Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets.

Looking forward to lepton-flavor-violating ALPs

We assess the status of past and future experiments on lepton flavor violating (LFV) muon and tau decays into a light, invisible, axion-like particle (ALP), a. We propose a new experimental setup for MEG II, the MEGII-fwd, with a forward calorimeter placed downstream from the muon stopping target. Searching for μ → ea decays MEGII-fwd is maximally sensitive to LFV ALPs, if these have nonzero couplings to right-handed leptons. The experimental set-up suppresses the (left-handed) Standard Model background in the forward direction by controlling the polarization purity of the muon beam. The reach of MEGII-fwd is compared with the present constraints, the reach of Mu3e and the Belle-II reach from τ → ℓa decays. We show that a dedicated experimental campaign for LFV muon decays into ALPs at MEG II and Mu3e will be able to probe the ALP parameter space in an unexplored region well beyond the existing astrophysical constraints. We study the implications of these searches for representative LFV ALP models, where the presence of a light ALP is motivated by neutrino masses, the strong CP problem and/or the SM flavor puzzle. To this extent we discuss the majoron in low-scale seesaw setups and introduce the LFV QCD axion, the LFV axiflavon and the leptonic familon, paying particular attention to the cases where the LFV ALPs constitute cold dark matter.

A Rapid Target Searching Scheme for a Small Satellite with Device Limits

In recent years, small satellites, which are more and more popular and affordable, have been already widely applied in observation and monitoring missions. However, it is a tough problem to meet the different mission requirements with such limits as space, energy, and devices. In this paper, we propose a practical target searching scheme for a small satellite which suffers from the device limits during the first 7.5 seconds after the launch separation. Due to the device limits at the beginning of the separation, the initial attitude of the satellite and the position of the target that the following observation task is based on are both unknown. In order to solve this problem, a backward integral strategy used to estimate the initial attitude and a target searching method intended to ensure the satellite acquires the target rapidly are included in the scheme. Simulation results proved that this scheme enabled the satellite, regardless of the initial conditions, to acquire the target within the limited observation time.

Systematic Literature Review of Swarm Robotics Strategies Applied to Target Search Problem with Environment Constraints

Target searching is a well-known but difficult problem in many research domains, including computational intelligence, swarm intelligence, and robotics. The main goal is to search for the targets within the specific boundary with the minimum time that is required and the obstacle avoidance that has been equipped in place. Swarm robotics (SR) is an extension of the multi-robot system that particularly discovers a concept of coordination, collaboration, and communication among a large number of robots. Because the robots are collaborating and working together, the task that is given will be completed faster compared to using a single robot. Thus, searching for single or multiple targets with swarm robots is a significant and realistic approach. Robustness, flexibility, and scalability, which are supported by distributed sensing, also make the swarm robots strategy suitable for target searching problems in real-world applications. The purpose of this article is to deliver a systematic literature review of SR strategies that are applied to target search problems, so as to show which are being explored in the fields as well as the performance of current state-of-the-art SR approaches. This review extracts data from four scientific databases and filters with two established high-indexed databases (Scopus and Web of Science). Notably, 25 selected articles fell under two main categories in environment complexity, namely empty space and cluttered. There are four strategies which have been compiled for both empty space and cluttered categories, namely, bio-inspired mechanism, behavior-based mechanism, random strategy mechanism, and hybrid mechanism.

A Heuristic Lowest Unknown-Degree Target Search Strategy Under Non-Structured Environment for Multi-Agent Systems

Multi-target searching is a hotspot and foundation topic in multi-agent systems research. However, most of the research is based on simple environment or known environment, which greatly limits the application of target search. In the non-structured environment, the searching result can be greatly affected by the complex terrain constraints and detectability of targets especially when we have no prior knowledge about the environment. In the paper, a novel search strategy combining maximum visibility and particle swarm optimization is proposed for the target search problem in a completely unknown and non-structural environment. The strategy utilizes the concept of visibility to describe how well the agent detects the map, and guides the agent to perform online path planning to complete the search task. In addition, considering the limited communication distance and communication bandwidth, the strategy introduces a cooperative mechanism for each agent to improve the search efficiency. Finally, in the experimental part, the search strategy is compared with the commonly used search strategies. Compared with the methods combining advantages, the proposed strategy can still achieve similar results, which proves the feasibility and efficiency of the strategy.