Hybrid algorithms to solve linear systems of equations with limited qubit resources

The solution of linear systems of equations is a very frequent operation and thus important in many fields. The complexity using classical methods increases linearly with the size of equations. The HHL algorithm proposed by Harrow et al. achieves exponential acceleration compared with the best classical algorithm. However, it has a relatively high demand for qubit resources and the solution $\left| x \right\rangle$ is in a normalized form. Assuming that the eigenvalues of the coefficient matrix of the linear systems of equations can be represented perfectly by finite binary number strings, three hybrid iterative phase estimation algorithms (HIPEA) are designed based on the iterative phase estimation algorithm in this paper. The complexity is transferred to the measurement operation in an iterative way, and thus the demand of qubit resources is reduced in our hybrid algorithms. Moreover, the solution is stored in a classical register instead of a quantum register, so the exact unnormalized solution can be obtained. The required qubit resources in the three HIPEA algorithms are different. HIPEA-1 only needs one single ancillary qubit. The number of ancillary qubits in HIPEA-2 is equal to the number of nondegenerate eigenvalues of the coefficient matrix of linear systems of equations. HIPEA-3 is designed with a flexible number of ancillary qubits. The HIPEA algorithms proposed in this paper broadens the application range of quantum computation in solving linear systems of equations by avoiding the problem that quantum programs may not be used to solve linear systems of equations due to the lack of qubit resources.Comment: 22 pages, 6 figures, 6 tables, 48 equation

High Order Projection Plane Method for Evaluation of Supersingular Curved Boundary Integrals in BEM

Boundary element method (BEM) is a very promising approach for solving various engineering problems, in which accurate evaluation of boundary integrals is required. In the present work, the direct method for evaluating singular curved boundary integrals is developed by considering the third-order derivatives in the projection plane method when expanding the geometry quantities at the field point as Taylor series. New analytical formulas are derived for geometry quantities defined on the curved line/plane, and unified expressions are obtained for both two-dimensional and three-dimensional problems. For the two-dimensional boundary integrals, analytical expressions for the third-order derivatives are derived and are employed to verify the complex-variable-differentiation method (CVDM) which is used to evaluate the high order derivatives for three-dimensional problems. A few numerical examples are given to show the effectiveness and the accuracy of the present method

Cascaded Multi-task Adaptive Learning Based on Neural Architecture Search

Cascading multiple pre-trained models is an effective way to compose an end-to-end system. However, fine-tuning the full cascaded model is parameter and memory inefficient and our observations reveal that only applying adapter modules on cascaded model can not achieve considerable performance as fine-tuning. We propose an automatic and effective adaptive learning method to optimize end-to-end cascaded multi-task models based on Neural Architecture Search (NAS) framework. The candidate adaptive operations on each specific module consist of frozen, inserting an adapter and fine-tuning. We further add a penalty item on the loss to limit the learned structure which takes the amount of trainable parameters into account. The penalty item successfully restrict the searched architecture and the proposed approach is able to search similar tuning scheme with hand-craft, compressing the optimizing parameters to 8.7% corresponding to full fine-tuning on SLURP with an even better performance

Plugin Speech Enhancement: A Universal Speech Enhancement Framework Inspired by Dynamic Neural Network

The expectation to deploy a universal neural network for speech enhancement, with the aim of improving noise robustness across diverse speech processing tasks, faces challenges due to the existing lack of awareness within static speech enhancement frameworks regarding the expected speech in downstream modules. These limitations impede the effectiveness of static speech enhancement approaches in achieving optimal performance for a range of speech processing tasks, thereby challenging the notion of universal applicability. The fundamental issue in achieving universal speech enhancement lies in effectively informing the speech enhancement module about the features of downstream modules. In this study, we present a novel weighting prediction approach, which explicitly learns the task relationships from downstream training information to address the core challenge of universal speech enhancement. We found the role of deciding whether to employ data augmentation techniques as crucial downstream training information. This decision significantly impacts the expected speech and the performance of the speech enhancement module. Moreover, we introduce a novel speech enhancement network, the Plugin Speech Enhancement (Plugin-SE). The Plugin-SE is a dynamic neural network that includes the speech enhancement module, gate module, and weight prediction module. Experimental results demonstrate that the proposed Plugin-SE approach is competitive or superior to other joint training methods across various downstream tasks

MetaLoc: Learning to Learn Wireless Localization

The existing indoor fingerprinting-based localization methods are rather accurate after intensive offline calibrations for a specific environment, and they are built based either on the received signal strength (RSS) or the channel state information (CSI). However, a well-calibrated localization method (which can be a pure statistical signal processing method or an emerging data-driven method) will present poor generalization abilities in changing environments, which results in large losses in knowledge and human effort. To break the environment-specific localization bottleneck, we propose a novel data-driven fingerprinting-based localization framework empowered by the model-agnostic meta-learning (MAML), named MetaLoc. Specifically, MetaLoc is characterized by its ability to rapidly adapt itself to a new, possibly unseen, environment with very little calibration. The underlying data-driven localization model is a deep neural network, and we leverage historical data previously collected from various well-calibrated environments to train an optimal set of meta-parameters as an initialization to the new environments. Furthermore, we develop two MetaLoc paradigms in the proposed MetaLoc based on the different ways of obtaining meta-parameters. The centralized paradigm using vanilla MAML is much easier to implement, while the distributed paradigm incorporates domain shifts into the vanilla MAML to accelerate the convergence speed of the training process. The experimental results obtained for both synthetic- and real datasets demonstrate MetaLoc's strengthes in terms of localization error, robustness and cost-effectiveness compared with various baseline methods

Modified Glucose-Insulin-Potassium Regimen Provides Cardioprotection With Improved Tissue Perfusion in Patients Undergoing Cardiopulmonary Bypass Surgery

Background Laboratory studies demonstrate glucose-insulin-potassium (GIK) as a potent cardioprotective intervention, but clinical trials have yielded mixed results, likely because of varying formulas and timing of GIK treatment and different clinical settings. This study sought to evaluate the effects of modified GIK regimen given perioperatively with an insulin-glucose ratio of 1:3 in patients undergoing cardiopulmonary bypass surgery. Methods and Results In this prospective, randomized, double-blinded trial with 930 patients referred for cardiac surgery with cardiopulmonary bypass, GIK (200 g/L glucose, 66.7 U/L insulin, and 80 mmol/L KCl) or placebo treatment was administered intravenously at 1 mL/kg per hour 10 minutes before anesthesia and continuously for 12.5 hours. The primary outcome was the incidence of in-hospital major adverse cardiac events including all-cause death, low cardiac output syndrome, acute myocardial infarction, cardiac arrest with successful resuscitation, congestive heart failure, and arrhythmia. GIK therapy reduced the incidence of major adverse cardiac events and enhanced cardiac function recovery without increasing perioperative blood glucose compared with the control group. Mechanistically, this treatment resulted in increased glucose uptake and less lactate excretion calculated by the differences between arterial and coronary sinus, and increased phosphorylation of insulin receptor substrate-1 and protein kinase B in the hearts of GIK-treated patients. Systemic blood lactate was also reduced in GIK-treated patients during cardiopulmonary bypass surgery. Conclusions A modified GIK regimen administered perioperatively reduces the incidence of in-hospital major adverse cardiac events in patients undergoing cardiopulmonary bypass surgery. These benefits are likely a result of enhanced systemic tissue perfusion and improved myocardial metabolism via activation of insulin signaling by GIK. Clinical Trial Registration URL: clinicaltrials.gov. Identifier: NCT01516138

Bone marrow-derived mesenchymal stem cell-conditioned medium ameliorates diabetic foot ulcers in rats

Objectives: This study aimed to explore the effects of bone marrow-derived Mesenchymal Stem Cell-Conditioned Medium (MSC-CM) treating diabetic foot ulcers in rats. Methods: Models of T2DM rats were induced by a high-fat diet and intraperitoneal injection of STZ in SD rats. Models of Diabetic Foot Ulcers (DFUs) were made by operation on hind limbs in diabetic rats. Rats were divided into four groups (n = 6 for each group), i.e., Normal Control group (NC), Diabetes Control group (DM-C), MSC-CM group and Mesenchymal Stem Cells group (MSCs). MSC-CM group was treated with an injection of conditioned medium derived from preconditioned rats' bone marrow MSCs around ulcers. MSCs group were treated with an injection of rats' bone marrow MSCs. The other two groups were treated with an injection of PBS. After the treatment, wound closure, re-epithelialization (thickness of the stratum granulosums of the skin, by H&E staining), cell proliferation (Ki67, by IHC), angiogenesis (CD31, by IFC), autophagy (LC3B, by IFC and WB; autolysosome, by EM) and pyroptosis (IL-1β, NLRP3, Caspase-1, GSDMD and GSDMD-N, by WB) in ulcers were evaluated. Results: After the treatment wound area rate, IL-1β by ELISA, and IL-1β, Caspase-1, GSDMD and GSDMD-N by WB of MSC-CM group were less than those of DM group. The thickness of the stratum granulosums of the skin, proliferation index of Ki67, mean optic density of CD31 and LC3B by IFC, and LC3B by WB of MSC-CM group were more than those of DM group. The present analysis demonstrated that the injection of MSC-CM into rats with DFUs enhanced the wound-healing process by accelerating wound closure, promoting cell proliferation and angiogenesis, enhancing cell autophagy, and reducing cell pyroptosis in ulcers. Conclusions: Studies conducted indicate that MSC-CM administration could be a novel cell-free therapeutic approach to treat DFUs accelerating the wound healing process and avoiding the risk of living cells therapy