@inproceedings{7402, abstract = {Graph planning gives rise to fundamental algorithmic questions such as shortest path, traveling salesman problem, etc. A classical problem in discrete planning is to consider a weighted graph and construct a path that maximizes the sum of weights for a given time horizon T. However, in many scenarios, the time horizon is not fixed, but the stopping time is chosen according to some distribution such that the expected stopping time is T. If the stopping time distribution is not known, then to ensure robustness, the distribution is chosen by an adversary, to represent the worst-case scenario. A stationary plan for every vertex always chooses the same outgoing edge. For fixed horizon or fixed stopping-time distribution, stationary plans are not sufficient for optimality. Quite surprisingly we show that when an adversary chooses the stopping-time distribution with expected stopping time T, then stationary plans are sufficient. While computing optimal stationary plans for fixed horizon is NP-complete, we show that computing optimal stationary plans under adversarial stopping-time distribution can be achieved in polynomial time. Consequently, our polynomial-time algorithm for adversarial stopping time also computes an optimal plan among all possible plans.}, author = {Chatterjee, Krishnendu and Doyen, Laurent}, booktitle = {34th Annual ACM/IEEE Symposium on Logic in Computer Science}, isbn = {9781728136080}, location = {Vancouver, BC, Canada}, pages = {1--13}, publisher = {IEEE}, title = {{Graph planning with expected finite horizon}}, doi = {10.1109/lics.2019.8785706}, year = {2019}, } @article{7404, abstract = {The formation of neuronal dendrite branches is fundamental for the wiring and function of the nervous system. Indeed, dendrite branching enhances the coverage of the neuron's receptive field and modulates the initial processing of incoming stimuli. Complex dendrite patterns are achieved in vivo through a dynamic process of de novo branch formation, branch extension and retraction. The first step towards branch formation is the generation of a dynamic filopodium-like branchlet. The mechanisms underlying the initiation of dendrite branchlets are therefore crucial to the shaping of dendrites. Through in vivo time-lapse imaging of the subcellular localization of actin during the process of branching of Drosophila larva sensory neurons, combined with genetic analysis and electron tomography, we have identified the Actin-related protein (Arp) 2/3 complex as the major actin nucleator involved in the initiation of dendrite branchlet formation, under the control of the activator WAVE and of the small GTPase Rac1. Transient recruitment of an Arp2/3 component marks the site of branchlet initiation in vivo. These data position the activation of Arp2/3 as an early hub for the initiation of branchlet formation.}, author = {Stürner, Tomke and Tatarnikova, Anastasia and Müller, Jan and Schaffran, Barbara and Cuntz, Hermann and Zhang, Yun and Nemethova, Maria and Bogdan, Sven and Small, Vic and Tavosanis, Gaia}, issn = {1477-9129}, journal = {Development}, number = {7}, publisher = {The Company of Biologists}, title = {{Transient localization of the Arp2/3 complex initiates neuronal dendrite branching in vivo}}, doi = {10.1242/dev.171397}, volume = {146}, year = {2019}, } @article{7405, abstract = {Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.}, author = {Dura-Bernal, Salvador and Suter, Benjamin and Gleeson, Padraig and Cantarelli, Matteo and Quintana, Adrian and Rodriguez, Facundo and Kedziora, David J and Chadderdon, George L and Kerr, Cliff C and Neymotin, Samuel A and McDougal, Robert A and Hines, Michael and Shepherd, Gordon MG and Lytton, William W}, issn = {2050-084X}, journal = {eLife}, publisher = {eLife Sciences Publications}, title = {{NetPyNE, a tool for data-driven multiscale modeling of brain circuits}}, doi = {10.7554/elife.44494}, volume = {8}, year = {2019}, } @inproceedings{7411, abstract = {Proofs of sequential work (PoSW) are proof systems where a prover, upon receiving a statement χ and a time parameter T computes a proof ϕ(χ,T) which is efficiently and publicly verifiable. The proof can be computed in T sequential steps, but not much less, even by a malicious party having large parallelism. A PoSW thus serves as a proof that T units of time have passed since χ was received. PoSW were introduced by Mahmoody, Moran and Vadhan [MMV11], a simple and practical construction was only recently proposed by Cohen and Pietrzak [CP18]. In this work we construct a new simple PoSW in the random permutation model which is almost as simple and efficient as [CP18] but conceptually very different. Whereas the structure underlying [CP18] is a hash tree, our construction is based on skip lists and has the interesting property that computing the PoSW is a reversible computation. The fact that the construction is reversible can potentially be used for new applications like constructing proofs of replication. We also show how to “embed” the sloth function of Lenstra and Weselowski [LW17] into our PoSW to get a PoSW where one additionally can verify correctness of the output much more efficiently than recomputing it (though recent constructions of “verifiable delay functions” subsume most of the applications this construction was aiming at).}, author = {Abusalah, Hamza M and Kamath Hosdurg, Chethan and Klein, Karen and Pietrzak, Krzysztof Z and Walter, Michael}, booktitle = {Advances in Cryptology – EUROCRYPT 2019}, isbn = {9783030176556}, issn = {1611-3349}, location = {Darmstadt, Germany}, pages = {277--291}, publisher = {Springer International Publishing}, title = {{Reversible proofs of sequential work}}, doi = {10.1007/978-3-030-17656-3_10}, volume = {11477}, year = {2019}, } @article{7412, abstract = {We develop a framework for the rigorous analysis of focused stochastic local search algorithms. These algorithms search a state space by repeatedly selecting some constraint that is violated in the current state and moving to a random nearby state that addresses the violation, while (we hope) not introducing many new violations. An important class of focused local search algorithms with provable performance guarantees has recently arisen from algorithmizations of the Lovász local lemma (LLL), a nonconstructive tool for proving the existence of satisfying states by introducing a background measure on the state space. While powerful, the state transitions of algorithms in this class must be, in a precise sense, perfectly compatible with the background measure. In many applications this is a very restrictive requirement, and one needs to step outside the class. Here we introduce the notion of measure distortion and develop a framework for analyzing arbitrary focused stochastic local search algorithms, recovering LLL algorithmizations as the special case of no distortion. Our framework takes as input an arbitrary algorithm of such type and an arbitrary probability measure and shows how to use the measure as a yardstick of algorithmic progress, even for algorithms designed independently of the measure.}, author = {Achlioptas, Dimitris and Iliopoulos, Fotis and Kolmogorov, Vladimir}, issn = {1095-7111}, journal = {SIAM Journal on Computing}, number = {5}, pages = {1583--1602}, publisher = {SIAM}, title = {{A local lemma for focused stochastical algorithms}}, doi = {10.1137/16m109332x}, volume = {48}, year = {2019}, } @article{7413, abstract = {We consider Bose gases consisting of N particles trapped in a box with volume one and interacting through a repulsive potential with scattering length of order N−1 (Gross–Pitaevskii regime). We determine the ground state energy and the low-energy excitation spectrum, up to errors vanishing as N→∞. Our results confirm Bogoliubov’s predictions.}, author = {Boccato, Chiara and Brennecke, Christian and Cenatiempo, Serena and Schlein, Benjamin}, issn = {1871-2509}, journal = {Acta Mathematica}, number = {2}, pages = {219--335}, publisher = {International Press of Boston}, title = {{Bogoliubov theory in the Gross–Pitaevskii limit}}, doi = {10.4310/acta.2019.v222.n2.a1}, volume = {222}, year = {2019}, } @article{7420, abstract = {β1-integrins mediate cell–matrix interactions and their trafficking is important in the dynamic regulation of cell adhesion, migration and malignant processes, including cancer cell invasion. Here, we employ an RNAi screen to characterize regulators of integrin traffic and identify the association of Golgi-localized gamma ear-containing Arf-binding protein 2 (GGA2) with β1-integrin, and its role in recycling of active but not inactive β1-integrin receptors. Silencing of GGA2 limits active β1-integrin levels in focal adhesions and decreases cancer cell migration and invasion, which is in agreement with its ability to regulate the dynamics of active integrins. By using the proximity-dependent biotin identification (BioID) method, we identified two RAB family small GTPases, i.e. RAB13 and RAB10, as novel interactors of GGA2. Functionally, RAB13 silencing triggers the intracellular accumulation of active β1-integrin, and reduces integrin activity in focal adhesions and cell migration similarly to GGA2 depletion, indicating that both facilitate active β1-integrin recycling to the plasma membrane. Thus, GGA2 and RAB13 are important specificity determinants for integrin activity-dependent traffic.}, author = {Sahgal, Pranshu and Alanko, Jonna H and Icha, Jaroslav and Paatero, Ilkka and Hamidi, Hellyeh and Arjonen, Antti and Pietilä, Mika and Rokka, Anne and Ivaska, Johanna}, issn = {1477-9137}, journal = {Journal of Cell Science}, number = {11}, publisher = {The Company of Biologists}, title = {{GGA2 and RAB13 promote activity-dependent β1-integrin recycling}}, doi = {10.1242/jcs.233387}, volume = {132}, year = {2019}, } @article{7422, abstract = {Biochemical reactions often occur at low copy numbers but at once in crowded and diverse environments. Space and stochasticity therefore play an essential role in biochemical networks. Spatial-stochastic simulations have become a prominent tool for understanding how stochasticity at the microscopic level influences the macroscopic behavior of such systems. While particle-based models guarantee the level of detail necessary to accurately describe the microscopic dynamics at very low copy numbers, the algorithms used to simulate them typically imply trade-offs between computational efficiency and biochemical accuracy. eGFRD (enhanced Green’s Function Reaction Dynamics) is an exact algorithm that evades such trade-offs by partitioning the N-particle system into M ≤ N analytically tractable one- and two-particle systems; the analytical solutions (Green’s functions) then are used to implement an event-driven particle-based scheme that allows particles to make large jumps in time and space while retaining access to their state variables at arbitrary simulation times. Here we present “eGFRD2,” a new eGFRD version that implements the principle of eGFRD in all dimensions, thus enabling efficient particle-based simulation of biochemical reaction-diffusion processes in the 3D cytoplasm, on 2D planes representing membranes, and on 1D elongated cylinders representative of, e.g., cytoskeletal tracks or DNA; in 1D, it also incorporates convective motion used to model active transport. We find that, for low particle densities, eGFRD2 is up to 6 orders of magnitude faster than conventional Brownian dynamics. We exemplify the capabilities of eGFRD2 by simulating an idealized model of Pom1 gradient formation, which involves 3D diffusion, active transport on microtubules, and autophosphorylation on the membrane, confirming recent experimental and theoretical results on this system to hold under genuinely stochastic conditions.}, author = {Sokolowski, Thomas R and Paijmans, Joris and Bossen, Laurens and Miedema, Thomas and Wehrens, Martijn and Becker, Nils B. and Kaizu, Kazunari and Takahashi, Koichi and Dogterom, Marileen and ten Wolde, Pieter Rein}, issn = {1089-7690}, journal = {The Journal of Chemical Physics}, number = {5}, publisher = {AIP Publishing}, title = {{eGFRD in all dimensions}}, doi = {10.1063/1.5064867}, volume = {150}, year = {2019}, } @article{7423, abstract = {We compare finite rank perturbations of the following three ensembles of complex rectangular random matrices: First, a generalised Wishart ensemble with one random and two fixed correlation matrices introduced by Borodin and Péché, second, the product of two independent random matrices where one has correlated entries, and third, the case when the two random matrices become also coupled through a fixed matrix. The singular value statistics of all three ensembles is shown to be determinantal and we derive double contour integral representations for their respective kernels. Three different kernels are found in the limit of infinite matrix dimension at the origin of the spectrum. They depend on finite rank perturbations of the correlation and coupling matrices and are shown to be integrable. The first kernel (I) is found for two independent matrices from the second, and two weakly coupled matrices from the third ensemble. It generalises the Meijer G-kernel for two independent and uncorrelated matrices. The third kernel (III) is obtained for the generalised Wishart ensemble and for two strongly coupled matrices. It further generalises the perturbed Bessel kernel of Desrosiers and Forrester. Finally, kernel (II), found for the ensemble of two coupled matrices, provides an interpolation between the kernels (I) and (III), generalising previous findings of part of the authors.}, author = {Akemann, Gernot and Checinski, Tomasz and Liu, Dangzheng and Strahov, Eugene}, issn = {0246-0203}, journal = {Annales de l'Institut Henri Poincaré, Probabilités et Statistiques}, number = {1}, pages = {441--479}, publisher = {Institute of Mathematical Statistics}, title = {{Finite rank perturbations in products of coupled random matrices: From one correlated to two Wishart ensembles}}, doi = {10.1214/18-aihp888}, volume = {55}, year = {2019}, } @article{7436, abstract = {For an ordinary K3 surface over an algebraically closed field of positive characteristic we show that every automorphism lifts to characteristic zero. Moreover, we show that the Fourier-Mukai partners of an ordinary K3 surface are in one-to-one correspondence with the Fourier-Mukai partners of the geometric generic fiber of its canonical lift. We also prove that the explicit counting formula for Fourier-Mukai partners of the K3 surfaces with Picard rank two and with discriminant equal to minus of a prime number, in terms of the class number of the prime, holds over a field of positive characteristic as well. We show that the image of the derived autoequivalence group of a K3 surface of finite height in the group of isometries of its crystalline cohomology has index at least two. Moreover, we provide a conditional upper bound on the kernel of this natural cohomological descent map. Further, we give an extended remark in the appendix on the possibility of an F-crystal structure on the crystalline cohomology of a K3 surface over an algebraically closed field of positive characteristic and show that the naive F-crystal structure fails in being compatible with inner product. }, author = {Srivastava, Tanya K}, issn = {1431-0643}, journal = {Documenta Mathematica}, pages = {1135--1177}, publisher = {EMS Press}, title = {{On derived equivalences of k3 surfaces in positive characteristic}}, doi = {10.25537/dm.2019v24.1135-1177}, volume = {24}, year = {2019}, } @inproceedings{7437, abstract = {Most of today's distributed machine learning systems assume reliable networks: whenever two machines exchange information (e.g., gradients or models), the network should guarantee the delivery of the message. At the same time, recent work exhibits the impressive tolerance of machine learning algorithms to errors or noise arising from relaxed communication or synchronization. In this paper, we connect these two trends, and consider the following question: Can we design machine learning systems that are tolerant to network unreliability during training? With this motivation, we focus on a theoretical problem of independent interest-given a standard distributed parameter server architecture, if every communication between the worker and the server has a non-zero probability p of being dropped, does there exist an algorithm that still converges, and at what speed? The technical contribution of this paper is a novel theoretical analysis proving that distributed learning over unreliable network can achieve comparable convergence rate to centralized or distributed learning over reliable networks. Further, we prove that the influence of the packet drop rate diminishes with the growth of the number of parameter servers. We map this theoretical result onto a real-world scenario, training deep neural networks over an unreliable network layer, and conduct network simulation to validate the system improvement by allowing the networks to be unreliable.}, author = {Yu, Chen and Tang, Hanlin and Renggli, Cedric and Kassing, Simon and Singla, Ankit and Alistarh, Dan-Adrian and Zhang, Ce and Liu, Ji}, booktitle = {36th International Conference on Machine Learning, ICML 2019}, isbn = {9781510886988}, location = {Long Beach, CA, United States}, pages = {12481--12512}, publisher = {IMLS}, title = {{Distributed learning over unreliable networks}}, volume = {2019-June}, year = {2019}, } @article{7451, abstract = {We prove that the observable telegraph signal accompanying the bistability in the photon-blockade-breakdown regime of the driven and lossy Jaynes–Cummings model is the finite-size precursor of what in the thermodynamic limit is a genuine first-order phase transition. We construct a finite-size scaling of the system parameters to a well-defined thermodynamic limit, in which the system remains the same microscopic system, but the telegraph signal becomes macroscopic both in its timescale and intensity. The existence of such a finite-size scaling completes and justifies the classification of the photon-blockade-breakdown effect as a first-order dissipative quantum phase transition.}, author = {Vukics, A. and Dombi, A. and Fink, Johannes M and Domokos, P.}, issn = {2521-327X}, journal = {Quantum}, publisher = {Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften}, title = {{Finite-size scaling of the photon-blockade breakdown dissipative quantum phase transition}}, doi = {10.22331/q-2019-06-03-150}, volume = {3}, year = {2019}, } @inbook{7453, abstract = {We illustrate the ingredients of the state-of-the-art of model-based approach for the formal design and verification of cyber-physical systems. To capture the interaction between a discrete controller and its continuously evolving environment, we use the formal models of timed and hybrid automata. We explain the steps of modeling and verification in the tools Uppaal and SpaceEx using a case study based on a dual-chamber implantable pacemaker monitoring a human heart. We show how to design a model as a composition of components, how to construct models at varying levels of detail, how to establish that one model is an abstraction of another, how to specify correctness requirements using temporal logic, and how to verify that a model satisfies a logical requirement.}, author = {Alur, Rajeev and Giacobbe, Mirco and Henzinger, Thomas A and Larsen, Kim G. and Mikučionis, Marius}, booktitle = {Computing and Software Science}, editor = {Steffen, Bernhard and Woeginger, Gerhard}, isbn = {9783319919072}, issn = {0302-9743}, pages = {452--477}, publisher = {Springer Nature}, title = {{Continuous-time models for system design and analysis}}, doi = {10.1007/978-3-319-91908-9_22}, volume = {10000}, year = {2019}, } @inproceedings{7468, abstract = {We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference in structured energy minimization problems. The method optimizes a Lagrangean relaxation of the original energy minimization problem using a multi plane block-coordinate Frank-Wolfe method that takes advantage of the specific structure of the Lagrangean decomposition. We show empirically that our method outperforms state-of-the-art Lagrangean decomposition based algorithms on some challenging Markov Random Field, multi-label discrete tomography and graph matching problems.}, author = {Swoboda, Paul and Kolmogorov, Vladimir}, booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition}, isbn = {9781728132938}, issn = {10636919}, location = {Long Beach, CA, United States}, publisher = {IEEE}, title = {{Map inference via block-coordinate Frank-Wolfe algorithm}}, doi = {10.1109/CVPR.2019.01140}, volume = {2019-June}, year = {2019}, } @article{7476, abstract = {The sebaceous gland (SG) is an essential component of the skin, and SG dysfunction is debilitating1,2. Yet, the cellular bases for its origin, development and subsequent maintenance remain poorly understood. Here, we apply large-scale quantitative fate mapping to define the patterns of cell fate behaviour during SG development and maintenance. We show that the SG develops from a defined number of lineage-restricted progenitors that undergo a programme of independent and stochastic cell fate decisions. Following an expansion phase, equipotent progenitors transition into a phase of homeostatic turnover, which is correlated with changes in the mechanical properties of the stroma and spatial restrictions on gland size. Expression of the oncogene KrasG12D results in a release from these constraints and unbridled gland expansion. Quantitative clonal fate analysis reveals that, during this phase, the primary effect of the Kras oncogene is to drive a constant fate bias with little effect on cell division rates. These findings provide insight into the developmental programme of the SG, as well as the mechanisms that drive tumour progression and gland dysfunction.}, author = {Andersen, Marianne Stemann and Hannezo, Edouard B and Ulyanchenko, Svetlana and Estrach, Soline and Antoku, Yasuko and Pisano, Sabrina and Boonekamp, Kim E. and Sendrup, Sarah and Maimets, Martti and Pedersen, Marianne Terndrup and Johansen, Jens V. and Clement, Ditte L. and Feral, Chloe C. and Simons, Benjamin D. and Jensen, Kim B.}, issn = {1465-7392}, journal = {Nature Cell Biology}, number = {8}, pages = {924--932}, publisher = {Springer Nature}, title = {{Tracing the cellular dynamics of sebaceous gland development in normal and perturbed states}}, doi = {10.1038/s41556-019-0362-x}, volume = {21}, year = {2019}, } @inproceedings{7479, abstract = {Multi-exit architectures, in which a stack of processing layers is interleaved with early output layers, allow the processing of a test example to stop early and thus save computation time and/or energy. In this work, we propose a new training procedure for multi-exit architectures based on the principle of knowledge distillation. The method encourage searly exits to mimic later, more accurate exits, by matching their output probabilities. Experiments on CIFAR100 and ImageNet show that distillation-based training significantly improves the accuracy of early exits while maintaining state-of-the-art accuracy for late ones. The method is particularly beneficial when training data is limited and it allows a straightforward extension to semi-supervised learning,i.e. making use of unlabeled data at training time. Moreover, it takes only afew lines to implement and incurs almost no computational overhead at training time, and none at all at test time.}, author = {Bui Thi Mai, Phuong and Lampert, Christoph}, booktitle = {IEEE International Conference on Computer Vision}, isbn = {9781728148038}, issn = {15505499}, location = {Seoul, Korea}, pages = {1355--1364}, publisher = {IEEE}, title = {{Distillation-based training for multi-exit architectures}}, doi = {10.1109/ICCV.2019.00144}, volume = {2019-October}, year = {2019}, } @unpublished{7524, abstract = {We prove a lower bound for the free energy (per unit volume) of the two-dimensional Bose gas in the thermodynamic limit. We show that the free energy at density $\rho$ and inverse temperature $\beta$ differs from the one of the non-interacting system by the correction term $4 \pi \rho^2 |\ln a^2 \rho|^{-1} (2 - [1 - \beta_{\mathrm{c}}/\beta]_+^2)$. Here $a$ is the scattering length of the interaction potential, $[\cdot]_+ = \max\{ 0, \cdot \}$ and $\beta_{\mathrm{c}}$ is the inverse Berezinskii--Kosterlitz--Thouless critical temperature for superfluidity. The result is valid in the dilute limit $a^2\rho \ll 1$ and if $\beta \rho \gtrsim 1$.}, author = {Deuchert, Andreas and Mayer, Simon and Seiringer, Robert}, booktitle = {arXiv:1910.03372}, pages = {61}, publisher = {ArXiv}, title = {{The free energy of the two-dimensional dilute Bose gas. I. Lower bound}}, year = {2019}, } @inproceedings{7542, abstract = {We present a novel class of convolutional neural networks (CNNs) for set functions,i.e., data indexed with the powerset of a finite set. The convolutions are derivedas linear, shift-equivariant functions for various notions of shifts on set functions.The framework is fundamentally different from graph convolutions based on theLaplacian, as it provides not one but several basic shifts, one for each element inthe ground set. Prototypical experiments with several set function classificationtasks on synthetic datasets and on datasets derived from real-world hypergraphsdemonstrate the potential of our new powerset CNNs.}, author = {Wendler, Chris and Alistarh, Dan-Adrian and Püschel, Markus}, issn = {1049-5258}, location = {Vancouver, Canada}, pages = {927--938}, publisher = {Neural Information Processing Systems Foundation}, title = {{Powerset convolutional neural networks}}, volume = {32}, year = {2019}, } @article{7550, abstract = {We consider an optimal control problem for an abstract nonlinear dissipative evolution equation. The differential constraint is penalized by augmenting the target functional by a nonnegative global-in-time functional which is null-minimized in the evolution equation is satisfied. Different variational settings are presented, leading to the convergence of the penalization method for gradient flows, noncyclic and semimonotone flows, doubly nonlinear evolutions, and GENERIC systems. }, author = {Portinale, Lorenzo and Stefanelli, Ulisse}, issn = {1343-4373}, journal = {Advances in Mathematical Sciences and Applications}, number = {2}, pages = {425--447}, publisher = {Gakko Tosho}, title = {{Penalization via global functionals of optimal-control problems for dissipative evolution}}, volume = {28}, year = {2019}, } @unpublished{7552, abstract = {There is increasing evidence that protein binding to specific sites along DNA can activate the reading out of genetic information without coming into direct physical contact with the gene. There also is evidence that these distant but interacting sites are embedded in a liquid droplet of proteins which condenses out of the surrounding solution. We argue that droplet-mediated interactions can account for crucial features of gene regulation only if the droplet is poised at a non-generic point in its phase diagram. We explore a minimal model that embodies this idea, show that this model has a natural mechanism for self-tuning, and suggest direct experimental tests. }, author = {Bialek, William and Gregor, Thomas and Tkačik, Gašper}, booktitle = {arXiv:1912.08579}, pages = {5}, publisher = {ArXiv}, title = {{Action at a distance in transcriptional regulation}}, year = {2019}, }