Quantum machine learning: a classical perspective

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication alongside the increasing size of datasets are motivating a growing number of researchers to explore the possibility of harnessing the power of quantum computation to speed-up classical machine learning algorithms. Here we review the literature in quantum machine learning and discuss perspectives for a mixed readership of classical machine learning and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in machine learning are identified as promising directions for the field. Practical questions, like how to upload classical data into quantum form, will also be addressed.Comment: v3 33 pages; typos corrected and references adde

Simulating Turing Machines with Polarizationless P Systems with Active Membranes

We prove that every single-tape deterministic Turing machine working in t(n) t(n) time, for some function t:N→N t:N→N , can be simulated by a uniform family of polarizationless P systems with active membranes. Moreover, this is done without significant slowdown in the working time. Furthermore, if logt(n) log⁡t(n) is space constructible, then the members of the uniform family can be constructed by a family machine that uses O(logt(n)) O(log⁡t(n)) space.Ministerio de Economía y Competitividad TIN2012-3743

Refined saddle-point preconditioners for discretized Stokes problems

This paper is concerned with the implementation of efficient solution algorithms for elliptic problems with constraints. We establish theory which shows that including a simple scaling within well-established block diagonal preconditioners for Stokes problems can result in significantly faster convergence when applying the preconditioned MINRES method. The codes used in the numerical studies are available online

Selection of silk-binding peptides by phage display

Peptides that bind to silkworm-derived silk fibroin fiber were selected from a phage-displayed random peptide library. The selected silk-binding peptides contained a consensus sequence QSWS which is important for silk-binding as confirmed by binding assays using phage and synthetic peptides. With further optimization, we anticipate that the silk-binding peptides will be useful for functionalization of silk for biomaterial applications

Fast interior point solution of quadratic programming problems arising from PDE-constrained optimization

Interior point methods provide an attractive class of approaches for solving linear, quadratic and nonlinear programming problems, due to their excellent efficiency and wide applicability. In this paper, we consider PDE-constrained optimization problems with bound constraints on the state and control variables, and their representation on the discrete level as quadratic programming problems. To tackle complex problems and achieve high accuracy in the solution, one is required to solve matrix systems of huge scale resulting from Newton iteration, and hence fast and robust methods for these systems are required. We present preconditioned iterative techniques for solving a number of these problems using Krylov subspace methods, considering in what circumstances one may predict rapid convergence of the solvers in theory, as well as the solutions observed from practical computations

Identifying chemokines as therapeutic targets in renal disease: Lessons from antagonist studies and knockout mice

Chemokines, in concert with cytokines and adhesion molecules, play multiple roles in local and systemic immune responses. In the kidney, the temporal and spatial expression of chemokines correlates with local renal damage and accumulation of chemokine receptor-bearing leukocytes. Chemokines play important roles in leukocyte trafficking and blocking chemokines can effectively reduce renal leukocyte recruitment and subsequent renal damage. However, recent data indicate that blocking chemokine or chemokine receptor activity in renal disease may also exacerbate renal inflammation under certain conditions. An increasing amount of data indicates additional roles of chemokines in the regulation of innate and adaptive immune responses, which may adversively affect the outcome of interventional studies. This review summarizes available in vivo studies on the blockade of chemokines and chemokine receptors in kidney diseases, with a special focus on the therapeutic potential of anti-chemokine strategies, including potential side effects, in renal disease. Copyright (C) 2004 S. Karger AG, Basel

Acquiring Tetanus After Hemorrhoid Banding and Other Gastrointestinal Procedures

Tetanus after hemorrhoidal banding is an extremely rare but serious complication of the procedure. We describe the second reported case of this complication and review the literature concerning tetanus after different gastrointestinal procedures. Although a rare complication, practicing physicians need to be aware of the clinical presentation of this deadly disease when encountered in at-risk patient populations. Such cases also reemphasize the importance of primary tetanus immunization and follow-up boosters for all vulnerable patients

Psychological interventions in asthma

Asthma is a multifactorial chronic respiratory disease characterised by recurrent episodes of airway obstruction. The current management of asthma focuses principally on pharmacological treatments, which have a strong evidence base underlying their use. However, in clinical practice, poor symptom control remains a common problem for patients with asthma. Living with asthma has been linked with psychological co-morbidity including anxiety, depression, panic attacks and behavioural factors such as poor adherence and suboptimal self-management. Psychological disorders have a higher-than-expected prevalence in patients with difficult-to-control asthma. As psychological considerations play an important role in the management of people with asthma, it is not surprising that many psychological therapies have been applied in the management of asthma. There are case reports which support their use as an adjunct to pharmacological therapy in selected individuals, and in some clinical trials, benefit is demonstrated, but the evidence is not consistent. When findings are quantitatively synthesised in meta-analyses, no firm conclusions are able to be drawn and no guidelines recommend psychological interventions. These inconsistencies in findings may in part be due to poor study design, the combining of results of studies using different interventions and the diversity of ways patient benefit is assessed. Despite this weak evidence base, the rationale for psychological therapies is plausible, and this therapeutic modality is appealing to both patients and their clinicians as an adjunct to conventional pharmacological treatments. What are urgently required are rigorous evaluations of psychological therapies in asthma, on a par to the quality of pharmaceutical trials. From this evidence base, we can then determine which interventions are beneficial for our patients with asthma management and more specifically which psychological therapy is best suited for each patient

Translating HbA1c measurements into estimated average glucose values in pregnant women with diabetes

Aims/hypothesis This study aimed to examine the relationship between average glucose levels, assessed by continuous glucose monitoring (CGM), and HbA1c levels in pregnant women with diabetes to determine whether calculations of standard estimated average glucose (eAG) levels from HbA1c measurements are applicable to pregnant women with diabetes. Methods CGM data from 117 pregnant women (89 women with type 1 diabetes; 28 women with type 2 diabetes) were analysed. Average glucose levels were calculated from 5–7 day CGM profiles (mean 1275 glucose values per profile) and paired with a corresponding (±1 week) HbA1c measure. In total, 688 average glucose–HbA1c pairs were obtained across pregnancy (mean six pairs per participant). Average glucose level was used as the dependent variable in a regression model. Covariates were gestational week, study centre and HbA1c. Results There was a strong association between HbA1c and average glucose values in pregnancy (coefficient 0.67 [95% CI 0.57, 0.78]), i.e. a 1% (11 mmol/mol) difference in HbA1c corresponded to a 0.67 mmol/l difference in average glucose. The random effects model that included gestational week as a curvilinear (quadratic) covariate fitted best, allowing calculation of a pregnancy-specific eAG (PeAG). This showed that an HbA1c of 8.0% (64 mmol/mol) gave a PeAG of 7.4–7.7 mmol/l (depending on gestational week), compared with a standard eAG of 10.2 mmol/l. The PeAG associated with maintaining an HbA1c level of 6.0% (42 mmol/mol) during pregnancy was between 6.4 and 6.7 mmol/l, depending on gestational week. Conclusions/interpretation The HbA1c–average glucose relationship is altered by pregnancy. Routinely generated standard eAG values do not account for this difference between pregnant and non-pregnant individuals and, thus, should not be used during pregnancy. Instead, the PeAG values deduced in the current study are recommended for antenatal clinical care