TY - JOUR AB - 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. AU - Akemann, Gernot AU - Checinski, Tomasz AU - Liu, Dangzheng AU - Strahov, Eugene ID - 7423 IS - 1 JF - Annales de l'Institut Henri Poincaré, Probabilités et Statistiques SN - 0246-0203 TI - Finite rank perturbations in products of coupled random matrices: From one correlated to two Wishart ensembles VL - 55 ER - TY - JOUR AB - X and Y chromosomes can diverge when rearrangements block recombination between them. Here we present the first genomic view of a reciprocal translocation that causes two physically unconnected pairs of chromosomes to be coinherited as sex chromosomes. In a population of the common frog (Rana temporaria), both pairs of X and Y chromosomes show extensive sequence differentiation, but not degeneration of the Y chromosomes. A new method based on gene trees shows both chromosomes are sex‐linked. Furthermore, the gene trees from the two Y chromosomes have identical topologies, showing they have been coinherited since the reciprocal translocation occurred. Reciprocal translocations can thus reshape sex linkage on a much greater scale compared with inversions, the type of rearrangement that is much better known in sex chromosome evolution, and they can greatly amplify the power of sexually antagonistic selection to drive genomic rearrangement. Two more populations show evidence of other rearrangements, suggesting that this species has unprecedented structural polymorphism in its sex chromosomes. AU - Toups, Melissa A AU - Rodrigues, Nicolas AU - Perrin, Nicolas AU - Kirkpatrick, Mark ID - 7421 IS - 8 JF - Molecular Ecology SN - 0962-1083 TI - A reciprocal translocation radically reshapes sex‐linked inheritance in the common frog VL - 28 ER - TY - CONF AB - 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). AU - Abusalah, Hamza M AU - Kamath Hosdurg, Chethan AU - Klein, Karen AU - Pietrzak, Krzysztof Z AU - Walter, Michael ID - 7411 SN - 0302-9743 T2 - Advances in Cryptology – EUROCRYPT 2019 TI - Reversible proofs of sequential work VL - 11477 ER - TY - JOUR AB - Background Synaptic vesicles (SVs) are an integral part of the neurotransmission machinery, and isolation of SVs from their host neuron is necessary to reveal their most fundamental biochemical and functional properties in in vitro assays. Isolated SVs from neurons that have been genetically engineered, e.g. to introduce genetically encoded indicators, are not readily available but would permit new insights into SV structure and function. Furthermore, it is unclear if cultured neurons can provide sufficient starting material for SV isolation procedures. New method Here, we demonstrate an efficient ex vivo procedure to obtain functional SVs from cultured rat cortical neurons after genetic engineering with a lentivirus. Results We show that ∼108 plated cortical neurons allow isolation of suitable SV amounts for functional analysis and imaging. We found that SVs isolated from cultured neurons have neurotransmitter uptake comparable to that of SVs isolated from intact cortex. Using total internal reflection fluorescence (TIRF) microscopy, we visualized an exogenous SV-targeted marker protein and demonstrated the high efficiency of SV modification. Comparison with existing methods Obtaining SVs from genetically engineered neurons currently generally requires the availability of transgenic animals, which is constrained by technical (e.g. cost and time) and biological (e.g. developmental defects and lethality) limitations. Conclusions These results demonstrate the modification and isolation of functional SVs using cultured neurons and viral transduction. The ability to readily obtain SVs from genetically engineered neurons will permit linking in situ studies to in vitro experiments in a variety of genetic contexts. AU - Mckenzie, Catherine AU - Spanova, Miroslava AU - Johnson, Alexander J AU - Kainrath, Stephanie AU - Zheden, Vanessa AU - Sitte, Harald H. AU - Janovjak, Harald L ID - 7406 JF - Journal of Neuroscience Methods SN - 0165-0270 TI - Isolation of synaptic vesicles from genetically engineered cultured neurons VL - 312 ER - TY - CONF AB - 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. AU - Yu, Chen AU - Tang, Hanlin AU - Renggli, Cedric AU - Kassing, Simon AU - Singla, Ankit AU - Alistarh, Dan-Adrian AU - Zhang, Ce AU - Liu, Ji ID - 7437 SN - 9781510886988 T2 - 36th International Conference on Machine Learning, ICML 2019 TI - Distributed learning over unreliable networks VL - 2019-June ER -