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In this study, a novel approach is demonstrated for converting calorimeter images from fast simulations to those akin to comprehensive full simulations, utilizing conditional Generative Adversarial Networks (GANs). The concept of pix2pix is tailored for CALPAGAN, where images from fast simulations serve as the basis(condition) for generating outputs that closely resemble those from detailed simulations. The findings indicate a strong correlation between the generated images and those from full simulations, especially in terms of key observables like jet transverse momentum distribution, jet mass, jet subjettiness, and jet girth. Additionally, the paper explores the efficacy of this method and its intrinsic limitations. This research marks a significant step towards exploring more efficient simulation methodologies in High Energy Particle Physics.

We present a new statistical model for predicting discrete events continuously in time, called Reactive Point Processes (RPP’s). RPP’s are a natural fit for many domains where time-series data are available, and their development was motivated by the important problem of predicting serious events (fires, explosions, power failures) in the underground electrical grid of New York City (NYC). RPP’s capture several important properties of this domain: •There is an instantaneous rise in vulnerability to future serious events immediately following an occurrence of a past serious event, and this rise in vulnerability gradually fades back to the baseline level. This is a type of self-exciting property. •There is an instantaneous decrease in vulnerability due to an inspection or repair. The effect of this inspection fades gradually over time. This is a self-regulating property. •The cumulative effect of events or inspections can saturate, ensuring that vulnerability levels never stray too far beyond their baseline level. This captures diminishing returns of many events or inspections in a row.

The problem of “approximating the crowd” is that of estimating the crowd’s majority opinion by querying only a subset of it. Algorithms that approximate the crowd can intelligently stretch a limited budget for a crowdsourcing task. We present an algorithm, “CrowdSense,” that works in an online fashion where items come one at a time. CrowdSense dynamically samples subsets of the crowd based on an exploration/exploitation criterion. The algorithm produces a weighted combination of the subset’s votes that approximates the crowd’s opinion. We then introduce two variations of CrowdSense that make various distributional approximations to handle distinct crowd characteristics. In particular, the first algorithm makes a statistical independence approximation of the labelers for large crowds, whereas the second algorithm finds a lower bound on how often the current subcrowd agrees with the crowd’s majority vote. Our experiments on CrowdSense and several baselines demonstrate that we can reliably approximate the entire crowd’s vote by collecting opinions from a representative subset of the crowd.

Reactive point processes (RPPs) are a new statistical model designed for predicting discrete events in time based on past history. RPPs were developed to handle an important problem within the domain of electrical grid reliability: short-term prediction of electrical grid failures (“manhole events”), including outages, fires, explosions and smoking manholes, which can cause threats to public safety and reliability of electrical service in cities. RPPs incorporate self-exciting, self-regulating and saturating components. The self-excitement occurs as a result of a past event, which causes a temporary rise in vulner ability to future events. The self-regulation occurs as a result of an external inspection which temporarily lowers vulnerability to future events. RPPs can saturate when too many events or inspections occur close together, which ensures that the probability of an event stays within a realistic range. Two of the operational challenges for power companies are (i) making continuous-time failure predictions, and (ii) cost/benefit analysis for decision making and proactive maintenance. RPPs are naturally suited for handling both of these challenges. We use the model to predict power-grid failures in Manhattan over a short-term horizon, and to provide a cost/benefit analysis of different proactive maintenance programs., Con Edison, MIT Energy Initiative (Seed Fund), National Science Foundation (U.S.) (CAREER Grant IIS-1053407)

We summarize the first major effort to use analytics for preemptive maintenance and repair of an electrical distribution network. This is a large-scale multiyear effort between scientists and students at Columbia University and the Massachusetts Institute of Technology and engineers from the Consolidated Edison Company of New York (Con Edison), which operates the world’s oldest and largest underground electrical system. Con Edison’s preemptive maintenance programs are less than a decade old and are made more effective with the use of analytics developing alongside them. Some of the data we used for our projects are historical records dating as far back as the 1880s, and some of the data are free-text documents typed by Con Edison dispatchers. The operational goals of this work are to assist with Con Edison’s preemptive inspection and repair program and its vented-cover replacement program. This has a continuing impact on the public safety, operating costs, and reliability of electrical service in New York City.

Reactive point processes (RPP’s) are a new statistical model designed for predicting discrete events, incorporating self-exciting, self-regulating, and saturating components. The self-excitement occurs as a result of a past event, which causes a temporary rise in vulnerability to future events. The self-regulation occurs as a result of an external “inspection” which temporarily lowers vulnerability to future events. RPP’s can saturate when too many events or inspections occur close together, which ensures that the probability of an event stays within a realistic range. RPP’s were developed to handle an important problem within the domain of electrical grid reliability: short term prediction of electrical grid failures (“manhole events”), including outages, fires, explosions, and smoking manholes, which can cause threats to public safety and reliability of electrical service in cities. For the self-exciting, self-regulating, and saturating elements of the model, we develop both a nonparametric estimation strategy and introduce a class of flexible parametric functions reflecting how the influence of past events and inspections on vulnerability levels gradually fades over time. We use the model to predict power grid failures in Manhattan over a short term horizon.

We demonstrate that there are machine learning algorithms that can achieve success for two separate tasks simultaneously, namely the tasks of classification and bipartite ranking. This means that advantages gained from solving one task can be carried over to the other task, such as the ability to obtain conditional density estimates, and an order-of-magnitude reduction in computational time for training the algorithm. It also means that some algorithms are robust to the choice of evaluation metric used; they can theoretically perform well when performance is measured either by a misclassification error or by a statistic of the ROC curve (such as the area under the curve). Specifically, we provide such an equivalence relationship between a generalization of Freund et al.'s RankBoost algorithm, called the "P-Norm Push," and a particular cost-sensitive classification algorithm that generalizes AdaBoost, which we call "P-Classification." We discuss and validate the potential benefits of this equivalence relationship, and perform controlled experiments to understand P-Classification's empirical performance. There is no established equivalence relationship for logistic regression and its ranking counterpart, so we introduce a logistic-regression-style algorithm that aims in between classification and ranking, and has promising experimental performance with respect to both tasks., National Science Foundation (U.S.) (grant number IIS-1053407)

We use molecular dynamics and simple thermodynamic arguments to model the interaction between catalyst nanoparticles and carbon nanotube caps, and we illustrate how the competition between cap strain energy and adhesion plays a role in the lifting of these caps from the catalyst surface prior to tube elongation. Given a particular cap structure, we show that there is a lower bound on the catalyst size from which the cap can lift. This lower bound depends on the cap’s spontaneous curvature and bending rigidity, as well as the catalyst binding strength, and it explains the mismatch between single-walled carbon nanotube and catalyst diameters observed in prior experiments. These findings offer new insight into the nucleation of carbon nanotubes, and they may lead to the design of catalysts that can better control nanotube structure., Royal Society of New Zealand. Marsden Fund (Contract No. IRL0602), MacDiarmid Institute for Advanced Materials and Nanotechnology, New Zealand. Ministry of Research, Science, and Technology, Fulbright New Zealand