TY - JOUR AB - We demonstrate that releasing atoms into free space from an optical lattice does not deteriorate cavity-generated spin squeezing for metrological purposes. In this work, an ensemble of 500000 spin-squeezed atoms in a high-finesse optical cavity with near-uniform atom-cavity coupling is prepared, released into free space, recaptured in the cavity, and probed. Up to ∼10 dB of metrologically relevant squeezing is retrieved for 700μs free-fall times, and decaying levels of squeezing are realized for up to 3 ms free-fall times. The degradation of squeezing results from loss of atom-cavity coupling homogeneity between the initial squeezed state generation and final collective state readout. A theoretical model is developed to quantify this degradation and this model is experimentally validated. AU - Wu, Yunfan AU - Krishnakumar, Rajiv AU - Martínez-Rincón, Julián AU - Malia, Benjamin K. AU - Hosten, Onur AU - Kasevich, Mark A. ID - 8319 IS - 1 JF - Physical Review A SN - 24699926 TI - Retrieval of cavity-generated atomic spin squeezing after free-space release VL - 102 ER - TY - JOUR AB - Markov decision processes (MDPs) are the defacto framework for sequential decision making in the presence of stochastic uncertainty. A classical optimization criterion for MDPs is to maximize the expected discounted-sum payoff, which ignores low probability catastrophic events with highly negative impact on the system. On the other hand, risk-averse policies require the probability of undesirable events to be below a given threshold, but they do not account for optimization of the expected payoff. We consider MDPs with discounted-sum payoff with failure states which represent catastrophic outcomes. The objective of risk-constrained planning is to maximize the expected discounted-sum payoff among risk-averse policies that ensure the probability to encounter a failure state is below a desired threshold. Our main contribution is an efficient risk-constrained planning algorithm that combines UCT-like search with a predictor learned through interaction with the MDP (in the style of AlphaZero) and with a risk-constrained action selection via linear programming. We demonstrate the effectiveness of our approach with experiments on classical MDPs from the literature, including benchmarks with an order of 106 states. AU - Brázdil, Tomáš AU - Chatterjee, Krishnendu AU - Novotný, Petr AU - Vahala, Jiří ID - 15055 IS - 06 JF - Proceedings of the 34th AAAI Conference on Artificial Intelligence KW - General Medicine SN - 2374-3468 TI - Reinforcement learning of risk-constrained policies in Markov decision processes VL - 34 ER - TY - JOUR AB - Vaccinia virus–related kinase (VRK) is an evolutionarily conserved nuclear protein kinase. VRK-1, the single Caenorhabditis elegans VRK ortholog, functions in cell division and germline proliferation. However, the role of VRK-1 in postmitotic cells and adult life span remains unknown. Here, we show that VRK-1 increases organismal longevity by activating the cellular energy sensor, AMP-activated protein kinase (AMPK), via direct phosphorylation. We found that overexpression of vrk-1 in the soma of adult C. elegans increased life span and, conversely, inhibition of vrk-1 decreased life span. In addition, vrk-1 was required for longevity conferred by mutations that inhibit C. elegans mitochondrial respiration, which requires AMPK. VRK-1 directly phosphorylated and up-regulated AMPK in both C. elegans and cultured human cells. Thus, our data show that the somatic nuclear kinase, VRK-1, promotes longevity through AMPK activation, and this function appears to be conserved between C. elegans and humans. AU - Park, Sangsoon AU - Artan, Murat AU - Han, Seung Hyun AU - Park, Hae-Eun H. AU - Jung, Yoonji AU - Hwang, Ara B. AU - Shin, Won Sik AU - Kim, Kyong-Tai AU - Lee, Seung-Jae V. ID - 15057 IS - 27 JF - Science Advances TI - VRK-1 extends life span by activation of AMPK via phosphorylation VL - 6 ER - TY - JOUR AB - The actin cytoskeleton, a dynamic network of actin filaments and associated F-actin–binding proteins, is fundamentally important in eukaryotes. α-Actinins are major F-actin bundlers that are inhibited by Ca2+ in nonmuscle cells. Here we report the mechanism of Ca2+-mediated regulation of Entamoeba histolytica α-actinin-2 (EhActn2) with features expected for the common ancestor of Entamoeba and higher eukaryotic α-actinins. Crystal structures of Ca2+-free and Ca2+-bound EhActn2 reveal a calmodulin-like domain (CaMD) uniquely inserted within the rod domain. Integrative studies reveal an exceptionally high affinity of the EhActn2 CaMD for Ca2+, binding of which can only be regulated in the presence of physiological concentrations of Mg2+. Ca2+ binding triggers an increase in protein multidomain rigidity, reducing conformational flexibility of F-actin–binding domains via interdomain cross-talk and consequently inhibiting F-actin bundling. In vivo studies uncover that EhActn2 plays an important role in phagocytic cup formation and might constitute a new drug target for amoebic dysentery. AU - Pinotsis, Nikos AU - Zielinska, Karolina AU - Babuta, Mrigya AU - Arolas, Joan L. AU - Kostan, Julius AU - Khan, Muhammad Bashir AU - Schreiner, Claudia AU - Testa Salmazo, Anita P AU - Ciccarelli, Luciano AU - Puchinger, Martin AU - Gkougkoulia, Eirini A. AU - Ribeiro, Euripedes de Almeida AU - Marlovits, Thomas C. AU - Bhattacharya, Alok AU - Djinovic-Carugo, Kristina ID - 15061 IS - 36 JF - Proceedings of the National Academy of Sciences SN - 0027-8424 TI - Calcium modulates the domain flexibility and function of an α-actinin similar to the ancestral α-actinin VL - 117 ER - TY - JOUR AB - We call a continuous self-map that reveals itself through a discrete set of point-value pairs a sampled dynamical system. Capturing the available information with chain maps on Delaunay complexes, we use persistent homology to quantify the evidence of recurrent behavior. We establish a sampling theorem to recover the eigenspaces of the endomorphism on homology induced by the self-map. Using a combinatorial gradient flow arising from the discrete Morse theory for Čech and Delaunay complexes, we construct a chain map to transform the problem from the natural but expensive Čech complexes to the computationally efficient Delaunay triangulations. The fast chain map algorithm has applications beyond dynamical systems. AU - Bauer, U. AU - Edelsbrunner, Herbert AU - Jablonski, Grzegorz AU - Mrozek, M. ID - 15064 IS - 4 JF - Journal of Applied and Computational Topology SN - 2367-1726 TI - Čech-Delaunay gradient flow and homology inference for self-maps VL - 4 ER - TY - JOUR AB - We consider the least singular value of a large random matrix with real or complex i.i.d. Gaussian entries shifted by a constant z∈C. We prove an optimal lower tail estimate on this singular value in the critical regime where z is around the spectral edge, thus improving the classical bound of Sankar, Spielman and Teng (SIAM J. Matrix Anal. Appl. 28:2 (2006), 446–476) for the particular shift-perturbation in the edge regime. Lacking Brézin–Hikami formulas in the real case, we rely on the superbosonization formula (Comm. Math. Phys. 283:2 (2008), 343–395). AU - Cipolloni, Giorgio AU - Erdös, László AU - Schröder, Dominik J ID - 15063 IS - 1 JF - Probability and Mathematical Physics KW - General Medicine SN - 2690-1005 TI - Optimal lower bound on the least singular value of the shifted Ginibre ensemble VL - 1 ER - TY - CONF AB - We introduce a new graph problem, the token dropping game, and we show how to solve it efficiently in a distributed setting. We use the token dropping game as a tool to design an efficient distributed algorithm for the stable orientation problem, which is a special case of the more general locally optimal semi-matching problem. The prior work by Czygrinow et al. (DISC 2012) finds a locally optimal semi-matching in O(Δ⁵) rounds in graphs of maximum degree Δ, which directly implies an algorithm with the same runtime for stable orientations. We improve the runtime to O(Δ⁴) for stable orientations and prove a lower bound of Ω(Δ) rounds. AU - Brandt, Sebastian AU - Keller, Barbara AU - Rybicki, Joel AU - Suomela, Jukka AU - Uitto, Jara ID - 15074 T2 - 34th International Symposium on Distributed Computing TI - Brief announcement: Efficient load-balancing through distributed token dropping VL - 179 ER - TY - CONF AB - We consider the following dynamic load-balancing process: given an underlying graph G with n nodes, in each step t≥ 0, one unit of load is created, and placed at a randomly chosen graph node. In the same step, the chosen node picks a random neighbor, and the two nodes balance their loads by averaging them. We are interested in the expected gap between the minimum and maximum loads at nodes as the process progresses, and its dependence on n and on the graph structure. Variants of the above graphical balanced allocation process have been studied previously by Peres, Talwar, and Wieder [Peres et al., 2015], and by Sauerwald and Sun [Sauerwald and Sun, 2015]. These authors left as open the question of characterizing the gap in the case of cycle graphs in the dynamic case, where weights are created during the algorithm’s execution. For this case, the only known upper bound is of 𝒪(n log n), following from a majorization argument due to [Peres et al., 2015], which analyzes a related graphical allocation process. In this paper, we provide an upper bound of 𝒪 (√n log n) on the expected gap of the above process for cycles of length n. We introduce a new potential analysis technique, which enables us to bound the difference in load between k-hop neighbors on the cycle, for any k ≤ n/2. We complement this with a "gap covering" argument, which bounds the maximum value of the gap by bounding its value across all possible subsets of a certain structure, and recursively bounding the gaps within each subset. We provide analytical and experimental evidence that our upper bound on the gap is tight up to a logarithmic factor. AU - Alistarh, Dan-Adrian AU - Nadiradze, Giorgi AU - Sabour, Amirmojtaba ID - 15077 T2 - 47th International Colloquium on Automata, Languages, and Programming TI - Dynamic averaging load balancing on cycles VL - 168 ER - TY - CONF AB - Two plane drawings of geometric graphs on the same set of points are called disjoint compatible if their union is plane and they do not have an edge in common. For a given set S of 2n points two plane drawings of perfect matchings M1 and M2 (which do not need to be disjoint nor compatible) are disjoint tree-compatible if there exists a plane drawing of a spanning tree T on S which is disjoint compatible to both M1 and M2. We show that the graph of all disjoint tree-compatible perfect geometric matchings on 2n points in convex position is connected if and only if 2n ≥ 10. Moreover, in that case the diameter of this graph is either 4 or 5, independent of n. AU - Aichholzer, Oswin AU - Obmann, Julia AU - Patak, Pavel AU - Perz, Daniel AU - Tkadlec, Josef ID - 15082 T2 - 36th European Workshop on Computational Geometry TI - Disjoint tree-compatible plane perfect matchings ER - TY - JOUR AB - 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. AU - Javanmard, Adel AU - Mondelli, Marco AU - Montanari, Andrea ID - 6748 IS - 6 JF - Annals of Statistics SN - 1932-6157 TI - Analysis of a two-layer neural network via displacement convexity VL - 48 ER -