Network Traffic Classification Based on External Attention by IP Packet Header

As the emerging services have increasingly strict requirements on quality of service (QoS), such as millisecond network service latency ect., network traffic classification technology is required to assist more advanced network management and monitoring capabilities. So far as we know, the delays of flow-granularity classification methods are difficult to meet the real-time requirements for too long packet-waiting time, whereas the present packet-granularity classification methods may have problems related to privacy protection due to using excessive user payloads. To solve the above problems, we proposed a network traffic classification method only by the IP packet header, which satisfies the requirements of both user's privacy protection and classification performances. We opted to remove the IP address from the header information of the network layer and utilized the remaining 12-byte IP packet header information as input for the model. Additionally, we examined the variations in header value distributions among different categories of network traffic samples. And, the external attention is also introduced to form the online classification framework, which performs well for its low time complexity and strong ability to enhance high-dimensional classification features. The experiments on three open-source datasets show that our average accuracy can reach upon 94.57%, and the classification time is shortened to meet the real-time requirements (0.35ms for a single packet).Comment: 12 pages, 5 figure

TransMUSE: Transferable Traffic Prediction in MUlti-Service Edge Networks

The Covid-19 pandemic has forced the workforce to switch to working from home, which has put significant burdens on the management of broadband networks and called for intelligent service-by-service resource optimization at the network edge. In this context, network traffic prediction is crucial for operators to provide reliable connectivity across large geographic regions. Although recent advances in neural network design have demonstrated potential to effectively tackle forecasting, in this work we reveal based on real-world measurements that network traffic across different regions differs widely. As a result, models trained on historical traffic data observed in one region can hardly serve in making accurate predictions in other areas. Training bespoke models for different regions is tempting, but that approach bears significant measurement overhead, is computationally expensive, and does not scale. Therefore, in this paper we propose TransMUSE, a novel deep learning framework that clusters similar services, groups edge-nodes into cohorts by traffic feature similarity, and employs a Transformer-based Multi-service Traffic Prediction Network (TMTPN), which can be directly transferred within a cohort without any customization. We demonstrate that TransMUSE exhibits imperceptible performance degradation in terms of mean absolute error (MAE) when forecasting traffic, compared with settings where a model is trained for each individual edge node. Moreover, our proposed TMTPN architecture outperforms the state-of-the-art, achieving up to 43.21% lower MAE in the multi-service traffic prediction task. To the best of our knowledge, this is the first work that jointly employs model transfer and multi-service traffic prediction to reduce measurement overhead, while providing fine-grained accurate demand forecasts for edge services provisioning

Iteratively Coupled Multiple Instance Learning from Instance to Bag Classifier for Whole Slide Image Classification

Whole Slide Image (WSI) classification remains a challenge due to their extremely high resolution and the absence of fine-grained labels. Presently, WSIs are usually classified as a Multiple Instance Learning (MIL) problem when only slide-level labels are available. MIL methods involve a patch embedding process and a bag-level classification process, but they are prohibitively expensive to be trained end-to-end. Therefore, existing methods usually train them separately, or directly skip the training of the embedder. Such schemes hinder the patch embedder's access to slide-level labels, resulting in inconsistencies within the entire MIL pipeline. To overcome this issue, we propose a novel framework called Iteratively Coupled MIL (ICMIL), which bridges the loss back-propagation process from the bag-level classifier to the patch embedder. In ICMIL, we use category information in the bag-level classifier to guide the patch-level fine-tuning of the patch feature extractor. The refined embedder then generates better instance representations for achieving a more accurate bag-level classifier. By coupling the patch embedder and bag classifier at a low cost, our proposed framework enables information exchange between the two processes, benefiting the entire MIL classification model. We tested our framework on two datasets using three different backbones, and our experimental results demonstrate consistent performance improvements over state-of-the-art MIL methods. Code will be made available upon acceptance

Electronic correlations and flattened band in magnetic Weyl semimetal candidate Co3Sn2S2

The interplay between electronic correlations and topological protection may offer a rich avenue for discovering emergent quantum phenomena in condensed matter. However, electronic correlations have so far been little investigated in Weyl semimetals (WSMs) by experiments. Here, we report a combined optical spectroscopy and theoretical calculation study on the strength of electronic correlations in a kagome magnet Co3Sn2S2 and the influence of electronic correlations on its WSM state expected within a single-particle picture. The electronic kinetic energy estimated from our optical data is about half of that obtained from single-particle ab initio calculations, which indicates intermediate-strength electronic correlations in this system. Furthermore, comparing the energy ratios between the interband-transition peaks at high energies in the experimental and single-particle-ab-initio-calculation derived optical conductivity spectra with the electronic bandwidth renormalization factors obtained by many-body calculations enables us to estimate the Coulomb-interaction strength (U ~ 4 eV) of electronic correlations in Co3Sn2S2. Our many-body calculations with U ~ 4 eV show that a WSM state, which is characterized by bulk Weyl cones and surface Fermi arcs, survives in this correlated electron system. Besides, a sharp experimental optical conductivity peak at low energy, which is absent in the single-particle-ab-initio-calculation-derived optical conductivity spectrum but is consistent with the optical conductivity peaks obtained by many-body calculations, indicates that an electronic band connecting the two Weyl cones is flattened by electronic correlations and emerges near the Fermi energy in Co3Sn2S2. Our work paves the way for exploring flat-band-generated quantum phenomena in WSMs

Fabrication and Characterization of 5 vol.% (Al

Hybrid composites are fabricated by adding two reinforcements into matrix materials so that the expected excellent properties can be achieved through the combined advantages of short fibres, and different size particles (micron or nano), which provide a high degree of design freedom. In this paper, hybrid preforms were produced with the different size reinforcement of the Al2O3 particles and short fibres. The Al-Si alloy-based hybrid composites reinforced by 5 vol. % Al2O3 particles and 8 vol. % Al2O3 fibres were fabricated by preform-squeezing casting route. The structure and performance of composite materials were studied with Transmission Electron Microscopy (TEM) and Scanning Electron Microscopy (SEM). The results show that the reinforcements, both particles and fibres, distribute homogeneously in the matrix materials, and the properties of composites are found to improve in comparison with the matrix Al-Si alloy

Drop impact on a sticky porous surface with gas discharge - Supplementary material

This collection contains the raw data (Microsoft Excel xlsx-format) for the plots as well as the computational finite-element model (Comsol Multiphysics mph-format and model description in html) for the following publication: Weimar, L., Hu, L., Baier, T., & Hardt, S. (2022). Drop impact on a sticky porous surface with gas discharge: transformation of drops into bubbles. Journal of Fluid Mechanics, 953, A6. https://doi.org/10.1017/jfm.2022.921

Recent advances on the circadian gene PER2 and metabolic rhythm of lactation of mammary gland

Due to regulation by circadian rhythm, the lactation of the mammary gland has rhythmicity. As one of prominent members of period protein family which regulates biological rhythms, PER2 plays an important role in developing the milk duct and maintaining the polarity and the morphology of the mammary epithelium; at the same time, it is also closely related with the metabolism of milk protein and milk fat. This paper summarized recent researches on PER2 gene and related researches on mammary gland development and metabolism to provide some information for the studies of the theory and technology on physiological functions of the mammary gland and milk quality control

Nano-composites Argile/polyéthylène avec propriétés barrière améliorées pour la conservation à long terme des graines

International audienceThere are numerous threats to the biodiversity of commercial and non-commercial plant species. The easiest way to prevent biodiversity loss is to conserve their genetic resources in seed banks. Depending on seeds sensitivity, it is vital to provide seeds packaging protection with high water vapor barrier components. Currently, most of container materials for seed banking are made from glass or tri-laminated foil. Glass containers are heavy and easily broken during handling and transport, whereas tri-laminated foil pouches are opaque and subjected to puncture by sharp seeds. High density polyethylene (HDPE) containers are rarely used for long term seed storage due to their permeability to moisture vapor. Considering the numerous advantages of HDPE such as low cost, lightweight, chemical inertness and easy processing, HDPE with enhanced barrier properties is suitable for long-term seed storage. According to previous research work, the addition of nanoclays into polymer matrix leads to the tortuous path that delayed the diffusion of gaseous molecules as water vapour. The present study aims to evaluate the effect of different clays on the overall properties of HDPE, especially water vapor barrier properties. The water vapor barrier of tested samples was found to be influenced by several factors: the aspect ratio of clays, the crystallinity of HDPE, the interface between clays and HDPE and the storage conditions.Il y a des nombreuses menaces qui pèsent sur la biodiversité des espèces végétales commerciales et non commerciales. La meilleure façon de prévenir la perte de biodiversité est de conserver leurs ressources génétiques dans des banques de semences. Selon la sensibilité des graines, il est essentiel d’utiliser des contenants composés de matériaux faisant hautement barrage à la vapeur d’eau. Actuellement, la plupart des conteneurs pour les banques de semences est en verre ou en complexe laminé aluminium /plastique. Les récipients en verre sont lourds et fragiles pendant les manutentions et le transport, alors que les sachets laminés aluminium/plastique sont opaques et sensibles à la perforation par des graines acérées. Les contenants en polyéthylène haute densité (PEHD) sont rarement utilisés pour le stockage long terme de semences en raison de leur perméabilité à la vapeur d’eau. Étant donné les nombreux avantages du PEHD tels que l’inertie chimique, la légèreté, leur faible coût ainsi que leur mise en oeuvre facile, le PEHD doté de propriétés de barrière renforcées est adapté au stockage à long terme des semences. Comme démontré dans les travaux de recherche précédents, l’ajout de nano-argiles dans la matrice polymère conduit à la mise en place de chemins tortueux qui retardent la diffusion des molécules gazeuses comme la vapeur d’eau. La présente étude vise à évaluer l’effet des différentes argiles sur les propriétés globales de HDPE et en particulier les propriétés de barrière de vapeur d’eau. La capacité barrière de vapeur d’eau des échantillons testés s’est avérée être influencée par plusieurs facteurs : l’allongement des argiles, la cristallinité du PEHD, l’interface entre les argiles et le PEHD ainsi que les conditions de stockage