Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline

Edge computing solutions that enable the extraction of high level information from a variety of sensors is in increasingly high demand. This is due to the increasing number of smart devices that require sensory processing for their application on the edge. To tackle this problem, we present a smart vision sensor System on Chip (Soc), featuring an event-based camera and a low power asynchronous spiking Convolutional Neuronal Network (sCNN) computing architecture embedded on a single chip. By combining both sensor and processing on a single die, we can lower unit production costs significantly. Moreover, the simple end-to-end nature of the SoC facilitates small stand-alone applications as well as functioning as an edge node in a larger systems. The event-driven nature of the vision sensor delivers high-speed signals in a sparse data stream. This is reflected in the processing pipeline, focuses on optimising highly sparse computation and minimising latency for 9 sCNN layers to $3.36\mu s$. Overall, this results in an extremely low-latency visual processing pipeline deployed on a small form factor with a low energy budget and sensor cost. We present the asynchronous architecture, the individual blocks, the sCNN processing principle and benchmark against other sCNN capable processors

Speck:A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline

Edge computing solutions that enable the extraction of high-level information from a variety of sensors is in increasingly high demand. This is due to the increasing number of smart devices that require sensory processing for their application on the edge. To tackle this problem, we present a smart vision sensor System on Chip (SoC), featuring an event-based camera and a low-power asynchronous spiking Convolutional Neural Network (sCNN) computing architecture embedded on a single chip. By combining both sensor and processing on a single die, we can lower unit production costs significantly. Moreover, the simple end-to-end nature of the SoC facilitates small stand-alone applications as well as functioning as an edge node in larger systems. The event-driven nature of the vision sensor delivers high-speed signals in a sparse data stream. This is reflected in the processing pipeline, which focuses on optimising highly sparse computation and minimising latency for 9 sCNN layers to 3.36{\mu}s for an incoming event. Overall, this results in an extremely low-latency visual processing pipeline deployed on a small form factor with a low energy budget and sensor cost. We present the asynchronous architecture, the individual blocks, and the sCNN processing principle and benchmark against other sCNN capable processors

Speck:A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline

Edge computing solutions that enable the extraction of high-level information from a variety of sensors is in increasingly high demand. This is due to the increasing number of smart devices that require sensory processing for their application on the edge. To tackle this problem, we present a smart vision sensor System on Chip (SoC), featuring an event-based camera and a low-power asynchronous spiking Convolutional Neural Network (sCNN) computing architecture embedded on a single chip. By combining both sensor and processing on a single die, we can lower unit production costs significantly. Moreover, the simple end-to-end nature of the SoC facilitates small stand-alone applications as well as functioning as an edge node in larger systems. The event-driven nature of the vision sensor delivers high-speed signals in a sparse data stream. This is reflected in the processing pipeline, which focuses on optimising highly sparse computation and minimising latency for 9 sCNN layers to 3.36{\mu}s for an incoming event. Overall, this results in an extremely low-latency visual processing pipeline deployed on a small form factor with a low energy budget and sensor cost. We present the asynchronous architecture, the individual blocks, and the sCNN processing principle and benchmark against other sCNN capable processors

Low-Carbon and Nanosheathed ZnCo2O4 Spheroids with Porous Architecture for Boosted Lithium Storage Properties

Multielectronic reaction electrode materials for high energy density lithium-ion batteries (LIBs) are severely hindered by their inherent sluggish kinetics and large volume variations, leading to rapid capacity fade. Here, a simple method is developed to construct low-carbon and nanosheathed ZnCo2O4 porous spheroids (ZCO@C-5). In this micro/nanostructure, an ultrathin amorphous carbon layer (~2 nm in thickness) is distributed all over the primary nanosized ZCO particles (~20 nm in diameter), which finally self-assembles into porous core (ZCO)-shell(carbon) micron spheroids. The nanoencapsulation and macro/mesoporous architecture can not only provide facile electrolyte penetration and rapid ion/electron transfer but also better alleviate volumetric expansion effect to avoid pulverization of ZCO@C-5 spheroids during repeat charge/discharge processes. As expected, the three-dimensional porous ZCO@C-5 composites exhibit high reversible capacity of 1240 mAh g−1 cycle at 500 mA g−1, as well as excellent long-term cycling stability and rate capability. The low-carbon and nanoencapsulation strategy in this study is simple and effective, exhibiting great potential for high-performance LIBs

Enhanced Regeneration and Hepatoprotective Effects of Interleukin 22 Fusion Protein on a Predamaged Liver Undergoing Partial Hepatectomy

Liver ischemia-reperfusion injury (IRI) and regeneration deficiency are two major challenges for surgery patients with chronic liver disease. As a survival factor for hepatocytes, interleukin 22 (IL-22) plays an important role in hepatoprotection and the promotion of regeneration after hepatectomy. In this study, we aim to investigate the roles of an interleukin 22 fusion protein (IL-22-FP) in mice with a predamaged liver after a two-third partial hepatectomy (PHx). Predamaged livers in mice were induced by concanavalin A (ConA)/carbon tetrachloride (CCl4) following PHx with or without IL-22-FP treatment. A hepatic IRI mouse model was also used to determine the hepatoprotective effects of IL-22-FP. In the ConA/CCl4 model, IL-22-FP treatment alleviated liver injury and accelerated hepatocyte proliferation. Administration of IL-22-FP activated the hepatic signal transducer and activator of transcription 3 (STAT3) and upregulated the expression of many mitogenic proteins. IL-22-FP treatment prior to IRI effectively reduced liver damage through decreased aminotransferase and improved liver histology. In conclusion, IL-22-FP promotes liver regeneration in mice with predamaged livers following PHx and alleviates IRI-induced liver injury. Our study suggests that IL-22-FP may represent a promising therapeutic drug against regeneration deficiency and liver IRI in patients who have undergone PHx

Immune‐related interaction perturbation networks unravel biological peculiars and clinical significance of glioblastoma

Abstract The immune system is an interacting network of plentiful molecules that could better characterize the relationship between immunity and cancer. This study aims to investigate the behavioral patterns of immune‐related interaction perturbation networks in glioblastoma. An immune‐related interaction‐perturbation framework was introduced to characterize four heterogeneous subtypes using RNA‐seq data of TCGA/CGGA glioblastoma tissues and GTEx normal brain tissues. The stability and robustness of the four subtypes were validated in public datasets and our in‐house cohort. In the four subtypes, C1 was an inflammatory subtype with high immune infiltration, low tumor purity, and potential response to immunotherapy; C2, an invasive subtype, was featured with dismal prognosis, telomerase reverse transcriptase promoter mutations, moderate levels of immunity, and stromal constituents, as well as sensitivity to receptor tyrosine kinase signaling inhibitors; C3 was a proliferative subtype with high tumor purity, immune‐desert microenvironment, sensitivity to phosphatidylinositol 3′‐kinase signaling inhibitor and DNA replication inhibitors, and potential resistance to immunotherapy; C4, a synaptogenesis subtype with the best prognosis, exhibited high synaptogenesis‐related gene expression, prevalent isocitrate dehydrogenase mutations, and potential sensitivity to radiotherapy and chemotherapy. Overall, this study provided an attractive platform from the perspective of immune‐related interaction perturbation networks, which might advance the tailored management of glioblastoma

Enhanced regeneration and hepatoprotective effects of interleukin 22 fusion protein on a pre-damaged liver undergoing partial hepatectomy.pdf

All the details about each data table is described in the uploaded file

Operando diagnostic detection of interfacial oxygen ‘breathing’ of resistive random access memory by bulk-sensitive hard X-ray photoelectron spectroscopy

The HfO2-based resistive random access memory (RRAM) is one of the most promising candidates for non-volatile memory applications. The detection and examination of the dynamic behavior of oxygen ions/vacancies are crucial to deeply understand the microscopic physical nature of the resistive switching (RS) behavior. By using synchrotron radiation based, non-destructive and bulk-sensitive hard X-ray photoelectron spectroscopy (HAXPES), we demonstrate an operando diagnostic detection of the oxygen ‘breathing’ behavior at the oxide/metal interface, namely, oxygen migration between HfO2 and TiN during different RS periods. The results highlight the significance of oxide/metal interfaces in RRAM, even in filament-type devices