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1. SoK: Where's the 'up'?! A Comprehensive (bottom-up) Study on the Security of Arm Cortex-M Systems

Author

Tan, Xi, Ma, Zheyuan, Pinto, Sandro, Guan, Le, Zhang, Ning, Xu, Jun, Lin, Zhiqiang, Hu, Hongxin, and Zhao, Ziming

Subjects
Computer Science - Cryptography and Security, Computer Science - Hardware Architecture, C.0, K.6.5
Abstract

Arm Cortex-M processors are the most widely used 32-bit microcontrollers among embedded and Internet-of-Things devices. Despite the widespread usage, there has been little effort in summarizing their hardware security features, characterizing the limitations and vulnerabilities of their hardware and software stack, and systematizing the research on securing these systems. The goals and contributions of this paper are multi-fold. First, we analyze the hardware security limitations and issues of Cortex-M systems. Second, we conducted a deep study of the software stack designed for Cortex-M and revealed its limitations, which is accompanied by an empirical analysis of 1,797 real-world firmware. Third, we categorize the reported bugs in Cortex-M software systems. Finally, we systematize the efforts that aim at securing Cortex-M systems and evaluate them in terms of the protections they offer, runtime performance, required hardware features, etc. Based on the insights, we develop a set of recommendations for the research community and MCU software developers., Comment: To Appear in the 18th USENIX WOOT Conference on Offensive Technologies, August 12-13, 2024

Published
2024

3. Monitoring and Management of Home-Quarantined Patients With COVID-19 Using a WeChat-Based Telemedicine System: Retrospective Cohort Study

Author

Xu, Hui, Huang, Sufang, Qiu, Chun, Liu, Shangkun, Deng, Juan, Jiao, Bo, Tan, Xi, Ai, Ling, Xiao, Yaru, Belliato, Mirko, and Yan, Li

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundMost patients with coronavirus disease (COVID-19) who show mild symptoms are sent home by physicians to recover. However, the condition of some of these patients becomes severe or critical as the disease progresses. ObjectiveThe aim of this study was to evaluate a telemedicine model that was developed to address the challenges of treating patients with progressive COVID-19 who are home-quarantined and shortages in the medical workforce. MethodsA telemedicine system was developed to continuously monitor the progression of home-quarantined patients with COVID-19. The system was built based on a popular social media smartphone app called WeChat; the app was used to establish two-way communication between a multidisciplinary team consisting of 7 medical workers and 188 home-quarantined individuals (including 74 confirmed patients with COVID-19). The system helped patients self-assess their conditions and update the multidisciplinary team through a telemedicine form stored on a cloud service, based on which the multidisciplinary team made treatment decisions. We evaluated this telemedicine system via a single-center retrospective study conducted at Tongji Hospital in Wuhan, China, in January 2020. ResultsAmong 188 individuals using the telemedicine system, 114 (60.6%) were not infected with COVID-19 and were dismissed. Of the 74 confirmed patients with COVID-19, 26 (35%) recovered during the study period and voluntarily stopped using the system. The remaining 48/76 confirmed patients with COVID-19 (63%) used the system until the end of the study, including 6 patients whose conditions progressed to severe or critical. These 6 patients were admitted to hospital and were stabilized (one received extracorporeal membrane oxygenation support for 17 days). All 74 patients with COVID-19 eventually recovered. Through a comparison of the monitored symptoms between hospitalized and nonhospitalized patients, we found prolonged persistence and deterioration of fever, dyspnea, lack of strength, and muscle soreness to be diagnostic of need for hospitalization. ConclusionsBy continuously monitoring the changes in several key symptoms, the telemedicine system reduces the risks of delayed hospitalization due to disease progression for patients with COVID-19 quarantined at home. The system uses a set of scales for quarantine management assessment that enables patients to self-assess their conditions. The results are useful for medical staff to identify disease progression and, hence, make appropriate and timely treatment decisions. The system requires few staff to manage a large cohort of patients. In addition, the system can solicit help from recovered but self-quarantined medical workers to alleviate shortages in the medical workforce and free healthy medical workers to fight COVID-19 on the front line. Thus, it optimizes the usage of local medical resources and prevents cross-infections among medical workers and patients.

Published
2020
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4. Simulating Photosynthetic Energy Transport on a Photonic Network

Author

Tang, Hao, Shang, Xiao-Wen, Shi, Zi-Yu, He, Tian-Shen, Feng, Zhen, Wang, Tian-Yu, Shi, Ruoxi, Wang, Hui-Ming, Tan, Xi, Xu, Xiao-Yun, Wang, Yao, Gao, Jun, Kim, M. S., and Jin, Xian-Min

Subjects
Quantum Physics, Physics - Biological Physics
Abstract

Quantum effects in photosynthetic energy transport in nature, especially for the typical Fenna-Matthews-Olson (FMO) complexes, are extensively studied in quantum biology. Such energy transport processes can be investigated as open quantum systems that blend the quantum coherence and environmental noises, and have been experimentally simulated on a few quantum devices. However, the existing experiments always lack a solid quantum simulation for the FMO energy transport due to their constraints to map a variety of issues in actual FMO complexes that have rich biological meanings. Here we successfully map the full coupling profile of the seven-site FMO structure by comprehensive characterization and precise control of the evanescent coupling of the three-dimensional waveguide array. By applying a stochastic dynamical modulation on each waveguide, we introduce the base site energy and the dephasing term in colored noises to faithfully simulate the power spectral density of the FMO complexes. We show our photonic model well interprets the issues including the reorganization energy, vibrational assistance, exciton transfer and energy localization. We further experimentally demonstrate the existence of an optimal transport efficiency at certain dephasing strength, providing a window to closely investigate environment-assisted quantum transport.

Published
2023
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13. Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study

Author

Ru, Boshu, Tan, Xi, Liu, Yu, Kannapur, Kartik, Ramanan, Dheepan, Kessler, Garin, Lautsch, Dominik, and Fonarow, Gregg

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Patient Safety, Heart Disease, Cardiovascular, 7.3 Management and decision making, Management of diseases and conditions, Good Health and Well Being, deep learning, machine learning, hospital readmission, heart failure, heart failure with reduced ejection fraction, worsening heart failure event, Bidirectional Encoder Representations From Transformers, BERT, clinical registry, medical claims, real-world data, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundHeart failure (HF) is highly prevalent in the United States. Approximately one-third to one-half of HF cases are categorized as HF with reduced ejection fraction (HFrEF). Patients with HFrEF are at risk of worsening HF, have a high risk of adverse outcomes, and experience higher health care use and costs. Therefore, it is crucial to identify patients with HFrEF who are at high risk of subsequent events after HF hospitalization.ObjectiveMachine learning (ML) has been used to predict HF-related outcomes. The objective of this study was to compare different ML prediction models and feature construction methods to predict 30-, 90-, and 365-day hospital readmissions and worsening HF events (WHFEs).MethodsWe used the Veradigm PINNACLE outpatient registry linked to Symphony Health's Integrated Dataverse data from July 1, 2013, to September 30, 2017. Adults with a confirmed diagnosis of HFrEF and HF-related hospitalization were included. WHFEs were defined as HF-related hospitalizations or outpatient intravenous diuretic use within 1 year of the first HF hospitalization. We used different approaches to construct ML features from clinical codes, including frequencies of clinical classification software (CCS) categories, Bidirectional Encoder Representations From Transformers (BERT) trained with CCS sequences (BERT + CCS), BERT trained on raw clinical codes (BERT + raw), and prespecified features based on clinical knowledge. A multilayer perceptron neural network, extreme gradient boosting (XGBoost), random forest, and logistic regression prediction models were applied and compared.ResultsA total of 30,687 adult patients with HFrEF were included in the analysis; 11.41% (3184/27,917) of adults experienced a hospital readmission within 30 days of their first HF hospitalization, and nearly half (9231/21,562, 42.81%) of the patients experienced at least 1 WHFE within 1 year after HF hospitalization. The prediction models and feature combinations with the best area under the receiver operating characteristic curve (AUC) for each outcome were XGBoost with CCS frequency (AUC=0.595) for 30-day readmission, random forest with CCS frequency (AUC=0.630) for 90-day readmission, XGBoost with CCS frequency (AUC=0.649) for 365-day readmission, and XGBoost with CCS frequency (AUC=0.640) for WHFEs. Our ML models could discriminate between readmission and WHFE among patients with HFrEF. Our model performance was mediocre, especially for the 30-day readmission events, most likely owing to limitations of the data, including an imbalance between positive and negative cases and high missing rates of many clinical variables and outcome definitions.ConclusionsWe predicted readmissions and WHFEs after HF hospitalizations in patients with HFrEF. Features identified by data-driven approaches may be comparable with those identified by clinical domain knowledge. Future work may be warranted to validate and improve the models using more longitudinal electronic health records that are complete, are comprehensive, and have a longer follow-up time.

Published
2023

17. Phagocytosis-initiated tumor hybrid cells acquire a c-Myc-mediated quasi-polarization state for immunoevasion and distant dissemination

Author

Chou, Chih-Wei, Hung, Chia-Nung, Chiu, Cheryl Hsiang-Ling, Tan, Xi, Chen, Meizhen, Chen, Chien-Chin, Saeed, Moawiz, Hsu, Che-Wei, Liss, Michael A., Wang, Chiou-Miin, Lai, Zhao, Alvarez, Nathaniel, Osmulski, Pawel A., Gaczynska, Maria E., Lin, Li-Ling, Ortega, Veronica, Kirma, Nameer B., Xu, Kexin, Liu, Zhijie, Kumar, Addanki P., Taverna, Josephine A., Velagaleti, Gopalrao V. N., Chen, Chun-Liang, Zhang, Zhao, and Huang, Tim Hui-Ming

Published
2023
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19. Generating Haar-uniform Randomness using Stochastic Quantum Walks on a Photonic Chip

Author

Tang, Hao, Banchi, Leonardo, Wang, Tian-Yu, Shang, Xiao-Wen, Tan, Xi, Zhou, Wen-Hao, Feng, Zhen, Pal, Anurag, Li, Hang, Hu, Cheng-Qiu, Kim, M. S., and Jin, Xian-Min

Subjects
Quantum Physics, Physics - Optics
Abstract

As random operations for quantum systems are intensively used in various quantum information tasks, a trustworthy measure of the randomness in quantum operations is highly demanded. The Haar measure of randomness is a useful tool with wide applications such as boson sampling. Recently, a theoretical protocol was proposed to combine quantum control theory and driven stochastic quantum walks to generate Haar-uniform random operations. This opens up a promising route to converting classical randomness to quantum randomness. Here, we implement a two-dimensional stochastic quantum walk on the integrated photonic chip and demonstrate that the average of all distribution profiles converges to the even distribution when the evolution length increases, suggesting the 1-pad Haar-uniform randomness. We further show that our two-dimensional array outperforms the one-dimensional array of the same number of waveguide for the speed of convergence. Our work demonstrates a scalable and robust way to generate Haar-uniform randomness that can provide useful building blocks to boost future quantum information techniques., Comment: 6 pages and 4 figures for main text ; 16 pages and 16 figures for supplementary note

Published
2021
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50. AXL-initiated paracrine activation of pSTAT3 enhances mesenchymal and vasculogenic supportive features of tumor-associated macrophages

Author

Hung, Chia-Nung, Chen, Meizhen, DeArmond, Daniel T., Chiu, Cheryl H.-L., Limboy, Catherine A., Tan, Xi, Kusi, Meena, Chou, Chih-Wei, Lin, Li-Ling, Zhang, Zhao, Wang, Chiou-Miin, Chen, Chun-Liang, Mitsuya, Kohzoh, Osmulski, Pawel A., Gaczynska, Maria E., Kirma, Nameer B., Vadlamudi, Ratna K., Gibbons, Don L., Warner, Steve, Brenner, Andrew J., Mahadevan, Daruka, Michalek, Joel E., Huang, Tim H.-M., and Taverna, Josephine A.

Published
2023
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