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The linear response of synchronized chaotic units with delayed couplings and feedback to small external perturbations is investigated in the context of communication with chaos synchronization. For iterated chaotic maps, the distribution of…

Chaotic Dynamics · Physics 2013-02-05 Wolfgang Kinzel , Johannes Kestler , Ido Kanter

The expected high sensitivities of future neutrino oscillation experiments will allow precision tests of unified theories of flavour. In order to compare GUT scale predictions for neutrino masses, leptonic mixing angles and CP violating…

High Energy Physics - Phenomenology · Physics 2007-05-23 Stefan Antusch

Magnitude pruning is a common, effective technique to identify sparse subnetworks at little cost to accuracy. In this work, we ask whether a particular architecture's accuracy-sparsity tradeoff can be improved by combining pruning…

Computer Vision and Pattern Recognition · Computer Science 2021-05-03 Rajiv Movva , Jonathan Frankle , Michael Carbin

Light adaptation or brightness correction is a key step in improving the contrast and visual appeal of an image. There are multiple light-related tasks (for example, low-light enhancement and exposure correction) and previous studies have…

Computer Vision and Pattern Recognition · Computer Science 2023-04-07 Kai-Fu Yang , Cheng Cheng , Shi-Xuan Zhao , Xian-Shi Zhang , Yong-Jie Li

Comparing the luminosity distance measurements to its theoretical predictions is one of the cornerstones in establishing the modern cosmology. However, as shown in Biern & Yoo, its theoretical predictions in literature are often plagued…

Cosmology and Nongalactic Astrophysics · Physics 2016-10-12 Jaiyul Yoo , Fulvio Scaccabarozzi

Detectability of failures of linear programming (LP) decoding and its potential for improvement by adding new constraints motivate the use of an adaptive approach in selecting the constraints for the LP problem. In this paper, we make a…

Information Theory · Computer Science 2007-07-13 Mohammad H. Taghavi N. , Paul H. Siegel

Power systems are very large and complex, it can be influenced by many unexpected events this makes power system optimization problems difficult to solve, hence methods for solving these problems ought to be an active research topic. This…

Neural and Evolutionary Computing · Computer Science 2024-05-03 Soufiane Bouabbadi

We investigate the impact of extra vector-like GUT multiplets on the predicted value of the strong coupling. We find in particular that Yukawa couplings between such extra multiplets and the MSSM Higgs doublets can resolve the familiar…

High Energy Physics - Phenomenology · Physics 2012-08-10 Ivan Donkin , Arthur Hebecker

We consider the phenomenological implications of a class of compactified string theories which naturally reproduces the flavour multiplet structure of the Standard Model. The implications for gauge unification depends on which of three…

High Energy Physics - Phenomenology · Physics 2009-10-30 Witold Pokorski , Graham G. Ross

Bundle adjustment (BA) is a technique for refining sensor orientations of satellite images, while adjustment accuracy is correlated with feature matching results. Feature match-ing often contains high uncertainties in weak/repeat textures,…

Computer Vision and Pattern Recognition · Computer Science 2021-07-02 Xiao Ling , Xu Huang , Rongjun Qin

The creation of nanoscale lasers that operate above a coherent threshold is a challenging problem. We propose a way to circumvent this issue using systems in which a strong coupling regime is achieved between the light and the active…

The unification of gauge coupling constants in the minimal supersymmetric model (MSSM) is unaffected at the one-loop level by the inclusion of additional mass-degenerate SU(5) multiplets. Perturbativity puts an upper limit on the number of…

High Energy Physics - Phenomenology · Physics 2016-09-01 Ralf Hempfling

In this paper, we present perturbation analysis and randomized algorithms for the total least squares (TLS) problems. We derive the perturbation bound and check its sharpness by numerical experiments. Motivated by the recently popular…

Numerical Analysis · Mathematics 2014-11-12 Pengpeng Xie , Yimin Wei , Hua Xiang

We derive the 1-loop Renormalization Group Equations for the parameters of the Minimal Supersymmetric Standard Model (MSSM) taking into account the successive decoupling of each sparticle below its threshold. This is realized by a step…

High Energy Physics - Phenomenology · Physics 2009-10-28 A. B. Lahanas , K. Tamvakis

We study unification in the Randall-Sundrum scenario for solving the hierarchy problem, with gauge fields and fermions in the bulk. We calculate the one-loop corrected low-energy effective gauge couplings in a unified theory, broken at the…

High Energy Physics - Phenomenology · Physics 2009-09-15 Kaustubh Agashe , Antonio Delgado , Raman Sundrum

We study Radiative Electroweak Symmetry Breaking in the Minimal Supersymmetric Standard Model (MSSM). We employ the 2-loop Renormalization Group equations for running masses and couplings taking into account sparticle threshold effects. The…

High Energy Physics - Phenomenology · Physics 2007-05-23 K. Tamvakis

We compare the ``unified approach'' for the estimation of upper limits with an approach based on the Bayes theory, in the special case that no events are observed. The ``unified approach'' predicts, in this case, an upper limit that…

High Energy Physics - Experiment · Physics 2007-05-23 P. Astone , G. Pizzella

We study Radiative Electroweak Symmetry Breaking in the Minimal Supersymmetric Standard Model (MSSM). We employ the 2-loop Renormalization Group equations for running masses and couplings taking into account sparticle threshold effects. The…

High Energy Physics - Phenomenology · Physics 2009-09-24 A. Dedes , A. B. Lahanas , K. Tamvakis

We estimate the significance of the prediction for the gauge couplings in the MSSM with an underlying unification. The correlation between the couplings covers only (0.2-2)% of the a priori reasonable region of the parameter space, while…

High Energy Physics - Phenomenology · Physics 2014-11-17 Dumitru M. Ghilencea , Graham G. Ross

Understanding the uncertainty of a neural network's (NN) predictions is essential for many purposes. The Bayesian framework provides a principled approach to this, however applying it to NNs is challenging due to large numbers of parameters…

Machine Learning · Statistics 2020-02-27 Tim Pearce , Felix Leibfried , Alexandra Brintrup , Mohamed Zaki , Andy Neely