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Ivy Liu

Publications and source records attributed to Ivy Liu.

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Bounded Ratios of Lorentzian Polynomials II: The Complete Quadratic Local-to-Global Classification

Every quadratic Hessian slice of a Lorentzian polynomial yields bounded monomial ratios among the normalized coefficients of the polynomial. We determine exactly for which pairs $(n,d)$ these quadratic-slice ratios generate the full bounded-ratio cone for every $M$-convex support $S\subseteq\Delta_n^d$. For $d\geq 2$, this quadratic local-to-global principle holds universally if and only if \[ n\leq 3,\qquad d=2,\qquad\text{or}\qquad (n,d)=(4,3). \] In every remaining case, the principle fails already for Lorentzian polynomials with full support: for cubics in $n\geq 5$ variables and for polynomials of degree $d\geq 4$ in $n\geq 4$ variables. We identify the two minimal obstructions, at $(n,d)=(4,4)$ and $(n,d)=(5,3)$, and propagate them using cut-cone certificates and variable-lifting arguments. The quartic bounded ratio extends to every higher degree by differentiation, while perturbing a square-transportation direction yields an explicit degree-uniform family of separating functionals. As a conceptual byproduct, we show that transportation costs on finite median graphs give polar directions in arbitrary dimension and degree.

math.CO

Bounded Ratios of Lorentzian Polynomials I: The Ternary Theory and Optimal Bounding Constants

We study bounded ratios and optimal bounding constants among the normalized coefficients of ternary Lorentzian polynomials. For every fixed $M$-convex support and in arbitrary degree, we give an explicit presentation of the bounded-ratio cone in terms of quadratic Hessian slices. We then express the optimal bounding constants through a variational formula combining local support functions with linear compatibility constraints between slices. For full support, we determine all compatibility relations in arbitrary degree; in degree three, this yields explicit optimal constants for every two-generator section. Finally, we compare the resulting Lorentzian bounds with those for volume polynomials and rank-three matroid basis profiles.

math.CO

Qwen-Image-2.0 Technical Report

We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite recent progress, existing models still struggle with ultra-long text rendering, multilingual typography, high-resolution photorealism, robust instruction following, and efficient deployment, especially in text-rich and compositionally complex scenarios. Qwen-Image-2.0 addresses these challenges by coupling Qwen3-VL as the condition encoder with a Multimodal Diffusion Transformer for joint condition-target modeling, supported by large-scale data curation and a customized multi-stage training pipeline. This enables strong multimodal understanding while preserving flexible generation and editing capabilities. The model supports instructions of up to 1K tokens for generating text-rich content such as slides, posters, infographics, and comics, while significantly improving multilingual text fidelity and typography. It also enhances photorealistic generation with richer details, more realistic textures, and coherent lighting, and follows complex prompts more reliably across diverse styles. Extensive human evaluations show that Qwen-Image-2.0 substantially outperforms previous Qwen-Image models in both generation and editing, marking a step toward more general, reliable, and practical image generation foundation models.

cs.CV

Biomedical active matter: Emergence and breakdown of collective functionalities

Living systems are made of active materials with microscopic components that work together to perform macroscopic biological tasks. The breakdown of these collective functionalities leads to diseases, which, conversely, could be treated by exploiting self-organization in healthcare technologies. Here, we review recent advances in this rapidly growing field of biomedical active matter. The main themes are (1) collective self-assembly and spatiotemporal coordination; (2) collective motion, transport, and navigation; (3) collective sensing, signaling, and communication; and (4) collective adaptation, evolution, and learning. We discuss these emerging processes in a wide range of systems, including protein folding, biomolecular condensates, cytoskeleton dynamics, intracellular flows, bacterial biofilms, quorum sensing, cilia synchronization, wound healing, biolocomotion, neurons, endocrine signalling, and cardiovascular flow networks. For each, we highlight medical conditions associated with reduced collective functionality and how they may be treated using microrobotic swarms, bioinspired metamaterials, diagnostics, lab-on-chip devices, organoids, and other active and adaptive matter innovations.

physics.bio-ph

NNN: Next-Generation Neural Networks for Marketing Measurement

We present NNN, an experimental Transformer-based neural network approach to marketing measurement. Unlike Marketing Mix Models (MMMs) which rely on scalar inputs and parametric decay functions, NNN uses rich embeddings to capture both quantitative and qualitative aspects of marketing and organic channels (e.g., search queries, ad creatives). This, combined with its attention mechanism, potentially enables NNN to model complex interactions, capture long-term effects, and improve sales attribution accuracy. We show that L1 regularization permits the use of such expressive models in typical data-constrained settings. Evaluating NNN on simulated and real-world data demonstrates its efficacy, particularly through considerable improvement in predictive power. In addition to marketing measurement, the NNN framework can provide valuable, complementary insights through model probing, such as evaluating keyword or creative effectiveness.

cs.LG

Surrogate method for partial association between mixed data with application to well-being survey analysis

This paper is motivated by the analysis of a survey study of college student wellbeing before and after the outbreak of the COVID-19 pandemic. A statistical challenge in well-being survey studies lies in that outcome variables are often recorded in different scales, be it continuous, binary, or ordinal. The presence of mixed data complicates the assessment of the associations between them while adjusting for covariates. In our study, of particular interest are the associations between college students' wellbeing and other mental health measures and how other risk factors moderate these associations during the pandemic. To this end, we propose a unifying framework for studying partial association between mixed data. This is achieved by defining a unified residual using the surrogate method. The idea is to map the residual randomness to the same continuous scale, regardless of the original scales of outcome variables. It applies to virtually all commonly used models for covariate adjustments. We demonstrate the validity of using such defined residuals to assess partial association. In particular, we develop a measure that generalizes classical Kendall's tau in the sense that it can size both partial and marginal associations. More importantly, our development advances the theory of the surrogate method developed in recent years by showing that it can be used without requiring outcome variables having a latent variable structure. The use of our method in the well-being survey analysis reveals (i) significant moderation effects (i.e., the difference between partial and marginal associations) of some key risk factors; and (ii) an elevated moderation effect of physical health, loneliness, and accommodation after the onset of COVID-19.

stat.ME

Statistical generalized derivative applied to the profile likelihood estimation in a mixture of semiparametric models

There is a difficulty in finding an estimate of variance of the profile likelihood estimator in the joint model of longitudinal and survival data. We solve the difficulty by introducing the ``statistical generalized derivative''. The derivative is used to show the asymptotic normality of the estimator without assuming the second derivative of the density function in the model exists.

math.ST