Background: The Phoridae are one of the most poorly studied families of Diptera insects in Russia. They are small flies that play an important role in ecosystems. Methods: This dataset presents the results of a study on Phoridae conducted between 2019 and 2024 in European Russia. The overall study area covered 400,000 km2. Results: A total of 16,265 specimens were reliably identified, representing 272 species and 22 genera from 180 localities. Of these, 2673 specimens were females (16.4%), while the remaining 83.6% were males. Conclusions: The genus Megaselia Rondani accounted for 200 species (73.5%) and 12,120 specimens (74.5%). Ten species were particularly common: Megaselia pusilla, M. angusta agg., Triphleba opaca, Diplonevra funebris, M. brevicostalis, M. plurispinulosa, M. flavicans, M. lutea, M. minuta, and M. lactipennis. The highest number of localities was recorded for M. angusta agg. (37.2%), M. flavicans (27.8%), and M. brevicostalis (25.0%). In terms of collection methods, the majority of both specimens and species were captured using Malaise traps and pan traps. The highest species richness and specimen abundance were recorded in floodplain habitats, steppified areas, and meadows. In contrast, forested sites showed lower species diversity and abundance.
We present a comprehensive list of additions to Disney’s 1989 key ʻScuttle Flies. Diptera: Phoridae. Genus Megaseliaʼ, which has become the de facto fundamental reference for the identification of European species of Megaselia. The present update extends Disney’s key to cover all Palearctic species, with 393 additions and an indication of where they fit in the key. The couplets in Disney (1989) where these species key are indicated. We also report the discovery of two specimens of M. romphaea (Schmitz, 1947) and provide an updated description of this species, as the original account is outdated and difficult to consult. In addition, we describe four new species of Megaselia from Germany – M. bruna Caruso, Bøggild & Grundmann sp. nov., M. curta Caruso, Bøggild & Grundmann sp. nov., M. robertoi Caruso sp. nov., and M. splendida Caruso, Bøggild &; Grundmann sp. nov. – using the streamlined method of description developed specifically for the genus.
This work deals with the taxonomy of genus Compsoctena from India and Bangladesh, based on fresh material and historical collections housed in the Lepidoptera section of the Zoological Survey of India, Kolkata. Three new species, Compsoctena kushabhadrae sp. nov., C. kamarajisp. nov. (India), and C. faridahsani sp. nov. (Bangladesh) are described as new. The following new combinations are proposed based on primary types housed in the Natural History Museum, London: Compsoctena accurata (Meyrick, 1922), comb. nov.; Compsoctena autochthonia (Meyrick, 1931) comb. nov.; Compsoctena certatrix (Meyrick, 1916), comb. nov.; Compsoctena coagulata (Meyrick, 1919), comb. nov.; Compsoctena colonica (Meyrick, 1916) comb. nov.; Compsoctena cremata (Meyrick, 1916), comb. nov.; Compsoctena deposita (Meyrick, 1919) comb. nov.; Compsoctena devincta (Meyrick, 1916) comb. nov.; Compsoctena expedita (Meyrick, 1907) comb. nov.; Compsoctena exsecrata (Meyrick, 1937) comb. nov.; Compsoctena gregaria (Meyrick, 1916) comb. nov.; Compsoctena infensa (Meyrick, 1916) comb. nov.; Compsoctena isopeda (Meyrick, 1907) comb. nov.; Compsoctena jactata (Meyrick, 1937) comb. nov.; Compsoctena lignosa (Meyrick, 1917) comb. nov.; Compsoctena meliphaea (Meyrick, 1916) comb. nov.; Compsoctena multiplex (Meyrick, 1917) comb. nov.; Compsoctena nota (Meyrick, 1919) comb. nov.; Compsoctena obtrectans (Meyrick, 1930) comb. nov.; Compsoctena paraclasta (Meyrick, 1922) comb. nov.; Compsoctena phaeogenes (Meyrick, 1919) comb. nov.; Compsoctena pericrossa (Meyrick, 1907) comb. nov.; Compsoctena praecepta (Meyrick, 1919), comb. nov.; Compsoctena ptyalistis (Meyrick, 1937) comb. nov.; Compsoctena semota (Meyrick, 1938) comb. nov.; Compsoctena subacta (Meyrick, 1919) comb. nov.; Compsoctena tylota (Meyrick, 1916) comb. nov., and Compsoctena vorticosa (Meyrick, 1930) comb. nov. Although all these species were originally described under the genus Melasina, their current taxonomic placement is reviewed and discussed in detail. Additionally, three new country records, Compsoctena thwaitesii (Walsingham, 1887), and C. anasactis (Meyrick, 1907) from India, and Compsoctena pulla Sobczyk & Breithaupt, 2023 from Bangladesh are provided. An updated checklist of Compsoctena from India and Bangladesh is given arranging the species into ten species groups based on male wing maculation.
Aiming towards improving current computational models of humor detection, we propose a new multimodal dataset of stand-up comedies, in seven languages: English, French, Spanish, Italian, Portuguese, Hungarian and Czech. Our dataset of more than 330 hours %, is at the time of writing the biggest available for this type of task, and the most diverse. The whole dataset is automatically annotated in laughter (from the audience), and the subpart left for model validation is manually annotated.% Contrary to contemporary approaches, we do not frame the task of humor detection as a binary sequence classification, but as word-level sequence labeling, in order to take into account all the context of the sequence and to capture the continuous joke tagging mechanism typically occurring in natural conversations. As par with unimodal baselines results, we propose a method for e propose a method to enhance the automatic laughter detection based on Audio Speech Recognition errors. Our code and data are available online: https://tinyurl.com/EMNLPHumourStandUpAnonym
Multiple visions of 6G networks elicit Artificial Intelligence (AI) as a central, native element. When 6G systems are deployed at a large scale, end-to-end AI-based solutions will necessarily have to encompass both the radio and the fiber-optical domain. This paper introduces the Decentralized Multi-Party, Multi-Network AI (DMMAI) framework for integrating AI into 6G networks deployed at scale. DMMAI harmonizes AI-driven controls across diverse network platforms and thus facilitates networks that autonomously configure, monitor, and repair themselves. This is particularly crucial at the network edge, where advanced applications meet heightened functionality and security demands. The radio/optical integration is vital due to the current compartmentalization of AI research within these domains, which lacks a comprehensive understanding of their interaction. Our approach explores multi-network orchestration and AI control integration, filling a critical gap in standardized frameworks for AI-driven coordination in 6G networks. The DMMAI framework is a step towards a global standard for AI in 6G, aiming to establish reference use cases, data and model management methods, and benchmarking platforms for future AI/ML solutions.
