OPTICAL COHERENCE TOMOGRAPHY FOR NEUROSURGEY AND CANCER RESEARCH

Optical Coherence Tomography (OCT) provides non-labeling, real-time and high resolution images, which has the potential to transform the paradigm of surgical guidance and preclinical animal studies. The design and development of OCT devices for neurosurgery guidance and novel imaging algorithms for monitoring anti-cancer therapy have been pursued in this work. A forward-imaging needle-type OCT probe was developed which can fit into minimally invasive tools (I.D. ~ 1mm), detect the at-risk blood vessels, and identify tissue micro-landmarks. This promising guidance tool improves the safety and the accuracy of needle-based procedures, which are currently performed without imaging feedback. Despite the great imaging capability, OCT is limited by the shallow imaging depth (1-2 mm). In order to address this issue, the first MRI compatible OCT system has been developed. The multi-scale and multi-contrast MRI/OCT imaging combination significantly improves the accuracy of intra-operative MRI by two orders (from 1mm to 0.01 mm). In contrast to imaging systems, a thin (0.125 mm), low-cost (1/10 cost of OCT system) and simple fiber sensor technology called coherence gated Doppler (CGD) was developed which can be integrated with many surgical tools and aid in the avoidance of intracranial hemorrhage. Furthermore, intra-vital OCT is a powerful tool to study the mechanism of anti-cancer therapy. Photo-immunotherapy (PIT) is a low-side-effect cancer therapy based on an armed antibody conjugate that induces highly selective cancer cell necrosis after exposure to near infrared light both in vitro and in vivo. With novel algorithms that remove the bulk motion and track the vessel lumen automatically, OCT reveals dramatic hemodynamic changes during PIT and helps to elucidate the mechanisms behind the PIT treatment. The transformative guidance tools and the novel image processing algorithms pave a new avenue to better clinical outcomes and preclinical animal studies

Supervised Collective Classification for Crowdsourcing

Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainly unsupervised methods that are unaware of the quality of crowdsourced data. In this paper, we propose a supervised collective classification algorithm that aims to identify reliable labelers from the training data (e.g., items with known labels). The reliability (i.e., weighting factor) of each labeler is determined via a saddle point algorithm. The results on several crowdsourced data show that supervised methods can achieve better classification accuracy than unsupervised methods, and our proposed method outperforms other algorithms.Comment: to appear in IEEE Global Communications Conference (GLOBECOM) Workshop on Networking and Collaboration Issues for the Internet of Everythin

Exploring the Benefits of Differentially Private Pre-training and Parameter-Efficient Fine-tuning for Table Transformers

For machine learning with tabular data, Table Transformer (TabTransformer) is a state-of-the-art neural network model, while Differential Privacy (DP) is an essential component to ensure data privacy. In this paper, we explore the benefits of combining these two aspects together in the scenario of transfer learning -- differentially private pre-training and fine-tuning of TabTransformers with a variety of parameter-efficient fine-tuning (PEFT) methods, including Adapter, LoRA, and Prompt Tuning. Our extensive experiments on the ACSIncome dataset show that these PEFT methods outperform traditional approaches in terms of the accuracy of the downstream task and the number of trainable parameters, thus achieving an improved trade-off among parameter efficiency, privacy, and accuracy. Our code is available at github.com/IBM/DP-TabTransformer.Comment: submitted to ICASSP 202

High temperature reactive ion etching of iridium thin films with aluminum mask in CF4/O2/Ar plasma

Reactive ion etching (RIE) technology for iridium with CF4/O2/Ar gas mixtures and aluminum mask at high temperatures up to 350 °C was developed. The influence of various process parameters such as gas mixing ratio and substrate temperature on the etch rate was studied in order to find optimal process conditions. The surface of the samples after etching was found to be clean under SEM inspection. It was also shown that the etch rate of iridium could be enhanced at higher process temperature and, at the same time, very high etching selectivity between aluminum etching mask and iridium could be achieved