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  1. Few techniques are available for elucidating the nature of forces that drive subcellular behaviors. Here we develop two complementary ones: 1) femtosecond stereotactic laser ablation (FESLA), which rapidly creates complex cuts of subcellular structures, thereby allowing precise dissection of when, where, and in what direction forces are generated; and 2) assessment of subcellular fluid flows, by comparing direct flow measurements, using microinjected fluorescent nanodiamonds, to large-scale fluid-structure simulations of different models of force transduction. We apply these to study centrosomes in Caenorhabditis elegans early embryos, and use the data to construct a biophysically-based model of centrosome dynamics. Taken together, wemore »demonstrate that cortical pulling forces provide a general explanation for many behaviors mediated by centrosomes, including pronuclear migration/centration and rotation, metaphase spindle positioning, asymmetric spindle elongation and spindle oscillations. In sum, this work establishes new methodologies for disentangling the forces responsible for cell biological phenomena.« less
    Free, publicly-accessible full text available November 1, 2022
  2. Abstract We select 48 multiflare gamma-ray bursts (GRBs) (including 137 flares) from the Swift/XRT database and estimate the spectral lag with the discrete correlation function. It is found that 89.8% of the flares have positive lags and only 9.5% of the flares show negative lags when fluctuations are taken into account. The median lag of the multiflares (2.75 s) is much greater than that of GRB pulses (0.18 s), which can be explained by the fact that we confirm that multiflare GRBs and multipulse GRBs have similar positive lag–duration correlations. We investigate the origin of the lags by checking themore »E peak evolution with the two brightest bursts and find the leading models cannot explain all of the multiflare lags and there may be other physical mechanisms. All of the results above reveal that X-ray flares have the same properties as GRB pulses, which further supports the observation that X-ray flares and GRB prompt-emission pulses have the same physical origin.« less
    Free, publicly-accessible full text available November 1, 2022
  3. Free, publicly-accessible full text available April 1, 2023
  4. Meila, M. ; Zhang, T. (Ed.)
    In this paper, we propose conjugate energy-based models (CEBMs), a new class of energy-based models that define a joint density over data and latent variables. The joint density of a CEBM decomposes into an intractable distribution over data and a tractable posterior over latent variables. CEBMs have similar use cases as variational autoencoders, in the sense that they learn an unsupervised mapping from data to latent variables. However, these models omit a generator network, which allows them to learn more flexible notions of similarity between data points. Our experiments demonstrate that conjugate EBMs achieve competitive results in terms of imagemore »modelling, predictive power of latent space, and out-of-domain detection on a variety of datasets.« less
    Free, publicly-accessible full text available July 1, 2022
  5. III, H.D. ; Singh, A. (Ed.)
    We develop amortized population Gibbs (APG) samplers, a class of scalable methods that frame structured variational inference as adaptive importance sampling. APG samplers construct high-dimensional proposals by iterating over updates to lower-dimensional blocks of variables. We train each conditional proposal by minimizing the inclusive KL divergence with respect to the conditional posterior. To appropriately account for the size of the input data, we develop a new parameterization in terms of neural sufficient statistics. Experiments show that APG samplers can be used to train highly-structured deep generative models in an unsupervised manner, and achieve substantial improvements in inference accuracy relative tomore »standard autoencoding variational methods.« less
    Free, publicly-accessible full text available July 1, 2022
  6. Wim Uijttewaal, Mário J. (Ed.)
    Freshwater mussels are bivalve mollusks that inhabit the substrates of rivers. Fully three-dimensional large eddy simulations are used to investigate flow, turbulence and the capacity of the flow to dislocate an isolated, partially-buried, isolated freshwater mussel placed in a ful-ly-developed incoming turbulent open channel flow. The mussel is aligned with the flow di-rection, which corresponds to normal conditions in rivers containing mussel beds. Its sub-mergence depth is about 60% of the mussel height. The paper focuses on quantifying the ef-fect of the active filtering flow through the incurring and excurring siphons. Simulation re-sults are discussed for two limiting cases withmore »no active filtering and with a filtering flow dis-charge that is close to the maximum value recorded for the investigated freshwater mussel species. It is shown that the active filtering increases the turbulent kinetic energy in the wake and slightly decreases the mean streamwise drag acting on the mussel shell. The paper also discusses the main types of large-scale coherent structures generated by partially-burrowed mussels aligned with the flow, how they are affected by the filtered flow and the effects of these eddies on the bed shear stress, sediment entrainment/deposition phenomena and nutri-ent transport« less
  7. Do algorithms for drawing graphs pass theTuringTest?That is, are their outputs indistinguishable from graphs drawn by humans? We address this question through a human-centred experiment, focusing on ‘small’ graphs, of a size for which it would be reasonable for someone to choose to draw the graph manually. Overall, we find that hand-drawn layouts can be distinguished from those generated by graph drawing algorithms, although this is not always the case for graphs drawn by force- directed or multi-dimensional scaling algorithms, making these good candidates for Turing Test success. We show that, in general, hand-drawn graphs are judged to be ofmore »higher quality than automatically generated ones, although this result varies with graph size and algorithm.« less
  8. Free, publicly-accessible full text available August 1, 2022
  9. The Boolean Satisfiability (SAT) problem is the canonical NP-complete problem and is fundamental to computer science, with a wide array of applications in planning, verification, and theorem proving. Developing and evaluating practical SAT solvers relies on extensive empirical testing on a set of real-world benchmark formulas. However, the availability of such real-world SAT formulas is limited. While these benchmark formulas can be augmented with synthetically generated ones, existing approaches for doing so are heavily hand-crafted and fail to simultaneously capture a wide range of characteristics exhibited by real-world SAT instances. In this work, we present G2SAT, the first deep generativemore »framework that learns to generate SAT formulas from a given set of input formulas. Our key insight is that SAT formulas can be transformed into latent bipartite graph representations which we model using a specialized deep generative neural network. We show that G2SAT can generate SAT formulas that closely resemble given real-world SAT instances, as measured by both graph metrics and SAT solver behavior. Further, we show that our synthetic SAT formulas could be used to improve SAT solver performance on real-world benchmarks, which opens up new opportunities for the continued development of SAT solvers and a deeper understanding of their performance.« less