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Gao, Shang

Publications and source records attributed to Gao, Shang.

6 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Phonon excitations in Eu 2 Ir 2 O 7 probed by inelastic x-ray scattering

The study of phonon dynamics and its interplay with magnetic ordering is crucial for understanding the unique quantum phases in the pyrochlore iridates. Here, through inelastic x-ray scattering on a single crystal sample of the pyrochlore iridate Eu 2 Ir 2 O 7 , we map out the phonon excitation spectra in Eu 2 Ir 2 O 7 and compare them with the theoretical phonon spectra calculated using the density functional theory. Further, possible phonon renormalization across the magnetic long-range order transition is observed in our experiments, which is consistent with the results of the previous Raman scattering experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Cubic double perovskites host noncoplanar spin textures

Magnetic materials with noncoplanar magnetic structures can show unusual physical properties driven by nontrivial topology. Topologically-active states are often multi-q structures, which are challenging to stabilize in models and to identify in materials. Here, we use inelastic neutron-scattering experiments to show that the insulating double perovskites Ba 2 YRuO 6 and Ba 2 LuRuO 6 host a noncoplanar 3-q structure on the face-centered cubic lattice. Quantitative analysis of our neutron-scattering data reveals that these 3-q states are stabilized by biquadratic interactions. Our study identifies double perovskites as a highly promising class of materials to realize topological magnetism, elucidates the stabilization mechanism of the 3-q state in these materials, and establishes neutron spectroscopy on powder samples as a valuable technique to distinguish multi-q from single-q states, facilitating the discovery of topologically-nontrivial magnetic materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Path-BigBird: An AI-Driven Transformer Approach to Classification of Cancer Pathology Reports

PURPOSE Surgical pathology reports are critical for cancer diagnosis and management. To accurately extract information about tumor characteristics from pathology reports in near real time, we explore the impact of using domain-specific transformer models that understand cancer pathology reports. METHODS We built a pathology transformer model, Path-BigBird, by using 2.7 million pathology reports from six SEER cancer registries. We then compare different variations of Path-BigBird with two less computationally intensive methods: Hierarchical Self-Attention Network (HiSAN) classification model and an offthe-shelf clinical transformer model (Clinical BigBird). We use five pathology information extraction tasks for evaluation: site, subsite, laterality, histology, and behavior. Model performance is evaluated by using macro and micro F 1 scores. RESULTS We found that Path-BigBird and Clinical BigBird outperformed the HiSAN in all tasks. Clinical BigBird performed better on the site and laterality tasks. Versions of the Path-BigBird model performed best on the two most difficult tasks: subsite (micro F 1 score of 72.53, macro F 1 score of 35.76) and histology (micro F 1 score of 80.96, macro F 1 score of 37.94). The largest performance gains over the HiSAN model were for histology, for which a Path-BigBird model increased the micro F 1 score by 1.44 points and the macro F 1 score by 3.55 points. Overall, the results suggest that a Path-BigBird model with a vocabulary derived from wellcurated and deidentified data is the best-performing model. CONCLUSION The Path-BigBird pathology transformer model improves automated information extraction from pathology reports. Although Path-BigBird outperforms Clinical BigBird and HiSAN, these less computationally expensive models still have utility when resources are constrained.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Spinon continuum in the Heisenberg quantum chain compound Sr 2 ⁢V 3 ⁢O 9

Magnetic excitations in the spin chain candidate Sr 2 ⁢V 3 ⁢O 9 have been investigated by inelastic neutron scattering on a single crystal sample. A spinon continuum with a bandwidth of ~22 meV is observed along the chain formed by alternating magnetic V 4+ and nonmagnetic V 5+ ions, which reveals the importance of the orbital degree of freedom in determining the chain axis as identified by prior electronic structure calculations. Incipient magnetic Bragg peaks due to weak ferromagnetic interchain couplings emerge when approaching the magnetic transition at T N ~ 5.3 K, while the excitations remain gapless within the instrumental resolution. Comparisons to the Bethe ansatz, density matrix renormalization group calculations, and effective field theories confirm Sr 2 ⁢V 3 ⁢O 9 as a host of weakly coupled S = 1/2 chains dominated by antiferromagnetic intrachain interactions of ~7.1(1) meV.

1-dimensional spin chains↗
DOE OSTI2023

Deep learning uncertainty quantification for clinical text classification

Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network’s confidence, in-depth analyses are needed to establish whether they are well calibrated. In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount—that is, the number of electronic pathology reports for which the model’s predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining—thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

59 BASIC BIOLOGICAL SCIENCES↗