A stroboscopic averaging algorithm for highly oscillatory delay problems

We propose and analyze a heterogenous multiscale method for the efficient integration of constant-delay differential equations subject to fast periodic forcing. The stroboscopic averaging method (SAM) suggested here may provide approximations with \(\mathcal{O}(H^2+1/\Omega^2)\) errors with a computational effort that grows like \(H^{-1}\) (the inverse of the stepsize), uniformly in the forcing frequency Omega

Bogdanov-Takens resonance in time-delayed systems

We analyze the oscillatory dynamics of a time-delayed dynamical system subjected to a periodic external forcing. We show that, for certain values of the delay, the response can be greatly enhanced by a very small forcing amplitude. This phenomenon is related to the presence of a Bogdanov- Takens bifurcation and displays some analogies to other resonance phenomena, but also substantial differences.Comment: 14 pages, 8 figure

A stroboscopic averaging algorithm for highly oscillatory delay problems

We propose and analyse a heterogeneous multiscale method for the efficient integration of constant-delay differential equations subject to fast periodic forcing. The stroboscopic averaging method suggested here may provide approximations with O(H2+1/Ω2) errors with a computational effort that grows like H−1 (the inverse of the step size), uniformly in the forcing frequency Ω⁠.J.M. Sanz-Serna has been supported by projects MTM2013-46553-C3-1-P from Ministerio de Economía y Comercio, and MTM2016-77660-P(AEI/FEDER, UE) from Ministerio de Economía, Industria y Competitividad, Spain. Beibei Zhu has been supported by the National Natural Science Foundation of China (Grant No. 11371357 and No. 11771438). She is grateful to Universidad Carlos III de Madrid for hosting the stay in Spain that made this work possible and to the Chinese Scholarship Council for providing the necessary funds. The authors are thankful to M. A. F. Sanjuán and A. Daza for bringing to their attention the vibrational resonance phenomenon, the toggle switch problem and other highly-oscillatory systems with delay

Synthesis, Structures and Properties of Indium(In)-based Oxide Thermoelectric Materials

The effects of different dopants (Hf, Ga, Lu and Lu/Sn) on the thermoelectric performance of In2O3 have been investigated. These materials were synthesized by using a co-precipitation method followed by spark plasma sintering (SPS) process or conventional sintering method. The total thermal conductivity consists of two parts: lattice thermal conductivity and electronic thermal conductivity. Lattice thermal conductivity was found to dominate thermal conductivity in these systems; however when the magnitude of electronic thermal conductivity increase exceeded some threshold, point defect scattering would be suppressed and thermal conductivity would be increased, which were found in Hf doped In2O3 and Lu/Sn co-doped In2O3. In the former system, the highest zT value was obtained in x=0.002 Hf doped In2O3 at 973K (~0.2), almost 2 times higher than that of In2O3. ZT values of this sample from different synthesis methods indicated density would not change the thermoelectric performance of this compound. In Lu/Sn co-doped system, thermoelectric performance of In2O3 could be improved by manipulating the ratio of Sn/Lu. However, co-doping Lu/Sn did not have an advantage in improving the thermoelectric property of In2O3 compared with single Sn doping in this study. In the isovalent Ga and Lu doping systems, opposite change of thermoelectric performance was observed; namely that Ga doping slightly increased zT of In2O3 especially in the high temperatures (T>850K) while Lu largely decreased thermoelectric performance of In2O3. This was attributed to charge carrier concentration increasing in Ga doped In2O3 while dropping in Lu doped system. The Doping-Induced Strain Model was established to explain the phenomenon. The relation between charge carrier concentration and crystal structure change has been roughly quantified, indicating dopant with smaller radius than that of In would increase the charge carrier concentration while the one with larger radius would decrease it. Moreover the secondary phases in Ga doped In2O3 (GaInO3) and Hf doped In2O3 decreased thermal conductivity and improved thermoelectric property. Another In-based oxide In5SnSbO12 was then investigated in this dissertation. The thermal conductivity of pristine In5SnSbO12 was much lower than that of In2O3 due to its more complex crystal structure. The zT of this compound was comparable to that of In2O3, indicating In5SnSbO12 was a promising thermoelectric material. Ga doping in In5SnSbO12 resulted in the formation of secondary phase (Ga2In6Sn2O16), which significantly increased Hall mobility and helped improve the relative density of this compound from 60% to 90%

High-order stroboscopic averaging methods for highly oscillatory delay problems

We introduce and analyze a family of heterogeneous multiscale methods for the numerical integration of highly oscillatory systems of delay differential equations with constant delays. The methodology suggested provides algorithms of arbitrarily high accuracy.The authors are indebted to A. Murua for the discussion that started this project. M.P.C and J.M.S. were supported by project MTM2016-77660-P (AEI/FEDER, UE) funded by MINECO (Spain). M.P.C. was also supported by project VA024P17 (Consejería de Educación, Junta de Castilla y León, ES, cofinanced by FEDER funds). B.Z. was supported by the National Center for Mathematics and Interdisciplinary Sciences, CAS and the National Natural Science Foundation of China (Grant No. 11771438 and Grant No. 11901564) and by China Postdoctoral Science Foundation (Grant No. 2018M641505)

Testing for Asymmetric Employer Learning and Statistical Discrimination

We test the implications of a statistical discrimination model with asymmetric learning. Firms receive signals of productivity over time and may use race to infer worker's productivity. Incumbent employers have more information about workers productivity than outside employers. Using data from the NLSY79, we find evidence of asymmetric learning. In addition, employers statistically discriminate against non-college educated black workers at time of hiring. We also find that employers directly observe most of the productivity of college graduates at hiring, and learn very little over time about these workers

Estimating the Quality of Reprogrammed Cells Using ES Cell Differentiation Expression Patterns

Somatic cells can be reprogrammed to a pluripotent state by over-expression of defined factors, and pluripotency has been confirmed by the tetraploid complementation assay. However, especially in human cells, estimating the quality of Induced Pluripotent Stem Cell(iPSC) is still difficult. Here, we present a novel supervised method for the assessment of the quality of iPSCs by estimating the gene expression profile using a 2-D “Differentiation-index coordinate”, which consists of two “developing lines” that reflects the directions of ES cell differentiation and the changes of cell states during differentiation. By applying a novel liner model to describe the differentiation trajectory, we transformed the ES cell differentiation time-course expression profiles to linear “developing lines”; and use these lines to construct the 2-D “Differentiation-index coordinate” of mouse and human. We compared the published gene expression profiles of iPSCs, ESCs and fibroblasts in mouse and human “Differentiation-index coordinate”. Moreover, we defined the Distance index to indicate the qualities of iPS cells, which based on the projection distance of iPSCs-ESCs and iPSCs-fibroblasts. The results indicated that the “Differentiation-index coordinate” can distinguish differentiation states of the different cells types. Furthermore, by applying this method to the analysis of expression profiles in the tetraploid complementation assay, we showed that the Distance index which reflected spatial distributions correlated the pluripotency of iPSCs. We also analyzed the significantly changed gene sets of “developing lines”. The results suggest that the method presented here is not only suitable for the estimation of the quality of iPS cells based on expression profiles, but also is a new approach to analyze time-resolved experimental data

GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework

Many gait recognition methods first partition the human gait into N-parts and then combine them to establish part-based feature representations. Their gait recognition performance is often affected by partitioning strategies, which are empirically chosen in different datasets. However, we observe that strips as the basic component of parts are agnostic against different partitioning strategies. Motivated by this observation, we present a strip-based multi-level gait recognition network, named GaitStrip, to extract comprehensive gait information at different levels. To be specific, our high-level branch explores the context of gait sequences and our low-level one focuses on detailed posture changes. We introduce a novel StriP-Based feature extractor (SPB) to learn the strip-based feature representations by directly taking each strip of the human body as the basic unit. Moreover, we propose a novel multi-branch structure, called Enhanced Convolution Module (ECM), to extract different representations of gaits. ECM consists of the Spatial-Temporal feature extractor (ST), the Frame-Level feature extractor (FL) and SPB, and has two obvious advantages: First, each branch focuses on a specific representation, which can be used to improve the robustness of the network. Specifically, ST aims to extract spatial-temporal features of gait sequences, while FL is used to generate the feature representation of each frame. Second, the parameters of the ECM can be reduced in test by introducing a structural re-parameterization technique. Extensive experimental results demonstrate that our GaitStrip achieves state-of-the-art performance in both normal walking and complex conditions.Comment: Accepted to ACCV202

DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition

Gait recognition is a biometric technology that recognizes the identity of humans through their walking patterns. Compared with other biometric technologies, gait recognition is more difficult to disguise and can be applied to the condition of long-distance without the cooperation of subjects. Thus, it has unique potential and wide application for crime prevention and social security. At present, most gait recognition methods directly extract features from the video frames to establish representations. However, these architectures learn representations from different features equally but do not pay enough attention to dynamic features, which refers to a representation of dynamic parts of silhouettes over time (e.g. legs). Since dynamic parts of the human body are more informative than other parts (e.g. bags) during walking, in this paper, we propose a novel and high-performance framework named DyGait. This is the first framework on gait recognition that is designed to focus on the extraction of dynamic features. Specifically, to take full advantage of the dynamic information, we propose a Dynamic Augmentation Module (DAM), which can automatically establish spatial-temporal feature representations of the dynamic parts of the human body. The experimental results show that our DyGait network outperforms other state-of-the-art gait recognition methods. It achieves an average Rank-1 accuracy of 71.4% on the GREW dataset, 66.3% on the Gait3D dataset, 98.4% on the CASIA-B dataset and 98.3% on the OU-MVLP dataset