Search

Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as O(T2 × polylog(n)), where n is the size of the models and T is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems.

Noisy quantum simulation is challenging since one has to take into account the stochastic nature of the process. The dominating method for it is the density matrix approach. In this paper, we evaluate conditions for which this method is inferior to a substantially simpler way of simulation. Our approach uses stochastic ensembles of quantum circuits, where random Kraus operators are applied to original quantum gates to represent random errors for modeling quantum channels. We show that our stochastic simulation error is relatively low, even for large numbers of qubits. We implemented this approach as a part of the QTensor package. While usual density matrix simulations on average hardware are challenging at $n>15$, we show that for up to $n\lesssim 30$, it is possible to run embarrassingly parallel simulations with $, 6 pages, 6 figures

Recently, deep neural network (DNN) based adaptive optics systems were proposed to address the issue of latency in existing wavefront sensorless (WFS-less) aberration correction techniques. Intensity images alone are sufficient for the DNN model to compute the necessary wavefront correction, removing the need for iterative processes and allowing practical real-time aberration correction to be implemented. Specifically, we generate the desired aberration correction phase profiles utilizing a DNN based system that outputs a set of coefficients for 27 terms of Zernike polynomials. We present an experimental realization of this technique using a spatial light modulator (SLM) on real physical turbulence-induced aberration. We report an aberration correction rate of 20 frames per second in this laboratory setting, accelerated by parallelization on a graphics processing unit. There are a number of issues associated with the practical implementation of such techniques, which we highlight and address in this paper.

Background The Centers for Medicare & Medicaid Services (CMS) implemented the Hospital-Acquired Conditions (HACs) Initiative in October 2008; the CMS no longer reimbursed hospitals for fall injury. The effects of this payment change on fall and fall injury rates are not well described, nor its effect on physical restraint use. Objective The aim of this study was to examine the effects of the 2008 HACs Initiative on the rates of falls, injurious falls, and physical restraint use. Design/setting This was a nine-year retrospective cohort study (July 2006-December 2015) involving 2,862 adult medical, medical-surgical, and surgical nursing units from 734 hospitals. Measurements Annual rates of change in falls, injurious falls, and physical restraint use during the two years before the payment rule went into effect were compared with one-, four-, and seven-year rates of annual change after implementation, adjusting for unit- and facility-level covariates. Stratified analyses were conducted according to bed size and teaching status. Results Compared with prior to the payment change, there was stable acceleration in the one-, four-, and seven-year annual rates of decline in falls as follows: -2.1% (-3.3%, -0.9%), -2.2% (-3.2%, -1.1%), and -2.2% (-3.4%, -1.0%) respectively. For injurious falls, there was an increasing acceleration in the annual declines, achieving statistical significance only at seven years post CMS change as follows: -3.2% (-5.5%, -1.0%). Physical restraint use prevalence decreased from 1.6% to 0.6%. Changes in the rates of falls, injurious falls, and restraint use varied according to hospital bed size and teaching status. Conclusions and relevance Since the HACs Initiative, there was at best a modest decline in the rates of falls and injurious falls observed primarily in larger, major teaching hospitals. An increase in restraint use was not observed. Falls remain a difficult patient safety problem for hospitals, and further research is required to develop cost-effective, generalizable strategies for their prevention.

In this paper, we develop methods for longitudinal quantile regression when there is monotone missingness. In particular, we propose pattern mixture models with a constraint that provides a straightforward interpretation of the marginal quantile regression parameters. Our approach allows sensitivity analysis which is an essential component in inference for incomplete data. To facilitate computation of the likelihood, we propose a novel way to obtain analytic forms for the required integrals. We conduct simulations to examine the robustness of our approach to modeling assumptions and compare its performance to competing approaches. The model is applied to data from a recent clinical trial on weight management.

Objectives: The purpose of this study was to provide normative data on fall prevalence in U.S. hospitals by unit type and to determine the 27-month secular trend in falls before the implementation of the Centers for Medicare and Medicaid Service (CMS) rule, which does not reimburse hospitals for care related to injury resulting from hospital falls. Methods: We used data from the National Database of Nursing Quality Indicators (NDNQI) collected between July 1, 2006, and September 30, 2008, to estimate prevalence and secular trends of falls occurring in adult medical, medical-surgical, and surgical nursing units. More than 88 million patient days (pd) of observation were contributed from 6100 medical, surgical, and medical-surgical nursing units in 1263 hospitals across the United States. Results: A total of 315,817 falls occurred (rate = 3.56 falls/1000 pd) during the study period, of which, 82,332 (26.1%) resulted in an injury (rate = 0.93/1000 pd). Both total fall and injurious fall rates were highest in medical units (fall rate = 4.03/1000 pd; injurious fall rate = 1.08/1000 pd) and lowest in surgery units (fall rate = 2.76/1000 pd; injurious fall rate = 0.67/1000 pd). Falls (0.4% decrease per quarter, P < 0.0001) and injurious falls (1% decrease per quarter, P < 0.0001) both decreased over the 27-month study. Conclusions: In this large sample, fall and injurious fall prevalence varied by nursing unit type in U.S. hospitals. Over the 27-month study, there was a small, but statistically significant, decrease in falls (P < 0.0001) and injurious falls (P < 0.0001).

With an increase in the number of administrations of test forms, there is also an increase in the complexity of the quality assurance procedures needed to maintain the stability of the reported scores. Many traditional methods for quality control (QC; Allalouf, Educational Measurement: Issues and Practice 26(1):36–46, 2007) are not sufficient for detecting unusual scores in a rapid flow of administrations, for which new QC approaches have been proposed in recent years. In this study we investigate data from 15 consecutive administrations that follow a specific equating design (or braiding plan) and propose a linear mixed effects model for the detection of abnormal results. In this project we investigate the effects of the braiding plans and estimate the effects of several identified factors that influence the means and variances of the scale scores. This analysis is appropriate for tests with a continuous or almost continuous administration mode. The data are from a global standardized assessment of English skills. Reading and Listening sections of the English test were modeled separately. Test-takers’ education level, major, years abroad, years of study, and whether they repeated the test turned out to have significant effects on the scores. In addition, a formula for a prediction interval for the scaled mean score of certain subgroups was proposed to detect unusual administrations.

Bed alarm systems intended to prevent hospital falls have not been formally evaluated.To investigate whether an intervention aimed at increasing bed alarm use decreases hospital falls and related events.Pair-matched, cluster randomized trial over 18 months. Nursing units were allocated by computer-generated randomization on the basis of baseline fall rates. Patients and outcome assessors were blinded to unit assignment; outcome assessors may have become unblinded. (ClinicalTrials.gov registration number: NCT00183053)16 nursing units in an urban community hospital.27 672 inpatients in general medical, surgical, and specialty units.Education, training, and technical support to promote use of a standard bed alarm system (intervention units); bed alarms available but not formally promoted or supported (control units).Pre-post difference in change in falls per 1000 patient-days (primary end point); number of patients who fell, fall-related injuries, and number of patients restrained (secondary end points).Prevalence of alarm use was 64.41 days per 1000 patient-days on intervention units and 1.79 days per 1000 patient-days on control units (P = 0.004). There was no difference in change in fall rates per 1000 patient-days (risk ratio, 1.09 [95% CI, 0.85 to 1.53]; difference, 0.41 [CI, -1.05 to 2.47], which corresponds to a greater difference in falls in control vs. intervention units) or in the number of patients who fell, injurious fall rates, or the number of patients physically restrained on intervention units compared with control units.The study was conducted at a single site and was slightly underpowered compared with the initial design.An intervention designed to increase bed alarm use in an urban hospital increased alarm use but had no statistically or clinically significant effect on fall-related events or physical restraint use.National Institute on Aging.

Importance In 2008, Medicare implemented the Hospital-Acquired Conditions (HACs) Initiative, a policy denying incremental payment for 8 complications of hospital care, also known as never events . The regulation’s effect on these events has not been well studied. Objective To measure the association between Medicare’s nonpayment policy and 4 outcomes addressed by the HACs Initiative: central line–associated bloodstream infections (CLABSIs), catheter-associated urinary tract infections (CAUTIs), hospital-acquired pressure ulcers (HAPUs), and injurious inpatient falls. Design, Setting, and Participants Quasi-experimental study of adult nursing units from 1381 US hospitals participating in the National Database of Nursing Quality Indicators (NDNQI), a program of the American Nurses Association. The NDNQI data were combined with American Hospital Association, Medicare Cost Report, and local market data to examine adjusted outcomes. Multilevel models were used to evaluate the effect of Medicare’s nonpayment policy on never events. Exposures United States hospitals providing treatment for Medicare patients were subject to the new payment policy beginning in October 2008. Main Outcomes and Measures Changes in unit-level rates of HAPUs, injurious falls, CLABSIs, and CAUTIs after initiation of the policy. Results Medicare’s nonpayment policy was associated with an 11% reduction in the rate of change in CLABSIs (incidence rate ratio [IRR], 0.89; 95% CI, 0.83-0.95) and a 10% reduction in the rate of change in CAUTIs (IRR, 0.90; 95% CI, 0.85-0.95), but was not associated with a significant change in injurious falls (IRR, 0.99; 95% CI, 0.99-1.00) or HAPUs (odds ratio, 0.98; 95% CI, 0.96-1.01). Consideration of unit-, hospital-, and market-level factors did not significantly alter our findings. Conclusions and Relevance The HACs Initiative was associated with improvements in CLABSI and CAUTI trends, conditions for which there is strong evidence that better hospital processes yield better outcomes. However, the HACs Initiative was not associated with improvements in HAPU or injurious fall trends, conditions for which there is less evidence that changing hospital processes leads to significantly better outcomes.