Efficient Covariance Matrix Update for Variable Metric Evolution Strategies

International audienceRandomized direct search algorithms for continuous domains, such as Evolution Strategies, are basic tools in machine learning. They are especially needed when the gradient of an objective function (e.g., loss, energy, or reward function) cannot be computed or estimated efficiently. Application areas include supervised and reinforcement learning as well as model selection. These randomized search strategies often rely on normally distributed additive variations of candidate solutions. In order to efficiently search in non-separable and ill-conditioned landscapes the covariance matrix of the normal distribution must be adapted, amounting to a variable metric method. Consequently, Covariance Matrix Adaptation (CMA) is considered state-of-the-art in Evolution Strategies. In order to sample the normal distribution, the adapted covariance matrix needs to be decomposed, requiring in general $\Theta(n^3)$ operations, where $n$ is the search space dimension. We propose a new update mechanism which can replace a rank-one covariance matrix update and the computationally expensive decomposition of the covariance matrix. The newly developed update rule reduces the computational complexity of the rank-one covariance matrix adaptation to $\Theta(n^2)$ without resorting to outdated distributions. We derive new versions of the elitist Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the multi-objective CMA-ES. These algorithms are equivalent to the original procedures except that the update step for the variable metric distribution scales better in the problem dimension. We also introduce a simplified variant of the non-elitist CMA-ES with the incremental covariance matrix update and investigate its performance. Apart from the reduced time-complexity of the distribution update, the algebraic computations involved in all new algorithms are simpler compared to the original versions. The new update rule improves the performance of the CMA-ES for large scale machine learning problems in which the objective function can be evaluated fast

The Novel Human Influenza A(H7N9) Virus Is Naturally Adapted to Efficient Growth in Human Lung Tissue

A novel influenza A virus (IAV) of the H7N9 subtype has been isolated from severely diseased patients with pneumonia and acute respiratory distress syndrome and, apparently, from healthy poultry in March 2013 in Eastern China. We evaluated replication, tropism, and cytokine induction of the A/Anhui/1/2013 (H7N9) virus isolated from a fatal human infection and two low-pathogenic avian H7 subtype viruses in a human lung organ culture system mimicking infection of the lower respiratory tract. The A(H7N9) patient isolate replicated similarly well as a seasonal IAV in explanted human lung tissue, whereas avian H7 subtype viruses propagated poorly. Interestingly, the avian H7 strains provoked a strong antiviral type I interferon (IFN-I) response, whereas the A(H7N9) virus induced only low IFN levels. Nevertheless, all viruses analyzed were detected predominantly in type II pneumocytes, indicating that the A(H7N9) virus does not differ in its cellular tropism from other avian or human influenza viruses. Tissue culture-based studies suggested that the low induction of the IFN-β promoter correlated with an efficient suppression by the viral NS1 protein. These findings demonstrate that the zoonotic A(H7N9) virus is unusually well adapted to efficient propagation in human alveolar tissue, which most likely contributes to the severity of lower respiratory tract disease seen in many patients

Irinotecan and temozolomide in combination with dasatinib and rapamycin versus irinotecan and temozolomide for patients with relapsed or refractory neuroblastoma (RIST-rNB-2011): a multicentre, open-label, randomised, controlled, phase 2 trial

Background Neuroblastoma is the most common extracranial solid tumour in children. Relapsed or refractory neuroblastoma is associated with a poor outcome. We assessed the combination of irinotecan–temozolomide and dasatinib–rapamycin (RIST) in patients with relapsed or refractory neuroblastoma. Methods The multicentre, open-label, randomised, controlled, phase 2, RIST-rNB-2011 trial recruited from 40 paediatric oncology centres in Germany and Austria. Patients aged 1–25 years with high-risk relapsed (defined as recurrence of all stage IV and MYCN amplification stages, after response to treatment) or refractory (progressive disease during primary treatment) neuroblastoma, with Lansky and Karnofsky performance status at least 50%, were assigned (1:1) to RIST (RIST group) or irinotecan–temozolomide (control group) by block randomisation, stratified by MYCN status. We compared RIST (oral rapamycin [loading 3 mg/m2 on day 1, maintenance 1 mg/m2 on days 2–4] and oral dasatinib [2 mg/kg per day] for 4 days with 3 days off, followed by intravenous irinotecan [50 mg/m2 per day] and oral temozolomide [150 mg/m2 per day] for 5 days with 2 days off; one course each of rapamycin–dasatinib and irinotecan–temozolomide for four cycles over 8 weeks, then two courses of rapamycin–dasatinib followed by one course of irinotecan–temozolomide for 12 weeks) with irinotecan–temozolomide alone (with identical dosing as experimental group). The primary endpoint of progression-free survival was analysed in all eligible patients who received at least one course of therapy. The safety population consisted of all patients who received at least one course of therapy and had at least one post-baseline safety assessment. This trial is registered at ClinicalTrials.gov, NCT01467986, and is closed to accrual. Findings Between Aug 26, 2013, and Sept 21, 2020, 129 patients were randomly assigned to the RIST group (n=63) or control group (n=66). Median age was 5·4 years (IQR 3·7–8·1). 124 patients (78 [63%] male and 46 [37%] female) were included in the efficacy analysis. At a median follow-up of 72 months (IQR 31–88), the median progression-free survival was 11 months (95% CI 7–17) in the RIST group and 5 months (2–8) in the control group (hazard ratio 0·62, one-sided 90% CI 0·81; p=0·019). Median progression-free survival in patients with amplified MYCN (n=48) was 6 months (95% CI 4–24) in the RIST group versus 2 months (2–5) in the control group (HR 0·45 [95% CI 0·24-0·84], p=0·012); median progression-free survival in patients without amplified MYCN (n=76) was 14 months (95% CI 9–7) in the RIST group versus 8 months (4–15) in the control group (HR 0·84 [95% CI 0·51–1·38], p=0·49). The most common grade 3 or worse adverse events were neutropenia (54 [81%] of 67 patients given RIST vs 49 [82%] of 60 patients given control), thrombocytopenia (45 [67%] vs 41 [68%]), and anaemia (39 [58%] vs 38 [63%]). Nine serious treatment-related adverse events were reported (five patients given control and four patients given RIST). There were no treatment-related deaths in the control group and one in the RIST group (multiorgan failure). Interpretation RIST-rNB-2011 demonstrated that targeting of MYCN-amplified relapsed or refractory neuroblastoma with a pathway-directed metronomic combination of a multkinase inhibitor and an mTOR inhibitor can improve progression-free survival and overall survival. This exclusive efficacy in MYCN-amplified, relapsed neuroblastoma warrants further investigation in the first-line setting

Optimierung von Fahrerassistenzsystemen

Diese Arbeit gliedert sich in einen methodischen und einen anwendungsorientierten Teil, wobei sich der methodische Teil mit der Verbesserung der zeitlichen Effizienz von Evolutionsstrategien (ESs) und Support-Vektor-Maschinen (SVMs) befasst. In diesem Teil wird die Berechnungskomplexität der Covariance-Matrix-Adaptation-ES (CMA-ES)auf das theoretische Minimum reduziert und ein neues Approximationsverfahren für SVMs entwickelt, das eine größere Leistungsfähigkeit als bestehende Techniken aufweist. Im anwendungsorientierten Teil werden Komponenten für Fahrerassistenzsysteme entwickelt und optimiert. Dabei spielen die im methodischen Teil entwickelten Verfahren eine zentrale Rolle. Mit der CMA-ES werden die optimalen Parameter eines Fahrspurfindungssystems bestimmt. Des Weiteren wird ein effizientes und robustes System zur Fluchtpunktschätzung entwickelt. Abschließend wird für die Fußgängerdetektion mit SVMs die Technik der evolutionären Mehrzieloptimierung vorgeschlagen und eingesetzt