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

Referring Camouflaged Object Detection

In this paper, we consider the problem of referring camouflaged object detection (Ref-COD), a new task that aims to segment specified camouflaged objects based on some form of reference, e.g., image, text. We first assemble a large-scale dataset, called R2C7K, which consists of 7K images covering 64 object categories in real-world scenarios. Then, we develop a simple but strong dual-branch framework, dubbed R2CNet, with a reference branch learning common representations from the referring information and a segmentation branch identifying and segmenting camouflaged objects under the guidance of the common representations. In particular, we design a Referring Mask Generation module to generate pixel-level prior mask and a Referring Feature Enrichment module to enhance the capability of identifying camouflaged objects. Extensive experiments show the superiority of our Ref-COD methods over their COD counterparts in segmenting specified camouflaged objects and identifying the main body of target objects. Our code and dataset are publicly available at https://github.com/zhangxuying1004/RefCOD

Optogenetic mapping of cerebellar inhibitory circuitry reveals spatially biased coordination of interneurons via electrical synapses

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Cell 7 (2014): 1601–1613, doi:10.1016/j.celrep.2014.04.047.We used high-speed optogenetic mapping technology to examine the spatial organization of local inhibitory circuits formed by cerebellar interneurons. Transgenic mice expressing channelrhodopsin-2 exclusively in molecular layer interneurons allowed us to focally photostimulate these neurons, while measuring resulting responses in postsynaptic Purkinje cells. This approach revealed that interneurons converge upon Purkinje cells over a broad area and that at least seven interneurons form functional synapses with a single Purkinje cell. The number of converging interneurons was reduced by treatment with gap junction blockers, revealing that electrical synapses between interneurons contribute substantially to the spatial convergence. Remarkably, gap junction blockers affected convergence in sagittal slices, but not in coronal slices, indicating a sagittal bias in electrical coupling between interneurons. We conclude that electrical synapse networks spatially coordinate interneurons in the cerebellum and may also serve this function in other brain regions.This work was supported by a CRP grant from the National Research Foundation of Singapore and by the World Class Institute (WCI) Program of the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology of Korea (NRF grant number WCI 2009-003)

Clonal analysis of gliogenesis in the cerebral cortex reveals stochastic expansion of glia and cell autonomous responses to Egfr dosage

Development of the nervous system undergoes important transitions, including one from neurogenesis to gliogenesis which occurs late during embryonic gestation. Here we report on clonal analysis of gliogenesis in mice using Mosaic Analysis with Double Markers (MADM) with quantitative and computational methods. Results reveal that developmental gliogenesis in the cerebral cortex occurs in a fraction of earlier neurogenic clones, accelerating around E16.5, and giving rise to both astrocytes and oligodendrocytes. Moreover, MADM-based genetic deletion of the epidermal growth factor receptor (Egfr) in gliogenic clones revealed that Egfr is cell autonomously required for gliogenesis in the mouse dorsolateral cortices. A broad range in the proliferation capacity, symmetry of clones, and competitive advantage of MADM cells was evident in clones that contained one cellular lineage with double dosage of Egfr relative to their environment, while their sibling Egfr-null cells failed to generate glia. Remarkably, the total numbers of glia in MADM clones balance out regardless of significant alterations in clonal symmetries. The variability in glial clones shows stochastic patterns that we define mathematically, which are different from the deterministic patterns in neuronal clones. This study sets a foundation for studying the biological significance of stochastic and deterministic clonal principles underlying tissue development, and identifying mechanisms that differentiate between neurogenesis and gliogenesis.</jats:p

Intramuscular vitamin A injection in newborn lambs enhances antioxidant capacity and improves meat quality

IntroductionVitamin A (VA) and its metabolite, retinoic acid (RA) possess several biological functions. This report investigated whether neonatal intramuscular VA injection affected antioxidative activity and meat quality in longissimus dorsi (LD) muscle of lambs.MethodsLambs were injected with 0 (control) or 7,500 IU VA palmitate into the biceps femoris muscle on day 2 after birth. At 3, 12, and 32 weeks of age, blood samples were collected in the jugular vein for serum levels of RA and muscle samples were collected in the biceps femoris for analysis of relative mRNA expression of enzyme contributors to retinoid metabolism. All animals were harvested at 32 weeks of age and muscle samples were collected to explore the role of VA on the meat quality and antioxidant capacity of lambs.Results and discussionOur results indicated that VA increased the redness, crude protein, and crude fat (p &lt; 0.05), without affecting moisture, ash, and amino acid composition in LD muscle (p &gt; 0.05). In addition, VA increased catalase (CAT) activity and decreased malondialdehyde (MDA) levels in LD muscle (p &lt; 0.05). Meanwhile, greater levels of CAT and NRF2 mRNA and protein contents with VA treatment were observed in LD muscle (p &lt; 0.05), partly explained by the increased level of RA (p &lt; 0.05). Collectively, our findings indicated that VA injection at birth could improve lamb meat quality by elevating the redness, crude protein, crude fat, and antioxidative capacity in LD muscle of lambs

Polysialylated NCAM and EphrinA/EphA regulate synaptic development of gabaergic interneurons in prefrontal cortex

A novel function for the neural cell adhesion molecule (NCAM) was identified in ephrinA/EphA-mediated repulsion as an important regulatory mechanism for development of GABAergic inhibitory synaptic connections in mouse prefrontal cortex. Deletion of NCAM, EphA3, or ephrinA2/3/5 in null mutant mice increased the numbers and size of perisomatic synapses between GABAergic basket interneurons and pyramidal cells in the developing cingulate cortex (layers II/III). A functional consequence of NCAM loss was increased amplitudes and faster kinetics of miniature inhibitory postsynaptic currents in NCAM null cingulate cortex. NCAM and EphA3 formed a molecular complex and colocalized with the inhibitory presynaptic marker vesicular GABA transporter (VGAT) in perisomatic puncta and neuropil in the cingulate cortex. EphrinA5 treatment promoted axon remodeling of enhanced green fluorescent protein-labeled basket interneurons in cortical slice cultures and induced growth cone collapse in wild-type but not NCAM null mutant neurons. NCAM modified with polysialic acid (PSA) was required to promote ephrinA5-induced axon remodeling of basket interneurons in cortical slices, likely by providing a permissive environment for ephrinA5/EphA3 signaling. These results reveal a new mechanism in which NCAM and ephrinAs/EphA3 coordinate to constrain GABAergic interneuronal arborization and perisomatic innervation, potentially contributing to excitatory/inhibitory balance in prefrontal cortical circuitry. © 2012 The Author

Wideband Power Spectrum Sensing: a Fast Practical Solution for Nyquist Folding Receiver

The limited availability of spectrum resources has been growing into a critical problem in wireless communications, remote sensing, and electronic surveillance, etc. To address the high-speed sampling bottleneck of wideband spectrum sensing, a fast and practical solution of power spectrum estimation for Nyquist folding receiver (NYFR) is proposed in this paper. The NYFR architectures is can theoretically achieve the full-band signal sensing with a hundred percent of probability of intercept. But the existing algorithm is difficult to realize in real-time due to its high complexity and complicated calculations. By exploring the sub-sampling principle inherent in NYFR, a computationally efficient method is introduced with compressive covariance sensing. That can be efficient implemented via only the non-uniform fast Fourier transform, fast Fourier transform, and some simple multiplication operations. Meanwhile, the state-of-the-art power spectrum reconstruction model for NYFR of time-domain and frequency-domain is constructed in this paper as a comparison. Furthermore, the computational complexity of the proposed method scales linearly with the Nyquist-rate sampled number of samples and the sparsity of spectrum occupancy. Simulation results and discussion demonstrate that the low complexity in sampling and computation is a more practical solution to meet the real-time wideband spectrum sensing applications

Distributed UAV Swarm Augmented Wideband Spectrum Sensing Using Nyquist Folding Receiver

Distributed unmanned aerial vehicle (UAV) swarms are formed by multiple UAVs with increased portability, higher levels of sensing capabilities, and more powerful autonomy. These features make them attractive for many recent applica-tions, potentially increasing the shortage of spectrum resources. In this paper, wideband spectrum sensing augmented technology is discussed for distributed UAV swarms to improve the utilization of spectrum. However, the sub-Nyquist sampling applied in existing schemes has high hardware complexity, power consumption, and low recovery efficiency for non-strictly sparse conditions. Thus, the Nyquist folding receiver (NYFR) is considered for the distributed UAV swarms, which can theoretically achieve full-band spectrum detection and reception using a single analog-to-digital converter (ADC) at low speed for all circuit components. There is a focus on the sensing model of two multichannel scenarios for the distributed UAV swarms, one with a complete functional receiver for the UAV swarm with RIS, and another with a decentralized UAV swarm equipped with a complete functional receiver for each UAV element. The key issue is to consider whether the application of RIS technology will bring advantages to spectrum sensing and the data fusion problem of decentralized UAV swarms based on the NYFR architecture. Therefore, the property for multiple pulse reconstruction is analyzed through the Gershgorin circle theorem, especially for very short pulses. Further, the block sparse recovery property is analyzed for wide bandwidth signals. The proposed technology can improve the processing capability for multiple signals and wide bandwidth signals while reducing interference from folded noise and subsampled harmonics. Experiment results show augmented spectrum sensing efficiency under non-strictly sparse conditions

FLM-101B: An Open LLM and How to Train It with $100K Budget

Large language models (LLMs) have achieved remarkable success in NLP and multimodal tasks, among others. Despite these successes, two main challenges remain in developing LLMs: (i) high computational cost, and (ii) fair and objective evaluations. In this paper, we report a solution to significantly reduce LLM training cost through a growth strategy. We demonstrate that a 101B-parameter LLM with 0.31T tokens can be trained with a budget of 100K US dollars. Inspired by IQ tests, we also consolidate an additional range of evaluations on top of existing evaluations that focus on knowledge-oriented abilities. These IQ evaluations include symbolic mapping, rule understanding, pattern mining, and anti-interference. Such evaluations minimize the potential impact of memorization. Experimental results show that our model, named FLM-101B, trained with a budget of 100K US dollars, achieves performance comparable to powerful and well-known models, e.g., GPT-3 and GLM-130B, especially on the additional range of IQ evaluations. The checkpoint of FLM-101B is released at https://huggingface.co/CofeAI/FLM-101B