Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the RASP data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.Comment: 18 pages, to appear in IEEE TKDE, accepted in December 201

Towards Axiomatic, Hierarchical, and Symbolic Explanation for Deep Models

This paper aims to show that the inference logic of a deep model can be faithfully approximated as a sparse, symbolic causal graph. Such a causal graph potentially bridges the gap between connectionism and symbolism. To this end, the faithfulness of the causal graph is theoretically guaranteed, because we show that the causal graph can well mimic the model's output on an exponential number of different masked samples. Besides, such a causal graph can be further simplified and re-written as an And-Or graph (AOG), which explains the logical relationship between interactive concepts encoded by the deep model, without losing much explanation accuracy

CiGNN: A Causality-informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation:A Causality-informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation

Causality holds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly integrates causality and graph neural network (GNN) for cuffless continuous BP estimation. The first stage concentrates on the generation of a causal graph between BP and wearable features from the the perspective of causal inference, so as to identify features that are causally related to BP variations. This stage is pivotal for the identification of novel causal features from the causal graph beyond pulse transit time (PTT). We found these causal features empower better tracking in BP changes compared to PTT. For the second stage, a spatio-temporal GNN (STGNN) is utilized to learn from the causal graph obtained from the first stage. The STGNN can exploit both the spatial information within the causal graph and temporal information from beat-by-beat cardiac signals for refined cuffless continuous BP estimation. We evaluated the proposed method with three datasets that include 305 subjects (102 hypertensive patients) with age ranging from 20-90 and BP at different levels, with the continuous Finapres BP as references. The mean absolute difference (MAD) for estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 3.77 mmHg and 2.52 mmHg, respectively, which outperformed comparison methods. In all cases including subjects with different age groups, while doing various maneuvers that induces BP changes at different levels and with or without hypertension, the proposed CiGNN method demonstrates superior performance for cuffless continuous BP estimation. These findings suggest that the proposed CiGNN is a promising approach in elucidating the causal mechanisms of cuffless BP estimation and can substantially enhance the precision of BP measurement

Explainability for Large Language Models: A Survey

Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. Therefore, understanding and explaining these models is crucial for elucidating their behaviors, limitations, and social impacts. In this paper, we introduce a taxonomy of explainability techniques and provide a structured overview of methods for explaining Transformer-based language models. We categorize techniques based on the training paradigms of LLMs: traditional fine-tuning-based paradigm and prompting-based paradigm. For each paradigm, we summarize the goals and dominant approaches for generating local explanations of individual predictions and global explanations of overall model knowledge. We also discuss metrics for evaluating generated explanations, and discuss how explanations can be leveraged to debug models and improve performance. Lastly, we examine key challenges and emerging opportunities for explanation techniques in the era of LLMs in comparison to conventional machine learning models

Roadmap to a Comprehensive Clinical Data Warehouse for Precision Medicine Applications in Oncology

Leading institutions throughout the country have established Precision Medicine programs to support personalized treatment of patients. A cornerstone for these programs is the establishment of enterprise-wide Clinical Data Warehouses. Working shoulder-to-shoulder, a team of physicians, systems biologists, engineers, and scientists at Rutgers Cancer Institute of New Jersey have designed, developed, and implemented the Warehouse with information originating from data sources, including Electronic Medical Records, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology and Pathology archives, and Next Generation Sequencing services. Innovative solutions were implemented to detect and extract unstructured clinical information that was embedded in paper/text documents, including synoptic pathology reports. Supporting important precision medicine use cases, the growing Warehouse enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information of patient tumors individually or as part of large cohorts to identify changes and patterns that may influence treatment decisions and potential outcomes

Psoriasis comorbid with atherosclerosis meets in lipid metabolism

Psoriasis (PSO) is a common skin disease affecting approximately 1%–3% of the population, and the incidence rate is increasing yearly. PSO is associated with a dramatically increased risk of cardiovascular disease, the most common of which is atherosclerosis (AS). In the past, inflammation was considered to be the triggering factor of the two comorbidities, but in recent years, studies have found that lipid metabolism disorders increase the probability of atherosclerosis in patients with psoriasis. In this review, we discuss epidemiological studies, clinical treatment methods, risk factors, and lipid metabolism of psoriasis and atherosclerosis comorbidities

The burden of heatwave-related preterm births and associated human capital losses in China

Frequent heatwaves under global warming can increase the risk of preterm birth (PTB), which in turn will affect physical health and human potential over the life course. However, what remains unknown is the extent to which anthropogenic climate change has contributed to such burdens. We combine health impact and economic assessment methods to comprehensively evaluate the entire heatwave-related PTB burden in dimensions of health, human capital and economic costs. Here, we show that during 2010-2020, an average of 13,262 (95%CI 6,962-18,802) PTBs occurred annually due to heatwave exposure in China. In simulated scenarios, 25.8% (95%CI 17.1%-34.5%) of heatwave-related PTBs per year on average can be attributed to anthropogenic climate change, which further result in substantial human capital losses, estimated at over $1 billion costs. Our findings will provide additional impetus for introducing more stringent climate mitigation policies and also call for more sufficient adaptations to reduce heatwave detriments to newborn