Investigating the efficacy of bisphosphonates treatment against multiple myeloma induced bone disease using a computational model

Multiple myeloma (MM)-induced bone disease is mortal for most MM patients. Bisphosphonates are first-line treatment for MM-induced bone disease, since it can inhibit osteoclast activity and the resultant bone resorption by suppressing the differentiation of osteoclast precursors into mature osteoclasts, promoting osteoclast apoptosis and disrupting osteoclast function. However, it is still unclear whether bisphosphonates have an anti-tumour effect. In our previous work, a computational model was built to simulate the pathology of MM-induced bone disease. This paper extends this proposed computational model to investigate the efficacy of bisphosphonates treatment and then clear the controversy of this therapy. The extended model is validated through the good agreement between simulation results and experimental data. The simulation results suggest that bisphosphonates indeed have an anti-tumour effect

Towards Bridging the Performance Gaps of Joint Energy-based Models

Can we train a hybrid discriminative-generative model within a single network? This question has recently been answered in the affirmative, introducing the field of Joint Energy-based Model (JEM), which achieves high classification accuracy and image generation quality simultaneously. Despite recent advances, there remain two performance gaps: the accuracy gap to the standard softmax classifier, and the generation quality gap to state-of-the-art generative models. In this paper, we introduce a variety of training techniques to bridge the accuracy gap and the generation quality gap of JEM. 1) We incorporate a recently proposed sharpness-aware minimization (SAM) framework to train JEM, which promotes the energy landscape smoothness and the generalizability of JEM. 2) We exclude data augmentation from the maximum likelihood estimate pipeline of JEM, and mitigate the negative impact of data augmentation to image generation quality. Extensive experiments on multiple datasets demonstrate that our SADA-JEM achieves state-of-the-art performances and outperforms JEM in image classification, image generation, calibration, out-of-distribution detection and adversarial robustness by a notable margin

Observation of electric current induced by optically injected spin current

A normally incident light of linear polarization injects a pure spin current in a strip of 2-dimensional electron gas with spin-orbit coupling. We report observation of an electric current with a butterfly-like pattern induced by such a light shed on the vicinity of a crossbar shaped InGaAs/InAlAs quantum well. Its light polarization dependence is the same as that of the spin current. We attribute the observed electric current to be converted from the optically injected spin current caused by scatterings near the crossing. Our observation provides a realistic technique to detect spin currents, and opens a new route to study the spin-related science and engineering in semiconductors.Comment: 15 pages, 4 figure