Robust Model Predictive Control with Recursive State Estimation under Set-Membership Uncertainty

The robustness against uncertainties is a critical part in control system design. While robust control typically incorporates process noise into the formulation, state estimate error is neglected, which may cause the controller to fail in real-world applications. This paper presents a robust model predictive controller with state estimation for constrained linear systems. Unknown but bounded disturbances and partial state information are considered. To handle the partial observability of the system states, a recursive state estimator is utilized to provide the state feedback and the bounds on state estimate error. The resulting controller is guaranteed to satisfy the hard constraints for all possible realizations of the process and measurement noise within the given sets. The effectiveness of the proposed algorithm is illustrated in a numerical example.Comment: 6 pages, 4 figure

Delphi Study – Future Migration Scenarios for Europe

This report describes the methodology, implementation and results of a two-round Delphi survey looking at predictions made by a panel of experts focusing on migration-related: drivers, composition and policies for the EU in the next 10 years. The survey has been carried out amongst experts with experience in policymaking or advising policymakers in the area of migration. This report documents responses collected in both rounds of the survey. The first round was carried out in March 2021, whereas the second round was collected in June-August 2021. This work constitutes Task 3.2 in Work Package 3 of the Future Migration Scenarios for Europe (FUME) project

Aquatic virus culture collection: an absent (but necessary) safety net for environmental microbiologists

Viruses are recognised as the most abundant biological entities on the planet. In addition to their role in disease, they are crucial components of co-evolutionary processes, are instrumental in global biogeochemical pathways such as carbon fluxes and nutrient recycling, and in some cases act regionally on climate processes. Importantly, viruses harbour an enormous, as of yet unexplored genetic and metabolic potential. Some viruses infecting microalgae harbour hundreds of genes, including genes involved in cellular metabolic pathways. Collectively, these attributes have given rise to new fields of research: environmental virology and viral ecology. While traditionally the potential of viruses was recognised by isolating novel viruses into culture and subsequent sequencing of their genomes in the laboratory, advancements in next-generation sequencing technologies now allow for direct sequencing of viral genomes from their natural setting, bypassing the need for culturing. Nevertheless, the lack of associated biological reference material with most of these novel environmental genomes is problematic as there are limitations to what can be achieved with sequence data alone. Where aquatic viruses do exist in culture, they are most often kept privately within research institutes and are not available to the wider research community. Many are thus at risk of being lost because research teams rarely have secure long term resources to ensure continued propagation. Culture collections do exist for medically and agriculturally important viruses causing disease, but collections focusing on viruses infecting aquatic algae and bacteria are non-existent. We therefore highlight here the need for a centralised depository for aquatic viruses and present arguments indicating the benefits such a collection would have for the scientific community of environmental virologists

Occlusion-Aware Crowd Navigation Using People as Sensors

Autonomous navigation in crowded spaces poses a challenge for mobile robots due to the highly dynamic, partially observable environment. Occlusions are highly prevalent in such settings due to a limited sensor field of view and obstructing human agents. Previous work has shown that observed interactive behaviors of human agents can be used to estimate potential obstacles despite occlusions. We propose integrating such social inference techniques into the planning pipeline. We use a variational autoencoder with a specially designed loss function to learn representations that are meaningful for occlusion inference. This work adopts a deep reinforcement learning approach to incorporate the learned representation for occlusion-aware planning. In simulation, our occlusion-aware policy achieves comparable collision avoidance performance to fully observable navigation by estimating agents in occluded spaces. We demonstrate successful policy transfer from simulation to the real-world Turtlebot 2i. To the best of our knowledge, this work is the first to use social occlusion inference for crowd navigation.Comment: 7 pages, 4 figure

The Sample Complexity of Dictionary Learning

A large set of signals can sometimes be described sparsely using a dictionary, that is, every element can be represented as a linear combination of few elements from the dictionary. Algorithms for various signal processing applications, including classification, denoising and signal separation, learn a dictionary from a set of signals to be represented. Can we expect that the representation found by such a dictionary for a previously unseen example from the same source will have L_2 error of the same magnitude as those for the given examples? We assume signals are generated from a fixed distribution, and study this questions from a statistical learning theory perspective. We develop generalization bounds on the quality of the learned dictionary for two types of constraints on the coefficient selection, as measured by the expected L_2 error in representation when the dictionary is used. For the case of l_1 regularized coefficient selection we provide a generalization bound of the order of O(sqrt(np log(m lambda)/m)), where n is the dimension, p is the number of elements in the dictionary, lambda is a bound on the l_1 norm of the coefficient vector and m is the number of samples, which complements existing results. For the case of representing a new signal as a combination of at most k dictionary elements, we provide a bound of the order O(sqrt(np log(m k)/m)) under an assumption on the level of orthogonality of the dictionary (low Babel function). We further show that this assumption holds for most dictionaries in high dimensions in a strong probabilistic sense. Our results further yield fast rates of order 1/m as opposed to 1/sqrt(m) using localized Rademacher complexity. We provide similar results in a general setting using kernels with weak smoothness requirements

Effectiveness and characteristics of a new technology to reduce ammonia, carbon dioxide, and particulate matter pollution in poultry production with artificial turf floor

Ammonia (NH3), carbon dioxide (CO2), and particulate matter (PM) are three major aerial pollutants that threaten the health of workers and animals in poultry production. An experiment was conducted in four laying hen rooms, with 735 to 740 hens per room, to study a new technology using artificial turf (AstroTurf®) floor for mitigation of the three pollutants. Air was sampled at three locations in each room to measure ammonia and carbon dioxide concentrations with an Innova 1412 multi-gas monitor for 83 days. Particulate matter was measured at one location at bird height in each room using a Dylos DC1700 Air Quality Monitor for 35 days. Ventilation rates in all rooms were monitored with RM Young anemometers. Compared with two wood shavings rooms, the two artificial turf rooms significantly (p\u3c0.01) reduced concentrations of ammonia by 51.0%, carbon dioxide by 13.5%, small particles by 77.5%, and large particles by 83.6%. They also significantly (p\u3c0.01) reduced ammonia and carbon dioxide emission rates by 38.4% and 8.3%, respectively. The artificial turf rooms’ lower ammonia concentrations and emissions were a result of lower manure pH. The artificial turf rooms also retained more nitrogen in manure. Lower carbon dioxide concentrations and emissions were partially attributed to less carbon dioxide released from manure. Lower PM concentrations were related to reduced PM sources on floor surfaces. Artificial turf rooms had smaller in-room ammonia and carbon dioxide concentration gradients. Artificial turf is a promising new technology to improve indoor air quality in and reduce pollutant emissions from poultry production

Rethinking Polyp Segmentation from an Out-of-Distribution Perspective

Unlike existing fully-supervised approaches, we rethink colorectal polyp segmentation from an out-of-distribution perspective with a simple but effective self-supervised learning approach. We leverage the ability of masked autoencoders -- self-supervised vision transformers trained on a reconstruction task -- to learn in-distribution representations; here, the distribution of healthy colon images. We then perform out-of-distribution reconstruction and inference, with feature space standardisation to align the latent distribution of the diverse abnormal samples with the statistics of the healthy samples. We generate per-pixel anomaly scores for each image by calculating the difference between the input and reconstructed images and use this signal for out-of-distribution (ie, polyp) segmentation. Experimental results on six benchmarks show that our model has excellent segmentation performance and generalises across datasets. Our code is publicly available at https://github.com/GewelsJI/Polyp-OOD.Comment: Technical repor

Biogeographic responses of the copepod Calanus glacialis to a changing Arctic marine environment

Author Posting. © The Author(s), 2017. This is the author's version of the work. It is posted here under a nonexclusive, irrevocable, paid-up, worldwide license granted to WHOI. It is made available for personal use, not for redistribution. The definitive version was published in Global Change Biology 24 (2018): e159-e170, doi:10.1111/gcb.13890.Dramatic changes have occurred in the Arctic Ocean over the past few decades, especially in terms of sea ice loss and ocean warming. Those environmental changes may modify the planktonic ecosystem with changes from lower to upper trophic levels. This study aimed to understand how the biogeographic distribution of a crucial endemic copepod species, Calanus glacialis, may respond to both abiotic (ocean temperature) and biotic (phytoplankton prey) drivers. A copepod individual-based model coupled to an ice-ocean-biogeochemical model was utilized to simulate temperature- and food-dependent life cycle development of C. glacialis annually from 1980 to 2014. Over the 35-year study period, the northern boundaries of modeled diapausing C. glacialis expanded poleward and the annual success rates of C. glacialis individuals attaining diapause in a circumpolar transition zone increased substantially. Those patterns could be explained by a lengthening growth season (during which time food is ample) and shortening critical development time (the period from the first feeding stage N3 to the diapausing stage C4). The biogeographic changes were further linked to large scale oceanic processes, particularly diminishing sea ice cover, upper ocean warming, and increasing and prolonging food availability, which could have potential consequences to the entire Arctic shelf/slope marine ecosystems.This study was funded by National Science Foundation Arctic System Science (ARCSS) Program (PLR-1417677, PLR-1417339, and PLR-1416920)