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Menghan Li

Publications and source records attributed to Menghan Li.

10 recordsLinked to original sources

Canonical Mandelbrot Cascades on Curves Are Rajchman

We settle the Rajchman problem for canonical scalar dyadic Mandelbrot cascades at the minimal Kahane--Peyri\`ere integrability threshold. If $\mu$ is the cascade on $[0,1]$, then $\widehat{\mu}(\xi)\to 0$ as $|\xi|\to\infty$, almost surely on non-extinction. For every fixed nondegenerate $C^2$ embedded arc $\gamma:[0,1]\to\mathbb{R}^2$, the pushforward $\gamma_\#\mu$ is likewise Rajchman almost surely on non-extinction. The analogous conclusion holds for the scalar cascade on the parameter circle pushed forward by any fixed nondegenerate $C^2$ Jordan curve. No moment condition of order strictly greater than one is imposed; in particular, the results include the regime $\mathbb{E}[W^q]=\infty$ for every $q>1$. The proof combines a spine-based lower-deviation principle, adaptive terminal approximation, and predictable capping to obtain almost-sure estimates uniform over large frequency annuli without higher moments. For curved pushforwards, an endpoint-safe phase decomposition controls direction-dependent stationary regions, including those meeting the endpoints of an arc, and couples the geometric and probabilistic arguments through a common dyadic kernel. Combined with the exact Fourier-dimension formulas for the corresponding models, the theorems show that Rajchman decay persists at zero Fourier dimension.

math.PR

Learning Roller-Skating Motions of Humanoid Robots Based on Adversarial Motion Priors

Humanoid roller-skating is difficult because the robot must coordinate whole-body balance, rolling contacts, and velocity-dependent posture regulation. This paper presents an adversarial motion prior based reinforcement learning framework for two humanoid roller-skating gaits: Pump Glide skating and Push Glide skating. The two gait datasets are collected independently through motion capture and retargeted to the humanoid robot separately. The retargeted data are then smoothed and resampled into reference motion states for AMP training. The two gaits are learned by independent AMP training pipelines with separate reference datasets, separate policies, and independent reward architectures. Simulation experiments are designed to evaluate gait quality, velocity tracking, turning, and gait-specific reward ablations.

cs.RO

Exact Fourier dimensions of dyadic Mandelbrot cascades under minimal integrability

We determine the Fourier dimension of dyadic Mandelbrot cascades under the minimal Kahane-Peyriere integrability condition. The interval theorem is proved in a vector-valued dyadic cascade model in which sibling weights may have arbitrary dependence. For every balanced energy-admissible vector law, almost surely on non-extinction, dim_F(mu)=dim_E(mu)=dim_2(mu)=D_E(X). In the canonical scalar case, under W>=0, E W=1, E[W log_2^+ W] 1} max{0, (q-1-log_2 E[W^q])/q}. The interval and circle formulas share a light-tail/heavy-tail dichotomy but have different mechanisms: energy dimension for the interval, and minimum lower local dimension for the circle. The circle lower bound follows from a finite-moment annular Fourier theorem.

math.PR

Observation of field-odd and field-free superconducting diode effects in $\mathrm{Mo}_2\mathrm{C}$ nanoflakes

The superconducting diode effect (SDE) enables nonreciprocal supercurrent flow, holding immense potential for ultra-low-power quantum electronics. Intrinsic SDE typically requires materials with inherent symmetry breakings. Here, we report the discovery of SDE in chemical vapor deposition-grown molybdenum carbide ($\mathrm{Mo}_2\mathrm{C}$) nanoflakes, a material traditionally considered centrosymmetric. Strikingly, this system uniquely hosts both field-odd and field-free SDEs. Transport measurements reveal a field-odd SDE with tunable efficiency exceeding 40% at 4 K under a perpendicular in-plane magnetic field. In a separate sample, a robust field-free SDE persists under zero-field and field-coolings. Out-of-plane field sweeps confirm the intrinsic nature of these phenomena. We propose that domain-boundary supercurrents or charge density wave-like orders drive this unexpected combination of symmetry breakings. Our findings establish air-stable $\mathrm{Mo}_2\mathrm{C}$ as an ideal platform for nonreciprocal superconducting electronics operating at liquid-helium temperatures, expanding the search for SDE into nominally centrosymmetric superconductors.

cond-mat.supr-con

From Link Diversity to Cross-Band Feedback Collaboration: A New Perspective on Hybrid Optical-RF Systems

We suggest a re-examination of the conventional view that hybrid optical-radio frequency (O-RF) systems are primarily diversity-driven networks that switch between RF and optical links for robustness. Instead, we uncover a new architectural opportunity: repurposing the optical downlink to enable real-time feedback channel coding over the RF uplink, where structured decoder feedback is delivered from the access point to guide the transmitter's coding strategy. This insight marks a conceptual paradigm shift from passive link diversity to active cross-band collaboration, where the wideband, interference-free optical wireless communication (OWC) is no longer merely a downlink backup but a functional enabler of uplink reliability. To realize this vision, we propose a novel architecture, O-RF with Cross-Band Feedback (O-RF-CBF), that exploits the optical downlink feedback to facilitate adaptive RF uplink coding. Numerical results reveal that O-RF-CBF achieves significant uplink throughput gains over traditional O-RF systems. Our findings highlight that inter-band synergy, not redundancy, is the key to unlocking the full potential of hybrid wireless networks.

cs.IT

SAFER: Advancing Safety Alignment via Efficient Ex-Ante Reasoning

Recent advancements in large language models (LLMs) have accelerated progress toward artificial general intelligence, yet their potential to generate harmful content poses critical safety challenges. Existing alignment methods often struggle to cover diverse safety scenarios and remain vulnerable to adversarial attacks. In this work, we propose SAFER, a framework for Safety Alignment via eFficient Ex-Ante Reasoning. Our approach instantiates structured Ex-Ante reasoning through initial assessment, rule verification, and path calibration, and embeds predefined safety rules to provide transparent and verifiable safety judgments. Specifically, our approach consists of two training stages: (1) supervised fine-tuning with synthetic traces to teach the multi-stage Ex-Ante reasoning, and (2) step-level reasoning preference optimization to jointly enhance safety, utility, and efficiency. Experiments on multiple open-source LLMs demonstrate that SAFER significantly enhances safety performance while maintaining helpfulness and response efficiency.

cs.CL

Nonlinear Expectation Inference for Efficient Uncertainty Quantification and History Matching of Transient Darcy Flows in Porous Media with Random Parameters Under Distribution Uncertainty

The uncertainty quantification of Darcy flows using history matching is important for the evaluation and prediction of subsurface reservoir performance. Conventional methods aim to obtain the maximum a posterior or maximum likelihood estimate (MLE) using gradient-based, heuristic or ensemble-based methods. These methods can be computationally expensive for high-dimensional problems since forward simulation needs to be run iteratively as physical parameters are updated. In the current study, we propose a nonlinear expectation inference (NEI) method for efficient history matching and uncertainty quantification accounting for distribution or Knightian uncertainty. Forward simulation runs are conducted on prior realisations once, and then a range of expectations are computed in the data space based on subsets of prior realisations with no repetitive forward runs required. In NEI, no prior probability distribution for data is assumed. Instead, the probability distribution is assumed to be uncertain with prior and posterior uncertainty quantified by nonlinear expectations. The inferred result of NEI is the posterior subsets on which the expected flow rates are consistent with observation. The accuracy and efficiency of the new method are validated using single- and two-phase Darcy flows in 2D and 3D heterogeneous reservoirs.

math.NA

Optical Integrated Sensing and Communication with Light-Emitting Diode

This paper presents a new optical integrated sensing and communication (O-ISAC) framework tailored for cost-effective Light-Emitting Diode (LED) for enhanced Internet of Things (IoT) applications. Unlike prior research on ISAC, which predominantly focused on radio frequency (RF) band, O-ISAC capitalizes on the inherent advantages of the optical spectrum, including the ultra-wide license-free bandwidth, immunity to RF interference, and energy efficiency -- attributes crucial for IoT communications. The communication and sensing in our O-ISAC system unfold in two phases: directionless O-ISAC and directional O-ISAC. In the first phase, distributed optical access points emit non-directional light for communication and leverage small-aperture imaging principles for sensing. In the second phase, we put forth the concept of optical beamforming, using collimating lenses to concentrate light, resulting in substantial performance enhancements in both communication and sensing. Numerical and simulation results demonstrate the feasibility and impressive performance of O-ISAC benchmarked against optical separate communication and sensing systems.

cs.IT

Cover Reproducible Steganography via Deep Generative Models

Whereas cryptography easily arouses attacks by means of encrypting a secret message into a suspicious form, steganography is advantageous for its resilience to attacks by concealing the message in an innocent-looking cover signal. Minimal distortion steganography, one of the mainstream steganography frameworks, embeds messages while minimizing the distortion caused by the modification on the cover elements. Due to the unavailability of the original cover signal for the receiver, message embedding is realized by finding the coset leader of the syndrome function of steganographic codes migrated from channel coding, which is complex and has limited performance. Fortunately, deep generative models and the robust semantic of generated data make it possible for the receiver to perfectly reproduce the cover signal from the stego signal. With this advantage, we propose cover-reproducible steganography where the source coding, e.g., arithmetic coding, serves as the steganographic code. Specifically, the decoding process of arithmetic coding is used for message embedding and its encoding process is regarded as message extraction. Taking text-to-speech and text-to-image synthesis tasks as two examples, we illustrate the feasibility of cover-reproducible steganography. Steganalysis experiments and theoretical analysis are conducted to demonstrate that the proposed methods outperform the existing methods in most cases.

cs.CR

The Eclipse Integrated Computational Environment

Problems in modeling and simulation require significantly different workflow management technologies than standard grid-based workflow management systems. Computational scientists typically interact with simulation software in a feedback driven way were solutions and workflows are developed iteratively and simultaneously. This work describes common activities in workflows and how combinations of these activities form unique workflows. It presents the Eclipse Integrated Computational Environment as a workflow management system and development environment for the modeling and simulation community. Examples of the Environment's applicability to problems in energy science, general multiphysics simulations, quantum computing and other areas are presented as well as its impact on the community.

cs.SE