scholarly journals Unsupervised event classification with graphs on classical and photonic quantum computers

2021 ◽  
Vol 2021 (8) ◽  
Author(s):  
Andrew Blance ◽  
Michael Spannowsky
Keyword(s):  
Anomaly Detection ◽  
Clustering Algorithm ◽  
High Energy ◽  
New Physics ◽  
Continuous Variable ◽  
Quantum Computers ◽  
Detection Model ◽  
Novel Method ◽  
Formula Complexity ◽  
Boson Sampling

Abstract Photonic Quantum Computers provide several benefits over the discrete qubit-based paradigm of quantum computing. By using the power of continuous-variable computing we build an anomaly detection model to use on searches for New Physics. Our model uses Gaussian Boson Sampling, a #P-hard problem and thus not efficiently accessible to classical devices. This is used to create feature vectors from graph data, a natural format for representing data of high-energy collision events. A simple K-means clustering algorithm is used to provide a baseline method of classification. We then present a novel method of anomaly detection, combining the use of Gaussian Boson Sampling and a quantum extension to K-means known as Q-means. This is found to give equivalent results compared to the classical clustering version while also reducing the $$ \mathcal{O} $$ O complexity, with respect to the sample’s feature-vector length, from $$ \mathcal{O}(N) $$ O N to $$ \mathcal{O}\left(\log (N)\right) $$ O log N .

scholarly journals Anomaly detection in multi-class time series

2021 ◽  
Vol 2113 (1) ◽  
pp. 012062
Author(s):  
Weihong Wang ◽  
Zhuolin Wu ◽  
Xuan Liu ◽  
Lei Jia ◽  
Xiaoguang Wang
Keyword(s):  
Image Processing ◽  
Time Series ◽  
Anomaly Detection ◽  
Performance Indicators ◽  
Clustering Algorithm ◽  
Limited Resources ◽  
Detection Model ◽  
Class Time ◽  
Operation And Maintenance ◽  
Maintenance Systems

Abstract For modern operation and maintenance systems, they are usually required to monitor multiple types and large quantities of machine’s key performance indicators (KPIs) at the same time with limited resources. In this paper, to tackle these problems, we propose a highly compatible time series anomaly detection model based on K-means clustering algorithm with a new Wavelet Feature Distance (WFD). Our work is inspired by some ideas from image processing and signal processing domain. Our model detects abnormalities in the time series datasets which are first clustered by K-means to boost the accuracy. Our experiments show significant accuracy improvements compared with traditional algorithms, and excellent compatibilities and operating efficiencies compared with algorithms based on deep learning.

scholarly journals Autoencoders for unsupervised anomaly detection in high energy physics

2021 ◽  
Vol 2021 (6) ◽  
Author(s):  
Thorben Finke ◽  
Michael Krämer ◽  
Alessandro Morandini ◽  
Alexander Mück ◽  
Ivan Oleksiyuk
Keyword(s):  
Anomaly Detection ◽  
Particle Physics ◽  
High Energy Physics ◽  
High Energy ◽  
New Physics ◽  
Promising Tool ◽  
Unsupervised Anomaly Detection ◽  
Model Independent ◽  
Jet Images ◽  
Energy Physics

Abstract Autoencoders are widely used in machine learning applications, in particular for anomaly detection. Hence, they have been introduced in high energy physics as a promising tool for model-independent new physics searches. We scrutinize the usage of autoencoders for unsupervised anomaly detection based on reconstruction loss to show their capabilities, but also their limitations. As a particle physics benchmark scenario, we study the tagging of top jet images in a background of QCD jet images. Although we reproduce the positive results from the literature, we show that the standard autoencoder setup cannot be considered as a model-independent anomaly tagger by inverting the task: due to the sparsity and the specific structure of the jet images, the autoencoder fails to tag QCD jets if it is trained on top jets even in a semi-supervised setup. Since the same autoencoder architecture can be a good tagger for a specific example of an anomaly and a bad tagger for a different example, we suggest improved performance measures for the task of model-independent anomaly detection. We also improve the capability of the autoencoder to learn non-trivial features of the jet images, such that it is able to achieve both top jet tagging and the inverse task of QCD jet tagging with the same setup. However, we want to stress that a truly model-independent and powerful autoencoder-based unsupervised jet tagger still needs to be developed.

scholarly journals TS-PADM: Anomaly Detection Model of Wireless Sensors Based on Spatial-Temporal Feature Points

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fengjiao Wang ◽  
Ruixing Li ◽  
Hua Wang ◽  
Hengliang Zhu ◽  
Naixue Xiong
Keyword(s):  
Anomaly Detection ◽  
Smart Home ◽  
Recall Rate ◽  
Sensor Data ◽  
Detection Model ◽  
Data Flows ◽  
Boosted Decision Tree ◽  
Data Points ◽  
Novel Method ◽  
Temporal And Spatial

In the practical application, sensor data collection is an essential means for the system to perceive the intrinsic features of data. The anomaly detection of data points can improve data quality and explore the potential information of data. The anomaly detection can be classified as two basic types, that is, classification and clustering. Those methods usually depend on the spatial correlation of data and have high computation complexity, so they are not suitable for the smart home and another mini-Internet of Things (IoT) environment. To overcome these problems, we propose a novel method for anomaly detection. In this paper, we first define the temporal and spatial feature of data flows; then, a time series denoising autoencoder (TSDA) is proposed to extract the discriminative high-dimensional characteristics to represent the data points. Moreover, a probability statistics-based anomaly detection model (PADM) was proposed for identifying the abnormal data. Extensive experimental results demonstrated that our method has fewer parameters and is easy to adjust and optimize. More importantly, our approach has higher precision and recall rate than the gradient boosted decision tree and XGBoot.

scholarly journals Efficient verification of Boson Sampling

Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 578
Author(s):  
Ulysse Chabaud ◽  
Frédéric Grosshans ◽  
Elham Kashefi ◽  
Damian Markham
Keyword(s):  
Large Class ◽  
Single Mode ◽  
Continuous Variable ◽  
Quantum Circuits ◽  
Quantum Computers ◽  
Classical Simulation ◽  
Efficient Verification ◽  
Quantum Speedup ◽  
Important Milestone ◽  
Boson Sampling

The demonstration of quantum speedup, also known as quantum computational supremacy, that is the ability of quantum computers to outperform dramatically their classical counterparts, is an important milestone in the field of quantum computing. While quantum speedup experiments are gradually escaping the regime of classical simulation, they still lack efficient verification protocols and rely on partial validation. Here we derive an efficient protocol for verifying with single-mode Gaussian measurements the output states of a large class of continuous-variable quantum circuits demonstrating quantum speedup, including Boson Sampling experiments, thus enabling a convincing demonstration of quantum speedup with photonic computing. Beyond the quantum speedup milestone, our results also enable the efficient and reliable certification of a large class of intractable continuous-variable multimode quantum states.

scholarly journals The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics

2021 ◽  
Author(s):  
Gregor Kasieczka ◽  
Benjamin Nachman ◽  
David Shih ◽  
Oz Amram ◽  
Anders Andreassen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Supervised Learning ◽  
High Energy Physics ◽  
High Energy ◽  
New Physics ◽  
Lessons Learned ◽  
Detection Methods ◽  
Generative Adversarial Networks ◽  
New Paradigm

Abstract A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

Efficient mixed clustering algorithm and its application in anomaly detection

2010 ◽  
Vol 30 (7) ◽  
pp. 1916-1918 ◽  
Author(s):  
Jian-guo LI ◽  
Xue-gang HU
Keyword(s):  
Anomaly Detection ◽  
Clustering Algorithm

scholarly journals Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4237
Author(s):  
Hoon Ko ◽  
Kwangcheol Rim ◽  
Isabel Praça
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Feature Detection ◽  
Trading System ◽  
Judgment Accuracy ◽  
Energy Trading ◽  
Real Time Processing ◽  
Detection Model ◽  
Time Required ◽  
The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

scholarly journals Vector boson fusion at multi-TeV muon colliders

2020 ◽  
Vol 2020 (9) ◽  
Author(s):  
Antonio Costantini ◽  
Federico De Lillo ◽  
Fabio Maltoni ◽  
Luca Mantani ◽  
Olivier Mattelaer ◽  
...  
Keyword(s):  
Cross Sections ◽  
Vector Boson ◽  
High Energy ◽  
New Physics ◽  
Vector Boson Fusion ◽  
Weak Boson ◽  
Wide Range ◽  
Lepton Colliders ◽  
Muon Colliders ◽  
New Phenomena

Abstract High-energy lepton colliders with a centre-of-mass energy in the multi-TeV range are currently considered among the most challenging and far-reaching future accelerator projects. Studies performed so far have mostly focused on the reach for new phenomena in lepton-antilepton annihilation channels. In this work we observe that starting from collider energies of a few TeV, electroweak (EW) vector boson fusion/scattering (VBF) at lepton colliders becomes the dominant production mode for all Standard Model processes relevant to studying the EW sector. In many cases we find that this also holds for new physics. We quantify the size and the growth of VBF cross sections with collider energy for a number of SM and new physics processes. By considering luminosity scenarios achievable at a muon collider, we conclude that such a machine would effectively be a “high-luminosity weak boson collider,” and subsequently offer a wide range of opportunities to precisely measure EW and Higgs couplings as well as discover new particles.

scholarly journals Suspended Sediment Modeling Using a Heuristic Regression Method Hybridized with Kmeans Clustering

2021 ◽  
Vol 13 (9) ◽  
pp. 4648
Author(s):  
Rana Muhammad Adnan ◽  
Kulwinder Singh Parmar ◽  
Salim Heddam ◽  
Shamsuddin Shahid ◽  
Ozgur Kisi
Keyword(s):  
Clustering Algorithm ◽  
Suspended Sediments ◽  
Ecological Impacts ◽  
Accurate Estimation ◽  
Data Sets ◽  
The Novel ◽  
Neuro Fuzzy ◽  
Adaptive Regression ◽  
Novel Method ◽  
Model Training

The accurate estimation of suspended sediments (SSs) carries significance in determining the volume of dam storage, river carrying capacity, pollution susceptibility, soil erosion potential, aquatic ecological impacts, and the design and operation of hydraulic structures. The presented study proposes a new method for accurately estimating daily SSs using antecedent discharge and sediment information. The novel method is developed by hybridizing the multivariate adaptive regression spline (MARS) and the Kmeans clustering algorithm (MARS–KM). The proposed method’s efficacy is established by comparing its performance with the adaptive neuro-fuzzy system (ANFIS), MARS, and M5 tree (M5Tree) models in predicting SSs at two stations situated on the Yangtze River of China, according to the three assessment measurements, RMSE, MAE, and NSE. Two modeling scenarios are employed; data are divided into 50–50% for model training and testing in the first scenario, and the training and test data sets are swapped in the second scenario. In Guangyuan Station, the MARS–KM showed a performance improvement compared to ANFIS, MARS, and M5Tree methods in term of RMSE by 39%, 30%, and 18% in the first scenario and by 24%, 22%, and 8% in the second scenario, respectively, while the improvement in RMSE of ANFIS, MARS, and M5Tree was 34%, 26%, and 27% in the first scenario and 7%, 16%, and 6% in the second scenario, respectively, at Beibei Station. Additionally, the MARS–KM models provided much more satisfactory estimates using only discharge values as inputs.

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