Sample size, lag order and critical values of seasonal unit root tests

This paper presents a response surface analysis for the distributions of the popular tests for seasonal unit roots in quarterly observed time series variables developed by Hylleberg et al. (1990). Approximate asymptotic distributions are obtained, and response surface coefficients for 1%-, 5%- and 10%-level critical values are reported, permitting simple computation of accurate critical values for any sample size and lag order. Five test statistics are considered, along with five different specifications of the deterministic component in the test regression; allowance is also made for the lag order to be determined endogenously, using commonly applied selection methods. Dependence of the critical values and the probability density functions on the sample size and lag order is also investigated

A Model-Based, Bayesian Solution for Characterization of Complex Damage Scenarios in Aerospace Composite Structures

Ultrasonic damage detection and characterization is commonly used in nondestructive evaluation (NDE) of aerospace composite components. In recent years there has been an increased development of guided wave based methods. In real materials and structures, these dispersive waves result in complicated behavior in the presence of complex damage scenarios. Model-based characterization methods utilize accurate three dimensional finite element models (FEMs) of guided wave interaction with realistic damage scenarios to aid in defect identification and classification. This work describes an inverse solution for realistic composite damage characterization by comparing the wavenumber-frequency spectra of experimental and simulated ultrasonic inspections. The composite laminate material properties are first verified through a Bayesian solution (Markov chain Monte Carlo), enabling uncertainty quantification surrounding the characterization. A study is undertaken to assess the efficacy of the proposed damage model and comparative metrics between the experimental and simulated output. The FEM is then parameterized with a damage model capable of describing the typical complex damage created by impact events in composites. The damage is characterized through a transdimensional Markov chain Monte Carlo solution, enabling a flexible damage model capable of adapting to the complex damage geometry investigated here. The posterior probability distributions of the individual delamination petals as well as the overall envelope of the damage site are determined

Modelling Silicate - Nitrate - Ammonium Co-Limitation of Algal Growth and the Importance of Bacterial Remineralisation Based on an Experimental Arctic Coastal Spring Bloom Culture Study

Arctic coastal ecosystems are rapidly changing due to climate warming, which makes modelling their productivity crucially important to better understand future changes. System primary production in these systems is highest during the pronounced spring bloom, typically dominated by diatoms. Eventually the spring blooms terminate due to silicon or nitrogen limitation. Bacteria can play an important role for extending bloom duration and total CO2 fixation through ammonium regeneration. Current ecosystem models often simplify the effects of nutrient co-limitations on algal physiology and cellular ratios and neglect bacterial driven regeneration, leading to an underestimation of primary production. Detailed biochemistry- and cell-based models can represent these dynamics but are difficult to tune in the environment. We performed a cultivation experiment that showed typical spring bloom dynamics, such as extended algal growth via bacteria ammonium remineralisation, and reduced algal growth and inhibited chlorophyll synthesis under silicate limitation, and gradually reduced nitrogen assimilation and chlorophyll synthesis under nitrogen limitation. We developed a simplified dynamic model to represent these processes. The model also highlights the importance of organic matter excretion, and post bloom ammonium accumulation. Overall, model complexity is comparable to other ecosystem models used in the Arctic while improving the representation of nutrient co-limitation related processes. Such model enhancements that now incorporate increased nutrient inputs and higher mineralization rates in a warmer climate will improve future predictions in this vulnerable system

Leptogenesis with Dirac Neutrinos

We describe a "neutrinogenesis" mechanism whereby, in the presence of right-handed neutrinos with sufficiently small pure Dirac masses, (B+L)-violating sphaleron processes create the baryon asymmetry of the Universe, even when B=L=0 initially. It is shown that the resulting neutrino mass constraints are easily fulfilled by the neutrino masses suggested by current experiments. We present a simple toy model which uses this mechanism to produce the observed baryon asymmetry of the Universe. (PostScript Errors corrected in latest Version).Comment: 4 pages, Latex (using amsmath,feynmp,graphicx), 4 figure

CAManim: Animating end-to-end network activation maps

Deep neural networks have been widely adopted in numerous domains due to their high performance and accessibility to developers and application-specific end-users. Fundamental to image-based applications is the development of Convolutional Neural Networks (CNNs), which possess the ability to automatically extract features from data. However, comprehending these complex models and their learned representations, which typically comprise millions of parameters and numerous layers, remains a challenge for both developers and end-users. This challenge arises due to the absence of interpretable and transparent tools to make sense of black-box models. There exists a growing body of Explainable Artificial Intelligence (XAI) literature, including a collection of methods denoted Class Activation Maps (CAMs), that seek to demystify what representations the model learns from the data, how it informs a given prediction, and why it, at times, performs poorly in certain tasks. We propose a novel XAI visualization method denoted CAManim that seeks to simultaneously broaden and focus end-user understanding of CNN predictions by animating the CAM-based network activation maps through all layers, effectively depicting from end-to-end how a model progressively arrives at the final layer activation. Herein, we demonstrate that CAManim works with any CAM-based method and various CNN architectures. Beyond qualitative model assessments, we additionally propose a novel quantitative assessment that expands upon the Remove and Debias (ROAD) metric, pairing the qualitative end-to-end network visual explanations assessment with our novel quantitative "yellow brick ROAD" assessment (ybROAD). This builds upon prior research to address the increasing demand for interpretable, robust, and transparent model assessment methodology, ultimately improving an end-user's trust in a given model's predictions

MetaCAM: Ensemble-Based Class Activation Map

The need for clear, trustworthy explanations of deep learning model predictions is essential for high-criticality fields, such as medicine and biometric identification. Class Activation Maps (CAMs) are an increasingly popular category of visual explanation methods for Convolutional Neural Networks (CNNs). However, the performance of individual CAMs depends largely on experimental parameters such as the selected image, target class, and model. Here, we propose MetaCAM, an ensemble-based method for combining multiple existing CAM methods based on the consensus of the top-k% most highly activated pixels across component CAMs. We perform experiments to quantifiably determine the optimal combination of 11 CAMs for a given MetaCAM experiment. A new method denoted Cumulative Residual Effect (CRE) is proposed to summarize large-scale ensemble-based experiments. We also present adaptive thresholding and demonstrate how it can be applied to individual CAMs to improve their performance, measured using pixel perturbation method Remove and Debias (ROAD). Lastly, we show that MetaCAM outperforms existing CAMs and refines the most salient regions of images used for model predictions. In a specific example, MetaCAM improved ROAD performance to 0.393 compared to 11 individual CAMs with ranges from -0.101-0.172, demonstrating the importance of combining CAMs through an ensembling method and adaptive thresholding.Comment: 9 page

Leptogenesis, CP violation and neutrino data: What can we learn?

A detailed analytic and numerical study of baryogenesis through leptogenesis is performed in the framework of the standard model of electroweak interactions extended by the addition of three right-handed neutrinos, leading to the seesaw mechanism. We analyze the connection between GUT-motivated relations for the quark and lepton mass matrices and the possibility of obtaining a viable leptogenesis scenario. In particular, we analyze whether the constraints imposed by SO(10) GUTs can be compatible with all the available solar, atmospheric and reactor neutrino data and, simultaneously, be capable of producing the required baryon asymmetry via the leptogenesis mechanism. It is found that the Just-So^2 and SMA solar solutions lead to a viable leptogenesis even for the simplest SO(10) GUT, while the LMA, LOW and VO solar solutions would require a different hierarchy for the Dirac neutrino masses in order to generate the observed baryon asymmetry. Some implications on CP violation at low energies and on neutrinoless double beta decay are also considered.Comment: 36 pages, 6 figures; new references added, final version to appear in Nucl. Phys.

Portfolio Vol. V N 3

Koons, Marilyn. Apology . Poem. 5. Koons, Marilyn. A Woman\u27s Request Poem. 5. Davidson, Sally. Of the Present . Poem. 5. Koons, Marilyn. Escape in Memory . Poem. 5. Stander, Marianna. Self Portrait . Picture. 5. Morton, John. Consumer\u27s Victory . Prose. 6. Rhu, Helen. Prize Winning Poem . Poem. 8. Rhu, Helen. To the Victor . Poem. 8. Rhu, Helen. Fantasy at Midnight . Poem. 8. Tomlin, Bonnie. The Drag . Picture. 8. Morse, Kay. In Spite of all... Prose. 9. Metcalf, Carolyn. Isolation . Cartoon. 12. Koons, Marilyn. In Black and White . Prose. 14. Harvey, Dick. Through Enemy Eyes . Prose. 15. Vercoe, Mary. Storm .Poem. 16. Vercoe, Mary. Refuge .Poem. 16. Vercoe, Mary. Recovery .Poem. 16. Vercoe, Mary. Temporary Address .Poem. 16. Hill, Jacque. Weary Words . Poem. 17. Brannon, Earl W. The Fall . Poem. 17. T.W. Gardenias . Poem. 17. Hayne, Barbara. Window Tears . Poem. 17. Burrows, Pete. Family Portrait . Prose. 18. Seagrave, Leslie. Retribution . Prose. 19. Benson, Virginia. The Moon Came Up . Prose. 21. Reynolds, Virginia. Matter Over Mind . Prose. 22

Alternatives to Seesaw

The seesaw mechanism is attractive not only because it "explains'' small neutrino mass, but also because of its packaging with the SUSY-GUT, leptogenesis, Dark Matter, and electroweak symmetry breaking. However, this package has the flavor, CP, and gravitino problems. I discuss two alternatives to the seesaw mechanism. In one of them, the anomaly-mediated supersymmetry breaking solves these problems, while predicts naturally light Dirac neutrinos. In the other, the light Majorana neutrinos arise from supersymmetry breaking with right-handed neutrinos below TeV, and the Dark Matter and collider phenomenology are significantly different.Comment: Talk at Fujihara Seminar On Neutrino Mass And Seesaw Mechanism (SEESAW 1979-2004), 23-25 Feb 2004, KEK. 14 pages, uses espcrc2.st

Saving fourth generation and baryon number by living long

Recent studies of precision electroweak observables have led to the conclusion that a fourth generation is highly constrained. However, we point out that a long-lived fourth generation can reopen a large portion of the parameter space. In addition, it preserves baryon and lepton asymmetries against sphaleron erasure even if $B-L=0$. It opens up the possibility of exact $B-L$ symmetry and hence Dirac neutrinos. The fourth generation can be observed at the LHC with unique signatures of long-lived particles in the near future.Comment: 4 pages, 3 figure