projected shadow appearance through task-agnostic self-exploration. Given a target shadow image or video, the robot optimizes its hand configurations through gradient-based search over 1 the learned self-model and refines the solution through collision-aware simulation to obtain physically feasible motions. For dynamic shadow performance, we further introduce expressive-region objectives, temporal smoothness regularization, and keyframe-based optimization to preserve visually important motion cues while reducing optimization complexity. We demonstrate robotic shadow expression across sign-language gestures, hand-shadow puppetry, and animal motion imitation in both simulation and physical experiments. These results establish a framework for enabling robots to manipulate projected visual abstractions of themselves for communication and visual storytelling.

Adaptive Identity Anchoring: Closed-Loop Keyframe Placement for Synthetic Paired Supervision in Video Face Swapping

2026-07-23T15:37:55Z

Video face swapping has no natural paired supervision: no real footage exists of one person's face performing another person's video. The strongest current answer, DreamID-V's SyncID-Pipe, mints pairs by replacing the identity in exactly two frames of a real clip -- the first and the last -- and regenerating the rest from a pose sequence alone. Pose carries no appearance evidence of the swapped-in identity, so over long clips, occlusions, and extreme pose excursions the synthesized identity has a long unanchored span on which to drift; no published ablation examines anchor count or placement. We propose Adaptive Identity Anchoring (AIA): (i) generalize the synthesizer to arbitrary anchor sets, architecturally natural for diffusion-forcing-style transformers where conditioning on a frame is clamping its tokens to zero noise; (ii) place anchors by a closed feedback loop that scores every generated frame against the real reference identity and inserts an image-face-swapped anchor at the worst-scoring frame until the pair passes a threshold or exhausts a budget; (iii) reuse the loop's verdict as an automatic data filter. A second pathology, the beauty-filter look of over-smoothed skin, has the same root cause: micro-texture, like identity, is priced by none of the pipeline's objectives. We therefore pair AIA with Reality-Referenced Texture Restoration: matched re-graining from each real frame's non-face regions, band-split transfer of sub-identity micro-texture from the real footage, and a second, spectral acceptance channel refereed by the footage's own spectrum. Identity-anchor density, we argue, is a controllable quality dial, and we specify falsifiable experiments -- drift-versus-gap curves, uniform-versus-adaptive placement at matched budgets, student training on AIA-minted data, and texture ablations with a human beauty-filter study -- that would validate or refute the proposal.

Data Annotations as Pedagogical Hints: From Subjective Labels to Critical Thinking

2026-07-22T13:51:56Z

Machine learning courses often use pre-labeled datasets, hiding the subjectivity of human annotation. This creates students with an overly trusting view of AI data and models, undervaluing interpretive diversity. We investigated whether manual data annotation tasks teach students about subjective labeling. Study Design: An annotation activity was implemented at two universities: Fontys (Netherlands) and IT University Copenhagen (Denmark). Students annotated skin lesion images for hair coverage on a 3-point scale. Surveys were collected from 43 participants measuring their understanding of annotation ambiguity, data quality, bias, fairness, implementation barriers, and pedagogical effectiveness. Key Findings: Self-reported familiarity with course content increased substantially across all concepts. Most students recognised that personal interpretation affects annotations. Students rated the activity as more effective than traditional lectures for understanding bias. Participants were motivated to learn more. Main Drawbacks: Emotional unease from viewing medical images was the primary issue. Many students still requested clearer guidelines to reduce disagreement, suggesting they hadn't internalised that disagreement from different perspectives is a learning feature, not a bug. Recommendations for Future Iterations: Ensure sufficient interpretive ambiguity in materials. Reduce repetitive annotation workload. Mitigate emotional unease from sensitive content. Explicitly frame disagreement as a learning opportunity rather than a problem to solve. Manual data annotations effectively teach students that human judgment shapes model behavior and that disagreement reflects domain complexity, not just noise.

CanonicalPhys: Pose-Robust Remote Photoplethysmography via Canonical-Space Priors

2026-07-17T14:31:03Z

Deep remote photoplethysmography (rPPG) attains sub-bpm heart-rate error on frontal, stationary faces yet degrades sharply under head pose: on MMPD, the state-of-the-art FactorizePhys backbone's MAE grows $1.60\times$ from frontal ($|\text{yaw}|{<}15^\circ$) to large-yaw ($|\text{yaw}|{\geq}45^\circ$) frames. We argue that pose is a \emph{coordinate-structural} nuisance rather than a data-augmentation problem: in image coordinates the same pixel maps to different anatomy at different poses, blocking three priors otherwise natural for rPPG, namely the dichromatic reflection model, pulse-phase invariance across skin regions, and the POS/CHROM chromaticity projection, each of which presumes a stable anatomy-to-pixel mapping. We introduce \textbf{CanonicalPhys}, which prepends a differentiable four-point homography that fixes four facial anchors at canonical positions; in this canonical frame the three priors become expressible as a per-pixel Lambertian weight, a cross-ROI temporal consistency loss, and knowledge distillation from windowed POS, none of which adds trainable parameters over the backbone. At an identical parameter count, CanonicalPhys reduces MMPD's frontal-to-large-yaw MAE degradation from $1.60\times$ to $1.33\times$ and flattens the mild-yaw bin from $1.32\times$ to $1.07\times$ (across CanonicalPhys variants), with matched cross-dataset MAE reductions of up to $32\%$ on pose-rich targets. Code: https://github.com/infraface/CanonicalPhys

Frequency Selection in Bayesian Spectral Modeling of Time Series Data with Applications to Wearable Device Measurements

2026-07-16T16:00:44Z

This paper introduces a Bayesian spike-and-slab framework for spectral analysis of time series data. The proposed method combines frequency selection and dimensionality reduction with a refined grid of candidate frequencies, enabling high-resolution recovery of oscillatory components while promoting sparsity through a structured spike-and-slab prior. A stochastic search algorithm efficiently explores the posterior space, yielding posterior inclusion probabilities that quantify the relevance of each frequency. We extend the framework to multivariate signals via a hierarchical prior on frequency inclusion patterns, allowing the model to capture both shared and component-specific rhythms across multiple time series. Extensive simulation studies demonstrate the method's robustness and superior performance in frequency estimation and spectral power reconstruction compared to existing approaches. Applied to actigraphy data from individuals with partial-onset seizures, the univariate model identifies clinically relevant circadian and ultradian rhythms. In a second application, for the joint analysis of physical activity and skin temperature from a healthy individual, the multivariate model reveals partially overlapping rhythmic components consistent with known physiological coupling. This work establishes a powerful and interpretable approach to spectral analysis, with broad applicability to wearable data, chronobiology, and personalized health monitoring.

Machine learning prediction of the convergence criterion for a topological invariant of finite non-Hermitian chains

2026-07-07T06:54:16Z

A topological invariant based on polar-decomposition of matrices correctly captures the topology of finite non-Hermitian chains exhibiting the non-Hermitian skin effect, provided that an appropriate crop-length parameter is chosen. This parameter, which sets the cutoff used in the calculation of the invariant, is usually chosen empirically and becomes especially important near topological phase transitions, where finite-size effects are strongest. Here we show that the required crop-length is controlled by physical decay (localization) lengths. For nearest-neighbor and pure longer-range hopping Hatano-Nelson-type chains, the crop-length is set mainly by a single localization length and is well approximated by a scalar multiple of that length. For more general longer-range hopping models, it is governed instead by a multichannel root structure of the characteristic polynomial. Random-forest regression captures finite-size and near-boundary corrections while preserving this decay-length interpretation. Trained on one set of Hamiltonians, the predictor accurately generalizes to unseen Hamiltonians and complex base energies, reproducing crop-lengths across full phase diagrams. We further show that the predictions learned from clean nearest-neighbor hopping chains remain stable under moderate hopping disorder. These results provide a practical and physically interpretable way to choose the crop-length, which in turn determines when the real-space invariant can reliably capture the topology of finite non-Hermitian chains.

Spectral Subsurface Scattering from RGB via Biophysical Skin Inversion

2026-06-25T23:30:57Z

In this paper we present a spectral optical inversion for skin for path tracing-based rendering of subsurface scattering. Skin is a complex multilayered medium, with appearance determined by the mixture of biophysical chromophores. However, current methods rely on medium homogeneization, with optical parameters obtained via albedo inversion from a reflectance texture and hand-tuned scattering distance and anisotropy. This results into significant art-skilled manual labor for authoring, and an inaccurate scattering profile for skin. To solve these problems, we generalize existing albedo inversion techniques, and propose a framework that predicts full-spectral skin scattering parameters from a single RGB diffuse albedo. Our method builds upon a new mixture-of-media representation, that approximates the aggregated multilayered appearance of skin by mixing the aggregated scattering of three uncorrelated media. We train a chained neural decoder that maps RGB diffuse albedo to the optical properties of the mixture of media, including anisotropy, scattering radius and scattering albedo. Then, we show this mixture can be used in a random-walk-based path tracer with minimal modifications, by simply randomly selecting the medium to traverse.

Non-Hermitian skin effect induced by spatial noncommutativity

2026-06-11T06:43:17Z

In all known schemes for the non-Hermitian skin effect, the non-Hermitian ingredient that drives the skin localization, whether asymmetric hopping or gain and loss, is invariably introduced by hand as an independent model parameter along the skin direction. Here we show that when two spatial coordinates do not commute, the skin effect can break free of this paradigm: a gain-loss potential applied along one coordinate automatically generates non-reciprocity along the other through the coordinate noncommutativity, driving all eigenstates to pile up exponentially at a boundary. We term this phenomenon the noncommutative skin effect. The inverse skin length is proportional to the noncommutativity parameter and is given by an analytic formula, exact in the thermodynamic limit and verified by exact diagonalization of lattice models; the reflection symmetry of the imaginary potential furnishes an exact criterion for the presence or absence of the effect, valid rigorously for finite-size systems. For a sinusoidal imaginary potential, the skin direction of all eigenstates flips collectively at parameter points fixed purely by geometry. Because the flip point is independent of the potential strength, the reversal constitutes a zero-crossing measurement scheme intrinsically robust against systematic errors, from which the noncommutativity parameter can be extracted directly. The qualitative transition of the eigenstates from uniform to exponentially localized renders the effect a nonperturbative probe of spatial noncommutativity, and the Peierls-phase structure of its lattice model is in principle accessible to cold-atom synthetic dimensions, photonic resonators, and topolectrical circuits.

CD-RCM: Generalizable Continuous-Depth Novel View Synthesis for Reflectance Confocal Microscopy

2026-06-10T19:54:23Z

Reflectance confocal microscopy (RCM) provides noninvasive, cellular-resolution "optical biopsies" of human skin \emph{in vivo} by acquiring en-face images at successive depths, forming a sparse z-stack. Due to optical limitations, these stacks are anisotropic 3D volumes with lateral resolution (0.5 $μ$m) $\sim$6 times higher compared to axial resolution, which is defined by the optical sectioning (3 $μ$m), limiting the interpretation of tissue. Our goal is to provide continuous-depth visualization by interpolating intermediate sections and making the 3D volume isotropic. Such a representation permits arbitrary-direction sectioning, including histopathology-like cross-sectional examination, without requiring per-patient optimization. To that end, we introduce the first RCM-specific novel-view synthesis (NVS) approach, CD-RCM, a feedforward model that predicts realistic, unseen depths from sparsely sampled RCM stacks. Classical neural rendering methods focus on reconstruction from surface-level multi-view observations. In contrast to surface-level camera views, RCM can acquire optically sectioned en-face images of tissue beyond the surface up to 200 $μ$m. However, during visualization of the RCM stacks, observations of the shallower sections (towards the surface) obscure the deeper ones. This unique axial imaging geometry and layer-dependent anatomical organization motivated our development of a tailored architectural and training framework that explicitly accounts for RCM's depth-resolved, occlusive imaging physics. Experiments demonstrate that CD-RCM achieves high-fidelity novel-view synthesis with sub-second inference time.

Intelligent Skin Cancer Detection Using a Multispectral Metasurface and a Hybrid

2026-06-09T17:30:48Z

Skin cancer is among the most prevalent malignancies worldwiAdbe satnradcitts early detection is essential for improving patient survival and reducing treatment costs Conventional dermoscopic and visual imaging techniques are primarily limited to the visible spectrum and often fail to capture subtle spectral signatures associated with early stage malignancies This study proposes an innovative framework that integrates a multispectral metasurface for imaging with a hybrid deep learning architecture based on Convolutional Neural Networks and Vision Transformers The designed metasurface enables noninvasive acquisition of rich spectral information highly sensitive to tissue alterations while the hybrid CNN ViT model simultaneously extracts local and global features to robustly classify skin lesions Simulation-based evaluations demonstrate that the proposed method achieves approximately 98 accuracy 95 percentages sensitivity and 99 perentage specificity surpassing conventional RGB-based and single-architecture approaches Qualitative analyses using attention maps reveal that the model focuses on clinically relevant lesion regions improving interpretability Overall the results indicate that combining metasurface based multispectral imaging with hybrid deep learning can introduce a new generation of diagnostic tools in dermatology and pave the way for portable fast and highly accurate clinical systems

Appearance-Invariant Detection of Suggestive Motion via Laban Movement Descriptors

2026-06-11T16:42:25Z

Content moderation in online multiplayer 3D virtual environments is increasingly automated, yet detection has focused on images, video, and audio, leaving suggestive motion a blind spot. We present a motion-only classification pipeline that detects suggestive and explicit movement from SMPL skeleton trajectories using Laban Movement Analysis (LMA) descriptors. On a dataset spanning everyday, artistic, suggestive, and explicit movement (17+ hours of video), a logistic regression trained on 61-feature LMA descriptors reaches 68% binary SFW/NSFW accuracy (70% random forest) under a leak-free evaluation protocol. At this level, our descriptor performs comparably to a learned video model trained on the same motion re-rendered as appearance-free video, a gray figure with no clothing, skin, or scene. The indirectness (tortuosity) of each joint's trajectory, measured as the ratio of the joint's path length to its net displacement, peaks at the suggestive tier, showing that the Direct-to-Indirect polarity of Laban's Space factor provides an interpretable marker of the shift from functional to suggestive motion. Ultimately, Laban-based kinematic descriptors offer a lightweight, interpretable approach to suggestive-motion detection: every decision decomposes into named, theory-grounded features. Because the classifier operates on pose trajectories alone, moderation can run directly on avatar poses in virtual environments, with no appearance data.

Filling-Sensitive Spectral Complexity from Hilbert-Space Holonomy in Fragmented Non-Hermitian Systems

2026-05-19T12:10:39Z

We show that Hilbert-space holonomy provides a geometric organizing principle for spectral reality in fragmented non-Hermitian many-body systems, complementary to conventional symmetry protection. In two minimal fragmented models, complex spectra can arise only within the most symmetric sectors: half filling in the fermion model and zero magnetization in the spin chain. Adding or removing a single particle, or flipping a single spin, renders the spectra entirely real despite unchanged periodic boundary conditions, reminiscent of boundary-condition sensitivity in systems with a non-Hermitian skin effect. We explain this by viewing nonreciprocal hopping amplitudes as a discrete gauge field on the Krylov graph: trivial holonomy permits a diagonal similarity transformation to the Hermitian limit, whereas nontrivial holonomy obstructs it and allows complex spectra. In certain regimes, trivial holonomy admits an emergent-boundary interpretation, and longer-range models exhibit finite real and complex regions governed by the same criterion.

Uncertainty-Guided Dual-Domain Learning for Reliable Skin Lesion Segmentation

2026-05-10T15:20:58Z

Accurate skin lesion segmentation is vital for dermoscopic Computer-Aided Diagnosis. However, visual ambiguity and morphological irregularity often defeat spatial modeling, necessitating multi-domain architectures. Existing paradigms frequently overlook the active use of prediction uncertainty, leading to deterministic frameworks that suffer from blind cross-domain fusion and overfit to label noise. To address these issues, we propose the Uncertainty-Guided Dual-Domain Network (UGDD-Net). UGDD-Net introduces a novel "Glance-and-Gaze" mechanism to transform uncertainty into an active guiding signal. Specifically, the Uncertainty-Guided Bi-directional Feature Fusion (UGBFF) module uses pixel-level uncertainty to modulate spatial-spectral interactions. The Uncertainty-Guided Graph Refinement (UGGR) module constructs a topology-aware graph to propagate reliable semantic consensus and refine uncertain nodes. Finally, the Uncertainty-Guided Margin-Adaptive Loss (UGML) enforces strict constraints on confident pixels while relaxing penalties on uncertain ones to improve statistical calibration. Extensive experiments on ISIC2017, ISIC2018, PH2, and HAM10000 datasets demonstrate that UGDD-Net achieves state-of-the-art performance, especially on "Hard Samples". Our uncertainty maps align with expert inter-observer variability, providing robust interpretability for human-machine collaborative diagnosis.

Optimize-at-Capture: Highly-adaptive Exposure Controlling for In-Vehicle Non-contact Heart-rate Monitoring

2026-05-06T01:40:33Z

Remote photoplethysmography (rPPG) holds great promise for continuous heart-rate monitoring of drivers in intelligent vehicles. However, its performance is severely degraded by the highly dynamic illumination changes. A critical yet overlooked factor is the lack of exposure controlling during video acquisition -- most existing systems rely on either fixed exposure settings or camera build-in auto-exposure, both of which fail to maintain stable facial brightness under rapidly changing lighting conditions during driving. To address this gap, we propose a highly-adaptive exposure controlling framework that proactively adjusts exposure parameters based on predictive modeling of historical skin reflections. Unlike standard auto-exposure, our method is specifically optimized for rPPG measurement, ensuring the skin region of interest (ROI) remains within the optimal dynamic range for rPPG signal extraction. As an important contribution of this study, we introduce ExpDrive, a public in-vehicle physiological monitoring dataset comprising synchronized facial video and reference ECG from 48 subjects captured under real driving conditions. Extensive experiments demonstrate that our method consistently outperforms fixed exposure and standard auto-exposure strategies. Specifically, it reduces the Mean Absolute Error (MAE) by 6.31 bpm (from 14.1 to 7.79 bpm) and significantly increases the success rate by 32.3 percentage points (p < 0.001) (from 24.9% to 57.2%) across challenging driving scenarios. Notably, it clearly improved the performance of non-contact heart-rate monitoring in both low-light (rainy) and high-glare (sunny) conditions, validating the efficacy of exposure-aware acquisition design.

Observation of Universal Spectral Moments and the Dynamic Dispersive-to-Proliferative Transition

2026-04-30T08:38:46Z

In non-Hermitian systems, spectra can be maximally boundary-sensitive, yet bulk physics need not be. Here we experimentally show that spectral moments provide boundary-robust bulk observables in finite non-Hermitian lattices, even when the spectra undergo dramatic geometry-dependent reshaping due to the skin effect. Using a unified acoustic platform with full spectral reconstruction and time-domain access, we probe one-, two- and three-dimensional lattices and demonstrate that spectral moments remain nearly invariant across distinct boundary geometries while the corresponding complex spectra differ strongly. To connect the thermodynamic theorem to realistic finite systems, we develop a loop-counting theory that identifies the physical origin of finite-size deviations in terms of missing boundary loops, quantitatively captures the corrections, and predicts a scaling law, which we verify experimentally. Beyond acoustic spectroscopy, we reveal a counterintuitive dynamical consequence of moment invariance: a dispersive-to-proliferative bulk transition governed by bulk moment structure rather than spectral boundary sensitivity. As a result, local bulk dynamics can remain stable (dispersive) even in a $\mathcal{PT}$-broken spectral regime, challenging the conventional expectation that $\mathcal{PT}$ breaking necessarily implies feedback-induced dynamical instability (proliferation) through exponentially amplifying spectral components. These results establish spectral moments as practical bulk descriptors for finite non-Hermitian matter and open a route to extracting and controlling intrinsic bulk behavior in realistic wave-based non-Hermitian devices.

Skull-Conforming Acoustic Holographic Lenses for Transcranial Targeting

2026-04-23T01:59:27Z

Transcranial focused ultrasound (tFUS) offers noninvasive access to deep brain circuits but remains limited by skull-induced phase aberration, acoustic impedance mismatch, and poor volumetric control of intracranial pressure fields. Conventional phased-array and planar holographic strategies compensate aberrations electronically or computationally, yet do not resolve geometric and coupling inconsistencies imposed by subject-specific cranial morphology. We introduce personalized skull-conforming acoustic holograms that physically encode individualized wavefront corrections into a conformal acoustic interface. Within a subject-specific volumetric holography (SSVH) framework, cranial geometry and therapeutic constraints are embedded into a physics-based optimization pipeline for holographic phase synthesis. The resulting lens is integrated with a skull- and skin-conforming coupling layer that enhances impedance continuity, reduces reflection losses, and stabilizes spatial alignment, enabling simultaneous aberration mitigation and efficient transcranial transmission. Numerical simulations across multiple subjects and targets demonstrate consistent volumetric focusing and reliable target coverage while maintaining pressure fields within safety limits. Experimental validation using an ex vivo human skull confirms accurate fabrication, effective acoustic coupling, and faithful reconstruction of designed three-dimensional acoustic fields. By unifying wavefront engineering with anatomical conformity, this work establishes skull-conforming acoustic holography as a scalable strategy for high-fidelity, anatomically adaptive transcranial ultrasound targeting.

Spatial Extremes at Scale: A Case Study of Surface Skin Temperature and Heat Risk in the United States

2026-04-20T21:08:56Z

Understanding and mapping extreme heat is critical for risk management and public health planning, particularly in regions with complex terrain and heterogeneous climate. We present a case study of extreme heat in the Four Corners region of the United States, using high-resolution surface skin temperature data from the North American Land Data Assimilation System to characterize spatially heterogeneous and seasonally varying extremes across complex terrain, and to assess their implications for heat-related public health risks. Spatial extremes exhibit complex dependencies across geographic regions, which require sophisticated statistical models to capture. While recent advances in spatial extreme value modeling provide flexible representations of joint tail dependencies, statistical inference remains computationally demanding, especially for datasets with a large number of locations. To address this, we propose a random scale mixture process that facilitates Bayesian inference of spatial extremes, and develop scalable inference strategies that leverage advances in spatial modeling and amortized learning. We evaluate the proposed inference methods through large-scale simulation studies, representing the first such extensive study in spatial extremes, and a high-resolution surface skin temperature application in the Four Corners region. Surface skin temperature is particularly useful as a predictor for air temperature, for studying heatwaves and related environmental phenomena, and to calculate heat indices reflecting downstream health risks at any location. Our findings provide insights into efficient, data-driven approaches for modeling spatial extremes, and serve as guidelines for practitioners in the fields of climate science, environmental risk assessment, and beyond.

FreqPhys: Repurposing Implicit Physiological Frequency Prior for Robust Remote Photoplethysmography

2026-04-01T06:25:42Z

Remote photoplethysmography (rPPG) enables contactless physiological monitoring by capturing subtle skin-color variations from facial videos. However, most existing methods predominantly rely on time-domain modeling, making them vulnerable to motion artifacts and illumination fluctuations, where weak physiological clues are easily overwhelmed by noise. To address these challenges, we propose FreqPhys, a frequency-guided rPPG framework that explicitly leverages physiological frequency priors for robust signal recovery. Specifically, FreqPhys first applies a Physiological Bandpass Filtering module to suppress out-of-band interference, and then performs Physiological Spectrum Modulation together with adaptive spectral selection to emphasize pulse-related frequency components while suppress residual in-band noise. A Cross-domain Representation Learning module further fuses these spectral priors with deep time-domain features to capture informative spatial--temporal dependencies. Finally, a frequency-aware conditional diffusion process progressively reconstructs high-fidelity rPPG signals. Extensive experiments on six benchmarks demonstrate that FreqPhys yields significant improvements over state-of-the-art approaches, particularly under challenging motion conditions. It highlights the importance of explicitly modeling physiological frequency priors. The source code will be released.

Separation induced transition in a low pressure turbine under varying compressibility

2026-03-04T05:58:57Z

The present study investigates influence of compressibility on separation induced transition in a low pressure turbine cascade using high fidelity direct numerical simulations of the T106A blade. Simulations are performed for inlet Mach numbers, Ms ranging from 0.15 to 0.35 at a fixed Reynolds number and high incidence, representative of off design LPT operation. A dispersion relation preserving numerical framework is employed to accurately capture instability waves, separation bubbles, and separation induced transition to turbulence. A comprehensive analysis is carried out using surface pressure and skin friction distributions, boundary layer integral parameters, spectral analyses, and budgets of compressible enstrophy. Increasing Ms systematically reduces streamwise extent of both leading edge and trailing edge separation bubbles and promotes earlier transition and reattachment, consistent with trends observed under increased free stream disturbances. Despite shorter separation regions, suction side momentum thickness at trailing edge increases from Ms = 0.15 to 0.35, indicating higher profile losses at elevated Ms. Spectral analyses demonstrate a redistribution of turbulent spatial and temporal scales, with energy injection occurring at progressively larger scales as Ms increases. Flow field visualizations reveal a transition pathway that shifts from two dimensional spanwise rolls and intermittent turbulent spots at low Ms to streak dominated, bypass like transition at higher Ms.

Image-based investigation of the zebrafish developmental process using in vivo dynamic and multi-contrast optical coherence tomography

2026-02-13T06:32:38Z

We demonstrate in vivo dynamic optical coherence tomography (DOCT) imaging of zebrafish development from 2 weeks to 12 months post-fertilization, integrated with polarization-sensitive OCT (PS-OCT), OCT angiography (OCTA), and histological validation. Two DOCT algorithms were utilized: logarithmic intensity variance and late OCT correlation decay speed, which characterize the occupancy of dynamic scatterers and their motion speeds, respectively. Our results show that skin stripes exhibit high DOCT signals and it varies among the pigment-cell types. Furthermore, the combination of DOCT and PS-OCT captures the maturation of these stripes. In addition, DOCT and OCTA successfully visualized the developmental progression of blood and lymphatic vessels, as well as spinal tissues.

Thermal Imaging for Contactless Cardiorespiratory and Sudomotor Response Monitoring

2026-09-02T18:29:39Z

Human-machine interfaces in industrial automation need sensing modules that monitor operator actions and physiological state. This is important in factories, vehicles, machinery cabins, and human-robot collaboration, where workload, stress, fatigue, or reduced attention can affect safety. RGB monitoring is limited by low light, shadows, and privacy concerns, while thermal infrared imaging captures skin temperature dynamics without visible illumination. This paper studies thermal video as a contactless computer vision modality for estimating electrodermal activity (EDA), heart rate (HR), and breathing rate (BR), with the goal of supporting adaptive human-machine interfaces and operator-state awareness. We propose a signal-processing pipeline that tracks facial regions, aggregates thermal signals, and separates slow sudomotor trends from faster cardiorespiratory components. HR is estimated using orthogonal matrix image transformation (OMIT) across multiple facial regions, while BR is estimated from nasal and cheek thermal signals using spectral peak detection. We characterize 288 ROI-method configurations against contact references with lag-tolerant metrics using 31 sessions from the public SIMULATOR STUDY 1 (SIM1) driver monitoring dataset. The best fixed EDA configuration reaches a mean absolute correlation of $0.40 \pm 0.23$ against palm EDA, with individual sessions reaching $0.89$. BR estimation achieves $3.1 \pm 1.1$ bpm mean absolute error, while HR estimation yields $13.8 \pm 7.5$ bpm MAE, limited by the $7.5$ Hz thermal camera frame rate. The results show that thermal video provides useful respiratory and sudomotor cues, while revealing limitations caused by ROI selection, polarity changes, latency, and subject variability. These findings provide baseline design guidance for thermal computer vision as an auxiliary sensing layer in adaptive industrial HMI systems.

Neutral Prompts, Non-Neutral People: Quantifying Gender and Skin-Tone Bias in Gemini Flash 2.5 Image and GPT Image 1.5

2026-02-12T16:21:03Z

This study quantifies gender and skin-tone bias in two widely deployed commercial image generators - Gemini Flash 2.5 Image (NanoBanana) and GPT Image 1.5 - to test the assumption that neutral prompts yield demographically neutral outputs. We generated 3,200 photorealistic images using four semantically neutral prompts. The analysis employed a rigorous pipeline combining hybrid color normalization, facial landmark masking, and perceptually uniform skin tone quantification using the Monk (MST), PERLA, and Fitzpatrick scales. Neutral prompts produced highly polarized defaults. Both models exhibited a strong "default white" bias (>96% of outputs). However, they diverged sharply on gender: Gemini favored female-presenting subjects, while GPT favored male-presenting subjects with lighter skin tones. This research provides a large-scale, comparative audit of state-of-the-art models using an illumination-aware colorimetric methodology, distinguishing aesthetic rendering from underlying pigmentation in synthetic imagery. The study demonstrates that neutral prompts function as diagnostic probes rather than neutral instructions. It offers a robust framework for auditing algorithmic visual culture and challenges the sociolinguistic assumption that unmarked language results in inclusive representation.

DIAMOND-SSS: Diffusion-Augmented Multi-View Optimization for Data-efficient SubSurface Scattering

2026-01-17T12:02:14Z

Subsurface scattering (SSS) gives translucent materials -- such as wax, jade, marble, and skin -- their characteristic soft shadows, color bleeding, and diffuse glow. Modeling these effects in neural rendering remains challenging due to complex light transport and the need for densely captured multi-view, multi-light datasets (often more than 100 views and 112 OLATs). We present DIAMOND-SSS, a data-efficient framework for high-fidelity translucent reconstruction from extremely sparse supervision -- even as few as ten images. We fine-tune diffusion models for novel-view synthesis and relighting, conditioned on estimated geometry and trained on less than 7 percent of the dataset, producing photorealistic augmentations that can replace up to 95 percent of missing captures. To stabilize reconstruction under sparse or synthetic supervision, we introduce illumination-independent geometric priors: a multi-view silhouette consistency loss and a multi-view depth consistency loss. Across all sparsity regimes, DIAMOND-SSS achieves state-of-the-art quality in relightable Gaussian rendering, reducing real capture requirements by up to 90 percent compared to SSS-3DGS.

Localization Landscape in Non-Hermitian and Floquet quantum systems

2026-01-15T14:40:23Z

We propose a generalization of the Filoche--Mayboroda localization landscape that extends the theory well beyond the static, elliptic and Hermitian settings while preserving its geometric interpretability. Using the positive operator $H^\dagger H$, we obtain a landscape that predicts localization across non-Hermitian, Floquet, and topological systems without computing eigenstates. Singular-value collapse reveals spectral instabilities and skin effects, the Sambe formulation captures coherent destruction of tunneling, and topological zero modes emerge directly from the landscape. Applications to Hatano--Nelson chains, driven two-level systems, and driven Aubry--André--Harper models confirm quantitative accuracy, establishing a unified predictor for localization in equilibrium and driven quantum matter.

Fusion2Print: Deep Flash-Non-Flash Fusion for Contactless Fingerprint Matching

2026-04-05T23:00:57Z

Contactless fingerprint recognition offers a hygienic and convenient alternative to contact-based systems, enabling rapid acquisition without latent prints, pressure artifacts, or hygiene risks. However, contactless images often show degraded ridge clarity due to illumination variation, subcutaneous skin discoloration, and specular reflections. Flash captures preserve ridge detail but introduce noise, whereas non-flash captures reduce noise but lower ridge contrast. We propose Fusion2Print (F2P), the first framework to systematically capture and fuse paired flash-non-flash contactless fingerprints. We construct a custom paired dataset, FNF Database, and perform manual flash-non-flash subtraction to isolate ridge-preserving signals. A lightweight attention-based fusion network also integrates both modalities, emphasizing informative channels and suppressing noise, and then a U-Net enhancement module produces an optimally weighted grayscale image. Finally, a deep embedding model with cross-domain compatibility, generates discriminative and robust representations in a unified embedding space compatible with both contactless and contact-based fingerprints for verification. F2P enhances ridge clarity and achieves superior recognition performance (AUC=0.999, EER=1.12%) over single-capture baselines (Verifinger, DeepPrint).

Circuit-free cardiovascular monitoring via skin-interfaced nanophotonics

2026-08-31T22:55:44Z

Nanoscale mechano-optical surfaces enable electronics-free strain sensing, attractive for skin-interfaced devices, yet reported implementations require laser/spectrometer interrogation, negating this advantage. Here, we report electrically passive mechanical transduction of arterial pulsation into diffractive colour shifts read by unmodified smartphone cameras, enabled by a dual-function monolithic poly-dimethylsiloxane (PDMS) film. Using large-area double-sided nanoimprinting, we achieve a strain-sensitive nanophotonic surface on one face of the film and a bio-inspired 3D structural adhesive on the other. We measure strain-dependent optical response and reproduce it in colour-mixing optical simulations. In uniaxial cyclic loading tests, 2% strain produces a 9% RGB-intensity modulation, stable over 1000 cycles. Further, 3D structuring improves adhesive shear strength by 65% on skin over flat PDMS. Hand-held smartphone recordings in humans ($n=13$) resolve sub-beat hemodynamics in agreement with clinical reference (per-beat waveform $ρ=0.94 \pm 0.03$), exceeding established non-invasive techniques such as active reflectance photoplethysmography (PPG), imaging PPG, and piezoelectric pulse-force sensors in simultaneous recordings. Importantly, mechanical transduction at the elastomer--air interface presents an optical cardiovascular monitoring approach agnostic to dermal-melanin, a known PPG confounder. Together, these advances establish camera-readable mechanochromic elastomers as versatile materials platforms for wearable cardiovascular monitoring, point-of-care diagnostics, and electronics-free human-machine interfaces.

Volatile Organic Compounds for Stress Detection: A Scoping Review and Exploratory Feasibility Study with Low-Cost Sensors

2025-12-24T11:08:55Z

Volatile organic compounds (VOCs) represent a novel but underexplored modality for emotion recognition. This paper presents a systematic evidence synthesis and exploratory investigation of VOC-based affective computing using low-cost sensors. Study 1, a systematic scoping review following PRISMA-ScR guidelines, analyzed 16 studies from 610 records across breath, sweat, skin, and urine biosources. Evidence indicates that stress and affective states are reflected in VOC signatures (aldehydes, ketones, fatty acids, sulfur compounds), though with considerable heterogeneity. Current research relies predominantly on laboratory-grade GC-MS or PTR-MS, while wearable sensors provide pattern-level outputs without compound-specific identification - a critical gap for practical systems. Study 2 (n=25) investigated whether low-cost TVOC sensors (BME688, ENS160) combined with physiological monitoring (HR, HRV, GSR) can detect laboratory-induced stress. Exploratory analysis revealed that high cardiovascular reactors exhibited elevated TVOC during arithmetic stress (d=1.38), though requiring replication in larger samples. Substantial interindividual variability emerged (CV>80%), with coupling patterns moderated by baseline emission levels and temporal lags of 30-80 seconds. Random Forest-based multimodal classification achieved 77.3% accuracy (5-fold CV). SHAP analysis indicated VOC sensors contributed 24.9% of model performance. Leave-one-subject-out validation yielded 65.3% accuracy, highlighting the need for individual calibration. This work provides three contributions: (1) comprehensive mapping of VOC biomarker evidence and technological gaps, (2) initial demonstration that low-cost sensors can capture stress-related VOC patterns in multimodal fusion, and (3) identification of key implementation challenges. Findings require replication in larger samples (n>=50).

Simulation-Driven Deep Learning Framework for Raman Spectral Denoising Under Fluorescence-Dominant Conditions

2025-12-19T17:54:57Z

Raman spectroscopy enables non-destructive, label-free molecular analysis with high specificity, making it a powerful tool for biomedical diagnostics. However, its application to biological tissues is challenged by inherently weak Raman scattering and strong fluorescence background, which significantly degrade signal quality. In this study, we present a simulation-driven denoising framework that combines a statistically grounded noise model with deep learning to enhance Raman spectra acquired under fluorescence-dominated conditions. We comprehensively modeled major noise sources. Based on this model, we generated biologically realistic Raman spectra and used them to train a cascaded deep neural network designed to jointly suppress stochastic detector noise and fluorescence baseline interference. To evaluate the performance of our approach, we simulated human skin spectra derived from real experimental data as a validation case study. Our results demonstrate the potential of physics-informed learning to improve spectral quality and enable faster, more accurate Raman-based tissue analysis.

FacePhys: State of the Heart Learning

2025-12-06T03:54:12Z

Vital sign measurement using cameras presents opportunities for comfortable, ubiquitous health monitoring. Remote photoplethysmography (rPPG), a foundational technology, enables cardiac measurement through minute changes in light reflected from the skin. However, practical deployment is limited by the computational constraints of performing analysis on front-end devices and the accuracy degradation of transmitting data through compressive channels that reduce signal quality. We propose a memory efficient rPPG algorithm - \emph{FacePhys} - built on temporal-spatial state space duality, which resolves the trilemma of model scalability, cross-dataset generalization, and real-time operation. Leveraging a transferable heart state, FacePhys captures subtle periodic variations across video frames while maintaining a minimal computational overhead, enabling training on extended video sequences and supporting low-latency inference. FacePhys establishes a new state-of-the-art, with a substantial 49\% reduction in error. Our solution enables real-time inference with a memory footprint of 3.6 MB and per-frame latency of 9.46 ms -- surpassing existing methods by 83\% to 99\%. These results translate into reliable real-time performance in practical deployments, and a live demo is available at https://www.facephys.com/.

Unified Bulk-Entanglement Correspondence in Non-Hermitian Systems

2025-12-10T23:07:27Z

The non-Hermitian skin effect (NHSE) fundamentally invalidates the conventional bulk-boundary correspondence (BBC), leading topological diagnostics into a crisis. While the non-Bloch polarization $P_β$ defined on the generalized Brillouin zone restores momentum-space topology, a direct, robust real-space bulk probe has remained elusive. We resolve this by establishing a universal correspondence between $P_β$ and the entanglement polarization $χ$ of the biorthogonal ground state. Introducing a quasi-reciprocal Hamiltonian $\tilde{H}$ that removes the NHSE while preserving bulk topology, we rigorously prove the fundamental identity $P_β \equiv χ(\tilde{H})\pmod 1$ in the thermodynamic limit under the quasi-locality assumption. Crucially, we demonstrate that this equivalence transcends the locality constraints that limit traditional topological invariants. While the conventional Resta polarization fails when $\tilde{H}$ becomes non-local due to the divergence of position variance, we reveal that $χ(\tilde{H})$ remains robustly quantized, protected by the Fredholm index of Toeplitz operators. Our work thus identifies entanglement as the unique real-space diagnostic capable of capturing non-Bloch topology beyond the breakdown of locality, successfully restoring the BBC across diverse non-Hermitian systems such as line-gap, point-gap, and gapless phases, thereby unifying the geometric and entanglement paradigms in non-Hermitian physics.

Non-invasive load measurement in the human tibia via spectral analysis of flexural waves

2026-05-01T01:32:52Z

Forces transmitted by bones are routinely studied in human biomechanics, but it is challenging to measure them non-invasively, especially outside of laboratory settings. We introduce a technique for non-invasive, in vivo measurement of tibial compressive force using flexural waves propagating in the tibia. Modelling the tibia as an axially compressed Euler-Bernoulli beam, we show that tibial flexural waves have load-dependent frequency spectra. Specifically, under physiological conditions, peak locations in the wave acceleration spectra vary linearly with the compressive force on the tibia and may be used as proxies for the compressive force. We test the validity of this technique using a proof-of-concept wearable system that generates flexural waves via a skin-mounted mechanical transducer and measures the spectra of these waves using a skin-mounted accelerometer. In agreement with beam theory, data from 9 participants demonstrate linear relationships between tibial compressive force and spectral peak location, with Pearson correlation coefficients $r=0.82 - 0.99$ (mean $r=0.93$) for medial-lateral swaying and $r=0.81 - 0.98$ (mean $r=0.93$) for walking trials. This flexural wave-based technique could give rise to a new class of wearable sensors for non-invasive physiological bone load monitoring and measurement, impacting research in human locomotion and sports medicine.

Reducing Latency and Noise in PPG-Based SpO2 Measurements: A Kalman Filtering Approach Towards Acute Hypoxia Detection

2025-10-06T20:28:54Z

Photoplethysmography (PPG) is a common tool for monitoring cardiopulmonary health. Relying on absorption or reflectance of light by hemoglobin in the blood, the measured PPG waveform can be analyzed per heart beat using physiological assumptions to extract metrics ranging from heart rate to specific blood oxygenation (SpO2). This has led to the widespread use of PPG for bedside clinical monitoring to wearable consumer health monitoring. However, PPG is notoriously noisy and the measured absorption or reflectance of light is sensitive to factors such as body movement and contact with the skin. To reduce the noise in the PPG-derived SpO2, we developed combined traditional methods of estimating SpO2 from the PPG waveform with a new method to extract changes in SpO2 from the PPG waveform in a Kalman filter, and demonstrated its ability to better estimate SpO2 in humans undergoing controlled hypoxia (down to 14% atmospheric oxygen). The Kalman filter reduced variability in SpO2 to 4.30%SpO2 compared to the beat-to-beat SpO2 variability of 12.59%SpO2. This mirrored current methods of window-averaging the beat-to-beat SpO2, with a 30s window-average reducing SpO2 variability to 4.73%. However, current window-average methods also introduce delays, with 10s and 30s window-averaging introducing delays of 5s and 14s respectively compared to the beat-to-beat SpO2. The Kalman filter reduced this delay to within 3s of the beat-to-beat SpO2, highlighting its ability to reduce noise while maintaining SpO2 dynamics. This capability is particularly useful in reliably detecting clinically meaningful, but transient, hypoxic states, such as those observed during apnea.

Multiple many-body localization transitions in a driven non-Hermitian quasiperiodic chain

2025-09-18T07:38:50Z

We investigate the fate of a many-body localized phase in a non-Hermitian quasiperiodic model of hardcore bosons subjected to periodic driving. While in general, the many-body localized system is known to thermalize with increasing driving period due to Floquet heating, in this case, we demonstrate that the initially localized system first delocalizes and then localizes again, resulting in a re-entrant many-body localization (MBL) transition as a function of the driving period. Strikingly, further increase in the driving period results in a series of localization-delocalization transitions leaving behind traces of extended regimes (islands) in between MBL phases. Furthermore, non-Hermiticity renders the extended islands boundary-sensitive, resulting in a Floquet many-body skin effect under open boundaries. We present numerical evidence from spectral and dynamic studies, confirming these findings. Our study opens new pathways for understanding the interplay between non-Hermiticity and quasiperiodicity in driven systems.

Differential Physiological Responses to Proxemic and Facial Threats in Virtual Avatar Interactions

2025-08-14T12:25:19Z

Proxemics, the study of spatial behavior, is fundamental to social interaction and increasingly relevant for virtual reality (VR) applications. While previous research has established that users respond to personal space violations in VR similarly as in real-world settings, phase-specific physiological responses and the modulating effects of facial expressions remain understudied. We investigated physiological and subjective responses to personal space violations by virtual avatars, to understand how threatening facial expressions and interaction phases (approach vs. standing) influence these responses. Sixteen participants experienced a 2x2 factorial design manipulating Personal Space (intrusion vs. respect) and Facial Expression (neutral vs. angry) while we recorded skin conductance response (SCR), heart rate variability (HRV), and discomfort ratings. Personal space boundaries were individually calibrated using a stop-distance procedure. Results show that SCR responses are significantly higher during the standing phase compared to the approach phase when personal space was violated, indicating that prolonged proximity within personal space boundaries is more physiologically arousing than the approach itself. Angry facial expressions significantly reduced HRV, reflecting decreased parasympathetic activity, and increased discomfort ratings, but did not amplify SCR responses. These findings demonstrate that different physiological modalities capture distinct aspects of proxemic responses: SCR primarily reflects spatial boundary violations, while HRV responds to facial threat cues. Our results provide insights for developing comprehensive multi-modal assessments of social behavior in virtual environments and inform the design of more realistic avatar interactions.

Floquet Non-Bloch Formalism for a Non-Hermitian Ladder: From Theoretical Framework to Topolectrical Circuits

2026-01-21T19:31:35Z

Periodically driven systems intertwined with non-Hermiticity opens a rich arena for topological phases that transcend conventional Hermitian limits. The physical significance of these phases hinges on obtaining the topological invariants that restore the bulk-boundary correspondence, a task well explored for static non-Hermitian (NH) systems, while it remains elusive for the driven scenario. Here, we address this problem by constructing a generalized Floquet non-Bloch framework that analytically captures the spectral and topological properties of time-periodic NH systems. Employing a high-frequency Magnus expansion, we analytically derive an effective Floquet Hamiltonian and formulate the generalized Brillouin zone for a periodically driven quasi-one-dimensional system, namely, the Creutz ladder with a staggered complex potential. Our study demonstrates that the skin effect remains robust (despite the absence of non-reciprocal hopping) across a broad range of driving parameters, and is notably amplified in the low-frequency regime due to emergent longer-range couplings. We further employ a symmetric time frame approach that generates chiral-partner Hamiltonians, whose invariants, when appropriately combined, account for the full edge-state structure. To substantiate the theoretical framework, we propose a topolectrical circuit (TEC) that serves as a viable experimental setting. Apart from capturing the skin modes, the proposed TEC design faithfully reproduces the presence of distinct Floquet edge states, as revealed through the voltage and impedance profiles, respectively. Thus, our work not only offers a theoretical framework for exploring NH-driven systems, but also provides an experimentally feasible TEC architecture for realizing these phenomena stated above in a laboratory.

Anomalous Scaling Behaviors of the Green's Function in Critical Skin Effects

2025-07-28T13:52:13Z

We study the Green's functions in non-Hermitian systems exhibiting the critical non-Hermitian skin effect (critical NHSE) using a double-chain Hatano-Nelson model with inter-chain coupling $Δ$. For $Δ=0$, the system decouples into two independent chains, and the Green's functions follow predictable patterns based on the GBZ theory. For small $Δ$ ($Δ=1/10000$), in conventional regions with trivial OBC spectral winding numbers, inter-chain coupling induces a zigzag scaling structure in Green's functions due to competition between the two chains, explainable by first-order perturbation theory. In anomalous regions with non-trivial winding numbers, Green's functions match GBZ predictions in the bulk but diverge near boundaries, with residual contributions from the $Δ=0$ GBZ accounting for the deviations. These results reveal the unique non-perturbative features of critical NHSE and highlight the limitations of GBZ theory in capturing finite-size and boundary effects, emphasizing the need to consider both bulk and boundary dynamics in such systems.

EchoForce: Continuous Grip Force Estimation from Skin Deformation Using Active Acoustic Sensing on a Wristband

2025-07-27T23:10:08Z

Grip force is commonly used as an overall health indicator in older adults and is valuable for tracking progress in physical training and rehabilitation. Existing methods for wearable grip force measurement are cumbersome and user-dependent, making them insufficient for practical, continuous grip force measurement. We introduce EchoForce, a novel wristband using acoustic sensing for low-cost, non-contact measurement of grip force. EchoForce captures acoustic signals reflected from subtle skin deformations by flexor muscles on the forearm. In a user study with 11 participants, EchoForce achieved a fine-tuned user-dependent mean error rate of 9.08% and a user-independent mean error rate of 12.3% using a foundation model. Our system remained accurate between sessions, hand orientations, and users, overcoming a significant limitation of past force sensing systems. EchoForce makes continuous grip force measurement practical, providing an effective tool for health monitoring and novel interaction techniques.

HumanMaterial: Human Material Estimation from a Single Image via Progressive Training

2025-07-24T12:59:42Z

Full-body Human inverse rendering based on physically-based rendering aims to acquire high-quality materials, which helps achieve photo-realistic rendering under arbitrary illuminations. This task requires estimating multiple material maps and usually relies on the constraint of rendering result. The absence of constraints on the material maps makes inverse rendering an ill-posed task. Previous works alleviated this problem by building material dataset for training, but their simplified material data and rendering equation lead to rendering results with limited realism, especially that of skin. To further alleviate this problem, we construct a higher-quality dataset (OpenHumanBRDF) based on scanned real data and statistical material data. In addition to the normal, diffuse albedo, roughness, specular albedo, we produce displacement and subsurface scattering to enhance the realism of rendering results, especially for the skin. With the increase in prediction tasks for more materials, using an end-to-end model as in the previous work struggles to balance the importance among various material maps, and leads to model underfitting. Therefore, we design a model (HumanMaterial) with progressive training strategy to make full use of the supervision information of the material maps and improve the performance of material estimation. HumanMaterial first obtain the initial material results via three prior models, and then refine the results by a finetuning model. Prior models estimate different material maps, and each map has different significance for rendering results. Thus, we design a Controlled PBR Rendering (CPR) loss, which enhances the importance of the materials to be optimized during the training of prior models. Extensive experiments on OpenHumanBRDF dataset and real data demonstrate that our method achieves state-of-the-art performance.

Non-Abelian Gauge Effect for 2-D Non-Hermitian Hatano-Nelson Model in Cylinder Type

2025-06-19T00:38:15Z

Non-Abelian gauge offers a powerful route to engineer novel topological phenomena. Here, we systematically investigate a two-dimensional (2D) non-Hermitian Hatano-Nelson model incorporating SU(2) non-Abelian gauge, demonstrating the emergence of Hopf-link bulk braiding topology in the complex energy spectrum solely with x-direction nearest-neighbor couplings. Because of the limitations of exceptional point (EP) topology in fully capturing the rich non-Hermitian skin effect (NHSE) under non-Abelian influence, we introduce a novel polarization parameter derived from the generalized Brillouin zone (GBZ). This parameter quantitatively discerns left-, right-, and notably, bipolar skin modes, with its accuracy corroborated by directly encoding real-space eigenstate. Our findings reveal that non-Abelian gauge provides unprecedented influence over NHSE, compared with Abelian gauge and without gauge cases. Furthermore, we uncover unique characteristics of zero-imaginary-energy eigenstates at these topological boundaries, including pronounced degeneracy and bipolar localization which is unaffected by size effects, investigated via dynamical evolution and Inverse Participation Ratio (IPR). This work establishes a new paradigm for synthesizing and manipulating non-Hermitian topological phases driven by non-Abelian structures, opening avenues for topological engineering and holding promise for experimental realization in synthetic dimensional platforms.

ViVo: A Dataset for Volumetric Video Reconstruction and Compression

2025-06-09T16:40:15Z

As research on neural volumetric video reconstruction and compression flourishes, there is a need for diverse and realistic datasets, which can be used to develop and validate reconstruction and compression models. However, existing volumetric video datasets lack diverse content in terms of both semantic and low-level features that are commonly present in real-world production pipelines. In this context, we propose a new dataset, ViVo, for VolumetrIc VideO reconstruction and compression. The dataset is faithful to real-world volumetric video production and is the first dataset to extend the definition of diversity to include both human-centric characteristics (skin, hair, etc.) and dynamic visual phenomena (transparent, reflective, liquid, etc.). Each video sequence in this database contains raw data including fourteen multi-view RGB and depth video pairs, synchronized at 30FPS with per-frame calibration and audio data, and their associated 2-D foreground masks and 3-D point clouds. To demonstrate the use of this database, we have benchmarked three state-of-the-art (SotA) 3-D reconstruction methods and two volumetric video compression algorithms. The obtained results evidence the challenging nature of the proposed dataset and the limitations of existing datasets for both volumetric video reconstruction and compression tasks, highlighting the need to develop more effective algorithms for these applications. The database and the associated results are available at https://vivo-bvicr.github.io/

A Unified Framework for the Non-Hermitian Localization: Boundary-Insensitive Modes and Electric-Magnetic Analogy

2026-05-15T08:59:56Z

The non-Hermitian skin effect is fundamentally characterized by its sensitivity to boundary conditions, reflected in changes to the energy spectrum and boundary-localized eigenstates. Here, we demonstrate that a spatially inhomogeneous imaginary scalar potential field induces a skin effect that is insensitive to boundary conditions. Both the spectrum and eigenstate distribution remain invariant, a behavior not captured by existing theories. We attribute this anomaly to translational symmetry breaking induced by spatially varying imaginary potentials in finite systems. We further formulate a theory that universally predicts localization in single-particle non-Hermitian systems. This framework classifies skin effects into two fundamental types: electric, driven by imaginary scalar potentials, and magnetic, driven by imaginary vector potentials, and reveals a phase transition between them, where eigenstates become fully delocalized. Our work provides a unified theory for non-Hermitian localization, allowing full control over skin modes via potential engineering in various platforms like photonic crystals and cold-atom systems.

Human-Imperceptible Physical Adversarial Attack for NIR Face Recognition Models

2025-04-22T12:10:25Z

Near-infrared (NIR) face recognition systems, which can operate effectively in low-light conditions or in the presence of makeup, exhibit vulnerabilities when subjected to physical adversarial attacks. To further demonstrate the potential risks in real-world applications, we design a novel, stealthy, and practical adversarial patch to attack NIR face recognition systems in a black-box setting. We achieved this by utilizing human-imperceptible infrared-absorbing ink to generate multiple patches with digitally optimized shapes and positions for infrared images. To address the optimization mismatch between digital and real-world NIR imaging, we develop a light reflection model for human skin to minimize pixel-level discrepancies by simulating NIR light reflection. Compared to state-of-the-art (SOTA) physical attacks on NIR face recognition systems, the experimental results show that our method improves the attack success rate in both digital and physical domains, particularly maintaining effectiveness across various face postures. Notably, the proposed approach outperforms SOTA methods, achieving an average attack success rate of 82.46% in the physical domain across different models, compared to 64.18% for existing methods. The artifact is available at https://anonymous.4open.science/r/Human-imperceptible-adversarial-patch-0703/.

LightHeadEd: Relightable & Editable Head Avatars from a Smartphone

2025-04-13T17:51:56Z

Creating photorealistic, animatable, and relightable 3D head avatars traditionally requires expensive Lightstage with multiple calibrated cameras, making it inaccessible for widespread adoption. To bridge this gap, we present a novel, cost-effective approach for creating high-quality relightable head avatars using only a smartphone equipped with polaroid filters. Our approach involves simultaneously capturing cross-polarized and parallel-polarized video streams in a dark room with a single point-light source, separating the skin's diffuse and specular components during dynamic facial performances. We introduce a hybrid representation that embeds 2D Gaussians in the UV space of a parametric head model, facilitating efficient real-time rendering while preserving high-fidelity geometric details. Our learning-based neural analysis-by-synthesis pipeline decouples pose and expression-dependent geometrical offsets from appearance, decomposing the surface into albedo, normal, and specular UV texture maps, along with the environment maps. We collect a unique dataset of various subjects performing diverse facial expressions and head movements.

A Telecentric Offset Reflective Imaging System (TORIS) for Terahertz Imaging and Spectroscopy

2025-04-07T17:05:35Z

Terahertz (THz) imaging has emerged as a promising technology in medical diagnostics due to its non-ionizing radiation and high sensitivity to water content. However, conventional THz imaging systems face limitations such as slow mechanical scanning, restricted field of view, and poor telecentricity. To overcome these challenges, we introduce the Telecentric Offset Reflective Imaging System (TORIS), a novel dual-mirror scanning design optimized for high-speed, distortion-free imaging. The system employs a telecentric f-theta lens and is validated using ray tracing and physical optics simulations. It achieves uniform resolution across a 50 mm x 50 mm field of view without the need for mechanical translation stages. Broadband spectral imaging of a USAF resolution test target across WR-2.2 (325-500 GHz) and WR-1.5 (500-700 GHz) frequency bands demonstrates consistent beam focus and minimal distortion, with a maximum deviation of 2.7 degrees from normal incidence and a beam waist of 2.1 lambda at the field edge in the WR-1.5 band. The system's sensitivity to hydration dynamics is further validated through imaging of wet tissue paper, capturing temporal changes in water content. In vivo imaging of human skin after capsaicin patch application reveals localized hydration variations due to biochemical response and adhesive removal. These findings confirm the system's potential for real-time hydration sensing and dermatological evaluation. TORIS sets a new benchmark in THz imaging, with applications in clinical diagnostics, wound assessment, and material characterization.

Optical Convolutional Spectrometer

2025-02-12T19:43:45Z

Optical spectrometers are fundamental across numerous disciplines in science and technology. However, miniaturized versions, while essential for in situ measurements, are often restricted to coarse identification of signature peaks and inadequate for metrological purposes. Here, we introduce a new class of spectrometer, leveraging the convolution theorem as its mathematical foundation. Our convolutional spectrometer offers unmatched performance for miniaturized systems and distinct structural and computational simplicity, featuring a centimeter-scale footprint for the fully packaged unit, low cost (~$10) and a 2400 cm-1 (approximately 500 nm) bandwidth. We achieve excellent precision in resolving complex spectra with sub-second sampling and processing time, demonstrating a wide range of applications from industrial and agricultural analysis to healthcare monitoring. Specifically, our spectrometer system classifies diverse solid samples, including plastics, pharmaceuticals, coffee, flour and tea, with 100% success rate, and quantifies concentrations of aqueous and organic solutions with detection accuracy surpassing commercial benchtop spectrometers. We also realize the non-invasive sensing of human biomarkers, such as skin moisture (mean absolute error; MAE = 2.49%), blood alcohol (1.70 mg/dL), blood lactate (0.81 mmol/L), and blood glucose (0.36 mmol/L), highlighting the potential of this new class of spectrometers for low-cost, high-precision, portable/wearable spectral metrology.

Experimentally Probing Non-Hermitian Spectral Transition and Eigenstate Skewness

2025-01-14T14:40:05Z

Non-Hermitian (NH) systems exhibit intricate spectral topology arising from complex-valued eigenenergies, with positive/negative imaginary parts representing gain/loss. Unlike the orthogonal eigenstates of Hermitian systems, NH systems feature left and right eigenstates that form a biorthogonal basis and can differ significantly, showcasing pronounced skewness between them. These characteristics give rise to unique properties absent in Hermitian systems, such as the NH skin effect and ultra spectral sensitivity. However, conventional experimental techniques are inadequate for directly measuring the complex-valued spectra and left and right eigenstates -- key elements for enhancing our knowledge of NH physics. This challenge is particularly acute in higher-dimensional NH systems, where the spectra and eigenstates are highly sensitive to macroscopic shapes, lattice geometry, and boundary conditions, posing greater experimental demands compared to one-dimensional systems. Here, we present a Green's function-based method that enables the direct measurement and characterization of both complex-valued energy spectra and the left and right eigenstates in arbitrary NH lattices. Using active acoustic crystals as the experimental platform, we observe spectral transitions and eigenstate skewness in two-dimensional NH lattices under both nonreciprocal and reciprocal conditions, with varied geometries and boundary conditions. Our approach renders complex spectral topology and left eigenstates experimentally accessible and practically meaningful, providing new insights into these quantities. The results not only confirm recent theoretical predictions of higher-dimensional NH systems but also establish a universal and versatile framework for investigating complex spectral properties and NH dynamics across a wide range of physical platforms.

TexHOI: Reconstructing Textures of 3D Unknown Objects in Monocular Hand-Object Interaction Scenes

2025-02-04T01:47:23Z

Reconstructing 3D models of dynamic, real-world objects with high-fidelity textures from monocular frame sequences has been a challenging problem in recent years. This difficulty stems from factors such as shadows, indirect illumination, and inaccurate object-pose estimations due to occluding hand-object interactions. To address these challenges, we propose a novel approach that predicts the hand's impact on environmental visibility and indirect illumination on the object's surface albedo. Our method first learns the geometry and low-fidelity texture of the object, hand, and background through composite rendering of radiance fields. Simultaneously, we optimize the hand and object poses to achieve accurate object-pose estimations. We then refine physics-based rendering parameters - including roughness, specularity, albedo, hand visibility, skin color reflections, and environmental illumination - to produce precise albedo, and accurate hand illumination and shadow regions. Our approach surpasses state-of-the-art methods in texture reconstruction and, to the best of our knowledge, is the first to account for hand-object interactions in object texture reconstruction.

Polarimetric BSSRDF Acquisition of Dynamic Faces

2024-12-29T07:57:25Z

Acquisition and modeling of polarized light reflection and scattering help reveal the shape, structure, and physical characteristics of an object, which is increasingly important in computer graphics. However, current polarimetric acquisition systems are limited to static and opaque objects. Human faces, on the other hand, present a particularly difficult challenge, given their complex structure and reflectance properties, the strong presence of spatially-varying subsurface scattering, and their dynamic nature. We present a new polarimetric acquisition method for dynamic human faces, which focuses on capturing spatially varying appearance and precise geometry, across a wide spectrum of skin tones and facial expressions. It includes both single and heterogeneous subsurface scattering, index of refraction, and specular roughness and intensity, among other parameters, while revealing biophysically-based components such as inner- and outer-layer hemoglobin, eumelanin and pheomelanin. Our method leverages such components' unique multispectral absorption profiles to quantify their concentrations, which in turn inform our model about the complex interactions occurring within the skin layers. To our knowledge, our work is the first to simultaneously acquire polarimetric and spectral reflectance information alongside biophysically-based skin parameters and geometry of dynamic human faces. Moreover, our polarimetric skin model integrates seamlessly into various rendering pipelines.

Machine Learning-based sEMG Signal Classification for Hand Gesture Recognition

2024-11-23T21:29:51Z

EMG-based hand gesture recognition uses electromyographic~(EMG) signals to interpret and classify hand movements by analyzing electrical activity generated by muscle contractions. It has wide applications in prosthesis control, rehabilitation training, and human-computer interaction. Using electrodes placed on the skin, the EMG sensor captures muscle signals, which are processed and filtered to reduce noise. Numerous feature extraction and machine learning algorithms have been proposed to extract and classify muscle signals to distinguish between various hand gestures. This paper aims to benchmark the performance of EMG-based hand gesture recognition using novel feature extraction methods, namely, fused time-domain descriptors, temporal-spatial descriptors, and wavelet transform-based features, combined with the state-of-the-art machine and deep learning models. Experimental investigations on the Grabmyo dataset demonstrate that the 1D Dilated CNN performed the best with an accuracy of $97\%$ using fused time-domain descriptors such as power spectral moments, sparsity, irregularity factor and waveform length ratio. Similarly, on the FORS-EMG dataset, random forest performed the best with an accuracy of $94.95\%$ using temporal-spatial descriptors (which include time domain features along with additional features such as coefficient of variation (COV), and Teager-Kaiser energy operator (TKEO)).

Hamiltonian non-Hermicity: accurate dynamics with the multiple Davydov D$_2$ Ansätze

2024-11-01T18:41:14Z

We examine the applicability of the numerically accurate method of time dependent variation with multiple Davydov Ansatze (mDA) to non-Hermitian systems. Three systems of interest includes: a non-Hermitian system of dissipative Landau-Zener transitions, a non-Hermitian, multimode Jaynes-Cummings model, and a dissipative Holstein-Tavis-Cummings model, where complex many-body dynamics are accurately captured by the mDA method. Our findings highlight the versatility of the mDA as a powerful numerical tool for investigating complex many-body non-Hermitian systems, which can be extended to explore diverse phenomena such as skin effects, excited-state dynamics, and spectral topology in the non-Hermitian field.

Electro-Mechanical Contact Interactions Between Human Finger and Touchscreen Under Electroadhesion

2024-09-27T13:20:33Z

Electroadhesion (EA) has potential in robotics, automation, space missions, textiles, and tactile displays, but its physics remains underexplored due to limited models and experimental data. This thesis develops an electro-mechanical model to estimate electrostatic forces between human finger and touchscreen under EA and compares it to experimentally measured friction forces. The model aligns well with the data, showing that the electrostatic force changes mainly due to charge leakage from the Stratum Corneum at frequencies below 250 Hz and its electrical properties above 250 Hz. Additionally, a novel approach using electrical impedance measurements estimates electrostatic forces by subtracting skin and touchscreen impedances from the total impedance. This method is the first to experimentally estimate the average air gap between finger and voltage-induced capacitive touchscreen. The effect of electrode polarization impedance, particularly at low frequencies, was also studied, revealing its role in the charge leakage phenomenon. Tactile perception via EA was investigated using DC and AC voltage signals on a touchscreen with 10 participants of varying finger moisture levels. Results showed that AC voltage detection thresholds were significantly lower than for DC, explained by charge leakage at lower frequencies. Participants with moist fingers exhibited higher threshold levels, supported by impedance measurements. The thesis also investigated how touchscreen top coatings influence tactile perception, focusing on EA-free interactions. Psychophysical experiments and physical measurements demonstrated that coating materials significantly affect tactile perception, likely due to molecular interactions. These findings offer insights into finger-touchscreen interactions under EA and have potential applications in designing robotic systems and haptic interfaces using this technology.

Machine Learning Model for Complete Reconstruction of Diagnostic Polarimetric Images from partial Mueller polarimetry data

2024-09-19T20:12:59Z

The translation of imaging Mueller polarimetry to clinical practice is often hindered by large footprint and relatively slow acquisition speed of the existing instruments. Using polarization-sensitive camera as a detector may reduce instrument dimensions and allow data streaming at video rate. However, only the first three rows of a complete 4x4 Mueller matrix can be measured. To overcome this hurdle we developed a machine learning approach using sequential neural network algorithm for the reconstruction of missing elements of a Mueller matrix from the measured elements of the first three rows. The algorithm was trained and tested on the dataset of polarimetric images of various excised human tissues (uterine cervix, colon, skin, brain) acquired with two different imaging Mueller polarimeters operating in either reflection (wide-field imaging system) or transmission (microscope) configurations at different wavelengths of 550 nm and 385 nm, respectively. The reconstruction performance was evaluated using various error metrics, all of which confirmed low error values. The execution time of the trained neural network algorithm was about 300 microseconds for a single image pixel. It suggests that a machine learning approach with parallel processing of all image pixels combined with the partial Mueller polarimeter operating at video rate can effectively substitute for the complete Mueller polarimeter and produce accurate maps of depolarization, linear retardance and orientation of the optical axis of biological tissues, which can be used for medical diagnosis in clinical settings.

Entanglement Spectrum Dynamics as a Probe for Non-Hermitian Bulk-Boundary Correspondence in Systems with Periodic Boundaries

2025-04-12T06:37:17Z

It has recently been established that open quantum systems may exhibit a strong spectral sensitivity to boundary conditions, known as the non-Hermitian/Liouvillian skin effect (NHSE/LSE), making the topological properties of the system boundary-condition sensitive. In this Letter, we ask the query: Can topological phase transitions of open quantum systems, captured by open boundary conditioned invariants, be observed in the dynamics of a system in a periodic boundary condition, even in the presence of NHSE/LSE? We affirmatively respond to this question, by considering the quench dynamics of entanglement spectrum in a periodic open quantum fermionic system. We demonstrate that the entanglement spectrum exhibits zero-crossings only when this periodic system is quenched from a topologically trivial to non-trivial phase, defined from the spectrum in open boundary conditions, even in systems featuring LSE. Our results reveal that non-Hermitian topological phases leave a distinctive imprint on the unconditional dynamics within a subsystem of fermionic systems.

LSSF-Net: Lightweight Segmentation with Self-Awareness, Spatial Attention, and Focal Modulation

2024-09-03T03:06:32Z

Accurate segmentation of skin lesions within dermoscopic images plays a crucial role in the timely identification of skin cancer for computer-aided diagnosis on mobile platforms. However, varying shapes of the lesions, lack of defined edges, and the presence of obstructions such as hair strands and marker colors make this challenge more complex. \textcolor{red}Additionally, skin lesions often exhibit subtle variations in texture and color that are difficult to differentiate from surrounding healthy skin, necessitating models that can capture both fine-grained details and broader contextual information. Currently, melanoma segmentation models are commonly based on fully connected networks and U-Nets. However, these models often struggle with capturing the complex and varied characteristics of skin lesions, such as the presence of indistinct boundaries and diverse lesion appearances, which can lead to suboptimal segmentation performance.To address these challenges, we propose a novel lightweight network specifically designed for skin lesion segmentation utilizing mobile devices, featuring a minimal number of learnable parameters (only 0.8 million). This network comprises an encoder-decoder architecture that incorporates conformer-based focal modulation attention, self-aware local and global spatial attention, and split channel-shuffle. The efficacy of our model has been evaluated on four well-established benchmark datasets for skin lesion segmentation: ISIC 2016, ISIC 2017, ISIC 2018, and PH2. Empirical findings substantiate its state-of-the-art performance, notably reflected in a high Jaccard index.

Mpox Detection Advanced: Rapid Epidemic Response Through Synthetic Data

2024-07-25T04:33:19Z

Rapid development of disease detection models using computer vision is crucial in responding to medical emergencies, such as epidemics or bioterrorism events. Traditional data collection methods are often too slow in these scenarios, requiring innovative approaches for quick, reliable model generation from minimal data. Our study introduces a novel approach by constructing a comprehensive computer vision model to detect Mpox lesions using only synthetic data. Initially, these models generated a diverse set of synthetic images representing Mpox lesions on various body parts (face, back, chest, leg, neck, arm) across different skin tones as defined by the Fitzpatrick scale (fair, brown, dark skin). Subsequently, we trained and tested a vision model with this synthetic dataset to evaluate the diffusion models' efficacy in producing high-quality training data and its impact on the vision model's medical image recognition performance. The results were promising; the vision model achieved a 97% accuracy rate, with 96% precision and recall for Mpox cases, and similarly high metrics for normal and other skin disorder cases, demonstrating its ability to correctly identify true positives and minimize false positives. The model achieved an F1-Score of 96% for Mpox cases and 98% for normal and other skin disorders, reflecting a balanced precision-recall relationship, thus ensuring reliability and robustness in its predictions. Our proposed SynthVision methodology indicates the potential to develop accurate computer vision models with minimal data input for future medical emergencies.