Search trajectories illuminated

This article summarizes our recent journal paper entitled "Search trajectory networks: A tool for analysing and visualising the behaviour of metaheuristics", where we propose a graph-based, data-driven modeling tool (STNs) to visualize and analyze the dynamics of any type of metaheuristic (evolutionary, swarm-based or single-point).

Recommendation of Search Trajectories to Travel Package for Real Time

Ongoing years have seen an expanded interest in recommender frameworks. Notwithstanding huge advancement in this field, there still stay various roads to investigate. Surely, this work gives an investigation of misusing on the web travel data for customized travel bundle suggestion. A basic test along this line is to address the remarkable attributes of movement information, which recognize travel bundles from customary things for proposal. With that in mind, in this work, we initially dissect the qualities of the current travel bundles and build up a traveler region season subject (TAST) model. This TAST model can address travel bundles and sightseers by various subject disseminations, where the point extraction is molded on both the vacationers and the inherent highlights (i.e., areas, travel periods) of the scenes. GPS empowers cell phones to constantly give new freedoms to improve our day by day lives. For instance, the information gathered in applications made by Uber or Public Transport Authorities can be utilized to design transportation courses, gauge limits, and proactively recognize low inclusion zones.

Identification of Exploration and Exploitation Balance in the Silkmoth Olfactory Search Behavior by Information-Theoretic Modeling

Insects search for and find odor sources as their basic behaviors, such as when looking for food or a mate. This has motivated research to describe how they achieve such behavior under turbulent odor plumes with a small number of neurons. Among different insects, the silk moth has been studied owing to its clear motor response to olfactory input. In past studies, the “programmed behavior” of the silk moth has been modeled as the average duration of a sequence of maneuvers based on the duration of periods without odor hits. However, this model does not fully represent the fine variations in their behavior. In this study, we used silk moth olfactory search trajectories from an experimental virtual reality device. We achieved an accurate input by using optogenetic silk moths that react to blue light. We then modeled such trajectories as a probabilistic learning agent with a belief of possible source locations. We found that maneuvers mismatching the programmed behavior are related to larger entropy decrease, that is, they are more likely to increase the certainty of the belief. This implies that silkmoths include some stochasticity in their search policy to balance the exploration and exploitation of olfactory information by matching or mismatching the programmed behavior model. We believe that this information-theoretic representation of insect behavior is important for the future implementation of olfactory searches in artificial agents such as robots.

Corpus-Level End-to-End Exploration for Interactive Systems

A core interest in building Artificial Intelligence (AI) agents is to let them interact with and assist humans. One example is Dynamic Search (DS), which models the process that a human works with a search engine agent to accomplish a complex and goal-oriented task. Early DS agents using Reinforcement Learning (RL) have only achieved limited success for (1) their lack of direct control over which documents to return and (2) the difficulty to recover from wrong search trajectories. In this paper, we present a novel corpus-level end-to-end exploration (CE3) method to address these issues. In our method, an entire text corpus is compressed into a global low-dimensional representation, which enables the agent to gain access to the full state and action spaces, including the under-explored areas. We also propose a new form of retrieval function, whose linear approximation allows end-to-end manipulation of documents. Experiments on the Text REtrieval Conference (TREC) Dynamic Domain (DD) Track show that CE3 outperforms the state-of-the-art DS systems.

Diverse food-sensing neurons trigger idiothetic local search in Drosophila

ABSTRACTResources are often sparsely clustered in nature. Thus, foraging animals may benefit from remembering the location of a newly discovered food patch while continuing to explore nearby [1, 2]. For example, after encountering a drop of yeast or sugar, hungry flies often perform a local search consisting of frequent departures and returns to the food site [3, 4]. Fruit flies, Drosophila melanogaster, can perform this food-centered search behavior in the absence of external stimuli or landmarks, instead relying solely on internal (idiothetic) cues to keep track of their location [5]. This path integration behavior may represent a deeply conserved navigational capacity in insects [6, 7], but the neural pathways underlying food-triggered searches remain unknown. Here, we used optogenetic activation to screen candidate cell classes and found that local searches can be initiated by diverse sensory neurons including sugar-sensors, water-sensors, olfactory-receptor neurons, as well as hunger-signaling neurons of the central nervous system. Optogenetically-induced searches resemble those triggered by actual food and are modulated by starvation state. Furthermore, search trajectories exhibit key features of path integration: searches remain tightly centered around the fictive-food site, even during long periods without reinforcement, and flies re-center their searches when they encounter a new fictive-food site. Flies can even perform elaborate local searches within a constrained maze. Together, these results suggest that flies enact local searches in response to a wide variety of food-associated cues, and that these sensory pathways may converge upon a common neural system for path integration. Optogenetically induced local searches in Drosophila can now serve as a tractable system for the study of spatial memory and navigation in insects.

Principles of transcription factor traffic on folded chromatin

All organisms regulate the transcription of their genes. To understand this process, it is essential to know how transcription factors find their targets in the genome. In addition to the DNA sequence, several variables have a known influence, but overall the binding patterns of transcription factors distribution remains mostly unexplained in animal genomes. Here we investigate the role of the chromosome conformation in shaping the search path of transcription factors. Using molecular dynamics simulations, we uncover the main principles of their diffusion on folded chromatin. Chromosome contacts play a conflicting role: at low density they enhance the traffic of transcription factors, but a high density they lower the traffic by volume exclusion. Consistently, we observe that in human cells, highly occupied targets, where protein binding is promiscuous, are found at sites engaged in chromosome loops within uncompact chromatin. In summary, those results provide a theoretical framework to understand the search trajectories of transcription factors and highlight the key contribution of genome conformation.

“Nearest Zero-point” Algorithm for Cooperative Robotic Search Missions

<p><span lang="EN-AU">Four path planning and data exchange algorithms for cooperative search and coverage robotic missions are proposed and modified. The introduced methods are simulated using C++ programming environment and the results are discussed in detail for environments with static obstacles. It has been shown that using the <strong>“nearest zero-point”</strong> algorithm can greatly optimize the mission duration and also overlapping of the search trajectories. Finally, the results are compared with several existing algorithms.</span></p>