This study aimed to evaluate the clinical characteristics and identify risk factors for surgical site infection (SSI) following abdominal wall reconstruction using biological mesh.
Skin-inspired electronics emerges as a new paradigm due to the increasing demands for conformable and high-quality skin-sensor-silicon (SSS) interfacing in wearable, electronic skin and health monitoring applications. Advances in ultra-thin, flexible, stretchable and conformable materials have made skin electronics feasible. In this paper, we prototyped an active electrode (with a thickness ≤ 2 um), which integrates the electrode with a thin-film transistor (TFT) based amplifier, to effectively suppress motion artifacts. The fabricated ultra-thin amplifier can achieve a gain of 32 dB at 20 kHz, demonstrating the feasibility of the proposed active electrode. Using atrial fibrillation (AF) detection for electrocardiogram (ECG) as an application driver, we further develop a simulation framework taking into account all elements including the skin, the sensor, the amplifier and the silicon chip. Systematic and quantitative simulation results indicate that the proposed active electrode can effectively improve the signal quality under motion noises (achieving ≥30 dB improvement in signal-to-noise ratio (SNR)), which boosts classification accuracy by more than 19% for AF detection.
Increasing evidence demonstrates that mammals have different reactions to hypoxia with varied oxygen dynamic patterns. It takes ∼24 h for tri-gas incubator to achieve steady cell hypoxia, which fails to recapitulate ultrafast oxygen dynamics of intestinal ischemia/reperfusion (IR) injury. Inspired from the structure of native intestinal villi, we engineered an intestinal organoid chip embedded with engineered artificial microvessels based on co-axial microfluidic technology by using pH-responsive ZIF-8/sodium alginate scaffold. The chip was featured on: (i) eight times the oxygen exchange efficiency compared with the conventional device, tri-gas incubator, (ii) implantation of intestinal organoid reproducing all types of intestinal epithelial cells, and (iii) bio-responsiveness to hypoxia and reoxygenation (HR) by presenting metabolism disorder, inflammatory reaction, and cell apoptosis. Strikingly, it was found for the first time that Olfactomedin 4 (Olfm4) was the most significantly down-regulated gene under a rapid HR condition by sequencing the RNA from the organoids. Mechanistically, OLFM4 played protective functions on HR-induced cell inflammation and tissue damage by inhibiting the NF-kappa B signaling activation, thus it could be used as a therapeutic target. Altogether, this study overcomes the issue of mismatched oxygen dynamics between in vitro and in vivo, and sets an example of next-generation multisystem-interactive organoid chip for finding precise therapeutic targets of IR injury.
High-performance low-cost flexible hybrid electronics (FHE) are desirable for internet of things (IoT). Carbon-nanotube (CNT) thin-film transistor (TFT) is a promising candidate for high-performance FHE because of its high carrier mobility (25cm 2 /V.s), superior mechanical flexibility/stretchability, and material compatibility with low-cost printing and solution processes. Flexible sensors and peripheral CNT-TFT circuits, such as decoders, drivers and sense amplifiers, can be printed and integrated with thinned (<;50μm) silicon chips on soft, thin, and flexible substrates for appealing product designs and form factors. Here we report: 1) process design kit (PDK) to enable FHE design automation, from device modeling to physical verification, and 2) open-source and solution-process proven intellectual property (IP) blocks, including Pseudo-CMOS [1] digital logic and analog amplifiers on flexible substrates, as shown in Figure 1. The proposed FHE-PDK and circuit design IP are fully compatible with silicon design EDA tools, and can be readily used for co-design with both CNT-TFT circuits and silicon chips.
Vision transformers have recently demonstrated great success in various computer vision tasks, motivating a tremendously increased interest in their deployment into many real-world IoT applications. However, powerful ViTs are often too computationally expensive to be fitted onto real-world resource-constrained platforms, due to (1) their quadratically increased complexity with the number of input tokens and (2) their overparameterized self-attention heads and model depth. In parallel, different images are of varied complexity and their different regions can contain various levels of visual information, e.g., a sky background is not as informative as a foreground object in object classification tasks, indicating that treating those regions equally in terms of model complexity is unnecessary while such opportunities for trimming down ViTs' complexity have not been fully exploited. To this end, we propose a Multi-grained Input-Adaptive Vision Transformer framework dubbed MIA-Former that can input-adaptively adjust the structure of ViTs at three coarse-to-fine-grained granularities (i.e., model depth and the number of model heads/tokens). In particular, our MIA-Former adopts a low-cost network trained with a hybrid supervised and reinforcement learning method to skip the unnecessary layers, heads, and tokens in an input adaptive manner, reducing the overall computational cost. Furthermore, an interesting side effect of our MIA-Former is that its resulting ViTs are naturally equipped with improved robustness against adversarial attacks over their static counterparts, because MIA-Former's multi-grained dynamic control improves the model diversity similar to the effect of ensemble and thus increases the difficulty of adversarial attacks against all its sub-models. Extensive experiments and ablation studies validate that the proposed MIA-Former framework can (1) effectively allocate adaptive computation budgets to the difficulty of input images, achieving state-of-the-art (SOTA) accuracy-efficiency trade-offs, e.g., up to 16.5\% computation savings with the same or even a higher accuracy compared with the SOTA dynamic transformer models, and (2) boost ViTs' robustness accuracy under various adversarial attacks over their vanilla counterparts by 2.4\% and 3.0\%, respectively. Our code is available at https://github.com/RICE-EIC/MIA-Former.
With the continuous advancement of processors, modern micro-architecture designs have become increasingly complex. The vast design space presents significant challenges for human designers, making design space exploration (DSE) algorithms a significant tool for $\mu$-arch design. In recent years, efforts have been made in the development of DSE algorithms, and promising results have been achieved. However, the existing DSE algorithms, e.g., Bayesian Optimization and ensemble learning, suffer from poor interpretability, hindering designers' understanding of the decision-making process. To address this limitation, we propose utilizing Fuzzy Neural Networks to induce and summarize knowledge and insights from the DSE process, enhancing interpretability and controllability. Furthermore, to improve efficiency, we introduce a multi-fidelity reinforcement learning approach, which primarily conducts exploration using cheap but less precise data, thereby substantially diminishing the reliance on costly data. Experimental results show that our method achieves excellent results with a very limited sample budget and successfully surpasses the current state-of-the-art. Our DSE framework is open-sourced and available at https://github.com/fanhanwei/FNN\_MFRL\_ArchDSE/\ .
Deep Neural Networks (DNNs) are pervasively applied in many artificial intelligence (AI) applications. The high performance of DNNs comes at the cost of larger size and higher compute complexity. Recent studies show that DNNs have much redundancy, such as the zero-value parameters and excessive numerical precision. To reduce computing complexity, many redundancy reduction techniques have been proposed, including pruning and data quantization. In this paper, we demonstrate our co-optimization of the DNN algorithm and hardware which exploits the model redundancy to accelerate DNNs.
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