Twist-3 contribution to the pion electromagnetic form factor

Non-leading contribution to the pion electromagnetic form factor which comes from the pion twist-3 wave function is analyzed in the modified hard scattering approach (MHSA) proposed by Li and Sterman. This contribution is enhanced significantly due to bound state effect (the twist-3 wave function is independent of the fractional momentum carried by the parton and has a large factor $\sim m_\pi^2/m_0$ with $m_\pi$ being the pion meson mass and $m_0$ being the mean u- and d-quark masses). Consequently, although it is suppressed by the factor $1/Q^2$, the twist-3 contribution is comparable with and even larger than the leading twist (twist-2) contribution at intermediate energy region of $Q^2$ being $2 \sim 40 {GeV}^2$.Comment: 10 pages, 2 fgures, latex. More discussions on the Sudakov effect added, references added. To appear in European Physical Journal C (Zeitschrift fur Physik C

Find a Reasonable Ending for Stories: Does Logic Relation Help the Story Cloze Test?

Natural language understanding is a challenging problem that covers a wide range of tasks. While previous methods generally train each task separately, we consider combining the cross-task features to enhance the task performance. In this paper, we incorporate the logic information with the help of the Natural Language Inference (NLI) task to the Story Cloze Test (SCT). Previous work on SCT considered various semantic information, such as sentiment and topic, but lack the logic information between sentences which is an essential element of stories. Thus we propose to extract the logic information during the course of the story to improve the understanding of the whole story. The logic information is modeled with the help of the NLI task. Experimental results prove the strength of the logic information.Comment: Student Abstract in AAAI-201

Order-Free RNN with Visual Attention for Multi-Label Classification

In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification. While approaches based on the use of either model exist (e.g., for the task of image captioning), training such existing network architectures typically require pre-defined label sequences. For multi-label classification, it would be desirable to have a robust inference process, so that the prediction error would not propagate and thus affect the performance. Our proposed model uniquely integrates attention and Long Short Term Memory (LSTM) models, which not only addresses the above problem but also allows one to identify visual objects of interests with varying sizes without the prior knowledge of particular label ordering. More importantly, label co-occurrence information can be jointly exploited by our LSTM model. Finally, by advancing the technique of beam search, prediction of multiple labels can be efficiently achieved by our proposed network model.Comment: Accepted at 32nd AAAI Conference on Artificial Intelligence (AAAI-18

7-ketocholesterol stimulates differentiation of lens epithelial cells

PURPOSE: To establish if oxysterols stimulate differentiation of lens epithelial cells (LEC). METHODS: Primary cultures of lens epithelial cells were incubated with 7-ketocholesterol (7-keto), 25-hydroxycholesterol (25-OH) or cholesterol at 10 microg/ml for 10 days. Cells incubated with 100 ng/ml basic fibroblast growth factor (b-FGF) were used as positive controls for differentiation. The expression of the differentiation marker p57KIP2, proliferation marker PCNA (Proliferating Cell Nuclear Antigen) and fibers specific proteins gamma-crystallin, CP49, MIP26 following treatment with oxysterols was determined by western blot. Differentiation into fiber cells was further confirmed by counting the number of lentoid bodies formed following incubation with 7-keto. RESULTS: LEC incubated with 7-keto presented higher levels of p57KIP2 and showed expression of fiber specific proteins such as MIP26 and CP49, compared to cells incubated with 25-OH or cholesterol. The differentiation marker p57KIP2 increased over time for cells incubated with 7-keto while there was a decline on the amount of the proliferation marker PCNA. The expression of the fiber specific proteins gamma-crystallin, MIP26 and CP49 was detected after 5 days of incubation with 7-keto. Differentiation was accompanied by a seven-fold increase in the number of lentoid bodies formed. CONCLUSIONS: Results show for the first time that 7-keto inhibits proliferation and stimulates differentiation of lens epithelial cells into fiber cells. The presence of 7-keto in the lens may disrupt the highly regulated differentiation program of LEC, compromising normal lens growth and transparenc

Inverse scattering procedures for the reconstruction of one-dimensional permittivity range profile

In the present work we have presented a reliable and efficient algorithm for the data inversion, which is based on a fully nonlinear data model in conjunction with an optimization technique. The reconstruction of the permittivity range profile has been tested both on synthetic and real data to validate the electromagnetic code as well as to assess the accuracy and efficiency of the reconstruction procedure. We have studied the resolution of the algorithm and its robustness to the noise, demonstrating the ability of our procedure to be able to recognize the presence of high discontinuities even independently from the discretization fixed by the user. As a part of the ongoing improvement of the presented method, we have addressed the implementation of a new optimization algorithm, namely the particle swarm optimization, which has been customized and enhanced for our purposes. Finally, a detailed description of a fast and efficient procedure to evaluate the green’s function for a multilayered medium has been given. This is the groundwork useful for the next step toward a more reliable and versatile forward solver to be implemented in the inversion procedure

VBF vs. GGF Higgs with Full-Event Deep Learning: Towards a Decay-Agnostic Tagger

We study the benefits of jet- and event-level deep learning methods in distinguishing vector boson fusion (VBF) from gluon-gluon fusion (GGF) Higgs production at the LHC. We show that a variety of classifiers (CNNs, attention-based networks) trained on the complete low-level inputs of the full event achieve significant performance gains over shallow machine learning methods (BDTs) trained on jet kinematics and jet shapes, and we elucidate the reasons for these performance gains. Finally, we take initial steps towards the possibility of a VBF vs. GGF tagger that is agnostic to the Higgs decay mode, by demonstrating that the performance of our event-level CNN does not change when the Higgs decay products are removed. These results highlight the potentially powerful benefits of event-level deep learning at the LHC.Comment: 21 pages+appendices, 16 figures; added references, updated Pythia shower scheme for VBF, and added Appendix C for version

The effect of mechanism design on the performance of a quadruped walking machine

The objective of this paper is to investigate the effect of mechanism design on the performance of a quadruped walking machine. For studying the effect of mechanism design on the performance of a quadruped walking machine, four designs with different crank and leg arrangements are proposed and analyzed. The performance of the walking machine, including the stance leg sequence, foot trajectory, pitch angle, and dynamic response of the quadruped walking machine are investigated and compared with the existing design. The results show that the phrase angle between front and rear legs on the same side should be 0° or 90° and the one between the legs on the different sides should be 180°. And, the design with the front and rear legs bent in the same direction has better performance in dynamic responses. The results of this study can serve as a reference for future design and optimization of quadruped walking machines