NetGPT: Generative Pretrained Transformer for Network Traffic

Pretrained models for network traffic can utilize large-scale raw data to learn the essential characteristics of network traffic, and generate distinguishable results for input traffic without considering specific downstream tasks. Effective pretrained models can significantly optimize the training efficiency and effectiveness of downstream tasks, such as traffic classification, attack detection, resource scheduling, protocol analysis, and traffic generation. Despite the great success of pretraining in natural language processing, there is no work in the network field. Considering the diverse demands and characteristics of network traffic and network tasks, it is non-trivial to build a pretrained model for network traffic and we face various challenges, especially the heterogeneous headers and payloads in the multi-pattern network traffic and the different dependencies for contexts of diverse downstream network tasks. To tackle these challenges, in this paper, we make the first attempt to provide a generative pretrained model for both traffic understanding and generation tasks. We propose the multi-pattern network traffic modeling to construct unified text inputs and support both traffic understanding and generation tasks. We further optimize the adaptation effect of the pretrained model to diversified tasks by shuffling header fields, segmenting packets in flows, and incorporating diverse task labels with prompts. Expensive experiments demonstrate the effectiveness of our NetGPT in a range of traffic understanding and generation tasks, and outperform state-of-the-art baselines by a wide margin

Enhancing Nanoparticle-Based Visible Detection by Controlling the Extent of Aggregation

Visible indication based on the aggregation of colloidal nanoparticles (NPs) is highly advantageous for rapid on-site detection of biological entities, which even untrained persons can perform without specialized instrumentation. However, since the extent of aggregation should exceed a certain minimum threshold to produce visible change, further applications of this conventional method have been hampered by insufficient sensitivity or certain limiting characteristics of the target. Here we report a signal amplification strategy to enhance visible detection by introducing switchable linkers (SLs), which are designed to lose their function to bridge NPs in the presence of target and control the extent of aggregation. By precisely designing the system, considering the quantitative relationship between the functionalized NPs and SLs, highly sensitive and quantitative visible detection is possible. We confirmed the ultrahigh sensitivity of this method by detecting the presence of 20 fM of streptavidin and fewer than 100 CFU/mL of Escherichia coli

The Crest Phenotype in Chicken Is Associated with Ectopic Expression of HOXC8 in Cranial Skin

The Crest phenotype is characterised by a tuft of elongated feathers atop the head. A similar phenotype is also seen in several wild bird species. Crest shows an autosomal incompletely dominant mode of inheritance and is associated with cerebral hernia. Here we show, using linkage analysis and genome-wide association, that Crest is located on the E22C19W28 linkage group and that it shows complete association to the HOXC-cluster on this chromosome. Expression analysis of tissues from Crested and non-crested chickens, representing 26 different breeds, revealed that HOXC8, but not HOXC12 or HOXC13, showed ectopic expression in cranial skin during embryonic development. We propose that Crest is caused by a cis-acting regulatory mutation underlying the ectopic expression of HOXC8. However, the identification of the causative mutation(s) has to await until a method becomes available for assembling this chromosomal region. Crest is unfortunately located in a genomic region that has so far defied all attempts to establish a contiguous sequence

Interfacial memristors in Al–LaNiO3 heterostructures

International audienceMemristive devices are promising circuit elements that enable novel computational approaches which go beyond the von-Neumann paradigms. Here by tuning the chemistry at the Al–LaNiO3 (LNO) interface, a metal–metal junction, we engineer good switching behavior with good electroresistance (ON–OFF resistance ratios of 100), and repeatable multiple resistance states. The active material responsible for such a behavior is a self-formed sandwich of an AlxOy layer at the interface obtained by grabbing oxygen by Al from LNO. Using aberration corrected electron microscopy and transport measurements, it is confirmed that the memristive hysteresis occurs due to the electric field driven O2− (or ) cycling between LNO (reservoir) and the interlayer, which drives the redox reactions forming and dissolving Al nanoclusters in the AlxOy matrix. This work provides clear insights into and details on precise oxygen control at such interfaces and can be useful for newer opportunities in oxitronics

Decrease of 5-Hydroxymethylcytosine Is Associated with Progression of Hepatocellular Carcinoma through Downregulation of TET1

<div><p>DNA methylation is an important epigenetic modification and is frequently altered in cancer. Convert of 5-methylcytosine (5 mC) to 5-hydroxymethylcytosine (5 hmC) by ten-eleven translocation (TET) family enzymes plays important biological functions in embryonic stem cells, development, aging and disease. Recent reports showed that level of 5 hmC was altered in various types of cancers. However, the change of 5 hmC level in hepatocellular carcinoma (HCC) and association with clinical outcome were not well defined. Here, we reported that level of 5 hmC was decreased in HCC tissues, as compared with non-tumor tissues. Clincopathological analysis showed the decreased level of 5 hmC in HCC was associated with tumor size, AFP level and poor overall survival. We also found that the decreased level of 5 hmC in non-tumor tissues was associated with tumor recurrence in the first year after surgical resection. In an animal model with carcinogen DEN-induced HCC, we found that the level of 5 hmC was gradually decreased in the livers during the period of induction. There was further reduction of 5 hmC in tumor tissues when tumors were developed. In contrast, level of 5 mC was increased in HCC tissues and the increased 5 mC level was associated with capsular invasion, vascular thrombosis, tumor recurrence and overall survival. Furthermore, our data showed that expression of TET1, but not TET2 and TET3, was downregulated in HCC. Taken together, our data indicated 5 hmC may be served as a prognostic marker for HCC and the decreased expression of TET1 is likely one of the mechanisms underlying 5 hmC loss in HCC.</p></div

Ultralow‐power in‐memory computing based on ferroelectric memcapacitor network

Abstract Analog storage through synaptic weights using conductance in resistive neuromorphic systems and devices inevitably generates harmful heat dissipation. This thermal issue not only limits the energy efficiency but also hampers the very‐large‐scale and highly complicated hardware integration as in the human brain. Here we demonstrate that the synaptic weights can be simulated by reconfigurable non‐volatile capacitances of a ferroelectric‐based memcapacitor with ultralow‐power consumption. The as‐designed metal/ferroelectric/metal/insulator/semiconductor memcapacitor shows distinct 3‐bit capacitance states controlled by the ferroelectric domain dynamics. These robust memcapacitive states exhibit uniform maintenance of more than 104 s and well endurance of 109 cycles. In a wired memcapacitor crossbar network hardware, analog vector‐matrix multiplication is successfully implemented to classify 9‐pixel images by collecting the sum of displacement currents (I = C × dV/dt) in each column, which intrinsically consumes zero energy in memcapacitors themselves. Our work sheds light on an ultralow‐power neural hardware based on ferroelectric memcapacitors