Unsupervised Spectral Ranking For Anomaly Detection

Anomaly detection is the problem of finding deviations from expected normal patterns. A wide variety of applications, such as fraud detection for credit cards and insurance, medical image monitoring, network intrusion detection, and military surveillance, can be viewed as anomaly detection. For anomaly detection, obtaining accurate labels, especially labels for anomalous cases, is costly and time consuming, if not practically infeasible. This makes supervised anomaly detection less desirable in the domain of anomaly detection. In this thesis, we propose a novel unsupervised spectral ranking method for anomaly detection (SRA). Based on the 1st non-principal eigenvectors from Laplacian matrices, the proposed SRA can generate anomaly ranking either with respect to a single majority class or with respect to multiple majority classes. The ranking type is based on whether the percentage of the smaller class instances (positive or negative) is larger than the expected upper bound of the anomaly ratio. We justify the proposed spectral ranking by establishing a connection between the unsupervised support vector machine optimization and the spectral Laplacian optimization problem. Using both synthetic and real data sets, we show that our proposed SRA is a meaningful and effective alternative to the state-of-art unsupervised anomaly ranking methods. In addition, we show that, in certain scenarios, unsupervised SRA method surpasses the state-of-art unsupervised anomaly ranking methods in terms of performance and robustness of parameter tuning. Finally, we demonstrate that choosing appropriate similarity measures remains crucial in applying our proposed SRA algorithm

Data-Driven Models: An Alternative Discrete Hedging Strategy

Options hedging is a critical problem in financial risk management. The prevailing approach in financial derivative pricing and hedging has been to first assume a parametric model describing the underlying price dynamics. An option model function is then calibrated to current market option prices and various sensitivities are computed and used to hedge the option risk. It has been recognized that computing hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing the variance of the option hedging risk, as it fails to capture the model parameter dependence on the underlying price. We propose several data-driven approaches to directly learn a hedging function from the historical market option and underlying data by minimizing certain measures of the local hedging risk and total hedging risk. This thesis will focus on answering the following questions: 1) Can we efficiently build direct data-driven models for discrete hedging problems that outperform existing state-of-art parametric hedging models based on the market prices? 2) Can we incorporate feature selection and feature extraction into the data-driven models to further improve the performance of the discrete hedging? 3) Can we build efficient models for both the one-step local risk hedging problem and multi-step total risk hedging problem based on the state-of-art learning framework such as deep learning framework and kernel learning framework? Using the S&P 500 index daily options data for more than a decade ending in August 2015, we first propose a direct data-driven approach based on kernel learning framework and we demonstrate that the proposed method outperforms the parametric minimum variance hedging method, as well as minimum variance hedging corrective techniques based on stochastic volatility or local volatility models. Furthermore, we show that the proposed approach achieves significant gain over the implied Black-Sholes delta hedging for weekly and monthly hedging. Following the direct data-driven kernel learning approach, we propose a robust encoder-decoder Gated Recurrent Unit (GRU) model, for optimal discrete option hedging. The proposed model utilizes the Black-Scholes model as a pre-trained model and incorporates sequential information and feature selection. Using the S&P 500 index European option market data from January 2, 2004, to August 31, 2015, we demonstrate that the weekly and monthly hedging performance of the proposed model significantly surpasses that of the data-driven minimum variance (MV) method, the regularized kernel data-driven model, and the SABR-Bartlett method. In addition, the daily hedging performance of the proposed model also surpasses that of MV methods based on parametric models, the kernel method, and the SABR-Bartlett method. Lastly, we design multi-step data-driven models to hedge the option discretely until the expiry. We utilize the SABR model and Local Volatility Function (LVF) to augment existing market data and thus alleviate the problem of scarcity in market option prices. The augmented market data is used to train a sufficient total risk hedging model

Bayesian inference of momentum and length dependence of jet energy loss

Using a simple model for medium modification of the jet function through a parameterized form of the jet energy loss distribution, we carry out a comprehensive Bayesian analysis of the world data on single inclusive jet spectra in heavy-ion collisions at both RHIC and LHC energies. We extract the average jet energy loss $\langle \Delta E\rangle$ as a function of jet transverse momentum $p_T$ for each collision system and centrality independently. Assuming jet energy loss is proportional to the initial parton density $\rho \sim dN_{\rm ch}/d\eta/\pi R_{\rm eff}^2$ as estimated from the pseudorapidity density of charged hadron multiplicity $dN_{\rm ch}/d\eta$ and the effective system size $R_{\rm eff}\sim N_{\rm part}^{1/3}$ given by the number of participant nucleons $N_{\rm part}$, the scaled average jet energy loss $\langle \Delta E\rangle/\rho \sim R_{\rm eff}^{0.59} p_T^{0.13}\ln p_T$ for jet cone-size $R=0.4$ is found to have a momentum dependence that is slightly stronger than a logarithmic form while the system size or length dependence is slower than a linear one. The fluctuation of jet energy loss is, however, independent of the initial parton density or the system size. These are consistent with results from Monte Carlo simulations of jet transport in a fast expanding quark-gluon plasma in high-energy heavy-ion collisions.Comment: 9 pages with 10 figure

eHIJING: an Event Generator for Jet Tomography in Electron-Ion Collisions

We develop the first event generator, the electron-Heavy-Ion-Jet-Interaction-Generator (eHIJING), for the jet tomography study of electron-ion collisions. In this generator, energetic jet partons produced from the initial hard scattering undergo multiple collisions with the nuclear remnants with a collision rate that is proportional to the transverse-momentum-dependent (TMD) gluon densities in the nucleus. Medium-modified QCD parton splittings within the higher-twist and generalized higher-twist framework are utilized to simulate parton showering in the nuclear medium that takes into account the non-Abelian Landau-Pomeranchuck-Midgal interference in gluon radiation induced by multiple scatterings. The TMD gluon distribution inside the nucleus is given by a simple model inspired by the physics of gluon saturation. Employing eHIJING, we revisit hadron production in semi-inclusive deep inelastic scattering (SIDIS) as measured by EMC, HERMES as well as recent CLAS experiments. eHIJING with both the higher-twist and generalized higher-twist framework gives reasonably good descriptions of these experimental data. Predictions for experiments at the future electron-ion colliders are also provided. It is demonstrated that future measurements of the transverse momentum broadening of single hadron spectra can be used to map out the two dimensional kinematic ($Q^2, x_B$) dependence the jet transport parameter $\hat{q}$ in cold nuclear matter.Comment: 27 pages, 27 figure

Homotopic connectivity in drug-naive, first-episode, early-onset schizophrenia

BackgroundThe disconnection hypothesis of schizophrenia has been extensively tested in adults. Recent studies have reported the presence of brain disconnection in younger patients, adding evidence to support the neurodevelopmental hypothesis of schizophrenia. Because of drug confounds in chronic and medicated patients, it has been extremely challenging for researchers to directly investigate abnormalities in the development of connectivity and their role in the pathophysiology of schizophrenia. The present study aimed to examine functional homotopy - a measure of interhemispheric connection - and its relevance to clinical symptoms in first-episode drug-naive early-onset schizophrenia (EOS) patients.</p

Design and Testing of an Online Fertilizing Amount Detection Device Based on the Moment Balance Principle

Based on the principle of moment balance, this paper designs a fertilizer application amount online detection device, which is mainly composed of two major parts: the fertilizer guide mechanism and the fertilizer metering and discharging mechanism.Under the electromagnetic reversing and buffering of the fertilizer guide mechanism, the fertilizer discharged into the device falls alternately into the storage box of the two metering units of the metering and discharging mechanism. Once the gravity of the fertilizer in the storage box is greater than the suction of the electromagnetic sucker, the fertilizer discharging board is automatically opened for fertilizer discharge, and the metering pulse signal is accumulated once. Meanwhile, the fertilizer guide plate is driven by the electromagnetic commutator to reverse the material, and then another storage box is started for fertilizer storage and metering. In this approach, online detection of fertilizer flow can be realized by repeatedly guiding and reversing and metering the incoming fertilizer. According to the single metering fertilizer quality and the number of metering pulse signals, the fertilization amount can be calculated in real-time.The performance of the device was verified by bench test. The test results indicated that: The established fertilizer application detection model is a quadratic function (R2&gt;0.98), and the verification error was less than 3.73% in the detection of alternating cycle fertilizer discharge; the coefficient of determination (R2) and the root mean square error (RMSE) reached 0.992 and 9.858 respectively, indicating high detection accuracy of the device is

Deep learning assisted jet tomography for the study of Mach cones in QGP

Mach cones are expected to form in the expanding quark-gluon plasma (QGP) when energetic quarks and gluons (called jets) traverse the hot medium at a velocity faster than the speed of sound in high-energy heavy-ion collisions. The shape of the Mach cone and the associated diffusion wake are sensitive to the initial jet production location and the jet propagation direction relative to the radial flow because of the distortion by the collective expansion of the QGP and large density gradient. The shape of jet-induced Mach cones and their distortions in heavy-ion collisions provide a unique and direct probe of the dynamical evolution and the equation of state of QGP. However, it is difficult to identify the Mach cone and the diffusion wake in current experimental measurements of final hadron distributions because they are averaged over all possible initial jet production locations and propagation directions. To overcome this difficulty, we develop a deep learning assisted jet tomography which uses the full information of the final hadrons from jets to localize the initial jet production positions. This method can help to constrain the initial regions of jet production in heavy-ion collisions and enable a differential study of Mach-cones with different jet path length and orientation relative to the radial flow of the QGP in heavy-ion collisions

Decoupled advection-dispersion method for determining wall thickness of slurry trench cutoff walls

Low-permeability slurry trench cutoff walls are commonly constructed as barriers for containment of subsurface point-source pollution or as part of seepage-control systems on contaminated sites. A method to estimate wall thickness in slurry wall design is proposed based on decoupling the advective and dispersive components of contaminant fluxes through the wall. The relative error of the result obtained by the proposed method compared with that by an analytical solution was found to increase as the ratio of the specified breakthrough exit concentration (c*) to the source concentration (c0) increased. For c*/c0 of less than 0.1, which covers common practical situations, the relative error was not greater than 4% and was always conservative, indicating that the proposed method provides sufficient accuracy for design. For a given breakthrough criterion (i.e., c*/c0), the relative error was low for the scenarios having either a low or high column Peclet number, where either dispersion or advection dominates the contaminant migration, respectively, and the relative error was high for the scenario having an intermediate column Peclet number, in which case the coupling effect of advective and dispersive migrations is relatively high