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Michael Kane

Publications and source records attributed to Michael Kane.

10 recordsLinked to original sources
arXiv2025

Luminosity measurement with the LHCb RICH detectors in Run 3

The LHCb Ring-Imaging Cherenkov detectors are built to provide charged hadron identification over a large range of momentum. The upgraded detectors are also capable of providing an independent measurement of the luminosity for the LHCb experiment during LHC Run 3. The modelling of the opto-electronics chain, the application of the powering strategy during operations, the calibration procedures and the proof of principle of a novel technique for luminosity determination are presented. In addition, the preliminary precision achieved during the 2023 data-taking year for real-time and offline luminosity measurements is reported.

hep-ex↗
arXiv2023

Valuing Post-Revenue Biopharmaceutical Assets with Pfizer's Current Portfolio as a Case Study

This research paper addresses the critical challenge of accurately valuing post-revenue drug assets in the biotechnology and pharmaceutical sectors, a key factor influencing a wide range of strategic operations and investment decisions. Recognizing the importance of reliable valuations for stakeholders such as pharmaceutical companies, venture capitalists, and private equity firms, this study introduces a novel model for forecasting future sales of post-revenue biopharmaceutical assets. The proposed model leverages historical sales data, a resource known for its high quality and availability in company financial records, to produce distributional estimates of cumulative sales for individual assets. These estimates are instrumental in calculating the Net Present Value of each asset, thereby facilitating more informed and strategic investment decisions. A practical application of this model is demonstrated through its implementation in analyzing Pfizer's portfolio of post-revenue assets. This precision highlights the model's potential as a valuable tool in the financial assessment and decision-making processes within the biotech and pharmaceutical industries, offering a methodical approach to identifying investment opportunities and optimizing capital allocation.

q-fin.PR↗
arXiv2023

Toward A Dynamic Comfort Model for Human-Building Interaction in Grid-Interactive Efficient Buildings: Supported by Field Data

Controlling building electric loads could alleviate the increasing grid strain caused by the adoption of renewables and electrification. However, current approaches that automatically setback thermostats on the hottest day compromise their efficacy by neglecting human-building interaction (HBI). This study aims to define challenges and opportunities for developing engineering models of HBI to be used in the design of controls for grid-interactive efficient buildings (GEBs). Building system and measured and just-in-time surveyed psychophysiological data were collected from 41 participants in 20 homes from April-September. ASHRAE Standard 55 thermal comfort models for building design were evaluated with these data. Increased error bias was observed with increasing spatiotemporal temperature variations. Unsurprising, considering these models neglect such variance, but questioning their suitability for GEBs controlling thermostat setpoints, and given the observed 4{\deg}F intra-home spatial temperature variation. The results highlight opportunities for reducing these biases in GEBs through a paradigm shift to modeling discomfort instead of comfort, increasing use of low-cost sensors, and models that account for the observed dynamic occupant behavior: of the thermostat setpoint overrides made with 140-minutes of a previous setpoint change, 95% of small changes ( 2{\deg}F) were made with 120-minutes, while 95% of larger changes ( 10{\deg}F) were made within only 70-minutes.

cs.CY↗
arXiv2021

Bayesian local exchangeability design for phase II basket trials

We propose an information borrowing strategy for the design and monitoring of phase II basket trials based on the local multisource exchangeability assumption between baskets (disease types). In our proposed local-MEM framework, information borrowing is only allowed to occur locally, i.e., among baskets with similar response rate and the amount of information borrowing is determined by the level of similarity in response rate, whereas baskets not considered similar are not allowed to share information. We construct a two-stage design for phase II basket trials using the proposed strategy. The proposed method is compared to competing Bayesian methods and Simon's two-stage design in a variety of simulation scenarios. We demonstrate the proposed method is able to maintain the family-wise type I error rate at a reasonable level and has desirable basket-wise power compared to Simon's two-stage design. In addition, our method is computationally efficient compared to existing Bayesian methods in that the posterior profiles of interest can be derived explicitly without the need for sampling algorithms.

stat.ME↗
arXiv2021

Economic controls co-design of hybrid microgrids with tidal/PV generation and lithium ion/flow battery storage

Islanded microgrids powered by renewable energy require costly energy storage systems due to the uncontrollable generators. Energy storage needs are amplified when load and generation are misaligned on hourly, monthly, or seasonal timescales. Diversification of both loads and generation can smooth out such mismatches. The ideal type of battery to smooth out remaining generation deficits will depend on the duration(s) that energy is stored. This study presents a controls co-design approach to design an islanded microgrid, showing the benefit of hybridizing tidal and solar generation and hybridizing lithium-ion and flow battery energy storage. The optimization of the microgrid's levelized cost of energy is initially studied in grid-search slices to understand convexity and smoothness, then a particle swarm optimization is proposed and used to study the sensitivity of the hybrid system configuration to variations in component costs. The study highlights the benefits of controls co-design, the need to model premature battery failure, and the importance of using battery cost models that are applicable across orders of magnitude variations in energy storage durations. The results indicate that such a hybrid microgrid would currently produce energy at five times the cost of diesel generation, but flow battery innovations could bring this closer to only twice the cost while using 100% renewable energy.

eess.SY↗
arXiv2021

Infrastructure Resilience Curves: Performance Measures and Summary Metrics

Resilience curves are used to communicate quantitative and qualitative aspects of system behavior and resilience to stakeholders of critical infrastructure. Generally, these curves illustrate the evolution of system performance before, during, and after a disruption. As simple as these curves may appear, the literature contains underexplored nuance when defining "performance" and comparing curves with summary metrics. Through a critical review of 273 publications, this manuscript aims to define a common vocabulary for practitioners and researchers that will improve the use of resilience curves as a tool for assessing and designing resilient infrastructure. This vocabulary includes a taxonomy of resilience curve performance measures as well as a taxonomy of summary metrics. In addition, this review synthesizes a framework for examining assumptions of resilience analysis that are often implicit or unexamined in the practice and literature. From this vocabulary and framework comes recommendations including broader adoption of productivity measures; additional research on endogenous performance targets and thresholds; deliberate consideration of curve milestones when defining summary metrics; and cautionary fundamental flaws that may arise when condensing an ensemble of resilience curves into an "expected" trajectory.

cs.DC↗
arXiv2020

Numerical tolerance for spectral decompositions of random matrices

We precisely quantify the impact of statistical error in the quality of a numerical approximation to a random matrix eigendecomposition, and under mild conditions, we use this to introduce an optimal numerical tolerance for residual error in spectral decompositions of random matrices. We demonstrate that terminating an eigendecomposition algorithm when the numerical error and statistical error are of the same order results in computational savings with no loss of accuracy. We also repair a flaw in a ubiquitous termination condition, one in wide employ in several computational linear algebra implementations. We illustrate the practical consequences of our stopping criterion with an analysis of simulated and real networks. Our theoretical results and real-data examples establish that the tradeoff between statistical and numerical error is of significant import for data science.

stat.CO↗
arXiv2016

iotools: High-Performance I/O Tools for R

The iotools package provides a set of tools for Input/Output (I/O) intensive datasets processing in R (R Core Team, 2014). Efficent parsing methods are included which minimize copying and avoid the use of intermediate string representations whenever possible. Functions for applying chunk-wise operations allow for computing on streaming input as well as arbitrarily large files. We present a set of example use cases for iotools, as well as extensive benchmarks comparing comparable functions provided in both core-R as well as other contributed packages.

stat.CO↗
arXiv2016

Efficient Thresholded Correlation using Truncated Singular Value Decomposition

Efficiently computing a subset of a correlation matrix consisting of values above a specified threshold is important to many practical applications. Real-world problems in genomics, machine learning, finance other applications can produce correlation matrices too large to explicitly form and tractably compute. Often, only values corresponding to highly-correlated vectors are of interest, and those values typically make up a small fraction of the overall correlation matrix. We present a method based on the singular value decomposition (SVD) and its relationship to the data covariance structure that can efficiently compute thresholded subsets of very large correlation matrices.

stat.CO↗