@article{9470,
abstract = {A key step in understanding the genetic basis of different evolutionary outcomes (e.g., adaptation) is to determine the roles played by different mutation types (e.g., SNPs, translocations and inversions). To do this we must simultaneously consider different mutation types in an evolutionary framework. Here, we propose a research framework that directly utilizes the most important characteristics of mutations, their population genetic effects, to determine their relative evolutionary significance in a given scenario. We review known population genetic effects of different mutation types and show how these may be connected to different evolutionary outcomes. We provide examples of how to implement this framework and pinpoint areas where more data, theory and synthesis are needed. Linking experimental and theoretical approaches to examine different mutation types simultaneously is a critical step towards understanding their evolutionary significance.},
author = {Berdan, Emma L. and Blanckaert, Alexandre and Slotte, Tanja and Suh, Alexander and Westram, Anja M and Fragata, Inês},
issn = {1365294X},
journal = {Molecular Ecology},
number = {12},
pages = {2710--2723},
publisher = {Wiley},
title = {{Unboxing mutations: Connecting mutation types with evolutionary consequences}},
doi = {10.1111/mec.15936},
volume = {30},
year = {2021},
}
@article{9467,
abstract = {Turbulence in the flow of fluid through a pipe can be suppressed by buoyancy forces. As the suppression of turbulence leads to severe heat transfer deterioration, this is an important and undesirable phenomenon in both heating and cooling applications. Vertical flow is often considered, as the axial buoyancy force can help drive the flow. With heating measured by the buoyancy parameter 𝐶, our direct numerical simulations show that shear-driven turbulence may either be completely laminarised or it transitions to a relatively quiescent convection-driven state. Buoyancy forces cause a flattening of the base flow profile, which in isothermal pipe flow has recently been linked to complete suppression of turbulence (Kühnen et al., Nat. Phys., vol. 14, 2018, pp. 386–390), and the flattened laminar base profile has enhanced nonlinear stability (Marensi et al., J. Fluid Mech., vol. 863, 2019, pp. 50–875). In agreement with these findings, the nonlinear lower-branch travelling-wave solution analysed here, which is believed to mediate transition to turbulence in isothermal pipe flow, is shown to be suppressed by buoyancy. A linear instability of the laminar base flow is responsible for the appearance of the relatively quiescent convection driven state for 𝐶≳4 across the range of Reynolds numbers considered. In the suppression of turbulence, however, i.e. in the transition from turbulence, we find clearer association with the analysis of He et al. (J. Fluid Mech., vol. 809, 2016, pp. 31–71) than with the above dynamical systems approach, which describes better the transition to turbulence. The laminarisation criterion He et al. propose, based on an apparent Reynolds number of the flow as measured by its driving pressure gradient, is found to capture the critical 𝐶=𝐶𝑐𝑟(𝑅𝑒) above which the flow will be laminarised or switch to the convection-driven type. Our analysis suggests that it is the weakened rolls, rather than the streaks, which appear to be critical for laminarisation.},
author = {Marensi, Elena and He, Shuisheng and Willis, Ashley P.},
issn = {14697645},
journal = {Journal of Fluid Mechanics},
publisher = {Cambridge University Press},
title = {{Suppression of turbulence and travelling waves in a vertical heated pipe}},
doi = {10.1017/jfm.2021.371},
volume = {919},
year = {2021},
}
@article{9468,
abstract = {Motivated by the successful application of geometry to proving the Harary--Hill conjecture for “pseudolinear” drawings of $K_n$, we introduce “pseudospherical” drawings of graphs. A spherical drawing of a graph $G$ is a drawing in the unit sphere $\mathbb{S}^2$ in which the vertices of $G$ are represented as points---no three on a great circle---and the edges of $G$ are shortest-arcs in $\mathbb{S}^2$ connecting pairs of vertices. Such a drawing has three properties: (1) every edge $e$ is contained in a simple closed curve $\gamma_e$ such that the only vertices in $\gamma_e$ are the ends of $e$; (2) if $e\ne f$, then $\gamma_e\cap\gamma_f$ has precisely two crossings; and (3) if $e\ne f$, then $e$ intersects $\gamma_f$ at most once, in either a crossing or an end of $e$. We use properties (1)--(3) to define a pseudospherical drawing of $G$. Our main result is that for the complete graph, properties (1)--(3) are equivalent to the same three properties but with “precisely two crossings” in (2) replaced by “at most two crossings.” The proof requires a result in the geometric transversal theory of arrangements of pseudocircles. This is proved using the surprising result that the absence of special arcs (coherent spirals) in an arrangement of simple closed curves characterizes the fact that any two curves in the arrangement have at most two crossings. Our studies provide the necessary ideas for exhibiting a drawing of $K_{10}$ that has no extension to an arrangement of pseudocircles and a drawing of $K_9$ that does extend to an arrangement of pseudocircles, but no such extension has all pairs of pseudocircles crossing twice.
},
author = {Arroyo Guevara, Alan M and Richter, R. Bruce and Sunohara, Matthew},
issn = {08954801},
journal = {SIAM Journal on Discrete Mathematics},
number = {2},
pages = {1050--1076},
publisher = {Society for Industrial and Applied Mathematics},
title = {{Extending drawings of complete graphs into arrangements of pseudocircles}},
doi = {10.1137/20M1313234},
volume = {35},
year = {2021},
}
@article{8546,
abstract = {Brain neurons arise from relatively few progenitors generating an enormous diversity of neuronal types. Nonetheless, a cardinal feature of mammalian brain neurogenesis is thought to be that excitatory and inhibitory neurons derive from separate, spatially segregated progenitors. Whether bi-potential progenitors with an intrinsic capacity to generate both lineages exist and how such a fate decision may be regulated are unknown. Using cerebellar development as a model, we discover that individual progenitors can give rise to both inhibitory and excitatory lineages. Gradations of Notch activity determine the fates of the progenitors and their daughters. Daughters with the highest levels of Notch activity retain the progenitor fate, while intermediate levels of Notch activity generate inhibitory neurons, and daughters with very low levels of Notch signaling adopt the excitatory fate. Therefore, Notch-mediated binary cell fate choice is a mechanism for regulating the ratio of excitatory to inhibitory neurons from common progenitors.},
author = {Zhang, Tingting and Liu, Tengyuan and Mora, Natalia and Guegan, Justine and Bertrand, Mathilde and Contreras, Ximena and Hansen, Andi H and Streicher, Carmen and Anderle, Marica and Danda, Natasha and Tiberi, Luca and Hippenmeyer, Simon and Hassan, Bassem A.},
issn = { 22111247},
journal = {Cell Reports},
number = {10},
publisher = {Elsevier},
title = {{Generation of excitatory and inhibitory neurons from common progenitors via Notch signaling in the cerebellum}},
doi = {10.1016/j.celrep.2021.109208},
volume = {35},
year = {2021},
}
@article{9550,
abstract = {We prove that the energy of any eigenvector of a sum of several independent large Wigner matrices is equally distributed among these matrices with very high precision. This shows a particularly strong microcanonical form of the equipartition principle for quantum systems whose components are modelled by Wigner matrices. },
author = {Bao, Zhigang and Erdös, László and Schnelli, Kevin},
issn = {20505094},
journal = {Forum of Mathematics, Sigma},
publisher = {Cambridge University Press},
title = {{Equipartition principle for Wigner matrices}},
doi = {10.1017/fms.2021.38},
volume = {9},
year = {2021},
}
@article{9548,
abstract = {We extend the notion of the minimal volume ellipsoid containing a convex body in Rd to the setting of logarithmically concave functions. We consider a vast class of logarithmically concave functions whose superlevel sets are concentric ellipsoids. For a fixed function from this class, we consider the set of all its “affine” positions. For any log-concave function f on Rd, we consider functions belonging to this set of “affine” positions, and find the one with the minimal integral under the condition that it is pointwise greater than or equal to f. We study the properties of existence and uniqueness of the solution to this problem. For any s∈[0,+∞), we consider the construction dual to the recently defined John s-function (Ivanov and Naszódi in Functional John ellipsoids. arXiv preprint: arXiv:2006.09934, 2020). We prove that such a construction determines a unique function and call it the Löwner s-function of f. We study the Löwner s-functions as s tends to zero and to infinity. Finally, extending the notion of the outer volume ratio, we define the outer integral ratio of a log-concave function and give an asymptotically tight bound on it.},
author = {Ivanov, Grigory and Tsiutsiurupa, Igor},
issn = {1559-002X},
journal = {Journal of Geometric Analysis},
publisher = {Springer},
title = {{Functional Löwner ellipsoids}},
doi = {10.1007/s12220-021-00691-4},
year = {2021},
}
@article{9429,
abstract = {De novo loss of function mutations in the ubiquitin ligase-encoding gene Cullin3 lead to autism spectrum disorder (ASD). In mouse, constitutive haploinsufficiency leads to motor coordination deficits as well as ASD-relevant social and cognitive impairments. However, induction of Cul3 haploinsufficiency later in life does not lead to ASD-relevant behaviors, pointing to an important role of Cul3 during a critical developmental window. Here we show that Cul3 is essential to regulate neuronal migration and, therefore, constitutive Cul3 heterozygous mutant mice display cortical lamination abnormalities. At the molecular level, we found that Cul3 controls neuronal migration by tightly regulating the amount of Plastin3 (Pls3), a previously unrecognized player of neural migration. Furthermore, we found that Pls3 cell-autonomously regulates cell migration by regulating actin cytoskeleton organization, and its levels are inversely proportional to neural migration speed. Finally, we provide evidence that cellular phenotypes associated with autism-linked gene haploinsufficiency can be rescued by transcriptional activation of the intact allele in vitro, offering a proof of concept for a potential therapeutic approach for ASDs.},
author = {Morandell, Jasmin and Schwarz, Lena A and Basilico, Bernadette and Tasciyan, Saren and Dimchev, Georgi A and Nicolas, Armel and Sommer, Christoph M and Kreuzinger, Caroline and Dotter, Christoph and Knaus, Lisa and Dobler, Zoe and Cacci, Emanuele and Schur, Florian KM and Danzl, Johann G and Novarino, Gaia},
issn = {2041-1723},
journal = {Nature Communications},
keywords = {General Biochemistry, Genetics and Molecular Biology},
number = {1},
publisher = {Springer Nature},
title = {{Cul3 regulates cytoskeleton protein homeostasis and cell migration during a critical window of brain development}},
doi = {10.1038/s41467-021-23123-x},
volume = {12},
year = {2021},
}
@article{9431,
abstract = {Inositol hexakisphosphate (IP6) is an assembly cofactor for HIV-1. We report here that IP6 is also used for assembly of Rous sarcoma virus (RSV), a retrovirus from a different genus. IP6 is ~100-fold more potent at promoting RSV mature capsid protein (CA) assembly than observed for HIV-1 and removal of IP6 in cells reduces infectivity by 100-fold. Here, visualized by cryo-electron tomography and subtomogram averaging, mature capsid-like particles show an IP6-like density in the CA hexamer, coordinated by rings of six lysines and six arginines. Phosphate and IP6 have opposing effects on CA in vitro assembly, inducing formation of T = 1 icosahedrons and tubes, respectively, implying that phosphate promotes pentamer and IP6 hexamer formation. Subtomogram averaging and classification optimized for analysis of pleomorphic retrovirus particles reveal that the heterogeneity of mature RSV CA polyhedrons results from an unexpected, intrinsic CA hexamer flexibility. In contrast, the CA pentamer forms rigid units organizing the local architecture. These different features of hexamers and pentamers determine the structural mechanism to form CA polyhedrons of variable shape in mature RSV particles.},
author = {Obr, Martin and Ricana, Clifton L. and Nikulin, Nadia and Feathers, Jon-Philip R. and Klanschnig, Marco and Thader, Andreas and Johnson, Marc C. and Vogt, Volker M. and Schur, Florian KM and Dick, Robert A.},
issn = {2041-1723},
journal = {Nature Communications},
keywords = {General Biochemistry, Genetics and Molecular Biology, General Physics and Astronomy, General Chemistry},
number = {1},
publisher = {Nature Research},
title = {{Structure of the mature Rous sarcoma virus lattice reveals a role for IP6 in the formation of the capsid hexamer}},
doi = {10.1038/s41467-021-23506-0},
volume = {12},
year = {2021},
}
@article{9540,
abstract = {The hexameric AAA-ATPase Drg1 is a key factor in eukaryotic ribosome biogenesis and initiates cytoplasmic maturation of the large ribosomal subunit by releasing the shuttling maturation factor Rlp24. Drg1 monomers contain two AAA-domains (D1 and D2) that act in a concerted manner. Rlp24 release is inhibited by the drug diazaborine which blocks ATP hydrolysis in D2. The mode of inhibition was unknown. Here we show the first cryo-EM structure of Drg1 revealing the inhibitory mechanism. Diazaborine forms a covalent bond to the 2′-OH of the nucleotide in D2, explaining its specificity for this site. As a consequence, the D2 domain is locked in a rigid, inactive state, stalling the whole Drg1 hexamer. Resistance mechanisms identified include abolished drug binding and altered positioning of the nucleotide. Our results suggest nucleotide-modifying compounds as potential novel inhibitors for AAA-ATPases.},
author = {Prattes, Michael and Grishkovskaya, Irina and Hodirnau, Victor-Valentin and Rössler, Ingrid and Klein, Isabella and Hetzmannseder, Christina and Zisser, Gertrude and Gruber, Christian C. and Gruber, Karl and Haselbach, David and Bergler, Helmut},
issn = {2041-1723},
journal = {Nature Communications},
keywords = {General Biochemistry, Genetics and Molecular Biology, General Physics and Astronomy, General Chemistry},
number = {1},
publisher = {Springer Nature},
title = {{Structural basis for inhibition of the AAA-ATPase Drg1 by diazaborine}},
doi = {10.1038/s41467-021-23854-x},
volume = {12},
year = {2021},
}
@article{9407,
abstract = {High impact epidemics constitute one of the largest threats humanity is facing in the 21st century. In the absence of pharmaceutical interventions, physical distancing together with testing, contact tracing and quarantining are crucial in slowing down epidemic dynamics. Yet, here we show that if testing capacities are limited, containment may fail dramatically because such combined countermeasures drastically change the rules of the epidemic transition: Instead of continuous, the response to countermeasures becomes discontinuous. Rather than following the conventional exponential growth, the outbreak that is initially strongly suppressed eventually accelerates and scales faster than exponential during an explosive growth period. As a consequence, containment measures either suffice to stop the outbreak at low total case numbers or fail catastrophically if marginally too weak, thus implying large uncertainties in reliably estimating overall epidemic dynamics, both during initial phases and during second wave scenarios.},
author = {Scarselli, Davide and Budanur, Nazmi B and Timme, Marc and Hof, Björn},
issn = {20411723},
journal = {Nature Communications},
number = {1},
publisher = {Springer Nature},
title = {{Discontinuous epidemic transition due to limited testing}},
doi = {10.1038/s41467-021-22725-9},
volume = {12},
year = {2021},
}
@article{9428,
abstract = {Thermalization is the inevitable fate of many complex quantum systems, whose dynamics allow them to fully explore the vast configuration space regardless of the initial state---the behaviour known as quantum ergodicity. In a quest for experimental realizations of coherent long-time dynamics, efforts have focused on ergodicity-breaking mechanisms, such as integrability and localization. The recent discovery of persistent revivals in quantum simulators based on Rydberg atoms have pointed to the existence of a new type of behaviour where the system rapidly relaxes for most initial conditions, while certain initial states give rise to non-ergodic dynamics. This collective effect has been named ”quantum many-body scarring’by analogy with a related form of weak ergodicity breaking that occurs for a single particle inside a stadium billiard potential. In this Review, we provide a pedagogical introduction to quantum many-body scars and highlight the emerging connections with the semiclassical quantization of many-body systems. We discuss the relation between scars and more general routes towards weak violations of ergodicity due to embedded algebras and non-thermal eigenstates, and highlight possible applications of scars in quantum technology.},
author = {Serbyn, Maksym and Abanin, Dmitry A. and Papić, Zlatko},
issn = {1745-2481},
journal = {Nature Physics},
number = {6},
pages = {675–685},
title = {{Quantum many-body scars and weak breaking of ergodicity}},
doi = {10.1038/s41567-021-01230-2},
volume = {17},
year = {2021},
}
@article{9462,
abstract = {We consider a system of N trapped bosons with repulsive interactions in a combined semiclassical mean-field limit at positive temperature. We show that the free energy is well approximated by the minimum of the Hartree free energy functional – a natural extension of the Hartree energy functional to positive temperatures. The Hartree free energy functional converges in the same limit to a semiclassical free energy functional, and we show that the system displays Bose–Einstein condensation if and only if it occurs in the semiclassical free energy functional. This allows us to show that for weak coupling the critical temperature decreases due to the repulsive interactions.},
author = {Deuchert, Andreas and Seiringer, Robert},
issn = {10960783},
journal = {Journal of Functional Analysis},
number = {6},
publisher = {Elsevier},
title = {{Semiclassical approximation and critical temperature shift for weakly interacting trapped bosons}},
doi = {10.1016/j.jfa.2021.109096},
volume = {281},
year = {2021},
}
@article{9547,
abstract = {With the wider availability of full-color 3D printers, color-accurate 3D-print preparation has received increased attention. A key challenge lies in the inherent translucency of commonly used print materials that blurs out details of the color texture. Previous work tries to compensate for these scattering effects through strategic assignment of colored primary materials to printer voxels. To date, the highest-quality approach uses iterative optimization that relies on computationally expensive Monte Carlo light transport simulation to predict the surface appearance from subsurface scattering within a given print material distribution; that optimization, however, takes in the order of days on a single machine. In our work, we dramatically speed up the process by replacing the light transport simulation with a data-driven approach. Leveraging a deep neural network to predict the scattering within a highly heterogeneous medium, our method performs around two orders of magnitude faster than Monte Carlo rendering while yielding optimization results of similar quality level. The network is based on an established method from atmospheric cloud rendering, adapted to our domain and extended by a physically motivated weight sharing scheme that substantially reduces the network size. We analyze its performance in an end-to-end print preparation pipeline and compare quality and runtime to alternative approaches, and demonstrate its generalization to unseen geometry and material values. This for the first time enables full heterogenous material optimization for 3D-print preparation within time frames in the order of the actual printing time.},
author = {Rittig, Tobias and Sumin, Denis and Babaei, Vahid and Didyk, Piotr and Voloboy, Alexey and Wilkie, Alexander and Bickel, Bernd and Myszkowski, Karol and Weyrich, Tim and Křivánek, Jaroslav},
issn = {14678659},
journal = {Computer Graphics Forum},
number = {2},
pages = {205--219},
publisher = {Wiley},
title = {{Neural acceleration of scattering-aware color 3D printing}},
doi = {10.1111/cgf.142626},
volume = {40},
year = {2021},
}
@article{9541,
abstract = {The Massively Parallel Computation (MPC) model is an emerging model that distills core aspects of distributed and parallel computation, developed as a tool to solve combinatorial (typically graph) problems in systems of many machines with limited space. Recent work has focused on the regime in which machines have sublinear (in n, the number of nodes in the input graph) space, with randomized algorithms presented for the fundamental problems of Maximal Matching and Maximal Independent Set. However, there have been no prior corresponding deterministic algorithms. A major challenge underlying the sublinear space setting is that the local space of each machine might be too small to store all edges incident to a single node. This poses a considerable obstacle compared to classical models in which each node is assumed to know and have easy access to its incident edges. To overcome this barrier, we introduce a new graph sparsification technique that deterministically computes a low-degree subgraph, with the additional property that solving the problem on this subgraph provides significant progress towards solving the problem for the original input graph. Using this framework to derandomize the well-known algorithm of Luby [SICOMP’86], we obtain O(log Δ + log log n)-round deterministic MPC algorithms for solving the problems of Maximal Matching and Maximal Independent Set with O(nɛ) space on each machine for any constant ɛ > 0. These algorithms also run in O(log Δ) rounds in the closely related model of CONGESTED CLIQUE, improving upon the state-of-the-art bound of O(log 2Δ) rounds by Censor-Hillel et al. [DISC’17].},
author = {Czumaj, Artur and Davies, Peter and Parter, Merav},
issn = {1549-6333},
journal = {ACM Transactions on Algorithms},
number = {2},
publisher = {ACM},
title = {{Graph sparsification for derandomizing massively parallel computation with low space}},
doi = {10.1145/3451992},
volume = {17},
year = {2021},
}
@phdthesis{9418,
abstract = {Deep learning is best known for its empirical success across a wide range of applications
spanning computer vision, natural language processing and speech. Of equal significance,
though perhaps less known, are its ramifications for learning theory: deep networks have
been observed to perform surprisingly well in the high-capacity regime, aka the overfitting
or underspecified regime. Classically, this regime on the far right of the bias-variance curve
is associated with poor generalisation; however, recent experiments with deep networks
challenge this view.
This thesis is devoted to investigating various aspects of underspecification in deep learning.
First, we argue that deep learning models are underspecified on two levels: a) any given
training dataset can be fit by many different functions, and b) any given function can be
expressed by many different parameter configurations. We refer to the second kind of
underspecification as parameterisation redundancy and we precisely characterise its extent.
Second, we characterise the implicit criteria (the inductive bias) that guide learning in the
underspecified regime. Specifically, we consider a nonlinear but tractable classification
setting, and show that given the choice, neural networks learn classifiers with a large margin.
Third, we consider learning scenarios where the inductive bias is not by itself sufficient to
deal with underspecification. We then study different ways of ‘tightening the specification’: i)
In the setting of representation learning with variational autoencoders, we propose a hand-
crafted regulariser based on mutual information. ii) In the setting of binary classification, we
consider soft-label (real-valued) supervision. We derive a generalisation bound for linear
networks supervised in this way and verify that soft labels facilitate fast learning. Finally, we
explore an application of soft-label supervision to the training of multi-exit models.},
author = {Bui Thi Mai, Phuong},
pages = {125},
publisher = {IST Austria},
title = {{Underspecification in Deep Learning}},
doi = {10.15479/AT:ISTA:9418},
year = {2021},
}
@inproceedings{9543,
abstract = {We consider the problem ofdistributed mean estimation (DME), in which n machines are each given a local d-dimensional vector xv∈Rd, and must cooperate to estimate the mean of their inputs μ=1n∑nv=1xv, while minimizing total communication cost. DME is a fundamental construct in distributed machine learning, and there has been considerable work on variants of this problem, especially in the context of distributed variance reduction for stochastic gradients in parallel SGD. Previous work typically assumes an upper bound on the norm of the input vectors, and achieves an error bound in terms of this norm. However, in many real applications, the input vectors are concentrated around the correct output μ, but μ itself has large norm. In such cases, previous output error bounds perform poorly. In this paper, we show that output error bounds need not depend on input norm. We provide a method of quantization which allows distributed mean estimation to be performed with solution quality dependent only on the distance between inputs, not on input norm, and show an analogous result for distributed variance reduction. The technique is based on a new connection with lattice theory. We also provide lower bounds showing that the communication to error trade-off of our algorithms is asymptotically optimal. As the lattices achieving optimal bounds under l2-norm can be computationally impractical, we also present an extension which leverages easy-to-use cubic lattices, and is loose only up to a logarithmic factor ind. We show experimentally that our method yields practical improvements for common applications, relative to prior approaches.},
author = {Davies, Peter and Gurunanthan, Vijaykrishna and Moshrefi, Niusha and Ashkboos, Saleh and Alistarh, Dan-Adrian},
booktitle = {9th International Conference on Learning Representations},
location = {Online},
title = {{New bounds for distributed mean estimation and variance reduction}},
year = {2021},
}
@inproceedings{9416,
abstract = {We study the inductive bias of two-layer ReLU networks trained by gradient flow. We identify a class of easy-to-learn (`orthogonally separable') datasets, and characterise the solution that ReLU networks trained on such datasets converge to. Irrespective of network width, the solution turns out to be a combination of two max-margin classifiers: one corresponding to the positive data subset and one corresponding to the negative data subset. The proof is based on the recently introduced concept of extremal sectors, for which we prove a number of properties in the context of orthogonal separability. In particular, we prove stationarity of activation patterns from some time onwards, which enables a reduction of the ReLU network to an ensemble of linear subnetworks.},
author = {Bui Thi Mai, Phuong and Lampert, Christoph},
booktitle = {9th International Conference on Learning Representations},
location = {Online},
title = {{The inductive bias of ReLU networks on orthogonally separable data}},
year = {2021},
}
@article{9449,
abstract = {Spin qubits are considered to be among the most promising candidates for building a quantum processor. Group IV hole spin qubits are particularly interesting owing to their ease of operation and compatibility with Si technology. In addition, Ge offers the option for monolithic superconductor–semiconductor integration. Here, we demonstrate a hole spin qubit operating at fields below 10 mT, the critical field of Al, by exploiting the large out-of-plane hole g-factors in planar Ge and by encoding the qubit into the singlet-triplet states of a double quantum dot. We observe electrically controlled g-factor difference-driven and exchange-driven rotations with tunable frequencies exceeding 100 MHz and dephasing times of 1 μs, which we extend beyond 150 μs using echo techniques. These results demonstrate that Ge hole singlet-triplet qubits are competing with state-of-the-art GaAs and Si singlet-triplet qubits. In addition, their rotation frequencies and coherence are comparable with those of Ge single spin qubits, but singlet-triplet qubits can be operated at much lower fields, emphasizing their potential for on-chip integration with superconducting technologies.},
author = {Jirovec, Daniel and Hofmann, Andrea C and Ballabio, Andrea and Mutter, Philipp M. and Tavani, Giulio and Botifoll, Marc and Crippa, Alessandro and Kukucka, Josip and Sagi, Oliver and Martins, Frederico and Saez Mollejo, Jaime and Prieto Gonzalez, Ivan and Borovkov, Maksim and Arbio, Jordi and Chrastina, Daniel and Isella, Giovanni and Katsaros, Georgios},
issn = {1476-4660},
journal = {Nature Materials},
publisher = {Springer Nature},
title = {{A singlet-triplet hole spin qubit in planar Ge}},
doi = {10.1038/s41563-021-01022-2},
year = {2021},
}
@article{9571,
abstract = {As the size and complexity of models and datasets grow, so does the need for communication-efficient variants of stochastic gradient descent that can be deployed to perform parallel model training. One popular communication-compression method for data-parallel SGD is QSGD (Alistarh et al., 2017), which quantizes and encodes gradients to reduce communication costs. The baseline variant of QSGD provides strong theoretical guarantees, however, for practical purposes, the authors proposed a heuristic variant which we call QSGDinf, which demonstrated impressive empirical gains for distributed training of large neural networks. In this paper, we build on this work to propose a new gradient quantization scheme, and show that it has both stronger theoretical guarantees than QSGD, and matches and exceeds the empirical performance of the QSGDinf heuristic and of other compression methods.},
author = {Ramezani-Kebrya, Ali and Faghri, Fartash and Markov, Ilya and Aksenov, Vitalii and Alistarh, Dan-Adrian and Roy, Daniel M.},
issn = {15337928},
journal = {Journal of Machine Learning Research},
number = {114},
pages = {1−43},
publisher = {Journal of Machine Learning Research},
title = {{NUQSGD: Provably communication-efficient data-parallel SGD via nonuniform quantization}},
volume = {22},
year = {2021},
}
@article{9558,
abstract = {We show that turbulent dynamics that arise in simulations of the three-dimensional Navier--Stokes equations in a triply-periodic domain under sinusoidal forcing can be described as transient visits to the neighborhoods of unstable time-periodic solutions. Based on this description, we reduce the original system with more than 10^5 degrees of freedom to a 17-node Markov chain where each node corresponds to the neighborhood of a periodic orbit. The model accurately reproduces long-term averages of the system's observables as weighted sums over the periodic orbits.
},
author = {Yalniz, Gökhan and Hof, Björn and Budanur, Nazmi B},
issn = {1079-7114},
journal = {Physical Review Letters},
number = {24},
publisher = {American Physical Society},
title = {{Coarse graining the state space of a turbulent flow using periodic orbits}},
doi = {10.1103/PhysRevLett.126.244502},
volume = {126},
year = {2021},
}