Status of searches for electroweak-scale supersymmetry after LHC Run 2

The second period of datataking at the Large Hadron Collider (LHC) has provided a large dataset of proton–proton collisions that is unprecedented in terms of its centre-of-mass energy of 13 TeV and integrated luminosity of almost 140 fb[Formula: see text]. These data constitute a formidable laboratory for the search for new particles predicted by models of supersymmetry. The analysis activity is still ongoing, but a host of results on supersymmetry had already been released by the general purpose LHC experiments ATLAS and CMS. In this paper, we provide a map into this remarkable body of research, which spans a multitude of experimental signatures and phenomenological scenarios. In the absence of conclusive evidence for the production of supersymmetric particles we discuss the constraints obtained in the context of various models. We finish with a short outlook on the new opportunities for the next runs that will be provided by the upgrade of detectors and accelerator.

An Intelligent Visualisation Tool to Analyse the Sustainability of Road Transportation

Road transport is an integral part of economic activity and is therefore essential for its development. On the downside, it accounts for 30% of the world’s GHG emissions, almost a third of which correspond to the transport of freight in heavy goods vehicles by road. Additionally, means of transport are still evolving technically and are subject to ever more demanding regulations, which aim to reduce their emissions. In order to analyse the sustainability of this activity, this study proposes the application of novel Artificial Intelligence techniques (more specifically, Machine Learning). In this research, the use of Hybrid Unsupervised Exploratory Plots is broadened with new Exploratory Projection Pursuit techniques. These, together with clustering techniques, form an intelligent visualisation tool that allows knowledge to be obtained from a previously unknown dataset. The proposal is tested with a large dataset from the official survey for road transport in Spain, which was conducted over a period of 7 years. The results obtained are interesting and provide encouraging evidence for the use of this tool as a means of intelligent analysis on the subject of developments in the sustainability of road transportation.

Using Variational Autoencoder to Develop and Validate a Compact, Deep Representation of Digital Clock Drawing Test for Classifying Dementia

The Clock Drawing Test (CDT) is an inexpensive tool to screen for dementia. In this study, we examined if a semi-supervised deep learning (DL) system using Variational Autoencoder (VAE) can extract atypical clock features from a large dataset of unannotated CDTs (n=13,580) and use them to classify dementia (n=18) from non-dementia (n=20) peers. The classification model built with VAE latent space features adequately classified dementia from non-dementia (0.78 Area Under Receiver Operating Characteristics (AUROC)). The VAE-identified atypical clock features were then reviewed by domain experts and compared with existing literature on clock drawing errors. This study shows that a semi-supervised deep learning (DL) analysis of the CDT can extract important clock drawing anomalies that are predictive of dementia.

Where to Go Next? Examining the Effect of Franchise Expansion and Location on Game-Level Attendance in Major League Soccer

Using a large dataset with over 4,000 game-level observations from Major League Soccer over the period 2006 to 2019 we investigate the determinants of attendance demand. Focusing on franchise expansion and location effects, we find that some decisions made by the organization had positive impacts on league revenues. While going to cities with a large population and already hosting nearby NFL or NBA teams is positively associated with game attendance, the presence of geographically close MLB and NHL teams is detrimental to MLS revenues. Our results suggest a need for a more nuanced and selective approach to MLS expansion policy.

Techniques for Selecting the Optimal Parameters of One-Class Support Vector Machine Classifier for Reduced Samples

Usually, the One-Class Support Vector Machine (OC-SVM) requires a large dataset for modeling effectively the target class independently to other classes. For finding the OC-SVM model, the available dataset is subdivided into two subsets namely training and validation, which are used for training and validating the optimal parameters. This approach is effective when a large dataset is available. However, when training samples are reduced, parameters of the OC-SVM are difficult to find in absence of the validation subset. Hence, this paper proposes various techniques for selecting the optimal parameters using only a training subset. The experimental evaluation conducted on several real-world benchmarks proves the effective use of the new selection parameter techniques for validating the model of OC-SVM classifiers versus the standard validation techniques

Classification of Genuinity in Job Posting Using Machine Learning

To avoid fraudulent Job postings on the internet, we target to minimize the number of such frauds through the Machine Learning approach to predict the chances of a job being fake so that the candidate can stay alert and make informed decisions if required. The model will use NLP to analyze the sentiments and pattern in the job posting and TF-IDF vectorizer for feature extraction. In this model, we are going to use Synthetic Minority Oversampling Technique (SMOTE) to balance the data and for classification, we used Random Forest to predict output with high accuracy, even for the large dataset it runs efficiently, and it enhances the accuracy of the model and prevents the overfitting issue. The final model will take in any relevant job posting data and produce a result determining whether the job is real or fake. Keywords: Natural Language Processing (NLP), Term Frequency-Inverse Document Frequency (TF-IDF), Synthetic Minority Oversampling Technique (SMOTE), Random Forest.

Multimodal Biometric Person Authentication Using Face, Ear and Periocular Region Based on Convolution Neural Networks

Biometrics is an active area of research because of the increase in need for accurate person identification in numerous applications ranging from entertainment to security. Unimodal and multimodal are the well-known biometric methods. Unimodal biometrics uses one biometric modality of a person for person identification. The performance of an unimodal biometric system is degraded due to certain limitations such as: intra-class variations and nonuniversality. The person identification using more than one biometric modality of a person is multimodal biometrics. This method of identification has gained more interest due to resistance on spoof attacks and more recognition rate. Conventional methods of feature extraction have difficulty in engineering features that are liable to more variations such as illumination, pose and age variations. Feature extraction using convolution neural network (CNN) can overcome these difficulties because large dataset with robust variations can be used for training, where CNN can learn these variations. In this paper, we propose multimodal biometrics at feature level horizontal fusion using face, ear and periocular region biometric modalities and apply deep learning CNN for feature representation and also we propose face, ear and periocular region dataset that are robust to intra-class variations. The evaluation of the system is made by using proposed database. Accuracy, Precision, Recall and [Formula: see text] score are calculated to evaluate the performance of the system and had shown remarkable improvement over existing biometric system.