@unpublished{9699, abstract = {We investigate the structural similarities between liquid water and 53 ices, including 20 known crystalline phases. We base such similarity comparison on the local environments that consist of atoms within a certain cutoff radius of a central atom. We reveal that liquid water explores the local environments of the diverse ice phases, by directly comparing the environments in these phases using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, the lattice energies, and vibrational properties of the ices. The finding that the local environments characterising the different ice phases are found in water sheds light on water phase behaviors, and rationalizes the transferability of water models between different phases.}, author = {Monserrat, Bartomeu and Brandenburg, Jan Gerit and Engel, Edgar A. and Cheng, Bingqing}, booktitle = {arXiv}, title = {{Extracting ice phases from liquid water: Why a machine-learning water model generalizes so well}}, doi = {10.48550/arXiv.2006.13316}, year = {2020}, } @misc{9706, abstract = {Additional file 2: Supplementary Tables. The association of pre-adjusted protein levels with biological and technical covariates. Protein levels were adjusted for age, sex, array plate and four genetic principal components (population structure) prior to analyses. Significant associations are emboldened. (Table S1). pQTLs associated with inflammatory biomarker levels from Bayesian penalised regression model (Posterior Inclusion Probability > 95%). (Table S2). All pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S3). Summary of lambda values relating to ordinary least squares GWAS and EWAS performed on inflammatory protein levels (n = 70) in Lothian Birth Cohort 1936 study. (Table S4). Conditionally significant pQTLs associated with inflammatory biomarker levels from ordinary least squares regression model (P < 7.14 × 10− 10). (Table S5). Comparison of variance explained by ordinary least squares and Bayesian penalised regression models for concordantly identified SNPs. (Table S6). Estimate of heritability for blood protein levels as well as proportion of variance explained attributable to different prior mixtures. (Table S7). Comparison of heritability estimates from Ahsan et al. (maximum likelihood) and Hillary et al. (Bayesian penalised regression). (Table S8). List of concordant SNPs identified by linear model and Bayesian penalised regression and whether they have been previously identified as eQTLs. (Table S9). Bayesian tests of colocalisation for cis pQTLs and cis eQTLs. (Table S10). Sherlock algorithm: Genes whose expression are putatively associated with circulating inflammatory proteins that harbour pQTLs. (Table S11). CpGs associated with inflammatory protein biomarkers as identified by Bayesian model (Bayesian model; Posterior Inclusion Probability > 95%). (Table S12). CpGs associated with inflammatory protein biomarkers as identified by linear model (limma) at P < 5.14 × 10− 10. (Table S13). CpGs associated with inflammatory protein biomarkers as identified by mixed linear model (OSCA) at P < 5.14 × 10− 10. (Table S14). Estimate of variance explained for blood protein levels by DNA methylation as well as proportion of explained attributable to different prior mixtures - BayesR+. (Table S15). Comparison of variance in protein levels explained by genome-wide DNA methylation data by mixed linear model (OSCA) and Bayesian penalised regression model (BayesR+). (Table S16). Variance in circulating inflammatory protein biomarker levels explained by common genetic and methylation data (joint and conditional estimates from BayesR+). Ordered by combined variance explained by genetic and epigenetic data - smallest to largest. Significant results from t-tests comparing distributions for variance explained by methylation or genetics alone versus combined estimate are emboldened. (Table S17). Genetic and epigenetic factors identified by BayesR+ when conditioning on all SNPs and CpGs together. (Table S18). Mendelian Randomisation analyses to assess whether proteins with concordantly identified genetic signals are causally associated with Alzheimer’s disease risk. (Table S19).}, author = {Hillary, Robert F. and Trejo-Banos, Daniel and Kousathanas, Athanasios and McCartney, Daniel L. and Harris, Sarah E. and Stevenson, Anna J. and Patxot, Marion and Ojavee, Sven Erik and Zhang, Qian and Liewald, David C. and Ritchie, Craig W. and Evans, Kathryn L. and Tucker-Drob, Elliot M. and Wray, Naomi R. and McRae, Allan F. and Visscher, Peter M. and Deary, Ian J. and Robinson, Matthew Richard and Marioni, Riccardo E. }, publisher = {Springer Nature}, title = {{Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults}}, doi = {10.6084/m9.figshare.12629697.v1}, year = {2020}, } @misc{9708, abstract = {This research data supports 'Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors'. A Readme file for plotting each figure is provided.}, author = {Hartstein, Mate and Hsu, Yu-Te and Modic, Kimberly A and Porras, Juan and Loew, Toshinao and Le Tacon, Matthieu and Zuo, Huakun and Wang, Jinhua and Zhu, Zengwei and Chan, Mun and McDonald, Ross and Lonzarich, Gilbert and Keimer, Bernhard and Sebastian, Suchitra and Harrison, Neil}, publisher = {Apollo - University of Cambridge}, title = {{Accompanying dataset for 'Hard antinodal gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors'}}, doi = {10.17863/cam.50169}, year = {2020}, } @misc{9776, author = {Grah, Rok and Friedlander, Tamar}, publisher = {Public Library of Science}, title = {{Supporting information}}, doi = {10.1371/journal.pcbi.1007642.s001}, year = {2020}, } @misc{9777, author = {Grah, Rok and Friedlander, Tamar}, publisher = {Public Library of Science}, title = {{Maximizing crosstalk}}, doi = {10.1371/journal.pcbi.1007642.s002}, year = {2020}, } @misc{9779, author = {Grah, Rok and Friedlander, Tamar}, publisher = {Public Library of Science}, title = {{Distribution of crosstalk values}}, doi = {10.1371/journal.pcbi.1007642.s003}, year = {2020}, } @misc{9780, abstract = {PADREV : 4,4'-dimethoxy[1,1'-biphenyl]-2,2',5,5'-tetrol Space Group: C 2 (5), Cell: a 24.488(16)Å b 5.981(4)Å c 3.911(3)Å, α 90° β 91.47(3)° γ 90°}, author = {Schlemmer, Werner and Nothdurft, Philipp and Petzold, Alina and Riess, Gisbert and Frühwirt, Philipp and Schmallegger, Max and Gescheidt-Demner, Georg and Fischer, Roland and Freunberger, Stefan Alexander and Kern, Wolfgang and Spirk, Stefan}, publisher = {CCDC}, title = {{CCDC 1991959: Experimental Crystal Structure Determination}}, doi = {10.5517/ccdc.csd.cc24vsrk}, year = {2020}, } @misc{9798, abstract = {Fitness interactions between mutations can influence a population’s evolution in many different ways. While epistatic effects are difficult to measure precisely, important information is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from a class of simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effect sizes, mutational bias and maladaptation of the wild type. We illustrate our approach by reanalysing a large dataset of mutant effects in a yeast snoRNA. Though characterized by some large epistatic effects, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have limited influence on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape and the distribution of mutations, and so is expected to vary in consistent ways between new mutations, standing variation and fixed mutations.}, author = {Fraisse, Christelle and Welch, John J.}, publisher = {Royal Society of London}, title = {{Simulation code for Fig S2 from the distribution of epistasis on simple fitness landscapes}}, doi = {10.6084/m9.figshare.7957472.v1}, year = {2020}, } @misc{9799, abstract = {Fitness interactions between mutations can influence a population’s evolution in many different ways. While epistatic effects are difficult to measure precisely, important information is captured by the mean and variance of log fitnesses for individuals carrying different numbers of mutations. We derive predictions for these quantities from a class of simple fitness landscapes, based on models of optimizing selection on quantitative traits. We also explore extensions to the models, including modular pleiotropy, variable effect sizes, mutational bias and maladaptation of the wild type. We illustrate our approach by reanalysing a large dataset of mutant effects in a yeast snoRNA. Though characterized by some large epistatic effects, these data give a good overall fit to the non-epistatic null model, suggesting that epistasis might have limited influence on the evolutionary dynamics in this system. We also show how the amount of epistasis depends on both the underlying fitness landscape and the distribution of mutations, and so is expected to vary in consistent ways between new mutations, standing variation and fixed mutations.}, author = {Fraisse, Christelle and Welch, John J.}, publisher = {Royal Society of London}, title = {{Simulation code for Fig S1 from the distribution of epistasis on simple fitness landscapes}}, doi = {10.6084/m9.figshare.7957469.v1}, year = {2020}, } @article{6488, abstract = {We prove a central limit theorem for the difference of linear eigenvalue statistics of a sample covariance matrix W˜ and its minor W. We find that the fluctuation of this difference is much smaller than those of the individual linear statistics, as a consequence of the strong correlation between the eigenvalues of W˜ and W. Our result identifies the fluctuation of the spatial derivative of the approximate Gaussian field in the recent paper by Dumitru and Paquette. Unlike in a similar result for Wigner matrices, for sample covariance matrices, the fluctuation may entirely vanish.}, author = {Cipolloni, Giorgio and Erdös, László}, issn = {2010-3271}, journal = {Random Matrices: Theory and Application}, number = {3}, publisher = {World Scientific Publishing}, title = {{Fluctuations for differences of linear eigenvalue statistics for sample covariance matrices}}, doi = {10.1142/S2010326320500069}, volume = {9}, year = {2020}, } @article{6563, abstract = {This paper presents two algorithms. The first decides the existence of a pointed homotopy between given simplicial maps 𝑓,𝑔:𝑋→𝑌, and the second computes the group [𝛴𝑋,𝑌]∗ of pointed homotopy classes of maps from a suspension; in both cases, the target Y is assumed simply connected. More generally, these algorithms work relative to 𝐴⊆𝑋.}, author = {Filakovský, Marek and Vokřínek, Lukas}, issn = {1615-3383}, journal = {Foundations of Computational Mathematics}, pages = {311--330}, publisher = {Springer Nature}, title = {{Are two given maps homotopic? An algorithmic viewpoint}}, doi = {10.1007/s10208-019-09419-x}, volume = {20}, year = {2020}, } @article{6593, abstract = {We consider the monotone variational inequality problem in a Hilbert space and describe a projection-type method with inertial terms under the following properties: (a) The method generates a strongly convergent iteration sequence; (b) The method requires, at each iteration, only one projection onto the feasible set and two evaluations of the operator; (c) The method is designed for variational inequality for which the underline operator is monotone and uniformly continuous; (d) The method includes an inertial term. The latter is also shown to speed up the convergence in our numerical results. A comparison with some related methods is given and indicates that the new method is promising.}, author = {Shehu, Yekini and Li, Xiao-Huan and Dong, Qiao-Li}, issn = {1572-9265}, journal = {Numerical Algorithms}, pages = {365--388}, publisher = {Springer Nature}, title = {{An efficient projection-type method for monotone variational inequalities in Hilbert spaces}}, doi = {10.1007/s11075-019-00758-y}, volume = {84}, year = {2020}, } @article{6649, abstract = {While Hartree–Fock theory is well established as a fundamental approximation for interacting fermions, it has been unclear how to describe corrections to it due to many-body correlations. In this paper we start from the Hartree–Fock state given by plane waves and introduce collective particle–hole pair excitations. These pairs can be approximately described by a bosonic quadratic Hamiltonian. We use Bogoliubov theory to construct a trial state yielding a rigorous Gell-Mann–Brueckner–type upper bound to the ground state energy. Our result justifies the random-phase approximation in the mean-field scaling regime, for repulsive, regular interaction potentials. }, author = {Benedikter, Niels P and Nam, Phan Thành and Porta, Marcello and Schlein, Benjamin and Seiringer, Robert}, issn = {1432-0916}, journal = {Communications in Mathematical Physics}, pages = {2097–2150}, publisher = {Springer Nature}, title = {{Optimal upper bound for the correlation energy of a Fermi gas in the mean-field regime}}, doi = {10.1007/s00220-019-03505-5}, volume = {374}, year = {2020}, } @article{6748, abstract = {Fitting a function by using linear combinations of a large number N of `simple' components is one of the most fruitful ideas in statistical learning. This idea lies at the core of a variety of methods, from two-layer neural networks to kernel regression, to boosting. In general, the resulting risk minimization problem is non-convex and is solved by gradient descent or its variants. Unfortunately, little is known about global convergence properties of these approaches. Here we consider the problem of learning a concave function f on a compact convex domain Ω⊆ℝd, using linear combinations of `bump-like' components (neurons). The parameters to be fitted are the centers of N bumps, and the resulting empirical risk minimization problem is highly non-convex. We prove that, in the limit in which the number of neurons diverges, the evolution of gradient descent converges to a Wasserstein gradient flow in the space of probability distributions over Ω. Further, when the bump width δ tends to 0, this gradient flow has a limit which is a viscous porous medium equation. Remarkably, the cost function optimized by this gradient flow exhibits a special property known as displacement convexity, which implies exponential convergence rates for N→∞, δ→0. Surprisingly, this asymptotic theory appears to capture well the behavior for moderate values of δ,N. Explaining this phenomenon, and understanding the dependence on δ,N in a quantitative manner remains an outstanding challenge.}, author = {Javanmard, Adel and Mondelli, Marco and Montanari, Andrea}, issn = {1941-7330}, journal = {Annals of Statistics}, number = {6}, pages = {3619--3642}, publisher = {Institute of Mathematical Statistics}, title = {{Analysis of a two-layer neural network via displacement convexity}}, doi = {10.1214/20-AOS1945}, volume = {48}, year = {2020}, } @article{6761, abstract = {In resource allocation games, selfish players share resources that are needed in order to fulfill their objectives. The cost of using a resource depends on the load on it. In the traditional setting, the players make their choices concurrently and in one-shot. That is, a strategy for a player is a subset of the resources. We introduce and study dynamic resource allocation games. In this setting, the game proceeds in phases. In each phase each player chooses one resource. A scheduler dictates the order in which the players proceed in a phase, possibly scheduling several players to proceed concurrently. The game ends when each player has collected a set of resources that fulfills his objective. The cost for each player then depends on this set as well as on the load on the resources in it – we consider both congestion and cost-sharing games. We argue that the dynamic setting is the suitable setting for many applications in practice. We study the stability of dynamic resource allocation games, where the appropriate notion of stability is that of subgame perfect equilibrium, study the inefficiency incurred due to selfish behavior, and also study problems that are particular to the dynamic setting, like constraints on the order in which resources can be chosen or the problem of finding a scheduler that achieves stability.}, author = {Avni, Guy and Henzinger, Thomas A and Kupferman, Orna}, issn = {0304-3975}, journal = {Theoretical Computer Science}, pages = {42--55}, publisher = {Elsevier}, title = {{Dynamic resource allocation games}}, doi = {10.1016/j.tcs.2019.06.031}, volume = {807}, year = {2020}, } @article{6796, abstract = {Nearby grid cells have been observed to express a remarkable degree of long-rangeorder, which is often idealized as extending potentially to infinity. Yet their strict peri-odic firing and ensemble coherence are theoretically possible only in flat environments, much unlike the burrows which rodents usually live in. Are the symmetrical, coherent grid maps inferred in the lab relevant to chart their way in their natural habitat? We consider spheres as simple models of curved environments and waiting for the appropriate experiments to be performed, we use our adaptation model to predict what grid maps would emerge in a network with the same type of recurrent connections, which on the plane produce coherence among the units. We find that on the sphere such connections distort the maps that single grid units would express on their own, and aggregate them into clusters. When remapping to a different spherical environment, units in each cluster maintain only partial coherence, similar to what is observed in disordered materials, such as spin glasses.}, author = {Stella, Federico and Urdapilleta, Eugenio and Luo, Yifan and Treves, Alessandro}, issn = {1098-1063}, journal = {Hippocampus}, number = {4}, pages = {302--313}, publisher = {Wiley}, title = {{Partial coherence and frustration in self-organizing spherical grids}}, doi = {10.1002/hipo.23144}, volume = {30}, year = {2020}, } @article{6808, abstract = {Super-resolution fluorescence microscopy has become an important catalyst for discovery in the life sciences. In STimulated Emission Depletion (STED) microscopy, a pattern of light drives fluorophores from a signal-emitting on-state to a non-signalling off-state. Only emitters residing in a sub-diffraction volume around an intensity minimum are allowed to fluoresce, rendering them distinguishable from the nearby, but dark fluorophores. STED routinely achieves resolution in the few tens of nanometers range in biological samples and is suitable for live imaging. Here, we review the working principle of STED and provide general guidelines for successful STED imaging. The strive for ever higher resolution comes at the cost of increased light burden. We discuss techniques to reduce light exposure and mitigate its detrimental effects on the specimen. These include specialized illumination strategies as well as protecting fluorophores from photobleaching mediated by high-intensity STED light. This opens up the prospect of volumetric imaging in living cells and tissues with diffraction-unlimited resolution in all three spatial dimensions.}, author = {Jahr, Wiebke and Velicky, Philipp and Danzl, Johann G}, issn = {1046-2023}, journal = {Methods}, number = {3}, pages = {27--41}, publisher = {Elsevier}, title = {{Strategies to maximize performance in STimulated Emission Depletion (STED) nanoscopy of biological specimens}}, doi = {10.1016/j.ymeth.2019.07.019}, volume = {174}, year = {2020}, } @article{6906, abstract = {We consider systems of bosons trapped in a box, in the Gross–Pitaevskii regime. We show that low-energy states exhibit complete Bose–Einstein condensation with an optimal bound on the number of orthogonal excitations. This extends recent results obtained in Boccato et al. (Commun Math Phys 359(3):975–1026, 2018), removing the assumption of small interaction potential.}, author = {Boccato, Chiara and Brennecke, Christian and Cenatiempo, Serena and Schlein, Benjamin}, issn = {1432-0916}, journal = {Communications in Mathematical Physics}, pages = {1311--1395}, publisher = {Springer}, title = {{Optimal rate for Bose-Einstein condensation in the Gross-Pitaevskii regime}}, doi = {10.1007/s00220-019-03555-9}, volume = {376}, year = {2020}, } @article{6944, abstract = {We study the problem of automatically detecting if a given multi-class classifier operates outside of its specifications (out-of-specs), i.e. on input data from a different distribution than what it was trained for. This is an important problem to solve on the road towards creating reliable computer vision systems for real-world applications, because the quality of a classifier’s predictions cannot be guaranteed if it operates out-of-specs. Previously proposed methods for out-of-specs detection make decisions on the level of single inputs. This, however, is insufficient to achieve low false positive rate and high false negative rates at the same time. In this work, we describe a new procedure named KS(conf), based on statistical reasoning. Its main component is a classical Kolmogorov–Smirnov test that is applied to the set of predicted confidence values for batches of samples. Working with batches instead of single samples allows increasing the true positive rate without negatively affecting the false positive rate, thereby overcoming a crucial limitation of single sample tests. We show by extensive experiments using a variety of convolutional network architectures and datasets that KS(conf) reliably detects out-of-specs situations even under conditions where other tests fail. It furthermore has a number of properties that make it an excellent candidate for practical deployment: it is easy to implement, adds almost no overhead to the system, works with any classifier that outputs confidence scores, and requires no a priori knowledge about how the data distribution could change.}, author = {Sun, Rémy and Lampert, Christoph}, issn = {1573-1405}, journal = {International Journal of Computer Vision}, number = {4}, pages = {970--995}, publisher = {Springer Nature}, title = {{KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications}}, doi = {10.1007/s11263-019-01232-x}, volume = {128}, year = {2020}, } @article{6976, abstract = {Origami is rapidly transforming the design of robots1,2, deployable structures3,4,5,6 and metamaterials7,8,9,10,11,12,13,14. However, as foldability requires a large number of complex compatibility conditions that are difficult to satisfy, the design of crease patterns is limited to heuristics and computer optimization. Here we introduce a systematic strategy that enables intuitive and effective design of complex crease patterns that are guaranteed to fold. First, we exploit symmetries to construct 140 distinct foldable motifs, and represent these as jigsaw puzzle pieces. We then show that when these pieces are fitted together they encode foldable crease patterns. This maps origami design to solving combinatorial problems, which allows us to systematically create, count and classify a vast number of crease patterns. We show that all of these crease patterns are pluripotent—capable of folding into multiple shapes—and solve exactly for the number of possible shapes for each pattern. Finally, we employ our framework to rationally design a crease pattern that folds into two independently defined target shapes, and fabricate such pluripotent origami. Our results provide physicists, mathematicians and engineers with a powerful new design strategy.}, author = {Dieleman, Peter and Vasmel, Niek and Waitukaitis, Scott R and van Hecke, Martin}, issn = {1745-2481}, journal = {Nature Physics}, number = {1}, pages = {63–68}, publisher = {Springer Nature}, title = {{Jigsaw puzzle design of pluripotent origami}}, doi = {10.1038/s41567-019-0677-3}, volume = {16}, year = {2020}, }