Detection of Stock Price Manipulation Using Kernel Based Principal Component Analysis and Multivariate Density Estimation

Stock price manipulation uses illegitimate means to artificially influence market prices of several stocks. It causes massive losses and undermines investors’ confidence and the integrity of the stock market. Several existing research works focused on detecting a specific manipulation scheme using supervised learning but lacks the adaptive capability to capture different manipulative strategies. This begets the assumption of model parameter values specific to the underlying manipulation scheme. In addition, supervised learning requires the use of labelled data which is difficult to acquire due to confidentiality and the proprietary nature of trading data. The proposed research establishes a detection model based on unsupervised learning using Kernel Principal Component Analysis (KPCA) and applied increased variance of selected latent features in higher dimensions. A proposed Multidimensional Kernel Density Estimation (MKDE) clustering is then applied upon the selected components to identify abnormal patterns of manipulation in data. This research has an advantage over the existing methods in overcoming the ambiguity of assuming values of several parameters, reducing the high dimensions obtained from conventional KPCA and thereby reducing computational complexity. The robustness of the detection model has also been evaluated when two or more manipulative activities occur within a short duration of each other and by varying the window length of the dataset fed to the model. The results show a comprehensive assessment of the model on multiple datasets and a significant performance enhancement in terms of the F-measure values with a significant reduction in false alarm rate (FAR) has been achieved

Stock Price Manipulation Detection based on Autoencoder Learning of Stock Trades Affinity

Stock price manipulation, a major problem in capital markets surveillance, uses illegitimate means to influence the price of traded stocks in order to reap illicit profit. Most of the existing attempts to detect such manipulations have either relied upon annotated trading data, using supervised methods, or have been restricted to detecting a specific manipulation scheme. There have been a few unsupervised algorithms focusing on general detection yet none of them explored the innate affinity among the stock trades, be it normal or manipulative. This paper proposes a fully unsupervised model based on the idea of learning the relationship among stock prices in the form of an affinity matrix. The proposed affinity matrix based features are used to train an under-fitting autoencoder in order to learn an efficient representation of the normal stock prices. A kernel density estimate of the normal trading data is used as the reconstruction error of the autoencoder. During the detection phase, the normal dataset has been injected with synthetic manipulative trades. A kernel density estimation based clustering technique is then used to detect manipulative trades based on their autoencoder representation. The proposed approach is validated on benchmark stock price data from the LOBSTER project and the obtained results show dramatic improvements in the detection performance over existing price manipulation detection techniques

A Multi-Institutional Meningioma MRI Dataset for Automated Multi-Sequence Image Segmentation

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients

Analytical model of multi-junction solar cell

Multi-junction solar cells (MJSCs) are a current trend in the field of solar cells and form the backbone of concentrated photovoltaic systems. They are an attractive option because of their high efficiency, better power production and cost effectiveness. The aim of this paper is to present a general mathematical model of MJSC, suitable for computer simulation. This model investigates cell characterization curves including current density and power curves as a function of voltage for different concentration levels and number of junctions. The effect of varying material properties of junctions and tunneling layers is also analyzed. Two different types of MJSCs have been tested on the model, including InGaP–GaAs dual-junction solar cell with tunneling layer of InGaP and InGaP–GaAs–Ge triple-junction solar cell with tunneling layers of GaAs. Paper also presents the simulation results which are in agreement with practical conclusions

Analytical model of multi-junction solar cell

Multi-junction solar cells (MJSCs) are a current trend in the field of solar cells and form the backbone of concentrated photovoltaic systems. They are an attractive option because of their high efficiency, better power production and cost effectiveness. The aim of this paper is to present a general mathematical model of MJSC, suitable for computer simulation. This model investigates cell characterization curves including current density and power curves as a function of voltage for different concentration levels and number of junctions. The effect of varying material properties of junctions and tunneling layers is also analyzed. Two different types of MJSCs have been tested on the model, including InGaP–GaAs dual-junction solar cell with tunneling layer of InGaP and InGaP–GaAs–Ge triple-junction solar cell with tunneling layers of GaAs. Paper also presents the simulation results which are in agreement with practical conclusions

Whole Genome Sequencing of Methicillin-Resistant Staphylococcus epidermidis Clinical Isolates Reveals Variable Composite SCCmec ACME among Different STs in a Tertiary Care Hospital in Oman

Staphylococcus epidermidis has been recently recognized as an emerging nosocomial pathogen. There are concerns over the increasing virulence potential of this commensal due to the capabilities of transferring mobile genetic elements to Staphylococcus aureus through staphylococcal chromosomal cassette (SCCmec) and the closely related arginine catabolic mobile element (ACME) and the copper and mercury resistance island (COMER). The potential pathogenicity of S. epidermidis, particularly from blood stream infections, has been poorly investigated. In this study, 24 S. epidermidis isolated from blood stream infections from Oman were investigated using whole genome sequence analysis. Core genome phylogenetic trees revealed one third of the isolates belong to the multidrug resistance ST-2. Genomic analysis unraveled a common occurrence of SCCmec type IV and ACME element predominantly type I arranged in a composite island. The genetic composition of ACME was highly variable among isolates of same or different STs. The COMER-like island was absent in all of our isolates. Reduced copper susceptibility was observed among isolates of ST-2 and ACME type I, followed by ACME type V. In conclusion, in this work, we identify a prevalent occurrence of highly variable ACME elements in different hospital STs of S. epidermidis in Oman, thus strongly suggesting the hypothesis that ACME types evolved from closely related STs

Techno-economic sustainability assessment for bio-hydrogen production based on hybrid blend of biomass:A simulation study

This study investigates the potential use of a blend of different types of biomass for sustainable bio-hydrogen production through steam gasification. Simulation models were developed and optimized using Aspen Plus V.11 to achieve optimal bio-hydrogen production while minimizing carbon monoxide production and maintaining a set amount of carbon dioxide concentration in the syngas. The effects of varying the composition of the feedstock material and steam to biomass ratio on hydrogen yield were investigated for five different blends, including leather and municipal solid waste. The study found that the composition of the feedstock played a crucial role in gasification, with higher calorific values for blends containing higher leather content. Additionally, the study showed that specific energy consumption decreased with an increase in total heat duty for four out of the five blends, with Case V having the lowest specific energy consumption of 39.28 kW/kmol of bio H2. The economic analysis further established the potential for the effective utilization of hybrid biomass for renewable bio-hydrogen production. The optimal case V provides total capital cost of USD 1.42 × 106 and the operating cost of the proposed system accounts to USD 2.99 × 105/yr. The unit production cost involved for the optimal case is USD 0.66/kg of H2. Overall, this study provides a basis for further investigation of hybrid biomass blends as a source of renewable energy.</p

The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts

The development of machine learning models for electrocatalysts requires a broad set of training data to enable their use across a wide variety of materials. One class of materials that currently lacks sufficient training data is oxides, which are critical for the development of Oxygen Evolution Reaction (OER) catalysts. To address this, we developed the Open Catalyst 2022 (OC22) dataset, consisting of 62,331 Density Functional Theory (DFT) relaxations (~9,854,504 single point calculations) across a range of oxide materials, coverages, and adsorbates. We define generalized total energy tasks that enable property prediction beyond adsorption energies; we test baseline performance of several graph neural networks; and we provide pre-defined dataset splits to establish clear benchmarks for future efforts. In the most general task, GemNet-OC sees a ~32% improvement in energy predictions when combining the chemically dissimilar Open Catalyst 2020 Dataset (OC20) and OC22 datasets via fine-tuning. Similarly, we achieved a ~19% improvement in total energy predictions on OC20 and a ~9% improvement in force predictions in OC22 when using joint training. We demonstrate the practical utility of a top performing model by capturing literature adsorption energies and important OER scaling relationships. We expect OC22 to provide an important benchmark for models seeking to incorporate intricate long-range electrostatic and magnetic interactions in oxide surfaces. The dataset and baseline models are open sourced, and a public leaderboard has been made available to encourage continued community developments on the total energy tasks and data.Comment: 48 pages, 14 figure