A rise-to-threshold signal for a relative value deliberation

Whereas progress has been made in identifying neural signals related to rapid, cued decisions, less is known about how brains guide and terminate more ethologically relevant deliberations, where an animal's own behavior governs the options experienced over minutes. Drosophila search for many seconds to minutes for egg-laying sites with high relative value and neurons, called oviDNs, exist whose activity fulfills necessity and sufficiency criteria for initiating the egg-deposition motor program. Here we show that oviDNs express a calcium signal that rises over seconds to minutes as a fly deliberates whether to lay an egg. The calcium signal dips when an egg is internally prepared (ovulated), rises at a rate related to the relative value of the current substrate being experienced, and reaches a consistent peak just prior to the abdomen bend for egg deposition. We provide perturbational evidence that the egg-deposition motor program is initiated once this signal hits a threshold and that sub-threshold variation in the signal regulates the time spent deliberating and, ultimately, the option chosen. These results argue that a rise-to-threshold signal guides Drosophila to lay eggs on substrate options with high relative value, with each egg-laying event representing a self-paced decision similar to real-world decisions made by humans and other mammals.

Response thresholds alone cannot explain empirical patterns of division of labor in social insects

The effects of heterogeneity in group composition remain a major hurdle to our understanding of collective behavior across disciplines. In social insects, division of labor (DOL) is an emergent, colony-level trait thought to depend on colony composition. Theoretically, behavioral response threshold models have most commonly been employed to investigate the impact of heterogeneity on DOL. However, empirical studies that systematically test their predictions are lacking because they require control over colony composition and the ability to monitor individual behavior in groups, both of which are challenging. Here, we employ automated behavioral tracking in 120 colonies of the clonal raider ant with unparalleled control over genetic, morphological, and demographic composition. We find that each of these sources of variation in colony composition generates a distinct pattern of behavioral organization, ranging from the amplification to the dampening of inherent behavioral differences in heterogeneous colonies. Furthermore, larvae modulate interactions between adults, exacerbating the apparent complexity. Models based on threshold variation alone only partially recapitulate these empirical patterns. However, by incorporating the potential for variability in task efficiency among adults and task demand among larvae, we account for all the observed phenomena. Our findings highlight the significance of previously overlooked parameters pertaining to both larvae and workers, allow the formulation of theoretical predictions for increasing colony complexity, and suggest new avenues of empirical study.

The problem of determining the threshold for statistical analysis by the POT method: Application to wave data on the Moroccan Atlantic coast

In a study of extreme waves by the Peak Over Threshold (POT) method, the determination of the threshold of data censoring is an essential step. A wrong choice of the threshold can lead to erroneous results of the wave height design and consequently a bad design of maritime structures such as breakwaters for deep sea ports. In this study, we analyzed the influence of the threshold variation on the results of the hundred-year return period waves, generally considered for the design of maritime structures. The sensitivity study allowed us to confirm that the exponential model is the best probability distribution to describe wave data in two points on the Moroccan Atlantic coast for the wave data period from 1958 to 2019. This study also confirmed that a wrong choice of the statistical distribution and a wrong choice of the threshold lead to significant errors in the estimation of design wave height.

Traffic Flow Prediction using Combination of Support Vector Machine and Rough Set

Congestion is the primary issue related to traffic flow. Avoiding congestion after getting into is not possible. So the only way is to make the informed decision by knowing the traffic situation in advance. This can be achieved with the help of traffic flow prediction. In the proposed work, short term traffic flow prediction is performed using support vector machine in combination with rough set. Traffic data used for analysis is collected from three adjacent intersections of Nagpur city and traffic flow is predicted at downstream junction. The work has attempted to study the effect of aggregation intervals and past samples on the prediction performance using MSE threshold variation. Rough set is used as a post processor to validate the prediction result. Accurate and timely prediction can provide reliability for optimized traffic control and guidance.

Research on Key Technology of Web Hierarchical Topic Detection and Evolution Based on Behaviour Tracking Analysis

In the development background of today’s big data era, the research direction of Web hierarchical topic detection and evolution characterized by the semistructured or unstructured data has caught wide attention for academicians. This paper proposes an idea of Web hierarchical topic detection and evolution based on behaviour tracking analysis taking the network big data as the research object, and expounds main implementation methods, which include the instance analysis of the usage mode, the instance analysis of the seed, the set analysis of similar instance supporting the topics, the set analysis of similar instance supporting the events, the evolution analysis of the event, and expounds the algorithm of Web hierarchical topic detection and evolution based on behaviour tracking analysis. The process of experimental analysis is organized as follows, first of all, the experiment analyses the quality of topic detection, the accuracy rate with the number of instance concerned and the seed threshold variation trend, the accuracy rate with the number of instance concerned and the probability threshold variation trend, secondly, the experiment analyses the quality of topic evolution, the accuracy rate with the variation trend of parameter adjustment, the accuracy rate with the number of instance concerned and the similar threshold variation trend, finally, the experiment analyses the time consuming to solve main research problem under different method, the qualitative result of topic detection and evolution under different data set. The results of experimental analysis show the idea is feasible, verifiable and superior, which plays a major role in reconfiguring Web hierarchical topic corpus and providing an intelligent big data warehouse for the network information evolution application.