followers of honey bee tremble and waggle dances exhibit similar behaviors

The function of the honey bee tremble dance and how it attracts signal receivers is poorly understood. We tested the hypothesis that tremble followers and waggle followers exhibit the same dance-following behavior. If correct, this could unify our understanding of dance following, provide insight into dance information transfer, and offer a way to identify the signal receivers of tremble dance information. Followers showed similar initial attraction to and tracking of dancers. However, waggle dancers were faster than tremble dancers, and follower-forward, -sideways, and -angular velocities were generally similar to the velocities of their respective dancers. Waggle dancers attracted followers from 1.3-fold greater distances away than tremble dancers. Both follower types were attracted to the lateral sides of dancers, but tremble followers were more attracted to the dancer's head, and waggle followers were more attracted to the dancer's abdomen. Tremble dancers engaged in 4-fold more brief food exchanges with their followers than waggle dancers. The behaviors of both follower types are therefore relatively conserved. Researchers can now take the next steps, observing tremble followers to determine their subsequent behaviors and testing the broader question of whether follower attraction and tracking is conserved in a wide range of social insects

Automatic methods for long-term tracking and the detection and decoding of communication dances in honeybees

The honeybee waggle dance communication system is an intriguing example of abstract animal communication and has been investigated thoroughly throughout the last seven decades. Typically, observables such as waggle durations or body angles are extracted manually either directly from the observation hive or from video recordings to quantify properties of the dance and related behaviors. In recent years, biology has profited from automation, improving measurement precision, removing human bias, and accelerating data collection. We have developed technologies to track all individuals of a honeybee colony and to detect and decode communication dances automatically. In strong contrast to conventional approaches that focus on a small subset of the hive life, whether this regards time, space, or animal identity, our more inclusive system will help the understanding of the dance comprehensively in its spatial, temporal, and social context. In this contribution, we present full specifications of the recording setup and the software for automatic recognition of individually tagged bees and the decoding of dances. We discuss potential research directions that may benefit from the proposed automation. Lastly, to exemplify the power of the methodology, we show experimental data and respective analyses from a continuous, experimental recording of 9 weeks duration

The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective

In the Russo-Ukrainian war, propaganda is produced by Russian state-run news outlets for both international and domestic audiences. Its content and form evolve and change with time as the war continues. This constitutes a challenge to content moderation tools based on machine learning when the data used for training and the current news start to differ significantly. In this follow-up study, we evaluate our previous BERT and SVM models that classify Pro-Kremlin propaganda from a Pro-Western stance, trained on the data from news articles and telegram posts at the start of 2022, on the new 2023 subset. We examine both classifiers’ errors and perform a comparative analysis of these subsets to investigate which changes in narratives provoke drops in performance

WeiPer: OOD Detection using Weight Perturbations of Class Projections

Recent advances in out-of-distribution (OOD) detection on image data show that pre-trained neural network classifiers can separate in-distribution (ID) from OOD data well, leveraging the class-discriminative ability of the model itself. Methods have been proposed that either use logit information directly or that process the model's penultimate layer activations. With "WeiPer", we introduce perturbations of the class projections in the final fully connected layer which creates a richer representation of the input. We show that this simple trick can improve the OOD detection performance of a variety of methods and additionally propose a distance-based method that leverages the properties of the augmented WeiPer space. We achieve state-of-the-art OOD detection results across multiple benchmarks of the OpenOOD framework, especially pronounced in difficult settings in which OOD samples are positioned close to the training set distribution. We support our findings with theoretical motivations and empirical observations, and run extensive ablations to provide insights into why WeiPer works

Electric signal synchronization as a behavioural strategy to generate social attention in small groups of mormyrid weakly electric fish and a mobile fish robot

African weakly electric fish communicate at night by constantly emitting and perceiving brief electrical signals (electric organ discharges, EOD) at variable inter-discharge intervals (IDI). While the waveform of single EODs contains information about the sender’s identity, the variable IDI patterns convey information about its current motivational and behavioural state. Pairs of fish can synchronize their EODs to each other via echo responses, and we have previously formulated a ‘social attention hypothesis’ stating that fish use echo responses to address specific individuals and establish brief dyadic communication frameworks within a group. Here, we employed a mobile fish robot to investigate the behaviour of small groups of up to four Mormyrus rume and characterized the social situations during which synchronizations occurred. An EOD-emitting robot reliably evoked social following behaviour, which was strongest in smaller groups and declined with increasing group size. We did not find significant differences in motor behaviour of M. rume with either an interactive playback (echo response) or a random control playback by the robot. Still, the robot reliably elicited mutual synchronizations with other fish. Synchronizations mostly occurred during relatively close social interactions, usually when the fish that initiated synchronization approached either the robot or another fish from a distance. The results support our social attention hypothesis and suggest that electric signal synchronization might facilitate the exchange of social information during a wide range of social behaviours from aggressive territorial displays to shoaling and even cooperative hunting in some mormyrids