ترغب بنشر مسار تعليمي؟ اضغط هنا

Reply to Local Filtering Fundamentally Against Wide Spectrum

291   0   0.0 ( 0 )
 نشر من قبل Jianwei Miao
 تاريخ النشر 2014
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

After carefully studying the comment by Wang et al. (arXiv:1408.6420), we found it includes several mistakes and unjustified statements and Wang et al. lack very basic knowledge of dislocations. Moreover, there is clear evidence indicating that Wang et al. significantly misrepresented our method and claimed something that they actually did not implement.



قيم البحث

اقرأ أيضاً

The so-called optimal filter analysis of a microcalorimeters x-ray pulses is statistically optimal only if all pulses have the same shape, regardless of energy. The shapes of pulses from a nonlinear detector can and do depend on the pulse energy, how ever. A pulse-fitting procedure that we call tangent filtering accounts for the energy dependence of the shape and should therefore achieve superior energy resolution. We take a geometric view of the pulse-fitting problem and give expressions to predict how much the energy resolution stands to benefit from such a procedure. We also demonstrate the method with a case study of K-line fluorescence from several 3d transition metals. The method improves the resolution from 4.9 eV to 4.2 eV at the Cu K$alpha$ line (8.0keV).
98 - D. Sornette 2010
In a Forum published in EOS Transactions AGU (2009) entitled Lies, damned lies and statistics (in Geology), Vermeesch (2009) claims that statistical significant is not the same as geological significant, in other words, statistical tests may be misle ading. In complete contradiction, we affirm that statistical tests are always informative. We detail the several mistakes of Vermeesch in his initial paper and in his comments to our reply. The present text is developed in the hope that it can serve as an illuminating pedagogical exercise for students and lecturers to learn more about the subtleties, richness and power of the science of statistics.
121 - Duo Sun , Tao Zhou , Jian-Guo Liu 2009
In this Brief Report, we propose a new index of user similarity, namely the transferring similarity, which involves all high-order similarities between users. Accordingly, we design a modified collaborative filtering algorithm, which provides remarka bly higher accurate predictions than the standard collaborative filtering. More interestingly, we find that the algorithmic performance will approach its optimal value when the parameter, contained in the definition of transferring similarity, gets close to its critical value, before which the series expansion of transferring similarity is convergent and after which it is divergent. Our study is complementary to the one reported in [E. A. Leicht, P. Holme, and M. E. J. Newman, Phys. Rev. E {bf 73} 026120 (2006)], and is relevant to the missing link prediction problem.
In this paper, we propose a novel method to compute the similarity between congeneric nodes in bipartite networks. Different from the standard Person correlation, we take into account the influence of nodes degree. Substituting this new definition of similarity for the standard Person correlation, we propose a modified collaborative filtering (MCF). Based on a benchmark database, we demonstrate the great improvement of algorithmic accuracy for both user-based MCF and object-based MCF.
107 - Jie Ren , Tao Zhou , 2008
Recommender systems are significant to help people deal with the world of information explosion and overload. In this Letter, we develop a general framework named self-consistent refinement and implement it be embedding two representative recommendat ion algorithms: similarity-based and spectrum-based methods. Numerical simulations on a benchmark data set demonstrate that the present method converges fast and can provide quite better performance than the standard methods.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا