Analog Multi-Party Computing: Locally Differential Private Protocols for Collaborative Computations

We consider a fully decentralized scenario in which no central trusted entity exists and all clients are honest-but-curious. The state-of-the-art approaches to this problem often rely on cryptographic protocols, such as multiparty computation (MPC), that require mapping real-valued data to a discrete alphabet, specifically a finite field. These approaches, however, can result in substantial accuracy losses due to computation overflows. To address this issue, we propose A-MPC, a private analog MPC protocol that performs all computations in the analog domain. We characterize the privacy of individual datasets in terms of $(\epsilon, \delta)$-local differential privacy, where the privacy of a single record in each client's dataset is guaranteed against other participants. In particular, we characterize the required noise variance in the Gaussian mechanism in terms of the required $(\epsilon,\delta)$-local differential privacy parameters by solving an optimization problem. Furthermore, compared with existing decentralized protocols, A-MPC keeps the privacy of individual datasets against the collusion of all other participants, thereby, in a notably significant improvement, increasing the maximum number of colluding clients tolerated in the protocol by a factor of three compared with the state-of-the-art collaborative learning protocols. Our experiments illustrate that the accuracy of the proposed $(\epsilon,\delta)$-locally differential private logistic regression and linear regression models trained in a fully-decentralized fashion using A-MPC closely follows that of a centralized one performed by a single trusted entity

Back-end of line compatible transistors for hybrid CMOS applications

The low-temperature back-end of line (BEOL) compatible transparent amorphous oxide semiconductor (TAOS) TFTs and poly-Si TFTs are the suitable platforms for three-dimensional (3D) integration hybrid CMOS technologies. The n-channel amorphous indium tungsten oxide (a-IWO) ultra-thin-film transistors (UTFTs) have been successfully fabricated and demonstrated in the category of indium oxide based thin film transistors (TFTs). We have scaled down thickness of a-IWO channel to 4nm. The proposed a-IWO UTFTs with low operation voltages exhibit good electrical characteristics: near ideal subthreshold swing (S.S.) ~ 63mV/dec., high field-effect mobility (FE) ~ 25.3 cm2/V-s. In addition, we also have fabricated the novel less metal contamination Ni-induced lateral crystallization (LC-NILC) p-channel poly-Si TFTs. The matched electrical characteristics of n-channel and p-channel devices with low operation voltage and low IOFF are exhibiting the promising candidate for future hybrid CMOS applications

Electroacupuncture relieves portal hypertension by improving vascular angiogenesis and linking gut microbiota in bile duct ligation rats

The pathological increase in the intrahepatic resistance and decrease peripheral vascular tone in the development of portal hypertension (PHT). PHT has been linked to lower microbial diversity and weakened intestinal barrier, and interplay alters inflammatory signaling cascades. Electroacupuncture (EA) may ameliorate the inflammatory response and limit arterial vasodilatation and portal pressure. This study addresses the possible mechanisms underlying putative hemodynamics effects of EA in PHT rats. PHT was induced by bile duct ligation (BDL) over 7 days in rats. BDL rats were treated with low-frequency EA (2 Hz) at acupoint, ST36, 10 min once daily for 7 consecutive days. EA significantly reduced portal pressure and enhanced maximum contractile responses in the aorta, and blunts the angiogenesis cascade in PHT rats. EA decreased the aortic angiogenesis signaling cascade, reflected by downregulated of ICAM1, VCAM1, VEGFR1, and TGFβR2 levels. In addition, EA preserved claudin-1, occludin, and ZO-1 levels in BDL-induced PHT model. Furthermore, EA demonstrates to have a positive effect on the gut Bacteroidetes/Firmicutes ratio and to reduce pro-inflammatory cytokines and endotoxins. These results summarize the potential role of EA in the gut microbiota could potentially lead to attenuate intestine injury which could further contribute to vascular reactivity in PHT rats

Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration

The brightfield microscope is instrumental in the visual examination of both biological and physical samples at sub-millimeter scales. One key clinical application has been in cancer histopathology, where the microscopic assessment of the tissue samples is used for the diagnosis and staging of cancer and thus guides clinical therapy. However, the interpretation of these samples is inherently subjective, resulting in significant diagnostic variability. Moreover, in many regions of the world, access to pathologists is severely limited due to lack of trained personnel. In this regard, Artificial Intelligence (AI) based tools promise to improve the access and quality of healthcare. However, despite significant advances in AI research, integration of these tools into real-world cancer diagnosis workflows remains challenging because of the costs of image digitization and difficulties in deploying AI solutions. Here we propose a cost-effective solution to the integration of AI: the Augmented Reality Microscope (ARM). The ARM overlays AI-based information onto the current view of the sample through the optical pathway in real-time, enabling seamless integration of AI into the regular microscopy workflow. We demonstrate the utility of ARM in the detection of lymph node metastases in breast cancer and the identification of prostate cancer with a latency that supports real-time workflows. We anticipate that ARM will remove barriers towards the use of AI in microscopic analysis and thus improve the accuracy and efficiency of cancer diagnosis. This approach is applicable to other microscopy tasks and AI algorithms in the life sciences and beyond

Deep learning-based survival prediction for multiple cancer types using histopathology images

Prognostic information at diagnosis has important implications for cancer treatment and monitoring. Although cancer staging, histopathological assessment, molecular features, and clinical variables can provide useful prognostic insights, improving risk stratification remains an active research area. We developed a deep learning system (DLS) to predict disease specific survival across 10 cancer types from The Cancer Genome Atlas (TCGA). We used a weakly-supervised approach without pixel-level annotations, and tested three different survival loss functions. The DLS was developed using 9,086 slides from 3,664 cases and evaluated using 3,009 slides from 1,216 cases. In multivariable Cox regression analysis of the combined cohort including all 10 cancers, the DLS was significantly associated with disease specific survival (hazard ratio of 1.58, 95% CI 1.28-1.70, p<0.0001) after adjusting for cancer type, stage, age, and sex. In a per-cancer adjusted subanalysis, the DLS remained a significant predictor of survival in 5 of 10 cancer types. Compared to a baseline model including stage, age, and sex, the c-index of the model demonstrated an absolute 3.7% improvement (95% CI 1.0-6.5) in the combined cohort. Additionally, our models stratified patients within individual cancer stages, particularly stage II (p=0.025) and stage III (p<0.001). By developing and evaluating prognostic models across multiple cancer types, this work represents one of the most comprehensive studies exploring the direct prediction of clinical outcomes using deep learning and histopathology images. Our analysis demonstrates the potential for this approach to provide prognostic information in multiple cancer types, and even within specific pathologic stages. However, given the relatively small number of clinical events, we observed wide confidence intervals, suggesting that future work will benefit from larger datasets