Use of Deep Neural Networks for Uncertain Stress Functions with Extensions to Impact Mechanics

Stress-strain curves, or more generally, stress functions, are an extremely important characterization of a material's mechanical properties. However, stress functions are often difficult to derive and are narrowly tailored to a specific material. Further, large deformations, high strain-rates, temperature sensitivity, and effect of material parameters compound modeling challenges. We propose a generalized deep neural network approach to model stress as a state function with quantile regression to capture uncertainty. We extend these models to uniaxial impact mechanics using stochastic differential equations to demonstrate a use case and provide a framework for implementing this uncertainty-aware stress function. We provide experiments benchmarking our approach against leading constitutive, machine learning, and transfer learning approaches to stress and impact mechanics modeling on publicly available and newly presented data sets. We also provide a framework to optimize material parameters given multiple competing impact scenarios.Comment: Index Terms: Stress, Uncertainty, Impact Mechanics, Deep Learning, Neural Network. 10 pages, 9 figures, 6 table

Ariel - Volume 1 Number 2

Copyright 1969 Arie

Testing formula satisfaction

We study the query complexity of testing for properties defined by read once formulae, as instances of massively parametrized properties, and prove several testability and non-testability results. First we prove the testability of any property accepted by a Boolean read-once formula involving any bounded arity gates, with a number of queries exponential in \epsilon and independent of all other parameters. When the gates are limited to being monotone, we prove that there is an estimation algorithm, that outputs an approximation of the distance of the input from satisfying the property. For formulae only involving And/Or gates, we provide a more efficient test whose query complexity is only quasi-polynomial in \epsilon. On the other hand we show that such testability results do not hold in general for formulae over non-Boolean alphabets; specifically we construct a property defined by a read-once arity 2 (non-Boolean) formula over alphabets of size 4, such that any 1/4-test for it requires a number of queries depending on the formula size

Ariel - Volume 2 Number 5

Editors Delvyn C. Case, Jr. Paul M. Fernhoff News Editors Richard Bonanno Robin A. Edwards Features Editors Stephen P. Flynn Steven A. Ager Lay-Out Editor Carol Dolinskas Contributing Editors Michael J. Blecker W. Cherry Light Eugenia Miller Lin Sey Edwards Jack Guralnik Tom Williams James Noco

Isoperimetric Inequalities in Simplicial Complexes

In graph theory there are intimate connections between the expansion properties of a graph and the spectrum of its Laplacian. In this paper we define a notion of combinatorial expansion for simplicial complexes of general dimension, and prove that similar connections exist between the combinatorial expansion of a complex, and the spectrum of the high dimensional Laplacian defined by Eckmann. In particular, we present a Cheeger-type inequality, and a high-dimensional Expander Mixing Lemma. As a corollary, using the work of Pach, we obtain a connection between spectral properties of complexes and Gromov's notion of geometric overlap. Using the work of Gunder and Wagner, we give an estimate for the combinatorial expansion and geometric overlap of random Linial-Meshulam complexes

Testing non-uniform k-wise independent distributions over product spaces (extended abstract)

A distribution D over Σ1× ⋯ ×Σ n is called (non-uniform) k-wise independent if for any set of k indices {i 1, ..., i k } and for any z1zki1ik, PrXD[Xi1Xik=z1zk]=PrXD[Xi1=z1]PrXD[Xik=zk]. We study the problem of testing (non-uniform) k-wise independent distributions over product spaces. For the uniform case we show an upper bound on the distance between a distribution D from the set of k-wise independent distributions in terms of the sum of Fourier coefficients of D at vectors of weight at most k. Such a bound was previously known only for the binary field. For the non-uniform case, we give a new characterization of distributions being k-wise independent and further show that such a characterization is robust. These greatly generalize the results of Alon et al. [1] on uniform k-wise independence over the binary field to non-uniform k-wise independence over product spaces. Our results yield natural testing algorithms for k-wise independence with time and sample complexity sublinear in terms of the support size when k is a constant. The main technical tools employed include discrete Fourier transforms and the theory of linear systems of congruences.National Science Foundation (U.S.) (NSF grant 0514771)National Science Foundation (U.S.) (grant 0728645)National Science Foundation (U.S.) (Grant 0732334)Marie Curie International Reintegration Grants (Grant PIRG03-GA-2008-231077)Israel Science Foundation (Grant 1147/09)Israel Science Foundation (Grant 1675/09)Massachusetts Institute of Technology (Akamai Presidential Fellowship

Safety Outcomes During Pediatric GH Therapy : Final Results From the Prospective GeNeSIS Observational Program

Altres ajuts: Financial Support: GeNeSIS was sponsored by Eli Lilly and Company (Indianapolis, IN). In compliance with the Uniform Requirements for Manuscripts, established by the International Committee of Medical Journal Editors, the sponsor of this study did not impose any impediment, directly or indirectly, on the publication of the study's results. Disclosure Summary: C.J.C. and N.J. are employees and stockholders ofEliLilly and Company (Indianapolis, IN).W.F.B. and A.G.Z. are former employees and are stockholders of Lilly. C.L.D., T.H., M.M., and R.G.R. are former members of the GeNeSIS International Advisory Board; S.L., J.P.S., A.R.-U., and M.P. have served as regional advisors. B.P. has consulted for Eli Lilly Italia SpA, and E.C. has received grant support from Lilly. W.F.B. also reports heis aconsultant forAmmonett Pharma,Lilly Germany, and Merck KGaA Darmstadt. C.L.D. also reports receipt of grants, consultancy honoraria, and speaker fees from Lilly,EMD Serono, and Sandoz; grants fromOpko Prolor, Pfizer, and Versatis; honoraria and speaker fees from Roche; honoraria from Pfizer; and speaker fees from Novo Nordisk. The remaining authors have nothing to disclose.Safety concerns have been raised regarding premature mortality, diabetes, neoplasia, and cerebrovascular disease in association with GH therapy. To assess incidence of key safety outcomes. Prospective, multinational, observational study (1999 to 2015). A total of 22,311 GH-treated children from 827 investigative sites in 30 countries. Children with growth disorders. GH treatment. Standardized mortality ratio (SMR) and standardized incidence ratio (SIR) with 95% CIs for mortality, diabetes, and primary cancer using general population registries. Predominant short stature diagnoses were GH deficiency (63%), idiopathic short stature (13%), and Turner syndrome (8%), with mean ± SD follow-up of 4.2 ± 3.2 years (∼92,000 person-years [PY]). Forty-two deaths occurred in patients with follow-up, with an SMR (95% CI) of 0.61 (0.44, 0.82); the SMR was elevated for patients with cancer-related organic GH deficiency [5.87 (3.21, 9.85)]. Based on 18 cases, type 2 diabetes mellitus (T2DM) risk was elevated [SIR: 3.77 (2.24, 5.96)], but 72% had risk factors. In patients without cancer history, 14 primary cancers were observed [SIR: 0.71 (0.39, 1.20)]. Second neoplasms occurred in 31 of 622 cancer survivors [5.0%; 10.7 (7.5, 15.2) cases/1000 PY] and intracranial tumor recurrences in 67 of 823 tumor survivors [8.1%; 16.9 (13.3, 21.5) cases/1000 PY]. All three hemorrhagic stroke cases had risk factors. GeNeSIS (Genetics and Neuroendocrinology of Short Stature International Study) data support the favorable safety profile of pediatric GH treatment. Overall risk of death or primary cancer was not elevated in GH-treated children, and no hemorrhagic strokes occurred in patients without risk factors. T2DM incidence was elevated compared with the general population, but most cases had diabetes risk factors. Safety of GH therapy was assessed in a pediatric observational study. Death and primary cancer rates were not higher than in the general population; T2DM rate was higher owing to risk factors

Semi-supervised prediction of protein subcellular localization using abstraction augmented Markov models

<p>Abstract</p> <p>Background</p> <p>Determination of protein subcellular localization plays an important role in understanding protein function. Knowledge of the subcellular localization is also essential for genome annotation and drug discovery. Supervised machine learning methods for predicting the localization of a protein in a cell rely on the availability of large amounts of labeled data. However, because of the high cost and effort involved in labeling the data, the amount of labeled data is quite small compared to the amount of unlabeled data. Hence, there is a growing interest in developing <it>semi-supervised methods</it> for predicting protein subcellular localization from large amounts of unlabeled data together with small amounts of labeled data.</p> <p>Results</p> <p>In this paper, we present an Abstraction Augmented Markov Model (AAMM) based approach to semi-supervised protein subcellular localization prediction problem. We investigate the effectiveness of AAMMs in exploiting <it>unlabeled</it> data. We compare semi-supervised AAMMs with: (i) Markov models (MMs) (which do not take advantage of unlabeled data); (ii) an expectation maximization (EM); and (iii) a co-training based approaches to semi-supervised training of MMs (that make use of unlabeled data).</p> <p>Conclusions</p> <p>The results of our experiments on three protein subcellular localization data sets show that semi-supervised AAMMs: (i) can effectively exploit unlabeled data; (ii) are more accurate than both the MMs and the EM based semi-supervised MMs; and (iii) are comparable in performance, and in some cases outperform, the co-training based semi-supervised MMs.</p