@inproceedings{6012, abstract = {We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task.}, author = {Sahoo, Subham and Lampert, Christoph and Martius, Georg S}, booktitle = {Proceedings of the 35th International Conference on Machine Learning}, location = {Stockholm, Sweden}, pages = {4442--4450}, publisher = {ML Research Press}, title = {{Learning equations for extrapolation and control}}, volume = {80}, year = {2018}, } @inproceedings{6011, abstract = {We establish a data-dependent notion of algorithmic stability for Stochastic Gradient Descent (SGD), and employ it to develop novel generalization bounds. This is in contrast to previous distribution-free algorithmic stability results for SGD which depend on the worst-case constants. By virtue of the data-dependent argument, our bounds provide new insights into learning with SGD on convex and non-convex problems. In the convex case, we show that the bound on the generalization error depends on the risk at the initialization point. In the non-convex case, we prove that the expected curvature of the objective function around the initialization point has crucial influence on the generalization error. In both cases, our results suggest a simple data-driven strategy to stabilize SGD by pre-screening its initialization. As a corollary, our results allow us to show optimistic generalization bounds that exhibit fast convergence rates for SGD subject to a vanishing empirical risk and low noise of stochastic gradient. }, author = {Kuzborskij, Ilja and Lampert, Christoph}, booktitle = {Proceedings of the 35 th International Conference on Machine Learning}, location = {Stockholm, Sweden}, pages = {2815--2824}, publisher = {ML Research Press}, title = {{Data-dependent stability of stochastic gradient descent}}, volume = {80}, year = {2018}, } @techreport{5686, author = {Danowski, Patrick}, pages = {5}, title = {{An Austrian proposal for the Classification of Open Access Tuples (COAT) - Distinguish different Open Access types beyond colors}}, doi = {10.5281/zenodo.1244154}, year = {2018}, } @inproceedings{6589, abstract = {Distributed training of massive machine learning models, in particular deep neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace. Several families of communication-reduction methods, such as quantization, large-batch methods, and gradient sparsification, have been proposed. To date, gradient sparsification methods--where each node sorts gradients by magnitude, and only communicates a subset of the components, accumulating the rest locally--are known to yield some of the largest practical gains. Such methods can reduce the amount of communication per step by up to \emph{three orders of magnitude}, while preserving model accuracy. Yet, this family of methods currently has no theoretical justification. This is the question we address in this paper. We prove that, under analytic assumptions, sparsifying gradients by magnitude with local error correction provides convergence guarantees, for both convex and non-convex smooth objectives, for data-parallel SGD. The main insight is that sparsification methods implicitly maintain bounds on the maximum impact of stale updates, thanks to selection by magnitude. Our analysis and empirical validation also reveal that these methods do require analytical conditions to converge well, justifying existing heuristics.}, author = {Alistarh, Dan-Adrian and Hoefler, Torsten and Johansson, Mikael and Konstantinov, Nikola H and Khirirat, Sarit and Renggli, Cedric}, booktitle = {Advances in Neural Information Processing Systems 31}, location = {Montreal, Canada}, pages = {5973--5983}, publisher = {Neural Information Processing Systems Foundation}, title = {{The convergence of sparsified gradient methods}}, volume = {Volume 2018}, year = {2018}, } @article{7, abstract = {Animal social networks are shaped by multiple selection pressures, including the need to ensure efficient communication and functioning while simultaneously limiting disease transmission. Social animals could potentially further reduce epidemic risk by altering their social networks in the presence of pathogens, yet there is currently no evidence for such pathogen-triggered responses. We tested this hypothesis experimentally in the ant Lasius niger using a combination of automated tracking, controlled pathogen exposure, transmission quantification, and temporally explicit simulations. Pathogen exposure induced behavioral changes in both exposed ants and their nestmates, which helped contain the disease by reinforcing key transmission-inhibitory properties of the colony's contact network. This suggests that social network plasticity in response to pathogens is an effective strategy for mitigating the effects of disease in social groups.}, author = {Stroeymeyt, Nathalie and Grasse, Anna V and Crespi, Alessandro and Mersch, Danielle and Cremer, Sylvia and Keller, Laurent}, issn = {1095-9203}, journal = {Science}, number = {6417}, pages = {941 -- 945}, publisher = {AAAS}, title = {{Social network plasticity decreases disease transmission in a eusocial insect}}, doi = {10.1126/science.aat4793}, volume = {362}, year = {2018}, } @article{19, abstract = {Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression, and antibiotic potency, which rationalizes our observation that a multitude of genes and even innate antibiotic defense mechanisms have expression that is critically nonoptimal under antibiotic treatment. First, we developed a pooled-strain drug-diffusion assay and screened Escherichia coli overexpression and knockout libraries, finding that resistance to a range of 31 antibiotics could result from changing expression of a large and functionally diverse set of genes, in a primarily but not exclusively drug-specific manner. Second, by synthetically controlling the expression of single-drug and multidrug resistance genes, we observed that their fitness-expression functions changed dramatically under antibiotic treatment in accordance with a log-sensitivity relation. Thus, because many genes are nonoptimally expressed under antibiotic treatment, many regulatory mutations can contribute to resistance by altering expression and by activating latent defenses.}, author = {Palmer, Adam and Chait, Remy P and Kishony, Roy}, issn = {0737-4038}, journal = {Molecular Biology and Evolution}, number = {11}, pages = {2669 -- 2684}, publisher = {Oxford University Press}, title = {{Nonoptimal gene expression creates latent potential for antibiotic resistance}}, doi = {10.1093/molbev/msy163}, volume = {35}, year = {2018}, } @article{6, abstract = {Lesion and electrode location verification are traditionally done via histological examination of stained brain slices, a time-consuming procedure that requires manual estimation. Here, we describe a simple, straightforward method for quantifying lesions and locating electrodes in the brain that is less laborious and yields more detailed results. Whole brains are stained with osmium tetroxide, embedded in resin, and imaged with a micro-CT scanner. The scans result in 3D digital volumes of the brains with resolutions and virtual section thicknesses dependent on the sample size (12-15 and 5-6 µm per voxel for rat and zebra finch brains, respectively). Surface and deep lesions can be characterized, and single tetrodes, tetrode arrays, electrolytic lesions, and silicon probes can also be localized. Free and proprietary software allows experimenters to examine the sample volume from any plane and segment the volume manually or automatically. Because this method generates whole brain volume, lesions and electrodes can be quantified to a much higher degree than in current methods, which will help standardize comparisons within and across studies.}, author = {Masís, Javier and Mankus, David and Wolff, Steffen and Guitchounts, Grigori and Jösch, Maximilian A and Cox, David}, journal = {Journal of visualized experiments}, publisher = {MyJove Corporation}, title = {{A micro-CT-based method for characterising lesions and locating electrodes in small animal brains}}, doi = {10.3791/58585}, volume = {141}, year = {2018}, } @misc{13055, abstract = {Dataset for manuscript 'Social network plasticity decreases disease transmission in a eusocial insect' Compared to previous versions: - raw image files added - correction of URLs within README.txt file }, author = {Stroeymeyt, Nathalie and Grasse, Anna V and Crespi, Alessandro and Mersch, Danielle and Cremer, Sylvia and Keller, Laurent}, publisher = {Zenodo}, title = {{Social network plasticity decreases disease transmission in a eusocial insect}}, doi = {10.5281/ZENODO.1322669}, year = {2018}, } @article{22, abstract = {Conventional ultra-high sensitivity detectors in the millimeter-wave range are usually cooled as their own thermal noise at room temperature would mask the weak received radiation. The need for cryogenic systems increases the cost and complexity of the instruments, hindering the development of, among others, airborne and space applications. In this work, the nonlinear parametric upconversion of millimeter-wave radiation to the optical domain inside high-quality (Q) lithium niobate whispering-gallery mode (WGM) resonators is proposed for ultra-low noise detection. We experimentally demonstrate coherent upconversion of millimeter-wave signals to a 1550 nm telecom carrier, with a photon conversion efficiency surpassing the state-of-the-art by 2 orders of magnitude. Moreover, a theoretical model shows that the thermal equilibrium of counterpropagating WGMs is broken by overcoupling the millimeter-wave WGM, effectively cooling the upconverted mode and allowing ultra-low noise detection. By theoretically estimating the sensitivity of a correlation radiometer based on the presented scheme, it is found that room-temperature radiometers with better sensitivity than state-of-the-art high-electron-mobility transistor (HEMT)-based radiometers can be designed. This detection paradigm can be used to develop room-temperature instrumentation for radio astronomy, earth observation, planetary missions, and imaging systems.}, author = {Botello, Gabriel and Sedlmeir, Florian and Rueda Sanchez, Alfredo R and Abdalmalak, Kerlos and Brown, Elliott and Leuchs, Gerd and Preu, Sascha and Segovia Vargas, Daniel and Strekalov, Dmitry and Munoz, Luis and Schwefel, Harald}, issn = {23342536}, journal = {Optica}, number = {10}, pages = {1210 -- 1219}, title = {{Sensitivity limits of millimeter-wave photonic radiometers based on efficient electro-optic upconverters}}, doi = {10.1364/OPTICA.5.001210}, volume = {5}, year = {2018}, } @article{5677, abstract = {Recently, contract-based design has been proposed as an “orthogonal” approach that complements system design methodologies proposed so far to cope with the complexity of system design. Contract-based design provides a rigorous scaffolding for verification, analysis, abstraction/refinement, and even synthesis. A number of results have been obtained in this domain but a unified treatment of the topic that can help put contract-based design in perspective was missing. This monograph intends to provide such a treatment where contracts are precisely defined and characterized so that they can be used in design methodologies with no ambiguity. In particular, this monograph identifies the essence of complex system design using contracts through a mathematical “meta-theory”, where all the properties of the methodology are derived from a very abstract and generic notion of contract. We show that the meta-theory provides deep and illuminating links with existing contract and interface theories, as well as guidelines for designing new theories. Our study encompasses contracts for both software and systems, with emphasis on the latter. We illustrate the use of contracts with two examples: requirement engineering for a parking garage management, and the development of contracts for timing and scheduling in the context of the Autosar methodology in use in the automotive sector.}, author = {Benveniste, Albert and Nickovic, Dejan and Caillaud, Benoît and Passerone, Roberto and Raclet, Jean Baptiste and Reinkemeier, Philipp and Sangiovanni-Vincentelli, Alberto and Damm, Werner and Henzinger, Thomas A and Larsen, Kim G.}, issn = {1551-3939}, journal = {Foundations and Trends in Electronic Design Automation}, number = {2-3}, pages = {124--400}, publisher = {Now Publishers}, title = {{Contracts for system design}}, doi = {10.1561/1000000053}, volume = {12}, year = {2018}, }