Octopus: A Heterogeneous In-network Computing Accelerator Enabling Deep Learning for network

Deep learning (DL) for network models have achieved excellent performance in the field and are becoming a promising component in future intelligent network system. Programmable in-network computing device has great potential to deploy DL for network models, however, existing device cannot afford to run a DL model. The main challenges of data-plane supporting DL-based network models lie in computing power, task granularity, model generality and feature extracting. To address above problems, we propose Octopus: a heterogeneous in-network computing accelerator enabling DL for network models. A feature extractor is designed for fast and efficient feature extracting. Vector accelerator and systolic array work in a heterogeneous collaborative way, offering low-latency-highthroughput general computing ability for packet-and-flow-based tasks. Octopus also contains on-chip memory fabric for storage and connecting, and Risc-V core for global controlling. The proposed Octopus accelerator design is implemented on FPGA. Functionality and performance of Octopus are validated in several use-cases, achieving performance of 31Mpkt/s feature extracting, 207ns packet-based computing latency, and 90kflow/s flow-based computing throughput

MqsR/MqsA Toxin/Antitoxin System Regulates Persistence and Biofilm Formation in Pseudomonas putida KT2440

Bacterial toxin/antitoxin (TA) systems have received increasing attention due to their prevalence, diverse structures, and important physiological functions. In this study, we identified and characterized a type II TA system in a soil bacterium Pseudomonas putida KT2440. This TA system belongs to the MqsR/MqsA family. We found that PP_4205 (MqsR) greatly inhibits cell growth in P. putida KT2440 and Escherichia coli, the antitoxin PP_4204 (MqsA) neutralizes the toxicity of the toxin MqsR, and the two genes encoding them are co-transcribed. MqsR and MqsA interact with each other directly in vivo and MqsA is a negative regulator of the TA operon through binding to the promoter. Consistent with the MqsR/MqsA pair in E. coli, the binding of the toxin MqsR to MqsA inhibits the DNA binding ability of MqsA in P. putida KT2440. Disruption of the mqsA gene which induces mqsR expression increases persister cell formation 53-fold, while overexpressing mqsA which represses mqsR expression reduces persister cell formation 220-fold, suggesting an important role of MqsR in persistence in P. putida KT2440. Furthermore, both MqsR and MqsA promote biofilm formation. As a DNA binding protein, MqsA can also negatively regulate an ECF sigma factor AlgU and a universal stress protein PP_3288. Thus, we revealed an important regulatory role of MqsR/MqsA in persistence and biofilm formation in P. putida KT2440

Neutrophil heterogeneity and aging: implications for COVID-19 and wound healing

Neutrophils play a critical role in the immune response to infection and tissue injury. However, recent studies have shown that neutrophils are a heterogeneous population with distinct subtypes that differ in their functional properties. Moreover, aging can alter neutrophil function and exacerbate immune dysregulation. In this review, we discuss the concept of neutrophil heterogeneity and how it may be affected by aging. We then examine the implications of neutrophil heterogeneity and aging for COVID-19 pathogenesis and wound healing. Specifically, we summarize the evidence for neutrophil involvement in COVID-19 and the potential mechanisms underlying neutrophil recruitment and activation in this disease. We also review the literature on the role of neutrophils in the wound healing process and how aging and neutrophil heterogeneity may impact wound healing outcomes. Finally, we discuss the potential for neutrophil-targeted therapies to improve clinical outcomes in COVID-19 and wound healing

ADD 2023: the Second Audio Deepfake Detection Challenge

Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks

Efficient and bright warm-white electroluminescence from lead-free metal halides.

Solution-processed metal-halide perovskites are emerging as one of the most promising materials for displays, lighting and energy generation. Currently, the best-performing perovskite optoelectronic devices are based on lead halides and the lead toxicity severely restricts their practical applications. Moreover, efficient white electroluminescence from broadband-emission metal halides remains a challenge. Here we demonstrate efficient and bright lead-free LEDs based on cesium copper halides enabled by introducing an organic additive (Tween, polyethylene glycol sorbitan monooleate) into the precursor solutions. We find the additive can reduce the trap states, enhancing the photoluminescence quantum efficiency of the metal halide films, and increase the surface potential, facilitating the hole injection and transport in the LEDs. Consequently, we achieve warm-white LEDs reaching an external quantum efficiency of 3.1% and a luminance of 1570 cd m-2 at a low voltage of 5.4 V, showing great promise of lead-free metal halides for solution-processed white LED applications

Roadmap on Superoscillations

Superoscillations are band-limited functions with the counterintuitive property that they can vary arbitrarily faster than their fastest Fourier component, over arbitrarily long intervals. Modern studies originated in quantum theory, but there were anticipations in radar and optics. The mathematical understanding—still being explored—recognises that functions are extremely small where they superoscillate; this has implications for information theory. Applications to optical vortices, sub-wavelength microscopy and related areas of nanoscience are now moving from the theoretical and the demonstrative to the practical. This Roadmap surveys all these areas, providing background, current research, and anticipating future developments

Roadmap on superoscillations

Superoscillations are band-limited functions with the counterintuitive property that they can vary arbitrarily faster than their fastest Fourier component, over arbitrarily long intervals. Modern studies originated in quantum theory, but there were anticipations in radar and optics. The mathematical understanding—still being explored—recognises that functions are extremely small where they superoscillate; this has implications for information theory. Applications to optical vortices, sub-wavelength microscopy and related areas of nanoscience are now moving from the theoretical and the demonstrative to the practical. This Roadmap surveys all these areas, providing background, current research, and anticipating future developments