Event-based Methods Research Articles

Incorporating Topological and Age Uncertainty into Event-Based Biogeography of Sand Spiders Supports Paleo-Islands in Galapagos and Ancient Connections among Neotropical Dry Forests

Event-based biogeographic methods, such as dispersal-extinction-cladogenesis, have become increasingly popular for attempting to reconstruct the biogeographic history of organisms. Such methods employ distributional data of sampled species and a dated phylogenetic tree to estimate ancestral distribution ranges. Because the input tree is often a single consensus tree, uncertainty in topology and age estimates are rarely accounted for, even when they may affect the outcome of biogeographic estimates. Even when such uncertainties are taken into account for estimates of ancestral ranges, they are usually ignored when researchers compare competing biogeographic hypotheses. We explore the effect of incorporating this uncertainty in a biogeographic analysis of the 21 species of sand spiders (Sicariidae: Sicarius) from Neotropical xeric biomes, based on a total-evidence phylogeny including a complete sampling of the genus. Using a custom R script, we account for uncertainty in ages and topology by estimating ancestral ranges over a sample of trees from the posterior distribution of a Bayesian analysis, and for uncertainty in biogeographic estimates by using stochastic maps. This approach allows for counting biogeographic events such as dispersal among areas, counting lineages through time per area, and testing biogeographic hypotheses, while not overestimating the confidence in a single topology. Including uncertainty in ages indicates that Sicarius dispersed to the Galapagos Islands when the archipelago was formed by paleo-islands that are now submerged; model comparison strongly favors a scenario where dispersal took place before the current islands emerged. We also investigated past connections among currently disjunct Neotropical dry forests; failing to account for topological uncertainty underestimates possible connections among the Caatinga and Andean dry forests in favor of connections among Caatinga and Caribbean + Mesoamerican dry forests. Additionally, we find that biogeographic models including a founder-event speciation parameter (“+J”) are more prone to suffer from the overconfidence effects of estimating ancestral ranges using a single topology. This effect is alleviated by incorporating topological and age uncertainty while estimating stochastic maps, increasing the similarity in the inference of biogeographic events between models with or without a founder-event speciation parameter. We argue that incorporating phylogenetic uncertainty in biogeographic hypothesis-testing is valuable and should be a commonplace approach in the presence of rogue taxa or wide confidence intervals in age estimates, and especially when using models including founder-event speciation.

Distributed Event Triggering Algorithm for Multi-Agent System over a Packet Dropping Network.

The availability of wireless networked control systems (WNCSs) has increased the interest in controlling multi-agent systems. Multiple feedback loops are closed over a shared communication network in such systems. An event triggering algorithm can significantly reduce network usage compared to the time triggering algorithm in WNCSs, however, the control performance is insecure in an industrial environment with a high probability of the packet dropping. This paper presents the design of a distributed event triggering algorithm in the state feedback controller for multi-agent systems, whose dynamics are subjected to the external interaction of other agents and under a random single packet drop scenario. Distributed event-based state estimation methods were applied in this work for designing a new event triggering algorithm for multi-agent systems while retaining satisfactory control performance, even in a high probability of packet drop condition. Simulation results for a multi-agent application show the main benefits and suitability of the proposed event triggering algorithm for multi-agent feedback control in WNCSs with packet drop imperfection.

Learning From Images: A Distillation Learning Framework for Event Cameras.

Event cameras have recently drawn massive attention in the computer vision community because of their low power consumption and high response speed. These cameras produce sparse and non-uniform spatiotemporal representations of a scene. These characteristics of representations make it difficult for event-based models to extract discriminative cues (such as textures and geometric relationships). Consequently, event-based methods usually perform poorly compared to their conventional image counterparts. Considering that traditional images and event signals share considerable visual information, this paper aims to improve the feature extraction ability of event-based models by using knowledge distilled from the image domain to additionally provide explicit feature-level supervision for the learning of event data. Specifically, we propose a simple yet effective distillation learning framework, including multi-level customized knowledge distillation constraints. Our framework can significantly boost the feature extraction process for event data and is applicable to various downstream tasks. We evaluate our framework on high-level and low-level tasks, i.e., object classification and optical flow prediction. Experimental results show that our framework can effectively improve the performance of event-based models on both tasks by a large margin. Furthermore, we present a 10K dataset (CEP-DVS) for event-based object classification. This dataset consists of samples recorded under random motion trajectories that can better evaluate the motion robustness of the event-based model and is compatible with multi-modality vision tasks.

A Survey on Object Detection for the Internet of Multimedia Things (IoMT) using Deep Learning and Event-based Middleware: Approaches, Challenges, and Future Directions

An enormous amount of sensing devices (scalar or multimedia) collect and generate information (in the form of events) over the Internet of Things (IoT). Present research on IoT mainly focus on the processing of scalar sensor data events and barely considers the challenges posed by multimedia based events. In this paper, we systematically review the existing solutions available for the Internet of Multimedia Things (IoMT) by analyzing sensing, networking, service, and application-level services provided by IoT. We present state-of-the-art event-based middleware methods and their suitability for multimedia event processing methods. We observe that existing IoT event-based middleware solutions focus on structured (scalar) events and possess only domain-specific characteristics for unstructured (multimedia) events. A case study for object detection is also presented to demonstrate the requirements associated with the processing of multimedia events within smart cities, even with common image recognition based applications. In order to validate the existing issues in the detection of objects, we also presented an evaluation of object detection models using existing datasets. At the end of each section, we shed light on trends, gaps, and possible solutions based on our analysis, experiments, and review of the existing research. Finally, we summarize the challenges and future research directions for the generalized multimedia event processing (by taking detection of each and every object as an example) based on applications using IoMT. Our experiments demonstrate that existing models are very slow to respond to any unseen class, and existing rich datasets do not have a sufficient number of classes to meet the requirements of real-time applications of smart cities. We show that although there is a significantly large technical literature on IoT, and research on IoMT is also quite actively growing, there have not been much research efforts directed towards the processing of multimedia events. As an example, although deep learning techniques have been shown to achieve impressive performance in applications like image recognition, the methods are deficient in detecting new (previously unseen) objects for multimedia based applications in smart cities. In light of these facts, it becomes imperative to conduct research on bringing together the abilities of event-based middleware for IoMT, and low response-time based online training and adaptation techniques.

Cyberattacks Against Event-Based Analysis in Micro-PMUs: Attack Models and Counter Measures

The recent advent of distribution-level phasor measurement units (D-PMUs), a.k.a., micro-PMUs, has introduced a wide range of new applications in power distribution systems. A sub-class of such emerging applications are called event-based methods. These methods focus on the analysis of events in the stream of micro-PMU measurements to achieve situational awareness, enhance load modeling, integrate distributed energy resources, etc. In this article, we explore a scenario, where a cyberattack compromises the micro-PMU measurements during an event. Such a targeted attack could be limited in scope but result in a major impact on the operation of the power grid by highly deviating the outcome of the event-based methods. First, we investigate and model two types of such attacks, event-unsynchronized (basic) attacks and event-synchronized (advanced) attacks. We then conduct a geometric analysis to understand each attack type, in a setting where the events are represented in the phasor domain in a differential mode . Next, we introduce a novel method to detect the presence of the attack and then identify which micro-PMUs are compromised so as to discard the compromised measurements as a defense mechanism. The proposed approach makes critical use of magnitude as well as phase angle measurements from micro-PMUs. The method is tested on the IEEE 33-bus power distribution test system.

Wolbachia infection in wild mosquitoes (Diptera: Culicidae): implications for transmission modes and host-endosymbiont associations in Singapore

BackgroundWolbachia are intracellular bacterial endosymbionts found in most insect lineages. In mosquitoes, the influence of these endosymbionts on host reproduction and arboviral transmission has spurred numerous studies aimed at using Wolbachia infection as a vector control technique. However, there are several knowledge gaps in the literature and little is known about natural Wolbachia infection across species, their transmission modes, or associations between various Wolbachia lineages and their hosts. This study aims to address these gaps by exploring mosquito-Wolbachia associations and their evolutionary implications.MethodsWe conducted tissue-specific polymerase chain reaction screening for Wolbachia infection in the leg, gut and reproductive tissues of wild mosquitoes from Singapore using the Wolbachia surface protein gene (wsp) molecular marker. Mosquito-Wolbachia associations were explored using three methods—tanglegram, distance-based, and event-based methods—and by inferred instances of vertical transmission and host shifts.ResultsAdult mosquitoes (271 specimens) representing 14 genera and 40 species were screened for Wolbachia. Overall, 21 species (51.2%) were found positive for Wolbachia, including five in the genus Aedes and five in the genus Culex. To our knowledge, Wolbachia infections have not been previously reported in seven of these 21 species: Aedes nr. fumidus, Aedes annandalei, Uranotaenia obscura, Uranotaenia trilineata, Verrallina butleri, Verrallina sp. and Zeugnomyia gracilis. Wolbachia were predominantly detected in the reproductive tissues, which is an indication of vertical transmission. However, Wolbachia infection rates varied widely within a mosquito host species. There was no clear signal of cophylogeny between the mosquito hosts and the 12 putative Wolbachia strains observed in this study. Host shift events were also observed.ConclusionsOur results suggest that the mosquito-Wolbachia relationship is complex and that combinations of transmission modes and multiple evolutionary events likely explain the observed distribution of Wolbachia diversity across mosquito hosts. These findings have implications for a better understanding of the diversity and ecology of Wolbachia and for their utility as biocontrol agents.Graphical

Stereo dense depth tracking based on optical flow using frames and events

ABSTRACT Event cameras are biologically inspired sensors that asynchronously detect brightness changes in the scene independently for each pixel. Their output is a stream of events which is reported with a low latency and high temporal resolution of a microsecond, making them superior to standard cameras in highly dynamic scenarios when they are sensitive to motion blur. Event cameras can be used in a wide range of applications, one of them being depth estimation, in both stereo and monocular settings. However, most known event-based depth estimation methods yield sparse depth maps due to the nature of the sparse event stream. We present a novel method that fuses information from both events and standard frames, as well as odometry, to exploit the advantages of both sensors. We propose to estimate dense disparity from standard frames at the point of their availability, predict the disparity using odometry information, and track the disparity asynchronously using optical flow of events between the standard frames. We present the performance of the method through several experiments in various setups, including synthetic data, KITTI dataset enhanced with events, MVSEC dataset, as well as our own stereo event camera recordings.

Event-Based Processing of Single Photon Avalanche Diode Sensors

Single Photon Avalanche Diode sensor arrays operating in direct time of flight mode can perform 3D imaging using pulsed lasers. Operating at high frame rates, SPAD imagers typically generate large volumes of noisy and largely redundant spatio-temporal data. This results in communication bottlenecks and unnecessary data processing. In this work, we propose a neuromorphic processing solution to this problem. By processing the spatio-temporal patterns generated by the SPADs in a local, event-based manner, the proposed $128\times 128$ pixel sensor-processor system reduces the size of output data from the sensor by orders of magnitude while increasing the utility of the output data in the context of challenging recognition tasks. To test the proposed system, the first large scale complex SPAD imaging dataset is captured using an existing $32\times 32$ pixel sensor. The generated dataset consists of 24000 recordings and involves high-speed view-invariant recognition of airplanes with background clutter. The frame-based SPAD imaging dataset is converted via several alternative methods into event-based data streams and processed using the proposed $125\times 125$ receptive field neuromorphic processor as well as a range of feature extractor networks and pooling methods. The output of the proposed event generation methods are then processed by an event-based feature extraction and classification system implemented in FPGA hardware. The event-based processing methods are compared to processing the original frame-based dataset via frame-based but otherwise identical architectures. The results show the event-based methods are superior to the frame-based approach both in terms of classification accuracy and output data-rate.

AMAE: Adaptive Motion-Agnostic Encoder for Event-Based Object Classification

Event cameras, with low power consumption, high temporal resolution, and high dynamic range, have been used increasingly in computer vision. These superior characteristics enable event cameras to perform low-energy and high-response object classification tasks in challenging scenarios. Nevertheless, specific encoding methods for event-based classification are required owing to the unconventional output of event cameras. Existing event-based encoding methods have focused on extracting semantic and motion information in event signals. However, two main problems exist in these methods: (i) the motion information of event signals leads to mispredictions by the classifiers. (ii) effective evaluation methods to validate the motion robustness of event-based classification models have yet to be proposed. In this work, we introduce an adaptive motion-agnostic encoder for event streams to address the first problem. The proposed encoder would allow us to extract indistinguishable semantic information from an object in different motion circumstances. In addition, we propose a novel motion inconsistency evaluation method to assess the motion robustness of the classification models. We apply our method to several benchmark datasets and evaluate it using motion consistency and inconsistency testing methods. Classification performance shows that our proposed encoder outperforms state-of-the-art methods by a large margin.

Cophylogenetic patterns in algal symbionts correlate with repeated symbiont switches during diversification and geographic expansion of lichen-forming fungi in the genus Sticta (Ascomycota, Peltigeraceae)

Species in the fungal genus Sticta form symbiotic associations primarily with either green algae or cyanobacteria, but tripartite associations or photosymbiodemes involving both types of photobionts occur in some species. Sticta is known to associate with green algae in the genus Symbiochloris. However, previous studies have shown that algae from other genera, such as Heveochlorella, may also be suitable partners for Sticta. We examined the diversity of green algal partners in the genus Sticta and assessed the patterns of association between the host fungus and its algal symbiont. We used multi-locus sequence data from multiple individuals collected in Australia, Cuba, Madagascar, Mauritius, New Zealand, Reunion and South America to infer phylogenies for fungal and algal partners and performed tests of congruence to assess coevolution between the partners. In addition, event-based methods were implemented to examine which cophylogenetic processes have led to the observed association patterns in Sticta and its green algal symbionts. Our results show that in addition to Symbiochloris, Sticta associates with green algae from the genera Chloroidium, Coccomyxa, Elliptochloris and Heveochlorella, the latter being the most common algal symbiont associated with Sticta in this study. Geography plays a strong role in shaping fungal-algal association patterns in Sticta as mycobionts associate with different algal lineages in different geographic locations. While fungal and algal phylogenies were mostly congruent, event-based methods did not find any evidence for cospeciation between the partners. Instead, the association patterns observed in Sticta and associated algae, were largely explained by other cophylogenetic events such as host-switches, losses of symbiont and failure of the symbiont to diverge with its host. Our results also show that tripartite associations with green algae evolved multiple times in Sticta.

Cophylogenetic analysis of the relationship between anemonefish Amphiprion (Perciformes: Pomacentridae) and their symbiotic host anemones (Anthozoa: Actiniaria)

ABSTRACTThe association between anemonefish and anemone is a classical example of mutualism in coral reefs. Although mutualism is probably the key innovation that triggered the adaptive radiation of anemonefish into a wide range of habitats, the coevolutionary history between the groups has not been thoroughly tested in a phylogenetic framework. We examined the evolutionary history of the association via distance-based (Parafit and PACo) and event-based methods (Core-PA, Jane). Mitochondrial DNA sequences (COI mtDNA, Cytb, 16S rDNA and 12S rDNA) were used to reconstruct the phylogenies of tropical Amphiprion species and their host anemones by using maximum likelihood with best-fit models selected. Neither distance-based analyses nor event-based analyses revealed global significant congruence between the phylogenies of the hosts and the symbionts, and thus no evidence for coevolution between anemone-anemonefish. However, at the individual pair level, the fish showed some dependence on anemone hosts. Even though living in close association and benefiting from each other, the change of genetic composition of one species (anemonefish) does not always evolve in response to changes in the other (anemones). These findings expand our understanding of the pattern and the role of evolutionary events to allow a better prediction of the future of the anemonefish-anemone relationship.

Hydrological Modeling of Upper OumErRabia Basin (Morocco), Comparative Study of the Event-Based and Continuous-Process HEC-HMS Model Methods

Human population growth and land-use changes raise demand and competition for water resources. The Upper OumErRabia River Basin is experiencing high rangeland and matorral conversion to irrigated agricultural land expansion. Given Morocco’s per capita water availability, River-basin hydrologic modelling could potentially bring together agricultural, water resources and conservation objectives. However, not everywhere have hydrological models considered events and continuous assessment of climatic data. In this study, HEC-HMS modelling approach is used to explore the event-based and continuous-process simulation of land-use and land cover change (LULCC) impact on water balance. The use of HEC-GeoHMS facilitated the digital data processing for coupling with the model. The basin’s physical characteristics and the hydro-climatic data helped to generate a geospatial database for HEC-HMS model. We analyzed baseline and future scenario changes for the 1980-2016 period using the SCS Curve-Number and the Soil Moisture Accounting (SMA) loss methods. SMA was coupled with the Hargreaves evapotranspiration method. Model calibration focused on reproducing observed basin runoff hydrograph. To evaluate the model performance for both calibration and validation, the Coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), Root Mean Square Error (RSR) and Percent Bias (PBIAS) criteria were exploited. The average calibration NSE values were 0.740 and 0.585 for event-based (daily) and continuous-process (annual) respectively. The R2, RSR and PBIAS values were 0.624, 0.634 and +16.7 respectively. This is rated as good performance besides the validation simulations were satisfactory for subsequent hydrologic analyses. We conclude that the basin’s hydrologic response to positive and negative LULCC scenarios is significant both positive and negative scenarios. The study findings provide useful information for key stakeholders/decision-makers in water resources.

Systematic evaluation of differential splicing tools for RNA-seq studies.

Differential splicing (DS) is a post-transcriptional biological process with critical, wide-ranging effects on a plethora of cellular activities and disease processes. To date, a number of computational approaches have been developed to identify and quantify differentially spliced genes from RNA-seq data, but a comprehensive intercomparison and appraisal of these approaches is currently lacking. In this study, we systematically evaluated 10 DS analysis tools for consistency and reproducibility, precision, recall and false discovery rate, agreement upon reported differentially spliced genes and functional enrichment. The tools were selected to represent the three different methodological categories: exon-based (DEXSeq, edgeR, JunctionSeq, limma), isoform-based (cuffdiff2, DiffSplice) and event-based methods (dSpliceType, MAJIQ, rMATS, SUPPA). Overall, all the exon-based methods and two event-based methods (MAJIQ and rMATS) scored well on the selected measures. Of the 10 tools tested, the exon-based methods performed generally better than the isoform-based and event-based methods. However, overall, the different data analysis tools performed strikingly differently across different data sets or numbers of samples.

Cophylogenetic relationships betweenDactylogyrus(Monogenea) ectoparasites and endemic cyprinoids of the north‐eastern European peri‐Mediterranean region

The study of host-parasite coevolution is one of the cornerstones of evolutionary biology. The majority of fish ectoparasites belonging to the genus Dactylogyrus (Monogenea) exhibit a high degree of host specificity. Therefore, it is expected that their evolutionary history is primarily linked with the evolutionary history of their cyprinoid fish hosts and the historical formation of the landmasses. In the present study, we used a cophylogenetic approach to investigate coevolutionary relationships between endemic Cyprinoidea (Cyprinidae and Leuciscidae) from selected regions in southern Europe and their respective Dactylogyrus species. A total of 49 Dactylogyrus species including endemic and non-endemic species were collected from 62 endemic cyprinoid species in the Balkan and Apennine Peninsulas. However, 21 morphologically identified Dactylogyrus species exhibited different genetic variants (ranging from two to 28 variants per species) and some of them were recognized as cryptic species on the basis of phylogenetic reconstruction. Phylogenetic analyses revealed several lineages of endemic and non-endemic Dactylogyrus species reflecting some morphological similarities or host affinities. Using distance-based and event-based cophylogenetic methods, we found a significant coevolutionary signal between the phylogenies of parasites and their hosts. In particular, statistically significant links were revealed between Dactylogyrus species of Barbini (Cyprinidae) and their hosts belonging to the genera Aulopyge, Barbus and Luciobarbus. Additionally, a strong coevolutionary link was found between the generalist parasites D. alatus, D. sphyrna, D. vistulae, and their hosts, and between Dactylogyrus species of Pachychilon (Leuciscidae) and their hosts. Cophylogenetic analyses suggest that host-switching played an important role in the evolutionary history of Dactylogyrus parasitizing endemic cyprinoids in southern Europe. We propose that the high diversification of phylogenetically related cyprinoid species in the Mediterranean area is a process facilitating the host switching of specific parasites among highly diverse congeneric cyprinoids.

Online event-triggered adaptive critic design for non-zero-sum games of partially unknown networked systems

In this work, an event-triggered adaptive control scheme is proposed for the multi-player non-zero-sum (NZS) games of the nonlinear continuous-time networked systems with partially unknown dynamics. A fuzzy logic model (FLM)-based identifier is established to reconstruct the unknown system dynamics. To obtain the approximate Nash equilibrium solution, the critic neural networks (NN) are utilized to approximate the value functions and the control policies. Different from the classical event-based methods, a novel state-dependent event triggering condition is derived to reduce the recomputations and communications of the system input signals and to guarantee the control stability in the meanwhile. Moreover, by the means of the Lyapunov theorem, the uniform ultimate boundedness (UUB) properties of the system state and the critic NN weight estimation errors are proved. Finally, the theoretical analysis is substantiated by two numerical examples.

Event-Triggered Global Finite-Time Control for a Class of Uncertain Nonlinear Systems

This paper focuses on the problem of global finite-time stabilization for a class of uncertain nonlinear systems with event-triggered inputs. The existing event-based design methods can only partially compensate for the effects of the event-triggered errors and cannot completely counteract them to achieve finite-time control. For this reason, a new method about event triggering mechanism and event-triggered controller codesign is presented based on the idea of backstepping design and the sign function technique. It is proved that the event-triggered control system is the Zeno-free and the newly proposed control strategy ensures the global finite-time stability of the closed-loop systems via Lyapunov analyses and finite-time stability theory, which improves the existing results of only boundedness or asymptotic stability. Finally, two examples are performed to demonstrate the validity of the proposed strategy.

A Hybrid Event Detection Approach for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is a practical method to provide appliance-level electricity consumption information. Event detection, as an important part of event-based NILM methods, has a direct impact on the accuracy of the ultimate load disaggregation results in the entire NILM framework. This paper presents a hybrid event detection approach for relatively complex household load datasets that include appliances with long transients, high fluctuations, and/or near-simultaneous actions. The proposed approach includes a base algorithm based on moving average change with time limit, and two auxiliary algorithms based on derivative analysis and filtering analysis. The structure, steps, and working principle of this approach are described in detail. The proposed approach does not require additional information about household appliances, nor does it require any training sets. Case studies on different datasets are conducted to evaluate the performance of the proposed approach in comparison with several existing approaches including log likelihood ratio detector with maxima (LLD-Max) approach, active window-based (AWB) approach, and generalized likelihood ratio (GLR) approach. Results show that the proposed approach works well in detecting events in complex household load datasets and performs better than the existing approaches.

Resource-Aware IoT Control: Saving Communication Through Predictive Triggering

The Internet of Things (IoT) interconnects multiple physical devices in large-scale networks. When the 'things' coordinate decisions and act collectively on shared information, feedback is introduced between them. Multiple feedback loops are thus closed over a shared, general-purpose network. Traditional feedback control is unsuitable for design of IoT control because it relies on high-rate periodic communication and is ignorant of the shared network resource. Therefore, recent event-based estimation methods are applied herein for resource-aware IoT control allowing agents to decide online whether communication with other agents is needed, or not. While this can reduce network traffic significantly, a severe limitation of typical event-based approaches is the need for instantaneous triggering decisions that leave no time to reallocate freed resources (e.g., communication slots), which hence remain unused. To address this problem, novel predictive and self triggering protocols are proposed herein. From a unified Bayesian decision framework, two schemes are developed: self triggers that predict, at the current triggering instant, the next one; and predictive triggers that check at every time step, whether communication will be needed at a given prediction horizon. The suitability of these triggers for feedback control is demonstrated in hardware experiments on a cart-pole, and scalability is discussed with a multi-vehicle simulation.

Multi-constrained bus holding control in time windows with branch and bound and alternating minimization

This work proposes a periodic bus holding control method where the bus holding times of all running trips are computed simultaneously within each optimization time period; thus, increasing the coordination among running buses for avoiding bus bunching. This paper considers the adverse effects of the bus holding control in the in-vehicle travel times of on-board passengers and performs holistic bus holding decisions by modelling the bus holding problem as a discrete, nonlinear, constrained optimization problem. Given the computational complexity of the bus holding problem, an alternating minimization approach is introduced for computing the optimal holding times at each optimization instance. The performance of the periodic control method is evaluated against the performance of event-based control methods using 5-month automated vehicle location and automated passenger count data from bus line 1 in Stockholm for contacting simulation-based experiments.

Frozen Antarctic path for dispersal initiated parallel host-parasite evolution on different continents

After the break-up of Gondwana dispersal of organisms between America, Australia and Africa became more complicated. One of the possible remaining paths led through Antarctica, that was not yet glaciated and it remained habitable for many organisms. This favourable climate made Antarctica an important migration corridor for organisms with good dispersal ability, such as Aculeata (Hymenoptera), till the Oligocene cooling. Here we tested how cooling of Antarctica impacted global dispersal of Aculeata parasites (Strepsiptera: Xenidae). Our data set comprising six nuclear genes from a broad sample of Xenidae. Bayesian dating was used to estimate divergence times in phylogenetic reconstruction. Biogeography was investigated using event-based analytical methods: likelihood-based dispersal–extinction–cladogenesis and Bayesian models. The Bayesian model was used for reconstruction of ancestral host groups. Biogeographical methods indicate that multiple lineages were exchanged between the New World and the Old World + Australia until the Antarctica became completely frozen over. During the late Paleogene and Neogene periods, several lineages spread from the Afrotropics to other Old World regions and Australia. The original hosts of Xenidae were most likely social wasps. Within one lineage of solitary wasp parasites, parallel switch to digger wasps (Sphecidae) occurred independently in the New World and Old World regions. The biogeography and macroevolutionary history of Xenidae can be explained by the combination of dispersal, lineage extinction and climatic changes during the Cenozoic era. A habitable Antarctica and the presence of now-submerged islands and plateaus that acted as a connection between the New World and Old World + Australia provided the possibility for biotic exchanges of parasites along with their hymenopteran hosts. Although Xenidae are generally host specialists, there were significant host switches to unrelated but ecologically similar hosts during their evolution. There is little or no evidence for cophylogeny between strepsipteran parasites and hymenopteran lineages.