We propose a method for obtaining statistically guaranteed confidence bands for functional machine learning techniques: surrogate models which map between function spaces, motivated by the need build reliable PDE emulators. The method constructs nested confidence sets on a low-dimensional representation (an SVD) of the surrogate model's prediction error, and then maps these sets to the prediction space using set-propagation techniques. The result are conformal-like coverage guaranteed prediction sets for functional surrogate models. We use zonotopes as basis of the set construction, due to their well studied set-propagation and verification properties. The method is model agnostic and can thus be applied to complex Sci-ML models, including Neural Operators, but also in simpler settings. We also elicit a technique to capture the truncation error of the SVD, ensuring the guarantees of the method.

Lagrangian decomposition (LD) is a relaxation method that provides a dual bound for constrained optimization problems by decomposing them into more manageable sub-problems. This bound can be used in branch-and-bound algorithms to prune the search space effectively. In brief, a vector of Lagrangian multipliers is associated with each sub-problem, and an iterative procedure (e.g., a sub-gradient optimization) adjusts these multipliers to find the tightest bound. Initially applied to integer programming, Lagrangian decomposition also had success in constraint programming due to its versatility and the fact that global constraints provide natural sub-problems. However, the non-linear and combinatorial nature of sub-problems in constraint programming makes it computationally intensive to optimize the Lagrangian multipliers with sub-gradient methods at each node of the tree search. This currently limits the practicality of LD as a general bounding mechanism for constraint programming. To address this challenge, we propose a self-supervised learning approach that leverages neural networks to generate multipliers directly, yielding tight bounds. This approach significantly reduces the number of sub-gradient optimization steps required, enhancing the pruning efficiency and reducing the execution time of constraint programming solvers. This contribution is one of the few that leverage learning to enhance bounding mechanisms on the dual side, a critical element in the design of combinatorial solvers. To our knowledge, this work presents the first generic method for learning valid dual bounds in constraint programming.

Lagrangian relaxation is a versatile mathematical technique employed to relax constraints in an optimization problem, enabling the generation of dual bounds to prove the optimality of feasible solutions and the design of efficient propagators in constraint programming (such as the weighted circuit constraint). However, the conventional process of deriving Lagrangian multipliers (e.g., using subgradient methods) is often computationally intensive, limiting its practicality for large-scale or time-sensitive problems. To address this challenge, we propose an innovative unsupervised learning approach that harnesses the capabilities of graph neural networks to exploit the problem structure, aiming to generate accurate Lagrangian multipliers efficiently. We apply this technique to the well-known Held-Karp Lagrangian relaxation for the travelling salesman problem. The core idea is to predict accurate Lagrangian multipliers and to employ them as a warm start for generating Held-Karp relaxation bounds. These bounds are subsequently utilized to enhance the filtering process carried out by branch-and-bound algorithms. In contrast to much of the existing literature, which primarily focuses on finding feasible solutions, our approach operates on the dual side, demonstrating that learning can also accelerate the proof of optimality. We conduct experiments across various distributions of the metric travelling salesman problem, considering instances with up to 200 cities. The results illustrate that our approach can improve the filtering level of the weighted circuit global constraint, reduce the optimality gap by a factor two for unsolved instances up to a timeout, and reduce the execution time for solved instances by 10%.

COVID-19 posed plenty of challenges for folks around the world, but parents of school-aged children had a particularly hard time. As millions of students were forced to attend virtual school, many parents were too busy with their own WFH woes to keep constant tabs on what their kids were doing. Many schools, likewise, were ill-prepared for the realities of digital learning, and keeping kids engaged in virtual lessons was difficult. It came as no surprise, then, when declines in learning relative to previous years were observed in students. In June 2021, market research firm Ipsos surveyed US parents with school-aged kids on the future of education.

This paper presents a machine learning approach to model the electric consumption of electric vehicles at macroscopic level, i.e., in the absence of a speed profile, while preserving microscopic level accuracy. For this work, we leveraged a high-performance, agent-based transportation tool to model trips that occur in the Greater Chicago region under various scenario changes, along with physics-based modeling and simulation tools to provide high-fidelity energy consumption values. The generated results constitute a very large dataset of vehicle-route energy outcomes that capture variability in vehicle and routing setting, and in which high-fidelity time series of vehicle speed dynamics is masked. We show that although all internal dynamics that affect energy consumption are masked, it is possible to learn aggregate-level energy consumption values quite accurately with a deep learning approach. When large-scale data is available, and with carefully tailored feature engineering, a well-designed model can overcome and retrieve latent information. This model has been deployed and integrated within POLARIS Transportation System Simulation Tool to support real-time behavioral transportation models for individual charging decision-making, and rerouting of electric vehicles.

This paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to efficiently identify the most cost-effective medium and heavy duty truck (MDHDT) powertrain technology, from a total cost of ownership (TCO) perspective, for given trips. We employ a machine learning based approach to efficiently estimate the energy consumption of various candidate vehicles over given routes, defined as sequences of links (road segments), with little information known about internal dynamics, i.e using high level macroscopic route information. A complete recommendation logic is then developed to allow for real-time optimum assignment for each route, subject to the operational constraints of the fleet. We show how this framework can be used to (1) efficiently provide a single trip recommendation with a top-$k$ vehicles star ranking system, and (2) engage in more general assignment problems where $n$ vehicles need to be deployed over $m \leq n$ trips. This new assignment system has been deployed and integrated into the POLARIS Transportation System Simulation Tool for use in research conducted by the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Consortium

When she was a practicing occupational therapist, Elizabeth Fain started noticing something odd in her clinic: Her patients were weak. More specifically, their grip strengths, recorded via a hand-held dynamometer, were "not anywhere close to the norms" that had been established back in the 1980s. Fain knew that physical activity levels and hand-use patterns had changed a lot since then. Jobs had become increasingly automated, the professional and service sectors had grown, all sorts of measures of physical activity (like the likelihood that a child walks to school1) had declined, and the personal computer age had dawned. But to see the numbers decline so steeply and quickly was still a surprise, and not just to her.

Of all the prejudices of pundits, presentism is the strongest. It is the assumption that what is happening now is going to keep on happening, without anything happening to stop it. If the West has broken down the Berlin Wall and McDonald's opens in St. Petersburg, then history is over and Thomas Friedman is content. If, by a margin so small that in a voice vote you would have no idea who won, Brexit happens; or if, by a trick of an antique electoral system designed to give country people more power than city people, a Donald Trump is elected, then pluralist constitutional democracy is finished. The liberal millennium was upon us as the year 2000 dawned; fifteen years later, the autocratic apocalypse is at hand. You would think that people who think for a living would pause and reflect that whatever is happening usually does stop happening, and something else happens in its place; a baby who is crying now will stop crying sooner or later. Exhaustion, or a change of mood, or a passing sound, or a bright light, something, always happens next. But for the parents the wait can feel the same as forever, and for many pundits, too, now is the only time worth knowing, for now is when the baby is crying and now is when they're selling your books.

When she was a practicing occupational therapist, Elizabeth Fain started noticing something odd in her clinic: Her patients were weak. More specifically, their grip strengths, recorded via a hand-held dynamometer, were "not anywhere close to the norms" that had been established back in the 1980s. Fain knew that physical activity levels and hand-use patterns had changed a lot since then. Jobs had become increasingly automated, the professional and service sectors had grown, all sorts of measures of physical activity (like the likelihood that a child walks to school1) had declined, and the personal computer age had dawned. But to see the numbers decline so steeply and quickly was still a surprise, and not just to her.

For thousands of years humans believed that authority came from the gods. Then, during the modern era, humanism gradually shifted authority from deities to people. Jean-Jacques Rousseau summed up this revolution in Emile, his 1762 treatise on education. When looking for the rules of conduct in life, Rousseau found them "in the depths of my heart, traced by nature in characters which nothing can efface. I need only consult myself with regard to what I wish to do; what I feel to be good is good, what I feel to be bad is bad." Humanist thinkers such as Rousseau convinced us that our own feelings and desires were the ultimate source of meaning, and that our free will was, therefore, the highest authority of all.