Random two-frame phase-shifting interferometry via minimization of coefficient of variation

Random two-frame phase-shifting interferometry (PSI) is an advanced technique to retrieve the phase information from as few as two interferograms with unknown phase steps. Because of the advantages of no requirement for accurate phase shifters and much less time for data acquisition and processing, random two-frame PSI is attracting more and more interest in fast and high-precision optical metrology. However, reconstructing the phase from only two interferograms is challenging because it is an ill-posed problem in essence, especially when the phase step is unknown. Although some solutions have been proposed for this problem to date, most of them require complicated preprocessing or special usage preconditions for interferograms to be demodulated. In this letter, we developed an elegant phase reconstruction method for random two-frame PSI, which is much different from frameworks of existing methods. In the proposed approach, the phase of random two-frame PSI can be accurately reconstructed using the phase step value which minimizes the coefficient of variation (CV) of the modulation term of interferograms. Sufficient numerical simulations and experimental data demonstrate the high accuracy and high efficiency of this CV minimization (CVM) method. Moreover, its performance is not limited by the number of fringes in interferograms, in contrast to existing state-of-the-art approaches. We anticipate extensive applications of the CVM method in random two-frame PSI in the future

Random two-frame phase-shifting interferometry via minimization of coefficient of variation

Random two-frame phase-shifting interferometry (PSI) is an advanced technique to retrieve the phase information from as few as two interferograms with unknown phase steps. Because of the advantages of no requirement for accurate phase shifters and much less time for data acquisition and processing, random two-frame PSI is attracting more and more interest in fast and high-precision optical metrology. However, reconstructing the phase from only two interferograms is challenging because it is an ill-posed problem in essence, especially when the phase step is unknown. Although some solutions have been proposed for this problem to date, most of them require complicated preprocessing or special usage preconditions for interferograms to be demodulated. In this letter, we developed an elegant phase reconstruction method for random two-frame PSI, which is much different from frameworks of existing methods. In the proposed approach, the phase of random two-frame PSI can be accurately reconstructed using the phase step value which minimizes the coefficient of variation (CV) of the modulation term of interferograms. Sufficient numerical simulations and experimental data demonstrate the high accuracy and high efficiency of this CV minimization (CVM) method. Moreover, its performance is not limited by the number of fringes in interferograms, in contrast to existing state-of-the-art approaches. We anticipate extensive applications of the CVM method in random two-frame PSI in the future

WAVEFORM-OPTIMIZED WIRELESS POWER TRANSFER FOR IMPLANTABLE MEDICAL DEVICES

Ph.DDOCTOR OF PHILOSOPH

Fighting against fast speckle decorrelation for light focusing inside live tissue by photon frequency shifting

Light focusing inside live tissue by digital optical phase conjugation (DOPC) has drawn increasing interest due to its potential biomedical applications in optogenetics, microsurgery, phototherapy, and deep-tissue imaging. However, fast physiological motions in a live animal, including blood flow and respiratory motions, produce undesired photon perturbation and thus inevitably deteriorate the performance of light focusing. Here, we develop a photon-frequency-shifting DOPC method to fight against fast physiological motions by switching the states of a guide star at a distinctive frequency. Therefore, the photons tagged by the guide star are well detected at the specific frequency, separating them from the photons perturbed by fast motions. Light focusing was demonstrated in both phantoms in vitro and mice in vivo with substantially improved focusing contrast. This work puts a new perspective on light focusing inside live tissue and promises wide biomedical applications

When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method

While large language models (LLMs) often adopt finetuning to unlock their capabilities for downstream applications, our understanding on the inductive biases (especially the scaling properties) of different finetuning methods is still limited. To fill this gap, we conduct systematic experiments studying whether and how different scaling factors, including LLM model size, pretraining data size, new finetuning parameter size and finetuning data size, affect the finetuning performance. We consider two types of finetuning -- full-model tuning (FMT) and parameter efficient tuning (PET, including prompt tuning and LoRA), and explore their scaling behaviors in the data-limited regime where the LLM model size substantially outweighs the finetuning data size. Based on two sets of pretrained bilingual LLMs from 1B to 16B and experiments on bilingual machine translation and multilingual summarization benchmarks, we find that 1) LLM finetuning follows a powerbased multiplicative joint scaling law between finetuning data size and each other scaling factor; 2) LLM finetuning benefits more from LLM model scaling than pretraining data scaling, and PET parameter scaling is generally ineffective; and 3) the optimal finetuning method is highly task- and finetuning data-dependent. We hope our findings could shed light on understanding, selecting and developing LLM finetuning methods.Comment: ICLR2

Research Progress on Formaldehyde‐Free Wood Adhesive Derived from Soy Flour

Soy‐based adhesives have been regarded as the most suitable candidates for wood industry. For a widespread use of soy‐based adhesives, new technologies need to be developed to improve the water resistance. An overview on the methods to improve water resistance of soy‐based adhesives is presented. Denaturants were once considered necessary to modify soy protein. However, water‐resistant soy adhesives could be prepared by simply removing water‐soluble carbohydrates and low molecular peptides from soy flour. In addition, proper grafting and cross‐linking agents help to prepare water‐resistant soy‐based adhesives, which are used widely to bond interior wood composites. In particular, a new type of polyamidoamine (PADA) resin and an itaconic acid‐based polyamidoamine‐epichlorohydrin (IA‐PAE) resin were synthesized to perform as cross‐linking agents for soy‐based adhesives. This review concludes that soy‐based adhesives have great potential for use in numerous applications. However, future work is still needed to make soy‐based adhesives more competitive with synthetic adhesives

Study on the Effectiveness of the Equity Incentive Plan of Private Enterprises in Zhuhai City: Taking TongWang Technology as an Example

The study of the effectiveness of equity incentive has always been the focus of the academic circle. Whether it is foreign or domestic, there is no conclusive evidence at present. This paper is divided into three parts: Firstly, it summarized the literature research conclusion of equity incentive at home and abroad. Secondly, the author analyzes the incentive scheme and the implementation effect of the equity incentive scheme of the same-looking science and technology as an example. Finally, the author puts forward the feasibility of improving equity incentive measures for private enterprises in China

Unsupervised Text Style Transfer with Deep Generative Models

We present a general framework for unsupervised text style transfer with deep generative models. The framework models each sentence-label pair in the non-parallel corpus as partially observed from a complete quadruplet which additionally contains two latent codes representing the content and style, respectively. These codes are learned by exploiting dependencies inside the observed data. Then a sentence is transferred by manipulating them. Our framework is able to unify previous embedding and prototype methods as two special forms. It also provides a principled perspective to explain previously proposed techniques in the field such as aligned encoder and adversarial training. We further conduct experiments on three benchmarks. Both automatic and human evaluation results show that our methods achieve better or competitive results compared to several strong baselines

Efficiency Analysis of Equity Incentive in Private Listed Companies-By Taking the Example of By-Health

At present, more and more private listed companies in our country realize the importance of corporate governance structure and equity structure, and also start to adopt a variety of equity incentive models to stimulate the business operators. How to evaluate the effect of private listed company's equity incentive measures? By taking the example of By-Health, this paper analyzes the effect of implementing equity incentive from financial performance, manager's behavior and market performance from three aspects: financial performance, manager's behavior and market performance, and puts forward relevant suggestions. With a view to providing useful reference and reference for improving the management of private enterprises and implementing equity incentives smoothly