Parallel processing of streaming media on heterogeneous hosts using work stealing

Master'sMASTER OF SCIENC

Bicoptor 2.0: Addressing Challenges in Probabilistic Truncation for Enhanced Privacy-Preserving Machine Learning

This paper primarily focuses on analyzing the problems and proposing solutions for the probabilistic truncation protocol in existing PPML works from the perspectives of accuracy and efficiency. In terms of accuracy, we reveal that precision selections recommended in some of the existing works are incorrect. We conduct a thorough analysis of their open-source code and find that their errors were mainly due to simplified implementation, more specifically, fixed numbers are used instead of random numbers in probabilistic truncation protocols. Based on this, we provide a detailed theoretical analysis to validate our views. We propose a solution and a precision selection guideline for future works. Regarding efficiency, we identify limitations in the state-of-the-art comparison protocol, Bicoptor's (S\&P 2023) DReLU protocol, which relies on the probabilistic truncation protocol and is heavily constrained by the security parameter to avoid errors, significantly impacting the protocol's performance. To address these challenges, we introduce the first non-interactive deterministic truncation protocol, replacing the original probabilistic truncation protocol. Additionally, we design a non-interactive modulo switch protocol to enhance the protocol's security. Finally, we provide a guideline to reduce computational and communication overhead by using only a portion of the bits of the input, i.e., the key bits, for DReLU operations based on different model parameters. With the help of key bits, the performance of our DReLU protocol is further improved. We evaluate the performance of our protocols on three GPU servers, and achieve a 10x improvement in DReLU protocol, and a 6x improvement in the ReLU protocol over the state-of-the-art work Piranha-Falcon (USENIX Sec 22). Overall, the performance of our end-to-end (E2E) privacy-preserving machine learning (PPML) inference is improved by 3-4 times.Comment: 17 pages, 5 figure

Quantitative Analysis of Molecular Transport in the Extracellular Space Using Physics-Informed Neural Network

The brain extracellular space (ECS), an irregular, extremely tortuous nanoscale space located between cells or between cells and blood vessels, is crucial for nerve cell survival. It plays a pivotal role in high-level brain functions such as memory, emotion, and sensation. However, the specific form of molecular transport within the ECS remain elusive. To address this challenge, this paper proposes a novel approach to quantitatively analyze the molecular transport within the ECS by solving an inverse problem derived from the advection-diffusion equation (ADE) using a physics-informed neural network (PINN). PINN provides a streamlined solution to the ADE without the need for intricate mathematical formulations or grid settings. Additionally, the optimization of PINN facilitates the automatic computation of the diffusion coefficient governing long-term molecule transport and the velocity of molecules driven by advection. Consequently, the proposed method allows for the quantitative analysis and identification of the specific pattern of molecular transport within the ECS through the calculation of the Peclet number. Experimental validation on two datasets of magnetic resonance images (MRIs) captured at different time points showcases the effectiveness of the proposed method. Notably, our simulations reveal identical molecular transport patterns between datasets representing rats with tracer injected into the same brain region. These findings highlight the potential of PINN as a promising tool for comprehensively exploring molecular transport within the ECS

Self-reductive synthesis of MXene/Na0.55Mn1.4Ti0.6O4 hybrids for high-performance symmetric lithium ion batteries.

Increasing environmental problems and energy challenges have created an urgent demand for the development of green and efficient energy-storage systems. The search for new materials that could improve the performance of Li-ion batteries (LIBs) is one of today's most challenging tasks. Herein, a stable symmetric LIB based on the bipolar material-MXene/Na0.55Mn1.4Ti0.6O4 was developed. This bipolar hybrid material showed a typical MXene-type layered structure with high conductivity, containing two electrochemically active redox couples, namely, Mn4+/Mn3+ (3.06 V) and Mn2+/Mn (0.25 V). This MXene/Na0.55Mn2O4-based symmetric full cell exhibited the highest energy density of 393.4 W h kg−1 among all symmetric full cells reported so far, wherein it is bestowed with a high average voltage of 2.81 V and a reversible capacity of 140 mA h g−1 at a current density of 100 mA g−1. In addition, it offers a capacity retention of 79.4% after 200 cycles at a current density of 500 mA g−1. This symmetric lithium ion full battery will stimulate further research on new LIBs using the same active materials with improved safety, lower costs and a long life-span

Unique corrosion resistance of ultrahigh pressure Mg-25Al binary alloys.

Differing from as-cast and solid-solution alloys with coarse eutectic phases (Mg17Al12), a single-phase structure is attained in Mg-25wt.%Al alloy after ultrahigh-pressure solid-solution (USS, 800 oC, 4GPa). This USSed Mg-25wt.%Al sample exhibits a prominent age-hardening response due to the nano-scaled Mg17Al12 particles. Three testing methods confirm that USS-aged Mg-25wt.%Al alloy shows good corrosion resistance, which overwhelms the majority of Mg-based alloys reported so far, near to high purity Mg. The main reason is attributed to the formation of Al-rich oxide layer, wherein residual stress and pitting corrosion are eliminated. It provides a new avenue for developing corrosion resistant Mg alloys

IRE1α promotes cell apoptosis and an inflammatory response in endoplasmic reticulum stress-induced rheumatoid arthritis fibroblast-like synovial cells by enhancing autophagy

Endoplasmic reticulum (ER) stress can induce autophagy via the unfolded protein response (UPR), and autophagy can regulate the activation of inflammasomes. Inositol-requiring enzyme 1α (IRE1α) is a transducer of the UPR in cells with ER stress. Here, we investigated the role of IRE1α and its impact on ER stress in rheumatoid arthritis fibroblast-like synovial cells (RA-FLSs). RA-FLSs were isolated from rheumatoid arthritis (RA) patients and stimulated with thapsigargin (TG) to produce ER stress cells. ER stress-, autophagy and the expression of apoptosis-associated factors were investigated by western blotting and the qRT-PCR. Cellular ROS levels were assessed by flow cytometry. ELISAs were performed to determine the concentrations of inflammatory mediators. TG treatment promoted IRE1α, GRP78, CHOP, and ATP6 mRNA and protein expression. ROS generation was increased in TG-induced RA-FLSs; additionally, TG was found to induce cell inflammation by upregulating the expression of inflammasome markers and the concentrations of inflammatory mediators. The levels of autophagy markers, apoptosis-associated proteins, and mRNA were increased in TG-stimulated RA-FLSs. However, transfection with si-IRE1α suppressed TG-induced increases in ROS generation, inflammation levels, cell apoptosis, and autophagy in RA-FLSs. Treatment with the autophagy activator RAPA attenuated the protective effects of IRE1α silencing on TG-induced RA-FLS apoptosis and inflammatory damage. Our findings showed that in RA-FLSs, IRE1α silencing alleviated ER stress-induced inflammation and apoptosis caused by autophagy