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An outlier in a dataset is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Detection of such outliers is important for many applications and has recently attracted much attention in the data mining research community. In this paper, we present a new method to detect outliers by discovering frequent patterns (or frequent itemsets) from the data set. The outliers are defined as the data transactions that contain less frequent patterns in their itemsets. We define a measure called FPOF (Frequent Pattern Outlier Factor) to detect the outlier transactions and propose the FindFPOF algorithm to discover outliers. The experimental results have shown that our approach outperformed the existing methods on identifying interesting outliers.

Topological photonic systems, with their ability to host states protected against disorder and perturbation, allow us to do with photons what topological insulators do with electrons. Topological photonics can refer to electronic systems coupled with light or purely photonic setups. By shrinking these systems to the nanoscale, we can harness the enhanced sensitivity observed in nanoscale structures and combine this with the protection of the topological photonic states, allowing us to design photonic local density of states and to push towards one of the ultimate goals of modern science: the precise control of photons at the nanoscale. This is paramount for both nano-technological applications and also for fundamental research in light matter problems. For purely photonic systems, we work with bosonic rather than fermionic states, so the implementation of topology in these systems requires new paradigms. Trying to face these challenges has helped in the creation of the exciting new field of topological nanophotonics, with far-reaching applications. In this prospective article we review milestones in topological photonics and discuss how they can be built upon at the nanoscale. I. OVERVIEWOne of the ultimate goals of modern science is the precise control of photons at the nanoscale. Topological nanophotonics offers a promising path towards this aim.A key feature of topological condensed matter systems is the presence of topologically protected surface states immune to disorder and impurities. These unusual properties can be transferred to nanophotonic systems, allowing us to combine the high sensitivity of nanoscale systems with the robustness of topological states. We expect that this new field of topological nanophotonics will lead to a plethora of new applications and increased physical insight.In this perspective, as presented schematically in FIG. 1, we begin (section II) by exploring topology in electronic systems. We aim this section towards readers who are new to the topic, so begin at an introductory level where no prior knowledge of topology is assumed.In section III we introduce light, first by discussing how topological electronic systems can interact with light (section III A), then move onto the topic of topological photonic analogues (section III B), in which purely photonic platforms are used to mimic the physics of topological condensed matter systems.In section IV we discuss various paths via which topological photonics can be steered into the nanoscale. Excellent and extensive reviews already exist on topological photonics [1][2][3][4], and many platforms showcasing unique strengths and limitations are currently being studied in the drive towards new applications in topological photonics such as cold atoms [5], liquid helium [6], polaritons [7], acoustic [8] and mechanical systems [9] but in this work * marie.rider16@imperial.ac.uk † www.GianniniLab.com FIG. 1. Schematic overview Schematic showing the topics covered in this perspective.we restrict ourselves to nanostructures. We discuss the effo...

A surface-enhanced Raman scattering-based mapping technique is reported for the highly sensitive and reproducible analysis of multiple mycotoxins. Raman images of three mycotoxins, ochratoxin A (OTA), fumonisin B (FUMB), and aflatoxin B1 (AFB1) are obtained by rapidly scanning the surface-enhanced Raman scattering (SERS) nanotags-anchoring mycotoxins captured on a nanopillar plasmonic substrate. In this system, the decreased gap distance between nanopillars by their leaning effects as well as the multiple hot spots between SERS nanotags and nanopillars greatly enhances the coupling of local plasmonic fields. This strong enhancement effect makes it possible to perform a highly sensitive detection of multiple mycotoxins. In addition, the high uniformity of the densely packed nanopillar substrate minimizes the spot-to-spot fluctuations of the Raman peak intensity in the scanned area when Raman mapping is performed. Consequently, this makes it possible to gain a highly reproducible quantitative analysis of mycotoxins. The limit of detections (LODs) are determined to be 5.09, 5.11, and 6.07 pg mL for OTA, FUMB, and AFB1, and these values are approximately two orders of magnitude more sensitive than those determined by the enzyme-linked immunosorbent assays. It is believed that this SERS-based mapping technique provides a facile tool for the sensitive and reproducible quantification of various biotarget molecules.

Stance detection is an important task, which aims to classify the attitude of an opinionated text towards a given target. Remarkable success has been achieved when sufficient labeled training data is available. However, annotating sufficient data is labor-intensive, which establishes significant barriers for generalizing the stance classifier to the data with new targets. In this paper, we proposed a Semantic-Emotion Knowledge Transferring (SEKT) model for cross-target stance detection, which uses the external knowledge (semantic and emotion lexicons) as a bridge to enable knowledge transfer across different targets. Specifically, a semantic-emotion heterogeneous graph is constructed from external semantic and emotion lexicons, which is then fed into a graph convolutional network to learn multi-hop semantic connections between words and emotion tags. Then, the learned semantic-emotion graph representation, which serves as prior knowledge bridging the gap between the source and target domains, is fully integrated into the bidirectional long short-term memory (BiLSTM) stance classifier by adding a novel knowledgeaware memory unit to the BiLSTM cell. Extensive experiments on a large real-world dataset demonstrate the superiority of SEKT against the state-of-the-art baseline methods.

Positioning probe molecules at electromagnetic hot spots with nanometer precision is required to achieve highly sensitive and reproducible surfaceenhanced Raman spectroscopy (SERS) analysis. In this article, molecular positioning at plasmonic nanogaps is reported using a high aspect ratio (HAR) plasmonic nanopillar array with a controlled surface energy. A largearea HAR plasmonic nanopillar array is generated using a nanolithographyfree simple process involving Ar plasma treatment applied to a smooth polymer surface and the subsequent evaporation of metal onto the polymer nanopillars. The surface energy can be precisely controlled through the selective removal of an adsorbed self-assembled monolayer of low surfaceenergy molecules prepared on the plasmonic nanopillars. This process can be used to tune the surface energy and provide a superhydrophobic surface with a water contact angle of 165.8° on the one hand or a hydrophilic surface with a water contact angle of 40.0° on the other. The highly tunable surface wettability is employed to systematically investigate the effects of the surface energy on the capillary-force-induced clustering among the HAR plasmonic nanopillars as well as on molecular concentration at the collapsed nanogaps present at the tops of the clustered nanopillars.

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