相关论文: FLAME: Fitting Ly$\alpha$ Absorption lines using M…

As data requirements continue to grow, efficient learning increasingly depends on the curation and distillation of high-value data rather than brute-force scaling of model sizes. In the case of a hyperspectral image (HSI), the challenge is…

We have performed an extensive Voigt profile analysis of the neutral hydrogen (HI) and metal absorption present in a sample of 18 high resolution, high signal-to-noise QSO spectra observed with VLT/UVES. We use this analysis to separate the…

We present spectroscopic observations for quasars Q0107-025A, Q0107-025B, and Q0107-0232. For the first time, all data obtained with the Hubble Space Telescope's GHRS and FOS for these three quasars are combined. Ly-alpha absorbers selected…

Diffusion policies are expressive yet incur high inference latency. Flow Matching (FM) enables one-step generation, but integrating it into Maximum Entropy Reinforcement Learning (MaxEnt RL) is challenging: the optimal policy is an…

Recent works improving LLM math reasoning with synthetic data have used unique setups, making comparison of data synthesis strategies impractical. This leaves many unanswered questions about the roles of different factors in the synthetic…

Next-generation solar spectrographs increasingly record dense wavelength windows in which tens to hundreds of spectral lines are sampled at each spatial location and time step. This expands the scope for multi-line, multi-height diagnostics…

We have compiled, from the literature, a sample of Ly ${\alpha}$ forest lines in the spectra of 69 QSOs, all observed with a resolution between 60 to 100 km s$^{-1}$. The sample is studied for proximity effect. We have tried to account for…

We describe algorithms that detect 21cm line HI self-absorption (HISA) in large data sets and extract it for analysis. Our search method identifies HISA as spatially and spectrally confined dark HI features that appear as negative residuals…

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach. Instrumental to this framework, Feature Similarity…

Large-scale foundation models have demonstrated exceptional performance in language and vision tasks. However, the numerous dense matrix-vector operations involved in these large networks pose significant computational challenges during…

Wireless federated learning (WFL) suffers from heterogeneity prevailing in the data distributions, computing powers, and channel conditions of participating devices. This paper presents a new Federated Learning with Adjusted leaRning ratE…

The development of a Large Airfoil Model (LAM), a transformative approach for answering technical questions on airfoil aerodynamics, requires a vast dataset and a model to leverage it. To build this foundation, a novel probabilistic machine…

The helium I line intensity ratio (LIR) method is used to measure the electron density ($n_e$) and temperature ($T_e$) of fusion-relevant plasmas. Although the collisional-radiative model (CRM) has been used to predict $n_e$ and $T_e$,…

Line intensity mapping (LIM) is a promising observational method to probe large-scale fluctuations of line emission from distant galaxies. Data from wide-field LIM observations allow us to study the large-scale structure of the universe as…

We use hydrodynamic cosmological simulations to study damped Lyman-alpha (DLA) and Lyman limit (LL) absorption at redshifts z=2-4 in five variants of the cold dark matter scenario. Our standard simulations resolve the formation of dense…

Flow Matching (FM) models achieve remarkable results in generative tasks. Building upon diffusion models, FM's simulation-free training paradigm enables simplicity and efficiency but introduces a train-inference gap: model outputs cannot be…

Fully Homomorphic Encryption (FHE) allows for computation directly on encrypted data and enables privacy-preserving neural inference in the cloud. Prior work has focused on models with dense inputs (e.g., CNNs), with less attention given to…

We present a method that uses a Bloom filter transform to preprocess data for machine learning. Each sample is encoded into a compact bit-array representation using hash-based encoding, producing a fixed-length feature space that reduces…

We present a new approach to measure the power-law temperature density relationship $T=T_0 (\rho / \bar{\rho})^{\gamma -1}$ and the UV background photoionization rate $\Gamma_{\rm HI}$ of the IGM based on the Voigt profile decomposition of…

Recently, developing an automatic reading system for analog measuring instruments has gained increased attention, as it enables the collection of numerous state of equipment. Nonetheless, two major obstacles still obstruct its deployment to…