TY - CONF AB - This paper investigates the power of preprocessing in the CONGEST model. Schmid and Suomela (ACM HotSDN 2013) introduced the SUPPORTED CONGEST model to study the application of distributed algorithms in Software-Defined Networks (SDNs). In this paper, we show that a large class of lower bounds in the CONGEST model still hold in the SUPPORTED model, highlighting the robustness of these bounds. This also raises the question how much does preprocessing help in the CONGEST model. AU - Foerster, Klaus-Tycho AU - Korhonen, Janne AU - Rybicki, Joel AU - Schmid, Stefan ID - 6935 SN - 9781450362177 T2 - Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing TI - Does preprocessing help under congestion? ER - TY - JOUR AB - Autoregulation is the direct modulation of gene expression by the product of the corresponding gene. Autoregulation of bacterial gene expression has been mostly studied at the transcriptional level, when a protein acts as the cognate transcriptional repressor. A recent study investigating dynamics of the bacterial toxin–antitoxin MazEF system has shown how autoregulation at both the transcriptional and post-transcriptional levels affects the heterogeneity of Escherichia coli populations. Toxin–antitoxin systems hold a crucial but still elusive part in bacterial response to stress. This perspective highlights how these modules can also serve as a great model system for investigating basic concepts in gene regulation. However, as the genomic background and environmental conditions substantially influence toxin activation, it is important to study (auto)regulation of toxin–antitoxin systems in well-defined setups as well as in conditions that resemble the environmental niche. AU - Nikolic, Nela ID - 138 IS - 1 JF - Current Genetics TI - Autoregulation of bacterial gene expression: lessons from the MazEF toxin–antitoxin system VL - 65 ER - TY - JOUR AB - We construct planar bi-Sobolev mappings whose local volume distortion is bounded from below by a given function f∈Lp with p>1. More precisely, for any 1<q<(p+1)/2 we construct W1,q-bi-Sobolev maps with identity boundary conditions; for f∈L∞, we provide bi-Lipschitz maps. The basic building block of our construction are bi-Lipschitz maps which stretch a given compact subset of the unit square by a given factor while preserving the boundary. The construction of these stretching maps relies on a slight strengthening of the celebrated covering result of Alberti, Csörnyei, and Preiss for measurable planar sets in the case of compact sets. We apply our result to a model functional in nonlinear elasticity, the integrand of which features fast blowup as the Jacobian determinant of the deformation becomes small. For such functionals, the derivation of the equilibrium equations for minimizers requires an additional regularization of test functions, which our maps provide. AU - Fischer, Julian L AU - Kneuss, Olivier ID - 151 IS - 1 JF - Journal of Differential Equations TI - Bi-Sobolev solutions to the prescribed Jacobian inequality in the plane with L p data and applications to nonlinear elasticity VL - 266 ER - TY - JOUR AB - The cerebral cortex is composed of a large variety of distinct cell-types including projection neurons, interneurons and glial cells which emerge from distinct neural stem cell (NSC) lineages. The vast majority of cortical projection neurons and certain classes of glial cells are generated by radial glial progenitor cells (RGPs) in a highly orchestrated manner. Recent studies employing single cell analysis and clonal lineage tracing suggest that NSC and RGP lineage progression are regulated in a profound deterministic manner. In this review we focus on recent advances based mainly on correlative phenotypic data emerging from functional genetic studies in mice. We establish hypotheses to test in future research and outline a conceptual framework how epigenetic cues modulate the generation of cell-type diversity during cortical development. This article is protected by copyright. All rights reserved. AU - Amberg, Nicole AU - Laukoter, Susanne AU - Hippenmeyer, Simon ID - 27 IS - 1 JF - Journal of Neurochemistry TI - Epigenetic cues modulating the generation of cell type diversity in the cerebral cortex VL - 149 ER - TY - JOUR AB - Tissue morphogenesis is driven by mechanical forces that elicit changes in cell size, shape and motion. The extent by which forces deform tissues critically depends on the rheological properties of the recipient tissue. Yet, whether and how dynamic changes in tissue rheology affect tissue morphogenesis and how they are regulated within the developing organism remain unclear. Here, we show that blastoderm spreading at the onset of zebrafish morphogenesis relies on a rapid, pronounced and spatially patterned tissue fluidization. Blastoderm fluidization is temporally controlled by mitotic cell rounding-dependent cell–cell contact disassembly during the last rounds of cell cleavages. Moreover, fluidization is spatially restricted to the central blastoderm by local activation of non-canonical Wnt signalling within the blastoderm margin, increasing cell cohesion and thereby counteracting the effect of mitotic rounding on contact disassembly. Overall, our results identify a fluidity transition mediated by loss of cell cohesion as a critical regulator of embryo morphogenesis. AU - Petridou, Nicoletta AU - Grigolon, Silvia AU - Salbreux, Guillaume AU - Hannezo, Edouard B AU - Heisenberg, Carl-Philipp J ID - 5789 JF - Nature Cell Biology SN - 14657392 TI - Fluidization-mediated tissue spreading by mitotic cell rounding and non-canonical Wnt signalling VL - 21 ER - TY - JOUR AB - The abelian sandpile serves as a model to study self-organized criticality, a phenomenon occurring in biological, physical and social processes. The identity of the abelian group is a fractal composed of self-similar patches, and its limit is subject of extensive collaborative research. Here, we analyze the evolution of the sandpile identity under harmonic fields of different orders. We show that this evolution corresponds to periodic cycles through the abelian group characterized by the smooth transformation and apparent conservation of the patches constituting the identity. The dynamics induced by second and third order harmonics resemble smooth stretchings, respectively translations, of the identity, while the ones induced by fourth order harmonics resemble magnifications and rotations. Starting with order three, the dynamics pass through extended regions of seemingly random configurations which spontaneously reassemble into accentuated patterns. We show that the space of harmonic functions projects to the extended analogue of the sandpile group, thus providing a set of universal coordinates identifying configurations between different domains. Since the original sandpile group is a subgroup of the extended one, this directly implies that it admits a natural renormalization. Furthermore, we show that the harmonic fields can be induced by simple Markov processes, and that the corresponding stochastic dynamics show remarkable robustness over hundreds of periods. Finally, we encode information into seemingly random configurations, and decode this information with an algorithm requiring minimal prior knowledge. Our results suggest that harmonic fields might split the sandpile group into sub-sets showing different critical coefficients, and that it might be possible to extend the fractal structure of the identity beyond the boundaries of its domain. AU - Lang, Moritz AU - Shkolnikov, Mikhail ID - 196 IS - 8 JF - Proceedings of the National Academy of Sciences TI - Harmonic dynamics of the Abelian sandpile VL - 116 ER - TY - CONF AB - Learning disentangled representations is considered a cornerstone problem in representation learning. Recently, Locatello et al. (2019) demonstrated that unsupervised disentanglement learning without inductive biases is theoretically impossible and that existing inductive biases and unsupervised methods do not allow to consistently learn disentangled representations. However, in many practical settings, one might have access to a limited amount of supervision, for example through manual labeling of (some) factors of variation in a few training examples. In this paper, we investigate the impact of such supervision on state-of-the-art disentanglement methods and perform a large scale study, training over 52000 models under well-defined and reproducible experimental conditions. We observe that a small number of labeled examples (0.01--0.5\% of the data set), with potentially imprecise and incomplete labels, is sufficient to perform model selection on state-of-the-art unsupervised models. Further, we investigate the benefit of incorporating supervision into the training process. Overall, we empirically validate that with little and imprecise supervision it is possible to reliably learn disentangled representations. AU - Locatello, Francesco AU - Tschannen, Michael AU - Bauer, Stefan AU - Rätsch, Gunnar AU - Schölkopf, Bernhard AU - Bachem, Olivier ID - 14184 T2 - 8th International Conference on Learning Representations TI - Disentangling factors of variation using few labels ER - TY - CONF AB - We consider the problem of recovering a common latent source with independent components from multiple views. This applies to settings in which a variable is measured with multiple experimental modalities, and where the goal is to synthesize the disparate measurements into a single unified representation. We consider the case that the observed views are a nonlinear mixing of component-wise corruptions of the sources. When the views are considered separately, this reduces to nonlinear Independent Component Analysis (ICA) for which it is provably impossible to undo the mixing. We present novel identifiability proofs that this is possible when the multiple views are considered jointly, showing that the mixing can theoretically be undone using function approximators such as deep neural networks. In contrast to known identifiability results for nonlinear ICA, we prove that independent latent sources with arbitrary mixing can be recovered as long as multiple, sufficiently different noisy views are available. AU - Gresele, Luigi AU - Rubenstein, Paul K. AU - Mehrjou, Arash AU - Locatello, Francesco AU - Schölkopf, Bernhard ID - 14189 T2 - Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence TI - The incomplete Rosetta Stone problem: Identifiability results for multi-view nonlinear ICA VL - 115 ER - TY - CONF AB - Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks. In this paper, we investigate the usefulness of different notions of disentanglement for improving the fairness of downstream prediction tasks based on representations. We consider the setting where the goal is to predict a target variable based on the learned representation of high-dimensional observations (such as images) that depend on both the target variable and an \emph{unobserved} sensitive variable. We show that in this setting both the optimal and empirical predictions can be unfair, even if the target variable and the sensitive variable are independent. Analyzing the representations of more than \num{12600} trained state-of-the-art disentangled models, we observe that several disentanglement scores are consistently correlated with increased fairness, suggesting that disentanglement may be a useful property to encourage fairness when sensitive variables are not observed. AU - Locatello, Francesco AU - Abbati, Gabriele AU - Rainforth, Tom AU - Bauer, Stefan AU - Schölkopf, Bernhard AU - Bachem, Olivier ID - 14197 SN - 9781713807933 T2 - Advances in Neural Information Processing Systems TI - On the fairness of disentangled representations VL - 32 ER - TY - CONF AB - A broad class of convex optimization problems can be formulated as a semidefinite program (SDP), minimization of a convex function over the positive-semidefinite cone subject to some affine constraints. The majority of classical SDP solvers are designed for the deterministic setting where problem data is readily available. In this setting, generalized conditional gradient methods (aka Frank-Wolfe-type methods) provide scalable solutions by leveraging the so-called linear minimization oracle instead of the projection onto the semidefinite cone. Most problems in machine learning and modern engineering applications, however, contain some degree of stochasticity. In this work, we propose the first conditional-gradient-type method for solving stochastic optimization problems under affine constraints. Our method guarantees O(k−1/3) convergence rate in expectation on the objective residual and O(k−5/12) on the feasibility gap. AU - Locatello, Francesco AU - Yurtsever, Alp AU - Fercoq, Olivier AU - Cevher, Volkan ID - 14191 SN - 9781713807933 T2 - Advances in Neural Information Processing Systems TI - Stochastic Frank-Wolfe for composite convex minimization VL - 32 ER - TY - CONF AB - A disentangled representation encodes information about the salient factors of variation in the data independently. Although it is often argued that this representational format is useful in learning to solve many real-world down-stream tasks, there is little empirical evidence that supports this claim. In this paper, we conduct a large-scale study that investigates whether disentangled representations are more suitable for abstract reasoning tasks. Using two new tasks similar to Raven's Progressive Matrices, we evaluate the usefulness of the representations learned by 360 state-of-the-art unsupervised disentanglement models. Based on these representations, we train 3600 abstract reasoning models and observe that disentangled representations do in fact lead to better down-stream performance. In particular, they enable quicker learning using fewer samples. AU - Steenkiste, Sjoerd van AU - Locatello, Francesco AU - Schmidhuber, Jürgen AU - Bachem, Olivier ID - 14193 SN - 9781713807933 T2 - Advances in Neural Information Processing Systems TI - Are disentangled representations helpful for abstract visual reasoning? VL - 32 ER - TY - CONF AB - The key idea behind the unsupervised learning of disentangled representations is that real-world data is generated by a few explanatory factors of variation which can be recovered by unsupervised learning algorithms. In this paper, we provide a sober look at recent progress in the field and challenge some common assumptions. We first theoretically show that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases on both the models and the data. Then, we train more than 12000 models covering most prominent methods and evaluation metrics in a reproducible large-scale experimental study on seven different data sets. We observe that while the different methods successfully enforce properties ``encouraged'' by the corresponding losses, well-disentangled models seemingly cannot be identified without supervision. Furthermore, increased disentanglement does not seem to lead to a decreased sample complexity of learning for downstream tasks. Our results suggest that future work on disentanglement learning should be explicit about the role of inductive biases and (implicit) supervision, investigate concrete benefits of enforcing disentanglement of the learned representations, and consider a reproducible experimental setup covering several data sets. AU - Locatello, Francesco AU - Bauer, Stefan AU - Lucic, Mario AU - Rätsch, Gunnar AU - Gelly, Sylvain AU - Schölkopf, Bernhard AU - Bachem, Olivier ID - 14200 T2 - Proceedings of the 36th International Conference on Machine Learning TI - Challenging common assumptions in the unsupervised learning of disentangled representations VL - 97 ER - TY - JOUR AB - We theoretically study the shapes of lipid vesicles confined to a spherical cavity, elaborating a framework based on the so-called limiting shapes constructed from geometrically simple structural elements such as double-membrane walls and edges. Partly inspired by numerical results, the proposed non-compartmentalized and compartmentalized limiting shapes are arranged in the bilayer-couple phase diagram which is then compared to its free-vesicle counterpart. We also compute the area-difference-elasticity phase diagram of the limiting shapes and we use it to interpret shape transitions experimentally observed in vesicles confined within another vesicle. The limiting-shape framework may be generalized to theoretically investigate the structure of certain cell organelles such as the mitochondrion. AU - Kavcic, Bor AU - Sakashita, A. AU - Noguchi, H. AU - Ziherl, P. ID - 5817 IS - 4 JF - Soft Matter SN - 1744-683X TI - Limiting shapes of confined lipid vesicles VL - 15 ER - TY - JOUR AB - We consider the space of probability measures on a discrete set X, endowed with a dynamical optimal transport metric. Given two probability measures supported in a subset Y⊆X, it is natural to ask whether they can be connected by a constant speed geodesic with support in Y at all times. Our main result answers this question affirmatively, under a suitable geometric condition on Y introduced in this paper. The proof relies on an extension result for subsolutions to discrete Hamilton-Jacobi equations, which is of independent interest. AU - Erbar, Matthias AU - Maas, Jan AU - Wirth, Melchior ID - 73 IS - 1 JF - Calculus of Variations and Partial Differential Equations SN - 09442669 TI - On the geometry of geodesics in discrete optimal transport VL - 58 ER - TY - CONF AB - Learning meaningful and compact representations with disentangled semantic aspects is considered to be of key importance in representation learning. Since real-world data is notoriously costly to collect, many recent state-of-the-art disentanglement models have heavily relied on synthetic toy data-sets. In this paper, we propose a novel data-set which consists of over one million images of physical 3D objects with seven factors of variation, such as object color, shape, size and position. In order to be able to control all the factors of variation precisely, we built an experimental platform where the objects are being moved by a robotic arm. In addition, we provide two more datasets which consist of simulations of the experimental setup. These datasets provide for the first time the possibility to systematically investigate how well different disentanglement methods perform on real data in comparison to simulation, and how simulated data can be leveraged to build better representations of the real world. We provide a first experimental study of these questions and our results indicate that learned models transfer poorly, but that model and hyperparameter selection is an effective means of transferring information to the real world. AU - Gondal, Muhammad Waleed AU - Wüthrich, Manuel AU - Miladinović, Đorđe AU - Locatello, Francesco AU - Breidt, Martin AU - Volchkov, Valentin AU - Akpo, Joel AU - Bachem, Olivier AU - Schölkopf, Bernhard AU - Bauer, Stefan ID - 14190 SN - 9781713807933 T2 - Advances in Neural Information Processing Systems TI - On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset VL - 32 ER - TY - JOUR AB - We present an efficient algorithm for a problem in the interface between clustering and graph embeddings. An embedding ϕ : G → M of a graph G into a 2-manifold M maps the vertices in V(G) to distinct points and the edges in E(G) to interior-disjoint Jordan arcs between the corresponding vertices. In applications in clustering, cartography, and visualization, nearby vertices and edges are often bundled to the same point or overlapping arcs due to data compression or low resolution. This raises the computational problem of deciding whether a given map ϕ : G → M comes from an embedding. A map ϕ : G → M is a weak embedding if it can be perturbed into an embedding ψ ϵ : G → M with ‖ ϕ − ψ ϵ ‖ < ϵ for every ϵ > 0, where ‖.‖ is the unform norm. A polynomial-time algorithm for recognizing weak embeddings has recently been found by Fulek and Kynčl. It reduces the problem to solving a system of linear equations over Z2. It runs in O(n2ω)≤ O(n4.75) time, where ω ∈ [2,2.373) is the matrix multiplication exponent and n is the number of vertices and edges of G. We improve the running time to O(n log n). Our algorithm is also conceptually simpler: We perform a sequence of local operations that gradually “untangles” the image ϕ(G) into an embedding ψ(G) or reports that ϕ is not a weak embedding. It combines local constraints on the orientation of subgraphs directly, thereby eliminating the need for solving large systems of linear equations. AU - Akitaya, Hugo AU - Fulek, Radoslav AU - Tóth, Csaba ID - 6982 IS - 4 JF - ACM Transactions on Algorithms TI - Recognizing weak embeddings of graphs VL - 15 ER - TY - THES AB - Hybrid automata combine finite automata and dynamical systems, and model the interaction of digital with physical systems. Formal analysis that can guarantee the safety of all behaviors or rigorously witness failures, while unsolvable in general, has been tackled algorithmically using, e.g., abstraction, bounded model-checking, assisted theorem proving. Nevertheless, very few methods have addressed the time-unbounded reachability analysis of hybrid automata and, for current sound and automatic tools, scalability remains critical. We develop methods for the polyhedral abstraction of hybrid automata, which construct coarse overapproximations and tightens them incrementally, in a CEGAR fashion. We use template polyhedra, i.e., polyhedra whose facets are normal to a given set of directions. While, previously, directions were given by the user, we introduce (1) the first method for computing template directions from spurious counterexamples, so as to generalize and eliminate them. The method applies naturally to convex hybrid automata, i.e., hybrid automata with (possibly non-linear) convex constraints on derivatives only, while for linear ODE requires further abstraction. Specifically, we introduce (2) the conic abstractions, which, partitioning the state space into appropriate (possibly non-uniform) cones, divide curvy trajectories into relatively straight sections, suitable for polyhedral abstractions. Finally, we introduce (3) space-time interpolation, which, combining interval arithmetic and template refinement, computes appropriate (possibly non-uniform) time partitioning and template directions along spurious trajectories, so as to eliminate them. We obtain sound and automatic methods for the reachability analysis over dense and unbounded time of convex hybrid automata and hybrid automata with linear ODE. We build prototype tools and compare—favorably—our methods against the respective state-of-the-art tools, on several benchmarks. AU - Giacobbe, Mirco ID - 6894 TI - Automatic time-unbounded reachability analysis of hybrid systems ER - TY - GEN AB - The spread of adaptive alleles is fundamental to evolution, and in theory, this process is well‐understood. However, only rarely can we follow this process—whether it originates from the spread of a new mutation, or by introgression from another population. In this issue of Molecular Ecology, Hanemaaijer et al. (2018) report on a 25‐year long study of the mosquitoes Anopheles gambiae (Figure 1) and Anopheles coluzzi in Mali, based on genotypes at 15 single‐nucleotide polymorphism (SNP). The species are usually reproductively isolated from each other, but in 2002 and 2006, bursts of hybridization were observed, when F1 hybrids became abundant. Alleles backcrossed from A. gambiae into A. coluzzi, but after the first event, these declined over the following years. In contrast, after 2006, an insecticide resistance allele that had established in A. gambiae spread into A. coluzzi, and rose to high frequency there, over 6 years (~75 generations). Whole genome sequences of 74 individuals showed that A. gambiae SNP from across the genome had become common in the A. coluzzi population, but that most of these were clustered in 34 genes around the resistance locus. A new set of SNP from 25 of these genes were assayed over time; over the 4 years since near‐fixation of the resistance allele; some remained common, whereas others declined. What do these patterns tell us about this introgression event? AU - Barton, Nicholas H ID - 9805 TI - Data from: The consequences of an introgression event ER - TY - JOUR AB - Despite their different origins, Drosophila glia and hemocytes are related cell populations that provide an immune function. Drosophila hemocytes patrol the body cavity and act as macrophages outside the nervous system whereas glia originate from the neuroepithelium and provide the scavenger population of the nervous system. Drosophila glia are hence the functional orthologs of vertebrate microglia, even though the latter are cells of immune origin that subsequently move into the brain during development. Interestingly, the Drosophila immune cells within (glia) and outside the nervous system (hemocytes) require the same transcription factor Glide/Gcm for their development. This raises the issue of how do glia specifically differentiate in the nervous system and hemocytes in the procephalic mesoderm. The Repo homeodomain transcription factor and pan-glial direct target of Glide/Gcm is known to ensure glial terminal differentiation. Here we show that Repo also takes center stage in the process that discriminates between glia and hemocytes. First, Repo expression is repressed in the hemocyte anlagen by mesoderm-specific factors. Second, Repo ectopic activation in the procephalic mesoderm is sufficient to repress the expression of hemocyte-specific genes. Third, the lack of Repo triggers the expression of hemocyte markers in glia. Thus, a complex network of tissue-specific cues biases the potential of Glide/Gcm. These data allow us to revise the concept of fate determinants and help us understand the bases of cell specification. Both sexes were analyzed.SIGNIFICANCE STATEMENTDistinct cell types often require the same pioneer transcription factor, raising the issue of how does one factor trigger different fates. In Drosophila, glia and hemocytes provide a scavenger activity within and outside the nervous system, respectively. While they both require the Glide/Gcm transcription factor, glia originate from the ectoderm, hemocytes from the mesoderm. Here we show that tissue-specific factors inhibit the gliogenic potential of Glide/Gcm in the mesoderm by repressing the expression of the homeodomain protein Repo, a major glial-specific target of Glide/Gcm. Repo expression in turn inhibits the expression of hemocyte-specific genes in the nervous system. These cell-specific networks secure the establishment of the glial fate only in the nervous system and allow cell diversification. AU - Trébuchet, Guillaume AU - Cattenoz, Pierre B AU - Zsámboki, János AU - Mazaud, David AU - Siekhaus, Daria E AU - Fanto, Manolis AU - Giangrande, Angela ID - 8 IS - 2 JF - Journal of Neuroscience TI - The Repo homeodomain transcription factor suppresses hematopoiesis in Drosophila and preserves the glial fate VL - 39 ER - TY - JOUR AB - In this paper, we introduce a quantum version of the wonderful compactification of a group as a certain noncommutative projective scheme. Our approach stems from the fact that the wonderful compactification encodes the asymptotics of matrix coefficients, and from its realization as a GIT quotient of the Vinberg semigroup. In order to define the wonderful compactification for a quantum group, we adopt a generalized formalism of Proj categories in the spirit of Artin and Zhang. Key to our construction is a quantum version of the Vinberg semigroup, which we define as a q-deformation of a certain Rees algebra, compatible with a standard Poisson structure. Furthermore, we discuss quantum analogues of the stratification of the wonderful compactification by orbits for a certain group action, and provide explicit computations in the case of SL2. AU - Ganev, Iordan V ID - 5 IS - 3 JF - Journal of the London Mathematical Society TI - The wonderful compactification for quantum groups VL - 99 ER -