Search for the Higgs Boson Using Neural Networks in Events with Missing Energy and b-Quark Jets in pp̅ Collisions at √s=1.96 TeV

We report on a search for the standard model Higgs boson produced in association with a $W$ or $Z$ boson in $p\bar{p}$ collisions at $\sqrt{s} = 1.96$ TeV recorded by the CDF II experiment at the Tevatron in a data sample corresponding to an integrated luminosity of 2.1 fb$^{-1}$. We consider events which have no identified charged leptons, an imbalance in transverse momentum, and two or three jets where at least one jet is consistent with originating from the decay of a $b$ hadron. We find good agreement between data and predictions. We place 95% confidence level upper limits on the production cross section for several Higgs boson masses ranging from 110$\gevm$ to 150$\gevm$. For a mass of 115$\gevm$ the observed (expected) limit is 6.9 (5.6) times the standard model prediction.

Search for standard model Higgs boson production in association with a W boson using a neural network discriminant at CDF

We present a search for standard model Higgs boson production in association with a W boson in proton-antiproton collisions at a center of mass energy of 1.96 TeV. The search employs data collected with the CDF II detector that correspond to an integrated luminosity of approximately 1.9 inverse fb. We select events consistent with a signature of a single charged lepton, missing transverse energy, and two jets. Jets corresponding to bottom quarks are identified with a secondary vertex tagging method, a jet probability tagging method, and a neural network filter. We use kinematic information in an artificial neural network to improve discrimination between signal and background compared to previous analyses. The observed number of events and the neural network output distributions are consistent with the standard model background expectations, and we set 95% confidence level upper limits on the production cross section times branching fraction ranging from 1.2 to 1.1 pb or 7.5 to 102 times the standard model expectation for Higgs boson masses from 110 to $150 GeV/c^2, respectively.

Detecting Earnings Management Using Neural Networks

Detecting Earnings Management Using Neural Networks. Trying to balance between relevant and reliable accounting data, generally accepted accounting principles (GAAP) allow, to some extent, the company management to use their judgment and to make subjective assessments when preparing financial statements. The opportunistic use of the discretion in financial reporting is called earnings management. There have been a considerable number of suggestions of methods for detecting accrual based earnings management. A majority of these methods are based on linear regression. The problem with using linear regression is that a linear relationship between the dependent variable and the independent variables must be assumed. However, previous research has shown that the relationship between accruals and some of the explanatory variables, such as company performance, is non-linear. An alternative to linear regression, which can handle non-linear relationships, is neural networks. The type of neural network used in this study is the feed-forward back-propagation neural network. Three neural network-based models are compared with four commonly used linear regression-based earnings management detection models. All seven models are based on the earnings management detection model presented by Jones (1991). The performance of the models is assessed in three steps. First, a random data set of companies is used. Second, the discretionary accruals from the random data set are ranked according to six different variables. The discretionary accruals in the highest and lowest quartiles for these six variables are then compared. Third, a data set containing simulated earnings management is used. Both expense and revenue manipulation ranging between -5% and 5% of lagged total assets is simulated. Furthermore, two neural network-based models and two linear regression-based models are used with a data set containing financial statement data from 110 failed companies. Overall, the results show that the linear regression-based models, except for the model using a piecewise linear approach, produce biased estimates of discretionary accruals. The neural network-based model with the original Jones model variables and the neural network-based model augmented with ROA as an independent variable, however, perform well in all three steps. Especially in the second step, where the highest and lowest quartiles of ranked discretionary accruals are examined, the neural network-based model augmented with ROA as an independent variable outperforms the other models.

FGF signaling in neurogenesis and patterning of the developing midbrain and anterior hindbrain

Embryonic midbrain and hindbrain are structures which will give rise to brain stem and cerebellum in the adult vertebrates. Brain stem contains several nuclei which are essential for the regulation of movements and behavior. They include serotonin-producing neurons, which develop in the hindbrain, and dopamine-producing neurons in the ventral midbrain. Degeneration and malfunction of these neurons leads to various neurological disorders, including schizophrenia, depression, Alzheimer s, and Parkinson s disease. Thus, understanding their development is of high interest. During embryogenesis, a local signaling center called isthmic organizer regulates the development of midbrain and anterior hindbrain. It secretes peptides belonging to fibroblast growth factor (FGF) and Wingless/Int (Wnt) families. These factors bind to their receptors in the surrounding tissues, and activate various downstream signaling pathways which lead to alterations in gene expression. This in turn affects the various developmental processes in this region, such as proliferation, survival, patterning, and neuronal differentiation. In this study we have analyzed the role of FGFs in the development of midbrain and anterior hindbrain, by using mouse as a model organism. We show that FGF receptors cooperate to receive isthmic signals, and cell-autonomously promote cell survival, proliferation, and maintenance of neuronal progenitors. FGF signaling is required for the maintenance of Sox3 and Hes1 expression in progenitors, and Hes1 in turn suppresses the activity of proneural genes. Loss of Hes1 is correlated with increased cell cycle exit and premature neuronal differentiation. We further demonstrate that FGF8 protein forms an antero-posterior gradient in the basal lamina, and might enter the neuronal progenitors via their basal processes. We also analyze the impact of FGF signaling on the various neuronal nuclei in midbrain and hindbrain. Rostral serotonergic neurons appear to require high levels of FGF signaling in order to develop. In the absence of FGF signaling, these neurons are absent. We also show that embryonic meso-diencephalic dopaminergic domain consists of two populations in the anterior-posterior direction, and that these populations display different molecular profiles. The anterior diencephalic domain appears less dependent on isthmic FGFs, and lack several genes typical of midbrain dopaminergic neurons, such as Pitx3 and DAT. In Fgfr compound mutants, midbrain dopaminergic neurons begin to develop but soon adopt characteristics which highly resemble those of diencephalic dopaminergic precursors. Our results indicate that FGF signaling regulates patterning of these two domains cell-autonomously.