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Digitally Fast Programmable Optical Signal Processing Devices

Non

Linear Scaling Calculations of Excitation Energies with Active-Space Particle-Particle Random Phase Approximation

We developed an efficient active-space particle-particle random phase approximation (ppRPA) approach to calculate accurate charge-neutral excitation energies of molecular systems. The active-space ppRPA approach constrains both indexes in particle and hole pairs in the ppRPA matrix, which only selects frontier orbitals with dominant contributions to low-lying excitation energies. It employs the truncation in both orbital indexes in the particle-particle and the hole-hole spaces. The resulting matrix, the eigenvalues of which are excitation energies, has a dimension that is independent of the size of the systems. The computational effort for the excitation energy calculation, therefore, scales linearly with system size and is negligible compared with the ground-state calculation of the (N-2)-electron system, where N is the electron number of the molecule. With the active space consisting of 30 occupied and 30 virtual orbitals, the active-space ppRPA approach predicts excitation energies of valence, charge-transfer, Rydberg, double and diradical excitations with the mean absolute errors (MAEs) smaller than 0.03 eV compared with the full-space ppRPA results. As a side product, we also applied the active-space ppRPA approach in the renormalized singles (RS) T-matrix approach. Combining the non-interacting pair approximation that approximates the contribution to the self-energy outside the active space, the active-space $G_{\text{RS}}T_{\text{RS}}$@PBE approach predicts accurate absolute and relative core-level binding energies with the MAE around 1.58 eV and 0.3 eV, respectively. The developed linear scaling calculation of excitation energies is promising for applications to large and complex systems

Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods

This paper investigates the problem of online statistical inference of model parameters in stochastic optimization problems via the Kiefer-Wolfowitz algorithm with random search directions. We first present the asymptotic distribution for the Polyak-Ruppert-averaging type Kiefer-Wolfowitz (AKW) estimators, whose asymptotic covariance matrices depend on the function-value query complexity and the distribution of search directions. The distributional result reflects the trade-off between statistical efficiency and function query complexity. We further analyze the choices of random search directions to minimize the asymptotic covariance matrix, and conclude that the optimal search direction depends on the optimality criteria with respect to different summary statistics of the Fisher information matrix. Based on the asymptotic distribution result, we conduct online statistical inference by providing two construction procedures of valid confidence intervals. We provide numerical experiments verifying our theoretical results with the practical effectiveness of the procedures

Precise tuning of polymeric fiber dimensions to enhance the mechanical properties of alginate hydrogel matrices

Hydrogels based on biopolymers, such as alginate, are commonly used as scaffolds in tissue engineering applications as they mimic the features of the native extracellular matrix (ECM). However, in their native state, they suffer from drawbacks including poor mechanical performance and a lack of biological functionalities. Herein, we have exploited a crystallization-driven self-assembly (CDSA) methodology to prepare well-defined one-dimensional micellar structures with controlled lengths to act as a mimic of fibrillar collagen in native ECM and improve the mechanical strength of alginate-based hydrogels. Poly(ε-caprolactone)-b-poly(methyl methacrylate)-b-poly(N, N-dimethyl acrylamide) triblock copolymers were self-assembled into 1D cylindrical micelles with precise lengths using CDSA epitaxial growth and subsequently combined with calcium alginate hydrogel networks to obtain nanocomposites. Rheological characterization determined that the inclusion of the cylindrical structures within the hydrogel network increased the strength of the hydrogel under shear. Furthermore, the strain at flow point of the alginate-based hydrogel was found to increase with nanoparticle content, reaching an improvement of 37% when loaded with 500 nm cylindrical micelles. Overall, this study has demonstrated that one-dimensional cylindrical nanoparticles with controlled lengths formed through CDSA are promising fibrillar collagen mimics to build ECM scaffold models, allowing exploration of the relationship between collagen fiber size and matrix mechanical properties

Pharmacokinetics of mequindox after intravenous and intramuscular administration to goat

Pharmacokinetics and bioavailability of mequindox were determined after single intravenous (i.v.) or intramuscular (i.m.) administrations of 7 mg/kg body weight (b.w.) to 10 healthy adult goats. Plasma mequindox concentrations were measured by high performance liquid chromatography. Pharmacokinetics were best described by a two-compartment open model and an one-compartment open model for i.v. and i.m. groups, respectively. The elimination half-life and volume of distribution after i.v. and i.m. administrations were statistically different (t1/2β, 1.8 to 1.5 h, P < 0.05 and Vd, 0.35 to 0.45 L·kg-1, P < 0.05, respectively). Mequindox was rapidly (t1/2a, 0.28 h) and almost completely absorbed (F, 99.8%) after i.m. administration. In conclusion, 2~3 times daily i.v. and i.m. administration of mequindox (7 mg/kg b.w.) in goats may be useful in treatment of infectious diseases caused by sensitive pathogens. The plasma disposition kinetics of mequindox in goats is reported for the first time.Key words: Mequindox, pharmacokinetics, high performance liquid chromatography (HPLC), goats

FedSoup: Improving Generalization and Personalization in Federated Learning via Selective Model Interpolation

Cross-silo federated learning (FL) enables the development of machine learning models on datasets distributed across data centers such as hospitals and clinical research laboratories. However, recent research has found that current FL algorithms face a trade-off between local and global performance when confronted with distribution shifts. Specifically, personalized FL methods have a tendency to overfit to local data, leading to a sharp valley in the local model and inhibiting its ability to generalize to out-of-distribution data. In this paper, we propose a novel federated model soup method (i.e., selective interpolation of model parameters) to optimize the trade-off between local and global performance. Specifically, during the federated training phase, each client maintains its own global model pool by monitoring the performance of the interpolated model between the local and global models. This allows us to alleviate overfitting and seek flat minima, which can significantly improve the model's generalization performance. We evaluate our method on retinal and pathological image classification tasks, and our proposed method achieves significant improvements for out-of-distribution generalization. Our code is available at https://github.com/ubc-tea/FedSoup.Comment: Accepted by MICCAI202