An investigation into a wavelet accelerated gauge fixing algorithm

We introduce an acceleration algorithm for coulomb gauge fixing, using the compactly supported wavelets introduced by Daubechies. The algorithm is similar to Fourier acceleration. Our provisional numerical results for $SU(3)$ on $8^{4}$ lattices show that the acceleration based on the DAUB6 transform can reduce the number of iterations by a factor up to 3 over the unaccelerated algorithm. The reduction in iterations for Fourier acceleration is approximately a factor of 7.Comment: Resubmitted as a uuencode-compressed-tar postscript file. A Daubechies wavelet transform will transform a vector of length $N$ in $O(N)$ operations, and not in O(N log N) operations as we incorrectly stated in the first version of this pape

Corrigendum: Determinants of Non-paid Task Division in Gay-, Lesbian-, and Heterosexual-Parent Families With Infants Conceived Using Artificial Reproductive Techniques.

[This corrects the article DOI: 10.3389/fpsyg.2020.00914.]

Renormalization of the Lattice HQET Isgur-Wise Function

We compute the perturbative renormalization factors required to match to the continuum Isgur-Wise function, calculated using lattice Heavy Quark Effective Theory. The velocity, mass, wavefunction and current renormalizations are calculated for both the forward difference and backward difference actions for a variety of velocities. Subtleties are clarified regarding tadpole improvement, regulating divergences, and variations of techniques used in these renormalizations.Comment: 28 pages, 0 figures, LaTeX. Final version accepted for publication in Phys. Rev. D. (Minor changes.

A calculation of the BBB_{B} parameter in the static limit

We calculate the $B_{B}$ parameter, relevant for $\overline{B}^0$ -- $B^0$ mixing, from a lattice gauge theory simulation at $\beta = 6.0$. The bottom quarks are simulated in the static theory, the light quarks with Wilson fermions. Improved smearing functions produced by a variational technique, MOST, are used to reduce statistical errors and minimize excited-state contamination of the ground-state signal. We obtain $B_B(4.33 GeV) = 0.98^{+4}_{-4}$ (statistical) $^{+3}_{-18}$ (systematic) which corresponds to $\widehat{B}_B = 1.40^{+6}_{-6}$ (statistical) $^{+4}_{-26}$ (systematic) for the one-loop renormalization-scheme-independent parameter. The systematic errors include the uncertainty due to alternative (less favored) treatments of the perturbatively-calculated mixing coefficients; this uncertainty is at least as large as residual differences between Wilson-static and clover-static results. Our result agrees with extrapolations of results from relativistic (Wilson) heavy quark simulations.Comment: 39 pages (REVTeX) including 10 figures (PostScript); Final version accepted for publication: Added new section for clarity; Included comparison to recent results by other groups; slight numerical changes; Essential conclusions remain the sam

A text become provisional: revisiting the capital of the ruins

This essay is a reexamination of Samuel Beckett's The Capital of the Ruins, the untransmitted radio script written for Raidió Éireann (now Raidió Teilifís Éireann) in 1946 following his work with the Irish Red Cross in Saint Lô. The first half of this essay is concerned with the archival and publishing history of the text. This section examines the variants introduced by various editors or publishers and makes a case for a definitive edition of the text based on the edited photocopy of the typescript held in the Beckett International Foundation archive at the University of Reading. The second half of this essay then uses this close attention to the text to reconsider the focus of The Capital of the Ruins and the extent to which the piece is more firmly directed towards socio-political aspects of post-neutrality Ireland than has previously been identified

Greater male variability in daily energy expenditure develops through puberty

The authors also gratefully acknowledge funding from the Chinese Academy of Sciences (grant no. CAS153E11KYSB20190045) to J.R.S. and the US National Science Foundation (grant no. BCS-1824466) awarded to H.P. Acknowledgements Yvonne Schönbeck provided important information about morphometric measurements for Dutch children. A chat over dinner with Karsten Koehler, Eimear Dolan and Danny Longman brought up a number of thoughts that influenced this manuscript. The DLW database, which can be found at https://doublylabelled-waterdatabase.iaea.org/home, is hosted by the IAEA and generously supported by Taiyo Nippon Sanso and SERCON. We are grateful to the IAEA and these companies for their support and especially to Takashi Oono for his tremendous efforts at fundraising on our behalf.Peer reviewedPublisher PD

NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

NeuroBench:Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking

The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics

NeuroBench:A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of nearly 100 co-authors across over 50 institutions in industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we present initial performance baselines across various model architectures on the algorithm track and outline the system track benchmark tasks and guidelines. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community