A New Model for Family Resource Allocation Among Siblings: Competition, Forbearance, and Support

Previous research analyzing within-family education resource allocation usually employs the sibship and birth order of a child as explanatory variables. We argue in this paper that to correctly characterize the resource competition and support scenario within a family, one should identify the Sex, Seniority, and most importantly Age Difference of a child’s sibling structure, and hence we call our analysis a SSAD model of family resource allocation. We show that siblings with different combinations of SSAD may play distinct roles in family resource allocation. Ignoring such facts may distort the significance and/or direction of the prediction. We support our analysis with empirical evidence using data from Taiwan.

Multivariate dynamic kernels for financial time series forecasting

The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44781-0_40We propose a forecasting procedure based on multivariate dynamic kernels, with the capability of integrating information measured at different frequencies and at irregular time intervals in financial markets. A data compression process redefines the original financial time series into temporal data blocks, analyzing the temporal information of multiple time intervals. The analysis is done through multivariate dynamic kernels within support vector regression. We also propose two kernels for financial time series that are computationally efficient without a sacrifice on accuracy. The efficacy of the methodology is demonstrated by empirical experiments on forecasting the challenging S&P500 market.Peer ReviewedPostprint (author's final draft

Between steps: Intermediate relaxations between big-M and convex hull formulations

This work develops a class of relaxations in between the big-M and convex hull formulations of disjunctions, drawing advantages from both. The proposed "P-split" formulations split convex additively separable constraints into P partitions and form the convex hull of the partitioned disjuncts. Parameter P represents the trade-off of model size vs. relaxation strength. We examine the novel formulations and prove that, under certain assumptions, the relaxations form a hierarchy starting from a big-M equivalent and converging to the convex hull. We computationally compare the proposed formulations to big-M and convex hull formulations on a test set including: K-means clustering, P_ball problems, and ReLU neural networks. The computational results show that the intermediate P-split formulations can form strong outer approximations of the convex hull with fewer variables and constraints than the extended convex hull formulations, giving significant computational advantages over both the big-M and convex hull

Spiral bevel and circular arc helical gears: Tooth contact analysis and the effect of misalignment on circular arc helical gears

A computer aided method for tooth contact analysis was developed and applied. Optimal machine-tool settings for spiral bevel gears are proposed and when applied indicated that kinematic errors can be minimized while maintaining a desirable bearing contact. The effect of misalignment for circular arc helical gears was investigated and the results indicted that directed pinion refinishing can compensate the kinematic errors due to misalignment

New generation methods for spur, helical, and spiral-bevel gears

New methods for generating spur, helical, and spiral-bevel gears are proposed. These methods provide the gears with conjugate gear tooth surfaces, localized bearing contact, and reduced sensitivity to gear misalignment. Computer programs have been developed for simulating gear meshing and bearing contact

Partition-based formulations for mixed-integer optimization of trained ReLU neural networks

This paper introduces a class of mixed-integer formulations for trained ReLU neural networks. The approach balances model size and tightness by partitioning node inputs into a number of groups and forming the convex hull over the partitions via disjunctive programming. At one extreme, one partition per input recovers the convex hull of a node, i.e., the tightest possible formulation for each node. For fewer partitions, we develop smaller relaxations that approximate the convex hull, and show that they outperform existing formulations. Specifically, we propose strategies for partitioning variables based on theoretical motivations and validate these strategies using extensive computational experiments. Furthermore, the proposed scheme complements known algorithmic approaches, e.g., optimization-based bound tightening captures dependencies within a partition

Don't bleach chaotic data

A common first step in time series signal analysis involves digitally filtering the data to remove linear correlations. The residual data is spectrally white (it is ``bleached''), but in principle retains the nonlinear structure of the original time series. It is well known that simple linear autocorrelation can give rise to spurious results in algorithms for estimating nonlinear invariants, such as fractal dimension and Lyapunov exponents. In theory, bleached data avoids these pitfalls. But in practice, bleaching obscures the underlying deterministic structure of a low-dimensional chaotic process. This appears to be a property of the chaos itself, since nonchaotic data are not similarly affected. The adverse effects of bleaching are demonstrated in a series of numerical experiments on known chaotic data. Some theoretical aspects are also discussed.Comment: 12 dense pages (82K) of ordinary LaTeX; uses macro psfig.tex for inclusion of figures in text; figures are uufile'd into a single file of size 306K; the final dvips'd postscript file is about 1.3mb Replaced 9/30/93 to incorporate final changes in the proofs and to make the LaTeX more portable; the paper will appear in CHAOS 4 (Dec, 1993

Combining Multi-Fidelity Modelling and Asynchronous Batch Bayesian Optimization

Bayesian Optimization is a useful tool for experiment design. Unfortunately, the classical, sequential setting of Bayesian Optimization does not translate well into laboratory experiments, for instance battery design, where measurements may come from different sources and their evaluations may require significant waiting times. Multi-fidelity Bayesian Optimization addresses the setting with measurements from different sources. Asynchronous batch Bayesian Optimization provides a framework to select new experiments before the results of the prior experiments are revealed. This paper proposes an algorithm combining multi-fidelity and asynchronous batch methods. We empirically study the algorithm behavior, and show it can outperform single-fidelity batch methods and multi-fidelity sequential methods. As an application, we consider designing electrode materials for optimal performance in pouch cells using experiments with coin cells to approximate battery performance

AC-coupled GaAs microstrip detectors with a new type of integrated bias resistors

Full size single-sided GaAs microstrip detectors with integrated coupling capacitors and bias resistors have been fabricated on 3'' substrate wafers. PECVD deposited SiO_2 and SiO_2/Si_3N_4 layers were used to provide coupling capacitaces of 32.5 pF/cm and 61.6 pF/cm, respectively. The resistors are made of sputtered CERMET using simple lift of technique. The sheet resistivity of 78 kOhm/sq. and the thermal coefficient of resistance of less than 4x10^-3 / degree C satisfy the demands of small area biasing resistors, working on a wide temperature range.Comment: 20 pages, 9 figures, to be published in NIM