1,218 research outputs found

    Targeting fidelity of pharmaceutical systems models by optimization of precision on parameter estimates

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    Quantitative models have gained momentum to drive the development of pharmaceutical processes. The assessment of the prediction fidelity of these models is key to provide interpretability of process phenomena and to enable decision-making. Evaluating parametric uncertainty is paramount when the focus is on systems models, which combine different sub-models together, and, thus, parameters related to previous units may strongly impact the prediction of one final output. A framework is proposed to assess reliability in model predictions, where the precision of parameter estimates is explicitly optimized to target pre-set tolerance requirements on process key performance indicators and product critical quality attributes. A direct compression systems model for the manufacturing of oral solid dosage products is used as a case study. Results show that the proposed methodology is effective at guaranteeing the target model fidelity and at quantifying the maximum acceptable uncertainty in the estimates of model parameters

    Full and fast calibration of the Heston stochastic volatility model

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    This paper presents an algorithm for a complete and e cient calibration of the Heston stochastic volatility model. We express the calibration as a nonlinear least-squares problem. We exploit a suitable representation of the Heston characteristic function and modify it to avoid discontinuities caused by branch switchings of complex functions. Using this representation, we obtain the analytical gradient of the price of a vanilla option with respect to the model parameters, which is the key element of all variants of the objective function. The interdependency between the components of the gradient enables an e cient implementation which is around ten times faster than a numerical gradient. We choose the Levenberg-Marquardt method to calibrate the model and do not observe multiple local minima reported in previous research. Two-dimensional sections show that the objective function is shaped as a narrow valley with a flat bottom. Our method is the fastest calibration of the Heston model developed so far and meets the speed requirement of practical trading

    Historical Perspectives and Guidelines for Botulinum Neurotoxin Subtype Nomenclature

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    Botulinum neurotoxins are diverse proteins. They are currently represented by at least seven serotypes and more than 40 subtypes. New clostridial strains that produce novel neurotoxin variants are being identified with increasing frequency, which presents challenges when organizing the nomenclature surrounding these neurotoxins. Worldwide, researchers are faced with the possibility that toxins having identical sequences may be given different designations or novel toxins having unique sequences may be given the same designations on publication. In order to minimize these problems, an ad hoc committee consisting of over 20 researchers in the field of botulinum neurotoxin research was convened to discuss the clarification of the issues involved in botulinum neurotoxin nomenclature. This publication presents a historical overview of the issues and provides guidelines for botulinum neurotoxin subtype nomenclature in the future.Peer reviewe

    Replay Attacks and Defenses Against Cross-shard Consensus in Sharded Distributed Ledgers

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    We present a family of replay attacks against sharded distributed ledgers targeting cross-shard consensus protocols, such as the recently proposed Chainspace and Omniledger. They allow an attacker, with network access only, to double-spend or lock resources with minimal efforts. The attacker can act independently without colluding with any nodes, and succeed even if all nodes are honest; most of the attacks can also exhibit themselves as faults under periods of asynchrony. These attacks are effective against both shard-led and client-led cross-shard consensus approaches. We present Byzcuit-a new cross-shard consensus protocol that is immune to those attacks. We implement a prototype of Byzcuit and evaluate it on a real cloud-based testbed, showing that our defenses impact performance minimally, and overall performance surpasses previous works
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