Communication-Efficient Stochastic Zeroth-Order Optimization for Federated Learning

Federated learning (FL), as an emerging edge artificial intelligence paradigm, enables many edge devices to collaboratively train a global model without sharing their private data. To enhance the training efficiency of FL, various algorithms have been proposed, ranging from first-order to second-order methods. However, these algorithms cannot be applied in scenarios where the gradient information is not available, e.g., federated black-box attack and federated hyperparameter tuning. To address this issue, in this paper we propose a derivative-free federated zeroth-order optimization (FedZO) algorithm featured by performing multiple local updates based on stochastic gradient estimators in each communication round and enabling partial device participation. Under non-convex settings, we derive the convergence performance of the FedZO algorithm on non-independent and identically distributed data and characterize the impact of the numbers of local iterates and participating edge devices on the convergence. To enable communication-efficient FedZO over wireless networks, we further propose an over-the-air computation (AirComp) assisted FedZO algorithm. With an appropriate transceiver design, we show that the convergence of AirComp-assisted FedZO can still be preserved under certain signal-to-noise ratio conditions. Simulation results demonstrate the effectiveness of the FedZO algorithm and validate the theoretical observations.Comment: This work was accepted to Transaction on Signal Processin

Diff-ID: An Explainable Identity Difference Quantification Framework for DeepFake Detection

Despite the fact that DeepFake forgery detection algorithms have achieved impressive performance on known manipulations, they often face disastrous performance degradation when generalized to an unseen manipulation. Some recent works show improvement in generalization but rely on features fragile to image distortions such as compression. To this end, we propose Diff-ID, a concise and effective approach that explains and measures the identity loss induced by facial manipulations. When testing on an image of a specific person, Diff-ID utilizes an authentic image of that person as a reference and aligns them to the same identity-insensitive attribute feature space by applying a face-swapping generator. We then visualize the identity loss between the test and the reference image from the image differences of the aligned pairs, and design a custom metric to quantify the identity loss. The metric is then proved to be effective in distinguishing the forgery images from the real ones. Extensive experiments show that our approach achieves high detection performance on DeepFake images and state-of-the-art generalization ability to unknown forgery methods, while also being robust to image distortions

Effects of Nanoparticle Size and Radiation Energy on Copper-Cysteamine Nanoparticles for X-ray Induced Photodynamic Therapy

The Copper-cysteamine (Cu-Cy) nanoparticle is a novel sensitizer with a potential to increase the effectiveness of radiation therapy for cancer treatment. In this work, the effect of nanoparticle size and the energy of X-rays on the effectiveness of radiation therapy are investigated. The effect of the particle size on their performance is very complicated. The nanoparticles with an average size of 300 nm have the most intense photoluminescence, the nanoparticles with the average size of 100 nm have the most reactive oxygen species production upon X-ray irradiation, while the nanoparticles with the average size of 40 nm have the best outcome in the tumor suppression in mice upon X-ray irradiation. For energy, 90 kVp radiation resulted in smaller tumor sizes than 250 kVp or 350 kVp radiation energies. Overall, knowledge of the effect of nanoparticle size and radiation energy on radiation therapy outcomes could be useful for future applications of Cu-Cy nanoparticles

High-performance and Scalable Software-based NVMe Virtualization Mechanism with I/O Queues Passthrough

NVMe(Non-Volatile Memory Express) is an industry standard for solid-state drives (SSDs) that has been widely adopted in data centers. NVMe virtualization is crucial in cloud computing as it allows for virtualized NVMe devices to be used by virtual machines (VMs), thereby improving the utilization of storage resources. However, traditional software-based solutions have flexibility benefits but often come at the cost of performance degradation or high CPU overhead. On the other hand, hardware-assisted solutions offer high performance and low CPU usage, but their adoption is often limited by the need for special hardware support or the requirement for new hardware development. In this paper, we propose LightIOV, a novel software-based NVMe virtualization mechanism that achieves high performance and scalability without consuming valuable CPU resources and without requiring special hardware support. LightIOV can support thousands of VMs on each server. The key idea behind LightIOV is NVMe hardware I/O queues passthrough, which enables VMs to directly access I/O queues of NVMe devices, thus eliminating virtualization overhead and providing near-native performance. Results from our experiments show that LightIOV can provide comparable performance to VFIO, with an IOPS of 97.6%-100.2% of VFIO. Furthermore, in high-density VMs environments, LightIOV achieves 31.4% lower latency than SPDK-Vhost when running 200 VMs, and an improvement of 27.1% in OPS performance in real-world applications

cuZK: Accelerating Zero-Knowledge Proof with A Faster Parallel Multi-Scalar Multiplication Algorithm on GPUs

Zero-knowledge proof is a critical cryptographic primitive. Its most practical type, called zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK), has been deployed in various privacy-preserving applications such as cryptocurrencies and verifiable machine learning. Unfortunately, zkSNARK like Groth16 has a high overhead on its proof generation step, which consists of several time-consuming operations, including large-scale matrix-vector multiplication (MUL), number-theoretic transform (NTT), and multi-scalar multiplication (MSM). Therefore, this paper presents cuZK, an efficient GPU implementation of zkSNARK with the following three techniques to achieve high performance. First, we propose a new parallel MSM algorithm. This MSM algorithm achieves nearly perfect linear speedup over the Pippenger algorithm, a well-known serial MSM algorithm. Second, we parallelize the MUL operation. Along with our self-designed MSM scheme and well-studied NTT scheme, cuZK achieves the parallelization of all operations in the proof generation step. Third, cuZK reduces the latency overhead caused by CPU-GPU data transfer by 1) reducing redundant data transfer and 2) overlapping data transfer and device computation. The evaluation results show that our MSM module provides over 2.08× (up to 2.94×) speedup versus the state-of-the-art GPU implementation. cuZK achieves over 2.65× (up to 4.86×) speedup on standard benchmarks and 2.18× speedup on a GPU-accelerated cryptocurrency application, Filecoin

Infrared Imaging of Magnetic Octupole Domains in Non-collinear Antiferromagnets

Magnetic structure plays a pivotal role in the functionality of antiferromagnets (AFMs), which not only can be employed to encode digital data but also yields novel phenomena. Despite its growing significance, visualizing the antiferromagnetic domain structure remains a challenge, particularly for non-collinear AFMs. Currently, the observation of magnetic domains in non-collinear antiferromagnetic materials is feasible only in Mn$_{3}$Sn, underscoring the limitations of existing techniques that necessitate distinct methods for in-plane and out-of-plane magnetic domain imaging. In this study, we present a versatile method for imaging the antiferromagnetic domain structure in a series of non-collinear antiferromagnetic materials by utilizing the anomalous Ettingshausen effect (AEE), which resolves both the magnetic octupole moments parallel and perpendicular to the sample surface. Temperature modulation due to the AEE originating from different magnetic domains is measured by the lock-in thermography, revealing distinct behaviors of octupole domains in different antiferromagnets. This work delivers an efficient technique for the visualization of magnetic domains in non-collinear AFMs, which enables comprehensive study of the magnetization process at the microscopic level and paves the way for potential advancements in applications.Comment: National Science Review in pres

Prognostic values of ALDOB expression and 18F-FDG PET/CT in hepatocellular carcinoma

PurposeThe glycolytic enzyme fructose 1,6-bisphosphate aldolase B (ALDOB) is aberrantly expressed and impacts the prognosis in hepatocellular carcinoma (HCC). Hepatic ALDOB loss leads to paradoxical upregulation of glucose metabolism, favoring hepatocellular carcinogenesis. Nevertheless, the relationship between ALDOB expression and 18F-fluorodeoxyglucose (18F-FDG) uptake, and their effects on HCC prognosis remain unclear. We evaluated whether ALDOB expression is associated with 18F-FDG uptake and their impacts on HCC prognosis prediction.MethodsChanges in ALDOB expression levels and the prognostic values in HCC were analyzed using data from The Cancer Genome Atlas (TCGA) database. Ultimately, 34 patients with HCC who underwent 18F-FDG positron emission tomography/computed tomography (PET/CT) preoperatively were enrolled in this retrospective study. ALDOB expression was determined using immunohistochemistry (IHC) staining, and the maximum standardized uptake value (SUVmax) of HCC was calculated from the 18F-FDG PET/CT scans. The relationship between ALDOB expression and SUVmax was examined, and their impacts on overall survival were evaluated using Cox proportional hazards models and Kaplan–Meier survival analysis. ALDOB overexpression in HUH7 and 7721 cells was used to analyze its role in tumor metabolism.ResultsAccording to TCGA database, the ALDOB mRNA level was downregulated in HCC compared to normal tissue, and significantly shortened overall survival in HCC patients. ALDOB protein expression was similarly decreased in IHC findings in HCC than that in adjacent normal tissues (P<0.05) and was significantly associated with tumor size (P<0.001), high tumor-node-metastasis stage (P=0.022), and elevated SUVmax (P=0.009). ALDOB expression in HCC was inversely correlated with SUVmax (r=-0.454; P=0.012), and the optimal SUVmax cutoff value for predicting its expression was 4.15. Prognostically, low ALDOB expression or SUVmax ≥3.9 indicated shorter overall survival time in HCC. Moreover, COX regression analysis suggested high SUVmax as an independent prognostic risk factor for HCC (P=0.036). HCC patients with negative ALDOB expression and positive SUVmax (≥3.9) were correlated with worse prognosis. ALDOB overexpression in HCC cells significantly decreases 18F-FDG uptake and lactate production.ConclusionSUVmax in HCC patients is inversely correlated with ALDOB expression, and 18F-FDG PET/CT may be useful for ALDOB status prediction. The combined use of ALDOB expression and 18F-FDG PET/CT data can provide additional information on disease prognosis in HCC patients

Conservative surgery in stage I placental site trophoblastic tumor: a report of 10 cases and literature review

Background and purpose: Placental site trophoblastic tumor (PSTT) is a rare type of malignant tumor. Because of its unique mode of invasion in the uterus and its insensitivity to chemotherapy, total hysterectomy is the primary recommendation. The purpose of this study was to investigate the feasibility and safety of conservative surgical treatment in patients with stage Ⅰ PSTT. Methods: The patients with stage Ⅰ PSTT admitted to Obstetrics and Gynecology Hospital of Fudan University from January 2015 to December 2021 were included, and those published on Pubmed and China National Knowledge Infrastructure (CNKI) from January 1990 to December 2021 were searched with the keywords of “placental site trophoblastic tumor” and “case”, “placental trophoblastic tumor” and “case” respectively. The clinicopathological data of the patients were collected and retrospectively analyzed. Results: A total of 10 cases admitted to Obstetrics and Gynecology Hospital of Fudan University were enrolled. The median age was 27 years. The most common symptom was irregular vaginal bleeding (70.0%). The median time of interval since antecedent pregnancy (ISAP) was 14.5 months. The median level of β-human chorionic gonadotrophin (β-hCG) was 124.51 mU/mL, and the diameter of the focus was 0.8-8.0 cm. All 10 patients admitted to Obstetrics and Gynecology Hospital of Fudan University achieved complete remission after initial treatment. The average follow-up time was 48.1 months and there was no recurrence. Three patients became pregnant naturally after treatment, including 2 cases of full-term pregnancy and delivery and 1 case of induced abortion because of unplanned pregnancy. Literature review of PSTT cases showed similar clinicopathological distribution and disease outcome. Conclusion: Conservative surgery could be an alternative choice for selected patients with stage Ⅰ PSTT, but more research is needed to provide evidence

Needs and views on healthy lifestyles for the prevention of dementia and the potential role for mobile health (mHealth) interventions in China: A qualitative study

Objectives Over the coming decades, China is expected to face the largest worldwide increase in dementia incidence. Mobile health (mHealth) may improve the accessibility of dementia prevention strategies, targeting lifestyle-related risk factors. Our aim is to explore the needs and views of Chinese older adults regarding healthy lifestyles to prevent cardiovascular disease (CVD) and dementia through mHealth, supporting the Prevention of Dementia using Mobile Phone Applications (PRODEMOS) study. Design Qualitative semi-structured interview study, using thematic analysis. Setting Primary and secondary care in Beijing and Tai’an, China. Participants Older adults aged 55 and over without dementia with an increased dementia risk, possessing a smartphone. Participants were recruited through seven hospitals participating in the PRODEMOS study, purposively sampled on age, sex, living area and history of CVD and diabetes. Results We performed 26 interviews with participants aged 55–86 years. Three main themes were identified: valuing a healthy lifestyle, sociocultural expectations and need for guidance. First, following a healthy lifestyle was generally deemed important. In addition to generic healthy behaviours, participants regarded certain specific Chinese lifestyle practices as important to prevent disease. Second, the sociocultural context played a crucial role, as an important motive to avoid disease was to limit the care burden put on family members. However, time-consuming family obligations and other social values could also impede healthy behaviours such as regular physical activity. Finally, there seemed to be a need for reliable and personalised lifestyle advice and for guidance from a health professional. Conclusions The Chinese older adults included in this study highly value a healthy lifestyle. They express a need for personalised lifestyle support in order to adopt healthy behaviours. Potentially, the PRODEMOS mHealth intervention can meet these needs through blended lifestyle support to improve risk factors for dementia and CVD