Magnon-mediated interlayer coupling in an all-antiferromagnetic junction

The interlayer coupling mediated by fermions in ferromagnets brings about parallel and anti-parallel magnetization orientations of two magnetic layers, resulting in the giant magnetoresistance, which forms the foundation in spintronics and accelerates the development of information technology. However, the interlayer coupling mediated by another kind of quasi-particle, boson, is still lacking. Here we demonstrate such a static interlayer coupling at room temperature in an antiferromagnetic junction Fe2O3/Cr2O3/Fe2O3, where the two antiferromagnetic Fe2O3 layers are functional materials and the antiferromagnetic Cr2O3 layer serves as a spacer. The N\'eel vectors in the top and bottom Fe2O3 are strongly orthogonally coupled, which is bridged by a typical bosonic excitation (magnon) in the Cr2O3 spacer. Such an orthogonally coupling exceeds the category of traditional collinear interlayer coupling via fermions in ground state, reflecting the fluctuating nature of the magnons, as supported by our magnon quantum well model. Besides the fundamental significance on the quasi-particle-mediated interaction, the strong coupling in an antiferromagnetic magnon junction makes it a realistic candidate for practical antiferromagnetic spintronics and magnonics with ultrahigh-density integration.Comment: 19 pages, 4 figure

Fast and Memory-Efficient Traffic Classification with Deep Packet Inspection in CMP Architecture

Abstract—Traffic classification is important to many network applications, such as network monitoring. The classic way to identify flows, e.g., examining the port numbers in the packet headers, becomes ineffective. In this context, deep packet inspection technology, which does not only inspect the packet headers but also the packet payloads, plays a more important role in traffic classification. Meanwhile regular expressions are replacing strings to represent patterns because of their expressive power, simplicity and flexibility. However, regular expressions mathcing technique causes a high memory usage and processing cost, which result in low throughout. In this paper, we analyze the application-level protocol distribution of network traffic and conclude its characteristic. Furthermore, we design a fast and memory-efficient system of a two-layer architecture for traffic classification with the help of regular expressions in multi-core architecture, which is differ-ent from previous one-layer architecture. In order to reduce the memory usage of DFA, we use a compression algorithm called CSCA to perform regular expressions matching, which can reduce 95 % memory usage of DFA. We also introduce some optimizations to accelerate the matching speed. We use real-world traffic and all L7-filter protocol patterns to make our experiments, and the results show that the system achieves at Gbps level throughout in 4-cores Servers. I

A prefiltering approach to regular expression matching for network security systems

Abstract. Regular expression (RegEx) matching has been widely used in various networking and security applications. Despite much effort on this important problem, it remains a fundamentally difficult problem. DFA-based solutions can achieve high throughput, but require too much memory to be executed in high speed SRAM. NFA-based solutions require small memory, but are too slow. In this paper, we propose Regex-Filter, a prefiltering approach. The basic idea is to generate the RegEx print of RegEx set and use it to prefilter out most unmatched items. There are two key technical challenges: the generation of RegEx print and the matching process of RegEx print. The generation of RegEx is tricky as we need to tradeoff between two conflicting goals: filtering effectiveness, which means that we want the RegEx print to filter out as many unmatched items as possible, and matching speed, which means that we want the matching speed of the RegEx print as high as possible. To address the first challenge, we propose some measurement tools for RegEx complexity and filtering effectiveness, and use it to guide the generation of RegEx print. To address the second challenge, we propose a fast RegEx print matching solution using Ternary Content Addressable Memory. We implemented our approach and conducted experiments on real world data sets. Our experimental results show that RegexFilter can speedup the potential throughput of RegEx matching by 21.5 times and 20.3 times for RegEx sets of Snort and L7-Filter systems, at the cost of less than 0.2 Mb TCAM chip

Investigation of Temperature-Dependent Magnetic Properties and Coefficient of Thermal Expansion in Invar Alloys

Invar Fe–Ni alloy is a prominent Ni steel alloy with a low coefficient of thermal expansion around room temperature. We investigate the correlation between magnetic properties and thermal expansion in cold-drawn Fe–36Ni wires with different heat treatment conditions, where the annealing parameters with furnace cooling (cooling from the annealing temperature of 300, 400, 500, 600, 700, 800, 900, and 1000 °C) are used. The variation trend of magnetic properties is consistent with that of thermal expansion for all samples, where the maximum appears at 600 °C -treated sample and 400 °C shows the minimum. The domain size and the area of domain walls determine the total energy of the domain wall, and the total energy directly determines the size of magnetostriction, which is closely related to the coefficient of thermal expansion. Also, the differential thermal analysis (DTA) shows endothermic and exothermic reactions represent crystalline transitions, which could possibly cause the abrupt change of magnetic properties and thermal expansion coefficient of materials. The results indicate that there is a certain relation between thermal expansion and magnetic properties. Besides the fundamental significance, our work provides an Invar alloy with a low coefficient of thermal expansion for practical use

Distilling Knowledge from Well-Informed Soft Labels for Neural Relation Extraction

Extracting relations from plain text is an important task with wide application. Most existing methods formulate it as a supervised problem and utilize one-hot hard labels as the sole target in training, neglecting the rich semantic information among relations. In this paper, we aim to explore the supervision with soft labels in relation extraction, which makes it possible to integrate prior knowledge. Specifically, a bipartite graph is first devised to discover type constraints between entities and relations based on the entire corpus. Then, we combine such type constraints with neural networks to achieve a knowledgeable model. Furthermore, this model is regarded as teacher to generate well-informed soft labels and guide the optimization of a student network via knowledge distillation. Besides, a multi-aspect attention mechanism is introduced to help student mine latent information from text. In this way, the enhanced student inherits the dark knowledge (e.g., type constraints and relevance among relations) from teacher, and directly serves the testing scenarios without any extra constraints. We conduct extensive experiments on the TACRED and SemEval datasets, the experimental results justify the effectiveness of our approach

High-Density Ultra-small Clusters and Single-Atom Fe Sites Embedded in Graphitic Carbon Nitride (g-C3N4) for Highly Efficient Catalytic Advanced Oxidation Processes

Ultra-small metal clusters have attracted great attention owing to their superior catalytic performance and extensive application in heterogeneous catalysis. However, the synthesis of high-density metal clusters is very challenging due to their facile aggregation. Herein, one-step pyrolysis was used to synthesize ultra-small clusters and single-atom Fe sites embedded in graphitic carbon nitride with high density (iron loading up to 18.2 wt %), evidenced by high-angle annular dark field-scanning transmission electron microscopy, X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, and Fe-57 Mossbauer spectroscopy. The catalysts exhibit enhanced activity and stability in degrading various organic samples in advanced oxidation processes. The drastically increased metal site density and stability provide useful insights into the design and synthesis of cluster catalysts for practical application in catalytic oxidation reactions

Ultrasmall and tunable TeraHertz surface plasmon cavities at the ultimate plasmonic limit

Abstract The ability to confine THz photons inside deep-subwavelength cavities promises a transformative impact for THz light engineering with metamaterials and for realizing ultrastrong light-matter coupling at the single emitter level. To that end, the most successful approach taken so far has relied on cavity architectures based on metals, for their ability to constrain the spread of electromagnetic fields and tailor geometrically their resonant behavior. Here, we experimentally demonstrate a comparatively high level of confinement by exploiting a plasmonic mechanism based on localized THz surface plasmon modes in bulk semiconductors. We achieve plasmonic confinement at around 1 THz into record breaking small footprint THz cavities exhibiting mode volumes as low as $${V}_{cav}/{\lambda }_{0}^{3} \sim 1{0}^{-7}-1{0}^{-8}$$ V c a v / λ 0 3 ~ 1 0 − 7 − 1 0 − 8 , excellent coupling efficiencies and a large frequency tunability with temperature. Notably, we find that plasmonic-based THz cavities can operate until the emergence of electromagnetic nonlocality and Landau damping, which together constitute a fundamental limit to plasmonic confinement. This work discloses nonlocal plasmonic phenomena at unprecedentedly low frequencies and large spatial scales and opens the door to novel types of ultrastrong light-matter interaction experiments thanks to the plasmonic tunability