Properties of new even and odd nonlinear coherent states with different parameters

We construct a class of nonlinear coherent states (NLCSs) by introducing a more general nonlinear function and study their non-classical properties, specifically the second-order correlation function $g^{(2)}(0)$, Mandel parameter $Q$, squeezing, amplitude squared squeezing and Wigner function of the optical field. The results indicate that the non-classical properties of the new types of even and odd NLCSs crucially depend on nonlinear functions. More concretely, we find that the new even NLCSs could exhibit the photon-bunching effect whereas the new odd NLCSs could show photon-antibunching effect. The degree of squeezing is also significantly affected by the parameter selection of these NLCSs. By employing various forms of nonlinear functions, it becomes possible to construct NLCSs with diverse properties, thereby providing a theoretical foundation for corresponding experimental investigations

A novel multifunctional biomedical material based on polyacrylonitrile:preparation and characterization

Wet spun microfibers have great potential in the design of multifunctional controlled release materials. Curcumin (Cur) and vitamin E acetate (Vit. E Ac) were used as a model drug system to evaluate the potential application of the drug-loaded microfiber system for enhanced delivery. The drugs and polyacrylonitrile (PAN) were blended together and spun to produce the target drug-loaded microfiber using an improved wet-spinning method and then the microfibers were successfully woven into fabrics. Morphological, mechanical properties, thermal behavior, drug release performance characteristics, and cytocompatibility were determined. The drug-loaded microfiber had a lobed “kidney” shape with a height of 50 ~ 100 μm and width of 100 ~ 200 μm. The addition of Cur and Vit. E Ac had a great influence on the surface and cross section structure of the microfiber, leading to a rough surface having microvoids. X-ray diffraction and Fourier transform infrared spectroscopy indicated that the drugs were successfully encapsulated and dispersed evenly in the microfilament fiber. After drug loading, the mechanical performance of the microfilament changed, with the breaking strength improved slightly, but the tensile elongation increased significantly. Thermogravimetric results showed that the drug load had no apparent adverse effect on the thermal properties of the microfibers. However, drug release from the fiber, as determined through in-vitro experiments, is relatively low and this property is maintained over time. Furthermore, in-vitro cytocompatibility testing showed that no cytotoxicty on the L929 cells was found up to 5% and 10% respectively of the theoretical drug loading content (TDLC) of curcumin and vitamin E acetate. This study provides reference data to aid the development of multifunctional textiles and to explore their use in the biomedical material field

CASOG: Conservative Actor-critic with SmOoth Gradient for Skill Learning in Robot-Assisted Intervention

Robot-assisted intervention has shown reduced radiation exposure to physicians and improved precision in clinical trials. However, existing vascular robotic systems follow master-slave control mode and entirely rely on manual commands. This paper proposes a novel offline reinforcement learning algorithm, Conservative Actor-critic with SmOoth Gradient (CASOG), to learn manipulation skills from human demonstrations on vascular robotic systems. The proposed algorithm conservatively estimates Q-function and smooths gradients of convolution layers to deal with distribution shift and overfitting issues. Furthermore, to focus on complex manipulations, transitions with larger temporal-difference error are sampled with higher probability. Comparative experiments in a pre-clinical environment demonstrate that CASOG can deliver guidewire to the target at a success rate of 94.00\% and mean backward steps of 14.07, performing closer to humans and better than prior offline reinforcement learning methods. These results indicate that the proposed algorithm is promising to improve the autonomy of vascular robotic systems.Comment: 13 pages, 5 figure, preprin

DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning

Model-based reinforcement learning (RL), which learns environment model from offline dataset and generates more out-of-distribution model data, has become an effective approach to the problem of distribution shift in offline RL. Due to the gap between the learned and actual environment, conservatism should be incorporated into the algorithm to balance accurate offline data and imprecise model data. The conservatism of current algorithms mostly relies on model uncertainty estimation. However, uncertainty estimation is unreliable and leads to poor performance in certain scenarios, and the previous methods ignore differences between the model data, which brings great conservatism. Therefore, this paper proposes a milDly cOnservative Model-bAsed offlINe RL algorithm (DOMAIN) without estimating model uncertainty to address the above issues. DOMAIN introduces adaptive sampling distribution of model samples, which can adaptively adjust the model data penalty. In this paper, we theoretically demonstrate that the Q value learned by the DOMAIN outside the region is a lower bound of the true Q value, the DOMAIN is less conservative than previous model-based offline RL algorithms and has the guarantee of security policy improvement. The results of extensive experiments show that DOMAIN outperforms prior RL algorithms on the D4RL dataset benchmark, and achieves better performance than other RL algorithms on tasks that require generalization.Comment: 13 pages, 6 figure

Symmetry-breaking-induced nonlinear optics at a microcavity surface

Second-order nonlinear optical processes lie at the heart of many applications in both classical and quantum regimes1,2,3. Inversion symmetry, however, rules out the second-order nonlinear electric-dipole response in materials widely adopted in integrated photonics (for example, SiO_2, Si and Si_3N_4). Here, we report nonlinear optics induced by symmetry breaking at the surface of an ultrahigh-Q silica microcavity under a sub-milliwatt continuous-wave pump. By dynamically coordinating the double-resonance phase matching, a second harmonic is achieved with an unprecedented conversion efficiency of 0.049% W^(−1), 14 orders of magnitude higher than that of the non-enhancement case. In addition, the nonlinear effect from the intrinsic symmetry breaking at the surface can be identified unambiguously, with guided control of the pump polarization and the recognition of the second-harmonic mode distribution. This work not only extends the emission frequency range of silica photonic devices, but also lays the groundwork for applications in ultra-sensitive surface analysis

Syntheses, Structures and Insulin-Like Activity of Two Oxidovanadium(V) Complexes with Similar Nicotinohydrazone Ligands

Two new oxidovanadium(V) complexes, [VOL1(HQ)] (1) and [VOL2(SAH)] (2), were prepared by the reaction of [VO(acac)2] (where acac = acetylacetonate) with N'-(3-ethoxy-2-hydroxybenzylidene)nicotinohydrazide (H2L1) and 8-hydroxyquinoline (HHQ), and N'-(2-hydroxy-4-methoxybenzylidene)nicotinohydrazide (H2L2) and salicylhydroxamic acid (HSAH), respectively, in methanol. Crystal and molecular structures of the complexes were determined by elemental analysis, infrared spectroscopy and single crystal X-ray diffraction. The V atoms in both complexes are in octahedral coordination. Thermal stability of the complexes was studied. Both complexes can decrease the blood glucose level in alloxan-diabetic mice, but the blood glucose level in the treated normal mice was not altered. 搜索 复

CROP: Conservative Reward for Model-based Offline Policy Optimization

Offline reinforcement learning (RL) aims to optimize policy using collected data without online interactions. Model-based approaches are particularly appealing for addressing offline RL challenges due to their capability to mitigate the limitations of offline data through data generation using models. Prior research has demonstrated that introducing conservatism into the model or Q-function during policy optimization can effectively alleviate the prevalent distribution drift problem in offline RL. However, the investigation into the impacts of conservatism in reward estimation is still lacking. This paper proposes a novel model-based offline RL algorithm, Conservative Reward for model-based Offline Policy optimization (CROP), which conservatively estimates the reward in model training. To achieve a conservative reward estimation, CROP simultaneously minimizes the estimation error and the reward of random actions. Theoretical analysis shows that this conservative reward mechanism leads to a conservative policy evaluation and helps mitigate distribution drift. Experiments on D4RL benchmarks showcase that the performance of CROP is comparable to the state-of-the-art baselines. Notably, CROP establishes an innovative connection between offline and online RL, highlighting that offline RL problems can be tackled by adopting online RL techniques to the empirical Markov decision process trained with a conservative reward. The source code is available with https://github.com/G0K0URURI/CROP.git