@article{10585, abstract = {Recently it was shown that anyons on the two-sphere naturally arise from a system of molecular impurities exchanging angular momentum with a many-particle bath (Phys. Rev. Lett. 126, 015301 (2021)). Here we further advance this approach and rigorously demonstrate that in the experimentally realized regime the lowest spectrum of two linear molecules immersed in superfluid helium corresponds to the spectrum of two anyons on the sphere. We develop the formalism within the framework of the recently experimentally observed angulon quasiparticle}, author = {Brooks, Morris and Lemeshko, Mikhail and Lundholm, Douglas and Yakaboylu, Enderalp}, issn = {2218-2004}, journal = {Atoms}, keywords = {anyons, quasiparticles, Quantum Hall Effect, topological states of matter}, number = {4}, publisher = {MDPI}, title = {{Emergence of anyons on the two-sphere in molecular impurities}}, doi = {10.3390/atoms9040106}, volume = {9}, year = {2021}, } @inproceedings{13147, abstract = {We investigate fast and communication-efficient algorithms for the classic problem of minimizing a sum of strongly convex and smooth functions that are distributed among n different nodes, which can communicate using a limited number of bits. Most previous communication-efficient approaches for this problem are limited to first-order optimization, and therefore have \emph{linear} dependence on the condition number in their communication complexity. We show that this dependence is not inherent: communication-efficient methods can in fact have sublinear dependence on the condition number. For this, we design and analyze the first communication-efficient distributed variants of preconditioned gradient descent for Generalized Linear Models, and for Newton’s method. Our results rely on a new technique for quantizing both the preconditioner and the descent direction at each step of the algorithms, while controlling their convergence rate. We also validate our findings experimentally, showing faster convergence and reduced communication relative to previous methods.}, author = {Alimisis, Foivos and Davies, Peter and Alistarh, Dan-Adrian}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, isbn = {9781713845065}, issn = {2640-3498}, location = {Virtual}, pages = {196--206}, publisher = {ML Research Press}, title = {{Communication-efficient distributed optimization with quantized preconditioners}}, volume = {139}, year = {2021}, } @inproceedings{13146, abstract = {A recent line of work has analyzed the theoretical properties of deep neural networks via the Neural Tangent Kernel (NTK). In particular, the smallest eigenvalue of the NTK has been related to the memorization capacity, the global convergence of gradient descent algorithms and the generalization of deep nets. However, existing results either provide bounds in the two-layer setting or assume that the spectrum of the NTK matrices is bounded away from 0 for multi-layer networks. In this paper, we provide tight bounds on the smallest eigenvalue of NTK matrices for deep ReLU nets, both in the limiting case of infinite widths and for finite widths. In the finite-width setting, the network architectures we consider are fairly general: we require the existence of a wide layer with roughly order of N neurons, N being the number of data samples; and the scaling of the remaining layer widths is arbitrary (up to logarithmic factors). To obtain our results, we analyze various quantities of independent interest: we give lower bounds on the smallest singular value of hidden feature matrices, and upper bounds on the Lipschitz constant of input-output feature maps.}, author = {Nguyen, Quynh and Mondelli, Marco and Montufar, Guido}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, isbn = {9781713845065}, issn = {2640-3498}, location = {Virtual}, pages = {8119--8129}, publisher = {ML Research Press}, title = {{Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks}}, volume = {139}, year = {2021}, } @inproceedings{10665, abstract = {Formal verification of neural networks is an active topic of research, and recent advances have significantly increased the size of the networks that verification tools can handle. However, most methods are designed for verification of an idealized model of the actual network which works over real arithmetic and ignores rounding imprecisions. This idealization is in stark contrast to network quantization, which is a technique that trades numerical precision for computational efficiency and is, therefore, often applied in practice. Neglecting rounding errors of such low-bit quantized neural networks has been shown to lead to wrong conclusions about the network’s correctness. Thus, the desired approach for verifying quantized neural networks would be one that takes these rounding errors into account. In this paper, we show that verifying the bitexact implementation of quantized neural networks with bitvector specifications is PSPACE-hard, even though verifying idealized real-valued networks and satisfiability of bit-vector specifications alone are each in NP. Furthermore, we explore several practical heuristics toward closing the complexity gap between idealized and bit-exact verification. In particular, we propose three techniques for making SMT-based verification of quantized neural networks more scalable. Our experiments demonstrate that our proposed methods allow a speedup of up to three orders of magnitude over existing approaches.}, author = {Henzinger, Thomas A and Lechner, Mathias and Zikelic, Dorde}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence}, isbn = {978-1-57735-866-4}, issn = {2374-3468}, location = {Virtual}, number = {5A}, pages = {3787--3795}, publisher = {AAAI Press}, title = {{Scalable verification of quantized neural networks}}, volume = {35}, year = {2021}, } @inproceedings{10667, abstract = {Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network's prediction. We consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with infinite time horizon systems. Compared to the existing sampling-based approaches, which are inapplicable to the infinite time horizon setting, we train a separate deterministic neural network that serves as an infinite time horizon safety certificate. In particular, we show that the certificate network guarantees the safety of the system over a subset of the BNN weight posterior's support. Our method first computes a safe weight set and then alters the BNN's weight posterior to reject samples outside this set. Moreover, we show how to extend our approach to a safe-exploration reinforcement learning setting, in order to avoid unsafe trajectories during the training of the policy. We evaluate our approach on a series of reinforcement learning benchmarks, including non-Lyapunovian safety specifications.}, author = {Lechner, Mathias and Žikelić, Ðorđe and Chatterjee, Krishnendu and Henzinger, Thomas A}, booktitle = {35th Conference on Neural Information Processing Systems}, location = {Virtual}, title = {{Infinite time horizon safety of Bayesian neural networks}}, doi = {10.48550/arXiv.2111.03165}, year = {2021}, } @article{13358, abstract = {DNA nanotechnology offers a versatile toolbox for precise spatial and temporal manipulation of matter on the nanoscale. However, rendering DNA-based systems responsive to light has remained challenging. Herein, we describe the remote manipulation of native (non-photoresponsive) chiral plasmonic molecules (CPMs) using light. Our strategy is based on the use of a photoresponsive medium comprising a merocyanine-based photoacid. Upon exposure to visible light, the medium decreases its pH, inducing the formation of DNA triplex links, leading to a spatial reconfiguration of the CPMs. The process can be reversed simply by turning the light off and it can be repeated for multiple cycles. The degree of the overall chirality change in an ensemble of CPMs depends on the CPM fraction undergoing reconfiguration, which, remarkably, depends on and can be tuned by the intensity of incident light. Such a dynamic, remotely controlled system could aid in further advancing DNA-based devices and nanomaterials.}, author = {Ryssy, Joonas and Natarajan, Ashwin K. and Wang, Jinhua and Lehtonen, Arttu J. and Nguyen, Minh‐Kha and Klajn, Rafal and Kuzyk, Anton}, issn = {1521-3773}, journal = {Angewandte Chemie International Edition}, keywords = {General Chemistry, Catalysis}, number = {11}, pages = {5859--5863}, publisher = {Wiley}, title = {{Light‐responsive dynamic DNA‐origami‐based plasmonic assemblies}}, doi = {10.1002/anie.202014963}, volume = {60}, year = {2021}, } @inbook{13360, abstract = {Inorganic nanoparticles (NPs) exhibit a wide range of fascinating physicochemical properties, many of which can be controlled by modulating the NP–NP coupling. Controlling the self-assembly of NPs using light has traditionally been achieved by functionalizing their surfaces with monolayers of photoswitchable molecules, which can be reversibly isomerized between two or more states upon exposure to different wavelengths of light. NPs whose assembly can be controlled by light in a reversible fashion can find interesting applications. The chapter deals with systems comprising mixtures of non-photoswitchable NPs and small-molecule photoacids and photobases. Examples of light-controlled self-assembly of NPs hitherto reported have been categorized into six distinct approaches. These are: functionalizing NPs with monolayers of photoswitchable molecules, light-controlled adsorption/desorption of photoswitchable molecules onto NPs, and light-induced electron transfer between the particle's inorganic core and the NP-bound ligands.}, author = {Bian, Tong and Chu, Zonglin and Klajn, Rafal}, booktitle = {Out‐of‐Equilibrium (Supra)molecular Systems and Materials}, editor = {Giuseppone, Nicolas and Walther, Andreas}, isbn = {9783527346158}, pages = {241--273}, publisher = {Wiley}, title = {{Controlling Self‐Assembly of Nanoparticles Using Light}}, doi = {10.1002/9783527821990.ch9}, year = {2021}, } @article{13357, abstract = {Coulombic interactions can be used to assemble charged nanoparticles into higher-order structures, but the process requires oppositely charged partners that are similarly sized. The ability to mediate the assembly of such charged nanoparticles using structurally simple small molecules would greatly facilitate the fabrication of nanostructured materials and harnessing their applications in catalysis, sensing and photonics. Here we show that small molecules with as few as three electric charges can effectively induce attractive interactions between oppositely charged nanoparticles in water. These interactions can guide the assembly of charged nanoparticles into colloidal crystals of a quality previously only thought to result from their co-crystallization with oppositely charged nanoparticles of a similar size. Transient nanoparticle assemblies can be generated using positively charged nanoparticles and multiply charged anions that are enzymatically hydrolysed into mono- and/or dianions. Our findings demonstrate an approach for the facile fabrication, manipulation and further investigation of static and dynamic nanostructured materials in aqueous environments.}, author = {Bian, Tong and Gardin, Andrea and Gemen, Julius and Houben, Lothar and Perego, Claudio and Lee, Byeongdu and Elad, Nadav and Chu, Zonglin and Pavan, Giovanni M. and Klajn, Rafal}, issn = {1755-4349}, journal = {Nature Chemistry}, keywords = {General Chemical Engineering, General Chemistry}, number = {10}, pages = {940--949}, publisher = {Springer Nature}, title = {{Electrostatic co-assembly of nanoparticles with oppositely charged small molecules into static and dynamic superstructures}}, doi = {10.1038/s41557-021-00752-9}, volume = {13}, year = {2021}, } @misc{13072, abstract = {CpGs and corresponding mean weights for DNAm-based prediction of cognitive abilities (6 traits)}, author = {McCartney, Daniel L and Hillary, Robert F and Conole, Eleanor LS and Trejo Banos, Daniel and Gadd, Danni A and Walker, Rosie M and Nangle, Cliff and Flaig, Robin and Campbell, Archie and Murray, Alison D and Munoz Maniega, Susana and del C Valdes-Hernandez, Maria and Harris, Mathew A and Bastin, Mark E and Wardlaw, Joanna M and Harris, Sarah E and Porteous, David J and Tucker-Drob, Elliot M and McIntosh, Andrew M and Evans, Kathryn L and Deary, Ian J and Cox, Simon R and Robinson, Matthew Richard and Marioni, Riccardo E}, publisher = {Zenodo}, title = {{Blood-based epigenome-wide analyses of cognitive abilities}}, doi = {10.5281/ZENODO.5794028}, year = {2021}, } @misc{13068, abstract = {Source data and source code for the graphs in "Spatiotemporal dynamics of self-organized branching pancreatic cancer-derived organoids".}, author = {Randriamanantsoa, Samuel and Papargyriou, Aristeidis and Maurer, Carlo and Peschke, Katja and Schuster, Maximilian and Zecchin, Giulia and Steiger, Katja and Öllinger, Rupert and Saur, Dieter and Scheel, Christina and Rad, Roland and Hannezo, Edouard B and Reichert, Maximilian and Bausch, Andreas R.}, publisher = {Zenodo}, title = {{Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids}}, doi = {10.5281/ZENODO.5148117}, year = {2021}, }