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This paper describes a traffic signal control strategy that allows motorists who travel at a recommended speed on suburban arterial two-way roads with a common cycle time to make every traffic signal. A road-to-traveler-feedback-device (RTFD) advises motorists how fast they should travel to do this. Signalized arterial roads where vehicles that travel at the recommended speed make every traffic signal are termed Ride-the-Green-Wave (RGW) roads. Left-turn-arounds enable vehicles to turn left from two-way RGW-roads to intersecting/orthogonal two-way RGW-road while allowing maximum flow at the intersection. The traffic signal control technique that enables vehicles that travel at the recommended speed to make every traffic signal has been verified using a simulation program (RGW-SIM). In addition to introducing novel traffic signal control strategies, the methods presented in this paper have implications for road network design, public transport control, connected and automated vehicles and environmental impacts., Comment: 51 pages, 30 figures, 5 tables

The output of a machine learning algorithm can usually be represented by one or more multivariate functions of its input variables. Knowing the global properties of such functions can help in understanding the system that produced the data as well as interpreting and explaining corresponding model predictions. A method is presented for representing a general multivariate function as a tree of simpler functions. This tree exposes the global internal structure of the function by uncovering and describing the combined joint influences of subsets of its input variables. Given the inputs and corresponding function values, a function tree is constructed that can be used to rapidly identify and compute all of the function's main and interaction effects up to high order. Interaction effects involving up to four variables are graphically visualized.

We present a catalog of 97 uniformly-vetted candidates for quadruple star systems. The candidates were identified in TESS Full Frame Image data from Sectors 1 through 42 through a combination of machine learning techniques and visual examination, with major contributions from a dedicated group of citizen scientists. All targets exhibit two sets of eclipses with two different periods, both of which pass photocenter tests confirming that the eclipses are on-target. This catalog outlines the statistical properties of the sample, nearly doubles the number of known multiply-eclipsing quadruple systems, and provides the basis for detailed future studies of individual systems. Several important discoveries have already resulted from this effort, including the first sextuply-eclipsing sextuple stellar system and the first transiting circumbinary planet detected from one sector of TESS data., Comment: 56 pages, 27 figures, 4 tables

Many regression and classification procedures fit a parameterized function $f(x;w)$ of predictor variables $x$ to data $\{x_{i},y_{i}\}_1^N$ based on some loss criterion $L(y,f)$. Often, regularization is applied to improve accuracy by placing a constraint $P(w)\leq t$ on the values of the parameters $w$. Although efficient methods exist for finding solutions to these constrained optimization problems for all values of $t\geq0$ in the special case when $f$ is a linear function, none are available when $f$ is non-linear (e.g. Neural Networks). Here we present a fast algorithm that provides all such solutions for any differentiable function $f$ and loss $L$, and any constraint $P$ that is an increasing monotone function of the absolute value of each parameter. Applications involving sparsity inducing regularization of arbitrary Neural Networks are discussed. Empirical results indicate that these sparse solutions are usually superior to their dense counterparts in both accuracy and interpretability. This improvement in accuracy can often make Neural Networks competitive with, and sometimes superior to, state-of-the-art methods in the analysis of tabular data.

We report the discovery of a sextuply-eclipsing sextuple star system from TESS data, TIC 168789840, also known as TYC 7037-89-1, the first known sextuple system consisting of three eclipsing binaries. The target was observed in Sectors 4 and 5 during Cycle 1, with lightcurves extracted from TESS Full Frame Image data. It was also previously observed by the WASP survey and ASAS-SN. The system consists of three gravitationally-bound eclipsing binaries in a hierarchical structure of an inner quadruple system with an outer binary subsystem. Follow-up observations from several different observatories were conducted as a means of determining additional parameters. The system was resolved by speckle interferometry with a 0."42 separation between the inner quadruple and outer binary, inferring an estimated outer period of ~2 kyr. It was determined that the fainter of the two resolved components is an 8.217 day eclipsing binary, which orbits the inner quadruple that contains two eclipsing binaries with periods of 1.570 days and 1.306 days. MCMC analysis of the stellar parameters has shown that the three binaries of TIC 168789840 are "triplets", as each binary is quite similar to the others in terms of mass, radius, and Teff. As a consequence of its rare composition, structure, and orientation, this object can provide important new insight into the formation, dynamics, and evolution of multiple star systems. Future observations could reveal if the intermediate and outer orbital planes are all aligned with the planes of the three inner eclipsing binaries.

The goal of regression analysis is to predict the value of a numeric outcome variable y given a vector of joint values of other (predictor) variables x. Usually a particular x-vector does not specify a repeatable value for y, but rather a probability distribution of possible y--values, p(y|x). This distribution has a location, scale and shape, all of which can depend on x, and are needed to infer likely values for y given x. Regression methods usually assume that training data y-values are perfect numeric realizations from some well behaived p(y|x). Often actual training data y-values are discrete, truncated and/or arbitrary censored. Regression procedures based on an optimal transformation strategy are presented for estimating location, scale and shape of p(y|x) as general functions of x, in the possible presence of such imperfect training data. In addition, validation diagnostics are presented to ascertain the quality of the solutions., Comment: 33 pages, 19 figures

Often machine learning methods are applied and results reported in cases where there is little to no information concerning accuracy of the output. Simply because a computer program returns a result does not insure its validity. If decisions are to be made based on such results it is important to have some notion of their veracity. Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods. In situations where inaccuracies are detected boosted contrast trees can often improve performance. A special case, distribution boosting, provides an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values., Comment: 18 pages, 20 figures

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications.

\texttt{rCOSA} is a software package interfaced to the R language. It implements statistical techniques for clustering objects on subsets of attributes in multivariate data. The main output of COSA is a dissimilarity matrix that one can subsequently analyze with a variety of proximity analysis methods. Our package extends the original COSA software (Friedman and Meulman, 2004) by adding functions for hierarchical clustering methods, least squares multidimensional scaling, partitional clustering, and data visualization. In the many publications that cite the COSA paper by Friedman and Meulman (2004), the COSA program is actually used only a small number of times. This can be attributed to the fact that thse original implementation is not very easy to install and use. Moreover, the available software is out-of-date. Here, we introduce an up-to-date software package and a clear guidance for this advanced technique. The software package and related links are available for free at: \url{https://github.com/mkampert/rCOSA}, Comment: Accepted for publication by the Journal of Classification