Monolithically integrated active optical devices

Considerations relevant to the monolithic integration of optical detectors, lasers, and modulators with high speed amplifiers are discussed. Some design considerations for representative subsystems in the GaAs-AlGaAs and GaInAs-InP materials systems are described. Results of a detailed numerical design of an electro-optical birefringent filter for monolithic integration with a laser diode is described, and early experimental results on monolithic integration of broadband MESFET amplifiers with photoconductive detectors are reported

Characterization of gsp-Mediated Growth Hormone Excess in the Context of McCune-Albright Syndrome

McCune-Albright syndrome (MAS) is a disorder characterized by the triad of café-au-lait skin pigmentation, polyostotic fibrous dysplasia of bone, and hyperfunctioning endocrinopathies, including GH excess. The molecular etiology of the disease is postzygotic activating mutations of the GNAS1 gene product, Gsα. The term gsp oncogene has been assigned to these mutations due to their association with certain neoplasms. The aim of this study was to estimate the prevalence of GH excess in MAS, characterize the clinical and endocrine manifestations, and describe the response to treatment. Fifty-eight patients with MAS were screened, and 22 with stigmata of acromegaly and/or elevated GH or IGF-I underwent oral glucose tolerance testing. Twelve patients (21%) had GH excess, based on failure to suppress serum GH on oral glucose tolerance test, and underwent a TRH test, serial GH sampling from 2000-0800 h, and magnetic resonance imaging of the sella. We found that vision and hearing deficits were more common in patients with GH excess (4 of 12, 33%) than those without (2 of 56, 4%). Of interest, patients with a history of precocious puberty and GH excess who had reached skeletal maturity achieved normal adult height despite a history of early epiphyseal fusion. All 9 patients tested had an increase in serum GH after TRH, 11 of 12 (92%) had hyperprolactinemia, and all 8 tested had detectable or elevated nighttime GH levels. Pituitary adenoma was detected in 4 of 12 (33%) patients. All patients with elevated IGF-I levels were treated with cabergoline (7 patients), long-acting octreotide (LAO; 8 patients), or a combination of cabergoline and LAO (4 patients). In six of the seven patients (86%) treated with cabergoline, serum IGF-I decreased, but not to the normal range. In the eight patients treated with LAO alone, IGF-I decreased, and, in four, returned to the normal range. The remaining 4 patients were treated with a combination of cabergoline and LAO. For them, symptoms of GH excess diminished, and IGF-I decreased further, but did not enter the normal range. GH excess is common in MAS and results in a distinct clinical phenotype characterized by inappropriately normal stature, TRH responsiveness, prolactin cosecretion, small or absent pituitary tumors, a consistent but inadequate response to treatment with cabergoline, and an intermediate response to LAO

CONVERGENCE OF AMERICAN OPTION VALUES FROM DISCRETE- TO CONTINUOUS-TIME FINANCIAL MODELS 1

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75553/1/j.1467-9965.1994.tb00059.x.pd

Gate complexity using dynamic programming

The relationship between efficient quantum gate synthesis and control theory has been a topic of recent interest in the quantum computing literature. Motivated by this work, we describe how the dynamic programming technique from optimal control may be used in principle to determine gate complexity and for the optimal synthesis of quantum circuits. We illustrate the dynamic programming methodology using a simple example on the Lie group SU(2)

Theodicy and End-of-Life Care

Acknowledgments The section on Islamic perspective is contributed by information provided by Imranali Panjwani, Tutor in Theology & Religious Studies, King's College London.Peer reviewedPublisher PD

Quadratic optimal functional quantization of stochastic processes and numerical applications

In this paper, we present an overview of the recent developments of functional quantization of stochastic processes, with an emphasis on the quadratic case. Functional quantization is a way to approximate a process, viewed as a Hilbert-valued random variable, using a nearest neighbour projection on a finite codebook. A special emphasis is made on the computational aspects and the numerical applications, in particular the pricing of some path-dependent European options.Comment: 41 page

Optimal treatment allocations in space and time for on-line control of an emerging infectious disease

A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America

Effect of time-correlation of input patterns on the convergence of on-line learning

We studied the effects of time correlation of subsequent patterns on the convergence of on-line learning by a feedforward neural network with backpropagation algorithm. By using chaotic time series as sequences of correlated patterns, we found that the unexpected scaling of converging time with learning parameter emerges when time-correlated patterns accelerate learning process.Comment: 8 pages(Revtex), 5 figure