Miracle-dz/UniFAR-FTUs huggingface dataset:
Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (cma-es)

This paper presents a novel evolutionary optimization strategy based on the deran-domized evolution strategy with covariance matrix adaptation (CMA-ES). This new approach is intended to reduce the number of generations required for convergence to the optimum. Reducing the number of generations, i.e., the time complexity of the algorithm, is important if a large population size is desired: (1) to reduce the effect of noise; (2) to improve global search properties; and (3) to implement the algorithm on (highly) parallel machines. Our method results in a highly parallel algorithm which scales favorably with large numbers of processors. This is accomplished by efficiently incorporating the available information from a large population, thus significantly reducing the number of generations needed to adapt the covariance matrix. The original version of the CMA-ES was designed to reliably adapt the covariance matrix in small populations but it cannot exploit large populations efficiently. Our modifications scale up the efficiency to population sizes of up to ¡ £ , where £ is the problem dimension. This method has been applied to a large number of test problems, demonstrating that in many cases the CMA-ES can be advanced from quadratic to linear time complexity.
INDUCTION MOTOR CONTROL AND SPEED ESTIMATION VIA ADAPTIVE AND ARTIFICIAL INTELLIGENCE METHOD UPRAVLJANJE I ESTIMACIJA BRZINE ELEKTROMOTORNOG POGONA PRIMENOM ADAPTIVNE METODE I METODE VEŠTA Č KE INTELIGENCIJE

This paper shows the optimization of the parameters of PI controllers in the application of vector control of a three-phase induction motor by genetic algorithm for the set optimality criterion. There is also the projecting procedure as well as the comparative analysis of the performances of speed estimator based on MRAS observer and speed estimator based on ANN. The basis of the research is a mathematical model of vector control of a three-phase induction motor, which was developed in MATLAB/Simulink. The experimental analysis of the results was carried on the laboratory model of electric motor drive based on the dSPACE card. The application of the encoder provides a smaller measurement error, whereas the application of estimators is possible in the cases for which pinpoint accuracy is not required and for which the price and/or reliability are of greater interest.
A compact UWB dual slot antenna with dual notched characteristics

in this paper, a compact UWB dual slot antenna with dual notched characteristics is proposed. By introducing 50Ω Microstrip feed line proposed UWB antenna resonates below −10 dB bandwidth ranges from 3.1GHz to 10.6 GHz. U-Shaped slot etched onto ground plane to filter out 3.6GHz frequency for WIMAX application and C-Shaped slot of copper strip inserted into substrate to filter out 5.2GHz frequency for WLAN application.
Thrombolysisin Predicting the Outcome of Intravenous Collateral Score Complements Clot Location

BACKGROUND AND PURPOSE: Collateral circulation is an important determinant of stroke outcome. We studied the impact of leptomen-ingealcollateralcirculationwithrespecttothelocationofthethrombusinpredictingtheclinicaloutcomeofpatientstreatedwithintravenous thrombolytic therapy ( (cid:2) 3 hours) in a retrospective cohort. MATERIALS AND METHODS: Anterior circulation thrombus was detected with CT angiography in 105 patients. Baseline clinical and imaginginformationwascollected,andthesiteoftheocclusionwasrecorded.Collateralswereassessedbyusinga5-gradecollateralscore and were entered into logistic regression analysis to predict favorable clinical outcome (3-month modiﬁed Rankin Scale score of 0–2). RESULTS: Two-thirds of patients with a proximal occlusion displayed poor collateral ﬁlling (collateral score 0–1), whereas in more distal clot locations, approximately one-third had poor collaterals. Only 36% of patients with a proximal occlusion and good collaterals experiencedfavorableclinicaloutcome.Inmultivariateanalysis,bothclotlocationandcollateralscorewerehighlysigniﬁcant( P (cid:3) . 003and P (cid:3) . 001) and independent predictors of favorable clinical outcome. Good collateral status increased the odds of favorable clinical outcome about 9-fold (OR (cid:3) 9.3; 95% CI, 2.4–35.8). After dichotomization, a distal clot location had a larger odds ratio (OR (cid:3) 13.3; 95% CI, 3.0–60.0) compared with the odds ratio of good collaterals (OR (cid:3) 5.9; 95% CI, 1.8–19.0). CONCLUSIONS: A proximal occlusion in the anterior circulation is associated with poorer collateral status compared with a more distal occlusion. Both the clot location and collateral score are important and independent predictors of favorable clinical outcome of hyperacute stroke treated with intravenous thrombolysis. The location of the clot is a stronger
Simulations of Low Mach Number Reactive Flows Coupled with Electric Fields

: Applying electric ﬁelds to ﬂames can provide an effective, non-invasive control of the combustion process. In this work, an algorithm recently introduced to handle the interactions be-tween reactive ﬂows and an electric ﬁeld is extended to handle multiple dimensions and adaptive mesh reﬁnement (AMR), and applied to a series of laminar ﬂames. Experimental data allows to validate the ability of the algorithm to capture the mechanisms driving the ﬂame response to electric ﬁelds.
Political economy of environmental policy with overlapping generations

eScholarship provides open access, scholarly publishing services to the University of California and delivers a dynamic research platform to scholars worldwide. Abstract: A two-sector OLG model illuminates previously unexamined intergenerationaleffects of a tax that protects an environmental stock. A traded asset capitalizes the economic returns to future tax-induced environmental improvements, benefiting the current asset owners, the old generation. Absent a transfer, the tax harms the young generation by decreasing their real wage. Future generations benefit fromthe tax-induced improvement in environmental stock. The principalintergenerational conflict arising from public policy is between generationsalive at the time society imposes the policy, not between generations alive at different times. A Pareto-improving policy can be implemented under various political economy settings. Abstract A two-sector OLG model illuminates previously unexamined intergenerational e ﬀ ects of a tax that protects an environmental stock. A traded asset capitalizes the economic returns to future tax-induced environmental improvements, bene ﬁ ting the current asset owners, the old generation. Absent a transfer, the tax harms the young generation by decreasing their real wage. Future generations bene ﬁ t from the tax-induced improvement in environmental stock. The principal intergenerational con ﬂ ict arising from public policy is between generations alive at the time society imposes the policy, not between generations alive at di ﬀ erent times. A Pareto-improving policy can be implemented under various political economy settings.
Hybrid State Space-based Learning for Sequential Data Prediction with Joint Optimization

We investigate nonlinear prediction/regression in an online setting and introduce a hybrid model that effectively mitigates, via a joint mechanism through a state space formulation, the need for domain-specific feature engineering issues of conventional nonlinear prediction models and achieves an efficient mix of nonlinear and linear components. In particular, we use recursive structures to extract features from raw sequential sequences and a traditional linear time series model to deal with the intricacies of the sequential data, e.g., seasonality, trends. The state-of-the-art ensemble or hybrid models typically train the base models in a disjoint manner, which is not only time consuming but also sub-optimal due to the separation of modeling or independent training. In contrast, as the first time in the literature, we jointly optimize an enhanced recurrent neural network (LSTM) for automatic feature extraction from raw data and an ARMA-family time series model (SARIMAX) for effectively addressing peculiarities associated with time series data. We achieve this by introducing novel state space representations for the base models, which are then combined to provide a full state space representation of the hybrid or the ensemble. Hence, we are able to jointly optimize both models in a single pass via particle filtering, for which we also provide the update equations. The introduced architecture is generic so that one can use other recurrent architectures, e.g., GRUs, traditional time series-specific models, e.g., ETS or other optimization methods, e.g., EKF, UKF. Due to such novel combination and joint optimization, we demonstrate significant improvements in widely publicized real life competition datasets. We also openly share our code for further research and replicability of our results.
Functional requirements to mitigate the Risk of Harm to Patients from Artificial Intelligence in Healthcare

The Directorate General for Parliamentary Research Services of the European Parliament has prepared a report to the Members of the European Parliament where they enumerate seven main risks of Artificial Intelligence (AI) in medicine and healthcare: patient harm due to AI errors, misuse of medical AI tools, bias in AI and the perpetuation of existing inequities, lack of transparency, privacy and security issues, gaps in accountability, and obstacles in implementation. In this study, we propose fourteen functional requirements that AI systems may implement to reduce the risks associated with their medical purpose: AI passport, User management, Regulation check, Academic use only disclaimer, data quality assessment, Clinicians double check, Continuous performance evaluation, Audit trail, Continuous usability test, Review of retrospective/simulated cases, Bias check, eXplainable AI, Encryption and use of field-tested libraries, and Semantic interoperability. Our intention here is to provide specific high-level specifications of technical solutions to ensure continuous good performance and use of AI systems to benefit patients in compliance with the future EU regulatory framework.
Reply to the Comments of Reviewer #2

7 Despite the great success of machine learning, its application in climate dynamics has not been 8 well developed. One concern might be how well the trained neural networks could learn a dynamical 9 system and what will be the potential application of this kind of learning. In this paper, three 10 machine learning methods are used: reservoir computer (RC), back propagation based artificial 11 neural network (BP), and long short-term memory neural network (LSTM). It shows that the 12 coupling relations or dynamics among variables in linear or nonlinear systems can be inferred by RC 13 and LSTM, which can be further applied to reconstruct one time series from the other. Specifically, 14 we analyzed the climatic toy models to address two questions: (i) what factors significantly 15 influence machine learning reconstruction; and (ii) how to select suitable explanatory variables for 16 machine learning reconstruction. The results reveal that both linear and nonlinear coupling relations 17 between variables do influence the reconstruction quality of machine learning. If there is a strong 18 linear coupling between two variables, then the reconstruction can be bi-directional, and both of 19 these two variables can be an explanatory variable for reconstructing the other. When the linear 20 coupling among variables is absent, but with the significant nonlinear coupling, the machine 21
Integral quadratic forms : applications to algebraic geometry

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Spouses' Retirement and Hours of Work Outcomes: Evidence from Twofold Regression Discontinuity

Earlier studies conclude that spouses time their retirement closely together. Here, we exploit early retirement age legislation to identify the effect of own and spousal retirement on spouses' hours of work. The sample for the analysis includes over 85000 French couples. We conclude that hours of work fall significantly upon own and partner's retirement, for both spouses. The own effect is dramatically large and equal to a drop in hours worked of 65 to 77 per cent while the cross effects are small, suggesting an average reduction of one or two hours per week upon spousal retirement.
Antiplatelet therapy for preventing stroke in patients with non-valvular atrial fibrillation and no previous history of stroke or transient ischemic attacks.

BACKGROUND Atrial fibrillation (AF) carries an increased risk of stroke; antiplatelet agents are proven effective for stroke prevention in other settings. OBJECTIVES The objective of this review was to determine the efficacy and safety of antiplatelet therapy for prevention of stroke in patients with chronic non-valvular AF. SEARCH STRATEGY We searched the Cochrane Stroke Group Specialised Register of Trials, MEDLINE database (June 1999), and the database of the Antithrombotic Trialists Collaboration, as well as reference lists of relevant articles. SELECTION CRITERIA All randomized trials comparing antiplatelet therapies to placebo in patients with non-valvular AF and no history of transient ischemic attack (TIA) or stroke. DATA COLLECTION AND ANALYSIS Trials for inclusion were independently selected by two reviewers who also extracted each outcome and double-checked the data. The Peto method was used for combining odds ratios. All analysis were, as far as possible, "intention-to-treat". Since the published results of two trials included 3-8% of participants with prior stroke or TIA, unpublished results excluding these participants were obtained from the Atrial Fibrillation Investigators. MAIN RESULTS Among 1680 participants without prior stroke/TIA, randomized to aspirin (N = 838) or placebo in two trials, aspirin was associated with nonsignificantly lower risks of ischemic stroke (OR = 0.71, CI 95% 0. 46 - 1.10), all stroke (OR = 0.70, CI 95% 0.45 - 1.08) all disabling/fatal stroke (OR =0.88, CI 95% 0.48 - 1.58) and the constellation of stroke, MI or vascular death (OR = 0.76, CI 95% 0. 54 - 1.05 ). Considering all randomized participants including those with prior stroke or TIA, reductions in these events by aspirin were consistently smaller and marginally statistically significant: ischemic stroke (OR = 0.77, CI 95% 0.60-1.00), all stroke (OR = 0.76, CI 95% 0.61 - 0.93), all disabling/fatal stroke (OR = 0.87, CI 95% 0.64 - 1.19) and the combined outcome (OR = 0.79, CI 95% 0.64 - 0. 99). No increase in major hemorrhage was seen, but the number of hemorrhagic events was small. REVIEWER'S CONCLUSIONS Considering all randomized data, aspirin modestly (by about 20%) reduces stroke and major vascular events in nonvalvular AF. For primary prevention among AF patients with an average stroke rate of 4.5%/year, about 10 strokes would be prevented yearly for every 1000 given aspirin.
Cellsizeasdriverandsentinelofphytoplanktoncommunity structureandfunctioning HelmutHillebrand1,2,3 |EstebanAcevedo-Trejos4 |StefanieD.Moorthi1 | AlexeyRyabov5,6 |MarenStriebel1 |PatrickK.Thomas1 |Marie-LuiseSchneider1

1. Body size is a decisive functional trait in many organisms, especially for phyto-plankton, which span several orders of magnitude in cell volume. Therefore, the analysis of size as a functional trait driving species’ performance has received wide attention in aquatic ecology, amended in recent decades by studies documenting changes in phytoplankton size in response to abiotic or biotic factors in the environment. 2. We performed a systematic literature review to provide an overarching, partially quantitative synthesis of cell size as a driver and sentinel of phytoplankton ecology. We found consistent and significant allometric relationships between cell sizes and the functional performance of phytoplankton species (cellular rates of carbon fixation, respiration and exudation as well as resource affinities, uptake and content). Size scaling became weaker, absent or even negative when addressing C-or volume-specific rates or growth. C-specific photosynthesis and population growth rate peaked at intermediate cell sizes around 100 µ m 3 . 3. Additionally, we found a rich literature on sizes changing in response to warming, nutrients and pollutants. Whereas small cells tended to dominate under oligotrophic and warm conditions, there are a few notable exceptions, which indicates that other environmental or biotic constraints alter this general trend. Grazing seems a likely explanation, which we reviewed to understand both how size affects edibility and how size structure changes in response to grazing. Cell size also predisposes the strength and outcome of competitive interactions between algal species. Finally, we address size in a community context, where size-abundance scaling describes community composition and thereby the bio-diversity in phytoplankton assemblages.
A quantitative comparison and validation of finite-fault models: The 2011 Tohoku-Oki earthquake

Large earthquakes rupture faults over hundreds of kilometers within minutes. Finite-fault models elucidate these processes and provide observational constraints for understanding earthquake physics. However, finite-fault inversions are subject to nonuniqueness and substantial uncertainties. The diverse range of published models for the well-recorded 2011 M w 9.0 Tohoku-Oki earthquake aptly illustrates this issue, and details of its rupture process remain under debate. Here, we comprehensively compare 32 finite-fault models of the Tohoku-Oki earthquake and analyze the sensitivity of three commonly-used observational data types (geodetic, seismic, and tsunami) to the slip features identified. We first project all models to a realistic megathrust geometry and a 1-km subfault size. At this scale, we observe poor correlation among the models, irrespective of the data type. However, model agreement improves significantly when subfault sizes are increased, implying that their differences primarily stem from small-scale features. We then forward-compute geodetic and teleseismic synthetics and compare them with observations. We find that seismic observations are sensitive to rupture propagation, such as the peak-slip-rise time. However, neither teleseismic nor geodetic observations are sensitive to spatial slip features smaller than 64 km. In distinction, the synthesized seafloor deformation of all models exhibits poor correlation, indicating sensitivity to small-scale slip features. Our findings suggest that fine-scale slip features cannot be unambiguously resolved by remote or sparse observations, such as the three data types tested in this study. However, better resolution may become achievable from uniformly gridded dense offshore instrumentation.
Reliable FPGA-based Solar Energy Production

— This paper presents a stand-alone photovoltaic (PV) system with a hybrid storage unit operation under normal and faulty condition. An energy management method based on the use of a hybrid storage for the solar energy production system is presented. The storage system consists a battery and an Ultra Capacitor (UC) pack. The UCs are used for facing high frequency variation of the load/source, while the batteries are in charge of slow load /source variations. Generally, in a PV system boost converters are used to connect solar panels to the DC bus. These converter allows to achieve the maximum power of the PV source by applying a Maximum Power Point Tracking (MPPT) algorithm. On the other hand, the power electronic semiconductor switch faults are popular in DC-DC converters. In order to improve the micro-grid service continuity and reliability, a fast fault detection method based on the converter current shape and one redundancy based fault tolerant converter for PV source is proposed in this paper. The validity of the proposed energy management and associated control system, the fault detection method and fault tolerant strategy are confirmed by the simulations, HIL and experimental results.
Statistical generation of SST perturbations with spatio‐temporally coherent growing patterns

The medium‐range ensemble prediction system (EPS) at the Japan Meteorological Agency (JMA), like other operational EPSs, suffers from overconfident probabilistic forecasts in the lower troposphere and over the oceans in particular. To alleviate this issue, a new scheme to perturb the sea surface temperature (SST) is proposed and tested. The proposed method statistically generates SST perturbations that account for time‐varying errors of the SST that is prescribed to the control member. Despite its simplicity and purely statistical nature, the new method is able to produce growing perturbations whose spatial patterns appear to reflect ocean dynamics. Their growth with forecast lead‐time is completely flow‐independent but is statistically consistent with the past records of the error growth of the prescribed SST. Perturbing the SST with the proposed scheme is shown to improve the probabilistic forecast skills of JMA's medium‐range EPS. The improvement is statistically significant over the Tropics throughout the entire troposphere but the impact on the extratropical forecast is found to be neutral. The improvement is largely attributed to enhanced reliability which in turn is achieved through an increase in the ensemble spread. Perturbing SST incurs no harmful impact on the quality of ensemble mean forecast.
Evidence of SARS-Cov-2-specific memory B cells six months after vaccination with BNT162b2 mRNA vaccine

SARS-CoV-2 mRNA vaccines have demonstrated high efficacy and immunogenicity, but limited information is currently available on memory B cells generation and long-term persistence. Here, we investigated Spike-specific memory B cells and humoral responses in 145 subjects, up to six months after the BNT162b2 vaccine (Comirnaty) administration. Spike-specific antibody titers peaked 7 days after the second dose and significant titers and neutralizing activity were still observed after six months, despite a progressive decline over time. Concomitant to antibody reduction, Spike-specific memory B cells, mostly IgG class-switched, increased in blood of vaccinees and persisted six months after vaccination. Following in vitro restimulation, circulating memory B cells reactivated and produced Spike-specific antibodies. A high frequency of Spike-specific IgG+ plasmablasts, identified by computational analysis 7 days after boost, positively correlated with the generation of IgG+ memory B cells at six months. These data demonstrate that mRNA BNT162b2 vaccine elicits strong B cell immunity with Spike-specific memory B cells that still persist six months after vaccination, playing a crucial role for rapid response to SARS-CoV-2 virus encounter.