arXiv subjects

Boyang Fu

Publications and source records attributed to Boyang Fu.

5 recordsLinked to original sources

arXiv2026

Femtoscopy of Strange Baryons in Heavy-ion Collisions at RHIC-STAR

Studying the final state interactions and finding possible bound states is helpful for understanding the strong interactions and comprehending the equation-of-state (EoS) of the nuclear matter. In these proceedings, we present recent femtoscopy results of \pXi{}, \LaLa{}, \pOm{} femtoscopic correlations with high statistics Isobar (Ru+Ru, Zr+Zr) and Au+Au collisions measured by the STAR experiment. For the \pXi{} and \pOm{} pairs, the centrality dependence of source size and the scattering parameters are extracted with the Lednick\'y-Lyuboshitz approach. The results show that there is an attractive interaction in \pXi{} pairs and a bound state in \pOm{} pairs.

nucl-ex↗

arXiv2026

STRAND: Sequence-Conditioned Transport for Single-Cell Perturbations

Predicting how genetic perturbations change cellular state is a core problem for building controllable models of gene regulation. Perturbations targeting the same gene can produce different transcriptional responses depending on their genomic locus, including different transcription start sites and regulatory elements. Gene-level perturbation models collapse these distinct interventions into the same representation. We introduce STRAND, a generative model that predicts single-cell transcriptional responses by conditioning on regulatory DNA sequence. STRAND represents a perturbation by encoding the sequence at its genomic locus and uses this representation to parameterize a conditional transport process from control to perturbed cell states. Representing perturbations by sequence, rather than by a fixed set of gene identifiers, supports zero-shot inference at loci not seen during training and expands inference-time genomic coverage from ~1.5% for gene-level single-cell foundation models to ~95% of the genome. We evaluate STRAND on CRISPR perturbation datasets in K562, Jurkat, and RPE1 cells. STRAND improves discrimination scores by up to 33% in low-sample regimes, achieves the best average rank on unseen gene perturbation benchmarks, and improves transfer to novel cell lines by up to 0.14 in Pearson correlation. Ablations isolate the gains to sequence conditioning and transport, and case studies show that STRAND resolves functionally alternative transcription start sites missed by gene-level models.

q-bio.GN↗

arXiv2024

Measurement of $p$-$\Xi^{-}$ ($\bar{p}$-$\bar{\Xi}^{+}$) Correlation Function in Isobar and Au+Au Collisions at $\sqrt{s_{\mathrm{NN}}}$ = 200 GeV with the STAR Detector

Understanding the strong interactions between baryons, especially hyperon-nucleon ($Y$-$N$) interactions, is crucial for comprehending the equation-of-state (EoS) of the nuclear matter and inner structure of neutron star. In these proceedings, we present the measurements of $p$-$\Xi^{-}$ ($\bar{p}$-$\bar{\Xi}^{+}$) correlation functions with high statistics in Isobar (Ru+Ru, Zr+Zr) and Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 200 GeV by the STAR experiment. With the Lednick\'y-Lyuboshitz approach, the source size and strong interaction parameters of $p$-$\Xi^{-}$ ($\bar{p}$-$\bar{\Xi}^{+}$) pairs are extracted.

nucl-ex↗

arXiv2023

Structure responsible for the superconducting state in La3Ni2O7 at high pressure and low temperature conditions

Very recently, a new superconductor with Tc = 80 K was reported in nickelate (La3Ni2O7) at around 15 - 40 GPa conditions (Nature, 621, 493, 2023) [1], which is the second type of unconventional superconductor, beside the cuprates, with Tc above liquid nitrogen temperature. However, the phase diagram plotted in this report was mostly based on the transport measurement at low temperature and high pressure conditions, and the assumed corresponding X-ray diffraction (XRD) results was carried out at room temperature. This encouraged us to carry out in situ high pressure and low temperature synchrotron XRD experiments to determine which phase is responsible for the high Tc state. In addition to the phase transition from orthorhombic Amam structure to orthorhombic Fmmm structure, a tetragonal phase with space group of I4/mmm was discovered when the sample was compressed to 19 GPa at 40 K where the superconductivity takes palce in La3Ni2O7. The calculations based on this tetragonal structure reveal that the electronic states approached to the Fermi energy were mainly dominated by the eg orbitals (3dz2 and 3dx2-y2) of Ni atoms, which are located in the oxygen octahedral crystal field. The correlation between Tc and this structural evolution, especially Ni-O octahedra regularity and the in-plane Ni-O-Ni bonding angles, are analyzed. This work sheds new lights to identify what is the most likely phase responsible for superconductivity in the double layered nickelate.

cond-mat.supr-con↗

arXiv2020

Marginal Contribution Feature Importance -- an Axiomatic Approach for The Natural Case

When training a predictive model over medical data, the goal is sometimes to gain insights about a certain disease. In such cases, it is common to use feature importance as a tool to highlight significant factors contributing to that disease. As there are many existing methods for computing feature importance scores, understanding their relative merits is not trivial. Further, the diversity of scenarios in which they are used lead to different expectations from the feature importance scores. While it is common to make the distinction between local scores that focus on individual predictions and global scores that look at the contribution of a feature to the model, another important division distinguishes model scenarios, in which the goal is to understand predictions of a given model from natural scenarios, in which the goal is to understand a phenomenon such as a disease. We develop a set of axioms that represent the properties expected from a feature importance function in the natural scenario and prove that there exists only one function that satisfies all of them, the Marginal Contribution Feature Importance (MCI). We analyze this function for its theoretical and empirical properties and compare it to other feature importance scores. While our focus is the natural scenario, we suggest that our axiomatic approach could be carried out in other scenarios too.

cs.LG↗