Relatively low travel costs and abundant opportunities for research funding in Switzerland and other developed countries allow researchers large amounts of international travel and collaborations, leading to a substantial carbon footprint. Increasing willingness to tackle this issue, in combination with the desire of many academic institutions to become carbon-neutral, calls for an in-depth understanding of academic air travel. In this study, we quantified and analyzed the carbon footprint of air travel by researchers from the École Polytechnique Fédérale de Lausanne (EPFL) from 2014 to 2016, which is responsible for about one third of EPFL’s total CO₂ emissions. We find that the air travel impact of individual researchers is highly unequally distributed, with 10% of the EPFL researchers causing almost 60% of the total emissions from EPFL air travel. The travel footprint increases drastically with researcher seniority, increasing 10-fold from PhD students to professors. We found that simple measures such as restricting to economy class, replacing short trips by train and avoiding layovers already have the potential to reduce emissions by 36%. These findings can help academic institutions to implement travel policies which can mitigate the climate impact of their air travel.

We study a caching problem that resembles a lossy Gray-Wyner network: A source produces vector samples from a Gaussian distribution, but the user is interested in the samples of only one component. The encoder first sends a cache message without any knowledge of the user's preference. Upon learning her request, a second message is provided in the update phase so as to attain the desired fidelity on that component. The cache is efficient if it exploits as much of the correlation in the source as possible, which connects to the notions of Wyner's common information (for high cache rates) and Watanabe's total correlation (for low cache rates). For the former, we extend known results for 2 Gaussians to multivariates by showing that common information is a simple linear program, which can be solved analytically for circulant correlation matrices. Total correlation in a Gaussian setting is less well-studied. We show that for bivariates and using Gaussian auxiliaries it is captured in the dominant eigenvalue of the correlation matrix. For multivariates the problem is a more difficult optimization over a non-convex domain, but we conjecture that circulant matrices may again be analytically solvable.

We study a generalization of Wyner's Common Information to Watanabe's Total Correlation. The first minimizes the description size required for a variable that can make two other random variables conditionally independent. If independence is unattainable, Watanabe's total (conditional) correlation is measure to check just how independent they have become. Following up on earlier work for scalar Gaussians, we discuss the minimization of total correlation for Gaussian vector sources. Using Gaussian auxiliaries, we show one should transform two vectors of length d into d independent pairs, after which a reverse water filling procedure distributes the minimization over all these pairs. Lastly, we show how this minimization of total conditional correlation fits a lossy coding problem by using the Gray-Wyner network as a model for a caching problem.

Caching is a technique that alleviates networks during peak hours by transmitting partial information before a request for any is made. In a lossy setting of Gaussian databases, we study a single-user model in which good caching strategies minimize the data still needed on average once the user requests a file. The encoder decides on a caching strategy by weighing the benefit from two key parameters: the prior preference for a file and the correlation among the files. Considering uniform prior preference but correlated files, caching becomes an application of Wyner’s common information and Watanabe’s total correlation. We show this case triggers a split: caching Gaussian sources is a non-convex optimization problem unless one spends enough rate to cache all the common information between files. Combining both correlation and user preference we explicitly characterize the full trade-off when the encoder uses Gaussian codebooks in a database of two files: we show that as the size of the cache increases, the encoder should change strategy and increasingly prioritize user preference over correlation. In this specific case we also address the loss in performance incurred if the encoder has no knowledge of the user’s preference and show that this loss is bounded.

Concerning sub-Nyquist sampling, this paper continues research on the modulated wideband converter (MWC), which uses compressed sensing (CS) to sample multiband signals with unknown carriers below their Nyquist rate. The minimum sampling rate for blind acquisition of these signals is 2NB, with N the number of bands and B their bandwidth. The MWC performs at a rate 4NB; it features a factor 2 more sampling channels in parallel than the minimum. Investigated is a new CS paradigm, one that changes its compression matrix over time. It is believed that through this, the sampling rate can be brought to the minimum. Numerical simulations are performed to check the feasibility of the approach.