Runtime Improvements Research Articles

Efficient Global MOT Under Minimum-Cost Circulation Framework.

We developed a minimum-cost circulation framework for solving the global data association problem, which plays a key role in the tracking-by-detection paradigm of multi-object tracking (MOT). The global data association problem was extensively studied under the minimum-cost flow framework, which is theoretically attractive as being flexible and globally solvable. However, the high computational burden has been a long-standing obstacle to its wide adoption in practice. While enjoying the same theoretical advantages and maintaining the same optimal solution as the minimum-cost flow framework, our new framework has a better theoretical complexity bound and leads to orders of practical efficiency improvement. This new framework is motivated by the observation that minimum-cost flow only partially models the data association problem and it must be accompanied by an additional and time-consuming searching scheme to determine the optimal object number. By employing a minimum-cost circulation framework, we eliminate the searching step and naturally integrate the number of objects into the optimization problem. By exploring the special property of the associated graph, that is, an overwhelming majority of the vertices are with unit capacity, we designed an implementation of the framework and proved it has the best theoretical computational complexity so far for the global data association problem. We evaluated our method with 40 experiments on five MOT benchmark datasets. Our method was always the most efficient in every single experiment and averagely 53 to 1,192 times faster than the three state-of-the-art methods. When our method served as a sub-module for global data association methods utilizing higher-order constraints, similar running time improvement was attained. We further illustrated through several case studies how the improved computational efficiency enables more sophisticated tracking models and yields better tracking accuracy. We made the source code publicly available on GitHub with both Python and MATLAB interfaces.

Abstract TP260: Early Voluntary But Not Forced Exercise Improve Neural Remodeling After Cerebral Ischemia By Promotingmitochondrial Transport

Introduction: Neural remodeling occurs in the early stage after cerebral ischemia, and rehabilitation training promotes the recovery of neronal function to the greatest extent. Although the forced exercise within 6 hours after cerebral ischemia effectively promote long-term nerve remodeling, it enhanced the short-term ischemic injury. Instead, early voluntary exercise is relatively safe. The present study determined whether early voluntary exercise safely and effectively promote long-term functional recovery after stroke. Methods: A total of 60 adult male Sprague-Dawley rats were divided into 4 groups: 1) sham control, 2) stroke, 3) stroke with forced exercise from 6 hours to 28 days (30min every day), 4) stroke with voluntary exercise from 6 h to 28 d. Infarct volume and neurological deficit were evaluated. Long-term functional outcomes were determined by grid walk, rota-rod, adhesive tape touch, and Morris water maze. Levels of mRNA and proteins of neuroplasticity and synaptogenesis were determined by real-time PCR and Western blot. After 28 days of reperfusion, the rats were taken for measured with electron microscopy. Results . 1. Early forced exercise aggravated cerebral infarction, while early voluntary exercise reduced infarct volume. 2. As compared to forced exercise, voluntary exercise improved running time in rota-rod, shortened the time of removing adhesive tape and reduced the time of finding platform in water maze. 3. Voluntary exercise significantly stimulated production of irisin to brain and growth differentiation factor 10 (GDF10), suggesting voluntary exercise improve long-term function through release of GDF10. 4. Voluntary exercise promoted synaptophysin, brain-derived neurotrophic factorand postsynaptic density protein-95 expression, suggesting voluntary exercise improved neuroplastic and synaptogenic. 5. Electron microscopy showed that voluntary effectively improved the ultrastructure of mitochondria(32±3.1%) after stroke, alleviated swelling of mitochondria (20±2.5%). Conclusions: Voluntary exercise may become effective rehabilitation strategy, which not only induced short term neuroprotection by reducing infarct volume, but also promoted the long-term functional recovery after stroke.

A FE-Based Macro-Element for the Assessment of Masonry Structures: Linear Static, Vibration, and Non-Linear Cyclic Analyses

A Finite Element (FE) based macro–element is described for the mechanical response of masonry structures within different ranges of analysis. The macro–element is composed of discrete rigid quadrilateral FE plates whose adjoining interfaces are connected through FE trusses. It allows representing both elasticity and strength orthotropy, full material nonlinearity and damage through a scalar–based model. The possibility of coupling with a so–called FE2 (multi–scale) strategy is also addressed. Validation of the macro–element is conducted within linear static, vibration, and cyclic (nonlinear) problems, in which both static and dynamic ranges are explored. Results are compared with those retrieved from traditional FE continuous models. Advantages are highlighted, as well as its robustness to cope with convergence issues and suitability to be applied within more general and larger–scale scenarios, such as the analysis of anisotropic materials subjected to static and dynamic loading. Formal details are given for its reproducibility by academics and practitioners—eventually within other FE platforms—as the improved running times may be of utmost importance in dynamic problems or highly nonlinear (material) quasi–static analysis.

PyCharge: An open-source Python package for self-consistent electrodynamics simulations of Lorentz oscillators and moving point charges

PyCharge is a computational electrodynamics Python simulator that can calculate the electromagnetic fields and potentials generated by moving point charges and can self-consistently simulate dipoles modeled as Lorentz oscillators. To calculate the total fields and potentials along a discretized spatial grid at a specified time, PyCharge computes the retarded time of the point charges at each grid point, which are subsequently used to compute the analytical solutions to Maxwell's equations for each point charge. The Lorentz oscillators are driven by the electric field in the system and PyCharge self-consistently determines the reaction of the radiation on the dipole moment at each time step. PyCharge treats the two opposite charges in the dipole as separate point charge sources and calculates their individual contributions to the total electromagnetic fields and potentials. The expected coupling that arises between dipoles is captured in the PyCharge simulation, and the modified radiative properties of the dipoles (radiative decay rate and frequency shift) can be extracted using the dipole's energy at each time step throughout the simulation. The modified radiative properties of two dipoles separated in the near-field, which require a full dipole response to yield the correct physics, are calculated by PyCharge and shown to be in excellent agreement with the analytical Green's function results (<0.2% relative error, over a wide range of spatial separations). Moving dipoles can also be modeled by specifying the dipole's origin position as a function of time. PyCharge includes a parallelized version of the dipole simulation method to enable the parallel execution of computationally demanding simulations on high performance computing environments to significantly improve run time. Program summaryProgram Title: PyChargeCPC Library link to program files:https://doi.org/10.17632/77d5vt43h9.1Developer's repository link:https://github.com/MatthewFilipovich/pychargeLicensing provisions: GPLv3Programming language: Python 3.7 or newerSupplementary material: Documentation is available at pycharge.readthedocs.io. The PyCharge package and its dependencies can be installed from PyPI: pypi.org/project/pychargeNature of problem: Calculating the electromagnetic fields and potentials generated by complex geometries of point charges, as well as the self-consistent simulation of Lorentz oscillators.Solution method: PyCharge calculates the individual contributions from each point charge in the system to calculate the total electromagnetic fields and potentials, and computes the dipole moment of the Lorentz oscillators at each time step by solving their governing equation of motion.Additional comments including restrictions and unusual features: The parallel simulation method is implemented using the mpi4py package [1].

Lerna: transformer architectures for configuring error correction tools for short- and long-read genome sequencing

BackgroundSequencing technologies are prone to errors, making error correction (EC) necessary for downstream applications. EC tools need to be manually configured for optimal performance. We find that the optimal parameters (e.g., k-mer size) are both tool- and dataset-dependent. Moreover, evaluating the performance (i.e., Alignment-rate or Gain) of a given tool usually relies on a reference genome, but quality reference genomes are not always available. We introduce Lerna for the automated configuration of k-mer-based EC tools. Lerna first creates a language model (LM) of the uncorrected genomic reads, and then, based on this LM, calculates a metric called the perplexity metric to evaluate the corrected reads for different parameter choices. Next, it finds the one that produces the highest alignment rate without using a reference genome. The fundamental intuition of our approach is that the perplexity metric is inversely correlated with the quality of the assembly after error correction. Therefore, Lerna leverages the perplexity metric for automated tuning of k-mer sizes without needing a reference genome.ResultsFirst, we show that the best k-mer value can vary for different datasets, even for the same EC tool. This motivates our design that automates k-mer size selection without using a reference genome. Second, we show the gains of our LM using its component attention-based transformers. We show the model’s estimation of the perplexity metric before and after error correction. The lower the perplexity after correction, the better the k-mer size. We also show that the alignment rate and assembly quality computed for the corrected reads are strongly negatively correlated with the perplexity, enabling the automated selection of k-mer values for better error correction, and hence, improved assembly quality. We validate our approach on both short and long reads. Additionally, we show that our attention-based models have significant runtime improvement for the entire pipeline—18times faster than previous works, due to parallelizing the attention mechanism and the use of JIT compilation for GPU inferencing.ConclusionLerna improves de novo genome assembly by optimizing EC tools. Our code is made available in a public repository at: https://github.com/icanforce/lerna-genomics.

Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Images.

The random walker method for image segmentation is a popular tool for semi-automatic image segmentation, especially in the biomedical field. However, its linear asymptotic run time and memory requirements make application to 3D datasets of increasing sizes impractical. We propose a hierarchical framework that, to the best of our knowledge, is the first attempt to overcome these restrictions for the random walker algorithm and achieves sublinear run time and constant memory complexity. The goal of this framework is- rather than improving the segmentation quality compared to the baseline method- to make interactive segmentation on out-of-core datasets possible. The method is evaluated quantitatively on synthetic data and the CT-ORG dataset where the expected improvements in algorithm run time while maintaining high segmentation quality are confirmed. The incremental (i.e., interaction update) run time is demonstrated to be in seconds on a standard PC even for volumes of hundreds of gigabytes in size. In a small case study the applicability to large real world from current biomedical research is demonstrated. An implementation of the presented method is publicly available in version 5.2 of the widely used volume rendering and processing software Voreen (https://www.uni-muenster.de/Voreen/).

An Adaptive Agent-Specific Sub-Optimal Bounding Approach for Multi-Agent Path Finding

A Multi-Agent Path Finding (MAPF) problem involves multiple agents who want to reach their destinations without obstructing other agents. Although a MAPF problem needs to be solved for many real-world deployments, solving such a problem optimally is NP-hard. Many approaches have been proposed in the literature that offers sub-optimal solutions to this problem. For example, the Enhanced Conflict Based Search (ECBS) algorithm compromises the solution quality up to a constant factor to gain a notable runtime improvement. However, these algorithms use a fixed global sub-optimal bound for all agents, regardless of their preferences. In effect, with the increase in the number of agents, the runtime performance degrades. Against this backdrop, with the intent to further speed up the runtime, we propose an adaptive agent-specific sub-optimal bounding approach, called ASB-ECBS, that can be executed statically or dynamically. Specifically, ASB-ECBS can assign sub-optimal bound considering an individual agent’s requirement. Additionally, we theoretically prove that the solution cost of ASB-ECBS remains within the sub-optimal bound. Finally, our extensive empirical results depict a notable improvement in the runtime by using ASB-ECBS while reducing the search space compared to the state-of-the-art MAPF algorithms.

A multiscale high-cycle fatigue-damage model for the stiffness degradation of fiber-reinforced materials based on a mixed variational framework

Under fatigue-loading, short-fiber reinforced thermoplastic materials typically show a progressive degradation of the stiffness tensor. The stiffness degradation prior to failure is of primary interest from an engineering perspective, as it determines when fatigue cracks nucleate. Efficient modeling of this fatigue stage allows the engineer to monitor the fatigue-process prior to failure and design criteria which ensure a safe application of the component under investigation.We propose a multiscale model for the stiffness degradation in thermoplastic materials based on resolving the fiber microstructure. For a start, we propose a specific fatigue-damage model for the matrix, and the degradation of the thermoplastic composite arises from a rigorous homogenization procedure. The fatigue-damage model for the matrix is rather special, as its convex nature precludes localization, permits a well-defined upscaling, and is thus well-adapted to model the phase of stable stiffness degradation under fatigue loading. We demonstrate the capabilities of the full-field model by comparing the predictions on fully resolved fiber microstructures to experimental data.Furthermore, we introduce an associated model-order reduction strategy to enable component-scale simulations of the local stiffness degradation under fatigue loading. With model-order reduction in mind and upon implicit discretization in time, we transform the minimization of the incremental potential into an equivalent mixed formulation, which combines two rather attractive features. More precisely, upon order reduction, this mixed formulation permits precomputing all necessary quantities in advance, yet, retains its well-posedness in the process. We study the characteristics of the model-order reduction technique, and demonstrate its capabilities on component scale. Compared to similar approaches, the proposed model leads to improvements in runtime by more than an order of magnitude.

Modelling magnetised medium particle transport in the guiding centre limit with GEANT4

Monte Carlo codes are a standard tool for studying energetic particle propagation, secondary production, and radiation in astrophysical settings. In magnetised plasmas such as those found in solar active regions, the enormous disparity between particle gyroradii and system scales proves to be a major computational obstacle. To address this problem we have written a new module in Geant4 using the guiding centre (GC) approach in which the particle motion is averaged over a gyrofrequency. We describe the formulation and implementation of this method in particular dealing with the uncertainty in gyrophase so that particle velocities are well-defined for input to the modules handling reactions. As far as feasible, we compare the propagation and slowing down of primary protons, secondary particle production, and run times in the GC limit with the Newton–Lorentz approach, finding very good agreement between the two methods and orders of magnitude improvement in run times in the GC case. Finally, we present an illustrative solar physics application involving two interacting dipoles, which is only achievable using the GC approach.

An Ultra-Fast Digitally Reconstructed Radiograph (DRR) Implementation of the Siddon-Jacobs Algorithm using Parallel Computing: Runtime Improvement of an Intensity-Based 2D/3D Registration

Abstract A key component of an intensity-based 2D/3D registration is the digitally reconstructed radiograph (DRR) module, which creates 2D projections from pre-operative 3D data, e.g., CT and MRI scans. On average, an intensity-based 2D/3D registration requires ten iterations and the rendering of twelve DRR images per iteration. In a typical DRR implementation, the rendering time is about two seconds, and the registration runtime is four minutes. We present an implementation of the Siddon-Jacobs algorithm that uses a novel pixel-step approach to determine the pixel location of the rendering plane. In addition, we calculate the intensity of each pixel in the rendering plane using a parallel computing approach. The DRR rendering time is reduced to 10ms on average so that the registration runtime can be achieved in an average of 4.8 seconds.

Recurrent Early Filter Clotting during Continuous Veno-Venous Hemodialysis with Regional Citrate Anticoagulation is Linked to Systemic Thrombin Generation and Heparin Induced Thrombocytopenia Type II: A Retrospective Analysis

Objective: Regional Citrate Anticoagulation (RCA) for Continuous Renal Replacement Therapy (CRRT) is widely used and leads to an excellent clottingfree filter survival. Despite strict adherence to protocols, in some cases recurrent early filter-clotting occurs. The aim of this observational study was to evaluate the underlying causes and the efficacy of interventions in patients with early recurrent filter-clotting during RCA. Methods: In a retrospective analysis of a cohort of 1183 patients treated with RCA-CRRT we detected 12 patients with early filter-clotting unrelated to protocol violation or any obvious technical or medical reason. Results: All patients were systemically anticoagulated with low molecular weight or unfractionated heparin for at least 24h before initiation of Continuous Veno-Venous Hemodialysis with RCA (RCA-CVVHD). During RCA, all postfilter ionized calcium concentrations were in the target range (mean 0.33±0.05 mmol/L). At the time of the first clotting event, thrombocyte counts were 168±66/ nL. After the clotting events, the systemic anticoagulation was switched to argatroban in all patients. With systemic anticoagulation using argatroban filter lifetime of RCA-CVVHD increased significantly (p&lt;0.001) and clotting-events decreased from 0.61 to 0.10 per 24h. All patients were tested for HIT and 5/12 (42%) had a positive test for hep-PF4-antibodies. Application of argatroban significantly reduced early filter-clotting both in HIT-positive patients as well as in HIT-negative patients. At the time of the first clotting event, no patient had clinical signs of thrombosis or thromboembolism. However, during follow up a thromboembolic event occurred in three patients. Conclusion: In patients with recurrent early filter-clotting despite strict adherence to the citrate protocol undetected HIT or other causes of thrombin activation may be present. Therefore, patients with recurrent early filter clotting in RCA-CVVHD should be screened for HIT or other conditions that may activate thrombin. A significant improvement of filter run-time can be achieved by systemic administration of a thrombin inhibitor both in patients with and without HIT.

Recurrent Early Filter Clotting during Continuous Veno-Venous Hemodialysis with Regional Citrate Anticoagulation is Linked to Systemic Thrombin Generation and Heparin Induced Thrombocytopenia Type II: A Retrospective Analysis

Objective: Regional Citrate Anticoagulation (RCA) for Continuous Renal Replacement Therapy (CRRT) is widely used and leads to an excellent clottingfree filter survival. Despite strict adherence to protocols, in some cases recurrent early filter-clotting occurs. The aim of this observational study was to evaluate the underlying causes and the efficacy of interventions in patients with early recurrent filter-clotting during RCA. Methods: In a retrospective analysis of a cohort of 1183 patients treated with RCA-CRRT we detected 12 patients with early filter-clotting unrelated to protocol violation or any obvious technical or medical reason. Results: All patients were systemically anticoagulated with low molecular weight or unfractionated heparin for at least 24h before initiation of Continuous Veno-Venous Hemodialysis with RCA (RCA-CVVHD). During RCA, all postfilter ionized calcium concentrations were in the target range (mean 0.33±0.05 mmol/L). At the time of the first clotting event, thrombocyte counts were 168±66/ nL. After the clotting events, the systemic anticoagulation was switched to argatroban in all patients. With systemic anticoagulation using argatroban filter lifetime of RCA-CVVHD increased significantly (p&lt;0.001) and clotting-events decreased from 0.61 to 0.10 per 24h. All patients were tested for HIT and 5/12 (42%) had a positive test for hep-PF4-antibodies. Application of argatroban significantly reduced early filter-clotting both in HIT-positive patients as well as in HIT-negative patients. At the time of the first clotting event, no patient had clinical signs of thrombosis or thromboembolism. However, during follow up a thromboembolic event occurred in three patients. Conclusion: In patients with recurrent early filter-clotting despite strict adherence to the citrate protocol undetected HIT or other causes of thrombin activation may be present. Therefore, patients with recurrent early filter clotting in RCA-CVVHD should be screened for HIT or other conditions that may activate thrombin. A significant improvement of filter run-time can be achieved by systemic administration of a thrombin inhibitor both in patients with and without HIT.

An improved exact algorithm for minimum dominating set in chordal graphs

Minimum Dominating Set is among the most studied classical NP-hard problems. On general graphs, an O(1.4864n) exact algorithm for this problem is known. Minimum Dominating Set remains NP-hard on chordal graphs where the current asymptotically-fastest exact algorithm solves the problem in O(1.3687n) time and super-polynomial space. In this paper, a simple exact polynomial-space algorithm that exploits the local structure of neighborhoods of simplicial vertices in chordal graphs is presented, resulting in an improved running time in O(1.3384n).

Symmetry-Breaking Constraints for Grid-Based Multi-Agent Path Finding

We describe a new way of reasoning about symmetric collisions for Multi-Agent Path Finding (MAPF) on 4-neighbor grids. We also introduce a symmetry-breaking constraint to resolve these conflicts. This specialized technique allows us to identify and eliminate, in a single step, all permutations of two currently assigned but incompatible paths. Each such permutation has exactly the same cost as a current path, and each one results in a new collision between the same two agents. We show that the addition of symmetry-breaking techniques can lead to an exponential reduction in the size of the search space of CBS, a popular framework for MAPF, and report significant improvements in both runtime and success rate versus CBSH and EPEA* – two recent and state-of-the-art MAPF algorithms.

Effects of Acacia Honey Drink after Exercise-Induced Dehydration on Selected Physiological Parameters and Subsequent Running Performance

This study examined the effect of honey supplementation on exercise performance and biochemical markers in comparison to plain water and sports drink in the heat. Ten recreational athletes (Age: 22.2 ± 2.0 years, weight: 65.7 ± 5.3 kg; height: 170.4 ± 3.5 cm; VO2max: 51.5 ± 3.7 mL.kg−1.min−1) participated in this study. Participants ran at 70% of their VO2max for 1 h in a pre-load phase (Run-1), followed by a rehydration phase for 2 h and then a 20 min self-paced time trial (Run-2). After Run-1, participants drank either Acacia honey, sports drink or plain water with an amount equivalent to 150% of body weight loss. Subsequently, the participants performed the 20 min self-paced time trial. Acacia honey elicited an improved running time trial performance with a significantly (p&lt;0.05) longer distance ran compared to plain water trial, but it was not different from the sports drink trial. In addition, there was no significant difference in running performance between sports drink and plain water trials. Plasma glucose, insulin and free fatty acids were significantly (p&lt;0.05) higher in H and sports drink compared to the W trial during the rehydration phase. There were no significant differences in body weight changes, oxygen uptake, heart rate, rate of perceived exertion, tympanic temperature, plasma volume changes, plasma cortisol, urine osmolality, volume, and specific gravity among the three trials. Thus, Acacia honey can be recommended to be used as a rehydration drink for individuals who train and compete in the heat.

BC tree-based spectral sampling for big complex network visualization

Graph sampling methods have been used to reduce the size and complexity of big complex networks for graph mining and visualization. However, existing graph sampling methods often fail to preserve the connectivity and important structures of the original graph. This paper introduces a new divide and conquer approach to spectral graph sampling based on graph connectivity, called the BC Tree (i.e., decomposition of a connected graph into biconnected components) and spectral sparsification. Specifically, we present two methods, spectral vertex sampling BC_SV and spectral edge sampling BC_SS by computing effective resistance values of vertices and edges for each connected component. Furthermore, we present DBC_SS and DBC_GD, graph connectivity-based distributed algorithms for spectral sparsification and graph drawing respectively, aiming to further improve the runtime efficiency of spectral sparsification and graph drawing by integrating connectivity-based graph decomposition and distributed computing. Experimental results demonstrate that BC_SV and BC_SS are significantly faster than previous spectral graph sampling methods while preserving the same sampling quality. DBC_SS and DBC_GD obtain further significant runtime improvement over sequential approaches, and DBC_GD further achieves significant improvements in quality metrics over sequential graph drawing layouts.

Discrete Fréchet Distance under Translation

The discrete Fréchet distance is a popular measure for comparing polygonal curves. An important variant is the discrete Fréchet distance under translation, which enables detection of similar movement patterns in different spatial domains. For polygonal curves of length n in the plane, the fastest known algorithm runs in time Õ( n 5 ) [12]. This is achieved by constructing an arrangement of disks of size Õ( n 4 ), and then traversing its faces while updating reachability in a directed grid graph of size N := Õ( n 5 ), which can be done in time Õ(√ N ) per update [27]. The contribution of this article is two-fold. First, although it is an open problem to solve dynamic reachability in directed grid graphs faster than Õ(√ N ), we improve this part of the algorithm: We observe that an offline variant of dynamic s - t -reachability in directed grid graphs suffices, and we solve this variant in amortized time Õ( N 1/3 ) per update, resulting in an improved running time of Õ( N 4.66 ) for the discrete Fréchet distance under translation. Second, we provide evidence that constructing the arrangement of size Õ( N 4 ) is necessary in the worst case by proving a conditional lower bound of n 4 - o(1) on the running time for the discrete Fréchet distance under translation, assuming the Strong Exponential Time Hypothesis.

A convolutional neural-network framework for modelling auditory sensory cells and synapses

In classical computational neuroscience, analytical model descriptions are derived from neuronal recordings to mimic the underlying biological system. These neuronal models are typically slow to compute and cannot be integrated within large-scale neuronal simulation frameworks. We present a hybrid, machine-learning and computational-neuroscience approach that transforms analytical models of sensory neurons and synapses into deep-neural-network (DNN) neuronal units with the same biophysical properties. Our DNN-model architecture comprises parallel and differentiable equations that can be used for backpropagation in neuro-engineering applications, and offers a simulation run-time improvement factor of 70 and 280 on CPU or GPU systems respectively. We focussed our development on auditory neurons and synapses, and show that our DNN-model architecture can be extended to a variety of existing analytical models. We describe how our approach for auditory models can be applied to other neuron and synapse types to help accelerate the development of large-scale brain networks and DNN-based treatments of the pathological system.

Theoretical Analyses of Multi-Objective Evolutionary Algorithms on Multi-Modal Objectives

Previous theory work on multi-objective evolutionary algorithms considers mostly easy problems that are composed of unimodal objectives. This paper takes a first step towards a deeper understanding of how evolutionary algorithms solve multi-modal multi-objective problems. We propose the OneJumpZeroJump problem, a bi-objective problem whose single objectives are isomorphic to the classic jump functions benchmark. We prove that the simple evolutionary multi-objective optimizer (SEMO) cannot compute the full Pareto front. In contrast, for all problem sizes n and all jump sizes k in [4..n/2-1], the global SEMO (GSEMO) covers the Pareto front in Θ((n-2k)n^k) iterations in expectation. To improve the performance, we combine the GSEMO with two approaches, a heavy-tailed mutation operator and a stagnation detection strategy, that showed advantages in single-objective multi-modal problems. Runtime improvements of asymptotic order at least k^Ω(k) are shown for both strategies. Our experiments verify the substantial runtime gains already for moderate problem sizes. Overall, these results show that the ideas recently developed for single-objective evolutionary algorithms can be effectively employed also in multi-objective optimization.

A Progressive Approach to Scalar Field Topology.

This article introduces progressive algorithms for the topological analysis of scalar data. Our approach is based on a hierarchical representation of the input data and the fast identification of topologically invariant vertices, which are vertices that have no impact on the topological description of the data and for which we show that no computation is required as they are introduced in the hierarchy. This enables the definition of efficient coarse-to-fine topological algorithms, which leverage fast update mechanisms for ordinary vertices and avoid computation for the topologically invariant ones. We demonstrate our approach with two examples of topological algorithms (critical point extraction and persistence diagram computation), which generate interpretable outputs upon interruption requests and which progressively refine them otherwise. Experiments on real-life datasets illustrate that our progressive strategy, in addition to the continuous visual feedback it provides, even improves run time performance with regard to non-progressive algorithms and we describe further accelerations with shared-memory parallelism. We illustrate the utility of our approach in batch-mode and interactive setups, where it respectively enables the control of the execution time of complete topological pipelines as well as previews of the topological features found in a dataset, with progressive updates delivered within interactive times.