The transport sector accounts for about 20% of German CO2 emissions, with commuter traffic contributing a significant part. Particularly in rural areas, where public transport is inconvenient to use, private cars are a common choice for commuting and most commuters travel alone in their cars. Consolidation of some of these trips has the potential to decrease CO2 emissions and could be achieved, e.g., by offering ridesharing (commuters with similar origin-destination pairs share a car) or ridepooling (commuters are picked up by shuttle services). In this study, we present a framework to assess the potential of introducing new mobility modes like ridesharing and ridepooling for commuting towards several locations in close vicinity to each other. We test our framework on the case of student mobility to the University of Würzburg, a university with several campus locations and a big and rather rural catchment area, where existing public transport options are inconvenient and many students commute by car. We combine data on student home addresses and campus visitation times to create demand scenarios. In our case study, we compare the mobility modes of ridesharing and ridepooling to the base case, where students travel by car on their own. We find that ridesharing has the potential to greatly reduce emissions, depending on the percentage of students willing to use the service and their willingness to walk to the departure location. The benefit of ridepooling is less clear, materializing only if the shuttle vehicles are more energy efficient than the student cars.

When optimizing transportation systems, anticipating traffic flows is a central element. Yet, computing such traffic equilibria remains computationally expensive. Against this background, we introduce a novel combinatorial optimization augmented neural network architecture that allows for fast and accurate traffic flow predictions. We propose WardropNet, a neural network that combines classical layers with a subsequent equilibrium layer: the first ones inform the latter by predicting the parameterization of the equilibrium problem's latency functions. Using supervised learning we minimize the difference between the actual traffic flow and the predicted output. We show how to leverage a Bregman divergence fitting the geometry of the equilibria, which allows for end-to-end learning. WardropNet outperforms pure learning-based approaches in predicting traffic equilibria for realistic and stylized traffic scenarios. On realistic scenarios, WardropNet improves on average for time-invariant predictions by up to 72% and for time-variant predictions by up to 23% over pure learning-based approaches.

Adapting to regularities of the environment is critical for biological organisms to anticipate events and plan. A prominent example is the circadian rhythm corresponding to the internalization by organisms of the $24$-hour period of the Earth's rotation. In this work, we study the emergence of circadian-like rhythms in deep reinforcement learning agents. In particular, we deployed agents in an environment with a reliable periodic variation while solving a foraging task. We systematically characterize the agent's behavior during learning and demonstrate the emergence of a rhythm that is endogenous and entrainable. Interestingly, the internal rhythm adapts to shifts in the phase of the environmental signal without any re-training. Furthermore, we show via bifurcation and phase response curve analyses how artificial neurons develop dynamics to support the internalization of the environmental rhythm. From a dynamical systems view, we demonstrate that the adaptation proceeds by the emergence of a stable periodic orbit in the neuron dynamics with a phase response that allows an optimal phase synchronisation between the agent's dynamics and the environmental rhythm.

Hand-printed digits can be modeled as splines that are governed by about 8 control points. Images of digits can be produced by placing Gaussian ink generators uniformly along the spline. Real images can be recognized by finding the digit model most likely to have generated the data. For each digit model we use an elastic matching algorithm to minimize an energy function that includes both the defor(cid:173) mation energy of the digit model and the log probability that the model would generate the inked pixels in the image. If a uniform noise process is included in the model of image generation, some of the inked pixels can be rejected as noise as a digit model is fitting a poorly segmented image.

Deformable models are an attractive approach to recognizing non(cid:173) rigid objects which have considerable within class variability. How(cid:173) ever, there are severe search problems associated with fitting the models to data. We show that by using neural networks to provide better starting points, the search time can be significantly reduced. The method is demonstrated on a character recognition task. In previous work we have developed an approach to handwritten character recogni(cid:173) tion based on the use of deformable models (Hinton, Williams and Revow, 1992a; Revow, Williams and Hinton, 1993).

This paper analyzes the impact of COVID-19 related lockdowns in the Atlanta, Georgia metropolitan area by examining commuter patterns in three periods: prior to, during, and after the pandemic lockdown. A cellular phone location dataset is utilized in a novel pipeline to infer the home and work locations of thousands of users from the Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. The coordinates derived from the clustering are put through a reverse geocoding process from which word embeddings are extracted in order to categorize the industry of each work place based on the workplace name and Point of Interest (POI) mapping. Frequencies of commute from home locations to work locations are analyzed in and across all three time periods. Public health and economic factors are discussed to explain potential reasons for the observed changes in commuter patterns.

We all know that alcohol and tweeting is not always a good combination. Yet a surprising number of us indulge in this peculiar form of indiscretion. And this practice has given Nabil Hossain and pals at the University of Rochester an interesting idea. Today, these guys show how they've trained a machine to spot alcohol-related tweets. And they also show how to use this data to monitor alcohol-related activity and the way it is distributed throughout society.

We all know that alcohol and tweeting is not always a good combination. Yet a surprising number of us indulge in this peculiar form of indiscretion. And this practice has given Nabil Hossain and pals at the University of Rochester an interesting idea. Today, these guys show how they've trained a machine to spot alcohol-related tweets. And they also show how to use this data to monitor alcohol-related activity and the way it is distributed throughout society.

The combination of drinking, social media, and sharing has provided Rochester researchers with an innovative test case for analyzing ongoing behavior by Twitter users and then using this analysis to study patterns about drinking in different communities. In a new paper, PhD student Nabil Hossain reports that he and his collaborators have taught computers to analyze tweets about drinking in an effort to predict where Twitter users are when they report drinking. Heatmaps show concentrations of tweets while drinking from Monroe County, NY (left) and New York City. Hossain is a student in the computer science group led by Henry Kautz, the Robin and Tim Wentworth Director of the Goergen Institute for Data Science. He posted the paper on the arXiv.org

We all know that alcohol and tweeting is not always a good combination. Yet a surprising number of us indulge in this peculiar form of indiscretion. And this practice has given Nabil Hossain and pals at the University of Rochester an interesting idea. Today, these guys show how they've trained a machine to spot alcohol-related tweets. And they also show how to use this data to monitor alcohol-related activity and the way it is distributed throughout society.