Emergent Chirality and Hyperuniformity in an Active Mixture with Nonreciprocal Interactions.

We investigate collective dynamics in a binary mixture of programmable robots in experiments and simulations. While robots of the same species align their motion direction, interaction between species is distinctly nonreciprocal: species A aligns with B and species B antialigns with A. This nonreciprocal interaction gives rise to the emergence of collective chiral motion that can be stabilized by limiting the robot angular speed to be below a threshold. Within the chiral phase, increasing the robot density or extending the range of local repulsive interactions can drive the system through an absorbing-active transition. At the transition point, the robots exhibit a remarkable capacity for self-organization, forming disordered hyperuniform states.

Characterizing Pairwise U-Turn Behavior in Fish: A Data-Driven Analysis.

We applied the time-series clustering method to analyze the trajectory data of rummy-nose tetra (Hemigrammus rhodostomus), with a particular focus on their spontaneous paired turning behavior. Firstly, an automated U-turn maneuver identification method was proposed to extract turning behaviors from the open trajectory data of two fish swimming in an annular tank. We revealed two distinct ways of pairwise U-turn swimming, named dominated turn and non-dominated turn. Upon comparison, the dominated turn is smoother and more efficient, with a fixed leader-follower relationship, i.e., the leader dominates the turning process. Because these two distinct ways corresponded to different patterns of turning feature parameters over time, we incorporated the Toeplitz inverse covariance-based clustering (TICC) method to gain deeper insights into this process. Pairwise turning behavior was decomposed into some elemental state compositions. Specifically, we found that the main influencing factor for a spontaneous U-turn is collision avoidance with the wall. In dominated turn, when inter-individual distances were appropriate, fish adjusted their positions and movement directions to achieve turning. Conversely, in closely spaced non-dominated turn, various factors such as changes in distance, velocity, and movement direction resulted in more complex behaviors. The purpose of our study is to integrate common location-based analysis methods with time-series clustering methods to analyze biological behavioral data. The study provides valuable insights into the U-turn behavior, motion characteristics, and decision factors of rummy-nose tetra during pairwise swimming. Additionally, the study extends the analysis of fish interaction features through the application of time-series clustering methods, offering a fresh perspective for the analysis of biological collective data.

Perception of motion salience shapes the emergence of collective motions.

Despite the profound implications of self-organization in animal groups for collective behaviors, understanding the fundamental principles and applying them to swarm robotics remains incomplete. Here we propose a heuristic measure of perception of motion salience (MS) to quantify relative motion changes of neighbors from first-person view. Leveraging three large bird-flocking datasets, we explore how this perception of MS relates to the structure of leader-follower (LF) relations, and further perform an individual-level correlation analysis between past perception of MS and future change rate of velocity consensus. We observe prevalence of the positive correlations in real flocks, which demonstrates that individuals will accelerate the convergence of velocity with neighbors who have higher MS. This empirical finding motivates us to introduce the concept of adaptive MS-based (AMS) interaction in swarm model. Finally, we implement AMS in a swarm of ~102 miniature robots. Swarm experiments show the significant advantage of AMS in enhancing self-organization of the swarm for smooth evacuations from confined environments.

Exploring the criticality hypothesis using programmable swarm robots with Vicsek-like interactions.

A widely mentioned but not experimentally confirmed view (known as the 'criticality hypothesis') argues that biological swarm systems gain optimal responsiveness to perturbations and information processing capabilities by operating near the critical state where an ordered-to-disordered state transition occurs. However, various factors can induce the ordered-disordered transition, and the explicit relationship between these factors and the criticality is still unclear. Here, we present an experimental validation for the criticality hypothesis by employing real programmable swarm-robotic systems (up to 50 robots) governed by Vicsek-like interactions, subject to time-varying stimulus-response and hazard avoidance. We find that (i) not all ordered-disordered motion transitions correspond to the functional advantages for groups; (ii) collective response of groups is maximized near the critical state induced by alignment weight or scale rather than noise and other non-alignment factors; and (iii) those non-alignment factors act to highlight the functional advantages of alignment-induced criticality. These results suggest that the adjustability of velocity or directional coupling between individuals plays an essential role in the acquisition of maximizing collective response by criticality. Our results contribute to understanding the adjustment strategies of animal interactions from a perspective of criticality and provide insights into the design and control of swarm robotics.

Predicting the Glycemic Index of Biscuits Using Static In Vitro Digestion Protocols.

In vitro digestion methods that can accurately predict the estimated GI (eGI) values of complex carbohydrate foods, including biscuits, are worth exploring. In the current study, standard commercial biscuits with varied clinical GI values between 9~30 were digested using both the INFOGEST and single-enzyme digestion protocols. The digestion kinetic parameters were acquired through mathematical fitting by mathematical kinetics models. The results showed that compared with the INFOGEST protocol, the AUR180 deduced from digesting using either porcine pancreatin or α-amylase showed the best potential in predicting the eGI values. Accordingly, mathematical equations were established based on the relations between the AUR180 and the GI values. When digesting using porcine pancreatin, GI= 1.834 + 0.009 ×AUCR180 (R2= 0.952), and when digesting using only α-amylase, GI= 6.101 + 0.009 ×AUCR180 (R2=0.902). The AUR180 represents the area under the curve of the reducing-sugar content normalized to the total carbohydrates versus the digestion time in 180 min. The in vitro method presented enabled the rapid and accurate prediction of the eGI values of biscuits, and the validity of the formula was verified by another batch of biscuits with a known GI, and the error rate of most samples was less than 30%.

Selective interaction and its effect on collective motion.

Plenty of empirical evidence on biological swarms reveal that interaction between individuals is selective. Each individual's neighbor is selected based on one or more featured factors. Based on the self-propelled model, we develop a general probability neighbor selection framework to study the effect of four typical featured factors (i.e., distance, bearing, orientation change and bearing change). In this work, two common cases are involved to comprehensively analyze the impact of the four featured factors on the collective motion. One is the flocking, the other is the responsivity to stimulus. The impact of different selection strengths of the featured factors on both cases are investigated. The effect of noise on flocking and different stimulus intensities on responsivity to stimulus are analyzed. This study allows us to get the insight of selective interaction and suggests the potential solution to overcome the trade-off between flocking and responsivity quality.

Multimodal Optimization Enhanced Cooperative Coevolution for Large-Scale Optimization.

Cooperative coevolutionary (CC) algorithms decompose a problem into several subcomponents and optimize them separately. Such a divide-and-conquer strategy makes CC algorithms potentially well suited for large-scale optimization. However, decomposition may be inaccurate, resulting in a wrong division of the interacting decision variables into different subcomponents and thereby a loss of important information about the topology of the overall fitness landscape. In this paper, we suggest an idea that concurrently searches for multiple optima and uses them as informative representatives to be exchanged among subcomponents for compensation. To this end, we incorporate a multimodal optimization procedure into each subcomponent, which is adaptively triggered by the status of subcomponent optimizers. In addition, a nondominance-based selection scheme is proposed to adaptively select one complete solution for evaluation from the ones that are constructed by combining informative representatives from each subcomponent with a given solution. The performance of the proposed algorithm has been demonstrated by comparing five popular CC algorithms on a set of selected problems that are recognized to be hard for traditional CC algorithms. The superior performance of the proposed algorithm is further confirmed by a comprehensive study that compares 17 state-of-the-art CC algorithms and other metaheuristic algorithms on 20 1000-dimensional benchmark functions.