Synchronization of Fractional-order Chaotic Systems with Gaussian fluctuation by Sliding Mode Control

This paper is devoted to the problem of synchronization between fractional-order chaotic systems with Gaussian fluctuation by the method of fractional-order sliding mode control. A fractional integral (FI) sliding surface is proposed for synchronizing the uncertain fractional-order system, and then the sliding mode control technique is carried out to realize the synchronization of the given systems. One theorem about sliding mode controller is presented to prove the proposed controller can make the system synchronize. As a case study, the presented method is applied to the fractional-order Chen-L\"u system as the drive-response dynamical system. Simulation results show a good performance of the proposed control approach in synchronizing the chaotic systems in presence of Gaussian noise

Semi-Supervised Learning for Neural Machine Translation

While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low-resource languages, it is appealing to exploit monolingual corpora to improve NMT. We propose a semi-supervised approach for training NMT models on the concatenation of labeled (parallel corpora) and unlabeled (monolingual corpora) data. The central idea is to reconstruct the monolingual corpora using an autoencoder, in which the source-to-target and target-to-source translation models serve as the encoder and decoder, respectively. Our approach can not only exploit the monolingual corpora of the target language, but also of the source language. Experiments on the Chinese-English dataset show that our approach achieves significant improvements over state-of-the-art SMT and NMT systems.Comment: Corrected a typ

Radio Polarization of BL Lacertae objects

In this paper, using the database of the university of Michigan Radio Astronomy Observatory (UMRAO) at three (4.8 GHz, 8 GHZ, and 14.5 GHz) radio frequencies, we studied the polarization properties for 47 BL Lacertae objects(38 radio selected BL Lacertae objects, 7 X-ray selected BL Lacertae, and two inter-middle objects (Mkn 421 and Mkn 501), and found that (1) The polarizations at higher radio frequency is higher than those at lower frequency, (2) The variability of polarization at higher radio frequency is higher than those at lower frequency, (3) The polarization is correlated with the radio spectral index, and (4) The polarization is correlated with core-dominance parameter for those objects with known core-dominance parameters suggesting that the relativistic beaming could explain the polarization characteristic of BL Lacs.Comment: 5 pages, 3 figures, 1 table. PASJ, in pres

Characterization of Family IV UDG from Aeropyrum pernix and Its Application in Hot-Start PCR by Family B DNA Polymerase

Recombinant uracil-DNA glycosylase (UDG) from Aeropyrum pernix (A. pernix) was expressed in E. coli. The biochemical characteristics of A. pernix UDG (ApeUDG) were studied using oligonucleotides carrying a deoxyuracil (dU) base. The optimal temperature range and pH value for dU removal by ApeUDG were 55–65°C and pH 9.0, respectively. The removal of dU was inhibited by the divalent ions of Zn, Cu, Co, Ni, and Mn, as well as a high concentration of NaCl. The opposite base in the complementary strand affected the dU removal by ApeUDG as follows: U/C≈U/G>U/T≈U/AP≈U/->U/U≈U/I>U/A. The phosphorothioate around dU strongly inhibited dU removal by ApeUDG. Based on the above biochemical characteristics and the conservation of amino acid residues, ApeUDG was determined to belong to the IV UDG family. ApeUDG increased the yield of PCR by Pfu DNA polymerase via the removal of dU in amplified DNA. Using the dU-carrying oligonucleotide as an inhibitor and ApeUDG as an activator of Pfu DNA polymerase, the yield of undesired DNA fragments, such as primer-dimer, was significantly decreased, and the yield of the PCR target fragment was increased. This strategy, which aims to amplify the target gene with high specificity and yield, can be applied to all family B DNA polymerases

Role of Nrf2 in bone metabolism

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a transcription factor expressed in many cell types, including osteoblasts, osteocytes, and osteoclasts. Nrf2 has been considered a master regulator of cytoprotective genes against oxidative and chemical insults. The lack of Nrf2 can induce pathologies in multiple organs. Nrf2 deficiency promotes osteoclast differentiation and osteoclast activity, which leads to an increase in bone resorption. The role of Nrf2 in osteoblast differentiation and osteoblast activity is more complex. Nrf2 mediates anabolic effects within an ideal range. Nrf2 deletion suppresses load induced bone formation and delays fracture healing. Overall, Nrf2 plays an important role in the regulation of bone homeostasis in bone cells

Learning From Biased Soft Labels

Knowledge distillation has been widely adopted in a variety of tasks and has achieved remarkable successes. Since its inception, many researchers have been intrigued by the dark knowledge hidden in the outputs of the teacher model. Recently, a study has demonstrated that knowledge distillation and label smoothing can be unified as learning from soft labels. Consequently, how to measure the effectiveness of the soft labels becomes an important question. Most existing theories have stringent constraints on the teacher model or data distribution, and many assumptions imply that the soft labels are close to the ground-truth labels. This paper studies whether biased soft labels are still effective. We present two more comprehensive indicators to measure the effectiveness of such soft labels. Based on the two indicators, we give sufficient conditions to ensure biased soft label based learners are classifier-consistent and ERM learnable. The theory is applied to three weakly-supervised frameworks. Experimental results validate that biased soft labels can also teach good students, which corroborates the soundness of the theory