Entrainment of randomly coupled oscillator networks by a pacemaker

Entrainment by a pacemaker, representing an element with a higher frequency, is numerically investigated for several classes of random networks which consist of identical phase oscillators. We find that the entrainment frequency window of a network decreases exponentially with its depth, defined as the mean forward distance of the elements from the pacemaker. Effectively, only shallow networks can thus exhibit frequency-locking to the pacemaker. The exponential dependence is also derived analytically as an approximation for large random asymmetric networks.Comment: 4 pages, 3 figures, revtex 4, submitted to Phys. Rev. Let

On the effectiveness of partial variance reduction in federated learning with heterogeneous data

Data heterogeneity across clients is a key challenge in federated learning. Prior works address this by either aligning client and server models or using control variates to correct client model drift. Although these methods achieve fast convergence in convex or simple non-convex problems, the performance in over-parameterized models such as deep neural networks is lacking. In this paper, we first revisit the widely used FedAvg algorithm in a deep neural network to understand how data heterogeneity influences the gradient updates across the neural network layers. We observe that while the feature extraction layers are learned efficiently by FedAvg, the substantial diversity of the final classification layers across clients impedes the performance. Motivated by this, we propose to correct model drift by variance reduction only on the final layers. We demonstrate that this significantly outperforms existing benchmarks at a similar or lower communication cost. We furthermore provide proof for the convergence rate of our algorithm.Comment: Accepted to CVPR 202

Dynamic Structure Factor of Liquid and Amorphous Ge From Ab Initio Simulations

We calculate the dynamic structure factor S(k,omega) of liquid Ge (l-Ge) at temperature T = 1250 K, and of amorphous Ge (a-Ge) at T = 300 K, using ab initio molecular dynamics. The electronic energy is computed using density-functional theory, primarily in the generalized gradient approximation, together with a plane wave representation of the wave functions and ultra-soft pseudopotentials. We use a 64-atom cell with periodic boundary conditions, and calculate averages over runs of up to 16 ps. The calculated liquid S(k,omega) agrees qualitatively with that obtained by Hosokawa et al, using inelastic X-ray scattering. In a-Ge, we find that the calculated S(k,omega) is in qualitative agreement with that obtained experimentally by Maley et al. Our results suggest that the ab initio approach is sufficient to allow approximate calculations of S(k,omega) in both liquid and amorphous materials.Comment: 31 pages and 8 figures. Accepted for Phys. Rev.

The genetic basis of natural variation for iron homeostasis in the maize IBM population

BACKGROUND: Iron (Fe) deficiency symptoms in maize (Zea mays subsp. mays) express as leaf chlorosis, growth retardation, as well as yield reduction and are typically observed when plants grow in calcareous soils at alkaline pH. To improve our understanding of genotypical variability in the tolerance to Fe deficiency-induced chlorosis, the objectives of this study were to (i) determine the natural genetic variation of traits related to Fe homeostasis in the maize intermated B73 × Mo17 (IBM) population, (ii) to identify quantitative trait loci (QTLs) for these traits, and (iii) to analyze expression levels of genes known to be involved in Fe homeostasis as well as of candidate genes obtained from the QTL analysis. RESULTS: In hydroponically-grown maize, a total of 47 and 39 QTLs were detected for the traits recorded under limited and adequate supply of Fe, respectively. CONCLUSIONS: From the QTL results, we were able to identify new putative candidate genes involved in Fe homeostasis under a deficient or adequate Fe nutritional status, like Ferredoxin class gene, putative ferredoxin PETF, metal tolerance protein MTP4, and MTP8. Furthermore, our expression analysis of candidate genes suggested the importance of trans-acting regulation for 2’-deoxymugineic acid synthase 1 (DMAS1), nicotianamine synthase (NAS3, NAS1), formate dehydrogenase 1 (FDH1), methylthioribose-1-phosphate isomerase (IDI2), aspartate/tyrosine/aromatic aminotransferase (IDI4), and methylthioribose kinase (MTK)

Synthetic data shuffling accelerates the convergence of federated learning under data heterogeneity

In federated learning, data heterogeneity is a critical challenge. A straightforward solution is to shuffle the clients' data to homogenize the distribution. However, this may violate data access rights, and how and when shuffling can accelerate the convergence of a federated optimization algorithm is not theoretically well understood. In this paper, we establish a precise and quantifiable correspondence between data heterogeneity and parameters in the convergence rate when a fraction of data is shuffled across clients. We prove that shuffling can quadratically reduce the gradient dissimilarity with respect to the shuffling percentage, accelerating convergence. Inspired by the theory, we propose a practical approach that addresses the data access rights issue by shuffling locally generated synthetic data. The experimental results show that shuffling synthetic data improves the performance of multiple existing federated learning algorithms by a large margin

Atomic layering at the liquid silicon surface: a first- principles simulation

We simulate the liquid silicon surface with first-principles molecular dynamics in a slab geometry. We find that the atom-density profile presents a pronounced layering, similar to those observed in low-temperature liquid metals like Ga and Hg. The depth-dependent pair correlation function shows that the effect originates from directional bonding of Si atoms at the surface, and propagates into the bulk. The layering has no major effects in the electronic and dynamical properties of the system, that are very similar to those of bulk liquid Si. To our knowledge, this is the first study of a liquid surface by first-principles molecular dynamics.Comment: 4 pages, 4 figures, submitted to PR

Challenges and pitfalls of experimental bariatric procedures in rats

Introduction: The impact of Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG) on obesity and obesity-related diseases is unquestionable. Up to now, the technical descriptions of these techniques in animals/rats have not been very comprehensive. Methods: For SG and RYGB, operating time, learning curve, and intraoperative mortality in relation to weight of the rat and type of anesthesia were recorded. Furthermore, a review of the literature on experimental approaches towards SG and RYGB in rats was carried out, merging in a detailed technical description for both procedures. Results: The data presented here revealed that the mean operating time for SG (69.4 +/- 22.2 min (SD)) was shorter than for RYGB (123.0 +/- 20.7 min). There is a learning curve for both procedures, resulting in a reduced operating time of up to 60% in SG and 35% in RYGB (p < 0.05; t-test). However, with increased weight, operating time increases to about 80 min for SG and about 120 min for RYGB. Obese rats have an increased intraoperative mortality rate of up to 50%. After gaseous anesthesia the mortality can be even higher. The literature search revealed 40 papers dealing with SG and RYGB in rats. 18 articles (45%) contained neither photographs nor illustrations; 14 articles (35%) did not mention the applied type of anesthesia. The mortality rate was described in 15 papers (37.5%). Conclusion: Experimental obesity surgery in rats is challenging. Because of the high mortality in obese rats operated under gaseous anesthesia, exercises to establish the techniques should be performed in small rats using intraperitoneal anesthesia. Copyright (C) 2012 S. Karger GmbH, Freibur

The Hessian Estimation Evolution Strategy

We present a novel black box optimization algorithm called Hessian Estimation Evolution Strategy. The algorithm updates the covariance matrix of its sampling distribution by directly estimating the curvature of the objective function. This algorithm design is targeted at twice continuously differentiable problems. For this, we extend the cumulative step-size adaptation algorithm of the CMA-ES to mirrored sampling. We demonstrate that our approach to covariance matrix adaptation is efficient by evaluation it on the BBOB/COCO testbed. We also show that the algorithm is surprisingly robust when its core assumption of a twice continuously differentiable objective function is violated. The approach yields a new evolution strategy with competitive performance, and at the same time it also offers an interesting alternative to the usual covariance matrix update mechanism