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Tseng, Huan-Hsin

Publications and source records attributed to Tseng, Huan-Hsin.

5 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

INSURE: An Information Theory iNspired diSentanglement and pURification modEl for Domain Generalization

Domain Generalization (DG) aims to learn a generalizable model on the unseen target domain by only training on the multiple observed source domains. Although a variety of DG methods have focused on extracting domain-invariant features, the domain-specific class-relevant features have attracted attention and been argued to benefit generalization to the unseen target domain. To take into account the class-relevant domain-specific information, in this paper we propose an Information theory iNspired diSentanglement and pURification modEl (INSURE) to explicitly disentangle the latent features to obtain sufficient and compact (necessary) class-relevant feature for generalization to the unseen domain. Specifically, we first propose an information theory inspired loss function to ensure the disentangled class-relevant features contain sufficient class label information and the other disentangled auxiliary feature has sufficient domain information. Additionally, we further propose a paired purification loss function to let the auxiliary feature discard all the class-relevant information and thus the class-relevant feature will contain sufficient and compact (necessary) class-relevant information. Moreover, instead of using multiple encoders, we propose to use a learnable binary mask as our disentangler to make the disentanglement more efficient and make the disentangled features complementary to each other. We conduct extensive experiments on five widely used DG benchmark datasets including PACS, VLCS, OfficeHome, TerraIncognita, and DomainNet. The proposed INSURE achieves state-of-the-art performance. We also empirically show that domain-specific class-relevant features are beneficial for domain generalization. The code is available at https://github.com/yuxi120407/INSURE .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Should regional ventilation function be considered during radiation treatment planning to prevent radiation-induced complications?

Purpose: To investigate the incorporation of pretherapy regional ventilation function in predicting radiation fibrosis (RF) in stage III nonsmall cell lung cancer (NSCLC) patients treated with concurrent thoracic chemoradiotherapy. Methods: Thirty-seven patients with stage III NSCLC were retrospectively studied. Patients received one cycle of cisplatin–gemcitabine, followed by two to three cycles of cisplatin–etoposide concurrently with involved-field thoracic radiotherapy (46–66 Gy; 2 Gy/fraction). Pretherapy regional ventilation images of the lung were derived from 4D computed tomography via a density change–based algorithm with mass correction. In addition to the conventional dose–volume metrics (V{sub 20}, V{sub 30}, V{sub 40}, and mean lung dose), dose–function metrics (fV{sub 20}, fV{sub 30}, fV{sub 40}, and functional mean lung dose) were generated by combining regional ventilation and radiation dose. A new class of metrics was derived and referred to as dose–subvolume metrics (sV{sub 20}, sV{sub 30}, sV{sub 40}, and subvolume mean lung dose); these were defined as the conventional dose–volume metrics computed on the functional lung. Area under the receiver operating characteristic curve (AUC) values and logistic regression analyses were used to evaluate these metrics in predicting hallmark characteristics of RF (lung consolidation, volume loss, and airway dilation). Results: AUC values for the dose–volume metrics in predicting lung consolidation, volume loss, and airway dilation were 0.65–0.69, 0.57–0.70, and 0.69–0.76, respectively. The respective ranges for dose–function metrics were 0.63–0.66, 0.61–0.71, and 0.72–0.80 and for dose–subvolume metrics were 0.50–0.65, 0.65–0.75, and 0.73–0.85. Using an AUC value = 0.70 as cutoff value suggested that at least one of each type of metrics (dose–volume, dose–function, dose–subvolume) was predictive for volume loss and airway dilation, whereas lung consolidation cannot be accurately predicted by any of the metrics. Logistic regression analyses showed that dose–function and dose–subvolume metrics were significant (P values ≤ 0.02) in predicting volume airway dilation. Likelihood ratio test showed that when combining dose–function and/or dose–subvolume metrics with dose–volume metrics, the achieved improvements of prediction accuracy on volume loss and airway dilation were significant (P values ≤ 0.04). Conclusions: The authors’ results demonstrated that the inclusion of regional ventilation function improved accuracy in predicting RF. In particular, dose–subvolume metrics provided a promising method for preventing radiation-induced pulmonary complications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Scalar-torsion mode in a cosmological model of the Poincaré gauge theory of gravity

We investigate the scalar-torsion mode in a cosmological model of the Poincaré gauge theory of gravity. We treat the geometric effect of torsion as an effective quantity, which behaves like dark energy, and study the effective equation of state (EoS) of the model. We concentrate on two cases with the constant curvature solution and positive kinetic energy, respectively. In the former, we find that the torsion EoS has different values in the various stages of the universe. In particular, it behaves like the radiation (matter) EoS of w{sub r} = 1/3 (w{sub m} = 0) in the radiation (matter) dominant epoch, while in the late time the torsion density is supportive for the accelerating universe. In the latter, our numerical analysis shows that in general the EoS has an asymptotic behavior in the high redshift regime, while it could cross the phantom divide line in the low redshift regime.

79 ASTRONOMY AND ASTROPHYSICS↗