arXiv subjects

Haiwen Wang

Publications and source records attributed to Haiwen Wang.

15 recordsLinked to original sources

arXiv2026

Metacrystals: Inversely-designed 3D-printed intelligent panels for 6G communications

Metasurfaces represent a promising platform for improving coverage in future communication systems. Passive designs are especially attractive because they need no power supply and can be manufactured at low cost. However, most passive metasurfaces work well only for one polarization, frequency band, or incidence angle, which limits their practical use. Here we propose passive intelligent panels, termed metacrystals, that overcome these limitations by enabling highly complex multiplexed responses to multiple incident waves simultaneously and independently. This capability is enabled by a compact volumetric architecture that goes beyond conventional metasurfaces by exploiting a finite, yet still modest, thickness to unlock substantially more degrees of freedom. Through simulations and experiments, we demonstrate all-dielectric metacrystals capable of simultaneously controlling anomalous reflection and absorption, both in transmission and reflection regimes. Designed using inverse topology optimization, these metacrystals combine structural integrity, straightforward scalability, and compatibility with low-cost 3D printing for operation up to 100 GHz.

physics.optics↗

arXiv2026

Reasoning through Verifiable Forecast Actions: Consistency-Grounded RL for Financial LLMs

Financial markets are characterized by extreme non-stationarity, low signal-to-noise ratios, and strong dependence on external information such as news, company fundamentals, and macroeconomic signals. Yet, existing approaches either abstract time-series into text or decouple forecasting from language-based reasoning, leading to a fundamental mismatch between qualitative reasoning and quantitative outcomes. To address this, we introduce StockR1, a time-series-enhanced LLM that unifies stock forecasting and financial reasoning through a verifiable forecast action. Based on a tool-call design, the model first emits a forecast action, which is a structured and interpretable representation of its qualitative market outlook. It then invokes a time-series decoder conditioned on this action to generate distributional future trajectories, leading to more informed question answering and financial reasoning. We optimize the full pipeline with reinforcement learning, where rewards jointly reflect answer validity, forecast accuracy, and consistency between generated actions and observed time-series dynamics. In addition, rewards are reweighted by a sample-level uncertainty scalar, encouraging the model to accommodate varying uncertainty in market dynamics. We evaluate StockR1 on financial question answering and stock forecasting over a large-scale 10-year benchmark. Our method consistently outperforms time-series baselines and general-purpose LLMs, improving reasoning accuracy by 17.7% (4B) and 25.9% (8B). These findings demonstrate that structuring the forecast actions establishes a powerful synergy between language reasoning and temporal prediction, enabling LLMs to reason through verifiable, interpretable, and numerically grounded decisions.

cs.LG↗

arXiv2025

Photonic Shankar skyrmion

We unveil a new topological quasiparticle of light in 3D space, named the photonic Shankar skyrmion. We show that an elliptically polarized field can be described by an SO(3) order parameter and it can form a texture in 3D space classified by the $π_3(SO(3))$ homotopy group, known as the Shankar skyrmion. We provide ways to construct the photonic Shankar skyrmion in static monochromatic waves and also in propagating wavepackets, the latter give rise to a flying topological quasiparticle. We demonstrate that the transition between the topological configuration and the trivial configuration gives rise to a novel topological singularity, which we call the $L^T$ surface. Such configurations of the electromagnetic field, under light-matter interaction, may lead to new phenomena in condensed matter physics and plasma physics, and are expected to find applications in quantum emulation and optical manipulation.

physics.optics↗

arXiv2025

Spatiotemporal steering of non-diffracting wavepackets

We study the dynamics of space-time non-diffracting wavepackets, commonly known as light bullets, in a spatiotemporally varying medium. We show that by spatiotemporal refraction, a monochromatic focused beam can be converted to a light bullet that propagates at a given velocity. By further designing the index profile of the spatiotemporal boundary, the group velocity and the propagation direction of the light bullet can be engineered in a programmable way. All effects mentioned above cannot be achieved by spatial or temporal boundaries, and are only possible with spatiotemporal boundaries. These findings provide unique ways to engineer the dynamics of electromagnetic wavepackets in space-time. Such wavepackets with engineered spacetime trajectories may find potential applications in the spatiotemporal control of material properties or particles, or for use as a way to emulate relativistic physics in the laboratory.

physics.optics↗

arXiv2025

Dynamic realization of emergent high-dimensional optical vortices

The dimensionality of vortical structures has recently been extended beyond two dimensions, providing higher-order topological characteristics and robustness for high-capacity information processing and turbulence control. The generation of high-dimensional vortical structures has mostly been demonstrated in classical systems through the complex interference of fluidic, acoustic, or electromagnetic waves. However, natural materials rarely support three- or higher-dimensional vortical structures and their physical interactions. Here, we present a high-dimensional gradient thickness optical cavity (GTOC) in which the optical coupling of planar metal-dielectric multilayers implements topological interactions across multiple dimensions. Topological interactions in high-dimensional GTOC construct non-trivial topological phases, which induce high-dimensional vortical structures in generalized parameter space in three, four dimensions, and beyond. These emergent high-dimensional vortical structures are observed under electro-optic tomography as optical vortex dynamics in two-dimensional real-space, employing the optical thicknesses of the dielectric layers as synthetic dimensions. We experimentally demonstrate emergent vortical structures, optical vortex lines and vortex rings, in a three-dimensional generalized parameter space and their topological transitions. Furthermore, we explore four-dimensional vortical structures, termed optical vortex sheets, which provide the programmability of real-space optical vortex dynamics. Our findings hold significant promise for emulating high-dimensional physics and developing active topological photonic devices.

physics.optics↗

arXiv2024

Photonic torons, topological phase transition and tunable spin monopoles

Creation and control of topological complex excitations play crucial roles in both fundamental physics and modern information science. Torons are a sophisticated class of 3D chiral polar topological structures with both skyrmionic quasiparticle textures and monopole point defects, so far only observed in liquid crystal nonpolar models. Here, we experimentally construct torons with the photonic spin of vector structured light and demonstrate the topological phase transitions among diverse topological states: torons, hopfions, skyrmioniums and monopole pairs. We can also continually tune the toron's chirality and the helical spin textures of emerging monopole pairs. The birth of photonic torons and tunable monopoles opens a flexible platform for studying nontrivial light-matter interaction and topological informatics.

physics.optics↗

arXiv2024

AceMap: Knowledge Discovery through Academic Graph

The exponential growth of scientific literature requires effective management and extraction of valuable insights. While existing scientific search engines excel at delivering search results based on relational databases, they often neglect the analysis of collaborations between scientific entities and the evolution of ideas, as well as the in-depth analysis of content within scientific publications. The representation of heterogeneous graphs and the effective measurement, analysis, and mining of such graphs pose significant challenges. To address these challenges, we present AceMap, an academic system designed for knowledge discovery through academic graph. We present advanced database construction techniques to build the comprehensive AceMap database with large-scale academic entities that contain rich visual, textual, and numerical information. AceMap also employs innovative visualization, quantification, and analysis methods to explore associations and logical relationships among academic entities. AceMap introduces large-scale academic network visualization techniques centered on nebular graphs, providing a comprehensive view of academic networks from multiple perspectives. In addition, AceMap proposes a unified metric based on structural entropy to quantitatively measure the knowledge content of different academic entities. Moreover, AceMap provides advanced analysis capabilities, including tracing the evolution of academic ideas through citation relationships and concept co-occurrence, and generating concise summaries informed by this evolutionary process. In addition, AceMap uses machine reading methods to generate potential new ideas at the intersection of different fields. Exploring the integration of large language models and knowledge graphs is a promising direction for future research in idea evolution. Please visit \url{https://www.acemap.info} for further exploration.

cs.DL↗

arXiv2022

Topological spin defects of light

Topological defects are found in a variety of systems, and their existence are robust under perturbations due to their topological nature. Here we introduce a new type of topological defects found in electromagnetic waves: topological spin defects. Such a defect is associated with a point where the electromagnetic spin density is zero, and generically have a nontrivial topological spin texture surrounding the defect point. Due to such spin texture, a topological spin defect possesses a quantized topological charge. We provide examples where isolated defect point, periodic or quasi-periodic defect lattices can be created. Such topological spin defect points may find applications in 3D imaging and nanoparticle manipulation.

physics.optics↗

arXiv2021

Universal behavior of the scattering matrix near thresholds in photonics

Scattering thresholds and their associated spectral square root branch points are ubiquitous in photonics. In this Letter, we show that the scattering matrix has a simple universal behavior near scattering thresholds. We use unitarity, reciprocity, and time-reversal symmetry to construct a two-parameter model for a two-port scattering matrix near a threshold. We demonstrate this universal behavior in three different optical systems, namely a photonic crystal slab, a planar dielectric interface, and a junction between metallic waveguides of different widths.

physics.optics↗

arXiv2021

Isotropic Topological Second-Order Spatial Differentiator Operating in Transmission Mode

Differentiation has widespread applications, particularly in image processing for edge detection. Significant advances have been made in using nanophotonic structures and metamaterials to perform such operations. In particular, a recent work demonstrated a topological differentiator in which the transfer function exhibits a topological charge, making the differentiation operation robust to variation of operating conditions. The demonstrated topological differentiator, however, operates in the reflection mode at off-normal incidence and is difficult to integrate into compact imaging systems. In this work, we design a topological differentiator that operates isotropically in transmission mode at normal incidence. The device exhibits an optical transfer function with a symmetry-protected, topological charge of $\pm 2$ and performs second-order differentiation. Our work points to the potential of harnessing topological concepts for optical computing applications.

physics.optics↗

arXiv2021

Controllable finite ultra-narrow quality-factor peak in a perturbed Dirac-cone band structure of a photonic crystal slab

We show that by using a perturbed photonic Dirac-cone, one can realize ultra-narrow and finite Q-factor peak in the wavevector space, with both the peak value and the width separately tunable. We also discuss a lower bound in the minimal viable width given a peak Q-value while maintaining sufficient Q differentiation among modes. The strong angular and frequency Q-selection finds applications in optical devices where strong angle- and frequency-selection is needed.

physics.optics↗

arXiv2020

$\mathcal{PT}$-Symmetric Topological Edge-Gain Effect

We demonstrate a non-Hermitian topological effect that is characterized by having complex eigenvalues only in the edge states of a topological material, despite the fact that the material is completely uniform. Such an effect can be constructed in any topological structure formed by two gapped sub-systems, e.g., a quantum spin-Hall system, with a suitable non-Hermitian coupling between the spins. The resulting complex-eigenvalued edge state is robust against defects due to the topological protection. In photonics, such an effect can be used for the implementation of topological lasers, in which a uniform pumping provides gain only in the edge lasing state. Furthermore, such a topological lasing model is reciprocal and is thus compatible with standard photonic platforms.

physics.optics↗

arXiv2020

Squeeze free space with nonlocal flat optics

There has been substantial interest in miniaturizing optical systems by flat optics. However, one essential optical component, free space, fundamentally cannot be substituted with conventional local flat optics with space-dependent transfer functions, since the transfer function of free space is momentum-dependent instead. Overcoming this difficulty is important to achieve utmost miniaturization of optical systems. In this work, we show that free space can be substituted with nonlocal flat optics operating directly in the momentum domain. We derive the general criteria for an optical device to replace free space, and provide a concrete design of a photonic crystal slab device. Such a device can substitute much thicker free space with a compression ratio as high as 144. Our work paves the way for utmost miniaturization of optical systems using combination of local and nonlocal flat optics.

physics.optics↗

arXiv2020

Author Name Disambiguation on Heterogeneous Information Network with Adversarial Representation Learning

Author name ambiguity causes inadequacy and inconvenience in academic information retrieval, which raises the necessity of author name disambiguation (AND). Existing AND methods can be divided into two categories: the models focusing on content information to distinguish whether two papers are written by the same author, the models focusing on relation information to represent information as edges on the network and to quantify the similarity among papers. However, the former requires adequate labeled samples and informative negative samples, and are also ineffective in measuring the high-order connections among papers, while the latter needs complicated feature engineering or supervision to construct the network. We propose a novel generative adversarial framework to grow the two categories of models together: (i) the discriminative module distinguishes whether two papers are from the same author, and (ii) the generative module selects possibly homogeneous papers directly from the heterogeneous information network, which eliminates the complicated feature engineering. In such a way, the discriminative module guides the generative module to select homogeneous papers, and the generative module generates high-quality negative samples to train the discriminative module to make it aware of high-order connections among papers. Furthermore, a self-training strategy for the discriminative module and a random walk based generating algorithm are designed to make the training stable and efficient. Extensive experiments on two real-world AND benchmarks demonstrate that our model provides significant performance improvement over the state-of-the-art methods.

cs.SI↗

arXiv2019

On Geometric Structure of Activation Spaces in Neural Networks

In this paper, we investigate the geometric structure of activation spaces of fully connected layers in neural networks and then show applications of this study. We propose an efficient approximation algorithm to characterize the convex hull of massive points in high dimensional space. Based on this new algorithm, four common geometric properties shared by the activation spaces are concluded, which gives a rather clear description of the activation spaces. We then propose an alternative classification method grounding on the geometric structure description, which works better than neural networks alone. Surprisingly, this data classification method can be an indicator of overfitting in neural networks. We believe our work reveals several critical intrinsic properties of modern neural networks and further gives a new metric for evaluating them.

cs.LG↗