arXiv subjects

Andrew Ma

Publications and source records attributed to Andrew Ma.

16 recordsLinked to original sources

arXiv2026

Generative Frontier Planning for Adaptive Peer-Referral Recruitment under Covariate-Dependent Arrivals

Peer-referral recruitment systems such as respondent-driven sampling are critical for studying and intervening on hidden populations affected by infectious diseases. To accelerate recruitment, public health agencies must adaptively allocate limited referral resources across multiple rounds, where current decisions shape both the number and the covariates of future recruits. Prior work makes this problem tractable by assuming that referrals are drawn i.i.d.\ from a homogeneous population, an assumption that ignores the homophily and shared context that drive real peer recruitment. We instead consider a more realistic model in which both referral capacity and the covariates of newly referred individuals are conditioned on the referrer, learned from data with a censored count model and a conditional generative model. The resulting planning problem is challenging because each candidate allocation induces a different distribution over future recruits. We propose \emph{Generative Frontier Planning} (GFP), a model-based planner that replaces per-step Monte-Carlo sampling with a deterministic backup over a latent covariate-coverage value surrogate. The surrogate is designed so that the expected value of the next frontier depends on the offspring generative model only through finite-dimensional summaries that are amortized offline, and so that the resulting per-round objective is monotone with diminishing returns. Together, these two properties make planning tractable: the deterministic backup eliminates Monte-Carlo sampling, and the diminishing-returns structure lets a marginal greedy allocation achieve a $(1-1/e)$-approximation for the per-round problem. On a simulation environment calibrated to a real respondent-driven sampling dataset, GFP outperforms random, reinforcement-learning, and i.i.d.\ dynamic-programming baselines across four discount factors.

cs.LG↗

arXiv2026

HI Gas and Star Formation in Major Galaxy Pairs from the FAST All-Sky HI Survey (FASHI)

Atomic hydrogen (HI) plays a fundamental role in fueling star formation in galaxies. However, the behavior of HI gas in interacting systems, particularly galaxy pairs, remains elusive. In this work, we investigate the HI content of major mergers by cross-matching the extragalactic HI catalog from the FAST All-Sky HI Survey (FASHI) with a previously established sample of isolated galaxy pairs. With the superior sensitivity of FAST, we have constructed the largest sample of major mergers with HI detections, consisting of $440$ galaxy pairs: $364$ spiral-spiral (S+S) and $76$ spiral-elliptical (S+E) systems. We examine the HI gas fraction ($f_{\mathrm{HI}}$), star formation rate (SFR) and HI star formation efficiency ($\mathrm{SFE_{HI}}=\mathrm{SFR}/M_{\rm HI}$) for individual galaxies in pairs. The control sample is matched in both stellar mass and redshift. We find that paired galaxies, particularly those in pairs with small projected separations ($d_{\mathrm{p}}<50\ h^{-1}\mathrm{kpc}$), exhibit systematically lower (by $8.8\%$) HI gas fractions compared to the control galaxies. The SFR is enhanced for galaxies in S+S pairs. $\mathrm{SFE_{HI}}$ is $\sim15\%$ higher for galaxies in S+S pairs than in the control galaxies, while spiral galaxies in S+E pairs show no significant difference in $\mathrm{SFE_{HI}}$ compared to the control sample. These findings suggest that the merging process triggers efficient HI gas depletion and enhances star formation, especially in close S+S pairs. Notably, our sample includes $26$ red spirals in paired systems. These galaxies exhibit HI deficiency and suppressed star formation activity compared to the isolated galaxies, indicating that interactions may affect quiescent spirals differently, potentially due to mechanisms similar to ellipticals.

astro-ph.GA↗

arXiv2026

Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions

Reinforcement learning (RL) with combinatorial action spaces remains challenging because feasible action sets are exponentially large and governed by complex feasibility constraints, making direct policy parameterization impractical. Existing approaches embed task-specific value functions into constrained optimization programs or learn deterministic structured policies, sacrificing generality and policy expressiveness. We propose a solver-induced \emph{latent spherical flow policy} that brings the expressiveness of modern generative policies to combinatorial RL while guaranteeing feasibility by design. Our method, LSFlow, learns a \emph{stochastic} policy in a compact continuous latent space via spherical flow matching, and delegates feasibility to a combinatorial optimization solver that maps each latent sample to a valid structured action. To improve efficiency, we train the value network directly in the latent space, avoiding repeated solver calls during policy optimization. To address the piecewise-constant and discontinuous value landscape induced by solver-based action selection, we introduce a smoothed Bellman operator that yields stable, well-defined learning targets. Empirically, our approach outperforms state-of-the-art baselines by an average of 20.6\% across a range of challenging combinatorial RL tasks.

cs.LG↗

arXiv2025

HI Content of Group Galaxies from the FAST All Sky HI Survey

We investigate the atomic gas (HI) content of galaxies in groups using early data from the FAST All Sky HI survey (FASHI). Taking advantage of FAST's blind, wide-area coverage and uniform sensitivity, we assemble a sample of $230$ group galaxies belonging to $182$ groups at $z\leq0.03$. These groups were identified using a halo-based group finder, and they have an median membership of $4$ galaxies. We also derived a matched control sample of isolated systems, and apply censored-data modeling to include both detections and non-detections. At fixed stellar mass and color, we find that the global median HI fraction of group galaxies differs from that of controls by only $-0.04$ dex ($95\%$ CI [$-0.18,\ 0.16$]), indicating at most a mild average offset. The signal is not uniform across populations: satellites are HI-poor (median $\Delta f_{\mathrm{HI}}=-0.12$ dex), whereas centrals are not HI-deficient (median $\Delta f_{\mathrm{HI}}=0.13$ dex). Group galaxies located within $0.5R_{180}$ and in denser systems (richness $>10$ or local density $\Sigma>10\ \mathrm{gal\ Mpc^{-2}}$) show stronger negative offsets, whereas galaxies in the outskirts are statistically indistinguishable from the controls. These results refine earlier reports of global group HI deficiency: with deeper blind data and uniform treatment of upper limits, we show that HI depletion is primarily confined to satellites and compact cores rather than being ubiquitous across groups.

astro-ph.GA↗

arXiv2025

Reconstructing the Hamiltonian from the local density of states using neural networks

Reconstructing a quantum system's Hamiltonian from limited yet experimentally observable information is interesting both as a practical task and from a fundamental standpoint. We pose and investigate the inverse problem of reconstructing a Hamiltonian from a spatial map of the local density of states (LDOS) near a fixed energy. We demonstrate high-quality recovery of Hamiltonians from the LDOS using supervised learning. In particular, we generate synthetic data from single-particle Hamiltonians in 1D and 2D, train convolutional neural networks, and obtain models that solve the inverse problem with remarkably high accuracy. Moreover, we are able to generalize beyond the training distribution and develop models with strong robustness to noise. Finally, we comment on possible experimental applications to scanning tunneling microscopy, where we propose that maps of the electronic local density of states might be used to reveal a sample's unknown underlying energy landscape.

cond-mat.dis-nn↗

arXiv2025

Learning simple heuristic rules for classifying materials based on chemical composition

In the past decade, there has been a significant interest in the use of machine learning approaches in materials science research. Conventional deep learning approaches that rely on complex, nonlinear models have become increasingly important in computational materials science due to their high predictive accuracy. In contrast to these approaches, we have shown in a recent work that a remarkably simple learned heuristic rule -- based on the concept of topogivity -- can classify whether a material is topological using only its chemical composition. In this paper, we go beyond the topology classification scenario by also studying the use of machine learning to develop simple heuristic rules for classifying whether a material is a metal based on chemical composition. Moreover, we present a framework for incorporating chemistry-informed inductive bias based on the structure of the periodic table. For both the topology classification and the metallicity classification tasks, we empirically characterize the performance of simple heuristic rules fit with and without chemistry-informed inductive bias across a wide range of training set sizes. We find evidence that incorporating chemistry-informed inductive bias can reduce the amount of training data required to reach a given level of test accuracy.

cond-mat.mtrl-sci↗

arXiv2025

Practical Efficiency of Muon for Pretraining

We demonstrate that Muon, the simplest instantiation of a second-order optimizer, explicitly expands the Pareto frontier over AdamW on the compute-time tradeoff. We find that Muon is more effective than AdamW in retaining data efficiency at large batch sizes, far beyond the so-called critical batch size, while remaining computationally efficient, thus enabling more economical training. We study the combination of Muon and the maximal update parameterization (muP) for efficient hyperparameter transfer and present a simple telescoping algorithm that accounts for all sources of error in muP while introducing only a modest overhead in resources. We validate our findings through extensive experiments with model sizes up to four billion parameters and ablations on the data distribution and architecture.

cs.LG↗

arXiv2025

Rethinking Reflection in Pre-Training

A language model's ability to reflect on its own reasoning provides a key advantage for solving complex problems. While most recent research has focused on how this ability develops during reinforcement learning, we show that it actually begins to emerge much earlier - during the model's pre-training. To study this, we introduce deliberate errors into chains-of-thought and test whether the model can still arrive at the correct answer by recognizing and correcting these mistakes. By tracking performance across different stages of pre-training, we observe that this self-correcting ability appears early and improves steadily over time. For instance, an OLMo2-7B model pre-trained on 4 trillion tokens displays self-correction on our six self-reflection tasks.

cs.CL↗

arXiv2025

Predicting band gap from chemical composition: A simple learned model for a material property with atypical statistics

In solid-state materials science, substantial efforts have been devoted to the calculation and modeling of the electronic band gap. While a wide range of ab initio methods and machine learning algorithms have been created that can predict this quantity, the development of new computational approaches for studying the band gap remains an active area of research. Here we introduce a simple machine learning model for predicting the band gap using only the chemical composition of the crystalline material. To motivate the form of the model, we first analyze the empirical distribution of the band gap, which sheds new light on its atypical statistics. Specifically, our analysis enables us to frame band gap prediction as a task of modeling a mixed random variable, and we design our model accordingly. Our model formulation incorporates thematic ideas from chemical heuristic models for other material properties in a manner that is suited towards the band gap modeling task. The model has exactly one parameter corresponding to each element, which is fit using data. To predict the band gap for a given material, the model computes a weighted average of the parameters associated with its constituent elements and then takes the maximum of this quantity and zero. The model provides heuristic chemical interpretability by intuitively capturing the associations between the band gap and individual chemical elements.

cond-mat.mtrl-sci↗

arXiv2024

On the conservation laws and the structure of the nonlinearity for SQG and its generalizations

Using a new definition for the nonlinear term, we prove that all weak solutions to the SQG equation (and mSQG) conserve the angular momentum. This result is new for the weak solutions of [Resnick, '95] and rules out the possibility of anomalous dissipation of angular momentum. We also prove conservation of the Hamiltonian under conjecturally optimal assumptions, sharpening a well-known criterion of [Cheskidov-Constantin-Friedlander-Shvydkoy, '08]. Moreover, we show that our new estimate for the nonlinearity is optimal and that it characterizes the mSQG nonlinearity uniquely among active scalar nonlinearities with a scaling symmetry.

math.AP↗

arXiv2023

Multimodal Foundation Models for Material Property Prediction and Discovery

Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials. Recently, publicly available material data repositories have grown rapidly. This growth encompasses not only more materials but also a greater variety and quantity of their associated properties. Existing machine learning efforts in materials science focus primarily on single-modality tasks, i.e. relationships between materials and a single physical property, thus not taking advantage of the rich and multimodal set of material properties. Here, we introduce Multimodal Learning for Materials (MultiMat), which enables self-supervised multi-modality training of foundation models for materials. We demonstrate our framework's potential using data from the Materials Project database on multiple axes: (i) MultiMat achieves state-of-the-art performance for challenging material property prediction tasks; (ii) MultiMat enables novel and accurate material discovery via latent space similarity, enabling screening for stable materials with desired properties; and (iii) MultiMat encodes interpretable emergent features that may provide novel scientific insights.

cs.LG↗

arXiv2022

Orthogonal Rational Approximation of Transfer Functions for High-Frequency Circuits

Rational function approximations find applications in many areas including macro-modeling of high-frequency circuits, model order reduction for controller design, interpolation and extrapolation of system responses, surrogate models for high-energy physics, and approximation of elementary mathematical functions. The unknown denominator polynomial of the model results in a non-linear problem, which can be replaced with successive solutions of linearized problems following the Sanathanan-Koerner (SK) iteration. An orthogonal basis can be obtained based on Arnoldi resulting in a stabilized SK iteration. We present an extension of the stabilized SK, called Orthogonal Rational Approximation (ORA), which ensures real polynomial coefficients and stable poles for realizability of electrical networks. We also introduce an efficient implementation of ORA for multi-port networks based on a block QR decomposition.

eess.SY↗

arXiv2022

Topogivity: A Machine-Learned Chemical Rule for Discovering Topological Materials

Topological materials present unconventional electronic properties that make them attractive for both basic science and next-generation technological applications. The majority of currently known topological materials have been discovered using methods that involve symmetry-based analysis of the quantum wavefunction. Here we use machine learning to develop a simple-to-use heuristic chemical rule that diagnoses with a high accuracy whether a material is topological using only its chemical formula. This heuristic rule is based on a notion that we term topogivity, a machine-learned numerical value for each element that loosely captures its tendency to form topological materials. We next implement a high-throughput procedure for discovering topological materials based on the heuristic topogivity-rule prediction followed by ab initio validation. This way, we discover new topological materials that are not diagnosable using symmetry indicators, including several that may be promising for experimental observation.

cond-mat.mtrl-sci↗

arXiv2020

A direct approach to nonuniqueness and failure of compactness for the SQG equation

We give an alternative proof of the nonuniqueness of weak solutions to the surface quasigeostrophic equation (SQG) first shown in [Buckmaster-Shkoller-Vicol, '16]. Our approach proceeds directly at the level of the scalar field. Furthermore, we prove that every smooth scalar field with compact support that conserves the integral can be realized as a weak limit of solutions to SQG.

math.AP↗

arXiv2018

Optimal control of interacting quantum systems based on the first-order Magnus approximation: Application to multiple dipole-dipole coupled molecular rotors

We develop a methodology for performing approximate optimal control simulations for quantum systems with multiple interacting degrees of freedom. The quantum dynamics are modeled using the first-order Magnus approximation in the interaction picture, where the interactions between different degrees of freedom are treated as the perturbation. We present a numerical procedure for implementing this approximation, which leverages the separability of the zeroth-order time evolution operator and the pairwise nature of common interactions for a reduced computational cost. This formulation of the first-order Magnus approximation is suitable to be combined with gradient-free methods for control field optimization; to this end, we adopt a Stochastic Hill Climbing algorithm. The associated computational costs are analyzed and compared with those of the exact simulation in the large N limit. For numerical illustrations, we perform approximate optimal control simulations for systems of two and three dipole-dipole coupled molecular rotors under the influence of a global control field. For the two rotor system, we optimize fields for both orientation and entanglement control objectives. For the three rotor system, we optimize fields for orienting rotors in the same direction as well as in opposite directions.

quant-ph↗

arXiv2017

Existence and non-existence of transition fronts in mixed ignition-monostable media

We study transition fronts for one-dimensional reaction-diffusion equations with compactly perturbed ignition-monostable reactions. We establish an almost sharp condition on reactions which characterizes the existence and non-existence of fronts. In particular, we prove that a strong inhomogeneity in the reaction prevents formation of transition fronts, while a weak inhomogeneity gives rise to a front. Our work extends results and methods introduced by J. Nolen, J.M. Roquejoffre, L. Ryzhik, and A. Zlato\v{s}.

math.AP↗