PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protection

With careful manipulation, malicious agents can reverse engineer private information encoded in pre-trained language models. Security concerns motivate the development of quantum pre-training. In this work, we propose a highly portable quantum language model (PQLM) that can easily transmit information to downstream tasks on classical machines. The framework consists of a cloud PQLM built with random Variational Quantum Classifiers (VQC) and local models for downstream applications. We demonstrate the ad hoc portability of the quantum model by extracting only the word embeddings and effectively applying them to downstream tasks on classical machines. Our PQLM exhibits comparable performance to its classical counterpart on both intrinsic evaluation (loss, perplexity) and extrinsic evaluation (multilingual sentiment analysis accuracy) metrics. We also perform ablation studies on the factors affecting PQLM performance to analyze model stability. Our work establishes a theoretical foundation for a portable quantum pre-trained language model that could be trained on private data and made available for public use with privacy protection guarantees.Comment: 5 pages, 3 figures, 3 table

Twin-S: A Digital Twin for Skull-base Surgery

Purpose: Digital twins are virtual interactive models of the real world, exhibiting identical behavior and properties. In surgical applications, computational analysis from digital twins can be used, for example, to enhance situational awareness. Methods: We present a digital twin framework for skull-base surgeries, named Twin-S, which can be integrated within various image-guided interventions seamlessly. Twin-S combines high-precision optical tracking and real-time simulation. We rely on rigorous calibration routines to ensure that the digital twin representation precisely mimics all real-world processes. Twin-S models and tracks the critical components of skull-base surgery, including the surgical tool, patient anatomy, and surgical camera. Significantly, Twin-S updates and reflects real-world drilling of the anatomical model in frame rate. Results: We extensively evaluate the accuracy of Twin-S, which achieves an average 1.39 mm error during the drilling process. We further illustrate how segmentation masks derived from the continuously updated digital twin can augment the surgical microscope view in a mixed reality setting, where bone requiring ablation is highlighted to provide surgeons additional situational awareness. Conclusion: We present Twin-S, a digital twin environment for skull-base surgery. Twin-S tracks and updates the virtual model in real-time given measurements from modern tracking technologies. Future research on complementing optical tracking with higher-precision vision-based approaches may further increase the accuracy of Twin-S

TWIN-S: A DIGITAL TWIN PARADIGM FOR SKULL BASE SURGERY

Digital twins are being used in various industries for design, predictive maintenance, improved operations, risk mitigation, and sustainability. In the field of medicine, digital twins provide a range of benefits to surgeons throughout the entire surgical process, from pre-operative planning to intra-operative guidance and post-operative record- keeping. They help to improve patient outcomes and reduce the risk of complications by enabling greater precision, accuracy, and risk mitigation. This essay introduces Twin-S, a digital twin framework designed to improve surgeons’ situational awareness during skull base surgery. The framework utilizes high-precision optical tracking and real-time simulation to model critical aspects of the surgery, such as surgical tools, patient anatomy, and surgical cameras. The calibration routines used in Twin-S ensure that the virtual representation accurately reflects real-world processes. Twin-S captures the real-world surgical progress and updates the virtual model at a frame rate of 28 with an average error of 1.39 mm during the drilling process. By providing temporally varying augmented Sim-to-Real overlays derived from the continuously updated virtual model, Twin-S offers situational awareness to the surgeon. Future research could concentrate on utilizing vision-based techniques to improve the accuracy of Twin-S. Incorporating additional sensor modalities, such as lidar and robot dynamics would enable Twin-S to model real-world processes more accurately and provide the surgeon with more comprehensive information

Clinical characteristics and disease predictors of a large Chinese cohort of patients with autosomal dominant polycystic kidney disease

OBJECTIVE: Autosomal dominant polycystic kidney disease (ADPKD) is a relentlessly progressing form of chronic kidney disease for which there is no cure. The aim of this study was to characterize Chinese patients with ADPKD and to identify the factors which predict cyst growth and renal functional deterioration. METHODS: To analyze disease predicting factors we performed a prospective longitudinal observational study in a cohort of 541 Chinese patients with ADPKD and an eGFR ≥ 30 ml/min/1.73 m(2). Patients were followed clinically and radiologically with sequential abdominal magnetic resonance imaging (MRI). Clinical characteristics and laboratory data were related to changes in estimated glomerular filtration rate (eGFR) and total kidney volume (TKV). A linear regression model was developed to analyze the factors which determine eGFR and TKV changes. RESULTS: The age range of this unselected cohort ranged from 4 to 77 years. Median follow-up time was 14.3 ± 10.6 months. Although inter-individual differences in eGFR and TKV were large, there was a consistent link between these two parameters. Baseline log10-transformed TKV and urinary protein/creatinine ratio were identified as the major predictors for a faster eGFR decline and were associated with a higher TKV growth rate. Interestingly, a lower thrombocyte count correlated significantly with lower eGFR (r = 0.222) and higher TKV (r = 0.134). CONCLUSIONS: This large cohort of Chinese patients with ADPKD provides unique epidemiological data for comparison with other cohorts of different ethnicity. In Chinese patients we identified a lower thrombocyte count as a significant predictor of disease progression. These results are important for the design of future clinical trials to retard polycystic kidney disease progression

Spaghetti plot for TKV over entire observation time in individual patients (n = 421).

<p>Spaghetti plot for TKV over entire observation time in individual patients (n = 421).</p