NEEL+: Supporting Predicates for Nested Complex Event Processing

Complex event processing (CEP) has become increasingly important in modern applications, ranging from supply chain management for RFID tracking to real-time intrusion detection. These monitoring applications must detect complex event pattern sequences in event streams. However, the state-of-art in the CEP literature such as SASE, ZStream or Cayuga either do not support the specification of nesting for pattern queries altogether or they limit the nesting of non-occurrence expressions over composite event types. A recent work by Liu et al proposed a nested complex event pattern expression language, called NEEL (Nested Complex Event Language), that supports the specification of the non-occurrence over complex expressions. However, their work did not carefully consider predicate handling in these nested queries, especially in the context of complex negation. Yet it is well-known that predicate specification is a critical component of any query language. To overcome this gap, we now design a nested complex event pattern expression language called NEEL+, as an extension of the NEEL language, specifying nested CEP queries with predicates. We rigorously define the syntax and semantics of the NEEL+ language, with particular focus on predicate scoping and predicate placement. Accordingly, we introduce a top-down execution paradigm which recursively computes a nested NEEL+ query from the outermost query to the innermost one. We integrate predicate evaluation as part of the overall query evaluation process. Moreover, we design two optimization techniques that reduce the computation costs for processing NEEL+ queries. One, the intra-query method, called predicate push-in, optimizes each individual query component of a nested query by pushing the predicate evaluation into the process of computing the query rather than evaluating predicates at the end of the computation of that particular query. Two, the inter-query method, called predicate shortcutting, optimizes inter-query predicate evaluation. That is, it evaluates the predicates that correlate different query components within a nested query by exploiting a light weight predicate short cut. The NEEL+ system caches values of the equivalence attributes from the incoming data stream. When the computation starts, the system checks the existence of the attribute value of the outer query component in the cache and the predicate acts as a shortcut to early terminate the computation. Lastly, we conduct experimental studies to evaluate the CPU processing resources of the NEEL+ System with and without optimization techniques using real-world stock trading data streams. Our results confirm that our optimization techniques when applied to NEEL+ in a rich variety of cases result in a 10 fold faster query processing performance than the NEEL+ system without optimization

Periodic solutions for a porous medium equation

In this paper, we study with a periodic porous medium equation with nonlinear convection terms and weakly nonlinear sources under Dirichlet boundary conditions. Based on the theory of Leray-Shauder fixed point theorem, we establish the existence of periodic solutions

Coherence-Assisted Superradiant Laser with Hz Linewidth and 10−1010^{-10}W Power

The superradiant laser, based on the clock transition between the electric ground state $^1$S$_0$ and the metastable state $^3$P$_0$ of fermionic alkaline-earth(-like) atoms, has been proposed to be a new promising light source with linewidth being the order of millihertz. However, due to the small $^1$S$_0$-to-$^3$P$_0$ transition strength, the steady-state power in that system is relatively low ($\sim 10^{-12}$W). In this work, we propose an alternative superradiant laser scheme based on a Raman-transition-induced coupling between the $^3$P$_0$ and $^3$P$_1$ states in bosonic alkaline-earth(-like) atoms, and achieve a laser with linewidth $\lesssim 2\pi\times1$Hz and power $\gtrsim 10^{-10}$W ($\sim 10^{3}$ photons in steady state) at a small pumping cost. The Raman beams play two significant roles in our scheme. First, the coherence between the dark and bright states induced by the Raman beams produce a new local minimum in the pumping-linewidth curve with pumping rate lower than $2\pi \times 10$kHz, which is beneficial for continuous output. Second, the Raman beams mix the long-lived $^3$P$_0$ state into the lasing state and thus reduce the linewidth. Our work greatly improves the output performance of the superradiant laser system with coherence induced by Raman transitions and may offer a firm foundation for its practical use in future

Image Denoising via Nonlinear Hybrid Diffusion

A nonlinear anisotropic hybrid diffusion equation is discussed for image denoising, which is a combination of mean curvature smoothing and Gaussian heat diffusion. First, we propose a new edge detection indicator, that is, the diffusivity function. Based on this diffusivity function, the new diffusion is nonlinear anisotropic and forward-backward. Unlike the Perona-Malik (PM) diffusion, the new forward-backward diffusion is adjustable and under control. Then, the existence, uniqueness, and long-time behavior of the new regularization equation of the model are established. Finally, using the explicit difference scheme (PM scheme) and implicit difference scheme (AOS scheme), we do numerical experiments for different images, respectively. Experimental results illustrate the effectiveness of the new model with respect to other known models

SaaFormer: Spectral-spatial Axial Aggregation Transformer for Hyperspectral Image Classification

Hyperspectral images (HSI) captured from earth observing satellites and aircraft is becoming increasingly important for applications in agriculture, environmental monitoring, mining, etc. Due to the limited available hyperspectral datasets, the pixel-wise random sampling is the most commonly used training-test dataset partition approach, which has significant overlap between samples in training and test datasets. Furthermore, our experimental observations indicates that regions with larger overlap often exhibit higher classification accuracy. Consequently, the pixel-wise random sampling approach poses a risk of data leakage. Thus, we propose a block-wise sampling method to minimize the potential for data leakage. Our experimental findings also confirm the presence of data leakage in models such as 2DCNN. Further, We propose a spectral-spatial axial aggregation transformer model, namely SaaFormer, to address the challenges associated with hyperspectral image classifier that considers HSI as long sequential three-dimensional images. The model comprises two primary components: axial aggregation attention and multi-level spectral-spatial extraction. The axial aggregation attention mechanism effectively exploits the continuity and correlation among spectral bands at each pixel position in hyperspectral images, while aggregating spatial dimension features. This enables SaaFormer to maintain high precision even under block-wise sampling. The multi-level spectral-spatial extraction structure is designed to capture the sensitivity of different material components to specific spectral bands, allowing the model to focus on a broader range of spectral details. The results on six publicly available datasets demonstrate that our model exhibits comparable performance when using random sampling, while significantly outperforming other methods when employing block-wise sampling partition.Comment: arXiv admin note: text overlap with arXiv:2107.02988 by other author

The Position and Function of Macroscopic Analysis in the Failure Analysis of Railway Fasteners

Macroscopic analysis plays an important role in failure analysis, which cannot be replaced by other analyzing methods. In recent years, with the development of characterization techniques, more and more engineers and technicians rely on the advanced analytical testing methods in the process of failure analysis, ignoring the methods and means of macroscopic analysis. This can easily lead to some wrong judgments. Therefore, this chapter will combine with the cases to explain the position and role of macroanalysis in the failure analysis of rail fastening clips and to offer references for engineers and technicians in relevant fields